June 2013
Financial literacy
and inclusion
Results oF oeCD/INFe
suRvey aCRoss CouNtRIes
aND by geNDeR
FINANCIAL LITERACY AND INCLUSION:
RESULTS OF OECD/INFE SURVEY ACROSS COUNTRIES AND BY GENDER
FOREWORD
Governments around the world are recognising the benefits to individuals and national economies
of having a financially literate population that has access to appropriate financial products with relevant
consumer protection in place. In recent years, the G20 has endorsed three sets of principles in this regard
on financial consumer protection, financial inclusion and national strategies for financial education,
indicating firm commitment towards full and safe financial integration1.
Measurement and analysis play important roles in designing and implementing such policies. A
measure of financial literacy can be used to indicate the level of need for financial education across the
population. More detailed analysis can be used to identify the aspects of financial literacy most in need
of work, and the groups of population that need targeted support. Demand side measures of financial
inclusion provide similar insights, indicating where the policy focus should be placed.
The process of measurement can be complex and expensive. Developing a questionnaire, designing
a survey method, collecting data and analysing that data to provide meaningful results is time consuming
and costly, and requires expert inputs. It is for this reason that the OECD International Network on
Financial Education (INFE), with the support of the Russian/World Bank/OECD Trust Fund on Financial
Literacy, agreed to develop such a questionnaire and method for use across countries – thus cutting costs
to individual countries, reducing the time needed to undertake a survey and providing the opportunity of
comparing data across countries2.
The resulting OECD/INFE Financial Literacy Core Questionnaire has proved a huge success. This
publication shows how it has been successfully employed in 14 countries3. The countries participating in
this first ever cross-national study of financial literacy and financial inclusion (comprising indicators of
product awareness, use and choice, as well as financial knowledge, attitudes, behaviour) must be
commended for their willingness to undertake the study. Many of them have already taken their first step
towards improving financial literacy levels as a result of using the questionnaire to create a financial
literacy survey; some have even repeated the measure in order to track progress and identify changes.
The success of the questionnaire is also evidenced by the fact that countries as widespread as Korea,
Iceland and Jamaica are now benefiting from the tried and tested questions to capture levels of financial
literacy amongst their adult populations. As more countries use the questionnaire, the added value from
cross-national comparisons will grow rapidly.
1
G20 Principles for Innovative Financial Inclusion (2010) developed by Global Partnership for Financial Inclusion;
G20 High-level Principles on Financial Consumer Protection developed by the OECD Task Force on Financial
Consumer Protection (2011); OECD/INFE High-level Principles on National Strategies for Financial Education
developed by the OECD International Network on Financial Education (2012).
2
The OECD/INFE has developed a detailed toolkit comprising a core questionnaire and methodological guidance
as well as a set of supplementary questions: see OECD/INFE (2013a).
3
Albania, Armenia, British Virgin Islands, Czech Republic, Estonia, Germany, Hungary, Ireland, Malaysia, Norway,
Peru, Poland, South Africa and UK.
3
The resulting survey data are rich and provide numerous insights into levels of financial literacy in
the population and the needs of different groups (Chapter 1). The same data are further analysed in
Chapter 2 to explore levels of financial inclusion, and the potential association between exclusion and low
levels of financial literacy. The final chapter (Chapter 3) reports analysis by gender using the OECD/INFE
data and other sources.
Annexes provide additional detail, including information about the lessons learned from countries
applying the Core Questionnaire and discussion about the approach taken to creating measures of
financial literacy.
This publication, drafted by the OECD Secretariat, gives an indication of the research possibilities and
policy relevance of the data collected but much more can be learned from in-depth analysis, whether
focusing on certain target groups (this publication includes a chapter on the needs of women, but future
work may look at young adults or those low levels of education) or particular issues of interest (such as
indicators of credit use or financial strain).
4
TABLE OF CONTENTS
FOREWORD......................................................................................................................................................3
EXECUTIVE SUMMARY...................................................................................................................................11
Financial knowledge...................................................................................................................................12
Financial behaviour ....................................................................................................................................13
Attitudes ....................................................................................................................................................13
Overall indicators of financial literacy .......................................................................................................14
Variations in financial literacy by socio-demographics..............................................................................15
Indicators of inclusion and exclusion .........................................................................................................15
Further analysis of variations in financial literacy levels by gender ..........................................................18
Conclusion ..................................................................................................................................................19
CHAPTER 1: THE FINANCIAL LITERACY OF ADULTS IN 14 COUNTRIES ................................................... 21
INTRODUCTION .............................................................................................................................................23
Participating countries and data collection ...............................................................................................24
The structure and content of this chapter.................................................................................................24
FINANCIAL KNOWLEDGE ...............................................................................................................................26
Survey questions designed to test knowledge ..........................................................................................26
A financial knowledge score ......................................................................................................................29
FINANCIAL BEHAVIOUR .................................................................................................................................34
Financial behaviour across different domains ...........................................................................................34
A score for financial behaviours ................................................................................................................40
FINANCIAL ATTITUDES ...................................................................................................................................45
Attitudes and preferences: short term or longer term? ............................................................................45
Combining the various attitudes................................................................................................................46
RELATIONSHIP BETWEEN BEHAVIOUR AND THE OTHER SCORES .................................................................48
COMBINED MEASURES OF FINANCIAL LITERACY ..........................................................................................51
Segmenting the population .......................................................................................................................51
Developing an overall measure of financial literacy ..................................................................................52
VARIATIONS BY SOCIO-DEMOGRAPHICS.......................................................................................................54
Gender .......................................................................................................................................................54
Age .............................................................................................................................................................58
Income .......................................................................................................................................................60
Education level ...........................................................................................................................................61
Attitude to risk ...........................................................................................................................................63
Multivariate analysis ..................................................................................................................................63
CONCLUSION .................................................................................................................................................67
5
CHAPTER 2: FINANCIAL INCLUSION AND FINANCIAL LITERACY. ANALYSIS OF DATA FROM THE OECD
FINANCIAL LITERACY MEASUREMENT EXERCISE IN 14 COUNTRIES ...................................................... 69
INTRODUCTION .............................................................................................................................................71
Financial Education, Financial Inclusion and the Need for Data................................................................71
PRODUCT AWARENESS..................................................................................................................................73
The relationship between awareness and financial knowledge................................................................75
PRODUCT HOLDING .......................................................................................................................................77
The relationship between awareness and holding ....................................................................................77
Indicators of product holding ....................................................................................................................77
Payment products ......................................................................................................................................78
Savings and investment products ..............................................................................................................79
Insurance....................................................................................................................................................79
Credit..........................................................................................................................................................79
Variations in financial literacy by product holding ....................................................................................79
ACTIVE PRODUCT CHOICE .............................................................................................................................84
RELYING ON FRIENDS AND FAMILY ...............................................................................................................86
FINANCIAL INCLUSION AND SOCIO-DEMOGRAPHICS ...................................................................................88
Variations in awareness by socio-demographics .......................................................................................88
Variations in payment product holding by socio-demographics ...............................................................89
Variations in savings product holding by socio-demographics..................................................................91
Variations in credit and insurance product holding by socio-demographics ............................................93
Variations in active product choice by socio-demographics .....................................................................94
CONCLUSION .................................................................................................................................................96
Product awareness ....................................................................................................................................96
Products held .............................................................................................................................................97
Product choice ...........................................................................................................................................99
Reliance on friends and family...................................................................................................................99
CHAPTER 3: GENDER DIFFERENCES IN FINANCIAL LITERACY ...............................................................101
INTRODUCTION ...........................................................................................................................................103
The policy interest in the financial literacy of women and girls ..............................................................103
FINANCIAL KNOWLEDGE OF WOMEN .........................................................................................................104
Gender differences in financial knowledge at young ages ......................................................................105
Less well-educated and low-income women have the lowest financial knowledge ...............................106
Gender differences are smaller but still significant after controlling for socio-demographic factors ....107
WOMEN’S FINANCIAL ATTITUDES ...............................................................................................................109
Women appear to be aware of their lack of financial knowledge ..........................................................109
Women have lower confidence than men in their financial knowledge and skills .................................109
Gender differences in interest for financial matters ...............................................................................110
Women are more risk-averse than men ..................................................................................................111
GENDER DIFFERENCES IN FINANCIAL BEHAVIOUR AND STRATEGIES .........................................................112
Women are more likely to have a budget ...............................................................................................112
Making ends meet: women tend to cut down on spending while men try to earn extra money...........115
Gender differences in product holding ....................................................................................................117
6
Gender differences in saving behaviour ..................................................................................................119
Women are less likely to choose financial products appropriately .........................................................123
FACTORS AFFECTING GENDER DIFFERENCES IN FINANCIAL LITERACY .......................................................127
Gender roles in household financial decision making have limited impact ............................................127
Women’s lower financial inclusion and access to finance.......................................................................127
CONCLUSION ...............................................................................................................................................129
REFERENCES ................................................................................................................................................131
ANNEX 1:
ANNEX 2:
ANNEX 3:
ANNEX 4:
TESTING THE CORE QUESTIONNAIRE AND DEVELOPING A SCORE ............................................135
FURTHER DETAIL OF COUNTRY LEVEL DATA ..............................................................................153
PRODUCT HOLDING BY COUNTRY..............................................................................................167
ANALYSIS BY SUBGROUPS OF WOMEN......................................................................................175
Tables
Table 1.
Table 2.
Table 3.
Table 4.
Table 5.
Table 6.
Table 7.
Table 8.
Table 9.
Table 10.
Table 11.
Table 12.
Table 13.
Table 14.
Table 15.
Table 16.
Table 17.
Table 18.
Table 19.
Table 20.
Table 21.
Table 22.
Table 23.
Table 24.
Table 25.
Table 26.
Table 27.
Table 28.
Table 29.
Table 30.
Table 31.
Table 32.
The 8 knowledge questions ...................................................................................................27
Correct responses to knowledge questions ..........................................................................28
Creating a knowledge score ..................................................................................................30
Positive financial behaviours by country ...............................................................................35
Actively saving or buying investments in the past 12 months ..............................................38
Creating a behaviour score ....................................................................................................41
High score on each of the financial literacy components .....................................................51
Regression results ..................................................................................................................65
Awareness by socio-demographics .......................................................................................89
Payment products by gender, age and education level ........................................................90
Savings holding by gender, age and income .........................................................................92
Active product choice by age, income and education level ..................................................95
Creating a knowledge score .................................................................................................140
Ranking of country according to scoring method used .......................................................142
Financial Behaviour Variables..............................................................................................146
Testing the behaviour score ................................................................................................148
Details of sample .................................................................................................................153
Financial knowledge: division, time-value of money, interest paid on a loan ....................155
Financial knowledge: interest plus principle, compound interest ......................................156
Financial knowledge: risk and return, inflation, diversification ..........................................157
Behaviour: Before I buy something I carefully consider whether I can afford it ................158
Behaviour: I pay my bills on time ........................................................................................158
Behaviour: I keep a close personal watch on my financial affairs .......................................159
Behaviour: I set long term financial goals and strive to achieve them ...............................159
Average behaviour score by country ...................................................................................160
Attitude: I find it more satisfying to spend money than to save it for the long term .........160
Attitude: I tend to live for today and let tomorrow take care of itself ...............................161
Attitude: Money is there to be spent ..................................................................................161
Average combined attitude scores ......................................................................................162
Financial literacy segments by gender ................................................................................163
Financial Literacy Segments by Income ...............................................................................164
Financial knowledge across subgroups of women ..............................................................175
7
Figures
Figure 1.
Figure 2.
Figure 3.
Figure 4.
Figure 5.
Figure 6.
Figure 7.
Figure 8.
Figure 9.
Figure 10.
Figure 11.
Figure 12.
Figure 13.
Figure 14.
Figure 15.
Figure 16.
Figure 17.
Figure 18.
Figure 19.
Figure 20.
Figure 21.
Figure 22.
Figure 23.
Figure 24.
Figure 25.
Figure 26.
Figure 27.
Figure 28.
Figure 29.
Figure 30.
Figure 31.
Figure 32.
Figure 33.
Figure 34.
Figure 35.
Figure 36.
Figure 37.
Figure 38.
Figure 39.
Figure 40.
Figure 41.
Figure 42.
Figure 43.
Figure 45.
Figure 46.
Figure 47.
Country groupings by average financial knowledge scores ..................................................31
Distribution of knowledge scores ..........................................................................................32
Financial knowledge: Percentage with a high score .............................................................33
Responsible and has a household budget .............................................................................37
Shopping around for financial products ................................................................................39
Borrowing to make ends meet ..............................................................................................40
Country groupings by average financial behaviour scores ....................................................42
Distribution of financial behaviour scores.............................................................................43
Financial behaviours: Percentage scoring 6 or more ............................................................44
Distribution of financial attitude scores ................................................................................46
Percentage of respondents with average score over 3 .........................................................47
Relationship between financial knowledge and behaviour ..................................................49
Financial behaviour and attitudes .........................................................................................50
Financial Literacy Segments ..................................................................................................52
Country groupings by average overall financial literacy scores ............................................53
High knowledge score by gender ..........................................................................................55
High behaviour score by gender ............................................................................................56
High attitude score by gender ...............................................................................................57
Mean overall score by gender ...............................................................................................58
Financial literacy segments by age ........................................................................................59
Average overall score by income...........................................................................................61
Financial literacy segments by education..............................................................................62
Average overall scores by risk aversion .................................................................................63
Proportion of respondents aware of at least 5 products ......................................................74
Average financial literacy by product awareness ..................................................................75
Product awareness by financial knowledge ..........................................................................76
Product holding .....................................................................................................................78
Average financial literacy by payment product.....................................................................80
Paying bills on time with and without a payment account ...................................................81
Average financial literacy by saving and investment ............................................................81
Understands the impact of compound interest and has savings or investments .................82
Average financial literacy by credit product..........................................................................83
Average financial literacy by insurance product ...................................................................83
Made an active product choice in the last 2 years ................................................................84
Average financial literacy by recent product choice .............................................................85
Relying on friends or family for borrowing and saving .........................................................86
Average financial literacy by reliance on family and friends .................................................87
Payment product holding by income ....................................................................................91
Holds savings or investment account by education ..............................................................93
Credit products by gender .....................................................................................................93
Product choice by gender ......................................................................................................94
Average financial knowledge score by gender ....................................................................105
Average financial knowledge score by gender (young people)...........................................106
Percentage ‘do not know’ replies by gender.......................................................................110
Responsibility for day-to-day money management decisions in the household ................113
Responsible for money management decision and has a household budget.....................114
8
Figure 48.
Figure 49.
Figure 50.
Figure 51.
Figure 52.
Figure 53.
Figure 54.
Figure 55.
Figure 56.
Figure 57.
Figure 58.
Figure 59.
Figure 60.
Figure 62.
Figure 63.
Figure 64.
Figure 65.
Figure 66.
Figure 67.
Figure 68.
Figure 69.
Figure 70.
Figure 71.
Figure 72.
Figure 73.
Figure 74.
Figure 75.
I keep a close personal watch on my financial affairs .........................................................114
Sometimes people find that their income does not quite cover their living costs .............115
Strategies for making ends meet: cutting back on spending ..............................................116
Strategies for making ends meet: earning extra money .....................................................117
Percentage of respondents who hold a payment product .................................................118
Percentage of respondents who hold an investment product............................................119
Percentage of respondents who hold a saving product ......................................................120
Covering living expenses .....................................................................................................121
Saving behaviour: Actively saving through formal products ...............................................122
Saving behaviour: Saving informally....................................................................................122
Has tried to compare financial products across providers ..................................................124
Information sources: made some attempt at taking an informed decision .......................125
Information sources: consulted independent professional info/advice .............................126
Products: Albania.................................................................................................................167
Products: Armenia ...............................................................................................................167
Products: Czech Republic ....................................................................................................168
Products: Estonia .................................................................................................................168
Products: Germany ..............................................................................................................169
Products: Hungary ...............................................................................................................169
Products: Ireland .................................................................................................................170
Products: Malaysia ..............................................................................................................171
Products: Norway ................................................................................................................171
Products: Peru .....................................................................................................................172
Products: Poland..................................................................................................................173
Products: South Africa .........................................................................................................173
Products: UK ........................................................................................................................174
Products: British Virgin Islands ............................................................................................174
9
EXECUTIVE SUMMARY
Many governments and international organisations recognise the importance of measuring levels of
financial literacy4 and collecting information about financial inclusion5 to inform policy responses.
In 2010, the OECD International Network on Financial Education (INFE) developed and fielded a
questionnaire designed to create an international, broad based and robust measure of financial literacy
as well as financial inclusion indicators. Reflecting the majority of national financial literacy
measurements, this OECD/INFE Financial Literacy Core Questionnaire (or Core Questionnaire for brevity)
focuses on those aspects of knowledge, attitudes and behaviours that are associated with the overall
concept of financial literacy. The OECD/INFE measurement toolkit comprises this core questionnaire as
well as methodological guidance and a set of supplementary questions (OECD/INFE, 2013a).
The Core Questionnaire incorporates a series of questions on financial products chosen specifically
to enable analysis of financial inclusion. These ask about product awareness, products currently held and
approaches to choosing financial products.
The questions are drawn from worldwide good practice, and asked of a representative sample of
adults in each country, using either telephone or face-to-face interviews6. Almost all the questions refer
to the individual answering the question, although some information is collected about the respondent’s
household, including the household income and the number of people living with the respondent.
The core questions have been picked because they are applicable to the vast majority of people and
they are suitable across a wide range of countries. While all of the questions retain the same format in
each country, some of the answer codes allow for country specific responses (such as methods of saving,
or types of financial product). This enables cross country comparisons that are contextually meaningful,
while maximising the potential to include all interested countries.
In 2010, the OECD invited countries to use the Core Questionnaire to measure financial literacy in
their country, and to submit the data to the OECD/INFE Secretariat as part of a project to test the
applicability of a standard financial literacy measures across countries and across socio-demographic
groups within countries. At this initial stage, 12 countries across 4 continents accepted the invitation:
4
Financial literacy is defined as: ‘A combination of awareness, knowledge, skill, attitude and behaviour
necessary to make sound financial decisions and ultimately achieve individual financial wellbeing.’ (Atkinson
and Messy, 2012).
5
The OECD/INFE has defined financial inclusion as follows: ‘The process of promoting affordable, timely and
adequate access to regulated financial products and services and broadening their use by all segments of
society through the implementation of tailored, existing and innovative approaches including financial
awareness and education, with a view to promote financial wellbeing as well as economic and social inclusion.’
6
The exception to this is Norway, where a subset of questions has been tested over the telephone, whilst the
full questionnaire has been implemented through an online survey.
11
Armenia; the Czech Republic; Estonia; Germany; Hungary; Ireland; Malaysia; Norway; Peru; Poland; South
Africa, and the United Kingdom. Albania joined the project at a later stage and the British Virgin Islands
(BVI) subsequently undertook a national survey using the Core Questionnaire and shared the data with
the OECD/INFE7.
The survey population consisted of adults aged 18 and over8. Each country aimed to interview at
least 1,000 individuals and data has been weighted to reflect the national population in terms of basic
demographics.
The data collection for this project began in the second half of 2010 and was completed in early
20119. Each participating country collected the results from their survey in an anonymised database
which was then submitted to the OECD Secretariat for analysis.
The results focus on the general pattern of financial literacy in different countries, identifying
commonalities and differences. The exercise is not designed to rank countries according to their levels of
financial literacy, although it is illustrative to draw certain comparisons across countries to highlight
variations.
Financial knowledge
A financially literate person will have some basic knowledge of key financial concepts. The Core
Questionnaire therefore includes 8 questions to test levels of knowledge in each10. The questions have
been chosen to cover a range of financial topics and to vary in difficulty, although none of them is
excessively complex and none of them requires expert knowledge.
A financial knowledge score has been created by summing correct answers and reporting them as a
score out of 100. The proportion of the population in each country that exhibited a relatively high level of
financial knowledge (defined as getting at least 3/4 of the questions correct) has also been reported. In
some countries, fewer than half of the respondents achieved this score, and no country had more than
70% of their population who could answer at least 3/4 of the questions.
Of particular concern is the relatively large proportion of people who could not calculate simple
interest earned on a savings account at the end of one year and then identify the impact of compounding
over 5 years. In Albania and Peru, fewer than 1 in 5 people were able to apply their knowledge to this
two-part question, and in every country except Norway, at least half of the population failed to identify
the impact of compounding.
There was also a worrying low level of awareness of the benefit of diversification, with at least a
third and in some cases over half of respondents in each country being unable to answer this question.
7
In cross-country tables and figures, the data from BVI is reported last to remind the reader that the country
was not formally part of the first measurement project, and has a smaller sample size.
8
Where countries have surveyed a wider population (from 15 years of age in some cases) responses are only
included from those aged 18 and over – in some cases this meant recalibrating the weights.
9
With the exception of data from Albania and BVI.
10
Most countries have used the questions as designed, although there are some variations which mean that
direct comparisons of overall scores between the other countries and Norway or Hungary should be made with
caution.
12
Financial behaviour
The way in which a person behaves will have a significant impact on their financial wellbeing. It is
therefore important to capture evidence of behaviour within a financial literacy measure. The Core
Questionnaire does this by asking a variety of questions in different styles, to find out about behaviours
such as thinking before making a purchase, paying bills on time and budgeting, saving and borrowing to
make ends meet.
The financial behaviour score counts positive behaviours exhibited and rescales the final score to
take values between 0 to 100; those exhibiting 2/3 of the behaviours are considered to have a relatively
high score. As with knowledge, in some countries fewer than 50% achieved a high score. BVI residents
showed the largest number of positive behaviours, with 71% exhibiting such a score.
There is a wide variation in behaviours within countries, and noticeable variation across countries.
However, of concern in all the countries surveyed is the lack of active, informed market participation:
very few people reported that they had shopped around and sought independent information or advice
to make a financial product choice in the last 2 years (UK participants were the most likely to have done
so, at 16%).
In some of the countries, the lack of active saving is also a concern, although here there are large
variations by country. In Hungary just 27% had been saving in the previous 12 months whilst in Malaysia
almost everyone had done so (97%). In all, only three countries found that more than 80% of their
population were actively saving.
The likelihood of setting long term goals also varies by country: more than 7 in 10 Peruvians
reported that they did set long term goals, compared with just 3 in 10 Albanians.
Whilst borrowing to make ends meet is not widespread, it is a problem for a large minority in certain
countries. In particular, almost half (47%) of Armenians had resorted to borrowing the last time their
income fell short of their expenditure; in Albania, Peru and South Africa over a quarter of respondents
had also done so.
There is considerable variation in behaviour within a country. For example, a large proportion of
Malaysian respondents were active savers and carefully considered their purchases, yet hardly any (3%)
had made a recent financial product choice after shopping around and seeking independent guidance. In
Norway, almost 9 in 10 people reported that they were keeping an eye on their financial affairs yet just 1
in 4 was budgeting: showing that more people look over their recent financial activities than plan future
ones.
Attitudes
Attitudes and preferences are considered to be an important element of financial literacy. If people
have a rather negative attitude towards saving for their future, for example, it is argued that they will be
less inclined to undertake such behaviour. Similarly, if they prefer to prioritise short-term wants then they
are unlikely to provide themselves with emergency savings or to make longer term financial plans.
13
The Core Questionnaire includes three attitude statements to gauge respondents’ attitudes towards
money and planning for the future11. The attitude questions ask people about whether they agree or
disagree with particular statements, to capture their disposition or preferences.
Very few respondents in Armenia (8%) and Poland (19%) got satisfaction from saving. In contrast,
64% of Peruvians and 61% of Albanians found saving satisfying. Albanians and Peruvians were also the
most conservative with money, with almost half of respondents (45%) disagreeing that money is there to
be spent. In contrast, just 2% of Armenians and 12% of Polish respondents tended to disagree with the
statement.
The average response to the three attitude statements provides an overall indicator of attitude.
Respondents with an average attitude indicator above 3 (a ‘high’ score), have attitudes that tend towards
the longer term. Analysis shows that there is a very wide variation in attitudes across countries: in
Armenia, just over 1 in 10 people have a positive attitude towards the longer term; compared with 71% in
Peru.
Overall indicators of financial literacy
Several approaches have been used to describe the data in terms of overall levels of financial
literacy:
11

Summing the raw scores financial knowledge, financial behaviour and financial attitudes into an
overall indicator of financial literacy and rescaling this from 0 to 100: The average of this
combined score across all participating countries is 63.2. Scores in the Czech Republic, Germany,
Hungary, Ireland, Norway, Malaysia, Peru, the UK and BVI are above this combined average (See
Figure below).

Looking at the proportion of the population achieving high scores on each of the three
components of financial literacy: In 8 of the countries surveyed, a larger proportion of the
population achieved a high knowledge score than a high behaviour score; indicating that levels
of financial literacy in these countries are higher in terms of knowledge than behaviour.
Conversely, in Germany, Malaysia, Norway, Peru, South Africa and BVI countrywide financial
literacy levels are higher in terms of behaviour; in most cases this is because a larger proportion
of the population exhibit 6 or more positive behaviours, rather than because knowledge is
exceptionally low.

Counting the number of high scores for financial knowledge, financial behaviour and financial
attitudes that each respondent achieved: In all of the countries surveyed there are some people
who did not achieve any high scores and others who achieved a high score on all 3 aspects of
financial literacy. However, typically people tend to have 1 or 2 strengths. Germany and BVI
stand out with over 30% of their populations achieving 3 high scores, indicating high levels of
financial literacy.
Norway only used the first question.
14
Country groupings by average overall financial literacy scores
Lower average
overall score
Higher average
overall score
Armenia
South Africa
Poland
Estonia
Albania
Czech Republic
Peru
UK
Norway
Ireland
Malaysia
Hungary
Germany
BVI
Shaded boxes indicate homogenous groups of countries, computed in IBM SPSS19 using Scheffe Homogenous Subsets. Average
scores range from 56.4 to 68.6.
Variations in financial literacy by socio-demographics
It is clear that levels of financial literacy vary within countries and it is therefore useful to know more
about how they vary across particular socio-economic groups.
The findings of analysis by socio-demographics show important gender differences, which are
further explored in Chapter 3.
There is also a noticeable variation in financial literacy by age and income. In most countries, middle
age is associated with higher levels of financial literacy, whilst the oldest and youngest respondents are
more likely to have no high scores. Regression analysis confirms that higher income respondents are
more likely to gain high scores than their lower income peers.
Similarly, there is also a positive relationship between education and financial literacy. Higher
educated individuals are more likely to exhibit positive behaviours and attitudes as well as show
advanced levels of knowledge.
Indicators of inclusion and exclusion
Financial inclusion relates specifically to awareness, access and use of financial products. The second
chapter therefore pays particular attention to the awareness (which is necessary in order to use a
product), use, and recent purchase of a range of financial products.
The Core Questionnaire includes a suggested list of product types – covering savings, investments,
pensions, current accounts, insurance and credit. This list has been edited by each of the participating
countries to be contextually relevant. For each country, a unique list of product types has been created
by national authorities or experts. Results from each country in this chapter should then be considered in
context- it may not be appropriate to compare across very different countries.
15
The data from the Financial Literacy Measurement survey has provided the OECD/INFE with a
unique opportunity to analysis the demand side of financial inclusion, and the possibility to explore the
extent to which this is associated with financial literacy.
Charts detailing the products listed in each questionnaire and showing percentages of respondents
reporting product awareness, current product holding and recent purchases are shown in Annex 2.
The indicators of financial inclusion focus on the number of products that respondents were aware
of, the products they held, and recent product choice. An indicator of potential exclusion is also reported,
looking at the extent to which respondents relied on friends and family for saving or borrowing.
The findings show a positive association between financial literacy and financial inclusion.
In all countries, people who were aware of at least 5 financial products had higher levels of financial
literacy than those who were less aware. In some countries, low levels of financial knowledge were
associated with low levels of financial product awareness, suggesting that financial education policies
could usefully provide general financial knowledge whilst also improving awareness of financial products.
Awareness of a range of products is a vital first step in removing demand side barriers to financial
inclusion. At a minimum consumers need to know:

That certain financial products exist;

the purpose of each category of product: e.g. to receive income, make payments, protect
savings from the impact of inflation or protect households from the consequences of
expenditure shocks;

which products are regulated or safe; and

where and how to access the relevant products (who are the suppliers, what are the access
requirements, etc).
Awareness may also include knowing which features should be taken into account when choosing
amongst various offers, and being aware of consumer rights and responsibilities when holding a
particular product.
The data reveals that individuals with a payment account were more likely to make timely bill
payments, an important finding that could be stressed in financial education programmes.
Analysis also shows that understanding of the benefit of receiving compound interest on savings was
higher amongst those with a savings or investment product than those who did not hold such a product.
However, many savers did not know about this benefit, and may have made inappropriate choices as a
result.
Furthermore, financially literate people were more likely to hold insurance, a finding that strongly
supports the argument that financial literacy leads to financial inclusion (since there is no reason to
assume that holding insurance would make someone more financially literate).
Financial literacy levels are also lower amongst people who turned to friends and family to borrow
money or save, suggesting that financial education could help them to identify more secure alternatives.
16
The analysis discussed in this chapter looks at the first of these issues: awareness of the existence of
the product. In particular, it shows that financial literacy was lower amongst respondents who were
aware of fewer than 5 products in all participating countries.
Financial inclusion depends on both the demand and supply of appropriate products. It is not
possible to directly capture demand for a product, but it is possible to explore product holding, which
indicates demand that has been met.
There are many financial products available, serving different needs and providing consumers with a
range of tools and services. This analysis focuses on 4 key categories: payment products; saving and
investment; insurance; and credit. Whilst some consumers may not require products from each of these
categories, they are nevertheless frequently used in combination, and provide a broad overview of
product use at a country level.
If awareness is considered to be an approximation for supply, the analysis shows that in some cases
supply exists, but demand is low –i.e. people are aware that certain products are available, but they are
not using them. This finding is particularly striking in Armenia and Peru; and may invite further
investigation.
If consumers have made a recent product choice, then they are engaged in the financial market. This
is a good indicator of the health of the market and the extent to which products appeal to the population
at large. Whether this reflects a genuine match between the needs of consumers and the design of
products, or whether the activity is triggered by advertising should be explored at the national level.
The analysis presented here shows that financial literacy was higher amongst those who made a
recent product choice than those who had not. However it should be remembered that 2 of the financial
behaviours captured in our measure of financial literacy relate to how a product was chosen, and so
some variation would be anticipated. Nevertheless the additional points available cannot explain the size
of the gap in countries such as Albania, Germany and Peru.
The financial literacy analysis in Chapter 1 shows that it is very unusual for consumers to make an
informed product choice based on shopping around and seeking independent information or guidance.
The way in which individuals choose a product may be influenced by a wide range of factors including the
speed with which they (believe they) need to make a decision, the availability of alternatives, the extent
to which information and advice is available and their previous experiences. However, it is clear that
financial education could usefully highlight the benefit of comparing across products, seeking impartial
opinions and regularly assessing the suitability of products held.
The final indicator counts people who are not using financial products to meet (some, or all of) their
existing needs. By identifying people who are either saving with friends and family or have resorted to
borrowing from them, it is possible to identify people who could benefit from improved financial
inclusion measures that ensure suitable products exist, help consumers to see the benefit of using formal
products and encourage them to access such products. However, in some countries, and some groups,
family and friends may be the first option for people to access financial support, and this behaviour would
not be seen as a sign of financial exclusion.
The analysis shows that in every country there was some reliance on friends and family. In some
countries over a third of respondents had turned to such a network either to borrow money or to put
money aside in the last 12 months. Furthermore, financial literacy was lower amongst those individuals.
17
Further analysis of variations in financial literacy levels by gender
In many countries, women display lower financial knowledge than men and are also less confident in
their financial knowledge and skills. Even though women appear to be better than men at short term
money management behaviour they have a number of vulnerabilities in other aspects of financial
behaviour. For instance, women are more likely to experience difficulties in making ends meet, in saving
and in accumulating financial resources. Moreover, women are more risk averse and less likely than men
to invest in risky assets, such as investments. Further, women show more difficulties than men in
choosing financial products appropriately. In particular, men are more likely than women to shop around
for financial products.
A number of factors appear to be related to gender differences in financial literacy. Gender
differences in financial literacy are strongly correlated with differences in socio-economic conditions of
men and women, suggesting that limited access to education, employment and formal financial markets
not only reduce women’s financial well-being per se, but also limit the extent to which women can
improve their knowledge, confidence and skills about economic and financial issues.
Overall, the analysis of the available evidence on gender differences in financial literacy – based on
various sources, including the OECD/INFE financial literacy survey and other studies – highlights a number
of issues:

Women display lower financial knowledge than men in most of the countries surveyed. In
particular, young women, widows, less well-educated and low-income women lack financial
knowledge the most. Gender differences in financial knowledge are in part, but not entirely,
related to demographic and socio-economic factors.

Available evidence suggests that women are less confident then men in their financial
knowledge and skills, less over-confident in financial matters, and more averse to financial risk.
They also appear to be less interested than men in financial matters.

Women appear to be better than men at short-term money management behaviour: they are
more likely than men to have a budget and to keep a close watch on their financial affairs.

Women are more likely to experience difficulties in making ends meet. In addition, women and
men have different coping strategies for making ends meet. If confronted with situations where
their income is not sufficient to cover living costs, women tend to cut expenses, while men
prefer finding ways for earning extra money.

Women and men display different saving behaviour. Consistently with the evidence that they
face more difficulties in making ends meet, women tend to save less and accumulate less
wealth than men, typically as a result of their weaker labour market position. Moreover, men
are more likely than women to be actively saving through formal financial products, while
women are more likely than men to be saving cash at home or in their wallet, or to be saving in
informal savings clubs. Women are also less likely than men to invest in risky assets.

