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The Social Science Journal
ISSN: 0362-3319 (Print) 1873-5355 (Online) Journal homepage: https://www.tandfonline.com/loi/ussj20
Financial education and student financial literacy:
A cross-country analysis using PISA 2012 data
José Manuel Cordero, María Gil-Izquierdo & Francisco Pedraja-Chaparro
To cite this article: José Manuel Cordero, María Gil-Izquierdo & Francisco Pedraja-Chaparro
(2020): Financial education and student financial literacy: A cross-country analysis using PISA 2012
data, The Social Science Journal, DOI: 10.1016/j.soscij.2019.07.011
To link to this article: https://doi.org/10.1016/j.soscij.2019.07.011
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Financial education and student financial literacy: A cross-country
analysis using PISA 2012 data
José Manuel Cordero
a
, María Gil-Izquierdo
b
, and Francisco Pedraja-Chaparro
a
a
Universidad de Extremadura, Av. Elvas sn, 06006 Badajoz, Spain;
b
Universidad Autónoma de Madrid, C/F. Tomás
y Valiente, 528049 Madrid, Spain
ABSTRACT
The aim of this research is to explore whether teaching basic financial
concepts at schools helps to improve students’ability to apply the knowl-
edge and skills that they learn to real-life situations involving financial
issues and decision making measured by a standardized financial literacy
assessment. To do this, we exploit the rich set of comparative data about
the countries participating in the PISA 2012 financial literacy module. Our
empirical analysis is based on multilevel (hierarchical) regression modeling
including country fixed effects. Our results suggest that the availability of
financial education is positively and significantly related to students’finan-
cial literacy, regardless of the strategy applied to teach financial concepts.
Nevertheless, it has a very small influence compared to the major role
played by other individual- and school-level factors. In addition, we find
that students receiving courses taught by specialists from private institu-
tions and non-governmental organizations achieve better results than
others receiving financial education training from their teachers.
ARTICLE HISTORY
Received 5 February 2019
Revised 18 July 2019
Accepted 18 July 2019
KEYWORDS
Education policy;
Cross-country analysis;
Financial literacy; Financial
education training;
Multilevel regressions
1. Introduction
Based on the belief that improved financial literacy will empower people to make better financial
decisions, interest in this field has increased worldwide in recent years (Aprea et al., 2016; Moreno-
Herrero, Salas-Velasco, & Sánchez-Campillo, 2018). This is particularly important at a time when
a large part of the population has easy access to increasingly complex financial products and services,
and people live longer. There is thus a need to effectively manage money to achieve lifelong financial
security (Klapper, Lusardi, & Van Oudheusden, 2015). As a result, financial education programs
figure prominently on the national public policy agenda of most countries (Appleyard &
Rowlingson, 2013; Lusardi & Mitchell, 2011). Likewise, some international institutions like the
World Bank (Xu & Zia, 2012) or the Organization for Economic Cooperation and Development
(OECD) with its International Network on Financial Education (INFE) have made efforts to collect
information about financial literacy, coordinate national programs and provide a policy forum for
governments to exchange views and experiences on financial literacy and financial education
(OECD, 2012).
The design of effective educational interventions needs to be preceded by a proper assessment of
the level of financial literacy (Bongini, Iannello, Rinaldi, Zenga, & Antonietti, 2018). This concept
can be interpreted as knowledge of key financial concepts related to the management of money,
loans and investment in different assets (Huang, Nam, & Sherraden, 2013; Hung, Parker, & Yoong,
2009; Remund, 2010). However, it also accounts for the skills, motivation and confidence to apply
CONTACT José Manuel Cordero jmcordero@unex.es
Supplementary material related to this article can be found, in the online version, at doi:http://https://doi.org/10.1016/j.soscij.
2019.07.011.
© 2020 Western Social Science Association
THE SOCIAL SCIENCE JOURNAL
https://doi.org/10.1016/j.soscij.2019.07.011
this knowledge in order to make effective decisions across a range of financial contexts. This should
improve people’s financial well-being and enable their participation in economic life (Atkinson &
Messy, 2012). Therefore, although financial knowledge is usually considered as the key dimension of
financial literacy (Huston, 2010), it also includes other relevant dimensions such as financial
attitudes and behaviors (Garg & Singh, 2018).
1
Prior research has mainly explored the relationship between financial literacy and several aspects
such as wealth accumulation (Behrman, Mitchell, Soo, & Bravo, 2012; Gustman, Steinmeier, &
Tabatabai, 2012), stock market participation (Abreu & Mendes, 2010; Christelis, Jappelli, & Padula,
2010; Van Rooij, Lusardi, & Alessie, 2011), savings and retirement planning (Lusardi & Mitchell,
2017), low-cost borrowing and fee awareness (Bucher-Koenen, Lusardi, Alessie, & Van Rooij, 2017)
or contracting personal loans and mortgages with better conditions (Disney & Gathergood, 2013;
Lusardi & Tufano, 2015).
Even though adults make most financial decisions, it is acknowledged that financial literacy has to
be cultivated at school so that students develop the skills needed to successfully manage their
finances in adulthood. Besides, there is robust evidence showing that young people’s levels of
financial literacy are consistently lower than other demographics (Allgood & Walstad, 2013;
Mandell, 2008). Therefore, students’financial literacy needs to be improved so that they can
participate in modern society as well as being beneficial for the economy as a whole (Lusardi &
Mitchell, 2014; Lusardi, 2015). For this purpose, it is essential to engage parents in financial
education as socialization agents for their children (Jorgensen & Savla, 2010; Shim, Barber, Card,
Xiao, & Serido, 2010).
In response to the poor levels of youth financial literacy, many countries have developed plans to
introduce contents related to financial education (FE) in school curricula, especially for low-income
or lesser educated populations (García, Grifoni, López, & Mejía, 2013; Messy & Monticone,2016;
OECD, 2015,2016). This should give the entire school-age population equal access to financial
education. To do so, they are adopting different strategies. These strategies range from a well-
developed framework to basic pilot programs. However, the most common option is a cross-
curricular approach providing some form of financial education by linking financial concepts with
some other learning areas to prevent curriculum overload. In most cases, schools adopt a flexible
approach to the integration of financial education into the curriculum, and teachers may also decide
whether or not to include aspects of financial literacy within their subjects. Therefore, there are a lot
of differences across territories and also among schools within the same territory (Atkinson & Messy,
2013; Grifoni & Messy, 2012).
