banking in emerging economies - madjid tavana in emerging economies ... analysis and to consult...
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© 2014, CMK Inc. or its subsidiaries. All rights reserved
Banking in Emerging Economies
Group Project: Big Data and Decision Analytics
Page 2 © CMK Konsulting Inc.
Contents
Abstract .......................................................................................................................................... 3
Problem Statement ....................................................................................................................... 4
Additional Information ................................................................................................................................. 5
Background Information .............................................................................................................................. 5
Specific Question ........................................................................................................................................... 5
How does a bank do this analysis? ............................................................................................. 6
What factors are they looking at? .............................................................................................. 8
What factors do they want to avoid?......................................................................................... 9
Method ........................................................................................................................................... 10
Filter ............................................................................................................................................................... 10
Organize ........................................................................................................................................................ 10
Norm ................................................................................................................................................................ 12
Decide .............................................................................................................................................................. 13
Analyse ........................................................................................................................................................... 13
Data/ Decide ................................................................................................................................. 14
Africa .............................................................................................................................................................. 18
Asia ................................................................................................................................................................. 19
Europe ............................................................................................................................................................ 19
Latin America .............................................................................................................................................. 20
Analysis ....................................................................................................................................... 20
All countries – Analysed Together .......................................................................................................... 22
Asia ................................................................................................................................................................. 23
Europe ............................................................................................................................................................ 23
Latin America .............................................................................................................................................. 24
Africa ............................................................................................................................................................. 24
Opportunities for Further Analysis ........................................................................................................ 25
Sources ......................................................................................................................................... 26
Legal Information ........................................................................................................................ 27
ABSTRACT
ABSTRACT
Page 3 © CMK Konsulting Inc.
Abstract
The CMK Konsulting Company was engaged by the customer, Dr. Tavana, to
evaluate countries for potential expansion. The conclusion is that five
countries are recommended as potential candidates. These are Estonia,
Czech Republic, Poland, Mexico and Slovenia. These five countries are
relatively equally distributed and the customer will face individual
challenges in each. No particular country is a specific favourite.
Additionally, China represents a special case. The extremely strong
economy may outweigh some of the additional challenges that would be
faced expanding into China. The customer is advised to make further
analysis and to consult experts with specific knowledge of bank expansion
in China. CMK would look forward to preforming this analysis in a separate
contract.
The analysis was done with the following methods. First, the FONDA
method was used by filtering the data, organizing it, followed by
normalizing the data. The decisions were made and finally analysed. The
detailed analysis is described in this report and is available in a separate
excel file.
Graphically and tabular representations of the key data are included in this
report in the decision and analysis chapters.
Figure 1 Beijing (source:china.com)
PROBLEM STATEMENT
PROBLEM STATEMENT
Page 4 © CMK Konsulting Inc.
Problem Statement
The customer, Dr. Tavana, prepared the following problem statement:
The determinants for the internationalization of banking are based on
country characteristics and market dynamics and bank-specific advantages.
Primarily the country characteristics consist of barriers to entry to provide
services, the type of activities allowed and the type of investment allowed,
as well as, regulations on capital flows and taxation and repatriation of
profits, or indeed, tax benefits offered by a government to attract foreign
banks to invest directly to encourage spill-over effects in the industry. The
size of the market for banking products and specifically the product
markets that are targeted and the potential growth of these are other
factors. In terms of the foreign financial market dynamics the potential
performance and profitability depends on the interaction and rivalry among
existing banks in the market, the number of these their nationality and their
size distribution, that is whether it a concentrated oligopolistic market with
a few large banks and some other smaller banks, or it is a highly
fragmented market. Different product markets and customer segments,
that is, individual or corporate customers, may have different levels of
rivalry and margins and hence profitability is different for each type. The
more a bank has distinctive competencies the more that they may be
transferred abroad. By definition when they have distinctive there must be
some competitive advantage for the bank.
