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Appendix For Online Publication

In document Math, girls and socialism (Page 36-49)

A

Descriptive statistics

A.1 PISA 2003-E Germany

Table A1: Descriptive statistics. PISA

West Germany East Germany

Mean S.D. Mean S.D.

Male Score Global 524.33 94.92 531.34 86.65 Female Score Global 505.43 91.49 515.00 83.33

Grade 8 0.14 0.35 0.09 0.29 Grade 9 0.61 0.49 0.61 0.49 Grade 10 0.25 0.44 0.30 0.46 Female 0.50 0.50 0.49 0.50 Wealth 0.17 0.95 -0.06 0.86 Age 15.69 0.28 15.71 0.28 Mother Educ. 3.41 1.59 4.03 1.24 Father Educ. 3.82 1.67 4.07 1.29

Father’s professionnal situation

Working Full-Time 0.24 0.43 0.63 0.48

Working Part-Time 0.46 0.50 0.18 0.38

Looking for Work 0.04 0.19 0.11 0.32

Other 0.26 0.44 0.07 0.26

Mother’s professionnal situation

Working Full-Time 0.84 0.37 0.81 0.40

Working Part-Time 0.07 0.26 0.06 0.24

Looking for Work 0.04 0.19 0.09 0.29

Other 0.06 0.23 0.04 0.19

Share of Female in School 0.51 0.09 0.50 0.07 Size of the community

Less than 3000 people 0.15 0.35 0.01 0.09 Between 3000 and 15000 0.02 0.14 0.14 0.35 Between 15000 and 100000 0.25 0.44 0.34 0.48 Between 100000 and 1000000 0.36 0.48 0.37 0.48

More than 1000000 0.22 0.41 0.14 0.35

Observations 16401 7922

Notes: The data comes from the German National Evalu- ation of Pisa. The sample is restricted to individuals who were born in Germany.

In TableA2, we relate the self-declared mathematics grades to the test scores obtained for each of the mathematics categories. Looking at the first line, we see in column 1 that individuals who declared a grade of 1 or 2 in mathematics (highest grades) scored on average 555 points on the global score whereas individuals who declared a grade strictly higher than 2 scored 499 points on average. The difference between the two quantities is of 55 points and is significant at the 1% level.

Table A2: Relationship Between Self-Declared Grades and Objective Test Scores

Reported Grade High Grade Low Grade Difference 1 or 2 >2

Objective Score (1) (2) (1)-(2)

Global 555.804 499.841 55.963***

(1.272)

Space and Shape 559.428 493.747 65.680***

(1.501) Relationships and Change 560.623 501.877 58.747***

(1.483) Uncertainty 537.516 484.614 52.902*** (1.351) Quantity 566.134 511.369 54.765*** (1.346) Problem Solving 556.167 508.261 47.905*** (1.289) Observations 6807 14162 21244

* p < 0.1, ** p < 0.05, *** p < 0.01. The data comes from the German National Evaluation of Pisa. The sample is restricted to individuals who were born in Germany. Column 1 is restricted to individuals who declared a grade in mathematics of 1 or 2 (Highest Grades). Column 2 is restricted to individuals who declared a grade strictly higher than 2 (low grades). Column 3 computes the difference between Column 1 and Column 2. Each line displays the objective test scores for one of the mathematics categories.

A.2 German Socio-Economic Panel

Table A3: Descriptive statistics. GSOEP

West Germany East Germany

Mean S.D. Mean S.D.

Panel A : Adults

High Grade in Math Male 0.49 0.50 0.55 0.50 High Grade in Math Female 0.42 0.49 0.53 0.50

Age 49.57 16.66 48.59 17.03

Female 0.52 0.50 0.52 0.50

Household Income (EUR) 3080.08 2575.18 2223.61 1322.18

Observations 14906 4799

Panel B : Teenagers

High Grade in Math Male 0.34 0.47 0.35 0.48 High Grade in Math Female 0.30 0.46 0.39 0.49

Age 17.10 0.39 17.11 0.41

Female 0.50 0.50 0.47 0.50

Household Income (EUR) 3238.77 1946.33 2479.61 1346.98

Observations 2618 827

Notes: The data comes from the German Socio-Economic Panel. The sample is restricted to individuals who were born in Germany. High reported grades: 1 and 2 on a 1-6 scale.