In this paper, we attempt to exploit this heterogeneity in the international context in order to
determine whether student training in financial concepts helps to enhance their financial literacy,
since larger variations in school and population characteristics generally improve the prospects of
detecting the influence of specific factors on student outcomes (Hanushek & Woessmann, 2014). To
do this, we adopt a multilevel regression approach, which allows us to avoid potential problems of
estimation bias associated with classic methods, such as OLS regression, caused by the correlation
between the values of student variables aggregated at school level or schools belonging to the same
country (Hox, 2010).
In our empirical analysis we use data from the OECD’s Programme for International Student
Assessment (PISA), which included a module on financial literacy for the first time in 2012. Students
from 18 countries participated in this optional PISA assessment. The assessment provides compar-
able information with regard to the financial competences of 15-year-olds worldwide by testing their
knowledge applied in everyday life situations rather than the reproduction of knowledge (OECD,
1
In the literature it is common to find that terms such as financial literacy, financial capability or financial competence are used
interchangeably, although there might be differences among them (see Taruma & Kuma (2015) for details). Nevertheless,
throughout this paper we always refer to the term financial literacy because this is the denomination used in the PISA
assessment, which constitutes our main source of information.
2J.M. CORDERO ET AL.
2013). The availability of such comparable data across countries is essential for understanding how
well prepared young people are for dealing with new and changing financial environments.
Moreover, the dataset includes extensive information about individual characteristics, socio-
economic background and school contexts. Consequently, the analysis can account for these factors.
This research falls within the scope of recent literature focusing on the assessment of the
effectiveness of youth financial education programs (Kaiser & Menkhoff, 2017; McCormick, 2009;
Miller, Reichelstein, Salas, & Zia, 2015; Xu & Zia, 2012). Most existing papers regarding this topic
refer to the specific context of the USA, where there is a long tradition of mandated personal finance
courses in high schools in many states (Bernheim, Garrett, & Maki, 2001). Thus, it is possible to
study the long-term consequences of financial education courses. For instance, several authors noted
that youth participating in such programs had positive attitudinal and behavioral outcomes (Boyce &
Danes, 1999; Lyons, Chang, & Scherpf, 2006). Moreover, larger quasi-experimental studies also
suggest that financial education may improve financial decision-making for high school students
(Brown, Grigsby, Van Der Klaauw, Wen, & Zafar, 2016). There are more recent empirical studies
analyzing other initiatives in different developed nations (e.g., Becchetti, Caiazza, & Coviello, 2013;
Bover, Hospido, & Villanueva, 2018; Lührmann, Serra-Garcia, & Winter, 2015; Romagnoli &
Trifilidis, 2013) and developing countries (e.g. Berry, Karlan, & Pradhan, 2018; Bruhn, Leão,
Legovini, Marchetti, & Zia, 2016; Frisancho, 2018; Jamison, Karlan, & Zinman, 2014).
All the above studies focus on specific programs with different characteristics implemented in
highly heterogeneous contexts. Therefore, the evidence about their effectiveness is frequently mixed
(Walstad, 2013). Thus, several authors are rather skeptical about the effective contributions of
financial education (Cole, Paulson, & Shastry, 2016; Fernandes, Lynch, & Netemeyer, 2014; Willis,
2008), while many others have found a positive correlation between its implementation and educa-
tional outcomes (Batty, Collins, & Odders-White, 2015; Varcoe, Martin, Devitto, & Go, 2005;
Walstad, Rebeck, & MacDonald, 2010).
As previous evidence is scant, the use of a cross-country approach opens up opportunities. There are
also challenges, since very few studies have used international data to analyze differences in financial
literacy across countries. For instance, Jappelli (2010) uses international panel data on 55 countries in
order to explore the macroeconomic and institutional variables that are more likely to explain interna-
tional differences in literacy across countries. Likewise, Nicolini, Cude, and Chatterjee (2013)alsouse
a similar approach to collect data about only four countries and construct a financial literacy index
based on the number of correct answers to similar questions in different national surveys.
In this article we attempt to take advantage of a common measure of financial literacy for
students from different countries, as well as data about diverse initiatives retrieved by means of
the same data collection process. Thus, we can examine whether students receiving some form of
training about financial concepts are higher achievers in the financial literacy test. Furthermore,
given that the PISA dataset also provides additional information about how financial education is
implemented at each school, we also explore whether the results achieved by students in the financial
literacy test are affected by the type of teaching strategies adopted or the sector (public or private) of
those who are responsible for teaching financial education.
The rest of the paper is structured as follows. Section 2 summarizes the situation of financial
education in countries participating in the financial literacy test in PISA 2012. Section 3 provides
a description of the dataset and the variables considered in our empirical analysis. Section 4 explains
the methodology employed in our study and Section 5 reports the main results. Finally, Section 6
outlines our main conclusions.
2. Financial education in countries participating in the PISA 2012 financial literacy test
As pointed out previously, the awareness of the importance of financial education has led many
countries to develop an increasing number of national strategies for financial education. Such
strategies represent a systematic approach to reinforce the financial literacy of their citizens. They
THE SOCIAL SCIENCE JOURNAL 3
started mostly in developed economies such as the USA, Japan, the United Kingdom, Australia, New
Zealand, the Netherlands or Singapore. However, similar initiatives have spread to other countries
with varying economic, financial and socio-demographic contexts since the beginning of the
economic crisis (Grifoni & Messy, 2012).
