An Assessment of the Young Lives Sampling Approach in Peru

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 examine how policies affect children's well-being and (iii) to inform the development and implementation of future policies and practices that will reduce childhood poverty. In Peru the Young Lives team used multi-stage, cluster-stratified, random sampling to select the two cohorts of children. This methodology, unlike the one applied in the other Young Lives countries, randomized households within a site as well as sentinel site locations. To ensure the sustainability of the study, and for resurveying purposes, a number of well-defined sites were chosen. These were selected with a pro-poor bias, ensuring that randomly selected clusters of equal population excluded districts located in the top five per cent of the poverty map developed in 2000 by the Fondo Nacional de Cooperacion para el Desar rollo (FONCODES, the National Fund for Development and Social Compensation). This paper assesses the sampling methodology by comparing the Young Lives sample with larger, nationally representative samples. The Peru team sought to: - analyse how the Young Lives children and households compare with other children in Peru 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 sub-population in Peru - determine whether different levels of living standards are represented within the data set.