An Assessment of the Young Lives Sampling Approach in Ethiopia

Young Lives is a longitudinal research project investigating the changing nature of childhood poverty. The study is tracking the development of 12,000 children in Ethiopia, Peru, India (Andhra Pradesh) and Vietnam through qualitative and quantitative research over a 15-year period. Since 2002, the study has been following two cohorts in each study country. The younger cohort consists of 2,000 children per study country aged between 6 and 18 months in 2002. The older cohort consists of 1,000 children per country aged between 7.5 and 8.5 in 2002. The key objectives of Young Lives are: (i) to improve the understanding of causes and consequences of childhood poverty, (ii) to inform the development and implementation of future policies and practices that will reduce childhood poverty.

The sampling methodology adopted by Young Lives is known as a sentinel site surveillance system. In Ethiopia, the Young Lives team used multi-stage, purposive and random sampling to select the two cohorts of children. This methodology randomised households within a study site while the sites themselves were chosen on the basis of predetermined criteria, informed by the Young Lives objectives. To ensure the sustainability of the study, and for resurveying purposes, a number of well-defined sites was chosen. The sites were selected with a pro-poor bias and to ensure a balanced representation of the Ethiopian regional diversity as well as rural/urban differences.

This paper assesses the sampling methodology by comparing the Young Lives sample with larger, nationally representative samples. In doing this, the Ethiopia team sought to:

analyse how the Young Lives children and households compare with other children in Ethiopia in terms of their living standards and other characteristics examine whether this may affect inferences between the data establish to what extent the Young Lives sample is a relatively poorer or richer subpopulation in Ethiopia determine whether different levels of living standards are represented within the dataset.