One of the main challenges of such strategies is to include financial competences in primary and
secondary school education programs to improve financial awareness from early ages. For example,
several US states have adopted mandates to include financial education in the secondary school
curriculum, while Australia has had a financial education mandate since 2011. However, only a few
countries have so far established a well-developed framework for introducing financial competences
into their education systems. Given that the main focus of this research is to analyze the influence of
the provision of financial education on students’financial literacy results, we start by examining the
proportion of students attending financial education courses in each country. Figure 1 shows this
information based on the responses provided by the principals of schools participating in the PISA
survey. Although the average percentage of students attending financial education courses is
relatively high (70%), we observe that there is a considerable between-country variation, ranging
from percentages above 80% in Australia, Belgium, the USA or New Zealand to less than a half in
Slovenia, Poland, Croatia, Italy or Spain.
Table A1 in Appendix summarizes the different financial education configurations in the curri-
cular design of all the countries participating in the PISA 2012 financial literacy test. It also includes
information about some pilot programs implemented in a number of countries to incorporate
financial competences into the curriculum before they launch a national strategy. For instance, the
Spanish and Italian central banks and a number of ministries promoted several experimental
programs with the aim of incorporating financial education into school curricula. In contrast,
such programs were mainly implemented by private financial institutions in Colombia.
Based on the content of Table A1, as well as the information provided by school principals, we
can take a step further and explore how financial education is incorporated into the curriculum.
Firstly, note that financial education courses are not compulsory in most countries. Exceptions are
frequently represented by schools located in specific regions or states where financial education is
established as a compulsory subject (e.g. the USA). As a result, the proportion of schools that can be
included in this category is relatively small in most countries, as indicated in Figure 2.
2
Figure 1. Availability of financial education in schools by countries.
Source: Own computation from PISA 2012.
2
The main exception is the Czech Republic where financial education has been compulsory at upper secondary school level since
2009 and at lower secondary school since 2013.
4J.M. CORDERO ET AL.
With regard to how financial education courses are incorporated into the curriculum, the most
common option is the cross-curricular approach, i.e., financial concepts are included as a part of
other subjects such as mathematics, humanities or social sciences, whereas it is less common for
financial education to be taught as a separate subject (e.g., New Zealand). Notice again that financial
education might be included at different levels of the education system. Thus we have found that
there are several countries where financial education concepts are studied in primary education
(Latvia, the Czech Republic, Shanghai-China, Estonia or Australia), whereas they are taught during
compulsory secondary education in other education systems (the Flemish Community of Belgium,
the Slovak Republic, Israel, Italy or Poland).
Nevertheless, our empirical analysis focuses on the strategies implemented in lower secondary
schools since our data source is the information provided by school principals participating in PISA
as explained below. According to this information, summarized as country averages in Figures 3 and 4,
we can identify some countries where the cross-curricular approach is clearly the main alternative (e.g.,
the Slovak Republic, the Czech Republic or Estonia) and others were the preferred option is to teach
financial education as a separate subject (e.g., the USA or New Zealand). However, it is also common
practice to combine the two strategies (e.g., Shanghai-China, Colombia or the Russian Federation).
Figure 2. Financial education as a compulsory subject by countries.
Source: Own computation from PISA 2012.
Figure 3. Financial education using a cross-curricular approach by countries.
Source: Own computation from PISA 2012.
THE SOCIAL SCIENCE JOURNAL 5
3. Data and variables
PISA 2012 was the first assessment of the financial knowledge of 15-year-old students around the
world. This was an optional assessment for countries and economies. Eighteen countries and
economies participated in the assessment of financial literacy. They include 13 OECD countries
and economies—Australia, the Flemish Community of Belgium, the Czech Republic, Estonia,
France, Israel, Italy, New Zealand, Poland, the Slovak Republic, Slovenia, Spain and the USA—and
five partner countries and economies—Colombia, Croatia, Latvia, the Russian Federation and
Shanghai-China. Specifically, a total 29,041 students completed the assessment of financial literacy
in 2012, representing a student population of about nine million 15-year-olds in the 18 participating
countries and economies.
The students participating in the financial literacy assessment were recruited and assessed
separately from and in addition to the other pupils participating in the core PISA assessment
(35 per school). In particular, eight additional 15-year-old students were selected randomly from
students enrolled in each participating school to take the financial literacy assessment. The test
comprised four 30-min clusters of test materials which each student had a total of two hours to
complete. Each booklet included two clusters of financial literacy items (a total of 40 questions with
five different levels of difficulty) that they had to complete in 60 min. These questions cover different
contents (e.g. identify different ways to pay for items, calculate correct change, work out which of
two different-sized consumer items would be better value for money according to their needs and
circumstances, understand that money can be borrowed or lent and the reasons for paying or
receiving interest, identify which providers are trustworthy, etc.). There are a wide array of response
formats (open-constructed response, constructed response and multiple-choice), and respondents
are usually required to perform some simple mathematical calculations to answer questions. In
general terms, questions and answers are quite short and direct, thus they only require a basic
proficiency in reading literacy.
3
Moreover, each booklet also includes one cluster of mathematics test
items and one cluster of reading items very similar to the core assessment in PISA. Therefore, data
about three different domains (financial literacy, mathematics and reading) is available for this
smaller sample of students.
The financial literacy assessment includes three different dimensions: contents, processes and
contexts. The content categories comprise the areas of knowledge and understanding that are
essential for performing a particular financial task. They include money and transactions, planning
Figure 4. Financial education taught as a separate subject by countries.
Source: Own computation from PISA 2012.
3
Figure A1 in Appendix contains some examples of PISA questions included in the test.
6J.M. CORDERO ET AL.
and management of finances, risk and reward, and financial landscape. The process categories refer
to cognitive processes and describe students’ability to recognize and apply key concepts in the
domain, as well as to understand, evaluate and suggest solutions. Finally, the contexts represent the
situations in which financial knowledge, skills and understanding are applied. The focus may be on
the individual, family or peer group, the community or the school or even on a global scale.
Since there is a huge amount of test material to be covered, it is impossible to ask every pupil each
test question. Therefore, the pool of items is divided into blocks or clusters of items. As the test has
to be administered over a maximum of two hours
4
, students are randomly assigned to complete one
particular test booklet, each of which includes several blocks. Therefore, each student responds to
only a fraction of what constitutes the total assessment pool. As a result, the measurement error for
individual proficiency is substantial. One way of accounting for the uncertainty associated with the
estimates and obtaining unbiased group-level estimates is to use multiple values representing the
likely distribution of student proficiency. Several random draws from the estimated ability distribu-
tion are selected from the distribution of proficiency estimates of every student (Mislevy, Beaton,
Kaplan, & Sheehan, 1992; Wu, 2005).