The decision to enter an emerging economy by a multinational bank is a
very important one, as many factors need to be considered. We consider
the following 24 countries in 4 continents: Africa (Tunisia, South Africa,
Morocco, Algeria); Asia (China, India, Vietnam); Europe (Bulgaria, Czech
Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia,
Slovenia, Turkey, Ukraine, Georgia, Russia); and Latin America (Argentina,
Brazil, Mexico). PESTEL analysis (political, economic, social, technological,
environmental, and legal analysis) is used to initially collect over 500
factors grouped into the political, economic, social, technological,
environmental, and legal categories. We then use FONDA to Filter,
Organize, Normalize, Decide, and Analyse the data. A minimum of 200
(Note: the customer changed the requirement to 400) factors for the above
mentioned 30 countries must be used in the final data set. More
information is available at: http://tavana.us/project/.
Figure 2 Mexico City (source:wikipedia.org)
PROBLEM STATEMENT
ADDITIONAL INFORMATION
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Additional Information
Furthermore, the following background information was provided to the
CMK team as input to the problem statement.
Background Information
The following data files were provided:
These files were collected by other consulting groups that have worked on
similar issues in the past and were provided by the customer in order to
reduce the team’s efforts and overall billable hours for this project. This is
reflected in a reduction to the offered costs. Please see the commercial
documentation.
Specific Question
The customer requested that the CMK specifically address the following
questions in their report:
Write about how a bank does this analysis?
What factors are they looking at?
What factors do they want to avoid?
a.xlsx b.xlsx c.xlsx d.xlsx e.xlsx
Figure 3 Prague (Wikipedia.org)
HOW DOES A BANK DO THIS ANALYSIS?
HOW DOES A BANK DO THIS ANALYSIS?
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How does a bank do this analysis?
Before a bank analyses which countries to expand into, they would have to
undertake several steps.
First, the bank would make the decision to expand abroad.
Then they would evaluate in which countries to expand to and how they
would expand.
They would create an expansion plan (Kahn p. 3) and…
Finally, the bank would execute the plan, evaluating progress continually.
The decision to expand abroad could be motivated by many factors:
The bank may want to provide services to local customers that do
business in the new country. An example of this may be that a Turkish
Bank could possibly want to expand into Germany. Germany is Turkey’s
largest trading partner and it is quite plausible that a Turkish Bank may
want to offer Turkish exporters or importers the possibility of complete
services for international transactions.
The bank may want to protect itself against being dependant on a
specific country’s economy. A Bank in a Euro country may want to invest
abroad in a non-Euro market in order to have a chance for growth when
the Euro-country’s growth is stagnant.
The bank may want to grow and may feel that the local market is
saturated and their growth is limited. An example is that a bank which is
highly skilled with specific products, such as an American bank that
supports tech start-ups, may want to move abroad into a foreign country
known to have strong Tech start-ups. An American Bank may be sick of
the competition in Silicon Valley and may choose to expand to India or
Israel in order to obtain new customers where they have reduced
competition.
The bank may simply not have faith that its current market will
constantly be strong (Kahn p. 4).
Figure 4 Analysis
(source: www.ultraoilforpets.com)
HOW DOES A BANK DO THIS ANALYSIS?
HOW DOES A BANK DO THIS ANALYSIS?
Page 7 © CMK Konsulting Inc.
Banks may have been providing services for foreign clients through their
national offices and may have decided that this was insufficient (Buch p.
5). Either they were losing clients who wanted local offices, or they saw
the potential for expansion. Perhaps through this route, the banks
already knew the foreign market and could more easily make the
decision to expand.
The banks may simply see the growth of global banking and may have
decided that if the bank itself does not grow and become global, it will
lose out to the global banks. These banks may have no other motivation
than to keep up with the trend of globalization.
The bank would then prepare to decide which country to expand to.
Based on the reasons to expand internationally, this decision may
involve evaluating many factors. It also may be very simple and limited a
go / nogo decision for a specific country.
If many countries are going to be evaluated, the bank may choose to do
this in house, or they may choose to hire a consulting company, such as
CMK Konsulting, to present this work for them. The consulting company
will interview the bank’s management to understand the goals of the
bank and then prepare a report (such as this report) and make a series
of recommendations to the bank – for countries which ideally meet the
bank’s needs.
Then, after (and if) the bank has chosen, they will create an expansion
plan to enter the new country. This plan will describe a series of
milestones necessary to expand. These will range across the board from
simply acquiring offices and hiring people, to completing the necessary
licencing, and finally to opening and beginning business.
The consulting company may also offer additional services to assist the
bank in executing the expansion plan. These can range from continuing
to act in an advisory role to more extensive services such as making
local connections, finding office locations, assisting in hiring and bringing
expats to the new market.
