IDEAS home Printed from https://ideas.repec.org/a/kap/enreec/v79y2021i3d10.1007_s10640-021-00572-y.html
   My bibliography  Save this article

Impact Evaluation of Alternative Irrigation Technology in Crete: Correcting for Selectivity Bias

Author

Listed:
  • Maria Vrachioli

    (Technical University of Munich)

  • Spiro E. Stefanou

    (United States Department of Agriculture)

  • Vangelis Tzouvelekas

    (University of Crete)

Abstract

The interest in promoting food and water security through development projects has led to the need to evaluate the impact of these projects. This study evaluates the impact from transitioning to a modern irrigation technology. Deciding to adopt or not an alternative irrigation technology (sprinklers) is not necessarily a random determination. Therefore, selection bias can be present and this can lead to biased estimates. In this study, we apply methodological specifications to correct for self-selectivity biases. Then, we measure and compare the technical efficiency scores from adopters and non-adopters. The empirical application uses data covering 56 small-scale greenhouse farms in Crete (Greece) for the cropping years 2009-2013. The results reveal that the average technical efficiency for farmers who adopted sprinkler irrigation is lower than the group of non-adopters, when the presence of selectivity bias cannot be rejected. This implies that either the farmers need more time to incorporate the know-how of the newly acquired technology or they become less motivated after the adoption. Consequently, agricultural water saving technologies need to be promoted in combination with support to the farmers, so they can cope with the lower performance in the first years after adoption.

Suggested Citation

  • Maria Vrachioli & Spiro E. Stefanou & Vangelis Tzouvelekas, 2021. "Impact Evaluation of Alternative Irrigation Technology in Crete: Correcting for Selectivity Bias," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 79(3), pages 551-574, July.
  • Handle: RePEc:kap:enreec:v:79:y:2021:i:3:d:10.1007_s10640-021-00572-y
    DOI: 10.1007/s10640-021-00572-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10640-021-00572-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10640-021-00572-y?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
    ---><---

