S.A.M.P.L.E . S mall A rea M ethods for P overty and L iving condition E stimates

1. S.A.M.P.L.E.Small Area Methods forPoverty and Living condition Estimates European Conference on Quality in Official Statistics, Rome 8-11 July 2008 The SAMPLE project: history, structure and perspectives UNIPI-DSMAE Monica Pratesi

2. Agenda • Hystory: from a problem to a solution Local Government Agencies information need on poverty • S.A.M.P.L.E project structure Work packages • Project perspectives

3. Hystory: from a problem to a solution

4. Local Government Agencies information need What is poverty? NUTS 3 LAU 1-2 level Standard Laeken Indicators, monetary-non monetary aspects Data sources at local level? Poverty and deprivation at NUTS 3 – LAU 1-2 level? EU-SILC + Administrative data files FORMULATE AN ADEQUATE POLICY EVALUATE QUALITY AND RELEVANCE OF DATA SOURCES DASHBOARD OF RELIABLE POVERTY INDICATORS

5. 50% mean income 60% median income Other definitions Poverty line Equivalized Income Poors Not poors Standard Poverty Indicators

6. Standard Poverty Indicators Poors Diffusion (or incidence) of Poverty = Poors + Not poors Head Count Ratio (HCR)

7. Standard Poverty Indicators Limits of the standard approach • Dicothomy poor/not poor is an oversemplification of reality. Poverty is not an attribute but a problem of graduation. • Income is not the only indicator of Living Conditions. • Income is not a reliable variable, especially in Italy; • Poverty is a multidimensional phenomenon to be analyzed through several indicators (also not monetary) of living conditions

8. Equivalized Income (y) Poors Not poors Income Cumulative Distribution Function 1 orange and grey lines: same HCR(Head Count ratio) but different situations! HCR yi yj Cumulative distribution function of Income is more informative than HCR

9. EU-SILC survey European Survey on Living Conditions based on a probability sample of households significant estimates of Laeken indicators at NUTS 2 level (Regione) Not significant estimates at NUTS 3 – LAU 1-2level (e. g. Pisa Province only 147 randomly selected households)

10. Administrative data files Administrative data files referred to people eligible for obtaining a service (i.e. medicare, pensions, enrollment in social programs) NUTS 3, LAU 1-2 level, details on subscribers Periodical and low costs estimates at NUTS 3 – LAU 1-2level but affected by self-selection bias

11. Many unanswered questions • Is it possible to formulate new indicators of poverty which extend the traditional indicators (Laeken Indicators) to additional monetary and non monetary aspects and have a longitudinal perspective (measurement of changes in poverty)? • Is it possible to improve the accuracy of the EU-SILC estimates of traditional and new indicators at NUTS 3 – LAU 1-2 level? • Is it possible data integration between EU-SILC and Administrative data files? Is it possible to measure and correct the Administrative data files self-selection bias? • In other words: • Is it possible to satisfy the LOCAL GOVERNMENT AGENCIES INFORMATION NEED? • DASHBOARD OF POVERTY INDICATORS AT NUTS3 – LAU1-2 LEVEL

12. …it is a good CHALLENGE • The answers are “yes”!! • The solutions requires cooperation between statistical methodologists, official statisticians and NGAs officers. • Integration of skills… • …the solutions requires small area estimation methodology • …also statistical matching methodology • … and ability to implement software procedures for the estimation • …a good knowledge of admnistrative data on poverty and deprivation • …EU-SILC oversampling at NUTS 3, LAU 1-2 level • …availability of sensible information at NUTS 3 and LAU 1-2level • …a clear definition of the indicators to include in the DASHBOARD • …ability to communicate the solutions to methodologists, official statisticians, NGAs officers

13. S.A.M.P.L.E project structure

14. S.A.M.P.L.E SEVENTH FRAMEWORK PROGRAMME THEME FP7-SSH-2007-1 Socio-economic sciences and the Humanities Part 8 Grant agreement for:Collaborative Project - Small or medium-scale focused research project Small Area Methods for Poverty and Living Conditions Estimates Grant Agreement n. 217565 Project Officer: Ian Perry Requested EC contribution: € 874.031,00 Starting date: 01/03/2008 - Duration in months: 36 Kick-off meeting: 16/05/2008, Pisa

