New evidence on the gender gap in mathematical achievement in Brazil
Camila Rafaela Alvarenga, Cícero Augusto Silveira Braga
Abstract
South American countries have some of the worst overall achievement scores in math compared to developed countries. Brazilian students have the lower relative score which boys outperform girls in all ages. In this work we aim to demonstrate that gender plays a decisive role in the difference in math achievement in Brazil. We performed an Oaxaca-Blinder decomposition of the math-gender gap in unconditional quantiles in order to measure which proportion can be attributed to actual gender differences in converting individual and contextual characteristics into math achievement. The average differences we observed are partially a function of socially constructed gender roles and stereotypes, which may affect each individual differently. Nevertheless, we found that gender differences in math achievement persist across primary and secondary education (increasing with school-grade), and they are not explained by individual, family and school characteristics, but mostly by unobservable gender differences in returns to these characteristics.
Keywords
References
Albert, A. A., & Porter, J. R. (1988). Children’s gender-role stereotypes: A sociological investigation of psychological models. In Sociological Forum. Dordrecht: Kluwer Academic Publishers, 3(2), 184-210.
Andrade, Márcia, Franco, Creso, & Carvalho, João Pitombeira de. (2016). Gênero e desempenho em matemática ao final do ensino médio: quais as relações?(pp. 1-16). São Paulo: ABEP.
Arruda, Luciana. (2002). Desvendando desigualdades de oportunidades em ciências e em matemática relacionadas ao gênero do aluno - uma aplicação de modelagem multinível ao SAEB 99. Revista Brasileira de Pesquisa em Educação em Ciências, 2(3), 84-96.
Baker, David, & Jones, Deborah. (1993). Creating gender equality: Cross-national gender stratification and mathematical performance. Sociology of Education, 66(2), 91-103. http://doi.org/10.2307/2112795
Bharadwaj, Prashant, De Giorgi, Giacomo, Hansen, David, & Neilson, Christopher A. (2016). The gender gap in mathematics: Evidence from Chile. Economic Development and Cultural Change, 65(1), 141-166. http://doi.org/10.1086/687983
Blinder, Alan. (1973). Wage discrimination: Reduced form and structural estimates. The Journal of Human Resources, 8(4), 436-455. http://doi.org/10.2307/144855
Contini, Dalit, Tommaso, Maria Laura, & Mendolia, Silvia. (2017). The gender gap in mathematics achievement: Evidence from Italian data. Economics of Education Review, 58, 32-42. http://doi. org/10.1016/j.econedurev.2017.03.001
Duflo, Esther. (2012). Women empowerment and economic development. Journal of Economic Literature, 50(4), 1051-1079. http://doi.org/10.1257/jel.50.4.1051
Firpo, Sergio, Fortin, Nicole, & Lemieux, Thomas. (2018). Decomposing wage distributions using recentered influence function regressions. Econometrics, 6(2), 28. http://doi.org/10.3390/econometrics6020028
Fortin, Nicole, Lemieux, Thomas, & Firpo, Sergio. (2011). Decomposition methods in economics. In Orley Ashenfelter & David Card (Eds.), Handbook of labor economics (Vol. 4, pp. 1-102).
