In an era characterized by the proliferation of new data sources and an unprecedented data revolution, the 2030 Agenda for Sustainable Development and the overall goal of leaving no-one behind have generated a tremendous increase in the demand of disaggregated data and statistics.

In this framework, traditional sample surveys can provide important information on the social, economic and environmental dimensions of target populations, representing the essential data source to produce the official estimates of about the 30% of Sustainable Development Goal (SDG) Indicators. However, these data sources alone are not enough to realize the ambitious goal of monitoring SDG Indicators by all relevant disaggregation dimensions and geographic locations. Indeed, most sample surveys are characterized by sample sizes that are either not large enough to guarantee reliable direct estimates for all sub-populations or that do not cover all possible disaggregation domains.

Issues of this kind can be addressed at different stages of the statistical production process. They can be tackled at the design stage, by adopting sampling strategies guaranteeing an observed set of sampling units for every disaggregation domain. Although potentially optimal, this approach normally results in an exponential increase of the sampling size and survey costs and complexity. Alternatively, data disaggregation can be addressed at the data analysis stage, by adopting indirect estimation approaches borrowing strength from related disaggregation domains and/or time periods, thus resulting in an increase of the effective sample size. 

Small Area Estimation (SAE) methods are among the possible indirect estimation approaches that can be adopted to deal with data disaggregation at the analysis stage. SAE techniques allow combining survey data with auxiliary information coming from additional data sources that are not affected by sampling error. Traditionally, SAE have relied on the integration of survey data with information from population and agricultural censuses through explicit models linking the variable of interest to a set of auxiliary variables. However, with more and more data made available to the statistical community from multiple innovative data sources, relying exclusively on auxiliary variables from official statistical sources does not sound as an optimal solution. In this respect, the 2030 Agenda explicitly stresses the need for new and enhanced data integration strategies, including the exploitation of the potential contribution to be made by administrative registers, geospatial information systems and other big data sources.

Within this framework, the present Invited Paper Session (IPS) will discuss the various potentialities offered by SAE and other indirect estimation techniques to produce granular disaggregated estimates of SDG and other national priority indicators, by integrating survey data with auxiliary information retrieved from traditional and/or innovative data sources. The speakers will present different examples of how SAE is used by international organizations and other national statistical offices to monitor the SDGs and other priority development objectives.

List of papers/presentations

1) Recent advances on Poverty Mapping: ECLAC’s leading role in Latin-America and the Caribbean. Mr. Rolando Ocampo, Director of the Economic Commission for Latin America and the Caribbean (ECLAC) Statistics Division. rolando.ocampo@cepal.org 

2) Using Integrated Data Sources for Small Area Estimation within Italian Social Surveys to monitor SDG Indicators. Mr. Stefano Falorsi, Head of the Methodological Division of the Italian National Statistical Institute (Istat). stfalors@istat.it 

3) Integrating survey data with alternative data sources for small area estimation of food and agriculture related SDG indicators. Ms. Clara Aida Khalil, Statistician with the Office of the Chief Statistician of the Food and Agriculture Organization (FAO) of the United Nations. ClaraAida.Khalil@fao.org 

4) A spatial multivariate Fay-Herriot model for producing commune-level agricultural indicators in Burkina Faso. Mr. Dramane Bako, Statistician with the Statistics Division of the Food and Agriculture Organization of the United Nations. Dramane.Bako@fao.org

Organiser: Yakob Seid

Chair: Pietro Gennari 

Speaker: Mr Rolando Ocampo Alcántar 

Speaker: Maria Giovanna Ranalli

Speaker: Yakob Seid