15. Beneficiaries

16. Aim of the project The aim of the SAMPLE project is 1) to identify and develop new indicators and models that will help the understanding of inequality and poverty with special attention to social exclusion and deprivation. 2) to develop models and implement procedures for estimating these indicators and their corresponding accuracy measures at the level of small area (NUTS 3 and LAU 1-2 level). DASHBOARD OF RELIABLE POVERTY INDICATORS

17. Aim of the project These goals will be achieved… …by combining data from national surveys (EU-SILC survey) with data from local administrative databases. In particular, Local Government Agencies (LGAs) often have rich administrative data, which can be used for monitoring actions aiming at tackling situations of social exclusion, vulnerability and deprivation. Such data include information on claimants of unemployment benefit and benefits from other social security programs with the involvement of stakeholders and Non-Governmental Organizations (NGOs) representing people experiencing poverty and which act to prevent poverty.

18. Structure of the project The project is structured in six parts corresponding to six main areas of research or development. Each part consists of a group of tasks (called Work Package – WP) and will be carried out by a set of participant entities: WP1-WP4: substantive part of the project, WP5, WP6: management and dissemination Data sources: Italian, Spanish, Polish, English administrative data and EU-SILC data Sub-contract with Italian National Statistical Institute (ISTAT) for EU-SILC wave 2008 over-sampling al LAU 1 level (Pisa province)

19. Structure of the project WP 1: New indicators and models for inequality and poverty with attention to social exclusion, vulnerability and deprivation The usual indicators of poverty (head count ratio, average income per capita, Gini coefficient) will be completed by the definition of fuzzy monetary and supplementary indicators. EU-SILC will be the initial main source of data. WP 2: Small area estimation of poverty and inequality indicators Estimation of the cumulative distribution function of the variable of interest combining mixed and M-quantile models. Development of small area estimates of usual and new poverty indicators taking into accout the spatial and temporal correlation.

20. Structure of the project WP 3: Integration of EU-SILC data with administrative data Indicators of poverty and deprivation based only on administrative files are referred only to people eligible for obtaining a service (i.e. medicare, pensions, enrollment in social programs). Therefore, the production of periodical and low cost estimates based on administrative data requires adjustment based also on final EU-SILC estimates. WP 4: Standardisation and application development – Software for living conditions estimates The software is a standalone application whose purpose is to give unskilled users (i.e. policy makers, social workers from public and private sector) a simple, easy-to-use assessment and reporting tool for main wealth and poverty indicators at LAU 1 and LAU 2 level (R code - free).

21. Structure of the project EU-SILC over-sampling The 2007 final sample size is 150 interviews in the province of Pisa The 2008 final sample size will be 800 interviews distributed by LAU 2 (20 additional Municipalities) proportionate to population size. • Data collection period: autumn 2008. • Data production process:managed and carried out by Istat that will provide final validated micro-data referred to LAU 1 LAU 2 level to the Consortium. The release will be in line with the EU regulation on disclosure and access to individual data. • Nonresponse procedures: defined by Istat according to the standard procedures of the EU-SILC survey protocol (selection of a larger sample, several attempts to abtain answers, weighting adjustment to correct nonresponse bias).

22. Structure of the project EU-SILC over-sampling: Final objectives • the construction of poverty and inequality measures at local level from several waves (called pooled estimates) and the comparison between different EUSILC waves results with focus on the local longitudinal changes • the definition of effective indicators for the local government at LAU 2 or • aggregations of the LAU 2 level. • Intermediate objectives • Understanding the components of the self-selection bias of the administrative data files on poverty and deprivation • Testing the accuracy of the poverty and deprivation indicators for the Province of Pisa • Validating the estimation procedures of the MSE of the poverty and deprivation indicators at small area level

23. Project perspectives

24. Envisioning the future • Social political perspective: • better understanding of inequality and poverty with special attention to social exclusion and deprivation through local level indicators. • Methodological perspective: • producing more reliable (accuracy measured) indicators at the level of small area (NUTS 3 and LAU 1-2 level). • Dissemination perspective: • several kind of actors: statisticians, local and european policy makers, social researchers, official statisticians, NGOs, citizens

25. Envisioning the future Next meetings and conferences: 2009: Elche (SAE 2009) 2010: place to be defined 2011: Pisa final meeting www.sample-project.eu