Masaryk University
Faculty of Economics and Administration
Department of Finance
and
Institute for Financial Market
European Financial Systems 2014
Proceedings
of the 11th
International
Scientific Conference
June 12-13, 2014
Lednice, Czech Republic
Suggested citation:
AUTHOR, A. Tittle of the paper. In: European Financial Systems 2014.
Proceedings of the 11th International Scientific Conference, Brno: Masaryk
University, 2014, pp. xx-xx. ISBN 978-80-210-6294-8.
Editors:
Oleg Deev
Veronika Kajurová
© 2014 Masarykova univerzita
ISBN 978-80-210-6294-8
All published papers have been reviewed before publishing. The proceedings have not
been amended or proofread. Editors are not responsible for the language used in the
papers.
Program Committee of the Conference
Chair: Ing. Petr Valouch, Ph.D. (Masaryk University)
Members:
 prof. Dr. Ing. Dana Dluhošová (VŠB – Technical University of Ostrava)
 prof. Ing. Eva Horvátová, CSc. (University of Economics in Bratislava)
 prof. Ing. Eva Kislingerová, CSc. (University of Economics, Prague)
 prof. Ing. Oldřich Rejnuš, CSc. (Masaryk University)
 doc. Ing. Jaroslav Sedláček, CSc. (Masaryk University)
 prof. Ing. Viktória Čejková, Ph.D. (Masaryk University)
 prof. Ing. Anna Majtánová, Ph.D. (University of Economics in Bratislava)
 doc. Ing. Peter Krištofík, Ph.D. (Matej Bel University, Banska Bystrica)
Organizing Committee of the Conference
Chair: Ing. Jan Krajíček, Ph.D.
Members:
 Ing. Veronika Kajurová
 Oleg Deev
Conference website: www.efs.econ.muni.cz
PREFACE
Dear readers,
It is my pleasure to introduce you a collection of papers from the 11th annual
international scientific conference The European Financial Systems 2014 organized
annually by Department of Finance of the Faculty of Economics and Administration,
Masaryk University in Brno, Czech Republic. This year's conference was focused
especially on the current issues related to the impacts of the financial crisis on financial
and non-financial institutions, new regulation rules and procedures on financial markets,
new accounting and tax challenges and trends and tendencies in banking and insurance
industry.
Because the collection of papers presents the latest scientific knowledge in this area, I
believe you will get a number of new insights usable both for your scientific, and
educational or practical activities. I would also like to express my conviction that we
meet each other in occasion of the 12th year of this conference held in 2015.
I wish you pleasant reading!
Petr Valouch
Chairman of the Program Committee
Session 8 „Financial Literacy and Decision Making“
Chair: Zuzana Brokešová (University of Economics, Bratislava)
Maroš Mikuš „Satisfaction of the Czech bank customers“
Jana Peliova „How Do Risk Free Investment Options Change our Decisions
Under Risk?“
Kristína Hudoková „Selected determinants of lifelong learning of
population in the Slovak Republic“
Bohuslava Doláková „Financial Literacy of Masaryk University Students in
"Financial Literacy“ Course"
Zsuzsanna Eszter Tóth „Using modern quality management tools to
improve finance and business university courses“
Zuzana Brokešová, Erika Pastoráková „The importance of financial literacy
in consumer´s optimal choice of insurance“
Tomáš Ondruška „Overconfidence as a Cognitive Bias and Its Implications
for Insurance Industry“
Martin Svoboda „ Financial Literacy of Masaryk University Students with
focus on Socially Disadvantages Citizens“
Satisfaction of the Czech bank customers
Lenka Gabčová1
, Valér Demjan2
, Anna Chochoľáková1
, Maroš Mikuš1
1
Tomas Bata University
Faculty of Management and Economics, Department of Enterprise Economics
Mostní 5139, 760 01 Zlín, Czech Republic
E-mail: l.gabcova@gmail.com
2
The Banking Institute of Praque
College of Banking, Department of Banking
Cesta na štadion 7, 974 01 Banská Bystrica, Slovak republic
E-mail: valer@valer.sk
Abstract: The aim of the article was to quantify and compare the overall level of
satisfaction of the Czech banking customers, the most important factors of satisfaction
and dissatisfaction of these customers and number of used banking products and banks
in 2014 compared to 2012. Research on the satisfaction of retail customers in the Czech
Republic was conducted through a questionnaire survey in 2012 on a sample of 323
respondents, and in 2014 on a sample of 292 survey participants. The research results
are evaluated through Pearson statistics. Our research confirmed that the Czech Republic
remains on a relatively low position with satisfying its bank customers with provided
services. The main reason for satisfaction was the ability to use electronic banking and
most important reason for dissatisfaction were high priced products and services.
Number of banking products used by the Czech banking clients did not change
significantly.
Keywords: commercial bank, bank customer’s satisfaction, satisfaction factors, factors of
dissatisfaction, Cross-selling index.
JEL codes: G21
1 Introduction
Customer satisfaction is an important factor in the performance and competitiveness of
banks. When analyzing the attributes of the banking client satisfaction, it is appropriate
to be based on the specific characteristics of commercial banking (Horvátová, 2013,
Chovancová, and Arendas, 2013, Cipovová, and Belás, 2012, Belás, Cipovová, Novák,
and Polách, 2012, Belás, and Polách, 2011, Horvátová, 2008, Belás, 2004), which
significantly affect business processes and shape attributes of bank customer
satisfaction.
Banking experience proves that achieving a reasonable rate of customer satisfaction
represents a challenge for the bank and it is a permanent process with varied results.
Bank customers in many countries show a considerable degree of dissatisfaction and
many banks admit that it’s necessary to increase the level of customer care.
In this article satisfaction of the Czech bank customers with the services of commercial
banks was examined, as well as the most important factors of satisfaction, respectively
customer dissatisfaction and use of banking products and banks in 2014 compared to
2012.
2 Important factors determining bank customer satisfaction
Customer satisfaction is a complex of various aspects, which operate in a coherent
manner and form attitudes of customers to the bank. In the process of forming of
customer satisfaction acting the economic factors, emotional attitudes and habits of
consumers. Customers‘ satisfaction determines their loyalty to the bank and willingness
to buy the bank’s products.
According to Chavan and Ahmad (2013) bank business increasingly depends on the
quality of the customer service provided and overall satisfaction of the customer.
Relationship marketing has become the most critical aspect to corporate banking
success.
Maddern, Maull, and Smart (2007) state, that the key elements in building customer
satisfaction are employee satisfaction and service quality. In this context Gounaris and
Boukis (2014) indicate, that the employee job satisfaction influences a customer´s
perception of quality and customer satisfaction. Many authors agree that it is the banks
skilled workers with high emotional quotient are the prerequisite for building loyalty
among clients. (Rostamy, 2009; Bidmead, 2007) Customers in business relationships
require a high degree of acceptance of their own needs from staff and accurate service
delivery. (Lages and Piercy, 2012)
According to Baumann, Elliott and Burton (2012) a combination of two factors, namely
emotional connection with the bank and economic benefits should protect the bank
against competition. In this context Wruuck (2013) submits, that the prices play a
central role for customer satisfaction and profitability. Satisfaction with products prices
does not automatically mean total and complete satisfaction, but it is one of the most
important preconditions. On the other hand Chen, Liu, Sheu and Yang (2012) argue that
people rarely try to get the best services; instead they seek only for a fair services and a
consistent treatment. In their presented study, the significant direct correlation between
a customer satisfaction and the fair approach has been proved.
According to Mandahachitara and Poolthang (2011) customer’s loyalty is very important,
because getting new customers also brings additional costs and vice versa with the
duration of customer relationship profitability of this relationship grows as well. Effective
tool for building true loyalty can be e.g. friendly approach to clients, availability of
banking products and services, intensive communication and activities in the field of
Corporate social responsibility.
On the contrary, Frearing and Minor (2013) in their study indicate, that in the
relationship with the customer there are phenomena that extend beyond loyalty. The
most outstanding is the effort to remain faithful. Despite the fact that the customer
exposed to daily offers from competition develops a conscious effort to remain as there is
a complete trust to the product from the client’s side. The second phenomenon is the
social ties obtained during use or consumption of the product. (although this second
factor was not confirmed as a follow-up research on the concept of satisfaction-loyalty).
The conclusion of this study is also surprising finding that high levels of satisfaction and
loyalty is not only associated with longtime customers. The highest levels of these factors
were declared by respondents in the early years of the relationship, means after one year
of contract. This also means that financial institutions may induce high levels of customer
satisfaction, loyalty and effort to remain with them for over more timely stages of mutual
relationship.
The hot topic is the analysis of the satisfaction of banks’ customers in context of online
banking. According to Liébana-Cabanillas, Munoz-Leiva and Rejón-Guardia (2013)
electronic banking has become an essential service to secure clients’ loyalty by ensuring
greater customer satisfaction and building stronger ties with them. Yang, Lu and Chau
(2013) report, that online channel service quality enhances service quality in the offline
channel. In this context Genser, Leeflang and Skiera (2012) indicate that online use
improves customer profitability by increasing customer revenue and decreasing cost to
serve. Authors found out that online customers conduct 2.50 more transactions per
month than do their offline counterparts and their customer profitability improves by
0.28 EUR per month.
3 Objectives, Methodology and Data
The aim of this article was to quantify and compare the overall level of satisfaction of the
Czech banking customers , the most important factors of satisfaction and dissatisfaction
of these customers and number of used banking products and banks in 2014 compared
to 2012.
Research on the satisfaction of retail customers in the Czech Republic was conducted
through a questionnaire survey in 2012 and then in 2014.
In our research, five scientific hypotheses were set. When establishing quantitative
criteria estimation techniques were used.
H1: The overall level of satisfaction of bank customers in the Czech Republic in 2014
compared to 2012 has not changed and is now less than 65%.
H2: Czech clients are the mostly satisfied with the availability of banking products and
services through electronic banking. Electronic banking is still the most important factor
of satisfaction for Czech customers. More than 60% of Czech clients are satisfied with
this factor.
H3: The most important factor of dissatisfaction of the Czech customers are high priced
banking products and services. The rate of dissatisfaction of Czech clients in regards to
prices is higher than 50%.
H4: The average value of CSI has increased by at least 10% in the last two years.
H5: Average number of banks being used by clients in the Czech Republic increased by at
least 10% in the last two years.
Within the questionnaire survey in 2012, in the Czech Republic a total of 323
respondents was approached, of which 37% were men and 63% were women. Age
structure of respondents was as follows: 32% were aged less than 30 years, 50% of
them were 30-50 years old and 18% were over 50. The education level of respondents
was as follows: 1% had primary education only, 62% had secondary education and 37%
were university educated bank customers.
In 2014, there were received responses from 292 respondents, of which 43% were men
and 57% were women. The age structure of those respondents was as follows: 38% of
respondents were aged under 30 years, 43% were aged from 31 to 50 years and 19% of
them were customers over 50. The education level of respondents was as follows: 3%
had primary education, 52% had secondary education and 45% were university educated
bank customers.
Established scientific assumptions in each table were examined through Pearson
statistics. P-value less than 5% leads to the rejection of the null hypothesis. Part of the
quantitative analysis is the use of indicators and descriptive statistics such as weighted
arithmetical average and the percentage figures.
4 Results and Discussion
Table 1. presented the results of overall satisfaction of the Czech bank customers in 2014
compared to 2012.
Table 1 Overall satisfaction of bank customers in the Czech Republic (CR)
Are you satisfied with
bank products and
services provided to you?
CR 2012
v %
CR v 2014
v %
p-value
1. Yes 62.23 66.44 0.3157
2. No 26.32 15.07 <0.01
3. I don´t know 11.45 18.49 0.0192
χ2=14.8059
p-value<0.01
The proportion of respondents satisfied with banking services can be considered to be the
same as it was in 2012 (p-value=0.3157). However, the structure of the responses
differs, because there was a shift of dissatisfied customers to a group of „do not know“.
On the basis of proportional test it cannot be claimed (p-value=0.6751), that this figure
is less than 65%.
According to the results of our research, the overall satisfaction rate of the Czech
customers is at the European average level. These results are comparable with some of
the published results of the satisfaction of bank customers. For example, in the United
Kingdom in 2012 the average satisfaction level of bank customers was 62%.
(www.dailymail.co.uk). According to the results of research conducted by Deloitte (2012,
b) in the Czech Republic, Poland and Slovakia the overall satisfaction rate is 50% and
more. The overall satisfaction rate of bank customers in Slovakia in 2012 was at 61%.
One reason for the relatively low satisfaction of bank customers in the Czech Republic is
also a way of managing the retail staff. Managers of private banks put intense pressure
on front office employees, which gets translated into an incorrect approach to clients
(Belás, 2012) and the level of moral attitudes of bank employees in the sales process is
relatively low (Belás, Burianová, Cipovová, and Červenka, 2013).
In Table 2 are compared the reasons for satisfaction of the Czech banking customers in
2014 compared to 2012
Table 2 Reasons for customer satisfaction in the banking sector of the Czech Republic
What satisfies you the most in the bank? (you can
provide up to 3 answers)
CR
2012
in %
CR
2014
in %
p-value
1. quick service in the branch 13.93 11.64 0.4670
2. the possibility of using electronic banking 74.61 77.74 0.4161
3. quality products and services 17.65 17.81 1.0000
4. convenient and friendly service in a branch 17.09 23.29 0.0620
5. availability of the branch 49.54 52.40 0.5299
6. developed network of ATMs 40.25 44.18 0.3660
7. other (account for free) 0 5.14 The
most significant factors of customer satisfaction in our study were the availability of
banking products and services through electronic banking, branch availability and
extensive network of ATMs.
Electronic banking is still the most important factor of satisfaction for Czech clients. Level
of satisfaction of Czech bank customers was not significantly altered (p-value=0.4161).
More than 60% of Czech customers are satisfied with this factor, what was confirmed by
the value of the test criteria (p-value<0.01).
According to research conducted by Ernst & Young (2012) 78% of banks’ clients in the
Czech Republic control their bank accounts by using the internet. This survey indicates
that despite the relatively high popularity of electronic banking more than one third of
clients keeps giving priority to their personal visit to a branch, which was confirmed in
our study, where only 52,40% of respondents in the Czech Republic said they were most
satisfied with the availability of the branch. For comparing it could be stated, that in
Slovakia this option was mentioned by 67% of respondents back in 2012. (Belás,
Burianová, Cipovová, and Červenka, 2013)
According to Liébana-Cabanillas, Munoz-Leiva and Rejón-Guardia (2013) user satisfaction
levels with online banking have increased in recent years, reaching more than 80%.
Authors also indicate, that online banking also was favored over other channels that
customer have at their disposal (55% selected the online channel as one that satisfied
them the most, while 28% chose branches, 13% chose ATMs, and only 2% chose call
center and mobile banking as the channel that satisfied them most.
In Table 3 are compared the reasons for dissatisfaction of banking customers in the
Czech Republic in 2014 compared to those from the year 2012.
Table 3 Reasons for dissatisfaction of bank customers in the Czech Republic
What dissatisfies you the most in the bank?
(you can provide up to 3 answers)
CR
2012
in %
CR
2014
in %
p-value
1. slow service in a branch 22.60 20.21 0.5326
2. poor quality of electronic banking 1.86 4.11 0.1517
3. high price of products and services 65.33 66.78 0.7677
4. poor accessibility of branch 9.60 5.38 0.0770
5. impersonal approach 13.62 14.73 0.7823
6. low acceptance of my needs 13.31 18.15 0.1237
7. other (arrogance of personnel, misuse of
financial illiteracy of clients)
0 16.78 <0.01
Structure responses in 2014 did not differ from responses in 2012. Most important factor
of discontent of Czech clients are still high prices of banking products and services. The
proportion of respondents who stated the discontent of high prices for banking products
and services is higher than 50%, as it was truly confirmed by the value of the test
criteria (p-value=0.01)
For comparison, it could be mentioned, that in Slovakia in 2012 this ground of
dissatisfaction was reported by 63% of respondents. (Belás, Burianová, Cipovová,
Červenka, 2013)
According to research by Ernst & Young (2012) a definite number one impulse for change
of bank is the amount of bank charges. 69% of the Czech bank clients would change
their main operational bank because of high costs (compared to a European average of
53%). Along with that bank clients in the Czech Republic and Slovakia consider many
bank fees as absurd. For example in 2013 about 40,000 bank customers from the Czech
Republic and Slovakia provided information, that the most ridiculous bank charges were
account management fee to the mortgage or consumer loan. Previously customers
identified the following fees as absurd: fee for early repayment of loan, fee for cash
withdrawal and deposit into their own bank account, fee for excessive deposit, and fee
for money withdrawal from bank’s owned ATMs and charge for incoming payments.