B

PISA-E 2003 Germany

B.1 Quantile Regressions

We run quantile regressions to investigate whether the gender gap increases at higher levels of performance and how this gap is attenuated in East Germany. To test whether coefficients between quantiles are different, we compute the p-value from a Wald Test between the 10th and 90th percentile. From the coefficients, it seems that the gender gap increases by the largest extent for Relationship and Change (p-value=0.000), followed by Uncertainty (p-value=0.003) and then Problem Solving (p-value = 0.005). For the Space

and Shape and Quantity categories, there is no significant increase in the gender gap in math between the 10th and 90th percentiles.

The attenuation of the gender gap exhibits a similar impact across all quantiles except for the Relationship and Change category (p-value = 0.074) where the gap is sharply attenuated in the 90th percentile as compared to the 10th one. While there seems to be an increase in the Uncertainty category, the difference is not significant (p-value= 0.23). The differences between the 10th and 90th percentiles in the impact of F emale ∗ East is not significant for the other categories.

Table A4: Maths Scores by Quantiles - PISA 2003-E Germany

(1) (2) (3) (4) (5)

Quantile 10 30 50 70 90

Dependent Variable: Space and Shape Score

Female -31.52*** -34.56*** -34.11*** -35.03*** -34.81*** (2.76) (1.76) (1.23) (1.80) (2.26) East 1.79 5.49 -0.39 2.04 -1.74 (6.00) (5.19) (4.51) (5.04) (6.20) Female*East -6.73 -3.32 -4.09 -1.19 1.12 (4.91) (2.55) (2.64) (2.84) (3.88) Dependent Variable: Relationships and Change Score

Female -24.45*** -30.57*** -31.79*** -33.68*** -38.43*** (2.51) (1.59) (1.43) (2.04) (2.35) East 15.96** 8.39* 9.56*** 4.60 -13.08** (7.58) (4.46) (3.40) (4.10) (6.27) Female*East -2.77 6.43** 6.17** 2.53 7.28** (5.53) (2.85) (3.01) (4.35) (3.00) Dependent Variable: Uncertainty Score

Female -33.19*** -38.27*** -41.82*** -41.82*** -43.48*** (2.24) (1.73) (1.59) (1.41) (2.43) East -2.99 -4.39 -6.90* -9.74** -22.32*** (4.95) (4.78) (4.00) (4.08) (5.99) Female*East 7.99** 10.19*** 13.10*** 11.83*** 14.61*** (3.43) (3.03) (1.94) (2.97) (4.24) Dependent Variable: Quantity Score

Female -23.78*** -23.83*** -24.22*** -24.57*** -26.48*** (1.99) (1.81) (1.49) (1.89) (1.54) East -3.05 -5.29 -3.13 -5.56 -11.46* (6.41) (6.17) (5.59) (4.77) (5.87) Female*East 5.33* 4.15* 5.65 4.29 2.54 (2.75) (2.43) (3.56) (3.37) (3.46) Dependent Variable: Problem Solving Score

Female -12.92*** -14.67*** -20.58*** -21.51*** -23.27*** (2.89) (1.88) (1.59) (1.87) (1.97) East -9.46 -6.94** -14.71*** -16.46*** -16.85*** (6.31) (2.91) (3.48) (3.95) (6.05) Female*East 7.02 3.82 9.04*** 9.49*** 7.10** (4.78) (2.73) (2.82) (3.16) (3.39) * p < 0.1, ** p < 0.05, *** p < 0.01. The data comes from the German National Evaluation of Pisa. The sample is restricted to individuals who were born in Germany. Standard errors are calculated with a bootstrap method. Individual controls: a quadratic in age, class, a quadratic in wealth, education and employment status of both parents. School controls: Size of location area, share of girls in the school and L¨ander fixed-effects.

B.2 Data Appendix - Student Questionnaire

• Thinking about your views on Mathematics: to what extent do you agree with the following statements:

– I enjoy reading about Mathematics

– Making an effort in mathematics is worth it because it will help me in the work I want to do later.

