IDEAS home Printed from https://ideas.repec.org/a/oup/ajagec/v93y2011i5p1374-1390.html
   My bibliography  Save this article

Bayesian Estimation of a Censored Linear Almost Ideal Demand System: Food Demand in Pakistan

Author

Listed:
  • Panagiotis Kasteridis
  • Steven T. Yen
  • Cheng Fang

Abstract

A censored linear almost ideal demand system for food is estimated with a Bayesian Markov chain Monte Carlo procedure, using a sample of urban households from Pakistan. All own-price elasticities but one are found to be negative, and all total food expenditure elasticities are found to be positive, with a high posterior probability. There is a mix of gross complements and substitutes among the food products, while net substitution is the predominant pattern. Household characteristics play a role in food expenditures, and regional differences exist. These demand elasticities can inform policy deliberations by the national government and international organizations. Copyright 2011, Oxford University Press.

Suggested Citation

  • Panagiotis Kasteridis & Steven T. Yen & Cheng Fang, 2011. "Bayesian Estimation of a Censored Linear Almost Ideal Demand System: Food Demand in Pakistan," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(5), pages 1374-1390.
    • Handle: RePEc:oup:ajagec:v:93:y:2011:i:5:p:1374-1390
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/ajae/aar059
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Sung, Jae-hoon & Miranowski, John A., 2015. "Adaptive Behavior of U.S. Farms to Climate and Risk," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205787, Agricultural and Applied Economics Association.
    2. Jing Li & Edward C. Jaenicke & Tobenna D. Anekwe & Alessandro Bonanno, 2018. "Demand for ready‐to‐eat cereals with household‐level censored purchase data and nutrition label information: A distance metric approach," Agribusiness, John Wiley & Sons, Ltd., vol. 34(4), pages 687-713, October.
    3. Lin, Biing-Hwan & Ver Ploeg, Michele & Kasteridis, Panagiotis & Yen, Steven T., 2014. "The roles of food prices and food access in determining food purchases of low-income households," Journal of Policy Modeling, Elsevier, vol. 36(5), pages 938-952.
    4. Kasteridis, Panagiotis & Yen, Steven, 2012. "U.S. demand for organic and conventional vegetables: a Bayesian censored system approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 56(3), pages 1-21.
    5. Bilgic, Abdulbaki & Yen, Steven T., 2013. "Household food demand in Turkey: A two-step demand system approach," Food Policy, Elsevier, vol. 43(C), pages 267-277.
    6. Widenhorn, Andreas & Salhofer, Klaus, 2014. "Using a Generalized Differenced Demand Model to Estimate Price and Expenditure Elasticities for Milk and Meat in Austria," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 63(02), pages 1-16, June.
    7. Ariane Kehlbacher & Richard Tiffin & Adam Briggs & Mike Berners-Lee & Peter Scarborough, 2016. "The distributional and nutritional impacts and mitigation potential of emission-based food taxes in the UK," Climatic Change, Springer, vol. 137(1), pages 121-141, July.
    8. Ramírez–Hassan, Andrés & López-Vera, Alejandro, 2024. "Welfare implications of a tax on electricity: A semi-parametric specification of the incomplete EASI demand system," Energy Economics, Elsevier, vol. 131(C).
    9. Andr'es Ram'irez-Hassan & Alejandro L'opez-Vera, 2021. "Semi-parametric estimation of the EASI model: Welfare implications of taxes identifying clusters due to unobserved preference heterogeneity," Papers 2109.07646, arXiv.org.
    10. McCullough, Ellen & Zhen, Chen & Shin, Soye & Lu, Meichen & Arsenault, Joanne, 2022. "The role of food preferences in determining diet quality for Tanzanian consumers," Journal of Development Economics, Elsevier, vol. 155(C).
    11. Jacobi Liana & Kwok Chun Fung & Ramírez-Hassan Andrés & Nghiem Nhung, 2024. "Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 403-434, April.
    12. Liana Jacobi & Nhung Nghiem & Andrés Ramírez‐Hassan & Tony Blakely, 2021. "Food Price Elasticities for Policy Interventions: Estimates from a Virtual Supermarket Experiment in a Multistage Demand Analysis with (Expert) Prior Information," The Economic Record, The Economic Society of Australia, vol. 97(319), pages 457-490, December.
    13. Widenhorn, Andreas & Salhofer, Klaus, 2014. "Using a Generalized Differenced Demand Model to Estimate Price and Expenditure Elasticities for Milk and Meat in Austria," Journal of International Agricultural Trade and Development, Journal of International Agricultural Trade and Development, vol. 63(2).
    14. Andrés Ramírez‐Hassan, 2021. "Bayesian estimation of the exact affine Stone index demand system: Replicating the Lewbel and Pendakur (2009) results," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(4), pages 484-491, June.
    15. Huang, Wei, 2022. "Demand for plant-based milk and effects of a carbon tax on fresh milk consumption in Sweden," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 518-529.
    16. Abdulbaki Bilgic & Steven T. Yen, 2014. "Demand for meat and dairy products by Turkish households: a Bayesian censored system approach," Agricultural Economics, International Association of Agricultural Economists, vol. 45(2), pages 117-127, March.

    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:oup:ajagec:v:93:y:2011:i:5:p:1374-1390. 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.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.