Sampling elusive populations: Applications of child labour

This Manual complements the earlier volume on Sampling for household-based surveys of child labour brought out by within the framework of the ILO-IPEC Statistical Information and Monitoring Programme on Child Labour (SIMPOC) and contributes to survey methodology that goes beyond the particular subject of child labour. Thus, anyone interested in issues and practical solutions to problems such as sampling from imperfect frames or sampling difficult populations would benefit from the contents of this Manual.

Presentation | 17 October 2014

Sampling has now become an absolutely necessary instrument for collecting the vast amount of information required for understanding the functioning of our society. It provides a solid basis for estimating unknown values and ratios, and for testing the validity of presumed relationships in different areas of science. The remarkable aspect of probability sampling is its ability to produce not only valid estimates of the parameters of interest but also under broad conditions of their margin of errors due to sample variability. This feature has no doubt greatly contributed to the wide acceptance of sampling as an objective tool of measurement among analysts and the public at large.

The present publication has a dual function. It complements the earlier volume on Sampling for household-based surveys of child labour brought out by within the framework of the ILO Statistical Information and Monitoring Programme on Child Labour (SIMPOC). While the earlier volume dealt with sampling issues in conventional, broad-based household surveys, the present volume deals with non- standard issues involved in the sample design of child labour in targeted sectors and activities, such child street vendors or child domestic workers.

The second function of the present document is its contribution to survey methodology that goes beyond the particular subject of child labour. Thus, anyone interested in issues and practical solutions to problems such as sampling from imperfect frames, or sampling difficult populations because they are relatively few or mobile or secretive, would benefit from the contents of this publication. It puts together in one document a vast amount of materials on various sampling schemes such as multiplicity sampling, adaptive cluster sampling, controlled selection and balanced sampling, snowball sampling, and capture-recapture sampling. In most cases, novel ideas are brought on the underlying theory and the methodologies are illustrated with real-life numerical examples.

The ILO Department of Statistics and the ILO Fundamental Principles and Rights at Work (FPRW) hope that national statistical offices as well as researchers and analysts would use the materials presented here to improve national data collection programmes on child labour and as a result help to eliminate child labour across the world. The ILO could not be more pleased if this manual would also serve the statistical community at large and the survey statisticians in particular.