Wealth and credit compliance:
does economic literacy matter?

Celeste Varuma,*, Alla Kolybana

aDepartment of Economics, Management and Industrial Engineering at the University of Aveiro,
Campus Universitário de Santiago, 3810-193, Aveiro, Portugal

Abstract

The purpose of this work is to empirically examine the influence of economic literacy upon
individuals’ over-indebtedness and households’ wealth. It may be argued that the lack of economic-
financial knowledge may have detrimental consequences, in particular reflected in higher exposure to
credit and financial risk. There is scarce literature testing the importance of economic-financial literacy
for individuals’ over-indebtedness and household wealth. This article provides empirical evidence on
the importance of financial literacy, for both individuals’ over-indebtedness and household wealth,
exploring the case of Portugal, a country about which there is scant empirical evidence on these
matters. © 2014 Academy of Financial Services. All rights reserved.

JEL classification: B26; D8; D14; D3; H63

Keywords: Economic literacy; Financial literacy; Over-indebtedness; Wealth

1. Introduction

A recent report on financial stability from the Bank of Portugal shows that the degree of
failure in the Portuguese economy continues to increase to record levels, with bad loans
reaching the highest levels in 15 years. In total lending to households, the amount classified
as doubtful debts amounted to €5,031 million Euros- the highest on records. The latest
figures show that out of €134,15 million of loans to households, 3.73% are of bad loans,
meaning deemed uncollectible by financial institutions. In loans to households, out of the

* Corresponding author. Tel.: 00351 234 370 200; fax: 00351 234 370 215.
E-mail address: camorim@ua.pt (C. Varum).

Financial Services Review 23 (2014) 325–339

1057-0810/14/$ – see front matter © 2014 Academy of Financial Services. All rights reserved.



€5,031 million classified doubtful, €2,229 million are in home loans and €1,539 million in
consumer credit (e.g., to buy cars or appliances).

Moreover, owing to the large debt ratio and job instability, personal bankruptcies jumped
to 2,400, a 79% increase in 2010. Two in five bankruptcies in Portugal are no longer in
business, but individuals. These facts are replicated in many Organization for Economic
Co-Operation and Development countries. DeVaney and Lytton (1995) explored the problem
with household bankruptcy. However, more detailed analyses are necessary to reduce this
trend.

The rapid growth in household debt and its link to the current financial crisis has
highlighted consumer weaknesses, and raises the question of whether individuals’ lack of
economic and financial knowledge led them to take out mortgages and revolving credit they
could not afford. Indeed, the acknowledged widespread lack of financial and economic
literacy casts serious doubts on the ability of individuals to make financial decisions.

In this article we seek to understand the relationship between both economic and/or
financial literacy and financial decision-making, as well as how they may combine to cause
over-indebtedness. The study is based upon data collected through a new survey focused
specifically on economic literacy, including a component of financial literacy. This article
contributes to the existing literature in three ways. First, our research allows us to measure
economic literacy, including financial matters, as well as individuals’ perception of over-
indebtedness. Rather than relying on existing debt indicators, we ask individuals to judge
their own debt levels (evaluate their capacity to pay their debts). Finally, we assess how
economic and financial literacy is linked to over-indebtedness and monthly household
income. Our sample comprises Portuguese citizens with respect to their economic and
financial literacy, and their judgments about the extent of their indebtedness and levels of
income.

In the next section we explore the nexus between economic literacy and over-indebted-
ness. Section 3 describes the methodology, while the results are reported in Section 4.
Section 5 concludes.

2. Economic literacy and over-indebtedness

There is wide recognition that people in general hold low levels of economic and financial
literacy. As the Organization for Economic Co-Operation and Development (2005) report on
financial literacy documents, the low levels of financial literacy are not exclusive to Portugal,
it extend to several countries. Similarly, the Survey of Health, Aging and Retirement in
Europe (SHARE) shows that respondents score poorly on financial numeracy and literacy
scales (Christelis, Jappelli, & Padula, 2010).