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

    References listed on IDEAS

    as
    1. Daniel Solís & Boris E. Bravo-Ureta & Ricardo E. Quiroga, 2007. "Soil conservation and technical efficiency among hillside farmers in Central America: a switching regression model ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 51(4), pages 491-510, December.
    2. González-Flores, Mario & Bravo-Ureta, Boris E. & Solís, Daniel & Winters, Paul, 2014. "The impact of high value markets on smallholder productivity in the Ecuadorean Sierra: A Stochastic Production Frontier approach correcting for selectivity bias," Food Policy, Elsevier, vol. 44(C), pages 237-247.
    3. Salvatore Di Falco & Marcella Veronesi & Mahmud Yesuf, 2011. "Does Adaptation to Climate Change Provide Food Security? A Micro-Perspective from Ethiopia," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(3), pages 825-842.
    4. Feder, Gershon & Just, Richard E & Zilberman, David, 1985. "Adoption of Agricultural Innovations in Developing Countries: A Survey," Economic Development and Cultural Change, University of Chicago Press, vol. 33(2), pages 255-298, January.
    5. Boris Bravo-Ureta & Daniel Solís & Víctor Moreira López & José Maripani & Abdourahmane Thiam & Teodoro Rivas, 2007. "Technical efficiency in farming: a meta-regression analysis," Journal of Productivity Analysis, Springer, vol. 27(1), pages 57-72, February.
    6. Yair Mundlak, 1961. "Empirical Production Function Free of Management Bias," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 43(1), pages 44-56.
    7. Subal Kumbhakar & Efthymios Tsionas & Timo Sipiläinen, 2009. "Joint estimation of technology choice and technical efficiency: an application to organic and conventional dairy farming," Journal of Productivity Analysis, Springer, vol. 31(3), pages 151-161, June.
    8. Alan Collins & Richard I. D. Harris, 2005. "The Impact Of Foreign Ownership And Efficiency On Pollution Abatement Expenditure By Chemical Plants: Some Uk Evidence," Scottish Journal of Political Economy, Scottish Economic Society, vol. 52(5), pages 747-768, November.
    9. Boris E. Bravo-Ureta, 2014. "Stochastic frontiers, productivity effects and development projects," Economics and Business Letters, Oviedo University Press, vol. 3(1), pages 51-58.
    10. Mariano, Marc Jim & Villano, Renato & Fleming, Euan, 2012. "Factors influencing farmers’ adoption of modern rice technologies and good management practices in the Philippines," Agricultural Systems, Elsevier, vol. 110(C), pages 41-53.
    11. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
    12. Boris Bravo-Ureta & William Greene & Daniel Solís, 2012. "Technical efficiency analysis correcting for biases from observed and unobserved variables: an application to a natural resource management project," Empirical Economics, Springer, vol. 43(1), pages 55-72, August.
    13. 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.
    14. Kaparakis, Emmanuel I & Miller, Stephen M & Noulas, Athanasios G, 1994. "Short-Run Cost Inefficiency of Commercial Banks: A Flexible Stochastic Frontier Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 26(4), pages 875-893, November.
    15. Ramírez, Octavio A. & Shultz, Steven D., 2000. "Poisson Count Models to Explain the Adoption of Agricultural and Natural Resource Management Technologies by Small Farmers in Central American Countries," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 32(1), pages 21-33, April.
    16. Konstantinos Chatzimichael & Dimitris Christopoulos & Spiro Stefanou & Vangelis Tzouvelekas, 2020. "Irrigation practices, water effectiveness and productivity measurement [Toward an understanding of technology adoption: risk, learning, and neighborhood effects]," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 47(2), pages 467-498.
    17. W. David Bradford & Andrew N. Kleit & Marie A. Krousel-Wood & Richard N. Re, 2001. "Stochastic Frontier Estimation Of Cost Models Within The Hospital," The Review of Economics and Statistics, MIT Press, vol. 83(2), pages 302-309, May.
    18. Carlos D. Mayen & Joseph V. Balagtas & Corinne E. Alexander, 2010. "Technology Adoption and Technical Efficiency: Organic and Conventional Dairy Farms in the United States," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(1), pages 181-195.
    19. Renato Villano & Boris Bravo-Ureta & Daniel Solís & Euan Fleming, 2015. "Modern Rice Technologies and Productivity in the Philippines: Disentangling Technology from Managerial Gaps," Journal of Agricultural Economics, Wiley Blackwell, vol. 66(1), pages 129-154, February.
    20. repec:zwi:journl:v:43:y:2012:i:1:p:55-72 is not listed on IDEAS
    21. 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.
    22. 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. Boris E. Bravo‐Ureta & Mario González‐Flores & William Greene & Daniel Solís, 2021. "Technology and Technical Efficiency Change: Evidence from a Difference in Differences Selectivity Corrected Stochastic Production Frontier Model," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(1), pages 362-385, January.
    2. Bravo-Ureta, Boris E. & Higgins, Daniel & Arslan, Aslihan, 2020. "Irrigation infrastructure and farm productivity in the Philippines: A stochastic Meta-Frontier analysis," World Development, Elsevier, vol. 135(C).