(http://www.bankovnipoplatky.com).
In Table 4 indicated the number of used banking products in the Czech Republic in 2012
and 2014. Data structure in Table 4 does not provide evidence that would change the
number of products used for the past two years. It also confirmed our hypothesis that
the average value of CSI has increased by at least 10% in the last two years. The
average value of CSI as per our data is relatively low, indicating that banks in the Czech
Republic have quite interesting sales opportunities.
Table 4 Number of banking products being used
How many banking products
(current account, consumer credit,
mortgage etc.) you are currently
using?
1
product
2
product
s
3
products
4
products
and
more
Number of respondents in 2012 in % 26.00 35.91 23.22 14.86
Number of respondents in 2014 in % 27.74 38.36 20.21 13.69
p-value 0.6940 0.5873 0.4200 0.7670
χ2=0.1546
The average value of
CSI*2012=2.27 CSI*2014=2.20
Note: * CSI (Cross Selling Index – number of products/1 bank client)
In Table 5 indicated the number of banks being used in the Czech Republic in 2012 and
2014.
Table 5 Number of banks used in the Czech Republic
Indicate the number of banks whose
products and services you use.
CR
2012
v %
CR
2014
v %
p-value
1 bank 48.30 55.48 0.0839
2 banks 36.53 33.22 0.4392
3 and more banks 15.17 11.30 0.1968
χ2=3.7332/p-value=0.1546
NBI2012=1.67/NBI2014=1.56
Structure of responses is not significantly different in the year 2014 compared to answers
back in 2012 (p-value=0.1546). The largest growth was registered for the answer of
using of one bank. This change is not significant (p-value=0.0839), however indicates
that to our research in 2014 have joined relatively more respondents who indicated that
they utilize services of only one bank.
5 Conclusions
The results of our research confirm the partial validity of the Hypothesis 1. The overall
level of satisfaction of bank customers in the Czech Republic in 2014 in comparison to
2012 did not change. However, we did not find evidence that it is less than 65%.
Our research has confirmed the validity of Hypothesis 2. Electronic banking is the most
important factor of satisfaction for the Czech customers. More than 60% of the Czech
banking customers are satisfied with this factor.
The most important factor of dissatisfaction of the Czech clients are still high prices of
banking products and services. The proportion of respondents who provided the answer
of dissatisfaction with high prices of products and services is higher than 50%. The
research thus confirmed the validity of Hypothesis 3.
Surprising finding was that the CSI value has decreased, thereby Hypothesis 4 was
rejected. Average number of banks being used by clients has slightly fallen, as a result of
this, the Hypothesis 5 had to be rejected as well.
Acknowledgments
Authors are thankful to the Internal Grant Agency of FaME TBU No. 005/IGA/FaME/2014:
Optimization of parameters of the financial performance of the commercial bank, for
financial support to carry out this research.
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How Do Risk Free Investment Options Change our Decisions Under
Risk?
Jana Peliova
University of Economics
Faculty of National Economy, Department of Finance
Dolnozemska cesta 1, 85235 Bratislava, Slovakia
E-mail: jana.pelioval@euba.sk
Abstract: Financial decisions of investors do not essentially depend only on the amount
of expected reward and it´s probability but as well as on the probability and amount of
potential loss. Appropriate presentation of investment options may influence investors'
decisions. The article aims to test the sensitivity of perception of economic subjects to
the probability and amount of potential losses in decisions under risk. We conducted a
laboratory experiment with 67 students of the University of Economics in Bratislava. The
results of economic experiment show that the introduction of a risk-free investment
opportunity increases the attractiveness of less risky option on the one hand but on the
other hand it motivates some of the risk-averse subjects to choose a risk-free
alternative. While approximately same share of population of male and female subjects
changed their decision after implementation of risk-free option, results support higher
level of risk-aversion among female population.
Keywords: decision, risk, investment
JEL codes: G02
1 Introduction
We can find questions about rationales for decision making of economic agents in Smith's
Theory of moral sentiments. He realized that people are more sensitive to decrease in
their wealth, as if their wealth increases. This range of problems is addressed in the area
of economic research in behavioral economics. It is based on the assumption that
economic agents do not behave rationally, and their decisions are often influenced by
factors other than utility maximization (Leonard and Hsu, 2001, Wilkinson- Klaes, 2012).
Two basic approaches to decision making are based on the theory of expected utility and
prospect theory.
The existence of gender differences in willingness to undertake risks has been
documented in a number of large scale surveys and in incentivized experiments (Byrnes,
Miller and Schafer 1999, Eckel and Grossman 2008, Croson and Gneezy 2009). These
studies generally conclude that men are more willing to take risks than women are.
Focusing on financial risk-taking, Charness and Gneezy (2012) reanalyze data from 15
experiments in which subjects decide on how much Money to invest into a risky asset as
opposed to cash. They conclude that there is a robust gender difference with men
investing more of their money into the risky asset in comparison to women. Market
specific survey on insurance market done by Pastorakova et al (2013) supports previous
findings on data from central Europe.
Fehr-Duda and de Gennaro (2006) used for analysis abstract and contextual
environment. They stated that gender differences in risk taking may be due to
differences in subjects’ valuations of outcomes or due to the way probabilities are
processed. Published results of their experiment indicate that men and women differ in
their probability weighting schemes; however, they did not find a significant difference in
the value functions. Women tend to be less sensitive to probability changes and also tend
to underestimate large probabilities of gains to a higher degree than do men, i.e. women
are more pessimistic in the gain domain. The combination of both effects results in
significant gender differences in average probability weights in lotteries framed as
investment decisions. The analysis concludes that women’s relative insensitivity to
probabilities combined with pessimism may indeed lead to higher risk aversion.
Such findings have an important message for real-world investment decisions. Due to
increasing education levels and labor force participation of women, together with their
longer lifespans relative to that of men, investment decisions of individuals, households
or firms are more and more often done by women (Jianakoplos and Bernasek 1998). If
women indeed are more risk-averse investors than men, this will have important
implications for equilibrium portfolio allocations, wealth levels, asset prices and rates of
return.
Financial decisions of investors do not essentially depend only on the amount of expected
reward and it´s probability but as well as on the probability and amount of potential loss.
The article aims to test the sensitivity of perception of economic subjects to the
probability and amount of potential losses in decisions under risk.
The remaining part of the paper is structured as follows. Section 2 describes the
experimental design we used and data collected. Section 3 presents the results and
section 4 concludes.
2 Methodology and Data
To elicit the risk preferences we conducted controlled experiment using a paper-based
questionnaire. We used an economic laboratory experiment conducted at the University
of Economics in Bratislava. 67 subjects participated in filling out the questionnaires; all of
them were master students in Finance program within Experimental economics course.
Gender structure of subject pool was significantly favoring female subjects (49) to male
subjects (18) copying the population structure of finance students. Subjects were
answering questions anonymously and payments were hypothetical (students
participation was considered for final grade). Questions presented in questionnaire are
listed in Table 1. Each question was printed on separate paper and answers were
collected by experimenter one by one and followed by demographic survey.
Table 1: Experimental questions
1. Imagine that your account is € 1,000 and you must choose one of the following
investment options for your money:
a) 30% probability of loss of € 300 and a 70% probability of profit € 500
b) 10% probability of loss of € 900 and a 90% probability of profit € 389
c) Random selection from a) or b) – choice does not matter
After marking the answers turn the paper blank side up, and place it on the opposite side
of the table.
2. Imagine that your account is € 1,000 and you must choose one of the following
investment options for your money:
a) 30% probability of loss of € 300 and a 70% probability of profit € 500
b) 10% probability of loss of € 900 and a 90% probability of profit € 389
c) 30 € with probability 100 %
After marking the answers turn the paper blank side up, and place it on the opposite
side of the table.
Source: Excerpt from paper based laboratory experiment questionnaire.
Expected values for options a) and b) in the first question are equal. Based on the
Expected Utility Theory we can calculate:
( ( (1)
( ( (2)
Any economic subject should be indifferent in deciding between option a) and option b).
Prospect theory proved that into the decision also psychological factors are entering. In
formulating this question, we therefore assumed that subjects will choose option A or B,
depending on whether their risk aversion is more influenced by the probability of
outcome (profit or loss), or the absolute level of potential income. For subjects that do
not have preference, we introduced the option c), to limit the forced choice of subjects
and to increase the explanatory power of the results.
In the second question, we replaced the indifferent c) option from the first question, by
guaranteed gain of € 30. This option provides significantly lower expected value than the
EV of options a) and b), but no risk of loss. This option also makes the decisions of
subjects more real as people often have to choose between financial products that are
more risky and have higher expected value and the options that are guaranteed with a
much lower expected value. The amount of € 30 was set so that it is lower than the
expected value of unguaranteed options and that the difference between risky and safe
options is large enough to compensate the profit guarantee.
3 Results and Discussion
The data consist of 134 decisions (two decisions from each subject). Aggregate behavior
is displayed graphically in Figure 1 and Figure 2 and summarized in Table 2. According to
the results presented in Table 2 none of the subjects in our sample has chosen option c)
in Question 1. All participating students had clear preferences and 54 of them (over 80
%) decided for option a) that has a higher probability of loss and at the same time a
lower amount of possible loss.
Table 2: Decision made by subjects
Option Question 1 Question 2
a) 54 80,6 39 58,21
b) 13 19,4 18 26,87
c) 0 0 10 14,93
Total 67 100 % 67 100 %
Source: experimental data
In Question 2 the option c) was replaced with a guaranteed gain of € 30, but this gain
significantly lower profit than the risky options a) and b), € 500 and € 389. In Question 2
the majority of subjects decided for option a), but it was only 58,21 % of all students.
For option c) decided 10 participants. The inclusion of safe option paradoxically increased
the proportion of students who chose option b) (the option with less probability of loss,
but with the higher absolute amount of loss). This trend was more noticeable in subgroup
of men, when the portion was doubled (from 22,22 % in Question 1 to 44,44 % in
Question 2)
Of the 10 subjects who decided for option c) in Question 2, eight of them were preferring
option a) in Question 1 and two of them option b). Thus the majority of the subjects who
chose option c) in Question 2 have opted for a) in Question 1. We assume that these
subjects were not deciding according to the probability of loss and in this respect they
considered option a) in question 1 as less risky.
Table 3: Decision made by male and female subjects
Option
Question 1 Question 2
Male % Female % Male % Female %
a) 14 77,78 40 81,63 9 50 30 61,22
b) 4 22,22 9 18,37 8 44,44 10 20,41
c) 0 0 0 0 1 5,56 9 18,37
Total 18 100% 49 100 % 18 100 % 49 100 %
Source: experiment data
Option a) (30 % probability of loss of € 300 and a 70 % probability of profit € 500) is
perceived as less risky than option b), even though the expected values of both options
are the same. On this basis, we can summarize that subjects in this case are more
sensitive to the amount of potential loss then to the probability of its occurrence.
The results presented in Table 3 express that the distribution of decisions in Question 1
between male and female subject is similar (for option a) decided slightly more female
(81,63 %) than male subjects (77,78 %) in the studied sample).
Figure 1: Distribution of decisions in Question 1
Source: Data from experiment; 1 = option a) 2 = option b) 3= option c)
Comparing Figure 1 and 2 we come to the interesting finding that while 9 women who
chose in the second question option c), was only 7 choosing in the first question you
option a) and 2 who decided for b). Within male subjects, there was a change decisions
between options more often in favor to move from a) to b) (4 men) than from a) to c) (1
man).
Figure 2: Distribution of decisions in Question 2
Source: Data from experiment; 1 = option a) 2 = option b) 3= option c)
0
20
40
60
80
Percent
.5 1 1.5 2
Options
Male
0
20
40
60
80
Percent
.5 1 1.5 2
Options
Female
0
20
40
60
Percent
.5 1 1.5 2 2.5 3
Options
Male
0
20
40
60
Percent
.5 1 1.5 2 2.5 3
Options
Female
4 Conclusions
Financial decisions of investors do not essentially depend only on the amount of expected
reward and it´s probability but as well as on the probability and amount of potential loss.
The existence of gender differences in willingness to undertake risks has been
documented in a number of papers, both experimental and large scale surveys. In most
of the analysis females are considered to be more risk averse, e.g. Fehr-Duda and de
Gennaro (2006) conclude that women’s relative insensitivity to probabilities combined
with pessimism may indeed lead to higher risk aversion.
Increasing education levels and participation of women in labor force, together with their
longer lifespans relative to that of men, investment decisions of individuals, households
or firms are more and more often done by women. Due to this fact, we analyzed in this
article gender differences in decisions under risk. To simulate real world we conducted
laboratory experiment.
The results of economic experiment report that the introduction of a risk-free investment
opportunity increases the attractiveness of option with lower probability of loss on the
one hand but on the other hand it motivates some of the risk-averse subjects to choose a
risk-free alternative. While approximately same share of population of male and female
subjects changed their decision after implementation of risk-free option, results support
higher level of risk-aversion among female population. Introduction of risk free option
increased number of male subjects deciding for option b) in Question 2. Female decided
to change their decision in Question 2 in favor of safe option changing from the option
with existing probability of loss (7 of them from option a) and 2 from option b)).
References
Byrnes, J.P., Miller, D.C., Schafer, W.D. (1999). Gender differences in risk taking:A metaanalysis,
Psychological Bulletin, vol.125 (3), pp. 367–383.
Charness, G., Gneezy, U. (2012). Strong Evidence for Gender Differences in Risk Taking,
Journal of Economic Behavior & Organization, vol. 83 (1), pp. 50–58.
Croson, R., Gneezy, U. (2009). Gender Differences in Preferences, Journal of Economic
Literature, vol. 47 (2), pp. 448–474.
Eckel, C. C., Grossman, P. J. (2008). Men, Women and Risk Aversion: Experimental
Evidence, In: Handbook of Experimental Economics Results, Vol. 1, Elsevier, 2008,
chapter 113, pp. 1061–1073.
Fehr-Duda, H., de Gennaro, M. Schubert, R. (2006). Gender, Financial Risk, and
Probability Weights, Theory and Decision, vol. 60(2-3), pp. 283-313.
Jianakoplos, N. A. Bernasek, A. (1998). Are Women More Risk Averse?, Economic
Inquiry, vol. 36 (4), pp. 620–630.
Leonard, T. and Hsu, J.S.J. (2001). Bayesian Methods. Cambridge: Cambridge University
Press.
Pastorakova, E. et al. (2013). Rodové rozdiely vo finančnom rozhodovaní v oblasti
životného poistenia na Slovensku, Ekonomický časopis, vol. 61(1), pp. 82-100.
Wilkinson, N., Klaes, M. (2012). An Introcuction to Behavioral Economics, Palgrave
Macmillan.
Selected determinants of lifelong learning of population in the
Slovak Republic
Kristína Hudoková, Anna Schultzová, Marcela Rabatinová
1
University of Economics in Bratislava
Faculty of National Economy, Department of Finance
Dolnozemská cesta 1, 852 35 Bratislava, Slovak Republic
E-mail: kristina.hudokoval@euba.sk, anna.schultzova@euba.sk, marcela.rabatinova@euba.sk
Abstract: The contribution is focused on the analysis of lifelong learning and training
programs aimed at the residents of the Slovak Republic. The main reason for the
examination of this issue is the fact that sustainable development, competitiveness and
dynamically developing economy is nowadays almost unachievable without increasing the
educational level of human capital. The aim of this paper is to explore dependencies and
the impact of selected determinants of lifelong learning at higher population's ability to
assert themselves in the labor market. We will examine this dependency through the
econometric method - regression analysis. In this paper statistical data will be used to
evaluate the most probable regression model of selected determinants of lifelong learning
in the Slovak Republic. The output will be the assessment of the extent of importance of
selected indicators on the lifelong learning of population.
