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ABSTRACT
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 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’ 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.
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.
Supplementary data
Supplemental data for this article can be accessed on the publisher’s website
Further reading
OECD (2017b) PISA 2012 financial literacy questions and answers, Paris: OECD Publishing.
Notes
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.
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.
3 Figure A1 in Appendix contains some examples of PISA questions included in the test.
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 (Citation2013) 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, Citation2011 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 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, Citation2019; Moreno-Herrero, Salas-Velasco, & Sánchez-Campillo, Citation2018).
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.
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, Citation2007). 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., Citation2009). Nevertheless, there are many statistical packages that support this procedure. In our case, we used the command mi impute in the Stata 14 software.
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%.
18 Estimates are bootstrapped by cluster (schools) using 50 replications to calculate approximate standard errors (see OECD, Citation2013 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.
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, Citation2013 for details).