IDEAS home Printed from https://ideas.repec.org/h/spr/conchp/978-981-99-4902-1_5.html
   My bibliography  Save this book chapter

Mixture Models: Identifying Consumption Classes in Post-liberalization India

In: Applied Econometric Analysis Using Cross Section and Panel Data

Author

Listed:
  • Sudeshna Maitra

    (York University)

Abstract

A mixture model is a probabilistic model that allows us to make inferences about the characteristics of sub-populations from observations on the overall population, without any information about the membership of individuals in the sub-populations or even the number of sub-populations. In this chapter, I present the theory of mixture models, and an application in which I identify consumption classes in urban India in 1999–00 (NSS). Suppose there are three sub-populations—a lower, a middle, and an upper consumption class—determined by the total number of different durables owned by households. I construct a three-component (or three-class) mixture model of household durable ownership, which is assumed to be distributed binomially by class. I then demonstrate the use of the Expectation Maximization (EM) algorithm to estimate the size of and mean durables owned by each class, as well as the probability that a household with a given number of durables belongs to a given class. Finally, I show how to assign households to classes using the mixture estimates, which allows further investigation of class-specific characteristics.

Suggested Citation

  • Sudeshna Maitra, 2023. "Mixture Models: Identifying Consumption Classes in Post-liberalization India," Contributions to Economics, in: Deep Mukherjee (ed.), Applied Econometric Analysis Using Cross Section and Panel Data, chapter 0, pages 135-166, Springer.
  • Handle: RePEc:spr:conchp:978-981-99-4902-1_5
    DOI: 10.1007/978-981-99-4902-1_5
    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 search for a similarly titled item that would be available.

    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:conchp:978-981-99-4902-1_5. 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.