IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i3p631-d1089603.html
   My bibliography  Save this article

Mapping Disaggregate-Level Agricultural Households in South Africa Using a Hierarchical Bayes Small Area Estimation Approach

Author

Listed:
  • Yegnanew A. Shiferaw

    (Department of Statistics, University of Johannesburg, Auckland Park Kingsway Campus, P.O. Box 524, Johannesburg 2006, South Africa)

Abstract

The first important step toward ending hunger is sustainable agriculture, which is a vital component of the 2030 Agenda. In this study, auxiliary variables from the 2011 Population Census are combined with data from the 2016 Community Survey to develop and apply a hierarchical Bayes (HB) small area estimation approach for estimating the local-level households engaged in agriculture. A generalized variance function was used to reduce extreme proportions and noisy survey variances. The deviance information criterion (DIC) preferred the mixed logistic model with known sampling variance over the other two models (Fay-Herriot model and mixed log-normal model). For almost all local municipalities in South Africa, the proposed HB estimates outperform survey-based estimates in terms of root mean squared error (MSE) and coefficient of variation (CV). Indeed, information on local-level agricultural households can help governments evaluate programs that support agricultural households.

Suggested Citation

  • Yegnanew A. Shiferaw, 2023. "Mapping Disaggregate-Level Agricultural Households in South Africa Using a Hierarchical Bayes Small Area Estimation Approach," Agriculture, MDPI, vol. 13(3), pages 1-17, March.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:631-:d:1089603
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/3/631/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/3/631/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Priyanka Anjoy & Hukum Chandra & Pradip Basak, 2019. "Estimation of Disaggregate-Level Poverty Incidence in Odisha Under Area-Level Hierarchical Bayes Small Area Model," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(1), pages 251-273, July.
    2. Thorpe, L.E., 2017. "Surveillance as Our Sextant," American Journal of Public Health, American Public Health Association, vol. 107(6), pages 847-848.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. repec:aph:ajpbhl:10.2105/ajph.2017.303803_3 is not listed on IDEAS
    5. Joseph Mulema & Idah Mugambi & Monica Kansiime & Hong Twu Chan & Michael Chimalizeni & Thi Xuan Pham & George Oduor, 2021. "Barriers and opportunities for the youth engagement in agribusiness: empirical evidence from Zambia and Vietnam," Development in Practice, Taylor & Francis Journals, vol. 31(5), pages 690-706, July.
    6. Shonosuke Sugasawa & Tatsuya Kubokawa & J. N. K. Rao, 2019. "Hierarchical Bayes small‐area estimation with an unknown link function," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 46(3), pages 885-897, September.
    7. Benavent, Roberto & Morales, Domingo, 2016. "Multivariate Fay–Herriot models for small area estimation," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 372-390.
    8. Shiferaw, Yegnanew A., 2019. "Time-varying correlation between agricultural commodity and energy price dynamics with Bayesian multivariate DCC-GARCH models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    9. Carletto, Calogero & Corral, Paul & Guelfi, Anita, 2017. "Agricultural commercialization and nutrition revisited: Empirical evidence from three African countries," Food Policy, Elsevier, vol. 67(C), pages 106-118.
    10. Geza, W. & Ngidi, M. S. C. & Slotow, R. & Mabhaudhi, Tafadzwanashe, 2022. "The dynamics of youth employment and empowerment in agriculture and rural development in South Africa: a scoping review," Papers published in Journals (Open Access), International Water Management Institute, pages 1-14(9):504.
    11. Wendy Geza & Mjabuliseni Simon Cloapas Ngidi & Rob Slotow & Tafadzwanashe Mabhaudhi, 2022. "The Dynamics of Youth Employment and Empowerment in Agriculture and Rural Development in South Africa: A Scoping Review," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    12. Molina, Isabel, 2022. "Disaggregating data in household surveys: Using small area estimation methodologies," Estudios Estadísticos 48107, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    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. Primrose Madende & Johannes I. F. Henning & Henry Jordaan, 2023. "Accounting for Heterogeneity among Youth: A Missing Link in Enhancing Youth Participation in Agriculture—A South African Case Study," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    2. Sarah Edore Edewor & Genesis B. Kollie & Ibukun James Olaoye, 2023. "Conditions Driving Youth Employment in Key Sectors of the Nigerian Economy," Sustainability, MDPI, vol. 15(7), pages 1-23, March.
    3. Shiferaw, Yegnanew A., 2024. "A spatial analysis of the digital gender gap in South Africa: Are there any fundamental differences?," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    4. 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.
    5. Héritier Nsenge Mpia & Simon Nyaga Mwendia & Lucy Waruguru Mburu, 2022. "Predicting Employability of Congolese Information Technology Graduates Using Contextual Factors: Towards Sustainable Employability," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    6. Reon Matemane & Tankiso Moloi & Michael Adelowotan, 2022. "Appraising Executive Compensation ESG-Based Indicators Using Analytical Hierarchical Process and Delphi Techniques," JRFM, MDPI, vol. 15(10), pages 1-19, October.
    7. Oratilwe Penwell Mokoena & Thembelihle Sam Ntuli & Tshepo Ramarumo & Solly Matshonisa Seeletse, 2023. "Sustainability of Rural Small-Scale Farmers Using a Thematic Content-Fed Analytic Hierarchy Process," Sustainability, MDPI, vol. 15(15), pages 1-22, August.
    8. Saurav Guha & Hukum Chandra, 2021. "Measuring and Mapping Disaggregate Level Disparities in Food Consumption and Nutritional Status via Multivariate Small Area Modelling," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 154(2), pages 623-646, April.
    9. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    10. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    11. Christina Leuker & Thorsten Pachur & Ralph Hertwig & Timothy J. Pleskac, 2019. "Do people exploit risk–reward structures to simplify information processing in risky choice?," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 76-94, August.
    12. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    13. Alessandri, Piergiorgio & Mumtaz, Haroon, 2019. "Financial regimes and uncertainty shocks," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 31-46.
    14. Svetlana V. Tishkovskaya & Paul G. Blackwell, 2021. "Bayesian estimation of heterogeneous environments from animal movement data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
    15. Rolf Sternberg & David Breitenbach, 2023. "Youth Entrepreneurship in Germany: Empirical Evidence on the How, the Why, the How Many, the Who and the When," Economies, MDPI, vol. 11(6), pages 1-30, June.
    16. Leonardo Oliveira Martins & Hirohisa Kishino, 2010. "Distribution of distances between topologies and its effect on detection of phylogenetic recombination," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 145-159, February.
    17. Tamal Ghosh & Malay Ghosh & Jerry J. Maples & Xueying Tang, 2022. "Multivariate Global-Local Priors for Small Area Estimation," Stats, MDPI, vol. 5(3), pages 1-16, July.
    18. Edwin Fourrier-Nicolai & Michel Lubrano, 2022. "Bayesian inference for non-anonymous Growth Incidence Curves using Bernstein polynomials: an application to academic wage dynamics," AMSE Working Papers 2227, Aix-Marseille School of Economics, France.
    19. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.
    20. Wu, Ji & Guo, Mengmeng & Chen, Minghua & Jeon, Bang Nam, 2019. "Market power and risk-taking of banks: Some semiparametric evidence from emerging economies," Emerging Markets Review, Elsevier, vol. 41(C).

    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:gam:jagris:v:13:y:2023:i:3:p:631-:d:1089603. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.