Keywords: determinants of further adult education, regression analysis, lifelong learning,
Gretl
JEL codes: I20, I25, H52
1 Introduction
Lifelong learning is increasingly becoming the dominant theme of education and
vocational training policies in all developed industrialized countries. Increasing the
educational level of human capital is necessary for achieving a sustainable development,
competitiveness and dynamically developing economy. For governments, the concept of
lifelong learning is an overarching policy framework which offers solutions to a number of
common economic and social problems (Rubenson, 2011). Despite the fact that lifelong
learning relates to all levels and forms of education, in the context of the development of
the knowledge-based society, a specific interest is put on the education of the adult
population. Support of further education of the adult population is perceived, in the
context of global changes and their consequences such are, for example, the labor
market changes, technological changes, demographic changes or economic crisis.
Education and vocational training belong among the highest political priorities of all the
Member States in the European Union, not just the Slovak Republic. Obtaining the new
knowledge, skills and abilities as well as their continuous updating is considered to be the
assumption of personal development of all citizens and their active participation in all
aspects of society through integration into the labor market. Education of the adult
population is more important nowadays because we are in an era of globalization
characterized by rapid changes, technological progress and integration. Learning provides
to adults new knowledge and skills, which does not only improve the quality of their own
lives, but also leads to the benefit of their families, community and society. Lifelong adult
education also plays an important role in reducing poverty, promoting sustainable
procedures in the field of environment and not least in improving the level of health and
nutrition.
More than one third of the EU population in the age range between 25 and 64 years
participate in formal or non-formal education or vocational training. In general, young
people participate more in further education, and there are only minor differences
between men and women. Non-formal education and vocational training, in contrast to
formal education, is characterized by less time-intensive courses. This is probably the
main reason for higher participation of adult population in non-formal education. The
majority of non-formal education and vocational training activities is related on the work
of adult learners (i.e. job-related learning activities). In the Slovak Republic, in 2011 (the
latest figure available from Eurostat), the proportion of job-related activities of nonformal
education and vocational training was 90.5%. However, there are difficulties
related to participation in further education and vocational training, such as work
schedule or family responsibilities.
2 Methodology and Data
Our contribution is focused on the compilation of the most probable regression model of
selected determinants of lifelong learning in the Slovak Republic. For examining the
dependencies and impact of selected determinants of lifelong learning we have used the
multiple regression model in the econometric program Gretl (Gnu Regression,
Econometrics and Time-series Library). The analyzed data are obtained mainly from
Eurostat. The period of years monitored is from 2004 to 2013. We have chosen the time
series as a dataset structure in the Gretl program. When using the time series for the
estimation of the regression function, it is necessary to calculate with the existence of the
apparent correlation, which is caused by the identical or similar trend of the time
trajectories of the non-stationary variables (Budayová, Bolek, Šupšák, 2013).
Model Specification
The multiple regression model works with more than one explanatory variable. The
general form of this model is as follows:
i=1, 2, …, N (1)
where index i represents the individual observations,
index k = 1, 2, ..., K represents the individual explanatory variables and
β0, β1, ..., βK are the parameters to estimate (Bil, Němec, Pospiš, 2009).
To estimate the unknown model parameters we have used the estimation method
Ordinary Least Squares, so-called OLS model. To assess the quality of the model we have
used the coefficient of determination R2
, which shows what proportion of the variability of
dependent variable we managed to explain with the considered regression dependence.
Coefficient of determination R2
is defined as the ratio of Explained Sum of Squares (ESS)
and Total Sum of Squares (TSS) (Lukáčik, Lukáčiková, Szomolányi, 2011):
(2)
The coefficient of determination can take values between 0 (weak fit) and 1 (good fit),
(0 ≤ R2
≤ 1), respectively in percentage terms from 0% to 100%:
 value of the coefficient of determination R2
= 0, i.e. estimated econometric model
does not explain any part of the variability of selected dependent variable;
 value of the coefficient of determination R2
= 1, i.e. estimated econometric model
explains 100% of the variability of selected dependent variable (Soukiazis, 2013).
Lifelong Learning Participation
In the introduction we have mentioned that mainly young people are participating in
further, respectively lifelong learning. To gain a better overview, we have considered that
it is necessary to give a more detailed view of the development on the amount of
participants in the lifelong learning, namely the non-formal education and vocational
training in the Slovak Republic for the years 2004 - 2013 (see Figure 1). The monitored
group, in this case, were the adults in two age groups, namely 15-24 years and 25-29
years, simultaneously with breakdown by sex.
Figure 1 Participation in non-formal education and training by sex and age
Source: own processing based on data from Eurostat available at www.epp.eurostat.ec.europa.eu
As is apparent from Figure 1, there are clear signs of improvement in the reduction of
differences between men and women. Participation of young adults in non-formal
education and vocational training is starting to have an increasing tendency again, after a
period of economic crisis.
3 Results and Discussion
OLS model with multiple selected variables
Before estimating the parameters of a linear model with multiple variables we have
verified the suitability of selected variables by using a statistical correlation coefficient
extent - we have done an analysis of correlation between variables.
Table 1 Correlation Coefficients, using the observations 2004 – 2013
5% critical value (two-tailed) = 0,6319 for n = 10
FED_NFE ACT UNE INAC
1,0000 0,9291 0,9239 0,9595 FED_NFE
1,0000 0,7820 0,8105 ACT
1,0000 0,9021 UNE
1,0000 INAC
Source: own processing in Gretl based on data from Eurostat available at
www.epp.eurostat.ec.europa.eu
The highest calculated value 0,9595 of correlation coefficient between a pair of variables
INAC and FED_NFE (the explanatory note below) confirms that among these variables
there is a strong direct linear correlation. The lowest calculated value of the correlation
coefficient between variables UNE and ACT, equal to the value 0,7820, this shows that
between the given variables there is a linear dependence (correlation), but weaker than
between INAC and FED_NFE.
Then we have specified the dependent variable FED_NFE (overall participation in formal
and non-formal education and vocational training) and independent variables:
 ACT - economically active population - comprises all persons of either sex who
represent the labor supply for the production of economic goods and services,
 UNE - unemployed - the proportion of the labor force that is without work, but is
available and looking for a job,
 INAC - inactive persons - people who are not classified as employed or
unemployed are defined as inactive (http://stats.oecd.org/glossary/, 2014).
0
1
2
3
4
5
6
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Participationratesin%
males 15-24 yr
females 15-24 yr
males 25-29 yr
females 25-29 yr
15-24 yr
25-29 yr
After that we have done a linear model estimation of parameters in the Gretl program
using the Ordinary Least Squares estimator, so-called OLS model.
Model 1 OLS, using observations 2004-2013 (T = 10)
Dependent variable: FED_NFE
HAC standard errors, bandwidth 1 (Bartlett kernel)
Coefficient Std. Error t-ratio p-value
const 2,37193 0,321272 7,3829 0,00032 ***
ACT 0,72395 0,0594227 12,1831 0,00002 ***
UNE 0,718061 0,198928 3,6097 0,01124 **
INAC 0,205256 0,0195498 10,4991 0,00004 ***
Mean dependent var 14,65000 S.D. dependent var 1,641984
Sum squared resid 0,129668 S.E. of regression 0,147008
R-squared 0,994656 Adjusted R-squared 0,991984
F(3, 6) 1566,167 P-value(F) 4,54e-09
Log-likelihood 7,537420 Akaike criterion -7,074841
Schwarz criterion -5,864500 Hannan-Quinn -8,402581
rho 0,484833 Durbin-Watson 0,994101
Source: own processing in Gretl based on data from Eurostat available at
www.epp.eurostat.ec.europa.eu
At the beginning it is necessary to perform verification of the econometric model.
Statistical verification includes testing the significance of individual parameters, testing
the significance of the model as a whole, evaluation of the coefficient of determination
and testing the fulfillment of linear model assumptions (eg. heteroscedasticity test,
autocorrelation test, multicollinearity test). First we have tested the significance of
individual parameters using the Student's t-statistics. We have formulated a pair of
hypotheses:
H0: β0 = 0 against H1: β0 ≠ 0,
We have found, that the calculated t-statistic parameter β0 in absolute value (|7,3829|)
is greater than the critical value t0,025(6) = 2,44691, so we can reject the null hypothesis
and conclude that the parameter β0 is on the 5% significance level statistically
significant. For all other parameters, result of the comparison is the same - we reject the
null hypothesis, i.e. on the 5% significance level the parameters are statistically
significant:
Table 2 Comparison of t-statistics and critical value
Parameter t-statistics Comparison
Critical
value
Result
ACT |12,1831
|
> 2,44691 reject H0
UNE |3,6097| > 2,44691 reject H0
INAC |10,4991| > 2,44691 reject H0
Source: own processing; results from Gretl based on data from Eurostat available at
www.epp.eurostat.ec.europa.eu
Subsequently, we have tested the significance of the model as a whole using the
F-statistics by comparing the calculated F-statistics with the critical value F0,05(3,6). First
we have formulated a pair of hypotheses:
H0: β1 = β2 = β3 = 0 against H1: ∃ β1 ≠ 0, pre i = 1, …, 3
Thereafter, using the program Gretl, we have found the critical value of F-statistics at
significance level α = 0,05 and degrees of freedom 3 and 6. The searched critical value
was 4,75706. By comparing of both obtained values we have found, that the calculated
F-statistic is greater than the critical value F0,05(3,6):
F = 1566,167 > 4,75706 = F0,05(3,6).
Therefore, we can reject the null hypothesis and conclude that the model as a whole (at
5% significance level) is statistically significant. This conclusion is confirmed by the
probabilistic value P-value (F), because its value of 4,54e-09
is less than 0,05 and
therefore we can reject the null hypothesis at 5% significance level.
For the reason to avoid the heteroskedasticity problem, we have used the method of the
HAC estimator in Model 1 (robust standard errors).
Table 3 Results from White's test for heteroskedasticity
Hypotheses Reject H0 if
Results from
Gretl
Result
Hetero-
skeda
sticity
H0: β1 = β2 = β3 = 0
(homoskedasti
city)
H1: β1 or β2 or β3 ≠
0
(heteroskedast
icity)
TR2
>χ2
(p)
or
p-
value<0,05
TR2
=5,455033
<
χ2
(6)=12,5916
and also
all p-
values>0,0
5
Not
r
e
j
e
c
t
H
0
Source: own processing; results from Gretl based on data from Eurostat available at
www.epp.eurostat.ec.europa.eu
The high coefficient of determination R2
= 0,994656 means that the estimated
econometric model explains 99,46% of the variability of the selected dependent variable.
On the other hand, this high value may indicate a high multicollinearity between
variables. For this reason, we have calculated the Variance Inflation Factor (VIF) with
Gretl, which should not be greater than 10. It is given by the formula:
(
j=1, 2, 3, …, k (3)
Table 4 Detection of multicollinearity by using the Variance Inflation Factor
Variable VIF Result
ACT 3,037 < 10 No
collinearity
problem
UNE 5,599 < 10
INAC 6,339 < 10
Source: own processing; results from Gretl based on data from Eurostat available at
www.epp.eurostat.ec.europa.eu
Table 5 Results from Breusch-Godfrey test for first-order autocorrelation
Hypotheses Reject H0 if
Results from
Gretl
Result
Autocorrelation
(Rho = 0,484833
indicates
positive
autocorrelati
on)
H0: β1, β2, β3 = 0
(no
autocorrelat
ion)
H1: β1, β2, β3 ≠ 0
(autocorrela
tion)
TR2
>χ2
or
p-
value<0,05
TR2
=
4,808908
>
χ2
(1)=
3,84146
or
all p-
values>0
,05
Not
r
e
j
e
c
t
H
0
Source: own processing; results from Gretl based on data from Eurostat available at
www.epp.eurostat.ec.europa.eu
The result of Model 1 is the following equation with basic information:
^FED_NFE = 2,37 + 0,724*ACT + 0,718*UNE + 0,205*INAC
(0,321) (0,0594) (0,199) (0,0195)
T = 10, R-squared = 0,995
(standard errors in parentheses)
OLS model with two selected variables
For the dependent variable we have chosen UNE - unemployed - the proportion of the
labor force that is without work, but is available and looking for a job. For independent
variable we have chosen UNE_RT – unemployment rate - represents the share of
unemployed people to the economically active people (i.e. workers and unemployed).
After that we have done a linear model estimation of parameters in the Gretl program
using the Ordinary Least Squares estimator, so-called OLS model.
Model 2 OLS, using observations 2004-2013 (T = 10)
Dependent variable: UNE
HAC standard errors, bandwidth 1 (Bartlett kernel)
Coefficient Std. Error t-ratio p-value
UNE_RT 0,14897 0,00992498 15,0096 <0,00001 ***
Mean dependent var 2,070000 S.D. dependent var 0,459589
Sum squared resid 1,501057 S.E. of regression 0,408392
R-squared 0,966457 Adjusted R-squared 0,966457
F(1, 9) 225,2879 P-value(F) 1,12e-07
Log-likelihood -4,707308 Akaike criterion 11,41462
Schwarz criterion 11,71720 Hannan-Quinn 11,08268
rho 0,439378 Durbin-Watson 1,040290
Source: own processing in Gretl based on data from Eurostat available at
www.epp.eurostat.ec.europa.eu
Table 6 Results of tests for verification of the econometric model in the program Gretl
Results of the verification tests
t-statistics parameters are significant
F-statistics model as a whole is significant
Heteroscedasticity heteroskedasticity is not present
Multicollinearity multicollinearity is not present
Autocorrelation no autocorrelation
Source: own processing; results from Gretl based on data from Eurostat available at
www.epp.eurostat.ec.europa.eu
The result of Model 2 is the following equation with basic information:
^UNE = + 0,149*UNE_RT
(0,00992)
T = 10, R-squared = 0,966
(standard errors in parentheses)
Outcomes of further education
In 2013, the Statistical Office of the Slovak Republic published a study which is focused
on selected indicators about lifelong adult education. The survey was realized in the form
of a questionnaire on a sample of 7472 respondents on the whole territory of Slovakia
(the total rate of return was 62%, i.e. 5000 questionnaires). The reference period was 12
months - from 1.10.2011 to 15.11.2011. On the chart below we can see the results of
the use of new knowledge and skills acquired through formal education.
Figure 2 Outcomes of new skills/knowledge acquired through formal education (%)
Source: Statistical Office of SR (2013): Selected indicators about lifelong adult education in the
Slovak republic
It is obvious that the greatest motivation to an individual is to engage in further learning
when the individual has achieved a promotion in their job - this trend can be observed
especially among women. Reasons such as better performance in the job, higher salary,
personal benefits, or getting a (new) job do not reach far such level as promotion in the
job. It is interesting that some respondents do not expect any outcomes from
participation in further education. We assume that it can be caused by reason of
involuntary participation in education, for example mandatory participation of the
unemployed people in courses organized by labor offices.
4 Conclusions
There are several determinants that affect the participation of adults in lifelong learning,
for example the type of household (one member per household, a household with
children), the highest level of education successfully completed, gender, employment
status, employment rate, respectively unemployment rate, expenditures on education,
etc. In our contribution, we have compiled the most probable regression model of
selected determinants of lifelong learning in the Slovak Republic.
In Model 1 we have analyzed the impact of individual independent variables and the way
in which they change (in our case the adult population divided according to labor status)
on the dependent variable, which was the total participation of adults in formal and nonformal
education (FED_NFE). The results show that if there was a unit increase in the
economically active population (ACT), whilst the values UNE (unemployed people) and
INAC (inactive persons) unchange, the FED_NFE will grow on average by 0,7240. When
there was a unit increase in the UNE, whilst the values ACT and INAC unchange, the
FED_NFE will grow on average by 0,7181. When there was a unit increase in the INAC,
whilst the values ACT and UNE unchange, the growth of the FED_NFE was on average by
0,2053. From the above findings we suggest, that the inactive population does not show
sufficient efforts to educational activities of the state. Therefore, it may be necessary to
reconsider the state support activities focused on this population group.
In Model 2 we have analyzed the impact of the change of one independent variable (in
our case it was the unemployment rate, i.e. UNE_RT) on the dependent variable, which
was the unemployed people (UNE). The outputs show that if there was a unit increase in
the UNE_RT, then UNE will grow on average by 0,1490. The result can be interpreted in
another way, because the individual parameters are representing elasticities. Then the
parameter β expresses, that if the unemployment rate increases by 1%, then the total
number of unemployed people that are participating in lifelong learning will increase on
average by 0,15%. Despite the fact, that the given parameter value is low, it indicates
the efforts of the unemployed people to participate in the further education and
vocational training. We can justify it by the fact, that the unemployed people want to
participate in lifelong learning (during the periods of high unemployment rate) to become
more attractive for employers and thereby increase their chances of getting a job.