Rapid growth in household debt raises the question of whether individuals’ lack of
economic and financial knowledge led them to take out mortgages and revolving credit they
could not afford.

In recent years, a growing body of literature has shown that financial knowledge affects
a wide range of financial behaviors (e.g., stock market participation, portfolio diversification,
participation and asset allocation, indebtedness, etc.) that in turn can affect wealth accumu-

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lation. Indeed, research in the area has typically focused on individuals’ knowledge of
economics and finance and its effects on financial decisions, usually related to savings,
retirement planning, or portfolio choice (Lusardi & Tufano, 2009). There is some indication
that economic and financial literacy may affect debt as well. We expect people with higher
levels of economic literacy lato sensu, to have better capacity to avoid excess indebtedness.
This knowledge influences ones’ ability to make simple decisions (e.g., regarding debt
contracts and other decisions in the context of everyday financial choices). However, little
research has been done on the relationship between economic and financial literacy and
indebtedness.

Economic literacy comprises a set of knowledge and competences allowing agents to
improve their personnel decisions in daily activities, this is a wide concept comprising
economic and financial concepts. In addition, economic literacy is about making trade-offs
between deficient resources (Dahl, 1998), getting a job and combatting inflation (Buchholz,
1998), understanding the forces that influence the quality of households lives (Farrell, 1999),
and increasing competence skills in complex global markets (Gupta, 2006). In summary,
economic knowledge will apply to daily decisions about financial to money management; it
comprises the ability to understand, communicate, manage, decide, and forecast the financial
issues (Remund, 2010). The foundation of financial decisions is general economic knowl-
edge. According to Lusardi and Mitchell (2007) and Pang (2010) financial literacy will give
the agent knowledge about economic concepts that will be used to plan, evaluate, and
accurately decide about financial aspects. In contrast, insufficient economic or financial
knowledge leads to low savings, mortgage defaults and financial mistakes, extra fees, or
excessive interest rates on credit card debt (Agarwal et al., 2009; Agarwal & Mazumder,
2011; Banks & Oldfield, 2007; Gerardi, Goette, & Meier, 2010). Moreover, economically
illiterate individuals may be intimidated by politicians (Gupta, 2006).

Moore (2003) reports that respondents with lower levels of financial literacy were more
likely to have costly mortgages. The results from Lusardi and Tufano (2009) reinforce this
concept as they have found a strong negative relation between literacy about debt-related
issues and debt loads. Generally, individuals with lower levels of debt literacy tended to
conduct high-cost transactions (incurring fees and high borrowing rates); the less financially
literate were either unable to judge their debt position or reported excessive debt loads. Along
this line, Perry and Morris (2005) found individuals who were more knowledgeable about
financial matters were generally more likely to engage in financially responsible behavior,
such as controlling their spending, budgeting, and planning for the future.

Other works explore the relationship between financial knowledge and money manage-
ment (Hilgert, Hogarth, & Beverly, 2003), retirement savings (Bucher-Koenen, 2009;
Lusardi, 2004; Lusardi & Mitchell, 2009), stock market participation (Christelis, Jappelli, &
Padula, 2010; Van Rooij, Lusardi, & Alessie, 2011; Yoong, 2011), financial results (Banks,
2010; Smith, McArdle, & Willis, 2010), and household wealth management (Bateman et al.,
2011; Lusardi, & Mitchell, 2007; Van Rooij, Lusardi, & Alessie, 2012; and others).

Hence, the effect of economic and financial literacy may reflect upon wealth. Bateman et
al. (2011) found that financial literacy score increases with household income. Van Rooij,
Lusardi, and Alessie (2012) show that financial literacy is positively correlated with house-
hold wealth accumulation because economic and financial comprehension enhances the

327C. Varum, A. Kolyban / Financial Services Review 23 (2014) 325–339



ability to form plans and make decisions and reduces the cost of collecting and treating
information. In this way, analysis of relationship between financial literacy and its effects on
household wealth has become important and relevant in the current policy environment.

