IDEAS home Printed from https://ideas.repec.org/a/spr/lsprsc/v18y2025i1d10.1007_s12076-025-00407-0.html
   My bibliography  Save this article

A spatial analysis of disparity in the prevalence of stunting rates among children aged under five between rural and urban areas in Peru

Author

Listed:
  • Anna Sikov

    (National University of Engineering
    Econometric Modelling and Data Analysis Research Group)

  • José Cerda-Hernandez

    (National University of Engineering
    Econometric Modelling and Data Analysis Research Group)

Abstract

The main objective of this article is to assess disparities in the prevalence of stunting rates among children aged 6–59 months between rural and urban areas in Peru, through the spatial analysis of the evolution of stunting rates in small geographic areas (districts and provinces), over the 2013–2023 period. The data from the National Demographic and Health Survey (ENDES) carried out in 2013, 2018 y 2023 was analyzed using the model-assisted approach. Specifically, in this research, the spatial small area modeling is employed in order to address the problem of a very low representation of rural areas in the survey, that results in missing or unreliable direct estimates of the prevalence of stunting rates in these areas. An inadequate representation of the rural areas where the problem of stunting is more critical, compared to the urban areas, may present an obstacle for identifying the regions where the situation is more pressing, and could result in an incorrect assessment of the nationwide magnitude of the childhood stunting and its evolution over time. The approach utilized in this article permits the derivation of the prevalence of stunting rates estimates in out-of-sample areas, and improving the direct estimates obtained in the areas with small sample sizes. This allows a more insightful look into the disparity between the urban and rural areas over the last decade. In particular, we have shown that despite a substantial reduction in the prevalence of stunting rates observed in all regions over the studied period, the disparities between the urban and rural areas remain large.

Suggested Citation

  • Anna Sikov & José Cerda-Hernandez, 2025. "A spatial analysis of disparity in the prevalence of stunting rates among children aged under five between rural and urban areas in Peru," Letters in Spatial and Resource Sciences, Springer, vol. 18(1), pages 1-15, December.
    • Handle: RePEc:spr:lsprsc:v:18:y:2025:i:1:d:10.1007_s12076-025-00407-0
    DOI: 10.1007/s12076-025-00407-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12076-025-00407-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12076-025-00407-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Carlos Mendez & Tifani Husna Siregar, 2023. "Regional unemployment dynamics in Indonesia: serial persistence, spatial dependence, and common factors," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-20, December.
    2. Anna Sikov & José Cerda-Hernandez, 2023. "Estimating the prevalence of anemia rates among children under five in Peruvian districts with a small sample size," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1779-1804, December.
    3. Isabel Molina & Nicola Salvati & Monica Pratesi, 2009. "Bootstrap for estimating the MSE of the Spatial EBLUP," Computational Statistics, Springer, vol. 24(3), pages 441-458, August.
    4. Monica Pratesi & Nicola Salvati, 2008. "Small area estimation: the EBLUP estimator based on spatially correlated random area effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(1), pages 113-141, February.
    5. Anna Sikov & José Cerda-Hernandez, 2024. "Prediction in non-sampled areas under spatial small area models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(4), pages 1079-1116, September.
    6. Miguel Boubeta & María José Lombardía & Domingo Morales, 2024. "Small area prediction of proportions and counts under a spatial Poisson mixed model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(4), pages 1193-1215, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dian Handayani & Henk Folmer & Anang Kurnia & Khairil Anwar Notodiputro, 2018. "The spatial empirical Bayes predictor of the small area mean for a lognormal variable of interest and spatially correlated random effects," Empirical Economics, Springer, vol. 55(1), pages 147-167, August.
    2. Tomasz Ża̧dło, 2015. "On longitudinal moving average model for prediction of subpopulation total," Statistical Papers, Springer, vol. 56(3), pages 749-771, August.
    3. Marhuenda, Yolanda & Molina, Isabel & Morales, Domingo, 2013. "Small area estimation with spatio-temporal Fay–Herriot models," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 308-325.
    4. Caterina Giusti & Lucio Masserini & Monica Pratesi, 2017. "Local Comparisons of Small Area Estimates of Poverty: An Application Within the Tuscany Region in Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 131(1), pages 235-254, March.
    5. Tomasz .Zk{a}d{l}o & Adam Chwila, 2024. "A step towards the integration of machine learning and small area estimation," Papers 2402.07521, arXiv.org.
    6. Miguel Boubeta & María José Lombardía & Domingo Morales, 2024. "Small area prediction of proportions and counts under a spatial Poisson mixed model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(4), pages 1193-1215, September.
    7. Jan Kordos, 2016. "Development Of Smallarea Estimation In Official Statistics," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 105-132, March.
    8. repec:csb:stintr:v:17:y:2016:i:1:p:105-132 is not listed on IDEAS
    9. Luis Pereira & Pedro Coelho, 2013. "Estimation of house prices in regions with small sample sizes," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 50(2), pages 603-621, April.
    10. Anna Sikov & José Cerda-Hernandez, 2024. "Prediction in non-sampled areas under spatial small area models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(4), pages 1079-1116, September.
    11. Rebecca C. Steorts & Timo Schmid & Nikos Tzavidis, 2020. "Smoothing and Benchmarking for Small Area Estimation," International Statistical Review, International Statistical Institute, vol. 88(3), pages 580-598, December.
    12. Kordos Jan, 2016. "Development of Small Area Estimation in Official Statistics," Statistics in Transition New Series, Statistics Poland, vol. 17(1), pages 105-132, March.
    13. Anna Sikov & José Cerda-Hernandez, 2023. "Estimating the prevalence of anemia rates among children under five in Peruvian districts with a small sample size," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1779-1804, December.
    14. David Buil-Gil & Juanjo Medina & Natalie Shlomo, 2021. "Measuring the dark figure of crime in geographic areas: Small area estimation from the Crime Survey for England and Wales [From Broken Windows to Busy Streets: A Community Empowerment Perspective’]," The British Journal of Criminology, Centre for Crime and Justice Studies, vol. 61(2), pages 364-388.
    15. Jan Pablo Burgard & Domingo Morales & Anna-Lena Wölwer, 2022. "Small area estimation of socioeconomic indicators for sampled and unsampled domains," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 287-314, June.
    16. N. Salvati & N. Tzavidis & M. Pratesi & R. Chambers, 2012. "Small area estimation via M-quantile geographically weighted regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 1-28, March.
    17. van der Weide, Roy & Blankespoor, Brian & Elbers, Chris & Lanjouw, Peter, 2024. "How accurate is a poverty map based on remote sensing data? An application to Malawi," Journal of Development Economics, Elsevier, vol. 171(C).
    18. Angelo Moretti, 2023. "Estimation of small area proportions under a bivariate logistic mixed model," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3663-3684, August.
    19. Elbers,Chris & Roy Van der Weide, 2025. "Non-Normal Empirical Bayes Prediction of Local Welfare," Policy Research Working Paper Series 11107, The World Bank.
    20. Szymkowiak Marcin & Młodak Andrzej & Wawrowski Łukasz, 2017. "Mapping Poverty at the Level of Subregions in Poland Using Indirect Estimation," Statistics in Transition New Series, Statistics Poland, vol. 18(4), pages 609-635, December.
    21. Peter A. Gao & Jonathan Wakefield, 2023. "A Spatial Variance‐Smoothing Area Level Model for Small Area Estimation of Demographic Rates," International Statistical Review, International Statistical Institute, vol. 91(3), pages 493-510, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:lsprsc:v:18:y:2025:i:1:d:10.1007_s12076-025-00407-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.