The document discusses risk and resilience in childhood using evidence from the Young Lives study. It makes three key points: 1) Exposure to risks in early childhood, like malnutrition or poverty, can negatively impact development, but some children demonstrate resilience by achieving good outcomes despite risks. Protective factors in their environment help promote resilience. 2) Data from Young Lives shows windows of opportunity for interventions - gaps in learning between advantaged and disadvantaged children emerge from ages 5-8, so focusing on quality of schooling during this period could help close divides. 3) Gender differences in outcomes vary in different contexts and domains - interventions must consider these local variations to effectively target risks at critical ages. Longitudinal data is

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.