Financial education and student financial literacy: A cross-country analysis using PISA 2012 data

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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 , Francisco Pedraja-Chaparro a a b

Universidad de Extremadura, Av. Elvas sn, 06006 Badajoz, Spain Universidad Autónoma de Madrid, C/F. Tomás y Valiente 5, 28049 Madrid, Spain

a r t i c l e

i n f o

Article history: Received 5 February 2019 Received in revised form 18 July 2019 Accepted 18 July 2019 Available online xxx Keywords: Education policy Cross-country analysis Financial literacy Financial education training Multilevel regressions

a b s t r a c t The aim of this research is to explore whether teaching basic financial concepts at schools helps to improve students’ ability to apply the knowledge and skills that they learn to reallife 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’ financial 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 institutions and non-governmental organizations achieve better results than others receiving financial education training from their teachers. © 2019 Western Social Science Association. Published by Elsevier Inc. All rights reserved.

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

∗ Corresponding author. E-mail address: [email protected] (J.M. Cordero).

& 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 this knowledge in order to make

https://doi.org/10.1016/j.soscij.2019.07.011 0362-3319/© 2019 Western Social Science Association. Published by Elsevier Inc. All rights reserved.

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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 (BucherKoenen, 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).

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.

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 comparable 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, 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

Please cite this article in press as: Cordero, J. M., et al. Financial education and student financial literacy: A cross-country analysis using PISA 2012 data. The Social Science Journal (2019), https://doi.org/10.1016/j.soscij.2019.07.011

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between its implementation and educational 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 international differences in literacy across countries. Likewise, Nicolini, Cude, and Chatterjee (2013) also use 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 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

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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. Fig. 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 curricular 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 Fig. 2.2 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

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.

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Fig. 1. Availability of financial education in schools by countries. Source: Own computation from PISA 2012.

Fig. 2. Financial education as a compulsory subject by countries. Source: Own computation from PISA 2012.

Fig. 3. Financial education using a cross-curricular approach by countries. Source: Own computation from PISA 2012.

Figs. 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). 3. Data and variables PISA 2012 was the first assessment of the financial knowledge of 15-year-old students around the world. This

Please cite this article in press as: Cordero, J. M., et al. Financial education and student financial literacy: A cross-country analysis using PISA 2012 data. The Social Science Journal (2019), https://doi.org/10.1016/j.soscij.2019.07.011

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Fig. 4. Financial education taught as a separate subject by countries. Source: Own computation from PISA 2012.

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 differentsized 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

3 Figure A1 in Appendix contains some examples of PISA questions included in the test.

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 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 hours4 , 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 distribution 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

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 itemresponse theory (IRT) model to the data (Rasch, 1980). This model takes

Please cite this article in press as: Cordero, J. M., et al. Financial education and student financial literacy: A cross-country analysis using PISA 2012 data. The Social Science Journal (2019), https://doi.org/10.1016/j.soscij.2019.07.011

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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 socioeconomic 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 Gramat¸ki (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 a school located in a rural area (e.g. Ali, Anderson, McRae, & Ramsay, 2016), as well as the average socio-economic status index (ESCS8 ) of students in the school as a proxy of the peer effect.

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). 8 This is an indicator of the economic, social and cultural status of students created by PISA analysts from three variables related to family

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 hold-

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 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 crosscurricular 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.

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Table 1 Variable description. Description Dependent variable PV1FLIT PV2FLIT PV3FLIT PV4FLIT PV5FLIT Covariates at student level Female Age Immigrant (first generation) Student did not receive pre-primary education Mother is university educated Father is university educated Less than 25 books at home More than 200 books at home Covariates at school level Private school School in rural area ESCS mean Specific variables related to financial education FE is available FE is compulsory FE taught separately FE taught using a cross-curricular approach FE taught by people from private institutions FE taught by people from public institutions FE taught by people from NGOs Covariates at country level Account Gifts Allowance GDPpc Banks Stocks

