Download Financial Literacy and Financial Behavior in Switzerland

Master Thesis
Financial Literacy and Financial Behavior in Switzerland
Degree Type
Master of Arts in Banking & Finance (MBF)
University of St. Gallen (HSG)
Supervisor
Prof. Dr. Martin Brown
Swiss Institute of Banking & Finance
Author
Roman Graf
Buchenweg 9
CH-8442 Hettlingen
Matriculation No.: 05-532-197
E-Mail: [email protected]
Date
August 20, 2012
Financial Literacy and Financial Behavior in Switzerland
Abstract
Financial literacy has become a well-recognized item on the agenda of not just international
organizations such as the OECD but also national policy makers. Attention about the topic
soared especially in the aftermath of the financial crisis with multiple studies focusing on
financial literacy in emerging and developed countries being published in recent years. In
Switzerland, however, no representative data about the financial knowledge of the population
has so far been available. This master thesis fills this gap while constituting the first
representative study on financial literacy levels of the adult population of the German
speaking part of Switzerland.
The release of this master thesis occurs at a time when risks and responsibilities are
increasingly shifted to individuals, the range of financial products offered to retail investors is
immense and demand for consumer protection is at the upraise. In such an environment
adequate financial knowledge of individuals becomes particularly important.
Financial literacy in this study was defined on the basis of three simple questions about
interest rates, inflation and risk. About 20% of the respondents were not able to correctly
answer a simple question about interest rates or inflation and around 27% did not know if an
investment in a single stock is more risky than an investment in a fund with multiple stocks.
Women, poorly educated respondents, foreigners and low income earners performed worst.
Overall, financial literacy levels in Switzerland are akin to or slightly higher than those in
other developed countries such as Germany or the Netherlands.
There is strong evidence that people with a better understanding of financial matters are
more likely to have an investment portfolio and a retirement account (Säule 3a). Impulsive
behaving individuals are more likely to have consumption credits while financial knowledge
seems not to have a critical relationship to consumption credit indebtedness.
Numerous private organizations have recently launched initiatives aimed at improving
financial knowledge in Switzerland. However, while considering the often significant efforts
undertaken in the field of financial literacy abroad, Switzerland is not able to distinguish itself
positively on an international scale. The low levels of financial literacy in deprived sections of
the population and the positive effects of financial literacy on financial behavior justify further
related research and the allocation of more attention to financial literacy by Swiss policy
makers.
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Financial Literacy and Financial Behavior in Switzerland
I
Table of Contents
I
Table of Contents....................................................................................................... II
II
List of Exhibits ........................................................................................................... VI
III
List of Tables ........................................................................................................... VII
IV
List of Abbreviations ................................................................................................ VIII
1.
Introduction ................................................................................................................ 1
1.1.
Problem Identification and Objective ..................................................................... 2
1.2.
Restrictions ........................................................................................................... 3
1.3.
Structure of the Master Thesis ............................................................................... 3
2.
Definition and Relevance of Financial Literacy ........................................................... 4
2.1.
Definition of Financial Literacy ............................................................................... 4
2.1.1.
Broad Definition of Literacy .............................................................................. 4
2.1.2.
Narrow Definition of Literacy ............................................................................ 4
2.1.3.
Financial Literacy ............................................................................................. 4
2.1.4.
Separation of Financial Literacy from Financial Education ............................... 5
2.1.5.
Financial Literacy in This Master Thesis ........................................................... 5
2.2.
Relevance of Financial Literacy in Switzerland ...................................................... 6
2.2.1.
Shift of Risk and Responsibility to Individuals ................................................... 6
2.2.2.
Increased Supply of and Access to Wide Range of Financial Products ...........10
2.2.3.
Raising Demand for Consumer Protection and More Extensive Regulation ....12
3.
Relevant Literature....................................................................................................16
3.1.
Demographic, Economic and Financial Characteristics ........................................16
3.1.1.
Gender ............................................................................................................16
3.1.2.
Age .................................................................................................................17
3.1.3.
Education ........................................................................................................18
3.1.4.
Labor Market Status ........................................................................................20
3.1.5.
Financial Income .............................................................................................21
3.1.6.
Financial Wealth..............................................................................................21
3.2.
Retirement Planning and Saving ..........................................................................22
3.2.1.
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United States ..................................................................................................23
II
Financial Literacy and Financial Behavior in Switzerland
3.2.2.
Europe ............................................................................................................23
3.2.3.
Other Countries ...............................................................................................24
3.3.
Investment Behavior .............................................................................................24
3.3.1.
Investment Likelihood......................................................................................24
3.3.2.
Investment Sophistication ...............................................................................25
3.4.
Personal Indebtedness .........................................................................................25
3.4.1.
Quality of Debt Contracts ................................................................................25
3.4.2.
Mortgage Debt ................................................................................................26
3.4.3.
Self-control ......................................................................................................26
3.5.
Economic and Financial Stability ..........................................................................26
3.5.1.
Self-Assessed vs. Actual Financial Literacy ....................................................27
3.6.
Financial Literacy in Switzerland ..........................................................................27
3.7.
Final Remarks ......................................................................................................28
4.
Empirical Analysis of Financial Literacy ....................................................................29
4.1.
Description of Dataset ..........................................................................................29
4.1.1.
4.2.
Financial Literacy Questions ...........................................................................29
Description of Survey Results ...............................................................................30
4.2.1.
Interest Rates Knowledge ...............................................................................31
4.2.2.
Inflation Knowledge .........................................................................................31
4.2.3.
Risk Diversification Knowledge .......................................................................31
4.2.4.
Results Across All Questions ..........................................................................32
4.3.
International Comparison of Survey Results .........................................................33
4.4.
Determinants of Financial Literacy........................................................................34
4.4.1.
Gender ............................................................................................................34
4.4.2.
Age .................................................................................................................36
4.4.3.
Nationality .......................................................................................................38
4.4.4.
Marital Status ..................................................................................................39
4.4.5.
Education ........................................................................................................39
4.4.6.
Labor Market Status ........................................................................................41
4.4.7.
Number of People Living in a Household.........................................................42
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Financial Literacy and Financial Behavior in Switzerland
4.4.8.
Household Income ..........................................................................................42
4.4.9.
Financial Wealth..............................................................................................42
4.4.10.
4.5.
Multivariate Regression Analysis of Financial Literacy..........................................43
4.5.1.
Multivariate Regression Results ......................................................................44
4.5.2.
Is There a Problem of Multicollinearity? ...........................................................44
5.
Financial Literacy and Financial Behavior .................................................................48
5.1.
Self-Assessed Personal Behavior Characteristics ................................................48
5.1.1.
Financial Engagement.....................................................................................48
5.1.2.
Financial Interest .............................................................................................49
5.1.3.
Risk Characteristics ........................................................................................50
5.1.4.
Financial Planning ...........................................................................................50
5.2.
Investment Portfolio ..............................................................................................51
5.2.1.
Univariate Statistics .........................................................................................51
5.2.2.
Multivariate Regression Results ......................................................................51
5.2.3.
Direction of Causality and Omitted Variables ..................................................52
5.2.4.
Discussions .....................................................................................................55
5.2.5.
Conclusions ....................................................................................................57
5.3.
Consumption Credit and Mortgage Debt...............................................................57
5.3.1.
Univariate Statistics .........................................................................................57
5.3.2.
Multivariate Regression Results ......................................................................58
5.3.3.
Discussions .....................................................................................................60
5.3.4.
Conclusions ....................................................................................................62
5.4.
6.
Who Knows the Most and Who Knows the Least? .......................................43
Retirement Account ..............................................................................................62
5.4.1.
Univariate Statistics .........................................................................................62
5.4.2.
Multivariate Regression Results ......................................................................63
5.4.3.
Direction of Causality and Omitted Variables ..................................................65
5.4.4.
Discussions .....................................................................................................67
5.4.5.
Conclusions ....................................................................................................69
Policy Implications and Conclusions .........................................................................70
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Financial Literacy and Financial Behavior in Switzerland
6.1.
Rationale for Improving Financial Literacy in Switzerland .....................................70
6.2.
Information Challenge ..........................................................................................71
6.2.1.
High Fragmentation of Data Collection in Switzerland .....................................71
6.2.2.
The Question About the Requirement of a National Body and Strategy ..........72
6.3.
Policy Implications and Action Items .....................................................................72
6.3.1.
What Has Been Done in Switzerland? .............................................................74
6.3.2.
Which Focus Groups and Topic Areas Have Been Neglected? .......................75
6.3.3.
What Has Been Done Abroad? .......................................................................75
6.3.4.
Potential Action Items......................................................................................77
6.4.
Conclusions ..........................................................................................................79
V
Sources...................................................................................................................... A
VI
Acknowledgement ...................................................................................................... L
VII
Glossary.....................................................................................................................M
VIII
Appendix .................................................................................................................... N
IX
Declaration of Authorship ........................................................................................ RR
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Financial Literacy and Financial Behavior in Switzerland
II
List of Exhibits
Exhibit 1: Gross Replacement Rates of Retirement Income from Public Pension .................. 7
Exhibit 2: Development of Occupational Retirement System – DBP vs. DCP ........................ 8
Exhibit 3: Private Retirement Savings from Vested Pension Benefits .................................... 9
Exhibit 4: Private Retirement Savings from Pillar 3a .............................................................10
Exhibit 5: Private Indebtedness and Insolvencies in Switzerland ..........................................11
Exhibit 6: Development and Distribution of Structured Products ...........................................12
Exhibit 7: Financial Literacy by Age ......................................................................................37
Exhibit 8: Gender and Age by Occupation ............................................................................41
Exhibit 9: Private Indebtedness by Impulsive Behavior .........................................................60
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Financial Literacy and Financial Behavior in Switzerland
III
List of Tables
Table 1: Responses to Financial Literacy Questions ............................................................30
Table 2: Pearson-Correlation Coefficients of Responses Across Questions .........................32
Table 3: International Comparison of Financial Literacy .......................................................33
Table 4: Statistical Test of Gender Gap in Financial Literacy ................................................34
Table 5: Financial Literacy by Demographic, Economic and Financial Characteristics .........35
Table 6: Statistical Test of Nationality Gap in Financial Literacy ...........................................38
Table 7: Statistical Test of Financial Literacy and Level of Education ...................................39
Table 8: Gradients of Education and Financial Literacy ........................................................40
Table 9: Characteristics with Highest and Lowest Financial Literacy ....................................43
Table 10: Multivariate Linear Regression - Financial Literacy ...............................................45
Table 11: Financial Literacy by Self-Assessed Personal Behavior Characteristics ...............48
Table 12: Statistical Test of Financial Engagement and Financial Literacy ...........................49
Table 13: Statistical Test of Financial Interest and Financial Literacy ...................................50
Table 14: Statistical Test of Financial Planning and Financial Literacy .................................51
Table 15: Statistical Test of Investment Portfolio Ownership and Financial Literacy .............51
Table 16: Multivariate Linear Regression – Investment Portfolio ..........................................53
Table 17: Statistical Test of Consumption Credit/ Mortgage Debt and Financial Literacy......57
Table 18: Multivariate Linear Regression – Consumption Credit resp. Mortgage Debt .........59
Table 19: Statistical Test of Retirement Account Ownership and Financial Literacy .............62
Table 20: Multivariate Linear Regression – Retirement Account...........................................64
Table 21: Statistical Diagnostic of Instrumental Variables .....................................................66
Table 22: Standardized Labor Force Participation Rates in Switzerland ...............................69
Table 23: Selected Financial Literacy Initiatives Undertaken in Switzerland .........................73
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Financial Literacy and Financial Behavior in Switzerland
IV
List of Abbreviations
ABACO
Adult Basic Accounting and Control of Over Indebtedness
AHV
Swiss Federal Old-Age and Survivors’ Insurance
ANZ
Australia and New Zealand Banking Group
ASIC
Australian Securities and Investments Commission
BfS
Swiss Federal Statistical Office
BSV
Swiss Federal Office for Social Insurance
BV
Swiss Federal Constitution
BVG
Swiss Federal Law on Occupational Pension Funds
BVV
Swiss Federal Decree on Tax Deduction of Qualified Retirement Savings
DBG
Swiss Federal Law on Direct Federal Taxes
DBP
Defined Benefit Plan
DCP
Defined Contribution Plan
D-EDK
Conference of Education Ministers of German-Speaking Switzerland
DK
Don’t Know/ Refuse to Answer
EFD
Swiss Federal Department of Finance
ELSA
English Longitudinal Study of Aging
EU
European Union
EU-SILC
European Union Statistics on Income and Living Conditions
FDP
Liberal Democratic Party
FED
Federal Reserve
FinLiCo
Financial Literacy Competencies for Adult Learners
FINMA
Swiss Financial Market Supervisory Authority
FINRA
Financial Industry Regulatory Authority
FLEC
Financial Literacy and Education Commission
FLI
Financial Literacy Index
FSB
Financial Stability Board
GFLI
Global Financial Literacy Initiative
HABE
Household Budget Survey
HH
Household
HRS
University of Michigan Health and Retirement Study
HSG
University of St. Gallen
IAS
International Accounting Standards
IBF
Institute Banking and Finance (ZHAW)
IFRS
International Financial Reporting Standards
INFE
International Network of Financial Education
IV
Instrumental Variable
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Financial Literacy and Financial Behavior in Switzerland
KAG
Federal Act on Collective Investment Schemes
MBA
Master of Business Administration
MiFID
Markets in Financial Instruments Directive
NAB
National Australia Bank
NBER
National Bureau of Economic Research
NLSY
National Longitudinal Survey of Youth
NPAC
The National Pensions Awareness Campaign
NZZ
Neue Zurcher Zeitung
OECD
Organization for Economic Cooperation and Development
OLS
Ordinary Least Square
PISA
Programme for International Student Assessment
PLZ
Postal Code
RMR
Ray Morgan Research
SAKE
Swiss Labor Force Survey
SAVE
Survey About Saving in Germany
SDFB
Swiss Design Institute for Finance and Banking
SEC
Securities and Exchange Commission
SEI
Self Evaluation Index
SER
State Secretariat for Education and Research
SIA
Swiss Insurance Association
SILC
Statistics on Income and Living Conditions
SHAPE
System for Household and Personal Statistics
SHARE
Survey of Health, Aging and Retirement in Europe
SIB&F
Swiss Institute of Banking & Finance (HSG)
SNB
Swiss National Bank
SSPA
Swiss Structured Products Association
SVEB
The Swiss National Umbrella Organization for Adult Education
UAE
United Arab Emirates
UK
United Kingdom
US
United States
VIF
Variance Inflation Factor
WB
World Bank
ZHAW
Zurich University of Applied Science
ZKB
State Bank of Zurich
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Financial Literacy and Financial Behavior in Switzerland
1.
Introduction
The global financial crisis has not only deteriorated capital positions of major financial
intermediaries but has also led to many retail investors suffering heavy losses within their
personal investments.1 The insolvency of Lehman Brothers in September 2008 and the fall of
the ponzi scheme of Bernard Madoff in December 2008 have battered many individual
investors.
In particular the bankruptcy of Lehman Brothers resulted in many retail investors losing
significant amounts of their investments and consequently led to an outcry in the Swiss
media (cf. NZZamSonntag, 25.01.2009, p. 29). Retail investors were among those suffering
heavily from the default of Lehman Brothers as many individuals were holding structured
investment products which were distributed by banks as “capital protection” but indeed posed
an exposure to the credit risk of Lehman Brothers as counterparty (cf. NZZ, 05.01.2009, p.
13).
The insufficient communication or misunderstanding between the distributing bank and the
retail investors eventually resulted in the Swiss Financial Market Supervisory Authority
(FINMA) publishing a discussion paper about investment product distribution of financial
intermediaries (cf. FINMA Distribution Report 2010). The discussion paper thereby refers to a
separate investigation which highlighted that retail investors were often not aware of the full
risk exposure while investing in “capital protected” structured products and often neglected
the counterparty risk of the issuing company. (FINMA, 2010, p. 30)
The financial crisis highlighted that the risk and reward structures of the newly emerged and
more complex financial products were not fully understood by many individual investors.
Hence, it seems obvious that financial literacy of retail investors plays an important role not
just for investors themselves but also for financial intermediaries with whom they are in
frequent contact. The significant attention paid to the regulation of the “production and
distribution of financial products to private clients” by FINMA in Switzerland highlights the
critical importance of the (lack of) financial knowledge of retail investors for the proper
working of financial intermediation. (FINMA, 2012A, p. 2)
On an international level, financial literacy has been identified by the Organization for
Economic Cooperation and Development (OECD) as gaining growing importance in a world
with sophisticated financial markets as well as demographic, economic and policy changes.
The OECD published a report named “Improving Financial Literacy, Analysis of Issues and
Policies” already in 2005 in which it compared financial knowledge among the populations of
selected member countries. On a European level, the European Union (EU) has released the
1
FINMA defines retail investors as private clients typically living in moderate income and wealth categories with usual but not specific finance
relevant knowledge. Retail investors can be segregated from professional wealth managers, authorized dealers and institutional clients such as
pension funds, insurance companies and investment funds. (FINMA, 2010, p. 2)
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EU directive MiFID which targets to improve investor protection, provides greater financial
market transparency and ensures the highest level of integrity of financial service providers.
It seems obvious, however, that for any regulator to be able to come up with the right dose of
consumer protection regulations a profound understanding of financial knowledge among
retail investors is critical. A high level of financial knowledge among retail investors may well
result in less stringent consumer protection regulation to still be effective – as highlighted by
a quote of Ann Radcliffe.
“A well-informed mind is the best security against the contagion of folly and of vice. The
vacant mind is ever on the watch for relief, and ready to plunge into error, to escape from the
languor of idleness.”
Ann Radcliffe (1764 - 1823), The Mysteries of Udolpho, 1764
1.1.
Problem Identification and Objective
Empirical studies focused on financial literacy in many emerging and developed countries
point towards low levels of financial competence across large parts of examined populations.
Many respondents proved not to be able to come up with correct solutions to basic interest
compounding, inflation and risk diversification questions.2
Those rather discomforting results have caused international organizations such as the
Worldbank (WB), Financial Stability Board (FSB) and especially the OECD with the
foundation of the International Network on Financial Education (INFE) in 2008 to allocate
more resources to this critical subject. Beside efforts of international organizations to
enhance awareness of financial literacy there have also been national governments rolling
out initiatives and building national strategies in the field of financial literacy.3
In Switzerland, consciousness of financial literacy has also been amplified as increased
media coverage and initiatives by organizations such as the Swiss National Bank (SNB)
testimony.4 However, currently there is still no representative data available analyzing the
financial knowledge of citizens in Switzerland.
2
Refer to studies such as Lusardi & Mitchell, 2011B, and Jappelli, 2010, or Table 3 for an international comparison of financial literacy scores.
3
Efforts of the Worldbank include the launch of the Global Program on Consumer Protection and Financial Literacy in November 2011 which
objective is to “help countries achieve concrete measurable improvements in consumer protection in financial services”. (Worldbank, 2012)
The G20 rereleased the framework “High-Level Principles on Financial Consumer Protection” in October 2011 to assist G20 countries and other
interested economies to enhance financial consumer protection. In their framework they highlight that “levels of financial literacy remain low in a
number of jurisdictions” and that “financial consumer protection should be reinforced and integrated with other financial inclusion and financial
education policies.” (OECD, 2011A, p. 4)
Countries like the United States (US) have created the President’s Advisory Council on Financial Capability to assist the American people in
understanding financial matters and making informed financial decisions, and thereby contribute to financial stability. The advisory council
suggests ways to coordinate and maximize the effectiveness of existing private and public sector efforts and identifies new approaches to increase
financial capability through financial education and financial access. Even earlier, in 2006, the US published its first financial literacy strategy which
the Financial Literacy and Education Commission (FLEC) has updated thereafter. Refer to FLEC, 2011, for more information about the “National
Strategy for Financial Literacy 2011”.
4
Refer to section 3.6. and especially 6.3.1. for information about efforts around financial literacy in Switzerland.
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This master thesis aims at filling this gap and constitutes the first representative study of
financial knowledge of the population of German-speaking Switzerland. The questions used
in the survey were built on best practice in the assessment of financial literacy scores among
individuals and allow for international comparability of results.
In more concrete terms, it is the objective to comprehensively describe the financial literacy
level of the German-speaking population in Switzerland as it currently stands and to define
“who knows what?”. Furthermore, socio-economic variables which are strongly linked to
mediocre financial knowledge will be identified and selected relationships between financial
literacy and financial behavior analyzed.
Financial behavior is thereby measured on the basis of retirement saving, investment
portfolio ownership and exposure to consumption and mortgage credits. The aim is to
investigate if financial literacy is a significant explanatory variable for retirement planning,
investment portfolio ownership and consumption and mortgage credit indebtedness.
1.2.
Restrictions
The master thesis focuses on financial literacy levels of individuals in Switzerland. Financial
literacy as relevant for companies and the economy as a whole will be disregarded. Financial
literacy is derived from three representative questions rather than extensive questionnaires
including areas such as investment knowledge or knowledge about the Swiss retirement
system. The master thesis focuses on the following items:

Description of financial literacy levels of individuals in Switzerland

Reconciliation of results to demographic, economic and financial characteristics

Relationship between financial literacy and investment, debt and retirement behavior
There will be some international comparisons of financial literacy scores.
1.3.
Structure of the Master Thesis
The next chapter of this master thesis includes a definition of financial literacy and describes
why financial literacy is relevant for policy makers in Switzerland. An overview of the relevant
literature in the field of financial literacy will constitute the third chapter.
In chapter four, there will be a description of the cross-sectional survey data analyzed in this
master thesis followed by an overview of the financial knowledge of the survey respondents.
The inductive survey results will be reconciled to socio-demographic, economic and financial
characteristics of survey respondents by univariate and multivariate analysis.
Chapter five aims at providing analysis in order to be able to better understand the
relationship between financial literacy and financial behavior in Switzerland.
Lastly, there will be concluding remarks and policy advice given in chapter six.
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2.
Definition and Relevance of Financial Literacy
This chapter defines the term financial literacy and highlights the relevance of financial
literacy for the various stakeholders in Switzerland.
2.1.
Definition of Financial Literacy
In order to set the foundation and draw the outline of this master thesis the term financial
literacy has to be clearly defined. Financial literacy makes use of the broadly applied term
literacy which will be analyzed first.
Literacy is defined in many different ways to reflect different theoretical orientations. It can be
distinguished between a broader and a narrower definition of literacy.
2.1.1. Broad Definition of Literacy
The broad definition of literacy places emphasis on literacy as a process of deriving meaning
from text. However, the broad definition also explicitly includes other language skills such as
listening and speaking as well as skills in regards to the interpretation of visual material, the
use and understanding of mathematical concepts and critical thinking. (De Lemos, 2002, p.
3)
Gough, 1995, p. 79, refers to the broad meaning of literacy as “being educated” as compared
to the narrow meaning of the “ability to read and write”. He also emphasises the closeliness
of the broader definition of literacy to “competence” or “knowledge”.
2.1.2. Narrow Definition of Literacy
The narrow definition of literacy is understood as the ability to read and write, i.e. to convert
the written text to the spoken word and vice versa. (De Lemos, 2002, p. 3)
2.1.3. Financial Literacy
The concept of financial literacy refers to the wider definition of literacy and makes use of the
term literacy in the form of knowledge or competence.5 Financial literacy can thereby be
defined as “the ability to make informed judgments and to take effective decisions regarding
the use and management of money.” (Noctor, Stoney and Stradling 1992, p. 4)
The aforementioned definition can be segregated into the two key elements “make informed
judgments” and “take effective decisions”. In order to be in a position to make informed
judgments a certain knowledge level and basic research skills are essential.6 (Cohen &
Nelson, 2011, p. 2)
5
Financial literacy is also often called or used as synonym for “financial capability”. (OECD, 2012A, p. 7)
6
Atkinson & Messy, 2011, p. 659, refer to the OECD/INFE framework and emphasize that “financial literacy includes knowledge, but also goes
further to include attitudes, behaviors, and skills.” They stress the importance of decision-making and that the final outcome of higher financial
literacy should be enhanced financial well-being.
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The ability to take effective decisions is dependent on the context and requires personal
confidence to act on the knowledge. Effective decisions are taken with an awareness of the
possible financial consequences they may bring. (Widdowson & Hailwood, 2007, p. 37)
The financial literacy definition has since been further adopted and extended with the aim to
incorporate a more detailed description of what it means to be financially literate. For
example “enabling people to make informed and confident decisions regarding all aspects of
their budgeting, spending and saving and their use of financial products and services, from
everyday banking through to borrowing, investing and planning for the future”. (RMR, 2003,
p. 1)
It is widely acknowledged that the definitions and measures of financial literacy vary
considerably across researchers and studies. There is especially considerable variation in
what should be included in the definition and measure with certain studies having included
specific knowledge, the ability or skills to apply that knowledge, perceived knowledge, good
financial behavior, or even certain financial experiences. (Fonseca, Mullen, Zamarro, &
Zissimopoulos, 2012, p. 92)
For some the meaning of financial literacy can therefore be as broad as covering an
understanding of economics and how household decisions are affected by economic
conditions and circumstances. For others it may be a term defined much narrower on the
basis of money management such as budgeting, saving, investing and insuring.
(Worthington, 2006, p. 62)
2.1.4. Separation of Financial Literacy from Financial Education
Financial literacy is not to be used synonym with financial education. Financial education can
be defined as the process of building knowledge, skills and attitudes to become financially
literate. (Cohen, 2011, p. 3) Financial education includes the development of skills and
confidence to become more aware of financial risks and opportunities, to make informed
choices and to know where to go for help. (OECD, 2005, p. 13)
Financial education therefore helps people to adopt good money management practices with
respect to earning, spending, saving, borrowing, and investing. Thus, financial education is a
tool to achieve financial literacy. (Cohen & Nelson, 2011, p. 3)
2.1.5. Financial Literacy in This Master Thesis
In this master thesis the term financial literacy refers to financial knowledge in form of a basic
understanding of interest rate compounding, inflation effects and risk diversification. The
focus lies on the financial literacy of members of the general public in Switzerland and its
effects on decision making in regards to borrowing, investing and retirement saving.
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Particular focus is given to financial knowledge of non-expert members of the public and its
effects on financial behavior.
In more concrete terms, financial literacy in this master thesis is defined on the basis of
knowledge about the effects of compounding interest rates on an initial investment, the
impact of inflation and interest on purchasing power of an individual and the relative risk
profiles of two investment opportunities.
2.2.
Relevance of Financial Literacy in Switzerland
Many countries have become increasingly concerned about the financial literacy of their
citizens as a result of dwindling public and private support systems, an ever aging population
and more and more sophisticated and complex financial marketplaces.
Moreover, the financial crisis led to the recognition that lack of financial literacy was
contributing to unwise financial decisions and that those decisions had tremendous negative
spill-over effects. (OECD, 2009A, p. 9) Due to the reasons above, financial literacy is now
considered among policy makers as an important pillar for the economic and financial
stability of a country.
Financial literacy thereby has its relevance at several levels. It has major implications for the
welfare of individuals and families as it impacts the management of their financial affairs. It
also effects the behavior of financial institutions and consequently impacts financial stability.
Finally, it influences the allocation of resources in the real economy and therefore the longerterm potential growth rate of an economy. (Widdowson & Hailwood, 2007, p. 2)
The following key trends in the economic and financial landscape have resulted in financial
literacy becoming highly important for policy makers in Switzerland: (cf. OECD, 2012A, p. 7)
 Shift of risk and responsibility to individuals
 Increased supply and easier access to an ever increasing range of financial products
 Raising demand for consumer protection and more extensive regulation
All three items are interlinked and any policy decision has to be judged with a comprehensive
view on all of them. Furthermore, an astute policymaker does not just have to know the level
of financial literacy among citizens but also its impact on financial behavior in order to be
able to judge potential regulatory measures on their effectiveness.
The three key trends causing financial literacy to become even more important for individuals
and regulators in future are analyzed in more detail in the following three sub-sections.
2.2.1. Shift of Risk and Responsibility to Individuals
In recent years a trend of shifting various risks away from the government and employers to
individuals has unveiled. Especially in the field of retirement benefits are individuals
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increasingly shouldering investment risks which were previously covered by the public or by
employers.
Public Pension (First Pillar) Ensures Relatively Small Fraction of Retirement Income
The Swiss retirement system is heavily based on occupational and individual savings and
benefits as compared to many other countries which systems are more geared towards state
or public based retirement benefits.
Exhibit 1 highlights the fact that the Swiss Federal Old-Age and Survivors’ Insurance (AHV)
will only provide benefits to insured individuals which constitute a small fraction of their last
salary. Switzerland ranks at the lower end compared to other OECD countries and the OECD
average. Especially, people with high active labor income levels depend heavily on
occupational and individual savings in order to be able to secure a constant standard of living
also during retirement.
Exhibit 1: Gross Replacement Rates of Retirement Income from Public Pension
Gross Replacement Rates of Public Pension in 2008 (in %)
100.0
80.0
60.0
40.0
20.0
0.0
0.5 Median
1 Median
1.5 Median
Source: OECD, 2011B
The pay-as-you-go systems around the world but also in Switzerland are facing serious
challenges such as demographic shifts, adverse developments in dependency ratios and
lower population and economic growth. (OECD, 2005, p. 12) Together with the experienced
difficulties in finding a common ground for structural reforms, it can be expected that the
increasing importance of individual retirement savings may gain even more momentum in
future.7
However, making citizens more reliant on the second and third retirement system pillars
results not just in reduced government expenses but also in more risks being carried by
individuals and more responsibilities being transferred to them. It seems paramount that
individuals are in possession of adequate financial knowledge in order to be able to cope
with those increased risks and responsibilities.
7
The insufficient benefits provided by the first pillar to many individuals in Switzerland is also highlighted by the development of supplementary
payments (Ergänzungsleistungen). Supplementary payments kick in when regular public pension installments are not ensuring minimum
coverage. The number of people in Switzerland collecting such benefits increased from 137’700 in 2000 to 168’200 in 2010. (BSV, 2011A)
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Beside the overall structural shift in the public retirement system there are also developments
within the occupational saving system which lead to more risks being carried by citizens
rather than institutions.
From Defined Benefit Plans (DBP) to Defined Contribution Plans (DCP)
One major shift of risk to individuals is tacitly happening with a trend of occupational
retirement systems from defined benefit plans (DBP) to defined contribution plans (DCP).
Exhibit 2 highlights that at year end 2010 there were only 211 pension funds left which
promised insured individuals pre-defined benefits for their retirement. Pension funds using
DCP shift the investment risk partially indirectly to insured employees and result in higher
uncertainty in terms of the size of retirement benefits with employees.8
Exhibit 2: Development of Occupational Retirement System – DBP vs. DCP
Number of Pension Funds
Number of Insured Employees
4'000'000
3'000
2'500
3'500'000
278
247
2'000
234
211
494'259
488'331
472'451
2'910'841
3'157'725
3'155'009
3'223'594
2007
2008
2009
2010
634'730
2'500'000
1'500
1'000
3'000'000
2'000'000
2'265
2'188
2'117
2'054
1'500'000
1'000'000
500
500'000
0
2007
2008
Defined Contribution
2009
2010
0
Defined Benefit
Defined Contribution
Defined Benefit
Source: BfS, 2012A
The trend towards DCP is part of a general development of the second pillar towards more
self-determination and a relative reduction in collectivism.9 The stronger focus on
individualism in the second pillar has resulted in an increased complexity within the system
and higher related costs. (Credit Suisse, 2012A, p. 12)
The enlarged room for self-determination of financial decisions in the second pillar comes
with increased risks carried by individuals and poses higher requirements on their financial
knowledge.
Increasing Number of Accounts for Vested Pension Benefits (Freizügigkeitskonten)
The increasing number of vested pension benefits accounts and total balances as shown in
Exhibit 3 also highlight the shift of risk and financial decision responsibility to individuals. 10
8
Adjustments in the accounting standard framework IFRS which is applied by the majority of listed companies in Switzerland is likely to increase
the trend of shifting risks to individuals. Especially, the changes in the international accounting standard IAS 19 Employee Benefits which requires
an immediate recognition of defined benefit costs and abolishes smoothing mechanism (“Korridormethode”) and which will come into effect at the
beginning of 2013 will contribute to the trend towards DBP and risk transfer to individuals.
9
Developments such as the Federal Law on Vested Pension Benefits (Feizügigkeitsgesetz), the promotion for home ownership
(Wohneigentumsförderungsverordnung) and the decree about the Partnership Act (Verordnung über die Umsetzung des Partnerschaftsgesetzes)
are highlighting the development towards self-determination.
10
Vested pension benefits represent claims of individuals who are temporarily not employed, changed employers and were not able to take their
full benefits with them to the new pension fund or individuals who became self-employed (list is not exhaustive).
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Individuals with claims in the form of vested pension benefits are facing financial decisions
on where to place their funds (i.e. with a bank, insurance company or a designated fund
(Auffangeinrichtung)) and how the funds should be invested. As the investment risk with
vested pension benefits is fully carried by the individual an adequate understanding of
financial methodologies is crucial.
Exhibit 3: Private Retirement Savings from Vested Pension Benefits
Vested Pension Benefits Savings (CHF m)
40'000
35'117
30'495
Vested Pension Benefits Accounts
37'649
2'000'000
31'462
1'514'402
30'000
1'500'000
20'000
1'000'000
10'000
500'000
0
1'578'280
1'667'636
1'728'904
0
2007
2008
Banks
Auffangeinrichtung BVG
2009
2010
Insurance Companies
Total
2007
2008
Banks
Auffangeinrichtung BVG
2009
2010
Insurance Companies
Total
Source: BSV, 2011A
Individual Savings (Third Pillar) Get More Important
Saving through individual efforts in the third pillar is significantly less regulated as compared
to saving through the occupational system. This discretion, however, comes with a
responsibility as individuals have to decide on various parameters such as when they want to
start saving, which provider they want to select (i.e. bank or insurance solution) and how the
savings should be invested. These decisions require a basic understanding of financial
matters such as the risk and reward relationship of investments and the effects of
compounding interests and inflation.
Exhibit 4 indicates that the tax incentivized saving in the third pillar enjoys increasing
popularity with Swiss inhabitants who allocated around CHF 39bn of savings with banks at
year end 2010 (excl. amounts invested in securities which the banks only manage on behalf
of savers).
Financial literacy as “key life skill” is of high importance while considering the increased risk
and financial decision power with individuals. As the developments within the retirement
system in Switzerland highlight, citizens have to understand what retirement benefits they
can expect from the public system, the occupational pension fund and what amount of
savings they have to accumulate on their own.
Especially the third pillar requires a basic financial understanding which should enable
people to make sound financial decisions. Data from BfS, 2012B, states that the life
expectancy with the age 65 in Switzerland has increased for women from 19.8 years in 1991
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Financial Literacy and Financial Behavior in Switzerland
to 22.2 years in 2010 and for men from 15.6 to 19.8 accordingly. Individuals have to ensure
that they have adequate savings for the longer period they can expect to spend in retirement.
Exhibit 4: Private Retirement Savings from Pillar 3a
Retirement Savings Accounts Pillar 3a
Retirement Savings Pillar 3a - Banks only
(CHF bn)
2'000'000
1'479'917 1'588'006
1'317'512 1'411'483
1'372'685 1'416'032
1'500'000
1'270'488 1'326'645
50
1'000'000
30
38.70
40
29.07
31.22
34.14
2007
2008
2009
20
500'000
10
0
0
2007
2008
No. of Bank Accounts
2009
2010
No. of Insurance Policies
2010
Retirement Savings Pillar 3a
Source: BSV, 2011B
2.2.2. Increased Supply of and Access to Wide Range of Financial Products
The recent years have resulted in financial intermediaries but also consumer goods
producers and retailers in Switzerland to become more innovative in developing and offering
consumption financing solutions. Many retailers have introduced “buy now pay later” offers
for items such as living accessories which entail financial risks and financial burdens that are
hard to assess for retail customers. Such payment offers require higher financial literacy
levels from consumers.
In 2008, according to BfS, 2012C, more than 10% of the population was already living in
households with at least one vehicle leasing contract and 2.5% of the population were buying
living accessories such as furniture or household devices on credit.11
Private Indebtedness and Insolvencies
Private indebtedness in form of personal credits (excl. mortgages) is less distinct in
Switzerland while compared to average indebtedness in Europe. BfS, 2012C, refers to data
from the EU-SILC survey which shows that in 2008 only 18% of people in Switzerland were
living in households which had outstanding non-mortgage debt as compared to the European
average of 28%.
However, in 2008, according to BfS, 2012C, around 14% of the population in Switzerland
was living in a household with at least one consumption credit and half of those households
accumulated total liabilities above CHF 10’000. BfS, 2012C, also shows that in 2008 8% of
the population in Switzerland was leaving in a household with at least one bank or post
11
BfS raised household data in compliance with the European Union Statistics on Income and Living Conditions (EU-SILC) 2008 module which
allows a consistent comparison of data on topics such as indebtedness among countries.
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account overdraft. Overdrafts and unpaid credit card bills were thereby especially common in
the younger population.12
Bank and post account overdrafts and credit card debt can be considered as more expensive
debt compared to consumption credits. It may well be that many debtors lack financial
knowledge to realize that fact and therefore make excessive use of credits which are
conveniently accessible.13
Exhibit 5 shows the share of the population in Switzerland living with financial stress and the
numbers of private insolvencies. It can be assumed that a private person who declared
insolvency was likely not in possession of adequate financial knowledge to make humble
financial decisions.
Exhibit 5: Private Indebtedness and Insolvencies in Switzerland
Proportion of People Leaving in Households
with Financial Stress in 2008 by Age
12%
10%
8%
6%
4%
2%
0%
10%
9%
7%
Number of Private Insolvencies
7'000
8%
7%
2%
1%
2%
1%
6'007
5'691
5'719
5'748
5'000
5%
2%
6'140
6'000
2%
0%
2%
4'000
3'271
3'000
2'000
1'000
At Least One Account Overdraft
At Least One Open Credit Card Bill in Last Three Months
0
2007
2008
2009
2010
2011
6.2012
Source: BfS, 2012C / Creditreform, 2012
Structured Products Held by Private Investors
Structured products are financial assets characterized through various elemental
components which are combined to generate a specific risk-return profile. (Blümke, 2009, p.
7) Those risk-return profiles may be considerably more difficult to understand for retail
investors compared to plain financial products such as stocks or bonds and therefore also
require higher financial literacy.
Maybe due to those advanced requirements in terms of financial knowledge, the Swiss
Structured Products Association (SSPA), 2012, p. 8, estimated that more than half of the
structured products held by private clients (refer to Exhibit 6) were bought by professional
wealth managers on their behalf. However, the access to structured products has been
12
Streuli, 2007, used data from an internet survey of 500 young adults aged 18-24 years conducted by the LINK institute in German-speaking
Switzerland and found that 38% of respondents had formal (e.g. credit institutions, overdue bills and leasing obligations) and/or informal (e.g.
families) monetary liabilities. Around one tenth of the respondents reported debt of above CHF 2’000 and with every one in seven respondents
debt outweighted monthly income.
The financial problems of the younger population is also highlighted by the over-representation in debt enforcement statistics. In the city of Zurich,
for instance, approx. 30% of enforcements concern citizens aged 31 years and younger. (Konferenz der Stadtammänner von Zurich, 2012, p. 7)
13
Numbers published by the SNB in 2012 show that the number of credit card transactions in Switzerland increased from 78.2m in 2007 to
104.3m in 2011. (SNB, 2012) This hike indicates that retail customers have found it easier to access financial products such as credit cards and
have used them more heavily in their daily life.
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facilitated through technological developments as well as increased advertising and any retail
investor who intends to can easily purchase structured products.14
Product complexity has also increased with the type of mortgages banks sell to its
customers. Credit Suisse, 2012B, p. 7, estimates that around 40% of Swiss households
currently own their homes what compares to 35% in 2000. In recent years, banks have been
increasingly innovative with new mortgage solutions such as LIBOR linked mortgages.15
The advancements around the developments of new and more complex financial products
highlight the importance of financial knowledge among individuals who are exposed to those
new constructions.
Exhibit 6: Development and Distribution of Structured Products
Structured Products in Client Portfolios of
Swiss Banks (Yearly Average CHF bn)
Structured Products and Retail Investors
400
7%
5%
300
34%
173
175
200
31
21
100
133
102
133
120
117
15
14
78
78
12
71
54%
0
2007
2008
2009
Privat clients Commercial clients
2010
2011
Institutional clients
Current Str. Prod. Holder
Never Held Str. Prod.
Former Str. Prod. Holder
Does Not Know Str. Prod.
Source: SNB, 2012 / Wilding et al., 2010
2.2.3. Raising Demand for Consumer Protection and More Extensive Regulation
The OECD recognized in its “Strategic Response to the Financial and Economic Crisis” in
2009 the need to enhance the consumer protection regulatory framework and financial
education tools that are aimed at protecting and better informing financial customers in their
interactions with financial services providers.
The OECD thereby emphasized the need for better regulatory standards and international
code of conducts on marketing financial services. Financial literacy is considered as a
necessary complement to (rather than a substitute for) a sound framework for financial
market regulation and prudential supervision. (OECD, 2009B, p. 3)
Being aware of the importance of adequate regulatory frameworks in order to prevent future
financial crisis from happening, policy responses have been underway in many countries.
However, those policy actions and reforms do too seldom include measures focused on
14
A representative survey of 1’994 individuals aged 18-74 years conducted by the University of Zurich in 2010 showed that 7% of the respondents
held at least one structured product in their portfolio (cf. Exhibit 6). Furthermore, 66% of the respondents reported to know structured products or a
category thereof. (Wilding, Volkart, Affolter, & Lautenschlager, 2010, p. 5)
15
The State Bank of Zurich (ZKB), for instance, mentioned in its annual report 2011 (p. 93) a strong customer demand of LIBOR mortgages and
reported that LIBOR mortgages accounted for 15% of its mortgage portfolio by year end 2011. This number is also in line with data published by
Comparis in 2012 which suggests a share of LIBOR mortgages which went up to 16% in 2011.
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financial education. It seems as if the positive externalities from better financially educated
citizens in form of contributions to more efficient, transparent and competitive practices by
financial institutes are not fully believed in. Nevertheless, the OECD highlighted in 2009 that
better educated citizens can also implicitly help in monitoring markets through their own
decisions, and therefore complement prudential supervision. (OECD, 2009B, p. 8)
Consumer Protection Measures in Switzerland
FINMA, the Swiss Financial Market Supervisory Authority, has announced on February 24,
2012 a package of measures to strengthen client protection. On the first page of the press
release, FINMA refers to the “asymmetrical power relationship between financial services
providers and clients” and proposes clear rules of business conduct for financial services
providers and better product documentation.16
At the core of the proposed new rules of business conduct for financial services providers is
an obligation for banks, insurers and portfolio managers to “inform all clients about the
content of a service and the characteristics of financial products, and to warn them about the
risks involved”. FINMA elaborates further that “clients should in future be clearly informed
about all the costs associated with a service or the purchase of a product”. (FINMA, 2012B,
p. 1)
In more concrete terms, FINMA obliges providers of standardized financial products such as
shares, bonds and structured products to draw up prospectus which “must contain all the key
details of the product and the provider and ensure transparency concerning the risks
associated with buying the product”. In anticipation of the low financial knowledge of retail
clients, FINMA calls for brief, roughly two to three-page product descriptions along the lines
of the simplified prospectuses produced for securities funds. (FINMA, 2012B, p. 2)
The implementation of those proposed measures requires a financial services act which in
reality may take years until it is through the Swiss parliament. FINMA also supports a further
strengthening of the customer rights in the civil law, which would allow retail customers to
more effectively enforce their claims, and which may be introduced more quickly.
Increasing transparency is only of value for retail customers who are in a position to
understand the additional information provisions. It is critical for financial regulation and
customer protection laws based on transparency that adequate financial knowledge is
available with retail customers.
16
There have also been actions undertaken in regards to the Federal Act on Collective Investment Schemes (Kollektivanlagengesetz (KAG)). On
March 2, 2012 the Federal Council (Bundesrat) adopted the dispatch on the partial revision of the KAG which is supposed to enter into force in
2013 following its parliamentary consultation. Adjustments to the act are necessary in order to safeguard the compliance of Swiss law with
international regulatory standards. Of importance from a financial literacy point of view are the new distribution concept and the definition of
qualified investors. (EFD, 2011, p. 6) Currently wealthy individuals (i.e. individuals with financial assets of at least CHF 2 Mio.) are considered
qualified investors. The same holds true for all individuals who entered into written asset management agreements with specific asset managers.
Under the revised KAG, wealthy individuals shall only be deemed qualified investors upon their specific declaration (“opting-in”). Furthermore, the
fact of a written asset management agreement shall no longer turn a private, non-qualified investor into a qualified investor. (Bundesblatt, 2012,
12.037) The adjustments therefore represent a step towards stronger retail investor protection.
