1. 1
Degrees of Debt
The relationship between formal educational attainment,
debt and debt relief amongst Austrians, a sequential
mixed-methods analysis
Authors:
Andrijevic Marina
Bärnthaler Richard
Dausendschön Alina
Lewitus Evan
Panhuber Lisa
Course:
Quantitative and Qualitative Methods II
January 21st, 2016

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Table of Contents
ABSTRACT 3
INTRODUCTION 3
STATE OF THE ART AND THEORETICAL FRAMEWORK 4
EMPIRICAL DESIGN 6
QUANTITATIVE ANALYSIS 7
THE RESULTS OF THE QUANTITATIVE ANALYSIS 7
TO SUMMARIZE THE RESULTS OF ALL REGRESSION MODELS 10
QUALITATIVE ANALYSIS 11
THE RESULTS OF THE QUALITATIVE ANALYSIS 12
DISCUSSION OFMIXING QUANTITATIVE AND QUALITATIVE RESULTS 16
CONCLUSION 17
REFERENCES 17
ANNEX1 19
OUTPUT OF THE QUANTITATIVE ANALYSIS 19
ANNEX2 25
INTERVIEW GUIDE 25
ANNEX3 27
PARTIAL TRANSCRIPTS 27
ANNEX4 30
INDIVIDUAL FRAMEWORK ANALYSES 30
ANNEX5 33
A COMMON FRAMEWORK ANALYSIS: COMPARING THE THREE DIFFERENT EXPERTS 33

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Abstract
The influence of education on debt is a phenomenon that is often neglected and inadequately
addressed in research. This paper illustrates how educational attainment affects debt profiles.
The predominant part of the existing research on the relation between educational attainment
and debt is mainly concerned either with the accumulation of student debt due to educational
attainment or with rough conclusions on the relation of educational attainment and the amount
of debt. This paper seeks to give a more concrete picture through the consideration of debt
profiles as multi-faceted concept that includes several characteristics of debt, beyond total
amount: the number of loans for housing or apartments, the housing debt to disposable
income ratio, the type of credit, financial pressure, the ability to deal with unexpected costs,
and delay of payments. The authors use a mixed-methods approach. Concerning the
quantitative research, a logistic regression analysis detects the main relations between the type
and amount of formal education, and certain characteristics of an individual’s debt profile.
Qualitative evidence is garnered through in-depth interviews with laypersons and experts; it
reveals why individuals with different levels of education acquire the specific debt profiles
that they do, and how individuals with different levels of education handle their debt. The
paper concludes that whilst quantitative analysis only allows for ambiguous results indicating
a relation between higher debt and formal educational attainment, the insights gained through
qualitative interviews demonstrate the relevance of education concerning debt.
Introduction
In this paper, we look at the relationship between formal educational attainment, debt and
debt relief through a sequential mixed-methods analysis. We try to answer two main
questions: First, how does the level of education influence the handling of debt and credits in
Austria? Second, what are the triggers and root causes that make people less successful in
managing their income and expenses?
Little research on the connections between education, debt and debt relief in Austria or any
Central and Eastern European (CEE) country is currently available or being conducted. Most
knowledge on this subject comes from the United States and the United Kingdom, and is
rarely the central focus of such studies: formal education serves as one demographic factor
amongst many. Data from the European Union’s 2012 Statistics on Income and Living
Conditions (EU-SILC), and the Austrian National Bank’s analysis of it in their report
“Characteristics of Household Debt in Austria”, enabled research in this area to be conducted.
Knowledge about financial literacy, while also based primarily on the United States, was also
used as an appropriate, although indirect, basis for this research project. While differences
between US, UK and Austrian debt and systems of financialization and debt repayment are
present, we believe they are minute enough to not distort our research or pose any significant
distortions.
To understand the logic of consumer behavior in taking out and handling debt, we start from
the idea of consumer rationality. Insights from behavioral economics point out that many
psychological factors need to be taken into account when analyzing economic decisions. In
their paper about household financial management, Hilgert et al. (2003) show how behavioral
economics offers a framework for studying behaviors that seem inconsistent or irrational – for
example, consumers who hold money in a savings account earning interest at 2 percent while
carrying balances on credit cards and paying 18 percent interest.

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To find trends and relationships between education, debt and debt relief, as well as the
potential explanations behind them, we use both quantitative and qualitative methods in a
sequential mixed-methods procedure. The EU-SILC 2012 survey serves as our quantitative
data, and semi-structured interviews of three experts working at three different Austrian debt
counseling agencies serves as our qualitative data. The results from the quantitative analysis
were used to construct the interview guideline used for all interviews.
We will start by looking at current literature on education and its connections to debt and debt
relief, focusing primarily on the notion of financial literacy as well as its causes and effects.
The overall research design will then be laid out. As our process of data collection was
sequential mixed methods, we will first analyze the quantitative and then the qualitative
method and results. We will discuss each methods’ results in light of the current literature and
data; and then discuss what the two methods can tell us together and how they enlighten
current research and can influence future research.
State of the Art and Theoretical Framework
As there are no studies concerning the direct link between heads’ of households (HOH),
educational attainment and debt profiles, we must approach the phenomena through indirect
routes. We turn to factors that influence and are influenced by educational attainment that in
turn affect financial behavior through their influence on the keystone variable of financial
literacy and debt literacy. Then we look at how financial and debt literacy are shown to affect
different aspects of debt, mostly with respect to mortgages, as they are generally consumers’
most significant lifetime loans.
Debt literacy is a specific type of financial literacy, and more directly relevant to our research.
It is measured by questions testing knowledge of fundamental concepts related to debt and by
self-assessed financial knowledge. (Lusardi and Tufano, 2009) Financial experiences are the
participants' reported experiences with traditional borrowing, alternative borrowing, and
investing activities. Overindebtedness is a self-reported measure. Lusardi and Tufano (2009)
conducted a study in the United States that found that debt literacy is low: only about one-
third of the population seems to comprehend interest compounding or the workings of credit
cards. Even after controlling for demographics, they found a strong relationship between debt
literacy and both financial experiences and debt loads. Specifically, individuals with lower
levels of debt literacy tend to transact in high-cost manners, incurring higher fees and using
high-cost borrowing.
Financial literacy is more broadly researched and understood, and will therefore be discussed
in more depth. It is defined as, “people's ability to process economic information and make
informed decisions about financial planning, wealth accumulation, debt, and pensions”
(Lusardi and Mitchell, 2013, p.6). To determine financial literacy, questions are asked
concerning: “(i) numeracy and capacity to do calculations related to interest rates, such as
compound interest; (ii) understanding of inflation; and (iii) understanding of risk
diversification” (Lusardi and Mitchell, 2013, p. 10). Answers are then analyzed
independently, as well as weighted and indexed into a financial literacy ‘score’. While not all
of those aspects relate directly to debt, we can both assume and show in the evidence that
greater information and thus understanding of financial tools and economic mechanisms will,
under the rational actor theory, lead to better decisions concerning potential debt.

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Most individuals cannot perform simple economic calculations and lack knowledge of basic
financial concepts, such as the working of interest compounding, the difference between
nominal and real values, and the basics of risk diversification. Knowledge of more complex
concepts, such as the difference between bonds and stocks, the working of mutual funds and
basic asset pricing is even scarcer. Financial illiteracy is widespread among the general
population and particularly acute among specific demographic groups, such as women,
African Americans, Hispanics, and those with low educational attainment. (Lusardi, 2008)
In their 2013 overview of financial literacy, Lusardi and Mitchell laid out the ‘state of the art’
for relationships between education and financial literacy. Thus far, it points to significant
correlations between higher educational attainment and greater financial literacy. Other
factors involved in determining both financial literacy and educational attainment include
variables that i) affect HOH willingness or ability to attain more education, and ii) are
affected by HOH educational attainment. The former includes: parents’ educational
attainment (Heineck and Riphahn, 2009; Ermisch and Francesconi, 2001), risk aversion
(Belzil and Leonardi, 2013; Dohmen et al., 2010), and family income and wealth (Ermisch
and Francesconi, 2001). The latter includes income, as well as patience and discount rates
(Harrison et al., 2002).
The factors common to both phenomena are cognitive ability and the cognitive ability of
peers (Cole and Shastry, 2008; McArdle et al., 2009), but only the former is significantly
evidenced. To make clear the possible indirect route that education can have on debt profiles,
we put forth the following example: HOH parents were highly educated, affecting HOH
cognitive ability, their cognitive ability affects their financial literacy as well as their ability to
attain higher education which also affects their financial literacy, affecting their ability to
make rational borrowing and debt-handling decisions.
In the next part of the equation, financial literacy is shown to have significant effects on credit
portfolios, mortgage types and arrears. Disney and Gathergood (2012, p.20) show that
“households with household heads who perform poorly on the financial literacy questions
hold a greater fraction of high cost credit in their portfolios and thereby have higher portfolio-
weighted average APRs.” While Cox et al. (2011) show that the less financially literate and
more risk averse choose traditional mortgages over the possibly more profitable or
situationally appropriate ‘alternative mortgage products’. Financial illiteracy can also lead to
overconfidence and ‘over-optimism’ in economic decision-making skills, which was shown to
correlate with a greater amount of mortgage arrears (Dawson and Henley, 2012).
Financial literacy, its causes and forms, do not lie just in the individual or the national
spheres, but are also determined by larger macro-economic trends. New international research
demonstrates that financial illiteracy is widespread when financial markets are well developed
as in Germany, the Netherlands, Sweden, Japan, Italy, New Zealand, and the United States, or
when they are changing rapidly as in Russia. (Lusardi and Mitchell, 2011) Further, across
these countries, it is shown that the older population believes itself well informed, even
though it is actually less well informed than average. Other common patterns are also evident:
women are less financially literate than men and are aware of this shortfall. More educated
people are more informed, yet education is far from a perfect proxy for literacy. There are
also ethnic/racial and regional differences: city-dwellers in Russia are better informed than
their rural counterparts, while in the U.S., African Americans and Hispanics are relatively less
financially literate than others. Moreover, the more financially knowledgeable are also those
most likely to plan for retirement.