Based upon the above we expect economic literacy to contribute to explanation regarding
over-indebtedness and wealth. These expectations are tested in the following sections.

3. Methodology

3.1. Survey design

We implement an extensive survey, fielded in April 2012, collecting information on
individuals’ economic and financial knowledge and demographic characteristics (e.g., age,
gender, nationality, education, and employment). We also assess attitudes towards econom-
ics as well as data on individuals’ judgments about their indebtedness. In addition, the survey
collects self-reported information on household income.

According to the literature, financial literacy is a branch of economic literacy; the first is
essentially money management, and the second is the ability to accurately understand and
decide about daily aspects. Our survey encompasses questions regarding economic and
financial knowledge. There are national surveys that measure financial knowledge; never-
theless, few specifically focus on economics. The assessment of economic and financial
knowledge comprises 29 multiple-choice questions, with 22 that measure economic knowl-
edge and seven focuses upon financial aspects specifically. The economic questions comprise
the following economic topics: Consumer Economy, Economy and Production, Financial
Economy, and the Economic Role of Government and International Economy. The financial
questions cover the concepts of: Euribor and Spread, the degree of risk of a term deposit,
relationship between inflation and interest rate, ability to pay a loan, identification of the
balance of demand deposit, and identification of changes in the balance of the demand
deposit. We follow an adapted version of the Economic Literacy Test developed by the
National Council on Economic Education (NCEE) to evaluate economic literacy. For the
question on financial literacy section we use the questions applied in a survey conducted by
the Bank of Portugal.

3.2. The sample

The study was applied to a sample of Portuguese adults with children at their charge,
regarding their economic knowledge and their judgments about the extent of their indebt-
edness. The questionnaires were collected among the parents of children attending five
different primary schools in Portugal. In total, 618 questionnaires, out of the 1,061 ques-
tionnaires distributed, were returned, which means a response rate of 58%. Table 1 reports
frequencies (percent) of demographic and socioeconomic variables. Most respondents in our
sample are women (70.9%) and have Portuguese nationality (93.2%). The majority of
participants (68%) have between 36 and 45 years. About 26% of respondents reported at least

328 C. Varum, A. Kolyban / Financial Services Review 23 (2014) 325–339



some kind of economic or financial education and 58.6% of them have higher education.
About 66% of survey respondents replied they were able to meet their credit commitments

3.3. Model

To explore the influence of economic literacy upon the ability to pay individual’s debts we
use a probit model. The dependent variable is binary, equaling 1 if the individual is able to
fulfill all his debts, and 0 if he reports difficulties in meeting his debts.

Table 1 Sample: demographic and socioeconomic characteristics

Demographic and socioeconomic variables %

Gender Female 70.9
Male 28.4
No answer 0.7

Nationality Portuguese 93.2
Other 6.6
No answer 0.2

Age 26–35 14.9
36–45 67.7
46–55 15.5
56–67 1.5
No answer 0.4

Education level From 0 to 9th grade 15.8
12th grade 24.4
Higher education 58.6
No answer 1.2

Attended some kind of training
in economics or finance

Yes 26.3
No 62.4
No answer 11.3

National qualification levels 1st group: Legislators, senior officials, and managers 4.4
2nd group: Professionals, including teaching professionals 37.8
3rd group: Technicians and associate professionals 11.8
4th group: Administrative staff 12.1
5th group: Service workers and shop and market sales

workers
10.2

6th group: Skilled agricultural workers and fishery
workers

0.5

7th group: Craft and related trade workers 3.2
8th group: Plant and machinery operators and assemblers 0.2
9th group: Elementary occupations 4.5
No answer 15.3

Income �1,000€ 19.4
1,001€–2,000€ 30.5
2,001€–6,000€ 37.3
6,001€–10,000€ 0.8
�10,001€ 0.3
No answer 11.6

Can you assess your capacity
to pay the debt?