– I look forward to Mathematics lessons.

– I do Mathematics because I enjoy it.

– Learning Mathematics is worthwhile for me because it improves my career prospects, chances.

– I am interested in the things is learn in Mathematics.

– Mathematics is an important subject for me because I need it for what I want to study later on.

– I will learn many things in Mathematics that will help me get a job.

• How confident do you feel about having to do the following Mathematics tasks? – Using a train timetable to work out how long it would take to get from one

place to another.

– Calculating how much cheaper a TV would be after a 30% discount.

– Calculating how many square feet of tile you need to cover a floor.

– Understanding graphs presented in newspapers.

– Solving an equation like 3x+5= 17.

– Finding the actual distance between two places on a map with a 1:100 scale.

– Solving an equation like 2(x+3)=(x + 3)(x - 3).

– Calculating the gas mileage of a car.

• Thinking about studying mathematics: To what extent do you agree with the fol- lowing statements?

– I often worry that it will be difficult for me in Mathematics classes.

– I am just not good at Mathematics.

– I get very tense when I have to do Mathematics homework.

– I get very nervous doing Mathematics problems.

– I learn Mathematics quickly.

– I have always believed that Mathematics is one of my best subjects.

– I feel helpless when doing a Mathematics problem.

– In my Mathematics class, I understand even the most difficult work.

– I worry that I will get poor grades in Mathematics.

• Thinking about your Mathematics classes: To what extent do you agree with the following statements?

– I would like to be the best in my class in Mathematics.

– In Mathematics I enjoy working with other students in groups.

– I try very hard in Mathematics because I want to do better on the exams than the others.

– When we work on a project in Mathematics, I think that it is a good idea to combine the ideas of all the students in a group.

– I make a real effort in Mathematics because I want to be one of the best.

– I do my best work in Mathematics when I work with other students.

– In Mathematics I always try to do better than the other students in my class.

– In Mathematics, I enjoy helping others to work well in a group.

– In Mathematics I learn most when I work with other students in my class.

– I do my best work in Mathematics when I try to do better than others

• There are different ways of studying Mathematics.To what extent do you agree with the following statements?

– When I study for a Mathematics test, I try to work out what are the most important parts to learn

– When I am solving Mathematics problems, I often think of new ways to get the answer.

– When I study Mathematics, I make myself check to see if I remember the work I have already one.

– When I study Mathematics, I try to figure out which concepts I still have not understood properly.

– I think about how the Mathematics I have learnt can be used in everyday life.

– I go over some problems in Mathematics so often that I feel I could solve them in my sleep.

– When I study for Mathematics, I learn as much as I can by heart.

– I try to understand new concepts in Mathematics by relating them to things I already know.

– In order to remember the method for solving a Mathematics problem, I go through examples again and again.

– When I cannot understand something in Mathematics, I always search for more information to clarify the problem.

– When I am solving a Mathematics problem, I often think about how the solution might be applied to other interesting questions.

– When I study Mathematics, I start by working out exactly what I need to learn.

– To learn Mathematics, I try to remember every step in a procedure.

– When learning Mathematics, I try to relate the work to things I have learnt in other subjects.

Respondents have to tick one answer on a 4 points scale, labeled as: 1 ’strongly agree’, 2 ’agree’, 3 ’disagree’ and 4 ’strongly disagree’. We recoded the variables in ascending order of positive attitude.

C

Robustness

C.1 Are Eastern and Western Schools Different?

Table A5: Teaching Methods per School - PISA-E 2003 Germany

(1) (2) (3) (4)

Panel A: Organization of Classes and Abilities

East 0.13 -0.05 0.16 -0.05

(0.14) (0.12) (0.12) (0.15) Observations 1175 1164 1171 1155

Panel B: Teachers and Innovation

East 0.13 -0.12 0.05

(0.11) (0.11) (0.12) Observations 1191 1191 1191

Panel C: Level of Difficulty

East 0.30** -0.09 0.03

(0.12) (0.12) (0.10) Observations 1187 1188 1188

Panel D: Cognitive vs Non-Cognitive Skills

East 0.13 -0.37*** 0.18*

(0.11) (0.12) (0.11) Observations 1194 1189 1190

* p < 0.1, ** p < 0.05, *** p < 0.01. The data comes from the German National Evaluation of Pisa. School level regressions. School controls: Size of location area, share of girls in the school and L¨ander fixed-effects.