The adult participation in formal and non-formal education and vocational training has an
increasing trend. This can be a result of the wide range and availability in the form of a
relatively large number of universities in Slovakia, various educational institutions,
educational facilities of enterprises, private educational institutions, but it can also be a
result of awareness of perspectives of better chance and the position of educated people
in the labor market.
Acknowledgments
Supported from the project VEGA no. 1/0008/11: The effectiveness of using tax revenues
and public expenditures in relation to the long-term sustainability of public finances.
References
Bil, J., Němec, D., Pospiš, M. (2009). Gretl - uživatelská příručka. Retrieved from:
http://www.thunova.cz/wp-content/uploads/CZU/Manual_gretl.pdf.
Budayová, J., Bolek, V., Šupšák, R. (2013). Analýza dopytu po finančných službách
v závislosti od príjmu domácností. In Ekonomika, financie a manažment podniku - rok
2013 : zborník vedeckých statí pri príležitosti 60. výročia založenia Fakulty podnikového
manažmentu EU v Bratislave [electronic source]. - Bratislava : Vydavateľstvo EKONÓM,
p. 1-11.
EUROSTAT. (2014). Education and Training Statistics. Retrieved from:
http://epp.eurostat.ec.europa.eu/portal/page/portal/education/data/database.
Lukáčik, M., Lukáčiková, A., Szomolányi, K. (2011). Ekonometrické modelovanie
v programoch EViews a Gretl. Bratislava: EKONÓM.
OECD. (2014). Glossary of statistical terms. Retrieved from:
http://stats.oecd.org/glossary/.
Rubenson, K. (2011). Adult Learning and Education. Oxford: Academic Press.
Soukiazis, E. (2013). The linear regression model (LRM). In Applied Macro-econometrics
– Summer course for PhD. students. Portugal: Faculty of Economics of the University of
Coimbra.
Závodná, S. (2013). Selected indicators about lifelong adult education in the Slovak
republic. Bratislava: Statistical Office of the Slovak Republic.
Financial Literacy of Masaryk University Students in "Financial
Literacy" Course
Bohuslava Doláková
Masaryk University
Faculty of Economics and Administration, Department of Finance
Lipová 41 a, 602 00 Brno, The Czech Republic
E-mail: 175975@mail.muni.cz
Abstract: The aim of the contribution “Financial Literacy of Masaryk University Students
in “Financial Literacy” Course” is to analyse the state of financial literacy among the
students who enrolled the very new course “Financial Literacy”. This course is intended to
be an introduction to the components of financial literacy (literacy monetary, price and
budget). My contribution focuses on the level of financial literacy in the time when
students were just being enrolled into the course. My question is what level of financial
literacy the students have in the time when they are getting started the course “Financial
Literacy”. The first part of my contribution is concentrated on the description of the basic
terms and a summary of theoretical knowledge about them. In the second part I provide
the research, which was conducted through a questionnaire survey. The survey was a
required condition for enrolment into the course as an entrance test. And in the final part
there are analysed the results of the survey based on the research question. From some
reasons not all registered students enrolled or passed the course but those who passed
have generally improved in their knowledge and abilities connected with financial literacy.
Keywords: Financial literacy, education, personal and family budget, money
JEL codes: A10, A22, A23
1 Introduction
The term financial literacy is relatively new and the needed skills are now stressed
everywhere. There are new subjects created at schools of all levels. Very important is to
begin with children and teach them how to handle money. At the university level there is
a special situation: young people did not have a financial literacy courses at previous
educational stages and there are courses created for them too. The aim of the
contribution “Financial Literacy of Masaryk University Students in “Financial Literacy”
Course” is to analyse the state of financial literacy among the students who enrolled the
very new course “Financial Literacy”. This course is intended to be an introduction to the
components of financial literacy (literacy monetary, price and budget). At the end of the
course students should be able actively participate in the activities of organizations that
address the issue of financial literacy. The first part of my contribution is concentrated on
the description of the basic terms and a summary of theoretical knowledge about them.
Financial Literacy (FG) is defined rather inconsistently; the individual subjects that are
concerned with it have different definitions. Also across the Anglo-American world there
is no uniform approach to the FG, the United States used the term "financial literacy," i.e.
financial literacy (Roulet, 2009), while in the UK are more likely to encounter the phrase
"financial capability", or rather financial competence (Atkinson, 2005). Both terms are
translated into Czech in the same words (Balabán, 2011), but their meaning is slightly
different. The financial competence and financial literacy are closely linked together, but
they cannot be used as interchangeable terms. Financial literacy refers in particular to
the knowledge, financial skills rather refers to the ability of adequately usage of these
acquired skills and knowledge. The concepts (including the previously mentioned financial
literacy and competence) are closely interconnected, as it is evident in Figure 1.
Figure 1 Interpenetration of the concepts related to financial literacy
Source: Gnan et al. (2007)
Firstly, Ministry of Finance begun to deal with the financial education and financial
literacy in the Czech Republic in connection with the consumer protection in the financial
market. Ministry of Finance defined the financial literacy in the National Strategy for
Financial Education (Ministry of Finance, 2007 updated 2010). It became the central
document for financial education in the Czech Republic. Financial literacy is a set of
knowledge, skills and abilities that are necessary for the citizen to financially secure
his/her and his/her family in contemporary society and was active in the market of
financial products and services. Financially literate citizen with the knowledge about
money and prices is able to responsibly manage personal and family budget, including
the management of financial assets and financial liabilities with respect to the changing
situation (Ministry of Finance, 2010).
As the main motto financial literacy the Ministry states: Citizens are not financial experts,
but they should be able to consider what is offered to them - the final solution is their
own responsibility.
Ministry of Finance structured the financial literacy into three main components:
- Financial (monetary) literacy,
- Price literacy,
- Budget literacy.
Figure 2 The basic scheme of financial literacy
Source: The author´s own work
•Knowledge•Skill
•Education•Literacy
Financial Economic
FinancialEconomic
Financial (monetary) literacy skills are required to manage cash and non-cash money
transactions as well as management tools for this purpose (e.g. current account,
payment instruments, etc.).
Price literacy skills are essential for understanding the price mechanisms and the
inflation.
Budget literacy skills are required to manage personal / family budget (e.g., the ability
to manage a budget, set financial goals and make decisions about the allocation of
financial resources), and includes the ability to manage different situations in life from a
financial point of view. Budget literacy includes not only general components but also two
specialized components: management of financial assets (e.g. deposits, investments and
insurance) and the management of financial liabilities (such as credits or leases)
(Ministry of Finance, 2010).
An integral part of the financial literacy is a macroeconomic policy, i.e. focusing on the
fundamental relationships between different sectors of the economy along with an
understanding of basic macroeconomic indicators (such as inflation, GDP and interest
rates). Necessary are also a basic awareness of the tax system and the role of taxation in
society, i.e., knowledge in the field of taxation.
The financial literacy is associated with numerical literacy, which is the ability to obtain,
use and interpret mathematical information and ideas in order to actively cope with the
mathematical demands that the life of an adult present. It is the ability to handle
numerical financial operations and work with numbers, graphs, tables, etc. in the context
of real life. Very important is also important information literacy, or the ability to look at
the context, understand, use and evaluate relevant information and legal literacy, i.e. the
orientation of the legal system knowledge of rights, obligations and opportunities Ministry
of Finance, 2010). The complementarity of the three additional literacies is illustrated on
Figure 3.
Figure 3: Continuity of additional literacy
Source: The author´s own work
2 Methodology and Data
The very new course “Financial Literacy” started in spring semester 2014. My
contribution focuses on the level of financial literacy in the time when students were just
being enrolled into the course. The course is open to all students of Masaryk University
Numerical
literacy
Legal literacy
Information
literacy
only the fields directly associated with the programme have the preference. The
presumption was that the main part of the sample would consist of relatively new
students of the Faculty of Economics and Administration – people who are assumed to be
interested in finance generally.
The course “Financial Literacy” requires active participation and many other duties:
 A prerequisite for the granting of credit, the active participation in seminars
(seminars), manufacture of team essays, passing tests of financial literacy and
cooperation in the exploration of financial literacy.
 Tests of financial literacy
o Entrance test of financial literacy at home subject - by the end of the first
week of teaching.
o Second Final verification of financial literacy test after elf Seminar - at the
latest by twelve Seminars (is.muni.cz).
Maybe so many duties to pass the course were partly a reason why some students
quitted the course and are not going to pass it.
My question is what level of financial literacy the students have in the time when they
are getting started the course “Financial Literacy”.
The research should answer the basic question:
How much does the course “Financial Literacy” generally help students to improve their
financial literacy?
What kind of students is interested in improvement of their financial literacy?
Is it possible to anticipate how many students will quit the course and try to prevent this
to happen?
In the next part I provide the research, which was conducted through a questionnaire
survey. The survey was a required condition for enrolment into the course as an entrance
test. Next I will analyse the final test to see how much the results changed and how
much did the students improve their abilities.
Questionnaire usually involved questions that tried to simulate the need to make a
decision within the context. It was necessary to find out the real costs and benefits
associated with the decision and compare them. Some questions tried the students
spending decisions, which entail collecting information, planning and budgeting. There
were questions associated with the different choices about the saving money – time,
interest rates and inflation were crucial for the decisions. On the other side there was
tested the ability to use the credit options and to diverse the risk as an investor. Specific
question were connected with debit and credit cards, real impact of taxes on the prices
and special knowledge related to the Czech bureaucracy (e.g. what office is responsible
for disability pension). Some questions (like the tasks with the interest rate) needed to
be calculated but there were other question when there were necessary to know the
financial terms (like debit and credit cards different). Some questions asked the same
topic but in a different way (the question changed from positive to negative) so it was
easier to eliminate some “good tips”. The questions were from all of the fields mentioned
at the beginning. They tested financial literacy completely: monetary, price, budget
literacy and numerical, information, legal literacy too.
The demands for Entrance and Final tests were very similar, to make the comparison and
potential improvement clearer. They were as same level as it was possible.
3 Results and Discussion
Before the beginning of the semester spring 2014 there were 90 students interested in
improvement of their financial literacy. Two of them finally decided to enrol the course,
so 88 students enrolled the course. There were more women than men, but the
difference was not very distinct it is clear from the Figure 4.
Figure 4 Gender of enrolled students
Source: The author´s own work based on survey results
Just after the enrolment 2 students definitely left their studies, so my research sample
starts with 86 people (it was getting be smaller as the course was in the process). The
students were mainly from the Bachelor´s Degree (see Figure 5). They probably took the
course as an opportunity to get some really practical information in one package.
Figure 5 Level of studies of enrolled students
Source: The author´s own work based on survey results
As it is very clear from the Figure 6 absolute majority of enrolled students were students
of Faculty of Economics and Administration. In combination with previous information
44%
56%
Gender (N = 88)
Men
Women
83%
9%
8%
Level of studies (N = 86)
Bachelor´s Degree
Master´s Degree
Follow-up Master´s
Degree
about the study level it is clear that Bachelor´s students of Faculty of Economics and
Administration took the chance to get the complete course which most probably was
missing in their previous education (the Financial Literacy has just started to be a part of
the curriculum of elementary and secondary schools so these students did not have any
specialized compulsory course of financial literacy). The rest of the MU students had little
interest or information about the course of Financial Literacy (the knowledge about the
course among all students was not a part of the research). 2 students were from Faculty
of Informatics and 2 from Faculty of Sport Studies. 3 students studied two faculties in
one time, every time it was Faculty of Economics and Administration and Faculty of Law
in 1 case, Faculty of Arts in 1 case and Faculty of Social Studies in 1 case. 1 student was
from Faculty of Education and 1 from Faculty of Science.
Figure 6 Original MU departments of enrolled students
Source: The author´s own work based on survey results
Enrolled students had to pass the entrance test. The test had 276 questions each per 1
point, no negative points. It was able to enter the test as many times as it was
necessary, so students did not have to fill all the questions in one time, they could have
done what they want, close it and then open the test again. The received minimum was
61 points, maximum 248. In the Figure 7 there are absolute values of students and from
them it is obvious there were some students with very low level of financial literacy. Most
of the students belonged to the average and quite a few were already financially
educated.
Figure 7 Entrance test results
77
2 2 1 1 1 1 1
0
20
40
60
80
100
FEA FI FSpS FEA+FL FEA+FA FEA+FSS FE FS
MU original faculty (N = 86)
2
11
41
32
0
10
20
30
40
50
0-100 101-150 151-200 201-250
Numberofstudents
Points
Entrance test (N = 86)
Source: The author´s own work based on survey results
During the semester another student decided to leave the studies completely and other 8
students did not fill the final test in time (maybe they have forgotten or had other duties,
but they left the research sample). In the Masaryk university there is a possibility for
students who did not pass the course to request for non-repeating of the course (which is
not compulsory), so the students who left the course don´t have to enrol it any more in
the future, just if they want. The range of obtained points was from 31 points to 259
points. The maximum was 276 points again. The students from the 0-100 points were
checked and it was realised they had just very few tries for the final test, some of them
just 1 – it is probable they have forgotten to finish it. As it can be seen on Figure 8 the
group of 201-250 points-students grew up a lot and it is very possible it was thanks to
the course – they learned the necessary things and did much better. Even 3 students
received more than 251 points, so overall the class get better.
Figure 8 Final test results
Source: The author´s own work based on survey results
As follows there are analysed the results of the survey based on the research question.
Generally the course “Financial Literacy” helps the students to orientate in word of
finance. Overall they did the better in final test than in the entrance test. Interesting
would be to directly compare individual student´s comparison. The student which was
the best by the entrance test remained the best from the group and got better and did
the best by the final test too.
As it was mentioned before the most interested in improvement of financial literacy
presented the students of Faculty of Economics and Administration. The rest of the
university was included just very little. It must be taken into account that the course is
very new and probably there was not much awareness about it. Maybe in next years it
will be participating by the students of other faculties too. Actually, it is kind of
presumption that the student of Faculty of Economics and Administration will gain the
information of financial literacy just in the compulsory courses in that faculty. It would be
logical to offer this course to the other faculties. But the problem of financial literacy is
that it is too general and most of the lecturers just assume students already have the
necessary information. It is not the student s fault because the financial literacy was not
taught at the school in the past. Maybe the feeling of inadequacy satisfied the students of
Faculty of Economics and Administration to enrol this course in such a big number.
4 5
18
47
3
0
5
10
15
20
25
30
35
40
45
50
0-100 101-150 151-200 201-250 251-300
Numberofstundents
Points
Final test (N = 77)
The course “Financial Literacy” has a big target – to improve the financial literacy of
many students. It is obvious they have to do quite a lot of work and study. The reasons
why overall 13 students decided not to finally enrol or fulfil the requirements are not
known but it has to be clearly stated that the course requires a lot of work to make sure
just really interested students will have a place there.
4 Conclusions
Very new course of the Faculty of Economics and Administration called “Financial
Literacy” is kind of answer on the demand that is visible every where. Basic and
secondary schools started their compulsory financial education and the university – the
centre of whole knowledge has to do as much as possible to educate its own students in
this field. The aim of the course is to familiarize students with the fundamentals of
financial literacy. Even when it was not compulsory a lot of students registered in the
course – the topic is very actual and has a direct connection to the praxis. From some
reasons not all registered students enrolled or passed the course but those who passed
have generally improved in their knowledge and abilities connected with financial literacy.
Majority of students enrolled in the course we the students of Faculty of Economics and
Administration. Maybe there could be some bigger offer of financial literacy courses even
for students of other faculties. In general, all Faculty of Economics and Administration
students must gain some amount of information (financial literacy included), so the
course should be more open to the other students. On the other hand who else should be
an expert on financial literacy than a student of Faculty of Economics and Administration?
Maybe it will take years to coordinate everything so well that all the university students
will have the deep financial literacy background from the basic and secondary school
that this course will be just for the other than Faculty of Economics and Administration
students. The results showed that this course was beneficial nearly for every enrolled
student, so it definitely has its reason.
Acknowledgments
Masaryk University Faculty of Economics and Administration, Department of Finance provided the
data for my research by sharing the results from Entrance and Final test of “Financial Literacy”
course.
References
Atkinson, A. Introducing financial capability skills - A pilot study with Fairbridge West,
Bristol, 2005. [online]. [cit. 2014-04-19]. Available from WWW:
<http://www.pfrc.bris.ac.uk/Reports/introducing_financial.pdf>.
