Longitudinal studies, such as the Young Lives study of childhood poverty, help us to analyse
welfare dynamics in ways that are not possible using time-series or cross-sectional samples.
However, analysis based on panel datasets can be heavily compromised by sample attrition.
On the one hand, the number of respondents who do not participate in each round of data
collection (wave non-response) will inevitably cumulate over time, resulting in falling sample
sizes, which will undermine the precision of any research undertaken using such samples.
On the other hand, unless it is random, attrition might lead to biased inferences. Analysts
often presuppose that attrition is correlated with observable characteristics such as
household education, health or economic well-being, resulting in samples that include only a
selected group of households. However, even if that is the case, non-random attrition does
not necessarily lead to attrition bias. Attrition bias is model-specific and, as previous studies
have shown, biases might be absent even if attrition rates are high.
We investigate the incidence and potential bias arising from attrition in Young Lives following
the completion of the second round of data collection. Young Lives is a study concerned with
analysing childhood poverty in four countries, Ethiopia, India, Vietnam and Peru. The study,
which measures a range of child, household, and household-member characteristics , is
following two cohorts of children in each country over 15 years – a younger cohort of 2,000
children who were born in 2001 to 2002 (i.e. aged 6 to 18 months when first surveyed) and
1,000 older children born in 1994-95 (i.e. aged 7.5 to 8.5 at the start of the survey).
Sample attrition is particularly concerning in the context of a longitudinal study such as Young
Lives where cohort sample sizes are modest and individuals are tracked over a relatively
long period of time. This paper seeks to:
• document the rates of attrition of the Young Lives study following completion of the
second round of data collection;
• investigate the extent to which sample attrition is non-random;
• analyse whether non-random attrition in the Young Lives sample might lead to attrition
bias.