Panel A: Which of the following options describe what your school does for 15-year-old students in Mathematics classes? 1-3 scale. Column 1: Mathematics classes study similar content, but at different levels of difficulty; Column 2: Different classes study different content or sets of Mathematics topics that have different levels of difficulty; Column 3: Students are grouped by ability within their Mathematics classes; Column 4: In mathematics classes, teachers use a pedagogy suitable for students with heterogeneous abilities (i.e. students are not grouped by ability). Panel B : How much do you agree with these statements about innovation in your school? 1-4 scale. Column 1: Mathematics teachers are interested in trying new methods and teaching practices; Column 2: There is a preference among Mathematics teachers to stay with well-known methods and practices; Column 3: There are frequent disagreements between innovative and traditional Mathematics teachers. Panel C : How much do you agree with these statements about teachers expectations in your school? 1-4 scale. Column 1: There is a consensus among Mathematics teachers that academic achievement must be kept as high as possible; Column 2: There is a consensus among Mathematics teachers that it is best to adapt academic standards to the students’ level and needs; Column

3: There are frequent disagreements between Mathematics teachers who consider each other to be too demanding or too lax. Panel D : How much do you agree with these statements about teaching goals in your school? 1-4 scale. Column 1: There is consensus among Mathematics teachers that the social and emotional development of the student is as important as their acquisition of Mathematical skills and knowledge in Mathematics classes;Column 2: There is consensus among Mathematics teachers that the development of Mathematical skills and knowledge in students is the most important objective in Mathe- matics classes; Column 3: There are frequent disagreements between Mathematics teachers who consider each other as too focused on skill acquisition or too focused on the affective development of the student.

C.2 Are the Results Driven by Different Economic Conditions?

Table A6: Economic Conditions and the Gender Gap in Mathematics - PISA-E 2003. Within West Germany

(1) (2) (3) (4) (5) (6)

Global Space Relationships Uncertainty Quantity Problem

and Shape and Change Solving

Panel A Indicator of economic conditions : Rate of unemployment

Female -31.09*** -33.03*** -33.25*** -40.29*** -22.07*** -19.92*** (1.85) (2.33) (2.37) (2.14) (2.15) (2.13) Unemployment Rate -3.98*** -3.67*** -4.65*** -4.11*** -3.88*** -4.10*** (0.36) (0.38) (0.42) (0.38) (0.38) (0.38) Unemployment Rate*Female 0.04 -0.11 0.29 0.34 -0.16 0.50 (0.26) (0.32) (0.32) (0.29) (0.31) (0.32) Observations 15886 15886 15886 15886 15886 15886

Panel B Indicator of economic conditions : Wealth

Female -30.73*** -33.07*** -31.75*** -38.20*** -23.04*** -16.07*** (1.29) (1.64) (1.65) (1.52) (1.56) (1.44) Wealth Lander 99.18 111.90 157.67* 109.01 62.33 79.68 (67.02) (69.82) (82.00) (69.70) (66.60) (67.68) Wealth Lander*Female -2.01 6.08 -8.86 -2.44 -6.55 12.65 (8.39) (10.73) (9.67) (9.55) (9.34) (11.24) Observations 15886 15886 15886 15886 15886 15886

* p < 0.1, ** p < 0.05, *** p < 0.01. The data comes from the German National Evaluation of Pisa. The sample is restricted to individuals who were born in Germany. Individual controls: a quadratic in age, class, a quadratic in wealth, education and employment status of both parents. School controls: Size of location area and share of girls in the school and L¨ander fixed-effects. The rate of unemployment is computed from the answers given by the students on their parent’s job status and aggregated at the L¨ander level. The variable wealth is developed by the OECD based on the answers given by the students on the size of their house, the equipment of their house, the number of books that their parents have. We use this variable and aggregate the answers given by the students at the L¨ander level. This variable is scaled from -6.17 to 3.15, a higher score meaning a higher plausible family wealth.