1st plausible value (PV) for financial literacy 2nd plausible value (PV) for financial literacy 3rd plausible value (PV) for financial literacy 4th plausible value (PV) for financial literacy 5th plausible value (PV) for financial literacy Dummy variable (DM) that takes value 1 if the student is a girl and 0 otherwise Student age DM: value 1 if the student was born in another country and 0 otherwise DM: value 1 if the student has not received pre-primary education and 0 otherwise DM: value 1 if student’s mother has a university degree and 0 otherwise DM: value 1 if student’s father has a university degree and 0 otherwise DM: value 1 if there are less than 25 books at home and 0 otherwise DM: value 1 if there are more than 200 books at home and 0 otherwise DM: value 1 if the school is private and 0 if it is public DM: value 1 if the school is located in a village or small town and 0 otherwise Average value of the ESCS index at school level DM: value 1 when FE is available at the school and 0 otherwise DM: value 1 when FE is compulsory at the school and 0 otherwise DM: value 1 if FE is taught as a separate subject and 0 otherwise DM: value 1 if FE is taught adopting a cross-curricular approach and 0 otherwise DM: value 1 if FE is provided by people from private sector institutions (e.g. banks, insurance companies) DM: value 1 if FE is provided by people from public institutions (e.g. Ministry of Finance, reserve bank) DM: value 1 if FE is provided by people from non-government organizations (NGOs) Percentage of students holding a bank account Percentage of students receiving money as gifts from friends or relatives Percentage of students receiving money from an allowance without having to do chores Gross domestic product per capita Number of commercial branches in the country Total value of stocks traded (% GDP)

ing 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 variables, 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 development, 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. 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

14 See OECD (2014b, pp. 99–109)OECD, 2014bOECD (2014b, pp. 99–109) for details.

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 finan-

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.

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8 Table 2 Descriptive statistics.

Dependent variable PV1FLIT PV2FLIT PV3FLIT PV4FLIT PV5FLIT Covariates at student level Female Age Immigrant (first generation) Student did not receive pre-primary education Mother is university educated Father is university educated Less than 25 books at home More than 200 books at home Covariates at school level Private school School in rural area ESCS mean Specific variables related to financial education FE is available FE is compulsory FE taught separated FE taught using a cross-curricular approach FE taught by people from private institutions FE taught by people from public institutions FE taught by people from NGOs Covariates at country level Account Gifts Allowance GDPpc Banks Stocks

Mean

SD

Min.

Max.

491.94 491.82 491.32 491.52 491.63

101.22 101.47 101.40 101.69 101.63

2.71 5.79 17.07 31.61 11.10

921.03 873.48 860.80 850.55 879.08

0.50 15.78 0.04 0.07 0.39 0.36 0.33 0.19

0.50 0.29 0.20 0.25 0.49 0.48 0.47 0.40

0.00 15.25 0.00 0.00 0.00 0.00 0.00 0.00

1.00 16.33 1.00 1.00 1.00 1.00 1.00 1.00

0.05 0.25 −0.08

0.21 0.43 0.65

0.00 0.00 −3.55

1.00 1.00 1.88

0.67 0.30 0.26 0.35 0.12 0.05 0.10

0.47 0.46 0.44 0.48 0.32 0.22 0.30

0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.00 1.00 1.00 1.00 1.00 1.00 1.00

0.55 0.83 0.47 31,570.85 43.85 44.97

0.23 0.09 0.18 17,768.64 17.61 54.20

0.20 0.61 0.31 7,885.00 17.34 0.46

0.93 0.93 0.84 67,678.00 88.20 264.50

cial education is provided at the school (e.g., how financial education courses are taught). For items where this contradiction 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.

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%.

Table 3 Intra-school correlation coefficients by countries.

