gender gaps in education - india
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Gender gaps in education - IndiaAnjini Kochar
Stanford Center for InternationalDevelopment
Stanford University
The views expressed in this paper are the views of the author and do not necessarily reflect the views or policies of the AsianDevelopment Bank (ADB), or its Board of Governors, or the governments they represent. ADB does not guarantee the accuracy ofthe data included in this paper and accepts no responsibility for any consequence of their use. The countries listed in this paper donot imply any view on ADB's part as to sovereignty or independent status or necessarily conform to ADB's terminology.
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Overview of talk
Data on gender disparities and trends
National, rural/urban, regional, caste
Gender disparities in achievement/quality
Evidence from within schools, Karnataka
Data on household expenditure on boys, girls
Rural/urban, regional
Theories
Gender differences in returns and costs of schooling
Family / cultural factors (son preference)Changing nature of the family (Karnataka)
Conclusion
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2007: Gender gaps in completed level of schooling, by age group and rural/urban
sector (NSS 64th round, major states)
Age group/
completed educ
Rural Urban
Male Female Male Female
11-13
primary 0.70 (0.46) 0.66 (0.47) 0.80 (0.40) 0.79 (0.41)
Currently - HPS 0.55 (0.50) 0.51 (0.50) 0.59 (0.49) 0.58 (0.49)
14-16
Primary 0.83 (0.37) 0.78 (0.41) 0.89 (0.31) 0.88 (0.32)Upper primary 0.51 (0.50) 0.46 (0.50) 0.65 (0.48) 0.66 (0.47)
Currently enroll -
secondary
0.35 (0.48) 0.30 (0.46) 0.42 (0.49) 0.40 (0.49)
17-19
Primary 0.83 (0.37) 0.72 (0.45) 0.90 (0.30) 0.89 (0.31)
Upper primary 0.63 (0.48) 0.52 (0.50) 0.75 (0.43) 0.76 (0.43)
Sec 0.38 (0.49) 0.31 (0.46) 0.54 (0.50) 0.59 (0.49)
Higher sec 0.12 (0.31) 0.12 (0.30) 0.22 (0.41) 0.28 (0.45)
Currently in Hsec 0.16 (0.37) 0.10 (0.31) 0.22 (0.41) 0.20 (0.40)
Rural: Comparison across cohorts suggests narrowing gaps over timeUrban: insignificant gender gap
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Trends in gender gaps in completed higher secondary (12 years) by region,
Rural India, (NSS), Ages 19-25
Same patterns: Narrowing of gender gaps in all regions; approximately equal gender gaps (2007-08) in
all regions
Greatest reductions in Central and West
With growth, growing disparities in education (male, female) across regions
0
.05
.1
.15
.2
.25
mean
ofchsec
North central East West South
1995 2007 1995 2007 1995 2007 1995 2007 1995 2007
Male Female
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Urban India (NSS) - Trends in gender gaps in completed higher secondary (12
years) by region, Ages 14-18
0
.1
.2
.3
.4
mean
ofchsec
North central East West South
1995 2007 1995 2007 1995 2007 1995 2007 1995 2007
Male Female
Gaps narrowed in all regions, except East
High growth regions are North, West, South
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Most recent data is from ASER 2011
Proportion out-of school children (India, rural)
Narrowing gender gaps, within each cohort
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Even in rural Bihar and Punjab
Rural Bihar Rural Punjab
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Data suggest that gaps appear at primary level, and then
sustained or narrowed at higher level
Ages 20-25, Rural India, 2007
Proportion of ages 20-25 (2007) completed primary: Males: 0.77; Females 0.57
- 20 percentage point difference
1995 survey: Gender gap in primary completions, ages 11-13 (1995): Males
0.63 (0.48); Females 0.47 (0.50) 16 percentage point difference
Gender gap in secondary completions, ages 20-25 (2007): Males 0.33 (0.17);
Females 0.22 (0.10) 11 percentage point difference
Gender gap in higher secondary completions, ages 20-25 (2007): Males 0.18(0.38); females 0.11 (0.31) 7 percentage point difference
Contrary to what one might want: everyone gets at least a primary education,
and gender gaps show up at higher levels here, gender gaps are lowest at low
levels, and then narrow over time
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Pattern is true of all regions
Rural India, ages 20-25 (2007-08, NSS)
Region Completed primary Completed higher secondary
Males Females Males Females
North 0.81
(0.40)
0.61
(0.49)
0.22
(0.41)
0.16
(0.36)
Central 0.71(0.45)
0.45(0.50)
0.15(0.36)
0.08(0.28)
East 0.72
(0.45)
0.59
(0.49)
0.12
(0.32)
0.08
(0.27)
West 0.87
(0.33)
0.72
(0.45)
0.23
(0.42)
0.15
(0.36)
South 0.83
(0.38)
0.67
(0.47)
0.22
(0.42)
0.16
