Small Area Estimation Methods to Monitor the Progress towards the Sustainable Development Goals at the Subnational Level in the European Union (SAESDGs-EU)

Descripción del proyecto

Indicadores compuestos de bienestar económico de zonas pequeñas

La Asamblea General de las Naciones Unidas destaca la necesidad de desglosar los indicadores de los Objetivos de Desarrollo Sostenible por grupos socioeconómicos. Las encuestas a gran escala, como las estadísticas comunitarias sobre la renta y las condiciones de vida (EU-SILC, por sus siglas en inglés), no están diseñadas para elaborar análisis fiables de estos grupos, por lo que es necesario recurrir a métodos de estimación que mejoren la precisión utilizando datos auxiliares. Además, evaluar el bienestar económico requiere diferentes indicadores. Teniendo esto en cuenta, el proyecto SAESDGs-EU, financiado por el Consejo Europeo de Investigación, tiene por objeto desarrollar un método de un solo paso para crear indicadores compuestos de bienestar económico en zonas pequeñas utilizando EU-SILC. El método abordará los problemas de calidad de los datos observados en los métodos actuales de dos etapas para estimaciones en zonas pequeñas. En el proyecto también se investigará el uso de fuentes de datos no convencionales, como datos de internet y de satélites, como información auxiliar.

Objetivo

The United Nations (UN) General Assembly highlights the need to disaggregate Sustainable Development Goals (SDGs) indicators by different socio-economic characteristics e.g. income, sex, age, race, ethnicity, migratory status, disability, and small geographic location, or other relevant characteristics (General Assembly resolution 68/261). However, large scale national sample surveys, i.e. the EU Statistics on Income and Leaving Conditions (EUSILC), which collect variables that are needed to produce such indicators, are not designed to be reliable at those disaggregation levels. Small area estimation (SAE) methods are thus needed to produce accurate and precise estimates at a subnational level. SAE methods combine different auxiliary data sources (e.g. the Census with survey data), thus ‘borrowing strength’ from related areas to make a better-quality small area estimates.
The focus of SAESDGs-EU is on economic well-being, which is closely related to the SDGs, as improving quality of life is a core objective of sustainable development. Many of the 17 SDGs explicitly target well-being by addressing economic, social, and environmental factors. Economic well-being is multidimensional, hence a variety of indicators should be considered when measuring it. In this project, we will develop a single-step method to produce small area composite indicators of economic well-being based on the EUSILC. This is a ground-breaking innovation, since currently first composite indicators are produced and then SAE methods are applied (or vice-versa), in a two-step approach. The literature has highlighted problems with this approach which impact the data quality of the final estimates. Given that SAE requires auxiliary information, we will also explore pitfalls and potentials of using non-traditional data sources as auxiliary information (e.g. web-data, satellite data). These are well known data sources that provide granular spatial information.