EU – FP7 - SSH-2007-1Grant Agreement no 217565

S.A.M.P.L.E.Small Area Methods for

Poverty and Living condition Estimates

European Conference on Quality in Official Statistics, Rome 8-11 July 2008

UNIPI-DSMAE

Monica Pratesi

The SAMPLE project: history, structure and perspectives

Hystory: from a problem to a solutionHystory: from a problem to a solution

Local Government Agencies information need on poverty Local Government Agencies information need on poverty

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

Work packagesWork packages

Project perspectivesProject perspectives

Agenda

Hystory: from a problem Hystory: from a problem to a solutionto a solution

NUTS 3LAU 1-2

level

What is poverty?

Standard Laeken Indicators, monetary-non monetary aspects

Data sources at local level?

EU-SILC + Administrative data files

EVALUATE QUALITY AND RELEVANCE OF DATA SOURCES

FORMULATE AN ADEQUATE POLICY

Poverty and deprivation Poverty and deprivation at NUTS 3 – LAU 1-2 level?at NUTS 3 – LAU 1-2 level?

Local Government Agencies Local Government Agencies information needinformation need

DASHBOARD OF RELIABLE POVERTY INDICATORS

Poverty line

50% mean income

60% median income

Other definitions

Poors Not poors

Standard Poverty Indicators

EquivalizedIncome

Diffusion (or incidence) of Poverty

= Poors

Poors + Not poors

Head Count Ratio (HCR)

Standard Poverty Indicators

Limits of the standard approach

1. Dicothomy poor/not poor is an oversemplification of reality.

Poverty is not an attribute but a problem of graduation.

2. Income is not the only indicator of Living Conditions.

a) Income is not a reliable variable, especially in Italy;

1. Poverty is a multidimensional phenomenon to be analyzed

through several indicators (also not monetary)

of living conditions

Standard Poverty Indicators

Income Cumulative Distribution Function

Cumulative distribution function of Income is more informative than HCR

orange and grey lines:

same HCR (Head Count ratio) but different situations!

1

yi yj EquivalizedIncome (y)

Poors Not poors

HCR

EU-SILC survey

European Survey on Living Conditions

1. based on a probability sample of households

2. significant estimates of Laeken indicators at

NUTS 2 level (Regione)

Not significant estimates at NUTS 3 – LAU 1-2 level(e. g. Pisa Province only 147 randomly selected households)

Administrative data files

Administrative data files

1. referred to people eligible for obtaining a service

(i.e. medicare, pensions, enrollment in social

programs)

2. NUTS 3, LAU 1-2 level, details on subscribers

Periodical and low costs estimates at NUTS 3 – LAU 1-2 level but affected by self-selection bias

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

…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 1-2 level

• …a clear definition of the indicators to include in the DASHBOARD

• …ability to communicate the solutions to methodologists, official

statisticians, NGAs officers

S.A.M.P.L.E project S.A.M.P.L.E project structurestructure

S.A.M.P.L.E

SEVENTH FRAMEWORK PROGRAMMETHEME 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. 217565Project Officer: Ian PerryRequested EC contribution: € 874.031,00

Starting date: 01/03/2008 - Duration in months: 36Kick-off meeting: 16/05/2008, Pisa

Beneficiaries

Beneficiary number

Beneficiary nameBeneficiary short name

Country

1 (Coordinator)

Università di Pisa – Dipartimento di Statistica e Matematica Applicata all’Economia

UNIPI - DSMAE Italy

2 Università di Siena – Centro Interdipartimentale di Ricerca sulla Distribuzione del Reddito

CRIDIRE Italy

3 Cathie Marsh Center for Census and Survey Research, University of Manchester

CCSR UK

4 Departamento de Estadìstica, Universidad Carlos III de Madrid

UC3M Spain

5 Centro de Investigación Operativa, Universidad Miguel Hernandez de Helce

UMH Spain

6 Warsaw School of Economics WSE Poland

7 Provincia di Pisa – U.O. Studi e Ricerche - Osservatorio per le Politiche Sociali – Ufficio Politiche Comunitarie

PP Italy

8 Simurg Ricerche SR Italy

9 Glowny Urzad Statystyczny, Poland GUS-CES Poland

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

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.

Aim of the project

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)

Structure of the project

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.

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.

WP 1: New indicators and models for inequality and poverty with attention

to social exclusion, vulnerability and deprivation

WP 2: Small area estimation of poverty and inequality indicators

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).

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).

Structure of the project

EU-SILC over-sampling:

Final objectives1. 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

2. the definition of effective indicators for the local government at LAU 2 oraggregations of the LAU 2 level.

Intermediate objectives • Understanding the components of the self-selection bias of the administrative data

files on poverty and deprivation1. Testing the accuracy of the poverty and deprivation indicators for the Province of Pisa2. Validating the estimation procedures of the MSE of the poverty and deprivation

indicators at small area level

Project perspectivesProject perspectives

Envisioning the future

1) Social political perspective:

better understanding of inequality and poverty with special attention

to social exclusion and deprivation through local level indicators.

2) Methodological perspective:

producing more reliable (accuracy measured) indicators at the level

of small area (NUTS 3 and LAU 1-2 level).

3) Dissemination perspective:

several kind of actors: statisticians, local and european policy

makers, social researchers, official statisticians, NGOs, citizens

Envisioning the future

Next meetings and conferences:

2009: Elche (SAE 2009)

2010: place to be defined

2011: Pisa final meeting

www.sample-project.eu