5
Each draw is related to as a plausible value. These plausible
values can be interpreted as the range of abilities that a student might reasonably have (see OECD
(2014a) for details). Specifically, five plausible values representing financial literacy are reported for
each student. We use these five available measures of performance as our dependent variables.
6
The
PISA scores are presented on a scale with a mean of 500 and a standard deviation of 100. To aid
interpretation, the OECD states that one year of schooling is approximately equivalent to
a difference of 40 PISA test points (OECD, 2010, p. 110).
The PISA dataset also includes a wide range of variables on student background, learning
experiences and attitudes drawn from the student questionnaire. Besides, it provides data about
school resources and policies completed by school principals. In our application we have selected
several control variables at student level based on previous literature. Specifically, we select the
number of books at home and parents’educational level,
7
since several studies found that socio-
economic characteristics are the strongest predictors of financial literacy scores (e.g. Lusardi &
Lopez, 2016). In addition, we include a variable representing gender (female), since we are interested
in testing whether there is a gender gap among high school-aged students as demonstrated in some
previous studies (e.g. Chambers & Asarta, 2018; Erner, Goedde-Menke, & Oberste, 2016; Jang, Hahn,
& Park, 2014). We also incorporate age as a potential determinant of financial literacy since birth
date might be relevant in explaining educational attainment (Pedraja, Santín, & Simancas, 2015),
although some previous studies have concluded that this factor is not relevant in determining
student knowledge outcomes in personal finance (Hill & Asarta, 2018). Finally, one student-level
variable that we consider represents immigrant students. We want to explore whether there is a gap
between immigrant and native pupils as suggested by Gramaţki (2017). Another variable represents
preschool attendance since there is also evidence that there might be divergences in financial literacy
results caused by early childhood education (see Cordero & Pedraja, 2019). Likewise, we consider
several control variables frequently identified in the literature as relevant factors affecting student
performance such as attending a private school (e.g. Hospido, Villanueva, & Zamarro, 2015)or
4
This limitation on testing time is based on considerations with respect to reducing student burden, minimizing interruptions of
the school schedule, and other factors.
5
Proficiency estimates are determined by applying a complex item-response theory (IRT) model to the data (Rasch, 1980). This
model takes into account the difficulty of each test question (see Von Davier and Sinharay (2013) for further details).
6
PISA analysts recommend that the econometric analysis with plausible values should be conducted five times, once for each
relevant plausible variable value. The results should then be averaged and significance tests adjusting for variation between the
five sets of results, computed (see OECD, 2014a, p. 147).
7
These variables offer more detailed information about family background than the composite socio-economic status index
available in PISA. This notably reduces the variability of original variables through the application of principal component
analysis. Moreover, the use of the PISA socio-economic status index would not allow us to distinguish the separate contributions
of each parent to the intergenerational transmission of socio-economic status (see Jerrim & Micklewright, 2011 for details).
THE SOCIAL SCIENCE JOURNAL 7
a school located in a rural area (e.g. Ali, Anderson, McRae, & Ramsay, 2016), as well as the average
socio-economic status index (ESCS
8
) of students in the school as a proxy of the peer effect.
More importantly, principals from all the participating schools provide the same information
about financial education at the school in their responses to the school questionnaire. This is the
main focus of this empirical research. In particular, data include a specific question about whether or
not there is availability of financial education for 15 year-old students.
9
We can use this information
to construct our main variable of interest (FE is available).
10
Likewise, school principals also report
how financial education courses are taught, including whether they are compulsory for students,
whether they are taught as a separate subject or by means of a cross-curricular approach, i.e., as part
of other subjects, and who provides financial education (teachers or people from different private,
public or non-government organizations).
11
Since we are also interested in studying different ways of
implementing financial education courses at schools, we have defined several dummy variables
according to this information.
12
Moreover, we also take into account data collected through several questions about students’
experience with money matters included at the end of the financial literacy test booklets. The
questionnaire covered multiple aspects such as having access to financial products or their sources
of money, as it is widely assumed that students develop financial skills and habits, as well as
economic concepts, through their personal experiences and learning by doing (Otto, 2013;
Grohmann & Menkhoff, 2017; OECD, 2017a). Unfortunately, student responses to these questions
were incomplete,
13
thus these variables contained a substantial proportion of missing values. In
order to account for this important information, we have calculated the average variable values at
country level using data on students who answered these questions, assuming that their responses
can be considered representative of the whole country. Specifically, we retrieved three variables.
These variables represent the percentages of students holding a bank account, having money coming
from gifts from friends or relatives and receiving money from an allowance (without having to do
chores).
14
Moreover, we also collected additional information at country level about several vari-
ables, including the gross domestic product per capita, which has also been examined in previous
literature as a potential determinant of financial literacy (e.g. Chambers & Asarta, 2018; Jappelli,
2010; Jappelli & Padula, 2013), or some proxy variables representing the level of financial develop-
ment, such as the number of commercial bank branches or the total value of stocks traded. These
data were collected from the World Bank Indicators database for the year 2012.
Table 1 contains the definition of all the variables considered in our empirical analysis, and Table 2
shows the descriptive statistics of all the variables classified in four blocks: student-related variables,
school-related variables, financial education-related variables and covariates at country level.
8
This is an indicator of the economic, social and cultural status of students created by PISA analysts from three variables related to
family background from students’questionnaire: the highest educational level of either of the student’s parents, the highest
occupational status of either of the student’s parents and an index of educational possessions with respect to household
economy.
9
The exact question included in the school questionnaire is: Which of the statements below best describes the situation for students
in <national modal grade for 15-year-olds>regarding the availability of financial education in your school? (Please tick only one
box): (a) Financial education is not available; (b) Financial education has been available for less than two years; (c) Financial
education has been available or two years or more.
