Anmerkungen:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Beschreibung:
In 2015, the United Nations (UN) set up 17 Sustainable Development Goals (SDGs) to be achieved by 2030 (General Assembly, 2015). The goals encompass indicators of various socioeconomic characteristics (General Assembly, 2015). To reach them, there is a need to reliably measure the indicators, especially at disaggregated levels. National Statistical Institutes (NSI) collect data on various socio-economic indicators by conducting censuses or sample surveys. Although a census provides data on the entire population, it is only carried out every 10 years in most countries and it requires enormous financial resources. Sample surveys on the other hand are commonly used because they are cheaper and require a shorter time to collect (Sarndal et al., 2003; Cochran, 2007). They are, therefore, essential sources of data on the country’s key socio-economic indicators, which are necessary for policy-making, allocating resources, and determining interventions necessary. Surveys are mostly designed for the national level and specific planned areas or domains. Therefore, the drawback is sample surveys are not adequate for data dis-aggregation due to small sample sizes (Rao and Molina, 2015). In this thesis, geographical divisions will be called areas, while other sub-divisions such as age-sex-ethnicity will be called domains in line with (Pfeffermann, 2013; Rao and Molina, 2015). One solution to obtain reliable estimates at disaggregated levels is to use small area estimation (SAE) techniques. SAE increases the precision of survey estimates by combining the survey data and another source of data, for example, a previous census, administrative data or other passively recorded data such as mobile phone data as used in Schmid et al. (2017). The results obtained using the survey data only are called direct estimates, while those obtained using SAE models will be called model-based estimates. The auxiliary data are covariates related to the response variable of interest (Rao and Molina, 2015). According to Rao and Molina (2015), an ...