I can meet my commitments 66.1
I cannot meet my commitments 20.2
I don’t have credits 12.0
No answer 1.8

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The main explanatory variables in the model are the level of economic-financial literacy,
economic literacy, and financial literacy measured by the score in the questionnaire (per-
centage of correct answers). Additionally, we have considered variables likely to affect the
probability of over-indebtedness. Concerning demographic variables we have: gender (dum-
my: 1 for male and 0 for female), age and nationality (1 for Portuguese, and 0 otherwise).
Income was measured regarding a Likert scale from 1 to 5. Education was proxied by EDC1,
EDC2, and EDC3 and previous training in Economics or Finance (dummy: 1 for yes, and 0
for no). The ranking of qualifications were defined in accordance to the National Classifi-
cation of Professional Activities, being that 1 corresponds to the highest level of qualification
and 9 the lowest.

A vector of variables was included to grasp individuals’ attitudes, interest towards
economic matters, and motivations. FOLLOW is a dummy with value 1 if the respondent

Table 2 Description of variables

Variables Description

Dependent variable
Able to meet credit 1 � meet credits; 0 � over-indebted

Explanatory variables
Economic and/or financial literacy

Economic or financial literacy Percentage of correct answers in economic
or financial questions (X*100/29)

Economic literacy Percentage of correct answers in economics
(X_ECO*100/22)

Financial literacy Percentage of correct answers in finance
(X_FIN*100/7)

Demographic characteristics
AGE Discrete variable for individual’s age
AGE2 Age squared variable
EDC1 Education level From 0 to 9 years schooling: 1 yes; 0 no
EDC2 12 years schooling: 1 yes; 0 no
EDC3 Higher education: 1 yes; 0 no
GENDER Gender 1 � male; 0 � female
NAC Nationality 1 � Portuguese; 0 � otherwise

Socioeconomic characteristics
INCOME 1 to 5, from lower levels of income to

higher levels of income
ECON Attended some kind of training in

economics or finance: 1 yes; 0 no
CNP 1 to 9: 1 corresponds to the activities

corresponding to the highest level of
qualifications and 9 to the lowest

Interest and motivation towards economic matters
FOLLOW Frequently follows economic matters in the

media: 1 yes; 0 no
IMPORT Degree of importance of economic

knowledge to several situations: 0 to 29
Attitudes regarding saving

SAVE Saves: 1 yes; 0 no.
AQGOODS Saves with medium objectives Saves to acquire durable goods: 1 yes; 0 no
RETIREMENT Saves with long term objectives Saves for retirement: 1 yes; 0 no

330 C. Varum, A. Kolyban / Financial Services Review 23 (2014) 325–339



follows various media regarding economic affairs and 0 otherwise. AQGOODS and
RETIREMENT are two variables that indicate the motivation to save. AQGOODS is a
dummy variable that takes value 1 if the respondent saves essentially for purchase of durable
goods and 0 otherwise, and RETIREMENT takes value 1 when the respondent saves for
retirement and 0 otherwise. SAVE is and a dummy variable that takes value 1 if the
respondent is able to save a percentage of his disposable income and 0 otherwise.

We measure the importance of the respondents knowledge of economics for various
situations. For each situation we considered a Likert scale of 1 (not important) to 4 (very
important). The importance attributed to economics is measured by a weight factor (from 1
to 29, in an increasing scale). Table 2 presents a summary of the variables.

4. Results

4.1. Descriptive findings

Table 3 reports percentage of correct answers on Economic and Financial literacy of
respondents according all explanatory variables. Based upon statistics presented in Table 3
youngest respondents and minorities exhibit poorer performance in economic and financial
literacy, which is particularly troubling. The average percentage of correct responses in-
creases with post-school education and monthly household income. The average percentage
of correct answers for economic questions is significantly higher than the average for
financial questions, showing that Portuguese citizen s have higher literacy level in Econom-
ics than in Finance.