Women show more difficulties than men in choosing financial products appropriately. In
particular, men are more likely than women to shop around for financial products. In some
countries, men are also more likely than women to take informed financial decisions and to use
independent advisors.
18
Conclusion
This book presents the findings of analysis of a set of questions asked to very different populations
around the world and provides simple, meaningful indicators of financial literacy and financial inclusion.
The cross-country nature of this analysis enables countries to create partnerships to tackle particular
issues at an international level, and also allows some countries to identify potential ‘benchmark
countries’ that have achieved a higher level of financial literacy across their population.
The research highlights areas for concern. For instance, in every country there is significant room for
improvement in terms of financial knowledge: understanding of some everyday financial concepts such as
compound interest and diversification is lacking amongst sizeable proportions of the population in every
country, and in most countries surveyed women are less knowledgeable than their male counterparts.
The findings also highlight a large proportion of individuals who could benefit from initiatives
designed to change their behaviour. In almost every country surveyed, at least 3 in 10 respondents
exhibited fewer than 2/3 of the positive behaviours discussed.
The analysis shows how financial knowledge and financial behaviour are associated in every country
– more knowledgeable individuals are more likely to exhibit positive financial behaviour.
The data has also created a unique opportunity to explore some of the demand side factors of
financial inclusion, including product awareness and holding, providing an important complement to
existing international financial inclusion measurements, which focus mostly on supply-side data from
financial institutions.
The analysis undertaken in Chapter 2 provides indications that the population of some countries
have low levels of financial inclusion on a number of indicators. The positive association between
financial inclusion and financial literacy suggests an important role for financial education in the drive to
increase levels of inclusion in some countries. Moreover, the various indicators of financial inclusion are
significantly associated with various socio-demographic factors such as gender, age, education, and
household income; suggesting that targeted financial education may reap the highest rewards.
Chapter 3 identifies variations in the levels of financial literacy by gender, and indicates a pressing
need for financial education targeted at women to address the gender gap. This is particularly important
in terms of increasing levels of financial knowledge amongst women and girls, and improving ability to
make ends meet and choose appropriate financial products.
A number of factors appear to be related to gender differences in financial literacy. Gender
differences in financial literacy are strongly correlated with differences in socio-economic conditions of
men and women, suggesting that limited access to education, employment and formal financial markets
not only reduce women’s financial well-being per se, but also limit the extent to which women can
improve their knowledge, confidence and skills about economic and financial issues.
The data hold a great deal of potential. The OECD will continue with analysis in order to inform the
work of its INFE, focusing particularly on variations in financial literacy by key socio-demographic groups,
levels of financial inclusion and financial access, as well as exploring in more detail the relationship
between various aspects of financial literacy.
19
Chapter 1:
THE FINANCIAL LITERACY OF ADULTS IN 14 COUNTRIES
INTRODUCTION
Financial literacy is rapidly being recognised as a core skill, essential for consumers operating in an
increasingly complex financial landscape. It is therefore no surprise that governments around the world
are interested in finding effective approaches to improve the level of financial literacy amongst their
population and that many are in the process of creating or leading a national strategy for financial
education to provide learning opportunities throughout a person’s life (OECD/INFE, 2013b).
The OECD defines financial education as follows:
Financial education is the process by which individuals improve their understanding of financial
products and concepts; and through information, instruction and/or objective advice develop
the skills and confidence to become more aware of financial risks and opportunities, to make
informed choices, to know where to go for help, and to take other effective actions to improve
their financial well-being and protection (OECD 2005a).
Financial education strategies benefit from empirical evidence to indicate the level of need amongst
the population as a whole and within particular subgroups (OECD/INFE 2012). The measurement of
financial literacy levels is therefore widely recognised as a priority for countries seeking to deliver
financial education in an efficient manner and evaluate its impact at a national level.
Such a measurement exercise allows policy makers to identify potential needs and gaps in relation to
specific aspects of financial literacy and provides information about which groups of people are in need of
most support. The results of the first financial literacy survey undertaken in a particular country can be
taken as a baseline, and used to set benchmarks for financial education initiatives. Subsequent waves of a
survey can be used to identify changes that have occurred during the interim period.
National financial literacy surveys are clearly important tools, but the potential gain from a survey
undertaken across a number of countries is much greater. Such an international study provides the
opportunity to compare levels of financial literacy and progress across populations and financial markets,
and is of huge interest to policy makers and other stakeholders seeking to understand why one country
appears to be achieving more than another and which interventions are most effective.
The OECD International Network on Financial Education (INFE) agreed to address the lack of
internationally comparable data through the design and testing of a purpose built survey instrument.
After many months of development and refinement, the OECD/INFE approved a core questionnaire, and
countries were invited to utilise this OECD/INFE Financial Literacy Core Questionnaire according to an
agreed methodology.
23
This instrument took as its starting point the following working definition:
Financial literacy is a combination of awareness, knowledge, skill, attitude and behaviour
necessary to make sound financial decisions and ultimately achieve individual financial
wellbeing.
The Core Questionnaire focuses on those aspects of knowledge, attitudes and behaviours that are
associated with the overall concept of financial literacy. The questions cover a range of contexts,
including accessing financial services, meeting immediate financial requirements and planning for the
future. Almost all the questions relate directly to the individual answering the question, although some
information is collected about the household, including the household income and the number of people
living with the respondent.
The core questions have been picked because they are applicable to the vast majority of people and
they are suitable across a wide range of countries. Each question is designed to be asked in the same way
in each country, but country-specific responses are possible (for example when respondents are asked
about their methods of saving, or types of financial product, their responses will reflect the local financial
market). This enables cross country comparisons that are contextually meaningful, while maximising the
potential to include all interested countries.
Participating countries and data collection
This chapter reports on 14 countries: of these, 12 originally volunteered to take part in a
measurement project using the Core Questionnaire (Armenia; Czech Republic; Estonia; Germany;
Hungary; Ireland; Malaysia; Norway; Peru; Poland; South Africa and the United Kingdom), whilst the 13 th,
Albania joined the project at a later stage with the guidance of our INFE Measurement Subgroup Expert
from Italy. The 14th country, The British Virgin Islands (BVI), did not participate in the project, but
undertook a national survey using the Core Questionnaire and shared the data with the OECD/INFE.
Each country aimed to interview at least 1,000 individuals and data has been weighted to reflect the
national population in terms of basic demographics. The data collection process began in the second half
of 2010 and was completed early 201112. Each participating country undertook a nationally
representative survey using the Core Questionnaire, collected the results in an anonymised database and
provided a dataset to the OECD Secretariat for analysis.
Additional discussion about the methods used to develop measures of financial literacy is presented
in Annex 1, whilst further information about the countries that have participated and the approaches that
they used can be found in the Annex 2 (Table 17).
The structure and content of this chapter
The following three sections focus on each of the three components of financial literacy: knowledge,
behaviour and attitudes. Specific questions are reported as well as average score across several questions
and the distribution of these scores.
12
With the exception of data from Albania and BVI, which were received in 2011. Note that the achieved sample
size for BVI is 535.
24
These sections are followed by analysis of the relationship between knowledge and behaviour, and
between attitudes and behaviour.
The chapter then discusses how the population can be segmented according to their strengths and
weaknesses in terms of financial literacy, and looks at overall levels of financial literacy by combining the
scores from each of the three components.
The final section describes how levels of financial literacy vary by key demographic factors.
The analysis is particularly powerful in showing general patterns in financial literacy around the
world. However, as highlighted in the text that follows, the reader should apply caution when making
specific cross-country comparisons from this data, as there are some variations in the questions used and
the methods employed.
25
FINANCIAL KNOWLEDGE
A financially literate person will have some basic knowledge of key financial concepts and the ability
to apply numeracy skills in financial situations. The Core Questionnaire therefore asks a range of
questions in relation to concepts such as simple and compound interest, risk and return, and inflation.
Survey questions designed to test knowledge
The Core Questionnaire includes 8 survey questions designed to test aspects of financial knowledge
that are considered to be relevant across countries (Table 1). These vary in style and content in order to
avoid undue biases that could be caused by different ways of processing information across certain types
of people or cultural norms. Whilst some knowledge questions allow a person to give a completely free
response others provide a list of possible answers, from which the respondent must choose their
response. The questionnaire also encourages respondents to say if they don't know the answer to
something, in order to dissuade them from guessing (this helps to ensure that the survey captures actual
levels of knowledge rather than lucky guesses).
In some countries questions were amended or substituted, as indicated in Table 113. To some extent,
this limits our ability to make cross-country comparisons and in particular, caution should be applied
when comparing results from Hungary and Norway with the other countries or with each other.
The questions can only provide meaningful information about the level of financial literacy of
individuals and populations if they are sufficiently varied to differentiate between high and low achievers
by combining a mixture of easy and more difficult problems. The analysis of responses to each question
shows that the spread of difficulty in the questionnaire is appropriate; differentiating well both within
countries and across countries. There are also a sufficient number of questions to provide a good
overview of a person’s basic knowledge, indicate general willingness to absorb financial information and
an ability to apply knowledge to particular problems14. A high score indicates that someone has a high
level of financial knowledge, but does not necessarily suggest that they are financial experts.
13
All countries made essential edits to currency units. In some cases this also required changing the amount to
reflect national prices.
14
An international survey is not intended to capture country-specific knowledge, such as understanding the tax
system within a county, or knowing about the retirement provision provided by the state. Countries wishing to
find out more about the levels of knowledge amongst their population are encouraged to draw on the INFE
Supplementary Questions: Additional, Optional Survey Questions available at www.financial-education.org.
26
Table 1. The 8 knowledge questions
Question as in the original version of questionnaire (and
response codes with correct response in bold text)
Division
Time-value of
money
Interest paid on a
loan
Calculation of
interest plus
principle
Compound interest
Risk and return
Definition of
inflation
Diversification
Imagine that five brothers are given a gift of $1000. If the
brothers have to share the money equally how much does
each one get? [Open response: $200]
Now imagine that the brothers have to wait for one year to
get their share of the X. In one year’s time will they be able to
buy: Multiple choice:
a) More, b) the same amount, or c) less than they could buy
today.
Interviewers also recorded 2 other responses which were
considered to be correct: it depends on inflation, it depends
on the types of things they want to buy
You lend X to a friend one evening and he gives you X back the
next day. How much interest has he paid on this loan? [Open
response: 0]
Suppose you put $100 into a savings account with a
guaranteed interest rate of 2% per year. You don’t make any
further payments into this account and you don’t withdraw
any money. How much would be in the account at the end of
the first year, once the interest payment is made? [Open
response: $102]
and how much would be in the account at the end of five
years? Would it be:
a) More than $110
b) Exactly $110
c) Less than $110
d) Or is it impossible to tell from the information given
An investment with a high return is likely to be high risk
[True/False]
High inflation means that the cost of living is increasing rapidly
[True/False]
It is usually possible to reduce the risk of investing in the stock
market by buying a wide range of stocks and shares
[True/False]
Changes to core questions
Armenia used a previous version of the
core questionnaire, which asked about a
lottery prize rather than a gift.
Not asked in Norway*.
Norway: Imagine that you get a gift of
1000kr, and you put it in the drawer at
home for 12 months. After one year how
much could you buy for this money?
Armenia had 4 options (excluding: It
depends on the types of things)
Not asked in Albania and Norway*. In
Malaysia respondents were asked what
return was earned on the loan. This
question is not used in the score for
Peru, due to problems with data coding.
Hungary changed the interest rate to 5%
Hungary: The options given were less
than simple interest, making it
impossible to know whether respondent
identified the impact of compound
interest or just calculated simple interest
RSA: If someone offers you the chance to
make a lot there is a chance that you will
lose a lot
Not asked in Norway
RSA: It is less likely that you will lose all
of your money if you save it in more than
one place (Yes)
Norway and BVI: To buy a single share
carries less risk than buying shares in
mutual funds (No)
*Norway asked three alternative questions which have been used in their score: What is the nominal interest rate? What is
meant by the effective interest rate? True/False: When you buy shares in a company you lend money to the company? Note that
these questions were slightly edited as a result of feedback from the initial project.
27
The results reported in Table 215 show that most people in most of the countries could use mental
arithmetic to undertake a simple division. However, despite the widespread ability across all of the
countries there are still quite wide variations in the proportion of respondents who gave an incorrect
answer, from just 1% in Malaysia through to 17% in the United Kingdom.
Table 2. Correct responses to knowledge questions
Proportion giving correct response (Cell percentages by country).
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway*
Peru
Poland
South Africa
UK
BVI**
Division
Timevalue
of
money
89%
86%
93%
93%
84%
96%
93%
93%
61%
90%
91%
79%
76%
84%
61%
83%
80%
86%
61%
78%
58%
62%
87%
63%
77%
49%
61%
74%
Interest
paid on
loan
87%
88%
84%
88%
95%
88%
93%
61%
85%
65%
90%
99%
Calculation
of interest
plus
principle
Compound
interest
and correct
answer to
previous
question
Risk
and
return
Definition
of
inflation
Diversification
40%
53%
60%
64%
64%
61%
76%
54%
75%
40%
60%
44%
61%
63%
10%
18%
32%
31%
47%
46%
29%
30%
54%
14%
27%
21%
37%
20%
77%
67%
81%
72%
79%
86%
84%
82%
61%
69%
48%
73%
77%
83%
81%
57%
70%
85%
87%
91%
88%
74%
68%
86%
80%
78%
94%
87%
63%
59%
54%
57%
60%
61%
47%
43%
51%
51%
55%
48%
55%
41%
Empty cells have no relevant observations, including those where the response was not recorded. See Table 5 for question text.
*The results reported for Norway under the Division column actually refer to an alternative questions posed: What is the
nominal interest rate. Norway also slightly reworded the time value of money question, as they had not asked the previous
question. Under interest for Norway, the table reports answers to: What is meant by the effective interest rate. **For
diversification Norway and BVI asked Buying a single company’s stock usually provides a safer return than a stock mutual fund.
Fewer respondents gave a logical answer to the follow-up question designed to identify those who
understand how inflation impacts on the value of fixed cash amount. In South Africa fewer than half
(49%) believed that the money would buy less in one year’s time despite the fact that inflation in that
country was over 4% at the time of the survey. Only in Armenia, Czech Republic, Estonia and Norway did
at least 4 out of 5 people give a correct response to this question.
The concept of interest being paid on a loan appears to be widely understood; indeed in BVI 99% of
respondents gave a correct response. The question requires the simplest possible arithmetic, but records
an open ended response to minimise the possibility that people guess the correct answer.
People found it harder to calculate a percentage than to undertake a division. Between 40%
(Albania, Peru) and 76% (Ireland) of respondents gave a correct response to the first saving related
question requiring calculation of interest plus principle (Table 19, Annex 2). The follow-up question was
found to be harder still, particularly in Albania, Armenia and Peru: only 10%, 18% and 14% respectively
showed that they could calculate simple interest and understand the impact of compounding.
15
Tables 18, 19, and 20 in Annex 2 provide a more detailed breakdown of responses to the knowledge questions.
28
Individuals in Hungary (86%) and Ireland (84%) were most likely to understand the basic concept of
risk and return, whilst fewer than half of the Polish respondents appear to have grasped the relationship
as described (48%) (Table 20, Annex 2)16. In all other countries over 60% of respondents gave a correct
answer to this question.
Most respondents knew that high inflation meant the cost of living was increasing, suggesting an
awareness of simple economic terms. It appears that in most countries people are more likely to know
the definition of inflation than know what impact it has on their spending power, but in Armenia
considerably more people understood the time value of money than recognised the definition.
The various diversification questions used in different countries proved to be challenging. Up to 37%
of respondents claimed not to know the answer to the question used in Norway, and no more than 61%
per cent of respondents in any of the participating countries gave a correct response (Hungary).
The extent to which people said that they didn't know the answer to a question varies by question,
and country. For example, in the Albania, South Africa and the UK around 1 in 10 respondents (10%, 10%,
8%) reported that they didn't know the answer to the division question; in Malaysia just 1 in 100 gave this
response. Furthermore, almost half of Albanians (45%) said they didn't know how much money would be
in a savings account at the end of the year, compared to just 2% in Poland and 3% in Norway.
A financial knowledge score
Analysis of the responses to each question by country indicates that the combination of knowledge
questions adequately identified high and low achievers in all countries. It also shows that relatively few
people refused to answer the questions17. The financial knowledge score therefore uses all 8 questions.
It would be possible to create a score for each respondent from the factor analysis, and this
approach is widely considered to be good practice when scoring complex data18. However, there is also a
strong argument for giving each component of financial knowledge equal weighting, as each has benefits
for individuals, and each has been identified as important by international experts. There is also some
sense in avoiding complex statistical approaches if these are likely to be applied or interpreted in
different ways in different countries or if problems with data from one country are likely to influence the
way the data from other countries is analysed.
The results that follow therefore report a simple count of correct answers which has been rescaled
to take values from 0 to 100. This approach is in keeping with the development process: the questions
within the Core Questionnaire were all chosen because they were considered to capture essential aspects
of financial knowledge.
16
Note that in Albania more than one in 5 appears to have got the question wrong. However, Albania did not
record ‘don't know’ responses.
17
In the case of the knowledge questions, don't know is considered to be a valid response – indeed
respondents were encouraged to say if they didn't know the answer - and so this will not cause a problem in
analysis.
18
This argument is based on the fact that a score that counts correct answers may be misinterpreted; people
may assume it is equally difficult to gain one additional point from anywhere on the scale when in fact some
questions are more difficult than others.
29
The process of counting correct answers began by assigning a value to the responses to each
question (Table 3).
Where countries have substituted questions, or reworded them, the replacement question has also
given a value of 1 to a correct response and 0 in all other cases. In the case of a country with fewer than 8
financial knowledge questions each score has been rescaled as necessary (typically multiplying by a factor
of 8/7).
Table 3. Creating a knowledge score
Question
Discussion
Value towards final score
Division
This is open response and a correct answer is therefore a
good indicator of applied numeracy
1 for correct response. 0 in all other
cases.
Time-value of money
This is multiple response and country/context specific
1 for responses c, d, e unless country
tells us otherwise
Interest paid on a loan
This is open response and a correct answer is therefore a
good indicator of understanding
1 for correct response. 0 in all other
cases.
Calculation of interest
plus principle
This is open response and a correct answer is therefore a
good indicator of applied numeracy
1 for correct response. 0 in all other
cases.
Compound interest
This is multiple response. Assumption is that if the
respondent couldn't calculate 2% they also cannot
calculate 5*2%.
1 for a correct response IFF the previous
response was also correct. 0 in all other
cases.
Risk and return
This is a yes/no question
1 for a correct response. 0 in all other
cases.
Definition of inflation
This is a yes/no question
1 for a correct response. 0 in all other
cases.
Diversification
This is a yes/no question
1 for a correct response. 0 in all other
cases.
It is important to be cautious about making firm conclusions about the similarities and differences of
countries from the data because of the different questions used in some cases. This chapter therefore
employs a statistically conservative approach to analysing across the countries in order to look for general
patterns by grouping the countries according to whether or not their average results are significantly
different. This approach does not in any way correct for the variation in questions asked, but on the
assumption that they are largely equivalent, it attempts to identify groups of similar countries. Countries
within the same subset do not have significantly different average scores. Countries that are present in
more than one subset have scores that are between the two groups – i.e. they are not significantly
different from either group.
Figure 1 below shows that the 14 countries fell into 7 groups based on levels of financial knowledge,
although there is considerable overlap, with only 5 countries falling neatly into one group. The figure
shows that average scores in South Africa put this country in the lowest scoring group, similar only to
30
Peru. At the other end of the figure, Malaysia, the UK, BVI, Czech Republic, Ireland, Germany and Estonia
also had similar scores, but with a significantly higher average. Hungary appears to be uniquely high
scoring, but it should be remembered that this will partly be capturing the changes made to one of the
more difficult questions.
Figure 1. Country groupings by average financial knowledge scores
Lower average
scores
Higher average
scores
South Africa
Peru
Albania
Armenia
Norway
Poland
Malaysia
UK
BVI
Czech Republic
Ireland
Germany
Estonia
Hungary
Shaded boxes indicate homogenous groups of countries, computed in IBM SPSS19 using Scheffe Homogenous Subsets on
knowledge scores rounded to the nearest whole number. The order of the countries in this figure reflects average financial
knowledge scores. Average scores range from 57.5 to 76.3.
The distribution of scores is also interesting (Figure 2). Some countries, such as Albania, Norway, and
South Africa had a relatively large proportion of the population with low scores (to the left of the
distributions). However, the modal (the most frequently achieved) score in most countries was more than
half (Albania, Armenia, Czech Republic, Estonia, Hungary, Ireland, Malaysia, Norway, Peru, Poland, UK
and BVI). Just one country, Germany, had the maximum value as the modal value.
Countries such as the Czech Republic, Estonia and Germany with a negatively skewed distribution
can be reassured that the majority of their population had basic financial knowledge. However, every
country has some proportion of the population that achieved a low score on the knowledge test, showing
that there was room for improvement in all countries.
31
Figure 2. Distribution of knowledge scores
30%
25%
20%
15%
10%
5%
0%
Albania
Armenia
Czech
Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South Africa
United
Kingdom
BVI
30%
25%
20%
15%
10%
5%
0%
Scores from 0 (far left column for each country) – 100 (far right column for each country).
32
The following chart (Figure 3) focuses specifically on the percentage of respondents that gained a
high score. In Albania, Armenia, Peru, Poland and South Africa fewer than half of the respondents gave
correct responses to at least ¾ of the questions.
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
40%
41%
49%
53%
57%
BVI
51%
33%
South Africa
60%
Poland
69%
Peru
58%
46%
Armenia
Albania
45%
61%
Norway
57%
United Kingdom
Figure 3. Financial knowledge: Percentage with a high score
Base: all respondents. Lighter shaded columns indicate countries where fewer than 50% achieved a high score.
33
FINANCIAL BEHAVIOUR
Behaviour is an essential element of financial literacy; and arguably the most important. The positive
outcomes from being financially literate are driven by behaviour such as planning expenditure and
building up a financial safety net; conversely, certain behaviours, such as over-using credit, can reduce
financial wellbeing. This section therefore focuses on a wide range of behaviours, with an emphasis on
those that can enhance or reduce financial wellbeing.
Financial behaviour across different domains
The OECD/INFE Financial Literacy Core Questionnaire asks the respondents about their behaviour
using different question styles, in order to capture the maximum amount of information. From the
responses to these questions, it is possible to derive information about the ways in which people manage
their money, including considering carefully whether they can afford something, paying bills on time, and
keeping a close watch over finances. Questions also ask whether they attempt to save and set long term
goals, if they are personally (or jointly) responsible for a household budget, how they choose financial
products and if they have recently borrowed to make ends meet (see Table 4 for a summary of
responses; more detailed information is provided in Annex 2).
Four of the questions use a qualitative scale, enabling people to provide more information about the
frequency of their behaviour. These scaled questions have been asked in the same way in each of the
participating countries except Norway and South Africa19. Comparisons across the majority of countries
should therefore be robust.
A financially literate person will always have an idea of the amount of money they can afford to
spend on a purchase. The first of the behaviour statements shows that people typically did consider
whether they could afford potential purchases (Table 21, Annex 2). This is especially the case in Armenia,
Malaysia and Peru. However, in Norway 14% percent of respondents put themselves below the midpoint
– indicating that they tended not to consider their purchases; 1 in 10 UK respondents also put themselves
at this end of the scale. It is also interesting that in Estonia and Poland around one in 5 respondents put
themselves at the midpoint on the scale, suggesting that they were aware that they sometimes made
purchases without considering affordability.
19
In Norway the questions were asked on a 7 point scale: recoded as follows for the purpose of comparisons:
1=1 (2, 3=2) (4=3) (5, 6=4) (7=5). In South Africa, a 5 point scale was used, but each of the scale points was
given a verbal description. Note that Armenia labelled the scale in reverse, and asked the questions alongside
some of the attitudinal questions.
34
Table 4. Positive financial behaviours by country
Cell percentages by country.
Behaviour statements
Financial product
choice
Carefully
considers
purchases
Pays
bills
on
time
Keeps
close
watch on
personal
financial
affairs
Sets
long
term
goals
and
strives
to
achieve
them
Responsible
and has a
household
budget
Has been
actively
saving or
buying
investments
in the past
year
…after
gathering
some info
… after
shopping
around and
using
independent
info or
advice
Has not
borrowed
to make
ends meet
Albania
Armenia
87%
77%
71%
30%
59%
42%
49%
2%
69%
91%
94%
81%
58%
51%
36%
42%
Czech
Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South
Africa
United
Kingdom
BVI
75%
85%
76%
36%
37%
72%
28%
10%
89%
68%
83%
78%
41%
28%
36%
24%
8%
78%
82%
96%
87%
61%
22%
86%
52%
5%
96%
86%
82%
71%
52%
31%
27%
48%
4%
86%
83%
85%
85%
56%
54%
53%
39%
10%
86%
92%
69%
78%
64%
74%
97%
39%
3%
79%
72%
79%
89%
59%
25%
71%
57%
5%
93%
91%
86%
82%
71%
49%
62%
52%
4%
73%
70%
78%
81%
46%
54%
51%
32%
2%
79%
83%
61%
65%
55%
43%
53%
56%
3%
74%
77%
89%
80%
43%
43%
68%
29%
16%
91%
87%
83%
80%
68%
43%
83%
70%
2%
87%
53%
The first 4 columns report people putting themselves at 4 or 5 on a scale from Never=1 to Always=5. The financial product
choice data is used in the final measure as follows: 1 point for gathering some information (column 7 above), 2 points for
shopping around and using independent information and advice (column 8 above).
Financial literacy also requires organisational skills in order that individuals meet their financial
commitments and thus avoid problems such as reduced access to affordable credit or fines for nonpayment. The questionnaire therefore asks each respondent whether they usually pay their bills on time
(Table 22 Annex 2). Most respondents reported that they did – putting themselves at 4 or 5 on the scale.
However, a sizeable proportion of respondents in South Africa (15%) and Norway (15%) indicated that
they never or rarely did so (less than 3 on the scale)20. A further 1 in 5 respondents in South Africa (19%)
as well as 19% of respondents in Malaysia put themselves at the midpoint, suggesting that they too were
not paying many bills on time. These negative responses may be due to a variety of reasons including lack
of money, lack of access to electronic payment facilities or a tendency to be disorganised or unwilling to
meet responsibilities on time, but in all cases they suggest that a sizeable proportion of consumers could
be either encouraged or supported to improve this behaviour.
A third behaviour statement asks respondents how often they keep a close personal watch on their
financial affairs (Table 23, Annex 2). Keeping an eye on financial affairs is important for a variety of
reasons. For those who use financial products, it is essential to be aware of anticipated withdrawals from
20
It is possible that people chose the ‘Never’ category because they had no bills to pay. In Norway such people
could have simply skipped the question.
35
an account and to check statements in order to address mistakes or fraudulent activity, such as duplicate
amounts being withdrawn through computer error or unauthorised use of credit cards. Even those who
do not use financial products need to oversee their financial affairs in order to keep their savings safe,
smooth their expenditure and pay bills on time.
Very few people claimed that they never keep an eye on their own finances – ranging from 1% in
Norway, Ireland, Germany and Peru to 8% in Hungary. However, in almost all the participating countries
more than one in ten respondents put themselves in category 3 – suggesting that were aware they could
do more. In South Africa around a third of all respondents (32%) put themselves on the lower part of the
scale (1, 2, or 3) suggesting a considerable need to help people see the value of watching over their own
finances.
In Peru, this question produced a very clear clustering effect; people tended to report that they
either always kept a close personal watch (68%), or that they hardly ever did (point 2 on the scale: 19%).
The final statement in this set relates to acting on longer term plans (Table 24, Annex 2). It asks
whether respondents ‘set long term financial goals and strive to achieve them’; it does not specify how
far away the goal should be, or how easy it might be to achieve. Long term financial goals may be related
to accruing money for specific expenses, such as education fees or a wedding. Alternatively they could
relate to investment strategies, saving for retirement, business ideas or career progression. The second
phrase in this statement indicates that the respondent should be attempting to reach their goal, rather
than simply thinking about it.
Despite the fact that everyone can benefit from considering their longer term financial needs this
particular behaviour does not appear to be widespread. As many as one in five people in the United
Kingdom (22%) said that they never set a long term financial goal and worked to achieve it.
It appears that Peruvians (55%), British Virgin Islanders (45%) and Armenians (43%) are the most
likely to set long term goals. More than 1 in 10 Estonians responded that they didn't know whether this
statement applied to them, perhaps indicating disengagement with long term planning. A further ¼
placed themselves at 1 or 2 on the scale – suggesting that setting goals was not something that they did.
A sizeable proportion of respondents in each country (ranging from 12% to 26%) put themselves at
the midpoint on this scale. This could be classified as ‘sometimes’. Interpreted in this way, it indicates
that people do not consistently work towards long term goals.
Further behaviour questions provide us with information about the extent to which an individual
takes responsibility for household finances and budgeting21. Responses to two questions have been
combined to assess how many people report that they a) have either personal or joint responsibility for
day to day money management decisions in their household and b) live in a household with a budget
(Figure 4). This has been done to ensure that someone is not considered to be actively using a budget if
they do not take on any responsibility for household finances22. The combination of the two sets of
responses shows a very wide variation across countries, with fewer than 1/4 of respondents in Germany
21
The term budget was explained to the respondent to ensure that they did not misinterpret it.
22
This measure will not capture those people who budget their own money but do not take responsibility for the
household as both the questions relate to household money management.
36
and Estonia being personally or jointly financially responsible and budgeting through to almost 3/4 of
those in Malaysia (74%)23.
Figure 4. Responsible and has a household budget
74%
43%
43%
43%
United Kingdom
BVI
54%
Poland
Malaysia
Ireland
Peru
25%
22%
Germany
Estonia
Czech Republic
Armenia
Albania
28%
31%
Norway
37%
49%
South Africa
54%
51%
Hungary
59%
Base: all respondents. % of respondents have some responsibility for financial decisions in a household with a budget.
Saving behaviour is considered to be an important component of financial literacy – building
financial security and reducing the reliance on credit. As the actual amount that a person can save, and
the length of time they can keep money to one side varies immensely, the financial literacy measure
focuses exclusively on whether or not respondents save money. Respondents were asked ‘In the past 12
months have you been saving money in any of the following ways?’. The questionnaire then lists a variety
of ways in which people typically save, in order to prompt recollection of any type of saving. This was
tailored to the country context but typically included saving money at home, using informal savings clubs,
putting money into savings accounts and buying investments. For the purpose of the international
comparison a variable has been created that counts all kinds of saving as active saving, except for the
passive approach of building up a balance in a current account24. This is an appropriate indicator of
behaviour, since it indicates that saving was intentional rather than a default position due to income
exceeding outgoings.
As can be seen from Table 5, discussing savings is a sensitive issue in some countries. Almost one in 5
respondents refused to answer this question in the Czech Republic (19%), as did 14% in South Africa. In
Poland a very large proportion claimed that they didn't know, which almost certainly indicates an
unwillingness to divulge such information.
Malaysia clearly has a culture of saving, 97% of respondents reported that they had been saving in
the past 12 months and nobody appeared to feel that this was sensitive information. Conversely, in
23
Data from Armenia indicates that everyone takes some responsibility for household money management.
24
The measure of savings used by the Czech Republic includes a product known as ‘pension insurance’. Whilst
this has some of the characteristics of a pension, it is widely considered to be a standard savings vehicle by
Czech consumers.
37
Hungary, people were very unlikely to have been saving (just 27% responded positively), although again,
nobody refused to answer.
Table 5.
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South Africa
United Kingdom
BVI
Actively saving or buying investments in the past 12 months
Refused
2%
Don't know
4%
19%
2%
3%
4%
2%
1%
1%
21%
9%
2%
1%
12%
14%
1%
No
52%
64%
6%
58%
13%
71%
46%
2%
29%
36%
16%
24%
29%
17%
Yes
42%
36%
72%
36%
86%
27%
53%
97%
71%
62%
51%
53%
68%
83%
Base: all respondents. Row percentages by country.
The way people behave when choosing financial products is also an important aspect of their overall
financial literacy. If people attempt to make an informed decision by shopping around or using
independent advice they are more likely to choose appropriate products that meet their needs in a cost
effective way, less likely to buy something inappropriate, and less likely to be subject to mis-selling or
fraud.
People do not typically choose financial products on a weekly, or even monthly, basis and so
respondents are asked about a product chosen in the last 2 years (excluding simple renewals)25. It is
important to note that this measure is specific to choosing products, and does not capture information
about people who checked that their existing products were still suitable, unless they went on to shop for
something new. Neither does it capture intention to behave – such as how they think they might choose
a product in the future.
The possible approaches to choosing financial products may vary by country (and countries were
able to add their own options to the questionnaire), but shopping around and gathering information are
the behaviours that are most relevant. In the derived variable used in the final score, respondents are
considered to have made some attempt to make an informed decision if they tried to compare across
providers (even if they found out that there were no other providers), or if they sought information from
someone.
Figure 5 shows that consumers in Germany, Ireland and the United Kingdom were most likely to
have made active financial product choices by shopping around and using independent information or
advice.
25
The score should be seen as a conservative estimate since some of those who refused may actually have
shopped around- i.e. their score could actually be higher. Some countries did not supply information to
differentiate between those who did not choose a product, and those who refused to answer this question.
38
Figure 5. Shopping around for financial products
0%
10%
20%
30%
40%
50%
60%
70%
80%
90% 100%
Albania
Armenia
No score
Czech Republic
Estonia
Germany
Hungary
1 point: Some
attempt to make
informed
decision
Ireland
Malaysia
Norway
2 points:
Shopped around
and used
independent info
or advice
Peru
Poland
South Africa
United Kingdom
BVI
Base: all respondents. Respondents who fall into the ‘no score’ category include those who chose a product without attempting
to make an informed decision and those who had not chosen a product.
Financially literate people will have strategies to smooth income flows and a tendency to avoid using
credit for essentials such as food and utilities. The extent to which these strategies are successful will
depend on the predictability of their income and expenditure as well as the extent to which they have the
necessary skills. It is not always possible to prevent shortfalls in income, but a reliance on credit for basic
living can become very dangerous and impossible to escape. A variable has therefore been created to
identify people who reported that sometimes their income didn't meet their needs, and that the last time
this happened they had to borrow to make ends meet.
Figure 6 shows that on the whole, respondents were unlikely to have resorted to credit use to make
ends meet. However, almost a half of Armenians had done so in the last 12 months (47%), indicating a
worrying vulnerability to income fluctuations and the risk of facing spiralling debt problems.
39
Figure 6. Borrowing to make ends meet
47%
14%
South Africa
Poland
Peru
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
9%
7%
4%
Armenia
26%
14%
Norway
11%
Albania
21%
21%
13%
BVI
27%
22%
United Kingdom
31%
Base: all respondents. This variable combines the responses to two questions: Sometimes people find that their income does
not quite cover their living costs. In the last 12 months, has this happened to you? & If so what did you do to make ends meet
the last time this happened? Note that the question does not record all the strategies used to make ends meet in the course
of 1 year, but only on the last occasion this occurred. The measure captures borrowing informally and formally, including (but
not limited to) from family, pawn brokers, savings and loans club as well as relying on an overdraft, taking a bank loan and
taking money from a flexible mortgage, It does not include late payment of bills.
A score for financial behaviours
Information from the results described above has been combined into an overall score. This looks at
the number of behaviours that are in evidence for each person, with the caveat that respondents who
refused to answer particular questions (or opted for ‘don’t know’ in some cases) may appear to be lower
scoring than is actually be the case26.
The questionnaire captures a wide range of financial behaviours at an international level.
Incorporating all of these measures into an overall score ensures a nuanced indicator that provides a
good indication as to the extent to which individuals are behaving in a financially literate way.
26
There are various other ways in which missing data could be handled, including deleting the observations or
replacing missing information with imputed variables. The approach taken will tend to underestimate overall
scores, a conservative approach which is appropriate for this study.
40
The score is created with 9 points for showing evidence of certain positive financial behaviours, as
shown in Table 6. For reporting purposes this has been rescaled from 0 to 100.
Table 6. Creating a behaviour score
Behaviour
Discussion
Value towards final score
Considered purchase
This is a scaled response.
1 point for respondents who put themselves at 4
or 5 on the scale. 0 in all other cases.
Timely bill payment
This is a scaled response.
1 point for respondents who put themselves at 4
or 5 on the scale. 0 in all other cases.
of
This is a scaled response.
1 point for respondents who put themselves at 4
or 5 on the scale. 0 in all other cases.
financial
This is a scaled response.
1 point for respondents who put themselves at 4
or 5 on the scale. 0 in all other cases.
Responsible and has a
household budget
This is a derived variable, created from the
responses to two questions.
1 point if personally or jointly responsible for
money management and has a budget. 0 in all
other cases.
Active saving
This question identifies a range of different ways
in which the respondent may save. People who
refused to answer score 0.
1 point for any type of active saving (excluding
letting money build up in a current account as this
is not active). 0 in all other cases
Choosing products
This is a derived variable drawing information
from 2 questions. It is only possible to score
points on this measure if the respondent had
chosen a product: those with no score on this
measure have either refused to answer, not
chosen a product, or not made any attempt to
make an informed decision.
1 point for people who had tried to shop around
or gather any information. 2 points for those who
had shopped around and gathered independent
information. 0 in all other cases.
Borrowing
ends meet
This is a derived variable that combines a
question about running short of money and one
that identifies a range of different ways in which
the respondent made ends meet the last time
they ran short of money. The derived variable
indicates people who are making ends meet
without borrowing (refusals will score 1).
0 if the respondent used credit to make ends
meet. 1 in all other cases.
Keeping watch
financial affairs
Long term
goal setting
to
make
41
As with the knowledge scores, a statistical approach has been used to group countries according to
their average financial behaviour scores (Figure 7). This provides an indicator of the extent to which
individuals are exhibiting behaviour relating to the various aspects of financial literacy discussed above.
Some countries have relatively similar financial behaviour scores; the analysis indicates there are 5
groups of countries that had similar average scores. Estonia exhibited significantly lower levels of
behaviour than all other countries except Albania. The highest numbers of positive financial behaviours
were apparent in Germany, Malaysia and BVI.
Figure 7. Country groupings by average financial behaviour scores
Lower behaviour
scores
Higher behaviour
scores
Estonia
Albania
Hungary
South Africa
Poland
Armenia
Czech Republic
UK
Norway
Ireland
Peru
Germany
Malaysia
BVI
Shaded boxes indicate homogenous groups of countries, computed in IBM SPSS19 using Scheffe Homogenous Subsets. Average
scores range from 50.0 to 67.8.
42
It is also noteworthy that Germany and Malaysia had nobody who scored zero (identifying those
with no positive financial behaviours), and just two countries had nobody who achieved a maximum
score (Armenia and BVI) (Table 25, Annex 2).
Figure 8 shows that large proportions of respondents in Germany, Malaysia and BVI scored relatively
highly (characterised by a lighter colour on the bar charts). However, the distributions all tend towards a
steep bell shape, indicating that there was a large proportion of the population of most countries who
only exhibited around half of the positive behaviours identified. South Africa has the flattest distribution,
showing that there was a wider range of behaviour in evidence across the country.
Figure 8. Distribution of financial behaviour scores
30%
20%
10%
0%
Albania
Armenia
Czech
Republic
Estonia
Germany
Hungary
Ireland
Peru
Poland
South Africa
United
Kingdom
BVI
0.3
0.2
0.1
0
Malaysia
Norway
Scores from 0 (far left column for each country) – 100 (far right column for each country).
43
Based on the distributions presented above and the fact that financial products are not chosen on a
regular basis, those exhibiting 2/3 of the identifiable behaviours are considered to have a high score on
this component of financial literacy. Analysis shows that only half of the countries had a majority of
people gaining such a high score (Figure 9).
Figure 9. Financial behaviours: Percentage scoring 6 or more
67%
71%
67%
59%
57%
60%
41%
38%
43%
South Africa
39%
43%
Poland
51%
48%
Base: all respondents. Lighter shaded columns indicate countries where fewer than 50% achieved a high score.
44
BVI
United
Kingdom
Peru
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech
Republic
Armenia
Albania
27%
FINANCIAL ATTITUDES
Attitudes and preferences are considered to be an important element of financial literacy. If people
have a rather negative attitude towards saving for their future, for example, it is argued that they will be
less inclined to undertake such behaviour. Similarly, if they prefer to prioritise short terms wants then
they are unlikely to provide themselves with emergency savings or to make longer term financial plans.
The section that follows therefore reports the responses to three statements focusing on attitudes
towards money, and particularly towards planning for the future.
Attitudes and preferences: short term or longer term?
The Core Questionnaire includes three scaled attitudinal questions: ‘I find it more satisfying to spend
money than to save it for the long term’, ‘I tend to live for today and let tomorrow take care of itself’, and
‘Money is there to be spent’. Detailed results from each of the attitude statements are reported in Tables
26, 27 and 28 in Annex 2.
Around a third of respondents in Germany, Hungary, Poland and the UK put themselves at the
midpoint of the first statement ‘I find it more satisfying to spend money than to save it for the long term’
– suggesting that they found equal satisfaction in spending and saving. People in South Africa were the
least likely to give a neutral response: just 14% put themselves at 3 on the scale, compared with 35% of
UK respondents. Armenians were the most likely to report that they completely agreed that spending
was more satisfying (56%), whilst Peruvian respondents were most likely to completely disagree with the
statement (50%), followed by BVI (41%).
The responses to the second attitude statement show that in most countries, respondents tended
not to live for the day (Table 27). In Armenia, Czech Republic, Hungary and Peru, over half of the
respondents completely disagreed with the statement. Despite this, almost 1 in 5 respondents in Armenia
and Poland completely agreed with this statement showing considerable polarisation in Armenia.
The third attitude statement relates specifically to people’s attitude towards money (Table 28). Here
a large proportion of people were ambivalent (42% of Hungarians put themselves at 3 on the scale).
Peruvians were the most conservative, with 31% completely disagreeing that money is there to be spent.
In contrast, just 1% of Armenians and 4% of Polish respondents disagreed with the statement; indeed in
Armenia 74% completely agreed.
45
Combining the various attitudes
Exploratory factor analysis indicates that the three attitude questions capture an underlying
attitude, indicating whether the respondent tends towards short-term gratification, or long term security.
An average attitude score has therefore been created by adding together the responses to each of the
three questions, and then dividing by 327.
The average score varies significantly across countries (Table 29, Annex 2). The highest average
attitudinal score across the 3 attitudes is 3.7 (Albania and Peru) suggesting that respondents in these
countries are more likely than other countries to have a positive attitude towards planning for the future.
The distribution of (rounded) scores is shown below (Figure 10). Estonia has a rather flat distribution
compared with the other countries (indicating a wide range of attitudes in the country). Armenia has a
very pointed distribution, showing that many people have the same attitudes. Poland exhibits large
clusters of people who typically put themselves at 1 or 2 on the scale, whilst Albania, Hungary, and Peru
there is a much more positive attitude towards the long term.
Figure 10. Distribution of financial attitude scores
60%
50%
40%
30%
20%
10%
0%
Albania
Armenia
Czech
Republic
Estonia
Peru
Poland
Germany
Hungary
Ireland
60%
50%
40%
30%
20%
10%
0%
Malaysia
Norway
South Africa United Kingdom
BVI
Scores from 1 (far left column for each country) – 5 (far right column for each country).
27
Only the first of the attitudinal questions used in Norway have been used in this analysis in order to compare
similar attitudes in our international comparison; some caution is therefore necessary when comparing scores
from the other countries with the Norwegian score. Norway included a second attitude statement in their
questionnaire: ‘credit can be used for food and overhead costs’. The vast proportion disagreed with this; just
15% agreed to some extent. This has not been included in the score as it is capturing something different.
46
As with the previous scores, it is also possible to identify those with higher scores (Figure 11). In this
case, the higher scores identify those individuals with attitudes towards planning for the future that are
considered to be positively related to financial wellbeing (which are described below as ‘positive
attitudes’).
In this instance, a high score is attributed to people whose average response across the attitude
statements is greater than 3, even if only by a small percentage28. It shows that in most of the
participating countries, people generally had positive attitudes towards financial matters. However, in
Armenia and Poland this was not the case; in Armenia just over 1 in 10 respondents had positive attitudes
and in Poland just over a quarter (27%) of respondents had generally positive attitudes.
Figure 11. Percentage of respondents with average score over 3
71%
69%
53%
49%
46%
67%
57%
54%
49%
United Kingdom
63%
62%
South Africa
69%
27%
BVI
Poland
Peru
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
Armenia
Albania
11%
Base: all respondents. Lighter shaded columns indicate countries where fewer than 50% with an average score over 3.
28
As 3 is the midpoint, these people are neutral. In order to have scored above 3 on average, they must have put
themselves at 4 on the scale on at least one of the three questions: in the case of Norway, they must have put
themselves at 4 or 5 on the single question used in the analysis.
47
RELATIONSHIP BETWEEN BEHAVIOUR AND THE OTHER SCORES
The literature often uses financial knowledge as a predictor of positive outcomes, which rely on
behaviours that are consistent with financial wellbeing – exactly the types of behaviours captured in our
measure of financial literacy. This section therefore reports some very preliminary analysis to see
whether behaviour scores increase with knowledge scores in each country.
Figure 12 shows that across the countries surveyed there was a positive relationship between
knowledge and behaviour – higher knowledge scores are associated with higher behaviour scores29.
However, the relationship does vary by country. For example, in Germany and Peru average behaviour
scores only increase very slightly across the range of knowledge scores, and there is almost no increase in
average behaviour scores in the higher knowledge categories in Estonia. Conversely, in Malaysia
behaviour increases sharply with increased levels of knowledge, so that average behaviour scores
amongst those with a high level of knowledge were almost double those of people with low levels of
knowledge.
These graphs also show that even people with very low levels of knowledge exhibited some positive
financial behaviour. Whilst it is reassuring that they were financially active, it is very likely that they could
benefit from additional knowledge in order to improve the quality of their decision making on matters
such as choosing appropriate financial products or saving for the future.
29
The apparent dip in behaviour as knowledge moves from 0 to positive numbers should not be a concern: it is
the result of analysing a very small number of people who had a score of 0.
48
Figure 12. Relationship between financial knowledge and behaviour
Average behaviour score by financial knowledge score
80
Albania
Average behaviour score
70
Armenia
60
50
Czech
Republic
40
Estonia
30
Germany
20
Hungary
10
Ireland
0
Financial knowledge score
80
Malaysia
Averagel behaviour score
70
Norway
60
Peru
50
40
Poland
30
South
Africa
United
Kingdom
BVI
20
10
0
Financial knowledge score
It is also argued that those with positive attitudes towards the long term are more likely to behave in
ways that are consistent with achieving long term goals, so further analysis looks at whether behaviour
scores increase with attitude scores.
49
Figure 13 indicates a generally positive association between attitudes and behaviour: people with
attitudes that tend towards short term gratification have lower behaviour scores than those with an
attitude score over 3.
There appears to be little relationship between attitudes and behaviour in BVI, and relatively little in
Armenia. At the other extreme, behaviour increases notably with attitudes in Czech Republic and Ireland:
for example average behaviour scores for Czechs with who prefer the short term are half those of people
with positive attitudes towards the longer term.
Figure 13. Relationship between attitudes and financial behaviour
Average behaviour score by attitude score
80
70
60
50
40
30
20
10
0
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
1
2
3
4
5
80
70
60
50
40
30
20
10
0
Ireland
Malaysia
Norway
Peru
Poland
South Africa
United Kingdom
1
2
3
4
50
5
BVI
COMBINED MEASURES OF FINANCIAL LITERACY
Financial literacy is a combination of knowledge, attitude and behaviour, and so it makes sense to
explore these three components in combination. This is done in two ways in this chapter- firstly by
looking at whether people achieve high scores on several components, and secondly by adding the scores
together. As these measures combine information from the scores developed in the previous chapters,
the reader should apply the same caution when comparing countries that have made changes to the Core
Questionnaire with other countries.
Segmenting the population
Table 7 summarises the proportion of respondents in each country achieving a high score on each of
the three financial literacy components. There is some variation in relative strengths and weaknesses of
respondents within countries: for example more than 4 times as many Armenians had a high knowledge
score (46%) than a high attitude score (11%).
Table 7. High score on each of the financial literacy components
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South Africa
United Kingdom
BVI
High knowledge
score
High behaviour
score
High attitude
score
45%
39%
69%
46%
41%
11%
57%
48%
62%
61%
27%
46%
58%
67%
63%
69%
38%
69%
60%
57%
49%
51%
67%
53%
50%
59%
57%
41%
60%
71%
49%
43%
27%
33%
43%
54%
53%
51%
49%
57%
71%
67%
Base: all respondents. Proportion scoring highly on each component (Cell percentages by country). Each of the columns reports
% of respondents gaining a high score.
In addition to this aggregate data, it is possible to look at the relative strengths and weaknesses of
individual respondents within countries, and use this to segment the population. The data has therefore
been segmented according to whether respondents gained a high score in 0, 1, 2 or 3 of the three
components described above. Figure 14 shows quite clearly that there are two typical patterns. Whilst all
countries have people in each of the clusters, some tend to have the highest proportion in the cluster
with one high score, whilst for others the highest proportion has two high scores. Germany and BVI stand
out for having large portions of the population scoring highly on each component, indicating a high
51
overall level of financial literacy, whilst Armenia, Poland and South Africa have relatively large
proportions of the population with low levels of financial literacy across the three measures.
It is of some concern that more than 10% of the population in 10 of the countries had no high scores
– the exceptions being Germany, Hungary, Peru and BVI.
Figure 14. Financial Literacy Segments
Number of high scores (far left 0 high scores; far right: 3 high scores)
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
None
1
2
3
Albania
Armenia
Czech
Republic
Estonia
Germany
Hungary
Ireland
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
None
1
2
3
Malaysia
Norway
Peru
Poland
South Africa
United
Kingdom
BVI
Developing an overall measure of financial literacy
In order to assess overall levels of financial literacy, the three raw scores described above for
knowledge, behaviour and attitudes have been summed to provide a single indicator. This takes into
account the various aspects of financial literacy, including financial planning for the future, choosing
financial products and managing money on a day-to-day basis. The final score, in its raw form can take a
minimum value of 1, and a maximum value of 22. This has been rescaled to create a score that can take
values between 1 – 100.
As the three raw scores have different maximum values, the combined score is implicitly weighted.
The most heavily weighted factor is behaviour. This is appropriate: behavioural questions make up a large
52
part of the questionnaire because financial behaviour is seen as a key component in financial literacy30.
Financial knowledge also makes up a large percentage of the final score. Financial knowledge and
behaviour are the two aspects of financial literacy most typically targeted by financial education
initiatives. The score also contains a small component of attitudes towards money, and particularly
towards planning for the future- those with more negative attitudes towards longer term planning
therefore cannot achieve very high overall scores.
Figure 15 shows that the countries can be grouped into 5 subsets with largely similar overall scores.
Armenia, South Africa and Poland showed the greatest room for improvement, whilst Malaysia, Hungary,
Germany and BVI had statistically similar, high scores.
Taking an average of the country scores (not shown)31, countries typically scored around 63.2. Scores
in the Czech Republic, Germany, Hungary, Ireland, Norway, Malaysia, Peru, the UK and BVI were above
this average.
Figure 15. Country groupings by average overall financial literacy scores
Lower average
overall score
Higher average
overall score
Armenia
South Africa
Poland
Estonia
Albania
Czech Republic
Peru
UK
Norway
Ireland
Malaysia
Hungary
Germany
BVI
Shaded boxes indicate homogenous groups of countries, computed in IBM SPSS19 using Scheffe Homogenous Subsets. Average
scores range from 56.4 to 68.6.
30
Exploratory factor analysis also suggests that knowledge and behaviour should be weighted more heavily than
attitudes.
31
This average figure gives each country equal weight, regardless of population or sample size.
53
VARIATIONS BY SOCIO-DEMOGRAPHICS
Several approaches have been used to look at the association between socio demographic factors
and the financial literacy of consumers. Firstly, tables are used to show the proportion of people within a
particular demographic who fall into each segment described above. The average overall score for each
demographic is also calculated; and finally multivariate linear regression analysis has been employed to
explore the associations across several demographic categories simultaneously.
This analysis focuses on key socio-demographic information: gender, age, education level, work
status and income. It also takes into consideration attitude towards risk and income stability, as initial
discussion with participating countries suggested that these may be important explanatory factors in
understanding variations in financial literacy.
Gender
This section briefly highlights some of the key results of analysis of financial literacy by gender.
Chapter 3 discusses gender differences in financial literacy and their implication for policy makers in more
detail.
Figure 16 shows that a larger proportion of male respondents gain high scores in knowledge than
women in 13 of the countries studied. This is particularly marked in Norway, Poland and the UK with
more than a 20 percentage point difference. There is no difference in the proportion of men and women
gaining high scores in Hungary – this is an important finding that cannot be explained by the different
wording of one question.
54
In 8 of the countries surveyed, fewer than half of women have gained a high knowledge score; in
comparison there are just 2 countries where less than 50% of men achieved such a score. It appears that
in almost all countries where the average level of knowledge is relatively high, women are being left
behind their male counterparts.
High knowledge score by gender
45%
Norway
Malaysia
Hungary
Germany
Estonia
Ireland
55
63%
53%
52%
29%
Base: all respondents.
67%
59%
35%
39%
36%
31%
40%
BVI
54%
48%
Male
United
Kingdom
54%
50%
Female
68%
South Africa
67% 69% 69%
41%
Czech Republic
Albania
35%
51% 52%
Armenia
51%
66%
57%
Poland
63%
Peru
Figure 16.
There are no differences in the proportions of men and women who gained high behaviour scores in
Germany and Hungary (Figure 17) and only 2 countries show a difference of more than 10 percentage
points (Norway and Ireland). In some countries, more men than women gained high marks (Albania,
Armenia, Malaysia, South Africa and the UK), whilst in others (Czech Republic, Estonia, Ireland and
Norway) women were more likely than men to achieve a high score.
It is intriguing that this preliminary analysis suggests that the gender differences that are so
apparent in the knowledge component are not entirely reflected in financial behaviours. This may be
because many financial behaviours are undertaken at a household level – such as saving or choosing
products - whilst knowledge is entirely at the individual level. Even when the survey is designed to collect
information about individuals it is inevitable that their behaviours will reflect household decisions.
Figure 17. High behaviour score by gender
Base: all respondents.
56
BVI
49%52%
43%43% 41%46%
United Kingdom
Peru
Norway
Malaysia
Ireland
Germany
Estonia
Hungary
37% 38%
30%
23%
Czech Republic
Armenia
Albania
51%
44%
78%
66%
South Africa
65%68% 62%
62%
61%
55% 59%
51%
67% 67%
42% 38%43%
35%
Male
Poland
Female
Analysis of the average attitude scores (Figure 18) shows that in most countries women were more
likely than men to have high attitude scores – showing that they typically had a more positive attitude
towards the longer term. Only in Albania and Poland were men more likely than women to have attitudes
that tend towards longer term preferences. In Armenia and South Africa there was no difference in
attitudes by gender.
Figure 18. High attitude score by gender
Female
72%
64%
66%
59%
65%
60%
73%
66%
47%45%
Male
57%
55%
49%
43%
61%
54%
75%
69%
69%66%
54%54% 52%
46%
26%28%
BVI
United Kingdom
South Africa
Poland
Peru
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
Armenia
Albania
11%11%
Base: all respondents. In Albania, Estonia, Germany, Poland and the UK women were more likely than men to have no high
scores. This is not the case in other countries. In Hungary, men were over-represented in the lowest segment (showing that they
were more likely than women to have no high scores), and the gender spread of the other categories is similar to the population
as a whole. In South Africa, women were slightly more likely to have just one strength, and less likely to fall into the highest
category.
57
Figure 19 below shows that in some, but not all countries there was a small variation in overall score
by gender. In no country did women score significantly more than men; almost certainly because of the
large differences in levels of knowledge in most countries. The Czech Republic, Estonia, Hungary, Ireland
and Malaysia exhibited no significant gender difference in overall score. Conversely, women scored
significantly less in Albania, Armenia, Germany, Norway, Poland, South Africa, the UK and BVI.
Figure 19. Mean overall score by gender
Female
BVI
United Kingdom
South Africa
Poland
Peru
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
70.1 67.1 66.4
67.6 70.0
65.4 65.8 65.5 66.6 61.4 64.7 63.2 65.2
65.0 64.5 60.5 60.7 66.6
62.4 66.6
56.8 59.8 57.2 58.9
Armenia
Albania
57.8 62.3 55.4 57.2
Male
Base: all respondents.
Age
It might be expected for financial literacy to increase with age, as people become more
knowledgeable, and their attitudes and behaviours change accordingly. However, there are mediating
factors that may reduce the financial literacy of the eldest respondents, such as:

