![Sample our Social Sciences journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/110822/TOC-Subject-Sample-2021-Social-Sciences.jpg"})
Abstract
Understanding why women are less financially literate than men is crucial for developing effective policies that decrease gender inequalities and improve women’s financial literacy, agency, and empowerment. Accordingly, this article adopts a multidimensional approach to measuring financial literacy in developing countries, aggregating three key components of financial literacy, namely financial behavior, financial attitude, and financial knowledge. Using data from Argentina, Chile, and Paraguay, the study finds that there are statistically significant gender differences in these countries, which is confirmed, except in the case of Chile, by an extensive econometric analysis. In turn, a traditional Oaxaca–Blinder decomposition indicates, when considering the three countries as a whole, that 56 percent of the gap can be attributed to unexplained factors, while 44 percent to differences in observable characteristics, implying that men’s rates of return on human capital components, in a broad sense, are significantly different from those experienced by women.
HIGHLIGHTS
A comprehensive approach to financial literacy in Argentina, Chile, and Paraguay assesses gender differences in financial behavior, financial attitude, and financial knowledge.
Financial literacy is relatively low across all three countries.
In Argentina and Paraguay, the gender gap in financial literacy is driven by financial knowledge.
Education and income are the largest contributors to the variance in financial literacy.
ACKNOWLEDGMENTS
We would like to thank Diana Margarita Mejía Anzola for her useful comments and clarifications on the data set used in this paper. We would also like to thank Virginia Queijo Von Heideken, Verónica Frisancho, Libertad González, David Cuberes, Florencia Caro Sachetti, and the participants in the webinars of the project “Boosting growth and economic development by reducing gender gaps in Southern Cone countries,” as well as three anonymous reviewers, for their helpful comments and suggestions.
SUPPLEMENTAL DATA
Supplemental data for this article can be accessed online at https://doi.org/10.1080/13545701.2023.2278798.
Notes
1 As emphasized by the World Bank (Citation2011: 3), gender equality matters in its own right; so just as development means less income-poverty or better access to justice, it should also mean fewer differences in well-being between men and women. Gender equality is also “smart economics:” it can improve economic efficiency and enhance other development outcomes.
2 “Let w, x, and y be three random variables. The joint distribution gives the percentage of the population with (w, x, y) or less. The marginal distribution of w gives the percentage of the population with w or less, and the same for x and y” (Alkire and Foster Citation2011a: 294).
3 It should be noted that the Southern Cone also includes Uruguay; but, to our knowledge, this country does not have micro data from a financial literacy survey similar to the one we used, so it was not considered in our analysis.
4 The datasets used in our analyses are publicly available and can be retrieved, for example, from: https://scioteca.caf.com/handle/123456789/1086.
5 An updated version of these guidelines can be retrieved from https://www.oecd.org/financial/education/2022-INFE-Toolkit-Measuring-Finlit-Financial-Inclusion.pdfhttps://www.oecd.org/financial/education/2022-INFE-Toolkit-Measuring-Finlit-Financial-Inclusion.pdf.
6 As for the surveys weights, according to people from CAF, who shared with us the micro data, and IPSOS, the company that was in charge of data collection, in each of the countries, these were estimated considering “the actual distribution by region, sex and age.”
7 As an example, see Appendix A for the list of questions asked in the survey conducted in Argentina.
8 It should be noted that the proposed aggregation method and the resulting overall measure are “neutral” with respect to the relationship between the financial domains considered in our analysis, in line with Sabine Alkire and James Foster (Citation2011b: 485).
9 See https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groupshttps://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.
11 See https://hdr.undp.org/data-center/thematic-composite-indices/gender-inequality-index#/indicies/GIIhttps://hdr.undp.org/data-center/thematic-composite-indices/gender-inequality-index#/indicies/GII.
12 See, for example, https://www3.weforum.org/docs/GGGR2015/cover.pdf; https://www3.weforum.org/docs/WEF_GGGR_2017.pdfhttps://www3.weforum.org/docs/WEF_GGGR_2017.pdf; https://www3.weforum.org/docs/WEF_GGGR_2022.pdfhttps://www3.weforum.org/docs/WEF_GGGR_2022.pdf.
13 See Tables C-1, C-2, and C-3 in Appendix C for the percentage of “correct” answers (or “high performance”) given to each of the twenty-seven questions considered in our analysis, according to the questionnaire design, for the sample as a whole and separately for men and women. It should be observed that since the data used come from household surveys, these questions may incorporate measurement errors that could introduce different biases for women and men; consequently, this issue could lead to problems in measuring the corresponding gender gap; as emphasized by Deaton (2018: 99), “measurement error in survey data is a fact of life, and while it is not always possible to counter its effects, it is always important to realize what those effects are likely to be, and to beware of inferences that are possible attributable to, or contaminated by, measurement error.”
14 One million guaranies (national currency of Paraguay) in the case of Paraguay.
15 One million, one hundred thousand in the case of Paraguay.
16 In the case of Chile, the wording of this statement changed as follows: “When you invest and want to make a lot of money, there is also the possibility of losing a lot of money.”