Finally, the bank will evaluate the success of the action plan and
constantly monitor KPIs to determine if the expansion is successful or
not.
Figure 5 Algiers
(source tourist-destination.com)
WHAT FACTORS ARE THEY LOOKING AT?
WHAT FACTORS ARE THEY LOOKING AT?
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What factors are they looking at?
Banks look at many factors before deciding upon expansion. According to
Bhattacharya, some of these factors include:
Following existing customers: Banks have a significant advantage
entering new markets if they already have customers in the new country.
Bhattacharya also states that banks have significant savings if they
have, for example, already evaluated customers for creditworthiness and
do not need to repeat this work. Bhattacharya notes, surprisingly, that
banks do not necessarily enter markets in line with international trade.
Poor performing inefficient local banks are also a target for international
banks that would like to expand. It is likely that a national banking
industry has reached some kind of complacency. When the market is
opened, international banks are more efficient and can quickly profit and
expand.
Local banks can be targeted when their stock earnings are low,
especially when foreign currency is stronger.
In some cases, countries open up their markets by reducing national
regulation. This is very attractive to banks as they often face difficult
regulation.
Banks seek to diversify risk by expanding internationally. Bhattacharya
shows evidence that even though banks may face lower earning after
expanding abroad, they reduce their risk. For many banks, this may be a
conscious decision.
Banks may also expand, if they are doing international business anyway.
Banks have lower costs monitoring the international business if they
have local businesses and local know-how.
Figure 6 Decision Factors
(source: footage.shutterstock.com)
WHAT FACTORS DO THEY WANT TO AVOID?
WHAT FACTORS DO THEY WANT TO AVOID?
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What factors do they want to avoid?
In 2013, Ernst and Young prepared a publication titled: Banking in emerging
markets seizing opportunities overcoming challenges. In this publication,
E&Y interviewed bankers active in ten rapidly growing markets about their
experiences. This publication suggests factors that banks want to avoid in
emerging markets:
Markets that have already transitions: Banks are most interested in
experiencing a period of rapid growth. There is much less potential for the
banks after the rapid growth is completed. Therefore, the banks want to
avoid new markets that have had very strong growth which has slowed
down.
Political Upheaval: The bank expansion can be severely delayed or
compromised in periods of political upheaval. The current unrest in the
Ukraine and in Syria would make both of these countries extremely
unattractive for expansion. The remaining Arab Spring countries such as
Egypt reflect similar political uncertainty.
Social Upheaval: Periods of social upheaval can be just as disruptive as
political upheaval, and often happens concurrently. E&Y quote bankers who
are concerned with investing in Nigeria due to kidnapping risk and
vandalism, as well as Columbia where trade unions have disrupted industrial
activities (such as coal and coffee).
Investment in Infrastructure: Banks doing business in emerging markets
spoke about the concern that governments were not investing in
infrastructure improvements. The banks believe strongly, that without
these investments, the economies will not improve and the expected
growth would not come.
Ernst and Young report that their sources state that they are concerned
about margin compression, increased competition, increased costs and
changes to regulations.
Margin compression: Banks express concern that national central banks are
reducing interest rates to stimulate the economy. This puts pressure across
the board to lower interest rates which reduce, in turn, the bank’s profit.
Figure 7 Factors to Avoid
(source: bizblogs.fullerton.edu)
WHAT FACTORS DO THEY WANT TO AVOID? / METHOD
FILTER / ORGANIZE
Page 10 © CMK Konsulting Inc.
Increased competition: The international banks expanding in emerging
markets are not the only ones, and other foreign banks (both global banks
and regional banks from neighbouring countries) increase competition for
customers and drive prices down.
Regulatory changes: More and more regulations are increasing the cost of
operations. These regulations are not only international, but national as
well.
Method
The customer requested that CMK Konsulting team perform their analysis
on the provided data set using the FONDA method. The steps of the FONDA
method are described in the following sections.
Filter
First, the CMK filtered the provided data sets. The purpose of this was to
bring the data set in a proper form to effectively organize it. The filtering
was done with the following. All the variables were checked and labelled:
‘1’ Necessary to include in the final analysis.