Balabán, Z. (2011). Slabikář finanční gramotnosti: učebnice základních 7 modulů finanční
gramotnosti. 2nd
ed. Praha: Cofet.
Gnan, E., Silgoner, M. A., Weber, B. (2007) „Volkswirtschafts- und
Finanzbildung:Konzepte, Ziele, Messung“. In Geldpolitik & Wirtschaft, Q3/7. Wien: OeNB.
Retreived from: http://www.oenb.at/dms/oenb/Publikationen/Volkswirtschaft/MOP-
GEWI/2007/Geldpolitik-und-Wirtschaft-Q3-07/chapters/gewi_2007_3_gnan_tcm14-
67338.pdf.
Masaryk University, Faculty of Economics and Administration, Financial Literacy Course,
Retrieved
from:https://is.muni.cz/auth/predmety/uplny_vypis.pl?fakulta=1456;obdobi=6008;pred
met=760701.
Ministry of Finance. (2007 updated 2010). National Strategy for Financial Education.
Retrieved from:
http://www.mfcr.cz/cps/rde/xbcr/mfcr/Narodni_strategie_Financniho_vzdelavani_MF201
0.pdf.
Roulet, Marguerite. (2009). Financial Literacy and Low-Income Noncustodial Parents.
Center for Family Policy and Practice. Retrieved from:
http://www.cffpp.org/publications/Policy_finance.pdf.
Using modern quality management tools to improve finance and
business university courses
Zsuzsanna Eszter Tóth, Tamás Jónás
Budapest University of Technology and Economics
Faculty of Economic and Social Sciences
Institute of Business, Department of Management and Corporate Economics
2 Magyar Tudósok körútja, Building Q, 1117, Budapest, Hungary
E-mail: tothzs@mvt.bme.hu, jonas@mvt.bme.hu
Abstract: The Institute of Business of the Budapest University of Technology and
Economics is one of the largest financial and business higher education centers in
Hungary both in terms of total student numbers and the number of undergraduate and
graduate majors and courses. In this paper we report a project aimed at improving some
of the core undergraduate finance and business courses. The specialty of this project is
that the opinion of students is taken into consideration in a way that not only the given
course but the whole survey and feedback process can be improved. Self-organizing
maps (a hexagonal topologic structure with 5x5 neurons with the survey results
considered as 22 dimensional vectors containing 11 importance and performance pairs)
and statistical techniques were used to analyze the survey results. After processing the
first evaluation questionnaires we have used problem solving techniques with active
involvement of the students in order to lay the foundation for long-term course
improvement actions and to identify the factors considered to be important by the
students coupled with low actual performance. Based on the results obtained by using
the developed questionnaire and feedback tool it is going to be used in all the
undergraduate and postgraduate courses of the institute in order to provide education for
our students that they also consider useful and important for their future jobs so their
general financial literacy and business knowledge is improved. An important byproduct of
the whole process is that students realize that their opinion is taken into consideration
and that they are customers of the financial and business education of the institute.
Furthermore as being an active part of such a quality improvement process students get
familiar with the use and importance of modern quality management tools.
Keywords: higher education, student satisfaction, PDCA, quality improvement actions,
self-organizing map
JEL codes: I21, I23, C10, C45
1 Introduction
The issue of quality in higher education has received increasing attention in the last
decade in Hungary. Hungarian higher education has become a mass-market service
which can be described by an increasing number of students and by an increasing
number and diversity of institutions. In Hungary, the need to measure the quality of
teaching at university level has become a hot topic recently. Students are now generally
recognized as the principal stakeholders of higher education. Student feedback of some
sort is usually collected by most institutions, though, there is little standardization in how
this kind of feedback is collected and what is done with it. There is still little
understanding of how to use and how to act upon the collected data.
At the Budapest University of Technology and Economics (BUTE) students’ feedbacks
have been collected institutionally in several forms. Firstly, underclassmen have been
asked about the motivations of their chosen career and about their social status since
1999. Secondly, graduates have provided details about starting their career since 1997.
In the third form of measurements active students have been asked to evaluate each and
every course they have attended during the semester since 1999. Formal measurement
of course quality at BUTE is conducted through course evaluations completed by students
at the end of a term. It is found to be useful to pinpoint the strengths of courses and
identify areas of improvement in order to understand the factors that contribute to
student satisfaction.
Based on the existing course evaluation questionnaire our aim was to get more detailed
picture about some of the finance and business courses lectured by our institute as a
pilot project. In the second phase of our research brainstorming sessions were organized
with the involvement of students so as to understand the problems highlighted by the
questionnaire. Finally, cause and effect diagrams were constructed in order to reveal the
root causes of the problems. As quality actions must derive from student concerns we
ended up with an improvement action plan.
2 Measuring student satisfaction at BUTE
During our research we followed the steps of a TQM based course evaluation process
proposed by Venkatraman (2007) aligned with the steps of the PDCA cycle representing
continuous improvement (see Tóth et al., 2013). With the objective of understanding
students’ perceptions about the quality of educational service, we surveyed students on
five of our finance and business courses in 2011, using a survey that was built for this
particular purpose. The survey consists of a questionnaire of 11 questions (see Figure 1).
Students were asked to express their opinions in two dimensions, namely, scoring the
importance and the performance related to each question using an ordinal scale from 1
to 6. The performance dimension of a question reflects how much the students are
satisfied with the educational performance in the particular field addressed, while the
importance category is used to express how much they find important this particular
topic similarly to Hernádi and Ormos (2012a and 2012b). The measurement was
repeated with the same questionnaire in 2012 in the case of the Business Statistics
course. The two-dimensional survey approach is built on the consideration that topics
having higher importance scores should have higher performance values as students
rightly expect higher service level in the areas which they consider more important.
Figure 2 shows the total sum of importance scores and the total sum of performance
scores for each question. Taking the five analyzed finance and business courses together
into consideration, the biggest disconnects between the importance and performance
dimensions are in the areas addressed by Question 5, 8, 9, 10 and 11 (see Figure 1).
Self-organizing maps
A self-organizing map (SOM) with 5x5 neurons in a hexagonal topologic structure was
trained with the survey results which can be considered as 22 dimensional vectors
containing 11 importance and performance pairs. SOM in Figure 3 represents the input
space by 25 typical vectors learnt by the neurons. The self-organizing property of the
map means that any two representative vectors with small distance between them are
located on the grid close to each other. Coloring of the map reflects the similarities
between representative vectors. The abbreviation of the corresponding subject name
such as BC1-BC5 (for the five analyzed finance and business courses) was associated to
each input vector. The most frequently appearing subject name abbreviation of input
vectors represented by each vector (neuron) in the grid is also shown in the map. It can
be seen from Figure 3 that subjects form clusters in the input space, that is, there is
certain homogeneity of students’ answers for the same subject, while the answer vectors
between different subjects are considerably dissimilar.
Figure 1 Survey questionnaire
Figure 2 Total scores of importance and performance questions
0
500
1000
1500
2000
2500
3000
1 2 3 4 5 6 7 8 9 10 11
SumofScores
Question
Sum of Importance Scores
Sum of Performance Scores
Figure 3 Self-organizing map with similarity coloring
Importance (I1-I11) and performance (P1-P11) components of the representative
vectors are visualized in Figure 4. These figures together with the SOM in Figure 3 serve
well for preliminary study purposes as some relations between the answer components
can be easily identified by using them. For example, the five neurons in the upper-left
corner of the SOM in Figure 3 belong to the Business Statistics course (see as BC1 in
Figure 3), and we can see from the component planes that in case of question 11 the
importance values of these neurons are high (red and orange colored), while their
performance values are low (blue colored). It suggests that there is a considerably large
disconnection between the importance and performance values for question 11 in case of
this special course. A number of similar ‘quick analyses’ becomes possible based on the
shown component planes. The conclusions that can be drawn from the component planes
are in line with the ones which can be made based on statistical analyses.
Figure 4 Importance and performance components of the representative vectors
Brainstorming and cause and effect analysis
Based on the five identified questions deriving from the evaluation of the survey
questionnaire three questions were raised for the brainstorming session:
 How do you think the lecturer could develop the comprehension and logical
structure of his/her classes? (Q5)
 What ideas come to your mind regarding the notes and educational
supplementary material? (Q8, Q9)
 What would be the best exam system and conditions that could be used to assess
realistically and fairly the students’ knowledge about a given subject? (Q10, Q11)
Six groups of students were asked to brainstorm as many ideas as they could to answer
the three brainstorming questions. The two groups involved, each of which consisted of 7
to 9 students, collected their answers separately from their own brainstorming sessions.
After combining and harmonizing the answers for the three questions, they were divided
into four categories according to the type of skill needed to be developed by the lecturer,
namely human, pedagogical, technical skills and subject knowledge.
The ideas emerging from the first brainstorming question listed mainly the
pedagogical/didactical preparedness and methods used by lecturers as problems. The
inaccurate didactical, education technological and methodological knowledge of lecturers
is mainly due to their lack of teaching qualifications and inappropriate teaching
techniques. The intensification of this knowledge would be an important aspect while
improving the quality of business courses. The development of pedagogical skills could
also contribute to resolve some noted problems concerning human skills (see Gyökér and
Finna, 2013). The second brainstorming question fostered ideas in connection with
technical skills. Some of the deficiencies could be solved by developing the computer
skills of lecturers. The other group of ideas (video records, audio books) would require a
more significant financial investment from the university. Based on students’ feedbacks
lecturers should improve their presentation skills and put greater emphasis on
demonstrating practical examples and case studies. The third brainstorming question
addresses the issue of student performance evaluation. The performance can be
measured in several ways see e.g. Ormos and Urbán (2012 and 2013). Most of the ideas
can be associated with pedagogical and technical skills. Students require more strictness
at exams and consistent sanctions for cheating. Students would need written, richly
explained evaluations, more practical examples, more consultations, trial exams, etc.
They also want more precise and more thorough evaluations (Bedzsula and Bérces,
2012, Tóth et al., 2013).
After the brainstorming session, cause and effects analyses were conducted with the
participation of students by constructing Ishikawa diagrams trying to find the root causes
of the aforementioned problems. The questions of the Ishikawa analysis were the
following:
1. Why don’t students find the lectures comprehensible? (see Figure 5)
2. What is the reason for the fact that the students are dissatisfied with the notes
and written supplementary teaching material?
3. What is the reason for the fact that the students often don’t find exams
appropriate?
The two diagrams per question, which were created based on the three questions for the
six groups have been combined and the results were illustrated by fishbone diagrams
which have confirmed the conclusions of the brainstorming session. They demonstrate
the causal relationships and the key problems concerning the three brainstorming
questions in order to identify the most effective and important improvement actions (see
Figure 5 as an example). Analyzing the cause and effect relationships it is conspicuous
that the three fields addressed by the questions are strongly interrelated as some causes
appear in more diagrams that allows us to conclude that there are a number of problems
raised by the students which can be solved easily either by the lecturer or by the
department responsible for the courses (Bedzsula and Bérces, 2012).
Survey results from 2011 – Business Statistics course
Chyba! Nenalezen zdroj odkazů.6 shows a considerable gap between the averages of
importance and performance scores in case of several questions. The conclusion that
there is a lack of expected strong positive correlation between the average scores of the
two survey dimensions is also supported by the correlation coefficient of 0.1328
calculated for averages of importance and performance scores. Based on these initial
results, we focused on the questions with the largest gaps between averages of their
importance and performance scores.
Based on the ideas of the brainstorming session and on the survey results of Business
Statistics in 2011, we constructed a cause and effect matrix with the involvement of a
group of Business Statistics students in order to set immediate goals.
Figure 5 Ishikawa diagram for Question 1 of cause and effect analysis
Figure 6 Averages of importance and performance scores in 2011
Actions defined
Based on the initial statistical analyses of survey data from 2011 and on the results of
brainstorming and the cause and effect matrix, the following actions were defined and
implemented on the Business Statistics course in 2012 in the spirit of continuous
improvement.
 Lecturers took part in the Lecturers’ program organized by the Institute of
Continuing Engineers Education at BUTE in order to improve their pedagogical
skills (related survey questions: 5, 8, 9, 10, 11);
 Regular consultations emphasizing the most important theoretical topics were
held one day before each midterm exam (related survey questions: 10, 11);
 Additional, comprehensive consultation materials were prepared for each
consultation. The consultation materials were made available for students in
presentation slides (related survey questions: 8, 9, 10, 11);
 Well-defined theoretical topics with outlines of required answers were prepared for
each midterm exam consultation (related survey questions: 8, 9, 10, 11);
 The typical calculation exercises required in the midterm exams were summarized
and overviewed during the consultations (related survey questions: 5, 8, 9);
 The weights of different sub-topics in the midterm exams were deliberately
harmonized with the time spent on discussing and lecturing the corresponding
sub-topic (related survey questions: 2);
 The entire course was taught by one lecturer instead of two or three lecturers
teaching dedicated blocks of the course (related survey questions: 2, 8, 9).
0
1
2
3
4
5
6
1 2 3 4 5 6 7 8 9 10 11
AverageScore
Question
Average Scores in 2011
Importance Performance
Impact of improvement actions
After implementation of the improvement actions discussed above, we conducted the
same survey at the end of the Business Statistics course in 2012 to see how the actions
taken had acted upon students’ satisfaction. Figure 7 shows the average scores for each
survey question in 2011 and 2012.
Figure 7 Average scores in 2011 and 2012
The importance and performance scores can be considered as random variables, and so
their averages can be taken as point estimates of their expected values. The graphs in
Chyba! Nenalezen zdroj odkazů.7 suggest two hypotheses. On the one hand, we may
assume that the gaps between expected values of importance and performance scores
significantly decreased from 2011 to 2012 especially in the case of questions to which the
actions taken are related. On the other hand, the average importance scores suggest
that there was no significant change in the means of importance scores, that is, students’
opinion about importance of topics addressed by survey questions did not change
significantly.
The hypotheses that means of importance scores did not change significantly were tested
in case of each survey question. Each H0 hypothesis was tested by applying the two
samples z-test as an approximate statistical test at significance level of 0.05. The results
of conducted tests are summarized in Chyba! Nenalezen zdroj odkazů.1. The change
from 2011 to 2012 in students’ opinion about the importance of topics addressed by
survey questions is statistically insignificant; the only one exception is question 6.
Table 1 Z-values and p-values of tests on equality of importance score means and on
equality of performance scores in 2011 and 2012
Question z-value (imp.) p-value (imp.) z-value (perf.) p-value (perf.)
1 0.1450 0.8847 -1.7961 0.0362
2 0.8825 0.3775 -6.0247 0.0000
3 1.5193 0.1287 -4.4889 0.0000
4 0.9639 0.3351 -5.2995 0.0000
5 1.0094 0.3128 -5.7277 0.0000
6 3.1319 0.0017 -6.9768 0.0000
7 1.9122 0.0559 -6.3250 0.0000
8 0.8230 0.4105 -4.1052 0.0000
9 1.2154 0.2242 -4.6541 0.0000
10 0.1811 0.8563 -14.6876 0.0000
11 0.1534 0.8781 -11.8384 0.0000
From the year-to-year average performance scores visible in Chyba! Nenalezen zdroj
odkazů.7, we may assume that there was a significant increase from 2011 to 2012 in
mean of performance score for each question. Similarly to the testing of importance
scores, the results of statistical tests regarding performance scores are also summarized
in Chyba! Nenalezen zdroj odkazů.1. Each p-value in Table 1 is less than 0.05 and so
for each survey question the null-hypothesis is rejected and the alternative hypothesis is
0
1
2
3
4
5
6
1 2 3 4 5 6 7 8 9 10 11
AverageScore
Question
Year to year average importance scores
Importance in 2011 Importance in 2012
0
1
2
3
4
5
6
1 2 3 4 5 6 7 8 9 10 11
AverageScore
Question
Year to year average performance scores
Performance in 2011 Performance in 2012
accepted at significance level of 0.05. It means that the mean of performance score for
each survey question increased significantly from 2011 to 2012.
The correlation coefficient between the average importance and performance score for
2012 is 0.8669. The same correlation coefficient for 2011 was 0.1328, that is, the
stochastic relationship between the importance and performance categories is much
stronger in 2012 than in 2011.