Australia Belgium Colombia Czech Republic Spain Estonia France Croatia Israel Italy Latvia New Zealand Poland Shanghai-China Russian Federation Slovak Republic Slovenia United States of America

Null model

Full model

0.2552 0.4390 0.3335 0.5069 0.1762 0.1840 0.5191 0.3695 0.4482 0.4775 0.2415 0.2435 0.2377 0.4263 0.3196 0.5427 0.5865 0.2448

0.1615 0.2769 0.1684 0.2910 0.1186 0.1153 0.3130 0.2265 0.2286 0.3647 0.1514 0.0419 0.1304 0.2634 0.2010 0.3812 0.4292 0.0887

Therefore, we assume that there are njk students nested within each of j = 1,. . ., Jk schools, nested in turn within each of k = 1,. . ., K countries. At level 1, the outcome Yijk for case i within level-2 unit j and level-3 unit k is represented using the following expression: Yijk = 0jk +

P 

pjk apjk + eijk

(1)

p=1

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Table 4 HLM estimates of factors related to financial literacy test scores (I).

Female Age Immigrant (first generation) Student did not receive pre-primary education Mother is university educated Father is university educated Less than 25 books at home More than 200 books at home Private school School in rural area ESCS mean FE is available

Model 1a

Model 1b

Model 2a

Model 2b

−8.446*** (1.147) 14.81*** (2.024) −15.04*** (3.026) −22.06*** (2.431) −1.728 (1.453) −0.268 (1.439) −36.53*** (1.411) 21.07*** (1.558) 12.59*** (3.090) −4.833*** (1.407) 51.86*** (1.063) 9.932*** (1.339)

−7.893*** (1.094) 17.53*** (1.959) −14.05*** (3.027) −19.49*** (2.374) −0.163 (1.387) −0.626 (1.435) −34.34*** (1.358) 21.26*** (1.486) 4.654 (2.825) −1.406 (1.322) 50.04*** (1.188) 3.059** (1.283)

264.1*** (32.10) 27,788 NO

238.8*** (32.39) 27,788 YES

−8.406*** (1.155) 14.69*** (2.035) −15.36*** (3.053) −22.35*** (2.435) −1.708 (1.476) −0.243 (1.447) −36.61*** (1.428) 21.03*** (1.560) 12.91*** (3.108) −5.029*** (1.416) 51.68*** (1.070) 9.809*** (1.470) −1.127 (1.465) 266.3*** (32.26) 27,788 NO

−7.865*** (1.101) 17.43*** (1.967) −14.46*** (3.049) −19.53*** (2.374) −0.0648 (1.405) −0.531 (1.441) −34.47*** (1.374) 21.23*** (1.490) 4.826 (2.837) −1.679 (1.333) 49.97*** (1.202) 3.221** (1.408) 1.164 (1.391) 240.6*** (32.50) 27,788 YES

FE is compulsory Constant Observations Country FE Standard errors in parentheses. *** p < 0.01. ** p < 0.05.

The ␲pjk are level-1 coefficients, with the corresponding level-1 predictors; eijk is the level-1 random effect, with the assumption that eijk ≈ N(0,  2 ). At level 2, the ␲pjk coefficients at level 1 are treated as outcomes to be predicted. Thus, we have the following expression: pjk = ˇp0k +

Qp 

ˇpqk Xqjk + rpjk

(2)

q=1

The ␤pqk are level-2 coefficients, the Xqjk level-2 predictors and rpjk 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 ␤pqk coefficients at level 2 are treated as outcomes to be predicted: ˇpqk = ˇpq0 +

SPQ 

pqs Wsk + upqk .

(3)

s=1

The ␥pqs are level-3 coefficients, the Wsk are level-2 predictors, and upqk 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 P covariance matrix with maximum dimension (QP + p=0

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

Q

(QP + 1). p=0 Throughout the following empirical analysis, we make the appropriate adjustment to the estimated standard 1)x

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).

18 Estimates are bootstrapped by cluster (schools) using 50 replications to calculate approximate standard errors (see OECD, 2013 for details).

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Table 5 HLM estimates of factors related to financial literacy test scores (II).

