Starting Together, Growing Apart: Gender gaps in learning from preschool to adulthood in four developing countries Abhijeet Singh University College London, Young Lives Sofya Krutikova Institute for Fiscal Studies, Young Lives Young Lives conference, Oxford 9 Sept 2016

1. Starting Together, Growing Apart:
Gender gaps in learning from preschool to adulthood in four
developing countries
Abhijeet Singh
University College London,
Young Lives
Sofya Krutikova
Institute for Fiscal Studies,
Young Lives
Young Lives conference, Oxford
9 Sept 2016

2. Introduction
The dynamics of learning inequalities
I Inequalities in educational opportunities and outcomes are of
central interest to social scientists and to policy-makers:
I these can translate into inequalities in later outcomes, e.g.
labour force participation, nature of employment and wages
I these could, if unrelated to productivity, indicate a
misallocation of resources
I perhaps most importantly, equality of opportunity (in which
education is key) has intrinsic value and remains a valuable
policy objective
I In this paper, we look at gender gaps in learning outcomes
from preschool age to early adulthood in four developing
countries
I This is the first part of a larger stream of research looking at
inequalities in skill formation (e.g. SES)

3. This paper
What we do
I We use YL panel data on student achievement from 5-19 years
to study gender differences in learning for children in Ethiopia,
India(Andhra Pradesh), Peru and Vietnam
I Specifically, we investigate for multiple learning domains:
I whether gender gaps exist and their magnitude in each context
I how these gaps evolve from preschool-age to adulthood
I what the sources of these differences are in terms of household
choices, investments and schooling
I We pay careful attention to issues of comparable measurement
and ordinality of test scores which are key for studying
inter-group inequalities
I Our overall objective is to provide a detailed assessment of
which inequalities matter, where, at what ages and what are
the sources by which they emerge

4. Why looking at learning gaps is important
Gender gaps in education in developing countries
I In developing countries most focus on gaps in access to
schooling (e.g. MDGs):
I ignores differences in learning levels
I Differences in enrolment have sharply reduced globally,
especially at primary levels
I Differences in learning may still be important:
I boys and girls may receive different investments at home
I boys and girls may go to schools of different quality
I boys and girls may be treated differently in the same
schools/classes
I Eventually, we may care more about differences in skills more
than differences in just enrolment
I With automatic promotion to higher grades, unclear if grade
progression is an adequate measure of skills

5. Main results
I Gender differences are not significant in early childhood to
primary school age in any country

6. Main results
I Gender differences are not significant in early childhood to
primary school age in any country
I Difference in learning attainment emerge in adolescence and
mostly persist to early adulthood
I Some differences apparent at 12, but usually small
I larger gaps at 15, favouring boys in Ethiopia and India, girls in
Vietnam
I Gender-based divergence, when present, is typically across the
distribution of initial achievement
I at 19, gaps which are observed are those that seem to have
persisted from the age of 15.
I Where gaps are significant, they are consistent across domains

7. Main results
I Gender differences are not significant in early childhood to
primary school age in any country
I Difference in learning attainment emerge in adolescence and
mostly persist to early adulthood
I Some differences apparent at 12, but usually small
I larger gaps at 15, favouring boys in Ethiopia and India, girls in
Vietnam
I Gender-based divergence, when present, is typically across the
distribution of initial achievement
I at 19, gaps which are observed are those that seem to have
persisted from the age of 15.
I Where gaps are significant, they are consistent across domains
I Gender gaps can be large in absolute magnitude, albeit smaller
than other forms of inequality or the absolute deficit in
learning levels
I Observed investments, sorting and differences in within-school
productivity explain about two-thirds of the divergence
between boys and girls in India and less in other countries.