In the light of our continuous improvement philosophy and following the Plan-Do-CheckAct
cycle of course evaluation, the following actions are considered as having the
potential to improve the educational performance of the Business Statistics course in the
future. The entire curriculum is large and comprehensive. We need to review the
structure of the curriculum and the lecture notes to ensure that the consecutive topics
are in a logical and consistent order so that there is no topic which requires knowledge
that is introduced later on. Calculation exercises are part of the lectures. Based on
feedback from students and their representatives, it would be definitely more effective if
the calculation exercises were discussed in smaller groups within seminars. Defining
optional project exercises based on cases from different companies would challenge the
students to solve some real-life problems using the tools and techniques learnt during
the course. These changes are to come in the forthcoming term, now that we are in the
phase of revising the whole course based on the aforementioned ideas. The applied
pedagogical methods need a thorough reviewing in the long run as the brainstorming
sessions highlighted these skills as urgent issues.
4 Conclusions
This kind of questionnaire structure and the validation of the presented dual approach
would not only highlight the areas that need to be improved, but also students’
involvement in improvement actions could have more aspects. The results may have
implications for management responsible for resource allocations to various areas of the
university services and infrastructure. Our aim is to make the necessary steps towards
long term improvements and analyze regularly whether the actions have solved the most
critical problems. This approach ensures that the voice of students is fully integrated into
quality improvement efforts and contributes to a better understanding of the students’
requirements. Moreover, students contribute actively to the improvement of courses by
acquiring the knowledge necessary for the application of modern quality management
tools at the same time.
References
Bedzsula, B., Bérces, R. (2012). A hallgatói vélemények gyűjtése, elemzése és azok
felhasználási lehetőségei a BME egyes kurzusai minőségfejlesztése érdekében. In: Topár,
J., ed., A műszaki menedzsment aktuális kérdései. Budapest: Műszaki Könyvkiadó, pp.
153-169.
Gyökér, I., Finna, H. (2013). Humán tőke és nemzetközi karrierek. Virtuális Intézet Közép
Európa Kutatására Közleményei, vol.22(3), pp. 205-215.
Hernádi, P., Ormos, M. (2012a). Capital Structure and Its Choice in Central and Eastern
Europe. Acta Oeconomica, vol.62(2) pp. 229-263.
Hernádi, P., Ormos, M. (2012b). What Managers Think of Capital Structure and How They
Act: Evidence from Central and Eastern Europe. Baltic Journal Of Economics, vol.12(2)
pp. 47-72.
Ormos, M., Urbán, A. (2013). Performance analysis of log-optimal portfolio strategies
with transaction costs. Quantitative Finance, vol.13(10) pp. 1587-1597.
Tóth, Zs. E., Jónás, T., Bérces, R., Bedzsula, B. (2013). Course evaluation by importanceperformance
analysis and improving actions at the Budapest University of Technology
and Economics. International Journal of Quality and Service Sciences, vol.5(1), pp. 66-
86.
Urbán, A., Ormos, M. (2012). Performance Analysis of Equally Weighted Portfolios: USA
and Hungary. Acta Polytechnica Hungarica, vol.9:(2) pp. 155-168.
Venkatraman, S. (2007). A framework for implementing TQM in higher education
programs. Quality Assurance in Higher Education, vol.15(1), pp. 92-112.
Market Structure and Performance of the Life Insurance Industry
in the Slovak Republic
Zuzana Brokešová, Tomáš Ondruška, Erika Pastoráková
University of Economics in Bratislava
Faculty of National Economy, Department of Insurance
Dolnozemská cesta 1, 852 35 Bratislava, Slovak Republic
E-mail: zuzana.brokesova@euba.sk
tomas.ondruska@euba.sk
erika.pastorakova@euba.sk
Abstract: Life insurance industry represents an integral part of the financial market in all
developed economies. In the Slovak Republic, its importance begun to increase during
the first decade of new millennium, when the life insurance exceeded the non-life
insurance measured by the amount of gross written premium. This newly elicited demand
also flew into the changes on the supply side of life insurance and the structure of the
Slovak insurance market varied significantly during this period. It has developed from
monopolistic market with one dominant insurance company and few very small ones to
competitive market with 19 insurance companies offering life insurance operated on the
market in 2012. However, have these changes also affected the performance of the
industry? And do lower concentration evoke higher performance? As from the wellperforming
life insurance industry benefits consumers, producers and whole economic
system alike, understand these tendencies is particularly important. The aim of the paper
is to answer these questions through the analysis of the data from Slovak life insurance
industry.
Keywords: life insurance, industry performance, market structure
JEL codes: G22, L10, L25
1 Introduction
Life insurance industry represents an integral part of the financial market in all developed
economies. For example in 2012, life insurance penetration was 3.57% in North America
and 4.58% in Western Europe (Swiss Re, 2013). However in the Slovak Republic,
insurance penetration continues to be far below from the desired benchmark, when this
ratio represented only 1.26% in 2012 (Swiss Re, 2013). The difference in the
performance of Slovak life insurance market is evident. However, due to the enhanced
growth in the amount of written premiums of the industry, this indicator slowly
converges to the level of life insurance penetration in western European countries.
Faulkner (2002) sees the reason for this development in continuous raise of the wealth
among the population on the one side as well as cultural and social development and
convergence of Slovak Republic to developed countries on the other side.
The importance of life insurance started to grow slowly after the origin of Slovak Republic
in 1993. While non-life insurance dominated among Slovak population for more than
decade, since 1995 and during the first decade of new millennium the role of life
insurance has started increasing faster and more dynamically. After all, the life insurance
consumption exceeded the non-life insurance measured by the amount of gross written
premium in 2008 for a first time. This increased interest and newly elicited demand also
flew into the changes on the supply side of life insurance. The structure of the Slovak
insurance market varied significantly during this period. From the starting point, when
only 6 life insurance companies (as a life insurance company, we consider all insurance
companies that offer life insurance products on the market) operated on this market in
1993, to 19 insurance companies offering life insurance products in 2012. Market
structure was developed from monopolistic market with the one dominant insurance
company and few very small in 1993 to medium concentrated and competitive market in
2012. However, have these significant changes also affected the performance of the
industry? And does lower concentration evoke higher performance?
The main aim of the paper is to reveal the presence of a link between the development of
market structure and market performance in Slovak republic during the period 1993 -
2012. Based on the Structure Conduct Performance paradigm, we suggest a statically
significant relationship between these two variables.
The remainder of the paper is organized as follows. In the first section, we provide the
basic theoretical background and a literature review of the Structure Conduct
Performance and results of previous research. In the second part, we discuss the
methodology used and the data. The next two sections cover the main results, their
analysis and the resulting conclusions.
2 Literature review
The Structure Conduct Performance (SCP) hypothesis explains performance via conduct
market structure (Mason, 1939; Bain, 1951). Seminal work in this area is done by Bain
(1951), who expects that concentrated markets encouraged oligopolistic behaviour
among the competitors. Further research supports his assumptions about the existence
of relation between market structure and market performance. However, direction of this
relation lacks consensus and varies across the literature.
Empirical investigation of SCP paradigm was mainly implemented on the banking system
data (e.g. Smirlock, 1985; Goldberg and Rai, 1996; Berger and Hannan, 1998; Maudos,
1998) and there is only a few studies, which analyse insurance industry data (e.g.
Cummins, Denenberg, and Scheel, 1972; Weiss, 1974; Jung, 1987; Carroll, 1993;
Chidambaran et al., 1997; Bajtelsmit and Bouzouita, 1998; Choi and Weiss, 2005; Pope
and Ma, 2008; Bikker, 2012). Only one of them, Cummins et al. (1972), focuses on life
insurance industry at our knowledge. In addition, banking studies are primarily focused
on individual companies, while majority of the insurance studies use country aggregated
data (Carroll, 1993; Bajtelsmit and Bouzouita, 1998; Chidambaran et al., 1997). Results
of these studies are ambiguous. For example, Bajtelsmit and Bouzouita (1998) focus on
automobile insurance and their results show positive and significant relation between
market performance and market concentration. According to Weiss (1974) and Choi and
Weiss (2005), collusive behaviour should be more frequently observed in highly
concentrated market, since only a relatively small number of firms must agree to collude.
Therefore, higher concentrated market may causes higher prices as well as higher profits
and performance (Ellickson, 2014). On contrary, Cummins et al. (1972) find also
statistical significant but invers relation between market concentration and market
performance. According to their analysis of the U.S. life insurance market, these
variables are inversely related, which indicates a positive relationship between market
concentration and profitability. However, these results are weaker in comparison to
banks and non-life insurers.
3 Methodology and Data
In the research, we focus on the verification of the SCP hypothesis in Slovak life
insurance industry during the period from 1993 to 2012. We use a linear regression
model on a country level with robust standard errors. Data are log-log transformed for
interpretation as elasticity.
Model specification is as follows:
(1)
where t refers to time, t (1, 2…, 20), refers to unobservable random disturbance and
, are regression coefficients of key explanatory variables used to evaluate SCP
hypothesis. We use underwriting profit in life insurance industry in Slovak Republic
(Profit_lifet) as a dependent variable. In our analysis, it represents an indicator of market
performance, while underwriting profit is a profit that insurance companies obtain from
direct insurance services, i.e. earned premium remaining after deduction of paid claims
and administrative costs linked to life insurance. By this profit, we can approximate real
profit from life insurance products in Slovak insurance market while these values are
adjusted from non-direct insurance operations of insurance companies. As for log
transformation, positive values are required and this assumption is not fully applicable to
all values, we have to add a constant to all observations. Life insurance market
concentration approximated by the Herfindahl-Hirschman index (HHI_life) represents an
explanatory variable. Herfindahl-Hirschman index is a sum of squared market shares of
firms, where increased value of index represents increased market concentration as well.
According to the U.S. Department of Justice, market with a result of less than 1,000 to
be a competitive marketplace while a result of 1,800 or greater indicated highly
concentrated marketplace (Rhoades, 1993). In the estimation, we also incorporate a
control variable for market growth - growth of Gross Written Premium (
( ). However, the role of market growth varies based
on the barriers to entry (Choi and Weiss, 2005). When the barriers to market entry are
low the market growth entices new insurers to enter the market. But on the other hand,
when the barriers to market entry are significantly high then the growth of market evoke
increasing in profitability of insurance companies.
As SCP hypothesis predicts, simultaneous cause-and-effect relationship between market
structure and market performance (Choi and Weiss, 2005), Augmented Durbin-WuHausman
(DWH) test are conducted for the Equation (1) to determine whether the
suspect variables are endogenous. In the time series sample, we rejected presence of
unit root by Augmented Dickey–Fuller test (p<0.001). Serial correlation in regressions
were rejected by Durbin–Watson statistic (p>0.05).
Dataset is obtained from the database of the National Bank of Slovakia and descriptive
statistics are shown in Table 1.
Table 1 Descriptive statistics
Variable Obs. Mean St. Dev Min Max
Profit_life 20 162 173.1 150 363.7 -23 927.15 581 774.8
HHI_life 20 2 858.97 2 196.598 1 231.266 7 271.633
gGWP_life 19 1.172472 0.1516657 0.9359061 1.551661
Source: Authors’ own calculations
4 Results and Discussion
The life insurance industry has developed dynamically in the Slovak Republic during the
analyzed period, which was driven mainly by socio-economic, law and institutional
changes. Step by step with these changes arose the integration efforts of Slovak Republic
into international organizations like OECD, NATO and the European Union. Necessary
harmonization of the legislation and integrated financial services supervisions had a
major impact on the changes that occurred in the private insurance industry. Since 1995,
the role of life insurance has started increasing faster and more dynamically. Changes in
needs of population started to turn mainly property and liability coverage oriented
insurance market on to market offering wide variety of life insurance products.
Improving life insurance industry performance also motivated new insurance companies
for enter to the market. Market benefited not only by the entry of new specialized foreign
companies that brought new know-how and labor opportunities but also by the increased
competition between new and established companies. Efficiency and competition on the
life insurance sector are important not only for companies but also for households to
keep premiums low and innovation and quality high (Bikker, 2012). Those changes did
not result only in the increased number of insurance companies but in overall benefits in
supply as well as demand for life insurance products. Attractiveness of the life insurance
had increased especially during the years 2006 and 2011, when clients could use tax
advantage of life insurance. Entrant insurance companies offering life insurance products
brought also new types of policies for consumers with wider coverage and services that
were usual in developed economies. Majority of these “new” companies were subsidiary
firms of international companies with foreign capital. Turning point in the Slovak
insurance industry was the merge of the dominant insurance company Slovenská
poisťovna, a.s. (with life insurance market share 28,32% in 2002 followed by
Kooperativa poisťovňa, a.s. with 11,86%) and Allianz poisťovňa, a.s.(with life insurance
market share 5,43% in 2002) in 2003. The life insurance market share of new merged
Allianz – Slovenská poisťovňa, a.s. was 30,08% in 2003. But its market share in life
insurance was decreasing during the following years and nearly equalized the share
around 20% with the Kooperativa poisťovňa, a.s. in 2012. However, half of the life
insurance market according to gross written premium was controlled by three insurance
companies.
Main indicators of life insurance market development as gross written premium, share of
life insurance, insurance penetration and density are shown in reduced form of four years
periods in Table 2. Chosen period is illustrated by not only growing numbers of entities
offering life insurance products but also by the increase in the gross written premium,
share of life insurance, insurance penetration and density.
Table 2 Indicators of life insurance industry in the Slovak Republic
Variable 1993 1997 2001 2005 2009 2012
Gross Written Premium (millions
EUR)
65.43 156.79 457.41 731,33 1 062.1 1 165.6
Share of Life Insurance (%) 23.47 26.94 42.90 42.50 52.39 55.13
Insurance Penetration (%) 0.38 0.51 0.94 1.85 1.69 1.63
Insurance Density (EUR) 1.28 29.13 85.02 135.75 196.02 215.55
Market share of foreign life
insurance companies in the
domestic market
N/A 25.14 55.95 92.43 91.56 90.20
Underwriting Profit
(thousands EUR)
-16.23 24.18 151.88 295.89 221.38 122.31
Herfindahl-Hirschman index 7272 5199 1739 1391 1377 1231
Number of life insurance
companies
6 22 22 21 17 19
Source: National Bank of Slovakia, OECD
From the Table 2, it is evident that during the analyzed period, the gross written
premium in life insurance industry has increased by almost eighteen times. Also the
share of the life insurance on the whole industry has risen from les then quarter in 1993
to more than 55% in 2012. Market performance measured as underwriting profit of life
insurance industry has increased, while life insurance market concentration measured by
Herfindahl-Hirschman index has decreased (see Figure 1).
Figure 1 Market performance and market concentration
Source: Authors’ own calculations
468
101214
1993 1997 2001 2005 2009 2013
year
lnHHI_life lnProfit_life
Regressions results supported results of Figure 1. In table 3, we can see inverse relation
between market performance and market structure. Due to identified endogenity, with
respect to the concentration variables and gross written premium growth in Equation (1)
by Augmented Durbin-Wu-Hausman (DWH) test, we decided beside the Ordinary least
square regression (OLS regression) conduct also Instrumental variables (2SLS)
regression. In this model, we assumed that market concentration in life insurance
industry (ln_HHI) is in relation with the amount of gross written premium in life
insurance industry (lngGWP_life) and the year change of absolute value of life insurance
companies (lngIC_life). We decided to implement this variable mainly due to fact that
traditional SCP hypothesis omits the possibility of insurance companies to entry the
market (Choi and Weiss, 2005). However, due to the observed development in Slovak
Republic, the number of insurance companies varies during this period. The industry has
also witnessed tremendous growth in the number of life insurance entities, mainly
between 1993 and 1996, when their number more than doubled.
Table 3 Regression results
lnProfit_life OLS Regression
Instrumental
variables (2SLS)
regression
lnHHI_life -2.743438** -2.713257***
(0.9283807) (0.8076294)
lngGWP_life 4.683789 1.405165
(1.463963) (1.436591)
Constant 32.31828*** 32.09582***
(6.752665) (5.870782)
R-squared 0.6603 0.6602
Prob > chi2 0.0282 0.0031
Note: ** and *** denote significance at the 5% and 1% level, respectively. Regarding the
Instrumental variables (2SLS) regression: Instrumented: ln_HHI_life, Instruments: lnGWP_life,
lngIC_life
Source: Authors’ own calculations
In general, similar results were obtained by OLS regression as well as Instrumental
variables (2SLS) regression. Both models show that with decreasing market
concentration increases profit, which supports the results of Cummins et al. (1972). Our
results also support empirical evidence of Zhang and Zhu (2005) and Sliwinski et al.
