Transcript ASER data – informing policy debates

ASER data –
informing policy debates
Round Table Discussion : Cambridge University – 12 June 2014
Speakers:
Baela Raza Jamil, Monazza Aslam and Shaheen Sardar Ali
Evidence based policy
• Education research – undergoing revolution.
• ASER – example of large scale initiatives driven by
desire to base policies on scientific evidence.
• An important aspect of evidence-based policy is the
use of scientifically rigorous studies using quality
data to identify programs and practices capable of
improving policy-relevant outcomes.
Fostering Evidence-based policy and
research
Quality Data
Political
Support
Good
Analytical
Skills
Using ASER data to highlight key
messages through…
•
•
•
•
Annual Reports;
Policy Briefs;
Working Papers;
Studies using ASER data published in peer reviewed
journals;
• Newspaper articles;
• Economic Survey of Pakistan;
• Blogs, discussion forums, twitter & other social
media
Policy briefs – food for thought?
Education and
Learning in Pakistan
• A Story of Disparities
(Monazza Aslam and
Shenila Rawal)
Right to Education
• The Impact of Parents’
Literacy on Children’s
Education Access and
Learning
Expanding Equitable
Early Childhood Care
Standardizing Early
Childhood Education
• An Urgent Need! (Sahar
Saeed)
• Student Achievement and
Teacher Qualifications
(Zara Khan)
The Link of Pupil
Teacher Ratio (PTR)
Comparative Analysis
of Urban Polarization
• To Student Learning
Achievements in Pakistan
(Ravish Amjad)
• Education for Sustainable
Development (Huma Zia)
Learning – a story of disparities
• Education systems around the world are
characterised by persistent inequalities that
manifest in different ways - by gender, by class, by
religion, caste or ethnicity and in others through
socio-economic status or disability.
• Inequalities in access and in differences in learning
and achievement and consequently, differences in
the life they eventually lead.
• Pakistan – beset by inequalities since 1947.
Until recently…
• it has not been possible to document the exact
extent of differences in learning outcomes by
gender and especially across different districts in
the country.
• The availability of the Annual Status of Education
Report (ASER) data, however, allows us to
overcome this constraint and paint a picture of the
nature of gender gaps in key learning outcomes
across 32 districts in the country.
Progressive data collection…
• The ASER team has collected data on learning
outcomes (as well as other indicators such as
proportion of out of school children, the incidence of
tuition-taking, parental education levels etc.) effectively
since 2010.
• The coverage of districts across the country has been
progressive with 32 districts covered in 2010, 85 in
2011, 136 in 2012 and 138 in 2013.
• For the first time, it has become possible to generate a
data set that has collected information over
consecutive years on crucial education indicators.
Aslam and Rawal (2014)
• Use data from 2011, 2012 and 2013 to document gender gaps in key
learning outcomes.
• Data rendered to averages at the district level, resultant 32 data points
(representing the 32 districts) on which we have data across the two time
periods.
• Note: ASER survey does not revisit the same households across two time
periods and even villages are replaced over any two consecutive years.
While not a perfect panel, the datasets at hand do allow for a descriptive
analysis that can nevertheless be informative and indicative if not
necessarily able to establish causal linkages.
• ASER selects its villages through Probability Proportional to Size (PPS)
methodology. A sample of 30 villages is selected from one district such that
20 new villages are selected every year and 10 villages from the previous
year are retained for comparative analysis purposes.
A story of disparities in learning levels across districts:
% unable to read in 2011 and 2013
What can we say?
While we cannot draw causal inferences from these basic descriptive
statistics, two findings are very clear from the charts:
• The pattern indicates wide differences across districts in terms of
childrens’ competencies in both their local language
(Urdu/Sindhi/Pashto) and mathematics outcomes.
• While there appears to be some improvement in both reading and
mathematics competencies among the tested children over the
short time period being studied, it is clear that children in these
rural districts face severe learning challenges in basic literacy and
numeracy competencies – a very large proportion of children of
school-going age are illiterate and do not possess the basic literacy
and numeracy skills needed to make them productive contributors
to society.
A story of wide, widening and persistent gender
gaps in learning outcomes
Mathematics Achievement
Level
Of Child
2011
M
F
Nothing
17.1
24.5
Numbers 1-9
14.8
16.8
Numbers 10-99
21.0
21.6
Subtraction (2 digit)
18.1
16.1
Division (2 Digits)
27.4
22.2
2012
M
F
***
20.3
28.1
**
13.8
2013
M
F
15.6
20.9
***
13.1
14.4
16.6
**
20.9
20.0
23.1
21.6
**
**
15.9
12.5
**
18.4
16.3
**
***
29.7
24.4
***
28.8
24.7
***
***
A story of differences in access to
private tuition
• Shadow education sector – emerging ‘industry’ in the region.
• children are coached, out of school hours, to achieve better in statewide exams.
• Has raised pertinent questions regarding the equity, efficiency and
social justice implications of the rise of this education sector.
• The option of giving (for the teachers) and receiving (for the pupils)
tuition outside of normal school hours changes the incentive
structure of the provision of high quality instruction within the
standard school system which in turn has implications for equity and
social justice.
• The relationship between private tutoring and student achievement
is also increasingly gaining policy attention as it calls into question
the quality of schooling during usual school-hours.
District-wise data reveal:
• The incidence of private tuition taking is systematically high, especially in
certain districts.
• For example, as many as 25% of the male children aged 5-16 sampled in
Sheikhupura report taking private tuitions outside school hours.
• There is significant diversity in uptake of private tuitions across the districts
with some displaying very high uptake as compared to others.
• In almost all of the districts in 2013, the incidence of tuition taking appears
to display a pro-male gender bias in that boys are on average more likely to
be taking private tuitions as compared to girls.
• In some instances the gaps are especially large – for example in
Sheikhupura, Multan, Rawalpindi, Mianwali, Islamabad, Faisalabad and
Gotki among others.
% taking private tuition(aged 6-16)
by gender and district
Analyses such as above raise questions
and generate debate…
• Differences in access to human capital are perhaps one of
the most critical dimension of inequality of opportunity.
• The short descriptive analysis in the brief discussed here
has shown the extent to which educational inequalities exist
and manifest themselves in the form of large gender gaps or
differences across districts, even within the same provinces.
• What is more, the analysis has clearly shown that the last
few years have seen no systematic improvement in key
educational indicators. Gender and regional differences are
two of the most persistent contenders generating multiple
disadvantages.
Glass half full?
• The glass half full picture certainly shows Pakistan
having made great strides in improving educational
access to girls and more broadly to children in
different regions in rural areas, the glass half empty
picture certainly indicates worryingly persistent
gaps that still prevail.
• This is especially worrying because these gaps exist
in what children know (or rather do not know),
something that has the ability to alter their life
chances.
Targeted policy
• Policy clearly needs to target the more systemic and
deep rooted issues that both generate and perpetuate
biases.
• In terms of educational access (or even access to
private tutoring) for instance it could mean using
targeted social policy and media campaigns to alter
cultural prejudice against girls.
• There is clearly a need for policy reform to target girls’
learning outcomes and identify at-risk individuals to
provide support to ensure meaningful learning.
What does this mean for global learning
goals?
Data like ASER and the subsequent analyses highlight:
• The need to focus on the marginalised – be it by gender,
by caste, by socio-economic status etc;
• Measuring outcomes is critical – having well defined
targets essential;
• Measuring progress (or lack thereof) is critical;
• Debate is crucial – but it needs to be based on scientific,
evidence-based research that feeds further into
discussions surrounding the post 2015 agenda for
education indicators.