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Putting Children First: Session 2.2.C Ilze Plavgo - Inequality in education in Ethiopia [24-Oct-17]

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Putting Children First: Identifying solutions and taking action to tackle poverty and inequality in Africa.
Addis Ababa, Ethiopia, 23-25 October 2017

This three-day international conference aimed to engage policy makers, practitioners and researchers in identifying solutions for fighting child poverty and inequality in Africa, and in inspiring action towards change. The conference offered a platform for bridging divides across sectors, disciplines and policy, practice and research.

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Putting Children First: Session 2.2.C Ilze Plavgo - Inequality in education in Ethiopia [24-Oct-17]

  1. 1. Mechanisms behind inequality in educational opportunities: The case of Ethiopia Ilze Plavgo ilze.plavgo@eui.eu European University Institute, Florence (EUI) 24/10/2017 Conference “Putting Children First: Identifying Solutions and Taking Action to Tackle Child Poverty and Inequality in Africa”: 23-25 Oct 2017, Addis Ababa, Ethiopia 1
  2. 2. Outline of the presentation • Motivation • Research question • Theory • Data • Methodology • Findings • Conclusions and policy implications
  3. 3. Motivation Rational choice theory: lifting school fees should increase enrolment and promote intergenerational mobility of education (i.e., reduce intergenerational inequality) However: – Primary school enrolment rates increasing BUT – Primary school success rates stagnating AND – Intergenerational education inequality rising/ stagnating Year of fee abolition reform: 2003 in Kenya 1997 in Tanzania, Uganda and Ethiopia 0% 20% 40% 60% 80% 100% 2000-2004 2005-2010 2011-2015 %ofchildrenattendingschool (age7-12) Year of survey Educational expansion: school attendance Kenya Tanzania Uganda Ethiopia 0.00 0.10 0.20 0.30 0.40 0.50 2000-2004 2005-2010 2011-2015 Coefficientofassociation Year of survey Intergenerational education inequality: association of caretaker-child educational attainment (age 14) Kenya Tanzania Uganda Ethiopia Source: Own calculations based on DHS data
  4. 4. Research questions Mechanisms behind inequality in primary school progression: • Does primary school progression differ by socioeconomic status (SES)? Is this association the same for urban and rural areas? • Do chances to successfully progress in primary school vary by level of cognitive ability at primary school starting age ? • Does level of cognitive ability differ by SES? (primary effects of SES) • Do chances to successfully progress in primary school vary by families’ SES, net of children’s cognitive ability ? (secondary effects, compensatory effects of SES)
  5. 5. Raymond Boudon (1974): Theory of primary and secondary effects of social origin Parental background seen as the main determinant of inequality in educational opportunities because of two mechanisms: (1) family conditions affect children’s cognitive ability and scholastic achievement (primary effects), and (2) families are the main agents making choices about children’s school attendance and continuation (secondary effects). J. Goldthorpe (2007); Fabrizio Bernardi (2014): Compensatory advantage theory Educational choices made by better-off families are less sensitive to children’s cognitive ability and scholastic achievement compared to worse-off families. Main reasons: • Preventing downward intergenerational social mobility • Disposing more resources to cope with such prior disadvantageous events as low cognitive ability & health issues Theory Figure 1: Causal mechanism describing primary and secondary effects of social origin
  6. 6. Data • Young Lives longitudinal survey data • Ethiopia, younger cohort born in 2000 • Data collected in 2002, 2006, 2009, and 2013, when children of younger cohort were of age 1, 4/5, 7/8, and 11/12. Main outcome of interest: Chances to successfully progress in primary school over 4 years (between age 7/8 and 11/12)  Sample limited to children aged 7/8 enrolled in school (77%), analysing primary school progression of these same children 4 years later (when aged 11/12) School history for children between age 7/8 (survey: 2009) and age 11/12 (survey: 2013) Total Rural Urban Freq. Percent Freq. Percent Freq. Percent Enrolled (at age 7/8 & 11/12) 1328 73 690 65 638 86 Drop-outs (enrolled at age 7/8, not at 11/12) 69 4 60 6 9 1 Latecomers (enrolled at 11/12 but not at 7/8) 382 21 290 27 92 12 Never enrolled 29 2 25 2 4 1 Total 1808 100 1065 100 743 100
