IDEAS home Printed from
   My bibliography  Save this paper

Partial Identification of Poverty Measures with Contaminated Data


  • Juan Carlos Chavez-Martin del Campo


Much of the statistical analysis for poverty measurement regards the data employed to estimate poverty statistics as error-free observations. However, it is amply recognized that surveys responses are not perfectly reliable and that the quality of the data is often poor, especially for developing countries. Robust estimation addresses this problem by searching for poverty measures that are not highly sensitive to errors in the data. However, given the assumptions of robust estimation, the rationale for point estimation is not apparent. In the present study we tackle the problem by implementing a different strategy. Since a particular poverty measure is not point identified under the assumptions of robust estimation and some outcomes that are possible ex ante are ruled out ex post, we apply a fully non-parametric method to show that for the family of additively separable poverty measures it is possible to find identification regions under very mild assumptions. We investigate the sensitivity of the bounds of these identification regions to contamination for the class of Pa poverty measures, showing that there exists an a-ordering for the elasticities of these bounds with respect to the amount of contamination. We apply two conceptually different confidence intervals for partially identified poverty measures: the first type of confidence interval covers the entire identification region, while the other covers each element of the identification region with fixed probability. The methodology developed in the paper is applied to analyze rural poverty in Mexico

Suggested Citation

  • Juan Carlos Chavez-Martin del Campo, 2004. "Partial Identification of Poverty Measures with Contaminated Data," Econometric Society 2004 Latin American Meetings 221, Econometric Society.
  • Handle: RePEc:ecm:latm04:221

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Molinari, Francesca, 2010. "Missing Treatments," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 82-95.
    2. Nicoletti, Cheti & Peracchi, Franco & Foliano, Francesca, 2011. "Estimating Income Poverty in the Presence of Missing Data and Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 61-72.

    More about this item


    Poverty Measurement; Bounds; Partial Identification; Contamination Model; Identification Regions; Confidence Intervals;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty


    Access and download statistics


    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:ecm:latm04:221. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum). General contact details of provider: .

    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.

    We have no references for this item. You can help adding them by using 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.