8. Contribution
I Our central contribution is to provide the most extensive
description and decompositions of gender-based differences in
achievement in developing countries:
I no internationally comparable evidence using panel data
I nothing in LMICs covering an equally wide age range
I or able to simultaneously evaluate the possible importance of
both school and household factors.
I A further core contribution relates to measurement:
I specifically, how to construct scores comparably over time,
across a wide variation in ages, contexts and achievement
I Acknowledging issues of ordinality in test scores
I wider importance than just this paper but the first such
application to inter-group differences in developing countries

9. Data
This paper uses the Young Lives dataset in Ethiopia, India(Andhra
Pradesh), Peru and Vietnam
I two cohorts of children (born in 1994/95 and 2001/02)
I Four rounds of data - 2002, 2006, 2009 and 2014
I Detailed tests of academic achievement in multiple rounds:
I Measures of quantitative and language skills
I Designed to pick up a broad range of achievement
I Testing at home, not conditional on enrolment or attendance
I particularly important if there are gender-based patterns in
enrolment or attendance
I Rich household data, with information on schools attended, in
each round
I important because within-household allocation matters for
gender differences

10. Ages of children in Young Lives
Round 1 Round 2 Round 3 Round 4
05101520
Ageinyears
O
ct2002
D
ec
2006
N
ov
2009
N
ov
2013
Time
Younger cohort Older cohort
Graph shows median age of children and time of interview across countries
By age of children
Timing of survey rounds

11. Measuring learning outcomes comparably
I A comparable assessment of inequalities in learning requires a
comparable metric in which to measure achievement
I We use Item Response Theory to generate these comparable
measures
I decades long history in education and psychometrics e.g.
PISA, TIMSS and well-known US datasets (NAEP, ECLS-K)
I given a (partial) overlap across different assessments, tests
scores can be linked on a common metric
I We link assessments using common items across
rounds/countries/ages:
I Preschool quantitative ability: linked across countries at 5
I Math: linked across countries and across ages from 8–19 years
I Receptive vocabulary: linked within-language from 5–15
years
I Reading: linked within-language at 12 and 19 years

12. Gaps in enrolment and grade progression
Panel A: Proportion enrolled
Age Year Ethiopia India Peru Vietnam
Female Male Female Male Female Male Female Male
5 2006 0.04 0.03 0.45 0.44 0.01 0.01 0.01 0.00
8 2009 0.8 0.78 0.99 1.00 0.99 0.99 1.00 1.00
12 2014 0.98 0.96*** 0.97 0.98 1.00 1.00 0.99 0.99
12 2006 0.98 0.97 0.92 0.94 0.99 0.99 0.98 0.97
15 2009 0.93 0.9 0.8 0.87*** 0.95 0.91* 0.82 0.74***
19 2014 0.65 0.56** 0.42 0.57*** 0.51 0.52 0.52 0.43**
Panel B: Highest grade completed
Age Year Ethiopia India Peru Vietnam
Female Male Female Male Female Male Female Male
8 2009 0.69 0.64 1.83 1.57*** 1.31 1.32 1.76 1.73
12 2014 3.89 3.75* 5.67 5.31*** 6.04 6.05 5.75 5.7*
12 2006 3.38 3.31 5.75 5.7 4.98 4.88 5.63 5.61
15 2009 5.84 5.51** 8.39 8.4 7.88 7.79 8.45 8.25***

13. Quantitative skills
Preschool age gaps at 5
0.2.4.6.81
Proportionbelow
−4 −2 0 2 4
CDA score
Ethiopia
0.2.4.6.81
Proportionbelow
−4 −2 0 2 4
CDA score
India
0.2.4.6.81
Proportionbelow
−4 −2 0 2 4
CDA score
Peru
0.2.4.6.81
Proportionbelow
−4 −2 0 2 4
CDA score
Vietnam
Girls Boys

14. Quantitative skills
Gaps in from preschool to early adulthood
−.20.2.4.6
Standarddeviations
5y(2006)
8y(2009)
12y(2013)
12y(2006)
15y(2009)
19y(2013)
Ethiopia
−.20.2.4.6
Standarddeviations
5y(2006)
8y(2009)
12y(2013)
12y(2006)
15y(2009)
19y(2013)
India
−.20.2.4.6
Standarddeviations
5y(2006)
8y(2009)
12y(2013)
12y(2006)
15y(2009)
19y(2013)
Peru
−.20.2.4.6
Standarddeviations
5y(2006)
8y(2009)
12y(2013)
12y(2006)
15y(2009)
19y(2013)
Vietnam
Test scores are linked across countries/ages from 8−19y, normalized w.r.t. the pooled 8y sample.

15. Panel-based analyses of divergence
I The cross-sectional analyses above show that differences seem
mostly to arise in middle schools/adolescence
I But three key questions remain:
I is divergence concentrated among boys/girls at particular ends
of the ability distribution?
I where gaps are observed at two successive ages, how much
reflects persistence vs. the creation of fresh divergence?
I would the conclusions about fresh divergence or not be robust
to transformations of the (ordinal) test scores?
I We use the panel dimension of the data to investigate these
questions non-parametrically.