C.3 Are the Results Driven by Different Religion?

Figure A1: The Geographic Distribution of Religious Affiliation in Germany, in the 17th and 20th Century

Table A7: Religion and the Gender Gap in Mathematics - GSOEP Self-Declared Grade. Within West Germany

Dependent Variable: High Self-Declared Math Grade (0-1 scale)

(1) (2) (3)

Female -0.06** -0.07*** -0.07***

(0.03) (0.02) (0.01)

Share of Protestants -0.29

(0.23) Female * Share of Protestants -0.04

(0.16)

Share of Catholics 0.24

(0.19)

Female * Share of Catholics 0.02

(0.12)

Protestant/Catholic Ratio -0.01

(0.00)

Female * Protestant/Catholic Ratio 0.00

(0.00)

Observations 11020 11020 11020

* p < 0.1, ** p < 0.05, *** p < 0.01. Linear probability model. The data comes from the German Socio-Economic Panel. Standard errors clustered at the household level are given in parentheses. Sample restricted to individuals born before 1971. East=1 if the household head lived in the GDR before 1990. Controls: age, household Income and L¨ander fixed-effects. Question: Can you remember your last report card ? What grade did you have in mathematics (1-6 scale with 1 being the highest grade)? Answer originally on a 1-6 scale recoded on a 0-1 scale, 1 corresponding to a grade of 1 or 2. The share of religious individuals is computed from the answers given by individuals and aggregated at the L¨ander level.

D

PISA Europe

D.1 Country List

We examine the gender gap in mathematics in Western and Eastern Europe. We selected all the countries who participated at least once in the PISA international as- sessments from 2000 until 2012. Countries included in the Western group are: Austria, Belgium, Denmark, Finland, France, Iceland, Ireland, Italy, Luxembourg, Netherlands,

Norway, Portugal, Spain, Switzerland, Sweden and United-Kingdom. Countries included in the Eastern group are: Albania, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Macedonia, Moldova, Montenegro, Poland, Romania, Serbia, Slovakia, Slovenia, Russia and Yugoslavia.

D.2 Reading Results

Table A8: Reading Scores - PISA Europe 2000 to 2012

Dependent Variable: Global Reading Score (1) Female 33.06*** (0.39) Former Socialist -36.90*** (3.65) Female*Former Socialist 10.41*** (0.67) Observations 724784 * p < 0.1, ** p < 0.05, *** p < 0.01. The data comes from the 2000, 2003, 2006, 2009 and 2012 waves of PISA. Controls: quadratic in age, class, quadratic in household wealth, education of both parents as well as country and year fixed effects. Sample restricted to national born individuals.

D.3 Quantile Regression

We ran quantile regressions and computed the p-value from a Wald Test between the 10th and 90th percentile. As compared to the 10th percentile, the gender gap in math score is significantly greater at the 90th percentile (p-value = 0.000 with a t-stat of 19.55). The attenuation of this gap, related to the coefficient F emale ∗ East in Eastern European countries is significant at every percentile but slightly smaller at the 90th percentile as compared to the 10th one (p-value=0.093).

Table A9: Maths Scores by Quantiles - PISA Europe 2000-2012

Dependent Variable: Global Mathematics Score

(1) (2) (3) (4) (5) Quantile 10 30 50 70 90 Female -9.81*** -14.34*** -17.58*** -20.31*** -22.39*** (0.48) (0.38) (0.32) (0.36) (0.51) East -1.71 -5.31** -8.96*** -9.59*** -7.47*** (3.39) (2.58) (2.37) (2.4) (2.62) Female*East 8.68*** 10.9*** 11.01*** 10.75*** 7.19*** (0.71) (0.59) (0.53) (0.62) (0.75) Observations 726142 726142 726142 726142 726142

* p < 0.1, ** p < 0.05, *** p < 0.01. The data comes from the 2000, 2003, 2006, 2009 and 2012 waves of PISA. Standard errors are calculated with a bootstrap method. Controls: quadratic in age, class, quadratic in household wealth, education of both parents as well as country and year fixed effects. Sample restricted to national born individuals.

In document Math, girls and socialism (Page 36-49)

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