### Sammanfattning

The paper presents current developments on the estimation of poverty

indicators for domains and small areas in the context of the EU FP7 project

AMELI (Advanced Methodology for European Laeken Indicators). The indicators

considered are the Gini coeffi cient, relative median at-risk-of poverty gap

(poverty gap for short) and quintile share ratio (S20/S80 ratio). The corresponding

estimators to be compared are of direct, synthetic or composite type. It appears

that a direct (default) estimator can be quite ineffi cient, and a more effi cient

indirect synthetic estimator is often seriously biased. Composite estimators

are constructed as a linear combination of the direct and indirect estimators.

Underlying the indirect estimators, we use mixed models with domain-specifi c

random intercepts. Unit-level auxiliary data are incorporated into the estimation

procedure. In our case, these data are based on statistical registers maintained

by Statistics Finland. To cover realistic sampling designs, both equal and unequal

probability sampling designs are covered. By using selected indicators on bias and

accuracy, the relative performance of the estimators is assessed with Monte Carlo

simulation experiments based on real data.

indicators for domains and small areas in the context of the EU FP7 project

AMELI (Advanced Methodology for European Laeken Indicators). The indicators

considered are the Gini coeffi cient, relative median at-risk-of poverty gap

(poverty gap for short) and quintile share ratio (S20/S80 ratio). The corresponding

estimators to be compared are of direct, synthetic or composite type. It appears

that a direct (default) estimator can be quite ineffi cient, and a more effi cient

indirect synthetic estimator is often seriously biased. Composite estimators

are constructed as a linear combination of the direct and indirect estimators.

Underlying the indirect estimators, we use mixed models with domain-specifi c

random intercepts. Unit-level auxiliary data are incorporated into the estimation

procedure. In our case, these data are based on statistical registers maintained

by Statistics Finland. To cover realistic sampling designs, both equal and unequal

probability sampling designs are covered. By using selected indicators on bias and

accuracy, the relative performance of the estimators is assessed with Monte Carlo

simulation experiments based on real data.

Originalspråk | engelska |
---|---|

Titel på gästpublikation | Proceedings of the Q2010 Conference : European Conference on Quality in Official Statistics |

Antal sidor | 1 |

Förlag | Tilastokeskus |

Utgivningsdatum | 2010 |

Sidor | 164 |

Status | Publicerad - 2010 |

MoE-publikationstyp | B3 Ej refererad artikel i konferenshandlingar |

### Vetenskapsgrenar

- 112 Statistik

### Citera det här

Lehtonen, R., & Veijanen, A. (2010). Estimation of poverty indicators for domains with unit-level auxiliary information. I

*Proceedings of the Q2010 Conference: European Conference on Quality in Official Statistics*(s. 164). Tilastokeskus.