(2013) that monopolistic insurance markets are less developed than competitive and also
the level of performance of the market is significantly lower. Therefore, the social and
economic transformations, the break-up of monopolies and growing competition in the
Slovak Republic have contributed to the increase of life insurance industry performance.
5 Conclusions
The paper examines the relationships among market structure and performance of
Slovak life insurance industry over the period 1993–2012. We performed OLS regression
and Instrumental variables (2SLS) regression to identify the statistically significant
relation between the development of market structure and market performance. Both
models proved that decreasing market concentration increases profit, which supports
previous results of Cummins et al. (1972). In addition, our results support empirical
evidence of Zhang and Zhu (2005) and Sliwinski et al. (2013) that monopolistic
insurance markets are less developed than competitive and likewise the level of
performance of the industry is significantly lower. Improving of the Slovak life insurance
industry performance caused several changes in the market. As the positive changes in
life insurance industry during the analyzed period, we consider: increased market share
and gross written premium; growth in the number of insurance entities and increased
competitiveness; wider coverage and services; and better performance. On the other
hand, changing conditions caused also the negative consequences on the market
performance linked primarily with fusions and acquisitions. These increase the market
concentration, which according to our results may lead to the decrease of market
performance and affects life insurance companies as well as their clients. Regulation
authorities, therefore, should be very careful with the mergers and acquisitions
permission in the area of life insurance.
It is important to note that while in the manufacturing industry, the benefits of merger
and acquisition transactions lie primarily on the acquisition of know-how as well as on
mutual research and development to reduce costs (Péliová and Kováč, 2009), in case of
life insurance companies and other financial institutions mergers and acquisitions are on
the front burden the enhance of a market position and gain of the potential of the clients.
This is the reason why the interest of the regulators should be concerned on the fusions
and acquisitions and moreover our results are confirming that the monopolistic markets
cause less efficiency and performance from which neither insurance companies nor
clients benefit. For the better performance of the insurance industry, as the significant
part of the economy, is necessary to focus on the effects of the fusions and acquisitions
to the market structure.
The main limitation of our paper is the aggregated character of used dataset, where the
results are based on very few observations. Therefore, our current results about the
presence of relation between market structure and market performance development are
not robust. However in the further research, we would like to confirm our results by
extended model based on individual companies’ data and incorporation of the analysis of
efficiency changes role in this relation. In addition, deeper analysis of endogenity among
analyze variables will be in the area of our further interest.
Acknowledgments
This paper is part of a research project No. 1/0431/14 entitled ‘Insurance Relationship as
a Key Element of the Insurance Sector Operation in the Context of Socio-economic
Changes’ provided by the Ministry of Education, Science, Research and Sport of the
Slovak Republic.
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Overconfidence as a Cognitive Bias and Its Implications for
Insurance Industry
Tomáš Ondruška
University of Economics in Bratislava
Faculty of National Economy, Department of Insurance
Dolnozemská cesta 1, 852 35 Bratislava, Slovak Republic
E-mail: tomas.ondruska@euba.sk
Abstract: The behavior of individuals is influenced by many biases that cause their
deviation from the rationality. One of them, discussed extensively in the financial
industry is cognitive bias - overconfidence. It is a firmly established feature of an
individual behavior in the psychological point of view. On the other hand in the
economics it is extensively explained by inefficient market outcomes connected to a
biased sense of self-confidence. In short, the bias is shown for numerous real-world
situations and cause changes in consumers’ behavior. However, what are implications of
overconfidence for the insurance sector? Are there significant differences in the presence
of the overconfidence between men and women? Our results based on the survey of 870
respondents show that we cannot confirm the presence of the overconfidence in the
overall sample. However, we found the reason of this situation - significant difference
between men and women and their perceived and actual financial literacy. While in group
of men we confirmed presence of the overconfidence, in the group of women, we found
out the opposite effect - underrate. Paper also provides implications of these phenomena
in the insurance market.
Keywords: bias, consumer, financial literacy, insurance, overconfidence
JEL codes: D03, G22
1 Introduction
The behavior and decision-making of individuals in insurance matters is often influenced
by biases. One of them is the overconfidence. The term refers to a systematic error of
judgment made by individuals when they assess the correctness of their responses to
questions relating to intellectual or perceptual problems (Pallier et al., 2002, p. 258). It is
a cognitive deviation from rationality, which rises in individuals’ minds unfounded belief
in their own knowledge, intuition and judgment (Baláž, 2009). In the behavior of the
individuals is the subjective confidence in their judgments reliably greater than its
objective accuracy.
Overconfidence is associated with two phenomena - the illusion of knowledge and illusion
of control. The illusion of knowledge is closely connected to the fact that more
information reduces accuracy and increases confidence of individuals (Hall et al., 2007).
Individuals mistakenly deluding that the more information they have, the more they will
also know about some issue. Although there is not currently a problem for individuals to
obtain some information, it should be noted that in the case of complex decisions (such
as the decision-making of insurance policy) the presence of the illusion of knowledge may
have negative consequences. Individuals may have enough information about a certain
insurance product, but even the number of identified information cannot substitute their
own experience and other skills that can improve their judgment. Similarly, it is not
certain whether the person is able to correctly understand and process obtained
information. On the other hand there is the illusion of control that describes the tendency
for individuals to behave as if they might have some control when in fact they have none
(Langer, 1975). This phenomenon is undoubtedly associated with insurance since the
insurance industry inherently handles with uncertain events and so the awareness of its
presence is important. Individuals influenced by the illusion of control may underestimate
their insurance protection against some events. Also their conviction about knowing more
than others is enhanced. Individuals as the result of this illusion dive into some risky
activities that may unfold against them. Overconfidence can cause that individuals do not
buy insurance protection or if they buy it is with insufficient coverage that do not reflect
their real needs.
Therefore the main aim of this paper is to verify the presence of overconfidence on the
sample of survey respondents with respect to gender perspective and to suggest
recommendations how to eliminate its negative impact on the insurance industry. We
expect the presence of the overconfidence in our research sample (Cordell et al., 2011;
Matsumoto et al., 2013) and also we assume that the extent of the overconfidence will
be higher in case of men (Barber & Odean, 2001).
The paper is divided into four chapters. The remaining chapters include the description of
used methodology and data; result of our analysis with discussion of practical
applications of our findings; and conclusion.
2 Methodology and Data
Data were obtained via survey questionnaire about consumers of insurance products and
their attitudes towards insurance. Survey was conducted in electronic and paper form.
We used this method to investigate the presence of overconfidence in the insurance
market. The research sample consisted of citizens of the Slovak Republic in the age
structure from 18 to 61. Data collection was carried out in two periods, first from 1st
June
2013 to 31st
July 2013 and the second from 15th
November 2013 to 15th
December 2013.
Overall, the survey involved 1 044 respondents, from which we obtained 870 random
respondents who represent the distribution of the Slovak population by gender and age
according to the Statistical Office of the Slovak Republic. Sample consists of 438 men
(50,34%) and 432 women (49,66%) and age groups were represented as follows:
15,10% respondents from 18 to 24 years; 38,00% from 25 to 39 years and 46,90%
respondents from 40 to 61 years.
Since the survey was designed broadly, for the identification of the overconfidence in
behavior of respondents we used their declared financial literacy. We were interested in
respondents’ knowledge of the world of finance. They could assess themselves whether
they are very well, good or bad financially versed. We provided in survey also description
of each of these three levels of financial literacy for the better understanding. Answers to
this question are the basis for respondents’ self-evaluation of the financial literacy.
One part of survey consisted of a questions that we used as a proxy to review the
knowledge of insurance issues. Specifically, for that purpose we used two questions
(statements), and respondents should decide whether they are true or false. We used
answers to following questions: "If I pay an insurance premium regularly, in the case of
occurrence of the insured damage, insurance company will always pay me the full
amount of loss" and "I do not consider the increase in the amount of damage incurred
during the insured accident with “small” amount as wrong" as the proxy for a real
financial literacy. The correct answer to both questions is "no", because in the first case,
the insured person have to take into account also deductible, or decreased value of the
property covered by insurance policy. In the second case, the unjustified increase of
damage is dishonest act, in many countries classified as an insurance fraud.
The number of correct answers to these questions represent for us a dummy variable for
the actual degree of financial literacy, and on the basis of those answers, an individual
could earn a maximum of 2 (very good) and the minimum 0 points (bad). Again, we get
three-point range, which is the starting point for us to determine the actual level of
financial literacy, similarly to their self-classification. Based on these two observations,
we determined the difference in the level of their own financial literacy (perceived) and
literacy based on our test (actual). We were able to quantify the difference due to the
numerical coding of these categories (coding in IBM SPSS: 0 for very good; 1 for good;
and 2 for bad) as the difference between own, perceived level and the actual, calculated
level. This difference can only take whole number values in the range from -2 to 2, which
means that for value -2 respondent overestimated his financial literacy by 2 levels and
demonstrated his overconfidence, and vice versa in case of the value of 2.
We tested the presence of overconfidence in our research sample by Student's T-test.
T-test compares sample means by calculating Student’s t and displays the two-tailed
probability of the difference between the means (Field, 2009). Student's T-test can be
used to test the null hypotheses, which claims that the mean of a sample is equal to the
assigned constant (Rimarčík, 2000). The T-test help us answer the question whether, on
a random sample the mean of a sample is equal to the chosen constant, respectively,
whether it is larger or smaller than the selected number. Indeed, if the P-value is less
than the chosen significance level (usually 5 %, respectively 0.05), the null hypothesis is
rejected and we accept the alternative one. This means that the difference between a
specified constant and the calculated average of the sample is too large to be just the
result of random selection and therefore it is statistically significant.
The survey data were analyzed by the IBM SPSS Statistics program. However, we
focused not only on the whole sample but we also examined gender differences in the
presence of the overconfidence between men and women. The results of our analysis are
discussed in the following chapter.
3 Results and Discussion
Overconfidence is without any doubt the phenomenon that affects not only consumers
but also the insurers and other market subjects. That is the reason why the research in
this area is necessary. Awareness of the overconfidence may be beneficial for both sides
of insurance market - demand as well as supply. We use T-test to test assumptions that
respondents in the research sample are overconfident. Statistics of the one-sample test
are below in Table 1.
Table 1 One-Sample Statistics - Change in financial literacy (level)
N Mean Std. Deviation Std. Error Mean
Both 870 -,04 ,890 ,030
Women 432 ,09 ,889 ,043
Men 438 -,16 ,874 ,042
Source: own calculations
The descriptive table displays the sample size, mean, standard deviation, and standard
error for each of the three samples (women, men, both). The sample means disperse
around the 0 (0 means that there is not big difference between actual and perceived
financial literacy) by what appears to be a small amount of variation. But we can see that
there is different sign of the mean between men and women. The test statistic Table 2
shows the results of the one-sample T-test.
Table 2 One-Sample Test - Change in financial literacy (level)
Test Value = 0
t df Sig.
(2-tailed)
Mean Difference 95% Confidence
Interval of Difference
Lower Upper
Both -1,258 869 ,209 -,038 -,10 ,02
Women 2,056 431 ,040 ,088 ,00 ,17
Men -3,883 437 ,000 -,162 -,24 -,08
Source: own calculations
The t column displays the observed t statistic for each sample, which is calculated as the
ratio of the mean difference divided by the standard error of the sample mean. The
column labeled Sig. (2-tailed) displays a probability from the t distribution with
appropriate degrees of freedom (df). The value listed is the probability of obtaining an
absolute value greater than or equal to the observed t statistic, if the difference between
the sample mean and the test value is purely random. The Mean Difference is obtained
by subtracting the test value (0 in our case) from each sample mean. The 95%
Confidence Interval of the Difference provides an estimate of the boundaries between
which the true mean difference lies in 95% of all possible random samples of
respondents. Following the results of the one-sample test we cannot confirm the
presence of the overconfidence in the overall sample. More importantly, we found the
reason of this situation. There is significant difference between men and women and their
perceived and actual financial literacy. While in one group we confirmed presence of the
overconfidence, in the second one we found out the opposite effect.
Since the confidence interval of men lies entirely below 0.0 (their actual knowledge of
insurance related issues is worse than they declared), men overestimated their financial
knowledge and in their behavior is present overconfidence. Similarly, because confidence
intervals of women lie entirely above 0.0; we can conclude that their difference in
perceived and actual financial literacy is significantly higher than 0 on the average. This
means that there is not argument to conclude that the overconfidence is present in
behavior of women. However, the positive sign mean that women underrated their
financial literacy, what is worth noticing. Cause of the underestimation of their own
experience and knowledge of the financial issues may lie in their smaller participation in
investing as well as engagement in financial markets. The financial world is often
(wrongly) considered as a male affair. Also in matters of finance men tend to feel greater
competence (Prince, 1993). As a result of simultaneous overconfidence of men and
underestimation of women experience, we failed to prove the effect of overconfidence in
the overall sample of respondents.
What are the implications of our findings for insurance industry? We can view these
implications in two perspectives, from the insurers’ point of view on the one side and
consumers on the other. We can discuss consumer related issues first. It is well-known
fact that the level of financial literacy in Slovak republic is very low (Pastoráková, 2012).
However, taking this fact into account, individuals (men in our sample) tend to
overestimate their knowledge and experience, what is characterized by the excessive
degree of their confidence in knowledge of financial matters. Insurance market is
represented by the large group of "non-professional" consumers, characterized by a lack
of correct information. Moreover due their low levels of financial literacy and presence of
the overconfidence in their behavior, they are easily influenced and also vulnerable. They
often underrate the process of insurance policy purchase and do not use service of
financial advisers. Alternatively, when potential clients use help of financial advisers, they
often do not ask questions about issues that they do not know or they are not sure about
them. There are few motives for this kind of behavior. Consumers pretend their
knowledge and do not want to ask question to deflate their ego, respectively they are
indifferent to their financial issues or they do not want to show their weak points. This
may lead to loss of trust in insurance services in future because consumers’ expectations
of the insurance were biased. After all whole insurance industry suffers by this loss of
trust and it is very hard to restore trust in industry. On the other hand we also need to
take into account different gender perspective in the show of overconfidence. Several
papers confirmed that the men’s overconfidence is higher in men related issues (Deaux &
Emswiller, 1974; Beyer & Bowden, 1997; Brokešová, 2013). Important decisions about
purchase of the insurance policy should men and women discuss together. The effect of
the overconfidence of men and the effect of underrating financial literacy of women may
be reduced by their mutual dissemination of knowledge and their considered decision.
Women are more patient in the information seeking, use consultation, and in the end
they compare findings. Authors Barber and Odean (2001) bring the advice that men and
women should make their decision in financial issues together to eliminate negative
decisions.
The overconfidence is not only matter of consumers. Insurers should also realize their
responsibility in bad decisions of consumers due their overconfidence. The crucial roles in
this problem hold financial advisors. However, the placed demands on the quality of
financial advisors are often insufficient (Pastoráková, 2006). Financial advisors should
concentrate their effort not only into volume of the sale but they should also provide
sufficient and comprehensible information to potential clients. We consider as appropriate
to ask random questions by financial advisors (agents/brokers) related to subject of the
selling process to ensure that consumer understands insurance terms and conditions.
This could help potential clients to understand complicated insurance products and to
avoid hesitation to ask questions. Mainly because the occurrence of the overconfidence is
higher in the more complex tasks (Fischhoff, et al., 1977), as insurance undoubtedly is.
Insurers should also raise public awareness of some kind of insurance products because
of the illusion of control, which is one of the aspects of the overconfidence. This is
necessary mostly in case of small probability events and in the private pension products
due to unsustainable state schemes. Beyond all the insurance companies should also
participate on the education of the public and their potential consumers to increase
financial literacy.
4 Conclusions
The overconfidence bias is a solidly established feature in behavior of individuals and
their decision making. This phenomenon affects not only consumers, but also the
insurers and other market subjects. Main aim of the paper was to verify the presence of
overconfidence on the sample of survey respondents with respect to gender perspective
and to suggest recommendations how to eliminate its negative impact on the insurance
industry. Using actual and perceived financial literacy as well as the Student's T-test we
tested the appearance of the overconfidence bias in the sample of 870 respondents. Our
results show that we cannot confirm the presence of the overconfidence in the overall
sample. However, we found the reason of this situation - significant difference between
men and women and their perceived and actual financial literacy. While in group of men
we confirmed presence of the overconfidence, in the group of women, we found out the
opposite effect - underrate.