Table 4 presents the descriptive statistics on the capability to pay credit payments and
economic and/or financial literacy. There is a strong correlation between ability to meet
commitments and economic or financial knowledge. Furthermore, a significant differences
between those that meet commitments and those that are over-indebted.

Descriptive statistics on capability to pay debts and demographic characteristics are
presented in Table 5. We find a significant relationship between demographic characteristics
and over-indebtedness. Descriptive findings show that men, Portuguese respondents, and
those with higher education are more likely to meet their credit obligations.

Table 6 corroborates the descriptive findings on capacity to pay their credit obligations
and socioeconomic characteristics. It is observed that fewer difficulties in paying debts can
be found among individuals who are able to save some money and among those who
frequently follow economic issues in media. Individuals with lower income and qualification
levels are more likely to have difficulties paying credits.

4.2. Econometric results

In our study we seek to understand whether people face difficulties paying their debts and
if this situation is related to their knowledge about economic matters. To proxy debt levels,
we ask individuals about their capacity to meet their credit obligations.

At first, we try to understand which factors influence the ability to meet credit. Table 7

331C. Varum, A. Kolyban / Financial Services Review 23 (2014) 325–339



Table 3 Percentage of correct answers to economic and financial questions

Characteristics % Correct answers
on economic
literacy

% Correct answers
on financial
literacy

Gender Female* 72.4 60.7
Male* 83.3 71.3

Nationality Portuguese* 76.9 65.7
Other* 56.9 36.6

Age 26–35* 61.7 52.0
36–45* 78.7 67.0
46–55* 75.8 61.8
56–67** 73.9 59.4

Education level From 0 to 9th grade** 46.8 44.5
12th grade* 72.8 60.5
Higher education* 84.0 70.5

Attended some kind of
training in economics or
finance

Yes* 85.6 73.6
No* 73.6 62.2

National qualification levels 1st group: Legislators, senior
officials, and managers*

84.7 69.2

2nd group: Professionals,
including teaching
professionals*

84.2 71.3

3rd group: Technicians and
associate professionals*

78.8 66.0

4th group: Administrative
staff*

78.8 70.2

5th group: Service workers
and shop and market sales
workers*

65.7 53.3

6th group: Skilled
agricultural workers and
fishery workers

71.0 21.8

7th group: Craft and related
trade workers

64.5 55.3

8th group: Plant and
machinery operators and
assemblers

27.0 0.00

9th group: Elementary
occupations**

47.9 42.5

Income �1,000€* 61.5 50.2
1,001€–2,000€* 77.4 65.7
2,001€–6,000€* 86.3 74.7
6,001€–10,000€ 94.0 84.5
�10,001€ 95.0 74.5

Frequently follows economic
issues in the media

Yes* 80.8 70.3
No* 65.7 49.3

To understand political
decisions

Very important* 83.0 71.1
Important* 77.7 66.6
Not very important* 70.2 58.7
Not important* 68.4 52.8

(Continued)

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presents the econometric results. Our dependent variable is binary; therefore, we will
consider binary choice models; assuming a homogeneous sample, probit estimation tends to
be the most accurate. This model shows no evidence of lack of fit based on the Hosmer-
Lemeshow statistic.

In Column 1 (first-stage estimation) we observe that higher schooling years and monthly
income also positively influence the probability to meet credits; furthermore, assigning a
proportion of the disposable income to saving also increases the probability of meeting credit
obligations.

In Column 2 we infer that higher schooling levels, higher monthly income, and saving
increase the probability of meeting credit commitments.

