Multivariate Small Area Estimation of Multidimensional Latent Economic Well‐being Indicators

Moretti, Angelo; Shlomo, Natalie; Sakshaug, Joseph W.

doi:10.1111/insr.12333

Cited by 7 publications

(9 citation statements)

References 44 publications

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“…Therefore, the multivariate SAE method might provide better dashboard estimates and averages if the correlation between the single variables is taken into account. These extensions are currently being carried out in Moretti, Shlomo, and Sakshaug (2017).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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Small Area Estimation of Latent Economic Well-being

Moretti

Shlomo

Sakshaug

2019

Sociological Methods & Research

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Small area estimation (SAE) plays a crucial role in the social sciences due to the growing need for reliable and accurate estimates for small domains. In the study of well-being, for example, policy makers need detailed information about the geographical distribution of a range of social indicators. We investigate data dimensionality reduction using factor analysis models and implement SAE on the factor scores under the empirical best linear unbiased prediction approach. We contrast this approach with the standard approach of providing a dashboard of indicators or a weighted average of indicators at the local level. We demonstrate the approach in a simulation study and a real data application based on the European Union Statistics for Income and Living Conditions for the municipalities of Tuscany.

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Section: Conclusion and Discussionmentioning

confidence: 99%

“…We acknowledge that in the presence of more complex multidimensional phenomena, one factor may not explain the total variability. Moretti, Shlomo, and Sakshaug (2017) investigate the issue of multiple latent factors under a multivariate SAE approach.…”

Section: Using Factor Scores For Data Dimensionality Reductionmentioning

confidence: 99%

Small Area Estimation of Latent Economic Well-being

Moretti

Shlomo

Sakshaug

2019

Sociological Methods & Research

Self Cite

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“…Various extensions of the basic area-level model have been developed in the literature [1,7,8,11,12]. One type of extension leads to extending the UFH model to a multivariate Fay-Herriot model to take advantage of the correlations between different characteristics of interest [7,8].…”

Section: Multivariate Fay Herriot Modelmentioning

confidence: 99%

“…It is essential to obtain an accurate estimator of MSE to reflect the true variability associated with the EBLUP estimators. MSE estimators have been studied using variance component estimators under the MFH model [11,12]. The performance of small area estimates under the MFH model was assessed by the coefficient of variation (CV %) and root MSE [10,13].…”

Section: Introductionmentioning

confidence: 99%

Multivariate small area estimation of undernutrition for children under five using official statistics

Yilema

Shiferaw

Zewotir

et al. 2022

SJI

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Surveys are mainly used to obtain reliable estimates for planned domains at national and regional levels. However, the unplanned domains (lower administrative layers) with small sample sizes must be estimated. The direct survey estimates of the non-planned domains with small sample sizes lead to large sampling variability. Thus, small area estimations dealt with managing this variability by borrowing the strength of neighboring areas. The target variables of the study were obtained from the 2016 Ethiopian demographic and health survey (EDHS) and the auxiliary variables taken from the 2007 population and housing census data. Multivariate Fay Herriot (MFH) model was used by incorporating the correlations among the target variables. The model diagnostic measures assured the normality assumption, and the consistency of multivariate small area estimates are valid. Multivariate EBLUPs of the target variables produced the lowest percent coefficient of variation (CV) and root mean square error (MSE). Therefore, multivariate EBLUP has improved the direct survey estimates of undernutrition (stunting, wasting, and underweight) for small sample sizes (even zero sample sizes). It also provided better estimates compared to the univariate EBLUPs. Generally, multivariate EBLUPs of undernutrition produced the best reliable, efficient, and precise estimates for small sample sizes in all zones. Zones are essential domains for planning and monitoring purposes in the country, and therefore these results provide valuable estimates for policymakers, planners, and legislative organs of the government. One of the novelties of this paper is estimating the non-sampled zones, and therefore the policymakers will give equal attention similar to the sampled zones.

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“…The combined use of latent variable models and small area models has been investigated in other works. For instance, Moretti et al (2020) work with continuous variables and propose a Factor analysis model combined to a unit-level small area model to predict a vector of means of factor scores that can be interpreted as indicators of multidimensional latent well-being in small areas; in Montanari and Ranalli (2010) the latent class model is used to classify the population according to different levels of disability and then local estimates of the number of people belonging to each class are obtained via a small area model. Both examples rely on the use of a 2-step approach, first the estimation of the latent variable and second the small area correction.…”

Section: Introductionmentioning

confidence: 99%

Social Integration of Second Generation Students in the Italian School System

Giovinazzi

2021

Soc Indic Res

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Cultural divides and prejudice complicate the processes of integration and acculturation of migrant families living in a foreign country. Evaluating the impact of such phenomenon can be crucial for social stability and policy making. In this context, the education system has a leading role in fostering and attaining social integration, in particular when it comes to younger sections of the migrant population. In this work, we propose a method for the construction of a quantitative indicator capturing social integration of second generation students in the Italian school system according to areas defined by nationality of the students and administrative region in which they attend school. The indicator, based on survey data, is estimated by means of a 2-step methodology. In the first step, we choose an individual qualitative variable capturing social integration at the unit level, and we compute a first direct estimate of the indicator as the proportion of highly integrated students in each area. Such variable is isolated following alternatively a proxy variable approach or a latent variable model approach. In the second step, we make use of two alternative small area models to improve the estimates, dealing with missing values, low sample size and high variability in smaller domains. At the end, the 2-step methodology results in 4 alternative versions of a synthetic indicator of social integration, that can be used to rank nationalities and administrative regions.

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Multivariate Small Area Estimation of Multidimensional Latent Economic Well‐being Indicators

Cited by 7 publications

References 44 publications

Small Area Estimation of Latent Economic Well-being

Small Area Estimation of Latent Economic Well-being

Multivariate small area estimation of undernutrition for children under five using official statistics

Social Integration of Second Generation Students in the Italian School System

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