Young Lives Dataset and Data Visualisation:

Challenges and Opportunities

Caroline Knowles, Communications ManagerAnne Yates, Data and Survey Manager

Young Lives

ESRC Research Methods FestivalOxford 8 July 2010

OVERVIEW OF PRESENTATION

What we do and how- Study design- Structure of data- Round 3 potential

Research and analysis- How we can use the

data- Some findings

Data visualisation- Why- Young Lives ‘virtual

village’- Next steps

YOUNG LIVES: A LONGITUDINAL STUDY OF CHILDHOOD POVERTY

• Combines data collection, analysis and policy engagement

- To improve understanding of the causes and consequences of childhood poverty

- To improve policies and practice for children

• 3 research themes:- Dynamics of poverty and uncertainty- Children’s education, schooling and time-

use- Children’s well-being and experiences of

poverty

• All survey data available to other researchers (archived with ESDS) – and methodology being documented

• Increasing power of data

A COLLABORATIVE PROJECT• Ethiopia: EDRI, Save the

Children-UK, and qualitative team

• India (Andhra Pradesh): CESS, SPMVV, and Save the Children-BRB

• Peru: GRADE and IIN• Vietnam: CAF-VASS, GSO,

and Save the Children-Vietnam

• UK: ODID (University of Oxford), the Open University, Institute of Education (London), and Save the Children-UK

• Plus long-term support of donors (DFID), Board(s) and stakeholders

WHAT WE DO

12,000 children in 4 countries over 15 years

Two age cohorts in each country:• 2,000 children born in 2000-01• 1,000 children born in 1994-95

From infancy to parenthood

Pro-poor sample: 20 sites in each country selected to reflect country diversity, rural-urban, livelihoods, ethnicity etc; roughly equal numbers of boys and girls

Ethiopia India Peru Vietnam

YC (2,000) OC(1,000)

Round 1(2002)

Round 2(2006)

Round 3(2009)

1 year old

5 years old

8 years old

Round 4(2012) 11 years old

8 years old

12 years old

15 years old

18 years old

Round 5(2015) 14 years old 21 years old

STRUCTURE OF PANEL

Ethiopia India Peru Vietnam

YC OC

Child questionnaire

(except R1)

Household questionnaire

Community questionnaire

Child questionnaire

Household questionnaire

Self-administered questionnaire (R3)

Caregiver questionnaire (R1)

STRUCTURE OF QUANTITATIVE DATA

TWO EXISTING ROUNDS OF PANEL DATA• Child, household and community

level data• Household data: similar to other

cross-sectional datasets (e.g. LSMS) but with questions to the caregiver on psychosocial competencies, social capital

• Detailed time-use data for all family members

• Child-level information on anthropometrics and from the caregiver

• Child testing of cognitive achievement (language, maths)

• Directly asking children! School and work, likes and dislikes, perceptions and aspirations

• Complimented by 2 rounds of Qual data

THE THIRD ROUND OF DATA (2009)Questionnaires change as children

grow – they are now aged 8 and 15

• Self-administered questionnaire (older cohort)

• More on educational history, psychosocial indicators, social capital

• Focus on social protection, detailed modules on e.g. Productive Safety Net Programme in Ethiopia, in order to evaluate impact

• Linking to existing data where possible – school data in India and Peru, job card in India (expanding issues/improving robustness)

• School survey to complement existing data

RESEARCH AND ANALYSIS: WHAT CAN YOUNG LIVES DO? WHAT

CAN’T WE DO?• Powerful tool for policy analysis• Can’t be used to monitor poverty• Mainly to focus on differences in

access, impacts and outcomes across children, not communities

• Looking at short- and long-run causes and consequences by exploiting longitudinal data and comparing cohorts over time

• Can develop narratives – what are the mechanisms and channels for change (i.e. we know the impact of shocks, but what really happens and why)

• Increasing value of dataset

NOT FOR MONITORING POVERTY

Sampled children and communities, both in qualitative and quantitative samples:

- not ‘statistically representative’- but broadly representative of diversity in

rural and urban population

So we cannot monitor childhood poverty -x % of children in India live in poor families, or –y % are malnourished due to the food price increases-z % of schools have poor quality

BUT WE CAN ANALYSE CHILDHOOD POVERTY

e.g. x% of children living in the 25% poorest families go to schools of poor quality, while among the 25% richest families, this is only y%

So • Can be used to look at the causes and consequences of childhood poverty• Analyse poverty dynamics• Use to speak to other issues / countries / states

E.G. PERSISTING POVERTY IN VIETNAM

Poverty persists in families with poorly educated parentsBetter-educated parents are more likely to escape poverty

Example from Vietnam:

entrenched poverty in some groups -

Maternal education below primary is increasingly linked to extreme poverty –

2002 - 47% are in poorest fifth2006 - 68% are in poorest fifth

POVERTY PROFILE (INDIA, OLDER COHORT)

POVERTY CYCLES BEGIN WITHEARLY NUTRITION

•High prevalence of ‘stunting’ (low weight and height for age) in all 4 countries (23.4% to 30.8%)

•Stunting is consistently higher in rural areas

•Child nutrition is strongly linked to maternal and paternal education, regardless of family wealth

•Poor child nutrition can impact on cognitive, educational and psychosocial outcomes

STUNTING IN YOUNG LIVES SAMPLE

STUNTING IN YOUNG LIVES INDIA SAMPLE

RELATIONSHIP BETWEEN STUNTING AND COGNITIVE DEVELOPMENT

• Research for UNESCO GMR: relationship between stunting at 6-18 months (YC, R1) and cognitive test scores at age 4 to 5 (R2). By age 7 to 8, the disadvantage this creates is likely to be equivalent to the loss of a full term of schooling (Sanchez 2009)

• Children from poorer families were up to one grade behind at age 12 in Ethiopia and Vietnam (OC, R2) (Dercon 2008)

• Can also affect psychosocial outcomes: children’s sense of self-esteem, agency and respect they receive (Dercon 2008)

Potential for policy• Any long-term improvements across sample? Compare YC in R3

with OC in R1 (at same age, 7 to 8 years). And also have sibling data in R3

• Impact of food price crisis? Compare R2 data (2006) with R3 (2009)

THE IMPORTANCE OF EARLY EDUCATION• Pre-school enrolment:

- 94% in Vietnam- 87% in Andhra Pradesh- 84% in Peru- 25% in Ethiopia (58% urban 4% rural)

• In Andhra Pradesh- Primary education enrolment

= 73% (2006)- Private sector = 36% of

enrolment (and growing…)- Attraction of English-

medium instruction

Quality – major issue (school survey)

GROWTH OF THE PRIVATE SECTOR IN INDIA (PRE-SCHOOL / URBAN

HOUSEHOLDS)

WHY DATA VISUALISATION?

VIRTUAL VILLAGE

• Goals of the Virtual Village• Communicate information• Aesthetic and functional

• Challenges • Balance between design and function• Deciding Themes/Key findings• Representing Urban/Rural • Four countries• Navigation/Format• Using aggregated data

VISITING THE VIRTUAL VILLAGEhttp://dh83.qeh.ox.ac.uk/younglives/virtual-village/

MOVING FORWARD

• Modify Virtual Village- Intro page for each icon- Translate for country specific use- Include videos- Incorporate Round 3 data

• Virtual Village in development education – tools for children / teaching materials

• Incorporate Nesstar and graphical representation of Round 3 data on Young Lives website

FINDING OUT MORE…www.younglives.org.uk

• methodology• datasets (ESDS International)• publications• child profiles and photos• e-newsletter