Table 3 Continued

Characteristics % Correct answers
on economic
literacy

% Correct answers
on financial
literacy

To get a better paid job Very important* 74.8 63.6
Important* 76.6 66.2
Not very important* 79.1 64.9
Not important* 68.0 49.9

To become an active citizen
and play a fuller part in
society

Very important* 77.6 67.7
Important* 75.9 64.3
Not very important* 79.0 62.7
Not important* 66.0 50.0

To make optimal choices
about investment and
saving

Very important* 80.9 69.0
Important* 69.5 59.2
Not very important* 58.3 40.8
Not important* 41.2 16.5

To make better decisions on
consumption now and in
the future

Very important* 78.8 67.1
Important* 76.0 64.5
Not very important* 72.8 58.2
Not important* 42.8 21.9

To make optimal choices
about loans or credits

Very important* 80.6 68.5
Important* 72.7 62.3
Not very important** 61.4 49.9
Not important* 45.4 15.5

To improve my wealth and
well-being

Very important* 77.7 65.8
Important* 77.5 66.2
Not very important* 74.9 63.0
Not important* 55.4 34.7

Are you able to save some
money?

Yes* 81.8 69.2
No* 69.2 58.8

Save to acquire durable
goods

Save* 78.6 65.9
Do not* 74.7 63.8

Retirement savings Save* 78.7 67.5
Do not* 75.5 63.3

Note. We calculate an average percentage of economic and financial literacies for each category of explanatory
variables. Maximum score is 100%. Moreover, we use paired t-test to compare financial and economic literacy
scores.

*1% significance level; **5% significance level.

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Column 3 shows that financial knowledge, higher schooling levels, higher saving increase
the probability of meeting contracts.

We find that financial literacy shows a very strong inverse relationship with over-
indebtedness. Those who report higher levels of financial literacy are more likely to belong
to the group who report having no difficulties paying off debt.

The effect is not only sizable, but it also increases with higher scores for self-assessed
literacy. Conversely, those who are less literate are much more likely to report having
difficulties with debt and there is an inverse relationship between financial literacy and too
much debt. Although the estimates are less sizable than for those who may have difficulties
with debt, the unsure also are much less likely to display high levels of literacy. These results
are consistent with results of Gerardi, Goette, and Meier (2010), which show significant
correlation between mortgage delinquency and numerical ability. Mortgage delinquency

Table 4 Capacity to pay credits and economic and/or financial literacy

Characteristics % Correct answers
on economic or
financial literacy

% Correct answers
on economic
literacy

% Correct answers
on financial
literacy

Meet credits 77.99 80.82 69.07
Over-indebted 65.30 68.47 55.31
F-test 50.54 44.70 39.25
p-value 0.00 0.00 0.00

Note. We use one-way analysis of variance to test the hypothesis that the means of economic and/or financial
literacy score among two groups are equal. The first group represents respondents, who can meet credits and
second who are over-indebted.

Table 5 Capacity to pay credits across demographic characteristics

Characteristics % Of respondents who
are able to pay credits

% Of over indebted
respondents

Gender
Male 81.88 18.13
Female 74.12 25.88
Pearson’s �2(1) 3.73
p-value 0.05

Nationality
Portuguese 77.47 22.53
Other 60.71 39.29
Pearson’s �2(1) 4.15
p-value 0.04

Education
From 0 to 9th grade 46.05 53.95
12th grade 74.05 25.95
Higher education 84.83 15.17
Pearson’s �2(2) 52.25
p-value 0.000

Note. We use �2 test of association to establish relationship between capacity to meet credits and demographic
factors.

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rates are greater among borrowers with lower financial literacy. Demographic variables are
correlated to debt loads as well. Those who are employed and have higher income and higher
wealth are much more likely to report they have the appropriate amount of debt. Finally,
women, and those with low income and wealth are more likely to be unable to judge their
debt load.

Monthly income is likely to influence the ability to meet debt. We explore what deter-
mines asymmetries of income among families, and investigates the effect of economic
knowledge. The relationship between monthly household income and economic and/or
financial literacy is confirmed in OLS regression analysis including the same explanatory
variables as used previously (see Table 8).