  7. 7. Variables Dependent variable: success in primary school progression; Independent variables: SES Success: Enrolled at age 7/8 and completed 3+ grades by age 11/12 Total Rural Urban Freq. Percent Freq. Percent Freq. Percent Failure (0-2 grades or dropped out) 280 20 222 30 58 9 Success (3+ grades) 1117 80 528 70 589 91 Total (excl. Latecomers + Never enrolled) 1397 100 750 100 647 100 Excluded: Latecomers + Never enrolled 411 23 315 30 96 13 Total 1808 100 1065 100 743 100 Economic capital: Household wealth (quartiles) Total Rural Urban Freq. Percent Freq. Percent Freq. Percent 1 Poorest 449 25 416 39 33 4 2 Poorer 459 25 361 34 98 13 3 Wealthier 455 25 226 21 229 31 4 Wealthiest 445 25 62 6 383 52 Total 1808 100 1065 100 743 100 Educational capital: Caretaker's education (in years) Total Rural Urban Freq. Percent Freq. Percent Freq. Percent 0 years 806 45 609 57 197 27 1-8 years 808 45 437 41 371 50 9-14 years 194 11 19 2 175 24 Total 1808 100 1065 100 743 100
  8. 8. 1. Descriptive analysis studying differences in primary school success rates by socioeconomic status (SES) and the level of cognitive ability 2. Multivariate analysis studying the effect of primary and secondary effects of social origin on school success/failure Multivariate OLS probability model to estimate the effect of social origin on school progression: 𝑃 𝑅2013 = 𝛽0 + 𝛽1 𝑆𝑂2009 + 𝛽 𝑥 𝑋 + 𝜀 (1) An additive linear OLS probability model to estimate the primary effects of social origin: 𝑃 𝑅2013 = 𝛽0 + 𝛽1 𝑆𝑂2009 + 𝛽2 𝐴2009 + 𝛽 𝑥 𝑋 + 𝜀 (2) where 𝐴2009 is ability test z-score at age 7-8 (year 2009). 3. Multivariate analysis with an interaction term studying compensatory effects of social origin A linear OLS probability model with an interaction term: 𝑃 𝑅2013 = 𝛽0 + 𝛽1 𝑆𝑂2009 + 𝛽2 𝐴2009 + 𝛽3 𝑆𝑂2009 × 𝐴2009 + 𝛽 𝑥 𝑋 + 𝜀 (3) where 𝑆𝑂2009 × 𝐴2009 is an interaction term between prior cognitive ability level and social origin Methodology
  9. 9. Does primary school progression differ by SES? Note: Only children who were in school at age 7/8 Own calculations; Source: Young Lives Ethiopia data. Obs. 1,397
  10. 10. Is the association between primary school progression and SES the same for urban and rural areas? Own calculations; Source: Young Lives Ethiopia data. Obs. 1,397 (Urban N=647; Rural N=750)
  11. 11. Does cognitive ability differ by SES? 26 42 26 25 9 33 23 5 25 29 33 23 15 29 25 8 25 25 26 26 24 25 25 26 24 5 15 26 52 13 27 61 0 20 40 60 80 100 Total Poorest Poorer Wealthier Wealthiest 0 years 1-8 years 9-14 years TotalWealth Caretaker's education Cognitive ability distribution by family’s SES at age 7/8 Lowest Mid-low Mid-high Highest
  12. 12. Does cognitive ability differ by SES? Does this differ by area of residence? Primary effects
  13. 13. Does primary school progression differ by cognitive ability?  Chances to successfully progress in primary school vary by level of cognitive ability at primary school starting age
  14. 14. Does association between school progression and ability differ by SES & area of residence? Are there compensatory effects? Association differs by SES, but not throughout the cognitive ability distribution Chances to successfully progress in primary school are high for children of all SES if cognitive ability is high Implication: Cognitive ability matters, also among children from poor socioeconomic backgrounds In urban areas, chances to progress in school are high for children of high SES also when ability is low Implication: High SES can compensate for low cognitive ability Secondary effects Secondary effects Compensatory effects
  15. 15. In Urban areas, differences substantial and statistically significant. Association with cognitive ability is weaker for children from wealthier families (compensatory effects in urban areas!) Does association between school progression and ability differ by SES & area of residence? Are there compensatory effects?