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Another aspect of financial behavior is risk taking. In that context, it is interesting to look at
how risk tolerance can vary. Grable (2000) defines financial risk tolerance as the “maximum
amount of uncertainty that someone is willing to accept when making a financial decision”.
His research tried to examine how different demographic, socioeconomic, and attitudinal
characteristics determine people’s attitude towards taking financial risk in “everyday money
matter”. The findings imply that males are more risk tolerant than females, older respondents
were more risk tolerant than younger respondents, married respondents were more risk
tolerant than single respondents, professionals (occupational status) were more risk tolerant
than those with lower incomes and finally, the respondents with higher attained education
were more risk tolerant than others.
It is debated whether education is a good proxy for financial literacy. Several authors
conclude that, “those without a college education are much less likely to be knowledgeable
about basic financial literacy concepts, as reported in several U.S. surveys and across
countries. Moreover, numeracy is especially poor for those with low educational attainment”
(Lusardi and Mitchell, 2013, p.20).
However, other research shows that when controlling for various internal and external factors,
formal educational effects become insignificant and/or ambiguous (Lusardi and Mitchell,
2011). When education and financial literacy are included in multivariate regression models,
both tend to be statistically significant, indicating that financial literacy has an effect beyond
education. Financial literacy is also higher among those who are working, and in some
countries among the self-employed, compared to those who do not work. This difference may
in part result from financial education programs offered in the workplace (as in the United
States); it could also be the effect of learning from colleagues or skills acquired on the job
(Lusardi and Mitchell, 2011).
Empirical Design
A sequential mixed-methods analysis was chosen as the most useful for answering our
research question, and thus understanding the relationship between educational attainment,
debt and debt relief. Neither method by itself could yield a complete answer to our research
question. Without the quantitative data and analysis, useful interview questions and probes
could not be developed. In addition, without the interview data and subsequent thematic
mapping under a framework analysis, the quantitative relationships and strengths of such
could not be explained within economic-institutional contexts.
We began with analyzing the EU-SILC data using logistic regressions in SPSS to test our
hypothesis, find variable relationships as well as their strength. The contribution to the model
was measured by R2, which measures the fit between the dependent and the independent
variable. Due to the nature of our variables - many are nominal, and do not fit statistical
assumptions of normality, linearity or homogeneity - logistic regression was found to be the
most useful quantitative tool for analysis. Because few of the variables were continuous, we
had to assess probabilities: for example, that a person with a certain level of formal education
was a member of a certain modeled category (e.g. has trouble paying back loans, has a
consumption loan).
To explore the meaning of such relationships, uncover potential directions of causation, find
new perspectives from which to view the data, as well as uncover possible gaps or
contradictions between survey and on-the-streets information, the semi-structured expert
interview was taken as our method of qualitative data collection. A guideline was developed

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to keep the interview topics consistent with each other, and with the quantitatively analyzed
variables and relationships, or lack thereof. As our understanding of the variables and
relationships was limited strictly to the data and previous literature, we kept the format semi-
structured to allow experts to explore uncovered issues and perspectives. The interviews were
then subjected to a framework analysis.
Framework analysis was chosen as the most appropriate qualitative tool to format and
understand our qualitative data. While other tools, such as discourse analysis, would provide
useful insights into the language and narrative of each expert, framework analysis allowed for
each piece of qualitative data to be understood within common themes; thus more consistently
and uniformly reflected against the quantitative data and current literature. Furthermore, the
consistency of topics, ensured by using a single reference-guideline and a semi-structured
interview format, easily enabled the establishment of common themes, which underpins the
process of framework analysis.
Quantitative Analysis
For the quantitative analysis, we looked into an extensive data set provided by the EU. The
EU Survey on Income and Living Conditions (EU SILC) is a survey on statistics regarding
income, poverty and living situations across the different member countries. For our purpose,
it includes questions on housing credits, consumption credits, material hardship but also
general information about the demographics of the persons interviewed. There are three
different datasets, one includes the answers of the interviewees regarding the situation of their
household, one contains responses regarding the personal sphere and was conducted on an
individual level for each household member and one gives insights into the situation of
children in the household also on an individual level.
As the aim of the research was to look at how people with different educational backgrounds
handle their debt, the questions regarding credits were of particular interest to us.
Respondents were asked to report if they are currently paying back any loans for housing,
how many loans and the amount of repayment per month. They also had to report if they are
having difficulties paying back these loans, if they feel pressure of paying back loans, which
type of loans they took on, and more generally, if they are struggling to cover unexpected
expenses. Additionally, we calculated the ratio of debts to disposable household income as a
further indicator of financial burden.
Yet, the results of this model should be interpreted with caution for two reasons. First, the
income of a person can change abruptly. A sudden drop in income due to unemployment or
illness can lead to a deterioration of the financial situation of a person, which is not
considered in these calculations. Second, the EU SILC questionnaire only provides data on
the amount of housing debt, data on consumption debt or debt taken on to start a business are
not included. This was especially detrimental for our analysis as these loans are considered
the main cause of financial trouble for individuals. The only items inquired were loans for
cars, household appliances and other loans, which were not defined in more detail. In
addition, even there the interviewees were only asked whether they have a loan of this type or
not, not asking the person for the amount, the number or their duration. Repayment duties for
credit card bills or informal loans from relatives or friends were either part of the EU SILC
questionnaire.
The results of the quantitative analysis

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We identified eight indicators asked in the questionnaire that we felt could help us answer our
research question. The indicators 1-6 cover general characteristics of a debt profile. Our goal
was to see if a difference in education also leads to a difference in the debt profile of a person.
The last two indicators are more focused on the problems that can arise with debt. Our
hypothesis was that people who are not able to finance unexpected costs have a limited
financial leeway, which could also lead to repayment problems as soon as they are urged to
take on debt. In order to simplify and adjust the questionnaire to our needs, we had to sum up
some questions to create a new indicator or we grouped some of the answer possibilities
together. The final dependent variables used for the regression analysis are:
1. Do you have a real estate loan? (yes/no)
2. Which type of loan are you using to cover your real estate investment?
(Bauspardarlehen, Landesdarlehen, Bank- oder sonstiger Kredit)
3. What is the total sum of your real estate loans? (in €)
4. Housing debt/total disposable income ratio. (in %)
5. Do you have a consumption loan? (yes/no)
6. Have you been in delay of payment with your credit repayment obligations in the past
12 months? (yes/no)
7. Do you feel financial pressure when it comes to service your debt? (yes/no)
8. Are you able to finance unexpected costs? (yes/no)
In order to estimate the likelihood that a certain demographic characteristic, in our case
education, influences the debt of a person, logit models were used. In most of the cases, the
dependent variable only had two answer possibilities, yes or no, which made it possible to use
binary logistic regressions. For the questions that were answered in continuous numbers,
namely the amount of loans in € and the percentage of the debt to income ratio, linear
regression was used and for the remaining equation regarding the type of the loan with three
answer possibilities multinomial logistic regression was used (Rodríguez, 2007).
Originally, the answer possibilities for our independent variable, level of education, were split
up into various forms of schooling. For the purpose of this inquiry, these categories were
aggregated in order to achieve an ordinal scale from lowest to highest level of education. The
categories Lehre and Meisterausbildung were combined, so were the categories
Krankenpflegeschule and Andere Berufsbildende Mittlere Schule and the categories AHS and
Berufsbildende Höhere Schule. Finally, we also did not differentiate between different
university degrees. This aggregation resulted in the following six categories:
● 0= no schooling
● 1= mandatory schooling
● 2= apprenticeship (including those with Meisterprüfung)
● 3= middle school (Krankenpflegeschule or andere berufsbildende mittlere Schule)
● 4= high school (AHS + berufsbildende höhere Schule)
● 5=university
Additional control variables were age, age square (as we wanted to control for the effect of
age has a non-linear relationship with the independent variable) and household income. As we
conducted the analysis in SPSS on a household level it was necessary to reduce varying
characteristics within households to a single factor, which is representative for the household
unit. Thus, different degrees of educational attainment among household members had to be
conflated to a single denominator. This paper assumes that decisions within a household (e.g.
financial decisions) are highly influenced by the person with the highest educational

