1. 1 ESSnet on Small Area Estimation Stefano Falorsi Istat stfalors@istat.it

2. 2 Partners Istituto Nazionale di Statistica (ISTAT) Italy Institut National de la Statistique France et des Etudes Economiques (INSEE) France Statistisches Bundesamt (DESTATIS) Germany Centraal Bureau voor de Statistiek (CBS) Netherlands Statistisk Sentralbyrå (SSB) Norway Główny Urząd Statystyczny (GUS) Poland Instituto Nacional de Estadística de España (INE) Spain Office for National Statistics (ONS) United Kingdom Swiss Federal Statistical Office (FSO) will soon join the project

3. 3 Description of the Project The main purposes of the ESSnet on Small Area Estimation (SAE) project are:  reassess SAE methods and experiences;  follow up existing academic projects, with particular focus on assimilating and applying SAE methods to survey data;  disseminate knowledge among NSIs;  provide tools and recommendations useful to allow the production of small area estimates. The different phases of the project are a series of theoretical and applicative activities in order to facilitate and promote the use of small area techniques in the production of statistical information.

4. 4 The results of the ESSnet project will be helpful  to detect the best practises  to define the guidelines to be followed for the applications of SAE  to give some advices for software tools users. Dissemination of results is another important step of the ESSnet project, therefore a web-site will be available in order to  share information and the results among the partners  be a forum platform in order to boost the communication among NSIs willing to apply SAE methods. Description of the Project

5. 5 5  WP1 - Project management  WP2 - State of the art The WP2 is aimed to provide a comprehensive overview of small area estimation in the ESS social surveys with respect to implementation, needs and expectations.  WP3 - Quality assessment The WP3 is aimed to review and develop suitable criteria to assess the quality of SAE methods.  WP4 - Software tools The WP4 is aimed to developed specific software tools, mainly in open source languages, starting from the available software produced in EURAREA and BIAS project. The project is composed by 7 work-packages: Description of the Project

6. 6 6  WP5 - Case studies The activities of this WP will be focused on specifying for each of the main survey framework a sub-set of the SAE methods. Relevant tools for diagnostics, identified in WP3, will be applied and case studies will be the ground for training the software or routines identified in WP4.  WP6 - Guidelines and recommendations The WP6 is aimed to summarise the activities and the results produced in the previous WPs so to provide practical guidelines in ESS social surveys context.  WP7 - Transfer of knowledge and knowhow The WP7 is aimed to transferring knowledge and know how to non participating NSIs and dissemination of results through a course and coaching. Description of the Project

7. 7 Basic diagram:  U = population  Ui = ith small area  s = sample from U  The part of the sample s that falls in the small area Ui is si = Ui ∩ s  The size ni of si is usually random SAE Core

8. 8 When to use SAE methods: Whenever direct estimates based only on the sampling units observed for each small area are not reliable (small sample size or sometimes even no observed units) How to use SAE methods: By means of explicit or implicit modelling When and How?

9. 9 How does SAE work? Borrowing Strength Borrowing Strength from?

10. 10 SAE is a collection of different methods:  synthetic methods, composite methods;  design based methods, model based methods;  fixed models, mixed models;  linear models, non-linear models;  methods based on models with spatial and/or temporal correlation structures;  … SAE methods

11. 11  Definitions of problems: how can RL or SM be useful for SAE?  Sharing methods: can the tools developed for SM and RL be already useful for SAE? Viceversa?  Tackling new common problems together: are there unexplored areas where we can work together? For instance, increasing the available auxiliary information making use of SM Possible outcomes  joint documents for the two ESSnets  Presentations on the ESSnet-DI workshop (Madrid, November 2011) Connections between SAE and DI?