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IIEP-UNESCO Strategic Debate: the impact of inequalities on learning achievement

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Towards progressive universalism: the impact of inequalities on learning achievement.
IIEP Strategic Debate - May 2017
Speaker: Pauline Rose, Director, Research for Equitable Access and Learning (REAL) Centre, University of Cambridge
Moderator: Suzanne Grant Lewis (Director IIEP)
Drawing on analysis of available large-scale datasets, this session will show how inequalities in learning between the rich and poor and, amongst the poor by gender, widen substantially over the primary school cycle. It will also identify that children with disabilities are most likely to be left behind. The evidence further demonstrates that access to higher education for children from poor households is strongly dependent on their learning in the early years. Analysis will be presented showing that, where children from poor backgrounds have the same opportunities as those from rich backgrounds, learning gaps narrow significantly. It will further identify the importance of changing the way in which public resources are allocated, to achieve ‘progressive universalism’. The Debate will conclude by identifying ways in which data collection could be improved in resource-poor environments to enable better monitoring of education SDGs related to learning, with a focus on tracking progress for the most disadvantaged groups.

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IIEP-UNESCO Strategic Debate: the impact of inequalities on learning achievement

  1. 1. Towards progressive universalism: the impact of inequalities on learning achievement Professor Pauline Rose REAL Centre, Faculty of Education University of Cambridge
  2. 2. Learning inequalities and progressive universalism SDG4 Indicator 4.1.1: Proportion of children in grades 2/3 achieving at least a minimum proficiency level in reading and maths We need to give greatest priority to those children most at risk of being excluded from learning so unequal opportunities in one generation do not lead to unequal outcomes for the next… We can accomplish this only through a progressive universalism that will combine a commitment to every child with more resources devoted to those children who need most help.
  3. 3. Data sources UNESCO Institute for Statistics database Demographic and Health Surveys Sample-based household surveys assessing children in and out of school: Citizen-led assessments Annual nationwide annual survey of children’s literacy and numeracy in India, Pakistan, Kenya, Tanzania and Uganda Sample size ranges from 87,000 in Uganda to 655,000 in India Young Lives Longitudinal study of children and young people in Ethiopia, India (Andhra Pradesh), Peru, Viet Nam Cohort of ~1000 children born around 1994; surveyed at age 8, 12, 15 & 19 Photo courtesy of Uwezo Uganda
  4. 4. House type Electricity Mobile phone Television Wealth Measuring wealth-based inequalities
  5. 5. Wide learning gap between rich and poor children, taking account of whether or not children are in school
  6. 6. Steep progress needed to achieve basic learning for all 0 20 40 60 80 100 2015 2020 2025 2030 Percentagewhohavelearned thebasics Rural India 0 20 40 60 80 100 2015 2020 2025 2030 Percentagewhohavelearned thebasics Rural Pakistan 0 20 40 60 80 100 2015 2020 2025 2030 Percentagewhohavelearned thebasics Uganda 0 20 40 60 80 100 2015 2020 2025 2030 Percentagewhohavelearned thebasics Tanzania 0 20 40 60 80 100 2015 2020 2025 2030 Percentagewhohavelearned thebasics Kenya Source: Author calculations based on ASER and UWEZO, 2012
  7. 7. Poorest girls are more likely to be out of primary school Source: Authors’ calculations based on ASER Pakistan
  8. 8. In rural Pakistan, the poorest girls who are out of school have no chance of learning Source: Authors’ calculations based on ASER Pakistan
  9. 9. In rural Pakistan, the poorest girls who are out of school have no chance of learning Source: Authors’ calculations based on ASER Pakistan
  10. 10. Source: Paper prepared for the International Commission on Financing Global Education Authors’ calculations based on ASER Pakistan, 2015 Access and learning for children with disabilities in rural Punjab, Pakistan
  11. 11. In rural India, learning gaps widen in early years 0 5 10 15 20 25 30 35 40 45 7 8 9 10 11 Percentagewhocandothebasics (divisionandreadingastory) Age Rich girl, both parents attended school Rich boy, both parents attended school Poor boy, neither parent attended school Poor girl, neither parent attended school Source: Authors’ calculations based on ASER, India By age 11 only around 7% of poor girls have achieved the basics
  12. 12. Poorer children are far more likely to keep up when they get similar schooling opportunities Models: (1) OLS, (2) school fixed effects, (3) class fixed effects Source: Uwezo 2013, child ages 10–13
  13. 13. Poorer children are far more likely to keep up when they get similar schooling opportunities Models: (1) OLS, (2) school fixed effects, (3) class fixed effects Source: Uwezo 2013, child aged 10–13
  14. 14. Poorer children are far more likely to keep up when they get similar schooling opportunities to the rich Models: (1) OLS, (2) school fixed effects, (3) class fixed effects Source: Uwezo 2013, child ages 10–13