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attainment. Consequently, a respective 'household's educational attainment' is assumed to
equal the highest educational attainment of an individual within this respective household.
This simplification is backed by many research findings; they illustrate that higher
educational attainment leads to a stronger influence on joint decisions (see for example
Carlsson et al., 2009), more decision (see for example Blood and Wolfe, 1960), and higher
levels of decision power (see for example Lührmann and Maurer, 2007) within a household.
The same simplification is necessary for the ‘household age’. In order to be consistent with
the former research findings we assume the age of an individual with the highest educational
attainment within a respective household to be representative for the ‘household age’. We
refrained from using more independent variables as the process of using just the characteristic
of the household member with the highest education is very likely to oversimplify the
household behavior as such and we suspect that the results might not reflect the dynamics
within a household.
In logistic regression, a tentative solution is chosen at the beginning and revised slightly in
each step to see if the likelihood increases. The process is repeated until the increase in the
likelihood function from one step to the next is negligible (DeMaris, 1992).
In this study we created five binary logistic regression models based on four predictors,
education (a categorical variable), income, age and age2 (continuous variables). As an
example, we look at the model that estimates how the predictor variables influence the
likelihood of having a real estate loan or not. For the two possible response levels (no=0,
yes=1), the maximum likelihood regression model is:
P(Y=1) = [1 + exp(Xib)]-1
where Yi is the state of case i, Xi is a vector of the predictor variables for case i, and b is a
vector of coefficients to be estimated. The term on the right side of the equation is the logit
transformation, that is, the logarithm of the odds. The predicted values Y, which are the
probabilities of having a real estate loan, will lie between 0 and 1 over the ranges of the X’s
(Ohlmacher & Davis, 2003).
The corresponding binary logistic regression equation can be written:
P(real estate loan) = 1/ 1 + exp[-(β0 + β1education + β2income + β3age + β4age2)]
The result of the regression was analyzed according to a number of criteria. First, we looked if
the Block Omnibus Test is significant; this supports our hypothesis that the predictor
independent variables contribute to the model. Second, we looked if the standard error of the
variables is larger than two. This would indicate problems of multicollinearity among the
independent variables and would require us to run the model a few times with varying
compositions of independent variables. Third, we checked if the probability of the variables is
lower than the level of significance of 0.05. Finally, we looked at the sign of the value of the
coefficient. A negative sign implies that a one unit increase of that variable decreased the
odds of the survey respondents having a real estate loan, a positive sign implies an increase.
In order to check the results we also used cross-tabulation and scatterplots to have an isolated
look at the relation between education and debt. A final look at the Nagelkerke pseudo R2
value shows how much of the variability of the variables can be explained by the model and is
thus an indicator if the model fits the data. Backhaus et al. (2003) suggest that a model with a
value lower than 0.2 is acceptable, 0.4 is good and above 0.5 is very good. The same applies

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to the multinomial logistic model. For the linear regression models, we first looked at the
respective scatterplot graph to identify patterns and check the F-Test. After running the linear
regression, we check the F-Test to see if the relationship between the variables is significant.
A look at the standardized coefficient reveals the type of the relation, positive or negative, and
which of the independent variables has a higher impact on the dependent variable.
To summarize the results of all regression models
In this section, we want to give a short summary of our results. Representative for all eight
models explained in the Annex 1, we present detailed results of Model 1 and Model 8 in the
following tables and a summary of all models afterwards.
Model 1: The correlation between the independent variables and the prevalence of real estate
loans in the sample group shows a clear picture. The result of the binary regression analysis
suggests that both a rise in income and education increase the likelihood of having a housing
loan. The low Nagelkerke R Square test (.153) however, poses some limitation to the validity
of the model. Furthermore, the result of the crosstab of education and real estate loan are
highly significant (Chi-Square Test .000 and Pearson R Test .000), implying a positive
correlation between education and the likelihood to have a real estate loan.
The coefficients of the binary logistic regression of the probability to have a real estate loan:
Variable Coefficient Standard Error Significance
Education ,062 ,026 ,016
Age ,134 ,013 ,000
Age Square -,002 ,000 ,000
Income ,000 ,000 ,000
Constant -3,920 ,278 ,000
Model 8: The Block Omnibus Test for Model 8 is significant and there is no indication of
multicollinearity among the independent variables. The probabilities for the variables
education and income are lower than the level of significance of 0.05. The positive sign for
both implies that a rise in income and a rise in the level of education increase the likelihood
that a person is able to cover unexpected costs. The results for age are not significant,
indicating that this variable has no direct impact on the independent variable. A look at the
crosstab also revealed a significant relationship between the education variable and the ability
to cover unexpected costs.
The results of Model No. 8: binary logistic regression - cover unexpected costs (pressure:
no/yes)
Variable Coefficient Standard Error Significance
Education ,394 ,030 ,000
Age ,017 ,011 ,132
Age Square ,000 ,000 ,242
Income ,041 ,002 ,000

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Constant -2,213 ,266 ,000
In summary, we got significant results for the independent variables education and income for
the questions 1, 3, 5, 6, 7, 8. This would imply that income and education do have an effect on
a person’s debt profile. The models suggest that an increase in education or an increase in
income increases the ability to repay loans and cover unexpected costs; it lowers the
perceived pressure of debt burden. Furthermore, it increases both the probability to have a real
estate and the sum of the housing loan and the probability to have a consumption loan.
However, there was no relationship detectable between type of credit and level of education
or income (question 2). The probability of the chi-square statistic was in both cases higher
than the level of significance (0.05). A look at the scatterplot for question 4, the relationship
between debt/income ratio and level of education, did not suggest a relationship between the
two variables. However, the F Test shows that the correlation between the two variables is
statistically significant. This result was very surprising for us. In addition, the result of the
linear regression model showed that the significance of education and income is below the
threshold of 0.05.
Yet, this was not the only surprise we faced when analyzing the data. For instance, we could
not explain the very low number of people reporting that they are late with paying back their
debt (97 out of 6000). This and other irregularities might be the result of deficiencies in the
data collection process or response biases of the interviewees. Finally, however we have to
report the limitations of our results: When considering the goodness-of-fit metric R2 most of
the regression models we set up did not fit the data to an appropriate degree. The pseudo R2
values we derived were in most cases below the threshold of 0.2. The only model that does
fulfill this criteria is model 8. In conclusion we have to state that the data set provided did not
match our research question perfectly, which reduces the validity of our results. Due to the
low goodness-of-fit results we conclude, that our hypothesis of a strong relationship between
education and the various characteristics of the debt profile is not supported, despite the
significant results on six out of the eight parts of the debt profile. This final conclusion
suggests that there must be other factors, which we were not able to include in our model, that
have a significant impact on the debt profile and debt management of a person. In order to
find out more about these missing variables we turned to experts in the field.
The detailed results of the regression models can be found in Annex 1.
Qualitative Analysis
Since the insights - which the quantitative analysis of the EU SILC data could yield - are
limited, three expert interviews were conducted in order to gain some deeper insight into how
education affects people’s ability to deal with credits. The selected experts are experienced
professionals in the field of debt counseling and financial consumer protection who have
worked at Schuldnerberatung Wien, ASB Schuldnerberatungen and Arbeiterkammer Wien for
several years. While the prior two possess expert knowledge regarding people who seek aid
because they cannot handle their debt anymore, the latter is particularly knowledgeable about
the contractual interaction between her clients and banks. Yet, it should be noted that these
experts, while providing some deep insights into certain people’s handling of debt, cannot
give a representative overview of the Austrian society. The clients of each institution have