  16. 16. Primary and secondary effects of SES on successful school progression URBAN Economic capital M 1 Wealth quartiles at age 7-8: 2nd (ref. 1st ) 0.13 Wealth quartiles at age 7-8: 3rd (ref. 1st ) 0.21*** Wealth quartiles at age 7-8: 4th (ref. 1st ) 0.23*** Cognitive ability PPVT test score, age 7-8 (std.) Child's gender: boy (ref. girl) -0.01 Child is the oldest (no other child aged 13-17 in hh) 0.06*** Number of children aged 0-5 in household -0.04** Number of children aged 6-12 in household -0.03** Caretakers' educ: 1-8 years; ref.. 0 years Caretakers' educ: 9-14 years; ref. 0 years Constant 0.73*** Observations 637 R-squared 0.06
  17. 17. Primary and secondary effects of SES on successful school progression URBAN Economic capital M 1 M 2 Wealth quartiles at age 7-8: 2nd (ref. 1st ) 0.13 0.11 Wealth quartiles at age 7-8: 3rd (ref. 1st ) 0.21*** 0.18** Wealth quartiles at age 7-8: 4th (ref. 1st ) 0.23*** 0.18** Cognitive ability PPVT test score, age 7-8 (std.) 0.04*** Child's gender: boy (ref. girl) -0.01 -0.02 Child is the oldest (no other child aged 13-17 in hh) 0.06*** 0.05** Number of children aged 0-5 in household -0.04** -0.03 Number of children aged 6-12 in household -0.03** -0.02* Caretakers' educ: 1-8 years; ref.. 0 years Caretakers' educ: 9-14 years; ref. 0 years Constant 0.73*** 0.74*** Observations 637 637 R-squared 0.06 0.08
  18. 18. RURAL Economic capital Educational capital M 1 M 2 M 1 M 2 Wealth quartiles at age 7-8: 2nd (ref. 1st ) 0.07* 0.05 Wealth quartiles at age 7-8: 3rd (ref. 1st ) 0.08* 0.06 Wealth quartiles at age 7-8: 4th (ref. 1st ) 0.03 -0.01 Cognitive ability PPVT test score, age 7-8 (std.) 0.12*** 0.12*** Child's gender: boy (ref. girl) -0.05 -0.05* -0.06* -0.05* Child is the oldest (no other child aged 13-17 in hh) 0.01 0 0.02 0.01 Number of children aged 0-5 in household -0.02 -0.02 -0.02 -0.02 Number of children aged 6-12 in household -0.02 -0.01 -0.02 -0.01 Caretakers' educ: 1-8 years; ref.. 0 years -0.01 0 Caretakers' educ: 9-14 years; ref. 0 years 0.16 0.09 Constant 0.72*** 0.76*** 0.77*** 0.79*** Observations 760 760 760 760 R-squared 0.01 0.05 0.01 0.05 Primary and secondary effects of SES on successful school progression
  19. 19. Summary 1. For children from the poorest families, only 66% successfully progress in primary school between age 7/8 and 11/12 (wealthiest: 91%) 2. For children from the lowest quartile of cognitive ability at age 7/8, only 65% successfully progress in primary school (highest: 96%) 3. Among poorest children, 42% belonged to the lowest and 5% to the highest quartile of cognitive ability at age 7/8 Among wealthiest, 9% from lowest and 52% from highest quartile of cognitive ability.  Inequality already in the initial stage when entering primary school (age 7/8) 4. In urban areas: Differences in cognitive ability at primary school starting age only part of the story: Life chances differ by SES. Different coping mechanisms: Compensatory effects for children from better-off families. Among poor, only children with high cognitive ability succeed to the same extent as children with average-low cognitive ability but from wealthier families. 5. In rural areas: Inequality in educational opportunities predominantly a primary effect of SES
  20. 20.  Even the most egalitarian education system alone is unlikely to weaken much the impact of social origin on opportunities (initial inequality in cognitive ability and differences in costs throughout primary school)  Class differentials in school preparedness are already manifest when children first enter the education system, and schools are not well equipped to remedy the problem  Conditions during early childhood are decisive: Primary cause of disadvantage in early childhood stems from inadequate cognitive and behavioural stimulus, linked to lack of financial and educational capital  Social investment case [A. Hemerijck, 2017] : equality of opportunity can be achieved by equalizing childhood living conditions by: – “Stock” policies: Cognitive stimulus through parental education and high-quality pre-school institutions; and – “Buffer” policies: Reducing child poverty, offsetting opportunity costs for having and schooling children Policy implications
  21. 21. 22 Thank you!