‘2’ To be discussed by CMK Konsulting and can possibly be included in
the final analysis
‘3’ Not included The data set shall not be included in the analysis.
This filtering step ended up with approximately 375 Necessary and
approximately 50 to be discussed variables.
These 425 variables were checked again for duplicates and reduced to a
final number of approximately 400 (as per the customer request).
Organize
The next step of the FONDA method is to organize the data. This was done
by dividing the data into positive attributes and negative attributes and
assigning each attribute a PESTEL category.
Figure 8 Method (source:
http://www.aaindustrial.com.au/)
METHOD
ORGANIZE
Page 11 © CMK Konsulting Inc.
Positive and Negative Attributes: Each of the factors was checked to
determine if it could be interpreted as pro- or contra- indication for a bank
to expand into a country. For example, GDP is considered a pro- indication
to expand into a country for the reason that a higher GDP indicates that the
economy is better and there is more capital available for the bank to
provide services for. The corruption index is an example of a contra-
indication. This is because a country with a high level of corruption may be
extremely dangerous, expensive (especially with respect to hidden costs),
may violate bank policy and would be highly risky. It should be noted that
there are some attributes which could be interpreted as either positive or
negative – one example of this is “import duties,” which could be perceived
as a threat or opportunity. To properly account for these borderline factors,
a holistic approach is required, looking at the totality of the data and
making educated judgments. Furthermore, some factors may have quite
different effects in different countries, and for this reason it is important to
consult local experts before making any final decisions about expanding
into a new territory.
PESTEL Category: Each data factor was then assigned to one of the six
PESTEL categories. Godfrey provides the diagram below and describes the
six PESTLE categories on this blog.
Figure 10 Pestel Catagories
(Godfrey Blog entry)
Figure 9 Marrakesh (source: telegraph.co.uk)
METHOD
ORGANIZE / NORMALIZE
Page 12 © CMK Konsulting Inc.
The following descriptions of the PESTEL categories are described by
Godrey as well:
Political – Category for issues related to the political state in the proposed
country. Relevant to the general political situation, to import/exports and
political orientation with respect to support (or hindrance) to specific fields.
Economic – Obviously, for a bank, the overall economic situation and all
factors related to it are extremely important. This includes, but isn’t limited
to: current and projected stock and bond performance, bank and bond
interest rates, economic indicators, specific performance in important
industries, and many more.
Social – Everything in the social category describes the overall data of the
population. How and what they are doing, who they are, important changes
and issues in which the social state of the people are similar and different
to other countries.
Technological – What technology is available in the country being evaluated
and what are the general technological trends. This applies to all branches
of technology: personal computing, computer use, industrial use of
technology etc.
Environmental – This applies both to the overall environmental quality as
well as to the climatic conditions.
Legal – for issues related to legal issues in the proposed country. The legal
issues may pertain to general law, import/export law, legal support (or
hindrance) to specific fields, standards and regulations as well as the
overall political situation.
Norm
Figure 12 Normed Data (source: www.alwyncosgrove.com)
Figure 11 Hanoi (source: dilemma-x.net)
METHOD
NORMALIZE / DECIDE / ANALYSE
Page 13 © CMK Konsulting Inc.
The data categories collected are in many different units. In order to
evaluate them together, these data are all normalized to the same scale –
from 0 to 1 (or 0% to 100%). This method allows us to consider data with
different units (and different scales) together. An example of different data
types would be ‘population’ and GDP. Both of these values are measured in
different units (number of people) compared with (currency). The ranges
are different as well. By norming or scaling all of the categories from 0 to 1
(or 0% to 100%) we can use these values together.
Decide
The data is presented with several different views for all countries together
as well as in four global regions.
Africa: Tunisia, Morocco, South Africa, Algeria
Asia: China, India, Vietnam
Europe: Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania,
Poland, Romania, Slovakia, Slovenia, Turkey, Ukraine, Russia, Georgia
Latin America: Argentina, Brazil, Mexico
Analyse
The data is then analysed and several conclusions are presented. For each
region countries are presented as recommendations and indicated to avoid.
Figure 13 Decision (source: litemind.com)
DATA/ DECIDE
DATA/ DECIDE
Page 14 © CMK Konsulting Inc.
Data/ Decide
The data for all countries was analysed according to the method described
above in the following steps.