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Consumer Protection Measures in the European Union
The main regulatory piece in relation to consumer protection for wholesale investors in the
EU is the MiFID framework. The EU introduced MiFID with the key objective to foster
harmonization as well as to boost innovation and competition across financial markets of
member states. It aims at building an integrated structure for a pan-European market for
investment services.17 (Skinner, 2007, p. 123)
Article 19 of MiFID includes rules about business conduct of investment firms with clients and
is of importance with regards to financial literacy. Investment firms are asked to provide
clients or potential clients with various kind of information including appropriate guidance on
and warning of the risks associated with investments in a comprehensible form. (MiFID Art.
19.3) Furthermore, investment firms are obliged to classify their clients and to carry out a
suitability test for each client before entering into business. (MiFID Art.19.4)
MiFID thereby requires firms to categorize clients as “eligible counterparties”, professional
clients or retail clients who are given increasing levels of protection. Clear procedures must
be in place to categorize clients and assess their suitability for each type of investment
product. However, the suitability of any investment advice or recommended financial
transaction must still be verified before being given.
Such far-reaching requirements concerning bank’s duties are not within the scope of existing
Swiss regulations. However, many measures proposed by FINMA, as previously highlighted
in the policy measures in Switzerland, have been formulated with view on the existing MiFID
rules. In its distribution report, for instance, FINMA refers to topics such as customer
segmentation and categorization as well as pre-contract information obligations. (cf. KPMG,
2011)
Consumer Protection Measures in the United States
In the US, policymakers reacted to the financial crisis with the implementation of the DoddFrank Wall Street Reform and Consumer Protection Act.18 The stated objective of the act
was thereby defined as:
“To promote the financial stability of the United States by improving accountability and
transparency in the financial system, to end "too big to fail", to protect the American taxpayer
by ending bailouts, to protect consumers from abusive financial services practices, and for
other purposes.” (OpenCongress, 2012)
17
MiFID, the European Commission’s Markets in Financial Instruments Directive (EU Directive 2004/39/EC), was implemented on November 1,
2007, replacing the Investment Services Directive. The directive applies to all 27 EU member states as well as Iceland, Norway and Liechtenstein.
MiFID must either be incorporated into local law or into the rules of the local regulatory handbook, depending on how financial regulation is applied
in a respective state.
18
The Dodd-Frank Act was signed into law in July 2010 and spans over 2’300 pages and affects almost every aspect of the US financial services
industry. For a thorough analysis of the Act please refer to Skadden, 2010.
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Title IX of the act reads “Investor Protections and Improvements to the Regulation of
Securities” and includes subtitle A “Increasing Investor Protection” which contains provisions
that authorize the Securities and Exchange Commission (SEC) to issue "point-of-sale
disclosure" rules when retail investors purchase investment products or services. These
disclosures include concise information on costs, risks, and conflicts of interest. (Skadden,
2010, p. 82) Furthermore, in determining the disclosure rules, the act authorizes the SEC to
do "investor testing" and rely on experts to study financial literacy among retail investors.
(Skadden, 2010, p. 82)
Title X named “Bureau of Consumer Financial Protection” establishes the Bureau of
Consumer Financial Protection which also incorporates the Office of Financial Education
which will develop programs to improve consumers’ financial literacy and familiarity with
consumer financial products. (Skadden, 2010, p. 176)
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3.
Relevant Literature
In recent years a significant number of empirical studies related to financial literacy have
been published. The following literature review aims to capture the relevant publications in a
structured way.19
Firstly, there will be a section referring to financial literacy in regards to demographic,
economic and financial characteristics. Secondly, there will be sections targeting the
relationship between financial literacy and financial behavior in regards to private
indebtedness, retirement planning and financial investment.
The bulk of the existing literature refers to studies outside Switzerland. A separate analysis
on research performed in Switzerland is included at the end of the literature review only
followed by some final remarks.
3.1.
Demographic, Economic and Financial Characteristics
The report “Improving Financial Literacy: Analysis of Issues and Policies” released by the
OECD in 2005 can be considered as the first major report about financial literacy and
financial education on an interntaional level. The report identified and analyzed exisiting
studies in the field of financial literacy in twelf member countries and concluded a low level of
financial understanding among consumers, especially with minorities, those less-educated
and those with low income.
3.1.1. Gender
There is mounting empirical evidence of a gender gap in financial literacy with significantly
lower financial literacy levels of women. Furthermore, women are more probable to state that
they do not know the answer compared to men. (Lusardi & Mitchell, 2011B) Multiple studies
report a gender gap with young20 and old21 people which exists across interest, inflation and
risk diversification knowledge.
19
Table A20 includes an overview of major surveys which were conducted in designated developed countries and through which financial literacy
data has been collected in the past. The majority of those surveys are performed repetitively and some of them are of longitudinal nature.
20
There are a number of studies reporting on the gender gap among the youth and students. An early study from Chen & Volpe, 1998, comprised
of 924 students from 13 different US campuses and results showed men, students with higher class rank, and business majors having higher
financial literacy scores. Later, Chen & Volpe, 2002, surveyed 1’800 students in 11 universities in the US and concluded that women have lower
financial literacy levels than men even after controlling for items such as class rank, work experience and age. They argue that women are more
interested in subjects such as English and Humanity and disfavor subjects closely related to numeracy. Other arguments from Ford & Kent, 2010,
show that women possess three sensibilities regarding financial markets and suggest that those sensibilities lessen women’s interest in financial
literacy. They name the sensibilites a strong feeling of intimidation, a lack of interest, and low situational awareness. Finally, Lusardi, Mitchell, &
Curto, 2010, analyzed data from a nationally representative sample of 7’417 respondents aged 23–28 years in the US between 2007 and 2008
and found a statistically significant gender gap. Contradictory results come from India where Koshal, Gupta, Goyal, & Choudhary, 2008, surveyed
494 MBA students and concluded that gender differences do not have an influence on economic literacy defined through an index consisting of
basic knowledge about economics, savings and investments. Furthermore, Lalonde & Schmidt, 2011, surveyed 278 college students of a liberal
arts college in the Northeastern United States and found that class rank, as indicated by its impact on the number of credit cards, and motivation
as measured by interest in personal finance, are the most significant predictors of financial literacy rather than gender.
21
There is a considerable amount of research available on the gender gap with the older population. Lusardi, Mitchell, & Curto, 2009, analyzed
data from 1’332 US HRS respondents over the age of 55 years and reported lower financial knowledge of women. Their findings are consistent
with Lusardi & Tufano, 2009, who found that gender differences are large among the young and continue to be strong among the old. Clark, Allen,
& Morrill, 2010, surveyed 1’501 workers nearing retirement in the US and found men performing better on questions about general financial
knowledge. Lusardi, Mitchell, & Curto, 2012, used a special-purpose module implemented in the HRS and confirmed lower financial knowledge of
female with a sample of US respondents aged 55 years or older.
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Lower financial literacy levels for women in the adult population have been reported by
reseachers in many geographies.22 Univariate statistics in empirical studies in Germany
(Müller & Weber, 2010 and Bucher & Lusardi, 2011), Italy (Monticone, 2010 and Fornero &
Monticone, 2011), the Netherlands (Alessie, Van Rooij, & Lusardi, 2011 and Van Rooij,
Lusardi, & Alessie, 2011), Sweden (Almenberg & Säve-Söderbergh, 2011) and the US (e.g.
Lusardi, 2008 (A+B), Bumcrot, Lin, & Lusardi, 2011, Lusardi & Mitchell, 2011A and Lusardi,
2012) all confirmed the existance of a gender gap.23
A closer examination of the gender gap in financial knowledge has been performed by
Fonseca et al., 2012. They confirmed statistically significant lower finanical literacy levels
(even after controlling for socio-demographic characteristics) of women while studying Rand
American Life Panel data of 2’500 respondents aged 18 years and older. They argue that a
possible mechanism through which gender differences are produced is household
specification where men is specializing in making household financial decisions thereby
acquiring financial knowledge while women specialize in other household functions.
Furthermore, they reported that men benefit more from education than women.
Another explanation for the gender gap is provided by Lusardi & Mitchell, 2011B, who
contemplated from the fact that there is no gender gap in Russia and East Germany
compared to West Germany that women have more difficulties in catching up with economic
and financial market developments than men do.
3.1.2. Age
Results from empirical research on financial literacy and age indicate an overall humpshaped pattern with low financial literacy levels among the young and the old. The “don’t
know/ refusal” answers usually follow a U-shaped pattern with young and old respondents
mostly evading to answer or confirming not to know.24
A distinct overall hump-shaped pattern between age and financial literacy and a general Ushaped pattern for “don’t know/ refusal” responses have been reported in univariate statistics
by researchers such as Almenberg & Säve-Söderbergh, 2011, for Sweden, Crossan, Feslier,
& Hurnard, 2011, for New Zealand, Bucher & Lusardi, 2011, for Germany, Fornero &
Monticone, 2011, for Italy and Lusardi & Mitchell, 2011A, for the US.
Agarwal, Driscoll, Gabaix, & Laibson, 2009, study lifecycle patterns in financial mistakes
using a proprietary database that measures ten different types of credit behavior. They found
22
For empirical studies confirming the gender gap outside Europe and the US, refer to the following publications: Australia (Worthington, 2006 and
Gallery, Gallery, Brown, Furneaux, & Palm, 2011), Japan (Sekita, 2011), UAE (Al-Tamimi & Bin Kalli, 2009), Russia (Klapper & Panos, 2011 and
Klapper, Lusardi, & Panos, 2012), Chile (Hastings & Mitchell, 2011), and New Zealand (Crossan et al., 2011).
23
A systematic and persistent difference in financial literacy between men and women is also highlighted in a paper by Bucher, Lusardi, Alessie, &
Von Rooij, 2012 which is fully dedicated to the low financial literacy levels of women across several countries.
24
Many studies are cross-sectional and are therefore not able to distinguish between age and cohort effects. The majority of those studies
assume that both effects exist (cf. Bucher & Lusardi, 2011 or Sekita, 2011).
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that financial mistakes follow a U-shaped pattern with age and the cost-minimizing
performance occurring around the age of 53 years.25
Financial Literacy Among The Young
Lusardi, Mitchell, & Curto, 2010, published a paper about financial literacy among the young
in the US in which they used data from the National Longitudinal Survey of Youth (NLSY)
which covered 7’417 respondents aged 23–28 years and reported that financial knowledge
among young adults is poor with fewer than one-third possessing basic knowledge of interest
rates, inflation and risk diversification. The results are also supported by findings of Mandell,
2008, who reported the results of the sixth biennial US National Jump$tart Coalition Survey
of High School Seniors and College Students in 2008. He emphasizes that financial literacy
levels of US high school students have fallen to its lowest levels ever since the survey was
performed first ten years ago.
Financial Literacy Among The Old
Lusardi, Mitchell, & Curto, 2009, analyzed data from the 2008 US HRS of people aged 55
years and older and found that people lack even a rudimentary understanding of stock and
bond prices, risk diversification, portfolio choice, and investment fees. A later study carried
out again by Lusardi, Mitchell, & Curto, 2012, confirms the results that many elderly people
lack a basic grasp of asset pricing, risk diversification, portfolio choice, and investment fees.
However, some studies (cf. Sekita, 2011, Almenberg & Säve-Söderbergh, 2011, Crossan et
al., 2011, and Bucher & Lusardi, 2011) found that inflation knowledge is better among
respondents above the age of 65 years as compared to respondents below 35 years. They
argue that elderly people can still remember the high inflation years in the 1970s and 1980s.
3.1.3. Education
There is an overwhelmingly positive and often monotonic relationship between level of
general education and financial literacy levels across a broad range of empirical studies.
Higher education thereby comes with better financial knowledge irrespective if defined in
relation to interest, inflation or risk diversification. Furthermore, the share of answer of “don’t
know/ refusal” is decreasing in education. However, research on specific financial education,
as compared to general education, and its impact on financial literacy and saving rates
shows controversial findings.26
Almenberg & Säve-Söderbergh, 2011, used a sample of 1‘300 adults aged 18-79 years in
Sweden and confirmed a strong positive relationship between financial literacy and
education. (cf. Sekita, 2011, Crossan et al., 2011, Bucher & Lusardi, 2011, Fornero &
25
Tannahill, 2012, emphasizes in an article in the Journal of Financial Service Professionals the changing financial literacy and decision making
abilities through the lifetime of financial services clients. He emphasizes in alignment with the findings of Agarwal et al., 2009, that financial
services providers should adjust their communication and practices to reflect on those changes.
26
Manz, 2011, discusses in an article the relationship between education and financial behavior and questions its causality.
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Monticone, 2011, Lusardi & Mitchell, 2011A, Alessie et al., 2011, and Klapper & Panos,
2011) They also investigated the college major of the respondents and concluded that
respondents with quantiative majors peformed best.27
School-Based Financial Education
Mainly in the US there has been some research performed which aimed at measuring the
impact of personal finance courses in school on financial literacy after passing of some time.
Bernheim, Garrett, & Maki, 2001, analyzed the effects of mandated consumer education and
household finance policies in US high schools from the 1960s to the 1980s on financial
decision making. They conducted 2’000 surveys of a nationally representative sample of
respondents between the age of 30 and 49 years in 1995 who graduated between 1964 and
1983 and found that mandated finance instructions ultimately elevate the rates at which
individuals save and accumulate wealth during their adult lives.28 However, survey results
indicated that many respondents could not remember whether or not they had taken a
course in money management or personal finance in high school.
On the contrary, Cole & Shastry, 2008, use US census, a survey conducted by the US
government, with 14 million observations and concluded that financial literacy education
programs (in contrast to education in general), mandated by state governments, did not have
an effect on individual savings decisions. They ask for rigorous evaluations of those financial
literacy education programs to measure their effectiveness.
Furthermore, Mandell, 2008, who reported the results of the sixth biennial US National
Jump$tart Coalition Survey of US High School Seniors and College Students in 2008 found
that high school financial education had little if any impact on savings. Mandell, 2008, argued
that some of what is learned in high school financial education classes may lie dormant in the
minds of the students until much later in life when they have sufficient resources to utilize
27
There have been multiple studies focused on financial literacy with university students. Murphy, 2005, analyzed 277 survey responses of US
undergraduate students attending a predominantly black institution and concluded that finance-related classes raised students’ financial literacy.
He also found gender and age differences which, however, were not statistically significant. Mandell, 2008, used the Jump$tart Survey of 2008 of
1’030 college students aged 18-23 years and analyzed financial literacy by majors. He found that Science, Social Science and Engineering
students performed especially well and better than Business students. Furthermore, the study showed that parents’ educational background is not
likely to be associated with a child’s financial literacy. Peng, Bartholomae, Fox, & Cravener, 2007, conducted research on 12’000 graduates of a
large Midwestern university in the US in 2005 and results pointed out that experience from the participation in financial education classes in
university improves the score of investment knowledge tests. Koshal et al., 2008, found that differences between Indian MBA students’ grades
does not have a statistically significant impact on economic literacy scores. Lalonde & Schmidt, 2011, questioned college students in a liberal arts
college in the Northeastern United States and found that class rank, as indicated by its impact on the number of credit cards, and motivation, as
measured by interest in personal finance, are the most significant predictors of financial literacy. Chinen & Endo, 2012, surveyed 361
undergraduate students at a public university in Sacramento, California, and investigated items measuring attitudes toward financial education
while in high school. They found a positive relationship between basic structure of finance and economy as part of financial education
requirements for high school students and scores on financial literacy and an inverse relationship for students preferring applied level finance (e.g.
asset management). Borodich, Deplazes, Kardash, & Kovzik, 2010, examined levels of financial literacy across 4’157 high school and university
students in the US, Belarus and Japan and found that Japanese students, overall, outperformed all others in the sample regardless of coursework
in personal finance or grade level. Sabri, 2011, provides an overview of financial literacy studies on college students in the US between 19872010.
28
This results are in line with those of the National Endowment for Financial Education, 2009, who surveyed nearly 16’000 college students on
their financial knowledge and financial behavior. They reconciled survey participants to the state in which they attended high school and
segregated the states into six categories based on the strictness of their financial education requirements. They reported that respondents who
went to high school in a state with mandated financial education generally had better financial literacy scores, as well as “more favorable” financial
behavior in regards to budgeting and use of credit.
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what they have learned. Following this logic, a course in personal finance may not have an
instant impact on financial literacy until the knowledge is actually applied.29
In a later study, Mandell & Klein, 2009, analyzed a sample of 400 students in the US of which
half did take a personal financial management course one to four years earlier while the
other half did not. The results strikingly revealed that those who took the course were no
more financially literate than those who had not. Furthermore, those who took the course did
not evaluate themselves to be more savings-oriented and did not appear to have better
financial behavior.
Employer-Based Financial Education
Bernheim & Garrett, 2003, used data from the 1994 US national survey of 2’055 households
with respondents aged 30-48 years to explore the effects of retirement seminars on
household retirement and savings behavior. They found that employees of firms that offer
financial education have significantly higher levels of 401(k) participation rates, contributions,
and balances as well as higher overall (including savings outside of retirement accounts)
saving rates.
However, Gale & Levine, 2010, examined existing literature about financial education,
financial literacy and savings in regards to employer-based, school-based, credit counseling,
or community-based education efforts in the US. They argue that it is difficult to control for all
of the factors determining saving and it may be that firms with better benefits attract workers
with longer planning horizons who are living in more stable financial circumstances. In such a
case the impact of employer-based financial education may be overstated.
3.1.4. Labor Market Status
Empirical research indicates that low levels of financial literacy are associated with people
who are non-employed or unemployed, if shown separately. (cf. Almenberg & SäveSöderbergh, 2011, Sekita, 2011, Bucher & Lusardi, 2011, Fornero & Monticone, 2011, and
Lusardi & Mitchell, 2011A) These results may in univariate statistics, however, be driven by
the fact that lower educated individuals and women are over-represented with non-employed
people.30 However, Lusardi & Mitchell, 2011B, argue that the difference may also stem from
financial education programs regularly being offered in the workplace in the US or from
learning from colleagues.
29
Lusardi & Mitchell, 2007, stress in their concluding remarks in an article about financial literacy and retirement preparedness that education
alone may not be sufficient to enhance retirement savings as findings showed that people had difficulty following through on planned actions. They
argue that it is essential to provide individuals with tools to change their behaviors, rather than simply delivering financial education.
Mandell & Klein, 2007, argue in an article about motivation and financial literacy on the basis of data from the Jump$tart Coalition Survey of US
High School Seniors that motivation to learn or retain financial skills can explain differences in financial literacy. They argue that the equal or low
financial literacy scores of those who have and have not taken relevant coursework can be explained by the fact that many students just don’t care
about their personal finances.
30
Alessie et al., 2011, highlight the fact that the non-employed cohort includes those who are unemployed but looking for a job, those who are not
able to work and receive a disability benefit, and housekeepers.
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Multiple studies report highest financial literacy levels with self-employed people. (cf.
Almenberg & Säve-Söderbergh, 2011, Bucher & Lusardi, 2011, Fornero & Monticone, 2011,
and Lusardi & Mitchell, 2011A) As mentioned by Fornero & Monticone, 2011, self-employed
individuals include small business owners and “liberal professions” such as lawyers who are
assumed to be wealthier and therefore also more accustomed to managing their personal
and business finances.31
Studies such as Lusardi & Mitchell, 2011A, who used data from 1’500 Americans, reported
that inflation knowledge is highest with retired respondents. (cf. Sekita, 2011, for Japan)
Congruent with the conclusion about elderly people they argue that retired people know more
about inflation as they were exposed to high inflation periods in the 1970s and 1980s
whereas the younger generation have not had such experience.
3.1.5. Financial Income
Multiple studies confirm a positive relationship between financial income and financial literacy
scores. (cf. Tennant, Wright, & Jackson, 2009, Almenberg & Säve-Söderbergh, 2011,
Lusardi & Mitchell, 2008, Delavande, Rohwedder, & Willis, 2008) However, the relationship
between financial income and financial literacy in an univariate analysis may be driven by the
underlying education of people with better educated people also earning more.
Bucher & Lusardi, 2011, used a sample of 1’117 individuals from the SAVE survey for
Germany and concluded that financial literacy is particularly low among East Germans with
low income. Furthermore, they found that in particular, individuals at the top of the income
distribution are more probable to have calculated saving and investment needs than
individuals at the bottom.
3.1.6. Financial Wealth
As with income, empirical studies such as Fonseca et al., 2012, reported that wealthier
individuals have higher levels of financial literacy.
Van Rooij, Lusardi, & Alessie, 2012, used a sample of 1’091 households in the Netherlands
and found a strong positive association between financial literacy and net worth, even after
controlling for many determinants of wealth. They provide evidence that the positive
relationship between financial literacy and wealth accumulation might be supported by the
fact that financially knowledgeable individuals are more likely to invest in stocks and have a
higher propensity to plan for retirement.
A positive relationship was also found by McArdle, Smith, & Willis, 2009, who analyzed a
subset of surveys from the US HRS and concluded that numeracy, as a broader definition of
31
For example in Switzerland, self-employed individuals are not covered by the occupational pension system. (Art. 3 BVG) As they have to
accommodate for retirement planning by themselves, they can be assumed to have more exposure to personal and business finance decisions
than other individuals.
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financial literacy, is by far the strongest predictor of wealth among all cognitive variables in
the US HRS sample. (cf. Smith, McArdle, & Willis, 2010)
Direction of Causality
It may well be that financial literacy is an explanatory variable for financial wealth, however,
one may also argue that financial behavior itself influences financial knowledge (reverse
causality). Financial wealth may thereby be a source of leaning.32
Behrman, Mitchell, Soo, & Bravo, 2010, were aware of unobserved factors such as ability,
intelligence, and motivation that could enhance financial literacy and surveyed 13’054
Chilean respondents aged 24-65 years. They applied an instrumental variable approach and
reported large causal effects of financial literacy on wealth.
Also following an instrumental variable approach, Monticone, 2010, analyzed 3’992
households in Italy and suggested that households with larger financial assets are more
likely to invest in financial knowledge. She concluded that household wealth affects financial
knowledge even after removing wealth endogeneity in the model.
Furthermore, Smith et al., 2010, used data from the US HRS and found a positive
relationship between numerical skills and household wealth. They assume that causality is
running the same way as the effects were also holding for lower wealth quartiles.
On the contrary, Gustman, Steinmeier, & Tabatabai, 2012, used a sample of older US
members of couple households and did not find any relationship between basic cognitive
skills and knowledge of retirement plan characteristics. They identified a positive relationship
between pension wealth and knowledge but argued that causality is likely to run from
pension wealth to pension knowledge.33
3.2.
Retirement Planning and Saving
There is significant empirical evidence that adults with a better financial understanding are
more likely than others to plan for retirement. Financial literacy and its impact on retirement
planning can be seen as a central area of research in the field of financial knowledge.
Studies are often assembled in the form of multivariate regression analysis in order to control
for socio-demographic variables and include instrumental variables to account for reverse
causality omitted varialbe effects.
Empirical evidence shows that improved levels of financial literacy can lead to positive
behavior change in form of retirement planning, enabling people to be more financially
secure in their retirement. Retirement planning is seen as critically important as it is a good
32
Hilgert, Hogarth, & Beverly, 2003, reported that respondents in the 2001 University of Michigan Survey of Consumers mentioned personal
financial experience as the single most important source of financial knowledge.
33
Multiple studies report that financial literacy is closely tied to retirement planning and retirement wealth accumulation (cf. Behrman et al., 2010,
Lusardi, 2008B, Klapper & Panos, 2011 and Fornero & Monticone, 2011).
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proxy for retirement wealth – people who have calculated how much they need to save for
their own retirement reach retirement age with as much as three times the wealth of those
wo did no calculations. (Lusardi & Mitchell, 2011B)
Lusardi & Mitchell, 2011B, concluded while considering financial literacy studies from eight
countries that the financially knowledgeable are more likely to plan for retirement. They
reported that especially the understanding of interest rates and risk diversification matters for
retirement planning.
3.2.1. United States
Lusardi & Mitchell, 2011A, used data from the National Financial Capability Survey of 1’200
representatives of the general adult US population and concluded that people who score
higher on the financial literacy questions are much more likely to plan for retirement, which is
likely to leave them better positioned for old age. In the same direction point the findings of
Clark, Allen, & Morrill, 2010, who surveyed 1’500 US workers nearing retirement and
concluded that many lack basic financial knowledge such as knowledge about company and
national retirement plans and that incorrect understanding negatively affects retirement
plans.
Agnew, Szykman, Utkus, & Young, 2007, surveyed 817 respondents in the US and reported
that financial literacy plays a critical role in improving 401(k) savings behavior through a
reduction in both the proportion of non-joiners in voluntary 401(k) plans and the proportion of
quitters in automatic enrollment plans.
3.2.2. Europe
Positive effects of financial literacy on retirement planning were found by Bucher & Lusardi,
2011, who used a sample of 1’117 individuals from the SAVE survey for Germany and
Almenberg & Säve-Söderbergh, 2011, who surveyed 1’300 Swedish adults aged 18–79
years and concluded that people who report having tried to plan for retirement had higher
levels of financial literacy.
Alessie et al., 2011, present evidence on financial literacy and retirement preparation in the
Netherlands based on two surveys conducted before and after the onset of the financial
crisis. They document that while financial knowledge did not increase from 2005 to 2010, in
2010 significantly more individuals reported to have thought about their retirement. Using
information on financial conditions and financial knowledge of relatives, they found a positive
causal effect of financial literacy on retirement preparation.
Findings in Italy by Fornero & Monticone, 2011, show that financial literacy enhances the
probability of participation in a pension fund, even after controlling for other potential
determinants of pension plan participation, such as risk preferences and the expected social
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security replacement rate and retirement age. They also confirm causality while addressing
potential financial literacy endogeneity.
3.2.3. Other Countries
Sekita, 2011, used data of 5’368 respondents aged 20-69 years in Japan and reported that
financial literacy increased the probability of having a retirement savings plan. On the
contrary, Crossan et al., 2011, questionned 850 individuals of the general adult population of
New Zealand in 2009 and reported that financial literacy is not significantly associated with
planning for retirement. They argue that the result reflect the dominant role of New Zealand’s
universal public pension system in providing retirement income security.
Hastings & Mitchell, 2011, analyzed a nationally representative survey of 14’243 Chileans
with an average age of 50 years and found that both financial literacy and short-run
impatience play important roles in determining retirement saving, even after controlling for
education and income.
3.3.
Investment Behavior
Research indicates that individuals with higher financial literacy levels are better able to
manage their money, participate in the stock market more often and perform better on
portfolio choices. Furthermore, people with advanced financial knowledge are more likely to
accumulate wealth.
3.3.1. Investment Likelihood
Yoong, 2010, used a sample of older American respondents in the Rand American Life
Panel and found that a lack of familiarity with finance can be a meaningful impediment to
financial participation in the form of stock market participation, even after accounting for
background controls including income, social factors, and behavioral proxies. Similar findings
are pointed out by Van Rooij et al., 2012, who used a sample of 1’091 households in the
Netherlands and found that home ownership and investments in stocks, mutual funds and
bonds are much more common among those individuals who score high on financial literacy.
Christelis, Jappelli, & Padura, 2010, used data from the 2004 Survey of Health, Aging and
Retirement in Europe (SHARE) which covered a representative sample of 32’184 individuals
aged 50 years or more in eleven European countries and investigated the relationship
between cognitive abilities (i.e. numerical skills, memory skills and verbal fluency) and stock
market participation. They found evidence that cognitive impairments reduce the propensity
to hold stocks while controlling for socio-economic characteristics such as education.
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3.3.2. Investment Sophistication
Calvet, Campbell, & Sodini, 2007, used data supplied by Statistics Sweden of the entire
population of Sweden (about 9 million residents) and reported that households with greater
financial sophistication (measured by crude proxies) tend to invest more efficiently but also
more aggressively. Similar findings are provided by Banks, O'Dea, & Oldfield, 2010, who
used data from the English Longitudinal Study of Ageing (ELSA) with a representative
sample of the English population aged 50 years and older and found that less numerate
individuals hold systematically different portfolios.
Müller & Weber, 2010, conducted an internet survey on investment fund choice in Germany
and reported a positive influence of financial literacy on the likelihood of investing in low-cost
fund alternatives.
Guiso & Jappelli, 2008, used data from an Unicredit Clients’ Survey which was conducted in
2007 with 1’686 individuals with a checking account (balance at least EUR 10’000) in one of
the banks of the Unicredit Group. They reported that lack of financial literacy is the main
variable explaining lack of portfolio diversification. Also about diversification, Abreu &
Mendes, 2010, analyzed 1’268 questionnaires of Portuguese citizens aged 18 years or older
with at least one bank account and found that investors’ educational levels and their financial
knowledge have a significant impact on diversification or the number of different assets
included in a portfolio.
Hastings & Tejeda-Ashton, 2008, collected 763 questionnaires in Mexico City and found that
financially literate respondents place much higher importance on fees relative to brand name
when selecting mutual funds.
3.4.
Personal Indebtedness
The findings of financial literacy and debt or indebtedness point into the direction that more
financially literate individuals are more likely to opt for less costly mortgages and avoid high
interest payments and superfluous fees. A number of empirical studies focused their
attention on the relationship between indebtedness and financial literacy as well as selfcontrol.
3.4.1. Quality of Debt Contracts
Moore, 2003, surveyed Washington State residents and showed evidence that financial
literacy affects borrowing behavior. For instance, the less literate are more likely to have
costly mortgages and are more often behind schedule in paying off mortgage loans.
Furthermore, Lusardi & Tufano, 2009, analyzed data of 1’000 respondents in the US and
reported that low knowledge about debt is the norm and that low level of financial literacy
among consumer credit users is associated with use of high cost credits.
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3.4.2. Mortgage Debt
Fornero & Monticone, 2011, reported on the basis of data from Italy that among home
owners, households currently having a mortgage proof to have higher financial literacy levels
than those who are not. As explanation they emphasize the learning opportunity of
contracting housing debt.
Gerardi, Goette, & Meier, 2010, analyzed several aspects of financial literacy and cognitive
ability in a survey of subprime mortgage borrowers who took out loans in 2006 and 2007.
They found a large negative correlation between numerical ability and mortgage
delinquencies as well as defaults which is robust after controlling for a broad set of sociodemographic variables.
3.4.3. Self-control
Gathergood, 2012, analyzed 1’234 UK household questionnaires with a positive balance on
at least one consumer credit item and found that UK customers’ lack of self-control and to a
lesser extend also financial illiteracy are positively associated with non-payment of consumer
credit and self-reported excessive financial burdens of debt.
In the same direction point results by Heidhues & Koszegi, 2010, who showed in a formal
analysis that non-sophisticated consumers underestimate their taste for immediate
gratification and therefore result in repaying debt in an ex-ante suboptimal back-loaded
manner more often than they predict or prefer.34
3.5.
Economic and Financial Stability
Financial literacy is important to economic and financial stability as more financial literate
individuals can make better informed decisions and demand higher quality services which
will encourage competition and innovation in the financial marketplace. (OECD, 2012A, p. 8)
Furthermore, financially knowledgeable individuals are more likely to take appropriate steps
to manage transferred risks in a humble way. All together, financially literate individuals lead
to the financial sector becoming more efficient resulting in less costly financial regulation and
supervision being applicable. (OECD, 2012A, p. 8)
Lusardi & Mitchell, 2011C, highlight in an outlook for financial literacy that poor financial
decision making can have substantial costs not just for individuals but also for societies at
large.35 They argue that financial illiteracy undermines the stability of the global financial
system and conclude that financial literacy skills are critical for the economic and social
welfare not only of this generation, but especially of those to come.
34
Hoelzl & Kapteyn, 2011, assemble in an editorial about “Financial Capability” the different perspectives on financial behavior. They emphasize
that financial capability may be approached in an interdisciplinary way from both psychology and economics. In their article they also refer to selfassessment to learn more about one’s risk aversion.
35
For an illustration on how financial illiteracy can cause harm for the whole society, refer to Volpe & Mumaw, 2010, who examined the
relationship between financial literacy and loose lending standards during the US real estate boom of 2001-2006.
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3.5.1. Self-Assessed vs. Actual Financial Literacy
Guiso & Jappelli, 2008, used data from an Unicredit Clients’ Survey and found that a large
number of investors claim to know much more about finance than what is actually the case.
This finding is relevant to assess the impact of recent regulation requiring financial
intermediaries to elicit the degree of investors’ financial sophistication (refer to section
2.2.3.). Investment firms thereby often perform surveys which, however, rather reflect
investors’ optimism, self-confidence and over-confidence instead of financial literacy.
3.6.
Financial Literacy in Switzerland
There is only little known about financial literacy in Switzerland as there is only limited
empirical research available focusing on Switzerland and Switzerland did not participate in
the OECD study in 2005.
The Institute Banking and Finance (IBF) of the Zurich University of Applied Science (ZHAW)
has engaged in the financial literacy topic and established a dedicated education platform on
financial literacy. Their aim is to build fundamental knowledge about financial literacy in
Switzerland and make basic information available in order to enable larger research projects
to be performed.36
Thomas Staeheli and Matthias Zobl completed a project report as part of their studies at the
ZHAW in June 2006 titled: „Erhebung über den Stand der Kenntnisse bezüglich Financial
Literacy in der Schweiz“.37
Staeheli & Zobl, 2008, surveyed 104 individuals through their private network, 108 ZHAW
students, 103 employees of the ZHAW and 45 employees of two Swiss companies. They
raised data about the way in which respondents were handling their money, how
respondents assessed their own financial knowledge, how distinct their real financial
knowledge was and which exposure to financial education (incl. information channels) they
already had.38
Staeheli & Zobl, 2008, created a „Self Evaluation Index“ (SEI) and a „Financial Literacy
Index“ (FLI) in order to compare financial literacy levels with self-assessed financial
knowledge. Their results showed a gender gap in financial literacy scores with women also
answering more frequently “don’t know” compared to men. Their results also indicated a
significant over-confidence in terms of financial knowledge with respondents which is
decreasing in financial literacy levels. Especially weak results were discovered in the areas
36
The IBF of the ZHAW and the independent financial planning company Investor’s Dialogue have founded a joint venture for financial literacy in
2006. Their aim is to optimize resources and conduct common research in the field of financial literacy. Furthermore, the initiative of the ZHAW in
the field of financial literacy aims at fostering information sharing with organizations such as the OECD but also the SNB which has launched their
economic education program “iconomix” in November 2007.
37
The report has been revised in May 2007 for publication purposes.
38
They used a financial literacy study by the Bertelsmann foundation (Leinert, 2004) and Commerzbank (Commerzbank, 2004), both focused on
Germany, for comparative purposes.
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of the relationship between risk and reward of investments but also in the knowledge about
the Swiss retirement system. However, more than 50% of respondents reported to be
interested in learning more about handling their finances.
The financial literacy study by Staeheli and Zobl gives some first insight into the financial
knowledge of the population in Switzerland. However, there has not been any systematic and
representative survey of financial knowledge conducted in Switzerland yet.39
While there have been diverse and heterogeneous initiatives and projects in the area of
financial literacy, Switzerland has not introduced any national strategy in relation to financial
literacy as compared to multiple other OECD countries (cf. Hieber, Probst, & Wüthrich, 2011,
and section 6.3.1. about efforts undertaken in the field of financial literacy in Switzerland).
3.7.
Final Remarks
The basic OECD report on “Improving Financial Literacy, Analysis of Issues and Policies”
issued in 2005 concludes a low level of financial understanding and awareness among
respondents. At the same time, however, the OECD referred in its report to the significant
differences in survey audiences, the varieties in approaches applied to measure financial
literacy and the heterogeneous survey methodologies followed in the studies analyzed.
Since the publication of the fundamental OECD report many new studies have been
published and convergence in the methodologies and measurement of financial literacy
could be observed.40 However, the findings did change only little with financial literacy levels
still remaining on a rather low level in many countries.
39
The study by Staeheli & Zobl, 2008, cannot be considered representative due to the low sample size, lack of randomness in the selection of the
sample, unsatisfactory geographical diversification and biases in socio-economic characteristics such as age (approx. 50% of respondents are
within the age of 19 and 29 years) and education (approx. 50% of respondents are university graduates).
In contrast, the University of Zurich conducted representative surveys about structured products and investment and stock-market behavior in
Switzerland (cf. Wilding et al., 2010, and Birchler, Volkart, Ettlin, & Hegglin, 2011 or section 6.2.). While those studies collected information about
the knowledge of the population in regards to areas such as the differences between stocks and bonds or the functioning of structured products,
they were self-assessment based. Self-assessment likely results in people overstating their knowledge as highlighted in the paper of Staeheli &
Zobl, 2008.
40
Refer to Table 3 and Exhibit A6 for an international comparison of findings based on consistent financial literacy definitions.
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4.
Empirical Analysis of Financial Literacy
This chapter starts with a description of the dataset and a general overview of the survey
results. Later in the chapter there will be univariate and multivariate analysis of the financial
literacy scores reconciled to demographic, economic and financial characteristics of survey
respondents.
4.1.
Description of Dataset
The dataset used in the empirical analysis has been gathered by GfK Switzerland AG, a
major Swiss market research institute which provides qualitative and quantitative market
research. GfK was thereby assigned by the Swiss Institute of Banking & Finance (SIB&F) of
the University of St. Gallen (HSG) to raise data on their behalf about the behavior of
depositors during the financial crisis in the German speaking part of Switzerland.
A questionnaire was constructed by the SIB&F which was used by GfK to conduct 1’500
telephone interviews of about 15 minutes in length with a representative sample of randomly
selected individuals aged 20-74 years. The interviews were held in April 2011 and there was
no remuneration of survey participants, neither in cash nor in any other form. The people
participating in the survey were not necessarily the household heads. The data is crosssectional with respondents not having received the survey questionnaire prior to the
interviews.41
Three financial literacy questions (refer to Table A1 in the appendix) were included at the end
of the questionnaire. They were chosen such as to reflect on learnings from comparable
studies conducted abroad. Simple descriptive statistics were performed in Microsoft Excel
while more advanced analysis such as regressions were undertaken in Stata (V12).
The questionnaire does not distinguish between “don’t know” and “refuse to answer” and this
study will use the abbreviation DK for those types of responses. The share of DK is 3% or 42
answers of the sample (n=1’500) for the question about interest rates, 4% or 63 answers for
the question about inflation and 13% or 195 for the risk diversification question. The sample
has not been adjusted for DK observations with analysis displaying them separately. Table
A2 describes the demographic and economic characteristics and Table A3 the financial
characteristics of the sample households. Furthermore, Exhibit A1 geographically plots all
survey participants on a Swiss map.
4.1.1. Financial Literacy Questions
The complete questionnaire comprised of 18 questions whereof three were directly related to
financial literacy. The three questions about financial literacy were chosen such as to allow
41
The survey was the first in its kind and there are no learning effects to be considered due to repetitive raising of information through similar or
equal questions.
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Financial Literacy and Financial Behavior in Switzerland
for international comparability of the results received. They were developed by Lusardi and
Mitchell for the US HRS in 2004 and can be classified in three categories:

Knowledge about interest rates

Knowledge about inflation

Knowledge about risk diversification
4.2.
Description of Survey Results
The consolidated responses to the three questions are highlighted in Table 1. On an
aggregate view, the shares of correct answers for the interest rates and inflation questions
are both about 79%. However, the results for the question about risk diversification shows a
full sample score of correct answers of only 73%. Especially the number of respondents who
said that they did not know the answer or refused to give an answer was high with 13% or
195 survey participants.42
Table 1: Responses to Financial Literacy Questions
Men
Questions
# HH
Women
in %
# HH
Whole Sample
in %
# HH
in %
Interest Rates
More than CHF 102 (correct answer)
Exactly CHF 102
Less than CHF 102
Don’t know/ refuse to answer
Number of observations
611
50
43
10
714
85.57
7.00
6.02
1.40
100.00
578
116
60
32
786
73.54
14.76
7.63
4.07
100.00
1'189
166
103
42
1'500
79.27
11.07
6.87
2.80
100.00
33
55
606
20
714
4.62
7.70
84.87
2.80
100.00
61
112
570
43
786
7.76
14.25
72.52
5.47
100.00
94
167
1'176
63
1'500
6.27
11.13
78.40
4.20
100.00
88
561
65
714
12.32
78.57
9.10
100.00
115
541
130
786
14.63
68.83
16.54
100.00
203
1'102
195
1'500
13.53
73.47
13.00
100.00
699
636
443
530
15
97.90
89.08
62.04
74.23
2.10
750
630
309
431
36
95.42
80.15
39.31
54.83
4.58
1’449
1’266
752
961
51
96.60
84.40
50.13
64.07
3.40
84
10
1
11.76
1.40
0.14
170
26
9
21.63
3.31
1.15
254
36
10
16.93
2.40
0.67
Inflation
More
Exactly the same
Less (correct answer)
Don’t know/ refuse to answer
Number of observations
Risk Diversification
Investment in single stock
Investment in mutual fund (correct answer)
Don’t know/ refuse to answer
Number of observations
Correct Answers Across Questions
At least one correct answer
At least two correct answers
Correct answer to all questions
Correct answer to interest and inflation question
No correct answer
“Don’t know/ refuse” Across Questions
At least one “don’t know/ refuse” answer
At least two “don’t know/ refuse” answer
Answer "don’t know/ refuse" to all questions
Source: Dataset, authors’ calculations
42
Under the assumption of plain guessing of survey participants the expected share of respondents answering all questions correctly would be
5.56% or 83 respondents. Furthermore, the expected score of correct answers would be 1.17 correct answers for each respondent while
assuming pure guessing. That compares to an average of 2.31 in the sample (i.e. 3’467 correct answers divided by 1’500 respondents).
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Financial Literacy and Financial Behavior in Switzerland
4.2.1. Interest Rates Knowledge
Question 1 about interest rates was answered correctly by 1’189 or 79% of all survey
respondents. Those respondents correctly argued that they would have more than CHF 102
in their account by the end of year five. Around 18% of respondents gave incorrect answers
while 3% said that they did not know the solution or did not want to answer the question at
all.
The question about interest rates has been solved best what may be due to the fact that in
many states such as the state of Zurich, interest calculation is part of the official school
curriculum. The official school curriculum in mathematics in the state of Zurich includes
sample interest rate questions for Secondary School Level I which are to a large extend
congruent with the question asked in the survey.43 (Bildungsdirektion des Kantons Zürich,
2010, p. 302)
Therefore, survey participants who went through the Secondary School Level I of the Swiss
school system should have already been familiarized with the interest rates question.44
4.2.2. Inflation Knowledge
Question 2 about inflation reveals 1’176 or 78% correct responses with participants stating
that they could buy less if inflation is above the nominal interest rate. Around 17% of the
survey participants were responding incorrectly and 4% refused to give an answer or
admitted that they did not know.
While considering again the school curriculum of the state of Zurich, also as a proxy for other
German speaking states in Switzerland, one can find the term inflation as part of “orientation
knowledge” at Secondary School Level I. The curriculum requires students to be able to use
the term inflation “in various situations and areas”. (Bildungsdirektion des Kantons Zürich,
2010, p. 94)
4.2.3. Risk Diversification Knowledge
Question 3 about risk diversification shows the poorest results with only 1’102 or 73% of the
survey participants being aware of the lower risk of mutual fund as compared to single stock
investments. Approximately 13% of responses were wrong and also 13% of the survey
participants said that they did not know or refused to answer the question.
43
There is no consistent school curricula in Switzerland as the organization of the school system is in the authority of the states (BV Art. 62).
However, the initiative “Projekt Lehrplan 21” of the Deutschschweizer Erziehungsdirektoren-Konferenz (D-EDK) aims at harmonizing the school
curriculums of all German-speaking states. This new school curriculum includes clarifications in the field of financial competences of pupils and
aims at incorporating mandatory educational objectives in the field of financial knowledge. The release date of the unified curriculum is supposed
to be spring 2014. (EDK, 2010)
44
Zemp, 2011, chairman of the Swiss teachers’ association, argues in an article that so far Secondary School Level I (secondary school) teachers
have not received the required technical and didactical education in the area of financial literacy. However, he also mentions that the Secondary
School Level II (professional education, grammar school) should result in students having fundamental competences in financial matters such as
simple risk return considerations.
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The low performance in the risk diversification question can be explained with the fact that
the knowledge of stock market risk diversification is not part of most Swiss school curricula.
Knowledge in this area would have to come from economic and financial education or
personal investment experience.
However, personal investment experience may be rather low as only 536 or 36% of survey
participants said that they had an investment portfolio (refer to Table A4). 84% of
respondents with an investment portfolio answered the risk diversification question correctly
compared to 68% without portfolio (refer to Table 15).
