Abstract

Statistics and data have the important role in realizing international development priorities, the Fundamental Principles of Official Statistics and the global indicator framework of the SDGs recommends disaggregating data according to income, sex, age, disability, geographic location and other relevant dimensions. So, The demand for small area data to support planning, decision making, and service delivery at a local area level, SAE is produced depending on the new statistics techniques that are more accurate than survey estimates. Poverty statistics are very important, and SDG 1 aims to end poverty in all forms everywhere. Sample sizes of survey data sets used for poverty estimation are rarely large enough to generate reliable estimates for highly disaggregated analysis. Accurate Municipal-level poverty estimates will be useful for evidence-based economic and social policies and programs for poverty reduction. This study works on studying the potential opportunities and the challenges in applying Small Area Estimation (SAE) techniques to estimate poverty indicators at the smallest geographical level(shiakha/village) in Egypt to get the main reasons for this problem. This helps policymakers to address this national phenomenon at this small geographical level and also target the poorest areas. The paper is applying SAE methods of poverty indicators:

1- Direct methods that applied without the explicit application of statistical models.
2- Indirect methods (regression models).
Data sources: Household income, expenditure, and consumption survey (HIECS2021- 2022).