1. Gender, math and equality of opportunities Marina Murat Giulia Pirani University of Modena and Reggio Emilia marina.murat@unimore.it Productivity, Investment in Human Capital and the Challenge of Youth Employment. Comparative Developments and Global Responses Bergamo (Italy), 16-18 December 2010

2. Motivations: school gender gap in mathematics Negative difference between scores of girls and boys in math across countries: –Trends in Mathematics and Science Study (TMSS) 1995, 1999, 2003, 2007, 2008. –Programme for International Student Assessment (PISA) 2000, 2003, 2006, 2009. Concerns fifteen years old. Positive gap in reading

3. Motivations. Gap in mathematics - Origin Cultural Guiso, Monte, Sapienza, Zingales (2008) (PISA 2003, 37 countries): gender gap in mathematics related to empowerment of women in society. –Measured by Gender Gap Index – GGI (World Economic Forum). Fryer, Levitt (2020): gap emerges after first years of primary school –Narrower or nil in Islamic countries! –Explanation: separate classes for boys and girls

4. Motivations. Culture, school gap and economic gap Gender differences in mathematics can lead to inequality of opportunities in the economy –Paglin M, Rufolo A (1990) ‘Relative wages are higher in the mathematics and science based sectors of the economy, where male workers are generally over- represented’. [empirical investigation on USA data]

5. Motivations: Culture, math and the economy This paper How does culture actually affect the scores of males and females? Is there a relation between gender gaps in mathematics and the economic-Gender Gap Index (econ-GGI)?

6. Index Data Descriptive statistics Estimation methods Results Conclusion

7. Data PISA 2006, 57 countries We consider several indicators of students’ performance at school: gender, characteristics, background, grade, study hours of mathematics and from PISA questionnaire: Importance of studying mathematics: In general, how important do you think it is for you to do well in mathematics? [A: from ‘very important’ to ‘not important at all’] Math: total importance and difference between boys and girls vary significantly across countries.

8. Data We also consider country variables: –Gdp, per capita gdp, GGI, econ-GGI, religion, market institutions.

9. Descriptive statistics. 57 countries Girls tend to repeat grades less than boys Girls: less hours of study of math at school, out of school and at home. On average, math is more important for boys than for girls. But wide variation across-countries

16. Estimation A. Measure of gap in each country –Gap 1: only dummy gender –Gap 2: gender + background –Gap 3: gender + background + school factors –Gap 4_ gender + background + school factors + importance of math

17. Estimation Regression of school gaps in mathematics on countries’ variables: –gdp, per-capita gdp, gdp growth –GGI and economic-GGI –Religion: percentage of Catholic, Protestant, Islamic population in countries –Market institutions: ex-socialist countries –Educational institutions: comprehensive school vs. school tracking

18. Estimation Pisa 2006, average scores standardized to 500 (OECD) with standard deviation 100. About 1/3 of standard deviation corresponds to 1 school year.

20. Table 5. Results (OLS, BIC) dependent variablegap1gap2gap3gap4 variables ln per capita gdp4.51 (-1.36) ** ln average scores 14.38 (-6.86) * total do well math 0.31 (-0.13) *0.3 (-0.12) * diff m-f do well math-0.47 (-0.14) ** ex-socialist7.49 (-1.92) ***5.86 (-2.02) **5.72 (-2.09) **5.76 (-2.05) ** catholic percentage-0.07 (-0.02) **-0.06 (-0.03) *-0.07 (-0.03) *-0.07 (-0.03) ** islamic percentage0.09 (-0.03) ** intercept-54.5 (-13.88) ***-12.67 (-1.41) ***-45.12 (- 10.87) ***-132.15 (-43.33) ** n° observ57 Adj. R 2 0.46 0.19 0.26 0.29 Standard errors of coefficients are reported in parentheses

21. Table 4. GMSZ and GMSZ + FL (OLS) dependent variable: GAP1GMSZGMSZ + FL variables ggi -10.11(-15.82) 8.83(-13.76) econ-ggi 38.52(13.56)*** ln per capita gdp -1.02(-1.64) 0.44(-1.53) 1.34(-1.48) islamic percentage 0.11(-0.05)**0.16(-0.05)*** intercept 7.24(-14.23) -50.31(20.4)1** n° observ57 Adj. R 2 0 0.08 0.21 Standard errors of coefficients are reported in parentheses

22. Conclusions School gender gap related to countries’ beliefs on the importance of mathematics Where math more important, valuation of boys and girls more similar More similar valuations and higher valuation, narrower school gender gaps

23. Conclusions Math important and narrow differences in valuation: Where? not necessarily developed countries. Not in Catholic countries (several in Western Europe). But: in ex-socialist, Protestant, Islamic countries.

24. Conclusions School gender gaps are more related to the economic-GGI than to the general GGI It does seem to affect economic empowerment more directly than social empowerment (except Islamic countries, where both are low)

25. Conclusions Policies: Educational systems: –lower separation between school types (with more girls in classical curricula and more boys in scientific curricula) –incentives for fair distribution of girls’ and boys’ choices regarding math lessons within schools (level of difficulty and hours) and, hence, study at home –Attention to teachers’ attitudes with respect to gender roles in the study of math

26. Conclusions Economic empowerment is a first and important step for more general social empowerment. A better performance of girls in mathematics would rise the average level of test scores in countries. Important in some countries of Western Europe. General economic implications.