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Challenges in small area estimation of poverty indicators

Challenges in small area estimation of poverty indicators. Risto Lehtonen, Ari Veijanen, Maria Valaste (University of Helsinki) , and Mikko Myrskylä ( Max Planck Institute for Demographic Research, Rostock). Ameli 2010 Conference, 25-26 February 2010, Vienna. Outline. Background

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Challenges in small area estimation of poverty indicators

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  1. Challenges in small area estimation of poverty indicators Risto Lehtonen, Ari Veijanen, Maria Valaste (University of Helsinki) , andMikko Myrskylä (Max Planck Institute for Demographic Research, Rostock) Ameli 2010 Conference, 25-26 February 2010, Vienna

  2. Outline • Background • Material and methods • Results • Discussion • References

  3. EU/FP7 Project AMELI • Advanced Methodology for European Laeken Indicators (2008-2011) • The project is supported by European Commission funding from the Seventh Framework Programme for Research • DoW: The study will include research on data quality including • Measurement of quality • Treatment of outliers and nonresponse • Small area estimation • The measurement of development over time

  4. Material and methods • Investigation of statistical properties (bias and accuracy) of estimators of selected Laeken indicators for population subgroups or domains and small areas • Method: Design-based Monte Carlo simulation experiments based on real data • Data: Statistical register data based on merging of administrative register data at the unit level (Finland)

  5. Laeken indicators based on binary variables • At-risk-of poverty rate • Direct estimators • Horvitz-Thompson estimators HT • Indirect estimators • Model-assisted GREG and MC estimators • Model-based EBLUP and EB estimators • Modelling framework • Generalized linear mixed models GLMM • Lehtonen and Veijanen (2009) • Rao (2003), Jiang and Lahiri (2006)

  6. Laeken indicators based on medians or quantiles • Indicators based on medians or quantiles of cumulative distribution function of the underlying continuous variable • Relative median at-risk-of poverty gap • Quintile share ratio (S20/S80 ratio) • Gini coefficient • Direct estimators DEFAULT • Synthetic estimators SYN • Expanded prediction SYN estimators EP-SYN • Composite estimators COMP • Simulation-based methods

  7. Generalized linear mixed models

  8. Design-based GREG type estimators for poverty rate

  9. Model-based estimators forpoverty rate

  10. Poverty gap for domains • Relative median at-risk-of poverty gap • Poverty gap in domain d describes the difference between the poor people's median income and the at-risk-of-poverty threshold t

  11. Estimators of poverty gap

  12. Estimators of poverty gap

  13. Estimators of poverty gap

  14. Estimators of poverty gap

  15. MSE estimation for direct estimator DEFAULT

  16. MSE estimation for SYN estimator

  17. Monte Carlo simulation • Fixed finite population of 1,000,000 persons • D = 70 domains of interest • Cross-classification of NUTS 3 with sex and age group (7x2x5) • Y-variables • Equivalized income (based on register data) • Binary indicator for persons in poverty • X-variables (binary or continuous variables) • house _owner (binary) • education_level (7 classes) and educ_thh • lfs_code (3 classes) and empmohh • socstrat (6 classes) • sex_class and age_class (5 age classes) • NUTS3

  18. Sampling designs • SRSWOR sampling • Sample size n = 5,000 persons • Stratified SRSWOR • Sample size n = 5,000 persons • Stratification by education level of HH head • H = 7 strata • Unequal inclusion probabilities • Design weights vary between strata • Min: 185, Max: 783 • K = 1000 independent samples

  19. Design bias Absolute relative bias ARB (%) Accuracy Relative root mean squared error RRMSE (%) Quality measures of estimators

  20. Discussion: Poverty rate • Indirect design-based estimator MLGREG • Design unbiased • Large variance in small domains • Small variance in large domains • Indirect model-based estimator EB • Design biased • Small variance also in small domains • Accuracy: EB outperformed MLGREG • Might be the best choice at least for small domains unless it is important to avoid design bias

  21. Discussion: Poverty gap • Direct estimator DEFAULT • Small design bias but large variance • Indirect model-based SYN • Very large bias but small variance • Indirect model-based EP-SYN based on expanded predictions • Much smaller bias and variance than in SYN • Composite (DEFAULT with EP-SYN) • Small domains: good compromise • Large domains: bias can still dominate the MSE

  22. Thank you for your attention!

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