IDEAS home Printed from https://ideas.repec.org/p/ags/aaea14/170713.html
   My bibliography  Save this paper

Technical Efficiency of Thai Jasmine Rice Farmers: Comparing Price Support Program Participants and Non-Participants

Author

Listed:
  • Duangbootsee, Uchook
  • Myers, Robert J.

Abstract

The rice price support program (PSP) in Thailand is designed to support rice prices and raise incomes of rice farmers. However, it has been argued that the program only attracts participation from certain types of farmers, in particular larger and more efficient farmers with higher farm incomes. This raises the question of whether there is a difference in the technical efficiency of program participants and non-participants. This paper investigates two issues: (a) what are the key determinants of farmers’ decision to participate in the PSP? and (b) do program participants and non-participants use different rice production technologies and have different levels of technical efficiency. We take a stochastic frontier approach to answering these questions but because farmers self-select into the PSP the standard stochastic frontier model may lead to biased estimation. In response we augment the standard stochastic frontier model with a participation equation explaining the decision to participate in the PSP, and then use Heckman’s two-step estimation and Greene’s sample selection stochastic production frontier model to explore levels of technical efficiency among participants and non-participants. Results indicate that the participation decision is governed by key factors that include land size and the financial position of the farm. Results also show there is no strong evidence to support the presence of selectivity bias in the stochastic frontier estimates. In addition, a likelihood-ratio test indicates that participants and non-participants use the same frontier production technology. The analysis of technical efficiency reveals that participants are more technically efficient than non-participants. The findings therefore suggest that larger farmers participate more in the PSP and that these program participants tend to be more technically efficient farmers.

Suggested Citation

  • Duangbootsee, Uchook & Myers, Robert J., 2014. "Technical Efficiency of Thai Jasmine Rice Farmers: Comparing Price Support Program Participants and Non-Participants," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170713, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea14:170713
    DOI: 10.22004/ag.econ.170713
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/170713/files/Technical%20EfficiencyR.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.170713?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. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    2. Kumbhakar, Subal C & Ghosh, Soumendra & McGuckin, J Thomas, 1991. "A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Farms," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(3), pages 279-286, July.
    3. Jeong-Dong Lee & Almas Heshmati (ed.), 2009. "Productivity, Efficiency, and Economic Growth in the Asia-Pacific Region," Contributions to Economics, Springer, number 978-3-7908-2072-0, May.
    4. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    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. Kenichi Kashiwagi & Erina Iwasaki, 2020. "Effect of agglomeration on technical efficiency of small and medium‐sized garment firms in Egypt," African Development Review, African Development Bank, vol. 32(1), pages 14-26, March.
    2. repec:aer:wpaper:178 is not listed on IDEAS
    3. Seog-Chan Oh & Jaemin Shin, 2021. "The Assessment of Car Making Plants with an Integrated Stochastic Frontier Analysis Model," Mathematics, MDPI, vol. 9(11), pages 1-21, June.
    4. Tom Kompas & Tuong Nhu Che & R. Quentin Grafton, 2004. "Technical efficiency effects of input controls: evidence from Australia's banana prawn fishery," Applied Economics, Taylor & Francis Journals, vol. 36(15), pages 1631-1641.
    5. Daniel Solís & Boris E. Bravo‐Ureta & Ricardo E. Quiroga, 2009. "Technical Efficiency among Peasant Farmers Participating in Natural Resource Management Programmes in Central America," Journal of Agricultural Economics, Wiley Blackwell, vol. 60(1), pages 202-219, February.
    6. Wu, Yanrui, 1995. "The productive efficiency of Chinese iron and steel firms A stochastic frontier analysis," Resources Policy, Elsevier, vol. 21(3), pages 215-222, September.
    7. Dhehibi, Boubaker & Lachaal, Lassaad & Elloumi, Mohamed & Messaoud, Emna B., 2007. "Measurement and Sources of Technical Inefficiency in the Tunisian Citrus Growing Sector," 103rd Seminar, April 23-25, 2007, Barcelona, Spain 9391, European Association of Agricultural Economists.
    8. Noel Uri, 2003. "The Effect of Incentive Regulation in Telecommunications in the United States," Quality & Quantity: International Journal of Methodology, Springer, vol. 37(2), pages 169-191, May.
    9. Roy, Manish & Mazumder, Ritwik, 2016. "Technical Efficiency of Fish Catch in Traditional Fishing: A Study in Southern Assam," Journal of Regional Development and Planning, Rajarshi Majumder, vol. 5(1), pages 55-68.
    10. Managi, Shunsuke & Opaluch, James J. & Jin, Di & Grigalunas, Thomas A., 2006. "Stochastic frontier analysis of total factor productivity in the offshore oil and gas industry," Ecological Economics, Elsevier, vol. 60(1), pages 204-215, November.
    11. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    12. Greene, William, 2007. "Functional Form and Heterogeneity in Models for Count Data," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(2), pages 113-218, August.
    13. Edward Ebo ONUMAH & Bernhard BRÜMMER & Gabriele HÖRSTGEN-SCHWARK, 2010. "Productivity of the hired and family labour and determinants of technical inefficiency in Ghana's fish farms," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 56(2), pages 79-88.
    14. William Griffiths & Xiaohui Zhang & Xueyan Zhao, 2010. "A Stochastic Frontier Model for Discrete Ordinal Outcomes: A Health Production Function," Department of Economics - Working Papers Series 1092, The University of Melbourne.
    15. Coelli, Tim J. & Battese, George E., 1996. "Identification Of Factors Which Influence The Technical Inefficiency Of Indian Farmers," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 40(2), pages 1-26, August.
    16. Roberto Furesi & Fabio Madau & Pietro Pulina, 2013. "Technical efficiency in the sheep dairy industry: an application on the Sardinian (Italy) sector," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 1(1), pages 1-11, December.
    17. Subal Kumbhakar & Efthymios Tsionas, 2008. "Scale and efficiency measurement using a semiparametric stochastic frontier model: evidence from the U.S. commercial banks," Empirical Economics, Springer, vol. 34(3), pages 585-602, June.
    18. Wollni, Meike & Brümmer, Bernhard, 2012. "Productive efficiency of specialty and conventional coffee farmers in Costa Rica: Accounting for technological heterogeneity and self-selection," Food Policy, Elsevier, vol. 37(1), pages 67-76.
    19. Christopher F. Parmeter & Hung-Jen Wang & Subal C. Kumbhakar, 2017. "Nonparametric estimation of the determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 47(3), pages 205-221, June.
    20. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    21. Helvoigt, Ted L. & Adams, Darius M., 2009. "A stochastic frontier analysis of technical progress, efficiency change and productivity growth in the Pacific Northwest sawmill industry," Forest Policy and Economics, Elsevier, vol. 11(4), pages 280-287, July.

    More about this item

    Keywords

    Agricultural and Food Policy;

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:aaea14:170713. 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/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.