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RISK AND RESILIENCE 
IN CHILDHOOD 
Jo Boyden and Abhijeet Singh 
Young Lives, University of Oxford
Rudolph Schaffer: 
“Whatever stresses an individual may 
have encountered in early years, he or 
she need not forever more be at the 
mercy of the past. . . . children’s 
resilience must be acknowledged 
every bit as much as their 
vulnerability” 
‘Social Development: an Introduction’ 
(1996:47)
Understanding risk and resilience 
• Risk: a stressor (deficit or ‘insult’) experienced by an individual that 
heightens probability of developmental or behavioural pathology 
• Cohort studies in LMICs highlight loss of developmental potential due to 
risk exposure: 
• the first 1,000 days of life are critical, when exposure heightens 
severity & persistence of effects 
• synergy between developmental domains compounds effects 
• Yet, Rutter (1972) argued that genetically influenced variations in 
environmental susceptibility may be important 
• ‘Ego resilience’ (Gamezy 1983) : 
• good outcomes despite high-risk status 
• sustained competence under threat 
• recovery from trauma (Masten, Best, & Garmezy, 1990) 
• Rutter has found that risk itself can lead to the development of protective 
processes that enhance resilience in children: Steeling effects
Evidence on risk and resilience 
• Resilience is highly influenced by “protective processes” in the wider environment 
(proximal and distal) 
• Risks and protective processes are context specific: 
• Structural dynamics – risk burden varies widely according to children’s social 
attributes (ethnicity, religion, gender, caste), location & HH economic status 
• Ideational systems – cultural norms & subjective understandings of risk and 
resilience make a significant difference 
• Sociality – relationships and institutional membership are key 
• Risks are domain-specific: some competencies (e.g. social & emotional) are far 
more responsive to environmental stimuli than others (e.g. sensory & motor 
functions) 
• Risks and resilience are both sensitive to timing 
• Questions that need answering: 
• Is recovery, partial or complete, possible? 
• What are the factors behind such recovery? Limitations? 
• Which are the critical ages for domain-specific interventions? 
• Conversely, which are the most critical domains for each age group?
Young Lives 
• Interdisciplinary, mixed methods, comparative study of a dual birth 
cohort that is researching the determinants & outcomes of childhood 
poverty 
• Following nearly 12,000 children in Ethiopia, India (Andhra Pradesh & 
Telangana), Peru, Vietnam, started 2001 & ends 2018 
• 80 sites selected non-randomly - pro-poor & representing country 
diversity (rural-urban, livelihoods, ethnicity etc.) 
• Respondents selected randomly as representative of their cohort at 
sentinel site level; roughly equal numbers of boys and girls 
• 5 survey rounds combine explanatory variables at the child, caregiver & 
community level with child outcome indicators ( + measures from 
international test batteries) - plus school surveys 
• 4 waves of qualitative research with a sub-sample of 200 children
Ethiopia India Peru Vietnam 
Younger 
cohort (b. 
2000/1) 
Round 1 (2002) 
Round 2 (2006) 
Round 3 (2009) 
1 year old 
5 years old 
8 years old 
Round 4 (2013) 12 years old 
Older 
cohort (b. 
1994/5) 
8 years old 
12 years old 
15 years old 
19 years old 
Round 5 (2016) 15 years old 22 years old 
Full sample of 
children & 
caregivers, plus 
selected younger 
siblings & 
community 
representatives 
Structure of panel
Example 1: Catch-up from stunting 
• Children in developing countries born close to developed 
country norms but falter in the first two years of life 
(Victora et al. 2010) 
• Faltering is often thought to be irreversible 
• Panel data show however that a number of stunted 
children do ‘catch-up’ i.e. recover from stunting. 
• Cebu (Adair, 1999), Young Lives in Ethiopia, India, Peru and 
Vietnam (e.g. Crookston et. al. 2010, Crookston et. al. 2013) 
• Surprisingly children who have recovered from stunting 
and never stunted show no difference in cognitive skills 
(Crookston et. al. 2010) 
• So, perhaps stunting not as hopeless as believed.
Catch-up growth: Why it matters 
• Factors that correlate with catch up are similar to those 
which prevent stunting in the first place 
• The initially worst-off are the most vulnerable (similar to risk in 
other domains, e.g. Dercon 2002) 
• Those stunted earliest least likely to catch-up (Adair, 1999) 
• Policy may have a role: 
• early remediation most effective, but some hope still for those 
left behind 
• A concrete illustration: Singh, Park and Dercon (2014) 
show that India’s Midday Meals help compensate for 
nutritional effects of droughts in early childhood 
• So catch-up may be manipulable by policy tools. 