4.2.4. Results Across All Questions
The results across all questions show that around 50% of respondents were correctly
answering all three questions and around 3% were unable to correctly answering one
question at all. Approximately 17% of the survey participants said that they did not know or
refused to answer at least one question.45
Table 2 includes pearson-correlation coefficients of correct and DK responses across pairs of
questions. The pearson-correlation coefficients in Table 2 are demonstrating a positive linear
relationship between correct as well as between DK answers to any pair of questions.46
However, the strength of the linear relationships are rather weak. There seems not to exist
strong evidence that some people answered all questions correctly while others responded
incorrectly or in the form of DK to all questions. Nevertheless, one can conclude that the
strongest linear relationship contains a pearson-correlation coefficient of 0.31 and is found
with people answering DK to the interest rates and inflation question.
Table 2: Pearson-Correlation Coefficients of Responses Across Questions
Correct Answers
Interest
Inflation
Risk
Interest
1.00
-
-
Inflation
0.12
1.00
-
Risk
0.17
0.09
1.00
DK Answers
Interest
Inflation
Risk
Interest
1.00
-
Inflation
0.31
1.00
-
Risk
0.16
0.12
1.00
Variables: Correct Answers and DK Answers are dummy variables. Correct Answers (1 = correct answer and 0 incorrect or DK), DK Answers (1 = DK and 0 = otherwise).
Sample: n =1’500 (for all pairs of variables)
Source: Dataset, authors’ calculations
45
Exhibit A2 to Exhibit A5 display the (share of) respondents who correctly answered all three questions respectively no question at all on a Swiss
map. Inhabitants of the cantons of Appenzell-Ausserrhoden, Basel-Stadt and Fribourg performed best when considering the share of respondents
who correctly answered all three questions.
46
Pearson-correlations measure the strength and direction of a linear relationship between two variables. The pearson-correlation coefficients can
range from -1 to +1, with -1 indicating a perfect negative linear relationship, +1 indicating a perfect positive linear relationship, and 0 indicating no
linear relationship at all. A variable correlated with itself will always have a pearson-correlation coefficient of 1.
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4.3.
International Comparison of Survey Results
Table 3 includes an international comparison of the survey results. While considering
comparable surveys performed recently in other countries such as Germany or the
Netherlands one can see that financial literacy levels in Switzerland are comparable to or
slightly higher than in other developed countries. Furthermore, the country comparison
highlights the gap in financial literacy between emerging countries such as Russia and
developed countries like Switzerland.
Table 3: International Comparison of Financial Literacy
Germany
Sweden
New
Zealand
Japan
USA
Russia
Italy
Netherlands
Switzerland
Interest Rates
More than CHF 102 (correct answer)
82.4
Exactly CHF 102
3.0
Less than CHF 102
3.7
Don’t know/ refuse to answer
Total
35.2*
49.2*
86.0
70.5
64.9
36.3
40.0
84.8
79.3
6.0
6.0
11.3
24.1
25.0
3.4
11.1
4.0
9.4
9.2
6.7
6.8
1.8
6.9
11.0
15.6*
4.0
14.1
14.5
32.9
28.2
10.0
2.8
100.0
100.00
100.0
100.0
100.0
100.0
100.0
100.0
100.0
7.0
5.8
11.2
4.4
6.2
2.7
6.3
7.0
5.0
9.0
50.8
3.8
5.7
11.1
78.4
Inflation
More
0.9
Exactly the same
3.8
24.0
Less (correct answer)
78.4
59.5
81.0
58.8
64.3
18.7
59.3
76.9
Don’t know/ refuse to answer
17.0
16.5
5.0
30.4
15.6
26.1
30.7
14.7
4.2
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
Total
Risk Diversification
Investment in single stock
5.9
13.1
Different
2.8
13.3
12.8
Different
13.3
13.5
Investment in mutual fund (correct answer)
61.8
68.4
Question
39.5
51.8
6.7
Question
51.9
73.5
Don’t know/ refuse to answer
32.3
18.4
57.7
34.9
35.5
34.8
13.0
n/a
n/a
n/a
n/a
45.0
n/a
n/a
100.0
100.0
100.0
100.0
100.0
100.0
100.0
Correct answer to all questions
53.2
21.4*
n/a
27.0
30.2
3.1
n/a
44.8
50.1
Correct answer to interest & inflation quest.
71.9
26.7*
73.0
49.2
46.2
21.8
31.5
73.4
64.1
No correct answer
10.3
14.7*
n/a
17.6
12.3
31.8
n/a
10.5
3.4
37.0
34.7*
7.0*
61.5
42.4
53.7
44.9*
37.6
16.9
8.4
3.2*
n/a
9.7
4.7
12.5
n/a
8.1
0.7
Paper
Telephone
Face-to
Face
Paper
Telephone
Face-toFace
Face-toFace &
Paper
Computer
(Internet)
Telephone
Exactly same risk
Total
Correct Answers Across Questions
“Don’t know/ refuse” Across Questions
At least one “don’t know/ refuse” answer
Answer "don’t know/ refuse" to all questions
Methodology
Interview methodology
Data
Sample size (no of observations)
1’059
1’302
850
5’268
1’488
1’366
3’992
1’665
1’500
Age range (years)
22-91
18-79
Adult
20-69
Adult
n/a
n/a
>25
20-74
Average age (years)
52.11
44.00
n/a
49.59
n/a
46.04
57.65
55.00
45.87
Gender characteristics (% of male)
47.00
50.00
43.06
49.11
n/a
42.20
63.00
53.00
47.60
Year of interviews
2009
2010
2009
2010
2009
2009
2007
2010
2011
Not
necessarily
Not
necessarily
Not
necessarily
Not
necessarily
Not
necessarily
Not
necessarily
Yes
Yes (with
partner)
Not
necessarily
Respondent
could not
refuse to
answer
* Open
ended
question
Don’t know
and refuse
to answer
added up
Results
without
Maori tribe
Almenberg &
SäveSöderbergh,
2011
Crossan et
al. 2011
Alessie et al.
2011
Dataset
Respondents are household heads
Comments and Specialties
Particularities of studies
* Risk
question
slightly
different
Separate
study on
youth47
Sources
Sources
Bucher &
Lusardi,
2011
Sekita, 2011
Lusardi &
Mitchell,
2011A
Klapper &
Panos, 2011
Fornero
Monticone,
2011
Source: As included above
47
Lusardi et al., 2010, included the same three questions in the 1997 NLSY wave 11 (refer ot Table A20) in 2007-2008 and reported the following
results for 7’417 respondents aged 23-27 years (49% female, 51% male):
Interest Rates: 79.3% correct/ 14.7% incorrect/ 5.9% don’t know, Inflation: 54.0%/ 30.4%/ 15.4%, Risk Diversification: 46.7%/ 15.8%/ 37.4%
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The population in Switzerland seems to have better knowledge about risk diversification
while performing about average on inflation and interest rates knowledge compared to other
developed countries. On an aggregate basis it becomes visible that Switzerland has with
3.4% a very low share of respondents who could not answer one single question correctly
compared to other countries. Furthermore, the respondents in Switzerland were much less
likely to answer that they did not know a response or refused to answer the question.
4.4.
Determinants of Financial Literacy
Table 5 includes financial literacy scores reconciled to demographic, economic and financial
characteristics of survey participants. The following sections analyze those determinants of
financial literacy in more depth.48
4.4.1. Gender
The results in Table 5 show a non-trivial gender gap in financial literacy levels between men
and women with men outperforming women in all three questions separately but also on
aggregate. 62% of men answered all three questions correctly compared to only 39% of
women. Furthermore, there is a statistically significant difference in the share of “don’t know/
refuse” answers with women (22%) being almost double as likely as men (12%) to say at
least once that they didn’t know or to refuse to answer the question.49
Level of Education
One reason for the significant gender gap in financial literacy can be seen in the better
education of men. As shown in Table A2, 18% resp. 26% of men stated to have an university
or university of applied science degree compared to only 8% resp. 13% of women. This overrepresentation of men with university degrees is supported by the fact that in 1990, only
33.2% of licentiate or degree graduates were women. (BfS, 2012D)
Table 4: Statistical Test of Gender Gap in Financial Literacy
Characteristics
Overall
Primary School
Secondary School
Professional Education
Grammar School
University (Applied)
University
Men
62.04
28.57
30.77
52.94
62.79
71.43
80.77
N
714
7
39
306
43
189
130
Women
39.31
16.67
25.76
36.48
36.56
54.46
59.32
N
786
12
66
455
93
101
59
Difference
22.73 ***
11.90
5.01
16.46 ***
26.23 ***
16.97 ***
21.45 ***
Test-Stat.
8.79
0.61
0.55
4.50
2.86
2.90
3.12
P-Value
0.00
0.54
0.58
0.00
0.00
0.00
0.00
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Financial Literacy: Financial literacy is measured on the share of respondents answering all three questions correctly (e.g. 39.31% of 786 women knew all answers).
Source: Dataset, authors’ calculations
48
Refer to the literature review in chapter 3 or Exhibit A6 for a comparison of typical relationships between demographic, economic and financial
characteristics and financial literacy as reported by other researchers.
49
The differences in the proportions of correct responses to all three questions as well as DK responses between men and women are statistically
significant on a 1% confidence level in an univariate statistic. The resulting t-values are 8.79 for the difference in the proportion of correct answers
and 5.09 for the difference in DK respectively. The gender difference in financial literacy, measured on individual question level, in aggregate or in
form of number of correct answers, remains statistically significant (1% confidence level) in a multivariate regression analysis with a number of
control variables (refer to Table 10).
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Table 5: Financial Literacy by Demographic, Economic and Financial Characteristics
Interest Rates
Characteristics
Whole Sample
N
Correct
DK
Inflation
Correct
Risk
DK
Correct
DK
Overall
All 3
≥ 1 DK
Correct
1’500
79.27
2.80
78.40
4.20
73.47
13.00
50.13
16.93
714
786
85.57
73.54
1.40
4.07
84.87
72.52
2.80
5.47
78.57
68.83
9.10
16.54
62.04
39.31
11.76
21.63
634
575
291
81.07
80.00
73.88
2.52
2.09
4.81
71.29
83.65
83.51
5.36
3.13
3.78
77.60
75.30
60.82
9.46
12.35
21.99
50.16
54.61
41.24
14.51
15.30
25.43
1’357
143
79.37
78.32
2.65
4.20
79.51
67.83
3.91
6.99
75.24
56.64
12.09
21.68
51.88
33.57
15.77
27.97
316
63
950
121
45
5
83.54
79.37
79.16
71.07
75.56
60.00
1.58
4.76
2.63
5.79
4.44
0.00
77.22
71.43
79.89
74.38
77.78
60.00
5.38
4.76
3.47
5.79
6.67
0.00
76.27
71.43
73.47
71.07
64.44
60.00
11.71
15.87
12.42
13.22
28.89
20.00
52.85
46.03
51.16
42.98
35.56
40.00
16.46
22.22
15.68
19.01
33.33
20.00
19
105
761
136
290
189
57.89
63.81
76.74
79.41
83.79
93.12
15.79
9.52
2.76
2.94
0.00
2.12
57.89
65.71
73.19
78.68
90.00
90.48
0.00
9.52
5.12
4.41
1.38
2.12
36.84
56.19
72.40
66.18
81.38
84.13
26.32
29.52
12.22
18.38
7.93
9.52
21.05
27.62
43.10
44.85
65.52
74.07
31.58
35.24
17.21
22.79
9.31
11.64
1’040
188
22
213
23
14
82.69
69.15
72.73
74.18
78.26
50.00
1.73
4.79
0.00
5.16
8.70
14.29
79.33
68.09
68.18
85.92
69.57
64.29
3.46
7.98
0.00
4.23
8.70
7.14
77.31
68.62
81.82
61.50
60.87
42.86
10.48
15.43
9.09
22.07
17.39
28.57
54.23
38.83
54.55
41.78
47.83
21.43
13.94
21.81
9.09
25.82
21.74
42.86
247
512
237
330
129
43
2
77.33
79.10
79.75
80.91
78.29
75.68
100.00
2.43
4.10
1.27
1.21
6.20
0.00
0.00
77.73
82.03
79.75
76.36
70.54
72.97
75.00
5.26
4.49
2.11
4.55
3.88
2.70
0.00
69.64
71.29
78.90
76.67
69.77
78.38
75.00
15.79
13.09
10.55
12.12
15.50
8.11
0.00
43.32
51.17
55.27
52.12
46.51
46.51
0.00
20.24
17.19
12.66
16.06
20.93
9.30
100.00
127
407
339
264
125
90
148
66.93
75.43
79.35
84.85
90.40
93.33
72.30
4.72
2.46
1.18
2.65
0.00
0.00
10.14
69.29
72.24
80.53
84.09
90.40
91.11
70.27
7.87
3.93
3.24
3.03
1.60
0.00
10.81
53.54
69.53
75.52
79.92
77.60
93.33
69.59
26.77
12.53
10.62
10.98
12.80
2.22
18.24
27.56
42.26
51.03
59.47
65.60
80.00
41.22
30.71
16.71
13.57
15.15
14.40
2.22
27.70
620
327
235
119
21
178
75.81
81.65
85.53
85.71
100.00
71.91
2.42
1.83
0.85
2.52
0.00
8.99
73.39
77.98
87.66
94.12
95.24
71.91
4.52
3.06
2.98
0.00
0.00
10.11
69.84
72.17
80.85
85.71
76.19
70.22
13.87
13.46
9.36
5.88
9.52
19.10
43.23
49.54
65.53
68.91
71.43
39.89
17.10
16.51
12.34
8.40
9.52
29.78
Gender
Male
Female
Age
20 to 39 years
40 to 59 years
60 to 74 years
Nationality
Swiss
Foreigners
Marital Status
Single
Permanent Relationship
Married
Divorced
Widowed
DK
Education
Primary School
Secondary School
Professional Education
Grammar School
University (Applied)
University
Labor Market Status
Employed
Housekeeper
Pupil/ Student
Pensioner
Unemployed
Other
Number of People in HH
1 Person
2 People
3 People
4 People
5 People
≥ 6 People
DK
Household Income
< CHF 4'500
CHF 4'500 - 7'000
CHF 7'000 - 9'000
CHF 9'000 - 12'000
CHF 12'000 - 15'000
> CHF 15'000
DK
Financial Wealth
< CHF 50'000
CHF 50'000 - 100'000
CHF 100'000 - 250'000
CHF 250'000 - 1 Mio.
> CHF 1 Mio.
DK
Financial Literacy: Financial literacy is measured as share of respondents in the sample answering the question(s) correctly.
Nationality: Foreigners include all respondents without Swiss citizenship. Survey participants responding to have other citizenships beside the Swiss citizenship have
been counted as Swiss (n=35). There are three respondents with multiple citizenships but no Swiss citizenship.
Education: Secondary School (Real-, Sekundar-, Bezirkschule). Professional education (Berufsschule, Gewerbeschule, KV). Grammar School (Mittelschule, Gymnasium,
Seminar). University (Applied) (Fachhochschule, HWV, Technikum), University (Universität, Hochschule, ETH/ Poly).
Financial Wealth: Excludes Real Estate Property.
Source: Dataset, authors’ calculations
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However, Table 4 highlights the comparison of financial literacy levels, on the basis of the
ratio of participants who correctly answered all three questions, between genders with the
same level of education. The two-dimensional statistic shows that a statistically significant
gender gap (1% confidence level) remains within all education cohorts beside primary and
secondary school.
Type of Occupation and Marital Status
Another reason for the difference in financial literacy levels among men and women may be
the current occupation. 23% of women mentioned that their current occupation is
housekeeping compared to less than 1% of men (refer to Table A2). It may therefore be that
financial matters are more often handled by men as a result of a traditional household role
allocation.
Furthermore, any survey based on household heads only would result in a bias towards
single and widowed women living in one person households.50 However, this study was not
based on household heads only and the likelihood of the selection of a man or woman was
equally probable. Furthermore, Table A2 indicates a higher probability of male participants
leaving in a one person household as compared to female participants. Table A2, however,
also shows that women are more often widowed and divorced.
Engagement and Interest in Financial Matters
Even women who claim to be highly engaged in the daily management of their money know
significantly less (share of all answers correct: 35%) than corresponding male respondents
(58%) - and strikingly also less than women who are not engaged at all (41%).51 Also the
women who said to have followed the financial crisis very closely fall short in financial
knowledge compared to their male counterparts (55% vs. 69%).52
4.4.2. Age
There is no uniform picture in terms of financial literacy when it comes to age. The interest
rates and risk questions have been solved best by the youngest cohort aged 20-39 years
while the inflation question as well as all three questions together have been answered best
by participants in the age range 40-59 years.53 Overall, one can say that individuals aged 60
years and older have lower financial literacy levels (41% vs. 52%) and do more often admit
50
Bucher & Lusardi, 2011, p. 570, refer to this effect as “gender selection bias”.
51
Difference in score between men and women is statistically significant on a 1% confidence level with t-value of 4.67.
52
Difference in score between men and women is statistically significant on a 5% confidence level with t-value of 2.00 (p-value 0.045).
53
The study represents a cross-sectional analysis and it is therefore not possible to distinguish between age and cohort effects. The authors’ view
is congruent with Bucher & Lusardi, 2011, who concluded that financial literacy is related to both effects.
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to not know the question or refuse to answer questions (25% vs. 15%) than their younger
counterparts.54
The chart in Exhibit 7 indicates a hump-shaped pattern of financial literacy and age for both
men and women. The function of financial literacy scores is concave with increasing overall
levels of financial knowledge until the age 40 years and decreasing levels thereafter. The
pattern on individual question level is somewhat different with correct answers to the interest
rates and risk questions slightly decreasing with age and correct answer to the inflation
question increasing with age.55
The results can be explained by the fact that the older generation has more experience with
inflation as Switzerland was incurring high inflation rates of up to 10% during the 1970s and
again rates in the range of 5% to 6% in 1990 and 1991. (Global Rates, 2012)
Exhibit 7: Financial Literacy by Age
Financial Literacy by Age
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74
All correct - Ratio Men
All correct - Ratio Women
All correct - Ratio Together
Financial Literacy: Financial literacy is measured as share of respondents answering all three questions correctly (Ratio: Based on two years of data due to outliers - e.g.
data for age 20 and 21, 22 and 23 etc. has been combined).
Source: Dataset, authors’ calculations
Education and Gender Effect
While splitting the sample in three age categories and analyzing the composition in terms of
education and gender one observes that there is a gender bias with women accounting for
58% of respondents in the age cohort 60-74 years. Furthermore, the education level is lower
with older respondents who have a lower share of university degree holders.
The lower education level with older respondents underpins the existence of a cohort effect.
OECD statistics show that in 2009 40.0% of 25-34 years old individuals in Switzerland had
attained at least tertiary education compared to only 28.3% of individuals aged 55-64 years.
These numbers are reflecting the development of the ratio of graduates from tertiary
educations to the population at the typical age of graduation which increased in Switzerland
from 11.9% in 2000 to 31.4% in 2007. (OECD, 2012B)
54
Both differences are statistically significant on a 1% confidence level with t-value of 3.38 resp. 4.30. However, as shown in the multivariate
regression analysis in Table 10 which includes age as a discrete variable and also incorporates a number of control variables, age appears only to
be of statistical significance for the interest rates and risk diversification questions. Moreover, coefficients are negligibly small.
55
The results are in line with other research such as Agarwal et al., 2009, or Almenberg & Säve-Söderbergh, 2011, who also found a humpshaped age pattern and better inflation knowledge with the older population (refer to Exhibit A6).
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It can therefore be expected that the low ratio of tertiary educated people in the older age
cohorts will gradually reduce over time and that the hump-shaped pattern of financial literacy
and age will presumably steadily reduce.56
4.4.3. Nationality
The country of citizenship split allocates better financial knowledge to Swiss. Only about 34%
of foreigners were able to correctly answer all three questions and 28% did at least once
admit to not know or refused to answer a question compared to 52% and 16% of Swiss.57
One reason for the difference in financial literacy between Swiss and foreigners is education.
While 15% of foreigners judge their highest education as primary or secondary school, only
8% of Swiss do so. Table 6 includes two-dimensional statistics of the differences in financial
knowledge between Swiss and foreigners within education cohorts.
Table 6: Statistical Test of Nationality Gap in Financial Literacy
Characteristics
Overall
Primary School
Secondary School
Professional Education
Grammar School
University (Applied)
University
Swiss
51.88
23.53
31.40
44.71
45.90
66.67
76.36
N
1‘357
17
86
700
122
267
165
Foreign
33.57
10.53
24.59
35.71
52.17
58.33
N
143
2
19
61
14
23
24
Difference
22.73 ***
23.53
20.87 *
20.12 ***
10.19
14.49
18.03 *
Test-Stat.
4.17
0.77
1.84
3.04
0.73
1.40
1.88
P-Value
0.00
0.44
0.07
0.00
0.47
0.16
0.06
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Financial Literacy: Financial literacy is measured as share of respondents answering all three questions correctly (e.g. 33.57% of 143 foreigners knew all answers).
Source: Dataset, authors’ calculations
The differences in financial literacy are statistically less significant than in the gender case.
However, it is still noteworthy that secondary school or professionally educated Swiss are
around 20 percentage points more likely than foreigners to know all three answers.
Selection Bias
The share of foreign respondents in the sample is 9.5% compared to a much higher ratio of
foreign citizens in Switzerland which is estimated by BfS to be 24.9% in 2010 for the age
category 20-74 years.58 (BfS, 2011A) The main reason for the lower representation of
foreigners in the sample is likely to be the fact that the questionnaire was held in German
and foreigners not being able to speak German were not eligible to take part in the survey.
The language challenge is also highlighted by the fact that the interviewers of the survey
quoted that the German knowledge of 12% of the foreign respondents was rather poor
compared to only 1% for Swiss respondents. Finally, there is some higher proportion of
56
The ratio of tertiary educated people for the age cohort 25-64 years already increased from 23.8% in 1998 to 36.9% in 2009 underpinning the
strong trend towards a higher share of tertiary educated people. (OECD, 2012B, p. 217)
57
Differences are statistically significant on a 1% confidence level with t-value of 4.17 resp. 3.70. As seen in Table 10 nationality remains
statistically significant in a multivariate setting with financial literacy measured in all forms but the answers to the interest rate question.
58
The BfS data is based on Swiss wide data while the questionnaire only considers the German speaking part of Switzerland. However, the
substantial difference in the share of foreigners cannot be explained with geographical reasons as, for instance, Zurich with a population above
one million citizens has an unemployment rate of just above 26% for the age cohort 20-74 years. (BfS, 2011A)
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foreigners in the age cohort 20-39 years and foreign respondents consisted of 59% men
compared to only 46% of Swiss respondents (refer to Table A2 for a nationality split of
foreigners).
4.4.4. Marital Status
Table 5 shows that financial knowledge based on all three questions is highest with singles
(53%) and lowest with widowed respondents (36%). Besides, a third of widowed respondents
answered to at least one question that they did not know or just refused to answer. The
pairwise correlation coefficients in Table A8 and A9 include some explanations for the poor
performance of widowed respondents. Widowed survey participants are less well educated
than any other marital status cohort and are on average much older.
High Financial Engagement with Widowed Participants
When asked about their level of financial engagement, 40% of widowed respondents
answered that they were highly engaged – the highest engagement level across all marital
status (overall: 25%). Surprisingly enough, only about 26% of singles, the lowest ratio apart
from the five respondents who did not disclose their marital status, did say that they were
highly engaged.
4.4.5. Education
Table 5 shows a positive relationship between education and financial literacy. On an overall
basis one can see that the higher the education of a respondent the better his or her financial
knowledge. As already indicated, the level of education is positively related to the level of
financial knowledge irrespective of gender (Table 4) and nationality (Table 6).
Comparison of Education Levels
Table 7 includes the catalog of education respondents could choose from and analyses the
deviation in financial literacy scores between differently well educated respondents. The
univariate statistic includes statistical evidence of differences in financial knowledge between
individuals with different educational backgrounds.
Table 7: Statistical Test of Financial Literacy and Level of Education
Characteristics
Primary vs. Secondary School
Secondary School vs. Prof. Education
Secondary School vs. Grammar School
Prof. Education vs. Grammar School
Grammar School vs. University (Applied)
Prof. Education vs. University (Applied)
University (Applied) vs. University
Score
21.05
27.62
27.62
43.10
44.85
43.10
65.52
N
19
105
105
761
136
761
290
Score
27.62
43.10
44.85
44.85
65.52
65.52
74.07
N
105
761
136
136
290
290
189
Difference
-6.57
-15.48 ***
-17.23 ***
-1.75
-20.67 ***
-22.42 ***
-8.55 **
Test-Stat.
-0.60
-3.02
-2.74
-0.38
-4.04
-6.50
-1.98
P-Value
0.55
0.00
0.01
0.70
0.00
0.00
0.05
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Financial Literacy: Financial literacy is measured as share of respondents answering all three questions correctly (e.g. 21.05% of 19 pr. school grad. knew all answers).
Source: Dataset, authors’ calculations
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It may seem straightforward that better educated people have better financial knowledge
than less well educated people. However, while considering the low level of complexity of the
questions one may be surprised not to see any saturation in financial literacy scores. The
share of university graduates which answered all three questions correctly is still more than
20 percentage points higher than with grammar school graduates or professionally educated
people.59
For Whom Does Education Matter Most?
The gradients in terms of education and financial literacy scores are high with university
graduates being 53 percentage points more likely to know all three answers than primary
school graduates.60 There is no other demographic or economic characteristic which has a
higher dispersion in consolidated financial literacy scores.
An investigation of the gradients and statistical relevance of education for gender, nationality
and age has been included in Table 8. Statistically, education matters most for male, Swiss
and people in the age range 40-59 years. The difference in statistical significance is driven
by the sample size in the respective cohorts which is especially low with foreigners (n=143).
Table 8: Gradients of Education and Financial Literacy
Characteristics
Overall
Primary vs. Secondary School
Secondary School vs. Prof. Ed.
Secondary vs. Grammar School
Prof. Ed. vs. Grammar School
Grammar School vs. Uni. (Ap.)
Prof. Ed. vs. Uni. (Applied)
6.6
15.5
17.2
1.8
20.7
22.4
Uni. (Applied) vs. University
***
***
***
***
8.6 **
Male
Female
2.2
22.2 ***
32.0 ***
9.9
8.6
18.5 ***
9.1
10.7 *
10.8
0.1
17.9 **
18.0 ***
9.3 *
4.9
Swiss
7.9
13.3
14.5
1.2
20.8
22.0
**
**
***
***
9.7 **
Foreign.
20-39 Y.
40-59 Y.
60-74 Y.
10.5
14.1
25.2 *
11.1
16.5
27.6 **
26.0
16.2
17.1
0.9
23.0 ***
23.9 ***
12.6
19.9
21.8
1.8
19.6
21.4
10.0
8.2
13.7
5.6
16.8
22.3 *
6.2
9.4
5.1
**
**
**
***
12.7
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Financial Literacy: Financial literacy is measured as share of respondents answering all three questions correctly (e.g. the value 6.6 indicates that the share of secondary
school graduates knowing all three answers is 6.6 percentage points higher than with primary school graduates).
Source: Dataset, authors’ calculations
The gender split shows that male secondary school graduates perform on average 22
percentage points worse than professional educated male and staggering 32 percentage
points worse than grammar school graduates (both statistically significant at 1% confidence
level). Both, men and women with an university of applied science degree are about 18
percentage points more likely to know all answers compared to their professionally educated
counterparts (both statistically significant at 1% confidence level).
There is statistical evidence (5% confidence level) that Swiss with professional education or
grammar school perform better than secondary school graduates. Even stronger statistical
59
Almenberg & Säve-Söderbergh, 2011, had information about college majors of their respondents and found graduates with quantiatitve majors
such as economics and engineering performing better. They asked the interest rate question as an open question making it more difficult for
participants and concluded that the type of major matters with the interest rate and inflation question but not the risk diversification question.
60
The compulsory Swiss schooling system goes in all states until year nine and includes the primary and secondary school (BV 62, Art. 8
Konkordat über die Schulkoordination). However, there were 19 respondents considering primary school as their highest level of education. Out of
those 19, eleven are above 60 years old and another two are foreigners. The Konkordat über die Schulkoordination was introduced in 1970 and at
that time some states were only considering seven years schooling as compulsory. These states were asked to extent the mandatory school
system in “a reasonable amount of time” to nine years. (Schweizerische Konferenz der kantonalen Erziehungsdirektoren, 1970, p.3)
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evidence (1% confidence level) exists for the difference between grammar school or
professional education and university of applied science.
Persistent Positive Effect of Education over Time
The age split in Table 8 reveals evidence that any further education after the secondary
school matters especially in the cohort of 40-59 years old people. At age 60-74 years, there
are still high gradients but accompanied with lower statistical evidence. Some of those
results may have therefore been driven by chance. However, while comparing the sample
size within the three categories one can see that the oldest cohort only includes 291
respondents (refer to Table A2). One may therefore argue that the lower statistical
significance is mainly due to a sample size effect and that the level of education also matters
in higher age categories.
4.4.6. Labor Market Status
The labor market split in Table 5 shows that employed people and students compete best in
financial literacy while housekeepers and pensioners are at the lower end of the scale.
Considering the DK scores, 26% of pensioners and 22% of housekeepers admitted not to
know the answer or refused to give an answer to at least one question, much above students
with 9% or employed individuals with 14%.61
Occupation – Driven by Gender and Age
There are significantly more women who are housekeeper (23% vs. 1%) while the proportion
of men being employed is higher (81% vs. 59%). It may intuitively make sense that the
occupation has an impact on the understanding of financial matters.62
Exhibit 8: Gender and Age by Occupation
Gender by Occupation
100%
80%
45%
60%
97%
50%
43%
50%
52%
50%
57%
50%
48%
77%
40%
20%
55%
0%
3%
23%
Men
Women
Source: Dataset, authors’ calculations
61
The general negative relationship between financial literacy (measured as dummy with 0 = all three answers correct) and DK scores (variable
taking values of 0-3) manifest itself in a spearman-correlation coefficient of negative 0.45 for all survey respondents (n=1’500).
62
Refer also to Alessie et al., 2011, p. 534, or Lusardi & Mitchell, 2011A, p. 515, who argue that employed people should have better financial
knowledge due to the attendance of employer sponsored seminars focused on financial and retirement matters.
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There are also considerable differences in the age of respondents from the various
occupations as shown in Exhibit 8. The median pensioner is 67 years old while the median
housekeeper is 39 years old. As explained in section 4.4.2. financial knowledge scores are
hump-shaped with age and the type of occupation may therefore explain part the age
pattern.
4.4.7. Number of People Living in a Household
The number of people living in a household seems to have a rather small impact on financial
literacy. However, 43% of people living in single person households and about 47% of
people living in households with five people or more than five people knew all three
questions what is below average while three people households performed best with 55%.
One reason for the poor performance of one person households may lie in the composition of
the respondents. One person household respondents comprise of 66% employed people and
26% pensioners compared to 80% resp. 2% for three people households. There is also an
under-representation of married respondents with single person households which only
account for 2% and an over-representation of singles with 61%. Explained alternatively, three
people households outperform other households as they comprise of higher fractions of
employed respondents and relatively lower proportions of widowed and divorced
respondents.
4.4.8. Household Income
Table 5 shows that higher monthly household income is strongly positively related to higher
financial literacy scores, on single question but also on aggregate level. The gradient is high
as on aggregate level the difference in financial literacy (measured on the share of people
answering all three questions correctly) is staggering 52 percentage points between the 127
respondents declaring a monthly household income of below CHF 4’500 and those 90
respondents saying their household was to earn more than CHF 15’000.
As shown in Table A3, men are over-represented in higher income brackets and graduates
from university and university of applied science account for 67% of people earning CHF
15’000 while only comprising 32% of the sample. The spearman-correlation coefficient
between household income and education is 0.37 underpinning the positive relationship
between the two characteristics (refer to Table A9).
4.4.9. Financial Wealth
The picture for financial wealth and financial literacy looks rather similar to the picture seen
for household income. Again, wealthier respondents scored better than their financially
poorer counterparts. The difference in the share of people knowing all answers in the lowest
and highest wealth category is 28 percentage points.
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Interestingly, the 178 respondents who did not declare their financial wealth performed worst
on an aggregate basis with only around 40% of respondents correctly answering all three
questions and almost 30% saying at least once that they did not know or refused to answer.
4.4.10. Who Knows the Most and Who Knows the Least?
Table 9 includes the single demographic, economic and financial characteristics which are
related to the highest respectively lowest aggregate financial literacy scores.
Characteristics
Interest Rates
Inflation
Risk
Overall
Highest Literacy
Gender
Age
Nationality
Marital Status
Education
Labor Market Status
Number of People in Household
Household Income
Financial Wealth
Male
20-39 Years
Swiss
Single
University
Employed
4 People
> CHF 15’000
> CHF 1 Mio.
Male
40-59 Years
Swiss
Married
University
Pensioner
2 People
> CHF 15’000
> CHF 1 Mio.
Male
20-39 Years
Swiss
Single
University
Pupil/ Student
3 People
> CHF 15’000
CHF 250k - 1 Mio.
Male
40-59 Years
Swiss
Single
University
Pupil/ Student
3 People
> CHF 15’000
> CHF 1 Mio.
Lowest Literacy
Table 9: Characteristics with Highest and Lowest Financial Literacy
Gender
Age
Nationality
Marital Status
Education
Labor Market Status
Number of People in Household
Household Income
Financial Wealth
Female
60-74 Years
Foreigner
Divorced
Primary School
Housekeeper
≥ 6 People
< CHF 4’500
< CHF 50’000
Female
20-39 Years
Foreigner
Per. Relationship
Primary School
Housekeeper
5 People
< CHF 4’500
< CHF 50’000
Female
60-74 Years
Foreigner
Widowed
Primary School
Unemployed
1 Person
< CHF 4’500
< CHF 50’000
Female
60-74 Years
Foreigner
Widowed
Primary School
Housekeeper
1 Person
< CHF 4’500
< CHF 50’000
Financial Literacy: Financial literacy is measured as the share of respondents in the sample who answered the question(s) correctly.
Characteristics: DK have not been considered as characteristic.
Source: Dataset, authors’ calculations
4.5.
Multivariate Regression Analysis of Financial Literacy
This section analysis the relationship between demographic, economic and financial
variables with financial literacy on the basis of a multivariate linear regression analysis. The
objective is to come up with valid statements about the effects of demographic, economic
and financial variables on financial literacy scores of survey respondents. Financial literacy
as dependent variable has thereby been defined in a number of different ways. Dummy
variables are used as certain variables are nominal and standard regression analysis would
assume them to be of continuous nature.63
Table 10 reports OLS estimates of the effect of demographic, economic and financial
variables on financial literacy scores. The multivariate regression analysis includes variables
for gender, age, nationality, marital status, education, occupation, household size, household
income and financial wealth. It allows to make a statement about the impact of one of those
variables on financial literacy while keeping the other variables stable (ceteris paribus view).
63
Dummy variables are defined as being categorical, non-ordinal and binary in nature (only take values of 0 or 1).
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4.5.1. Multivariate Regression Results
The multivariate linear regression results in Table 10 imply a statistically significant
relationship between gender, nationality, education, household income and financial wealth
with most definitions of financial literacy. Men, better educated individuals, people with higher
household income as well as financial wealth have on average higher levels of financial
literacy than their counterparts.64
Interestingly, age, nationality and education are of higher statistical relevance for the
relationship with the inflation and risk diversification questions than the interest rate question.
Furthermore, household income and financial wealth seem to be especially relevant for risk
diversification knowledge as indicated by high statistically significant coefficients.
The gender coefficient in the first column indicates that women are 17 percentage points less
likely to answer all three questions correctly compared to men. The standard error of the
estimate of 0.03 gives an indication about the accuracy of the predicted coefficient. It
specifies that with a probability of around 68% the real coefficient will have a value between
minus 14 and minus 20 percentage points.
In regards to household income one can say that individuals living in households with income
above CHF 15’000 have on average a 0.54 higher score of correct answers than those living
in a household with income below CHF 4’500. The statistical significance is on a 1% level
confirming that the coefficient is very likely different from zero and the variable has therefore
a statistically non-negligible relationship with the dependent variable financial literacy score.
4.5.2. Is There a Problem of Multicollinearity?
As displayed in Table 10 there is stronger statistical evidence of an existing relationship
between household income and financial literacy than with the degree of education and
financial literacy. However, one may argue that education is highly correlated with household
income and results are therefore impacted by multicollinearities.65
64
The results from the linear probability model in Table 10 are confirmed while applying a probit model (with average marginal effects) in order to
account for the dichotomous nature of the dependent variable (excl. Score Correct and DK Score definition of financial literacy as they are not of
dichotomous nature). Estimates for average marginal effects of the independent variables (i.e. coefficients) derived from probit regression analysis
are characterized through very similar levels of magnitude and statistical significance as those included in the linear probability model applied.
Refer to Table A11 for results of the probit regression.
The conclusions drawn from the linear probability model in Table 10 remain also valid while applying logit regression analysis (with average
marginal effects). Logit models allow to estimate the probability of occurrence of an event (e.g. probability of all answers correct). Estimates for
average marginal effects of the independent variables (i.e. coefficients) derived from logit regression analysis are characterized through very
similar levels of magnitude and statistical significance as those included in the linear probability model applied. Refer to Table A12 for results of
the logit regression.
The F-values are in the range of 3.91 to 10.23 with p-values of 0.00 in the multiple regression analysis in Table 10. They indicate that in all the
regression analysis in Table 10 financial literacy is unlikely to be independent of the set of explanatory variables used. There is essentially no
chance of getting a relationship this strong just by chance. R2 values stretch from around 0.07 to 0.16 highlighting that the set of independent
variables are able to explain around 7% (Interest Rate definition, column four) to 16% (Score Correct definition, column two) of the variation in
financial literacy.
65
The multicollinearity problem refers to the fact that in a multiple linear regression with multicollinearity, OLS regression coefficients are unstable
(sensitive to small changes in input data) or not even uniquely defined, have highly inflated variances, and are impossible to be interpreted
individually. (Sundberg, 2002, p. 365) The stronger the multicollinearity is, the greater the standard errors will be and the more difficult it gets to
determine which independent variable is actually producing which effect on the dependent variable. When high multicollinearity is present,
confidence intervals for coefficients tend to be very wide and t-statistics tend to be small. Coefficients will have to be larger in order to be
statistically significant, i.e. it will be harder to reject the null hypothesis when multicollinearity is present. (Anderson, Sweeney, & Williams, 2009, p.
644)
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Table 10: Multivariate Linear Regression - Financial Literacy
Multivariate OLS Regression – Financial Literacy
Gender
Women (d)
Age
Age
Nationality
Foreigner (d)
Marital Status
Single (d)
In Permanent Relationship (d)
Married (d)
Divorced (d)
Widowed (d)
Education
Primary School (d)
Secondary School (d)
Professional Education (d)
Grammar School (d)
University (Applied) (d)
University (d)
Occupation
Employed (d)
Housekeeper (d)
Pupil/ Student (d)
Pensioner (d)
Unemployed (d)
Household Size
Number of People in Household
Household Income
< CHF 4’500 (d)
CHF 4’500 - 7’000 (d)
CHF 7’000 - 9’000 (d)
CHF 9’000 - 12’000 (d)
CHF 12’000 - 15’000 (d)
> CHF 15’000 (d)
DK (d)
Financial Wealth
< CHF 50'000 (d)
CHF 50'000 - 100'000 (d)
CHF 100'000 - 250'000 (d)
CHF 250'000 - 1 Mio. (d)
> CHF 1 Mio. (d)
DK (d)
_cons
N
R2
F
All Correct
Score Correct
Interest Rates
Inflation
-0.17 ***
(0.03)
-0.22 ***
(0.04)
-0.07 ***
(0.02)
-0.08 ***
(0.02)
-0.07 ***
(0.02)
0.09 ***
(0.03)
-0.00
(0.00)
-0.00
(0.00)
-0.00
(0.00)
0.01 ***
(0.00)
-0.00 ***
(0.00)
0.00
(0.00)
-0.19 ***
(0.04)
-0.31 ***
(0.07)
-0.02
(0.04)
-0.09 ***
(0.04)
-0.20 ***
(0.04)
0.14 ***
(0.04)
omitted
omitted
omitted
omitted
-0.03
(0.07)
0.01
(0.04)
0.05
(0.05)
0.05
(0.08)
-0.13
(0.11)
-0.02
(0.07)
0.01
(0.09)
0.12
(0.13)
-0.03
(0.06)
-0.03
(0.03)
-0.05
(0.05)
0.04
(0.07)
-0.07
(0.06)
0.00
(0.03)
-0.03
(0.05)
-0.03
(0.07)
omitted
omitted
omitted
omitted
0.02
(0.12)
0.11
(0.12)
0.12
(0.12)
0.24 **
(0.12)
0.30 **
(0.12)
omitted
0.31
(0.20)
0.56
(0.19)
0.57
(0.20)
0.73
(0.19)
0.82
(0.19)
***
***
***
***
0.03
(0.10)
0.12
(0.10)
0.16
(0.10)
0.13
(0.10)
0.21 **
(0.10)
0.11
(0.10)
0.18
(0.10)
0.23
(0.10)
0.30
(0.10)
0.31
(0.10)
Risk Diversification
omitted
omitted
-0.03
(0.06)
0.01
(0.04)
0.09 *
(0.05)
0.11
(0.07)
omitted
*
**
***
***
DK Score
-0.01
(0.07)
-0.04
(0.04)
-0.04
(0.05)
0.03
(0.08)
omitted
0.17
(0.11)
0.26 **
(0.10)
0.18
(0.11)
0.29 ***
(0.11)
0.29 ***
(0.11)
0.16
(0.12)
-0.06
(0.12)
0.00
(0.12)
-0.12
(0.12)
-0.06
(0.12)
omitted
omitted
omitted
omitted
omitted
-0.12
(0.10)
-0.11
(0.11)
-0.05
(0.14)
-0.18 *
(0.11)
-0.01
(0.16)
-0.11
(0.17)
-0.07
(0.23)
-0.11
(0.17)
-0.03
(0.09)
-0.09
(0.09)
-0.16
(0.12)
-0.04
(0.09)
0.00
(0.09)
-0.02
(0.09)
0.01
(0.12)
-0.05
(0.09)
0.02
(0.09)
0.00
(0.10)
0.08
(0.13)
-0.01
(0.10)
-0.05
(0.10)
-0.00
(0.11)
-0.21
(0.14)
0.07
(0.11)
-0.01
(0.01)
-0.02
(0.02)
0.00
(0.01)
-0.01
(0.01)
-0.00
(0.01)
0.01
(0.01)
omitted
omitted
omitted
0.07
(0.04)
0.08
(0.05)
0.12
(0.05)
0.15
(0.05)
0.15
(0.06)
0.07
(0.06)
0.05
(0.04)
0.10
(0.05)
0.12
(0.05)
0.15
(0.05)
0.14
(0.06)
0.02
(0.06)
0.13
(0.05)
0.16
(0.05)
0.18
(0.05)
0.15
(0.06)
0.25
(0.07)
0.12
(0.06)
omitted
0.13
(0.05)
0.16
(0.05)
0.21
(0.06)
0.23
(0.06)
0.30
(0.07)
0.16
(0.07)
omitted
**
***
***
***
***
**
omitted
0.25
(0.08)
0.34
(0.09)
0.42
(0.09)
0.45
(0.10)
0.54
(0.12)
0.21
(0.11)
***
***
***
***
***
*
omitted
*
**
***
**
omitted
**
**
***
**
omitted
omitted
***
***
***
**
***
*
omitted
-0.15
(0.05)
-0.15
(0.05)
-0.11
(0.06)
-0.12
(0.06)
-0.20
(0.07)
-0.02
(0.07)
***
***
**
*
***
omitted
0.01
(0.03)
0.14 ***
(0.04)
0.14 ***
(0.05)
0.14
(0.11)
-0.04
(0.05)
0.02
(0.05)
0.19 ***
(0.06)
0.25 ***
(0.08)
0.24
(0.18)
-0.01
(0.09)
0.04
(0.03)
0.07 **
(0.03)
0.06
(0.04)
0.17 *
(0.09)
-0.02
(0.05)
-0.03
(0.03)
0.02
(0.03)
0.04
(0.04)
0.04
(0.09)
-0.03
(0.05)
0.00
(0.03)
0.10 ***
(0.03)
0.15 ***
(0.05)
0.03
(0.10)
0.04
(0.05)
-0.00
(0.03)
-0.05
(0.04)
-0.11 **
(0.05)
-0.11
(0.11)
0.07
(0.05)
0.44 ***
(0.17)
1.62 ***
(0.27)
0.74 ***
(0.14)
0.31 **
(0.14)
0.57 ***
(0.15)
0.31 *
(0.17)
1’480
0.1571
9.66
1’480
0.1649
10.23
1’480
0.0701
3.91
1’480
0.1160
6.80
1’480
0.1015
5.86
1’480
0.0952
5.45
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Dependent Variables: Financial literacy according to the following types of definitions: All Correct (dummy with 1 = all answers correct and 0 = not all answers correct),
Score Correct (value of 0-3 depending on amount of correct answers of respondent), Interest Rates, Inflation and Risk Diversification (dummy with 1 = correct answer and
0 = incorrect or DK answer), DK Score (value of 0-3 depending on amount of DK answers of respondent).