10
We collapsed information about responses (b) and (c) into a single option (availability of financial education), thus we can
construct a binary variable taking the value one if financial education was available and 0 if it was not.
11
The findings of several empirical studies suggest that financial education is positively related to students’financial literacy scores
when it is taught using a cross-curricular approach (e.g. Cordero & Pedraja, 2019; Moreno-Herrero, Salas-Velasco, & Sánchez-
Campillo, 2018).
12
The original information provided by school principals about whether financial education was taught as a separate or cross-
curricular subject refers to the number of hours per year, divided into five categories (not at all, 1–4, 5–19, 20–49 and more than
50). Nevertheless, we have defined only two dummy variables (FE taught separately and FE taught using a cross-curricular
approach), denoting that either teaching style is implemented if at least five hours are taught during the year.
13
This questionnaire was split into four parts or booklets. Each part was given to a quarter of the students. Consequently, not all
the students answered all the questions.
14
See OECD (2014b, pp. 99–109)OECD, 2014bOECD (2014b, pp. 99–109) for details.
8J.M. CORDERO ET AL.
Besides variable selection, we should note that the dataset needed to be manipulated for the purposes of
empirical analysis in order to avoid the usual problems derived from missing variable values. In our case,
we applied iterative multiple imputation by chained equations (Royston, 2009; Schafer, 1999). This
method uses all the variables available in the model to estimate unobserved data according to the
particular characteristics of each variable.
15
In addition to this procedure, we applied an additional
imputation approach to complete information about our core variable, the availability of financial
education courses, based on the responses that school principals gave to other related questions. We
enacted this procedure after detecting several cases where principals indicated that financial courses were
not available but then went on to answer other related questions indicating how financial education is
Table 1. Variable description.
Description
Dependent variable
PV1FLIT 1
st
plausible value (PV) for financial literacy
PV2FLIT 2
nd
plausible value (PV) for financial literacy
PV3FLIT 3
rd
plausible value (PV) for financial literacy
PV4FLIT 4
th
plausible value (PV) for financial literacy
PV5FLIT 5
th
plausible value (PV) for financial literacy
Covariates at student level
Female Dummy variable (DM) that takes value 1 if the student is a girl and 0 otherwise
Age Student age
Immigrant (first generation) DM: value 1 if the student was born in another country and 0 otherwise
Student did not receive pre-primary
education
DM: value 1 if the student has not received pre-primary education and 0 otherwise
Mother is university educated DM: value 1 if student’s mother has a university degree and 0 otherwise
Father is university educated DM: value 1 if student’s father has a university degree and 0 otherwise
Less than 25 books at home DM: value 1 if there are less than 25 books at home and 0 otherwise
More than 200 books at home DM: value 1 if there are more than 200 books at home and 0 otherwise
Covariates at school level
Private school DM: value 1 if the school is private and 0 if it is public
School in rural area DM: value 1 if the school is located in a village or small town and 0 otherwise
ESCS mean Average value of the ESCS index at school level
Specific variables related to financial education
FE is available DM: value 1 when FE is available at the school and 0 otherwise
FE is compulsory DM: value 1 when FE is compulsory at the school and 0 otherwise
FE taught separately DM: value 1 if FE is taught as a separate subject and 0 otherwise
FE taught using a cross-curricular
approach
DM: value 1 if FE is taught adopting a cross-curricular approach and 0 otherwise
FE taught by people from private
institutions
DM: value 1 if FE is provided by people from private sector institutions (e.g. banks,
insurance companies)
FE taught by people from public
institutions
DM: value 1 if FE is provided by people from public institutions (e.g. Ministry of Finance,
reserve bank)
FE taught by people from NGOs DM: value 1 if FE is provided by people from non-government organizations (NGOs)
Covariates at country level
Account Percentage of students holding a bank account
Gifts Percentage of students receiving money as gifts from friends or relatives
Allowance Percentage of students receiving money from an allowance without having to do
chores
GDPpc Gross domestic product per capita
Banks Number of commercial branches in the country
Stocks Total value of stocks traded (% GDP)
15
Multiple imputation has been demonstrated to be a better statistical option than other more simplistic techniques dealing with
missing values such as listwise deletion or replacement by the mean values (Manly & Wells, 2015; Van Ginkel, Van der Ark, &
Sijtsma, 2007). This method improves inference making, since it helps to provide more accurate estimations of the distribution
underlying the data. Note, however, that this procedure has some limitations. For instance, it might provide misleading results if
data are not randomly missing. Therefore, the model should be carefully constructed and include enough variables to avoid this
problem. In addition, the method is computationally intensive, since it requires running several algorithms repeatedly in order to
yield adequate results (Sterne et al., 2009). Nevertheless, there are many statistical packages that support this procedure. In our
case, we used the command mi impute in the Stata 14 software.
THE SOCIAL SCIENCE JOURNAL 9
provided at the school (e.g., how financial education courses are taught). For items where this contra-
diction was observed, we filled missing data using the responses given to related questions.
16
If we were
unable to complete missing values using this procedure, we followed a listwise deletion method. This led
to a slight reduction in the size of the original dataset from 29,041 to 27,788.
17
4. Methodology
As the data available in PISA are hierarchical (students nested into schools, schools nested into
countries), we adopt a multilevel (or hierarchical) regression approach (Gelman & Hill, 2006;
Goldstein, 1995). Using this model, we can avoid potential problems of estimation bias derived
from classic methods, such as OLS regression, because the values of the school variables of pupils
from the same school are correlated (Hox, 2010). In particular, we adopt a three-level approach in
order to account not only for divergences among schools, but also across countries.
Therefore, we assume that there are n
jk
students nested within each of j=1,…,J
k
schools, nested
in turn within each of k=1,…,Kcountries. At level 1, the outcome Y
ijk
for case iwithin level-2 unit
jand level-3 unit kis represented using the following expression:
Table 2. Descriptive statistics.
Mean SD Min. Max.