There is likely to be a cohort effect that impacts on older consumers. Older people, with
experience of a very different financial marketplace may find it difficult to keep up with the fast
pace of change in the financial market place, including the introduction of new technologies,
modified rules and regulations and newly developed financial products.

Cognitive deterioration may reduce the extent to which the oldest consumers can retain and
apply financial knowledge.
Figure 20 shows that respondents with 3 high scores were most likely to be aged around 30-60.
Conversely, it was common for the proportion of people with no high scores to be higher amongst the
youngest and oldest respondents, and dip in middle age32. However, this pattern was less apparent in
Armenia, Poland and South Africa. In Armenia, the number of people with no high scores rose steeply
with age, whilst in Poland and South Africa there was no clear relationship.
32
Note that if there was no difference by age one would expect the same sized bands of colour for each row.
Statistical tests confirm that in every country except BVI, average overall scores (not shown) vary significantly
by age.
58
Figure 20. Financial literacy segments by age
Proportion in each segment, by age (Equivalent to row percentages by country)
Czech
German
Republi
Ireland Hungary
Estonia
Armenia Albania
y
c
0%
10%
20%
30%
40%
South
Africa
Poland
Peru
Norway
Malaysi
a
0%
UK
60%
70%
80%
90%
100%
80%
90%
100%
18-19
40-49
70-79
18-19
40-49
70-79
18-19
40-49
70-79
18-19
40-49
70-79
20-29
50-59
80+
20-29
50-59
80+
20-29
50-59
80+
None
BVI
50%
10%
20%
30%
1
40%
2
3
50%
60%
70%
18-19
40-49
70-79
20-29
50-59
80+
20-29
50-59
80+
20-29
50-59
80+
20-29
50-59
80+
20-29
50-59
80+
20-29
50-59
80+
None
1
2
3
Base: all respondents. Each bar reports the proportion of people within that age group who fall into each of the financial
literacy segments.
59
Income
A high level of financial literacy is possible at all income levels. Income itself does not impact on the
ability of someone to gain knowledge, to form attitudes conducive to their own financial wellbeing or to
exhibit positive behaviours. However, low income is often seen as an explanation for certain behaviours –
such as borrowing to make ends meet, and used as a reason not to undertake actions such as saving or
making long term plans. Furthermore, low income may also be associated with other socio-demographic
factors that have been shown to be associated with financial literacy, such as age33.
The Core Questionnaire includes a single question to give a general impression of the household
income of each respondent. As it is often difficult to get people to divulge income, the question was left
quite general in order to maximise responses. This question was fine-tuned by participating countries to
ask whether income fell into one of three bands, reflecting income at, below or above national medians.
In the results below data is only analysed for the section of the population who responded to this
question34.
The segment with no high scores includes a high proportion of people who had low incomes (Table
31). Conversely, high income respondents were typically over-represented in the segment with 3 high
scores.
Overall the pattern indicates that respondents from the higher income households in each country
were more than likely to be in the highest scoring segments. This pattern was less marked in Estonia and
Norway but particularly noticeable in the Czech Republic, Hungary, Ireland, Malaysia and South Africa.
Further analysis by income confirms that in general higher income is associated with higher average
scores (Figure 21)35. However, in Armenia and Ireland, middle income consumers were, on average, the
most financially literate and in Norway there was very little difference between the middle, and high
income consumers.
It is quite possible that these differences are driven in whole, or in part, by mediating variables such
as age or education level; it is known that income varies by both age and education. However, this does
not reduce the importance of the findings. Improving financial literacy amongst the poorest would clearly
be welfare enhancing, helping them to make better use of their money, reducing the likelihood that they
will make inappropriate decisions or ill-informed choices and potentially helping them to identify ways of
increasing their income. It is also worth noting that the poorest consumers have less flexibility to ‘learn by
doing’, as they cannot afford to make mistakes.
33
It is for this reason that the findings from initial regression analysis are also reported to explore whether
income is associated with levels of financial literacy even after controlling for other factors.
34
In the regression analysis that follows, a ‘dummy’ variable is included to identify those who refused to state
their income, to check whether they are systematically different.
35
Additional analysis indicates that the differences in scores by income are significant in every country.
60
Figure 21. Average overall score by income
Low
Average
High income
BVI
United
Kingdom
South Africa
Poland
Peru
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
Armenia
Albania
80
70
60
50
40
30
20
10
0
Base: all respondents with valid data on income. Caution necessary: Just 31 Armenian respondents had high income and 61
respondents in Ireland.
Analysis by income stability (not shown) indicates that there were significant differences in average
overall scores by income stability in every participating country except Hungary and Ireland. In all
countries the average score was higher amongst those that report that their income is regular and
predictable, than those who said their income varied or that sometimes they did not receive it on time, if
at all.
Education level
The Core Questionnaire captures detailed information about each respondent’s highest level of
education. For the purpose of an international comparison, several categories have been combined in
order to provide insight into how financial literacy varies according to whether an individual has or has
not completed secondary school, or has continued formal education beyond secondary school level.
There was a relationship between increased levels of education and high financial literacy scores in
every country analysed (Figure 22). In Germany the relationship was particularly strong, and Malaysia and
Poland also showed a clear pattern with higher educated individuals more likely to have high financial
literacy scores.
This initial analysis suggests that general levels of education impact on more than just knowledge. It
is particularly striking in Armenia, Poland and Germany that many individuals with low levels of education
had no high scores (Figure 22). However, it should also be noted that some people have achieved high
scores despite low levels of education, indicating that high levels of financial literacy levels are possible
even amongst those who have not completed formal education.
61
Figure 22. Financial literacy segments by education
Proportion in each segment, by education (Equivalent to row percentages by country)
BVI
UK
South
Africa
Poland
Peru
Czech
Norway Malaysia Ireland Hungary Germany Estonia Republic Armenia Albania
0%
10%
20%
30%
Beyond secondary school
Complete secondary school
Less than complete secondary school
Beyond secondary school
Complete secondary school
Less than complete secondary school
Beyond secondary school
Complete secondary school
Less than complete secondary school
Beyond secondary school
Complete secondary school
Less than complete secondary school
Beyond secondary school
Complete secondary school
Less than complete secondary school
Beyond secondary school
Complete secondary school
Less than complete secondary school
Beyond secondary school
Complete secondary school
Less than complete secondary school
Beyond secondary school
Complete secondary school
Less than complete secondary school
Beyond secondary school
Complete secondary school
Less than complete secondary school
Beyond secondary school
Complete secondary school
Less than complete secondary school
Beyond secondary school
Complete secondary school
Less than complete secondary school
Beyond secondary school
Complete secondary school
Less than complete secondary school
Beyond secondary school
Complete secondary school
Less than complete secondary school
Beyond secondary school
Complete secondary school
Less than complete secondary school
40%
50%
60%
70%
80%
90%
100%
None
1
2
3
Base: all respondents with valid data on education. Caution necessary on small bases for less than complete: Armenia (65)
Germany (11) BVI (86).
62
Attitude to risk
During the initial stages of the questionnaire design, attitude to risk was identified as an important
explanatory variable for measures of financial literacy. Figure 23 therefore reports the average scores of
those who disagreed (i.e. responded 4, or 5 on the scale) to the risk statement ‘I am prepared to risk
some of my own money when saving or making an investment’. Only the Czech Republic exhibited a
noteworthy difference in scores by this attitude – risk averse respondents scored more than those who
were risk tolerant. However, statistical tests indicate that the relatively small differences in scores in
Armenia and Poland are also statistically significant. Interestingly, it was the risk tolerant who scored
slightly higher, on average, in Poland.
Figure 23. Average overall scores by risk aversion
Risk loving or neutral
Risk averse
BVI
United Kingdom
South Africa
Poland
Peru
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
Armenia
Albania
68.668.1 65.867.0 65.465.7 66.565.0
66.7
65.163.7 68.569.0
62.963.2 64.364.8 59.3
61.6
60.360.7 58.9
60.161.1
57.958.2
57.0
53.4
Base: all respondents
Multivariate analysis
Various characteristics are associated with financial literacy; these characteristics are not observed
in isolation and so it is useful to be able to undertake multivariate analysis to gain further insights into the
associations.
The findings discussed above show important variations by country, indicating that it is appropriate
to run a series of regression analyses for each country.
The first set of analyses undertaken controlled for characteristics that cannot be chosen or
influenced by the participant (age and gender, table not shown). This preliminary check indicated that
once the impact of age has been taken into account, men scored significantly higher than women in 9 of
the participating countries; there was no country where women scored significantly higher than men.
Financial literacy also appears to typically increase with age up to the age of 69 although in Malaysia
respondents over the age of 60 had significantly lower scores than those aged 20-29 (after controlling for
gender differences). In Armenia, young adults aged 18 -39 had significantly higher levels of financial
literacy than other age groups, once gender differences are controlled for.
63
The second set of analyses focused on a wider range of socio-demographic and personal
characteristics that may be associated with financial literacy (gender, age, income, education, attitude to
risk: Table 8). Gender remained significant in 8 of the survey countries even after controlling for these
other factors. Only in South Africa did the other factors appear to outweigh any apparent differences by
gender.
It is interesting that once other factors such as education and income are controlled for, older
participants appear to have been more financially literate in several countries - this indicates that it was
not simply the impact of age, but factors associated with it (such as reduced income or lower levels of
education) that resulted in lower scores. For example, in Ireland, individuals on a low income had
significantly lower scores, as did those with incomplete schooling, and once these were taken into
account, scores were significantly higher amongst all adults over 30 than amongst the comparison group.
In every participating country, education was significantly related to overall scores, even after
controlling for income, age and gender. In every country except the Czech Republic, scores were
significantly higher amongst respondents that had continued their education compared with those who
stopped when they completed secondary school.
Comparing those people who disagreed that they were prepared to risk some of their own money
when saving or making an investment (putting themselves at 4 or 5 on a scale from 1 to 5) with others
also highlighted differences by country. In Armenia, risk averse people were less likely to be financially
literate than their risk tolerant counterparts; whilst in the Czech Republic, Hungary, Norway and South
Africa, a risk averse attitude was associated with higher levels of financial literacy.
64
Table 8. Regression results indicating significant variables and direction of association
Gender
Albania
Male (+)
Armenia
Male (+)
Czech Republic
Estonia
Germany
Male (+)
Hungary
Ireland
Male (+)
Malaysia
Norway
Peru
Male (+)
Age
u20 (-)
30-39 (+)
40-49 (+)
50-59 (+)
60-69 (+)
60-69 (-)
70-79 (-)
80+(-)
High (+)
Income
stability
Stable (+)
Low (-)
Stable (+)
u20 (-)
30-39 (+)
50-59 (+)
60-69 (+)
30-39 (+)
Low (-)
High (+)
30-39 (+)
40-49 (+)
50-59 (+)
60-69 (+)
30-39 (+)
40-49 (+)
50-59 (+)
60-69 (+)
80+(-)
u20 (-)
30-39 (+)
40-49 (+)
50-59 (+)
60-69 (+)
70-79 (+)
80+(+)
Low (-)
High (+)
u20 (-)
30-39 (+)
40-49 (+)
50-59 (+)
70-79 (-)
80+(-)
u20 (-)
40-49 (+)
50-59 (+)
60-69 (+)
70-79 (+)
u20 (-)
30-39 (+)
40-49 (+)
50-59 (+)
60-69 (+)
Income
High (+)
Low (-)
High (+)
Education
Risk aversion
Incomplete
schooling (-)
Education beyond
secondary school (+)
Incomplete
schooling (-)
Education beyond
secondary school (+)
Incomplete
schooling (-)
Incomplete
schooling (-)
Education beyond
secondary school (+)
Stable (+)
Incomplete
schooling (-)
Education beyond
secondary school (+)
Incomplete
schooling (-)
Education beyond
secondary school (+)
Risk averse (-)
Risk
averse (+)
Stable (+)
Low (-)
Risk averse (+)
Incomplete
schooling (-)
Education beyond
secondary school (+)
Incomplete
schooling (-)
Education beyond
secondary school (+)
High (+)
Stable (+)
Incomplete
schooling (-)
Education beyond
secondary school (+)
Low (-)
High (+)
Stable (+)
Incomplete
schooling (-)
Education beyond
secondary school (+)
65
Risk averse (+)
Gender
Poland
Age
Male (+)
South Africa
United Kingdom
Male (+)
BVI
Male (+)
Income
Income
stability
Low (-)
30-39 (+)
40-49 (+)
50-59 (+)
60-69 (+)
Low (-)
High (+)
u20 (-)
30-39 (+)
40-49 (+)
50-59 (+)
60-69 (+)
70-79 (+)
80+(+)
Low (-)
High (+)
Education
Incomplete
schooling (-)
Education beyond
secondary school (+)
Incomplete
schooling (-)
Education beyond
secondary school (+)
Stable (+)
Risk aversion
Risk averse (+)
Incomplete
schooling (-)
Education beyond
secondary school (+)
Education beyond
secondary school (+)
The dependent variable is the overall financial literacy score at country level. Explanatory variables considered significant with p
values of 0.05 or less. (+) & (-) signs in parentheses signify a positive or negative association. Age comparison is 20-29.
66
CONCLUSION
This chapter shows that it is possible to apply the same set of questions to very different populations
around the world and create simple, meaningful indicators of financial literacy. Combining each of the
indicators provides a way of capturing overall levels of financial literacy.
The findings highlight reasons for concern. It appears that most people had some very basic financial
knowledge, but understanding of other, everyday financial concepts such as compound interest and
diversification was lacking amongst sizeable proportions of the population in every country. There is also
some indication that certain respondents were over-confident, in that they gave incorrect responses
rather than admitting that they did not know the answer. Furthermore, some countries need to work
particularly hard to ensure that women are not left behind: women had lower levels of financial
knowledge than men in almost every country studied.
At least three in ten adults in each of the countries surveyed failed to gain a high score on the
behaviour measure36. This suggests that certain people could benefit from initiatives designed to change
their behaviour. In some countries such an effort would be consistent with consumer attitudes that are
largely positive towards longer term plans, whilst in others policymakers would need to take into account
the short term preferences of the majority of the population.
Analysis of the relationship between behaviour and knowledge suggests a positive association in
every country: when knowledge increases so does behaviour. This does not prove causation, and much
more research is needed to understand the relationship between these variables. It may be that
improved knowledge leads to more active participation in financial markets and more positive
behaviours, but it is also possible that people who need to do something seek information, thus
increasing their knowledge. Alternatively, knowledge and behaviour may be mutually reinforcing, so that
people do something, learn a little, do more and continue to improve their level of knowledge in a
continuous cycle.
There is also a positive association between attitudes and behaviour. People with a positive attitude
towards the longer term were more likely to be exhibiting financial behaviours than those with a
preference for the short term. This relationship also warrants further investigation.
Analysis by socio-demographics suggests that inequality in opportunities may be preventing
individuals from being more financially literate. In particular, low levels of education and income are
associated with lower levels of financial literacy, suggesting that certain groups may currently be
excluded from activities and learning opportunities that would improve their financial wellbeing. The
evidence also gives further emphasis to the need to support women in several of the countries studied37.
36
With the marginal exception of BVI (29%).
37
See Chapter 3 for further analysis of financial literacy by gender, and OECD (2013c forthcoming) for further
discussion about ways to address gender differences in financial literacy.
67
It is clear that the data collected for this measurement project provides the first ever rich and
detailed insight into the financial literacy of diverse populations. Further analysis will help to address
questions raised by this initial analysis, such as the relationship between risk aversion and financial
literacy, and to further explore areas such as:

which aspects of knowledge and behaviour people struggle with most;

the issue of confidence in relation to financial knowledge (by analysing the ‘don’t know’
responses);

the relationship between knowledge and specific behaviours

the relationship between attitudes and specific behaviours

the relationship between financial literacy and individual’s socio-demographic characteristics,
with a particular focus on gender, age and education;

the extent to which people are financially included and active consumers;

the extent to which the measures of financial literacy can be used to predict outcomes such as
having sufficient savings to cover unexpected income shocks.
The results of this analysis provide evidence from which the participating countries can identify
needs and gaps and develop appropriate national policies and strategies. The data provides a sound
empirical evidence base from which to inform the current revision and elaboration of OECD
recommendations on financial education, including the OECD/INFE High-level Principles on National
Strategies for Financial Education (2012). It will also inform the development of targeted financial
education programmes.
It is intended that this first measurement project will lead to a regular programme of data collection,
analysis and reporting. The countries participating in the first measurement project will be encouraged to
repeat the survey in 3 to 5 years time and for other countries to use the questionnaire to conduct their
own survey. By analysing the data in a consistent way, a large dataset of indicators can be developed
across countries and over time.
68
Chapter 2
FINANCIAL INCLUSION AND FINANCIAL LITERACY. ANALYSIS OF DATA FROM THE
OECD FINANCIAL LITERACY MEASUREMENT EXERCISE IN 14 COUNTRIES
INTRODUCTION
Financial Education, Financial Inclusion and the Need for Data
Financial inclusion is recognised as an important policy issue in developed and developing countries,
and is part of the G20 agenda on development. The OECD started working on the demand side of
financial inclusion in 2003, devoting a chapter of its 2005 publication to the importance of financial
education for bringing the unbanked and under-banked into the financial system (OECD, 2005a). In
October 2010, upon the recommendation of its Advisory Board, the OECD International Network on
Financial Education (INFE) created an Expert Subgroup on the Role of Financial Education in Financial
Inclusion.
The subgroup agreed on the following working definition:
“Financial inclusion refers to the process of promoting affordable, timely and adequate access
to a range of regulated financial products and services and broadening their use by all segments
of society through the implementation of tailored existing and innovative approaches including
financial awareness and education with a view to promote financial wellbeing as well as
economic and social inclusion.”
International efforts at measuring financial inclusion so far have mostly focused on supply-side data.
Looking at the demand side of financial inclusion is important for several reasons. In particular, it helps to
put supply side data into perspective. For example, supply side information about current accounts may
tell us that there are 1.9 active accounts for every adult in the country, but the distribution of such
accounts across the population can only be explored through consumer data. Furthermore, supply side
data can only capture the provision of services; it cannot describe the awareness of such services or the
public attitude towards them.
There is clearly also benefit in combining information about both sides of the market in order to gain
a comprehensive overview of the state of financial inclusion within a country. This work is in its infancy,
although it has gained momentum with the creation of the Global Partnership for Financial Inclusion’s
(GPFI) expert subgroup on financial inclusion data and target setting.
This chapter reports research undertaken by the INFE to explore the relationship between financial
literacy and demand side issues, including awareness, holding and use of financial products.
The INFE agreed that the first and foremost consideration when analysing the financial literacy data
to inform work on financial inclusion should be to explore patterns at a national level. Whilst tables and
figures in this chapter present findings from a wide range of countries, readers are therefore encouraged
to consider each of the results in relation to specific countries and their circumstances.
The emphasis of the chapter is on the relationship between financial literacy and various indicators
of financial inclusion within each country, rather than creating an overall indicator of financial inclusion
that can be compared across countries.
71
This chapter provides country level information about important aspects of financial inclusion,
including product awareness, holding and recent choices. It proceeds by exploring the extent to which the
various indicators of financial inclusion are associated with financial literacy. This is followed by results of
analysis of the financial inclusion variables and various key socio-demographic factors including gender,
age and income. Conclusions about the extent to which the data is capturing something useful and the
lessons that can be drawn from the findings are discussed in the final section, along with suggestions for
next steps.
72
PRODUCT AWARENESS
Financial inclusion can only occur if products are available, and if people are aware of the products
that are offered. The Core Questionnaire therefore begins the product section by checking whether
respondents have heard of the main product types available in their country38.
Annex 3 contains country level charts of financial product awareness, holding and recent choice.
The products in these charts are sorted by product awareness; the product at the top of the chart is
therefore the most widely known product in that country39.
Country level charts show that across the participating countries, awareness was typically highest in
relation to various bank accounts, such as savings or current accounts. However, in Armenia, more people
were aware of retail credit than any other product.
Overall, product awareness appears to have been relatively low in Albania, with fewer than 4 in 5
people having heard of a savings account, and slightly over half aware of a current account. In South
Africa, too, some types of product awareness appear to have been rather low: whilst 9 in 10 had heard of
a bank account, the next most widely recognised product was a credit card, and only 65% of respondents
claimed to have heard of this product.
The more products people are aware of and the more they can be considered to be engaged with
financial services. In this regard, awareness of financial products can be seen as an important indicator of
the demand side factors influencing financial inclusion. A simple indicator may just look at awareness of
one product used widely around the world, or provide a count of the number of products that people are
aware of. Reflecting the belief that people need to be aware of the financial market place more generally
in order to identify suitable products, the second approach has been adopted, developing an indicator
based on a count of products. However, the number of products differs across countries, and the number
of products listed in the questionnaire also varies across countries. For these reasons it would not be
appropriate to simply count how many products an individual has heard of; the indicator therefore
identifies respondents who know of at least 5.
A very small minority of the products listed by individual countries are informal products, such as
Stokvels in South Africa. These have been included in the score for awareness to show a general
connection with financial services of all kinds. As no country has listed 5 informal products, respondents
must have heard of some formal products in order to be considered financially included on this indicator.
38
These products may not be available in all areas of the country. Country specific analysis will be necessary to
explore whether lack of awareness is primarily caused by poor coverage.
39
Whilst the questions were asked of everyone, some people did not provide a valid response. The percentages
reported here are based on all respondents and will therefore provide a conservative estimate of the actual
proportions being aware, holding or making recent purchases of the products listed.
73
Figure 24 indicates that in Albania around a third of those responding had heard of at least 5
financial products out of 9 listed. In Germany all respondents (100%) had heard of at least 5 financial
products out of the 13 listed in their questionnaire.
94%
84%
100%
87%
99%
95%
96%
98%
81%
97%
90%
BVI
88%
UK
Figure 24. Proportion of respondents aware of at least 5 products
62%
South Africa
Poland
Peru
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech
Republic
Armenia
Albania
34%
Base: all respondents: counting all positive responses. The proportion “unaware” therefore includes non-response,
refusal, don't know and not aware.
Average financial literacy by levels of awareness
Figure 25 looks at the average combined financial literacy scores at a country level, across 2 groups.
The first group is aware of fewer than 5 of the financial products available in their country (as listed on
the questionnaire), whilst the second group is made up of those that are aware of at least 5 such
products. Figure 25 shows that individuals who were aware of at least 5 financial products had higher
levels of financial literacy than the rest of the population. In some countries the difference in average
scores is high: in the Czech Republic for example, there is difference in averages of more than 4 points.
74
Figure 25. Average financial literacy by product awareness
65.5 60.3 64.5
49.8
60.2
52.3
61.6
53.8
64.7 66.6 68.9
BVI
40.5
51.5
UK*
67.3
South Africa
68.1
Peru
61.6 58.1
Norway
46.9
55.3
Malaysia*
Armenia
Albania
45.1
65.9
Hungary
57.9
Estonia
65.6
Czech Republic
57.9
Aware of 5 or more products
Poland
Aware of fewer than 5 products
Base: all respondents. Germany and Ireland had too few respondents who were not aware of 5 financial products to make
meaningful comparisons. The UK and Malaysia had fewer than 50 respondents who were not aware of 5 financial products and
so comparisons should be made with caution.
The relationship between awareness and financial knowledge
Logically, one might assume that people who are financially knowledgeable will also be aware of
financial products. Additional analysis of financial product awareness by knowledge has therefore been
undertaken to explore whether this is the case. This explores the potential association by looking at the
proportion of respondents with low and high levels of financial knowledge40 that were aware of at least 5
products.
As seen from Figure 26 below, in some countries higher levels of financial knowledge were
associated with product awareness. In Albania and South Africa the relationship was particularly marked:
for example, 43% of Albanians with a high level of financial knowledge were aware of at least 5 financial
products, compared to just 26% of those with a lower financial knowledge score. However, awareness
levels in Germany, Ireland, Peru and BVI appear to have been equally high irrespective of knowledge.
It is possible that the apparent lack of a relationship between awareness and knowledge in some
countries is driven by country specific policies. For example, in countries where it is impossible to receive
an income without some form of bank account, people will be aware of such products even if they know
very little about financial matters.
40
As explained in Chapter 1, respondents who answered 3/4 of the knowledge questions correctly are
considered to have a high level of financial knowledge.
75
Figure 26. Product awareness by financial knowledge
Low
93% 88%99%
83%
High financial knowledge score
100%
100% 99% 93% 98%98% 98%
91%98%
91%
90%
90% 100%
75%
74%
72%
76
BVI
UK
Poland
Peru
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
Armenia
Base: all respondents
South Africa
53%
43%
26%
Albania
95% 99% 89% 91%
79%
PRODUCT HOLDING
Arguably, a measure of product holding is the most important demand side indicator of financial
inclusion; and the most relevant for a combined measure of demand and supply side issues. A thorough
exploration of the pattern of product holding can provide clear evidence of widespread financial inclusion
or, alternatively high levels of exclusion. This can help policy makers to identify the size and nature of the
problem of financial exclusion, if there is one.
However, a measure of financial product holding cannot be considered to be a fully comprehensive
indicator of the success of financial inclusion initiatives. If the primary goal of financial inclusion strategies
is to improve access to appropriate products, current use may only provide a weak indicator of the
success of strategies and should be used in conjunction with other information. For instance, the
indicator may show that current product holding levels are high whilst other evidence such as consumer
complaints data indicates that products are falling short of meeting the needs of consumers.
Alternatively, current product holding may be found to be very low despite the availability of welldesigned products; in such cases it is worth checking if other barriers exist such as low levels of financial
literacy or lack of geographical proximity. Furthermore, consumers may have been incentivised to hold
products but make no use of them: for example in countries where income is received through a current
account it is important to know whether that account is being used effectively, or whether the money is
being withdrawn immediately in order for the account holder to manage a cash budget.
The relationship between awareness and holding
The country level analyses in Annex 3 show that more people were aware of a product than actually
held it, and that in some countries, and with some products, the gap was large.
The gap between awareness and holding is particularly noticeable in Armenia (where none of the
individual types of products listed was held by more than 16% of the population – see Annex 3) and on
some products in Peru (where fewer than half had a general purpose account for salary or savings despite
almost everybody being aware of such a product).
Elsewhere, certain products were held by almost all of those people who were aware of them: such
as bank/current accounts in Estonia, Germany and Norway or savings account in Malaysia and BVI.
Indicators of product holding
Indicators of product holding have been created by classifying products into 4 mutually exclusive
categories, either: payment, savings/investments (excluding pension products), insurance or credit in
order to give an overview of this aspect of financial inclusion in each of the countries surveyed. The
percentage of the population in each country that holds a product from each of these categories has then
be calculated (Figure 4)41. These indicators are not necessarily comparable across countries, as each
41
None of the product types listed is counted twice, although we acknowledge that, for example, a current
account may also provide credit facilities, and certain savings products may provide various payment options.
77
country chose their own list of products to include in the questionnaire, and each financial market has
product variants with different, and often overlapping, features.
Figure 27 and the analysis reported below shows that within countries there is often a wide variation
in the proportion holding each of the four categories of products42. To the extent to which it is possible to
make any country comparison, it appears that across countries there is variation in the proportions of
people holding products from within the same category.
Figure 27. Product holding
Payment product
Saving and/or investment
Insurance product
Credit
BVI
UK
South Africa
Poland
Peru
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
Armenia
Albania
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Base: all respondents.
The following sections discuss each of the product categories in more detail.
Payment products
An indicator has been created that identifies respondents who held products such as current
accounts that are primarily designed to receive and make payments. This measure does not count savings
products even if they have payment facilities, nor credit products with payment facilities, such as credit
cards, as there is incomplete information about this in the data. It should also be noted that none of the
countries listed mobile banking facilities in their questionnaire and so this indicator should be seen as a
measure of the use of traditional payment products.
Access to accounts with payment facilities is widely seen as an important aspect of financial
inclusion. Such accounts allow people to automate bill payments whilst also lowering the cost of
transactions and reducing the risk created by carrying cash. They almost certainly help with money
management by allowing consumers to automate bill payments and may therefore reduce the likelihood
of falling into arrears through accidental missed payments. Payment products can also be a prerequisite
for other services (financial and other) including access to mobile phones and consumer credit.
42
Some people did not disclose which products they held, and so the percentages should be taken as an
underestimate.
78
The proportion of respondents that held a payment product ranges from over 9 in 10 in Germany,
through to less than 1 in 10 in Armenia (see Figure 27 and Annex 3).
In 7 of the countries (Estonia, Germany, Hungary, Norway, Poland, South Africa and the UK), more
people held a payment product than savings, credit or insurance.
Savings and investment products
The indicator of savings products incorporates financial products such as savings accounts, credit
union accounts, unit trusts and stocks and shares. Using this broad approach to categorisation, savings
products were the most common of the four types of financial product analysed in 4 of the countries
surveyed (Albania, Czech Republic, Malaysia and BVI) and held by a majority of those populations, with
the exception of Albania (31%). They were also held by a majority of the population in Germany, Ireland,
Norway and the UK.
Insurance
In some countries, certain insurance products can be compulsory across particular groups of the
population: for example, car owners may have a legal obligation to hold car insurance whilst mortgage
holders may be required to hold life insurance or income-protection. Elsewhere, insurance may be seen
as a wise investment to protect against potential risks, or it may be treated as an alternative to a savings
product (indeed in some countries term life-insurance is often bought as a savings vehicle). This analysis
draws together reporting on insurance products as listed by participating countries.
Only in Ireland were insurance products the most common products held. However, the majority of
respondents had an insurance policy of some kind in Czech Republic, Germany, Hungary, Ireland, Norway,
Peru, the UK and BVI.
Credit
Access to credit is widely considered to be an important part of financial inclusion; allowing
individuals to smooth income, deal with unexpected events, invest in future income streams and develop
entrepreneurial ideas. Nevertheless, this does not mean that credit is an essential product for all
individuals; indeed amongst the countries surveyed, only in Armenia were credit products the most
widely held product type.
Variations in financial literacy by product holding
Average financial literacy by payment product holding
As noted above a payment product can help individuals to better manage their money. As financially
literate people will exhibit effective money management and timely bill payment, it is logical to assume
that payment product holding is likely to be associated with higher levels of financial literacy. The
direction of any association is unclear: on the one hand high levels of financial literacy may increase
demand for useful products, whilst on the other, access to such products may lead to more effective
money management and thus higher financial literacy.
79
As in Figure 25, Figure 28 focuses on the differences in average financial literacy scores across two
groups – this time those without a payment product, and those with. This shows that payment product
holders had an average financial literacy score that was higher than those without such a product.
Figure 28. Average financial literacy by payment product
52.6
71.0
61.1 58.665.6 65.3
53.1
BVI
60.9
UK
67.9
60.9
South Africa
65.7
59.7
Poland
Malaysia
57.1
Peru
68.6
68.0
57.6
Ireland
Germany
69.2
61.3
Hungary
68.9
57.260.9 53.3
Estonia
67.4
58.1
Czech Republic
Albania
63.7
56.1
Armenia
66.1
58.5
Holds a payment product
Norway
Doesn't hold a payment product
Base: all respondents. 30 respondents in Germany lacked a payment account and so the comparison relies on very small
numbers and should be treated with caution; conversely 41 respondents in Armenia had a payment account.
Further exploration of the relationship between timely bill payments and payment products
empirically is possible by looking at responses to the statement ‘I pay my bills on time’ in relation to
payment product holding (Figure 29). There was a positive relationship between paying bills on time and
having a payment account: in all countries a larger percentage of those with a payment product paid their
bills on time than those without such a product. This is particularly noticeable in Albania, Ireland,
Malaysia and South Africa. However, even amongst those without such facilities, the majority stated that
they also made timely payments showing that there are other factors that influence the propensity to pay
bills on time over and above financial inclusion.
80
Figure 29. Paying bills on time with and without a payment account
88%
79% 83%
80% 83% 73%
88%
80% 81%
73% 77%
90%
73%
68%
81% 85%
Poland
Peru
50%
Norway
Malaysia
Ireland
Hungary
Germany*
Estonia
Armenia*
Czech Republic
55%
Albania
88% 89%
80%
BVI
79%
Has a payment product
UK
73%
92% 96%
South Africa
Has no payment product
90% 94% 95%
Base: all respondents: Percentage of respondents responding to the behaviour statement ‘I pay my bills on time’ by putting
themselves at 4 or 5 on the scale from Never=1 to Always=5. *30 respondents in Germany lacked a payment account and so the
comparison relies on very small numbers and should be treated with caution; conversely 41 respondents in Armenia had a
payment account.
Average financial literacy by savings product holding
The financial literacy measure considers a wide range of financial behaviours. Amongst these is
active saving. Although the active saving measure is not related specifically to account holding, it is likely
that there is some relationship between financial literacy and savings account holding.
Once again the focus is on the differences in average financial literacy scores across two groups –
this time those without a saving product, and those with. Respondents with a savings or investment
product were typically more financially literate than those without (Figure 30). However, the size of the
difference is too large to be explained by the active savings measure alone. This indicates that savings
account holding is associated with higher levels of financial literacy.
Figure 30. Average financial literacy by saving and investment
Doesn't hold a saving or investment product
Base: all respondents. Note that in Armenia 16 respondents had a savings or investment product.
81
BVI
UK
South Africa
Poland
Peru
Ireland
Hungary
Germany
Estonia
47.3
67.1
66.9 64.168.3
66.9 66.968.9
65.5
56.761.0 57.3
56.4
56.0
54.3
Norway
75.4
69.0 65.0
67.4
64.9
59.4
59.0
59.0
Malaysia
65.8
Czech
Republic
Armenia
Albania
67.3
61.7
57.4
56.2
Holds a saving or investment product
In order to remove the potential influence of the active savings measure, additional analysis
considers whether there is evidence of a link between financial knowledge and savings. Figure 31 below
shows whether people who understood the impact of compound interest on savings were more likely to
hold money in savings or investments than those who didn’t. There was a positive association between
this aspect of knowledge and savings in most countries, although in South Africa it appears that it was not
knowledge but other factors such as income that mostly impacted on the likelihood of using a saving or
investment product.
The chart also highlights the fact that even people without a clear understanding of the benefit of
compounding were using saving or investment products. Such people may not have chosen the most
appropriate product for their needs.
Figure 31. Holds savings or investment account by knowledge of compound interest
Incorrect
98%
92%
97%
90%
83%
76%
Correct
97%
90%
95%
83% 87%
69%
86%
76%
51%
BVI
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
Armenia
Albania
UK
8%10%
1% 1%
34% 31%31%
20%
South Africa
20%
14%
Poland
20%25%
Peru
29%
Base: all respondents. Left hand column per country is the percentage of all respondents who gave an incorrect
response to the compound interest question that hold a savings product. Right hand column shows the percentage of
all respondents who gave a correct response that hold a savings product. Note that in Armenia 16 respondents had a
savings or investment product.
Average financial literacy by credit product holding
The difference in financial literacy amongst those with and without a credit product is typically
smaller than for those products discussed above (Figure 32). However, there are some significant
differences (see for example Ireland, Malaysia and South Africa), and the association is consistently
positive: those with credit products were more financially literature than those without. Of course, given
the potential pitfalls from holding credit it is reassuring that credit users were not less financially literate
than those without credit.
82
Figure 32. Average financial literacy by credit product
Doesn't hold a credit product
Holds a credit product
BVI
UK
South Africa
Poland
Peru
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech
Republic
Armenia
Albania
72.0
69.8 66.068.2
67.9
66.8 67.069.3
66.4
66.2 61.668.8
65.6
63.467.8 58.662.2 64.2
62.2
59.9
59.7
59.1
59.0
58.6
58.0
56.1
55.7
54.7
Base: all respondents.
Average financial literacy by insurance product holding
In some countries some insurance products (such as car insurance or household content insurance)
will be mandatory for those individuals concerned (i.e. car owners or those living in rented
accommodation). With a strong external influence on the propensity to hold insurance there may be little
or no difference in the level of financial literacy amongst holders and non-holders. However, in fact the
analysis shows that average levels of financial literacy are considerably higher amongst those holding
insurance in several of the countries surveyed (Czech Republic, Germany, Ireland, Malaysia, South Africa,
UK; Figure 33).
Figure 33. Average financial literacy by insurance product
55.5
63.0
65.4
55.5
57.6
Base: all respondents. The proportion with insurance in Armenia is too small to make a meaningful comparison
83
66.7 67.7 69.0
BVI
66.7 62.0 66.5
UK
59.7
South Africa
72.7
61.4
Poland
67.5
55.1
Norway
69.8
Malaysia
63.2
Ireland
69.6
Hungary
63.4 60.3
Estonia
58.1
Germany
70.4
58.2
Czech Republic
61.2
Albania
60.4
Holds an insurance product
Peru
Doesn't hold an insurance product
ACTIVE PRODUCT CHOICE
Financial inclusion requires active participation in the financial market place. In most countries
people could benefit from keeping an eye on the market, switching providers and choosing new products
that meet their evolving needs. The Core Questionnaire therefore asks respondents whether, in the last 2
years, they have actively chosen any of the products listed – even if they no longer hold these products.
They are asked to exclude simple (passive) renewals.
Figure 34 reports the proportion of respondents in each country that said they had made such a
product choice. This is a useful measure of inclusion, but as with the previous measures, it will
underestimate the true proportion of active consumers, since some people did not answer the question
and some products may not have been listed43. Over half the population of Albania, Germany, Hungary,
Ireland, Peru, South Africa, the UK and the British Virgin Islands had chosen some kind of financial
product in the last 2 years.
Figure 34. Proportion making an active product choice in the last 2 years
57%
60%
UK
BVI
Peru
Norway*
Malaysia
Ireland
Hungary
56%
South Africa
49%
46%
Germany
Czech Republic
67%
60%
48%
Estonia
39%
Armenia
Albania
39%
64%
Poland
70%
62%
Base: all respondents. *Norway did not ask this question.
The country level figures in Annex 3 show that, in Albania and Armenia, almost as many people had
made a recent purchase of the products listed as reported that they held these products (although it
should be remembered that overall percentages are relatively low).
In all countries surveyed, average financial literacy was higher amongst those who had made a
recent product choice than those who hadn’t (Figure 35). In some cases the difference in averages is
large.
43
Countries in the first measurement project were not required to separate out the refusals from the ‘no’
responses, and so we cannot consistently identify non-response on this question. It should also be
remembered that the list of products for this question was decided at the country level.
84
Figure 35. Average financial literacy by recent product choice
Has made a recent choice
Base: all respondents. *Norway did not ask this question.
85
BVI
UK
South Africa
Poland
67.8
67.6 67.069.7
61.6
60.5 60.1
57.7
55.1
54.9
Norway*
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
Armenia
Albania
71.4
71.0
70.2
68.7
67.7
64.7
63.2
62.4
62.3
61.9
61.2
61.1
59.0
58.7
54.7
53.7
Peru
Hasn't made a recent product choice
RELYING ON FRIENDS AND FAMILY
The previous sections focused on product awareness, choice and usage. The indicators capture the
level of inclusion within a country. However, the data can potentially also be used to develop an indicator
of exclusion. This section does this by identifying people who turned to friends and family for a loan when
they are unable to make ends meet or ask them to hold money on their behalf when they want to save.
This may provide a useful indication of people who did not have access to formal products that they felt
met their needs, or alternatively of people who had less need for formal products because extended
family networks provided valuable support – the most likely explanation will depend on the country
context.
The full meaning of this indicator at a national level must be considered with care. In some
countries, and amongst some groups, family and friends may be the first option for people to access
financial support, and this behaviour would not be taken as a sign of lack of access to financial services. It
would, however, still have policy implications: over reliance on family and friends can put financial
pressure on households and informal networks and is of limited economic benefit.
Figure 36 shows that a relatively large proportion of adults in Armenia (37%) and Malaysia (35%) had
turned to family or friends to keep their savings or help them to make ends meet. In other countries the
behaviour was less common.
Figure 36. Relying on friends or family for borrowing and/or saving
12%
18%
BVI
27%
UK
15%
South
Africa
Peru
Ireland
27%
5%
Norway
10%
Malaysia
10%
Hungary
Czech
Republic
Armenia
Albania
9%
12%
Germany
35%
17%
Poland
37%
Estonia
27%
Base: all respondents. Counting all those who reported that they borrowed from friends and family to make ends
meet plus all those who gave money to friends and family to save on their behalf.
There is also a difference in average financial literacy scores by whether or not the respondent has
turned to family and friends to borrow and/or save in most of the countries surveyed44. Financial literacy
is consistently lower amongst individuals who have done so.
44
Some of the difference can be explained by considering the development of the score, but not all of it. Those
who had not borrowed to make ends meet would exhibit a positive behaviour, as would those who had given
money to friends to look after as a savings strategy.
86
Figure 37. Average financial literacy by reliance on family and friends
Has not turned to family or friends
Borrowed or saved via family or friends
Base: all respondents.
87
BVI
UK
South Africa
Poland
Peru
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
Armenia
Albania
69.066.9
68.9
66.665.0 65.4
66.2
65.162.5
65.0
64.3 67.361.4 66.8
62.9
61.9
59.9
59.5
59.4
59.3
56.5
54.5
54.4
54.1
54.1
51.3
51.1
51.0
FINANCIAL INCLUSION AND SOCIO-DEMOGRAPHICS
The analysis presented so far shows clearly that financial exclusion is associated with lower levels of
financial literacy. This indicates a potential gain from developing financial education programmes
designed to raise the financial literacy of the financially excluded. In order to identify who would benefit
most from such financial education, this section explores the relationship between financial inclusion and
various socio-demographic factors. There is no additional analysis of the exclusion indicator by sociodemographics as the small proportions make it difficult to split the categories further.
Variations in awareness by socio-demographics
Additional analysis shows that similar proportions of men and women were aware of at least 5 of
the products listed in most of the countries analysed (Table 9). In Albania (29% vs 37%), Armenia (86% vs
90%), Malaysia (93% vs 98%), and South Africa (60% vs 64%), women had slightly lower levels of
awareness than their male counterparts.
Product awareness varied somewhat by age, typically being highest in young to middle age and
falling off in old age across the countries surveyed. This contrasts somewhat with financial literacy, which
is typically highest in middle age.
With the exception of Germany and Ireland, levels of awareness increased notably with income. In
South Africa, this finding is particularly stark, with just 50% of respondents from low income households
aware of at least 5 products, compared with 83% of those from the higher income group.
Awareness also varied by education in every country but Germany (awareness appears to be
practically universal amongst German adults). Adults who had continued their education beyond
secondary school were the most likely to be aware of 5 or more products.
88
Table 9. Awareness by socio-demographics
Low
Average
High**
37%
90%
95%
40%
91%
95%
37%
92%
97%
29%
79%
92%
19%
81%
92%
36%
93%
96%
47%
100%
99%
20%
54%
92%
38%
83%
96%
58%
93%
97%
82%
100%
87%
99%
93%
97%
98%
80%
59%
86%
100%
86%
99%
98%
94%
98%
83%
64%
85%
100%
91%
100%
96%
91%
97%
87%
63%
90%
100%
90%
99%
98%
98%
99%
85%
65%
76%
100%
81%
99%
88%
96%
97%
74%
55%
79%
99%
78%
99%
93%
92%
97%
74%
50%
86%
100%
91%
99%
96%
98%
99%
88%
76%
89%
100%
96%
98%
99%
99%
99%
92%
83%
72%
100%
77%
98%
88%
94%
97%
64%
52%
84%
100%
91%
100%
97%
96%
99%
84%
71%
88%
100%
93%
99%
99%
98%
98%
86%
82%
97%
92%
97%
88%
98%
88%
98%
90%
96%
91%
95%
86%
98%
94%
99%
95%
87%
68%
98%
87%
98%
94%
Beyond
secondary
50 and over
29%
86%
93%
Complete
secondary
30 to 49
Less than
complete
secondary***
Education level
18 to 29*
Income
Male
Albania
Armenia
Czech
Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South
Africa
UK
BVI
Age
Female
Gender
Table suppresses data for refusals on age, income and education level respectively. *66 respondents from BVI fall into the youngest
category. ** For Armenia the number of respondents with a high income is just 31 and for Ireland 61 respondents reported a high
income. ***Few respondents had less than complete secondary school education in Armenia (65) Germany (11) and BVI (86). All
analysis based on small numbers of respondents should be read with caution.
Variations in payment product holding by socio-demographics
Analysis of payment product holding by socio-demographics indicates that women were noticeably
less likely than men to hold a payment product in Albania (22% vs 29%), Malaysia (74% vs 82%), Peru
(45% vs 53%) and BVI (54% vs 65%) (Table 10). In Norway, women were slightly more likely than men to
have an account (90% vs 86%). Elsewhere the difference in payment product holding by gender was very
small.
Payment products were less likely to be held by young adults and very old respondents than those in
middle age across the countries surveyed. Those with lower levels of education were also less likely than
the more educated to hold payment products – indeed a gap of 20 percentage points is not uncommon,
as seen in Table 10.
89
Table 10. Payment products by gender, age and education level
18 to 29
30 to 49
50 and over
Less than
complete
secondary
Complete
secondary
Beyond
secondary
Education level
Male
Age
Female
Gender
Albania
22%
28%
25%
27%
25%
9%
21%
52%
Armenia
Czech
Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South Africa
UK
BVI
2%
70%
3%
73%
3%
70%
3%
84%
2%
61%
1%
61%
3%
80%
26%
86%
90%
96%
68%
76%
74%
90%
45%
67%
59%
83%
54%
90%
96%
71%
78%
81%
86%
53%
69%
65%
84%
65%
89%
91%
67%
70%
89%
88%
38%
74%
59%
83%
39%
93%
97%
86%
84%
82%
93%
55%
79%
68%
86%
57%
88%
97%
57%
74%
53%
84%
60%
56%
58%
81%
65%
88%
93%
58%
72%
64%
87%
36%
58%
53%
79%
44%
90%
96%
71%
94%
84%
92%
57%
76%
74%
86%
68%
93%
99%
89%
91%
96%
92%
74%
84%
75%
91%
71%
Table suppresses data for refusals on age and education level. *Very few people in Germany are lacking a payment account and so
the comparison relies on very small numbers and should be treated with caution; conversely very few people in Armenia have a
payment account.
Arguably the most noteworthy relationship between product holding and the socio-demographics
studied is with income (Figure 38). Figure 38 shows the percentage of low, average and high income
respondents that held a payment product in each of the participating countries. There was a clear
association between level of income and payment product holding in every country; the relationship was
particularly marked in Albania, Armenia, Hungary, Malaysia and Peru.
90
Figure 38. Payment product holding by income
Low
Average
High Income
BVI
UK
South Africa
Poland
Peru
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
Armenia
Albania
100%
80%
60%
40%
20%
0%
Base: all respondents who provided detail of their income. Low, middle and high income refer to income relative to national
median values. *Very few people in Germany lacked a payment account and so the comparison relies on very small numbers and