Explanatory Variables: Numbers in brackets are standard errors. (d) stands for dummy (0/1) variable.
Sample: The sample of n=1’500 has been adjusted for respondents answering: DK for marital status (n=5), Other for occupation (n=14) and DK for number of people in
household (n=2). Out of a total adjustment of n=21, 1 has been multiple counting (DK for marital status and number of people in household) resulting in a sample size for
the regression of n=1’480.
Source: Dataset, authors’ calculations
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There are various approaches which help detecting the degree of multicollinearity. As a first
measure one can test if the regression is characterized through a high R2 but few statistically
significant t-statistics. While considering the regression results in Table 10 one can dismiss
this statement. There is no strong indication of multicollinearity that can be derived from the
findings of a high R2 but few significant t-statistics in the multivariate regression analysis
applied. As further analytical models to identify potential multicollinearities, pairwise
correlations, variance inflation factors (VIF) and conditional indices of regressors will be
analyzed.
Pairwise Correlations Across Regressors
Table A8 and Table A9 show pairwise pearson- and spearman-correlation coefficients
between the independent variables used in the multivariate linear regression analysis in
Table 10.
The results reveal that there are hardly any pearson-correlation coefficients between
variables which are above |0.50| and which would therefore indicate a significant linear
relationship. It comes with no surprise that strongest linear effects relate to trivial
relationships such as the ones between age and labor market characteristics, age and
marital status or marital status and household size.
While considering financial characteristics it becomes visible that education has a positive
linear relationship to both household income and financial wealth. However, the correlation
coefficients remain rather low with, for instance, university graduates being negatively related
to household income between CHF 4’500 and CHF 7’000 (pearson-correlation coefficient 0.15) and positively related to household income above CHF 15’000 (pearson correlation
coefficient 0.21). The strongest linear relationship exists between DK answers for financial
wealth and DK answers for household income with a pearson-correlation coefficient of 0.68.
Variance Inflation Factors (VIF) and Condition Indices
Table A10 depicts VIF, eigenvalues and condition indices for the independent variables used
in the multivariate regression analysis in Table 10.
VIF quantify the severity of multicollinearity of coefficients in OLS regression analysis. They
are computed from the correlation matrix of the independent variables and provide an index
that measures how much the variance of an estimated regression coefficient is increased
because of the correlation between predictors and, hence, multicollinearity. (Rawlings,
Pantula, & Dickey, 1998, p. 372) Multicollinearity of coefficients becomes critical with VIF
values larger than 10. (Kutner, Nachtsheim, & Neter, 2004, p. 408)
Table A10 shows that VIF values are highest with education characteristics and the
occupation type Housekeeper. The VIF of the independent variable Professional Education,
for instance, is 22.92 and its square root 4.79. This means that the standard error for the
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coefficient Professional Education is 4.79 as large as it would be if it were uncorrelated with
the other independent variables.
The condition number is derived from the square root of the ratio of the largest to the
smallest eigenvalues of the independent variables. A number of around 10 indicates weak
dependencies that may be starting to affect the regression estimates while values of 30-100
indicate moderate to strong dependencies. (Rawlings et al., 1998, p. 371) The condition
number of 45.25 indicates that the estimates might have a moderate amount of numerical
error.66
It can be concluded that there is a degree of multicollinearity involved in the regression
analysis which has likely resulted in the education characteristics showing too large standard
errors and consequently too small t-statistics. Education may therefore be of slightly higher
statistical relevance as depicted in Table 10.67
66
However, the statistical standard errors are almost always much greater than the numerical errors (cf. Belsley, Kuh, & Welsch, 2005, p. 85ff).
67
While removing household income or financial wealth as independent variables from the multivariate regression analysis in Table 10 (financial
literacy definition of first column), education coefficients become more statistically significant. The effects are stronger while omitting income but
only result in the dummy University to become significant on a higher confidence level (1% vs. 5% while omitting either wealth or income).
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5.
Financial Literacy and Financial Behavior
This chapter analysis the relationship between financial literacy and financial behavior on the
basis of univariate statistics and multivariate linear regression analysis. The objective is to be
able to come up with valid statements about the causal effects of financial literacy on
financial behavior variables such as investment behavior, consumption and mortgage
indebtedness as well as retirement account saving.
Multivariate analysis are required in order to be able to simultaneously examine the
relationship between financial literacy, financial behavior and demographic, economic and
financial characteristics of survey respondents.
5.1.
Self-Assessed Personal Behavior Characteristics
Included in Table 11 are financial literacy scores reconciled to self-assessed personal
behavior characteristics of respondents. Those characteristics will be analyzed in more detail
and will be included as control variables in the subsequent multivariate regression analysis.
Table 11: Financial Literacy by Self-Assessed Personal Behavior Characteristics
Interest Rates
Correct
DK
Inflation
Correct
Risk
DK
Correct
DK
Overall
All 3
≥ 1 DK
Correct
Characteristics
N
Whole Sample
1’500
79.27
2.80
78.40
4.20
73.47
13.00
50.13
16.93
Financial Engagement
No Engagement
Low Engagement
High Engagement
Very High Engagement
DK
171
372
537
411
9
73.68
81.45
81.19
77.86
44.44
5.85
2.42
2.05
2.19
33.33
78.95
83.87
78.21
73.72
66.67
8.77
3.49
2.42
4.87
22.22
70.76
76.34
76.16
69.59
22.22
15.20
13.17
9.87
15.09
55.56
46.78
55.38
52.89
44.28
0.00
23.98
16.94
12.29
18.98
66.67
Financial Interest
Very High Interest
High Interest
Low Interest
No Interest
219
716
483
82
83.11
81.28
76.40
68.29
1.37
2.09
3.73
7.32
89.04
82.96
70.19
58.54
0.91
2.93
6.42
10.98
82.65
73.46
71.84
58.54
5.94
12.57
15.11
23.17
65.30
53.36
43.06
20.73
7.31
15.36
21.33
30.49
Risk Characteristics
High Risk Aversion
Moderate Risk Aversion
Low Risk Aversion
DK
994
410
65
31
78.67
81.22
81.54
67.74
3.12
0.73
3.08
19.35
79.98
77.32
72.31
54.84
4.33
2.44
4.62
22.58
72.64
77.32
76.92
41.94
14.99
6.10
7.69
13.00
50.00
52.68
49.23
22.58
19.42
8.54
12.31
58.06
Financial Planning
No Planning
Little Planning
Some Planning
Significant Planning
DK
142
267
495
582
14
76.76
74.53
80.61
81.62
50.00
2.11
4.12
2.22
2.23
28.57
78.17
78.65
79.60
77.84
57.14
3.52
4.49
3.03
4.98
14.29
64.08
72.66
75.96
74.91
35.71
16.90
11.99
11.31
12.89
57.14
42.25
46.44
51.72
53.09
21.43
19.01
16.85
15.15
16.84
64.29
Financial Literacy: Financial literacy is measured as the share of respondents in the sample who answered the question(s) correctly (correct) or who did not know (DK).
Source: Dataset, authors’ calculations
5.1.1. Financial Engagement
Survey participants were asked to judge the following statement:
“I am very engaged when it comes to the daily managing of my money.”
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The participants were able to give their view on a Likert-type scale of one (strong
disagreement) to four (strong agreement). There was also the opportunity to not answer the
question or to say that one does not know, what nine respondents actually did. The results
show 63% or 948 respondents claiming to be highly or very highly engaged in the financial
management of their money.68
Interestingly, individuals claiming themselves as being only engaged little have the best
financial knowledge of interest rates, inflation and risk diversification and also on an
aggregate basis. Second best score people with high engagement while very high
engagement is surprisingly connected to the poorest performance. However, Table 12 shows
that the differences in financial literacy between the various levels of engagement are rather
small as compared to other personal behavior characteristics.
Table 12: Statistical Test of Financial Engagement and Financial Literacy
Characteristics
Very High vs. High Engagement
High vs. Low Engagement
Low vs. No Engagement
Score
44.28
52.89
55.38
N
411
537
372
Score
52.89
55.38
46.78
N
537
372
171
Difference
-8.61 ***
-2.49
8.60 **
Test-Stat.
-2.63
-0.74
1.86
P-Value
0.01
0.46
0.06
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Financial Literacy: Financial literacy is measured as share of respondents answering all three questions correctly (e.g. 44.28% of 411 very highly financially engaged
respondents knew all answers).
Source: Dataset, authors’ calculations
5.1.2. Financial Interest
Survey participants were asked to judge the following statement:
“How closely have you followed the developments during the financial crisis?”
The participants were again able to categorize themselves in a Likert-type scale with four
options. The range went from bracket one for very close observation (i.e. active search for
information about financial crisis) to bracket four for no observation at all. Nobody made use
of the opportunity to refuse to answer or to say that one does not know.
Table 13 indicates that the relationship between financial interest and financial knowledge
seems strongly positive on an overall basis. People who followed the financial crisis very
closely (and who are therefore assumed to be highly interested in financial matters) are
performing significantly better with an overall score of 65% compared to counterparts with
high interest (53%), low interest (43%) and no interest (21%).69
68
Survey participants were also asked about how intensively they thought about their financial future. This alternative form of financial
engagement reveals comparable results with 74% of people who claim to be very highly financially engaged also partly or fully disagreeing with
the statement that they were not thinking about their financial future. This number is considerably lower among people without financial
engagement (64%) or low engagement (68%). Refer also to section 5.1.4. for more information.
69
The questionnaire also assessed the knowledge of respondents about the Swiss depositor insurance system (Einlegerschutz). The knowledge
about the depositor insurance system can also be taken as an indicator of financial interest of a respondent as there was a political debate about
changes in the system (i.e. tentative increase of coverage from CHF 30’000 to CHF 100’000) which was also closely followed by the Swiss press.
83.56% of respondents with very high financial interest confirmed the existence of a deposit insurance in Switzerland compared to 78.21% for
respondents with high interest, 59.21% for respondents with low interest and 53.66% for respondents with no interest. Furthermore, 65.30% of
very highly interest respondents knew that the deposit insurance coverage increased in the last three years compared 41.90% for highly interest
individuals, 22.57% of little interested people and 21.95% of individuals with no interest at all.
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Table 13: Statistical Test of Financial Interest and Financial Literacy
Characteristics
Very High Interest vs. High Interest
High Interest vs. Low Interest
Low Interest vs. No Interest
Score
65.30
53.36
43.06
N
219
716
483
Score
53.36
43.06
20.73
N
716
483
82
Difference
11.94 ***
10.30 ***
22.33 ***
Test-Stat.
3.12
3.50
3.82
P-Value
0.00
0.00
0.00
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Financial Literacy: Financial literacy is measured as share of respondents answering all three questions correctly (e.g. 65.30% of 219 respondents who were very highly
interested in financial matters knew all answers).
Source: Dataset, authors’ calculations
5.1.3. Risk Characteristics
Survey participants were asked to judge the following statement:
“While considering the capital in your bank accounts and investment portfolios - How much
risk are you ready to bear if you may lose part of that money.”
The participants were able to give their view on a Likert-type scale of one (no risk at all) to six
(high risk). There was also the opportunity to not answer the question or to say that one does
not know, what 31 respondents actually did. The six risk categories have been reduced to
three cohorts (while merging two cohorts into one) in order to better facilitate evaluation.
As shown in Table 11, there is no distinct pattern in terms of risk aversion and financial
literacy across questions. While people who were not willing or able to judge their risk
aversion performed poorest in all three questions and on an overall basis, moderately risk
averse people seem to have performed best overall. Interestingly, less than 73% of people
considering themselves as highly risk averse were able to answer the question about risk
diversification correctly – a number below average. It may be that people who are aware of
their inferior knowledge about risk diversification consequently behave in a risk adverse way.
5.1.4. Financial Planning
Survey participants were asked to judge the following statement:
“You are stuck in the day to day live and don’t think about the future in terms of financial
matters?”
The participants were again able to categorize themselves in a Likert-type scale with four
options. The range went from bracket one which states that the statement is totally wrong to
bracket four which states that the statement is fully correct. 14 respondents made use of the
opportunity to refuse to answer or to say that one does not know.
72% or 1’077 respondents argued that the statement is at least rather wrong and
consequently are assumed to carry out at least some financial planning. 409 or 27% of
respondents indicated that they did only little or no financial planning at all. However, Table
14 shows that the difference in financial literacy between the levels of financial planning are
rather small as compared to other personal characteristics. Consequently, they are not
statistically significantly different from zero while applying reasonable confidence levels.
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Table 14: Statistical Test of Financial Planning and Financial Literacy
Characteristics
Significant vs. Some Planning
Some vs. Little Planning
Little vs. No Planning
Score
53.09
51.72
46.44
N
582
495
267
Score
51.72
46.44
42.25
N
495
267
142
Difference
1.37
5.28
4.19
Test-Stat,
0.45
1.39
0.81
P-Value
0.65
0.16
0.42
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Financial Literacy: Financial literacy is measured as share of respondents answering all three questions correctly (e.g. 53.09% of 582 sign. planner knew all answers).
Source: Dataset, authors’ calculations
5.2.
Investment Portfolio
Firstly, the relationship between financial literacy and investment portfolio ownership will be
analyzed with univariate statistics. In a next step there will be a multivariate regression
analysis performed to investigate the relationship between the two variables in more detail.70
5.2.1. Univariate Statistics
36% or 536 of survey participants reported to currently have an investment portfolio. Table
15 shows that those respondents who said that they currently have an investment portfolio
have statistically significant (1% confidence level) better financial literacy scores on all three
questions separately but also on an aggregate level compared to the 964 respondents
without investment portfolio.
Furthermore, there is also strong statistical evidence that people without an investment
portfolio do more often admit to not know a response or refuse to answer. It appears as if
individuals who report to not know the answer actually recognize their financial illiteracy.
Table 15: Statistical Test of Investment Portfolio Ownership and Financial Literacy
Investment Portfolio
Overall All 3 Correct
Overall ≥1 DK
Interest Rates
Inflation
Risk Diversification
Yes
64.37
7.46
85.26
87.50
83.96
N
536
536
536
536
536
No
42.22
22.20
75.93
73.34
67.63
N
964
964
964
964
964
Difference
22.15 ***
-14.74 ***
9.33 ***
14.16 ***
16.33 ***
Test-Stat.
8.22
-7.29
4.27
6.39
6.86
P-Value
0.00
0.00
0.00
0.00
0.00
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Financial Literacy: Financial literacy is measured as share of respondents with resp. without investment portfolio answering the question(s) correctly.
Source: Dataset, authors’ calculations
5.2.2. Multivariate Regression Results
Table 16 reports OLS estimates of the effects of financial literacy and other demographic,
economic and financial variables on investment portfolio ownership. The dependent variable
investment portfolio ownership is a dummy variable with 0 for no investment portfolio
ownership and 1 for investment portfolio ownership.71
70
Table A4 shows demographic, economic and financial characteristics split between participants with and without investment portfolio.
71
The results from the linear probability model in Table 16 are confirmed while applying a probit or logit model (with average marginal effects) in
order to account for the dichotomous nature of the dependent variable. Estimates for coefficients of the independent variables derived from probit
or logit regression analysis are characterized through very similar levels of statistical significance as those included in the linear probability model
applied. However, the size of the coefficients for the financial wealth variables are somewhat lower and the coefficients for the financial interest
variables are somewhat higher in the probit and logit regressions as compared to the OLS regression in Table 16. Refer to Table A19 which
includes probit and logit regression results for the financial literacy definition all correct.
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The regression results in Table 16 imply a statistically significant relationship between
financial literacy and investment portfolio ownership. A higher level of financial literacy comes
with an increased likelihood of investment portfolio ownership in all aggregate financial
literacy measures. Interestingly, the inflation and risk diversification questions have higher
statistical relevance for the relationship with investment portfolio ownership than the interest
rates question which, however, in univariate statistics was still statistically significant.72
The coefficients indicate that a person who can answer all three questions correctly is 10
percentage points more likely to have an investment portfolio compared to a person who
cannot respond correctly to all questions. At the same time, for every additional question a
person admits to not know or refuses to answer, the likelihood of investment portfolio
ownership decreases by 13 percentage points.
A statistically significant relationship does also exist between investment portfolio ownership
and financial wealth. Coefficients are rather high and results in the first column are indicating
that a person with financial wealth above CHF 1 Mio. has a 37 percentage points higher
probability of owning an investment portfolio compared to a person with financial wealth
below CHF 50’000. Nationality also prevails as having a statistically significant relationship
with investment portfolio ownership in all types of financial literacy definitions. Swiss are
more likely to have investment portfolios.
Also of statistical significance is a person’s risk aversion. Individuals who are less risk averse
are more likely to have an investment portfolio. Nevertheless, financial interest, engagement
and planning seem to have lesser of an impact on investment portfolio ownership. However,
very high interest in financial matters is significantly positive related to a higher probability of
investment portfolio ownership.
The coefficients of the financial literacy variables are indicating that the relationship between
financial literacy and investment portfolio ownership is somewhat weaker than the one
between financial wealth and investment portfolio ownership. However, one cannot neglect
that a person answering one additional financial literacy question correctly has a seven
percentage point higher chance of owning an investment portfolio.
5.2.3. Direction of Causality and Omitted Variables
The OLS regression analysis in Table 16 provides evidence of a statistically significant
relationship between financial literacy and investment portfolio ownership. However,
estimates included in the regression analysis may suffer from serious restrictions and
limitations.
72
The F-values of around 16 and p-values of 0.00 in the multiple regression analysis in Table 16 indicate that investment portfolio ownership is
unlikely to be independent of the set of independent variables used. There is essentially no chance of getting a relationship this strong just by
chance. R2 values of around 0.27 show that the set of independent variables are able to explain around 27% of the variation in investment portfolio
ownership, a value which is considerably higher than those reported in multivariate analysis of comparable studies as reported in Exhibit A6. For a
discussion about multicollinearity (i.e. VIF, eigenvalues and condition indices) refer to Table A13.
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Table 16: Multivariate Linear Regression – Investment Portfolio
Multivariate OLS Regression – Investment Portfolio (d)
Financial Literacy - All Correct (d)
0.10
(0.03)
***
Financial Literacy - Score Correct
0.07
(0.02)
***
Financial Literacy - Interest Rates (d)
0.02
(0.03)
0.07
(0.03)
0.11
(0.03)
Financial Literacy - Inflation (d)
Financial Literacy - Risk Diversification (d)
**
***
Financial Literacy - Score DK
Gender
Women (d)
Age
Age
Nationality
Foreigner (d)
Education
Primary School (d)
Secondary School (d)
Professional Education (d)
Grammar School (d)
University (Applied) (d)
University (d)
Occupation
Employed (d)
Unemployed (d)
Household Size
Number of People in Household
Household Income
Household Income
Financial Wealth
< CHF 50'000 (d)
CHF 50'000 - 100'000 (d)
CHF 100'000 - 250'000 (d)
CHF 250'000 - 1 Mio. (d)
> CHF 1 Mio. (d)
Financial Interest
No Interest (d)
Low Interest (d)
High Interest (d)
Very High Interest (d)
Financial Engagement
No Engagement (d)
Low Engagement (d)
High Engagement (d)
Very High Engagement (d)
Risk Characteristics
High Risk Aversion (d)
Morderate Risk Aversion (d)
Low Risk Aversion (d)
Financial Planning
No Planning of Financial Future (d)
Little Planning of Financial Future (d)
Some Planning of Financial Future (d)
Significant Planning of Financial Future (d)
_cons
N
R2
F
-0.13
(0.03)
***
*
-0.04
(0.03)
-0.04
(0.03)
-0.04
(0.03)
-0.05
(0.03)
0.00
(0.00)
0.00
(0.00)
0.00
(0.00)
0.00
(0.00)
-0.09
(0.04)
**
-0.08
(0.04)
*
-0.08
(0.04)
*
-0.09
(0.04)
omitted
omitted
omitted
omitted
-0.03
(0.14)
0.01
(0.13)
0.06
(0.13)
0.08
(0.13)
-0.02
(0.14)
-0.05
(0.14)
-0.01
(0.13)
0.03
(0.13)
0.06
(0.13)
-0.02
(0.14)
-0.07
(0.14)
-0.04
(0.13)
0.02
(0.13)
0.04
(0.13)
-0.04
(0.14)
-0.02
(0.14)
0.01
(0.13)
0.06
(0.13)
0.09
(0.13)
0.02
(0.13)
-0.04
(0.04)
0.00
(0.06)
-0.05
(0.04)
-0.00
(0.06)
-0.05
(0.04)
0.00
(0.06)
-0.04
(0.04)
0.01
(0.06)
0.00
(0.01)
0.01
(0.01)
0.00
(0.01)
0.00
(0.01)
0.00
(0.01)
0.00
(0.01)
0.00
(0.01)
0.01
(0.01)
omitted
omitted
omitted
omitted
0.21
(0.03)
0.37
(0.04)
0.49
(0.05)
0.37
(0.11)
***
***
***
***
0.21
(0.03)
0.37
(0.04)
0.48
(0.05)
0.37
(0.11)
***
***
***
***
0.22
(0.03)
0.37
(0.04)
0.48
(0.05)
0.37
(0.11)
***
***
***
***
0.22
(0.03)
0.38
(0.04)
0.49
(0.05)
0.39
(0.11)
omitted
omitted
omitted
omitted
0.06
(0.06)
0.06
(0.06)
0.20
(0.07)
0.06
(0.06)
0.06
(0.06)
0.20
(0.07)
0.06
(0.06)
0.06
(0.06)
0.19
(0.07)
0.07
(0.06)
0.07
(0.06)
0.20
(0.07)
***
***
***
omitted
omitted
omitted
omitted
0.03
(0.04)
0.04
(0.04)
0.02
(0.04)
0.03
(0.04)
0.04
(0.04)
0.02
(0.04)
0.03
(0.04)
0.04
(0.04)
0.02
(0.04)
0.03
(0.04)
0.04
(0.04)
0.02
(0.04)
omitted
0.11
(0.03)
0.16
(0.06)
omitted
***
***
0.11
(0.03)
0.16
(0.06)
omitted
***
***
0.11
(0.03)
0.16
(0.06)
***
0.09
(0.03)
0.14
(0.06)
omitted
omitted
omitted
0.05
(0.05)
0.03
(0.04)
0.07
(0.04)
0.05
(0.05)
0.03
(0.04)
0.07
(0.04)
0.05
(0.05)
0.03
(0.04)
0.07
(0.04)
0.05
(0.05)
0.04
(0.04)
0.08
(0.04)
-0.25
(0.16)
-0.32
(0.17)
1'250
0.2702
16.15
1'250
0.2726
16.34
-0.29
(0.17)
1'250
0.2750
15.41
***
***
***
***
***
omitted
***
omitted
*
**
*
***
***
*
-0.19
(0.17)
1'250
0.2710
16.21
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Dependent Variable: Investment Portfolio Ownership (0 = no portfolio (n=792), 1 = portfolio (n=458)) as dummy variable.
Explanatory Variables: Numbers in brackets are standard errors. (d) stands for dummy (0/1) variable. Household Income is based on ordinal scale with values ranging
from 1 (lowest income) to 6 (highest income).
Sample: The sample of n=1’500 has been adjusted for respondents answering: Other for occupation (n=14), DK for number of people in household (n=2), DK for income
(n=148), DK for wealth (n=178), DK for planning for financial future (n=14), DK for engagement (n=9), DK for risk aversion (n=31) and missing value for consumption credit
(n=1). Out of a total adjustment of n=397, 115 have been double, 13 triple and 2 four-times counted resulting in a sample size for the regression of n=1’250.
Source: Dataset, authors’ calculations
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The variable financial literacy may itself be endogenous as people become more financial
literate due to their investment experience. It may well be that the ownership of an
investment portfolio results in a better understanding of finance due to learning effects
(reverse causality). Furthermore, financial literacy may have been measured with error and
certain influential variables may have been omitted in the model potentially leading to biased
estimates. Finally, respondents may have reacted sensitive to how questions were asked and
there exists the possibility of guessing.73 (cf. Van Rooij et al., 2011)
Instrumental Variable (IV)
In order to take account of those pitfalls an IV estimation can be applied. An IV approach
allows to re-estimate the impact of financial literacy on investment portfolio ownership while
controlling for potential reverse causality and effects from omitted variables.74
A valid instrumental variable has to be correlated with the (potentially) endogenous variable
financial literacy and uncorrelated with the error term. A valid instrument should therefore not
have a direct linkage to investment portfolio ownership but an indirect relationship through
the variable financial literacy. The requirement that valid instruments are uncorrelated with
the error term can in general not be statistically tested and therefore needs a strong
theoretical argument. (Schmidheiny, 2012, p. 7)
Valid instruments are typically derived from natural or random experiments. (Angrist &
Krueger, 2001) However, finding a valid and strong instrumental variable is often very hard in
practice. (cf. Van Rooij et al., 2012) Finding a valid instrument has proven to be particularly
challenging in the setting of the multivariate regression in Table 16 with many theoretically
sound instruments failing to fulfill the necessary statistical tests.
Statistical Diagnostic of Applied Instruments
Table A14 includes a list of instruments which have been applied but for which the statistical
diagnostic revealed that they were often weak and failed to be valid. The instrumental
variables tested have been identified while following the rational that financial literacy of
respondents depends on the financial knowledge of individuals living in their surroundings. It
is assumed that respondents who are surrounded by highly financial knowledgeable
individuals can learn from them and become more literate themselves. Furthermore, it is not
73
Reverse causality: Investment portfolio ownership in this study refers to both types of ownership - discretionary and non-discretionary.
Learning effects may be present in both cases. When opening a discretionary investment portfolio with a Swiss bank one supposedly receives
information about the risk and return characteristics of various asset classes due to compliance regulations. Furthermore, while running an
investment depot one receives notifications for coupon and dividend payments as well as end-of-year investment statements. On the other hand,
while giving a bank an investment mandate, one usually gets information about the portfolio development during the investment period with a
description of the underlying economic developments. Consequently, in both cases one could assume that an individual can improve its
knowledge about risk diversification, inflation and interest rates while owning an investment portfolio.
Omitted variable: An example of an omitted variable is a significant inheritance. An inheritance of a large amount of money has likely an impact
on investment portfolio ownership. However, it was not possible to include the variable in the model as no data about inheritances was collected.
74
Instrumental variable approaches have been followed by many researchers in conjunction with studies about financial literacy and retirement
planning. Refer to Bernheim et al., 2001, Behrman et al., 2010, Lusardi & Mitchell, 2011A, or Table A15.
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expected that the high density of financial knowledgeable individuals is connected with
investment portfolio ownership of a respondent.75
The statistical diagnostic reveals that none of the instruments identified boasts an F-statistic
above the often cited threshold of 10 which would allocate high strength to an instrument.
Hardly any of the instruments tested have a statistically significant relationship with financial
literacy as also shown by the small t-statistics in the first stage regression which was
performed for the “score correct” and “all correct” financial literacy definitions. Even less
significant are the t-statistics in the second stage or IV regression where investment portfolio
ownership is the dependent variable. Durbin-Wu-Hausman test statistics indicate that the null
hypothesis of the exogeneity of financial literacy cannot be rejected in any case tested while
applying a 5% confidence level.
Due to the high p-values in the Durbin-Wu-Hausman tests one may argue that, on the basis
of the instruments applied and the underlying rational, there are some doubts in the
assumption that financial literacy is endogenous. While considering the test results achieved
one is advised to better stay with OLS estimates than to use IV estimates.
5.2.4. Discussions
While it has been difficult to draw on an IV approach to proof the direction of causality there
are some promising findings of other researchers which can be referred to at this stage.
Considering again the statistically significant characteristics found in Table 16, it gets visible
that financial wealth has the highest positive coefficients followed by risk appetite and
financial literacy.
Fascinatingly, the findings in this study are reflecting the responses given in a related survey
of 2’000 individuals in Switzerland conducted by the University of Zurich in 2010 which asked
non-shareholders why they did not hold shares. Most people responded that they did not
have the money (37%) followed by the fact that they did not want to take risks (28%) and
lack of knowledge (18%). (Birchler, Volkart, Ettlin, & Hegglin, 2011, p. 21)
Taking into consideration the findings above one may reasonably conclude that the lack of
knowledge results in people not having investment portfolios rather than the lack of
investment portfolio ownership resulting in less knowledge of individuals. At least for the
people not having an investment portfolio, which are 792 of the 1’250 individuals included in
the regression analysis, it seems as causality is largely running from knowledge to
investment portfolio ownership rather than opposite.
75
The same logic has been followed by a number of other researchers. Table A15 includes an overview of instrumental variables which have been
used by other researchers in order to instrument financial literacy.
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Wealth and Investment Portfolio Ownership
The very large coefficients of financial wealth indicate that wealth is critically linked to
portfolio ownership while the very low coefficients for household income are surprisingly
pointing towards a negligible impact of income on portfolio ownership.76
Survey participants were asked about the gross monthly income of their household. The fact
that individuals with a portfolio are double as likely to be retired and above the age of 60
years compared to people without portfolio may give some information on why wealth is
important for investment portfolio ownership rather than income (refer also to Table A4).
There may also be discussions about the direction of causality with financial wealth being
seen as a result of investment portfolio ownership. This view is somewhat challenged by the
structure of the coefficients for the various wealth categories. The wealth category
coefficients show a statistically significant positive relationship with portfolio ownership which
is peaking at a coefficient of 0.49 in the category CHF 250’000-1 Mio. rather than increasing
up to the category >CHF 1 Mio..77 The wealth category coefficients show a concave structure
with a saturation point rather than a monotonous increasing structure. Furthermore, there are
already high coefficients at low wealth levels such as the wealth level CHF 50’000-100’000
which arguably provides evidence that higher wealth is causing individuals to have
investment portfolios and effects running opposite are potentially weaker.
Low Risk Aversion Comes with Higher Investment Portfolio Ownership
Individuals who reported to have little risk aversion are 16 percentage points more likely to
have an investment portfolio than individuals with a high risk aversion.78
It seems as there exists a paradigm that investing is inherently risky, irrespective of the fact
that investments may also be solely focused on secure bonds rather than more risky assets.
Such a misperception of the riskiness of investments was reported by Bachmann & Hens,
2011, who used data from a representative online survey of 1’000 people in Switzerland and
reported that half of the respondents assessed the risk of asset classes wrongly. It may
therefore well be that risk averse people believe that investing is inherently risky and therfore
do not have an investment portfolio.
However, the findings may also reflect the reaction of many individuals to the dotcom bubble
and the recent financial crisis. Many retail investors lost considerable amounts of money
76
Coefficients remain low and standard errors elevated while splitting income into dummy variables reflecting the same categories as shown in
Table 5.
77
However, Wald tests of equality of the coefficients indicate statistically significant differences for all preceding wealth categories apart from the
category >CHF 1 Mio. which is non-significantly different from the category CHF 250’000-1 Mio.. Furthermore, there is awareness of the different
sizes of the wealth buckets. Nevertheless, there is also the argument that professional wealth managers actively target individuals in higher wealth
categories also resulting in wealthier individuals becoming more likely to own investment portfolios rather than opposite.
78
While considering the characteristics of individuals in regards to risk aversion one can see that 2.7% of women compared to 6.2% of men
assessed themselves to have a low risk aversion. In terms of age, 3.4% of people above the age of 60 years considered themselves to have a low
risk aversion compared to 5.4% of people between the age of 20-39 years. Furthermore, graduates from primary or secondary school are much
more likely to assess themselves as having low risk aversion as compared to their better educated counterparts. Furthermore, high risk aversion is
decreasing in household income and to a large extent also in financial wealth.
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during those stock market meltdowns and consequently decided to close their investment
portfolios and became more risk averse.79
Those crises may thereby have resulted in a segregation with investors with a higher risk
appetite remaining invested and less risk seeking individuals closing their portfolios. The
relationship between risk aversion and portfolio ownership may well lose on intensity once
capital markets are becoming less volatile again as it seems as many (risk averse)
individuals have closed their portfolios as a reaction to past investment experience.
5.2.5. Conclusions
Financial wealth, risk appetite and financial literacy are positively related to investment
portfolio ownership while household income has less of an impact. While the direction of
causality is hard to be proven, related research underpins the view that financial knowledge
has a meaningful causal effect on investment portfolio ownership.
5.3.
Consumption Credit and Mortgage Debt
Firstly, the relationship between financial literacy and consumption credit as well as
mortgage debt will be analyzed with univariate statistics. In a next step there will be a
multivariate regression analysis performed to investigate the relationship between the two
variables in more detail.80
5.3.1. Univariate Statistics
Table 17: Statistical Test of Consumption Credit/ Mortgage Debt and Financial Literacy
Consumption Credit
Overall All 3 Correct
Overall ≥1 DK
Interest Rates
Inflation
Risk Diversification
Yes
45.07
19.72
74.65
69.01
74.65
N
Mortgage Debt
Overall All 3 Correct
Overall ≥1 DK
Interest Rates
Inflation
Risk Diversification
Yes
56.77
14.12
81.80
83.26
75.98
N
71
71
71
71
71
No
50.35
16.81
79.48
78.85
73.39
N
1’428
1’428
1’428
1’428
1’428
Difference
-5.28
2.91
-4.83
-9.84 **
1.26
Test-Stat.
-0.87
0.64
-0.98
-1.97
0.23
P-Value
0.39
0.52
0.33
0.05
0.81
No
44.53
19.31
77.12
74.29
71.34
N
687
687
687
687
687
Difference
12.24 ***
-5.19 ***
4.68 **
8.97 ***
4.64 **
Test-Stat.
4.72
-2.67
2.23
4.21
2.03
P-Value
0.00
0.01
0.03
0.00
0.04
813
813
813
813
813
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Financial Literacy: Financial literacy is measured as share of respondents with resp. without consumption credit or mortgage debt answering the question(s) correctly.
Source: Dataset, authors’ calculations
Only 5% or 71 individuals reported to currently have an outstanding consumption credit.
Table 17 shows that respondents with a consumption credit perform worse as compared to
79
The proportion of people holding shares directly decreased from 30% in 2000 to only 17% in 2010. (Birchler et al., 2011, p. 5) Furthermore, the
relationship between risk aversion and investments is also highlighted by the fact that in a study conducted by the University of Zurich, 87% of
participants reported “security” as the pivotal criteria in the selection of their investments in 2010. (Birchler et al., 2011, p. 41) It seems as even
those more risk seeking investors still in the market focused on security of their investments rather than performance.
80
Table A5 and A6 show demographic, economic and financial characteristics split between participants with and without consumption credit and
mortgage debt.
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their counterparts without credit. However, the difference in financial literacy is only
statistically significant (5% significant level) for the inflation question.
On the other hand, 46% or 687 of respondents reported to currently have an outstanding
mortgage. Table 17 shows that respondents with mortgage debt perform better than their
counterparts without a mortgage. The differences in financial literacy are statistically
significant in univariate statistics.
5.3.2. Multivariate Regression Results
Table 18 reports OLS estimates of the effect of financial literacy and other demographic,
economic and financial variables on consumption credit and mortgage indebtedness.81 The
dependent variables consumption credit and mortgage debt are dummy variables which take
a value of 0 for respondents with no debt and 1 for respondents with debt.82
It becomes visible that the characteristics of mortgage debtors are distinctly different from
consumption credit debtors. While mortgage debtors are on average more financial literate,
financial literacy coefficients for consumption credit indebtedness are negligible. On an
aggregate level, there is statistical evidence that a person who answered all three questions
correctly is five percentage points more likely to having a mortgage.83
In addition, there is statistically significant evidence that consumption credit borrowers are
more likely to be male and foreigners whereas gender matters less with mortgage debt and
mortgage borrowers are more likely to be Swiss.
In terms of financial characteristics, there is statistically significant evidence that mortgage
loans are especially common with the highest wealth cohort while with consumption credits
the negative coefficients indicate a decreasing probability of indebtedness with wealth levels
statistically significant up to CHF 250’000. It seems as if there is a saturation effect at wealth
levels of around CHF 250’000 with customers becoming less likely to still apply for
consumption credits at this level (Table A5 shows that only two respondents with financial
wealth above CHF 250’000 reported to have a consumption credit).
81
The only change in control variables to the multivariate regression analysis in Table 16 is that the variable Impulsive Behavior is replacing
Financial Planning. The substitution has been conducted as financial planning rather than impulsive behavior seemed to be of interest for
investment portfolio ownership and vice-versa for consumption and mortgage indebtedness. The questionnaire included a self-assessment
question about an individual’s impulsiveness in relation to its purchase experience. Participants were asked to judge their impulsiveness on a
Likert-type scale of one (no impulsive behavior) to four (very frequent impulsive behavior).
82
The results from the linear probability model in Table 18 are confirmed while applying a probit or logit model (with average marginal effects) in
order to account for the dichotomous nature of the dependent variables. Estimates for coefficients of the independent variables derived from probit
or logit regression analysis are characterized through very similar levels of statistical significance as those included in the linear probability model
applied. However, the magnitude of the OLS estimates in the consumption credit regression in Table 18 deviate somewhat from the probit and
logit estimates in Table A19 due to the small number of respondents reporting to have a consumption credit (n=61 after adjustments). The size of
the coefficients for the variables nationality and very frequent impulsive behavior are lower in the probit and logit regressions as compared to the
OLS regression in Table 18. Refer to Table A19 which includes probit and logit regression results for the financial literacy definition all correct.
83
The F-values of around 4 resp. 13 and p-values of 0.00 in the multiple regression analysis in Table 18 indicate that consumption credit resp.
mortgage indebtedness is unlikely to be independent of the set of regressors used. There is essentially no chance of getting relationships this
strong just by chance. R2 values of around 0.09 resp. 0.23 show that the set of independent variables are able to explain around 9% resp. 23% of
the variation in consumption credit resp. mortgage indebtedness. The data shows that the explanatory power is considerably higher in the
mortgage regression. However, R2 values of around 9% are still comparable to those reported in multivariate analysis of comparable studies as
reported in Exhibit A6. Table A16 analysis the multicollinearity of the regressors included in Table 18.
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Table 18: Multivariate Linear Regression – Consumption Credit resp. Mortgage Debt
Multivariate OLS Regression - Consumption Credit (d)
Financial Literacy - All Correct (d)
0.05
(0.03)
Financial Literacy - Score Correct
0.03
(0.02)
-0.01
(0.02)
-0.01
(0.02)
0.01
(0.01)
Financial Literacy - Inflation (d)
Financial Literacy - Risk Divers. (d)
0.03
(0.03)
0.02
(0.03)
0.03
(0.03)
Financial Literacy - Score DK
Age
Age
Nationality
Foreigner (d)
Education
Primary School (d)
Secondary School (d)
Professional Education (d)
Grammar School (d)
University (Applied) (d)
University (d)
Occupation
Employed (d)
Unemployed (d)
Household Size
Number of People in Household
Household Income
Household Income
Financial Wealth
< CHF 50'000 (d)
CHF 50'000 - 100'000 (d)
CHF 100'000 - 250'000 (d)
CHF 250'000 - 1 Mio. (d)
> CHF 1 Mio. (d)
Financial Interest
No Interest (d)
Low Interest (d)
High Interest (d)
Very High Interest (d)
Financial Engagement
No Engagement (d)
Low Engagement (d)
High Engagement (d)
Very High Engagement (d)
Risk Characteristics
High Risk Aversion (d)
Morderate Risk Aversion (d)
Low Risk Aversion (d)
Impulsive Behavior
No Impulsive Behavior (d)
Rare Impulsive Behavior (d)
Regular Impulsive Behavior (d)
Very Frequent Impulsive Behavior (d)
_cons
N
R2
F
**
-0.00
(0.01)
Financial Literacy - Interest Rates (d)
Gender
Women (d)
Multivariate OLS Regression - Mortgage Debt (d)
0.00
(0.01)
0.02
(0.02)
-0.03
(0.01)
*
-0.00
(0.00)
0.13
(0.02)
-0.03
(0.01)
**
-0.00
(0.00)
***
0.13
(0.02)
-0.03
(0.01)
**
-0.00
(0.00)
***
0.13
(0.02)
-0.03
(0.01)
-0.04
(0.03)
**
-0.00
(0.00)
***
0.12
(0.02)
***
-0.00
(0.03)
-0.01
(0.03)
-0.01
(0.03)
-0.01
(0.03)
0.01
(0.00)
***
0.01
(0.00)
***
0.01
(0.00)
***
0.01
(0.00)
***
-0.13
(0.04)
***
-0.13
(0.04)
***
-0.13
(0.04)
***
-0.13
(0.04)
***
omitted
omitted
omitted
omitted
omitted
omitted
omitted
omitted
0.06
(0.07)
0.07
(0.06)
0.07
(0.07)
0.04
(0.07)
0.06
(0.07)
0.06
(0.07)
0.08
(0.06)
0.07
(0.07)
0.04
(0.07)
0.06
(0.07)
0.06
(0.07)
0.07
(0.06)
0.07
(0.07)
0.04
(0.07)
0.06
(0.07)
0.06
(0.07)
0.08
(0.06)
0.07
(0.07)
0.04
(0.07)
0.06
(0.07)
0.02
(0.14)
0.03
(0.14)
0.03
(0.14)
0.05
(0.14)
-0.02
(0.14)
0.01
(0.14)
0.02
(0.14)
0.02
(0.14)
0.05
(0.14)
-0.02
(0.14)
0.01
(0.14)
0.02
(0.14)
0.03
(0.14)
0.05
(0.14)
-0.02
(0.14)
0.02
(0.14)
0.03
(0.14)
0.04
(0.14)
0.06
(0.14)
-0.01
(0.14)
0.01
(0.02)
-0.01
(0.03)
0.01
(0.02)
-0.01
(0.03)
0.01
(0.02)
-0.01
(0.03)
0.01
(0.02)
-0.01
(0.03)
0.01
(0.04)
-0.06
(0.06)
0.01
(0.04)
-0.06
(0.06)
0.01
(0.04)
-0.06
(0.06)
0.01
(0.04)
-0.06
(0.06)
0.00
(0.00)
0.00
(0.00)
0.00
(0.00)
0.00
(0.00)
0.10
(0.01)
***
0.10
(0.01)
***
0.10
(0.01)
***
0.10
(0.01)
***
0.00
(0.01)
0.00
(0.01)
0.00
(0.01)
0.00
(0.01)
0.05
(0.01)
***
0.05
(0.01)
***
0.05
(0.01)
***
0.06
(0.01)
***
omitted
-0.04
(0.02)
-0.05
(0.02)
-0.04
(0.02)
-0.06
(0.05)
omitted
***
***
-0.04
(0.02)
-0.05
(0.02)
-0.04
(0.02)
-0.05
(0.05)
omitted
***
***
-0.04
(0.02)
-0.05
(0.02)
-0.04
(0.02)
-0.05
(0.05)
omitted
***
***
-0.04
(0.02)
-0.05
(0.02)
-0.04
(0.02)
-0.05
(0.05)
***
***
omitted
omitted
omitted
omitted
0.05
(0.03)
0.04
(0.04)
-0.00
(0.05)
0.23
(0.11)
0.05
(0.03)
0.04
(0.04)
0.00
(0.05)
0.23
(0.11)
0.05
(0.03)
0.04
(0.04)
-0.00
(0.05)
0.23
(0.11)
0.05
(0.03)
0.04
(0.04)
0.00
(0.05)
0.24
(0.11)
**
**
**
omitted
omitted
omitted
omitted
omitted
omitted
omitted
omitted
-0.02
(0.03)
-0.04
(0.03)
-0.02
(0.03)
-0.02
(0.03)
-0.04
(0.03)
-0.02
(0.03)
-0.02
(0.03)
-0.04
(0.03)
-0.02
(0.03)
-0.02
(0.03)
-0.04
(0.03)
-0.02
(0.03)
0.02
(0.07)
0.02
(0.06)
0.03
(0.06)
0.02
(0.07)
0.02
(0.06)
0.03
(0.06)
0.03
(0.07)
0.02
(0.06)
0.03
(0.06)
0.03
(0.07)
0.02
(0.06)
0.03
(0.06)
omitted
omitted
omitted
omitted
omitted
-0.00
(0.02)
0.01
(0.02)
0.01
(0.02)
-0.00
(0.02)
0.01
(0.02)
0.01
(0.02)
-0.00
(0.02)
0.01
(0.02)
0.01
(0.02)
-0.00
(0.02)
0.01
(0.02)
0.01
(0.02)
0.08
(0.05)
0.15
(0.04)
0.15
(0.05)
omitted
omitted
omitted
omitted
omitted
0.00
(0.01)
0.01
(0.03)
0.00
(0.01)
0.01
(0.03)
-0.00
(0.01)
0.00
(0.03)
0.00
(0.01)
0.01
(0.03)
-0.05
(0.03)
-0.15
(0.06)
omitted
0.03
(0.01)
0.05
(0.03)
0.16
(0.05)
omitted
**
*
***
0.03
(0.01)
0.05
(0.03)
0.16
(0.05)
omitted
**
*
***
0.03
(0.01)
0.05
(0.03)
0.16
(0.05)
omitted
**
*
***
0.03
(0.01)
0.05
(0.03)
0.17
(0.05)
**
*
***
omitted
*
***
***
0.08
(0.05)
0.15
(0.04)
0.15
(0.05)
omitted
*
***
***
omitted
*
**
-0.05
(0.03)
-0.16
(0.06)
0.08
(0.05)
0.15
(0.04)
0.15
(0.05)
omitted
*
***
***
omitted
*
**
-0.05
(0.03)
-0.16
(0.06)
0.09
(0.05)
0.15
(0.04)
0.15
(0.05)
**
-0.06
(0.03)
-0.16
(0.06)
omitted
omitted
omitted
0.03
(0.03)
0.02
(0.06)
-0.06
(0.10)
0.03
(0.03)
0.02
(0.06)
-0.07
(0.10)
0.03
(0.03)
0.02
(0.06)
-0.07
(0.10)
0.03
(0.03)
0.01
(0.06)
-0.08
(0.10)
0.12
(0.08)
0.12
(0.08)
0.10
(0.08)
-0.99
(0.17)
1'248
0.0891
4.26
1'248
0.0893
4.27
1'248
0.0900
4.01
1'248
0.0910
4.36
1'248
0.2351
13.38
***
-1.02
(0.17)
1'248
0.2340
13.30
***
-1.02
(0.18)
1'248
0.2341
12.40
***
-0.97
(0.17)
1'248
0.2338
13.28
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Dependent Variables: Consumption Credit (0 = no credit (n=1’187), 1 = credit (n=61)) resp. Mortgage Debt (0 = no debt (n=673), 1 = debt (n=575)) as dummy variables.