Dependent variable
PV1FLIT 491.94 101.22 2.71 921.03
PV2FLIT 491.82 101.47 5.79 873.48
PV3FLIT 491.32 101.40 17.07 860.80
PV4FLIT 491.52 101.69 31.61 850.55
PV5FLIT 491.63 101.63 11.10 879.08
Covariates at student level
Female 0.50 0.50 0.00 1.00
Age 15.78 0.29 15.25 16.33
Immigrant (first generation) 0.04 0.20 0.00 1.00
Student did not receive pre-primary education 0.07 0.25 0.00 1.00
Mother is university educated 0.39 0.49 0.00 1.00
Father is university educated 0.36 0.48 0.00 1.00
Less than 25 books at home 0.33 0.47 0.00 1.00
More than 200 books at home 0.19 0.40 0.00 1.00
Covariates at school level
Private school 0.05 0.21 0.00 1.00
School in rural area 0.25 0.43 0.00 1.00
ESCS mean −0.08 0.65 −3.55 1.88
Specific variables related to financial education
FE is available 0.67 0.47 0.00 1.00
FE is compulsory 0.30 0.46 0.00 1.00
FE taught separated 0.26 0.44 0.00 1.00
FE taught using a cross-curricular approach 0.35 0.48 0.00 1.00
FE taught by people from private institutions 0.12 0.32 0.00 1.00
FE taught by people from public institutions 0.05 0.22 0.00 1.00
FE taught by people from NGOs 0.10 0.30 0.00 1.00
Covariates at country level
Account 0.55 0.23 0.20 0.93
Gifts 0.83 0.09 0.61 0.93
Allowance 0.47 0.18 0.31 0.84
GDPpc 31,570.85 17,768.64 7,885.00 67,678.00
Banks 43.85 17.61 17.34 88.20
Stocks 44.97 54.20 0.46 264.50
16
Specifically, we assume that financial education is available at the school if the variables representing financial education being
taught as a separate or a cross-curricular subject had the value one (this means that at least five hours were taught during
the year).
17
Therefore, the original dataset was reduced by only 1,253 observations, which is equivalent to less than 5%.
10 J.M. CORDERO ET AL.
Yijk ¼π0jk þX
P
p¼1
πpjkapjk þeijk (1)
The π
pjk
are level-1 coefficients, with the corresponding level-1 predictors; e
ijk
is the level-1
random effect, with the assumption that eijk Nð0;σ2Þ. At level 2, the π
pjk
coefficients at level 1 are
treated as outcomes to be predicted. Thus, we have the following expression:
πpjk ¼βp0kþX
Qp
q¼1
βpqkXqjk þrpjk (2)
The β
pqk
are level-2 coefficients, the X
qjk
level-2 predictors and r
pjk
is the level-2 random effect.
Taken as a vector, the r’s are assumed to have a multivariate normal distribution with a mean vector
of 0 and a covariance matrix with maximum dimension (P+1)x(P+ 1). At level 3, the β
pqk
coefficients at level 2 are treated as outcomes to be predicted:
βpqk ¼βpq0þX
SPQ
s¼1
γpqsWsk þupqk :(3)
The γ
pqs
are level-3 coefficients, the W
sk
are level-2 predictors, and u
pqk
is the level-3 random
effect. Taken as a vector, the u’s are assumed to have a multivariate normal distribution with a mean
vector of 0 and a covariance matrix with maximum dimension PP
p¼0ðQPþ1ÞxPQ
p¼0ðQPþ1Þ.
Throughout the following empirical analysis, we make the appropriate adjustment to the esti-
mated standard errors (bootstrapping standard errors by cluster).
18
Likewise, we also applied the
sampling weights included in PISA to correct for non-response bias, while also scaling the sample up
to the size of the national population (see Rutkowski, González, Joncas, & von Davier, 2010 for
details).
5. Results
This section reports the main results of estimating the multilevel model explained above to examine
the determinants of test scores in financial literacy. To do this, we employed HLM 7 software
(Raudenbush, Bryk, Cheong, Congdon, & du Toit, 2011) to estimate the parameters using the five
available plausible values and correctly compute the average sampling variance (Willms & Smith,
2005). First of all, we calculated the intra-school correlation coefficients (ICC). ICC measures the
extent to which the financial literacy test scores (dependent variable) are more similar among
students from the same school than students randomly distributed across all schools for each
country. As an initial step, we calculated the ICC for each country from a null model without any
regressors and then we also calculated the ICC from a model including the covariates at student and
school levels (full model).
19
These values are reported in the first and second columns of Table 3,
respectively, for the 18 analyzed countries. The results reveal that there is a sizeable variation in test
scores across schools in most countries, suggesting that schools can make a difference in enhancing
their students’financial literacy performance. In most countries, however, this variation is notably
smaller when we incorporate all the student- and school-level covariates. This suggests that these
variables also play a major role in explaining the financial literacy results.
Subsequently, we estimated regressions including all the student- and school-level covariates as
explanatory variables and adding also the variable representing the availability of financial education at
18
Estimates are bootstrapped by cluster (schools) using 50 replications to calculate approximate standard errors (see OECD, 2013
for details).
19
The variability of the random intercepts in a multilevel logistic model can be viewed as between-school variability that is due to
unexplained differences between schools. Therefore, the inclusion of additional explanatory variables should explain some of this
variability and thus reduce the level of unexplained between-school variability.
THE SOCIAL SCIENCE JOURNAL 11
the school. This variable constitutes our main focus of interest, since we want to explore its relationship
with student financial literacy. The results are reported in Table 4, which shows estimations with and
without country fixed effects (Models a and b, respectively). Different models are estimated depending
on how FE is provided (Models 1 include the FE is available variable, while Models 2 include the FE is
compulsory variable). Looking at the fixed-effect results
20
(Models 1b and 2b), we find that there is
a positive and significant relationship between this variable and students’financial literacy, although
the value of the parameter is very small (only 3 points on a scale with 100 points of standard deviation)
compared with parameters estimated for other covariates. In particular, this variable is much less
influential than peer socioeconomic level (50 points), which is equivalent to more than one year of
schooling. Therefore, the mere provision of financial education at school does not to appear to be able
to be regarded as a key factor for explaining divergences in the student financial knowledge. The same
applies to compulsory financial education, as none of the estimated parameters associated with this
variable are significant. This result contradicts previous evidence about mandated financial education
courses in the specific context of the USA (Tennyson & Nguyen, 2001).
