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- In the case of Chile, the 2017 National Social and Economic Survey (CASEN survey), which corresponds to a representative sample at the national, regional, national urban, and national rural levels, was used in conjunction with the 2017 Population and Housing Census.
- For Colombia, the Comprehensive Survey of Households of 2018, which is representative of the national, national urban, national rural, regional, departmental, and for the capitals of the country's departments, was used together with the 2018 National Population and Housing Census.
- In the case of Peru, the 2017 National Household Survey (ENAHO), which is representative at the national, urban, rural, and departmental levels, was used together with the twelfth Population Census, seventh Housing Census, and third Census of Indigenous Communities of 2017.
Small area estimation
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models
A unit-level model with adjustment for the complex sampling design is used to estimate average income. This model gives an approximation to the best empirical predictor (pseudo-EBP) based on the nested-error model, as proposed by Guadarrama, Molina, and Rao (2018). The method assumes that the transformed income variables follow a nested error model including random effects for the subdivision of interest d. According to Molina (2019), for FGT indicators, the best linear predictor (the one that minimizes the mean squared error) is given by the expected value of the elements that are not selected in the sample within the subdivision of interest d, conditional on the observed values of the selected elements. Since the available data do not make it possible to identify and link sample units with census units, a "census-EB" type of approach is used. In household surveys of Latin America, the ratio of the number of units selected in the sample relative to the country's population is very close to zero; so the census-EB predictor performs quite similarly to the pseudo-EBP.
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The ECLAC approach allows to focus on a single country and dissagregate disaggregate the estimates not only at the geographical level but also incorporating other subgroups of interest such as sex, zone, age, disability, ethnicity, and education. For example, the following map shows how the extreme poverty rate is distributed in Latin America the Peruvian case by education (columnsthe first column represents no education, the second column represents 1 to 6 years of education, third column 7 to 12 years, and fourth column more that 12 years of education) and ethnicity (rowsthe first row represents indigenous people, the second row represents Afro-Peruvian people and the third row is devoted to the rest of the population).
References
- I. Molina, I. and J.N.K. Rao, “Small Area Estimation of Poverty Indicators”. Canadian Journal of Statistics, vol. 38, No. 3, 2010.
- M. Guadarrama, I. Molina and J. N. K. Rao, “Small area estimation of general parameters under complex sampling designs”. Computational Statistics & Data Analysis, No. 121, 2017.
- I. Molina, “Desagregación de datos en encuestas de hogares: metodologías de estimación en áreas pequeñas”, Statistical Studies series, No. 97 (LC/TS.2018/82/Rev.1), Santiago, Economic Commission for Latin America and the Caribbean (ECLAC), 2019.
- Economic Commission for Latin America and the Caribbean (ECLAC), “Medición de la pobreza por ingresos: actualización metodológica y resultados”, Metodologías de la CEPAL, No. 2 (LC/PUB.2018/22-P), Santiago, 2018. [AUTHOR: Please confirm this reference. OK]
- J. Foster, J. Greer and E. Thorbecke, “A class of decomposable poverty measures”, Econometrica, vol. 52, No. 3, 1984. [AUTHOR: Please confirm this reference. OK.]
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