All the data was sorted into positive and negative factors coded red and
called threats and coded green and called opportunities.
Examples of positive factors are: Annual Growth Rate Industrial Value
Added, Business > total registered (#) by country, Income Receipts.
Examples of negative factors are: crime as a % of population, Firing
costs (weeks of wages), Closing a Business
Following this, the data was normalized so that the ranges of the factors
were between 0 and 1. This was done using the formula:
Normed Value =
Figure 14 Screenshot of data sorted into positive and negative factors (see excel file worksheet Transposed)
Value – Minimum Value
Maximum Value – Minimum Value
DATA/ DECIDE
DATA/ DECIDE
Page 15 © CMK Konsulting Inc.
The results after normalization appeared as follows in the Excel file:
Next, the threats and opportunities were averaged (threats averaged
separately to opportunities), and the Euclidean distance was calculated
using the following formula:
𝑑 = √(1 − 𝑜)2 + (0 − 𝑡)2
where o is the average opportunity value for a country, and t is the average
threat value for a country.
The ranked results were as follows:
Figure 15 Screenshot of normed data (see excel file worksheet Normalized)
Figure 16 Screenshot of averaged and ranked data
(see excel file worksheet Averaged & Ranked)
DATA/ DECIDE
DATA/ DECIDE
Page 16 © CMK Konsulting Inc.
The data was then sorted by rank:
Figure 17 Screenshot of averaged and ranked data (see excel file worksheet Sorted By Rank) (see
excel file worksheet Averaged & Ranked)
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
AlgeriaArgentina
Brazil
Bulgaria
China
Czech Republic
Estonia
Georgia
Hungary
India
Latvia
LithuaniaMexico
Morocco
Poland
Romania
Russia
Slovakia
Slovenia
S. Africa
Tunisia
Turkey
Ukraine
Vietnam
Figure 18 Screenshot of radar chart
(see excel file worksheet Radar
chart)
DATA/ DECIDE
DATA/ DECIDE
Page 17 © CMK Konsulting Inc.
A PESTEL analysis was also done and the PESTEL values were ranked.
Figure 19 Screenshot of PESTEL data (see excel file worksheet PESEL)
DATA/ DECIDE
DATA/ DECIDE / AFRICA
Page 18 © CMK Konsulting Inc.
Finally, the data was plotted on a scatter chart. Note that the individual
countries are not labelled. An ‘exploded’ version of this chart is
presented in the analysis section.
Africa
The ranks of the African countries are listed here:
Country Continent Avg. Opportunity
Score
Avg. Threat
Score
Euclidean
Distance Rank
Tunisia Africa 0.32 0.32 0.750 20
S. Africa Africa 0.36 0.43 0.770 21
Morocco Africa 0.32 0.38 0.776 22
Algeria Africa 0.30 0.43 0.822 24
Figure 20: Screenshot of scatter plot
(see excel file worksheet Scatter plot II)
DATA/ DECIDE
ASIA / EUROPE
Page 19 © CMK Konsulting Inc.
Asia
The ranks of the Asian countries are listed here:
Country Continent Avg. Opportunity
Score
Avg. Threat
Score
Euclidean
Distance Rank
China Asia 0.57 0.38 0.571 1
India Asia 0.45 0.42 0.694 9
Vietnam Asia 0.34 0.32 0.730 17
Europe
The ranks of the European countries are listed here:
Country Continent Avg. Opportunity
Score
Avg. Threat
Score
Euclidean
Distance Rank
Czech Rep. Europe 0.43 0.32 0.656 2
Estonia Europe 0.40 0.30 0.669 3
Poland Europe 0.40 0.31 0.679 5
Slovenia Europe 0.41 0.34 0.680 6
Slovakia Europe 0.38 0.28 0.680 7
Lithuania Europe 0.36 0.25 0.688 8
Latvia Europe 0.36 0.28 0.696 10
Hungary Europe 0.39 0.34 0.702 11
Bulgaria Europe 0.35 0.29 0.710 12
Georgia Europe 0.35 0.29 0.712 13
Russia Europe 0.41 0.41 0.716 15
Romania Europe 0.32 0.28 0.731 18
Turkey Europe 0.38 0.38 0.731 19
Ukraine Europe 0.33 0.39 0.779 23
Figure 21 Argentina City
(source: wallsave.com)
DATA/ DECIDE / ANALYSIS
LATIN AMERICA / ANALYSIS
Page 20 © CMK Konsulting Inc.