should be treated with caution; conversely very few people in Armenia have a payment account.
Variations in savings product holding by socio-demographics
There was no strong, consistent pattern between savings products and gender across the countries
studied (Table 11). In Estonia (24% vs 19%) and Norway (85% vs 78%) women were slightly more likely
than men to have a savings product. Elsewhere gender differences were very small, with the exception of
Poland, where just 18% of women and 30% of men had a savings or investment product, and to a lesser
extent the UK (71% vs 77%).
Savings product holding appears to have been highest amongst individuals aged 30 to 49, and was
strongly associated with income (Table 11).
The relationship between saving holding and income was also positive. In two countries, however
(South Africa and BVI), more middle income individuals held saving and investment products than either
those on high or low incomes.
91
Table 11. Savings holding by gender, age and income
18 to 29
30 to 49
50 and
over
Low
Average
High
Income
Male
Albania
Armenia
Czech
Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway*
Peru
Poland
South
Africa
UK
BVI
Age
Female
Gender
26%
1%
94%
35%
1%
94%
32%
1%
94%
28%
1%
97%
34%
1%
92%
21%
0%
92%
23%
2%
96%
53%
11%
97%
24%
92%
17%
79%
90%
85%
8%
18%
31%
19%
94%
16%
78%
93%
78%
9%
30%
31%
16%
87%
6%
69%
95%
83%
5%
23%
28%
25%
95%
18%
82%
92%
81%
10%
29%
34%
24%
94%
21%
80%
86%
80%
12%
20%
32%
13%
87%
8%
76%
87%
77%
4%
15%
28%
24%
95%
16%
85%
95%
84%
10%
28%
36%
33%
98%
33%
89%
98%
86%
18%
41%
30%
71%
88%
77%
91%
69%
91%
74%
88%
76%
89%
62%
90%
76%
91%
87%
90%
Table suppresses data for refusals on age and income respectively.
There was a positive association between savings and investment products and education in most
countries, which was particularly marked in Albania, Germany, Hungary, Poland and UK (Figure 39).
However, in the Czech Republic there was relatively little variation in saving and investment product
holding by education and in Peru and BVI there was no clear relationship.
92
Figure 39. Holds savings or investment account by education
Less than complete
Complete secondary school
Beyond secondary school
BVI
UK
South Africa
Poland
Peru
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
Armenia
Albania
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Base: all respondents that provided information about their level of education. Graph shows proportions holding
savings products by education. Note that in Armenia 16 respondents had a savings or investment product.
Variations in credit and insurance product holding by socio-demographics
No clear relationship could be found between the use of credit products and gender (Figure 40). In
some countries, approximately the same proportions of men and women reported holding some kind of
credit product, whilst in others women were rather less likely to hold them than their male counterparts.
In Estonia slightly more women than men held a credit product.
Figure 40. Credit products by gender
36% 41% 31% 39%
Poland
Peru
Norway
Malaysia
Ireland
Hungary
Germany
Estonia
Czech Republic
Armenia
Albania
16%
19% 24%
South Africa
43%
35% 33%
32% 29%
28%
69% 73% 72% 73%
59%
55% 52%
48% 49%
81% 84%
71% 72%
BVI
72% 76%
Male
UK
Female
Base: all respondents. Graph shows proportion of men and women holding credit products.
Women were slightly less likely than men to have insurance, but only Malaysia shows a marked
difference with 34% of women reporting that they held an insurance product compared with 48% of men.
93
As found with the other products, the use of both credit and insurance products varied by age and
was highest amongst 30 to 49 year olds (not shown). There was a strong positive association between
both credit use and income and insurance and income – credit holding and the use of insurance products
were highest amongst those with the highest incomes. The relationship with education was also positive.
Variations in active product choice by socio-demographics
In most of the countries surveyed there was very little difference between the proportions of men
and women who had made a recent product choice (Figure 41). In Albania, however, 53% of women had
made a recent choice out of the products listed compared with 68% of men, and in Peru 61% of women
but 71% of men had made such a choice. Nowhere were women more likely to be active financial
consumers than men.
Figure 41. Product choice by gender
55%57% 54%60% 60%61%
BVI
Peru
Norway*
Malaysia
46%51%
UK
71%
South Africa
61%
47%44%
Ireland
Hungary
Estonia
Czech Republic
Armenia
Albania
47%49%
38%41% 37%41%
67%72% 62%66%
59%60%
Germany
68%
53%
Male
Poland
Female
Base: all respondents. *Norway did not ask this question. Graph shows proportion of men and women that made a recent
product choice.
There was a similar relationship between product choice and age as with product holding – those in
the 30 to 49 year old age bracket were the most likely to be active (Table 12). A larger proportion of high
income respondents had made a recent choice than those in the lower income brackets, and the higher
educated were more likely to have made a choice than those who had not completed secondary school.
94
Table 12. Active product choice by age, income and education level
50 and over
Low
Average
High
Less than
complete
secondary
Complete
secondary
Beyond
Secondary
Education level
30 to 49
Income
18 to 29
Age
Albania
Armenia
Czech
Republic
Estonia
Germany
Hungary
Ireland
55%
46%
56%
59%
44%
46%
66%
27%
26%
45%
32%
32%
58%
44%
45%
87%
71%
55%
46%
26%
35%
67%
35%
45%
90%
42%
42%
52%
71%
66%
62%
49%
78%
70%
67%
41%
64%
58%
49%
48%
51%
63%
56%
45%
75%
64%
66%
52%
81%
76%
67%
41%
31%
56%
49%
48%
66%
66%
60%
49%
76%
72%
65%
Malaysia
Norway*
Peru
Poland
56%
50%
20%
33%
50%
62%
23%
47%
65%
55%
66%
75%
56%
69%
33%
63%
42%
71%
52%
77%
66%
57%
28%
60%
50%
69%
60%
South Africa
UK
BVI
55%
64%
77%
60%
61%
53%
52%
50%
64%
51%
46%
61%
64%
60%
60%
60%
66%
58%
52%
51%
59%
61%
53%
58%
64%
62%
60%
Analysis suppresses missing data. *Norway did not ask this question
95
CONCLUSION
The OECD/INFE financial literacy measurement survey has created a unique opportunity to explore
some of the demand side factors of financial inclusion, including product awareness and holding.
This analysis has provided insights into various aspects of financial inclusion in particular countries
from a demand-side perspective. While this may not offer a full picture of financial inclusion, it provides
an important complement to existing international financial inclusion measurements, which focus mostly
on supply-side data from financial institutions. The approach also has the advantage of being applicable
internationally, and already provides data from a range of developed and developing countries.
The analysis undertaken in this chapter provides indications that the population of some countries
have low levels of financial inclusion on a number of indicators. The positive association between
financial inclusion and financial literacy suggests an important role for financial education in the drive to
increase levels of inclusion in some countries. Moreover, the various indicators of financial inclusion are
significantly associated with various socio-demographic factors such as gender, age, education, and
household income; suggesting that targeted financial education may reap the highest rewards.
However, financial education does not work in isolation. Financial services must be accessible and
financial products must be well designed to provide users with safe, effective solutions to their financial
needs. Consumer protection must be in place to protect consumers from unfair practices and regulation
must be fit for purpose. With this structure in place, education can focus on informing consumers about
their choices, rights and responsibilities, and providing them with the skills necessary to undertake those
choices and manage their products appropriately.
Product awareness
The analysis presented here shows that basic awareness of the existence of products is high in many
countries; even if product-use lags behind. Countries with high levels of awareness may wish to know
more about the extent to which consumers are aware of the features of products, their benefits and
costs, and how they can be accessed.
There is a clear association between product awareness and financial literacy. Countries with low
levels of product awareness may therefore wish to develop awareness campaigns and financial education
strategies to improve both financial inclusion and financial education.
Awareness was lower amongst Albanians and South Africans with low levels of financial knowledge
than those with higher levels of knowledge. This indicates that there may be considerable benefit from
providing financial education that increases both financial product awareness and financial knowledge in
these countries.
The fact that the least aware adults were typically older, less well educated and poorer, suggests
that campaigns targeted on this segment of the population may be the most effective ways of increasing
overall awareness. Effectiveness of any awareness campaign will depend on presenting information in a
way that is engaging and easily understood by adults with little or no formal schooling. Repeated
96
messages are often seen as the most appropriate way of achieving awareness. Awareness campaigns also
need to be immediately relevant to the target audience, suggesting that targeting different groups with
different messages may be necessary.
Countries wishing to develop this analysis further may consider certain refinements. For example, it
would be possible to undertake country level analysis of awareness of specific categories of products
(such as regulated/unregulated; products available online, or in branch, products available to under 18s).
Alternatively, countries may experiment with the point at which consumers are considered to be
sufficiently aware; this analysis focuses on 5 product types, but there is no precedent for this and there
may be additional benefit from exploring other relationships such as how financial literacy changes with
the number of product types the respondent is aware of. It may also be beneficial to look at lack of
awareness, and focus on the types of products least likely to be in the public eye.
Equally, countries may wish to undertake more in-depth analysis of the relationship between
product awareness and financial knowledge. It may be that the cut-off used to identify a high level of
financial literacy (a score of at least 6 out of 8), is too high to identify the point at which people become
aware of financial products in some countries.
In countries such as Germany and Ireland, there is little benefit from exploring the relationship
between basic product awareness and other factors, since awareness appears to be almost universal. In
these countries research may be more fruitful if it focuses on the other aspects of awareness such as
whether individuals know the purpose of the products.
There are many possible reasons for the gap between awareness and product holding that need
exploring through more in-depth data collection. It may be that there are barriers to accessing the
products that people are aware of, including rigid identification requirements, cost, or poor product
design; it may even be that people do not believe that they can benefit from the products currently on
the market. Additional analysis at the country level could also take into account factors such as the
maturity of the financial market, the efficiency of regulation and consumer protection and the extent to
which consumers can access impartial information and advice.
Products held
Of the four product types analysed, payment products were the most commonly held in 7 of the
countries. This may reflect pressure or preference to make and receive payments electronically, but may
also indicate that a current account is seen as a gateway to other products in some countries.
The data also suggests that the majority of adults lack a basic payment account in Albania, Armenia,
and Peru. National campaigns to explain the benefit of such accounts may be necessary to increase their
use. Incentive schemes and/or redesigned products that encourage account opening may also have some
initial impact, but experience elsewhere suggests that consumers are likely to continue to largely operate
in cash without increased understanding of the benefits of electronic transactions and confidence with
the necessary technology.
The analysis shows that use of a payment account is associated with timely bill payments. To the
extent that this reflects the ease with which bill payments can be automated, it suggests that education
aimed to reduce the number of unbanked households should stress this benefit of payment products.
However, countries should explore the association further, as it may be that people with poor payment
histories have been refused an account, or cannot find one that meets their needs.
97
The analysis of payment product holding by socio-demographics suggests that in Albania, Malaysia,
Peru and BVI financial education aimed at increasing the use of such products may be more effective if it
includes provision tailored to the needs of women.
The data suggests that information about payment accounts is an important component of financial
education targeted on adults in low income households. This information should stress the overall benefit
of using an account, and help individuals to develop their skills to manage the account effectively and
thus minimise the cost of transactions and reduce the likelihood of additional fees.
The use of savings and investment products is relatively low in Albania, Armenia, Estonia, Hungary,
Peru, Poland and South Africa. With the exception of South Africa, there is some association between the
use of savings or investment products and knowledge of the impact of compound interest; suggesting
that account holding could be further encouraged by financial education that explains the longer term
benefits of formal saving. In South Africa it appears that it is not knowledge but other factors such as
income that mostly impact on the likelihood of using a saving or investment product.
Whilst there is an association between holding saving and investment products and understanding
the impact of compound interest, the analysis also shows that many people held savings despite their
lack of understanding the benefit of compound interest. Of course, compound interest is not the only
reason to save, but as an indicator of their level of financial knowledge in relation to savings it suggests
that something other than knowledge led to their desire to put money aside. Additional analysis would be
useful to explore the types of products that more knowledgeable people choose, and to consider how to
encourage the least knowledgeable to open savings accounts. It is also important to provide additional
knowledge to existing savers to make sure that they are making the most of the saving and investment
opportunities available to them. Qualitative data may be necessary to understand the relationship
between knowledge and the use of savings products in more detail.
The analysis of the various types of product holding by socio-demographics shows that there is some
association with financial inclusion and gender although the pattern is not marked on some product
indicators- such as credit and insurance. This may reflect the propensity of households to hold such
products as a couple: for example, a home loan or insurance on a car may be held in joint names.
The extent to which credit and insurance products are being underutilised is difficult to assess
through the OECD/INFE Financial Literacy Survey. However, it is clear that once again, the segment least
likely to use these products is older, less well educated and poor suggesting that both types of product
could be discussed in financial education campaigns aimed at this group.
Across the products analysed there is also an association with education level. This may be related to
specific knowledge gained through education, but is perhaps more likely to reflect the association
between education and income, and in turn the association with income and the various products.
The relationship between income and payment product holding needs to be analysed at the country
level. It may be that financial institutions are excluding low income individuals in a variety of ways (such
as keeping fees excessively high, marketing products in a way that suggests that are for a different type of
clientele, opening branches exclusively in high income neighbourhoods or setting minimum income
requirements on certain products). However, it is also possible that consumers have the impression that
there is nothing to gain from managing their money through a payment account: an attitude that could
potentially be countered through financial education.
98
Product choice
At least 1/3 of the population in each of the countries studied had made an active product choice.
However there is potential to improve engagement still further, with a large group of individuals
remaining passive or excluded. It is particularly noteworthy that the proportion of people who had made
a recent purchase of general insurance was much lower than the proportion holding insurance products.
As many insurance policies can be renewed on an annual basis, this lack of recent product choice
suggests that there were a lot of passive consumers in the countries surveyed who let their providers
renew their product automatically.
Reliance on friends and family
It is difficult to be certain that there is unmet demand for financial products. One way in which this
chapter has attempted to identify this is through an indicator that captures the extent to which people
turn to friends and family. If people are relying on friends to keep their money safe or to lend them
money in times of need, this may suggest that they do not have access to appropriate financial products
such as safe savings accounts or payment products with overdrafts. However, authorities should decide
for themselves whether this is an acceptable indicator or a crude measure of unmet demand amongst
their own population, particularly taking into account cultural factors.
In most of the countries studied, financial literacy was lower amongst those who had turned to
family and friends to borrow money or to safeguard savings than those who hadn’t. As those who are
aware of financial products are more financially literate than those with lower levels of awareness it
might tentatively be concluded from the various findings that people turn to friends and family because
their low level of financial literacy makes them less aware of the alternatives. This could be further
explored in additional research.
99
Chapter 3
GENDER DIFFERENCES IN FINANCIAL LITERACY45
45
See also OECD (2013c forthcoming).
101
INTRODUCTION
The policy interest in the financial literacy of women and girls
Both women and men need to be sufficiently financially literate to effectively participate in
economic activities and to take appropriate financial decisions for themselves and their families.
However, as women tend to live longer and earn less than men, therefore being more likely to face
financial hardship in old age, it is particularly important that they have the financial skills necessary to
ensure their financial wellbeing. Unfortunately, existing evidence indicates that their level of financial
knowledge tends to be lower than men’s, suggesting that they may not be well prepared for the
challenges they face.
The need to address the financial literacy of women and girls as a way to improve their financial
empowerment is gaining global relevance and is reflected in various initiatives at a national and
international level.
With the aims of improving policies and promoting gender equality in the economy in both member
and non-member countries, in 2010 the OECD launched a Gender Initiative to examine existing barriers to
gender equality in education, employment and entrepreneurship. The OECD Horizontal Project in Gender
Equality presented its Final Report on Gender Equality in Education, Employment and Entrepreneurship
to the Ministerial Council Meeting in May 2012 to inform, share policy experiences and good practices,
and help governments promote gender equality (OECD, 2012). The report also addresses gender
differences in financial literacy and how financial education can contribute to women’s financial
empowerment.
Moreover, in June 2012 the G20 Leaders meeting in Mexico recognised the need for women to gain
access to financial services and financial education. G20 Leaders also asked the OECD/INFE – together
with the Global Partnership for Financial Inclusion (GPFI) and the World Bank – to identify the barriers
they may face, and called for a progress report to be delivered by the next Summit (G20, 2012).
This chapter supports the work of the OECD/INFE in helping policy makers and relevant stakeholders
by identify key gender differences in levels of financial literacy. It draws on the OECD/INFE financial
literacy survey and other relevant data to explore differences in knowledge, behaviour, attitudes and also
looks at issues around product choice and holding46.
46
See OECD/INFE (2013c) and INFE (2013).
103
FINANCIAL KNOWLEDGE OF WOMEN
Analysis of financial knowledge by gender shows that women have lower average financial
knowledge scores than men in 13 of the 14 countries that participated in the OECD Financial Literacy
Measurement survey – the exception being Hungary (Figure 42).
This result is consistent with the overall international evidence on gender differences in financial
knowledge and understanding (Hung et al., 2012). Gender differences in financial knowledge are found
also by Lusardi and Mitchell (2011), who report cross-country comparable evidence from studies on eight
countries – Germany, Italy, Japan, the Netherlands, New Zealand, the Russian Federation, Sweden and
the United States. Using the same datasets exploited in Lusardi and Mitchell (2011), Bucher-Koenen et al.
(2012) further explore gender differences in financial knowledge in a subset of countries (the United
States, the Netherlands, and Germany). The overall evidence from these studies shows that in most
countries women have lower levels of financial knowledge than men based on short tests of financial
knowledge. However, in Russia and East Germany financial knowledge is very low for both men and
women, and gender differences are not significant (Bucher-Koenen and Lusardi, 2011, and Klapper and
Panos, 2011).
Other national financial literacy surveys have documented gender differences. In Azerbaijan and
Bulgaria, women achieved a lower share of correct answers than men to six numeracy and financial
knowledge questions (Alpha Research, 2010; Azerbaijan Micro-finance Association, 2009). The ANZ
Survey of Adult Financial Literacy in Australia provides evidence that females had lower scores than males
on a financial knowledge and numeracy index (ANZ, 2011). Similarly, the 2012 Survey on the Financial
Literacy of the Portuguese population shows that men obtained higher scores on average than women in
the knowledge of basic financial concepts (Banco de Portugal, 2011). In New Zealand, women made up a
higher proportion of the low knowledge group, and a lower proportion of the high knowledge group,
even though they improved over time (ANZ–Retirement Commission, 2009). Also women in the United
Kingdom scored lower on a financial knowledge test compared to men (Atkinson et al., 2006).
104
Figure 42. Average financial knowledge score by gender
75.5
62.6
71.4
67.0
BVI *
South Africa *
Poland *
Peru *
Norway *
Malaysia *
Ireland *
Hungary
70.7
60.5 58.2
60.3
56.0
54.4
52.7
Germany *
Estonia *
Czech Republic *
Armenia *
Albania *
61.8
54.9
65.9
61.9
Female
77.076.6 74.3
75.9
66.8
66.6 68.464.1 66.0
UK *
Male
74.7
72.3
68.9
67.5
Note: In countries indicated with an asterisk * the gender difference is statically significant at 5% level.
Gender differences in financial knowledge at young ages
Gender differences in financial knowledge may to some extent be the result of historical gender
inequalities. If this is the case, it is possible that gender differences in financial knowledge are smaller
among younger generations exposed to a more egalitarian environment.
However, analysis of financial knowledge amongst respondents aged 18 to 29 identifies gender
differences among young generations, even though differences are significant for fewer countries
(Armenia, Ireland, Norway, Peru, Poland and the UK – Figure 43).
Bucher-Koenen et al. (2012) show that in Germany, the Netherlands and the US gender differences
in financial knowledge exist within all age brackets, including among respondents younger than 35. A
survey of financial understanding of 8 to 18-year-olds in the Netherlands also finds that more boys than
girls can answer knowledge questions correctly (CentiQ, 2008). In a study among college students in the
US, Chen and Volpe (2002) find that young women have less knowledge about personal finance topics
than men. Also Lusardi et al. (2010) find gender differences in financial knowledge among US young
adults.
The evidence suggests that more research is necessary to understand whether there are factors
inducing gender differences in financial knowledge from a very young age, and to what extent these
gender differences are transmitted across generations.
105
Figure 43. Average financial knowledge score by gender (young people aged 18-29)
60.759.2
Norway *
Malaysia
Ireland *
Hungary
Germany
Estonia
Czech Republic
Armenia *
Albania
45.1
75.0
66.0
58.3
52.1
69.0
59.459.9
57.7
UK *
67.968.2
67.7
59.7
58.2
South Africa
74.572.3
Poland *
70.9
68.1
66.5
62.9
Female
Peru *
63.063.2
Male
77.878.8
Subsample of respondents aged 18-29. The data for the BVI have been excluded due to a very low number of
observations in the relevant age range. Note: In countries indicated with an asterisk * the gender difference is
statically significant at 5% level.
Less well-educated and low-income women have the lowest financial knowledge
In addition to looking at the differences in financial knowledge between women and men, it is also
interesting to focus on women only by investigating which subgroups of women exhibit the lowest
financial knowledge. This is relevant as it can help policy makers to identify the subgroups of women who
may be most in need of improving their financial literacy.
The results show that there are marked differences across women according to their education,
their occupational status and their household income, while there is hardly any difference by marital
status or age.
Women who are married have similar levels of knowledge to those who are single, cohabiting and
divorced women. This suggests that married/partnered women are not learning from their partners and
single women are not ‘learning by doing’. In spite of the limited difference across marital statuses, in
some countries (including Albania, the Czech Republic, Malaysia, and South Africa) widows show lower
financial knowledge than married women, even after controlling for age and income.
Even though the age pattern is not significant in many countries, in some of them (such as Ireland,
Norway, Peru and the United Kingdom) young women in the age group 18-29 have lower knowledge than
women in the 30-59 age bracket. Some evidence of a hump-shaped pattern of financial knowledge by age
comes also from other studies. In the US, women under the age of 35 are found to have lower financial
knowledge than women in the age group 36-65 (Bucher-Koenen et al., 2012). In Germany, women over
the age of 65 gave fewer correct answers than younger women, and widows gave fewer correct answers
than married/single women (Bucher-Koenen et al., 2012). Overall, these results suggest that young and
elderly women/widows have lower financial knowledge than middle aged ones (as is the case for the
population in general).
106
Finally, there appears to be a marked association of financial knowledge with women’s occupational
status, their education and their household income. In Albania, Ireland, Poland, South Africa and the UK
women who are not working (including the unemployed, the retired, students, as well as homemakers)
have lower financial knowledge than women who are employed. Similarly, women who live in households
with below median income, and who have not completed secondary school have the lowest financial
knowledge.
Gender differences are smaller but still significant after controlling for socio-demographic factors
Gender difference may, to some extent, be related to the different opportunities that women and
men have to experience with financial issues along their life, and may therefore be related to observable
demographic, social and economic factors.
Indeed, empirical evidence shows that gender differences in financial literacy are strongly correlated
with differences in socio-economic conditions of men and women. Bucher-Koenen et al. (2012) show that
taking into account age, marital status, income and education reduces the apparent gender difference in
the Netherlands, although it does not disappear completely. Analogously, a study on the US population
shows that accounting for education, income and current and past marital status reduce the observed
gap in financial knowledge by about 25% (Fonseca et al., 2010).
Also the analysis of the OECD/INFE survey data supports the evidence that gender differences in
financial knowledge remain even after taking into account the different socio-economic conditions of
men and women47. Figure 44 shows that in Albania, Armenia, Czech Republic, Germany, Ireland, Norway,
Peru, Poland and the UK the difference between the financial knowledge score of men and women is
lower – but still significant – after controlling for demographic, social and economic factors with respect
to the case when no explanatory variables are considered (with the exceptions of Hungary, where there is
no gender difference, and Estonia and the BVI, where the difference does not become smaller). Malaysia
and South Africa stand out as exception, as gender differences disappear once socio-demographic factors
are accounted for.
47
Based on a multivariate analysis, including a series of separate ordinary-least-squares regression analyses for
each country on the whole set of female and male respondents. The outcome variable is the overall financial
knowledge score and the explanatory variables are gender, age, marital status, education, household income
and occupational status.
107
BVI **
UK **
South Africa *
Poland **
Peru **
Norway **
Malaysia *
Ireland **
Hungary
Germany **
Estonia **
Czech Republic**
Armenia **
Albania **
Figure 44. Gender differences in financial knowledge controlling for socio-demographic factors
Gender difference (M-F) with no controls
Gender difference (M-F) controlling for socio-demographics
Notes: the graph is based on the coefficients from an ordinary least squares estimation for each country separately. The results
on the left column are based on a model including only a female dummy as explanatory variable; the results on the right
column are based on a model including as explanatory variables gender, age, marital status, income, education, and working
status. The gender difference is the difference between the average financial knowledge score for men and women. In columns
indicated with an asterisk * the gender difference is statically significant at 5% level.
108
WOMEN’S FINANCIAL ATTITUDES
Evidence from academic and policy research highlights different attitudes and preferences among
men and women in relation to financial issues. Even though this evidence is often limited to specific
countries or age groups, it helps to shed more light on the different approaches women and men take to
financial matters, and it offers some insights about possible strategies to address women’s vulnerabilities
in the financial domain.
Women appear to be aware of their lack of financial knowledge
Evidence suggests that women tend to be aware of their lack of knowledge. For instance, BucherKoenen et al. (2012) show that women’s self-reported levels of financial knowledge are lower than men’s
in Germany, the Netherlands and the US. Women also gave themselves lower ratings than men when
assessing their personal financial knowledge in a US study about understanding debt concepts (Lusardi
and Tufano, 2009).
Similarly, when studying US college students, Chen and Volpe (2002) found that young women’s selfrated knowledge was lower than that of young men. Similar findings have also been reported amongst
high school seniors in the United States (Capital One, 2009).
Women have lower confidence than men in their financial knowledge and skills
Women are more likely than men to answer “do not know” to financial knowledge questions,
suggesting that they are not only less knowledgeable about finance, but that they are also less confident
than men about their financial knowledge. The OECD/INFE data show that in most countries women were
significantly more likely than men to say they did not know the answer to a financial knowledge question
rather than attempt to answer it (with the exception of Hungary) (Figure 45)48. Women have also been
found to be more likely to answer ‘do not know’ to financial knowledge tests in a variety of other
countries, including Azerbaijan, Bulgaria, Germany, Italy, Japan, the Netherlands, New Zealand, the
Russian Federation, Sweden and the United States (Alpha Research, 2010; Azerbaijan Micro-finance
Association, 2009; Lusardi and Mitchell, 2011; and Bucher-Koenen et al., 2012).
Evidence also indicates that women have lower confidence then men in their ability with certain
financial issues decisions. The ‘Women Understanding Money’ research campaign conducted in Australia
in 2008 highlighted that women were less confident than men when it came to more complex issues like
investing, understanding financial language and ensuring enough money for retirement (Australian
Government and Financial Literacy Foundation, 2008).
Overall, the evidence on self-assessed knowledge and confidence provides interesting insights.
Women’s lower confidence with respect to men is consistent with the awareness of their lower
knowledge. If women can correctly recognise their limited knowledge, they may be more prudent in their
48
Men may be more likely than women to guess when they don't know the answer. The OECD/INFE
questionnaire minimises the likelihood of respondents making correct guesses by using several open response
questions.
109
financial behaviour. However, the combination of lower levels of knowledge and a lack of confidence also
means that women are less likely to be willing to deal with financial issues, or to access financial products
and services, and that they may not necessarily grasp potential opportunities for investment or for
income generation.
Figure 45. Percentage "do not know" replies by gender
Male
Female
21.5
19.8
17.5
16.8
15.1
11.6 11.3
14.7
12.9
9.7
8.9
8.9 9.3
14.4
11.1
11.1
9.1
14.2 14.7
13.7
9.9
11.1
6.7
BVI *
UK *
South Africa *
Poland *
Peru *
Norway *
Ireland *
Hungary
Germany *
Estonia *
Czech Republic *
Armenia *
4.5
Notes: The above score is computed as the percentage of “do not know” answers to seven questions. The figure
does not report result for Albania and Malaysia because “do not know” was not available as an answer in three
financial knowledge questions. In countries indicated with an asterisk * the gender difference is statically
significant at 5% level.
Gender differences in interest for financial matters
Lower knowledge and confidence may, to some extent, be related to a lack of interest in financial
matters. If women are less interested in finance than men it is natural that they may be less motivated to
learn. However, it may also be that women show limited interest in money matters because they feel
they have too little knowledge to engage in these issues.
In a survey of US college students, young women expressed less interest in personal finance than
young men. Although both girls and boys thought that personal finance literacy and planning would help
them improve their quality of life, male students were more likely than young females to feel that
personal finance was important (Chen and Volpe, 2002).
In the Netherlands inquisitiveness about money and financial issues – and therefore financial
motivation – is generally very limited among young people. Research found that, on average, no more
than a quarter of youths aged from 12 to 18 expressed a desire to learn more about financially-related
issues such as savings accounts, taxes, insurance and borrowing money. Boys expressed this interest a
little more often than girls (CentiQ, 2008).
In Australia, the ‘Women Understanding Money’ study found that women were not very willing in
learning more about everyday money management issues, where they felt more confident, but found
that women thought it was important to learn about more complex money management issues, such as
110
planning for the financial future, understanding rights and responsibilities when dealing with money and
ensuring enough money for retirement (Australian Government and Financial Literacy Foundation, 2008).
However, despite of their acknowledgement of the importance to learn, significant numbers of Australian
women held attitudes and beliefs that could limit improvements in money management knowledge and
skills. Women were more likely than men to find money stressful, uncomfortable or boring and less likely
to feel in control of their financial situation.
Women are more risk-averse than men
There is evidence from experiments and surveys showing that women are less likely to invest in risky
assets than men, and that women are more risk-averse compared to men. These results are found in a
variety of countries and settings, and are robust to controlling for women’s lower income, wealth, and
financial knowledge.
Using US sample data, Jianakoplos and Bernasek (1998) examined household holdings of risky assets
to determine whether there were gender differences in financial risk-taking. As wealth increased, the
proportion of wealth held as risky assets was estimated to increase by a smaller amount for single women
than for single men, leading to the conclusion that single women exhibited relatively more risk aversion in
financial decision making than single men. In another study about the US, Sundén and Surette (1998)
examined whether workers differ systematically by gender in the allocation of assets in DC plans. They
concluded that gender and marital status significantly affected how individuals chose to allocate assets in
defined-contribution plans, after controlling for a wide range of demographic, financial, and attitudinal
characteristics. Similarly to previous studies, Halko et al. (2011) studied the relation between gender and