17 As a reference, according to the “OECD/INFE 2020 International Survey of Adult Financial Literacy,” in which twenty-six countries participated (Austria; Bulgaria; Colombia; Croatia; Czech Republic; Estonia; France; Georgia; Germany; Hong Kong, China; Hungary; Indonesia; Italy; Korea; Malaysia; Malta; Moldova; Montenegro; Peru; Poland; Portugal; Republic of North Macedonia; Romania; Russia; Slovenia; and Thailand. OECD member countries that took part in the survey are: Austria; Colombia; Czech Republic; Estonia; France; Germany; Hungary; Italy; Korea; Poland; Portugal; and Slovenia), on average across the sample, understanding simple interest turned out to be the question most widely answered correctly (84.4 percent of all adults gave correct answer; and 87.5 percent of adults of OECD member countries). Understanding both simple and compound interest, however, proved to “be a very challenging concept,” as only about one-third of the respondents (26.3 percent average for the entire sample; 28.8 percent across OECD member countries participating in the survey) were able to show understanding of both. In turn, around 80 percent of the adults identified the correct meaning of inflation, while 77 percent of them gave a correct answer to the question on “risk and return” and considerably fewer (58.9 percent) to the question on “risk and diversification” (OECD Citation2020: 19).
18 We also computed the overall estimate of financial literacy in Argentina, Chile, and Paraguay using an equal weighting structure, as well as alternative fuzzy weighting schemes, and the same qualitative conclusions can be drawn from these estimates; the results are available upon request from the authors.
19 The OECD (Citation2020) also found that in Austria, Germany, Italy, Moldova, Montenegro, Portugal, and North Macedonia, there is a statistically significant gender gap in financial literacy, but the size in all cases is less than 5 percent, in relative terms. Interestingly, in Russia, on the contrary, women seem to do slightly better than men in financial literacy, and the gender gap is estimated at 2 percent, in relative terms.
20 Figures C-1, C-2, C-3, and C-4 in Appendix C show the corresponding weighted relative contribution of the absolute gender difference in each of the questions considered to the overall gender gap in financial literacy, country by country, as well as considering the three countries as a whole.
21 In Appendix D, we also present a graphical representation of gender differences in financial illiteracy.
22 Individuals are considered to be monetarily poor if their income is below the median of the income distribution; in our analysis, we have also considered as monetarily poor those individuals who did not provide information on their income (those under the category “No response”), so that here we are somewhat overestimating monetary poverty.
23 Ordinary least squares regression results for financial literacy for men and women separately are available from the authors upon request.
25 These results confirm those of Leora F. Klapper, Annamaria Lusardi, and Peter Van Oudheusde (Citation2015), although adopting another methodological strategy. The survey they used did not include Paraguay, but they found that, among the South American countries, Chile has the second highest adult financial literacy score (41), Uruguay having the highest one (45). The scores of the other South American countries are as follows: Argentina: 28; Brazil: 35; Colombia: 32; Costa Rica and the Dominican Republic: 35; Ecuador: 30; Guatemala: 26; Honduras: 23; Nicaragua: 20; Peru: 28. It is worthy to mention that the score of Chile is much smaller than that of countries in Western Europe, but it is similar to that of Poland, which had a score of 42.
26 According to the joint significance F-test [(4, 3630) = 85.35, with a p-value = 0.0000] performed, all education-related variables are collectively significant at 0.1 percent.
27 Interestingly, Fonseca et al. (Citation2012: 92), using data from the RAND American Life Panel, find that “greater financial decision making responsibility within couples is correlated with higher financial literacy for men, but not for women.”
28 In Argentina, all variables related to the individual’s employment status included in the model are collectively significant at 5 percent [F-test (3, 1206) = 3.16, p-value = 0.0239], while in Paraguay they are significant at 10 percent [F-test (3, 1185) = 2.09, p-value = 0.0995].
29 We used the Stata command called “oaxaca” (“Blinder-Oaxaca decomposition of outcome differentials”); the decomposition was based on separate linear regressions (OLS) for the two groups (men and women), and it is expressed from the viewpoint of men. The corresponding ordinary least squares regression results for financial literacy for men and women separately are available from the authors upon request.
30 “The first relationship suggests that in countries in which more women are enrolled in tertiary education, an individual woman’s education may lead to higher financial literacy. This ratio might also pick up other social norms linked to women’s financial literacy. The second relationship may be explained by the fact that in countries with a higher life expectancy for women, women invest more in their financial literacy, anticipating that they may one day be responsible for their finances alone” (Cupák et al. Citation2018: 105).
33 “The index measures the human capital of the next generation, defined as the amount of human capital that a child born today can expect to achieve in view of the risks of poor health and poor education currently prevailing in the country where that child lives” (World Bank Citation2018: 3).
34 See the Global Gender Gap Report 2016: https://www3.weforum.org/docs/GGGR16/WEF_Global_Gender_Gap_Report_2016.pdf.
Additional information
Funding
Notes on contributors
José Espinoza-Delgado
José Espinoza-Delgado received his PhD in Economics from the University of Goettingen in Germany. He is currently a guest researcher at the Center for Modern India Studies (CeMIS), in the research group “Development Economics,” at the University of Goettingen.
Jacques Silber
Jacques Silber received his PhD from the University of Chicago and is now Professor Emeritus of Economics at Bar-Ilan University, Israel.
![Supercharge Your Next Research Paper: Research and write your next paper with Jenni AI.]({"type":"image" , "src" : "/sda/112448/MPU-L1SN.png"})