16. Divergence in learning by initial ability
12-15 years
−1012−1012
0 50 100 0 50 100
ET IN
PE VN
Mathscores(2009)
Percentiles of Math scores (2006)
Mathematics
−2−101−2−101
0 50 100 0 50 100
ET IN
PE VN
Clozescores(2009)
Percentiles of PPVT scores (2006)
Cloze
Girls Boys

17. Investigating sources of divergence
I Panel analyses confirm that:
I divergence is concentrated in adolescent years;
I affects boys and girls across the ability distribution;
I and is not an artefact of scaling
I A key question is what accounts for this documented
divergence?
I We will investigate multiple channels in a standard regression
framework
I We focus on the period between 12-15 years
I I will only present results on quantitative skills right now

18. Where do the gaps come from?
Differences in household characteristics and child-specific investment
Ethiopia India Peru Vietnam
Female Male Diff Female Male Diff Female Male Diff Female Male Diff
Household level variables
Caregiver’s education level
— None 0.50 0.51 -0.00 0.68 0.69 -0.00 0.10 0.11 -0.01 0.09 0.11 -0.02
— Up to Grade 8 0.26 0.28 -0.02 0.19 0.21 -0.01 0.37 0.33 0.04 0.27 0.29 -0.02
— Grade 9-10 0.02 0.02 0.00 0.08 0.07 0.02 0.37 0.38 -0.01 0.46 0.42 0.04
— Grades 11-12 0.04 0.03 0.01 0.02 0.02 0.00 0.06 0.07 -0.01 0.13 0.13 -0.00
— Higher Education 0.17 0.16 0.01 0.01 0.02 -0.01 0.10 0.10 -0.01 0.06 0.06 0.00
Household size 6.36 6.34 0.02 5.02 5.08 -0.06 5.33 5.43 -0.10 4.65 4.43 0.22*
Urban 0.43 0.40 0.02 0.24 0.26 -0.02 0.76 0.78 -0.02 0.19 0.21 -0.02
Wealth index 0.35 0.35 -0.01 0.52 0.53 -0.01 0.59 0.59 -0.00 0.63 0.62 0.02
Child-specific investments
Enrolled at 15 years 0.91 0.88 0.04 0.74 0.81 -0.07* 0.95 0.91 0.04 0.81 0.73 0.08**
Child specific expenditure on 130.92 192.47 -61.55 1474.66 3162.62 -1687.97*** 308.99 347.62 -38.63 1935.70 1858.08 77.62
education (annual)
Height-for-age z-score -1.01 -1.74 0.73*** -1.69 -1.60 -0.08 -1.59 -1.38 -0.21** -1.40 -1.46 0.06
Height-for-age z-scores are defined as per WHO standards. The wealth index is
generated based on consumer durables, housing and access to services.

19. Do differences in investments explain learning divergence?
Specifications
Yia = ↵ + 1.malei (1)
+ 2.Yi,a 1 (2)
+ 3.enroli,a (3)
+ 4.EdExpia + 5.zhfaia (4)
where Yia is test score of child i at age a
male is a dummy variable (=1 for boys)
enrol is a dummy variable for enrolment
EdExp is expenditure on schooling, entered quadratically
zhfa is height-for-age z score
Although parsimonious, the controls summarize several potentially
important channels of divergence.

20. Do differences in investments explain learning divergence?
Results, 12-15 years, Mathematics
VARIABLES Dependent var: Math scores at 15 years
No controls +Lagged achievement +Enrolment +Expenditure + HAZ
Ethiopia 0.247*** 0.206*** 0.210*** 0.207*** 0.223***
(0.0555) (0.0594) (0.0534) (0.0530) (0.0583)
India 0.334*** 0.287*** 0.274*** 0.246*** 0.241***
(0.0599) (0.0622) (0.0552) (0.0577) (0.0573)
Peru -0.00284 -0.0503 -0.0296 -0.0336 -0.0421
(0.0553) (0.0526) (0.0475) (0.0472) (0.0471)
Vietnam -0.187*** -0.156*** -0.132*** -0.132*** -0.128**
(0.0377) (0.0391) (0.0453) (0.0452) (0.0476)
Cells contain the coefficient on male dummy variable from OLS regressions, run within
country sample, with sequential addition of control variables. Robust standard errors
in parentheses, clustered at the site level.

