الفهرس 
The use of small area samples for estimating population parameters
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Abstract
The demand on statistics which include small areas has increased for official statistics in the recent period. So there is needed to estimate the unemployment rate at shaikhia\ village level as the smallest geographic level instead of the government or country (Egypt) level. The study had a problem of misclassification of working status in Census data questionnaire, and how dealing with this problem of misclassification in Census. Labor Force Survey (LFS) estimates are inaccurate because the small sample size for the smallest geographic level although good classification is in LFS questionnaire. So the study extends the Census’ properties to solve small sample size in LFS. The aim of this study is to estimate the unemployment rate in shiakhia\ village level at the smallest geographic level in the Egypt to know the main and detailed reasons for unemployment; Also how to solve this problem with more accuracy and detail. So, the study uses Census data 2006, LFS 2006 by application on Qaliobia governorate that reflects the Egyptian society. The study applied the direct estimation with direct estimator and Broad area estimator with no auxiliary data, the indirect estimation with unit level estimator level (binary logistic regression). the Indirect estimation for unemployment is more accurate than direct estimate, because it depends on the statistical model; This study recommend to apply the small area estimation to help policymakers to address unemployment problem at small geographical level, draw policies and identify the gap between employed and unemployed across the geographical hierarchy (i.e.Shiakha\village) and so on for other phenomenon such as poverty