  15. 15. Higher education access almost non-existent for the poorest… 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% SaoTomeandPrincipe Tanzania Niger Rwanda Malawi Mozambique Liberia India Maldives BurkinaFaso Zambia Madagascar SierraLeone Senegal Coted'Ivoire Swaziland Zimbabwe Mali Kenya Ghana Congo,Dem.Rep. Ethiopia Gabon Lesotho Benin Namibia Congo Nigeria Guinea Cameroon Comoros Pakistan Bangladesh Nepal Higher education net enrolment rates, young people under 25 years, poorest 50% & richest 50% Poor Rich Source: Demographic and Health Surveys
  16. 16. … because so few complete secondary, or even primary, school 16 26 19 32 65 77 70 85 40 57 0 10 20 30 40 50 60 70 80 90 2000-2005 2010-2015 Primarycompletionrate(%) Primary completion rate in SSA Poorest girl Poorest boy Richest girl Richest boy Average 6 56 18 47 63 47 68 23 43 0 10 20 30 40 50 60 70 80 90 2000-2005 2010-2015 Lowersecondarycompletionrate(%) Lower secondary completion rate in SSA Poorest girl Poorest boy Richest girl Richest boy Average
  17. 17. Wealth and early learning: the strongest determinants of university access Source: Authors’ calculations based on Young Lives data 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Peru India Vietnam Ethiopia Chancesofaccesinghigher educationaged19 Richest children who are learning at age 8 Poorest children who are learning at age 8
  18. 18. Wealth and early learning: the strongest determinants of university access 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Peru India Vietnam Ethiopia Chancesofaccesinghigher educationaged19 Source: Authors’ calculations based on Young Lives data Richest children who are learning at age 8 Poorest children who are learning at age 8 Richest children who are NOT learning at age 8
  19. 19. Wealth and early learning: the strongest determinants of university access 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Peru India Vietnam Ethiopia Chancesofaccesinghigher educationaged19 Richest children who are learning at age 8 Poorest children who are learning at age 8 Richest children who are NOT learning at age 8 Poorest children who are NOT learning at age 8 Source: Authors’ calculations based on Young Lives data
  20. 20. In advocating progressive universalism, the Commission ..proposes that funds be allocated for the highest return activities and to those least able to pay. It implies strongly favoring of the allocation of public funding to the lower levels of the education ladder, and, within that, to those left behind because of poverty, disability, and social disadvantage. Progressive universalism and financing priorities
  21. 21. Who benefits from public spending on education? 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100% Liberia Congo Guinea Malawi Senegal D.R. Congo Burkina Faso Lesotho Sierra Leone Mali Ghana Benin Tanzania Niger Côte d'Ivoire Rwanda Cameroon Zambia Kenya Madagascar Togo Zimbabwe Swaziland Mozambique Ethiopia Maldives Gambia Namibia Bangladesh Comoros Nepal Group 1: poorest receive at least 50% of public expenditure spent on richest Group 3: poorest receive less than 10% of public expenditure spent on richest Group 2: poorest receive between 10% and 50% of public expenditure spent on richest Source: Ilie and Rose (2016) based on DHS and UNESCO Institute for Statistics database
  22. 22. 23 Who benefits from public spending on education? 0 0.5 1 1.5 2 2.5 Guinea Mali Burkina Faso Niger Mozambique Ethopia Senegal Cote d'Ivoire Tanzania Malawi Benin Rwanda Madagascar Cameroon Ghana Nepal Congo Kenya Lesotho Bangladesh India Zimbabwe Swaziland Namibia Pro-poor Pro-rich 0 5 10 15 20 25 30 Mozambique Ethopia Cote d'Ivoire Niger Burkina Faso Madagascar Tanzania Guinea Malawi Cameroon Mali Rwanda Lesotho Congo Benin Senegal Swaziland Zimbabwe Bangladesh Kenya India Ghana Namibia Nepal Pro-rich 0 20 40 60 80 100 Cameroon Zimbabwe Cote d'Ivoire Madagascar Burkina Faso Malawi Mozambique Benin Ethopia Namibia Tanzania Guinea Congo Mali Lesotho Senegal Swaziland Kenya Ghana India Bangladesh Niger Rwanda Nepal Pro-rich Ratio of education spending on richest 20% vs poorest 20% Primary education Secondary education Higher education Source: Ilie and Rose (2016) based on DHS and UNESCO Institute for Statistics database
  23. 23. Policy lessons to leave no one behind SDG monitoring Tackle disadvantage early Use data! Track progress from the early years Identify and implement policies associated with disadvantage in early years Use data on access, learning and financing to inform policy at local, national and global levels
  24. 24. Tackle disadvantage early – education strategies • Support early childhood programmes • Ensure teaching is at the right pace • Provide disadvantaged learners with best teachers • Provide appropriate learning materials • Empower all parents and communities to hold schools to account • Adopt ‘progressive universalism’ principles for distribution of education financing
  25. 25. Measurement lessons to leave no one behind + indicators Linkage Skills Comparative measures of wealth; include disability Link households and schools, panel data Add non-cognitive and advanced skills
  26. 26. REAL Centre research on leaving no one behind Evaluations of tracer studies of Speed Schools in Ethiopia and Complementary Basic Education in Ghana Cost effectiveness and scaling up Camfed’s adolescent girls’ programme in Tanzania and Zimbabwe Case studies of assessment to action for in 13 countries Teaching Effectively All Children: India and Pakistan

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