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specific demographic characteristics, which is why it is not advisable to overgeneralise to the
whole population. That said, the expert interviews did generate rich information regarding
their group of clients.
After drafting an interview guide (see Annex 2), the three interviews, which lasted
approximately 30 to 40 minutes each, were conducted by three different interviewers in the
first week of December (for the partial transcripts see Annex 3). Although the guide gave
some structure to the interview in order to make sure that all relevant topics were covered in
each interview, it was not followed word by word. This was to give the experts the freedom to
set their own focus on their fields of expertise. The interviews were recorded, supplemented
by field notes and subsequently summarized. As the analysis remains largely at the manifest
level and does not go into too much interpretation, only selected parts of the interviews were
transcribed.
In the first phase of the analysis, each of the interviewers conducted a framework analysis of
their respective expert interview in order to identify the main themes for every expert. This
helped considering all relevant aspects without being influenced by the outcomes of the other
interviews. Only in the second phase, did the three persons come together and identify
common themes of the three experts. While there was considerable overlap between the
interviews, there were also discrepancies. Figure 1 summarizes the outcome of the framework
analysis.
Framework analysis was chosen as the most appropriate qualitative tool to obtain a general
understanding of debt, education and debt relief from the expert perspective. While other
tools, such as discourse analysis, would provide useful insights into the language and
narrative of each expert, framework analysis allows for each piece of qualitative data to be
understood within common themes and thus more consistently and uniformly reflected
against the quantitative data and current literature. Furthermore, the consistency of topics,
ensured by using a single reference-guideline for all the three interviews, easily enabled the
establishment of common themes that underpins framework analysis.
The results of the qualitative analysis
In the following, the three experts’ lines of argumentation are analyzed separately (see Annex
4) before comparing and contrasting the themes across the three cases (see Annex 5). This
design was chosen in order not to lose each interview’s context and the coherence of the
arguments, while still being able to make references across the cases, identify common
themes and highlight differences.
Mag. (FH) Ferdinand Herndler is CEO of the ‘Schuldnerhilfe’ in Upper Austria. He is a
certified social worker and supervisor and has a degree in social sciences. For over two
decades, he has been working with indebted people and has thus acquired great experience in
the field. During the interview, Mr. Herndler gave us helpful information about the context
and social environment of his clients but also about the legal framework in Austria. He partly
confirmed the results of our quantitative analysis by arguing that the relationship between
educational level and type of debt, quantity of loans and amount of loans is not decisive and
from his experience does not play an important role. However, what he deems crucial when it
comes to managing debt, are the social environment, social values and reasonable foresight.
These areas are also more likely to differ between people with higher and lower education:
Some people, quite the ones with higher education, normally do not contact
the Schuldnerberatung, as they have different approaches to solution, this
means they know lawyers, tax consultants. But what is even more important,

13. 13
the people with higher education make use of expert knowledge much faster.
Our clients, mostly people with lower education, also have solution
approaches but they are not always that constructive. If people have higher
education they value expert knowledge more, see that it makes sense and talk
to them earlier. (ll. 1-7)
Moreover, he points out that it is not only the clients who should be blamed for the increases
in private bankruptcy cases. The dominant mindset of our high consumption society “one part
is material belonging” (ll. 10-11) and the methods used by companies to increase their sales
also play a role:
We also have to make the lenders and businesses more accountable. Twenty
years ago there was not that an amount of possibilities to buy products in
installments. (ll. 15-17)
Overall, Herndler argues that although education is an important factor, the root cause of
many cases lies in the mindset of the people and thus in the values of our society. The fast
pace of consumption and the deficiencies when it comes to anticipating the future
consequences of our actions today bring many people into trouble.
Some people think only in the short-term but enter middle-term contracts and
this does not fit together. If I enter middle-term contracts I have to think on
the middle-term. If I only think on the short-term I will struggle. (ll. 26-29)
Therefore, the Schuldnerhilfe in Upper Austria has a strong focus on prevention activities and
tries to enhance the financial literacy of the young. They cooperate with schools to create
appealing school materials for teachers to use in various subjects and they offer courses and
workshops themselves. People should be encouraged to talk about money, to understand the
dynamics of interest payments and understand the rules of the game and finally they should
also “question themselves do I really need this product and do I want this product” (ll. 36-37).
Benedikta Rupprecht works as a legal counsel for members of the workers’ chamber
(Arbeiterkammer Wien). She is a lawyer with a focus on loan contracts, which is why she
advises her clients, who are all members of the workers’ chamber, in matters related to credit
contracts; yet, she also worked at Schuldnerberatung under Alexander Maly’s guidance from
2003 to 2007. While her work gives her only limited insight into people’s ability to pay back,
it does enable her to judge how her clients approach the contractual side to credits. In
Rupprecht’s view, education plays a role in people’s planning horizons. More educated clients
tend to ask for advice regarding specific clauses of preliminary agreements in advance:
It is those that are more on their toes and where I get the impression on the
phone when the client contacts me or from the way people communicate with
me that they are probably more educated. Those who ask in advance are
rather academics (ll. 60-63).
Moreover, she stresses that the soft skills of the more educated play a role in their dealing
with banks and ultimately handling of credits. Not only do they possess the necessary
language skills to understand the often highly technical texts, which are often problematic for
immigrants, but they are also more confident when communicating with bankers.

14. 14
It is one of the factors that it is easier (to deal with banks) with a higher
degree. And surely in the communication with the bank. One might appear
more self-confident when defining the contractual position and when there is
anything unclear with the advisor, to (…) negotiate or to negotiate the
interest when overdrawing the account (ll. 39-44).
Rupprecht also mentions the importance of the social environment, heavily stressing the role
of the circle of friends with “lawyers or anyone who can jump to their side” (ll.15-16) to give
advice or help financially. Additionally, she argues that people with higher education have
less difficulty understanding how a credit is calculated. Those who call to ask for an
explanation of their bank statement are “typically those who probably do not have a
commercial profession or have not gone through any vocational training at all” (ll. 4-6).
However, she warns about generalizing; in her view, education is certainly not the only
determining factor of how people handle credit or debt, which is why the individual situation
must always be taken into account.
Alexander Maly, a certified social worker by education, has worked at 'Schuldnerberatung',
which has offered help to indebted private persons since the 1980s. Since 2006, he has acted
as the operational chief executive. He published numerous professional articles and books.
Moreover, Mr. Maly is a member of the ministerial working group "Insolvency Law" and a
lecturer at the university of applied science for social work in Vienna. According to
Alexander Maly there is a clear link between education and debt. However, in his view it is
not just about formal education. He rather takes a very normative stance with regards to this
topic by arguing:
If a general manager needs a new, big car every year, he is also uneducated
in this context" (ll 39-41).
Furthermore, he stresses that it is not just about the person itself, but also about the social
environment in which this individual is embedded. The lack of social relations, which could
absorb smaller financial losses, leads to an accumulation of debt - small debts increase
substantially over time. In fact, debts usually double all five years and in the context of
smaller claims they even triple within this period.
Mistakes that people make - such as the poor handling of money - cannot be
smoothed out by their environment. Every young person makes a financial
mistake at some point. Most of them are lucky, because their mistakes are
corrected by their environment. (...) The people that we are advising are
facing an environment, which is as destitute as them. Therefore, they are not
able to smooth out these mistakes. Consequently, mistakes that have been
made by 20 years-old individuals protract and manifold into the future. (ll. 2-
10)
Finally, Maly stresses the effect that education has on personal values. Higher educated
individuals are not just less susceptible to advertisements, but have also fundamentally
different values than people with lower education. It is crucial, however, to stress that he not
just considers the formal aspect of education in this context, but also informal education.
Education enables people to resist the temptations of advertisement. You
could even turn this argument around and create new status symbols: For
example, the status symbol of not having a TV. An uneducated person could

15. 15
not abide that. For them a TV is the window on the world. I have many
different windows on the world; I don't need television for that. (ll. 28-31)
All three experts argue that the social environment is decisive for the problem-solving
capabilities of people in debt, yet for somewhat differing reasons. Herndler and Rupprecht, on
the one hand, highlight that while friends and family of the lower-educated may be willing to
help, their support is often less effective than that of social contacts possessing relevant expert
knowledge and financial capabilities. Hence, the higher educated often have access to better
advice via their social networks. Maly (and to a lesser degree Rupprecht), on the other hand,
focuses more on the larger financial resources at the hand of friends and family of the more
educated.
An argument which is much more prevalent among the two debt counselors is business’
tendency to lure people into getting loans. The frequent offer of installment purchases, they
argue, is a rather new way of businesses to create increasing desire for material possessions,
which is especially prevalent among people with lower education who cannot identify the
hidden hosts. Rupprecht adds that contract and account information is oftentimes
intransparent and difficult to grasp for people with little financial knowledge or migrants from
other countries with different financial systems. Only 17 percent of Austrians feel confident to
manage their financial lives (Klafl, 2015).
Additionally, the two debt counselors argue that material possessions are considered a way of
belonging in today’s society, people with low education even more receptive. Hence, they
claim that the increasing pace of the product life cycle puts special pressure on people with
lower incomes. Furthermore, a peculiar habit has emerged: People have learned to go to banks
in case of financial problems. However, the banks’ only approach is refinancing (including
selling additional products such as insurances).
The three experts ascribe certain characteristics to people with higher education which enable
them to better deal with loans and debt: They are more receptive to expert knowledge, act
faster to limit damage, keep track of their payment obligations and aware of the consequences
of loans. While Rupprecht places great emphasis on their greater confidence when dealing
with banks, better language skills and a longer planning horizon, the other two experts also
claim that highly educated people tend to have non-material status symbols (e.g. the status
symbol of not having a car or TV) and thus, different values. Additionally, the latter argue
that more educated people are in a better position to resist advertisement.
The different possibilities for solutions identified by the experts can be split into two approach
categories: On the one hand, Maly and Rupprecht argue that prevention (e.g. financial
education) is necessary, but not sufficient. Instead, they focus on the responsibility of the
regulator (i.e. the state) and, as a consequence, of financial institutions and business.
Herndler, on the other hand, stresses the necessity of changing people’s mindsets, stating that
“we need more learning for life” (Herndler, 18:13), including a critical reflection on
consumption patterns and lifestyle choices. The three experts interviewed converged on the
point that formal education is by far not the only important factor in people’s handling of
debt, but that the social environment is a highly salient factor. Still, they all argued that formal
education equips individuals with better capabilities to grasp connections and consequences
of debt.
While the expert interviews could yield highly relevant insights into why some people have
more difficulty in dealing with debt and credit, their limitations must be kept in mind. The