stock holdings in Finland. They found that amongst individuals participating in the stock market, men
were more likely than women to have a portfolio with a large equity share, even after controlling for
other factors including financial knowledge and financial resources.
Two studies review experimental evidence on risk aversion. Charness and Gneezy (2011) conducted
a meta-analysis of 15 different studies on risk-taking in investment (conducted by different researchers in
different countries, with different instructions, durations, payments, and subject pools). Each study used
the same mechanism for eliciting risk preferences and gathered data by gender, but gender differences
were not the focus of the experiments. They found a very consistent result that women invested less, and
thus appeared to be more financially risk averse than men. Eckel and Grossman (2008) reviewed the
results from experimental measures of risk aversion for evidence of systematic differences in the
behaviour of men and women and again, in most studies, women were found to be more averse to risk
than men.
111
GENDER DIFFERENCES IN FINANCIAL BEHAVIOUR AND STRATEGIES
In addition to gender differences in financial knowledge, to some extent men and women also
display different financial behaviours. However gender patterns in financial behaviour are more complex
than those for financial knowledge. Women are not always outperformed by men in all domains: for
instance they are more likely than men to have a budget. Nevertheless, in several countries there are
areas where women show vulnerabilities, including product choice, information seeking and saving
behaviour.
Women are more likely to have a budget
Short-term money management is a crucial aspect of behaviour, and as such keeping a close watch
on every-day financial expenses is a first step in building long-term financial security and avoiding
unsustainable levels of debt.
Analysis of the OECD/INFE Financial Literacy Survey allows exploration of the extent to which an
individual takes responsibility for household finances and budgeting. This section first looks at who was
responsible for day-to-day money management in the household, and then considers whether this
person was using a budget.
Even though there are no striking gender differences, in many countries women were mainly
responsible for household money management in a large share of households. For instance, in the Czech
Republic, Peru, Poland, the UK and the British Virgin Islands (BVI) almost one third of married/cohabiting
respondents indicated that the wife/female partner had main day-to-day financial responsibility (Figure
46).
112
Figure 46. Responsibility for day-to-day money management decisions in the household
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Peru
Poland
South Africa
UK
BVI
Female partner
Male partner
Both partners
Other
Notes: Base: married and living with partner. ‘Other’ includes responses indicating that the person responsible for
money management is: the respondent and another family member; another family member; someone else;
nobody. The question about marital status was not asked in Norway.
Combining the responses to two questions that assess how many people report that they a) had
either personal or joint responsibility for day to day money management decisions in their household and
b) lived in a household with a budget shows that in several countries women were more likely to be
responsible for a household budget than men (Figure 47)49. This is in line with the fact that women are
significantly more likely than men to report that they keep a close watch on their financial affairs in
Estonia, Norway, Poland and the UK (Figure 48), even though gender differences are not very large.
49
This result holds also if we consider who has a budget without conditioning on having personal or joint
responsibility for day to day money management decisions in the household.
113
Figure 47. Responsible for money management decision and has a household budget
Male
Female
76%
72%
BVI
UK *
South Africa
Poland *
Malaysia
Peru *
29%
22%
Ireland *
Czech Republic
*
Armenia
Hungary *
35%
32%
26%
24%
23%
20%
Norway *
43%
30%
Albania
60%
53%
47%
46%
43%43% 39%46% 44%43%
60%
49%
Germany
52%50%
Estonia *
59%59%
Notes: Base: all respondents. In countries indicated with an asterisk * the gender difference is statically significant at 5% level.
Figure 48. I keep a close personal watch on my financial affairs
4.3 4.4 4.4 4.3
BVI
3.8 3.8
UK *
Poland *
Peru
Norway *
Malaysia
Ireland
Hungary
Germany
Estonia *
Czech Republic
4.4 4.5 4.2 4.3 4.3 4.5 4.5 4.4 4.3 4.4
4.3 4.4 4.1 4.3 4.2 4.4 4.5 4.5
4.0 4.1
Armenia
Albania *
4.0 3.9
Female
South Africa
Male
Notes: Base: all respondents. The graph indicates to what extent the respondents agree with the statement “I keep a close
personal watch on my financial affairs” on a scale from 1 to 5. The question has been recoded to reverse the original scale so
that 1 is never and 5 is always. * the gender difference is statically significant at 5% level.
This is consistent with evidence from other national studies showing that women appear to be
better than men at short-term money management behaviour. For instance, in the United Kingdom,
women outperformed men at keeping track of finances (Atkinson et al., 2006) and in Canada women
marginally outperformed men on making ends meet and keeping track of finances (although these
differences were not statistically significant) (McKay, 2011). In Portugal, women gained higher scores
than men in a composite index measuring short-term money management capability and saving
propensity (Banco de Portugal, 2011). Moreover, the results of the 2006 and 2009 Financial Knowledge
Survey in New Zealand suggest that men were less likely than women to control their expenses: women
114
were more likely than men to report that they kept a fairly close eye on their expenses, or that they used
written/electronic records. However, gender differences in the 2006 survey were smaller than in the
2009 one (ANZ–Retirement Commission, 2009). In the Netherlands, research conducted by the non-forprofit organisation Nibud showed that women were more likely than men to know what their balance is,
to give estimates of household expenses, and to plan their spending on costly items (Nibud, 2012).
Making ends meet: women tend to cut down on spending while men try to earn extra money
Further analysis suggests that men and women are not equally able to make ends meet and that
they engage in different strategies when they find that their income does not cover their living costs. First
analysis looks at the ability to make ends meet, and then at coping strategies.
The OECD/INFE survey results presented in Figure 49 show that there were not only significant
differences across countries, but also between men and women, in the ability of respondents to cover
their living expenses. In some countries, including Armenia, Germany, South Africa and the BVI women
were more likely than men to have experienced problems in covering living costs in the previous year.
Since making ends meet can be closely related to the allocation of resources within the household, checks
have been made to ensure that the results are very similar when the analysis is restricted to respondents
who do not have a partner (i.e. are single, divorced/separated, or widowed). A more in-depth data
analysis indicates that in the four countries where there was a gender difference, the difference can be
accounted for by controlling for socio-demographic factors (i.e. age, marital status, income, education,
work status).
Figure 49. Making ends meet
Male
56%57%
47%47%
36%37%
35%36%
49%
40% 43%
35%
30%33%
46%
37%
BVI *
UK
South Africa *
Poland
Peru
Norway
Malaysia
13%12%
Hungary
Germany *
22%
13%
Estonia
Czech Republic
Armenia *
Albania
25%24%
46%
41%
Ireland
59%60%
Female
72%
65%
Notes: Base: all respondents. The graph indicates the share of respondents who answered affirmatively to the question
“Sometimes people find that their income does not quite cover their living costs. In the last 12 months, has this happened to
you?” In countries indicated with an asterisk * the gender difference is statically significant at 5% level.
The Core Questionnaire asks respondents’ about their coping strategies if they report having had
problems in making ends meet in the previous year. The flexibility of the questionnaire allows for several
options, including drawing on existing resources, cutting expenses, borrowing formally or informally and
falling behind with payments.
115
To some extent men and women appear to have employed different strategies when they needed to
make ends meet. In particular, in most countries women are more likely than men to answer that they
spent less (Figure 50), while in most countries men are more likely than women to answer that they
worked overtime, earned extra money, or took a second job (Figure 51). Respondents did not display
differences across genders with respect to other strategies, such as running down existing savings,
borrowing, or falling behind with payments.
Making ends meet typically involves household resources and is not necessarily an individual
behaviour in married or cohabiting couples, but, results are qualitatively the same after excluding
respondents who are married or live with their partner from the analyses.
Figure 50. Strategies for making ends meet: cutting back on spending
BVI
UK
Ireland *
Hungary
Germany *
Estonia *
Czech Republic
Armenia
Albania
31%
South Africa
49%
45%
44% 46%
42%
41%
40% 40%
36%
31%28%
26%
41%
Poland
46%
Female
64%64%
Peru
60%
Male
Norway
55%
50%
80%
69% 70%
64%
Malaysia *
70%72% 74%73%
Notes: Base: respondents who said that in the 12 months before interview experienced situations where their income
did not cover living costs; all marital statuses. In countries indicated with an asterisk * the gender difference is statically
significant at 5% level.
These results have a number of implications, some of which may differ across countries. The
tendency to reduce expenses may be a wise choice for relatively high-income women, but for women
who already had problems in covering their living costs a reduction in expenses may make them
particularly vulnerable. Reducing current consumption may expose them, for instance, to malnutrition
and/or sickness, and may reduce the well-being of their children whenever women are responsible for
child-related expenses. In addition, cutting back on insurance and pension payments could undermine
their financial security in the future.
Moreover, the evidence that women are less likely than men to engage in working activities to
increase their income points to their vulnerability in the labour market as well as various potential
constraints, including a lack of policies seeking to reconcile work and family life (e.g. childcare, part-time
employment opportunities, etc.). To the extent that women’s difficulty in accessing economic
opportunities is related to a lack of financial knowledge and confidence, many of them could benefit from
financial education combined with entrepreneurial education. These could improve the extent to which
they can increase their income in a flexible way rather than having to cut expenditure when they fail to
make ends meet.
116
Figure 51. Strategies for making ends meet: earning extra money
BVI *
UK
South Africa *
24%
21%
11% 7%
8%
4% 7% 4%
Poland *
35%29%
Peru *
22%
13% 18%16%
Norway
10%15%
Malaysia *
18%18%
Ireland
10% 7%
Germany
Estonia
28%24% 28%
20%
Czech Republic
Armenia *
8% 2%
0%
Albania *
20%
Female
Hungary
Male
Base: respondents who said that in the 12 months before interview experienced situations where their income did not cover
living costs; all marital statuses. In countries indicated with an asterisk * the gender difference is statically significant at 5%
level.
Gender differences in product holding
Whenever access to safe and regulated financial products is possible, their appropriate use and
choice can be considered as indicators of savvy financial behaviour. It is interesting to look at gender
differences in product holding before considering how they are used and how they are chosen in the
following paragraphs.
The analysis of the INFE survey data reveals different gender patterns in the holding and use of
transactional, investment, and insurance products, in addition to sizeable differences between countries
(Table 3 in the Appendix collects detailed information on gender differences in product holding). Holding
of saving products and saving behaviour is discussed more extensively in the following sub-section.
Men are significantly more likely to hold transaction products (i.e., current account, ATM card, etc.)
especially in developing and emerging economies (e.g., Albania, Malaysia, Peru, South Africa and the BVI),
while there are no significant gender differences in more developed countries (i.e. Czech Republic,
Estonia, Germany, Hungary, Ireland, Poland and the UK – Figure 52). In Norway women are even more
likely than men to have payment products.
This is in line with the aggregate evidence showing that in most regions of the world women have
lower access than men to formal banking products and that the gender difference is larger in non-OECD
countries.
117
Figure 52. Percentage of respondents who hold a payment product
Male
96%
96%
90% 90%
71%
68%
73% 70%
Female
90%
86%
78%76% 82%74%
53%
45%
69%
67% 65%
59%
84%
83%
65%
54%
28%
22%
BVI *
UK
South Africa *
Poland
Peru *
Norway *
Malaysia *
Ireland
Hungary
Germany
Estonia
Czech Republic
Armenia
Albania *
3% 2%
Notes: In countries indicated with an asterisk * the gender difference is statically significant at 5% level.
Men are significantly more likely than women to hold investment products in most of the countries
analysed. However, as the holding of investment products is quite low in general, differences tend to be
very small in most countries (Figures 53). Gender differences are more pronounced in Germany and the
UK. Also in the European Union overall men are more likely than women to hold shares, bonds, and
investment funds (European Commission, 2012).
Differences in risky financial holdings among men and women have been found also in another study
about the UK. Women and men were equally likely to save into low-risk (and typically low-return) saving
vehicles, but men were more likely to save into higher-risk/higher-return products, like shares, personal
equity plans and unit trusts, compared to women (Westaway and McKay, 2007).
118
Figure 53. Percentage of respondents who hold an investment product
Female
45%
Peru *
Malaysia
Norway *
3% 2%
27% 30% 28%
8% 4% 8% 5%
South Africa *
13%
Ireland *
Hungary
5% 4%
Germany *
Estonia
7% 4% 7% 7%
Czech Republic
Armenia
Albania *
2% 0% 0% 0%
Poland *
19%
38%
29% 31% 31% 27%
BVI
34%
UK *
Male
Note: In countries indicated with an asterisk * the gender difference is statically significant at 5% level.
Gender differences in saving behaviour
Saving behaviour is also an important component of financial literacy, as it builds financial security
and reduces the reliance on credit. Exploring gender differences in saving behaviour is relevant because
economic, social and cultural factors may generate differences in the ability and propensity of men and
women to save. Moreover, women and men’s respective decision-making power within the household
may influence household decision-making patterns, and therefore also household overall saving decisions
(Floro and Seguino, 2002).
In analysing gender differences in saving behaviour, it is important to remember that saving typically
refers to the household and individual saving decisions are often part of the whole household decisionmaking. Bearing this caveat in mind, it is worth looking at gender differences in saving behaviour because
it can help identify women’s vulnerabilities in the financial domain, especially for non-married women.
According to the OECD/INFE survey, gender differences in saving products’ holding are not uniform
across countries. As shown in Figure 54 men are more likely to have savings products (including savings
accounts, term deposit, etc.) in Albania, Malaysia, Poland and the UK, whereas women are more likely to
hold formal savings products in Norway and South Africa. In most countries covered by the survey,
gender differences in saving holdings are not statistically significant (e.g., Armenia, Czech Republic,
Estonia, Germany, Hungary, Ireland, Peru, and the BVI). Also other studies found that women were about
as likely as men to have savings products (see Westaway and McKay, 2007 for the UK; Whitaker et al.,
2013 for the US).
119
Figure 54. Percentage of respondents who hold a saving product
Male
89%86%
Female
79%79%
93%88%
91%86%
80%
73%
74%
69%
49%46%
BVI
Norway *
Malaysia *
Ireland
Hungary
Germany
Estonia
Czech Republic
Armenia
Albania *
UK *
7% 7%
1% 1%
25%
25%29%
15%
South Africa *
15%14%
Poland *
15%20%
Peru
34%
26%
Notes: In countries indicated with an asterisk * the gender difference is statically significant at 5% level.
Another way to gauge the extent of people’s financial reserves is to look at how long they would be
able to cover their living expenses (without borrowing money or moving into a different house) in case
they lost their income. Results from the OECD/INFE survey shows that in Hungary, Peru, Poland, South
Africa, the UK and the BVI, non-married men would be able to cope for a longer period than non-married
women without their main source of income (Figure 55)50.
Gender differences in the ability to save and the amount of saving are likely to be driven by several
factors, whose respective importance may differ across countries. The existing evidence suggests that
women are typically able to accumulate significantly less in savings than men during their life cycle
because of lower earnings, due to lower labour market participation, occupational segregation, and a
higher likelihood to work part-time. This is related to the fact that in many households women are
responsible for the majority of unpaid work, including caring for children and the elderly. At the same
time, patterns of marriage and family formation have changed over time, with more women being single
for some periods in their life, with couples being more likely to separate or divorce, and with more
women heading sole parent households. All these factors may reduce women’s ability to save, especially
for retirement, and may result in women being more likely than men to face financial hardship later in life
(Lewis and Messy, 2012; Heathrose, 2012).
As women live longer, on average, than men, and typically retire at a younger age, different abilities
to save for the long term between men and women – whether through pensions or other saving vehicles
– may imply that women will be more likely to be less well off, to face poverty, or to depend on family
members or welfare in old age.
50
This is self-reported information. As men tend to be more over-confident than women, they may also be overoptimistic about how long they would be able to cope in case of an income loss.
120
Another factor potentially explaining gender differences in wealth accumulation is women’s higher
risk aversion, leading to more conservative portfolio allocations and lower investment returns. More
research would be needed to further explore the relation between women’s lower savings and gender
differences in preferences (especially risk and time preferences) and in financial knowledge.
Figure 55. Ability to cover living expenses in case of an income loss
80%
60%
40%
20%
Hungary
Peru
Poland
Female
South Africa
UK
< 1w
1w - 1m
1m - 3m
3m - 6m
> 6m
< 1w
1w - 1m
1m - 3m
3m - 6m
> 6m
< 1w
1w - 1m
1m - 3m
3m - 6m
> 6m
< 1w
1w - 1m
1m - 3m
3m - 6m
> 6m
< 1w
1w - 1m
1m - 3m
3m - 6m
> 6m
< 1w
1w - 1m
1m - 3m
3m - 6m
> 6m
0%
BVI
Male
Notes: Base: respondents who are not married or living with a partner. The graph reports the percentage of male and
female non-married respondents who report that they would be able to cover expenses for: less than a week (< 1w); at
least a week, but not one month (1w – 1m); at least a month, but not three months (1m – 3m); at least three months, but
not six months (3m – 6m); more than six months (> 6m). The graph only reports countries for which the distribution of the
outcome variable is significantly different between men and women at 5% level based on a Pearson's chi-squared test. In
the other countries the difference is not statistically significant.
Complementing the evidence about women’s and men’s ability to save, the OECD/INFE survey
suggests the presence of some differences in relation to how men and women save. Questions explore
whether respondents had been actively saving in the previous year and through which channels,
including saving cash at home, paying money to savings account or investment products, participating to
informal savings clubs, etc51.
In some countries, including Peru, Poland, South Africa, and the UK among non-married
respondents, men are more likely than women to be actively saving through formal financial products
(such as paying money into a savings account; buying financial investment products other than pension
funds; paying in for a term deposit, and/or paying into a building society contract - Figure 56). This is
consistent with the results of Figure 53, showing that in some of these countries women are also less
likely to use investment products.
On the contrary, non-married women are more likely than non-married men to be saving cash at
home or in their wallet, or to be saving in an informal savings club in Ireland, Malaysia, and Poland (Figure
51
Gender differences in saving by building up a balance on one’s current account and by giving money to one’s
family to save are not reported here. Men tend to be more likely to be passively saving by accumulating money
in their bank account in many countries. The option of saving by giving money to a family member was rarely
chosen. Men were more likely than women to choose this option in Estonia, Germany and Malaysia, but less
likely in Peru.
121
57)52. Such a propensity to save informally may be a result of several forces, including demand and supply
factors. Women may be unable to access formal financial institutions due to supply-side barriers (e.g.
geographical distance, high fees, minimum balance requirements, etc.), and/or they may lack sufficient
trust and knowledge to approach formal financial institutions and use financial products.
Figure 56. Saving behaviour: Actively saving through formal products
Male
80% 81%
67% 64%
66%
49%
12%
41%
22%
Poland *
South Africa *
11%
Peru *
Malaysia
Ireland
Hungary
Armenia
Germany
0% 0%
20%
11% 9%
5% 8%
Estonia
14% 14%
Czech Republic
8%
Albania *
17%
30%
26%
UK *
32% 35%
BVI *
71%
Female
Notes: Base: respondents who are not married or living with a partner. The question about marital status is not asked in
Norway. ‘Actively saving through formal products’ includes: including paying money into a savings account; buying financial
investment products other than pension funds; paying in for a term deposit, and/or paying in to a building society contract. In
countries indicated with an asterisk * the gender difference is statically significant at 5% level.
Figure 57. Saving behaviour: Saving informally
Male
Female
85%
75%
55%
52
31% 28%
BVI
23% 23%
South Africa
Poland *
Peru
45%
35%
25%
18%
Malaysia *
9%
Ireland *
Hungary
17% 13%
Germany
22% 23%
28%
UK
37% 41%
40%
Estonia
Armenia
Albania
25%
33% 32%
Czech Republic
38%
43% 40%
The ‘informal saving’ category in Malaysia also includes “buying poultry”. In Germany non-married men are
more likely than women to save through formal products (savings accounts, investments and building
societies), but also to save cash (at home/ in their wallet); saving in informal savings clubs was not among the
options in the German survey.
122
Notes: Base: respondents who are not married or living with a partner. The question about marital status is not asked
in Norway. ‘Informal saving’ includes: saving cash at home or in one’s wallet, and/or saving in an informal savings
club. In countries indicated with an asterisk * the gender difference is statically significant at 5% level.
Not only do women appear to be saving through different vehicles from men but they also seem to
have different motivations. A study about the UK found that there were gender differences in the saving
habits of men and women (Westaway and McKay, 2007). Women and men were equally likely to save
into low-risk (and typically low-return) saving vehicles, but men were more likely to save into higherrisk/higher-return products, like shares, personal equity plans and unit trusts, compared to women.
Moreover, the purpose of saving was different across gender: women were more likely to save for shortterm use (such as special events, holidays, home improvements, and children), whereas men were more
likely to save for long-term use (including for old-age, house purchase and cars).
As women live longer, on average, than men, and typically retire at a younger age, different longterm saving patterns between men and women – whether through pensions or other saving vehicles –
may imply that women will be more likely to be less well off, to face poverty, or to depend on family
members or welfare in old age.
Women are less likely to choose financial products appropriately
Product choice is also an area of financial behaviour that is relevant for consumers’ financial literacy.
If people attempt to make an informed decision by shopping around or using independent advice they
are more likely to choose appropriate products that meet their needs in a cost effective way, less likely to
buy something inappropriate, and less likely to be subject to mis-selling or fraud.
123
The OECD/INFE Financial Literacy Survey indicates whether respondents tried to compare financial
products across providers in their recent products choices. The results show that, in most of the countries
analysed, men were more likely than women to have shopped around for financial products (Figure 58)53.
Figure 58. Has tried to compare financial products across providers
27%
32%
BVI
33%
20%
UK *
27% 24%
49% 47%
41%
South Africa *
33%
24%
Poland
34%
21% 18%
Norway *
28%
Malaysia
34% 31%
37% 37%
Ireland
29% 25%
Hungary
12%
22% 20%
Czech Republic
16%
Armenia *
Albania *
22%
Estonia
33%
Germany *
42%
Female
Peru *
Male
Notes: Base: all respondents (Not comparing across providers also includes no recent product choice). In countries
indicated with an asterisk * the gender difference is statically significant at 5% level.
The OECD/INFE Financial Literacy Survey also allows us to examine which sources of information
respondents were most influenced from when deciding which products to take out. While the Core
Questionnaire lists several possible answers, some categories have been combined and the focus is on
whether individuals made some attempt at taking an informed decision using non-independent sources
(i.e., whether they gathered information from bank staff, friends, trusted individuals or from general
media coverage), and whether they consulted independent professional info/advice (including best buy
and specialist magazines).
Gender differences in the use of information sources were significant only in a few countries. In such
cases, women were usually less likely to choose financial products appropriately. Figure 59 shows that in
Albania, Armenia, Peru and South Africa men were more likely than women to have made an attempt at
taking an informed decision (i.e. by consulting either non-independent or independent sources of
information). On the contrary, women in Norway were more likely than men to have tried to take an
informed decision.
Of the respondents who tried to take an informed decision, men were more likely to have consulted
independent professional sources of information and advice in Germany, Norway, South Africa, and the
53
We know that women in some countries are less likely to hold various types of financial products. Since
respondents who did not compare across providers also include those who had not made any recent product
choice in the past two years, checks have been made to ensure that gender differences in shopping around are
qualitatively unchanged if respondents who did not make recent product choices are excluded. This avoids that
gender differences in shopping around are driven by gender differences in product holding and product choice.
124
UK. On the contrary, women in Ireland were more likely than men to have consulted an independent
source (Figure 60).
Other financial literacy surveys also highlight women’s difficulties with choosing products. The UK
financial capability survey showed that women had lower scores in the ‘choosing products’ domain,
which captured various aspects including information collection, the main source of information, product
choice, and reading products’ terms and conditions (Atkinson et al., 2006).
Figure 59. Information sources: made an attempt at taking an informed decision from independent or nonindependent sources
52%
40%
24% 19%
Peru *
Norway *
Malaysia
Ireland
Hungary
Germany
Estonia
14% 16%
Czech Republic
44%
51%
37% 33%
45%
BVI
49% 48%
South Africa *
25%
Armenia *
Albania *
36% 31%
44%
37% 40% 39% 34% 36% 41% 38% 38%
38% 36%
Poland
48%
Female
UK
Male
Notes: Base: all respondents. This is derived from a categorical variable taking three values: 0 = No attempt at taking an informed
decision (used only unsolicited info received by post, TV ads, other ads) / no recent product choice; 1 = Used only nonindependent sources (gathered info from bank staff, friends, trusted individuals or from general media coverage); 2 = Consulted
independent professional information/advice (Independent professional info/advice, best buy and specialist magazines). This
graph represents the share of female and male respondents who fall into categories 1 or 2. In countries indicated with an asterisk
* the gender difference is statically significant at 5% level.
Other national financial literacy surveys highlight women’s difficulty with choosing products more
broadly. The UK financial capability survey showed that women had lower scores in the ‘choosing
products’ domain, which captured various aspects including information collection, the main source of
information, product choice, and reading products’ terms and conditions (Atkinson et al., 2006).
Analogously, the financial capability survey conducted in Ireland shows that women scored lower than
men at staying informed (measured by a composite index based on respondents’ answers to four
variables about the importance of keeping up to date with financial matters, the number of financial
topics monitored, the frequency of monitoring financial topics, and the knowledge of whether specified
savings and investments are affected by the stock market) (Irish Financial Regulator, 2009).
A study by the UK Financial Services Authority (FSA) on women and personal finance highlighted that
men were much more likely than women to make decisions for savings accounts and mortgages based on
product characteristics and price, while having an existing relationship with a financial institution was the
most important factor in women’s decision-making. Moreover, the analysis indicated that women were
less likely than men to say that they read about financial products and services in newspapers (Graham
and Warren, 2001).
125
Using a survey of US households, Loibl and Hira (2011) showed that female investors were generally
more likely to practice a lower-information search strategy (based on the number of sources used and on
the frequency of use), using fewer online and mass media sources compared with male investors. This
result is confirmed in a multivariate analysis controlling for demographic and attitudinal factors (including
risk tolerance and self-confidence).
Women’s reluctance to use formal sources of advice may not only be a sign of low financial literacy,
but may also be related to the quality of the advice they receive. In an audit study of the market for
financial advice in the Boston area in the US, Mullainathan et al. (2012) show that some advisers refused
to offer any specific advice as long as the potential client had not transferred his or her account to the
company of the adviser. The results show that this behaviour was more pronounced towards females,
meaning that female investors were more frequently expected than male investors to engage with
advisors before being able to assess their quality. As the evidence on the supply side of financial advice,
especially in relation to the quality of advice, is quite limited, more research is needed to explore the
existence of any differential treatment to male and female investors, and why.
Figure 60. Information sources: consulted independent professional info/advice
Male
73%
82%
54%
50%
13% 9%
10% 8%
BVI
21% 19% 24% 20%
UK *
Malaysia
12%
South Africa *
16%
Poland
15% 12%
Ireland *
Estonia
Czech Republic
Armenia
Albania
7% 7% 3% 3%
Germany *
10%
41%
28%
Norway *
25%
20%
34%
Peru
35%
Hungary
44%
Female
Base: respondents who made some attempt at taking an informed decision. This is derived from a categorical
variable taking three values: 0 = No attempt at taking an informed decision (used only unsolicited info received by
post, TV ads, other ads) / no recent product choice; 1 = Used only non-independent sources (gathered info from
bank staff, friends, trusted individuals or from general media coverage); 2 = Consulted independent professional
information/advice (Independent professional info/advice, best buy and specialist magazines). This graph
represents the share of female and male respondents who fall into category 2 given that they made some attempt
at taking an informed decision (categories 1 and 2). In countries indicated with an asterisk * the gender difference is
statically significant at 5% level.
126
FACTORS AFFECTING GENDER DIFFERENCES IN FINANCIAL LITERACY
As shown in the previous section, women display on average lower financial knowledge and
confidence, and have more difficulties in some aspects of financial behaviour with respect to men. This
evidence calls for a better identification and understanding of what barriers affect gender differences in
financial literacy.
A number of elements can be considered as potentially driving such gender differences. In particular,
various factors may reduce women’s opportunities to learn about financial matters and to acquire
financial skills, as suggested also by previous research (Hung et al., 2012) and evidence from INFE
member surveys. Limited access to education, employment and formal financial markets not only reduce
women’s financial well-being per se, but also limit the extent to which women can improve their
knowledge, confidence and skills about economic and financial issues. These elements will be discussed
in the following paragraphs, together with examples of issues and policies from a selected number of
countries.
The literature on the potential causes of these gender differences in financial literacy is still in its
infancy and it is difficult to establish causal links. In practice, causality probably goes both ways: factors
limiting women’s economic opportunities may reduce their ability to gain more financial literacy, and at
the same time gender differences in financial literacy may reinforce disparities in other domains. This
highlights the need for policies addressing gender inequalities both in economic opportunities and in
financial literacy as a means to improve women’s financial well-being.
Gender roles in household financial decision making have limited impact
A potential explanation for women’s lower financial literacy may have to do with specialisation of
tasks within the household. The idea that men acquire greater financial knowledge and skills than women
because they specialise in financial decision making within the household (for various reasons, including
cultural and social norms) is consistent with the fact that men tend to display higher financial knowledge
than women across a wide range of countries.
However, the evidence regarding a link between gender differences in financial literacy and
specialisation within the household is at best mixed. Hsu (2011) finds that the financial literacy of older
married women in the US increases as they approach widowhood, thus supporting the idea of household
division of labour. Anticipating possible widowhood, women have incentives to begin learning. However,
Fonseca et al. (2010) and Bucher-Koenen et al. (2012), studying the US and Dutch population respectively,
do not find support for the specialisation hypothesis.
Women’s lower financial inclusion and access to finance
Women have been shown to face greater difficulties than men in accessing finance for business and
personal use. In most regions of the world, fewer women than men have an account at a formal financial
institution. Moreover, in some regions of the world women are more likely than men to use informal
127
products: in Sub-Saharan Africa more women savers report using an informal savings clubs and not a
formal financial institution to save as compared to men (Demirguc-Kunt and Klapper, 2012).
The evidence reviewed in the section highlights the existence of substantial gender disparities that
can affect gender differences in financial literacy. Even though financial education alone cannot address
all these challenges, nevertheless targeted financial education initiatives can address women’s needs and
help them to improve their awareness, knowledge, confidence and skills in dealing with financial issues.
128
CONCLUSION
The need to address the financial literacy of women and girls as a way to improve their financial
empowerment is gaining global relevance and is reflected in the G20 Leaders’ Declaration in June 2012
recognising the need for women to gain access to financial services and financial education.
This chapter identifies variations in the levels of financial literacy by gender, and indicates a pressing
need for financial education targeted at women to address the gender gap. This is particularly important
in terms of increasing levels of financial knowledge amongst women and girls, and improving ability to
make ends meet and choose appropriate financial products.
Women are often facing unequal economic and financial opportunities with respect to men in
developed and developing countries. This report identifies various women’s financial education barriers
and needs, and discusses financial education initiatives addressing these issues. It draws on previous
OECD/INFE work, including the financial literacy measurement survey and stock-taking exercises among
INFE members, as well as on extensive academic research and national evidence on financial education
programmes for women and girls.
Overall, the analysis of the evidence on gender differences in financial literacy highlights a number of
issues:

