IDEAS home Printed from https://ideas.repec.org/a/ags/aerrae/304153.html
   My bibliography  Save this article

Disaggregate-level disparity in the incidence of poverty in Chhattisgarh, India

Author

Listed:
  • Anjoy, Priyanka
  • Chandra, Hukum

Abstract

No abstract is available for this item.

Suggested Citation

  • Anjoy, Priyanka & Chandra, Hukum, 2020. "Disaggregate-level disparity in the incidence of poverty in Chhattisgarh, India," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 33(1), June.
    • Handle: RePEc:ags:aerrae:304153
    DOI: 10.22004/ag.econ.304153
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/304153/files/03-P-Anjoy.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.304153?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
    ---><---

    References listed on IDEAS

    as
    1. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    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. K. Shuvo Bakar & Nicholas Biddle & Philip Kokic & Huidong Jin, 2020. "A Bayesian spatial categorical model for prediction to overlapping geographical areas in sample surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 535-563, February.
    2. Jiming Jiang & En-Tzu Tang, 2011. "The best EBLUP in the Fay–Herriot model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(6), pages 1123-1140, December.
    3. Isabel Molina & Paul Corral & Minh Nguyen, 2022. "Estimation of poverty and inequality in small areas: review and discussion," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 1143-1166, December.
    4. J. N. K. Rao, 2015. "Inferential issues in model-based small area estimation: some new developments," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 491-510, December.
    5. Tamura, Karin Ayumi & Giampaoli, Viviana, 2013. "New prediction method for the mixed logistic model applied in a marketing problem," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 202-216.
    6. Risto Lehtonen & Ari Veijanen, 2011. "Percentile-adjusted estimation of poverty indicators for domains under outlier contamination," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 12(2), pages 345-356, October.
    7. Jan Pablo Burgard & Ralf Münnich, 2015. "Sae Teaching Using Simulations," Statistics in Transition New Series, Polish Statistical Association, vol. 16(4), pages 603-610, December.
    8. Sedeño-Noda, A. & González-Dávila, E. & González-Martín, C. & González-Yanes, A., 2009. "Preemptive benchmarking problem: An approach for official statistics in small areas," European Journal of Operational Research, Elsevier, vol. 196(1), pages 360-369, July.
    9. Lahiri Partha & Suntornchost Jiraphan, 2020. "A general Bayesian approach to meet different inferential goals in poverty research for small areas," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 237-253, August.
    10. Johannes Gräb & Michael Grimm, 2008. "Spatial inequalities explained - Evidence from Burkina Faso," Ibero America Institute for Econ. Research (IAI) Discussion Papers 173, Ibero-America Institute for Economic Research.
    11. Charlotte Articus & Jan Pablo Burgard, 2014. "A Finite Mixture Fay Herriot-type model for estimating regional rental prices in Germany," Research Papers in Economics 2014-14, University of Trier, Department of Economics.
    12. 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.
    13. Molina, Isabel, 2006. "Uncertainty under a multivariate nested-error regression model with logarithmic transformation," DES - Working Papers. Statistics and Econometrics. WS ws066117, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Steen Magnussen & Johannes Breidenbach, 2020. "Retrieval of among-stand variances from one observation per stand," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 66(4), pages 133-149.
    15. Miguel Boubeta & María José Lombardía & Domingo Morales, 2016. "Empirical best prediction under area-level Poisson mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 548-569, September.
    16. Lixia Diao & David D. Smith & Gauri Sankar Datta & Tapabrata Maiti & Jean D. Opsomer, 2014. "Accurate Confidence Interval Estimation of Small Area Parameters Under the Fay–Herriot Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 497-515, June.
    17. Newhouse,David Locke & Merfeld,Joshua David & Ramakrishnan,Anusha Pudugramam & Swartz,Tom & Lahiri,Partha, 2022. "Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning," Policy Research Working Paper Series 10175, The World Bank.
    18. Molina, Isabel, 2009. "Uncertainty under a multivariate nested-error regression model with logarithmic transformation," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 963-980, May.
    19. Jan Pablo Burgard & Joscha Krause & Domingo Morales, 2022. "A measurement error Rao–Yu model for regional prevalence estimation over time using uncertain data obtained from dependent survey estimates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 204-234, March.
    20. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.

    More about this item

    Keywords

    Agricultural and Food Policy;

    Statistics

    Access and download statistics

    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:ags:aerrae:304153. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aeraiea.html .

    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.