21. Can differential time use explain divergence?
Gender difference in time allocation
Ethiopia India Peru Vietnam
Age Year Male Female Male Female Male Female Male Female
12 2006 Caring for others 0.45 0.78 0.1 0.27 0.6 0.88 0.25 0.39
Domestic tasks and chores 1.65 2.86 0.55 1.24 0.98 1.16 1.01 1.41
Tasks on domestic farm/business 2.04 0.88 0.33 0.2 0.37 0.32 0.7 0.59
Work outside household 0.17 0.13 0.37 0.4 0.15 0.03 0.02 0.07
At school 5.35 5.54 6.12 6.08 5.47 5.64 4.37 4.43
Studying after school 1.75 1.72 2.02 1.83 1.82 2.08 2.69 3.03
Play/general leisure 3.03 2.56 4.31 3.79 2.32 2.16 5.99 5.47
Sleep 9.04 9.04 9.04 9.04 9.29 9.29 8.92 8.62
15 2009 Caring for others 0.48 0.92 0.1 0.45 0.67 0.82 0.11 0.23
Domestic tasks and chores 1.76 3.48 0.83 2.05 1.18 1.7 1.32 1.63
Tasks on domestic farm/business 2.23 0.43 0.54 0.45 0.66 0.69 1.26 0.93
Work outside household 0.51 0.32 1.05 1.02 0.58 0.23 0.59 0.48
At school 5.29 5.74 6.8 6.01 5.76 6.07 3.93 4.31
Studying after school 1.9 1.82 2.14 1.88 1.94 2.27 2.7 3.3
Play/general leisure 3.19 2.63 4.25 3.88 3.38 3.09 5.1 4.53
Sleep 8.65 8.66 8.3 8.26 8.94 8.86 8.91 8.46
19 2013 Caring for others 0.26 0.97 0.17 1.31 0.43 2.12 0.22 0.81
Domestic tasks and chores 1.22 3.19 1.11 2.65 1.02 2.05 1.08 1.82
Tasks on domestic farm/business 2.46 0.88 1.24 0.96 0.63 0.66 1.57 1.08
Work outside household 2.11 1.19 2.89 1.31 3.75 2.08 3.12 2.45
At school 3.42 3.77 4.24 3.19 3.84 3.34 2.47 3.01
Studying after school 1.58 1.65 1.24 1.13 1.47 1.49 1.09 1.32
Play/general leisure 4.54 3.75 5 5.07 3.79 3.43 6.12 5.23
Sleep 8.42 8.61 8.11 8.37 8.15 8.33 8.28 8.27

22. Can differential time use explain divergence?
Not really...
Ethiopia India Peru Vietnam
VARIABLES Dep var: Math
Male 0.202*** 0.237*** -0.0134 -0.130***
(0.0557) (0.0493) (0.0394) (0.0442)
Hours per day spent:
— in caring for hh members 0.0162 -0.0248 -0.000556 -0.0519
(0.0349) (0.0468) (0.0180) (0.0328)
—in hh chores -0.000571 0.0782** 0.0252 -0.0139
(0.0272) (0.0321) (0.0194) (0.0271)
—in domestic tasks - farming, business 0.0121 0.0505 0.0206 0.00679
(0.0267) (0.0315) (0.0166) (0.0216)
—in paid activity -7.98e-05 0.0405 0.0326** -0.0110
(0.0267) (0.0290) (0.0155) (0.0185)
—at school 0.0286 0.0834*** 0.0383** 0.0331
(0.0273) (0.0284) (0.0163) (0.0267)
—studying outside school 0.119*** 0.0992*** 0.0673*** 0.00273
(0.0272) (0.0247) (0.0206) (0.0207)
—leisure activities -0.00775 0.0539** 0.0180 -0.0195
(0.0245) (0.0261) (0.0153) (0.0188)
Observations 880 875 653 895
R-squared 0.397 0.386 0.429 0.364
Regressions include full set of controls previously included. Coefficients not reported
here.