Amsterdam: Elsevier. Fryer Junior, Roland, & Levitt, Steven. (2010). An empirical analysis of the gender gap in mathematics. American Economic Journal. Applied Economics, 2(2), 210-240. http://doi.org/10.1257/app.2.2.210
Gevrek, Zahide, Gevrek, Deniz, & Neumeier, Christian. (2020). Explaining the gender gaps in mathematics achievement and attitudes: The role of societal gender equality. Economics of Education Review, 76, 101978. http://doi.org/10.1016/j.econedurev.2020.101978
Gevrek, Zahide, & Seiberlich, Ruben. (2014). Semiparametric decomposition of the gender achievement gap: An application for Turkey. Labour Economics, 31, 27-44. http://doi.org/10.1016/j.labeco.2014.08.002
Greenwald, Anthony, & Banaji, Mahzarin. (1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102(1), 4-27. PMid:7878162. http://doi.org/10.1037/0033-295X.102.1.4
Guiso, Luigi, Monte, Ferdinando, Sapienza, Paola, & Zingales, Luigi. (2008). Culture, gender, and math. Science, 320(5880), 1164-1165. PMid:18511674. http://doi.org/10.1126/science.1154094
Hyde, Janet, Fennema, Elizabeth, & Lamon, Susan. (1990). Gender differences in mathematics performance: A meta-analysis. Psychological Bulletin, 107(2), 139-155. PMid:2138794. http://doi.org/10.1037/0033- 2909.107.2.139
Instituto Nacional de Estudos e Pesquisas Educacionais Anisio Teixeira – INEP. (2016). SAEB 2015. Retrieved in 2023, September 11, from http://portal.inep.gov.br/microdados
Kane, Jonathan & Mertz, Janet. (2012). Debunking myths about gender and mathematics performance. Notices of the American Mathematical Society, 59(1), 10-21. http://doi.org/10.1090/noti790
Koenker, Roger, & Bassett Junior, Gilbert. (1978). Regression quantiles. Econometrica, 46(1), 33-50. http:// doi.org/10.2307/1913643 Marks, Gary. (2008). Accounting for the gender gaps in student performance in reading and mathematics: Evidence from 31 countries. Oxford Review of Education, 34(1), 89-109. http://doi. org/10.1080/03054980701565279
Martin, Carol, Wood, Carolyn, & Little, Jane. (1990). The development of gender stereotype components. Child Development, 61(6), 1891-1904. PMid:2083503. http://doi.org/10.2307/1130845
Ng’ang’a, Alice, Mureithi, Leopold, & Wambugu, Anthony. (2018). Mathematics gender gaps in Kenya: Are resource differentials between boys and girls to blame? Cogent Education, 5(1), 1564163. http://doi.org /10.1080/2331186X.2018.1564163
Nosek, Brian, Banaji, Mahzarin, & Greenwald, Anthony. (2002). Math = Male, me = Female, therefore math = me. Journal of Personality and Social Psychology, 83(1), 44-59. PMid:12088131. http://doi. org/10.1037/0022-3514.83.1.44
Nosek, Brian, Smyth, Frederick, Sriram, Nataraja, Lindner, Nicole, Devos, Thierry, Ayala, Alfonso, Bar-Anan, Yoav, Bergh, Robin, Cai, Huajian, Gonsalkorale, Karen, Kesebir, Selin, Maliszewski, Norbert, Neto, Félix, Olli, Eero, Park, Jaihyun, Schnabel, Konrad, Shiomura, Kimihiro, Tulbure, Bogdan Tudor, Wiers, Reinout, Somogyi, Mónika, Akrami, Nazar, Ekehammar, Bo, Vianello, Michelangelo, Banaji, Mahzarin, & Greenwald, Anthony. (2009). National differences in gender-science stereotypes predict national sex differences in science and math achievement. Proceedings of the National Academy of Sciences of the United States of America, 106(26), 10593-10597. PMid:19549876. http://doi.org/10.1073/pnas.0809921106
Oaxaca, Ronald. (1973). Male-female wage differentials in urban labor markets. International Economic Review, 14(3), 693-709. http://doi.org/10.2307/2525981
Organisation for Economic Co-operation and Development – OECD. (2018). PISA 2018 database. Retrieved in 2023, September 11, from https://www.oecd.org/pisa/data/2018database/
Palermo, Gabrielle, Silva, Denise, & Novellino, Maria. (2014). Fatores associados ao desempenho escolar: Uma análise da proficiência em matemática dos alunos do 5º ano do ensino fundamental da rede municipal do Rio de Janeiro. Revista Brasileira de Estudos de População, 31(2), 367-394. http://doi.org/10.1590/ S0102-30982014000200007
Pinto, Marília. (2004). Quem são os meninos que fracassam na escola. Cadernos de Pesquisas, 34(121), 11-40. http://doi.org/10.1590/S0100-15742004000100002
Sohn, Kitae. (2012). A new insight into the gender gap in math. Bulletin of Economic Research, 64(1), 135- 155. http://doi.org/10.1111/j.1467-8586.2010.00358.x
Vinha, Luis, & Laros, Jacob. (2018). Dados ausentes em avaliações educacionais: Comparação de métodos de tratamento. Estudos Em Avaliação Educacional, 29(70), 156-187. World Economic Forum – WEF. (2019). The global gender gap report 2020. Geneva.
Submitted date:
09/11/2023
Accepted date:
06/04/2024
Facebook
Google+
Twitter
LinkedIn
Mendeley
StumbleUpon
CiteULike
Reddit
Email