Other parameters in the estimation are also noteworthy. For instance, we notice that the majority
of individual variables are significantly associated with the dependent variable. Specifically, financial
literacy test scores are clearly better for boys, older students, native pupils and students who attended
pre-primary school. Likewise, we find a strong relationship with the number of books at home. With
regard to the other school-level covariates, the findings suggest that there are no significant
divergences between private and public schools or between rural and urban schools.
Next, we tested whether implementing different strategies to teach financial education concepts
(Table 5), i.e. as a separate subject or using a cross-curricular approach, might influence financial
literacy results. However, we did not find any statistically significant relationship for either of these
variables in alternative Models 3a and 3b (without and with country fixed effects). Looking at who
teaches financial education, however, the estimated coefficients shown in Table 5 (Models 4a and 4b)
reveal that courses delivered by people from private institutions and non-governmental organizations
have a slight positive influence on results as opposed to training conducted by school teachers. Our
interpretation of this result is that specialists place more emphasis on the specific financial concepts
included in the PISA assessments than teachers who are, in many cases, not knowledgeable enough to
teach financial topics (Otter, 2010).
Table 3. Intra-school correlation coefficients by countries.
Null model Full model
Australia 0.2552 0.1615
Belgium 0.4390 0.2769
Colombia 0.3335 0.1684
Czech Republic 0.5069 0.2910
Spain 0.1762 0.1186
Estonia 0.1840 0.1153
France 0.5191 0.3130
Croatia 0.3695 0.2265
Israel 0.4482 0.2286
Italy 0.4775 0.3647
Latvia 0.2415 0.1514
New Zealand 0.2435 0.0419
Poland 0.2377 0.1304
Shanghai-China 0.4263 0.2634
Russian Federation 0.3196 0.2010
Slovak Republic 0.5427 0.3812
Slovenia 0.5865 0.4292
United States of America 0.2448 0.0887
20
We focus on the estimation of the fixed-effects model, since, if we disregard the nesting of observations within countries, we
would be ignoring the fact that individuals within the same country share unobserved characteristics (see Bryan & Jenkins, 2013
for details).
12 J.M. CORDERO ET AL.
The final step of our empirical analysis was to include several country-level variables in a three-level
hierarchical model. Table 6 reports these estimates, including at first only variables representing
students’experience with money matters (Model 5) and, subsequently, also adding economic indicators
(Model 6). For reasons of space, we do not report the estimated parameters for the student- and school-
level covariates because they were very similar to the values shown in Tables 4 and 5.
Of all these variables, the most remarkable result is for students receiving money from an allowance.
This has a major positive and significant influence on financial literacy test scores, even higher than the
aforementioned important role of schoolmates. This result contradicts previous evidence about the
influence of this strategy on saving behaviors (see Bucciol & Veronesi, 2014;Kim&Chatterjee,2013).
The other variables were not found to have any significant relationship to 15 year-old students’financial
knowledge. The fact that GDP is not statistically related is consistent with some previous evidence
existing in the literature (e.g. Chambers & Asarta, 2018). Note, however, that the small number of
countries available in our dataset (18) could constitute a potential explanation for this country-level
variable not being significant. In this respect, some authors suggest that at least 25 countries are required
in order to derive reliable estimates in three-level models (Bryan & Jenkins, 2013; Stegmueller, 2013).
6. Concluding remarks
This paper provides empirical evidence about the influence of providing financial education at schools
as a mechanism for improving young students’knowledge of financial issues. For this purpose, we
exploited the information provided by the financial literacy test conducted by students from 18
countries participating in PISA 2012. This was the first initiative that offered such comparable data
Table 4. HLM estimates of factors related to financial literacy test scores (I).
Model 1a Model 1b Model 2a Model 2b
Female −8.446*** −7.893*** −8.406*** −7.865***
(1.147) (1.094) (1.155) (1.101)
Age 14.81*** 17.53*** 14.69*** 17.43***
(2.024) (1.959) (2.035) (1.967)
Immigrant (first generation) −15.04*** −14.05*** −15.36*** −14.46***
(3.026) (3.027) (3.053) (3.049)
Student did not receive pre-primary education −22.06*** −19.49*** −22.35*** −19.53***
(2.431) (2.374) (2.435) (2.374)
Mother is university educated −1.728 −0.163 −1.708 −0.0648
(1.453) (1.387) (1.476) (1.405)
Father is university educated −0.268 −0.626 −0.243 −0.531
(1.439) (1.435) (1.447) (1.441)
Less than 25 books at home −36.53*** −34.34*** −36.61*** −34.47***
(1.411) (1.358) (1.428) (1.374)
More than 200 books at home 21.07*** 21.26*** 21.03*** 21.23***
(1.558) (1.486) (1.560) (1.490)
Private school 12.59*** 4.654 12.91*** 4.826
(3.090) (2.825) (3.108) (2.837)
School in rural area −4.833*** −1.406 −5.029*** −1.679
(1.407) (1.322) (1.416) (1.333)
ESCS mean 51.86*** 50.04*** 51.68*** 49.97***
(1.063) (1.188) (1.070) (1.202)
FE is available 9.932*** 3.059** 9.809*** 3.221**
(1.339) (1.283) (1.470) (1.408)
FE is compulsory −1.127 1.164
(1.465) (1.391)
Constant 264.1*** 238.8*** 266.3*** 240.6***
(32.10) (32.39) (32.26) (32.50)
Observations 27,788 27,788 27,788 27,788
Country FE NO YES NO YES
Standard errors in parentheses.
***p < 0.01.
**p < 0.05.
THE SOCIAL SCIENCE JOURNAL 13
in an international framework together with a rich dataset about the organization of financial
education at schools.
Our empirical findings suggest that the availability of financial education is positively and
significantly related to students’financial literacy test achievement. This result is robust to the
consideration of country fixed effects, although the influence is clearly smaller when we account
for the potential presence of significant differences among countries. This indicates that, as pointed
out by Nicolini et al. (2013), there are national and cultural differences that policymakers should
consider when developing financial literacy assessment tools for their respective countries.
