Statistical Matching in the Modernization of Social Statistics

1. Statistical Matching in the framework of the modernization of social statisticsAura Leulescu& Emilio Di MeglioEUROSTAT Unit F3 - Living conditions and social protection statistics

2. Key priorities in the EU context • to respond to cross-cutting and complex user needs by providing broad indicators on economic well-being and Quality of Life (Stiglitz Report, Europe 2020, GDP and beyond communication, OECD initiative on measuring well-being, etc.); • Demand for a comprehensive and coherent system of socio-economic statistics • to go beyond aggregates and capture heterogeneity in the population: multivariate distributions, sub-national statistics, vulnerable sub-groups; • Demand for micro-level statistical information that encompasses both social and economic aspects 2 2 2

3. Premises No single survey can provide all the necessary information No common identifiers allow record linkage at EU level Need for micro (meso)-level integrated statistical information from a coordinated network of surveys and data collection processes at EU level

4. Statistical matching? • High potential benefits: • Increased and better use of existing data at minimum costs, • Enhanced conceptual and statistical consistency across surveys, • Development of in house expertise in the domains of data matching transferable to other projects. • But also high risks: • Inherent limitations of statistical matching techniques and model-based imputation; • Need to consider both micro level data matching and meso-level data matching (small sub-populations could also be matched). 4

5. Matching project: 1) Scope This project should: • carry-out methodological work, identify and test statistical matching algorithms based on the “fitness for purpose” principle; • identify suitable criteria for assessing validity of findings based on both input quality and the robustness of the matching methods proposed; • produce methodological guidelines and recommendations for further implementation in Eurostat and/or MSs. 5 5

6. Matching project: 2) Investigation streams The project should assess the quality of the results and the relevance of the approach to cover specific needs: • Material well-being estimates based on wealth, consumption and income (matching of HFCS, HBS and SILC); • Quality of Life indicators that go beyond monetary resources (matching of SILC with LFS and EHIS and outside sources, such as ESS and EQLS); • Poverty estimates at regional level, linked to the monitoring of Europe 2020 (matching of data from SILC, EHIS and LFS). 6 6 6

7. Matching project: 3) Timeline • I phase: some preliminary analysis focused especially on setting the boundaries for the project • Dec 2010- July 2011 External contract for matching EU-SILC, ESS and EQLS • Dec 2010- April 2011 In-house matching exercise (review state of the art & preliminary analysis focused on the reconciliation datasets)‏ • II phase • May 2011- Dec 2012 Follow upof the in-house exercise • May 2011 Launch call of tender (according to preliminary results of the three investigation streams)‏ • November 2011 Signature contract(s) • December 2012 Recommendations for implementation 7

8. Matching project: 4) Organizational aspects The project is expected: to draw on both external contracts and the development of in-house expertise on matching techniques; to involve various stakeholders: concerned units in Eurostat, ECB, Eurofound, Commission users (DG EMPL, DG SANCO, DG REGIO) and academic experts; to develop synergies with ESS initiatives: Core social variables ESSnet on Data Integration ESSnet on Small Area Estimation

9. Matching exercise: ex-ante reconciliation 1 Main purpose: identify specific realistic objectives Identify target variables a) Income, consumption and wealth HFCS:value of assets and liabilities; EU-SILC: material deprivation, detailed income; HBS: food expenditure, leisure goods and services, transport expenditure; b) Quality of life indicators EQLS/ESS: social capital, quality of society, satisfaction variables LFS: job quality, training... SILC: standards of living c) Regional estimates Impute household disposable equivalized income in LFS

10. Matching exercise ex-ante reconciliation 2 Select matching/ stratification variables Predictive power (econometric models, correlations, multivariate analysis)‏ Data quality Consistency of concepts and statistical content Deal with different weights from the various surveys Define the observation level Individual Household Sub-population What type of auxiliary information we can use to validate results? overlap samples (NL); (partial) overlap variables (income classes in EQLS; some material deprivation; food consumption in HFCS)

11. Matching exercise: methods and quality assessment - Preliminary ideas • Matching algorithms • Hot deck techniques, regression based, multiple imputation? • Deal with complex survey designs (constraints)‏ • Create synthetic datasets versus estimate parameters (e.g. estimate frequencies by class of income & wealth); • How to assess quality/validity? • Checking the marginal and joint distributions of the donor/fused dataset; • Assess probability of good match (ex.: distribution distances donor-recipient)‏ • Need to assess the sensitivity of the results to changes in assumptions: • Simulation exercises; auxiliary information; theoretical validation; • Some applications: SPSD Canada (Liu& Kovacevic, 1997), ISTAT (Coli et al, 2006)‏ 11