Women display lower financial knowledge and confidence than men in many countries. In
particular, young women, elderly/widows, less well-educated and low-income women lack
financial knowledge the most;

Women appear to be more vulnerable than men in some aspects of financial behaviour, namely
in relation to making ends meet; saving strategies; and in choosing products.
A number of factors and barriers appear to affect women’s financial opportunities. Gender
differences in financial literacy are correlated with differences in socio-economic conditions of men and
women, suggesting that limited access to education, employment and formal financial markets not only
reduce women’s financial well-being per se, but also limit the extent to which women can improve their
knowledge, confidence and skills about economic and financial issues.
In light of these challenges, various countries have acknowledged the need to improve the financial
literacy of women and girls by implementing targeted national financial education policies and initiatives.
The most important policy goals of financial education initiatives for women include:

Addressing the needs of specific subgroups, such as young and old women, low-income women,
and small and micro female entrepreneurs; and
129

Improving women’s financial inclusion and financial strategies, including fostering the use of
formal saving accounts, preventing over-indebtedness, and supporting women in planning for
retirement.
130
REFERENCES
Alpha Research (2010), Financial Literacy Survey, Report on the Key Findings of the Survey Prepared for the World
Bank, Alpha Research, June 2010, Sofia, Bulgaria
http://siteresources.worldbank.org/FINANCIALSECTOR/Resources/World_Bank_Bulgaria_FinLit_Report.pdf
(Accessed on July 12, 2012)
ANZ (Australia and New Zealand Banking Group) (2011), Adult Financial Literacy in Australia. Full Report of the
results from the 2011 ANZ Survey. December 2011.
http://www.anz.com/resources/f/9/f9fc9800493e8ac695c3d7fc8cff90cd/2011-Adult-Financial-LiteracyFull.pdf.pdf?CACHEID=f9fc9800493e8ac695c3d7fc8cff90cd (Accessed on July 12, 2012)
ANZ–Retirement Commission (2009), 2009 ANZ–Retirement Commission Financial Knowledge Survey
http://www.financialliteracy.org.nz/research-/results-anz-retirement-commission-financial-knowledgesurvey/2009 (Accessed on July 12, 2012)
Atkinson, A, S. McKay, E. Kempson, and S. Collard (2006), "Levels of Financial Capability in the UK: Results of a
Baseline Survey", Consumer Research 47, Prepared for the Financial Services Authority by Personal Finance
Research Centre University of Bristol, Financial Services Authority. http://www.fsa.gov.uk/pubs/consumerresearch/crpr47.pdf (Accessed 13 June 2012).
Atkinson, A. and Messy, F-A. (2012), “Measuring Financial Literacy: Results of the OECD / International Network on
Financial Education (INFE) Pilot Study”, OECD Working Papers on Finance, Insurance and Private Pensions,
No. 15, OECD Publishing. http://dx.doi.org/10.1787/5k9csfs90fr4-en (Accessed 13 June 2012).
Australian Government and Financial Literacy Foundation (2008), Financial Literacy – Women Understanding
Money, Australian Government, Financial Literacy Foundation.
http://www.financialliteracy.gov.au/media/209296/women-understanding-money.pdf (Accessed 13 June
2012).
Azerbaijan Micro-finance Association (2009), Final Report - Results of the Financial Literacy Survey, Baku, Azerbaijan
Micro-finance Association, Azerbaijan: December 30, 2009.
http://siteresources.worldbank.org/INTECAREGTOPPRVSECDEV/Resources/Financial_Literacy_Survey_Repor
t_EN.pdf (Accessed online on June 20, 2012)
Banco de Portugal (2011), “Survey on the financial literacy of the Portuguese population”
http://clientebancario.bportugal.pt/ptPT/Publicacoes/InqueritoLiteraciaFinanceira/Documents/Survey%20on%20the%20Financial%20Literacy%20o
f%20the%20Portuguese%20Population%20(2010)%20-%20Final%20Report.pdf (Accessed on July 12, 2012)
Bucher-Koenen, Tabea, Annamaria Lusardi, Rob Alessie, and Maarten van Rooij (2012), “How financially literate are
women? Some new perspectives on the gender gap”. Netspar Panel Paper 31,
http://arno.uvt.nl/show.cgi?fid=126935
Capital One (2009), “Capital One Survey of High School Seniors Reveals Gender Gaps in Financial Literacy”,
http://phx.corporate-ir.net/phoenix.zhtml?c=70667&p=irol-newsArticle&ID=1301899&highlight (Accessed 6
February 2012).
131
CentiQ (2008), Summary. Financial understanding and behaviour of 8- to 18-year-olds in the Netherlands. CentiQ.
http://www.wijzeringeldzaken.nl/media/13194/summary_financial_understanding_%208to18_netherlands.
pdf
Charness G. and U. Gneezy (2011), “Strong Evidence for Gender Differences in Risk Taking”. Journal of Economic
Behavior and Organization, 83 (1), pp. 50-58.
http://www.sciencedirect.com/science/article/pii/S0167268111001521
Chen, H., and R.P. Volpe (2002), "Gender Differences in Personal Financial Literacy Among College Students,"
Financial Services Review, Vol. 11, No. 3, 2002, pp. 289-307.
http://www2.stetson.edu/fsr/abstracts2/Vol11_A18.pdf
Demirguc-Kunt, Asli, and Leora Klapper (2012), "Measuring Financial Inclusion: The Global Findex", World Bank
Policy Research WP 6025.
http://econ.worldbank.org/external/default/main?pagePK=64165259&theSitePK=469372&piPK=64165421&
menuPK=64166093&entityID=000158349_20120419083611 (Accessed on July 12, 2012)
Eckel, C. C. and P. J. Grossman (2008), Chapter 113 Men, Women and Risk Aversion: Experimental Evidence, In:
Charles R. Plott and Vernon L. Smith, Editor(s), Handbook of Experimental Economics Results, 2008, Volume
1, Pages 1061-1073. http://www.sciencedirect.com/science/article/pii/S1574072207001138 (Accessed 13
June 2012).
Floro, Maria, and Stephanie Seguino (2002), “Gender Effects on Aggregate Saving”, Policy Research Report on
Gender and Development, Working Paper Series No. 23, the World Bank
http://siteresources.worldbank.org/INTGENDER/Resources/wp23.pdf (Accessed on July 12, 2012.
Fonseca, Raquel, Kathleen J. Mullen, Gema Zamarro, and Julie Zissimopoulos (2010), “What explains the gender gap
in financial literacy”, RAND Working Paper No. WR-762, June 2010.
http://www.rand.org/content/dam/rand/pubs/working_papers/2010/RAND_WR762.pdf
G20 (2012), G20 Leaders Declaration, Los Cabos, Mexico.
http://www.g20.org/images/stories/docs/g20/conclu/G20_Leaders_Declaration_2012.pdf
Graham, M. and R. Warren (2011), Women and personal finance: the reality of the gender gap. Financial Services
Authority, Consumer Research 7. http://www.fsa.gov.uk/pubs/consumer-research/crpr07.pdf
Halko, M.-L., Kaustia, M., Alanko, E. (2011), “The gender effect in risky asset holdings”. Journal of Economic Behavior
and Organization, 83 (1), pp. 61-81. http://www.sciencedirect.com/science/article/pii/S0167268111001569
Hsu, Joanne W. (2011), “Aging and strategic learning: The impact of spousal incentives on financial literacy”,
Networks Financial Institute Working Paper, No. 2011-WP-06, Indiana State University.
http://www.federalreserve.gov/pubs/feds/2011/201153/201153abs.html
Hung, A., J. Yoong and E. Brown (2012), “Empowering Women Through Financial Awareness and Education”, OECD
Working Papers on Finance, Insurance and Private Pensions, No. 14, OECD Publishing.
http://dx.doi.org/10.1787/5k9d5v6kh56g-en (Accessed 13 June 2012).
Jianakoplos, N. A. and A. Bernasek (1998), “Are Women More Risk Averse?” Economic Inquiry. 36 (4), pp. 620–630,
October 1998. http://onlinelibrary.wiley.com/doi/10.1111/j.1465-7295.1998.tb01740.x/abstract (Accessed
13 June 2012).
132
INFE (2013), INFE Policy Guidance on Addressing Women’s and Girls’ Needs for Financial Awareness and Education,
in OECD/INFE set of criteria, principles, guidelines and policy guidance to improve financial education. Part 2:
addressing youth’s and women’s needs for financial education. OECD, Paris.
Kelly, S and H. Gong (2010), Saving Tomorrow - The savings and spending patterns of Australians, AMP.NATSEM
Income and Wealth Report, Issue 25, April 2010, AMP, Sydney.
http://www.melbourneinstitute.com/downloads/hilda/Bibliography/Other_Publications/AMP.NATSEM_Savi
ng_Tomorrow_Report.pdf
Klapper, L. and G. A. Panos (2011), “Financial literacy and retirement planning: the Russian case”, Journal of Pension
Economics and Finance, 10 (4): 599–618, October, 2011.
http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=8403939
Lewis, S. and F-A. Messy (2012), “Financial Education, Savings and Investments: An Overview”, OECD Working
Papers on Finance, Insurance and Private Pensions, No. 22, OECD Publishing.
http://dx.doi.org/10.1787/5k94gxrw760v-en
Loibl, C. and T. K. Hira (2011), “Know your subject: A gendered perspective on investor information search,” Journal
of Behavioral Finance, 12, pp.117-130.
http://www.tandfonline.com/doi/abs/10.1080/15427560.2011.600841#preview (Accessed on July 12, 2012)
Lusardi, A. and O. S. Mitchell (2011), "Financial literacy around the world: an overview", Journal of Pension
Economics and Finance, October 2011, 10 (4), pp. 497-508 .
http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=8403921 (Accessed 13 June
2012).
Lusardi, A., O. S. Mitchell and V. Curto (2010), “Financial literacy among the young”, Journal of Consumer Affairs,
44(2), pp. 358-380 http://onlinelibrary.wiley.com/doi/10.1111/j.1745-6606.2010.01173.x/abstract
Lusardi, A. and P. Tufano (2009), “Debt literacy, financial experiences, and overindebtedness”, NBER Working Paper,
no. 14808. http://www.nber.org/papers/w14808
McKay, S. (2011), "Understanding Financial Capability in Canada Analysis of the Canadian Financial Capability
Survey", Research paper prepared for the Task Force on Financial Literacy
http://publications.gc.ca/collections/collection_2011/fin/F2-213-2011-eng.pdf (Accessed on 19 January
2012).
Mullainathan, S., M. Noeth and A. Schoar (2012), “The Market for Financial Advice: An Audit Study”, NBER Working
Paper 17929, http://www.nber.org/papers/w17929
Nibud (2012), Mannen, vrouwen en geldzaken, Nibud Factsheet June 2012.
http://www.nibud.nl/fileadmin/user_upload/Documenten/PDF/onderzoeken/2012/Factsheet_man_vrouwe
n_geldzaken.pdf
OECD (2005a), Improving Financial Literacy: Analysis of Issues and Policies, OECD, Paris.
OECD (2005b), Recommendation of the Council on Principles and Good Practices on Financial Education and
Awareness.
OECD (2012), Gender Equality in Education, Employment and Entrepreneurship: Final Report to the MCM 2012
[C/MIN(2012)5], OECD, Paris. http://www.oecd.org/els/familiesandchildren/50423364.pdf
OECD/INFE (2012), High-Level Principles on National Strategies for Financial Education
133
OECD/INFE (2013a), OECD/INFE toolkit to measure financial literacy and inclusion: Guidance, Core questionnaire and
Supplementary Questions, OECD, Paris.
OECD/INFE (2013b), Current Status of National Strategies for Financial Education: OECD/INFE Comparative Analysis
and Relevant Practices
OECD/INFE (2013c), “Gender differences in financial literacy: identifying barriers and addressing women’s’ and girls’
needs through financial education” in OECD, Women and Financial Literacy: OECD/INFE Evidence, Survey and
Policy Responses, OECD, Paris.
Schmidt, Lucie and Purvi Sevak (2006), “Gender, Marriage, and Asset Accumulation in the United States”, Feminist
Economics, 12 (1-2), pp. 139-166 http://www.tandfonline.com/doi/abs/10.1080/13545700500508445
Siermiska, Eva M., Joachim R. Frick, and Markus M. Grabka (2008), “Examining the Gender Wealth Gap in Germany”,
DIW Discussion paper 806, Deutsches Institut für Wirtschaftsforschung, Berlin.
http://ssrn.com/abstract=1424899
Sundén, A. and B. Surette (1998), Gender differences in the allocation of assets in retirement savings plans.
American Economic Review Papers and Proceedings 88, 207–211.
http://www.jstor.org/stable/10.2307/116920 (Accessed on July 12, 2012).
Westaway, Jenny, and Stephen Mckay (2007), “Women’s financial assets and debt”, Fawcett Society, London.
http://www.fawcettsociety.org.uk/documents/Fawcett%20Assets%20Report.pdf (Accessed on July 12,
2012).
134
ANNEX 1: TESTING THE CORE QUESTIONNAIRE AND DEVELOPING A SCORE
Introduction
This annex outlines the lessons learned whilst developing the OECD/INFE Financial Literacy Core
Questionnaire and undertaking the first measurement project. It also provides detailed information about
the method used in analysing data collected from the OECD/INFE Financial Literacy Survey. It includes
information about the processes used to prepare the data for analysis, and the decisions made during the
analysis in order to create a measure of financial literacy that will provide cross-country comparisons. It is
based on the first 11 countries to submit data.
Design of the Core Questionnaire
The OECD/INFE Financial Literacy Survey uses a unique questionnaire, developed specifically to
capture financial literacy at an international level. The questionnaire is described below as the OECD/INFE
Financial Literacy Core Questionnaire, or Core Questionnaire for short. The OECD/INFE has defined
financial literacy as follows:
‘A combination of awareness, knowledge, skill, attitude and behaviour necessary to make sound
financial decisions and ultimately achieve individual financial wellbeing.’
The Core Questionnaire provides a number of ways in which the country can tailor responses to
national circumstances, in the interest of allowing flexibility across countries. It was designed to minimise
any negative reaction to sensitive questions – for example by carefully positioning them within the overall
set of questions - and to ensure that there was no unnecessary repetition or wordiness (hence asking
whether the respondent has a partner, before asking questions about the household). Probes and read
out categories should maximise the chance of getting the type of response intended and open ended
questions are used in some places to limit guessing.
Data collection and preparation process
Countries were invited to apply the questionnaire according to the recommended method and
submit their data to the OECD/INFE Secretariat for comparative analysis. The collection process was
therefore undertaken at a country level.
Once each country had finalised data collection and entry, the OECD Secretariat gave each dataset
consistent labels and checked the data for obvious errors or omissions in order to prepare them for
international comparison. This checking process continued throughout the analysis stage, as some issues
are only apparent once the data from one country is compared with other countries54.
54
Whilst it is very likely that there are differences across countries, it is also possible to identify potential
problems when one country appears to be extremely different. Often this is because of an alternative
approach to data coding (for example reversing the scale on an attitude statement). Where issues have been
identified in the data submitted, these have been addressed with the relevant agencies and revised datasets
have been submitted if necessary.
135
Data preparation also included deriving new variables from existing information. This included
gathering information that was spread over two or more questions and separating out multiple responses
that had been collected from a single question.
It is very unusual for every participant in a survey to answer every question, although every effort
should be made to keep refusals to a minimum. In some situations, an individual’s record may be
removed from the dataset because the responses are considered to be unreliable, or unusable. This was
necessary in a very small number of cases during the preparation of the data. Other missing data was
handled as appropriate during the analysis process (typically by taking the conservative approach of
assuming the respondent was not financially literate on that measure).
From survey questions to financial literacy scores
The Core Questionnaire asks a range of questions about issues that are widely considered to be
aspects of financial literacy. Each of these questions provides valuable information, but no single question
can adequately indicate levels of financial literacy. As the definition above highlights, financial literacy
incorporates a combination of factors.
Together, the questions provide valuable information about the knowledge, behaviour or attitudes of the
respondents. An overall financial literacy measure is expected to indicate something that can’t be
captured simply by asking the right question, it has to be created through combining information about a
respondent’s knowledge, behaviour and attitudes.
In order to incorporate information about a combination of factors it is necessary to develop a score
across several questions or segment the population according to their responses to particular questions
or groups of questions. Both of these approaches have been used successfully in a national context. As
the definition of financial literacy stresses that it is a combination of three components, namely
knowledge, behaviour and attitude, the approach taken with the OECD/INFE data incorporates measures
of each of these three components as well as combining them to develop an overall measure of financial
literacy.
It is important that the data preparation and analysis is undertaken in such a way that another
researcher will be to be able to achieve the same results using the same data. Similarly, it should be
possible to apply the same approach to new data to create comparable measures of financial literacy.
Two analysts at the OECD have worked together on the analysis, using IBM SPSS Statistics v19 to manage,
check and analyse the data.
Developing an internationally applicable method
The questions have a certain amount of flexibility in order to ensure that they are contextually
relevant across countries (this includes the possibility of changing the types of financial products being
discussed). This flexibility does not alter the substance of the question, and it is this that should be
captured in a measure of financial literacy. For example, a list of possible approaches to saving money
may vary by country, but the process under investigation is the act of saving, and this substantive issue
can be explored irrespective of the actual approaches taken.
Consistency is vital when comparing data across countries, and can be challenging when questions
have been contextualised. In order to consistently reflect the substance of the questions it is essential
that the responses to the questions are mapped to a central analytical framework and that multiple
136
responses are combined in the same way. Such an approach will ensure comparability across countries
and enable future comparisons.
The Core Questionnaire includes questions relating to managing money, planning ahead, choosing
financial products and staying informed. National surveys have often been analysed according to these
domains. However, exploratory factor analysis indicated that whilst there are considerable similarities
across countries, there is no single way of identifying which questions relate to regular money
management and which are capturing longer-term planning ahead behaviours. It is therefore difficult to
create comparable measures of the domains across countries. In contrast, knowledge, behaviour and
attitudes are concepts that do not vary by country, making comparisons meaningful and achievable.
137
KNOWLEDGE SCORE
Knowledge of key financial concepts and the ability to apply basic numeracy in financial situations
are seen as core requirements in order to consider a person to be financial literate. The Core
Questionnaire therefore asks a range of questions in relation to concepts such as simple and compound
interest, risk and return, and inflation. This is in contrast to some national surveys, which focus more on
knowledge of particular financial services available or issues such as tax and retirement funding.
Survey questions designed to test knowledge
The Core Questionnaire includes eight questions designed to test knowledge. These vary in style in
order to reduce potential biases that could be caused either by different ways of processing information
across certain groups of people or by cultural norms. In particular, some are open ended whilst others
provide alternative options from which the respondent must choose their response.
The knowledge questions also vary in content and complexity although none of them requires
technical (financial) knowledge. They have been chosen to reflect the kinds of knowledge that would help
individuals to make informed decisions and monitor their own finances.
During the design phase of the Core Questionnaire, it was argued that some of the questions were
too complex or abstract to ask amongst low income, financially excluded respondents. Alternative
wording was therefore provided to capture the same basic concept whilst minimising the risk of losing
comparability.
Lessons from the first measurement project
Level of difficulty
In order for the questions to provide meaningful information about the level of financial literacy of
individuals and populations, the questions must be able to differentiate high and low achievers. This
means that the question should be neither too hard nor too easy and should not cause the respondent to
refuse to answer.
Several country representatives expressed concern about the level of complexity of certain
knowledge questions used in the Core Questionnaire. Both points of view were heard – the questions
were too easy, and the questions were too difficult. If the questions were indeed too easy or too difficult,
then very few people would get them wrong, or right, respectively, and it would be impossible to create a
meaningful indicator of the level of financial knowledge.
Questions that are too easy may also potentially confuse respondents – they may think they are
missing some information, or that there is a trick in the question. The questionnaire includes a statement
to help overcome any such problem (‘the questions are not designed to trick you, so if you think you have
the right answer, you probably do’).
138
An additional concern with difficult questions is that people will be dissuaded from continuing to
answer the survey, which would negate the whole measurement process. To overcome this, and also to
reduce the possibility of people guessing, the questionnaire reminds respondents ‘if you don't know the
answer, just say so’.
The analysis indicates that the level of complexity was appropriate. Some questions were sufficiently
easy that most people were able to answer them (this is good, as it helps the respondent to feel at ease,
and it identifies the real outliers who can’t answer even the simplest question). Other questions were
more difficult, identifying those with a higher level of knowledge.
Just two countries used the alternative wording of the questions on Risk and Return, and
Diversification. South Africa used only the alternative wording whilst Malaysia tested both versions. The
Malaysian data shows a significant and high correlation between the two versions, providing reassurance
that they are capturing knowledge of the same underlying concept.
Very few people refused to answer the knowledge questions across all participating countries. Given
the range of countries participating and the heterogeneity of their populations, this indicates that the
questions are appropriate across a wide range of respondent types.
It is recommended that countries that are interested in using the Core Questionnaire, but still have
concerns about the level of difficulty of the knowledge questions draw on additional questions from
other sources to complement the questionnaire, including from our catalogue of Supplementary
Questions (available online). Drawing on additional questions will also provide countries with the
opportunity to ask about the same concept in more than one way, so providing greater opportunities to
check whether respondents really understand.
Changes to the question wording
Whilst the instructions for the measurement process made clear that questions should be translated
for meaning but should not be changed, some countries nevertheless made adjustments to the questions
before asking them. This created the need for additional tests to ensure that the changes had not
jeopardised the international comparability of the data. However, as it will be difficult to run similar tests
in future, it is strongly advised that countries using the questions keep to the original wording. Additional
questions can be added to the questionnaire if necessary to address country level issues or to collect
additional information about understanding of certain concepts.
A slight adjustment has been made to the finalised Core Questionnaire in response to these
unilateral changes. Specifically, the phrase ‘no fee’ has been added into the question designed to test the
calculation of interest plus principle, so that respondents in countries where savings accounts attract fees
do not take the cost of fees into consideration when answering.
The OECD/INFE also considered changing the wording of the Interest question, to reflect the fact
that the word interest may be culturally sensitive. However, as the question is specifically designed to
test whether people know how to identify interest on a payment, it was felt that a change was not
appropriate. Individual countries will need to decide whether they can use the word interest, or should
replace it with something less sensitive (such as return); they can be reassured that the results of the
analysis suggest that the overall results are not particularly sensitive to such a change. Changes to the
wording should be clearly identified in the data collection process, however, in order to make sure that
the correct response is identified during data coding.
139
The Time Value of Money question (QK3) has been adjusted to reflect the fact that it was difficult to
identify the correct response across respondents with very vivid memories of inflation. This also
addresses concerns voiced by analysts in South Africa who felt that the question may not be fully
capturing understanding of inflation. The question now includes the phrase ‘and inflation stays at X 55
percent’ to make the purpose of the question clear and ensure that it is relevant within countries.
Malaysia tested the alternative wording for the risk and reward and diversification questions by
asking every respondent both questions, and analysis shows that responses to the two alternatives were
significantly correlated. This is reassuring for countries wishing to rely on the alternative wording.
Developing a score
Analysis of the responses to each question by country indicates that the combination of knowledge
questions adequately identified high and low achievers in all countries. It also shows that relatively few
people refused to answer the questions56.
The score is therefore based on derived variables that identify correct answers to each of the
knowledge questions. The following approach was used to assign a value to the responses to each
question.
Table 13. Creating a knowledge score
Question
Discussion
Value towards final score
This is open response and a correct answer is therefore a good
indicator of applied numeracy
1 for correct response. 0 in all other cases.
Time-value of money
This is multiple response and context specific
1 for responses c, d, e unless country situation
suggests otherwise (in the final version of the
questionnaire, the correct response will depend
on X).
Interest paid on a loan
This is open response and a correct answer is therefore a good
indicator of understanding
1 for correct response. 0 in all other cases.
Calculation of
plus principle
This is open response and a correct answer is therefore a good
indicator of applied numeracy
1 for correct response. 0 in all other cases.
Compound interest
This is multiple response. It is assumed that if the respondent
couldn't calculate 2% they also cannot calculate 5*2%.
1 for a correct response IFF the previous
response was also correct. 0 in all other cases.
Risk and return
This is a yes/no question so guessing may occur
1 for a correct response. 0 in all other cases.
Definition of inflation
This is a yes/no question so guessing may occur
1 for a correct response. 0 in all other cases.
Diversification
This is a yes/no question so guessing may occur
1 for a correct response. 0 in all other cases.
Division
interest
55
X will be replaced by a valid percentage in each participating country.
56
The analysis also shows that some people responded ‘don't know’. In the case of the knowledge questions,
don't know is considered to be a valid response – indeed respondents were encouraged to say if they didn't
know the answer - and so this will not cause a problem in analysis.
140
There are several approaches that could be used to develop a score from these individual items.
After considering several factors influencing the development process it was agreed to develop a simple
count the number of correct responses. In particular:

Some questions are more difficult than others, and, arguably, some may be more important.
Should all correct answers be given equal weight?

Some experts have argued that the first question is numeracy, rather than financial literacy, and
have suggested that this should not go into the score.

Some countries have made adjustments to the questions or opted for the alternative wording.
How much does this affect the relative scores?
Two types of test were used to identify the most appropriate approach. Factor analysis was used to
look at the extent to which each question adds to an underlying financial knowledge factor. Several
additive knowledge scores were also created, to test the impact of using or omitting certain questions on
the overall score.
An attempt at undertaking confirmatory factor analysis of the knowledge questions highlighted the
difficulties in using such an approach to create a score for this survey. In particular, the analysis is
sensitive to decisions about which observations (or countries) and questions are included, and how
missing data is handled. The apparent weighting or importance of each item included varies accordingly.
It should also be noted that factor analysis seeks to correlate a range of factors with an unobserved,
underlying factor. If some of the questions appear to be important to experts, but do not correlate to this
artificially derived factor, then they are effectively deleted from the measure despite being valid
components of financial literacy.
Despite the challenges, the factor analysis provides some insights57. An analysis of seven questions
across 10 of the participating countries58 (excluding the compound interest question, which relies on the
correct response to the simple interest calculation making collinearity an issue) indicates that there is an
underlying construct related to knowledge of simple interest, risk diversification and the relationship
between risk and return. Neither knowledge of inflation nor interest on loans is well captured in this
construct.
It would be possible to create a score for each respondent from the factor analysis, and this
approach is widely considered to be good practice when scoring complex data59. However, there is also a
strong argument for giving each component of financial knowledge equal weighting, as each has benefits
for individuals, and each has been identified as important by international experts. There is also some
sense in avoiding complex statistical approaches if these are likely to be applied or interpreted in
57
Following the recommendations of Rowe 2006 to the extent possible, we used confirmatory factor analysis
with a one factor solution under Maximum Likelihood estimation using IBM SPSS 19.
58
South Africa was excluded because we wanted to test a specific set of questions, rather than the alternatively
worded questions. Albania and Peru was excluded because of late receipt of data. BVI was not part of the first
measurement project and so data is not included in any of these analyses.
59
This argument is based on the fact that a score that counts correct answers may be misinterpreted; people
may assume it is equally difficult to gain one additional point from anywhere on the scale when in fact some
questions are more difficult than others.
141
different ways in different countries or if changes/problems with data from one country are likely to
influence the way the data from other countries is analysed.
The final scores are therefore based on a simple count of correct answer, rescaled to 100, as may be
used in a school test. The questions within the Core Questionnaire were all chosen because they were
considered to capture essential aspects of financial knowledge and so have been given equal weighting in
the score.
The set of eight questions has been tested by excluding certain questions which may be having an
unintended influence on scores and looking at the this changes the ranking of country scores to see if the
change has a major impact on the apparent relative level of financial knowledge in any country (defined
as changing the relative position of a country by more than 2 places - e.g. dropping from 2nd to 5th relative to a score using all the knowledge questions with equal weighting).
Table 14. Financial knowledge scores
Ranking of country according to scoring method used
Knowledge score across all 8
questions
Knowledge
division
score
excluding
Knowledge
score
compounding
excluding
Knowledge score
interest on loan
Hungary
Hungary
Hungary
Hungary
Estonia
Germany
Estonia
Estonia
Germany
Estonia
Ireland
Germany
Ireland
UK
Czech Republic
Ireland
Czech Republic
Ireland
Germany
Czech Republic
UK
Czech Republic
UK
UK
Malaysia
Malaysia
Malaysia
Poland
Poland
Poland
Poland
Malaysia
Armenia
Armenia
Armenia
Armenia
South Africa
South Africa
South Africa
South Africa
excluding
Norway is excluded from this analysis because they have both substituted and omitted questions, making direct comparisons
difficult. Albania and Peru are also excluded as the analysis predates receipt of their data.
Excluding the division question has no major impact on the ranking of countries (because the
question was answered correctly by the majority of respondents). As the majority of countries and
experts felt that basic applied numeracy is an important aspect of financial literacy, this is kept in the
measure.
Excluding the compounding question, which was made easier in Hungary, makes very little
difference to the relative ranking of the countries, and Hungary does not move position. This question is
only considered to be correct if the previous question, on simple interest, was also answered correctly.
Removing the question indicates that the Hungarian population do have relatively high levels of financial
knowledge and are not simply being mis-graded through the reworded question.
Malaysia made a slight change to the question about interest on a loan, as the wording of the
question was not felt to be appropriate for Muslim consumers. Removing this question from the final
score also makes very little difference to the ranking of the countries.
142
The findings of this analysis suggest that it is appropriate to keep all eight knowledge questions.
However, for the ‘uncertain questions’ to add something, they must also have some explanatory power.
One way of testing this, is to see whether people who give correct answers to these questions have
higher levels of financial knowledge based on the other questions. This test confirmed that financial
knowledge (tested using the remaining questions) was higher amongst those that got the three uncertain
questions right than those who gave incorrect answers. Given these results all eight knowledge questions
are used in the final measure.
143
BEHAVIOUR SCORE
Survey questions designed to capture behaviour
The OECD/INFE Financial Literacy Core Questionnaire has a wide range of behavioural questions
asked in different ways to capture the maximum amount of information. This gathers information on the
way people manage their money, including whether they borrow to make ends meet, whether they
typically pay bills on time, if they are personally (or jointly) responsible for a household budget, if they
report that they keep a close watch over their finances and if they consider carefully whether they can
afford something, whether they attempt to save and set long term goals, and how they choose financial
products: including a question about shopping around and one about information and advice.
Lessons from the first measurement project
These questions are behavioural questions but they included quite sensitive financial questions –
such as whether the respondent is actively saving and whether they hold certain financial products. The
number of refusals on some questions indicates that some interviewers may need to be provided with
additional tools and guidance in order to maximise responses. In particular, the respondent must
genuinely believe that the responses will not be stored along with any personal information and that the
interviewer has no interest in their personal situation other than to record the information for research
purposes. The interviewer must also believe that there is no embarrassment in asking personal questions
of the respondent, and must neither show surprise nor make judgements or comments in relation to the
responses.
Given that some people refused to answer some of the savings questions, it was difficult to
incorporate all of the information about saving in the final measure. In particular, many people refused to
provide an answer to the question about how long they could continue to cover living expenses if they
lost their main source of income; this question has not been included in the financial literacy scores, but
could have been a useful indicator of the outcome of financial education. The question has been left in
the final version of the questionnaire because it can capture valuable information once non-responses
are minimised.
Changes to the Core Questionnaire
The first measurement project uncovered some issues with the instructions on the financial product
questions. In particular, several countries recorded the way in which a recent product was chosen but did
not have an indicator of which product was being discussed. Whilst this does not affect the way in which
the data is used in the financial literacy score, it does reduce the usefulness of the data for national
purposes. An instruction has therefore been added into the revised questionnaire to keep a record of the
question being discussed.
Country feedback also indicated that it was very difficult to implement rotation of products from the
list of options and that this caused mistakes to occur in the field. This instruction has therefore been
removed (this is a compromise; rotation is useful to avoid order effects in long lists).
144
There have been some changes to the behavioural statements in the final version of the
questionnaire – combining them with the attitude statements. The reasons for this are as follows:

The combination allows interviewers to work their way through just one set of statements with
one kind of answer code – reducing the time taken to ask the questions, and reducing the
complexity of the questions for respondents (previously the two sets of questions had
differently labelled scales)

Combining the questions reduces the chance of biases caused by repeated patterns. This is
because there will be a mixture of questions that require a 4 or 5 on the scale to indicate
financial literacy and those that require a 1 or 2.
The approach taken in particular countries also suggested that it was necessary to reconsider the use
of a 5 point scale on these questions. South Africa provided a verbal description of each of the points
(always, often, some of the time, seldom, never)60 whilst Norway used additional points (a 7 point scale).
The verbal description is not usually recommended when survey questions are going to be translated
because of the difficulty in translating qualitative nuances that are captured with words such as seldom.
Instead, the use of a show-card during face-to-face interviews can be considered. These would have a
scale from Always to Never, so that the respondent can place themselves on the scale in front of the
interviewer. The financial literacy score does not require 7 points on the scale and so the final version of
the questionnaire continues to use 5.
The original version of the questionnaire included an optional definition of budget that the
interviewer could read out if they felt that the respondent had not understood the word ‘budget’. It was
not clear from the analysis if this worked successfully in the field, and so reading the definition is now
obligatory in order to ensure consistency across countries.
60
Recent exploratory work undertaken by the World Bank under the Trust Fund suggests that people with low
levels of education find it difficult to put themselves on a scale. They have developed an alternative approach
using yes/no responses and follow-up questions.
145
Deriving variables and developing a score
In order to create a financial behaviour score several variables needed deriving as shown in the table
below:
Table 15. Financial Behaviour Variables
Derived variable for inclusion
in financial behaviour score
Personally or jointly
responsible for a household
budget
Carefully considers
affordability before making a
purchase
Pays bills on time
Keeps a close watch
Sets long term financial goals
and strive to achieve them
Has not borrowed to make
ends meet
Has been actively saving or
buying investments in the
past 12 months.
Making informed financial
product purchases
Questions used to create derived variable
(Paraphrased wording of questions from the
draft questionnaire)
QF1 – Who is responsible for day to day money
management decisions in your household?
QF2 – Do you have a household budget?
QM1 Before I buy something I carefully consider
whether I can afford it
QM1 I pay my bills on time
QM1 I keep a close personal watch on my
financial affairs
I set long term financial goals and strive to
achieve them
QM2 Sometimes people find that their income
does not quite cover their living costs
QM3 What did you do to make ends meet
QP1 in the past 12 months have you been
saving money in any of the following ways?
QC2 which statement best describes how you
last chose
QC3 which sources of information influences
your decision
Approach taken
Score of 1 IFF respondent is responsible for day to day money
management (personally or jointly) AND the household has a
budget.
Give a score of 1 if respondent puts themselves at 1 or 2 on the
scale
Score of 1 if respondent puts themselves at 1 or 2 on the scale
Score of 1 if respondents puts themselves at 1 or 2 on the scale
Score of 1 if respondent puts themselves at 1 or 2 on the scale
Score 1 unless QM3 response includes borrowing (Note that for
the purposes of this score following behind with bills is not
considered to be borrowing).
Score 1 if actively saving in any of the ways listed: i.e. any
option other than ‘building up a balance in current account’
which is not an active behaviour.
This is a 3 stage process.
a) Score 1 if considered several products from different
companies, or looked around at QC2.
b) Score 1 if took some advice at QC3- not independent; Score
2 if took independent advice.
c) Create the final score for this aspect which takes the value of
2 if a+b=3, and the value of 1 if a+b=1 or a+b=2.
The approach taken above was developed through discussion with various stakeholders and
following exploratory analysis.
There was considerable discussion about the household budget measure. It should be noted that the
final measure only identifies people who have some responsibility for the household budget. Other
respondents may be budgeting their own money; this information could be captured with an additional
question.
The behavioural questions include four that use a scale, enabling people to provide a more detailed
description of their behaviour. Only a small percentage of people refused to provide an answer to one or
more of the questions using a behavioural scale. A refusal has therefore been recoded to be scale point 3
before undertaking analysis. As noted above, these questions have been asked in the same way in every
country except Norway and South Africa. Where the scale is changed (and particularly if it is reversed) it
is important that care is taken to create the derived variable appropriately).
Financially literate people will have strategies to smooth income flows and a tendency to avoid using
credit for essentials. The extent to which these strategies are successful will depend on predictability of
both income and expenditure, as well as the skill involved. It is not always possible to prevent shortfalls in
income, but a reliance on credit for basic living can become very dangerous, and may create a situation
that becomes impossible to escape. A variable has therefore been created from QM2 and relevant
responses to QM3 to identify people who reported that sometimes their income doesn't meet their
needs, and that the last time this happened they had to borrow to make ends meet.
146
Respondents were asked ‘In the past 12 months have you been saving money in any of the following
ways?’ A list was then provided, which was tailored to the country context. For the purpose of the
international comparison a variable has been created that counts all kinds of saving as active saving,
except for building up a balance in a current account. This is an appropriate indicator of behaviour, since
it indicates that saving was intentional rather than just a default situation due to high income.
Discussing savings is a sensitive issue in some countries. Almost one in 5 respondents refused to
answer this question in the Czech Republic (19%), as did 14% in South Africa. In Poland a very large
proportion claimed that they didn't know, which almost certainly indicates an unwillingness to divulge
such information. These individuals have been considered to be non-savers in the score, which provides a
conservative approach to measuring the true level of financial literacy.
The way people behave when choosing financial products is also an important aspect of their
financial literacy. If people attempt to make an informed decision, by shopping around or using
independent advice they are more likely to choose appropriate products that meet their needs in a cost
effective way. People do not typically choose financial products on a weekly, or even monthly, basis. The
questionnaire therefore asks about a product chosen in the last 2 years. Even so, a large proportion had
not actively chosen a product in this timeframe61. This suggests that they could be encouraged or
supported to become more active consumers; it is therefore appropriate for them to get a low score on
this aspect of financial literacy.
The possible approaches to choosing financial products may vary by country (and countries are able
to add their own options to the questionnaire), but the general willingness to shop around to gather
information is the behaviour that is being captured. In the derived variable used in the final score,
respondents are considered to have made some attempt to make an informed decision if they tried to
compare across providers (even if they found out that there were no other providers), or if they sought
information from someone. Given the way that the derived variable is created, it is important that all
authorities using the questionnaire think carefully about the options that should be included in the survey
instrument, as this will directly influence the quality of the data.
Some individuals may assess their financial product holding regularly, and decide to keep their
current portfolio or renew rather than replace specific policies and products. The product choice score
does not take into account product monitoring, but this is something that could be captured through an
additional question and may partially explain low levels of product choice in some countries.
61
Some countries did not keep a record of respondents who refused to discuss product holding or product choice
and so the only way we could deal with this systematically for the financial behaviour score was to assume that
all non-responses were due to respondents that had not made a recent product choice.
147
Table 16. Testing the behaviour score
Ranking of country according to scoring method used
Behaviour score using 9
points
Behaviour score without
budgeting question
Behaviour score without
active saving
Malaysia
Germany
Ireland
UK
Czech Republic
Germany
Malaysia
UK
Ireland
Czech Republic
Ireland
Germany
Malaysia
UK
Armenia
Behaviour score without
any points for product
choice
Malaysia
Germany
Ireland
UK
Czech Republic
Armenia
Poland
Hungary
Armenia
Hungary
Czech Republic
Armenia
Poland
South Africa
Hungary
Estonia
South Africa
Poland
Estonia
Poland
South Africa
Estonia
South Africa
Hungary
Estonia
As reported above for knowledge, a simple test has been used to see whether the removal of certain
behavioural indicators affects the ranking of countries. The budget question was tested because of
concerns that it overlooks individuals who do not budget for their household. The active savings question
has been tested due to the relatively high level of missing data, whilst the product choice question has
been tested due to the low levels of product purchasing activity in some countries.
This indicates that the inclusion of the budgeting question and the active saving measure reduce the
relative score of Hungary. The active saving measure was not refused by many people in Hungary, and
the reduced ranking reflects the small proportion of individuals who reported that they were saving
suggesting that the ranking is appropriate. Other countries are not unduly affected by the behaviour
measures tested.
Additional tests show that average behaviour scores are higher amongst those who gained a score
on the omitted variable than those who did not or refused- another important indicator that the omitted
variable is a valid measure.
As behaviour is considered to be a key component of financial literacy, and as each of these
elements has been identified as a valuable and valid indicator they have all been kept in the final
measure. Comparative data should be read with caution, given the impact of missing information on
saving. The larger number of questions will help to provide a more nuanced indicator. It should be
remembered, however, that it will be less likely that an individual gets a top score in this section, through
a greater propensity to refuse, lack of responsibility for household finances or lack of recent product
choice.
As with the knowledge score, the behaviour score has been rescaled for reporting to take values
from 0 to 100.
148
ATTITUDINAL SCORE
Survey questions designed to capture attitudes
The Core Questionnaire includes three scaled attitudinal questions that are intended to be used to
measure financial literacy (and one that has been included as an explanatory factor).
Lessons from the first measurement project
People were generally happy to try to answer the attitude statements – very few refused, and a
small percentage claimed that they did not know what response to give.
Changes to the question wording
As with the behavioural scale questions, these attitudinal questions were asked in the same way in
every country except Norway and South Africa. In Norway the questions were asked on a 7 point, rather
than 5 point scale62. In addition, in Norway, just 1 of the three core questions was asked. In South Africa,
a 5 point scale was used, but each of the scale points was given a verbal description (completely agree,
agree, neither agree nor disagree, disagree, completely disagree).
In South Africans were far more likely to put themselves at 2 or 4, rather than 1 or 5 on the scale
(something that was not found in other countries). This may reflect the fact that South Africa labelled
each of the values. This is something that should be taken into account for future surveys.
Changes to the Core Questionnaire
In the revised version of the questionnaire, the behavioural statements have been combined with
the attitude statements, as discussed in the previous section on behaviours.
Developing a score
With very low levels of missing data, refusals have been recoded as neither agreeing nor disagreeing
(point 3 on the scale). Those who responded ‘don’t know’ have also been given a score at the midpoint
as they appear to be ambivalent.
The scale-points of the attitude questions have been used as scores (where necessary they have
been reversed so that 1 is the least financially literate attitude). The scores from each of the three
62
The Norwegian scale has been recoded as follows for the purpose of comparisons: 1=1 (2,3=2) (4=3) (5, 6=4)
(7=5).
149
questions have then be summed, before dividing by 3 to give an average attitude. Anyone achieving more
than 3 across the three questions has been identified as having a generally positive attitude63.
63
If 3 is the midpoint, then these people are neutral. In order to have scored above 3 on average, they must have
put themselves at 4 on the scale on at least one of the three questions.
150
AN OVERALL MEASURE OF FINANCIAL LITERACY
Segmenting the population
Some countries are particularly interested in categorising their population according to their
financial literacy. The approach applied by the OECD/INFE considers whether the respondent gained a
high score in 0, 1, 2 or 3 of the three components. This has the advantage of creating mutually exclusive
categories, and ones that are driven exclusively by responses to the financial literacy questions. The size
of the categories is of course related to decisions made about what constitutes a high score, but as the
purpose is to compare proportions in each category across groups, countries and times, the most
important aspect is that a) they genuinely represent ‘good enough’ levels of financial literacy and b) they
are easily replicable with different data.
Developing a single measure of financial literacy
In order to assess overall levels of financial literacy the three raw scores discussed above for
knowledge, behaviour and attitudes have also been summed64. This gives a simple measure that takes
into account the various aspects of financial literacy, including financial planning for the future, choosing
financial products and managing money on a day to day basis.
The three raw scores have different maximum values, and so the combined score is implicitly
weighted. The most heavily weighted factor is behaviour – which can take a value between 0 and 9. This
is appropriate: behavioural questions make up a large part of the questionnaire because financial
behaviour is seen as a key component in financial literacy65. Financial knowledge also makes up a large
percentage of the final score (scores range from 0-8). Financial knowledge and behaviour are the two
aspects of financial literacy most typically targeted by financial education initiatives. The score also
contains a small component of attitudes towards money, and particularly towards planning for the future
(the scores for attitude are based on the respondent’s average position on the scale and range from 1-5).
As the attitude score does not go to zero, the raw combined financial literacy score runs from a minimum
of 1 to a maximum of 22. This has been rescaled to take values from 1 to 100.
64
We have also rounded final values to whole numbers, in order to facilitate reporting.
65
Exploratory factor analysis also suggests that knowledge and behaviour should be weighted more heavily than
attitudes.
151
COLLECTING DATA ON SOCIO-DEMOGRAPHICS
It is important to be able to analyse financial literacy data by key demographics, in order to identify
target groups for financial education initiatives. Target groups typically reflect age, gender, income and
location. Work status may also be used to identify recipients of particular initiatives.
Across the participating countries, quite large proportions of the population refused to answer the
income question, despite the very broad categories. This means that most analysis by income is only
possible on a subset of respondents. However, it has been possible to include an indicator of missing
income data in the regressions in case they were systematically different from other respondents.
The rural/urban marker also caused difficulties in an international context. Changes have been made
to the final questionnaire to try to capture this data.
Following feedback about the use of 30 hours as an indication of full time work, the question on
work status has been edited, and it is now followed by a question on the number of hours worked. For
international comparisons it is recommended to use 30 hours, but this gives countries more detailed data
with which to explore financial literacy according to national working hour norms.
The questions have also been reordered to make sure that the demographic questions are asked in a
logical order (from education through to income), and to try to reduce the number of refusals to the
income question by making it shorter, and by noting that it is the last question.
152
ANNEX 2: FURTHER DETAIL OF COUNTRY LEVEL DATA
Table 17. Details of sample
Country
Survey details
Albania
 Age 18+
 Useable sample 1,000
 Three step random sample: 1) Primary Select ion Unit 2) Families selected 3) Random
selection of adult.
 Face-to-face interviews
 Main fieldwork in early 2011
 Only using OECD/INFE core questionnaire
 Weights created based on socio-demographic data.
Armenia
 Age 18+
 Useable sample 1,545
 Stratified sample: 1 stage in urban areas and 2 stages in rural areas. The sample size of each
selected areas was defined proportional to population size
 Face-to-face interviews
 Main Fieldwork October to November 2010.
 Only using OECD/INFE core questionnaire
 Weights created based on socio-demographic data.
Czech
Republic