    3. Boris E. Bravo-Ureta, 2014. "Stochastic frontiers, productivity effects and development projects," Economics and Business Letters, Oviedo University Press, vol. 3(1), pages 51-58.
    4. Abdul-Rahaman, Awal & Abdulai, Awudu, 2018. "Do farmer groups impact on farm yield and efficiency of smallholder farmers? Evidence from rice farmers in northern Ghana," Food Policy, Elsevier, vol. 81(C), pages 95-105.
    5. Kamiche Zegarra, J. & Bravo-Ureta, B., 2018. "Are users of market information efficient? A stochastic production frontier model corrected by sample selection," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 275870, International Association of Agricultural Economists.
    6. Sreejith Aravindakshan & Frederick Rossi & T. S. Amjath-Babu & Prakashan Chellattan Veettil & Timothy J. Krupnik, 2018. "Application of a bias-corrected meta-frontier approach and an endogenous switching regression to analyze the technical efficiency of conservation tillage for wheat in South Asia," Journal of Productivity Analysis, Springer, vol. 49(2), pages 153-171, June.
    7. Boris Bravo-Ureta & William Greene & Daniel Solís, 2012. "Technical efficiency analysis correcting for biases from observed and unobserved variables: an application to a natural resource management project," Empirical Economics, Springer, vol. 43(1), pages 55-72, August.
    8. Shaibu Baanni Azumah & Samuel Arkoh Donkoh & Joseph Agebase Awuni, 2019. "Correcting for sample selection in stochastic frontier analysis: insights from rice farmers in Northern Ghana," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 7(1), pages 1-15, December.
    9. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
    10. Ma, Wanglin & Renwick, Alan & Yuan, Peng & Ratna, Nazmun, 2018. "Agricultural cooperative membership and technical efficiency of apple farmers in China: An analysis accounting for selectivity bias," Food Policy, Elsevier, vol. 81(C), pages 122-132.
    11. Luis A. De los Santos‐Montero & Boris E. Bravo‐Ureta, 2017. "Productivity effects and natural resource management: econometric evidence from POSAF‐II in Nicaragua," Natural Resources Forum, Blackwell Publishing, vol. 41(4), pages 220-233, November.
    12. Begin, Rosemarie & Tamini, Lota D. & Doyon, Maurice, 2014. "L'effet du travail hors-ferme sur l'efficacité technique des fermes laitières québécoises: un modèle intégrant les biais de sélection sur les observables et inobservables," Working Papers 187233, University of Laval, Center for Research on the Economics of the Environment, Agri-food, Transports and Energy (CREATE).
    13. Mohammed, Sadick & Abdulai, Awudu, 2021. "The Impact of Extension Dissemination and Technology Adoption on Farmers' Efficiency and Welfare in Ghana," 2021 Conference, August 17-31, 2021, Virtual 315362, International Association of Agricultural Economists.
    14. Khanal, Uttam & Wilson, Clevo & Rahman, Sanzidur & Lee, Boon & Hoang, Vincent, 2020. "Smallholder farmers’ adaptation to climate change and its potential contribution to UN’s sustainable development goals of zero hunger and no poverty," MPRA Paper 106917, University Library of Munich, Germany, revised 07 Sep 2020.
    15. Bravo-Ureta, Boris E. & Jara-Rojas, Roberto & Lachaud, Michee A. & Moreira L., Victor H. & Scheierling, Susanne M., 2015. "Water and Farm Efficiency: Insights from the Frontier Literature," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 206076, Agricultural and Applied Economics Association.
    16. Cordero, José Manuel & Santín, Daniel & Sicilia, Gabriela, 2015. "Testing the accuracy of DEA estimates under endogeneity through a Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 244(2), pages 511-518.
    17. German Blanco, 2017. "Who benefits from job placement services? A two-sided analysis," Journal of Productivity Analysis, Springer, vol. 47(1), pages 33-47, February.
    18. Amponsah, Kwabena & Paliwal, Neha, 2015. "Technology And Managerial Gaps In The Adoption Of Improved Groundnut Varieties In Malawi And Uganda," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 206197, Agricultural and Applied Economics Association.
    19. González-Flores, Mario & Bravo-Ureta, Boris E. & Solís, Daniel & Winters, Paul, 2014. "The impact of high value markets on smallholder productivity in the Ecuadorean Sierra: A Stochastic Production Frontier approach correcting for selectivity bias," Food Policy, Elsevier, vol. 44(C), pages 237-247.
    20. Rahman, M Sadique & Kazal, Mohammad Mizanul Haque & Rayhan, Shah Johir, 2020. "Improved Management Practices Adoption And Technical Efficiency Of Shrimp Farmers In Bangladesh: A Sample Selection Stochastic Production Frontier Approach," Bangladesh Journal of Agricultural Economics, Bangladesh Agricultural University, vol. 41(1), July.

    More about this item

    Keywords

    Impact evaluation; Irrigation technology adoption; Sample selection; Stochastic frontier; Technical efficiency;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services
    • Q25 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Water

    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:kap:enreec:v:79:y:2021:i:3:d:10.1007_s10640-021-00572-y. 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: . General contact details of provider: http://www.springer.com .

    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: 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 hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.