An analysis of the OECD Programme for International Student Assessment on financial literacy
Date
2016
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Publisher
University of Delaware
Abstract
This study examines the financial knowledge of high school-aged students around the world using the OECD Programme for International Student Assessment (PISA) on Financial Literacy. The PISA Financial Literacy Assessment from 2012 marked the first internationally comparative assessment of the financial knowledge of high school-aged students. Multilevel modeling is used to examine whether or not a gender gap in financial knowledge is present, as well as the role that parents and countries may play in a student’s financial knowledge.
The possible gender gap in financial knowledge is first examined. Results indicate that a gender gap may or may not be present within the sample of students. Depending on the subsample used, either a traditional gender gap emerges, whereby male students possess more financial knowledge than female students, or no difference between male and female students is present. The traditional gender gap is present when examining parental characteristics, while examining country-level variables shows no gender gap in financial knowledge.
Characteristics of students’ parents are also examined to see what role parents may have in their child’s understanding of financial matters. I find that several parental characteristics are associated with a student’s financial knowledge. Both the mother’s and father’s highest levels of schooling, the mother’s employment status, discussing money matters with parents on a regular basis, and having a mother live in the student’s household are all correlated with a student’s financial knowledge. There is little evidence, however, that parental characteristics contribute to the gender gap in financial knowledge.
Given that the PISA 2012 Financial Literacy Assessment is internationally representative, country-level variables are also examined to determine if there exists a significant correlation between a student’s home country and his or her financial knowledge. However, after examining variables such as GDP per capita, the labor force participation rate, and the unemployment rate, I find no evidence of this type of correlation.
Multilevel modeling, or hierarchical linear modeling (HLM), is used to examine the data. To justify the use of multilevel modeling, a methodological comparison is undertaken to determine the best statistical approach for examining the data. Multilevel modeling results are compared to linear regression results across the sample of students. Comparisons of the two methodological approaches indicate that for the PISA 2012 data, multilevel modeling is best suited for the nested structure of the data.