The Spatial Fay-Herriot Model in Poverty Estimation

Abstract

Counteracting poverty is one of the objectives of the European Commission clearly emphasized in the Europe 2020 strategy. Conducting appropriate social policy requires knowledge of the extent of this phenomenon. Such information is provided through surveys on living conditions conducted by, among others, the Central Statistical Office (CSO). Nevertheless, the sample size in these surveys allows for a precise estimation of poverty rate only at a very general level - the whole country and regions. Small sample size at the lower level of spatial aggregation results in a large variance of obtained estimates and hence lower reliability. To obtain information in sparsely represented territorial sections, methods of small area estimation are used. Through using the information from other sources, such as censuses and administrative registers, it is possible to estimate distribution parameters with smaller variance than in the case of direct estimation. This paper attempts to estimate the poverty rate at LAU 1 level of Poland. This estimation will be possible through the use of data from different sources describing the living conditions of households and the use of the Fay-Herriot model with spatial correlation. As a result, estimates for previously unpublished levels of aggregation will be obtained.

A Hierarchical Bayesian approach to small area estimation of health insurance coverage in Ethiopian administrative zones for better policies and programs

“…We used the HB model-based SAE to estimate CBHI coverage in non-sampled zones, which borrowed auxiliary variables from census datasets. This approach is consistent with most studies in SAE contexts [ 39 , 78 , 79 ].…”

“…These include exploring spatiotemporal models using undernourished children in studies such as [ 34 – 36 ] and the Bayesian spatiotemporal approach to quantify SARS-CoV-2 [ 37 ]. Spatial SAE is also employed by [ 33 , 38 , 39 ]. In order to explore binary outcomes of response variables for SAE, a logit-normal model [ 40 ], logit-normal spatial model [ 41 ], HB logit-normal model [ 8 , 42 ], HB logit-normal spatial model [ 43 ], zero-inflated Binomial [ 44 ], and zero-inflated negative Binomial model [ 45 ] are used under the Fay-Herriot model.…”

Robust estimation of wages in small enterprises: the application to Poland’s districts

“…Fay and Herriot used their model to estimate income in small geographical units in the USA (Fay and Herriot, 1979). However, the model's application is wider than that -e.g., it can be used for poverty estimation (Wawrowski 2016). Let us assume that the direct estimate of the population mean is the sum of true values of the parameter and random error, which is expressed by the formula:…”

Perception of insecurity in municipalities in Mexico: A small area estimation approach

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