Labor figures for Mexico’s municipalities: Small Area Estimation

Author:

Orozco Edgar Vielma1,Rivera José Vences2,Mata Gustavo Aguilar2

Affiliation:

1. General of Sociodemographic Statistics, National Institute of Statistics and Geography (INEGI), Aguascalientes, 20276, México

2. Development of Statistical Processes, National Institute of Statistics and Geography (INEGI), Aguascalientes, 20276, México

Abstract

Labor figures for Mexico’s municipalities were estimated during 2018’s first quarter by using Small Area Estimation (SAE) techniques with the incorporation of a spatial component – given there is no recent information source with such a level of geographic disaggregation. To achieve this, combined information from different sources was used to build statistical models in which the Economically Active Population, the Employed Population and the Informal Employed Population were taken as variables object of estimation – this information was taken from the National Survey of Occupation and Employment (ENOE for its acronym in Spanish). Auxiliary variables were selected from population censuses, administrative records, and population projections. The results were contrasted with those calculated by applying the percentage structures of 2010 Population and Housing Census to the figures provided by ENOE at a federal entity level, and with the data in this survey (obtained by direct estimation for those municipalities which had a sufficient sample with acceptable coefficients of variation). It is observed that the results obtained by Small Area Estimation are plausible and register coefficients of variation below 10 percent.

Publisher

IOS Press

Subject

Statistics, Probability and Uncertainty,Economics and Econometrics,Management Information Systems

Reference15 articles.

1. Development of small area estimation in official statistics;Kordos;STATISTICS IN TRANSITION New Series,2016

2. From start to finish: a framework for the production of small area official statistics;Tzavidis;Journal of the Royal Statistical Society Serie A,2018

3. Estimates of income for small places: an application of james-stein procedures to census data;Fay;Journal of the American Statistical Association,1979

4. Micro-level estimation of poverty and inequality;Elbers;Econometrica Journal of the Econometric Society,2003

5. Small area estimation of poverty indicators;Molina;The Canadian Journal of Statistics,2010

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