16. 16
experts are from the specific fields of debt and legal counseling, meaning that they are
experienced with their client base only. This means that they cannot give an overview of how
the total Austrian population handles their loans. Nevertheless, the expert interviews still
provided us with some valuable information about how the education of that part of the
population with seeks professional help impacts their handling of credit.
Discussion of mixing quantitative and qualitative results
The benefit of using a mixed-methods analysis is to gain a greater understanding of the
phenomenon than would two separate analyses. Here we will discuss how the qualitative and
quantitative results correlate with and enlighten each other, as well as the current literature.
To see how our results line up with other research findings, we will explore if the qualitative
results match with the current literature. Our results show that formal education does not have
a singular or clear effect on debt profiles, confirming the aforementioned findings. However,
education can have effects on value systems and financial understanding. The expert
interviews revealed that there is often a greater weight given to materialistic values among the
lower educated, thus making them more likely to take on burdensome consumer debt. Those
with lower education also rarely have access to individuals with high levels of financial
literacy and/or connections, often leaving them to choose between the most exploitative or
costly options offered by banks and firms.
There are however, some discrepancies between the literature and insights from one of the
interviews concerning education’s influence on levels of materialism. However, this
relationship is subject to debate. Richins and Dawson (1992) showed that there is no
significant relationship between materialism and income, gender, age, education, or marital
status. Respondents who scored in the top quarter of their materialism scale were categorized
as having high levels of materialism, and respondents who scored in the bottom quarter of the
scale were categorized as having low levels of materialism.
Do the qualitative results match the quantitative data and results? The 2012 EU-SILC data
used in this study, and the Austrian National Bank paper (Beer and Schürz, 2007) based on
2003 EU-SILC data, showed that only a fraction of a percent of all people in debt had ever
been late on a debt repayment. However, the experts at the various debt counseling
organizations revealed that a much greater percentage of people have difficulties repaying
their debts. It is thus possible and likely that many survey-takers did not provide accurate
information concerning their repayments. Thus a significant falsehood or distortion of
knowledge within EU-SILC data was revealed through new qualitative data collection.
Lastly, what do the quantitative and qualitative results tell us together? Formal education
itself has an indirect effect on individuals’ debt profiles and debt relief abilities. However,
when it comes to a household’s ability to handle debt, a higher educational level may translate
into greater social access to experts, more receptivity to expert knowledge, as well as better
language skills and financial confidence. These factors influence the household's ‘financial
literacy’ and ability to find and more fully utilize debt and (re-)financialization options
offered by banks. Similarly, education may influence households’ ability to evaluate bank
offers that are motivated by profit and a desire to increase levels of spending and debt, and not
what is necessarily most feasible for the household.

17. 17
Conclusion
If we want to improve the indebtedness and debt-coping mechanisms within society and
across groups, focusing on formal education will not by itself be satisfactory. The qualitative
results contradict and fill in many gaps in the EU-SILC data, as well as the Austrian National
Bank’s “Characteristics of household debt in Austria” which also relied only on EU SILC
data
To gain a fuller understanding of a population’s debt situation by household, factors such as
social environment and values; macroeconomic trends (Lusardi and Mitchell, 2011); and
types and knowledge about various (re)financing instruments are necessary .
● We did find significant correlations between the debt profile and the education level.
Even if we isolate education from income. The age variable in most cases does not
have a significant effect on the debt.
● However, the value of the quantitative results is limited as the data did not perfectly
match the RQ and we had to aggregate the data of the individuals to the household
level which of course made us lose information on the way. Thus we should interpret
the results with caution.
● The interviews with the experts supported our findings in the quanti part. They did
argue that education does play a role, yet they pointed out a number of other factors
that we did not include in our quantitative analysis which also influence the handling
of debt and especially the ability to manage debt successfully.
● next to education and income the social environment, the values, the experiences and
the family background play an important role.
● limitation of quali: very specific field of expertise - their clients are really the high risk
group that is already in great trouble. Thus we cannot draw conclusions to the whole
group of people with lower education in Austria.
● Still insights of the research are interesting and we were happy that the mixed methods
approach was complementary and value-adding.
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Annex 1
Output of the quantitative analysis
Model 1: Do you have a real estate loan? (yes/no)
The correlation between the independent variables and the prevalence of real estate loans in
the sample group shows a clear picture. The result of the binary regression analysis suggests
that both a rise in income and education increase the likelihood of having a housing loan. The
low Nagelkerke R Square test (.153) however, poses some limitation to the validity of the
model. Furthermore, the result of the crosstab of education and real estate loan are highly
significant as well (Chi-Square Test .000 and Pearson R Test .000), implying a positive
correlation between education and the likelihood to have a real estate loan.
Regression coefficients table:
Test results of the Crosstab:

20. 20
Model 2: Which type of loan are you using to cover your real estate investment?
(Bauspardarlehen, Landesdarlehen, Bank- oder sonstiger Kredit)
There was no relationship detectable between type of credit and level of education or income.
The probability of the chi-square statistic was in both cases higher than the level of
significance (0.05). The level of education or the income did not influence the choice of
which credit or loan to take.
Multinomial logistic regression relationship between independent and dependent variables:
Model 3: What is the total sum of your real estate loans? (in €)
As the dependent variable in this case is continuous, we use a standard regression model. The
linear regression's F-test has the null hypothesis that there is no linear relationship between
the variables (in other words R²=0). The F-test for this regression is highly significant (Sig.=
0.000), thus we can assume that there is a linear relationship between the variables in our
model. In the Coefficients Table, we find that the coefficient Beta for income is higher than
the one for education, meaning that income has a higher impact than education.
Linear Regression Test Results:

21. 21
The Scatterplot also shows a relation between the level of education and the total amount of
loans taken out for housing:
Model 4: Housing debt/total disposable income ratio.
Result of the Regression model with four independent variables:

22. 22
Even though the regression model gives significant results for all variables, a look at the
scatterplot of the variable debt/income ratio and level of education suggests that there is no
clear relationship between these two variables. However, the F Test and the coefficient table
of the model with just the variable education also reveal that the correlation between the two
variables is statistically significant.
Scatterplot:
Regression model with only one independent variable:
Model 5: Do you have a consumption loan? (yes/no)
The probability for the variables education and income is lower than the level of significance
of 0.05. The negative sign for education implies that an increase in the level of education
decreases the likelihood of having a consumption loan. The positive sign for income implies
that a rise in income increases the likelihood that a person has a consumption loan.
Result of the Regression model:

23. 23
The Pearson Chi-Square Test for the Crosstab also indicates a significant relationship between
education and the likelihood to have a consumption loan. However, the Pearson R Test is not
significant.
Result of the Pearson R Test of the Crosstab:
Model 6: Have you been in delay of payment with your credit repayment obligations in the
past 12 months? (yes/no)
The Block Omnibus Test is significant which supports our hypothesis that the predictor
independent variables contribute to the model. A standard error larger than 2 would indicate
problems of multicollinearity among the independent variables, however, this is not the case.
The probability for the variables education and income is lower than the level of significance
of 0.05. The positive sign for both implies that a rise in income and a rise in the level of
education increase the likelihood that a person is not in delay of payment.
A look at the crosstab also revealed a significant relationship between the education variable
and the delay of payment.
Result of the Regression model:

24. 24
Model 7: Do you feel financial pressure when it comes to service your debt? (no/yes)
This regression offers significant results for the education and income variable. Age does not
have an influence on the perceived pressure of debt burden.
Result of the Regression model:
A crosstabulation of the relationship between level of education and the perceived pressure
also gives significant results.
Test Result of the Crosstab:
Model 8: Are you able to finance unexpected costs? (yes/no)
The Block Omnibus Test for Model 8 is significant and there is no indication of
multicollinearity among the independent variables. The probability for the variables education
and income is lower than the level of significance of 0.05. The positive sign for both implies
that a rise in income and a rise in the level of education increase the likelihood that a person is
able to cover unexpected costs. The results for age are not significant, indicating that this
variable has no direct impact on the independent variable. A look at the crosstab also revealed

25. 25
a significant relationship between the education variable and the ability to cover unexpected
costs.
Result of the Regression model:
Test Results of the Crosstab:
Annex 2
Interview Guide
Gleich zu Beginn: Vielen Dank für diesen Termin. Ihre Erfahrungen sind für unsere Arbeit
ein großer Gewinn und helfen uns immens bei der Interpretation unserer Daten. Dieses
Interview ist Teil eines Forschungsprojekts des Master-Studiengangs Socio-Ecological
Economics and Policy an der WU Wien. Wir beschäftigen uns dieses Semester intensiv mit
dem Thema Schulden und der Gefahr von Verarmung durch Schulden. Im Speziellen
interessieren wir uns für die Beziehung von Bildung, im Sinne von formaler Schulbildung
also dem höchsten abgeschlossenen Bildungsgrad, und deren Schuldenprofilen, also Art,

