What is it about?
The surveys offer reliable results mainly for aggregated population domains because more specific domains would cost more. However, the user claims it is his or her right to the information in specific domains for which official surveys are not designed. This information is reached by applying Small Area Estimation (SAE) techniques.
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Why is it important?
There is a growing demand of subnational level information in order to design and evaluate specific social programs, and for rational allocation of resources.
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This page is a summary of: Labor figures for Mexico’s municipalities: Small Area Estimation, Statistical Journal of the IAOS, June 2021, IOS Press, DOI: 10.3233/sji-200780.
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Labor figures for Mexico’s municipalities: Small Area Estimation
Labor figures for Mexico’s municipalities were estimated during 2018’s first quarter. The calculates were obtained by applying Small Area Estimation (SAE) techniques. The Spatial Empirical Best Linear Unbiased Prediction (SEBLUP) was used. To do this, we obtained the state level data from the National Survey of Occupation and Employment (ENOE, its acronym in Spanish) whereas the auxiliary variables for each municipality were obtained from the census of population and housing units and social security administrative records. The publication was released with Open Access Option in Issue 2, volume 37 of the Statistical Journal of the International Association for Official Statistics (SJIAOS).
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