Table 6 Capacity to pay credits across socioeconomic characteristics

Characteristics % Of respondents who
are able to pay credits

% Of over indebted
respondents

Attended some kind of training in economics
Yes 82.43 17.57
No 75.99 24.01
Pearson’s �2(1) 2.47
p-value 0.12

National qualification levels
1st group 64.00 36.00
2nd group 85.12 14.88
3rd group 76.56 23.44
4th group 84.06 15.94
5th group 63.27 36.73
6th group 33.33 66.67
7th group 66.67 33.33
8th group – –
9th group 45.00 55.00
Pearson’s �2(7) 33.92
p-value 0.00

Income level
�€1,000 41.41 58.59
€1,001–€2,000 79.17 20.83
€2,001–€6,000 91.67 8.33
€6,001–€10,000 100.00 0.00
� €10,000 100.00 0.00
Pearson’s �2(4) 99.72
p-value 0.00

Frequently follows economic issues in the media
Yes 81.16 18.84
No 68.25 31.75
Pearson’s �2(1) 11.34
p-value 0.00

Are you able to save some money?
Yes 95.17 4.83
No 55.79 44.21
Pearson’s �2(1) 108.94
p-value 0.00

Note. We use �2 test of association to establish relationship between capacity to meet credits and socioeco-
nomic factors.

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The OLS regression shows that both economic and financial knowledge is positively
correlated with monthly income. Age, nationality, schooling, and saving are shown to be
statistically significant. More concretely, they are positively related to the achievement of
higher income levels.

5. Conclusion

The results of this study enable us to conclude that many Portuguese households are not
able to meet their credit commitments. Moreover, our findings show that Portuguese indi-
viduals have limited financial knowledge. In particular, the youngest and those respondents
with lower level of education displayed the lowest levels of economic and/or financial
knowledge. These findings are consistent with results of Chen and Volpe (1998).

Most important, those who have the lower levels of financial literacy are more likely to
report problems with debt. Moreover, respondents with lower levels of economic and
financial literacy reported lower levels of monthly household income. All together, these
findings point to the fact that widespread lack of economic and financial knowledge is a
reasonable cause for concern.

Table 7 Capacity to meet credits and economic and/or financial literacy: Probit estimation

Explanatory variables Model 1 (z-statistic) Model 2 (z-statistic) Model 3 (z-statistic)

C �1.652 (�0.476) �1.832 (�0.532) �1.463 (�0.415)
Economic or financial literacy 0.012 (1.698)
Economic literacy 0.009 (1.252)
Financial literacy 0.011** (2.096)
AGE �0.031 (�0.203) �0.023 (�0.151) �0.033 (�0.210)
AGE2 �8.38E-05 (�0.046) �0.0002 (�0.094) �7.04E-05 (�0.038)
GEN 0.310 (1.412) 0.334 (1.527) 0.315 (1.438)
NAC �0.372 (�0.657) �0.292 (�0.525) �0.433 (�0.767)
EDC2 0.934** (2.346) 0.941** (2.362) 1.002** (2.517)
EDC3 0.871** (1.979) 0.891** (2.025) 0.922** (2.100)
ECON �0.266 (�1.229) �0.253 (�1.166) �0.237 (-1.106)
CNP �0.047 (0.709) 0.045 (0.682) 0.040 (0.597)
INCOME 0.536* (3.193) 0.549* (3.285) 0.535* (3.191)
FOLLOW 0.154 (0.735) 0.157 (0.752) 0.140 (0.667)
AQGOODS 0.336 (1.396) 0.340 (1.412) 0.375 (1.562)
RETIREMENT 0.020 (0.083) 0.018 (0.074) 0.051 (0.209)
SAVE 1.161* (5.570) 1.151* (5.540) 1.188* (5.681)
IMPORT 0.019 (0.702) 0.023 (0.859) 0.021 (0.768)
Total obs 357 357 357
McFadden R2 0.34090 0.33721 0.34526
LR statistic 121.4201 120.1074 122.9730
Prob(LR statistic) 0.00000 0.00000 0.00000
Hosmer-Lemeshow �2 (8) 11.72 8.15 12.90
Prob � �2 0.1641 0.4188 0.1153

Note. Dependent variable is “Able to meet credits.” All explanatory variables described in Table 2.
*1% significance level; **5% significance level.