• Focusing entirely on risks denies possibility of 
remediation
Understanding the ages for intervention: 
learning divergence 
• Two key questions relevant for child outcomes: 
• When do gaps emerge between advantaged and 
disadvantaged children? 
• This may indicate where remediation/prevention should focus 
• What are the sources of the gaps? 
• Indicates which domains should be targeted. 
• I study these questions focusing on learning divergence 
between children in four YL countries 
• Learning levels differ widely across countries and with 
direct relevance to growth and inequality
Learning from 5-8 years 
p10 p90 
400 450 500 550 600 
Math scores (2009) 
300 400 500 600 700 
CDA scores (2006) 
Ethiopia India 
Peru Vietnam
Timing and sources of gaps 
• 5-8 years a period of major divergence in learning levels 
• For observationally equivalent children 
• The quality of schooling, measured as learning gain per year 
of schooling, the major source of divergence: 
• Equalizing that wipes out divergence between Peru and Vietnam 
• Closes 70% of divergence between Vietnam and India 
• (Results based on value-added models and regression discontinuities) 
• Why this is important: 
• Early years undoubtedly important (achievement at 5 an important 
factor) but not all lost 
• Large randomized experimental literature explores channels of 
intervention 
• Panel-based overview highlights possibilities and domains of 
interventions – a conceptual underpinning of risk and resilience.
Understanding gender gaps: Dercon and Singh 
2013 
• Gender differences frequently context-specific: 
• Academic outcomes pro-boy biased in India and Ethiopia, pro-girl 
biased in Vietnam, and no distinguishable bias in Peru 
• Frequently domain-specific: 
• Nutritional outcomes (esp. BMI-for-age at15) pro-girl biased in most 
countries including where educational investments are distinctly pro-boy 
biased 
• Changing in nature: 
• A decade ago, gender bias in India was on enrolment (Kingdon 2005) 
but now it is in private schooling (Maitra et. al.) 
• Dynamic in emergence: 
• Agency at 12 predicts agency at 15 and educational outcomes 
• The type of foundational work that observational data (esp. 
panels) yield which precede (experimental) program-based 
planning
Coming back to the questions 
• Is recovery, partial or complete, possible? 
• What are the factors behind such recovery? 
Limitations? 
• Which are the critical ages for domain-specific 
interventions? 
• Conversely, which are the most critical domains 
for each age group? 
• These should be questions where there is a 
clear advantage for panel-based designs.

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Risk and resilience in childhood

  • 1. RISK AND RESILIENCE IN CHILDHOOD Jo Boyden and Abhijeet Singh Young Lives, University of Oxford
  • 2. Rudolph Schaffer: “Whatever stresses an individual may have encountered in early years, he or she need not forever more be at the mercy of the past. . . . children’s resilience must be acknowledged every bit as much as their vulnerability” ‘Social Development: an Introduction’ (1996:47)
  • 3. Understanding risk and resilience • Risk: a stressor (deficit or ‘insult’) experienced by an individual that heightens probability of developmental or behavioural pathology • Cohort studies in LMICs highlight loss of developmental potential due to risk exposure: • the first 1,000 days of life are critical, when exposure heightens severity & persistence of effects • synergy between developmental domains compounds effects • Yet, Rutter (1972) argued that genetically influenced variations in environmental susceptibility may be important • ‘Ego resilience’ (Gamezy 1983) : • good outcomes despite high-risk status • sustained competence under threat • recovery from trauma (Masten, Best, & Garmezy, 1990) • Rutter has found that risk itself can lead to the development of protective processes that enhance resilience in children: Steeling effects
  • 4. Evidence on risk and resilience • Resilience is highly influenced by “protective processes” in the wider environment (proximal and distal) • Risks and protective processes are context specific: • Structural dynamics – risk burden varies widely according to children’s social attributes (ethnicity, religion, gender, caste), location & HH economic status • Ideational systems – cultural norms & subjective understandings of risk and resilience make a significant difference • Sociality – relationships and institutional membership are key • Risks are domain-specific: some competencies (e.g. social & emotional) are far more responsive to environmental stimuli than others (e.g. sensory & motor functions) • Risks and resilience are both sensitive to timing • Questions that need answering: • Is recovery, partial or complete, possible? • What are the factors behind such recovery? Limitations? • Which are the critical ages for domain-specific interventions? • Conversely, which are the most critical domains for each age group?