Latin America
The ranks of the Latin American countries are listed here:
Country Continent Avg. Opportunity
Score
Avg. Threat
Score
Euclidean
Distance Rank
Mexico Latin America 0.41 0.32 0.673 4
Brazil Latin America 0.45 0.46 0.716 14
Argentina Latin America 0.39 0.37 0.717 16
Analysis
The countries are presented together on the scatter chart on the following
page. This chart contains two axis to show both the level of opportunities as
well as the level of threats.
y axis – levels of threats
x axis – levels of opportunities
The chart is further divided into four quadrants with specific labels:
Abandon Ship: Low Opportunities and High Threats
Three Sheets to the Wind: Low Opportunities and Low Threats
Loose Cannons: High Opportunities and High Threats
Smooth Sailing: High Opportunities and Low Threats
Figure 22 Bratislava (source: grayline.com)
ANALYSIS
ANALYSIS
Page 21 © CMK Konsulting Inc.
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60
Abandon Ship
Three Sheets to
the Wind Smooth Sailing
Loose Cannon
S. Africa Algeria
Ukraine
Morocco
China
Brazil
Turkey
Argentina
Tunisia
Romania
Vietnam
Bulgaria Georgia
Latvia Slovakia
Lithuania
Estonia
Poland
Mexico Czech Rep.
India Russia
Hungary Slovenia
Average
Average
Africa Europe Asia Latin America
Page 21 © CMK KKonsulting Inc.
ANALYSIS
ALL COUNTRIES – ANALYSED TOGETHER
Page 22 © CMK Konsulting Inc.
All countries – Analysed Together
Recommendations:
Countries recommended for bank expansion: The results clearly
recommend expansion in four countries:
Estonia
Czech Republic
Poland
Mexico
Slovenia
Hungary
It is interesting that these countries are ranked two to four. The number
one ranked country is China, which has a very high positive ranking, but too
high level of threats to fall into the ‘smooth sailing quadrant.’
Countries recommended avoiding:
Basically, all countries that fall into the ‘Abandon Ship’ quadrant should be
avoided. These are:
S. Africa
Morocco
Ukraine
Algeria
Turkey
These countries have a low level of opportunities and a high level of
threats.
Figure 23 Kiew (source:
skyscrapercity.com)
ANALYSIS
ASIA / EUROPE
Page 23 © CMK Konsulting Inc.
Asia
The recommended countries for expansion in Asia are maybe China and
Vietnam. Neither country is in a positive quadrant. China has a very high
level of opportunities, but a high level of threats as well. Vietnam has low
opportunities, but low threats as well.
The PESTEL analysis shows that China is especially strong, as is well known,
in economy and social rankings. This would be extremely beneficial for
expansion, however it has strong negative rankings as well in
environmental and legal, the legal would make the expansion extremely
challenging. For Vietnam the PESTEL analysis does not show any strong
rankings in the positive area and indicates a very weak economy which
would be a challenge for expansion.
The countries with a low ranking that should be avoided in Asia are India
which has a much higher level of threats, while still having opportunities.
The PESTEL analysis for India is extremely interesting with very high
positive and negative rankings almost across the board. Positive high
ranking are for everything except for technology and negative rankings are
extremely high for everything but technology and environmental. This
would make India a huge challenge for a Bank.
Europe
The recommended countries for expansion in Europe are Hungary, Poland,
Estonia and Czech Republic. All are countries with high opportunities and
low threats.
The PESTEL analysis shows that Hungary is strong in technology and legal.
Hungary doesn’t have strong weaknesses, but technology and
environmental. For Poland, moderately strong PESTEL values are across
the board with the highest ranking in social. The most dangerous negative
rankings are technology and environmental. The Czech Republic shows the
highest values in technology and legal and negative values only in
environmental. All of these countries can be strongly recommended.
The countries with a low ranking that should be avoided in Europe are the
Ukraine which not only has a high level of threats and low opportunities, but
now is in the middle of a major crisis.
The PESTEL analysis of the Ukraine backs up the current political instability
with political, economic, social and legal. Obviously, the Ukraine should be
avoided until some stability is reached.
