IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-13-9314-3_3.html
   My bibliography  Save this book chapter

Estimation of Poverty Rates by Calorie Decomposition Method

In: Applications of Regression Techniques

Author

Listed:
  • Manoranjan Pal

    (Indian Statistical Institute, Economic Research Unit)

  • Premananda Bharati

    (Indian Statistical Institute, Biological Anthropology Unit)

Abstract

In this chapter we use the member wise expected calorie consumption of the households to arrive at the poverty rates. For a given household, we compute the per capita average expenditure of each member of the household depending on whether the household belongs to rural or urban sector. The weighted sums of the expected amount of calories consumed by the members are then found separately for male and female members of the household, where the weight is the number of members in each category. In a similar manner we get the sum of calorie norms of members in the household. The calorie norm of the household is compared with the estimated calorie consumption to determine whether the household is poor. If a household is poor then it is given a dummy value ‘1’, otherwise it is given the value ‘0’. Weighted means of these dummy values give us the poverty ratios. This calculation is carried out separately for rural and urban India. The urban poverty ratios are found to be higher than the corresponding rural poverty ratios. This is because the activity status of people is not considered. The need for the daily calorie consumption of urban people is less because most of them they work less. All modern facilities like transport and machineries to give relief to work are more available to urban people. But in our calculations, we did not assume it. There may be other reasons also. Urban people take fast food in the streets more than rural people and these are often not reported.

Suggested Citation

  • Manoranjan Pal & Premananda Bharati, 2019. "Estimation of Poverty Rates by Calorie Decomposition Method," Springer Books, in: Applications of Regression Techniques, chapter 0, pages 49-58, Springer.
  • Handle: RePEc:spr:sprchp:978-981-13-9314-3_3
    DOI: 10.1007/978-981-13-9314-3_3
    as

    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
    for a similarly titled item that would be available.

    More about this item

    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:spr:sprchp:978-981-13-9314-3_3. 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.

    We have no bibliographic 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.

    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.