26. 26
Ausmaß, Verwendungszweck, etc. Ihre Organisation ist in diesem Bereich ein wichtiger
Akteur in Österreich. Es freut uns daher sehr, dass Sie uns als Experte/in für den qualitativen
Teil unserer Studie zur Verfügung stehen.
Dieses Interview wird aufgenommen und danach transkribiert. Zusätzlich werde ich während
des Interviews einige Notizen machen. Wir hoffen, dass dies für Sie in Ordnung ist? (Antwort
abwarten). Falls Sie anonym bleiben möchten, werden wir dies natürlich berücksichtigen.
(Reaktion abwarten).
Nehmen Sie sich bitte so viel Zeit zum Antworten, wie Sie für nötig halten, ich werde sie
nicht unterbrechen. Alles, was Sie zu sagen haben, ist relevant für unser Forschungsprojekt.
Wenn Sie keine weiteren Fragen haben, beginnen wir jetzt mit dem Interview.
1. Meine erste Frage bezieht sich auf ihr Kerngeschäft: Wie ist der Ablauf einer
typischen Schuldenberatung?
Nun einige Fragen zu den Klienten:
2. Welche Personen nutzen normalerweise Ihre Dienstleistung?
3. Gibt es einen typischen Kunden?
Wenn ja, was sind dessen Charakteristika? (Falls nicht bereits erwähnt)
Wenn nein: Heißt das, dass Ihre Kunden sowohl sozio-demographisch als auch was die
persönlichen Eigenschaften betrifft sehr unterschiedlich sind?
4. Gibt es eine Personengruppe die sehr selten oder nie zu Ihnen kommt?
5. Die Zahlen der ÖNB zeigen, dass Menschen mit hohem Bildungsgrad mehr Kredite
aufnehmen, die Daten der Schuldnerberatung zeigt, dass Personen mit niedriger
Bildung überrepräsentiert sind. Wie interpretieren Sie diese Zahlen?

27. 27
Nun zur Art der Schulden:
1. Welche Arten von Schulden und Kredite haben Ihre Kunden?
2. Hat die Bildung einer Person Ihrer Einschätzung nach einen relevanten Einfluss auf
ihren Umgang mit Krediten und Schulden?
3. Können Sie eine konkrete Beziehung zwischen dem Bildungsgrad Ihrer Kunden und
deren Krediten (sowohl die Art als auch die Menge) feststellen?
4. Können Sie eine Beziehung zwischen dem Bildungsgrad Ihrer Kunden und deren
Umgang mit Schulden feststellen?
5. Wie viel wissen Ihre Kunden über ihre eigenen Schulden? (Über die Möglichkeiten,
die Ihnen zur Verfügung stehen)
6. Erkennen Sie hier einen Unterschied zwischen verschiedenen Bildungsniveaus?
7. Es gibt eine enge Korrelation zwischen Einkommen und Bildung. Gibt es für Sie einen
Bildungseffekt auf Schulden wenn der Faktor Einkommen aus der Rechnung
genommen wird?
8. Wie und wann entscheiden Kunden, dass Sie Ihre Dienstleistung nicht mehr
benötigen?
9. Wir würden Ihnen jetzt noch gern einige kurze Fragen zu Ihrer Organisation stellen.
10. Haben sich die Dienstleistungen, die Sie anbieten, über die Zeit verändert?
11. Hat sich die Nachfrage verändert?
12. Zum Abschluss noch eine Frage im eigenen Interesse. Können Sie uns weitere
Einrichtungen oder Personen empfehlen, die Experten zum Thema Bildung und
Schulden sind?
Wir wären hiermit fertig mit dem Interview. Gibt es noch etwas, was Sie uns gern erzählen
würden? (Antwort abwarten). Vielen Dank für Ihre Zeit und Ihr Engagement. Sie haben uns
sehr geholfen bei unserem Projekt. Falls Sie gern das Endergebnis erhalten möchten, senden
wir Ihnen den Abschlussbericht gerne Ende Januar per E-Mail zu.
Annex 3
Partial Transcripts
Transcript Interview: Ferdinand Herndler
8:55: Dann gibt es auch wieder Personen, das heißt, die können durchaus akademischen
Abschluss haben, die sind aber in der Regel nicht bei der Schuldnerberatung sondern die
haben andere Zugänge wenn es um Lösungen geht. Das heißt man kennt Rechtsanwälte, man
kennt Steuerberater. Was aber noch viel wichtiger ist, ist dieses, dass Personen mit hoher

28. 28
Bildung, schneller Expertenwissen in Anspruch nehmen. Unser Klientel, Klientel mit geringer
Bildung, hat sehr viele, auch eigene Lösungsschritte, sie sind nicht immer sehr konstruktiv.
Wenn ich jetzt höhere Bildung habe, werte ich Expertenwissen höher, sehe dass das einen
Sinn hat und melde mich auch schneller dort. In der Regel tust du auch schneller Handlungen
setzen. Nicht nur schneller Fachwissen beiziehen externes, sondern auch schneller, wenn's
daneben läuft, Handlungen setzen.
12:40: Da muss ich jetzt ein bisschen weiter ausholen, sag ich einmal. Es ist so dass, ein Teil
halt ist, über materielles Dazugehören. Das trifft mitunter, sag ich einmal, des Öfteren
Personen mit geringer Bildung stärker. Das ist aber Voraussetzung dass nicht unbedingt
sinnvolle Handlungen gesetzt werden. Das heißt über das Dazugehören, oder bestimmte
Produkte haben zu müssen, ganz leicht stellt man das fest, wenn man das Smartphone
hernimmt, das ist schon lange ein Statussymbol.
15:34: Es ist gefährlich wenn man immer nur sagt, die Klienten sind schuld. Da muss man
auch ein bisschen die Kreditgeber oder die Wirtschaft in die Verantwortung nehmen. Es hat
vor 20 Jahren nicht die Menge oder nicht die Möglichkeiten gegeben, so viele Produkte auf
Raten zu kaufen. Und wenn man da jetzt dahinterschaut, was ist denn der Grund für einen
Ratengeschäft, oder was ist denn die Grundüberlegung? Die Grundüberlegung ist diese, das
heißt, wie verkaufe ich jemandem etwas der sich’s nicht leisten kann.
16:28: Schauen sie auf die Homepages was dort angeboten wird: 36 Raten. Das eine ist, ich
will das unbedingt haben, schau nicht so genau hin, und dann sind es Produkte die im
Weihnachtsgeschäft angeboten werden. Das Christkind hat einen Intervall von 12 Monaten –
das Ratengeschäft sind 36 Monate. Das heißt Weihnachten 2016 bediene ich es noch,
Weihnachten 2017 und Weihnachten 2018 bin ich erst fertig. Was tu ich nächstes Jahr? Und
das sind so diese, ich will nicht sagen Fallen, aber, so Stolpersteine, die halt recht schnell
vergessen werden oder halt ein sehr kurzfristiges Denken darübergelegt wird und aber
eigentlich mittelfristige Verträge abgeschlossen werden. Und das passt nicht zusammen.
Wenn ich mittelfristige Produkte abschließe, müsste ich auch mittelfristig denken. Wenn ich
aber kurzfristig denke dann falle ich da drüber.
26:30: Da müssen junge Menschen lernen, was sind die richtigen Fragen die sie stellen
müssen und sie müssen auch die Antworten verstehen, sonst bin ich ausgeliefert, sonst erzählt
mir nur der was. Zu dem dass du da die richtigen Fragen stellen kannst musst du ein paar
Sachen wissen. Und da musst du dann aber auch so ganz banale Sachen über mein Leben
wissen. Das heißt wie möchte ich mein Leben leben. Und immer auch die Entscheidung, sag
ich mal, will ich warten und ich spars an und kaufs dann, oder will ichs sofort und ich zahls
ab aber es kostet mir mehr. Die Entscheidung muss ich immer treffen. Und die zweite Sache
die sehr wichtig ist, auch eben zu überlegen, brauch ich das Produkt, will ich das Produkt.
Weil es gibt unheimlich viele Produkte und es geht eigentlich, heute ist es so, dass es ganz
viel geht um das Auswählen, um das Nein sagen.
Transcript Interview: Alexander Maly
02:12: Die Bildung macht sehr viel aus im Zusammenhang mit dem Risiko in die
Überschuldung zu geraten. Dazu kommt das Fehler die Menschen machen, ich habe vorher
gesagt eine Ursache ist der schlechte Umgang mit Geld - und das Problem ist, dass das jeder
einmal hat, jeder junge Erwachsene macht irgendwann mal einen Blödsinn in finanzieller
Sicht. Aber die meisten haben das Glück, dass ihre Fehler korrigiert werden von der
Umgebung. Von den Eltern oder so die dann sagen 'jetzt warst aber ziemlich deppert und