Explanatory Variables: Numbers in brackets are standard errors. (d) stands for dummy. Household Income based on ordinal scale ranging from 1 (lowest) to 6 (highest).
Sample: The sample of n=1’500 has been adjusted for respondents answering: Other for occupation (n=14), DK for number of people in household (n=2), DK for income
(n=148), DK for wealth (n=178), DK for planning for financial future (n=14), DK for engagement (n=9), DK for risk aversion (n=31), DK for impulsive behavior (n=2) and
missing value for consumption credit (n=1). Out of a total adjustment of n=399, 115 have been double, 13 triple and 2 four-times counted resulting in a sample size for the
regression of n=1’248.
Source: Dataset, authors’ calculations
Roman Graf
*
***
***
omitted
*
omitted
0.11
(0.08)
***
59/79
**
***
***
Financial Literacy and Financial Behavior in Switzerland
Interestingly, household income and household size are only of statistical relevance for
mortgage holders. While applying for a mortgage loan, banks in Switzerland consider
household income as one key factor in order to determine what size of mortgage a
household can carry. However, one would also have assumed that income plays an even
more decisive role with consumption credits which are typically not backed by any other form
of collateral.
From a personal behavior point of view it becomes observable that impulsive behavior is
strongly positively related to consumption credit indebtedness but not to mortgage debt. On
the other hand, engagement in the daily management of financial matters seems to be
relevant with mortgage holders only.
The same holds true for risk appetite which is only statistically negatively related to mortgage
debt. One may argue that owning property financed with mortgage debt is more risky than
renting and highly risk averse people pay down their mortgage faster than little risk averse
people. However, rather high statistically significant negative coefficients indicate that
mortgage owners are risk averse and potentially believe owning a property is less risky.
5.3.3. Discussions
As highlighted in Table A5 there are only 71 (resp. 61 after adjustments) individuals reporting
to currently have a consumption credit. This small sample size makes it hard to find
statistically significant relationships in multivariate regression analysis.
Self-Control and Impulsive Behavior84
Exhibit 9 highlights that impulsive behavior is strongly positively related to consumption credit
indebtedness and negatively related to mortgage debt.
Exhibit 9: Private Indebtedness by Impulsive Behavior
Consumption Credit Indebtedness
Mortgage Indebtedness
22%
25%
20%
47%
35%
40%
15%
26%
30%
12%
10%
5%
50%
47%
20%
7%
3%
10%
0%
0%
No Impulsive
Behavior
Rare
Impulsive
Behavior
Regular
Impulsive
Behavior
Very
Frequent
Impulsive
Behavior
No Impulsive
Behavior
Rare
Impulsive
Behavior
Regular
Impulsive
Behavior
Very
Frequent
Impulsive
Behavior
Indebtedness: Ratio of respondents with consumption resp. mortgage debt to total respondents in cohort.
Source: Dataset, authors’ calculations
84
In this study lack of self-control is used synonym to impulsive behavior. Self-control can be characterized as an intrapersonal decision timeinconsistency problem. (Gathergood, 2012, p. 590) The close interrelation between the two terms is also highlighted by the trade-off individuals
face between ‘excessive’ and ‘impulsive’ immediate consumption and a consumption–saving rule requiring the exercise of self-control for its
implementation. (Benhabib & Bisin, 2005, p. 462)
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Financial Literacy and Financial Behavior in Switzerland
Gathergood, 2012, examined the relationship between self-control, financial literacy and
over-indebtedness on consumer credit debt among UK consumers and provides empirical
evidence of a positive relationship between lack of self-control and greater use of quickaccess but high cost credit items. Further findings point out that both financial illiteracy and
lack of self-control have a positive impact on consumer over-indebtedness, with the lesser
exerting a stronger influence. Self-control problems have also been used as explanation for
high levels of credit card borrowings in theoretical literature. (cf. Heidhues & Koszegi, 2010)
As mentioned, previous research emphasizes that impulsive behavior is strongest related to
readily accessible credit products such as credit card borrowings. One would have assumed
that borrowing with consumption credits is less clear-cut and impulsiveness would therefore
play less of a role.
However, while there was no information collected about over-indebtendess of survey
respondents, the data about consumer credit indebtedness in Table 18 and Exhibit 9
confirms that impulsive behavior is prone to consumption credit debt. The same linkage is
not statistically proven for financial illiteracy in a multivariate analysis while the univariate
analysis in Table 17 allocates higher overall financial literacy scores to individuals without
consumption credit – however, on no stastically signficiant grounds.
Concluding on findings from this study one may argue that it is the behavior characteristic
impulsiveness which prevails as significant driver of consumption credit indebtedness rather
than financial knowledge.
Who Demonstrates Impulsive Behavior?
In order to better understand the characteristics of people acting in an impulsive manner a
multivariate regression analysis with the dependent variable impulsive behavior has been
included in Table A17. The dependent variable impulsive behavior is a dummy variable with
a value of 0 for respondents showing no impulsive behavior and 1 for respondents showing
at least some impulsive behavior. The analysis includes the same control variables as the
model in Table 18.
While refraining from drawing on conclusions about causality, impulsive behavior seems to
be more prominent with wealthier individuals and, unexpectedly, people assessing
themselves to be highly risk averse and very highly engaged in financial matters. Ironically,
as impulsive behavior is positively related to consumption credit indebtedness there is some
indirect effect resulting in very risk averse people potentially being more likely to end up
having consumption credit debt than less risk averse people.85
85
One may also argue that bad experience with (existing) consumption credits may result in impulsive individuals becoming more risk averse. A
higher risk aversion would thereby result from bad personal experiences of individuals. However, while considering the whole sample, the share of
consumption credit holders is 4.43% for the 994 highly risk averse people, 4.39% for the 410 moderately risk averse people, 7.69% for the 65 less
risk averse people and 12.90% for the people not declaring the level of their risk aversion. Univariate statistics are supporting the hypothesis that
more risk seeking individuals are more likely to have consumption credits.
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Financial Literacy and Financial Behavior in Switzerland
While financial literacy is statistically significant negatively related to impulsive behavior, the
general level of education does not imply a significant relationship. More financial literate
individuals are less likely to show impulsive behavior with a person answering all three
questions correctly being 5 percentage points less likely to act at least to a certain degree in
an impulsive manner.
5.3.4. Conclusions
It can be concluded that consumption credit indebtedness is positively related to impulsive
behavior while no significant relationship exists with financial literacy. Impulsive behavior at
the same time is more common among rich and highly risk averse people as well as less
financially literate individuals.
5.4.
Retirement Account
Firstly, the relationship between financial literacy and retirement account ownership will be
analyzed with univariate statistics. In a next step there will be a multivariate regression
analysis performed to investigate the relationship between the two variables in more detail.86
5.4.1. Univariate Statistics
41% or 610 respondents reported to have a retirement account with a bank.87 As indicated in
Table 19 there is strong statistical evidence (1% confidence level) that people with retirement
accounts know more about interest rates, inflation and risk diversification than people without
retirement accounts. There is an overall 16 percentage point higher share of people with
retirement account knowing all three questions as compared to people without an account.
Table 19: Statistical Test of Retirement Account Ownership and Financial Literacy
Retirement Account
Overall All 3 Correct
Overall ≥1 DK
Interest Rates
Inflation
Risk Diversification
Yes
59.84
11.48
83.93
84.26
81.15
N
610
610
610
610
610
No
43.48
20.67
76.07
74.38
68.20
N
890
890
890
890
890
Difference
16.36 ***
-9.19 ***
7.86 ***
9.88 ***
12.95 ***
Test-Stat.
6.22
-4.66
3.69
4.57
5.58
P-Value
0.00
0.00
0.00
0.00
0.00
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Financial Literacy: Financial literacy is measured as share of respondents with resp. without retirement account answering the question(s) correctly.
Source: Dataset, authors’ calculations
86
Table A7 shows demographic, economic and financial characteristics split between participants with and without retirement account.
87
A retirement account (Säule 3a Vorsorgekonto) receives special tax treatment under Swiss legislation and allows individuals to save for their
retirement in a tax efficient manner. Individuals saving through retirement accounts are able to annually claim payments to the accounts for tax
purposes but are charged with a tax charge (at a reduced tax rate) once they withdraw their savings. While opening a retirement account,
depositors have to decide upon the savings solutions they want to select. Many banks offer pure savings accounts with a variable interest rate but
also investment solutions which invest up to 50% into stocks. The numbers above only refer to the retirement accounts provided by banks while
disregarding retirement solutions from insurance companies (cf. Exhibit 4). The term retirement account in this master thesis is used as a proxy for
the tax preferred retirement accounts only (Säule 3a Vorsorgekonto).
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Financial Literacy and Financial Behavior in Switzerland
5.4.2. Multivariate Regression Results
Table 20 reports OLS estimates of the effect of financial literacy and other demographic,
economic and financial variables on retirement account ownership. 88 The dependent variable
retirement account ownership is a dummy variable which takes a value of 0 for respondents
with no retirement account and 1 for respondents with retirement account.89
Financial literacy has a statistically significant positive relationship with retirement account
ownership while being defined as score of correct answers or all answers correct. Answering
one additional question correctly results in a five percentage points higher chance of owning
a retirement account while keeping the other variables unchanged.90
Nationality prevails as having a statistically significant relationship with retirement account
ownership in all types of financial literacy definitions. Foreigners are less likely to have
retirement accounts. The absolute value of the coefficients of around 0.12 are high while
compared to financial literacy coefficients. Even stronger, however, is the relationship
between unemployment and saving through retirement accounts. The coefficient indicates
that an unemployed person is around 15 percentage point less likely to save through
retirement accounts.
Considering education one can see that higher education generally increases the likelihood
of saving account ownership. As also shown in Table A7, people educated at grammar
school or higher are relatively over-represented in the retirement account cohort. However,
education does not prevail as being statistical significantly related to retirement account
ownership in a multivariate setting. Financial wealth and household income have a positive
relationship with retirement account ownership which is especially significant at higher
household income and lower financial wealth levels.
88
The changes in control variables to the multivariate regression analysis in Table 16 are that the variable Risk Characteristics has been excluded
and the variable Household Income has been accounted for through dummy variables in place of an ordinal scale variable to increase explanatory
power. The Risk Characteristics variable has been excluded as risk aversion considerations seem to be of less interest while analyzing retirement
account ownership (the risk characteristics coefficients are not statistically significant when being included in the regression analysis in Table 20).
89
The results from the linear probability model in Table 20 are confirmed while applying a probit or logit model (with average marginal effects) in
order to account for the dichotomous nature of the dependent variable. Estimates for coefficients of the independent variables derived from probit
or logit regression analysis are characterized through very similar levels of statistical significance as those included in the linear probability model
applied. However, the size of the coefficients for the household income variables are somewhat higher and the coefficients for the financial wealth
variables are somewhat lower in the probit and logit regressions as compared to the OLS regression in Table 20. Refer to Table A19 which
includes probit and logit regression results for the financial literacy definition all correct.
Sample: Swiss law allows withdrawals of money from retirement accounts starting from the completed 60 iest birth year for men and the 59th for
women (BVV3). In order to circumvent adverse tax progression effects, many individuals gradually withdraw their retirement account deposits
starting already before their retirement. Any withdrawals are thereby taxed separately at a reduced tax rate (cf. DBG Art. 38 & DBG Art. 214).
Retirement account contributions of individuals who did not contribute to the occupational pension system (often individuals below the age of 25
years) result in a reduced tax deduction allowance within general allowances (cf. DBG Art. 212). The tax deduction allowance for insurance
nd
rd
nd
premiums and interest income is linked to contributions to the 2 or 3 pillar in Switzerland. As the compulsory 2 pillar contributions (and usually
also the voluntary 2nd pillar solutions) start with the 25th birth year there is hardly any tax benefit to be derived from contributions to the 3rd pillar
prior to the 25th birthday.
For the purpose of the multivariate regression analysis in Table 20, only individuals between the age of 20-65 years have been included. Even
though there are typically rather small tax benefits for individuals opening retirement accounts below the age of 25 years, they have still been
included in the analysis as some occupational pension systems may already accumulate savings before the compulsory 25 th birth year or some
individuals may have contributed to retirement accounts neglecting the reduced tax benefits. Out of the 35 individuals aged below 25 years in the
analysis, four reported to have a retirement account.
90
The F-values of around 6 and p-values of 0.00 in the multiple regression analysis in Table 20 indicate that retirement account ownership is
unlikely to be independent of the set of regressors used. There is essentially no chance of getting a relationship this strong just by chance. R2
values of around 0.15 show that the set of independent variables are able to explain around 15% of the variation in investment portfolio ownership.
Table A18 analysis the multicollinearity of the regressors included in Table 20.
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Financial Literacy and Financial Behavior in Switzerland
Table 20: Multivariate Linear Regression – Retirement Account
First Stage Regression
All Correct (d)
Multivariate OLS Regression - Retirement Account (d)
Financial Literacy - All Correct (d)
0.06
(0.03)
*
Financial Literacy - Score Correct
1.00
(0.52)
0.05
(0.02)
0.03
(0.04)
0.05
(0.04)
0.07
(0.03)
Financial Literacy - Inflation (d)
Financial Literacy - Risk Diversification (d)
*
Financial Literacy - Score DK
-0.04
(0.04)
Instrumental Variables
FDP (d)
Political Activism (d)
Age
Age
Nationality
Foreigner (d)
Education
Primary School (d)
Secondary School (d)
Professional Education (d)
Grammar School (d)
University (Applied) (d)
University (d)
Occupation
Employed (d)
Unemployed (d)
Household Size
Number of People in Household
Household Income
< CHF 4'500 (d)
CHF 4'500 - 7'000 (d)
CHF 7'000 - 9'000 (d)
CHF 9'000 - 12'000 (d)
CHF 12'000 - 15'000 (d)
> CHF 15'000 (d)
Financial Wealth
< CHF 50'000 (d)
CHF 50'000 - 100'000 (d)
CHF 100'000 - 250'000 (d)
CHF 250'000 - 1 Mio. (d)
> CHF 1 Mio. (d)
Financial Interest
No Interest (d)
Low Interest (d)
High Interest (d)
Very High Interest (d)
Financial Engagement
No Engagement (d)
Low Engagement (d)
High Engagement (d)
Very High Engagement (d)
Financial Planning
No Planning of Financial Future (d)
Little Planning of Financial Future (d)
Some Planning of Financial Future (d)
Significant Planning of Financial Future (d)
_cons
N
R2
F
F of instruments
Hansen J-Test
*
**
Financial Literacy - Interest Rates (d)
Gender
Women (d)
IV Regression
Ret. Account (d)
0.09
(0.05)
0.07
(0.05)
**
***
-0.00
(0.03)
0.00
(0.03)
-0.00
(0.03)
-0.01
(0.03)
-0.18
(0.03)
0.00
(0.00)
0.00
(0.00)
0.00
(0.00)
0.00
(0.00)
-0.00
(0.00)
-0.12
(0.05)
**
-0.12
(0.05)
**
-0.12
(0.05)
**
-0.13
(0.05)
***
-0.17
(0.05)
0.16
(0.10)
0.00
(0.00)
***
0.04
(0.11)
omitted
omitted
omitted
omitted
omitted
omitted
0.20
(0.17)
0.13
(0.16)
0.18
(0.17)
0.18
(0.16)
0.20
(0.17)
0.19
(0.17)
0.12
(0.16)
0.18
(0.17)
0.17
(0.16)
0.19
(0.17)
0.18
(0.17)
0.11
(0.16)
0.17
(0.17)
0.16
(0.16)
0.18
(0.17)
0.20
(0.17)
0.13
(0.16)
0.19
(0.17)
0.19
(0.16)
0.21
(0.17)
-0.08
(0.16)
-0.01
(0.16)
0.00
(0.16)
0.13
(0.16)
0.14
(0.16)
0.26
(0.16)
0.13
(0.14)
0.18
(0.15)
0.05
(0.17)
0.05
(0.18)
-0.05
(0.04)
-0.07
(0.09)
0.08
(0.06)
-0.09
(0.13)
-0.01
(0.01)
0.01
(0.02)
omitted
omitted
0.04
(0.04)
-0.15
(0.08)
**
0.00
(0.01)
***
***
***
***
0.12
(0.06)
0.17
(0.07)
0.20
(0.07)
0.26
(0.08)
0.20
(0.08)
***
***
0.14
(0.04)
0.23
(0.04)
0.23
(0.06)
0.05
(0.14)
*
***
***
***
**
0.12
(0.06)
0.17
(0.07)
0.20
(0.07)
0.26
(0.08)
0.20
(0.08)
***
***
0.14
(0.04)
0.23
(0.04)
0.23
(0.06)
0.05
(0.14)
*
omitted
*
***
***
***
***
omitted
***
0.04
(0.04)
-0.15
(0.09)
0.00
(0.01)
omitted
*
omitted
***
0.04
(0.04)
-0.15
(0.08)
0.00
(0.01)
omitted
**
omitted
0.14
(0.04)
0.23
(0.04)
0.23
(0.06)
0.05
(0.14)
*
0.00
(0.01)
omitted
0.12
(0.06)
0.18
(0.07)
0.21
(0.07)
0.27
(0.08)
0.21
(0.08)
0.04
(0.04)
-0.16
(0.08)
0.13
(0.06)
0.18
(0.07)
0.22
(0.07)
0.28
(0.08)
0.22
(0.08)
**
***
***
***
***
omitted
***
***
**
0.14
(0.04)
0.24
(0.04)
0.24
(0.06)
0.05
(0.14)
0.09
(0.06)
0.14
(0.06)
0.17
(0.07)
0.19
(0.07)
0.27
(0.08)
**
**
***
***
omitted
***
***
**
0.02
(0.04)
0.15
(0.04)
0.10
(0.06)
0.08
(0.12)
0.04
(0.08)
0.06
(0.10)
0.06
(0.11)
0.10
(0.13)
-0.03
(0.17)
omitted
***
*
0.12
(0.05)
0.09
(0.10)
0.13
(0.10)
-0.03
(0.19)
omitted
omitted
omitted
omitted
omitted
0.01
(0.07)
0.04
(0.07)
0.13
(0.08)
0.01
(0.07)
0.04
(0.07)
0.13
(0.08)
0.01
(0.07)
0.04
(0.07)
0.13
(0.08)
0.02
(0.07)
0.05
(0.07)
0.13
(0.08)
0.21
(0.08)
0.17
(0.07)
0.15
(0.07)
omitted
omitted
omitted
omitted
omitted
omitted
0.02
(0.05)
0.01
(0.05)
0.00
(0.05)
0.02
(0.05)
0.01
(0.05)
0.01
(0.05)
0.02
(0.05)
0.01
(0.05)
0.01
(0.05)
0.02
(0.05)
0.01
(0.05)
0.01
(0.05)
0.04
(0.05)
0.02
(0.05)
-0.00
(0.05)
-0.02
(0.07)
-0.01
(0.07)
0.00
(0.07)
omitted
omitted
omitted
omitted
omitted
omitted
0.05
(0.06)
0.08
(0.06)
0.11
(0.06)
0.05
(0.06)
0.08
(0.06)
0.11
(0.06)
0.05
(0.06)
0.08
(0.06)
0.11
(0.06)
0.05
(0.06)
0.08
(0.06)
0.11
(0.06)
-0.03
(0.06)
0.01
(0.06)
0.00
(0.05)
0.08
(0.08)
0.08
(0.08)
0.10
(0.08)
*
*
*
*
*
*
**
omitted
***
**
**
-0.05
(0.15)
-0.11
(0.13)
-0.12
(0.12)
-0.26
(0.20)
-0.32
(0.20)
-0.31
(0.20)
-0.24
(0.20)
0.14
(0.19)
-0.39
(0.21)
1‘127
0.1481
6.35
1‘127
0.1503
6.46
1‘127
0.1507
6.07
1‘127
0.1465
6.27
1’127
0.1634
9.65
3.58
1’127
*
0.06
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Dependent Variable: Retirement Account Ownership (0 = no account (n=621), 1 = account (n=506)) resp. All Correct as dummy variable in first stage regression.
Explanatory Variables: Numbers in brackets are standard errors. (d) stands for dummy (0/1) variable.
Sample: The sample of n=1’500 has been adjusted for respondents answering: Other for occupation (n=14), DK for number of people in household (n=2), DK for income
(n=148), DK for wealth (n=178), DK for planning for financial future (n=14), DK for engagement (n=9), DK for risk aversion (n=31), missing value for consumption credit
(n=1) and respondents with age above 65 years (n=161) . Out of a total adjustment of n=558, 116 have been double, 30 triple and 3 four-times counted resulting in a
sample size for the regression of n=1’127 (refer to footnote 89 for a discussion of the age adjustment).
Source: Dataset, authors’ calculations
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Financial interest, financial engagement and interestingly even financial planning seem to
have less of an impact on retirement account ownership. Coefficients are low and there is no
statistical evidence for a distinct impact of those variables on retirement account ownership.
5.4.3. Direction of Causality and Omitted Variables
The OLS regression analysis in Table 20 implies a statistically significant relationship
between financial literacy and retirement account ownership. However, estimates included in
the regression analysis may suffer from serious restrictions and limitations.
As with investment portfolio also with retirement account ownership there remains the
question about the direction in which causality is running. It again may well be that the
ownership of a retirement account results in a better understanding of finance due to learning
effects (reverse causality). Financial literacy may thereby be endogenous itself as people
become more financial literate due to their exposure to savings and investment decisions.
Furthermore, financial literacy may have been measured with error which could also have
contributed to biased estimates. Finally, respondents may have reacted sensitive to how
questions were asked and there exists the possibility of guessing. (cf. Van Rooij et al., 2011)
In order to take account of those limitations an IV estimation will be applied. An IV approach
allows to re-estimate the impact of financial literacy on investment portfolio ownership while
controlling for potential reverse causality and effects from omitted variables.
Instrumental Variable (IV)
A valid instrumental variable has to be highly correlated with the (potentially) endogenous
variable financial literacy and uncorrelated with the error term. A valid instrument should
therefore not have a direct linkage to retirement account ownership but an indirect
relationship through the variable financial literacy. (Schmidheiny, 2012, p. 7)
Financial literacy is instrumented by the exposure to financial knowledge and financial
interest of others in the same region. The assumption thereby is that people who are
exposed to financially knowledgeable and financially interested people become more
financially knowledgeable themselves. Furthermore, it is assumed that financial knowledge
and interest of others is beyond the scope of the respondents and does not directly impact
the fact if a respondent has a retirement account.
Political Attitude and Political Activism
The first hypothesis is that the exposure to financially knowledgeable individuals in regions
with a low share of liberal right-wing voters is smaller than in regions with a high share of
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liberal right-wing voters. The voting shares of the political party “FDP.Die Liberalen” in the
national elections in October 2011 have been taken as a proxy for financial knowledge.91
The second hypothesis is that financial interest and the desire to make one’s financial
knowledge known to others is more prominent with individuals who are politically active in
regards to votes about financial topics. The regional participation rates in the national vote
about the adjustment of the retirement rent conversion factor in March 2010 have been taken
as proxy for financial interest.92
Statistical Diagnostic of Instrumental Variables
The first stage regression highlights that the instrument reflecting the voting share of the
“FDP.Die Liberalen” has a positive statistically significant relationship with financial literacy
while political activism has less statistical relevance in the setting applied. The second stage
regression underpins that the instrumented financial literacy variable has a statistically
significant positive impact on retirement account ownership.
Table 21 includes a number of statistical tests which have been applied to test the validity
and strength of the instrumental variables applied.
Table 21: Statistical Diagnostic of Instrumental Variables
First Stage Regression Summary Statistics
Variable
Financial Literacy –
All Correct
R2
Adj. R2
0.1634
0.1397
Part. R2
0.0062
F-Stat.
3.58
P-Value
Durbin-WuHausman Test
F-Stat.
0.03
6.16
P-Value
Hansen J-Test
X2-Score
0.01
0.06
P-Value
0.81
Source: Dataset, authors’ calculations
The test results as included above will be analyzed on an item-by-item basis.
F-Statistic
The F-statistic allows to test the joint significance or strength of both instruments. The Fstatistic of 3.58 and p-value of 0.03 indicates that both instruments are jointly significantly
different from zero while applying a confidence level of up to 3%. However, the partial R2 is
just 0.0062 what raises some questions about the strength of the instruments. Furthermore,
Staiger & Stock, 1997, argue that instruments can be considered weak if F-statistics are
below 10 what is the case with the two instruments applied.93
91
FDP.Die Liberalen is the major liberal party in Switzerland and emerged from the merger performed on a national level in 2009 between the
FDP and the significantly smaller Swiss Liberal Party. Currently, FDP.Die Liberalen has 30 representatives in the national parliament and is the
third strongest party in Switzerland on a national level (Nationalrat) with a share of 15.1% of all seats. Voting shares of FDP.Die Liberalen have
been collected on postal code level and a dummy variable has been defined which takes a value of 1 for the postal codes which are in the highest
10% of FDP.Die Liberalen voting shares and 0 otherwise.
92
On March 7, 2010 there was a national vote about the adjustment of the rent conversion rate in the compulsory occupational pension system so
as to account for the changed demographic and economic landscape in Switzerland. Voting participation rates have been collected on postal code
level and a dummy variable has been defined which, consistently with the FDP.Die Liberalen voting shares, takes a value of 1 for the postal codes
which are in the highest 10% of voting participation rates and 0 otherwise.
93
There is awareness of the rather low F-statistics which are a result of the difficulties in finding a set of instruments with more predictive power.
However, at the same time there can be referred to other studies such as Bucher & Lusardi, 2011, which included instruments in their regression
analysis which were characterized through F-statistics for the instruments of around 4 as well.
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Durbin-Wu-Hausman Test
With the Durbin-Wu-Hausman Test the endogeneity of financial literacy can be tested. The
null hypothesis is that financial literacy is exogenous. The F-statistic of 6.16 and the
corresponding p-value of 0.01 indicate that the null hypothesis can be rejected up to a
confidence level of 1%. Consequently, it can be said with a reasonable high certainty that
financial literacy is endogenous in the setting applied.
Hansen J-Test
The Hansen J-Test examines the over-identifying restrictions. The test can be applied as two
instrumental variables (FDP voting share and vote participation rates) are used to instrument
one endogenous variable (financial literacy). The test assumes that one instrument is valid
(i.e. exogenous) and then tests for the validity of the other instrument. It allows to make a
statement about whether the instruments are uncorrelated with the error term in the second
stage regression or not. The X2-score of 0.06 and p-value of 0.81 does not allow to reject the
over-identifying restrictions while applying a meaningful confidence level. Hence, it cannot be
concluded that the instruments do not satisfy the orthogonality conditions and thus it cannot
be said that the instruments are not valid.
Interpretation
The IV approach applied gives some evidence that the direction of causality is going from
financial literacy to retirement account ownership rather than opposite. While analyzing the
IV regression results, however, one has to keep in mind the rather low F-statistic for the
instruments derived from the first stage regression.
5.4.4. Discussions
The old-age pension system in Switzerland is often considered as a well thought through
construction which is also referred to as an example of best practice by multiple international
organizations. (BSV, 2008) Irrespectively, 45% of 1’000 respondents of a representative
survey of eligible Swiss voters published by Credit Suisse mentioned that they look at the
old-age pension system in Switzerland with concern. The Swiss pension system thereby
ranks second highest on a scale reflecting the worries of Swiss, just after unemployment.
(Credit Suisse, 2010, p. 6)
Having those two findings in mind and considering that 506 out of 1’127 or 45% of
respondents reported to have a retirement savings account one may raise the question if
people hold retirement accounts mainly for tax or genuinely for retirement saving purposes.
This question is especially relevant as no significant relationship (on 5% confidence level or
smaller) could be found between financial planning or engagement and retirement account
ownership.
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Upper Income and Wealth Quintiles Use Retirement Accounts
Overall, it seems as household income and financial wealth are the key variables
determining if someone has a retirement account or not. The high coefficients of the different
financial income and wealth levels paired with statistical significance indicate their
importance.
There is an annual contribution cap to retirement accounts making the tax favored saving
especially interesting for medium income earners rather than very prosperous individuals for
whom the favorable tax effects loose on relative importance.94 However, the regression
results show the highest coefficients of around 0.27 with the income bracket CHF 12’00015’000. In terms of financial wealth and retirement account ownership, coefficients are
peaking at the wealth bracket CHF 100’000-250’000 resp. CHF 250’000-1 Mio. reaching
values of around 0.23. Out of those numbers one may conclude that tax efficient saving
through retirement accounts is especially favored by households in the upper (but not top)
income and wealth classes.95
Nationality Gap in Retirement Account Ownership
The eligibility for tax preferred saving through retirement accounts does not depend on any
nationality criteria. However, the process through which the derived tax benefits can be
claimed is different between Swiss and foreigners with certain visa types.96
The majority of foreigners living in Switzerland are in possession of permanent residence
permissions. However, there may still be a knowledge barrier or some foreigners may still
have their pension solution from their home country. They may consider Switzerland only as
their home for a temporary time and may therefore decide not to save for retirement through
Swiss retirement solutions. Furthermore, as many of them may not have gone through the
Swiss education system they are unlikely to know the Swiss retirement and tax system well.
Only the labor force is eligible for savings in the form of retirement accounts. Table 22
indicates that foreigners have higher labor force participation rates than Swiss irrespective of
94
The contribution cap to retirement accounts is dependent on the question if an individual is insured in the second pillar (BVG) or not. For 2012
the maximum contribution amounts were CHF 6’682 (8% of upper limit according to BVG 80 I) for individuals with BVG insurance coverage and
CHF 33’408 (40% of upper limit) for those without BVG coverage.
95
The gross monthly wage (median) in Switzerland was CHF 5’933 in 2010. (BfS, 2011B) The SNB reported average net wealth of households of
CHF 341’262 for 2010. However, the SNB includes real estate and pension assets while deducting liabilities such as mortgages to get average net
wealth. (SNB, 2011) The wealth number used in this study is a gross number neglecting real estate ownership and liabilities.
The conclusions are supported by Wald tests of the equality of the coefficients of two neighboring wealth brackets. The null hypothesis of equal
coefficients can thereby be rejected on a 5% confidence level with the wealth categories <CHF 50’000 and CHF 50’000-100’000 as well as CHF
50’000-100’000 and CHF 100’000-250’000. The corresponding F-values are 15.68 resp. 4.01 and the p-value 0.000 resp. 0.045. There is a
statistically significant difference between the coefficients of the wealth categories. Wald tests for income brackets result in the rejection of the null
hypothesis of equal coefficients for the income bracket <CHF 4’500 and CHF 4’500-7’000 (F-value: 3.99, p-value: 0.046).
96
Foreigners without permanent residence permission (Niederlassungsbewilligung C) working in Switzerland are subject to source taxes
(Quellensteuern) which are deducted monthly from their salary and handed in to the tax office by their employer. If such foreigners make
contributions to retirement accounts, they are asked to hand in an application with their contribution confirmation in order to receive reimbursement
of some of their deducted source taxes.
Foreigners with permanent residence permission or without permanent residence but with gross labor income exceeding a state set threshold (e.g.
state of Zurich CHF 120’000 annually) are subject to a tax assessment based on their income and wealth situation. They are required to hand in a
tax declaration as Swiss do (incl. any claim on retirement account contributions) and get their source tax paid considered in the tax assessment.
Foreigners and Swiss leaving Switzerland permanently are eligible to claim the savings in retirement accounts (with the regular tax implications
which applies with a withdrawal). However, a person leaving Switzerland may also decide to keep the money in the retirement account and claim it
when retiring. In this case a source tax (Quellensteuer) applies in place of the regular taxation, which, however, is usually more favorable. (BVV 3)
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gender. This findings are also consistent with our sample where out of the 109 foreigners
included in the regression analysis, 85.3% are employed compared to 79.2% of Swiss.
Table 22: Standardized Labor Force Participation Rates in Switzerland
Men
75.5
Women
60.8
- Swiss
73.5
- Swiss
59.7
- Foreigners
82.0
- Foreigners
65.5
Standardized Labor Force Participation Rate: Includes individuals with age 15 years and older.
Foreigners: Permanent residents (short term visitors only if at least 12 months in Switzerland).
Source: BfS, 2012E
As all respondents in the survey reported to be living in Switzerland for more than five years,
it may be of pivotal importance to better educate foreigners about the Swiss retirement and
tax system in order to ensure a higher coverage of foreigners also in the third pillar.
Furthermore, as highlighted in Table 20, unemployed people are far less likely to have a
retirement account. This fact is especially noteworthy as workers with no employment are no
longer insured against age in the second pillar. Longer periods of unemployment may
therefore cause critical gaps in a person’s retirement benefits.
5.4.5. Conclusions
Household income, financial wealth, significant financial planning and financial literacy are
statistically significantly positively related to retirement account ownership while foreign
nationality and unemployment have a negative relationship. An instrumental variable
regression provides evidence that causality is running from financial knowledge to retirement
account ownership rather than opposite. However, a low F-statistic for the instruments
applied proclaims the applied instruments to be rather weak.
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6.
Policy Implications and Conclusions
This chapter discusses implications for policy makers in Switzerland arising from the findings
about financial literacy as described previously. The aim is to outline the magnitude of efforts
in the area of financial literacy undertaken by policy makers abroad and to reflect on actions
already undertaken in Switzerland.
6.1.
Rationale for Improving Financial Literacy in Switzerland
As stated by Gale & Levine, 2010, improving financial literacy should be a first-order concern
for policy makers, with potential gains not only occurring for the individuals who would benefit
directly, but also for their family members and for the society at large.97
The more financially literate individuals and their families would experience a financially
secure working life and retirement while the society would also get its share through the
experience of social and economic gains from a reduced financial vulnerability of many of its
members. A better financial knowledge of the population in Switzerland would also bring
positive externalities in the form of improved decision making processes with individuals.
Such better decision making practices are of particular importance in Switzerland due to its
direct democracy and the derived high frequency of votes.
Findings of this study reveal that financial literacy levels in Switzerland are akin to other
developed countries such as Germany or the Netherlands. Even though there were no
national strategies on financial literacy propagated in Switzerland as was done in some
countries abroad, it seems as if we are not lacking behind on financial knowledge.98
However, even though the results do not call for any immediate actions, it seems to be the
wrong time to play down the importance of financial literacy in Switzerland. Instances such
as the fall of Lehman Brothers and its harmful consequences for many less financially
knowledgeable retail investors have clearly pointed out that there is still work to be done.
This holds especially true while recalling current domestic trends of transferring an increasing
number of risks to individuals.
Financial knowledge is particularly low among women, elderly, foreigners and poorly
educated individuals and some of those groups seem to be exposed the highest to the new
challenges. Furthermore, the positive externalities of financial literacy as seen in a higher
propensity to owning investment portfolios and retirement accounts underpin the rational for
improving financial literacy.
97
For a discussion about the relationship between financial literacy and welfare costs refer to Williams & Satchell, 2011.
98
One may argue that the financial literacy levels in Switzerland are surprisingly high while considering the rather small efforts undertaken in the
past to foster financial knowledge (refer to Table 23). Habschick, Seidl, & Evers, 2007, analyzed the financial literacy initiatives undertaken in the
EU-27 countries and concluded that Germany plays an active role in the field of financial literacy and constitutes the second-most active member
state after the UK. They identified 40 schemes in Germany which are mostly provided in classrooms and which mainly target children or young
adults. Hence, an explanation for the small difference in financial literacy between Switzerland and Germany may be the fact that the initiatives in
Germany may not yet have had its impact on the adult population which was used as focus group in this study.
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6.2.
Information Challenge
The survey as presented in this paper provides insight into the status quo of financial literacy
in Switzerland and its relationship to financial behavior. However, the study has limitations in
regards to statements about multiple aspects of financial literacy such as debt literacy or
investment knowledge and can be considered as providing the big picture rather than an
accurate drawing of the financial knowledge level in Switzerland.99
As indicated in the literature review, other researchers often defined financial literacy rather
broadly including knowledge about areas such as retirement, debt, insurance or numeracy.
Consequently, in order to get a more accurate picture of the areas where financial literacy is
lacking the most among the population in Switzerland, there should be follow-on studies with
a focus on the various aspects of financial literacy.
Furthermore, prospective studies should be focused on Switzerland as a whole as compared
to this study which was focused on the German speaking part of Switzerland only.
6.2.1. High Fragmentation of Data Collection in Switzerland
Table A20 highlights that many countries such as the US with its Jump$tart survey or
Australia with its ANZ survey have implemented financial literacy surveys which provide data
about the development of financial literacy levels across time and on granular level.
Longitudinal studies or regularly conducted surveys focused on financial literacy are still
missing in Switzerland. So far, information relating to financial knowledge has been gathered
in the form of add-ons to multiple studies with broad arrays of rationales. While this study
ensured international comparability of financial literacy results, there is still a high level of
fragmentation in information gathering in Switzerland when it comes to defining financial
literacy at a more granular level.100
Regularly conducted studies with consistent questions focused on financial literacy (no selfassessment) would be of critical importance to measure and track the success of any policy
measures undertaken in the field of financial literacy. As done with the HRS survey in the
US, it could be thought about attaching a financial literacy add-on in the Integrated System
for Household and Personal Statistics (SHAPE) framework managed by the BfS or the part
of the SHARE survey which covers the elderly population in Switzerland.101
99
The study does, for instance, provide information about the relationship between financial literacy and consumption credit indebtedness on the
basis if someone has a consumption credit. However, the survey does not provide information about the fact if an individual can be considered to
be in financial hardship because of the consumption credit or if the credit does just provide some bridge financing without causing any further
challenges with the debtor. In addition, the survey is cross-sectional and does not allow for comparisons across different points in time.
100
More granular information about financial knowledge in Switzerland has for instance been collected through representative surveys by the
University of Zurich for knowledge about structured products (cf. Wilding et al., 2010) or investment and stock-exchange knowledge (cf. Birchler et
al., 2011). However, their assessment of the knowledge of survey participants is based on self-assessment rather than derived from the results of
relevant questions.
101
The aim of the BfS is to build with the SHAPE framework an integrated statistical information system about individuals and households until
2019. SHAPE comprises of the HABE, SAKE and SILC surveys which are conducted continuously (i.e. SAKE) or annually (other surveys). As
postulated by BfS, 2007, p. 8, thematic surveys on topics such as education should be added to the SHAPE framework with data raising on a five
year interval. Financial literacy questions may be included in an add-on to the thematic survey about education.
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6.2.2. The Question About the Requirement of a National Body and Strategy
Exhibits A2 to A5 report aggregate financial literacy scores geographically and indicate that
improving financial literacy levels is rather a national than a local challenge in Switzerland.
This holds especially true as newly proposed consumer protection regulations as well as
changes in the retirement system have a national rather than local reach. While considering
the organizational setting in regards to improving financial literacy in countries abroad one
can see that many countries have introduced national committees or offices with broader or
narrower mandates.
One can argue about the reasonableness of establishing a national body also in Switzerland
which would be in charge of the coordination of potential policy measures in the field of
financial literacy and which may define a financial literacy strategy in Switzerland. Such a
national committee on financial literacy could define the term financial literacy in the form of
national standards, act as a coordinator in raising data about the level of financial literacy,
derive appropriate policy proposals and act as an evaluator of measures taken.102 (cf. Hieber
et al., 2011)
A nationwide approach might also be supported as the school curricula for German-speaking
Switzerland are established in combined efforts rather than on pure state level.
Consequently, a national body on financial literacy could undertake important lobbying
activities and provide valuable inputs for future school curricula. Besides, it could act as a
strategic player which is representing Switzerland in international committees such as the
OECD/ INFE. Furthermore, the national body could assess international initiatives and
propose best practice measures in the field of financial literacy in Switzerland.
6.3.
Policy Implications and Action Items
Even though there was no representative data available on financial knowledge in
Switzerland, various bodies have already undertaken financial literacy related initiatives. Not
surprisingly, however, those initiatives have not always targeted the audience characterized
by particular low financial knowledge levels.
102
National bodies and strategies in regards to financial literacy have already been defined in countries such as Australia and the US.
Australia: The Australian government has asked its financial regulator, the Australian Securities and Investments Commission (ASIC), to prepare
a national financial literacy strategy which was then endorsed by the Australian Government Financial Literacy Board. With the Australian
Government Financial Literacy Board, Australia has defined a non-statutory body that provides advice to government and ASIC on financial
literacy issues. The Board consists of 12 members who meet quarterly. Refer to ASIC, 2011, for further information.
US: The President’s Advisory Council on Financial Capability, established in 2010, can be considered the national body on financial literacy in the
US. The council belongs to the Department of the Treasury and its functions include the collection of information about financial literacy (e.g.
through the FLEC), the advising of the president in regards to policy measures as well as the reporting of the financial knowledge in the US. The
Financial Literacy and Education Commission (FLEC) has developed the 2011 national strategy to promote financial literacy and education. The
FLEC comprises of 21 federal entities. For further information about the organization and functions of the President’s Advisory Council on
Financial Capability refer to The White House, 2010, and for information about the national strategy to FLEC, 2011.
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Table 23: Selected Financial Literacy Initiatives Undertaken in Switzerland
Initiative
Kinder-Cash & Potz
Tuusig
iconomix
Money & Budget
Organization
Description
Audience
Zentris AG,
Pro Juventute
Focus: Responsible money management
Target Group: Children (4-12 years)
Concept: Comprehensive teaching material for kindergarten and primary
school level, workshops and material for parents.
Kinder-Cash, the 1x1 of money for kids provides tool which helps teach
money-lessons in an age appropriate way.
Potz Tuusig, teaching material for kits in comic form.
Reference: www.kinder-cash.ch & www.potz-tuusig.ch
Teachers &
Parents
Swiss National Bank
(SNB)
Focus: Enhance economics knowledge
Target Group: Secondary School Level II students
Concept: Iconomix is a web-based tool used in the teaching of economics.
It offers a range of teaching units that can be either downloaded or ordered.
It is primarily intended for use by school teachers of economics and
humanities.
Reference: www.iconomix.ch
Teachers
Budget Consulting
Switzerland
Focus: Promotion of sound financial behavior
Target Group: People from various social backgrounds
Concept: Providing of budget examples and consulting as well as material
tailored for school education in the field of financial behavior (dedicated
section for teachers).
Reference: www.budgetberatung.ch
Teenagers,
Adults &
Teachers
Money & Budget
Debt Consulting
Switzerland
Education material &
Web-Gateway to
Financial Literacy
Swiss Bankers
Association
Focus: Responsible money management
Target Group: Teenagers and adults
Concept: Webpage which provides in a section “prevention” extensive
material about money and education. Section “youth and money” includes
selection of learn clips and testimonies of teenagers ending up in debt trap.
Reference: www.schulden.ch and www.femmestische.ch
Focus: Provide online gateway for financial literacy
Target Group: Students, adults, parents and teachers
Concept: Provide training material for the secondary school level II and
various courses for bank employees. Launch “Webweiser Financial Literacy”
as financial literacy gateway.