(0.36)
Gender gaps much larger at primary level (those who did not complete primary), then at higher secondary level, suggesting
that gender differences in completed years of schooling is primarily because of gender differences in those completingprimary
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Urban India, ages 20-25 (2007-08, NSS)
Region Completed primary Completed higher secondary
Males Females Males Females
North 0.86
(0.35)
0.80
(0.40)
0.39
(0.49)
0.31
(0.59)
Central 0.82(0.38)
0.72(0.45)
0.33(0.47)
0.34(0.47)
East 0.86
(0.34)
0.78
(0.42)
0.36
(0.48)
0.31
(0.46)
West 0.92
(0.28)
0.88
(0.33)
0.39
(0.49)
0.39
(0.49)
South 0.92
(0.27)
0.87
(0.34)
0.39
(0.48)
0.39
(0.49)
Gender gaps much larger at primary level (those who did not complete primary), then at higher secondary level, suggesting
that gender differences in completed years of schooling is primarily because of gender differences in those completingprimary
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By caste - In North, South and West (high growth regions), gender gaps are
LARGER amongst upper castes, rural India, ages 14-18, 2007-08 (NSS)
Region Upper castes SC/ST
Males Females Males Females
Prop completed
upper primary
North 0.67
(0.47)
0.55
(0.50)
0.44
(0.50)
0.36
(0.48)
Central 0.50
(0.50)
0.43
(0.49)
0.39
(0.49)
0.32
(0.46)
East 0.49
(0.50)
0.52
(0.50)
0.40
(0.49)
0.31
(0.46)
West 0.72
(0.45)
0.59
(0.49)
0.58
(0.49)
0.52
(0.50)
South 0.78
(0.41)
0.71
(0.45)
0.64
(0.48)
0.63
(0.48)
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Alternative way of looking at gender gaps: Of currently enrolled,
percentage who are women (NSS, 2007-08)
Doesnt standardize for age: gender gaps may reflect differences in
acceptable ages at enrollment
Differences across levels reflect cohort effects
Currently
enrolled in
Rural Urban
Primary 45% 46%
Middle 46% 46%
Secondary 42% 46%
Higher
secondary
38% 46%
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Even here, trend is declining gender gaps
MHRD: Number of girls per 100 boys, class IX-XII
Region/State 2001-02 2005-06 Region/state 2001-02 2005-06
South West
A.P. 71 80 Gujarat 68 67
Karnataka 82 89 Maharashtra 76 92
Kerala 107 100
Tamil Nadu 84 97 Central
North Bihar 44 47
Punjab 83 84 MP 52 58
Haryana 68 76 UP 36 58
H.P. 86 88 Chattisgarh 57 65
Rajasthan 38 46 Jharkhand 54 62
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While data suggests narrowing gaps in levels of education, what
is the evidence regarding quality?
Learning gaps less research and data
Current (on-going) study of rural Karnataka schools (720
schools, 11 districts)
Very little evidence of a gender gap in learning in the South,
and, instead, a reverse gap favoring girls.
Government schools only, so results will be biased if
brighter boys go to private schools
Complete picture will require data which surveys all
schools in an area, and conducts same test in all schools
(eg. LEAPS, Pakistan)
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Test score Full
sample
Quartile of District EDI rank
1 (top) 2 3 4
Language (max=100)
Male 33.91
(0.33)
41.17
(0.83)
40.19
(0.60)
33.14
(0.81)
26.83
(0.50)
Female 37.01
(0.34)
45.61
(0.80)
43.57
(0.62)
39.60
(0.80)
26.78
(0.52)
F test for equality
Prob > F
42.39*
(0.00)
14.77*
(0.00)
15.27*
(0.00)
32.75*
(0.00)
0.00
(0.95)
Mathematics
(max=100)
Male 20.49
(0.22)
25.68
(0.64)
21.86
(0.40)
18.45
(0.49)
18.33
(0.33)
Female 21.13
(0.22)
27.79
(0.62)
22.60
(0.42)
18.25
(0.46)
18.44
(0.35)
F test for equality
Prob > F
4.01
(0.05)
5.43
(0.02)
1.64
(0.02)
0.09
(0.77)
0.04
(0.85)
Table 8: Test scores for grade 3 students, by gender and District EDI rank
Note: Language and math test scores based on grade specific curriculum. Sample size=11,447. Standard errors in parentheses.*Statistically significant different at 1% level
Test Scores, Rural Karnataka, Grade 3 2009-10 (Karnataka schooling project data)
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Learning gaps at higher levels
Difficult to interpret, because of greater selectivity of women
into higher education
Eg., MP, results from HSC-12 examinations:
Proportion Division 1: men - 0.24; women - 0.32
Proportion failing: men 0.18; women 0.12
But, proportion of female students (of those appearing for theexam): 0.39
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Possibility of gender gap in quality of schooling comes from data on schooling
expenditures 1995 and 2007 particularly in urban areas
Expenditure on schooling (all items), on children ages 12-15 currently enrolled
in higher primary school or higher (NSS Education Surveys).