29. 29
hoffentlich hast gelernt, aber wir reißen dich noch einmal raus aus dieser Situation und sei das
nächste Mal gescheiter'. Die Menschen die wir beraten deren Umgebung ist genauso mittellos
wie sie selbst. Das heißt die können solche Fehler dann gar nicht ausbügeln. Und daher
schleppen sich Fehler die man als 20-jähriger macht sehr weit und vervielfältigen sich.
13:46: Also die meisten Kunden die zu uns kommen habe irgendwann einmal gelernt: wenn
es Probleme gibt, geh zur Bank. Das haben sie blöderweise auch gemacht. Also das heißt sie
sind zur Bank gegangen und habe gesagt: 'ich habe ein Problem was soll ich tun?' und bis
2008 hat's immer nur eine Sanierungsmöglichkeit gegeben: die Bank hat eine Umschuldung
angeboten und manchmal noch Öl ins Feuer gegossen. Das heißt sie haben gesagt: 'Ja ok wir
sehen das Konto ist bis am Anschlag überzogen, einen kleinen Kredit gibt’s auch. Machen wir
eine Umschuldung. Wir decken den kleinen Kredit ab, wir decken die Kontoüberziehung ab
und vielleicht geben wir bisschen was drauf - einen bisschen größeren Kredit - sie wollen ja
vielleicht das Vorzimmer auch noch neu einrichten.' Das war die Lösung bis 2008. Wir haben
sogar begonnen Leute zu warnen zur Bank zu gehen, weil was ist oft passiert? Und im
übrigen, das passiert immer noch. Menschen gehen zur Bank, von uns vielleicht mit einem
klaren Verhandlungsauftrag - wir wollen ja das die Leute das selber in die Hand nehmen und
schalten uns nur ein wenn wir das Gefühl haben die werden nicht ernst genommen. Also wir
schicken die Leute hin und sie kommen zurück mit einem Bausparer, einer
Lebensversicherung, irgendeiner anderen Unfallversicherung.
16:23: Ich glaube es gibt sehr viele Gebildete Menschen die ein sehr schlechtes Einkommen
haben (...) die aber natürlich nicht überschuldet sind weil sie sozusagen die Gefahren kennen.
(...) Bildung ermöglicht es überhaupt den Verlockungen der Werbung zu widerstehen. Man
kann es sogar umdrehen und ein Statussymbol daraus machen. Ich leiste mir keinen Fernseher
zu haben. Das würde jemand mit schlechter Bildung nicht aushalten. Für ihn ist das das
Fenster zur Welt. Ich habe viele andere Fenster zur Welt, ich brauche das Fernsehen nicht.
Oder es ist mittlerweile ein Statussymbol in der Stadt kein Auto zu haben. Sagen Sie das
einem jungen Serben der an der untersten Bildungsschicht knappert und dem immer
eingeredet wird wenn er sich einen schwarzen BMW kauft ist er was besseres. Natürlich wird
er mit aller Gewalt diesen schwarzen BMW auf Kredit kaufen wollen, weil er dann das
Gefühl hat er ist etwas mehr. (...) Es gibt schreckliche Statussymbole und die Werbung
richtet sich natürlich immer an den Bauch, an das Gefühl und ja nicht an den Verstand. (...)
Und wer Werbung widerstehen kann ist gebildeter. Das ist ja der Zweck der Werbung - die
Bildungsschranken zu durchbrechen. Und wenn der Herr Generaldirektor jedes zweite Jahr
seinen Mercedes wechselt dann ist er meines Erachtens genauso ein Dummkopf und vielleicht
auch in dem Bereich ungebildet.
Transcript Interview: Benedikta Rupprecht
5:14: Und man hört dann wieder raus: Naja vielleicht hat man so mit...mit der
Zinsverrechnung direkt keine Erfahrung und das sein eben so die typischen, die
wahrscheinlich keine kaufmännische Ausbildung oder wahrscheinlich irgendeinen Beruf
gelernt haben auch oder...oder im...oder auch ungelernte Kräfte.
5:40: (Über Migranten): So wie die Oma oder die Tante mit dem Sparbuch die gibt’s sicher
viel weniger, sicher auch, aber sicher viel weniger.
8:05: Und umgekehrt, die, die dann hier vielleicht auch nicht zur Beratung kommen, vielleicht
die Reicheren oder womöglich die höheren Bildungsschichten, die auch mit den Verträgen
besser umgehen und besser sich auch rechtlich auskennen und im Freundeskreis Anwälte oder

30. 30
Juristen haben oder irgendwen, der hier schon hilfreich zur Seite springt und finanziell auch
dann sicher bei den Wohlhabenderen und Gebildeteren sicher schneller dann die
Eigentumswohnung finanzieren kann oder das in die Wege leitet wahrscheinlich eh öfter als
bei den weniger Gebildeten.
(Unterschiedlicher Umgang mit Krediten? Darüber, wer einen Konsumkredit aufnimmt):
Das...das sind sicher eher diejenigen, die eben die Klassischen halt, mit Lehrabschluss, viele
Hilfsarbeiter, das also wirklich. Ein Gutteil von denen haben keinen Beruf gelernt. Sein zwar
schon Jahrzehnte lang in einer Firma gewesen, haben auch schon gut verdient und so, aber
wenn’s dann dort mit der Firma geht in Konkurs oder so oder diejenige Person wird krank –
die finden dann ganz schwer mehr Anschluss im Job.
(Direkter Einfluss von Bildung auf Schulden?) (long silence)
12:02: Naja, also ich glaub’ schon, dass Bildung einen direkten Einfluss hat, aber wie weit,
das hängt dann halt tendenziell sicher, davon gehe ich schon aus, es hängt dann wirklich,
wahrscheinlich von der, wenn das eine höher gebildete Person ist, dann sagen wir mal, hängt
das auch von den Lebensumständen ab, wie man halt seine Finanz- und Kreditverträge, wie
man die handelt. Wenn’s dann halt...wofür man Kredite aufnimmt im Konsumbereich oder
auch wenn es vielleicht irgendwelche Fragen, Probleme etc. gibt. Wie man halt wirklich...wie
die Situation ist. Wenn man mit Kind, Familie, Beruf gestresst ist, so wird dann halt jemand
mit höherer Bildung da wahrscheinlich auch irgendwas schleifen lassen unter Umständen.
Das ist halt so einer der Faktoren, dass man sich mit höherem Bildungsabschluss leichter tut.
Und sicher in der Kommunikation mit der Bank schon. Dass man da noch vielleicht
selbstbewusster im Auftritt und seine Vertragsposition versucht zu definieren und wenn
irgendwas unklar ist mit dem Berater, das irgendwie so festzulegen oder zu verhandeln oder
vielleicht beim Kontoüberzug die Zinsen zu verhandeln.
13:05: Und ich stelle mir vor, dass das besser ist als wenn ich jetzt schlecht Deutsch spreche
oder irgendwie mich wenig auskenne oder so. Oder auch das nicht gewöhnt bin jetzt zu
verhandeln. Da wird man da schon eher in der Lage zu sein. Also ich glaube schon, dass man
da noch mal mehr sich auf die Füße stellt. Ich merk’s auch dann also bei Konsumenten, die
dann vorvertraglich, das ist auch so eine Konstellation, dass jemand halt oder beabsichtigt
einen Kreditvertrag abzuschließen, sei es jetzt mehr die Wohnungskredite, die größeren, aber
auch bei Auto-Leasing, da gibt’s auch mal Anfragen zu. Wenn sie den Vertrag dann
vorgelesen oder ausgedruckt bekommen, bevor sie ihn abgeschlossen haben. Das ist meistens
bei Wohnkrediten so. Und die halt die Klauseln nicht verstehen, die es wirklich lesen und die
sind wirklich völlig unverständlich teilweise und die sich dann über diese Klausel Sorgen
machen, wenn da steht: Das und das.
13:55: Also das sind schon die...die mehr auf Zack sein und wo man den Eindruck am
Telefon schon hat von der Kundenkontaktaufnahme oder so wie die Leute halt mit mir
kommunizieren hört man schon, dass die wahrscheinlich halt höher gebildet sein. Die, die im
Vorfeld anfragen, sind eher die Akademiker. Und wenn sie was schicken, sieht man das in der
Signatur dann eben, dass das eher weniger die einfachen Leute sind.
Annex 4
Individual Framework Analyses
Ferdinand Herndler