Reference: www.money-info.ch
Teenagers,
Parents &
Teachers
Students,
Adults, Parents
& Teachers
PostFinance
Focus: Support sound financial behavior
Target Group: Teenagers
Concept: EventManager is a modern online-game supporting sound
financial behavior. Topics covered are budgeting, financing and investing.
Reference: www.postfinance-eventmanager.ch
Teenagers
Money and
Education
FemmesTISCHE
Focus: Money and education – what parents can do
Target Group: Children
Concept: Comprehensive set of materials on how parents can educate their
children on how to deal with money. Set is available in many languages so
as to cover migrants and their families as well.
Reference: www.femmestische.ch/produkte/geld.html
Parents
“Budgetiert – Kapiert
in 90 Minuten”
Cooperation between
Plusminus and Swiss
Post
FinLiCo - Financial
Literacy
Competencies for
Adult Learners
The Swiss national
Umbrella
Organization for
Adult Education
(SVEB)
Consumer
information –
insurance basics
Swiss Insurance
Association (SIA)
EventManager
Financial Services
Provider vs.
Customer
Relationship
Swiss Design
Institute for Finance
and Banking (SDFB)
Guidebooks
Foundation for
Consumer Protection
Focus: Sound management of money (incl. budgeting and debt trap)
Target Group: Teenagers and students
Concept: School presentation including training material of 90 minutes.
Reference: www.post.ch/post-postdoc-budgetiert-kapiert-in-90minuten.pdf
Focus: Improve financial knowledge
Target Group: Adults
Concept: Toolbox for learners and guidance for teachers will be developed.
The international project runs from 2010-2012. SVEB is partnered by SER
State Secretariat for Education and Research which provides funding.
Reference: www.alice.ch/de/sveb/projekte
Focus: Improve insurance knowledge
Target Group: Adults
Concept: SIA provides on its website information on insurance basics. It
covers personal insurance but also non-life insurance.
Reference: www.svv.ch
Focus: Reflection of the financial institutions and customers relationship
Target Group: Financial institutions and consumers
Concept: The SFDB is included in the OECD database on financial
education programs. SFDB projects such as FLOW addresses the wideranging issues associated with the manner in which digital media influence
the interface between financial service providers and their customers.
Reference: www.sdfb.ch
Focus: Responsible money management
Target Group: Consumers
Concept: Provider of guidebooks covering topics such as savings,
investments and budgeting. Special publications cover youth and money as
well as how to deal with cell phone bills. The consumer protection
foundation also provides consulting.
Reference: www.konsumentenschutz.ch
Teachers
Adults
Adults
Financial
Institutions &
Consumers
Consumers
Source: OECD, 2012C, references as mentioned
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6.3.1. What Has Been Done in Switzerland?
An overview of selected initiatives undertaken in the field of financial literacy in Switzerland is
given in Table 23 which shows that there is no national strategy on how to improve financial
literacy but a set of initiatives and projects started by various public and private institutions.
Publicly and privately led initiatives covering target audiences of children, teenagers and
adults as well as customers have been undertaken. The initiatives are typically covering
financial literacy in the area of money handling and target to reduce private indebtedness,
especially among teenagers and young adults.
However, as highlighted in section 5.3. of this study no statistically significant relationship
between financial literacy and consumption credit indebtedness could be proven on the basis
of the data raised in the questionnaire. Findings from the analysis about consumption credit
indebtedness highlight the critical importance of impulsiveness rather than financial
knowledge while, however, not covering indebtedness in the form of leasing obligations or
credit card debt.
Nevertheless, one may argue that first-order policy measures and initiatives focusing on
consumer indebtedness should aim at defusing the impulsive behavior of individuals rather
than enhancing financial knowledge. While individuals can hardly be educated on selfcontrol, potential measures in this area could be seen in constraining the access to
consumption credits. In more concrete terms one could consider restricting the access to
credits at the point-of-sale or delaying the access to credits through processes which require
certain amounts of time.103
The Lack of Evaluation
As there was no representative data on financial literacy levels in Switzerland available, the
initiatives undertaken were mainly based on studies and data which unveiled problems in
areas such as youth indebtedness. Furthermore, there were no such things as systematic
evaluations of the effects of the existing programs, neither on financial literacy levels nor on
consequent financial behavior. However, as highlighted by the OECD, it is especially the
evaluation of the financial education programs which is seen as of critical importance.
(OECD, 2009C)
While considering the various initiatives it becomes clear that most of them are followed by
individuals voluntarily. On may argue that many initiatives are followed by (children of) better
educated individuals as their awareness of such programs is supposed to be higher than for
less educated people. Furthermore, programs targeted towards school curricula are often at
the mercy of the teachers if they are implemented in practice.
103
Comparable measures have also been proposed by Gathergood, 2012, who found evidence for an especially strong relationship between
impulsive behavior and indebtedness with readily accessible credit products in the UK.
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No Information about Financial Literacy of Youth
Unlike in many other countries, in Switzerland there has not been any data collected on
financial literacy of youth, neither in this study nor in any other. As there is no binding school
curricula on the topic of financial literacy, Switzerland will also not take part in the voluntary
OECD PISA 2012 financial literacy framework survey which will take place in 2012.104 (Manz,
2011, p. 60)
OECD PISA 2012 is the first large-scale international study which aims at assessing the
financial literacy of young people and which generates comparable data across countries.105
As a result, taking part in the OECD PISA financial literacy survey would have allowed local
policy makers to gather important information about the financial knowledge of students in
Switzerland and how they compare internationally.
6.3.2. Which Focus Groups and Topic Areas Have Been Neglected?
Disregarding the lacking information about the financial knowledge of youth, extensive focus
has been given to financial literacy of students and the use of money by teenagers. The
reason can be seen in increasing private indebtedness of young adults in Switzerland (refer
to section 2.2.2.).
While considering the demographic groups which were attributed low financial knowledge
levels one can see that especially people with the following characteristics have not been
given special consideration by the initiatives undertaken:106

Women

Poorly educated and low income groups

Elderly population
A glance at international efforts in the field of financial literacy will shed some light on how
Switzerland compares internationally and what weight has been given to better educating
women, disadvantaged and elderly people in other countries.
6.3.3. What Has Been Done Abroad?
There is no consistency in measures commenced by public and private policymakers and
organizations around the globe in regards to financial literacy. Actions undertaken have been
104
However, the school curricula “Projekt Lehrplan 21” is supposed to introduce mandatory lectures covering the area “money” within the subject
group “Wirtschaft, Arbeit, Haushalt” at Secondary School Level I. (EDK, 2010, p.12) Refer to section 4.2. or EDK, 2010, for further information.
105
Refer to Table A20 or OECD, 2012A, for more information about the OECD PISA 2012 financial literacy framework.
106
Foreigners have also not been targeted by any initiative explicitly. However, they have been excluded here as the financial literacy results of
foreigners as shown in this study are based on foreigners who declared to be living in Switzerland for more than five years. As those foreigners
were also able to answer the telephone questions in German, one would assume that they do not need to be targeted differently from Swiss.
However, special initiatives for foreigners may be applicable if further studies disclose major lack in financial knowledge among foreigners not yet
fully integrated in Switzerland. Germany, for instance, has with its “Fit in Finanzen” a mediation concept which explicitly targets foreigners.
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Financial Literacy and Financial Behavior in Switzerland
highly fragmented with some countries introducing promising financial literacy programs
while others have taken a rather hands-off approach.107
High Fragmentation of Initiatives in Europe
Habschick, Seidl, & Evers, 2007, analyzed the EU-27 member states in regards to their
measures and initiatives undertaken in the field of financial literacy while distributing 800
online questionnaires of which 440 were returned. They were able to identify 180 financial
literacy schemes in the countries analyzed and concluded that there had been little
information exchange between member states. While pointing out the fact that there were
already very successful and efficient methodologies in place in several member states, they
argued to concentrate on transnational cooperation in terms of defining content, curricula and
methodology of initiatives.
A closer reflection of the initiatives undertaken in the EU-27 member countries showed that
the distribution of financial literacy schemes varied among countries with most schemes
being found in the UK, Germany and Austria. Poland was at the forefront while considering
only states in Eastern Europe where many countries followed a hands-off approach. The
main target groups were children and young adults and two out of three schemes were
provided by intermediaries. (Habschick et al., 2007, p. 3)
Furthermore, Habschick et al., 2007, p. 21, report that one out of four schemes had targeted
specifically low-income or low-education groups and that every second initiative used
multiple instruments and channels with the internet being of particular importance.
Initiatives focused on women include "Women Entrepreneurs: Empowering through financial
literacy” in Malta, “Financial management in young households” in Germany or “Plan Your
Future” in Poland. The elderly population has been explicitly mentioned as target group in
schemes such as the European funded “Adult Basic Accounting and Control of Over
Indebtedness” (ABACO) project focusing on South Europe or in the campaign “The National
Pensions Awareness Campaign” (NPAC) in Ireland. The financial literacy program “Finance
& Pédagogie” in France covers especially poorly edcuated and disadvantaged people and
gives advice on general money handling matters.
Many Initiatives Focused on Youth in the United States
There has been much attention paid to financial literacy in the US with a large array of
initiatives rolled out from private and public organizations in the past. Due to the high number
of schemes in the US the focus of this snapshot will be on the Jump$tart coalition but also
the efforts undertaken by the Federal Reserve. While limiting the research on those two
107
The OECD International Gateway for Financial Education maintains a database of financial education programs undertaken in its member
countries. Those information can be accessed through www.financial-education.org/program.php.
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Financial Literacy and Financial Behavior in Switzerland
organizations, it becomes clear that students and youth in general are the single most
important focus groups in terms of financial education.108
The Jump$tart coalition maintains the national standards in personal finance education,
which serve as a program-design and evaluation framework for school administrators,
teachers, curriculum specialists, instructional materials developers, and educational
policymakers. Combined with the Jump$tart coalition's best practices guidelines, the
standards serve as a guide for state and local policymakers in implementing financial
education legislation. (Mandell, 2008, p. 11)
Measures undertaken in the field of financial education of youth by the Federal Reserve in
the US include "Life Smarts," a national quiz-based competition for high school students,
educational web sites, such as "FedVille," where students actively learn more about financial
concepts such as spending, saving, and earning interest, and FederalReserveEducation.Org,
a system-wide education web portal which offers easy access to a host of beneficial
resources geared toward students, parents, and teachers. (FED, 2012)
High Activism in Australia
Australia identified in its 2011 financial literacy strategy a number of financial literacy
programs across a range of sectors which are: community, indigenous, government,
workplace and international. Interestingly, many of those financial literacy initiatives are
sponsored or endorsed by the major Australian financial services companies.
Community programs include the campaign “Saver Plus” administered by The Brotherhood
of St Laurence and ANZ which aims to increase personal savings and financial capability of
people on low incomes or the initiative “Start Smart” an interactive program for primary and
secondary school students sponsored by the Commonwealth Bank Foundation. Indigenous
people are covered through the “Indigenous Money Mentor Network” which is run by NAB
and the Traditional Credit Union while government departments are active through a number
of programs mainly focused on money management, savings and investments. Some
employers such as the Australian Post and Westpac have special financial education
programs for employees.109
6.3.4. Potential Action Items
While looking across the border it seems as there are very few financial literacy schemes
undertaken in common efforts across national borders. Financial literacy has been dealt with
108
Financial literacy schemes are also administered by many other regional, state and national bodies such as the Consumer Financial Protection
Bureau, the President’s Advisory Council on Financial Literacy, the FLEC or the National Association of Insurance Commissioners. However,
initiatives undertaken by the national bodies mentioned are often aiming at children and students. The President’s Advisory Council, for instance,
seeks to assist the American people in understanding and addressing financial matters through various tools such as campaigns like the “National
Financial Literacy Challenge” (online test for high-school students) or the “MoneyMath” (lessons for life curriculum for 7-9 graders).
109
For a thorough description of the pathway undertaken in the Australian education system in regards to financial literacy, refer to the chapter B
of the 2011 National Financial Literacy Strategy of Australia named “Using educational pathways to build financial literacy“. (ASIC, 2011)
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Financial Literacy and Financial Behavior in Switzerland
mainly on national levels with countries formulating their own strategies and with some
countries following a “wait-and-see” strategy.
Switzerland is not able to positively distinguish itself from comparable developed countries
through its efforts carried out in the past. There is rather the impression that financial literacy
enjoys some more attention in countries such as the US, the UK or Germany than in
Switzerland. Switzerland is therefore well advised to reconsider its situation and define next
steps so as not to lose ground. Meaningful action could thereby be seen in:

Building of roundtables comprising of key stakeholders (e.g. initiative leaders, federal
and state education representatives)

Designation of coordinative national committee or related body for financial literacy

Definition of financial literacy in its various forms and formulation of strategy for data
collection and data management (e.g. financial literacy among youth)

International cooperation and information exchange
It seems paramount to sort out the fundamental institutional governance and framework first
and not to call for concrete financial literacy initiatives at the time being. Any initiative
launched instantly would be grounded on weak fundamentals in regards to which area of
financial literacy it should tackle resulting in potentially disappointing outcomes. This holds
especially true as it is vital that there exists a legitimate evaluation framework which is
capable of measuring the effectiveness and efficiency of any initiative implemented.
Especially, any calls asking for immediate actions in regards to the school education should
be considered with caution. As highlighted in the relevant literature about school-based
financial education, empirical findings provoke doubts about the scope of personal financial
management courses on the financial literacy of participants. (cf. Cole & Shastry, 2008,
Mandell & Klein, 2007, and Mandell, 2008)
While the positive relationship between general academic education and financial literacy
seems proven (also through results from this master thesis) there needs some further
research and investigation in regards to the effects of financial management courses. One
line of argumentation mentions the critical importance of fast application of acquired financial
knowledge. (Mandell, 2008)
Due to the considerable attention the topic financial literacy has received in many countries
abroad, policy makers in Switzerland are well advised to leverage on their findings.
International cooperation and information sharing seems crucial also in regards to
international initiatives which may be extended to Switzerland.110
110
The “Global Financial Literacy Initiative” (GFLI), for instance, is an international non-profit organization whose mission is to advance financial
literacy and financial responsibility in the US but also throughout the world. There may be numerous organizations and initiatives, private and
public, which are proven in practice, well-accepted among financial literacy specialists and also available for application in Switzerland.
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Financial Literacy and Financial Behavior in Switzerland
6.4.
Conclusions
The first representative study on financial knowledge of the population in Switzerland
confirms the expected. Switzerland is no role model but also not dragging behind other
developed countries in regards to the financial knowledge of its population.
While the personal characteristics of individuals connected to high financial literacy are rather
clear-cut there is some more uncertainty about the (causal) relationship of financial literacy
and financial behavior. Gender, nationality, education, household income and financial
wealth seem to be the main determinants of financial literacy.
Relationship wise, financial literacy seems to be positively connected to investment portfolio
and retirement account ownership while having no significant impact on consumption credit
indebtedness. Overcoming pitfalls seen in reverse causality of investment portfolio ownership
on financial literacy as well as omitted variables in the regression models have proven to be
demanding.
However, at least in the case for retirement accounts an instrumental variable approach
could be applied and provided some evidence of causality running from financial knowledge
to retirement account ownership rather than opposite. Furthermore, as many other
researchers (cf. Exhibit A6) have been able to find evidence of the direction of causality, one
may leverage on their findings and imply the same direction of causality. Consequently, one
may confidently conclude that financial literacy is causally leading to retirement account
ownership.
Engagement in regards to improving financial knowledge in Switzerland has been highest
with private organizations while public policymakers have largely remained on the sideline.
The initiatives undertaken to date (refer to Table 23) show that many non-profit organizations
have become aware of the topic financial literacy. However, while considering the catalog of
initiatives undertaken it becomes obvious that efficiency and effectiveness in building
financial knowledge in the population could be enhanced while better coordinating efforts. A
holistic approach aimed at improving financial literacy could thereby be implemented while
establishing a national coordinative body.
Switzerland cannot be considered a first-mover in regards to initiatives undertaken in
financial literacy. However, as the results in this study showed there was also no need to be
at the very front in terms of policy measures. What seems more important, however, is that at
a time where many countries increased their efforts, Switzerland is not losing ground but
does its homework. It may not harm to bring decision makers on a table, set out what has
been done so far and learn from what has been done abroad. International cooperation and
information sharing can be considered critical with many countries supposedly being in a
better position to evaluate on past policy measures.
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Financial Literacy and Financial Behavior in Switzerland
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Financial Literacy and Financial Behavior in Switzerland
VI
Acknowledgement
While writing this master thesis I could benefit from the support and from useful thoughtprovoking remarks of various people. Foremost, I would like to acknowledge the employees
of the Swiss Institute of Banking & Finance (SIB&F) who constructed the questionnaire and
who made the derived dataset available to me. Special thanks go to Matthias Hoffmann who
supported me with valuable thoughts in the process of writing this master thesis. Also
appreciated is the support of the NZZ which provided me with aggregate data about their
subscribers on a geographical basis. Many thanks go also to Prof. Dr. Martin Brown for his
assistance as referee of this document.
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Financial Literacy and Financial Behavior in Switzerland
VII
Glossary
Organization
Description
Conference of Education
Ministers of GermanSpeaking Switzerland (DEDK)
The D-EDK is part of the network of the Swiss Conference of State Education Ministers (EDK). DEDK consists of separate sub-sections for the northwestern, the central and the east part of
Switzerland.
The objective of the D-EDK is to foster collaboration in the fields of the new school curricula
(Lehrplan 21), the school television project, the promotion of specially gifted pupils and the area
around external evaluation. Furthermore, partnership is also sought with questions about the
coordination of new teaching materials and performance measurement in schools.
Further information: www.d-edk.ch
Financial Stability Board
(FSB)
The FSB has been established to coordinate at the international level the work of national
financial authorities and international standard setting bodies and to develop and promote the
implementation of effective regulatory, supervisory and other financial sector policies.
It brings together national authorities responsible for financial stability in significant international
financial centres, international financial institutions, sector-specific international groupings of
regulators and supervisors, and committees of central bank experts. The Swiss National Bank is
represented on the FSB.
Further information: www.financialstabilityboard.org
Jump$tart Coalition for
Personal Financial
Literacy
The Jump$tart Coalition is a non-profit coalition of national organizations seeking to advance the
financial literacy of pre-kindergarten through college-aged students. The Jump$tart Coalition
endeavors to provide youth with lifelong financial decision-making skills. By working together with
its 150 national partners, the Jump$tart Coalition provides advocacy, research, standards and
educational resources.
Further information: www.jumpstart.org
OECD International
Network of Financial
Education (INFE)
The OECD created the INFE Network in 2008 to promote and facilitate international co-operation
between policy makers and other stakeholders on financial education issues worldwide.
Currently, more than 170 institutions from 86 countries have joined the OECD INFE.
Further information: www.oecd.org
Swiss Design Institute for
Finance and Banking
(SDFB)
The SDFB is an interuniversity competence center for design. It conducts theoretical and applied
research to optimize the relationship between financial service providers and their customers. In
cooperation with partners from the Swiss banking and financial services industry, SDFB
researches and develops future-proof media solutions for people. SDFB is a cooperation
between chairs of the Zurich University of the Arts, the Zurich University, the ETH Zurich and the
University of St. Gallen.
Further information: www.sdfb.ch
State Secretariat for
Education and Research
(SER)
The SER within the Federal Department of Home Affairs is the federal government's specialized
agency for national and international matters concerning general and university education,
research and space. It coordinates related activities within the Federal Administration and
ensures cooperation with the cantons.
Further information: www.sbf.admin.ch
Swiss National Umbrella
Organization for Adult
Education (SVEB)
SVEB is a non-governmental organization, which represents nationwide private and state
institutions, associations, representatives responsible for adult education on a cantonal level,
institutions, in-house training departments and personnel managers. It also extends its reach to
individuals who are active in adult education (lifelong learning). SVEB promotes cooperation
among adult learning institutions, raises public awareness for lifelong learning and supports its
members in their activities.
Further information: www.alice.ch/en/sveb
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Financial Literacy and Financial Behavior in Switzerland
VIII
Appendix
Exhibit A1: Geographical Allocation of Survey Respondents
(n=1’500)
Source: Dataset, authors’ calculations
Exhibit A2: Geographical Allocation of Survey Respondents Answering All Questions Correctly
(n=752)
Source: Dataset, authors’ calculations
Exhibit A3: Geographical Allocation of Survey Respondents Answering No Question Correctly
(n=51)
Source: Dataset, authors’ calculations
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Financial Literacy and Financial Behavior in Switzerland
Exhibit A4: Geographical State Allocation of Share of Respondents Answering All Questions Correctly
Canton
CH-AR
CH-BS
CH-FR
CH-ZH
CH-BL
CH-ZG
CH-LU
CH-AG
CH-AI
CH-VS
CH-SO
CH-TG
CH-BE
CH-UR
CH-NW
CH-OW
CH-GR
CH-SZ
CH-SG
CH-SH
CH-GL
CH-GE
CH-JU
CH-NE
CH-TI
CH-VD
Share
0.77
0.62
0.57
0.55
0.52
0.52
0.52
0.51
0.50
0.50
0.49
0.48
0.48
0.47
0.46
0.46
0.45
0.44
0.40
0.28
0.25
n/a
n/a
n/a
n/a
n/a
Overall Share of Respondents Answering All Questions Correctly: 50.13% or 752 respondents.
White Squares: NE, VD, GE, TI and JU have not been included in survey as they belong to the French or Italian part of Switzerland.
Source: Dataset, authors’ calculations
Exhibit A5: Geographical State Allocation of Share of Respondents Answering No Question Correctly
Canton
CH-FR
CH-ZG
CH-NW
CH-LU
CH-SO
CH-VS
CH-SH
CH-GR
CH-BE
CH-TG
CH-AG
CH-SG
CH-ZH
CH-AR
CH-BS
CH-BL
CH-AI
CH-UR
CH-OW
CH-SZ
CH-GL
CH-GE
CH-JU
CH-NE
CH-TI
CH-VD
Share
0.09
0.08
0.08
0.06
0.06
0.06
0.06
0.05
0.04
0.04
0.04
0.03
0.03
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
n/a
n/a
n/a
n/a
n/a
Overall Share of Respondents Answering No Question Correctly: 3.40% or 51 respondents.
White Squares: NE, VD, GE, TI and JU have not been included in survey as they belong to the French or Italian part of Switzerland.
Source: Dataset, authors’ calculations
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Financial Literacy and Financial Behavior in Switzerland
Exhibit A6: Findings of Empirical Studies Covering Identical Three Financial Literacy Questions
Author(s)
Year
Journal
Title
2011
Master Thesis
Financial Literacy and
Behavior in Switzerland
Bucher & Lusardi
2011
Journal of Pension
Economics and
Finance
Almenberg &
Säve-Söderbergh
2011
Crossan et al.
Country
Sample
Findings
Gender
Age
Education
Income
Wealth
Debt
Retirem.
Invest
CH
Age: 20-74 (ø 45.87)
(n=1’500)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Financial Literacy and Retirement
Planning in Germany
GER
Age: 22-91 (ø 52.11)
(n=1’059)
Yes
Yes
Yes
Yes
N/A
N/A
Yes
N/A
Journal of Pension
Economics and
Finance
Financial Literacy and Retirement
Planning in Sweden
SWE
Age: 18–79 (ø 44.00)
(n=1’302)
Yes
Yes
Yes
Yes
N/A
N/A
Yes
N/A
2011
Journal of Pension
Economics &
Finance
Financial Literacy and Retirement
Planning in New Zealand
NZ
Age: Adult (ø n/a)
(n=850)
Yes
Yes
Yes
N/A
N/A
N/A
No
(public
pension)
N/A
Sekita
2011
Journal of Pension
Economics &
Finance
Financial Literacy and Retirement
Planning in Japan
JPN
Age: 20-69 (ø 49.59)
(n=5’268)
Yes
Yes
Yes
Yes
N/A
N/A
Yes
N/A
Lusardi & Mitchell
2011A
Journal of Pension
Economics and
Finance
Financial Literacy and Retirement
Planning in the United States
USA
Age: Adult (ø n/a)
(n=1’488)
Yes
Yes
Yes
Yes
N/A
N/A
Yes
N/A
Klapper & Panos
2011
Journal of Pension
Economics and
Finance
Financial Literacy and Retirement
Planning: the Russian Case
RUS
Age: n/a (ø 46.04)
(n=1’366)
Yes
Yes
Yes
N/A
N/A
N/A
Yes
N/A
Fornero &
Monticone
2011
Journal of Pension
Economics &
Finance
Financial Literacy and Pension Plan
Participation in Italy
ITA
Age: n/a (ø 57.69)
(n=3’992)
Yes
Yes
Yes
N/A
Yes
Yes
(Mortgage)
Yes
N/A
Alessie et al.
2011
Journal of Pension
Economics and
Finance
Financial Literacy and Retirement
Preparation in the Netherlands
NED
Age: >25 (ø 55.00)
(n=1’665)
Yes
No
Yes
N/A
N/A
N/A
Yes
N/A
Graf (this study)
Financial
Gender:
Yes: FL is lower among women
No: Other effects
N/A: Gender effect not analyzed in study
Age:
Yes: FL is lower among young and old adults, hump-shaped (at least one correct)
No: Other effects
N/A: Age effect not analyzed in study
Education:
Yes: FL is lower among poorer educated adults
No: Other effects
N/A: Education effect not analyzed in study
Income:
Yes: FL is lower among low income households
No: Other effects
N/A: Income effect not analyzed in study
Wealth:
Yes: FL is lower among poorer households
No: Other effects
N/A: Wealth effect not analyzed in study
Personal Indebtedness:
Yes: FL is negatively related to personal indebtedness (not mortgage debt)
No: Other effects
N/A: Debt effects not analyzed in study
Retirement Planning/Saving:
Yes: FL is positive related to retirement planning/ saving
No: Other effects
N/A: Retirement planning effects not analyzed in study
Investment Behavior:
Yes: FL is positively related to stock investments (experience) or investment sophistication/ fees
No: Other effects
N/A: Investment Behavior effects not analyzed in study
Source: As included above
Roman Graf
P
Financial Literacy and Financial Behavior in Switzerland
Table A1: Financial Literacy Questions
Interest Rates
Suppose you had CHF 100 in a savings account, the interest rate was 2% per year and
there are no account management fees. After 5 years, how much do you think you would
have in the account if you left the money to grow:
a) more than CHF 102
b) exactly CHF 102
c) less than CHF 102
(Don’t know/ Refusal)
Inflation
Imagine that the interest rate on your savings account was 1% per year and inflation was
2% per year. After 1 year, would you be able to buy more than, exactly the same as, or less
than today with the money in this account?
a) more
b) exactly the same
c) less
(Don’t know/ Refusal)
Risk Diversification
Which of the following investments do you consider to be less risky?
a) an investment in stocks of a single company
b) an investment in a mutual fund
(Don’t know/ Refusal)
Source: Questionnaire, authors’ translations
Roman Graf
Q
Financial Literacy and Financial Behavior in Switzerland
Table A2: Demographic and Economic Characteristics of Survey Respondents
Men
Characteristics
# HH
Women
in %
# HH
Whole Sample
In %
# HH
In %
714
100.00
786
100.00
1‘500
100.00
313
272
129
43.84
38.10
18.07
321
303
162
40.84
38.55
20.61
634
575
291
39.27
39.33
21.40
630
84
17
13
7
1
1
45
88.24
11.76
20.24
15.48
8.33
1.19
1.19
53.58
727
59
11
9
8
5
4
22
92.49
7.51
18.64
15.25
13.56
8.47
6.78
37.29
1‘357
143
28
22
15
6
5
67
90.47
9.53
19.58
15.38
10.49
4.20
3.50
46.85
182
30
465
26
10
1
25.49
4.20
65.13
3.64
1.40
0.14
134
33
485
95
35
4
17.05
4.20
61.70
12.09
4.45
0.51
316
63
950
121
45
5
21.07
4.20
63.33
8.07
3.00
0.33
Primary School
Secondary School
Professional Education
Grammar School
University (Applied Science)
University
7
39
306
43
189
130
0.98
5.46
42.86
6.02
26.47
18.21
12
66
455
93
101
59
1.53
8.40
57.89
11.83
12.85
7.51
19
105
761
136
290
189
1.27
7.00
50.73
9.07
19.33
12.60
Labor Market Status
Employed
Housekeeper
Pupil/ Student
Pensioner
Unemployed
Other
577
5
5
107
13
7
80.81
0.70
0.70
14.99
1.82
0.98
463
183
17
106
10
7
58.91
23.28
2.16
13.49
1.27
0.89
1’040
188
22
213
23
14
69.33
12.53
1.47
14.20
1.53
0.93
Number of People in Household
1 Person
2 People
3 People
4 People
5 People
>6 People
Don’t know/ Refusal
121
253
105
159
62
14
0
16.95
35.43
14.71
22.27
8.68
1.96
0.00
126
259
132
171
67
29
2
16.03
32.95
16.79
21.76
8.52
3.69
0.25
247
512
237
330
129
43
2
16.47
34.13
15.80
22.00
8.60
2.87
0.13
Whole Sample
Age
20 to 39 years
40 to 59 years
60 to 74 years
Nationality
Swiss
Foreigners1
- thereof Italian
- thereof German
- thereof Austrian
- thereof Portuguese
- thereof Spaniard
- thereof Other
Marital Status
Single
In Permanent Relationship
Married
Divorced
Widowed
Don’t know/ Refusal
Education
1
Foreigners include all respondents without Swiss citizenship. Survey participants responding to have other citizenships beside the Swiss citizenship have been counted
as Swiss (n=35). Foreigners with more than one foreign citizenship but no Swiss citizenship have been included in the “other” cohort.
Source: Dataset, authors’ calculations
Roman Graf
R
Financial Literacy and Financial Behavior in Switzerland
Table A3: Financial Characteristics of Survey Respondents
Male
Characteristics
Whole Sample
# HH
Female
In %
# HH
Whole Sample
In %
# HH
In %
714
100.00
786
100.00
1‘500
100.00
44
181
164
130
77
58
60
6.16
25.35
22.97
18.21
10.78
8.12
8.40
83
226
175
134
48
32
88
11.62
31.65
24.51
18.77
6.72
4.48
12.32
127
407
339
264
125
90
148
8.47
27.13
22.60
17.60
8.33
6.00
9.87
269
157
129
69
15
75
37.68
21.99
18.07
9.66
2.10
10.50
351
170
106
50
6
103
49.16
23.81
14.85
7.00
0.84
14.43
620
327
235
119
21
178
41.33
21.80
15.67
7.93
1.40
11.87
Household Income
< CHF 4'500
CHF 4'500 - 7'000
CHF 7'000 - 9'000
CHF 9'000 - 12'000
CHF 12'000 - 15'000
> CHF 15'000
Don’t know/ Refusal
Financial Wealth (ex. Real Estate)
< CHF 50'000
CHF 50'000 - 100'000
CHF 100'000 - 250'000
CHF 250'000 - 1 Mio.
> CHF 1 Mio.
Don’t know/ Refusal
Source: Dataset, authors’ calculations
Roman Graf
S
Financial Literacy and Financial Behavior in Switzerland
Table A4: Characteristics of Survey Respondents by Investment Portfolio
Questions
Whole Sample
With Portfolio
# HH
in %
Without Portfolio
# HH
in %
Whole Sample
# HH
in %
536
100.00
964
100.00
1‘500
100.00
297
239
55.41
44.59
417
547
43.26
56.74
714
786
47.60
52.40
187
201
148
34.89
37.50
27.61
447
374
143
46.37
38.80
14.83
634
575
291
39.27
39.33
21.40
510
26
95.15
4.85
847
117
87.86
12.14
1‘357
143
90.47
9.53
106
15
353
34
27
1
19.78
2.80
65.86
6.34
5.04
0.19
210
48
597
87
18
4
21.78
4.98
61.93
9.02
1.87
0.41
316
63
950
121
45
5
21.07
4.20
63.33
8.07
3.00
0.33
5
24
0.93
4.48
14
81
1.45
8.40
19
105
1.27
7.00
233
43.47
528
54.77
761
50.73
Gender
Male
Female
Age
20 to 39 years
40 to 59 years
60 to 74 years
Nationality
Swiss
Foreigners
Marital Status
Single
In Permanent Relationship
Married
Divorced
Widowed
Don’t know/ Refusal
Education
Primary School
Secondary School
Professional Education
Grammar School
University (Applied Science)
University
53
9.89
83
8.61
136
9.07
135
25.19
155
16.08
290
19.33
86
16.04
103
10.68
189
12.60
Labor Market Status
352
65.67
688
71.37
1’040
69.33
Housekeeper
62
11.57
126
13.07
188
12.53
Pupil/ Student
5
0.93
17
1.76
22
1.47
108
20.15
105
10.89
213
14.20
Unemployed
4
0.75
19
1.97
23
1.53
Other
5
0.93
9
0.93
14
0.93
29
5.41
98
10.17
127
8.47
CHF 4'500 - 7'000
118
22.01
289
29.98
407
27.13
CHF 7'000 - 9'000
127
23.69
212
21.99
339
22.60
CHF 9'000 - 12'000
102
19.03
162
16.80
264
17.60
CHF 12'000 - 15'000
59
11.01
66
6.85
125
8.33
> CHF 15'000
55
10.26
35
3.63
90
6.00
Don’t know/ Refusal
46
8.58
102
10.58
148
9.87
Employed
Pensioner
Household Income
< CHF 4'500
Financial Wealth (ex. Real Estate)
< CHF 50'000
94
17.54
526
54.56
620
41.33
CHF 50'000 - 100'000
132
24.63
195
20.23
327
21.80
CHF 100'000 - 250'000
145
27.05
90
9.34
235
15.67
CHF 250'000 - 1 Mio.
92
17.16
27
2.80
119
7.93
> CHF 1 Mio.
Don’t know/ Refusal
16
57
2.99
10.63
5
121
0.52
12.55
21
178
1.40
11.87
Source: Dataset, authors’ calculations
Roman Graf
T
Financial Literacy and Financial Behavior in Switzerland
Table A5: Characteristics of Survey Respondents by Consumption Credit
Questions
Whole Sample
With Credit
# HH
in %
Without Credit
# HH
in %
Whole Sample
# HH
in %
71
100.00
1’428
100.00
1‘500
100.00
43
28
60.56
39.44
670
758
46.92
53.08
714
786
47.60
52.40
45
25
1
63.38
35.21
1.41
589
549
290
41.25
38.45
20.31
634
575
291
39.27
39.33
21.40
23
48
32.39
67.61
1’308
120
91.60
8.40
1‘357
143
90.47
9.53
10
5
50
6
-
14.08
7.04
70.42
8.45
-
306
58
899
115
45
5
21.43
4.06
62.96
8.05
3.15
0.35
316
63
950
121
45
5
21.07
4.20
63.33
8.07
3.00
0.33
1
4
1.41
5.63
18
101
1.26
7.07
19
105
1.27
7.00
43
60.56
717
50.21
761
50.73
Gender
Male
Female
Age
20 to 39 years
40 to 59 years
60 to 74 years
Nationality
Swiss
Foreigners
Marital Status
Single
In Permanent Relationship
Married
Divorced
Widowed
Don’t know/ Refusal
Education
Primary School
Secondary School
Professional Education
Grammar School
6
8.45
130
9.10
136
9.07
University (Applied Science)
8
11.27
282
19.75
290
19.33
University
9
12.68
180
12.61
189
12.60
Labor Market Status
60
84.51
979
68.56
1’040
69.33
Housekeeper
6
8.45
182
12.75
188
12.53
Pupil/ Student
1
1.41
21
1.47
22
1.47
Pensioner
1
1.41
212
14.85
213
14.20
Unemployed
3
4.23
20
1.40
23
1.53
Other
-
-
14
0.98
14
0.93
5
7.04
122
8.54
127
8.47
CHF 4'500 - 7'000
22
30.99
385
26.96
407
27.13
CHF 7'000 - 9'000
21
29.58
318
22.27
339
22.60
CHF 9'000 - 12'000
14
19.72
250
17.51
264
17.60
CHF 12'000 - 15'000
3
4.23
122
8.54
125
8.33
> CHF 15'000
2
2.82
88
6.16
90
6.00
Don’t know/ Refusal
4
5.63
143
10.01
148
9.87
Employed
Household Income
< CHF 4'500
Financial Wealth (ex. Real Estate)
< CHF 50'000
55
77.46
565
39.57
620
41.33
CHF 50'000 - 100'000
8
11.27
319
22.34
327
21.80
CHF 100'000 - 250'000
2
2.82
233
16.32
235
15.67
CHF 250'000 - 1 Mio.
1
1.41
118
8.26
119
7.93
> CHF 1 Mio.
Don’t know/ Refusal
1
4
1.41
5.63
20
173
1.40
12.11
21
178
1.40
11.87
Source: Dataset, authors’ calculations
Roman Graf
U
Financial Literacy and Financial Behavior in Switzerland
Table A6: Characteristics of Survey Respondents by Mortgage Debt
Questions
Whole Sample
With Mortgage
# HH
in %
Without Mortgage
# HH
in %
Whole Sample
# HH
in %
687
100.00
813
100.00
1’500
100.00
321
366
46.72
53.28
393
420
48.34
51.66
714
786
47.60
52.40
190
342
155
27.66
49.78
22.56
444
233
136
54.61
28.66
16.73
634
575
291
39.27
39.33
21.40
34
653
4.95
95.05
109
704
13.41
86.59
1‘357
143
90.47
9.53
52
9
573
31
20
2
7.57
1.31
83.41
4.51
2.91
0.29
264
54
377
90
25
3
32.47
6.64
46.37
11.07
3.08
0.37
316
63
950
121
45
5
21.07
4.20
63.33
8.07
3.00
0.33
9
44
1.31
6.40
10
61
1.23
7.50
19
105
1.27
7.00
344
50.07
417
51.29
761
50.73
Gender
Male
Female
Age
20 to 39 years
40 to 59 years
60 to 74 years
Nationality
Swiss
Foreigners
Marital Status
Single
In Permanent Relationship
Married
Divorced
Widowed
Don’t know/ Refusal
Education
Primary School
Secondary School
Professional Education
Grammar School
62
9.02
74
9.10
136
9.07
143
20.82
147
18.08
290
19.33
85
12.37
104
12.79
189
12.60
Employed
461
67.10
579
71.22
1’040
69.33
Housekeeper
102
14.85
86
10.58
188
12.53
Pupil/ Student
2
0.29
20
2.46
22
1.47
115
16.74
98
12.05
213
14.20
Unemployed
3
0.44
20
2.46
23
1.53
Other
4
0.58
10
1.23
14
0.93
18
2.62%
109
13.41%
127
8.47
CHF 4'500 - 7'000
155
22.56%
252
31.00%
407
27.13
CHF 7'000 - 9'000
176
25.62%
163
20.05%
339
22.60
CHF 9'000 - 12'000
144
20.96%
120
14.76%
264
17.60
CHF 12'000 - 15'000
70
10.19%
55
6.77%
125
8.33
> CHF 15'000
52
7.57%
38
4.67%
90
6.00
Don’t know/ Refusal
72
10.48%
76
9.35%
148
9.87
< CHF 50'000
228
33.19%
392
48.22%
620
41.33
CHF 50'000 - 100'000
162
23.58%
165
20.30%
327
21.80
CHF 100'000 - 250'000
University (Applied Science)
University
Labor Market Status
Pensioner
Household Income
< CHF 4'500
Financial Wealth (ex. Real Estate)
127
18.49%
108
13.28%
235
15.67
CHF 250'000 - 1 Mio.
65
9.46%
54
6.64%
119
7.93
> CHF 1 Mio.
Don’t know/ Refusal
17
2.47%
4
0.49%
88
12.81
90
11.07
21
178
1.40
11.87
Source: Dataset, authors’ calculations
Roman Graf
V
Financial Literacy and Financial Behavior in Switzerland
Table A7: Characteristics of Survey Respondents by Retirement Account
Questions
Whole Sample
With Account
# HH
in %
Without Account
# HH
in %
Whole Sample
# HH
in %
610
100.00
890
100.00
1‘500
100.00
311
299
50.98
49.02
403
487
45.28
54.72
714
786
47.60
52.40
262
268
80
42.95
43.93
13.11
372
307
211
41.80
34.49
23.71
634
575
291
39.27
39.33
21.40
572
38
93.77
6.23
785
105
88.20
11.80
1‘357
143
90.47
9.53
115
23
431
31
10
-
18.85
3.77
70.66
5.08
1.64
-
201
40
519
90
35
5
22.58
4.49
58.31
10.11
3.93
0.56
316
63
950
121
45
5
21.07
4.20
63.33
8.07
3.00
0.33
3
28
0.49
4.59
16
77
1.80
8.65
19
105
1.27
7.00
276
45.25
485
54.49
761
50.73
Gender
Male
Female
Age
20 to 39 years
40 to 59 years
60 to 74 years
Nationality
Swiss
Foreigners
Marital Status
Single
In Permanent Relationship
Married
Divorced
Widowed
Don’t know/ Refusal
Education
Primary School
Secondary School
Professional Education
Grammar School
58
9.51
78
8.76
136
9.07
University (Applied Science)
143
23.44
147
16.52
290
19.33
University
102
16.72
87
9.78
189
12.60
Labor Market Status
485
79.51
555
62.36
1’040
69.33
Housekeeper
71
11.64
117
13.15
188
12.53
Pupil/ Student
5
0.82
17
1.91
22
1.47
41
6.72
172
19.33
213
14.20
Unemployed
5
0.82
18
2.02
23
1.53
Other
3
0.49
11
1.24
14
0.93
16
2.62
111
12.47
127
8.47
CHF 4'500 - 7'000
121
19.84
286
32.13
407
27.13
CHF 7'000 - 9'000
155
25.41
184
20.67
339
22.60
CHF 9'000 - 12'000
133
21.80
131
14.72
264
17.60
CHF 12'000 - 15'000
74
12.13
51
5.73
125
8.33
> CHF 15'000
59
9.67
31
3.48
90
6.00
Don’t know/ Refusal
52
8.52
96
10.79
148
9.87
< CHF 50'000
178
29.18
442
49.66
620
41.33
CHF 50'000 - 100'000
154
25.25
173
19.44
327
21.80
CHF 100'000 - 250'000
Employed
Pensioner
Household Income
< CHF 4'500
Financial Wealth (ex. Real Estate)
138
22.62
97
10.90
235
15.67
CHF 250'000 - 1 Mio.
67
10.98
52
5.84
119
7.93
> CHF 1 Mio.
Don’t know/ Refusal
8
65
1.31
10.66
13
113
1.46
12.70
21
178
1.40
11.87
Source: Dataset, authors’ calculations
Roman Graf
W
Financial Literacy and Financial Behavior in Switzerland
Table A8: Pairwise Pearson-Correlation Coefficients of Demographic, Economic and Financial Characteristics
Gender
Age
National.
Men
Age
Foreign.
Marital Status
Single
Perm.
Relation
Married
Divorced
Widowed
DK
Primary
Secondary
Education
Profes.
Grammar
Educ.
Uni
(Appl.)