Figure 24 Warsaw (source: immonet.at)
ANALYSIS
LATIN AMERICA / AFRICA
Page 24 © CMK Konsulting Inc.
Latin America
The recommended countries for expansion in Latin America are Mexico
which has a high level of opportunities and low threats.
The PESTEL analysis for Mexico shows extremely high ranking for political
and no strong negative rankings.
The countries with a low ranking that should be avoided in Latin America
are Brazil which, in spite of hosting the World Cup, has a high level of
threats while still have some opportunities.
The PESTEL analysis for Brazil shows extremely high ranking for Political,
Economic and Social but many high negative rankings including Political
and Legal. Brazil has some potential, but there would be a long rank.
Africa
The recommended countries for expansion in Africa are perhaps Tunisia
which has low threats and low opportunities.
The PESTEL analysis for Tunisia doesn’t show positive ranking and high
negative rankings Political, Economic and Technology.
The countries with a low ranking that should be avoided in Africa are South
Africa, Morocco and Algeria, all of which have low opportunities and high
threats.
The PESTEL analysis for the low ranking African countries show almost no
positive rankings in any category and many of the number one negative
rankings including the worst environmental score for Algeria, the worst
Economic score for Morocco and the worst Social and Technology scores
for South Africa.
Figure 25 Tallinn (source: visitaretallinn.it)
Figure 25 Tallinn (source: visitaretallinn.it)
ANALYSIS
OPPORTUNITIES FOR FURTHER ANALYSIS
Page 25 © CMK Konsulting Inc.
Opportunities for Further Analysis
While this study was limited in scope, there are many more ways that the
country data could be analysed. In particular, an extensive sensitivity
analysis could be performed to determine which factors or groups of
factors have the greatest impact on the results, and provide quick answers
to “what if” questions. As an example, in the accompanying Excel workbook,
there is a sheet called PESTEL which shows how one could compute the
average opportunity and threat scores using an alternative technique:
rather than simply averaging across all of the factors with equal weights,
we recognize that there are, for example, many more political than
technological factors. We therefore compute the average of each PESTEL
group first, and then take the average of the averages to come up with the
final opportunity and threat scores. This provides a balancing out effect,
which results in more tightly clustered data points, and arguably a more
accurate reflection of each country’s strengths and weaknesses. A further
step might be to assign weights to each category, so that the model could
tell us, for example, how the countries would be ranked if we were 1.5 times
as interested in social opportunities as we are concerned about legal
threats.
Further ways to enhance the model may include:
Removing a certain number or percentage of top and bottom values
from the data set for each factor, in order to discount outliers.
Using median rather than mean values as “average” threat and
opportunity scores.
Using more complex statistical techniques, such as second order
analysis, to determine country rankings.
“Drilling down” into each continent, comparing only the countries in
that region with each other.
While CMK Konsulting stands behind the conclusions and recommendations
presented in this paper, we recommend that a second phase of work be
done to provide additional insights into bank expansion options. Our
employees have a great deal of experience doing this type of work, and we
look forward to continuing to collaborate with our valued client, Dr. Tavana,
should he choose to engage us for a Phase II of this project.
Figure 26 Analysis (source: economist.com)
SOURCES
SOURCES
Page 26 © CMK Konsulting Inc.
Sources
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(http://www2.econ.iastate.edu/faculty/bhattacharya/
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Buch, Claudia M. (1999) : Why Do Banks Go Abroad? - Evidence from
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(http://www.econstor.eu/bitstream/10419/17810/1/303447842.pdf)
Ernst & Young Publication (2013): Banking in emerging markets seizing
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seizing-oppurtunities-overcoming-challenges/$FILE/Banking-in-emerging-
markets-seizing-oppurtunities-overcoming-challenges.pdf
Godfrey, Elli St.George Blog Post: PESTEL Analysis- Snapshot of Your
World, December 6, 2011
(http://www.abilitysuccessgrowth.com/2011/12/pestel-analysis-snapshot-of-
your-world-2/)
Khan, Asim “Deciding to Go International” Business Management Group,
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(http://www.themanager.org/strategy/Deciding_to_Go_International.pdf) Figure 27 Kapstadt (source: boalingua.de)
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Figure 28 Delhi
(source: economictimes.com)