We can view implications for insurance industry of our findings in two perspectives, from
the insurers’ point of view on the one side and consumers on the other. Due low levels of
financial literacy and presence of the overconfidence in consumers’ behavior, they are
easily influenced and also vulnerable. They often underrate the process of insurance
policy purchase and do not use service of financial advisers. Important decisions about
purchasing the insurance policy should men and women discuss together because of
different confidence in their financial literacy. The effect of the overconfidence of men
and the effect of underrating financial literacy of women may be reduced by their mutual
dissemination of knowledge and their considered decision. On the other hand, insurers
should also realize their responsibility in bad decisions of consumers due their
overconfidence, when the essential roles in this problem hold financial advisors.
Insurance companies should also participate in the education of the public and their
potential consumers to increase financial literacy. These are the key elements to build
the trust in relationships in the insurance market.
References
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Financial Literacy of Masaryk University Students with focus on
socially disadvantages citizens
Martin Svoboda
Masaryk University, Brno, Czech Republic
Faculty of Economics and Administration
Institute for financial market
Lipová 41a, Brno 60200
E-mail: svoboda@econ.muni.cz
Abstract: The aim of this paper is to analyze the current level of financial literacy of
Masaryk University (MU) students. The empirical survey was conducted among students
of the Faculty of Economics and Administration MU. It included both full-time and
combined-study students who enrolled in the courses “Basic Finance”, taught in the
Bachelor study program, and “Financial investment”, which is offered to university
students in the master’s degree study program. The questionnaire survey was conducted
within the Citi Foundation program and it was based on the project of “Empirical
verification of university students’ literacy”, whose aim was to test the financial literacy
of individual respondents, i.e. university students. The sub-objective was to identify
suitable students who are skilled enough to disseminate voluntarily financial literacy
within their communities and will focus primarily on socially disadvantaged citizens. For
this reason, the initial questionnaire survey was modified to incorporate questions that
can be closely connected with this group of socially disadvantaged citizens. The paper
also addresses the issue of possible specific requirements and different approaches of
financial literacy to a group of socially disadvantaged citizens. The first part focuses on
the summary of theoretical knowledge, definitions and description of the basic concepts.
The second part includes the research, which was conducted in the form of a
questionnaire survey. The final part analyzes the results of the research.
Keywords: Financial Literacy, Univerisity Students, Personal Budget, Money, Education,
Socially disadvantages citizens,
JEL codes: A10, A22, I23
1 Introduction
People of the Czech Republic, as well as nationals of other countries in the world, still lack a
sufficient level of financial literacy (Ministry of Finance 2010). This is true both for the
knowledge and attitudes or behavior. Besides the projects and programs of financial
education, which, however, still have a limited range with respect to the number of
inhabitants, many people can regard a request for advice from their immediate
neighborhood as a quick solution (Roulet 2009). Nevertheless, it is assumed that these
natural advisors will themselves have sufficient financial literacy and will be able and willing
to help. Given the fact that recent research in the Czech Republic and abroad shows that
financial literacy increases with education attainment (CNB 2010), it can be assumed that
university students or graduates can be a suitable group of people possessing a higher level
of financial literacy and the ability to provide correct advice.
Universities can offer the opportunity to engage their students in the process of financial
education of adults. University students were identified as suitable carriers and
disseminators of financial literacy as well as instructors of financial education in the society
(Atkinson 2005). As a large group present in all regions of the Czech Republic they can
provide a nationwide reach. Long-term sustainability is guaranteed through the annual
arrival of new students at universities. They are independent, respected and trustworthy
people, which is an important fact for disseminating financial education in disadvantaged
and vulnerable communities.
In their studies students receive financial education and skills needed for further
dissemination. They will subsequently present financial education independently or in
collaboration with non-profit organizations. Hence, students will be given the opportunity to
develop their knowledge and experience, and the opportunity to participate in volunteer
activities. At the same time they will learn responsibility towards the society and they will
also get to know its reverse side. The program should therefore encourage a further
development of the civil society and responsible behavior throughout the Czech population.
2 Methodology and Data
Research by The University of Economics (Hradil, Křížek and Dvořák, 2012) showed that
this target group possesses higher financial literacy than the adult 18+ population (MFČR
2010), but still with some deficits. The second major finding of the VŠE research is the
fact that 62% of university students or graduates believe that they would be able to
advise on personal finance. The ability to advise grows with the real financial literacy;
respondents’ answers suggest that those who understand finance relatively better would
rather help their neighborhood. Therefore it implies that it is appropriate for universities
to teach courses that (1) strengthen the financial literacy of students and (2) equip them
with the knowledge and skills necessary to effectively strengthen the financial literacy of
others.
The survey was based on the structure of the “Empirical verification of university
students’ literacy” project; the Faculty of Economics and Administration participated in
this structure through the Institute for Financial Market. The questionnaire was modified
by experts on financial literacy (Lusardi, Olivia and Vilsa, 2010) in order to find the level
of financial literacy at our faculty, and also for the subsequent selection of students who
are willing to engage in the dissemination of financial literacy among the socially
disadvantaged people of the Czech Republic.
The modification of the original questionnaire was as follows:
 The original questionnaire comprising 70 questions testing financial literacy in the
monetary, numerical, pricing, budgetary, information and legal areas was
expanded by 28 questions that explicitly focused on the issue of specific
requirements and different approaches to financial literacy of the socially
disadvantaged citizens.
 These 28 questions included 3 questions, which are the subject of international
comparisons. These are the questions on interest rates, portfolio diversification
and inflation (Lusardi 2009).
We wanted to verify whether the students are really equipped with the knowledge of the
issues concerning specific claims and different approaches to financial literacy of the
socially disadvantaged citizens so that they themselves would be able to effectively
strengthen their financial literacy. By incorporating 3 questions of international
comparisons we can expect that these questions will be among the easier ones and
students will have no problem to answer them correctly.
The survey of financial literacy included first-year students of the Bachelor’s degree study
program attending the course Basic Finance, third-year students of the Bachelor’s degree
study program attending the course Financial Markets, and first-year students of the
Master’s degree follow-up study program attending the course Financial Investment.
Students of both forms of study, i.e. combined and full-time, were invited to participate
(Masarykova Univerzita 2013). Overall, the survey conducted in 2013 included 918
students.
3 Results and Discussion
The total of 918 survey respondents included 473 women (51.5%) and 445 men
(48.5%). The selected sample of respondents was well (almost ideally) diversified from a
gender perspective. It was determined beforehand that the analysis will refrain from
monitoring gender differences. It is assumed that knowledge of men and women are at
the same level.
The survey included a relatively high percentage (12.3%) of respondents studying noneconomic
fields, which is shown in the Figure 1.
Figure 1 The ratio of students of economic and non-economic fields
Source: own calculations
It should be noted that most of the students studied the Financial and Insurance
Mathematics program or the Mathematics and Economics program at the Faculty of
Science MU. Given the fact that the students of these programs took several courses at
the Faculty of Economics and Administration and their study programs are very close to
finance, it was decided not to differentiate between students of economic and noneconomic
fields.
The conducted analysis also includes students of the joint inter-faculty bachelor's degree
program “Finance and Law”, offered by the Faculty of Economics and Administration and
the Faculty of Law MU, and treats them as students of an economic field. The main
guarantor of this study program is the Faculty of Economics and Administration;
therefore, the students are treated as studying an economic field (Svoboda, Krajíček and
Doláková 2013). Taking into account this as well as other specifics, the ratio of students
in non-economic fields would exceed 20%, so we can speak of a significant
representation of students from other faculties of Masaryk University.
The Faculty of Economics and Administration offers a wide range of degree Bachelor's,
Master's programmes. In order to reflect the dynamics of social and occupational reality,
the offer of studies provided by the faculty is diversified to a great extent. There are two
forms of studies available at the faculty. Apart from the traditional full-time mode of
studies, there is a form of studies designed to meet the needs of those with a more busy
schedule – the combined mode of studies. The combined form of studies is based on a
combination of direct teaching in blocks of lessons (mostly taking place during weekends)
and self-studies enhanced by the use of the latest information technologies and teaching
methods.
Full-time students accounted for more than three-quarters of the number (77.1%) while
students of the combined form of study were in the minority and their numbers reached
one quarter (22.9%). The exact structure of the monitored students based on their form
of study is presented in the following Table 1.
Table 1 Students structure according the mode and the subjects of studies
Respondents according the mode of studies Full-
time
Combined Total
Bachelor's programmes 655 194 849
Master's programmes 53 16 69
Total 708 210 918
Respondents according the mode of studies
(%)
Full-time Combined Total
Bachelor's programmes 92,5 92,4 92,5
Master's programmes 7,5 7,6 7,5
Total 100,0 100,0 100,0
Respondents according the subjects: N %
Financial markets (Bachelor's program.
combined)
53 5,8
Basics of finance (Bachelor's program.
combined)
141 15,4
Financial investing (Master's program.
combined)
16 1,7
Basics of finance (Bachelor's program. full-
time)
329 35,8
Financial markets (Bachelor's program. full-
time)
326 35,5
Financial investing (Master's program. full-
time)
53 5,8
Total 918 100
Source: own calculations based on results of study
From following analyses were excluded students that entered the test, answered only a
few – or even no –questions and saved the test with zero point gain. Such students were
in the test altogether 41.
Students could get as many as 18 points from knowledge-based questions that explicitly
focused on the issue of specific requirements and different approaches to financial
literacy of socially disadvantaged citizens; each correctly answered question was rated 1
point and if there were more variants of correct answers to one question, we assigned an
aliquot proportion of one point.
The number of points obtained is presented in Table 2.
Table 2 Total points
Mean 8,4635
Median 8,1667
Mode 7,17
Percentiles 25 6,0000
50 8,1667
75 11,000
Note: Mean – arithmetic average
Median – the value that divides the sample into 2 equal halves
Mode – the most frequently occurring value
Percentiles – who is better than 25% of people scored 6 points; who is better than 50%
of people scored 8.1667 points; who is better than 75% of people scored 11 points
Histogram presented in the Figure 2 shows data distribution using a bar chart. The height
of columns (y-axis) represents the frequency of scoring a certain number of points. The
x-axis shows the number of points.
Figure 2 Histogram
Source: own calculations
A detailed segmentation of the average scores was conducted within analyzing the data
files.
The results show that a higher score was reached by women, university graduates, and
people at the Master’s level of education in the combined form of study. People who are
willing to participate in financial education reached in average the highest score (9
points).
Table 3 Willingness to participate in financial education
Score - quartile lowest
quartile
low
quartile
higher
quartile
highest
quartile
Total
yes 20.5 21.8 25.7 28.8 24.1
no 18.7 15.1 11.4 13.8 14.7
maybe 49.4 53.1 56.9 53.1 53.1
I don’t know 11.4 10.1 6.0 4.4 8.0
Total 100 100 100 100 100
The results in the Table 3 shows that People who are willing to participate in financial
education reached in average the highest scores. These people belong to the highest
quartile – they reached a higher score than 75% of the others. Overall, the question was
answered positively by 24.1% of the respondents; the option “maybe” was chosen by
53.1% of them.
The following Table 4 shows ten easiest and ten hardest questions. The questionnaire
was structured into the following 19 sub-areas. The structure was: Introductory general
issues (questions 1-3), Quiz questions (questions 4-10), Payment (in domestic and
foreign currency) (questions 11-16), Pricing (questions 17-21), Inflation - the overall
macroeconomic situation (questions 22-25), Management households (questions 26-30),
Debts and debtors (questions 31-34), Surplus funds - investments (questions 35-38),
APR-annual percentage rate of charge (questions 39-41), Interest rates (questions 42-
46), Credit products - mortgages (questions 47-48), Insurance (questions 49-52),
Deposit insurance (questions 53-55), Where to go to complain (questions 56-59),
Contract, arbitration clauses, bills, contractual penalties (questions 60-62 ), Loss of credit
cards (questions 63-64), Socio-demographic questions (questions 65-70). A separate
segment consisted of 28 questions focused on the issues of socio-disadvantaged citizens
(questions 71-98).
The results indicate that four out of the five most difficult questions were from the field
focusing on the issue of specific requirements and different approaches to financial
literacy of socially disadvantaged citizens. These results are presented in Table 4.
Table 4 Complexity of questions
EASIEST
vo31 Imagine, that you take a loan and after some months of regular
repayment you lose your job and thus regular income. What will you
do?
836
vo64 What is the first thing to do, if credit card is stolen? 823
vo50 Novaks have in their property (flat) electronics in the value of some
tens of thousands and worry, that their new flat could be robbed. Which
insurance contract should they sign?
813
vo97 Assume that you have entered $100 on your account, which bears 2%
annually. How much money are you going to have on this account in
five years?
794
vo98 Imagine that the interest rate on your account is 1% annually and
inflation was 2 %in given year. How much can you buy with the money
from your account after one year?
787
vo23 If current inflation grew, what would happen to the money saved in
bank?
742
vo45 Debit card is payment card. 742
vo36 What is true about risk, rate of profit and liquidity? 733
vo63 If your payment card is stolen, what risk are you exposed to? 719
vo81 General rate of unemployment in Czech republic is higher for: 714
vo29 Employee working in Czech republic can lower his tax base by: 197
vo20 By comparing of prices in stone shops and internet shops: 170
vo12 What is not the advantage of payment system PayPal? 137
vo30 What are current taxes and contributions for social and health insurance
for employees, entrepreneurs and firms?
126
vo25 What is the relationship of central state authority? 116
vo73 How many personal bankruptcies were approximately declared in the
Czech Republic in 2011?
116
vo38 For investor who never bought securities and now for the first time
wants to buy for example shares of ČEZ, a.s. at the Stock Exchange
Praha, applies:
98
vo74 How many executions were approximately ordered by the courts in the
Czech Republic in 2012?
87
vo79 In case of forced sales of chattels Czech law does not exclude from the
enforcement:
52
vo82 When determining the eligibility for certain kinds of social benefits, such
as childbirth allowances or child benefits, the relevant indicator is:
46
MOST COMPLICATED
On the contrary, further results indicate that two of the three added questions, used for
international comparisons, occurred among the five easiest questions. Particularly, these
were questions on the issue of interest rates and inflation.
The questionnaire was evaluated as medium till very difficult (total 86,8% of people
answered like that). Only 0.7% of respondents considered the questionnaire to be very
simple and 3.4% of the respondents evaluated the questionnaire as very difficult. The
results shows the following Table 5.
Table 5 Difficulty of the questionnaire
What is the difficulty of the questionnaire according to you? %
Very simple 0,7
Moderately difficult 9,9
Medium difficult 52,9
Difficult 30,5
Very difficult 3,4
Don´t know 2,6
Total 100
4 Conclusions
Although research conducted in the past showed that university students have higher
financial literacy than the adult population, their financial literacy still may have some
deficits or limits. The fact that 62% of university students or graduates believe that they
would be able to advise on personal finance can be problematic if we thoroughly deal
with the issue of possible specific requirements and different approaches of financial
literacy to the group of socially disadvantaged people.
The following conclusions can be drawn from the analysis of the questionnaire survey,
which included in the issue of socially disadvantaged people:
 Socially disadvantaged people deal with more complex problems that go beyond
the area of personal finance, which the students themselves sometimes perceive
too narrowly.
 Students who are willing to participate in financial education reached in average
the highest score.
 Questions focused on the issue of specific requirements and different approaches
to financial literacy of socially disadvantaged people were among the most
complex ones.
The presented results could be analyzed more thoroughly in the future, for example in
terms of gender differences, more significant differentiation of economic and noneconomic
fields, or students of full-time and combined study programs.
Acknowledgments
The author wants to thank the Citi Foundation for the support.
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Masaryk University
Faculty of Economics and Administration
Department of Finance
and Institute for Financial Market
European Financial Systems 2014
Proceedings
of the 11th
International
Scientific Conference
Edited by Oleg Deev, Veronika Kajurová
Editorial Board: Petr Pirožek, Ladislav Blažek, Eva Hýblová, Jaroslav Maryáš,
Martin Kvizda, Jaroslav Nekuda, Daniel Němec, Vladimir Hyánek
Published by Masaryk University, Brno 2014
1st
edition, 2014, number of copies 120
Printed by: Tiskárna KNOPP s.r.o., Nádražní 219, 549 01 Nové Město nad Metují
ISBN 978-80-210-6294-8