31. 31
Client characteristics:
● Low education ("our clients, people with low education")
● Most important: following this low income and know prospect of increase (42%
below 1000€) also means that financial mistakes cannot be recovered by family
● Young (2/3 below 40) more venturesome, less experience,
● 1/3 unemployed
● 95% consumption debt which has little value itself (compared to real estate)
● Define themselves more via material possessions
● Have lost track of their various payment obligations (different creditors, time periods,
interest rates)
● Know their liabilities but not what happens if you don't pay back for some time
Type of credits:
● Informal debts of family and friends, mostly senseless because not structured and
reflected (often leads to trouble and relationship problems)
● Quantity more correlated to income than education
● Amount is irrelevant, debt/income ratio is decisive
Environmental factors in Austria:
● Struggling economy especially harmful for low educated people -> missing income
● Businesses who encourage people to buy things they can't afford -> tempting
installment purchases
● Execution order designed for people who do not want to pay back, but know applied
to people who cannot pay back
● GFK Study shows: Austrians feel they have too little knowledge on finance
● Paradigm shift: in the past financial literacy part of parental education, today
requested in schools - also supported from politics in Upper Austria
● Housing costs have increased (33% to 50% of income)
People with higher education:
● Many people have credits and get by fine
● Educated seek help at friends (lawyers, tax advisors)
● Most importantly: more receptive to experts knowledge
Reasons for debt problems:
● Feeling of belonging via material possessions
● Responsibility of businesses: installment purchases have increased a lot business
wants to sell stuff to people who can't afford it
● People don't have to wait until they can afford things
● People pay little attention to details
● People do not grasp full extent of installment purchases (duration, interest)
● People don't talk about money
● People with lower income cannot afford making mistakes
Prevention:
● Personal confrontation, discussion, scenarios
● Awareness building for long-term effects
● Which questions have to be asked, understand the rules of the game
● Ask yourself the question: do I really need this product? Select and say no (27:30)
● "We need more learning for life!" (18:13)

32. 32
● Start as early as possible
Factors next to education:
● Mindset and society values!
● Reflecting your life and your actions, anticipating future consequences
● Fast pace of society and consumption (balance of income and expenses)
Alexander Maly
Education:
● Different education systems (e.g. Turkish people often don't know consumption loans
and thus don't know how to deal with them)
● It is easier to sell something to people with lower education
● People with higher education often have different status symbols (values): e.g. the
status symbol of not having a TV or not having a car
● People with higher education are in a better position to resist advertisement
● However, it is not just about formal education: "if a general manager needs a new, big
car every year, he is uneducated in this context"
Environment:
● Most people in debt have an environment (e.g. parents, family, friends) that is not able
to smooth out their mistakes (the environment is as indebted as the person itself is) -
minor mistakes (overdrawing banking account or failing to pay cell phone contract for
example) become bigger and bigger over time (e.g. general fees, collection costs, court
fees): debts usually double all five years (in the context of small claims they even
triple)
Type of loan:
● Most problematic: consumption loans
● Especially 'hidden loans': e.g. for a TV, cell phone contract, etc.
Legal system:
● Attachment order: legislator does not take into account why a person is not able to pay
(maybe the person don't want to or it simply can't) - Regardless of the reason, people
have to pay interests, fees, and additional costs
● Only after the debt is so huge that people simply cannot repay it the second system
comes into play: the Insolvency Statute
Role of banks:
● 'Banks took over the role of credit sharks' (because they recognized that this is a niche
where they can make additional profit) and provided people with high-interest loans
who would have never received a loan before - especially until 2008
● Since the financial crisis in 2008 banks become more careful (tendency: the total
amount of people’s debt who use services of 'Schuldnerberatung' is declining)
● Habits of people: People have learned that if they have financial problems they have to
go to a bank. However, the bank's only approach is refinancing (including selling
additional products such as insurances)
Benedikta Rupprecht
Characteristics, which enable the more educated to better deal with credits:

33. 33
Soft skills:
● Language (technical, difficult particularly for immigrants)
● Problem-solving skills (help themselves)
● Confidence in dealing with banks (feel on equal footing)
○ Demand
○ Negotiate conditions
○ More self-confident
○ Used to such situations
Social capital:
● Friends (bankers/ lawyers) whom to ask for advice
● Usually richer environment who can help out financially
Planning horizon:
● Call more often in advance to ask about
○ Contractual conditions
○ Preliminary agreements
○ Specific clauses
Use of/ dependence on banks:
● Account overdraft: only visible on statement of account without further explanation;
other information material
→ people with more education have less difficulty understanding
● Understanding of how credit is calculated (e.g. impact of interest, extension of
payment)
→ advantage of people with commercial knowledge
● Difficulty to critically assess bank information
Exception:
● Not overgeneralize
● Even academics might not (want to) know about finance
○ Role of individual situation (e.g. stress, time)
● Foreign currency loans hit also the more educated, who perhaps even speculated with
them
Annex 5
A common framework analysis: Comparing the three different experts

34. 34
InstitutionalEnvironment
Businessencouragespeopletobuy
thingstheycannotafford(tempting
installmentpurchases);
Economiccrisisparticularlyharmful
forthelowereducated(missing
income);
GFKStudyshows:Austriansfeel
theyhavetoolittleknowledgeon
finance(missingingeneral
education).
"Bankstookovertheroleofcredit
sharks"(high-interestloansforthe
notcreditworthy);
Since2008bankshavebecomemore
careful(tendency:theamountofdebt
ofSchuldnerbertaung'sclientshas
beendeclining);
Differenteducationsystem(e.g.
Turkishpeopleoftendonotknow
(howtohandle)consumptionloans).
Exampleaccountoverdraft:Only
visibleonstatementofaccount,no
furtherexplanation(easierto
understandforbettereducated);
Understandingofhowinterest/
extensionforpayment/...is
calculated(advantageforpeoplewith
commercialknowledge);
Difficultytocriticallyassessbank
information.
SocialEnvironment
Educatedpeopleseek
helpfromexperts
(lawyers,taxadvisors),
peoplewithlower
educationrelyonless
structuredandless
reflectedhelp.
Socialenvironment(e.g.
family,friends)
Educatedpeoplehavea
socialenvironment(e.g.
friendswhoarelawyers/
bankers)whomtoask
foradvice.
TypeofLoans
95%consumptiondebt
whichhaslittle
equivalentvalue:
Informaldebtsof
familyandfriends;
Quantityofloansand
totalamountirrelevant
(debt/incomeratiois
decisiveforourcases:
toohigh).
Mostproblematic:
consumptionloansand
overdraft;
Especially'hidden
loans':e.g.cellphone
contract.
Overdrafthighly
problematicbecause
clientshavetobealert
themselves.
ClientDemographics
Loweducation,lowincome
andlowprospectsofincrease
inthefuture(cannotafford
makingcostlymistakes);
Young;
1/3ofclientsunemployed.
45%women,55%men;debt
ofca.€40000;salaryofca.
€1200(net);
Often:Decreasingincome
duetounemployment
(mostlypeoplewhowere
self-employedbefore),
peoplesimplycannothandle
money;
Lowesteducatedare
overrepresented:45%:just
mandatoryschooling,80%:
eithermandatoryschooling
orapprenticeship.Allmembersof
Arbeiterkammer(i.e.all
employees);
mostlytypical"workers",but
notonly.
Herndler(Managing
DirectorofASB
Schuldner-beratungen)
Maly(SocialWorkerat
Schuldnerberatung
Wien)
Rupprecht(Lawyerat
ArbeiterkammerWien,
DepartmentofConsumer
Protection)

35. 35
Problem-SolvingStrategies
Personalconfrontation,discussion,
considerdifferentscenarios;
Awarenessbuildingforlong-term
effectsofdebt
"weneedmorelearningforlife!";
Startwithfinancialeducationalready
inprimaryschool;
Wehavetohelppeopleaskthemselves
thequestion:DoIreallyneedthis
product?Selectandsayno.
Prevention(e.g.financialeducation)is
necessary,butnotsufficient-banks
mustassumeresponsibility
Arbeiterkammerhaslongdemanded
financialeducationinschools;
Notblametheindividualonly(should
haveeducatedthemselves),butmake
informationmoretransparentandaid
peopleinunderstanding(roleofbanks!
regulation!).
CharacteristicsoftheHigherEducated
Morereceptivetoexpertknowledge;
Actfastertolimitdamage;
Keeptrackoftheirpaymentobligations;
Awareoftheconsequencesofcredits
(exponentialinterest,longduration,)
"lowereducatedpeopletendtohaveshort-
termthinkingbutentermiddle-term
contracts";
Definethemselveslessviamaterial
possessions.
Betterabletoresistadvertisement;
Differentstatussymbols(values):e.g.not
possessingaTV;
Notonlyaboutformaleducation:"ifa
generalmanagerneedsanew,bigcar
everyyear,heisuneducatedinthis
context".
Longerplanninghorizon:Oftencalltoask
inadvance(loanconditions,preliminary
agreement,specificclauses);
Betterlanguageskills(oftentechnical
language,particularlydifficultto
understandforpeoplewithpooreducation
orimmigrants)
Problem-solvingskills(knowhowtohelp
themselves);
Confidenceindealingwithbanks(feelon
equalfooting,demand,negotiate
conditions,usedtoformalsituations)
SocietalValues
Creditworthinessisagoal
today;
fastpaceofconsumption
(balancingincomeand
expensesgetsharder);
peopledefinethemselvesvia
materialpossessions.
Problematicmaterialism;
Habits:Peoplehavelearned
togotobanksincaseof
financialproblems;banks'
reactionisusually
refinancing(including
sellingadditionalproducts).
Herndler
(ManagingDirector
ofASBSchuldner-
beratungen)
Maly(Social
Workerat
Schuldner-beratung
Wien)
Rupprecht(Lawyer
atArbeiter-
kammerWien,
Departmentof
Consumer
Protection)