Gender, math and equality of

opportunities Marina MuratGiulia 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

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

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

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]

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)?

Index

• Data

• Descriptive statistics

• Estimation methods

• Results

• Conclusion

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.

Data

• We also consider country variables: – Gdp, per capita gdp, GGI, econ-GGI, religion,

market institutions.

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

Total %Importance Importance lessons >6 hours selfstudy >6 hoursout of school >6 hoursgrade <9

THA 96.60 -14.09 -2.84 -0.59 0.34 1.94KGZ 79.43 -10.00 -4.45 -1.70 1.36 2.13ISR 83.36 -9.24 -1.71 0.39 -0.66 0.00ISL 91.68 -7.94 -3.82 -0.84 -0.18 0.00TUR 90.59 -7.67 0.78 -1.14 0.12 1.75BGR 85.15 -6.91 -1.88 -1.73 -0.48 4.16JOR 86.55 -6.44 -2.53 -1.78 0.25 -0.21ARG 81.62 -4.68 1.22 -0.79 0.50 5.50IDN 92.43 -4.08 -1.89 -1.21 -0.31 4.27MEX 91.86 -3.84 2.34 0.18 0.93 6.06ROU 87.46 -3.73 1.45 1.68 1.39 4.63EST 91.31 -3.16 2.03 -1.45 0.31 11.13POL 84.61 -3.11 -1.07 -0.98 0.54 3.86LVA 92.53 -2.69 -2.91 0.09 1.37 7.64RUS 88.90 -2.20 0.97 -0.69 0.24 1.78SWE 90.93 -0.73 2.20 0.08 0.25 0.72QAT 73.91 -0.55 -1.98 0.32 2.89 2.31CHL 89.52 -0.44 1.00 -0.02 0.36 1.21BRA 86.78 -0.42 1.08 -0.33 0.15 10.58HUN 81.20 -0.17 0.29 0.40 0.55 3.32ESP 87.00 -0.15 0.76 -0.32 0.14 3.19LTU 92.20 0.16 -1.29 0.36 0.29 3.64SVN 82.70 0.48 -1.15 0.09 0.24 0.36URY 76.25 0.98 1.90 -0.45 0.56 9.15CAN 85.48 1.12 -5.45 -1.49 0.76 0.05USA 91.38 2.15 -1.22 -1.26 -0.04 0.20TUN 78.05 2.52 1.27 1.13 1.28 9.51COL 91.58 2.66 1.22 0.93 1.35 6.31

Difference %male - %female

Table1. Importance and hours of study of mathematics. Grades.

Total %ImportanceImportancelessons >6 hoursselfstudy >6 hoursout of school >6 hoursgrade <9

BEL 75.30 3.32 2.15 -0.09 0.13 0.81HKG 73.95 3.49 0.07 1.25 1.85 2.98DNK 90.14 3.56 2.12 0.61 0.57 7.72NOR 85.64 4.46 1.16 0.36 0.63 0.00GRC 84.25 4.80 2.93 2.53 2.15 2.22SVK 85.33 4.80 -0.58 0.54 0.10 1.30MAC 53.86 5.56 -6.89 -0.57 -0.32 13.23DEU 86.73 5.72 1.17 -1.01 0.20 4.16AUS 77.28 5.79 1.92 0.69 0.52 0.03NLD 76.97 5.96 0.06 -0.01 -0.08 1.02PRT 70.47 6.11 -2.26 -1.43 -0.06 5.41NZL 85.62 6.24 -0.62 -0.07 0.22 0.00GBR 92.51 6.28 3.74 0.13 0.19 0.00SRB 70.57 6.29 1.65 0.29 0.34 1.50AUT 80.28 6.47 2.89 1.16 0.39 2.73JPN 86.36 7.15 1.26 0.29 0.41 0.00MNE 72.12 7.25 2.65 2.09 1.31 0.14IRL 81.78 7.36 0.99 0.80 0.89 1.04

KOR 87.57 7.67 -2.00 2.99 2.74 0.00AZE 77.48 8.08 1.97 1.77 2.49 1.78ITA 81.84 8.21 3.94 -0.05 0.31 1.44LUX 82.22 8.84 1.39 1.19 0.94 1.73FIN 84.70 9.02 -0.16 -0.17 0.14 4.47HRV 70.02 9.88 1.82 1.26 0.10 -0.46CZE 84.48 10.07 3.53 0.97 0.96 -0.40TAP 83.05 10.91 0.91 1.04 0.24 0.05FRA 85.16 12.73 1.05 0.96 0.48 0.99LIE 89.38 13.33 7.26 1.38 0.00 7.71CHE 81.34 17.03 1.18 0.35 0.37 3.77

Average 80.57 7.46 1.22 0.66 0.63 2.25

Difference %male - %female

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

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

Estimation

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

GENDER GAP IN MATHEMATICS

-33.00

-28.00

-23.00

-18.00

-13.00

-8.00

-3.00

2.00

7.00

12.00

DUMMY BACKG

Table 5. Results (OLS, BIC)

dependent variable gap1 gap2 gap3 gap4

variables          

ln per capita gdp 4.51 (-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-socialist 7.49 (-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 percentage 0.09 (-0.03) **                  

intercept -54.5 (-13.88) *** -12.67 (-1.41) *** -45.12 (-10.87) *** -132.15 (-43.33) **

n° observ 57     57     57     57    

Adj. R2 0.46     0.19     0.26     0.29    

Standard errors of coefficients are reported in parentheses

Table 4. GMSZ and GMSZ + FL (OLS)

dependent variable: GAP1 GMSZ GMSZ + FL GMSZ + 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) ***

intercept7.24 (-14.23)

    

 -50.31 (20.4)1 **

n° observ 57     57     57    

Adj. R2 0     0.08     0.21    

Standard errors of coefficients are reported in parentheses

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

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.

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)

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

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.