Giang118/Analysis-of-Data-by-Small-Area-Method

Estimates of two important indicators: the mean household equivalised income and the headcount ratio (HCR). The data are generated from the Eu-Silc data for Tuscany. In this region, the Local Labour Systems (LLSs) are the 57 areas of interests for LLS: this is the code corresponding to the small area SM_House: Squared Meters of the house (computed as the mean at municipality level) Work_Status: binary variable indicating is the reference person of the household works (value 1) or not (value 0) Gender: binary variable indicating is the reference person of the household is a male (value 1) or female (value 0) Year_Education: number of years in education of the reference person of the household Single: binary variable indicating is the reference person of the household is a single (value 1) or not (value 0) Weights: is the household survey weight. Methods : Horvitz and Thompson Estimator – HT – (Direct Estimator) Generalised Regression –GREG – Estimator (SAE) Empirical Best Linear Unbiased Prediction – EBLUP – (SAE) Fay Herriot - FH-EBLUP / Area EBLUP (SAE) The data are generated from the Eu-Silc data for Tuscany.