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Education concerning economic principles and credit issues are very important in helping
people avoid excess indebtedness, mortgage delinquencies and foreclosures, and bankrupt-
cies and borrowing that are costly. The proliferation of credit and debt instruments, often
with extensive information from written provisions and salespeople, can overwhelm bor-
rowers.

First, we focus attention on the important effect of economic and financial knowledge
upon individuals’ debt. Secondly, we consider the rich set of variables, attitude, and
experiences. Thirdly, we listen to individuals about their own debt levels. Finally, we design
a collaborative research project that blends scholarly research with timely market research.
Our conclusions suggest a complex set of interactions among literacy, experience, demo-
graphics, and debt loads.

Our work suggests that financial literacy is related to the choices that people make, and
this in turn affects their income and capacity to meet debt. We interpret this to mean that
additional research on economic and financial literacy—and education to enhance financial
literacy—can complement, and not substitute for, auto-default and other comparable ap-
proaches.

Table 8 Household income and economic and/or financial literacy: OLS estimation

Explanatory variables Model 1 ( t-statistic) Model 2 ( t-statistic) Model 3 ( t-statistic)

C �3.114* (�2.750) �3.183* (�2.804) �3.178* (�2.800)
Economic or financial literacy 0.008* (3.269)
Economic literacy 0.006* (2.858)
Financial literacy 0.005* (2.875)
AGE 0.129** (2.512) 0.132** (2.561) 0.138* (2.708)
AGE2 �0.001** (�2.115) �0.001** (�2.159) �0.001** (�2.298)
GEN 0.004 (0.062) 0.016 (0.246) 0.011 (0.176)
NAC 0.757* (4.725) 0.792* (4.975) 0.763* (4.736)
EDC2 0.281** (2.172) 0.286** (2.199) 0.310** (2.403)
EDC3 0.822* (5.977) 0.836* (6.066) 0.856* (6.270)
ECON �0.066 (�1.014) �0.065 (�0.989) �0.043 (�0.672)
CNP 0.005 (0.222) 0.005 (0.218) �0.003 (�0.121)
FOLLOW 0.026 (0.387) 0.033 (0.498) 0.021 (0.317)
AQGOODS 0.053 (0.748) 0.050 (0.694) 0.082 (1.145)
RETIREMENT �0.009 (�0.126) �0.012 (�0.156) 0.006 (0.082)
SAVE 0.399* (6.608) 0.397* (6.530) 0.421* (6.984)
IMPORT 0.007 (0.816) 0.009 (0.953) 0.010 (1.151)
Total obs 394 394 394
R2 0.48551 0.48216 0.48229
Adjusted R2 0.46650 0.46303 0.46316
F-statistic 25.5461 25.2060 25.2191
Prob(F-statistic) 0.00000 0.00000 0.00000
Breusch-Pagan �2(1) 0.26 0.32 0.10
Prob � �2 0.6068 0.5700 0.7542

Note. Dependent variable is “INCOME.” All explanatory variables described in Table 2.
*1% significance; **5% significance.

337C. Varum, A. Kolyban / Financial Services Review 23 (2014) 325–339



Acknowledgment

The study has been conducted under Research project “Economicando” (PTDC/EGE-
ECO/100923/2008), financed by FEDER funds through the Programa Operacional Fatores
de Competitividade - COMPETE and by national funds through the FCT - Fundação para a
Ciência e Tecnologia.

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