Nevertheless, we should underscore that the provision of training on financial issues at school
cannot be considered as a differential factor for predicting financial literacy results. The magnitude
of the influence of this variable is modest when compared to other family background or school
factors (especially the socio-economic composition). In fact, these variables appear to have a more
relevant influence on explaining divergences in student performance. A potential explanation for
financial education taught as part of the school curriculum not having a bigger impact in most
Table 5. HLM estimates of factors related to financial literacy test scores (II).
Model 3a Model 3b Model 4a Model 4b
Female −8.403*** −7.855*** −8.502*** −7.901***
(1.155) (1.102) (1.155) (1.102)
Age 14.73*** 17.43*** 14.57*** 17.33***
(2.036) (1.967) (2.037) (1.969)
Immigrant (first generation) −15.36*** −14.43*** −15.66*** −14.45***
(3.054) (3.055) (3.051) (3.051)
Student did not receive pre-primary education −22.36*** −19.54*** −22.16*** −19.59***
(2.434) (2.373) (2.432) (2.372)
Mother is university educated −1.697 −0.0654 −1.669 −0.0523
(1.475) (1.406) (1.474) (1.406)
Father is university educated −0.203 −0.534 −0.0396 −0.590
(1.448) (1.441) (1.449) (1.444)
Less than 25 books at home −36.58*** −34.46*** −36.58*** −34.46***
(1.430) (1.375) (1.428) (1.373)
More than 200 books at home 21.01*** 21.24*** 21.05*** 21.24***
(1.559) (1.489) (1.559) (1.489)
Private school 13.00*** 4.794 13.01*** 4.925
(3.119) (2.840) (3.127) (2.841)
School in rural area −5.089*** −1.658 −5.146*** −1.578
(1.422) (1.334) (1.423) (1.336)
ESCS mean 51.65*** 49.95*** 51.25*** 49.91***
(1.071) (1.203) (1.070) (1.202)
FE is available 10.27*** 3.324** 8.863*** 1.813
(1.626) (1.228) (1.667) (1.550)
FE is compulsory −0.858 1.171 −0.886 1.122
(1.490) (1.420) (1.492) (1.420)
FE taught separately −1.306 0.431 −1.903 −0.116
(1.576) (1.496) (1.588) (1.500)
FE taught using a cross-curricular approach −0.159 −0.885 −1.020 −1.382
(1.361) (1.353) (1.370) (1.354)
FE taught by people from private institutions 10.15*** 3.292
(2.091) (1.989)
FE taught by people from public institutions −5.136 −2.306
(3.068) (2.842)
FE taught by people from NGOs 2.876 5.389**
(2.352) (2.162)
Constant 265.7*** 240.8*** 268.4*** 242.3***
(32.27) (32.48) (32.29) (32.51)
Observations 27,788 27,788 27,788 27,788
Country FE NO YES NO YES
Standard errors in parentheses.
***p < 0.01.
**p < 0.05.
14 J.M. CORDERO ET AL.
countries could be the time (or distance) until students get to apply the concepts in practice. This
would lead to the knowledge acquired being diluted over time (McDermott, 2014).
Regarding the strategies for implementing financial education programs, our results suggest that
there are not significant differences in financial literacy results among schools using a cross-
curricular approach or teaching financial education as a separate subject. However, we find that
students taught by specialists from private institutions and non-governmental organizations achieve
better results than students who receive training provided by the teachers of their school. While
worrisome, this is not surprising bearing in mind that most countries that introduced financial
education in the years leading up to the first PISA assessment (2012) neither required nor promoted
teacher training in the field. Since then, guidance for teachers on how to develop and implement
financial literacy programs has become a key issue with a view to enhancing the effectiveness of
financial education under the premise that this type of action should have a decisive influence on
student achievement (Totenhagen et al., 2015). In fact, some previous studies have identified some
successful financial training initiatives for teachers in different countries (e.g. Koh, 2016;O’Neill &
Hensley, 2016; Swinton, DeBerry, Scafidi, & Woodard, 2007).
Despite these interesting results, there remain some issues that require further research, such as
exploring different types of professional development strategies for teachers for implementation (see
Compen, De Witte, & Schelfhout, 2018), examining the different effects of financial education
courses depending on whether they are taught during primary or secondary education or consider-
ing potential displacements caused by the incorporation of financial education into the school
curriculum. Unfortunately, the PISA dataset does not include enough reliable information about
Table 6. HLM estimates including country-level variables.
Model 5 Model 6
Student-level covariates X X
School-level covariates X X
FE is available 3.107** 3.202
(1.510) (1.573)
FE is compulsory 1.108 0.741
(1.420) (1.494)
FE taught separately −0.0915 0.248
(1.500) (1.578)
FE taught using a cross-curricular approach −1.408 −1.687
(1.354) (1.517)
FE taught by people from private institutions 3.294 2.837
(1.990) (2.189)
FE taught by people from public institutions −2.288 −1.094
(2.842) (3.117)
FE taught by people from NGOs 5.383** 6.991***
(2.162) (2.389)
Account 27.96 −19.97
(37.27) (32.48)
Gifts 48.95 37.65
(103.9) (32.92)
Allowance 102.3** 131.2**
(47.74) (64.56)
GDPpc 0.000552
(0.000547)
Banks 0.0206
(0.307)
Stocks −0.134
(0.108)
Constant 129.2 109.2
(81.22) (61.16)
Observations 27,788 27,788
Standard errors in parentheses.
***p < 0.01.
**p < 0.05.
THE SOCIAL SCIENCE JOURNAL 15
these issues, although the growing development of initiatives and pilot programs involving financial
education in many countries should allow researchers to make significant progress in gathering
empirical evidence about these issues in the near future.
Acknowledgments
The authors would like to express their gratitude to the Savings Banks Foundation (Fundación de las Cajas de
Ahorros –FUNCAS-) and the Spanish Ministry for Economy and Competitiveness for supporting this research
through grant ECO2017-83759-P.
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