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

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  • 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
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    References listed on IDEAS

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    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. repec:aph:ajpbhl:10.2105/ajph.2017.303803_3 is not listed on IDEAS
    3. Benavent, Roberto & Morales, Domingo, 2016. "Multivariate Fay–Herriot models for small area estimation," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 372-390.
    4. 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).
    5. Thorpe, L.E., 2017. "Surveillance as Our Sextant," American Journal of Public Health, American Public Health Association, vol. 107(6), pages 847-848.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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.
    12. 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.
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