Estonia







Germany
 Age 18+
 Useable sample 1,005
 Random sample based on the ADM-Basis for CATI samples, all private phone numbers
whether or not registered in directories.
 Telephone interviews 1,441 calls were made
 Main fieldwork November 2010 until January 2011
 Weights created based on socio-demographic data
Age 18+
Useable sample 1,005 (1,047 responded, but some incomplete scripts)
Face-to-face interviews
OECD/INFE core questionnaire combined with others: total 80 questions
Weights created based on socio-demographic data.
Piloted prior to full scale survey (main survey September 2010)
Report available in Czech http://www.mfcr.cz/cps/rde/xchg/mfcr/xsl/ft_finvzd_vyzkum_gramot.html
Age 18+
Useable sample 993
Multi-stage probability random sampling
by regions and types of settlement (urban/rural)
Face-to-face interviews
Main Fieldwork November to December 2010.
OECD/INFE core questionnaire combined with 10 others
 Weights created based on socio-demographic data
153
Country
Survey details
Hungary





Ireland
 Age 18+
 Useable sample 1,010
 Stratified random sampling, sampling points selected proportion to population, random
selection of adult in household
 Face-to-face interviews
 Piloted prior to full scale survey (main survey October to November 2010)
 Only using OECD/INFE core questionnaire
 Weights created based on socio-demographic data
Malaysia






Norway
 Age 18+
 Useable sample 2122
 Web panel (robustness checks are possible as certain questions have also been added to a
telephone omnibus).
 Weights created based on socio-demographic data
Peru
 Age 18+
 Useable sample 2254
 Weights created based on socio-demographic data
Poland




South Africa





Age 18+
Useable sample 998
(gross sample 3131)
Face-to-face interviews
Only using OECD/INFE core questionnaire
 Weights created based on socio-demographic data, individuals in household, number of
telephone lines.
Age 18+
Useable sample 1,046
Multi Stage Stratified Random Sampling. Response rate 65%
Face-to-face interviews
Data collection period September to October 2010
OECD/INFE Questions combined with others on household indebtedness and levels of literacy
 Weights created based on socio-demographic data
Age 18+
Useable sample 1008
Telephone omnibus survey
Questions used as part of a longer survey and also incorporating additional knowledge
questions from INFE supplementary questions
 Fieldwork September to December 2010
 Weights created based on socio-demographic data
Original sample of Age 16+ reweighted to be a sample of adults aged 18+.
Complex stratified multi-stage sample
Useable sample 3,112
Face-to-face interviews
Fieldwork November to December 2010.
 Weights created based on socio-demographic data
154
Country
Survey details
UK




BVI





Age 18+.
Useable sample 1579
Fieldwork September 2010.
Only using OECD/INFE core questionnaire
 Weights created based on socio-demographic data
Age 18+.
Useable sample 535.
Sample drawn from a random pool of anonymous telephone numbers: 1489 calls made.
Telephone interviews.
Only using OECD/INFE core questionnaire
 Data unweighted; tested for representativeness across key socio-demographics
Table 18. Financial knowledge: division, time-value of money, interest paid on a loan
Albania
Correct
Incorrect
Refused
Don’t know
Interest paid on a loan
Correct
Incorrect
Refused
Correct
Don’t know
Time-value of money
Incorrect
Refused
Don’t know
Division
1%
10%
-
89%
4%
25%
9%
61%
Armenia
-
4%
10%
86%
1%
9%
6%
83%
-
7%
5%
87%
Czech
Republic
Estonia
2%
3%
2%
93%
1%
6%
13%
80%
3%
8%
1%
88%
2%
3%
2%
93%
2%
7%
5%
86%
3%
9%
4%
84%
Germany
1%
5%
10%
84%
-
4%
35%
61%
1%
7%
3%
88%
2%
2%
96%
-
5%
16%
78%
-
3%
1%
95%
Hungary
Ireland
Malaysia
Norway*
4%
3%
93%
-
7%
35%
58%
6%
6%
1%
88%
1%
5%
1%
93%
2%
14%
23%
62%
1%
6%
1%
93%
1%
16%
23%
61%
-
20%
18%
61%
1%
3%
10%
87%
Peru
1%
3%
6%
90%
2%
7%
28%
63%
Poland
4%
1%
4%
91%
1%
8%
14%
77%
1%
3%
11%
85%
South
Africa
United
Kingdom
1%
10%
10%
79%
34%
49%
1%
12%
22%
65%
-
8%
17%
76%
-
6%
33%
61%
-
8%
2%
90%
14%
2%
84%
1%
16%
9%
74%
-
-
1%
99%
BVI
4%
13%
Base: all respondents. Column percentages by country. A dash [–] refers to more than 0 but less than 0.5%. Empty cells have no
relevant observations, including those where the response was not recorded. *The results reported for Norway under the Division
column actually refer to an alternative questions posed: What is the nominal interest rate. Norway also slightly reworded the time
value of money question, as they had not asked the previous question. Under interest for Norway responses to: ‘What is meant by
the effective interest rate’ are reported.
155
Table 19. Financial knowledge: interest plus principle, compound interest
Responses to knowledge question (Column percentages by country, weighted data, all respondents)
Calculation of interest plus principle
Compound interest AND Correct response to previous question *
Refused
Don’t know
Incorrect
Correct
No
Yes
Albania
2%
45%
13%
40%
90%
10%
Armenia
1%
14%
33%
53%
82%
18%
Czech Republic
6%
20%
14%
60%
68%
32%
Estonia
4%
20%
12%
64%
69%
31%
Germany
2%
17%
18%
64%
53%
47%
Hungary
-
18%
20%
61%
54%
46%
Ireland
-
15%
9%
76%
71%
29%
Malaysia
1%
29%
16%
54%
70%
30%
Norway
-
3%
22%
75%
46%
54%
Peru
4%
20%
36%
40%
86%
14%
Poland
2%
2%
36%
60%
73%
27%
South Africa
4%
28%
24%
44%
79%
21%
-
19%
19%
61%
63%
37%
27%
10%
63%
80%
20%
United Kingdom
BVI
Base: all respondents. Column percentages by country. A dash [–] refers to more than 0 but less than 0.5%. Empty cells have no
relevant observations, including those where the response was not recorded.
156
Table 20. Financial knowledge: risk and return, inflation, diversification
0%
81%
2%
1%
22%
4%
72%
Germany
1%
4%
16%
79%
Hungary
-
7%
6%
86%
-
10%
6%
84%
-
8%
82%
14%
Estonia
Ireland
81%
3%
10%
33%
57%
1%
13%
14%
70%
4%
1%
11%
2%
85%
0%
3%
10%
3%
6%
63%
12%
28%
59%
34%
7%
54%
2%
35%
6%
57%
87%
1%
11%
28%
60%
91%
-
25%
14%
61%
34%
18%
47%
29%
43%
3%
88%
1%
74%
27%
16%
15%
68%
1%
37%
51%
11%
4%
9%
86%
3%
29%
17%
51%
1%
12%
6%
80%
1%
31%
12%
55%
73%
1%
8%
13%
78%
1%
9%
42%
48%
14%
77%
0%
3%
4%
94%
0%
22%
23%
55%
11%
83%
7%
6%
87%
39%
41%
20%
11%
Norway**
1%
20%
18%
61%
1%
Peru
2%
16%
13%
69%
1%
Poland
2%
37%
13%
48%
South Africa
1%
7%
19%
United Kingdom
0%
9%
6%
BVI**
34%
12%
Malaysia
8%
True
67%
7%
3%
17%
False
16%
10%
1%
Czech Republic
Don’t know
16%
Armenia
Refused
2%
Diversification
True
77%
2%
False
Refused
21%
Albania
Don’t know
True
Definition of inflation
False
Don’t know
Refused
Risk and return
Base: all respondents. Column percentages by country. A dash [–] refers to more than 0 but less than 0.5%. Empty cells have
no relevant observations, including those where the response was not recorded.* If the respondent did not know how to
calculate simple interest over 1 year, it is unlikely that they could calculate 5 times this amount, and therefore it is assumed
that a correct answer to the follow-up question is a guess.**For diversification Norway and BVI asked Buying a single
company’s stock usually provides a safer return than a stock mutual fund: note that in this case False is the correct response.
157
Table 21. Behaviour: Before I buy something I carefully consider whether I can afford it
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South Africa
United Kingdom
BVI
Refused
-
Don’t know
1%
1%
-
1%
4%
1%
1%
1%
4%
Never
4%
1%
2%
4%
4%
2%
2%
1%
2%
1%
4%
3%
6%
2%
2
4%
2%
3%
4%
3%
2%
3%
1%
12%
2%
5%
3%
4%
2%
3
4%
6%
18%
21%
11%
10%
12%
6%
13%
6%
20%
10%
13%
5%
4
16%
9%
21%
20%
23%
18%
20%
14%
41%
11%
23%
21%
15%
15%
Always
70%
81%
54%
47%
59%
68%
63%
78%
31%
80%
47%
62%
62%
72%
Base: all respondents. Row percentages by country. A dash [–] refers to more than 0 but less than 0.5%. Empty cells have no
relevant observations, including those where the response was not recorded.
Table 22. Behaviour: I pay my bills on time
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South Africa
United
Kingdom
BVI
Refused
-
Don’t know
3%
1%
3%
0%
Never
3%
1%
1%
1%
2%
1%
4%
5%
1%
2%
9%
3%
2
6%
1%
2%
2%
2%
3%
5%
10%
1%
4%
6%
1%
3
11%
5%
9%
10%
3%
12%
11%
19%
5%
12%
14%
19%
6%
4
30%
13%
20%
19%
13%
16%
20%
30%
29%
18%
21%
26%
9%
Always
48%
81%
65%
64%
83%
66%
64%
39%
50%
68%
57%
35%
80%
2%
1%
3%
1%
0%
3%
1%
1%
2%
1%
-
4%
1%
2%
10%
19%
64%
Base: all respondents. Row percentages by country. A dash [–] refers to more than 0 but less than 0.5%. Empty cells have no
relevant observations, including those where the response was not recorded.
158
Table 23. Behaviour: I keep a close personal watch on my financial affairs
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South Africa
United
Kingdom
BVI
Refused
-
Don’t know
1%
1%
1%
1%
-
Never
3%
4%
3%
2%
1%
8%
1%
2%
1%
1%
2%
7%
4%
2
8%
4%
5%
4%
2%
5%
3%
2%
3%
19%
4%
8%
3%
3
17%
11%
15%
14%
9%
15%
10%
15%
6%
2%
12%
17%
12%
4
35%
16%
25%
23%
23%
16%
21%
29%
34%
23%
28%
15%
Always
36%
65%
51%
55%
64%
54%
64%
50%
55%
68%
58%
37%
65%
1%
1%
2%
2%
1%
1%
2%
-
-
4%
3%
2%
11%
20%
59%
Base: all respondents. Row percentages by country. A dash [–] refers to more than 0 but less than 0.5%. Empty cells have no
relevant observations, including those where the response was not recorded.
Table 24. Behaviour: I set long term financial goals and strive to achieve them
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South Africa
United
Kingdom
BVI
Refused
-
Don’t know
6%
4%
2%
-
1%
11%
1%
2%
1%
1%
1%
2%
1%
1%
2%
2%
1%
Never
15%
15%
18%
12%
11%
13%
12%
7%
4%
9%
14%
12%
22%
2
27%
9%
15%
13%
6%
10%
11%
7%
18%
5%
16%
9%
9%
3
22%
18%
26%
21%
22%
24%
20%
20%
18%
12%
22%
19%
25%
4
19%
15%
18%
19%
25%
21%
23%
34%
39%
16%
21%
24%
15%
Always
12%
43%
19%
22%
36%
31%
33%
30%
19%
55%
25%
32%
27%
6%
5%
3%
17%
23%
45%
1%
1%
Base: all respondents. Row percentages by country. A dash [–] refers to more than 0 but less than 0.5%. Empty cells have no
relevant observations, including those where the response was not recorded.
159
Table 25. Average behaviour score by country
Mean
54.4
55.6
57.8
50.0
65.6
54.4
62.2
66.7
61.1
63.3
55.6
55.6
61.1
67.8
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South Africa
United Kingdom
BVI
Min
0.0
0.0
0.0
0.0
11.1
0.0
0.0
11.1
0.0
0.0
0.0
0.0
0.0
0.0
Max
100.0
88.9
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
88.9
Base: all respondents.
Table 26. Attitude: I find it more satisfying to spend money than to save it for the long term
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South Africa
United Kingdom
BVI
Refused
Don’t know
1%
2%
2%
2%
1%
1%
-
2%
6%
1%
1%
1%
3%
1%
1%
6%
Completely
agree
7%
56%
10%
19%
8%
5%
15%
19%
4%
11%
28%
11%
17%
6%
2
3
4
10%
15%
14%
14%
12%
8%
20%
16%
17%
8%
17%
26%
12%
8%
20%
21%
26%
21%
30%
30%
27%
18%
21%
15%
33%
14%
35%
20%
24%
4%
18%
9%
20%
19%
14%
11%
35%
15%
11%
28%
14%
19%
Completely
disagree
37%
4%
27%
30%
29%
37%
24%
35%
23%
50%
8%
20%
21%
41%
Base: all respondents. Row percentages by country. A dash [–] refers to more than 0 but less than 0.5%. Empty cells have no
relevant observations, including those where the response was not recorded.
160
Table 27. Attitude: I tend to live for today and let tomorrow take care of itself
Refused
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South Africa
United Kingdom
BVI
Don’t know
1%
2%
1%
1%
1%
1%
3%
-
1%
1%
1%
1%
6%
Completely
agree
3%
19%
8%
16%
8%
6%
12%
10%
2
3
4
9%
10%
9%
12%
7%
8%
16%
13%
18%
11%
13%
18%
20%
18%
18%
20%
25%
7%
18%
14%
19%
16%
19%
16%
Completely
disagree
41%
53%
51%
35%
46%
52%
35%
41%
11%
19%
7%
15%
9%
5%
14%
19%
11%
6%
11%
20%
14%
24%
14%
15%
21%
35%
16%
21%
58%
24%
25%
34%
44%
Base: all respondents. Row percentages by country. A dash [–] refers to more than 0 but less than 0.5%. Empty cells have no
relevant observations, including those where the response was not recorded.
Table 28. Attitude: Money is there to be spent
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South Africa
United Kingdom
BVI
Refused
Don’t know
Completely
agree
2
3
4
Completely
disagree
1%
4%
2%
1%
1%
2%
7%
15%
74%
14%
28%
21%
12%
14%
21%
9%
13%
19%
16%
14%
13%
23%
22%
25%
11%
35%
24%
39%
42%
33%
29%
21%
1%
13%
9%
12%
14%
12%
11%
24%
1%
16%
16%
14%
19%
18%
15%
1%
1%
-
2%
1%
1%
4%
19%
38%
14%
21%
21%
11%
24%
26%
15%
13%
23%
25%
18%
34%
32%
14%
8%
22%
12%
14%
31%
4%
17%
17%
17%
-
Base: all respondents. Row percentages by country. A dash [–] refers to more than 0 but less than 0.5%. Empty cells have no
relevant observations, including those where the response was not recorded.
161
Table 29. Average combined attitude scores
Average combined score
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South Africa
United Kingdom
BVI
3.7
2.3
3.4
3.1
3.4
3.6
3.2
3.2
3.6
3.7
2.6
3.2
3.1
3.5
Base: all respondents. A dash [–] refers to more than 0 but less than 0.5%. Empty cells have no relevant observations, including
those where the response was not recorded.
162
Table 30. Financial literacy segments by gender
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
Poland
South Africa
UK
BVI
Female
Male
All
Female
Male
All
Female
Male
All
Female
Male
All
Female
Male
All
Female
Male
All
Female
Male
All
Female
Male
All
Female
Male
All
Female
Male
All
Female
Male
All
Female
Male
All
Female
Male
All
Female
Male
All
No Strengths
One strength
Two strengths
Strong on all
components
19%
8%
13%
33%
29%
32%
16%
15%
15%
19%
15%
17%
10%
6%
8%
7%
11%
9%
14%
13%
13%
11%
10%
10%
12%
12%
12%
11%
10%
10%
30%
22%
26%
21%
23%
22%
19%
14%
17%
10%
5%
8%
38%
35%
36%
44%
39%
42%
26%
28%
27%
40%
44%
42%
27%
28%
27%
31%
26%
29%
26%
32%
29%
32%
31%
32%
30%
29%
30%
28%
28%
28%
39%
38%
38%
44%
33%
39%
36%
30%
33%
25%
18%
22%
33%
41%
38%
20%
28%
24%
32%
33%
33%
30%
31%
30%
34%
32%
33%
36%
41%
38%
37%
35%
36%
33%
37%
35%
41%
36%
38%
44%
41%
42%
26%
30%
28%
25%
28%
27%
30%
34%
32%
33%
40%
36%
11%
16%
14%
2%
4%
3%
26%
24%
25%
11%
10%
10%
29%
34%
32%
25%
22%
24%
23%
20%
22%
25%
22%
23%
17%
23%
20%
18%
22%
20%
6%
11%
8%
10%
15%
13%
15%
22%
19%
32%
36%
34%
Base: all respondents. Row percentages by country
163
All respondents
(Unweighted
count of non
missing data)
410
590
1000
1042
503
1545
532
473
1005
580
413
993
548
457
1005
533
465
998
581
429
1010
423
623
1046
1126
991
2117
798
1456
2254
639
369
1008
1798
1219
3017
840
739
1579
305
230
535
Table 31. Financial Literacy Segments by Income
Number of high scores across three components
No high scores
Albania
Armenia
Czech Republic
Estonia
Germany
Hungary
Ireland
Malaysia
Norway
Peru
1
2
3
All
respondents
(Unweighted
count of non
missing data)
Low
19%
44%
33%
4%
380
Average
12%
39%
39%
10%
321
High
5%
26%
40%
29%
283
Total
12%
37%
37%
14%
984
Low
46%
39%
15%
1%
733
Average
20%
45%
31%
4%
755
High
23%
29%
46%
3%
31
Total
32%
42%
24%
3%
1519
Low
21%
29%
32%
19%
451
Average
11%
26%
35%
28%
332
High
7%
21%
34%
38%
139
Total
15%
26%
33%
25%
922
Low
19%
41%
31%
8%
363
Average
16%
42%
32%
10%
375
High
11%
46%
26%
17%
151
Total
16%
42%
31%
11%
889
Low
10%
40%
29%
20%
249
Average
9%
26%
35%
30%
347
High
4%
19%
34%
44%
384
Total
8%
28%
33%
32%
980
Low
14%
35%
36%
15%
319
Average
8%
25%
41%
26%
367
High
6%
16%
37%
41%
141
Total
10%
28%
38%
24%
827
Low
15%
32%
37%
17%
558
Average
5%
22%
42%
31%
176
High
6%
20%
36%
38%
61
Total
12%
28%
38%
22%
795
Low
16%
39%
32%
14%
431
Average
8%
31%
34%
27%
410
High
4%
17%
44%
36%
205
Total
10%
32%
35%
23%
1046
Low
11%
32%
43%
14%
349
Average
10%
28%
41%
21%
493
High
9%
26%
36%
28%
905
Total
10%
28%
39%
24%
1747
Low
13%
31%
40%
16%
822
Average
8%
25%
45%
22%
793
High
7%
20%
42%
31%
341
164
Poland
South Africa
UK
BVI
Total
10%
27%
42%
21%
1956
Low
36%
37%
23%
4%
455
Average
19%
43%
27%
12%
314
High
11%
35%
41%
12%
144
Total
26%
39%
27%
8%
913
Low
27%
46%
20%
7%
1347
Average
16%
31%
35%
18%
1327
High
11%
25%
31%
33%
137
Total
22%
39%
27%
13%
2811
Low
21%
39%
31%
9%
478
Average
14%
35%
29%
22%
453
High
11%
25%
35%
29%
444
Total
16%
33%
32%
20%
1375
Low
10%
25%
40%
24%
153
Average
6%
15%
36%
42%
151
High
5%
21%
31%
42%
153
Total
7%
21%
36%
36%
457
Base: all respondents. Row percentages by country. Caution should be taken when interpreting the results of the high income
groups in Armenia and Ireland due to small bases.
165
ANNEX 3: PRODUCT HOLDING BY COUNTRY
Figure 61. Products: Albania
Awareness
Holding
Recent purchase
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Savings Account
Current Account
Insurance product
Credit Card
Secured Loan
Treasury bill
Unsecured bank loan
Pension fund
Microfinance Loan
Figure 62. Products: Armenia
Awareness
Holding
Recent purchase
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Retail credit
Loan with gold coverage
Mortgage
Secured bank loan
Rural loans
Savings Account
Credit Card
Consumer loans
Current Account
Pension fund
Insurance product
Stocks and shares
Bonds
Overdraft loans
167
Figure 63. Products: Czech Republic
Awareness
Holding
Recent purchase
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Current Account
Additional Pension Insurance
Housing Focused Saving
Life Insurance
Proprietary Insurance
Mortgage
Credit Card
Savings Account
Shares
Consumer Lending
Debit Card
Debentures
Investment Fund
Income Loss Insurance
Charge over Property
Figure 64. Products: Estonia
Awareness
Holding
Recent purchase
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Bank Account
Insurance
Home Loan
Pension Fund
Credit Card
Small Loan
Term Deposit
Secured Bank Loan
Stocks and Shares
Unsecured Bank Loan
Shares and Investment Fund
Bonds
168
Figure 65. Products: Germany
Awareness
Holding
Recent purchase
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Savings Account
Life-Pension Insurance
Current Account
P&C Insurance
Credit Card
Building Society Contract
Shares
Mortgage
Short Term Savings
Investment Funds
Installment Credit
Fixed Interest Securities
Savings Plan
Figure 66. Products: Hungary
Awareness
Holding
Recent purchase
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Bank account
Insurance
Credit for housing
Private pension fund
Personal consumer loan
Term deposit
Cash loan
Credit card
Voluntary pension fund
Mortgage loan
State securities
Stocks
Investment funds
169
Figure 67. Products: Ireland
Awareness
Holding
Recent purchase
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Current account
Credit card
Mortgage
Car insurance
Home insurance
Credit union savings account
Savings account (bank or building society)
Private health insurance
Post office account
Credit union loan
Pension (including PRSA-AVC)
Life insurance (including mortgage protection)
Stocks and shares
Bank loan secured on property
Hire purchase loan
Unsecured bank loan
Bonds
Investment account
Serious illness insurance
Consumer hire-leasing
Income protection insurance
Loan insurance or payment protection insurance
170
Figure 68. Products: Malaysia
Awareness
Holding
Recent purchase
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Savings account
ATM card
EPF
Insurance
Home loan
Car loan
Credit card
Current account
Unit trust: permodalan nasional berhad
Fixed deposits
Pension funds: gov servants
Debit card
Unsecured bank loan
Stocks, shares and warrants
Unit trust: banks or private mutual
Bank loan secured on property
Overdraft
Pensions funds: private
Microfinance loan
Bonds
Figure 69. Products: Norway
Awareness
Holding
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%
Savings account
Credit card
Checking account
Mortgage
Insurance
Consumer loan
Pension fund
Investment products
Credit line/housing credit/flexible loan
Senior loan
171
Figure 70. Products: Peru
Awareness
Holding
Recent purchase
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Savings, salary account, zero account
Health insurance – ESSALUD
Pension fund – AFP
Credit, business card store
Compulsory Insurance: Traffic Accidents
Health insurance – SIS (comprehensive)
Life insurance
Pension fund – ONP
Loan, mortgage
Personal loan
Health insurance – EPS, insurance companies
Fixed term deposit
Current account, money order
Working capital loan
Personal accident insurance
Vehicle insurance
School insurance
Cooperative savings account
Commercial loan, SME
Car loan
Collective funds
Supplementary insurance for hazardous work
Cooperatives loan
Loan, credit for motorcycle taxi, motorbike
Investment account, mutual funds
Investments in the stock exchange
Insurance on debt
Loan for fixed assets
Collateral loan (not on housing)
Leasing
172
Figure 71. Products: Poland
Awareness
Holding
Recent purchase
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%
Current account
Credit card
Pension fund
Insurance
Mortgage
Saving account
Payment cards (not credit cards)
Bonds
Stocks and shares
Bank loan secured on property
Unsecured bank loan
Investment account, such as a unit trust
Microfinance loan
Structured products
Figure 72. Products: South Africa
Awareness
Holding
Recent purchase
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%
Bank account
Credit card
Post office savings
Policies (sanlam, Old Mutual)
Insurance (car and house, funeral)
Stokvels
Pension fund
House bond
Investment account
Bank loan secured on a property
Microfinance loan
Stocks and shares
Unsecured bank loan
Retail bonds
173
Figure 73. Products: UK
Awareness
Holding
Recent purchase
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Current account
Credit card
Life insurance
Savings account, other than a cash ISA
Mortgage
General insurance
Health or medical insurance
Cash ISA
Pension fund, company or personal
Stocks and shares
Bonds e.g. gilts and corporate bonds
Loan secured on property
Unsecured loan
Investment account e.g. unit trust, …
Insurance to protect income or mortgage
Microfinance loan
Figure 74. Products: British Virgin Islands
Awareness
Holding
Recent purchase
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%
Insurance
Credit card
Saving account
Pension fund
Current account
Mortgage
Stocks and shares
Bonds
Bank loan secured on property
Unsecured bank loan
Investment account
Microfinance loan
174
ANNEX 4: ANALYSIS BY SUBGROUPS OF WOMEN
6.533
-6.689
**
*
3.335
3.295
Norway
Malaysia
Ireland
1.506
-2.430
0.816
-1.783
-3.162
6.393
-0.112
BVI
-7.051
2.526
-0.246
0.402
UK
4.252
-2.701
South
Africa
2.267
2.753
0.154
-4.128
Poland
**
1.600
-4.557
Peru
4.501
6.526
Divorced
***
3.486
Hungary
9.458
Germany
0.000
Estonia
Czech
Republic
Marital status Married (ref.)
Living
Coeff
15.04
with a
.
4
partner
Signif
Armenia
Albania
Table 32. Financial knowledge across subgroups of women
2.844
14.85
2
0.929
2.885
**
14.40
7
3.835
12.39
6
***
Single
-0.750
-1.647
-3.953
-1.354
4.212
1.218
2.168
2.724
-5.699
19.381
**
1.418
-3.085
4.338
0.621
*
***
-9.093
-2.613
-4.405
*
Widow
*
Age 30-59 (ref.)
18-29
3.889
1.592
-4.109
-5.719
-4.106
2.110
*
***
*
9.281
6.206
***
***
2.042
***
3.384
5.507
**
**
0.204
60+
-4.119
2.941
7.355
-0.718
Working status (not working as reference category )
Working
6.218
2.619
2.914
-1.910
-0.462
-1.932
1.882
-8.381
0.523
5.345
0.164
2.864
1.404
-0.308
2.976
1.658
5.803
3.586
1.563
2.426
6.492
4.099
6.037
-3.467
***
*
**
***
***
-6.683
1.699
-4.759
8.793
4.852
*
***
**
**
Household income Median (ref.)
Below
median
1.646
5.461
6.607
***
**
7.779
4.048
1.070
12.065
1.192
-0.445
2.656
0.324
1.981
6.063
0.502
0.635
0.543
9.151
7.922
12.774
13.747
10.425
12.791
14.191
8.243
7.919
11.562
7.111
8.938
-7.637
**
***
***
***
***
***
***
**
***
***
***
**
5.906
3.260
3.748
5.190
2.481
9.285
5.577
6.592
4.845
0.371
5.715
6.439
Above
median
5.515
Education Secondary (ref.)
Below
secondar
-5.845
y
**
Above
secondar
y
7.415
-2.162
-0.695
-1.434
***
9.588
7.409
0.810
***
-4.206
Notes: ordinary least squares regressions. Coefficients: Empty cells: the explanatory variable is not available for this country (e.g. marital status not
available for Norway)/ no observations in the cell (e.g. the category “living with partner” was not coded in the Armenian survey). Dummies for
refusals/DK were included in the regressions, but not reported in the table. Omitted categories: marital status: married is the omitted category for
marital status; age: 30-59 omitted; working status: not working was omitted; household income: median omitted; education: complete secondary
school omitted. Significance levels: *** 1%; ** 5%; * 10%. The financial knowledge score is based on 8 questions and has been rescaled on a 0-100
scale.
175
The Russia Financial Literacy and Education Trust Fund was established in 2008 at the World
Bank with funding provided by the Ministry of Finance of the Russian Federation. The work
supported by the Trust Fund is jointly managed by the World Bank and the Organisation for
Economic Co‑operation and Development (OECD) and is directed toward improving public
policies and programs to enhance financial knowledge and capabilities in low‑ and middle‑
income countries. This effort has focused on the review of national strategies for financial
education, the development of methods for the measurement of financial knowledge and
capabilities, methods for evaluating the impact and outcome of programs, and research
applying these methods to programs in developing countries. The products of this program of
work can be found at the Trust Fund website at:
www.finlitedu.org
MINISTRY OF FINANCE OF
THE RUSSIAN FEDERATION