23. Does schooling quality explain gender gaps?
Sorting across schools
I The estimation above does not directly account for differences
in school quality
I except to the extent this is captured by school fees
I However, such sorting (as well as within-school differences) can
be potentially important in explaining gender-based divergence
I Grant and Behrman (2010) “...if girls are likely to attend
different types of schools than boys, tend to take different
classes than boys, are treated differently than boys in the same
classes...”
I e.g. sorting of boys into private schools in India
I The YL data have limited information on schools
I However, schools attended by students are uniquely identifiable
I enables us to control for school fixed effects, thus identifying
gender differences based comparing boys and girls attending
the same schools

24. Does schooling quality explain gender gaps?
Sorting across schools
(1) (2) (3)
VARIABLES Math Vocabulary Cloze
Ethiopia 0.230*** 0.226*** 0.0212
(0.0505) (0.0840) (0.0914)
India 0.151* 0.206** -0.0890
(0.0874) (0.0803) (0.104)
Vietnam -0.0568 0.00538 -0.184***
(0.0512) (0.0628) (0.0671)
Cells are coefficients on the male dummy with standard errors in parentheses from
regressions including all previous controls including lagged achievement, and a full
vector of school fixed effects. Coefficients for these variables are not reported here.

25. Do differences in within-school productivity explain
divergence?
I A final potential source that we can investigate is that of
within-school differences in the productivity of schooling for
boys and girls:
I could be e.g. if boys and girls were treated differently
I or e.g. due to gender-match between students and teachers
I Key specification
Yia = ↵ + 1.malei + 2.male ⇤ enroli,a + 3.Yi,a 1 + 4.Xi
+ 5.EdExpia + 6.HAZia + ✓s + ✏ia
I 2, if significant, will indicate gender-based differences in the
productivity of schools.
I We do not find significant evidence of such gaps (although, to
be fair, statistical power is an issue).

26. Summarizing the regression results
I Despite relatively rich measures of inputs, we are unable to
fully explain gaps in any settings
I sometimes forced into statistical insignificance but mostly
that’s a power issue
I The decomposition exercises are most informative in India
I clear differences in lagged achievement, in investments and in
enrolment in the same direction as learning gaps
I can explain up to 2/3 of the cross-sectional differences
I Decompositions less successful in other contexts
I partly because key measured inputs often aren’t different
across sexes
I sometimes, in fact, in the opposite direction (e.g. enrolment in
Ethiopia)
I time use does not add too much new information

27. Putting results into perspective
A comparison with SES-based gaps
.2.4.6.811.2
Standarddeviations
5y(2006)
8y(2009)
12y(2013)
12y(2006)
15y(2009)
19y(2013)
Ethiopia
.2.4.6.811.2
Standarddeviations
5y(2006)
8y(2009)
12y(2013)
12y(2006)
15y(2009)
19y(2013)
India
.2.4.6.811.2
Standarddeviations
5y(2006)
8y(2009)
12y(2013)
12y(2006)
15y(2009)
19y(2013)
Peru
.2.4.6.811.2
Standarddeviations
5y(2006)
8y(2009)
12y(2013)
12y(2006)
15y(2009)
19y(2013)
Vietnam
Test scores are linked across countries/ages from 8−19y, normalized w.r.t. the pooled 8y sample.
Always significant, in all countries, and MUCH larger

28. Main results
I Difference in learning attainment mostly emerge in adolescence
and mostly persist to early adulthood
I Gender-based divergence, when present, is typically across the
distribution of initial achievement
I at 19, gaps which are observed are those that seem to have
persisted from the age of 15.
I Where gaps are significant, they are usually consistent across
domains
I Observed investments, sorting and differences in within-school
productivity explain at most two-thirds of the divergence
between boys and girls in India and less in other countries.
I Gender gaps are smaller in magnitude than other forms of
inequality or the absolute deficit in learning levels

29. Discussion
Three main implications:
I For gender differences in learning, the key period to focus on is
in adolescence/post-primary education
I differences small and not systematic before but clear
divergence in this period
I Even systematic differences in inputs may be misleading as a
guide to whether differences exist in outcomes:
I In India, education expenditures, private school enrolment and
private tuition are all systematically gender-biased at all ages
but with no learning differences for many groups
I a rare upside to shockingly low productivity of inputs!
I If deciding priorities for educational policy, gender gaps in
learning seem much less pressing than other issues:
I the absolute deficit in learning across all children
I inequalities by other dimensions, e.g. SES

30. Comments and Questions?