  22. 22. 23 References Azubuike, O. B. and Briones, K. (2016). “Young Lives Rounds 1 to 4: Constructed files”. Young Lives Technical Note 35, Oxford Department of International Development, University of Oxford. Bernardi, F. (2014). “Compensatory Advantage as a Mechanism of Educational Inequality: A Regression Discontinuity Based on Month of Birth”. Sociology of Education, 87(2): 74-88. Blossfeld, H.-P. and von Maurice, J. (2011): Education as a lifelong process, in: H.-P. Blossfeld, H.-G. Roßbach, and J. von Maurice (eds.): Education as a lifelong process, Zeitschrift für Erziehungswissenschaft, Special Issue 14:19-34. Boudon, R. (1974). Education, opportunity, and social inequality: Changing prospects in Western society. New York: Wiley. Boyden, J., Porter, I. Z., Heissler, K. “Balancing School and Work with New Opportunities: Changes in Children’s Gendered Time Use in Ethiopia (2006-2013)”. Young Lives Working Paper 161, Oxford Department of International Development, University of Oxford. Breen, R., Goldthorpe, J. H. (1997). “Explaining Educational Differentials: Towards a Formal Rational Action Theory”. Rationality and Society, 9(3): 275-305. EDHS (2012), Ethiopia Demographic and Health Survey 2011, Central Statistical Agency, Addis Ababa, Ethiopia. ICF International Calverton, Maryland, USA.
  23. 23. 24 References Erikson, R., J. O. Jonsson (eds) (1996). Can Education Be Equalized? The Swedish Case in Comparative Perspective. Westview Press. Goldthorpe, J. H. (1996). “Class Analysis and the Reorientation of Class Theory: The Case of Persisting Differentials in Educational Attainmnet” in British Journal of Sociology 47(3): 481-505. Hackman, D. A., M. J. Farah, and M.J. Meaney (2010): Socioeconomic status and the brain: mechanistic insights from human and animal research, in: Nature Reviews/Neuroscience, Vol. 11 (September 2010): 651-659. Hemerijck, A. (2014). ‘Social Investment ‘stocks’, ‘flows’ and ‘buffers’’. Politiche Sociali, 1(1), 6-22. Hemerijck (ed.) (2017). The Uses of Social Investment. Oxford: Oxford University Press. Heckman, J. and Krueger, A. (2003) Inequality in America. Cambridge, MA: MIT Press. Ministry of Education (2015). Education Sector Development Programme V: 2015/16 – 2009/20. Programme Action Plan. Federal Ministry of Education: Addis Ababa. Tesfay, N., Malmberg, L.-E. (2014). “Horizontal inequalities in children’s educational outcomes in Ethiopia”. International Journal of Educational Development, 39: 110-120. Woldehanna, T., Mekonnen, A., Jones, N. (2008). Education choices in Ethiopia: What determines whether poor households send their children to school? Ethiopian Journal of Economics 17(1).

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