Female Age Immigrant (first generation) Student did not receive pre-primary education Mother is university educated Father is university educated Less than 25 books at home More than 200 books at home Private school School in rural area ESCS mean FE is available FE is compulsory FE taught separately FE taught using a cross-curricular approach

Model 3a

Model 3b

Model 4a

Model 4b

−8.403*** (1.155) 14.73*** (2.036) −15.36*** (3.054) −22.36*** (2.434) −1.697 (1.475) −0.203 (1.448) −36.58*** (1.430) 21.01*** (1.559) 13.00*** (3.119) −5.089*** (1.422) 51.65*** (1.071) 10.27*** (1.626) −0.858 (1.490) −1.306 (1.576) −0.159 (1.361)

−7.855*** (1.102) 17.43*** (1.967) −14.43*** (3.055) −19.54*** (2.373) −0.0654 (1.406) −0.534 (1.441) −34.46*** (1.375) 21.24*** (1.489) 4.794 (2.840) −1.658 (1.334) 49.95*** (1.203) 3.324** (1.228) 1.171 (1.420) 0.431 (1.496) −0.885 (1.353)

265.7*** (32.27) 27,788 NO

240.8*** (32.48) 27,788 YES

−8.502*** (1.155) 14.57*** (2.037) −15.66*** (3.051) −22.16*** (2.432) −1.669 (1.474) −0.0396 (1.449) −36.58*** (1.428) 21.05*** (1.559) 13.01*** (3.127) −5.146*** (1.423) 51.25*** (1.070) 8.863*** (1.667) −0.886 (1.492) −1.903 (1.588) −1.020 (1.370) 10.15*** (2.091) −5.136 (3.068) 2.876 (2.352) 268.4*** (32.29) 27,788 NO

−7.901*** (1.102) 17.33*** (1.969) −14.45*** (3.051) −19.59*** (2.372) −0.0523 (1.406) −0.590 (1.444) −34.46*** (1.373) 21.24*** (1.489) 4.925 (2.841) −1.578 (1.336) 49.91*** (1.202) 1.813 (1.550) 1.122 (1.420) −0.116 (1.500) −1.382 (1.354) 3.292 (1.989) −2.306 (2.842) 5.389** (2.162) 242.3*** (32.51) 27,788 YES

FE taught by people from private institutions FE taught by people from public institutions FE taught by people from NGOs Constant Observations Country FE Standard errors in parentheses. *** p < 0.01. ** p < 0.05.

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

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.

availability of financial education at 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 results20 (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

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).

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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). 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

11

Table 6 HLM estimates including country-level variables.

Student-level covariates School-level covariates FE is available FE is compulsory FE taught separately FE taught using a cross-curricular approach FE taught by people from private institutions FE taught by people from public institutions FE taught by people from NGOs Account Gifts Allowance

Model 5

Model 6

X X 3.107** (1.510) 1.108 (1.420) −0.0915 (1.500) −1.408

X X 3.202 (1.573) 0.741 (1.494) 0.248 (1.578) −1.687

(1.354) 3.294

(1.517) 2.837

(1.990) −2.288

(2.189) −1.094

(2.842) 5.383** (2.162) 27.96 (37.27) 48.95 (103.9) 102.3** (47.74)

(3.117) 6.991*** (2.389) −19.97 (32.48) 37.65 (32.92) 131.2** (64.56) 0.000552 (0.000547) 0.0206 (0.307) −0.134 (0.108) 109.2 (61.16) 27,788

GDPpc Banks Stocks Constant Observations

129.2 (81.22) 27,788

Standard errors in parentheses. *** p < 0.01. ** p < 0.05.

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 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 finan-

Please cite this article in press as: Cordero, J. M., et al. Financial education and student financial literacy: A cross-country analysis using PISA 2012 data. The Social Science Journal (2019), https://doi.org/10.1016/j.soscij.2019.07.011

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cial 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 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 considering potential displacements caused by the incorporation of financial education into the school curriculum. Unfortunately, the PISA dataset does not include enough reliable information about 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.

Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/ j.soscij.2019.07.011.

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Further reading OECD (2017b) PISA 2012 financial literacy questions and answers, Paris: OECD Publishing.

Please cite this article in press as: Cordero, J. M., et al. Financial education and student financial literacy: A cross-country analysis using PISA 2012 data. The Social Science Journal (2019), https://doi.org/10.1016/j.soscij.2019.07.011