  • 5. Young Lives • Interdisciplinary, mixed methods, comparative study of a dual birth cohort that is researching the determinants & outcomes of childhood poverty • Following nearly 12,000 children in Ethiopia, India (Andhra Pradesh & Telangana), Peru, Vietnam, started 2001 & ends 2018 • 80 sites selected non-randomly - pro-poor & representing country diversity (rural-urban, livelihoods, ethnicity etc.) • Respondents selected randomly as representative of their cohort at sentinel site level; roughly equal numbers of boys and girls • 5 survey rounds combine explanatory variables at the child, caregiver & community level with child outcome indicators ( + measures from international test batteries) - plus school surveys • 4 waves of qualitative research with a sub-sample of 200 children
  • 6. Ethiopia India Peru Vietnam Younger cohort (b. 2000/1) Round 1 (2002) Round 2 (2006) Round 3 (2009) 1 year old 5 years old 8 years old Round 4 (2013) 12 years old Older cohort (b. 1994/5) 8 years old 12 years old 15 years old 19 years old Round 5 (2016) 15 years old 22 years old Full sample of children & caregivers, plus selected younger siblings & community representatives Structure of panel
  • 7. Example 1: Catch-up from stunting • Children in developing countries born close to developed country norms but falter in the first two years of life (Victora et al. 2010) • Faltering is often thought to be irreversible • Panel data show however that a number of stunted children do ‘catch-up’ i.e. recover from stunting. • Cebu (Adair, 1999), Young Lives in Ethiopia, India, Peru and Vietnam (e.g. Crookston et. al. 2010, Crookston et. al. 2013) • Surprisingly children who have recovered from stunting and never stunted show no difference in cognitive skills (Crookston et. al. 2010) • So, perhaps stunting not as hopeless as believed.
  • 8. Catch-up growth: Why it matters • Factors that correlate with catch up are similar to those which prevent stunting in the first place • The initially worst-off are the most vulnerable (similar to risk in other domains, e.g. Dercon 2002) • Those stunted earliest least likely to catch-up (Adair, 1999) • Policy may have a role: • early remediation most effective, but some hope still for those left behind • A concrete illustration: Singh, Park and Dercon (2014) show that India’s Midday Meals help compensate for nutritional effects of droughts in early childhood • So catch-up may be manipulable by policy tools. • Focusing entirely on risks denies possibility of remediation
  • 9. Understanding the ages for intervention: learning divergence • Two key questions relevant for child outcomes: • When do gaps emerge between advantaged and disadvantaged children? • This may indicate where remediation/prevention should focus • What are the sources of the gaps? • Indicates which domains should be targeted. • I study these questions focusing on learning divergence between children in four YL countries • Learning levels differ widely across countries and with direct relevance to growth and inequality
  • 10. Learning from 5-8 years p10 p90 400 450 500 550 600 Math scores (2009) 300 400 500 600 700 CDA scores (2006) Ethiopia India Peru Vietnam
  • 11. Timing and sources of gaps • 5-8 years a period of major divergence in learning levels • For observationally equivalent children • The quality of schooling, measured as learning gain per year of schooling, the major source of divergence: • Equalizing that wipes out divergence between Peru and Vietnam • Closes 70% of divergence between Vietnam and India • (Results based on value-added models and regression discontinuities) • Why this is important: • Early years undoubtedly important (achievement at 5 an important factor) but not all lost • Large randomized experimental literature explores channels of intervention • Panel-based overview highlights possibilities and domains of interventions – a conceptual underpinning of risk and resilience.
  • 12. Understanding gender gaps: Dercon and Singh 2013 • Gender differences frequently context-specific: • Academic outcomes pro-boy biased in India and Ethiopia, pro-girl biased in Vietnam, and no distinguishable bias in Peru • Frequently domain-specific: • Nutritional outcomes (esp. BMI-for-age at15) pro-girl biased in most countries including where educational investments are distinctly pro-boy biased • Changing in nature: • A decade ago, gender bias in India was on enrolment (Kingdon 2005) but now it is in private schooling (Maitra et. al.) • Dynamic in emergence: • Agency at 12 predicts agency at 15 and educational outcomes • The type of foundational work that observational data (esp. panels) yield which precede (experimental) program-based planning
  • 13. Coming back to the questions • Is recovery, partial or complete, possible? • What are the factors behind such recovery? Limitations? • Which are the critical ages for domain-specific interventions? • Conversely, which are the most critical domains for each age group? • These should be questions where there is a clear advantage for panel-based designs.