Uni
Empl
oyed
House
keeper
Labor Market Status
Stud
Pens
ent
ioner
Unem
ployed
DK
HH
No
People
<4,5k
4,57k
Household Income
129-12k
15k
7-9k
>15k
DK
<50k
50100k
Financial Wealth
100250250k
1m
>1m
DK
Gender
Men (d)
Age
Age
-0.03
1.00
Nationality
Foreigner (d)
0.07
-0.11
1.00
Single (d)
0.10
-0.30
-0.01
1.00
-0.00
-0.10
0.03
-0.11
1.00
Married (d)
0.03
0.13
-0.00
-0.68
-0.28
1.00
Divorced (d)
-0.16
0.14
0.00
-0.15
-0.06
-0.39
1.00
Widowed (d)
-0.09
0.26
-0.02
-0.09
-0.04
-0.23
-0.05
1.00
DK (d)
-0.02
-0.02
-0.02
-0.03
-0.01
-0.07
-0.02
-0.01
1.00
Primary (d)
-0.02
0.11
0.00
-0.01
0.04
-0.01
-0.01
0.05
-0.01
1.00
Secondary (d)
-0.06
0.13
0.08
-0.02
-0.02
-0.00
0.02
0.04
-0.01
-0.03
1.00
Prof. Ed. (d)
-0.15
0.01
-0.05
-0.08
-0.03
0.05
0.03
0.03
0.03
-0.11
-0.28
1.00
Grammar (d)
-0.10
0.01
0.01
0.01
0.01
-0.04
0.01
0.05
-0.02
-0.04
-0.09
-0.32
1.00
Uni (Appl.) (d)
0.17
-0.06
-0.03
0.06
-0.00
-0.02
-0.03
-0.06
0.01
-0.06
-0.13
-0.50
-0.15
1.00
University (d)
0.16
-0.09
0.04
0.06
0.03
-0.02
-0.03
-0.07
-0.02
-0.04
-0.10
-0.39
-0.12
-0.19
Employed (d)
0.24
-0.41
0.07
0.11
0.06
-0.05
-0.00
-0.20
0.03
-0.07
-0.11
-0.07
-0.06
0.14
0.09
1.00
Housek. (d)
-0.34
-0.06
-0.05
-0.14
-0.07
0.20
-0.06
-0.03
-0.02
-0.01
0.05
0.14
0.02
-0.14
-0.10
-0.57
1.00
Student (d)
-0.06
-0.18
-0.00
0.18
-0.03
-0.11
-0.04
-0.02
-0.01
-0.01
-0.03
-0.08
0.08
-0.05
0.14
-0.18
-0.05
1.00
Pensioner (d)
0.02
0.66
-0.07
-0.12
-0.01
-0.04
0.08
0.29
-0.02
0.06
0.08
0.00
0.01
-0.03
-0.06
-0.61
-0.15
-0.05
1.00
Unempl. (d)
0.02
-0.02
0.07
0.10
-0.03
-0.09
0.00
0.04
-0.01
0.03
0.07
-0.02
0.04
-0.05
-0.01
-0.19
-0.05
-0.02
-0.05
1.00
DK (d)
0.00
0.01
-0.03
0.04
0.05
-0.04
-0.00
-0.02
-0.01
0.11
0.00
-0.04
0.04
0.02
-0.04
-0.15
-0.04
-0.01
-0.04
-0.01
1.00
No People
-0.03
-0.30
0.03
-0.39
-0.06
0.53
-0.20
-0.18
-0.03
-0.05
-0.05
0.04
-0.01
-0.02
0.03
0.07
0.25
0.04
-0.32
-0.03
-0.04
1.00
<4,5k (d)
-0.08
0.12
0.02
0.10
-0.00
-0.24
0.17
0.16
0.03
0.09
0.11
0.02
0.02
-0.10
-0.04
-0.20
-0.01
0.06
0.19
0.14
0.05
-0.21
1.00
4,5-7k (d)
-0.04
0.00
0.04
0.04
-0.06
-0.06
0.07
0.02
-0.00
-0.00
0.08
0.16
-0.02
-0.11
-0.15
-0.10
0.05
0.01
0.07
0.02
-0.01
-0.11
-0.19
1.00
7-9k (d)
0.01
-0.04
-0.02
-0.09
-0.00
0.13
-0.07
-0.03
-0.03
-0.05
-0.08
0.06
0.01
0.07
-0.10
0.06
0.02
-0.07
-0.05
-0.04
-0.02
0.11
-0.16
-0.33
1.00
9-12k (d)
0.01
-0.05
-0.00
-0.04
0.03
0.09
-0.07
-0.06
-0.02
-0.02
-0.04
-0.09
-0.02
0.09
0.07
0.13
-0.03
0.00
-0.11
-0.06
-0.04
0.14
-0.14
-0.28
-0.25
1.00
12-15k (d)
0.08
-0.03
0.00
0.01
0.03
0.02
-0.05
-0.04
-0.02
-0.03
-0.05
-0.10
0.01
0.05
0.13
0.07
-0.04
0.00
-0.04
-0.04
-0.00
0.04
-0.09
-0.18
-0.16
-0.14
1.00
>15k (d)
0.08
-0.03
-0.03
0.03
0.03
-0.00
-0.03
-0.04
-0.01
-0.03
-0.07
-0.15
0.03
0.05
0.21
0.12
-0.05
-0.03
-0.09
-0.01
0.00
0.05
-0.08
-0.15
-0.14
-0.12
-0.08
1.00
DK (d)
-0.04
0.06
-0.01
-0.00
0.01
-0.01
-0.01
0.01
0.07
0.06
0.03
-0.00
-0.03
-0.04
0.02
-0.07
0.02
0.03
0.04
0.01
0.06
-0.04
-0.10
-0.20
-0.18
-0.15
-0.10
-0.08
1.00
<50k (d)
-0.07
-0.28
0.12
0.06
0.03
-0.08
0.05
-0.05
-0.02
0.04
0.02
0.14
-0.03
-0.08
-0.12
0.09
0.02
0.10
-0.19
0.06
0.02
0.12
0.12
0.17
0.03
-0.03
-0.07
-0.12
-0.20
1.00
50-100k (d)
0.00
0.02
-0.05
0.00
0.00
-0.00
-0.00
0.00
0.00
-0.05
-0.02
-0.02
-0.02
0.05
0.01
0.04
-0.03
-0.04
-0.00
-0.03
-0.02
-0.02
-0.04
0.02
0.09
0.03
-0.01
-0.00
-0.14
-0.44
1.00
100-250k (d)
0.06
0.13
-0.04
-0.06
-0.01
0.08
-0.03
-0.00
-0.02
-0.03
-0.04
-0.05
0.04
0.06
0.00
0.01
-0.02
-0.05
0.06
-0.05
-0.04
-0.03
-0.05
-0.06
0.01
0.04
0.12
0.09
-0.12
-0.36
-0.23
1.00
250-1m (d)
0.06
0.21
-0.05
-0.02
-0.05
0.03
-0.01
0.04
-0.02
-0.01
-0.01
-0.10
0.00
0.02
0.13
-0.09
-0.03
-0.02
0.16
0.00
-0.00
-0.06
-0.03
-0.06
-0.04
0.06
0.03
0.16
-0.06
-0.25
-0.16
-0.13
1.00
>1m (d)
0.06
0.11
-0.02
-0.03
-0.03
0.02
0.01
0.05
-0.01
0.04
-0.03
-0.08
0.00
-0.00
0.13
-0.09
-0.03
-0.01
0.15
-0.01
0.05
-0.06
0.00
-0.05
-0.05
0.03
0.03
0.09
-0.00
-0.10
-0.06
-0.05
-0.04
1.00
DK (d)
-0.04
0.05
-0.03
-
0.01
0.00
-0.04
0.03
0.06
0.03
0.05
-0.02
0.01
-0.04
0.01
-0.09
0.07
-0.03
0.04
0.01
0.03
-0.05
-0.04
-0.15
-0.11
-0.10
-0.06
-0.08
0.68
-0.31
-0.19
-0.16
-0.11
-0.04
1.00
1’498
714
1’498
143
316
63
950
121
45
5
19
105
761
136
290
189
1’040
188
22
213
23
14
1’498
127
407
339
264
125
90
148
620
327
235
119
21
178
P.Relation. (d)
1.00
Marital Status
Education
Labor Market
Status
Household (HH)
Household Income
1.00
Financial Wealth
Sample (n)
Pearson-Correlation Coefficients: The table includes pairwise pearson-correlation coefficients between demographic, economic and financial characteristics of survey respondents. The characteristics are instrumented through dummy variables for all types of
characteristics other than age and number of people living in a household. 0/1 variables have to be used for all variables apart from the two mentioned as the assumptions underlying the pearson-correlation coefficients are interval level measurement. (O'Rourke,
Hatcher, & Stepanski, 2005, p. 153) The pairwise pearson-correlation coefficients have been calculated on the basis of listwise deletion neglecting the two DK responses mentioned below in all correlation coefficients (i.e. STATA command: correlate and not pwcorr has
been applied).
Sample: A total of 1’498 respondents have been considered in order to account for two DK responses with the characteristics “Number of people living in a household” which have been eliminated in order to be able to include household size as categorical variable.
Red Squares: The red shaded squares highlight pearson-correlation coefficients greater than |0.25| which are referring to a linear relationship between two variables among different types of characteristics.
Limitations: Assessing pairwise pearson-correlation coefficients comes with some shortcuts due to various reasons. Firstly, a characteristic may be a linear combination of several variables and consequently not be correlated with one single other variable. Secondly,
one has to define a cutoff point regarding statistical significance which should be lower the smaller the sample size of two pairs is. As a result, the examination of VIF is likely to be more prominent in order to get an understanding about multicollinearity between
independent variables. While measuring the effect of correlation with all other variables VIF provide strong insight into multicollinearity problems.
Source: Dataset, authors’ calculations
Roman Graf
X
Financial Literacy and Financial Behavior in Switzerland
Table A9: Pairwise Spearman-Correlation Coefficients of Demographic, Economic and Financial Characteristics
Gender
Age
National.
Men
Age
Foreigner
Marital Status
Single
Gender
Men (d)
1.00
Age
Age
0.01
1.00
Nationality
Foreigner (d)
0.07
-0.10
1.00
Single (d)
0.08
-0.32
-0.01
1.00
P. Relationship (d)
Perm.
Relationship
Married
Education
Divorced
Widowed
DK
Education
Labor Market Status
Employed
Housekeeper
-0.01
-0.12
0.03
-0.11
1.00
Married (d)
0.07
0.17
0.01
-0.68
-0.28
1.00
Divorced (d)
-0.18
0.15
-0.02
-0.16
-0.06
-0.40
1.00
Widowed (d)
-0.09
0.22
-0.01
-0.09
-0.04
-0.22
-0.05
1.00
DK (d)
-0.04
-0.02
-0.01
-0.02
-0.01
-0.05
-0.01
-0.01
1.00
Education (Ordinal)
0.21
-0.13
-0.02
0.09
0.03
-0.04
-0.04
-0.09
0.01
Employed (d)
0.22
-0.35
0.06
0.12
0.08
-0.06
-0.01
-0.19
0.03
0.15
1.00
Housekeeper (d)
-0.33
-0.07
-0.04
-0.15
-0.06
0.20
-0.07
-0.02
-0.01
-0.16
-0.57
1.00
Student (d)
Student
Pensioner
Unemployed
DK
Household
Size
Household
Income
Financial
Wealth
No People
Income
Wealth
Marital Status
Education
Labor Market
Status
1.00
-0.06
-0.17
-0.02
0.16
-0.03
-0.10
-0.04
-0.02
-0.00
0.11
-0.19
-0.04
1.00
Pensioner (d)
0.03
0.59
-0.06
-0.11
-0.02
-0.03
0.08
0.26
-0.02
-0.08
-0.62
-0.14
-0.05
1.00
Unemployed (d)
0.02
-0.02
0.09
0.08
-0.03
-0.08
0.01
0.06
-0.00
-0.04
-0.19
-0.04
-0.01
-0.05
1.00
DK (d)
0.04
0.01
-0.03
0.04
-0.02
-0.02
0.01
-0.02
-0.00
0.01
-0.14
-0.03
-0.01
-0.04
-0.01
1.00
-0.02
-0.25
0.05
-0.42
-0.05
0.55
-0.21
-0.18
0.01
0.02
0.08
0.24
0.06
-0.34
-0.02
-0.03
Household Size
No People
Household Income
Income (Ordinal)
0.14
-0.08
-0.04
-0.06
0.06
0.19
-0.18
-0.15
-0.05
0.37
0.28
-0.08
-0.05
-0.23
-0.12
-0.04
1.00
0.26
Financial Wealth
Wealth (Ordinal)
0.11
0.35
-0.13
-0.09
-0.05
0.10
-0.04
0.06
-0.01
0.21
-0.11
-0.06
-0.10
0.26
-0.06
-0.04
-0.12
0.27
1.00
Sample (n)
1'290
633
1'290
128
273
55
817
107
36
2
1'290
917
150
18
176
19
10
1'290
1'290
1'290
1.00
Spearman-Correlation Coefficients: The table includes pairwise spearman-correlation coefficients between demographic, economic and financial characteristics of survey respondents. The characteristics are instrumented through dummy variables for characteristics
which are of non-ordinary respectively of nominal nature. 0/1 variables have to be applied for certain characteristics such as gender or marital status because assumptions underlying the spearman-correlation coefficients only allow ordinary, interval or ratio scaled
variables. (O'Rourke et al., 2005, p. 154) The pairwise spearman-correlation coefficients have been calculated on the basis of listwise deletion neglecting the two DK responses mentioned below in all spearman-correlation coefficients.
Sample: Ordinary scaled characteristics have been included as categorical variables while eliminating DK answers for household size (n=2), income (n=148) and wealth (n=178) resulting in a total adjustment of 328. A final sample of 1’290 respondents have been
considered, resulting from an initial sample of 1’500 respondents and 328 adjustments of which 114 were double counts and 4 triple counts.
Red Squares: The red shaded squares highlight spearman-correlation coefficients greater than |0.25| which are referring to a linear relationship between two variables among different types of characteristics.
Limitations: Assessing pairwise spearman-correlation coefficients comes with some shortcuts due to various reasons. Firstly, a characteristic may be a linear combination of several variables and consequently not be correlated with one single other variable. Secondly,
one has to define a cutoff point regarding statistical significance which should be lower the smaller the sample size of two pairs is. As a result, the examination of VIF is likely to be more prominent in order to get an understanding about multicollinearity between
independent variables. While measuring the effect of correlation with all other variables VIF provide strong insight into multicollinearity problems.
Source: Dataset, authors’ calculations
Roman Graf
Y
Financial Literacy and Financial Behavior in Switzerland
Table A10: Multicollinearity Diagnostics – Multivariate OLS Regression Analysis in Table 10
Variable
Intercept
Women
Age
Foreigner
Permanent Relationship
Married
Divorced
Widowed
Secondary School
Professional Education
Grammar School
University (Applied)
University
Housekeeper
Student/ Pupil
Pensioner
Unemployed
Household Size
CHF 4'500 - 7'000
CHF 7'000 - 9'000
CHF 9'000 - 12'000
CHF 12'000 - 15'000
> CHF 15'000
DK
CHF 50'000 - 100'000
CHF 100'000 - 250'000
CHF 250'000 - 1 Mio.
> CHF 1 Mio.
DK
Mean VIF
VIF
SQRT VIF
Tolerance
R-Squared
1.28
2.53
1.05
1.20
2.61
1.52
1.33
6.69
22.92
8.30
15.08
11.25
14.63
8.64
2.05
9.82
1.95
3.43
3.54
3.26
2.21
2.07
2.88
1.29
1.36
1.34
1.13
2.04
4.91
1.13
1.59
1.03
1.10
1.62
1.23
1.15
2.59
4.79
2.88
3.88
3.35
3.82
2.94
1.43
3.13
1.40
1.85
1.88
1.81
1.49
1.44
1.70
1.13
1.17
1.16
1.06
1.43
0.78
0.40
0.95
0.83
0.38
0.66
0.75
0.15
0.04
0.12
0.07
0.09
0.07
0.12
0.49
0.10
0.51
0.29
0.28
0.31
0.45
0.48
0.35
0.78
0.74
0.75
0.88
0.49
0.22
0.61
0.05
0.17
0.62
0.34
0.25
0.85
0.96
0.88
0.93
0.91
0.93
0.88
0.51
0.90
0.49
0.71
0.72
0.69
0.55
0.52
0.65
0.22
0.26
0.25
0.12
0.51
Eigenvalue
7.80
1.78
1.57
1.47
1.22
1.18
1.11
1.06
1.05
1.03
1.00
0.98
0.96
0.91
0.88
0.85
0.82
0.76
0.67
0.59
0.37
0.32
0.27
0.16
0.08
0.06
0.03
0.01
0.00
Condition Number
Condition Index
1.00
2.09
2.23
2.30
2.53
2.57
2.65
2.71
2.73
2.76
2.79
2.82
2.84
2.92
2.98
3.02
3.08
3.20
3.41
3.62
4.58
4.96
5.42
7.09
9.58
11.77
17.62
24.60
45.25
45.25
Theory
As emphasized by Kmenta, 1986, p. 380, multicollinearity is a question of degree and not of kind and the meaningful distinction is not between the presence and the
absence of multicollinearity but between the various degrees of multicollinearities.
VIF: VIF quantify the severity of multicollinearity in OLS regression analysis. They provide an index that measures how much the variance (the square of the estimate's
standard deviation) of an estimated regression coefficient is increased because of multicollinearity.
The variance inflation factors are computed from the correlation matrix of the independent variables. Thus, the independent variables are centered and standardized to unit
length. The diagonal elements of the inverse of the correlation matrix are the variance inflation factors. (Rawlings et al., 1998, p. 372)
The link between
and multicollinearity (of the standardized and centered variables) is through the relationship:
is the coefficient of determination from the regression of
on the other independent variables (supporting regression analysis: =
). If there is a near-singularity
involving
and the other independent variables,
will be near 1.0 and
will be large. If
is orthogonal (i.e. uncorrelated) to the other independent variables,
will
be 0 and
will be 1.0. (Rawlings et al., 1998, p. 373) VIF values become critical if they are larger than 10. (cf. Kutner et al., 2004, p. 408)
SQRT VIF: The square root of the VIF shows how much larger the standard error is compared to what it would be if that variable was uncorrelated with the other
independent variables in the model.
Tolerance: The tolerance is representing the reciprocal value of the VIF. A tolerance close to 1 means that there is little multicollinearity whereas a value close to zero is
stressing that multicollinearity may be a threat. A tolerance value lower than 0.1 is comparable to a VIF of above 10.
R-Squared: R-Squared represent the coefficient of determination from the regression of
tolerance from 1.
on the other independent variables. Values equal the subtraction of the
Eigenvalue: Eigenvalues are computed by a multivariate statistical technique called the principal component analysis. The eigenvalues (or characteristic roots) are the
variances of the components. A zero eigenvalue means perfect multicollinearity among independent variables and very small eigenvalues imply severe multicollinearity.
Condition Index/ Condition Number: The condition number is the condition index with the largest value. The condition number equals the square root of the largest
eigenvalue divided by the smallest eigenvalue.
When no multicollinearity is existent, the eigenvalues and condition indices will all equal one and when multicollinearity increases condition indices will increase as well.
Rules of thumb suggest that a condition number of around 10 indicates weak dependencies that may be starting to affect the regression estimates. A condition number of
30-100 indicates moderate to strong dependencies. (Rawlings et al., 1998, p. 371)
Sample: n = 1’480 (Table 10 includes a breakdown of the sample composition)
Application/ Interpretation
The VIF for Professional Education is 22.92 and its square root 4.79. This means that the standard error for the coefficient Professional Education is 4.79 as large as it
would be if it were uncorrelated with the other independent variables. The value of 22.92 is derived from dividing 1 by 1 reduced by the coefficient of determination (i.e. R 2
of 0.96) of the linear multiple regression analysis with the dependent variable Professional Education and the independent variables as included above.
The condition number of 45.25 is achieved while taking the square root of the highest eigenvalue (i.e. 7.7986) divided by the smallest eigenvalue (i.e. 0.0038). The
condition number of 45.25 indicates that the estimates might have a moderate amount of numerical error. However, the statistical standard error is almost always much
greater than the numerical error (cf. Belsley et al., 2005, p. 85ff).
Source: Dataset, authors’ calculations
Roman Graf
Z
Financial Literacy and Financial Behavior in Switzerland
Table A11: Probit Regression (Average Marginal Effects) – Financial Literacy
Probit Regression – Financial Literacy
All Correct
Gender
Women (d)
Age
Age
Nationality
Foreigner (d)
Marital Status
Single (d)
In Permanent Relationship (d)
Married (d)
Divorced (d)
Widowed (d)
Education
Primary School (d)
Secondary School (d)
Professional Education (d)
Grammar School (d)
University (Applied) (d)
University (d)
Occupation
Employed (d)
Housekeeper (d)
Pupil/ Student (d)
Pensioner (d)
Unemployed (d)
Household Size
Number of People in Household
Household Income
< CHF 4’500 (d)
CHF 4’500 - 7’000 (d)
CHF 7’000 - 9’000 (d)
CHF 9’000 - 12’000 (d)
CHF 12’000 - 15’000 (d)
> CHF 15’000 (d)
DK (d)
Financial Wealth
< CHF 50'000 (d)
CHF 50'000 - 100'000 (d)
CHF 100'000 - 250'000 (d)
CHF 250'000 - 1 Mio. (d)
> CHF 1 Mio. (d)
DK (d)
N
Log Likelihood
Pseudo R2
LR chi2
Interest Rates
Inflation
Risk Diversification
-0.16 ***
(0.03)
-0.07 ***
(0.02)
-0.09 ***
(0.02)
-0.07 ***
(0.02)
-0.00
(0.00)
-0.00
(0.00)
0.01 ***
(0.00)
-0.00 ***
(0.00)
-0.19 ***
(0.04)
-0.03
(0.03)
-0.08 **
(0.03)
-0.17 ***
(0.03)
omitted
omitted
omitted
-0.03
(0.07)
0.01
(0.04)
0.05
(0.05)
0.05
(0.08)
-0.04
(0.06)
-0.04
(0.04)
-0.05
(0.04)
0.02
(0.07)
-0.06
(0.05)
0.01
(0.03)
-0.02
(0.04)
-0.05
(0.07)
omitted
omitted
omitted
0.01
(0.13)
0.11
(0.12)
0.12
(0.13)
0.23 *
(0.12)
0.29 **
(0.13)
omitted
0.02
(0.09)
0.08
(0.08)
0.11
(0.09)
0.09
(0.09)
0.22 **
(0.09)
0.08
(0.09)
0.15
(0.08)
0.19
(0.09)
0.29
(0.09)
0.30
(0.09)
omitted
-0.03
(0.06)
0.00
(0.04)
0.08 *
(0.05)
0.10
(0.07)
omitted
*
**
***
***
0.13
(0.10)
0.20 **
(0.10)
0.13
(0.10)
0.23 **
(0.10)
0.24 **
(0.10)
omitted
omitted
omitted
-0.12
(0.10)
-0.11
(0.11)
-0.05
(0.14)
-0.19 *
(0.11)
-0.03
(0.08)
-0.07
(0.09)
-0.17
(0.12)
-0.04
(0.09)
-0.01
(0.08)
-0.02
(0.08)
0.00
(0.11)
-0.04
(0.09)
0.02
(0.09)
0.00
(0.09)
0.07
(0.13)
-0.01
(0.09)
-0.01
(0.01)
0.00
(0.01)
-0.01
(0.01)
-0.00
(0.01)
omitted
omitted
omitted
0.06
(0.04)
0.07
(0.04)
0.11
(0.05)
0.16
(0.06)
0.19
(0.07)
0.06
(0.05)
0.04
(0.04)
0.09
(0.04)
0.11
(0.05)
0.16
(0.06)
0.16
(0.07)
0.01
(0.05)
0.10
(0.04)
0.13
(0.05)
0.16
(0.05)
0.12
(0.06)
0.30
(0.08)
0.10
(0.06)
omitted
0.13
(0.05)
0.17
(0.05)
0.21
(0.06)
0.23
(0.06)
0.33
(0.08)
0.15
(0.07)
***
***
***
***
***
**
omitted
0.01
(0.03)
0.14 ***
(0.04)
0.14 ***
(0.05)
0.14
(0.12)
-0.03
(0.05)
1’480
-899.79
0.1228
251.99
*
**
***
***
**
**
***
**
omitted
omitted
0.04
(0.03)
0.07 **
(0.03)
0.06
(0.05)
omitted
-0.03
(0.03)
0.02
(0.03)
0.10 *
(0.06)
omitted
-0.01
(0.04)
-0.03
(0.04)
1’460
-690.33
0.0724
107.81
1’460
-669.16
0.1226
186.98
**
***
***
**
***
*
omitted
0.00
(0.03)
0.10 ***
(0.04)
0.16 ***
(0.05)
0.01
(0.10)
0.04
(0.05)
1’480
-773.83
0.091
155.24
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Dependent Variables: Financial literacy according to the following types of definitions: All Correct (dummy with 1 = all answers correct and 0 = not all answers correct).
Explanatory Variables: Numbers in brackets are standard errors. (d) stands for dummy (0/1) variable. Interest Rates, Inflation: Wealth category >CHF 1 Mio. != 0 and
predicts success perfectly. Variable has been dropped and 20 observations not used (therefore n=1’460).
Sample: The sample of n=1’500 has been adjusted for respondents answering: DK for marital status (n=5), Other for occupation (n=14) and DK for number of people in
household (n=2). Out of a total adjustment of n=21, 1 has been multiple counting (DK for marital status and number of people in household) resulting in a sample size for
the regression of n=1’480.
Probit Model: Probit models are fitted by maximum likelihood compared to multivariate linear regression analysis which are fitted by ordinary least squares. The probit
model assumes that the probability density function of the error term follows a standard normal distribution. The interpretation of the coefficients (apart from the direction of
their effects, which can be derived from their sign, and the statistical significance, which can be derived from the z-values) is rather difficult in a probit model.
Consequently, marginal effects have been disclosed in the regression analysis above. Marginal effects for dummy (0/1) variables show how the probability of the
dependent variable taking the value one is predicted to change as the independent variable changes from zero to one, holding all other variables equal. In more concrete
terms average marginal effects (in contrast to conditional marginal effects) have been calculated. Average marginal effects are taken as no real person has mean values
on all the categorical independent variables (e.g. values between 0 and 1) and effects should therefore not be calculated at only one set of values, the means. With
average marginal effects, a marginal effect is computed for each case and then all calculated effects are averaged.
Source: Dataset, authors’ calculations
Roman Graf
AA
Financial Literacy and Financial Behavior in Switzerland
Table A12: Logit Regression (Average Marginal Effects) – Financial Literacy
Logit Regression – Financial Literacy
All Correct
Gender
Women (d)
Age
Age
Nationality
Foreigner (d)
Marital Status
Single (d)
In Permanent Relationship (d)
Married (d)
Divorced (d)
Widowed (d)
Education
Primary School (d)
Secondary School (d)
Professional Education (d)
Grammar School (d)
University (Applied) (d)
University (d)
Occupation
Employed (d)
Housekeeper (d)
Pupil/ Student (d)
Pensioner (d)
Unemployed (d)
Household Size
Number of People in Household
Household Income
< CHF 4’500 (d)
CHF 4’500 - 7’000 (d)
CHF 7’000 - 9’000 (d)
CHF 9’000 - 12’000 (d)
CHF 12’000 - 15’000 (d)
> CHF 15’000 (d)
DK (d)
Financial Wealth
< CHF 50'000 (d)
CHF 50'000 - 100'000 (d)
CHF 100'000 - 250'000 (d)
CHF 250'000 - 1 Mio. (d)
> CHF 1 Mio. (d)
DK (d)
N
Log Likelihood
Pseudo R2
LR chi2
Interest Rates
Inflation
Risk Diversification
-0.16 ***
(0.03)
-0.07 ***
(0.02)
-0.09 ***
(0.02)
-0.07 ***
(0.02)
-0.00
(0.00)
-0.00
(0.00)
0.01 ***
(0.00)
-0.00 ***
(0.00)
-0.19 ***
(0.04)
-0.03
(0.03)
-0.08 ***
(0.03)
-0.17 ***
(0.03)
omitted
omitted
omitted
omitted
-0.03
(0.07)
0.01
(0.04)
0.05
(0.05)
0.05
(0.08)
-0.04
(0.06)
-0.03
(0.04)
-0.04
(0.04)
0.03
(0.06)
-0.06
(0.05)
0.01
(0.03)
-0.02
(0.04)
-0.05
(0.07)
-0.03
(0.06)
0.00
(0.04)
0.08
(0.05)
0.10
(0.07)
omitted
omitted
omitted
omitted
0.02
(0.13)
0.11
(0.13)
0.13
(0.13)
0.24 *
(0.13)
0.31 **
(0.13)
omitted
0.02
(0.08)
0.08
(0.08)
0.11
(0.09)
0.08
(0.08)
0.23 **
(0.09)
0.08
(0.08)
0.15
(0.08)
0.19
(0.08)
0.30
(0.08)
0.31
(0.09)
*
**
***
***
0.13
(0.10)
0.19 **
(0.09)
0.12
(0.10)
0.23 **
(0.10)
0.24 **
(0.10)
omitted
omitted
omitted
-0.12
(0.10)
-0.11
(0.11)
-0.06
(0.14)
-0.19 *
(0.10)
-0.02
(0.08)
-0.07
(0.09)
-0.17
(0.11)
-0.03
(0.09)
-0.01
(0.07)
-0.02
(0.08)
0.01
(0.11)
-0.05
(0.08)
0.02
(0.08)
0.00
(0.09)
0.07
(0.13)
-0.01
(0.09)
-0.01
(0.01)
-0.00
(0.01)
-0.01
(0.01)
-0.00
(0.01)
omitted
omitted
omitted
0.04
(0.04)
0.09
(0.04)
0.11
(0.04)
0.16
(0.06)
0.15
(0.07)
0.01
(0.05)
0.10
(0.04)
0.13
(0.04)
0.16
(0.05)
0.12
(0.06)
0.32
(0.09)
0.10
(0.06)
omitted
0.14
(0.05)
0.17
(0.06)
0.22
(0.06)
0.23
(0.07)
0.34
(0.08)
0.16
(0.07)
***
***
***
***
***
**
omitted
0.01
(0.03)
0.14 ***
(0.04)
0.15 ***
(0.05)
0.16
(0.12)
-0.04
(0.05)
1’480
-899.55
0.1231
252.49
0.05
(0.04)
0.06
(0.04)
0.11 **
(0.05)
0.16 ***
(0.06)
0.20 **
(0.08)
0.06
(0.05)
**
**
***
**
omitted
omitted
0.04
(0.03)
0.07 **
(0.03)
0.06
(0.05)
omitted
-0.03
(0.03)
0.03
(0.04)
0.11 *
(0.06)
omitted
-0.01
(0.04)
-0.04
(0.04)
1’460
-690.84
0.0717
106.79
1’460
-669.61
0.1220
186.09
**
***
***
**
***
*
omitted
0.00
(0.03)
0.10 ***
(0.04)
0.17 ***
(0.05)
0.02
(0.10)
0.04
(0.05)
1’480
-773.69
0.091
155.52
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Dependent Variables: Financial literacy according to the following types of definitions: All Correct (dummy with 1 = all answers correct and 0 = not all answers correct).
Explanatory Variables: Numbers in brackets are standard errors. (d) stands for dummy (0/1) variable. Interest Rates, Inflation: Wealth category >CHF 1 Mio. != 0 and
predicts success perfectly. Variable has been dropped and 20 observations not used (therefore n=1’460).
Sample: The sample of n=1’500 has been adjusted for respondents answering: DK for marital status (n=5), Other for occupation (n=14) and DK for number of people in
household (n=2). Out of a total adjustment of n=21, 1 has been multiple counting (DK for marital status and number of people in household) resulting in a sample size for
the regression of n=1’480.
Logit Model: Logit models are fitted by maximum likelihood compared to multivariate linear regression analysis which are fitted by ordinary least squares. The logit model
assumes that the probability density function of the error term follows a logistic distribution. The interpretation of the coefficients (apart from the direction of their effects,
which can be derived from their sign, and the statistical significance, which can be derived from the z-values) is rather difficult in a logit model.
Consequently, marginal effects have been disclosed in the regression analysis above. Marginal effects for dummy (0/1) variables show how the probability of the
dependent variable taking the value one is predicted to change as the independent variable changes from zero to one, holding all other variables equal. In more concrete
terms average marginal effects (in contrast to conditional marginal effects) have been calculated. Average marginal effects are taken as no real person has mean values
on all the categorical independent variables (e.g. values between 0 and 1) and effects should therefore not be calculated at only one set of values, the means. With
average marginal effects, a marginal effect is computed for each case and then all calculated effects are averaged.
Source: Dataset, authors’ calculations
Roman Graf
BB
Financial Literacy and Financial Behavior in Switzerland
Table A13: Multicollinearity Diagnostics – Multivariate OLS Regression Analysis in Table 16
Variable
Intercept
Financial Literacy - All Correct
Women
Age
Foreigner
Secondary School
Professional Education
Grammar School
University (Applied)
University
Employed
Unemployed
Household Size
Household Income
CHF 50'000 - 100'000
CHF 100'000 - 250'000
CHF 250'000 - 1 Mio.
> CHF 1 Mio.
Very High Interest
High Interest
Low Interest
Low Engagement
High Engagement
Very High Engagement
Moderate Risk Aversion
Low Risk Aversion
Significant Planning
Some Planning
Little Planning
Mean VIF
VIF
SQRT VIF
Tolerance
1.19
1.31
2.06
1.07
7.65
29.96
10.63
20.33
14.6
2.04
2.85
1.28
1.50
1.27
1.39
1.40
1.15
3.48
5.89
5.23
2.58
2.89
2.64
1.08
1.06
3.33
3.26
2.47
4.84
1.09
1.15
1.43
1.03
2.77
5.47
3.26
4.51
3.82
1.43
1.69
1.13
1.22
1.13
1.18
1.18
1.07
1.86
2.43
2.29
1.60
1.70
1.62
1.04
1.03
1.83
1.81
1.57
0.84
0.76
0.49
0.94
0.13
0.03
0.09
0.05
0.07
0.49
0.35
0.78
0.67
0.79
0.72
0.72
0.87
0.29
0.17
0.19
0.39
0.35
0.38
0.92
0.95
0.30
0.31
0.40
R-Squared
Eigenvalue
10.35
0.16
1.58
0.24
1.37
0.51
1.24
0.06
1.14
0.87
1.10
0.97
1.07
0.91
1.04
0.95
1.01
0.93
0.97
0.51
0.95
0.65
0.94
0.22
0.90
0.33
0.82
0.21
0.79
0.28
0.75
0.29
0.67
0.13
0.62
0.71
0.45
0.83
0.34
0.81
0.31
0.61
0.18
0.65
0.11
0.62
0.09
0.08
0.08
0.05
0.06
0.70
0.04
0.69
0.02
0.60
0.00
Condition Number
Condition Index
1.00
2.56
2.75
2.89
3.01
3.07
3.12
3.15
3.20
3.26
3.31
3.32
3.38
3.55
3.63
3.72
3.94
4.10
4.80
5.56
5.78
7.53
9.54
10.50
11.28
12.82
15.90
21.69
54.08
54.08
Theory
As emphasized by Kmenta, 1986, p. 380, multicollinearity is a question of degree and not of kind and the meaningful distinction is not between the presence and the
absence of multicollinearity but between the various degrees of multicollinearities.
VIF: VIF quantify the severity of multicollinearity in OLS regression analysis. They provide an index that measures how much the variance (the square of the estimate's
standard deviation) of an estimated regression coefficient is increased because of multicollinearity.
The variance inflation factors are computed from the correlation matrix of the independent variables. Thus, the independent variables are centered and standardized to unit
length. The diagonal elements of the inverse of the correlation matrix are the variance inflation factors. (Rawlings et al., 1998, p. 372)
The link between
and multicollinearity (of the standardized and centered variables) is through the relationship:
is the coefficient of determination from the regression of
on the other independent variables (supporting regression analysis: =
). If there is a near-singularity
involving
and the other independent variables,
will be near 1.0 and
will be large. If
is orthogonal (i.e. uncorrelated) to the other independent variables,
will
be 0 and
will be 1.0. (Rawlings et al., 1998, p. 373) VIF values become critical if they are larger than 10. (cf. Kutner et al., 2004, p. 408)
SQRT VIF: The square root of the VIF shows how much larger the standard error is compared to what it would be if that variable was unco rrelated with the other
independent variables in the model.
Tolerance: The tolerance is representing the reciprocal value of the VIF. A tolerance close to 1 means that there is little multicollinearity whereas a value close to zero is
stressing that multicollinearity may be a threat. A tolerance value lower than 0.1 is comparable to a VIF of above 10.
R-Squared: R-Squared represent the coefficient of determination from the regression of
tolerance from 1.
on the other independent variables. Values equal the subtraction of the
Eigenvalue: Eigenvalues are computed by a multivariate statistical technique called the principal component analysis. The eigenvalues (or characteristic roots) are the
variances of the components. A zero eigenvalue means perfect multicollinearity among independent variables and very small eigenvalues imply severe multicollinearity.
Condition Index/ Number: The condition number is the condition index with the largest value. The condition number equals the square root of the largest eigenvalue
divided by the smallest eigenvalue.
When no multicollinearity is existent, the eigenvalues and condition indices will all equal one and when multicollinearity increases condition indices will increase as well.
Rules of thumb suggest that a condition number of around 10 indicates weak dependencies that may be starting to affect the regression estimates. A condition number of
30-100 indicates moderate to strong dependencies. (Rawlings et al., 1998, p. 371)
Sample: n = 1’250 (Table 16 includes a breakdown of the sample composition)
Application/ Interpretation
The VIF for Professional Education is 29.96 and its square root 5.47. This means that the standard error for the coefficient Professional Education is 5.47 as large as it
would be if it were uncorrelated with the other independent variables. The value of 29.96 is derived from dividing 1 by 1 reduced by the coefficient of determination (i.e. R2
of 0.97) of the linear multiple regression analysis with the dependent variable Professional Education and the independent variables as included above.
The condition number of 54.08 is achieved while taking the square root of the highest eigenvalue (i.e. 10.35) divided by the smallest eigenvalue (i.e. 0.00). The condition
number of 54.08 indicates that the estimates might have a moderate amount of numerical error. However, the statistical standard error is almost always much greater than
the numerical error (cf. Belsley et al., 2005, p. 85ff).
Source: Dataset, authors’ calculations
Roman Graf
CC
Financial Literacy and Financial Behavior in Switzerland
Table A14: Analysis of Selected Instrumental Variables Applied for Investment Portfolio OLS Regression
Table A14.1: Selection of Instrumental Variables Applied
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
No
31
32
33
34
35
Instrumental Variables - Data on Post Code (PLZ) Basis
Absolute number of the population of town/ village
Population growth of town/ village over the last 10 years (in %)
Share of foreigners in town/ village (in %)
Share of people aged 20-64 years in town/ village (in %)
Labor force participation rate of 15-64 years old people (in %)
Unemployment rate in town/ village (in %)
Share of employees in 1. economic sector (in %)
Share of employees in 3. economic sector (in %)
Share of companies in 1. economic sector (in %)
Share of companies in 3. economic sector (in %)
Share of empty rental apartments in town/ village (in %)
Density of dwelling - Wohndichte (resident per existent residency)
Share of home owners in town/ village (in %)
Share of people without education of people 15-64 years old (in %)
Share of secondary school II educated people of people 15-64 y. (in %)
Share of tertiary educated people of people 15-64 years old (in %)
Voting share of FDP in 2011 parliament votes in town/ village (in %)
Voting share of CVP in 2011 parliament votes in town/ village (in %)
Voting share of SVP in 2011 parliament votes in town/ village (in %)
Voting share of SP in 2011 parliament votes in town/ village (in %)
Participation rate in 2010 vote about retirement conversion factor (in %)
Participation rate in 2010 vote about tax fairness initiative (in %)
Share of “Yes” votes in vote about retirement conversion factor (in %)
Share of “Yes” votes in vote about tax fairness initiative (in %)
Tax burden in town/ village (federal, state and local tax combined)
Absolute amount of NZZ subscriptions (PLZ)
Relative amount of NZZ subscriptions (in % of population PLZ)
Relative amount of NZZ subscriptions (in % of population region 1)
Relative amount of NZZ subscriptions (in % of population region 2)
Comparis.ch – Number of mortgage rate requests (relative)
Instrumental Variables - Data on Driving Time Basis
Shortest driving time to one of 9 biggest cities in G-CH <45 min. - dummy
Shortest driving time to one of 9 biggest cities in G-CH <30 min. - dummy
Shortest driving time to one of 9 biggest cities in G-CH <15 min. - dummy
Shortest driving time to Zurich, Basel or Bern
Shortest driving time to Zurich, Basel or Bern <30 min. - dummy
Underlying Hypothesis
People in large cities are more literate.
People in cities which grow fast are more literate.
People in cities with less foreigners are more literate.
People in cities with more people aged 15-64 years are more literate.
People in cities with high labor force participation rate are more literate.
People in cities with low unemployment rate are more literate.
People in cities with low share of 1. sector empl. are more literate.
People in cities with high share of 3. sector empl. are more literate.
People in cities with low share of 1. sector comp. are more literate.
People in cities with high share of 3. sector comp. are more literate.
People in cities with low share of empty apartments are more literate.
People in cities with high dwelling density are more literate.
People in cities with high home ownership rates are more literate.
People in cities with low share of uneducated people are more literate.
People in cities with low share of secondary graduates are more literate.
People in cities with high share of tertiary educ. people are more literate.
People in cities with high share of FDP are more literate.
People in cities with high share of CVP are more literate.
People in cities with high share of SVP are more literate.
People in cities with low share of SP are more literate.
People in cities with high participation rates are more literate.
People in cities with high participation rates are more literate.
People in cities with high “Yes” voting shares are more literate.
People in cities with high “No” voting shares are more literate.
People in cities with low tax burden are more literate.
People in cities with high amount of NZZ subscribers are more literate.
People in cities with high amount of NZZ subscribers. are more literate.
People in cities with high amount of NZZ subscribers. are more literate.
People in cities with high amount of NZZ subscribers. are more literate.
People in cities with frequent comparis.ch activity are more literate.
Underlying Hypothesis
People living closer to large cities are more literate.
Application/ Interpretation
Selection: The listing above represents a small selection of variables which have been applied in attempts to find a valid and strong instrumental variable for financial
literacy. The majority of the variables above have also been tested on state level basis or, where possible, on regional basis (i.e. region 1 was defined as representing the
first digit of the PLZ and region 2 as the first two digits of the PLZ). Variables which indicated a t-statistic close to or above a 10% confidence level (i.e. t-values of |1.65|) in
the first or the second stage regression (refer to Table A14.2) were also implemented in the form of a dummy variables with the median or third quartile taken as
segregation criteria.
There have also been attempts undertaken to find valid instruments through the inclusion of multiple instrumental variables in over-identified models. However, the results
did not improve markedly.
Challenge: The main challenge in finding a valid and strong instrumental variable lies in the fact that the questionnaire was not constructed with regards to providing data
for an instrumental variable approach. As seen in Table A15, some researchers were in the comfortable position to be able to draw on data collected in the questionnaire
while defining an instrumental variable. As no data about the educational level of the parents or siblings nor usable information about the environment of the respondents
were collected, finding a suitable instrumental variable becomes challenging.
Hypothesis: The definitions of the hypothesis are congruent with the rational which was followed by other researchers (refer to Table A15). It is assumed that a person
who is surrounded by people with better financial knowledge becomes itself more financial knowledgeable while at the same time no correlation to investment portfolio
ownership exists. Hypothesis 27 states, for instance, that a person who is living in a town with a high relative share of NZZ subscribers becomes more financially literate.
The NZZ is the leading Swiss economic newspaper and it can be assume that subscribers of this newspaper are more financially knowledgeable than their counterparts.
At the same time, it can also be expected that the relative share of NZZ subscribers has no direct impact on investment portfolio ownership of a respondent and is itself
exogenous (refer to Table A14.2 for more information).
