Abstract

In this paper, we focus on respondent-driven sampling (RDS), which is a valuable survey methodology for both national and international organisations to estimate the size and characteristics of hidden (e.g., homeless people, undocumented immigrants) or hard-to-measure population groups (e.g., minorities, indigenous people).
The principle of “leaving no one behind” is at the heart of the 2030 Agenda, and a key requirement for many Sustainable Development Goals (SDG) indicators is to be available for the most vulnerable and marginalised population groups. Nevertheless, halfway through the implementation of the 2030 Agenda, most SDG indicators are still not available at the needed level of disaggregation to monitor the socioeconomic conditions of hidden and hard-to-count population groups. As a result, it is neither possible to produce reliable structural data on the needed disaggregation dimensions nor to monitor the developments of emerging phenomena that need to be approached with targeted evidence-based policy interventions.
The RDS methodology makes it possible to gather information on these populations by exploiting the relationships between their components. Moreover, the effectiveness of the RDS can be further increased by employing an integrated approach in which the RDS is used in conjunction with other information sources, such as administrative or geographical data.
In this paper, we address the estimation problem, and by approaching the RDS methodology as a particular indirect sampling technique, we propose three unbiased estimation methods as possible solutions. In particular, the first method assumes a random sampling of the initial individuals. In contrast, the second method, which considers purposive sample selection, creates a nonbiased estimation if the initial sample of respondents falls into all the clusters of networks that characterise the population of interest. Finally, leveraging the generalised capture-recapture estimation approach, we propose an estimator that accounts for the noncoverage of two independent indirect samplings.