RURAL URBAN
0
1,0
00
2,0
00
3,0
00
4,0
00
5,0
00
meanofedexp
1995 2007
1 2 1 20
1,0
00
2,0
00
3,0
00
4,0
00
5,0
00
meanofedexp
1995 2007
1 2 1 2
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Reflects enrollment in private schools, of far greater significance in urban
areas (NSS education surveys, 1995 and 2007)
Rural Urban
0
.1
.2
.3
.4
mean
ofpvt
1995 2007
1 2 1 2
0
.1
.2
.3
.4
mean
ofpvt
1995 2007
1 2 1 2
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Urban India: regional variation in education expenditure, 1995 & 2007
(ages 12-15, currently enrolled upper primary or higher)
particularly high in North and Central regions (traditional son preference)
0
,
,
,
,
1995 2007
1 2 3 4 5 1 2 3 4 5
1 2
0
,
,
,
1995 2007
1 2 3 4 5 1 2 3 4 5
1 2
Total Educational Expenditure School tuition costs
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Rural India: regional variation in education expenditure, 1995 & 2007
(ages 12-15, currently enrolled upper primary or higher)
Again high in North, but also in South
0
,
,
,
1995 2007
1 2 3 4 5 1 2 3 4 5
1 2
0
200
400
600
800
1995 2007
1 2 3 4 5 1 2 3 4 5
1 2
Total Educational exp. School tuition fees
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Conclusions from data
Reduction in gender gaps in completed schooling
Within cohort analysis suggests that difference in years ofschooling may reflect larger drop out rates by women at lowerlevels of schooling - challenge is at the elementary level
While gender disparities in years of schooling appear to benarrowing, emerging gaps in expenditure on schooling,particularly in regions that have traditionally shown sonpreference
Economic literature suggests that quality gap may influenceincome (and other outcomes) more than quantity gap(Hanushek et al 2007, Heckman and co-authors)
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Theory
Predicting narrowing gaps:
due to higher returns to schooling for women; increases inmaternal education; higher income elasticity of demand forfemale education (Maitra et al, 2012)
Munshi and Rosenzweig (2004) rates of return from pvtschooling are higher for women, but only amongst lower castes
reverse gender gap for lower castes, but no predictions forhigher castes
Increasing gaps:
Transition from agricultural to non-agricultural economy may
initially cause increasing gender gaps which will then benarrowed - inverted U hypothesis (Goldin) diff. in rates ofreturn in transition
Greater credit constraints as incomes rise, causing greatergender inequality in household expenditure.
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In India, general belief that effect of rates of return mediated by
gender differences in returns to parents from sons/daughters
Traditionally believed that this would exacerbate effect of anygender differences in return
May also generate increasing gender gaps
Das Gupta and Bhat (1997): with rising incomes andconsequent decline in fertility, a reduction in the parityeffect (discrimination against girls at higher parities) butincrease in the intensification effect (greaterdiscrimination at lower parities), with latter effectdominating.
Underlying reason: family systems with strongdisincentives against investments in girls
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Problem with that explanation: insufficient attention to changing
nature of the family
(survey data, Karnataka, 2009-10)
father Mother
Grandparents financially dependent
on children
61.55% 61.40%
Parents expect to be financially
dependent on children
49.38% 50.65%
In families which support their
parents, proportion of parents who
expect to depend on their children
58% 57%
Expect to reside by themselves or
with spouse
26% 29%
Expect to reside with children 54% 53%
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Family and gender gaps
Data (Karnataka) also do not suggest greater investment in older son, with who
parents are most likely to live with
Expected years of schooling do not vary across sons (even by caste)
Despite the fact that parents generally claim that older son is brightest
(particularly amongst lower castes)
Alternative explanation: Dependence on son reducedexpenditure on sons,
because of fears regarding commitment to provide support. Widening gaps as
parental dependence on children increases (work in progress).
May particularly take the form of increased expenditures on private
schooling, since returns to private schooling are really in the non-agricultural,
formal sector
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Conclusions and Policy Implications:
Particularly need research on whether gender gap is switching fromquantity of schooling to quality, and reasons for this
If gender gaps reflect family preferences, then programs which relyon community organizations may not be successful (NPEGEL,
operates through community and womens organizations)
If increasing gaps reflect credit constraints, then have to re-structure programs such as Incentive to Girls for SecondaryEducation (2008-09) to address these constraints rather than Rs.
3000 in fixed deposit, withdrawable at age 18,need to providefunds continuously through secondary stage.
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