Source: BfS, 2012F, authors’ calculations
Roman Graf
DD
Financial Literacy and Financial Behavior in Switzerland
Table A14.2: Summary Statistics of Selected Instrumental Variables Applied
1. Stage Regression
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
t-Statistic
All correct
t-Statistic
Score
-1.10
-0.28
0.57
-0.71
-1.33
0.59
-1.33
0.90
-1.31
1.10
1.17
-0.85
-0.31
-1.26
0.93
0.57
1.14
0.28
-1.55
0.37
0.35
0.52
-1.06
0.03
-0.10
-0.89
-1.75
-0.12
-1.21
0.83
0.34
2.21
0.29
-1.06
2.71
0.13
-1.01
0.72
-0.35
-1.98
0.20
-0.57
0.74
-0.55
0.66
0.41
0.09
-0.49
-0.14
-0.11
0.31
1.02
0.55
-0.92
0.80
0.18
0.45
-1.18
0.19
-0.21
-0.06
-0.85
0.88
-0.08
0.94
0.26
2.10
1.50
-0.86
2.24
2. Stage Regression
t-Statistic
All correct
0.96
0.17
0.07
0.69
-0.20
-0.52
-0.90
-0.71
-0.84
-0.71
-0.04
-0.45
-0.17
0.11
0.50
-0.11
0.51
0.28
-0.33
-0.34
-0.29
0.42
-0.50
-0.03
0.10
-0.51
0.41
0.05
0.82
0.26
-0.22
-0.60
-0.12
-0.61
-0.34
t-Statistic
Score
-0.13
0.20
0.07
0.36
-0.20
-0.20
-0.50
-0.61
-0.48
-0.53
-0.04
0.10
-0.19
0.09
-0.11
-0.10
0.49
0.52
-0.31
-0.60
-0.17
0.40
-0.51
-0.18
0.21
-0.06
0.40
-0.05
0.08
0.27
-0.19
-0.60
-0.14
-0.55
-0.34
F-Statistic
F-Statistic
All correct
1.21
0.08
0.33
0.50
1.78
0.35
1.77
0.80
1.71
1.21
1.36
0.71
0.10
1.59
0.86
0.33
1.31
0.08
2.40
0.14
0.12
0.27
1.13
0.00
0.01
0.80
3.08
0.02
1.47
0.70
0.12
4.87
0.08
1.13
7.35
Durbin-Wu-Hausman Test
F-Statistic
Score
0.02
1.02
0.52
0.12
3.93
0.04
0.33
0.55
0.30
0.44
0.17
0.01
0.24
0.02
0.01
0.10
1.04
0.30
0.84
0.65
0.03
0.21
1.39
0.03
0.04
0.00
0.73
0.77
0.01
0.89
0.07
4.43
2.26
0.74
5.03
F-Statistic
All correct
2.42
0.03
0.00
1.56
0.13
2.03
2.47
2.20
1.92
1.33
0.03
0.44
0.06
0.00
0.20
0.03
0.15
2.85
0.27
1.12
0.38
0.30
0.56
3.51
3.00
0.55
0.04
0.00
0.76
0.02
0.14
0.83
0.03
0.99
0.43
P-Value
All correct
0.12
0.87
1.00
0.21
0.72
0.15
0.12
0.14
0.17
0.25
0.86
0.51
0.81
0.97
0.65
0.86
0.70
0.09
0.60
0.29
0.54
0.59
0.45
0.06
0.08
0.46
0.84
0.97
0.38
0.88
0.71
0.36
0.86
0.32
0.51
F-Statistic
Score
2.94
0.00
0.00
1.67
0.19
1.91
2.26
2.18
1.71
1.23
0.01
0.31
0.07
0.01
0.32
0.02
0.15
2.71
0.22
1.27
0.36
0.30
0.61
3.59
2.96
0.43
0.08
0.03
1.02
0.23
0.13
0.86
0.10
0.97
0.40
P-Value
Score
0.09
0.95
0.98
0.20
0.66
0.17
0.13
0.14
0.19
0.27
0.92
0.58
0.79
0.93
0.57
0.88
0.70
0.10
0.64
0.26
0.55
0.59
0.43
0.06
0.09
0.51
0.78
0.87
0.31
0.88
0.71
0.35
0.75
0.32
0.53
Theory
1. Stage Regression: The first stage regression can be obtained while regressing all exogenous independent variables and the instrumental variables on the
(endogenous) dependent variable (which is financial literacy in this case). The first stage regression allows to calculate the predictions for all exogenous independent
variables and the instrumental variable. Weak instrumental variables are poor predictors of the endogenous variables in the first stage regression. The first stage
regression has been performed with the STATA command ivregress, first and the attachment vce(robust) in order to get consistent (i.e. asymptotically unbiased) results
(“vce” stands for variance-covariance estimates). Furthermore, in the case of the “all correct” definition of financial literacy, the stata command treatreg has also been
applied due to the binary nature of the endogenous variable. However, results did not change markedly.
2. Stage Regression (IV Regression): The second stage or instrumental variable regression can be obtained while regressing the predictions received in the first-stage
regression on the dependent variable (which is investment portfolio ownership in this case).
F-Statistic: The F-statistic of the instruments allows to test the strength of the instrumental variables and is therefore derived from the first stage regression analysis. As
there is always only one instrumental variable used for the (potentially) endogenous variable financial literacy, the F-statistics equal the square of the t-statistics (of the
instrumental variable) in the case above. With one single endogenous regressor (which is the case in the examples above), instruments can be declared to be weak if the
first stage F-statistic is less than 10. (Staiger & Stock,1997) When the instrumental variables are weak, the IV or two stage least squares estimators could be inconsistent
or have large standard errors. The F-statistics have been performed with the STATA command estat firststage.
Durbin-Wu-Hausman Test: The Durbin-Wu-Hausman test allows to make a statement about the endogeneity of a variable. The IV approach assumes financial literacy to
be endogenous while in fact financial literacy may be exogenous. In the exogeneity case, OLS estimates would be more efficient than IV estimates. The null hypothesis of
the Durbin-Wu-Hausman test is that financial literacy is exogenous. P-values of above 0.05 indicate that the null hypothesis cannot be rejected on a 5% confidence level
and that the exogeneity of financial literacy cannot be rejected in the model as well. The Durbin-Wu Hausman test has been performed with the STATA command estat
endogenous.
T-Statistic: The t-statistic is the estimated coefficient of a variable divided by its standard error. Thus, the t-statistic measures how many standard deviations away from
zero the estimated coefficient of a variable is. The t-statistic is used to test the hypothesis that the true value of the coefficient is non-zero, in order to confirm that the
independent variable really belongs in the model.
P-Value: The p-value of regression coefficients is the probability of observing a t-statistic that large or larger in magnitude given the null hypothesis that the true coefficient
value is zero. The p-values as included above are referring to the Durbin-Wu-Hausman test and the null hypothesis that financial literacy is exogenous. The p-values
thereby reflect the probability of observing an F-statistic as large or larger than the one received given the assumption that the null hypothesis is correct and financial
literacy is exogenous.
All correct: Dummy variable with 1 for respondents who answered all questions correct and 0 for respondents who could not answer all questions correct.
Score: Variable which takes values in the range of 0-3 depending on the amount of correct answers of a respondent.
Hansen J-Test (J Statistic): The Sargan Hansen test or J-test of over-identifying restrictions should be performed in any over-identified model estimated with instrumental
variables techniques. All the instances above, however, are not over-identified but just-identified (i.e. there is one instrument for one endogenous variable (financial
literacy)).
Sample: n=1’250 (some hypothesis could not be tested on a full sample basis (e.g. voting shares of parties) as data was not always available for all data points on PLZ
level).
Application/ Interpretation
The table above shows only few t-statistics in the first stage regression which are statistically significant on a 5% (critical value: |1.96|) or 1% confidence level (critical
value: |2.58|). The fact that the instrumental variables are weak is underpinned by the F-statistics which are all below the critical value of 10.
While considering the IV regression one can see that there is no instrument which exerts a statistically significant impact on investment portfolio ownership. T-values are
rather low and in many cases there is even a change in sign between the first stage and second stage regression.
Durbin-Wu-Hausman tests show that in non of the incidences above the null-hypothesis can be rejected while applying a 5% confidence level. On the basis of the
instruments used above it cannot be rejected that financial literacy is exogenous.
Source: Dataset, authors’ calculations
Roman Graf
EE
Financial Literacy and Financial Behavior in Switzerland
Table A15: Instruments Used by Other Researchers in Financial Literacy Studies
Empirical Paper
Dependent Variable
Lusardi & Mitchell, 2011A
Financial Literacy and Retirement
Planning in the United States
Retirement Planning
Bucher & Lusardi, 2011
Financial Literacy and Retirement
Planning in Germany
Retirement Planning
Alessie et al., 2011
Financial Literacy and Retirement
Planning in the Netherlands
Retirement Planning
Klapper & Panos, 2011
Financial Literacy and Retirement
Planning: The Russian Case
Retirement Planning
Sekita, 2011
Financial Literacy and Retirement
Planning in Japan
Retirement Planning
Fornero & Monticone, 2011
Financial Literacy and Pension Plan
Participation in Italy
Pension Plan
Participation
Crossan et al., 2011
Financial Literacy and Retirement
Planning in New Zealand
Almenberg & Säve-Söderbergh, 2011
Financial Literacy and Retirement
Planning in Sweden
Instrumental Variable (for Financial Literacy)
IV: Exposure of respondents to state mandated high school financial education
courses.
Description: Some states in the US introduced financial education courses in high
schools due to political reasons. Financial literacy is instrumented with the number of
years a mandate was in effect and also relevant to the respondent (number of mandate
years). Data was collected which indicated in which state a respondent was living in
his/ her senior year at high school and how many years of financial education he or she
was exposed to.
Over-identified Model: No
IV: Exposure to financial knowledge of others in the same region (instrumented by
voting shares of libertarian party (FDP) and leftist party (PDS/ Die Linke) in the 2005
national election at administrative district level) and financial education of parents,
alternatively.
Description: Rational is that people who are exposed to financially knowledgeable
people become more financially knowledgeable themselves (learning effects). Political
attitude at a regional level is used as a proxy for financial knowledge of others.
Alternatively, financial education of parents was used as well.
Over-identified Model: Yes
IV: Financial situation of siblings relative to respondents and financial understanding of
parents as perceived by respondents.
Description: Rational is that people who are exposed to financially knowledgeable
people become more financially knowledgeable themselves (learning effects). When
parents have better financial understanding, respondents should have higher financial
literacy as well. Furthermore, information about the financial situation of the oldest
sibling relative to the respondent was collected as well. When siblings were in worse
financial condition than the respondents, respondents were more likely to have higher
financial literacy.
Over-identified Model: Yes
IV: Total number of newspapers in circulation in every administrative region and total
number of universities in every administrative region.
Description: Rational is that people who are exposed to financially knowledgeable
people become more financially knowledgeable themselves (learning effects). When
there are more newspapers or universities, people should have better financial
knowledge and respondents are supposed to have higher financial literacy as well.
Over-identified Model: Yes
IV: Level of Japanese language ability in the form of the relative grade in school with
the age of 15 years (implemented as dummy). As additional IV, the average Japanese
language ability in the prefecture where the respondents were living were used.
Description: Respondents must know the meaning of words and be able to
comprehend the sentences in the financial literacy questions. The rational for the
average language ability in the prefecture is that people who are exposed to financially
knowledgeable people become more financially knowledgeable themselves (learning
effects).
Over-identified Model: Yes
IV: IV are relating to cost of learning and acquiring financial knowledge and
information. One IV is defined as dummy and refers to the fact if a household member
has a degree in economics. An additional IV is also defined as dummy and refers to
the fact if at least one household member uses of computer (either at home or at work).
Description: The hypothesis is that the presence of an economist and/ or a computer
user in the household makes it easier for the respondent to acquire knowledge and
information about financial matters.
Over-identified Model: Yes
Retirement Planning
No IV approach applied.
Retirement Planning
No IV approach applied.
Van Rooij et al., 2011
Financial Literacy and Stock Market
Participation
Stock Market
Participation
Van Rooij et al., 2012
Financial Literacy, Retirement
Planning and Household Wealth
Net Worth and
Retirement Planning
(separate regressions)
IV: Two types of instrumental variables have been used in the form of dummy
variables. One is referring to the financial situation of the oldest sibling and the other is
referring to the parents’ understanding of financial matters.
Description: They asked respondents about the financial experiences and relative
financial situation of their siblings and parents. The rational is that respondents can
learn from those around them, thus increasing their own financial literacy.
Over-identified Model: Yes
IV: IV are referring to the respondents economics education (i.e. the exposure to
economics education before entering the job) and are defined as dummy variables.
Description: Economic education has a strong predictive power for financial literacy,
as can be shown by the test on the relevance of the instruments in the first stage
regression. For both multivariate regression analysis (net worth and retirement
planning) the same instrumental variable was used.
Over-identified Model: No
Source: As included above
Roman Graf
FF
Financial Literacy and Financial Behavior in Switzerland
Table A16: Multicollinearity Diagnostics – Multivariate OLS Regression Analysis in Table 18
Variable
Intercept
Financial Literacy - All Correct
Women
Age
Foreigner
Secondary School
Professional Education
Grammar School
University (Applied)
University
Employed
Unemployed
Household Size
Household Income
CHF 50'000 - 100'000
CHF 100'000 - 250'000
CHF 250'000 - 1 Mio.
> CHF 1 Mio.
Very High Interest
High Interest
Low Interest
Low Engagement
High Engagement
Very High Engagement
Moderate Risk Aversion
Low Risk Aversion
Rare Impulsive Behavior
Regular Impulsive Behavior
Very Frequent Impulsive Behavior
Mean VIF
VIF
SQRT VIF
Tolerance
1.21
1.32
2.06
1.06
7.64
29.86
10.49
20.28
14.54
2.04
2.84
1.27
1.5
1.28
1.41
1.41
1.15
3.44
5.86
5.22
2.61
2.93
2.65
1.09
1.06
1.09
1.08
1.06
4.62
1.1
1.15
1.44
1.03
2.76
5.46
3.24
4.5
3.81
1.43
1.69
1.13
1.22
1.13
1.19
1.19
1.07
1.85
2.42
2.28
1.62
1.71
1.63
1.05
1.03
1.04
1.04
1.03
0.83
0.76
0.48
0.94
0.13
0.03
0.10
0.05
0.07
0.49
0.35
0.79
0.67
0.78
0.71
0.71
0.87
0.29
0.17
0.19
0.38
0.34
0.38
0.91
0.94
0.92
0.93
0.95
R-Squared
Eigenvalue
9.85
0.17
1.54
0.24
1.34
0.52
1.21
0.06
1.14
0.87
1.10
0.97
1.08
0.90
1.02
0.95
1.00
0.93
0.97
0.51
0.96
0.65
0.94
0.21
0.92
0.33
0.85
0.22
0.83
0.29
0.78
0.29
0.68
0.13
0.63
0.71
0.58
0.83
0.41
0.81
0.35
0.62
0.30
0.66
0.18
0.62
0.11
0.09
0.08
0.06
0.07
0.08
0.04
0.07
0.02
0.05
0.00
Condition Number
Condition Index
1.00
2.53
2.71
2.86
2.94
2.99
3.02
3.11
3.14
3.19
3.21
3.24
3.28
3.41
3.45
3.55
3.80
3.95
4.11
4.88
5.32
5.72
7.43
9.43
10.95
11.48
15.38
20.72
52.33
52.33
Theory
As emphasized by Kmenta, 1986, p. 380, multicollinearity is a question of degree and not of kind and the meaningful distinction is not between the presence and the
absence of multicollinearity but between the various degrees of multicollinearities.
VIF: VIF quantify the severity of multicollinearity in OLS regression analysis. They provide an index that measures how much the variance (the square of the estimate's
standard deviation) of an estimated regression coefficient is increased because of multicollinearity.
The variance inflation factors are computed from the correlation matrix of the independent variables. Thus, the independent variables are centered and standardized to unit
length. The diagonal elements of the inverse of the correlation matrix are the variance inflation factors. (Rawlings et al., 1998, p. 372)
The link between
and multicollinearity (of the standardized and centered variables) is through the relationship:
is the coefficient of determination from the regression of
on the other independent variables (supporting regression analysis: =
). If there is a near-singularity
involving
and the other independent variables,
will be near 1.0 and
will be large. If
is orthogonal (i.e. uncorrelated) to the other independent variables,
will
be 0 and
will be 1.0. (Rawlings et al., 1998, p. 373) VIF values become critical if they are larger than 10. (cf. Kutner et al., 2004, p. 408)
SQRT VIF: The square root of the VIF shows how much larger the standard error is compared to what it would be if that variable was unco rrelated with the other
independent variables in the model.
Tolerance: The tolerance is representing the reciprocal value of the VIF. A tolerance close to 1 means that there is little multicollinearity whereas a value close to zero is
stressing that multicollinearity may be a threat. A tolerance value lower than 0.1 is comparable to a VIF of above 10.
R-Squared: R-Squared represent the coefficient of determination from the regression of
tolerance from 1.
on the other independent variables. Values equal the subtraction of the
Eigenvalue: Eigenvalues are computed by a multivariate statistical technique called the principal component analysis. The eigenvalues (or characteristic roots) are the
variances of the components. A zero eigenvalue means perfect multicollinearity among independent variables and very small eigenvalues imply severe multicollinearity.
Condition Index/ Number: The condition number is the condition index with the largest value. The condition number equals the square root of the largest eigenvalue
divided by the smallest eigenvalue.
When no multicollinearity is existent, the eigenvalues and condition indices will all equal one and when multicollinearity increases condition indices will increase as well.
Rules of thumb suggest that a condition number of around 10 indicates weak dependencies that may be starting to affect the regression estimates. A condition number of
30-100 indicates moderate to strong dependencies. (Rawlings et al., 1998, p. 371)
Sample: n = 1’248 (Table 18 includes a breakdown of the sample composition)
Application/ Interpretation
The VIF for Professional Education is 29.86 and its square root 5.46. This means that the standard error for the coefficient Professional Education is 5.46 as large as it
would be if it were uncorrelated with the other independent variables. The value of 29.86 is derived from dividing 1 by 1 reduced by the coefficient of determination (i.e. R2
of 0.97) of the linear multiple regression analysis with the dependent variable Professional Education and the independent variables as included above.
The condition number of 52.33 is achieved while taking the square root of the highest eigenvalue (i.e. 9.85) divided by the smallest eigenvalue (i.e. 0.00). The condition
number of 52.33 indicates that the estimates might have a moderate amount of numerical error. However, the statistical standard error is almost always much greater than
the numerical error (cf. Belsley et al., 2005, p. 85ff).
Source: Dataset, authors’ calculations
Roman Graf
GG
Financial Literacy and Financial Behavior in Switzerland
Table A17: Multivariate Linear Regression – Impulsive Behavior
Multivariate OLS Regression - Impulsive Behavior (d)
Financial Literacy - All Correct (d)
-0.05
(0.03)
*
Financial Literacy - Score Correct
-0.04
(0.02)
**
Financial Literacy - Interest Rates (d)
-0.03
(0.03)
-0.03
(0.03)
-0.06
(0.03)
Financial Literacy - Inflation (d)
Financial Literacy - Risk Diversification (d)
**
Financial Literacy - Score DK
Gender
Women (d)
Age
Age
Nationality
Foreigner (d)
Education
Primary School (d)
Secondary School (d)
Professional Education (d)
Grammar School (d)
University (Applied) (d)
University (d)
Occupation
Employed (d)
Unemployed (d)
Household Size
Number of People in Household
Household Income
Household Income
Financial Wealth
< CHF 50'000 (d)
CHF 50'000 - 100'000 (d)
CHF 100'000 - 250'000 (d)
CHF 250'000 - 1 Mio. (d)
> CHF 1 Mio. (d)
Financial Interest
No Interest (d)
Low Interest (d)
High Interest (d)
Very High Interest (d)
Financial Engagement
No Engagement (d)
Low Engagement (d)
High Engagement (d)
Very High Engagement (d)
Risk Characteristics
High Risk Aversion (d)
Morderate Risk Aversion (d)
Low Risk Aversion (d)
_cons
N
R2
F
0.09
(0.03)
0.00
(0.03)
0.00
(0.00)
0.00
(0.03)
**
0.00
(0.00)
0.00
(0.03)
**
0.00
(0.00)
0.01
(0.03)
**
0.00
(0.00)
0.03
(0.05)
0.03
(0.05)
0.04
(0.05)
0.03
(0.05)
omitted
omitted
omitted
omitted
-0.01
(0.15)
0.02
(0.14)
-0.01
(0.15)
0.04
(0.14)
0.02
(0.15)
0.00
(0.15)
0.03
(0.14)
0.00
(0.15)
0.06
(0.14)
0.03
(0.15)
0.01
(0.15)
0.04
(0.14)
0.01
(0.15)
0.07
(0.14)
0.04
(0.15)
-0.02
(0.15)
0.02
(0.14)
-0.01
(0.15)
0.04
(0.14)
0.01
(0.15)
-0.02
(0.04)
-0.10
(0.06)
-0.02
(0.04)
-0.10
(0.06)
-0.02
(0.04)
-0.10
(0.06)
-0.02
(0.04)
-0.11
(0.06)
-0.01
(0.01)
-0.01
(0.01)
-0.01
(0.01)
-0.01
(0.01)
-0.01
(0.01)
-0.00
(0.01)
-0.00
(0.01)
-0.01
(0.01)
omitted
0.11
(0.03)
0.17
(0.04)
0.17
(0.05)
0.18
(0.12)
omitted
***
***
***
0.11
(0.03)
0.17
(0.04)
0.18
(0.05)
0.18
(0.11)
omitted
***
***
***
0.11
(0.03)
0.17
(0.04)
0.18
(0.05)
0.18
(0.12)
***
***
0.11
(0.03)
0.17
(0.04)
0.17
(0.05)
0.17
(0.11)
omitted
omitted
omitted
-0.01
(0.06)
0.06
(0.06)
0.06
(0.07)
-0.01
(0.06)
0.06
(0.06)
0.06
(0.07)
-0.00
(0.06)
0.06
(0.06)
0.06
(0.07)
-0.01
(0.06)
0.06
(0.06)
0.06
(0.07)
-0.08
(0.05)
-0.01
(0.05)
0.09
(0.05)
omitted
*
*
omitted
-0.08
(0.05)
-0.01
(0.05)
0.09
(0.05)
omitted
*
*
omitted
-0.08
(0.05)
-0.01
(0.05)
0.09
(0.05)
**
*
omitted
***
omitted
omitted
***
***
***
***
omitted
*
*
omitted
-0.08
(0.05)
-0.01
(0.05)
0.09
(0.05)
*
**
omitted
-0.11
(0.03)
-0.09
(0.07)
***
-0.11
(0.03)
-0.09
(0.07)
***
-0.11
(0.03)
-0.09
(0.07)
***
-0.10
(0.03)
-0.08
(0.07)
***
0.55
(0.17)
***
0.59
(0.17)
***
0.59
(0.18)
***
0.51
(0.17)
***
1'248
0.0745
3.94
1'248
0.0764
4.04
1'248
0.0768
3.76
1'248
0.0779
4.13
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Dependent Variable: Impulsive Behavior (0 = no impulsive behavior at all (n=850), 1 = at least some impulsive behavior (n=398)) as dummy variable.
Explanatory Variables: Numbers in brackets are standard errors. (d) stands for dummy. Household Income is based on ordinal scale with values ranging from 1 (lowest
income) to 6 (highest income).
Sample: The sample of n=1’500 has been adjusted for respondents answering: Other for occupation (n=14), DK for number of people in household (n=2), DK for income
(n=148), DK for wealth (n=178), DK for planning for financial future (n=14), DK for engagement (n=9), DK for risk aversion (n=31), DK for impulsive behavior (n=2) and
missing value for consumption credit (n=1). Out of a total adjustment of n=399, 115 have been double, 13 triple and 2 four-times counted resulting in a sample size for the
regression of n=1’248.
Source: Dataset, authors’ calculations
Roman Graf
HH
Financial Literacy and Financial Behavior in Switzerland
Table A18: Multicollinearity Diagnostics – Multivariate OLS Regression Analysis in Table 20
Variable
Intercept
Financial Literacy - All Correct
Women
Age
Foreigner
Secondary School
Professional Education
Grammar School
University (Applied)
University
Employed
Unemployed
Household Size
CHF 4’500 - 7’000
CHF 7’000 - 9’000
CHF 9’000 - 12’000
CHF 12’000 - 15’000
> CHF 15’000
CHF 50'000 - 100'000
CHF 100'000 - 250'000
CHF 250'000 - 1 Mio.
> CHF 1 Mio.
Very High Interest
High Interest
Low Interest
Low Engagement
High Engagement
Very High Engagement
Significant Planning
Some Planning
Little Planning
Mean VIF
VIF
SQRT VIF
Tolerance
1.19
1.31
1.43
1.08
7.58
32.96
11.25
22.56
16.51
1.54
1.59
1.21
4.17
4.29
4.02
2.65
2.53
1.25
1.36
1.36
1.15
3.57
6.37
5.70
2.69
2.99
2.70
3.92
3.85
2.84
5.25
1.09
1.14
1.20
1.04
2.75
5.74
3.35
4.75
4.06
1.24
1.26
1.10
2.04
2.07
2.01
1.63
1.59
1.12
1.17
1.17
1.07
1.89
2.52
2.39
1.64
1.73
1.64
1.98
1.96
1.69
0.84
0.77
0.70
0.93
0.13
0.03
0.09
0.04
0.06
0.65
0.63
0.83
0.24
0.23
0.25
0.38
0.40
0.80
0.74
0.73
0.87
0.28
0.16
0.18
0.37
0.33
0.37
0.25
0.26
0.35
R-Squared
Eigenvalue
10.15
0.16
1.84
0.23
1.32
0.30
1.27
0.07
1.17
0.87
1.12
0.97
1.10
0.91
1.06
0.96
1.04
0.94
1.03
0.35
0.99
0.37
0.96
0.17
0.93
0.76
0.88
0.77
0.87
0.75
0.84
0.62
0.80
0.60
0.73
0.20
0.64
0.26
0.60
0.27
0.42
0.13
0.36
0.72
0.34
0.84
0.18
0.82
0.10
0.63
0.09
0.67
0.07
0.63
0.05
0.75
0.04
0.74
0.02
0.65
0.00
Condition Number
Condition Index
1.00
2.35
2.77
2.82
2.95
3.01
3.03
3.09
3.13
3.15
3.20
3.25
3.31
3.39
3.42
3.48
3.56
3.72
4.00
4.13
4.90
5.32
5.49
7.57
9.92
10.82
12.47
14.23
15.75
21.69
55.61
55.61
Theory
As emphasized by Kmenta, 1986, p. 380, multicollinearity is a question of degree and not of kind and the meaningful distinction is not between the presence and the
absence of multicollinearity but between the various degrees of multicollinearities.
VIF: VIF quantify the severity of multicollinearity in OLS regression analysis. They provide an index that measures how much the variance (the square of the estimate's
standard deviation) of an estimated regression coefficient is increased because of multicollinearity.
The variance inflation factors are computed from the correlation matrix of the independent variables. Thus, the independent variables are centered and standardized to unit
length. The diagonal elements of the inverse of the correlation matrix are the variance inflation factors. (Rawlings et al., 1998, p. 372)
The link between
and multicollinearity (of the standardized and centered variables) is through the relationship:
is the coefficient of determination from the regression of
on the other independent variables (supporting regression analysis: =
). If there is a near-singularity
involving
and the other independent variables,
will be near 1.0 and
will be large. If
is orthogonal (i.e. uncorrelated) to the other independent variables,
will
be 0 and
will be 1.0. (Rawlings et al., 1998, p. 373) VIF values become critical if they are larger than 10. (cf. Kutner et al., 2004, p. 408)
SQRT VIF: The square root of the VIF shows how much larger the standard error is compared to what it would be if that variable was unco rrelated with the other
independent variables in the model.
Tolerance: The tolerance is representing the reciprocal value of the VIF. A tolerance close to 1 means that there is little multicollinearity whereas a value close to zero is
stressing that multicollinearity may be a threat. A tolerance value lower than 0.1 is comparable to a VIF of above 10.
R-Squared: R-Squared represent the coefficient of determination from the regression of
tolerance from 1.
on the other independent variables. Values equal the subtraction of the
Eigenvalue: Eigenvalues are computed by a multivariate statistical technique called the principal component analysis. The eigenvalues (or characteristic roots) are the
variances of the components. A zero eigenvalue means perfect multicollinearity among independent variables and very small eigenvalues imply severe multicollinearity.
Condition Index/ Number: The condition number is the condition index with the largest value. The condition number equals the square root of the largest eigenvalue
divided by the smallest eigenvalue.
When no multicollinearity is existent, the eigenvalues and condition indices will all equal one and when multicollinearity increases condition indices will increase as well.
Rules of thumb suggest that a condition number of around 10 indicates weak dependencies that may be starting to affect the regression estimates. A condition number of
30-100 indicates moderate to strong dependencies. (Rawlings et al., 1998, p. 371)
Sample: n = 1’127 (Table 20 includes a breakdown of the sample composition)
Application/ Interpretation
The VIF for Professional Education is 32.96 and its square root 5.74. This means that the standard error for the coefficient Professional Education is 5.74 as large as it
would be if it were uncorrelated with the other independent variables. The value of 32.96 is derived from dividing 1 by 1 reduced by the coefficient of determination (i.e. R2
of 0.97) of the linear multiple regression analysis with the dependent variable Professional Education and the independent variables as included above.
The condition number of 55.61 is achieved while taking the square root of the highest eigenvalue (i.e. 10.15) divided by the smallest eigenvalue (i.e. 0.00). The condition
number of 55.61 indicates that the estimates might have a moderate amount of numerical error. However, the statistical standard error is almost always much greater than
the numerical error (cf. Belsley et al., 2005, p. 85ff).
Source: Dataset, authors’ calculations
Roman Graf
II
Financial Literacy and Financial Behavior in Switzerland
Table A19: Probit and Logit Regression (Average Marginal Effects) – Financial Behavior
Investment Portfolio (d)
Probit
Logit
Financial Literacy
All Correct (d)
Gender
Women (d)
Age
Age
Nationality
Foreigner (d)
Education
Primary School (d)
Consumption Credit (d)
Probit
Logit
Mortgage Debt (d)
Probit
Logit
Retirement Account (d)
Probit
Logit
0.10 ***
(0.02)
0.10 ***
(0.02)
-0.00
(0.01)
-0.00
(0.01)
0.05 **
(0.03)
0.06 **
(0.03)
0.06 **
(0.03)
0.06 **
(0.03)
-0.04
(0.03)
-0.04
(0.03)
-0.03 *
(0.02)
-0.03 *
(0.02)
-0.00
(0.03)
-0.00
(0.03)
-0.00
(0.03)
-0.00
(0.03)
0.00
(0.00)
0.00
(0.00)
-0.00
(0.00)
-0.00
(0.00)
0.01 ***
(0.00)
0.01 ***
(0.00)
0.00
(0.00)
0.00
(0.00)
-0.11 **
(0.05)
-0.11 **
(0.05)
0.08 ***
(0.02)
0.07 ***
(0.02)
-0.13 ***
(0.05)
-0.14 ***
(0.05)
-0.13 ***
(0.05)
-0.13 ***
(0.05)
omitted
omitted
omitted
omitted
omitted
omitted
omitted
omitted
-0.04
(0.14)
0.01
(0.14)
0.05
(0.14)
0.08
(0.14)
-0.00
(0.14)
-0.05
(0.15)
0.00
(0.14)
0.04
(0.14)
0.07
(0.14)
-0.01
(0.14)
0.43
(13.80)
0.46
(13.80)
0.45
(13.80)
0.42
(13.80)
0.44
(13.80)
0.66
(43.24)
0.68
(43.24)
0.68
(43.24)
0.65
(43.24)
0.66
(43.24)
0.01
(0.13)
0.01
(0.12)
0.02
(0.13)
0.04
(0.13)
-0.03
(0.13)
-0.01
(0.13)
0.01
(0.12)
0.01
(0.13)
0.04
(0.12)
-0.04
(0.13)
0.22
(0.19)
0.14
(0.18)
0.20
(0.19)
0.19
(0.19)
0.21
(0.19)
0.22
(0.19)
0.14
(0.18)
0.20
(0.19)
0.19
(0.19)
0.21
(0.19)
-0.05
(0.04)
-0.00
(0.06)
-0.05
(0.04)
0.00
(0.06)
0.01
(0.02)
omitted
0.01
(0.02)
omitted
0.02
(0.04)
-0.06
(0.06)
0.02
(0.04)
-0.06
(0.06)
0.04
(0.04)
-0.15
(0.08) *
0.04
(0.04)
-0.16
(0.08) *
0.00
(0.01)
0.00
(0.01)
0.00
(0.01)
0.00
(0.01)
0.11 ***
(0.01)
0.11 ***
(0.01)
0.00
(0.01)
0.00
(0.01)
0.00
(0.01)
n/a
0.00
(0.01)
n/a
0.00
(0.01)
n/a
0.00
(0.01)
n/a
0.06 ***
(0.01)
n/a
0.06 ***
(0.01)
n/a
n/a
n/a
omitted
omitted
CHF 4’500 - 7’000 (d)
n/a
n/a
n/a
n/a
n/a
n/a
CHF 7’000 - 9’000 (d)
n/a
n/a
n/a
n/a
n/a
n/a
CHF 9’000 - 12’000 (d)
n/a
n/a
n/a
n/a
n/a
n/a
CHF 12’000 - 15’000 (d)
n/a
n/a
n/a
n/a
n/a
n/a
> CHF 15’000 (d)
n/a
n/a
n/a
n/a
n/a
n/a
Financial Wealth
< CHF 50'000 (d)
omitted
omitted
omitted
omitted
omitted
-0.05 **
(0.02)
-0.08 ***
(0.03)
-0.06
(0.04)
omitted
-0.05 **
(0.02)
-0.09 **
(0.04)
-0.07
(0.05)
omitted
omitted
omitted
Secondary School (d)
Professional Education (d)
Grammar School (d)
University (Applied) (d)
University
Occupation
Employed (d)
Unemployed (d)
Household Size
Number of People in Household
Household Income
Household Income
< CHF 4’500 (d)
CHF 50'000 - 100'000 (d)
CHF 100'000 - 250'000 (d)
CHF 250'000 - 1 Mio. (d)
> CHF 1 Mio. (d)
Financial Interest
No Interest (d)
Low Interest (d)
High Interest (d)
Very High Interest (d)
Financial Engagement
No Engagement (d)
Low Engagement (d)
High Engagement (d)
Very High Engagement (d)
Risk Characteristics
High Risk Aversion (d)
Morderate Risk Aversion (d)
Low Risk Aversion (d)
Financial Planning
No Planning of Financial Future (d)
0.20
(0.03)
0.31
(0.03)
0.42
(0.04)
0.30
(0.10)
omitted
***
***
***
***
omitted
0.09
(0.07)
0.08
(0.06)
0.21 ***
(0.07)
0.20
(0.03)
0.30
(0.03)
0.41
(0.04)
0.29
(0.10)
***
***
***
***
omitted
0.10
(0.07)
0.09
(0.07)
0.22 ***
(0.08)
-0.03
(0.03)
-0.05 *
(0.03)
-0.01
(0.03)
-0.02
(0.03)
-0.05 *
(0.03)
-0.02
(0.03)
omitted
omitted
omitted
omitted
0.03
(0.04)
0.03
(0.04)
0.02
(0.04)
0.03
(0.04)
0.04
(0.04)
0.02
(0.04)
-0.00
(0.03)
0.01
(0.02)
0.02
(0.02)
-0.00
(0.03)
0.00
(0.02)
0.01
(0.02)
omitted
omitted
omitted
omitted
0.00
(0.02)
0.00
(0.03)
0.00
(0.02)
0.01
(0.03)
0.10 ***
(0.03)
0.16 ***
(0.06)
0.10 ***
(0.03)
0.16 ***
(0.06)
0.05 *
(0.03)
0.04
(0.04)
-0.00
(0.05)
0.23 **
(0.12)
0.05 *
(0.03)
0.04
(0.04)
-0.00
(0.05)
0.22 *
(0.12)
omitted
omitted
omitted
0.02
(0.05)
0.01
(0.05)
0.01
(0.05)
0.02
(0.05)
0.01
(0.05)
0.01
(0.05)
n/a
n/a
n/a
n/a
n/a
n/a
omitted
omitted
omitted
0.09 *
(0.05)
0.15 ***
(0.04)
0.15 ***
(0.05)
omitted
-0.06 **
(0.03)
-0.17 **
(0.07)
-0.06 **
(0.03)
-0.16 **
(0.07)
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
Impulsive Behavior
No Impulsive Behavior (d)
n/a
n/a
omitted
omitted
omitted
Rare Impulsive Behavior (d)
n/a
n/a
Regular Impulsive Behavior (d)
n/a
n/a
Very Frequent Impulsive Behavior (d)
n/a
n/a
0.03
(0.03)
0.00
(0.06)
-0.09
(0.11)
1’250
-635.99
0.2256
370.54
1’250
-637.03
0.2243
368.47
1’248
-691.95
0.1965
338.49
***
***
***
***
0.13 ***
(0.03)
0.22 ***
(0.04)
0.22 ***
(0.06)
0.04
(0.14)
omitted
0.09 **
(0.05)
0.15 ***
(0.04)
0.15 ***
(0.05)
**
omitted
0.01
(0.07)
0.04
(0.07)
0.13
(0.08)
n/a
1’072
-192.72
0.1767
82.73
0.13 ***
(0.03)
0.22 ***
(0.04)
0.22 ***
(0.06)
0.04
(0.14)
omitted
n/a
1’072
-192.04
0.1796
84.08
omitted
0.01
(0.07)
0.04
(0.07)
0.13
(0.08)
n/a
N
Log Likelihood
Pseudo R2
2
LR chi
***
omitted
n/a
0.04 **
(0.02)
0.04 *
(0.02)
0.08 **
(0.03)
***
0.02
(0.06)
0.01
(0.06)
0.02
(0.07)
0.07
(0.05)
0.04
(0.05)
0.07
(0.05)
0.04 **
(0.02)
0.04 *
(0.02)
0.08 **
(0.03)
***
0.16
(0.07)
0.21
(0.07)
0.24
(0.07)
0.29
(0.08)
0.24
(0.09)
omitted
omitted
Significant Planning of Fin. Future (d)
***
0.02
(0.06)
0.01
(0.06)
0.02
(0.07)
0.06
(0.05)
0.04
(0.05)
0.07
(0.04)
Some Planning of Financial Future (d)
**
omitted
omitted
Little Planning of Financial Future (d)
0.16
(0.07)
0.21
(0.07)
0.24
(0.07)
0.29
(0.08)
0.24
(0.09)
0.05
(0.06)
0.09
(0.06)
0.11 *
(0.06)
0.05
(0.06)
0.09
(0.06)
0.11 *
(0.06)
omitted
n/a
n/a
0.03
(0.03)
0.00
(0.06)
-0.09
(0.11)
n/a
n/a
n/a
n/a
n/a
n/a
1’248
-691.67
0.1968
339.04
1’127
-686.07
0.1151
178.47
1’127
-685.85
0.1154
178.89
*** Significant at 1% confidence level, ** Significant at 5% confidence level, * Significant at 10% confidence level
Dependent Variable: Financial behavior characteristics as stated above in the form of dummy variables with 0 = no behavior (e.g. no investment portfolio ownership) and
1 = behavior (e.g. investment portfolio ownership).
Explanatory Variables: Numbers in brackets are standard errors. (d) stands for dummy (0/1) variable. Household Income is based on ordinal scale with values ranging
from 1 (lowest income) to 6 (highest income). The term n/a states that the variable has not been included in the regression analysis. Consumption credit: Wealth category
>CHF 1 Mio. != 0 and occupation category unemployed != 0 predicts failure perfectly. Variables have been dropped and 176 (168 resp. 8) observations not used.
Sample: A reconciliation of the underlying sample sizes is included in the description of the relevant multivariate linear regression tables in the main body of this thesis.
Source: Dataset, authors’ calculations
Roman Graf
JJ
Financial Literacy and Financial Behavior in Switzerland
Table A20: Selection of Repetitive Surveys Used for Financial Literacy Purposes in Developed Countries
Country
USA
USA
USA
USA
USA
USA
EU
UK
Survey
University of Michigan
Health and Retirement
Study (HRS)
Jump$tart Coalition
Survey
National Longitudinal
Survey of Youth
(NLSY)
Rand American Life
Panel
National Financial
Capability Survey
(excl. State-by-State
and Military Survey)
Consumer Financial
Literacy Survey
Survey of Health, Aging
and Retirement in
Europe (SHARE)
English Longitudinal
Study of Ageing (ELSA)
Roman Graf
Frequency
Description
Biennial
Type: Longitudinal panel study
Way: In-depth personal or telephone interviews
Sample: >26’000 Americans over the age of 50 years
Scope: Nationwide
Support: National Institute on Aging and Social Security Administration
Launch: 1992
Financial Literacy: First set of FL questions was included in the 2004 HRS and the scope
has been extended in 2006 and thereafter.
Biennial
(since 2000)
Type: Cross-sectional study
Way: Standardized questionnaire with 31 questions
Sample: 6’856 high school seniors from 385 randomly selected schools and 1’030
undergraduate students from US colleges/ universities (2008 survey)
Scope: Nationwide
Support: More than 150 Jump$tart coalition members comprising of government agencies,
non-profit organizations, educational institutions, financial associations and corporations.
Funding of surveys: Merrill-Lynch Foundation.
Launch: 1997 (in 2008 extended to college students)
Financial Literacy: 2008 survey included basic questions covering inflation, taxes,
investments (incl. risk considerations), credit cards, debt, retirement income, savings,
insurance and general financial matters.
1997 and
annual
basis
thereafter
Type: Longitudinal study
Way: Hour-long personal interviews of youth and one parent
Sample: Approx. 9’000 youth (12-16 years old in 1997)
Scope: Nationwide
Support: Bureau of Labor Statistics
Launch: 1979 resp. 1997
Financial Literacy: The three standard FL questions were included in the wave 11 of the
NLSY97 (no longitudinal comparison as only included once).
Monthly/ Six
Months
Type: Longitudinal study
Way: Interviews over internet
Sample: Approx. 5’000 respondents aged 18 years and older
Scope: Nationwide
Support: Rand Corporation (non-profit institution)
Launch: 1970s
Financial Literacy: Used by various researcher in the field of financial literacy such as
Fonseca et al., 2012, Yoong, 2010 (no longitudinal comparison as only included once).
One-off
Type: Cross-sectional study
Way: Telephone interviews
Sample: Approx. 1'500 US adults
Scope: Nationwide
Support: FINRA Investor Education Foundation commissioned study in consultation with the
US Department of Treasury and the President's Advisory Council on Financial Literacy.
Launch: 2009
Financial Literacy: Survey included basic question about inflation, interest, bond prices and
risk diversification as well as various sections on financial behavior in terms of credit card
usage, mortgage indebtedness, investment and savings behavior etc.
Annually
Type: Cross-sectional study
Way: Telephone interviews
Sample: 1’007 adults aged 18 years and older (2012 survey)
Scope: Nationwide
Support: Survey was prepared for National Foundation for Credit Counseling and The
Network Branded Prepaid Card Association
Launch: 2007
Financial Literacy: Survey includes questions about budget, bills, debt, savings, spending,
credit, mortgages, and personal finances (not exhaustive).
Biennial
Type: Longitudinal panel study
Way: Computer assisted interviews
Sample: Approx. 60’000 individuals from aged ≥ 50 years
Scope: 20 European countries (incl. Switzerland)
Support: Funding from European Commission, US National Institute on Aging, German
Federal Ministry of Education and Research and various national sources.
Launch: 2004 (SHARE baseline study with eleven countries)
Financial Literacy: Focus on aging process from economics, health and social network
angles.
Biennial
Type: Longitudinal panel study
Way: Telephone and face-to-face interviews
Sample: Approx. 15’000 individuals from aged ≥ 50 years (wave 4)
Scope: England
Support: Funding from US National Institute on Aging and consortium of UK government.
Launch: 2002
Financial Literacy: No financial literacy section but cognitive module.
KK
Financial Literacy and Financial Behavior in Switzerland
DE
AUS
OECD
SAVE Survey
ANZ Survey of Adult
Financial Literacy in
Australia
OECD PISA Financial
Literacy Assessment
Annually
Type: Longitudinal study
Way: Usually personal inverviews (but also paper questionnaires)
Sample: Around 2’250 observations (2009)
Scope: Germany (focus on savings behavior)
Support: Deutsche Forschungsgemeinschaft (German national science foundation)
Launch: 2001 (first wave)
Financial Literacy: In 2009, the questionnaire has been extended to include a module about
financial literacy which covered a respondents' degree of financial and cognitive ability.
Approx.
Biennial
Type: Cross-sectional study
Way: Telephone interviews
Sample: Approx. 3’500 adults aged 18 years and over (fourth survey, 2011)
Scope: Australia
Support: ANZ
Launch: 2002
Financial Literacy: Survey includes questions about use and understanding of payment and
transaction methods, money management, budgeting and planning, transaction products,
borrowing and debt, saving, investments and superannuation as well as insurance (not
exhaustive).
Triennial
(PISA)
Type: Longitudinal study
Way: Questionnaires (together with PISA assessment)
Sample: 15 years old students
Scope: 65 countries or regions are in PISA, 19 thereof will take part in financial literacy part
(Albania, Australia, Belgium (Flemish Community), Brazil, Shanghai-China, Colombia,
Croatia, Czech Republic, Estonia, France, Israel, Italy, Latvia, New Zealand, Poland, Slovak
Republic, Slovenia, Spain and United States)
Support: OECD
Launch: 2012 (results will be available in 2013)
Financial Literacy: Focus on demonstrating and applying knowledge and skills. Survey
consists of 60 minutes of main survey material with 40 items. The content covers (1) Money
and transactions, (2) Planning and managing finances, (3) Risk and reward, (4) Financial
landscape while scores depend on the following processes: (1) identify financial information,
(2) analyze information in a financial context, (3) evaluate financial issues, (4) apply financial
knowledge and understanding.
Questions cover constructed-response (open questions) and selected-response (multiple
choice questions) items.
Source: Web-pages of survey providers
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Financial Literacy and Financial Behavior in Switzerland
Questionnaire (Page 1-2)
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Financial Literacy and Financial Behavior in Switzerland
Questionnaire (Page 3-4)
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Financial Literacy and Financial Behavior in Switzerland
Questionnaire (Page 5-6)
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Financial Literacy and Financial Behavior in Switzerland
Questionnaire (Page 7-8)
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Financial Literacy and Financial Behavior in Switzerland
Questionnaire (Page 9)
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Financial Literacy and Financial Behavior in Switzerland
IX
Declaration of Authorship
" I hereby declare
-
that I have written this thesis without any help from others and without the use of
documents and aids other than those stated above,
-
that I have mentioned all used sources and that I have cited them correctly according
to established academic citation rules."
Date and Signature:
St. Gallen, August 20, 2012
Roman Graf
………………………………………………
RR