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Analyzing Italian citrus sector by semi-nonparametric frontier efficiency models

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  • Francesco Vidoli
  • Giancarlo Ferrara

Abstract

In this paper, we carried out an empirical productive analysis on agricultural Italian farms. In this research area, we propose a new approach of stochastic frontier analysis adopting a generalized additive model framework also compared with Stochastic semi-Nonparametric Envelopment of Z variables Data. By using the Italian National Institute of Agricultural Economics micro-data, we were able to map out the overall level of efficiency thereby focusing also on the evaluation of the differences observed due to presence of contextual variables. We obtained overall measures for the citrus sector that suggests an evaluation framework that can uphold policies to encourage and support farms. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Francesco Vidoli & Giancarlo Ferrara, 2015. "Analyzing Italian citrus sector by semi-nonparametric frontier efficiency models," Empirical Economics, Springer, vol. 49(2), pages 641-658, September.
  • Handle: RePEc:spr:empeco:v:49:y:2015:i:2:p:641-658
    DOI: 10.1007/s00181-014-0879-6
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    References listed on IDEAS

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    1. Madau, Fabio A., 2011. "Parametric Estimation of Technical and Scale Efficiencies in Italian Citrus Farming," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 12(1).
    2. Sperlich, Stefan & Tjøstheim, Dag & Yang, Lijian, 2002. "Nonparametric Estimation And Testing Of Interaction In Additive Models," Econometric Theory, Cambridge University Press, vol. 18(2), pages 197-251, April.
    3. Ernest Reig‐Martínez & José A. Gómez‐Limón & Andrés J. Picazo‐Tadeo, 2011. "Ranking farms with a composite indicator of sustainability," Agricultural Economics, International Association of Agricultural Economists, vol. 42(5), pages 561-575, September.
    4. Francesco D'Amico & Jose-Luis Fernandez, 2012. "Measuring Inefficiency in Long-term Care Commissioning: Evidence from English Local Authorities," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 34(2), pages 275-299.
    5. Coelli, Tim J., 1995. "Recent Developments In Frontier Modelling And Efficiency Measurement," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 39(3), pages 1-27, December.
    6. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    7. Frank Figge & Tobias Hahn, 2005. "The Cost of Sustainability Capital and the Creation of Sustainable Value by Companies," Journal of Industrial Ecology, Yale University, vol. 9(4), pages 47-58, October.
    8. Pasquale De Muro & Matteo Mazziotta & Adriano Pareto, 2011. "Composite Indices of Development and Poverty: An Application to MDGs," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 104(1), pages 1-18, October.
    9. Fan, Yanqin & Li, Qi & Weersink, Alfons, 1996. "Semiparametric Estimation of Stochastic Production Frontier Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 460-468, October.
    10. Cisilino, Federica & Madau, Fabio A., 2007. "Organic and Conventional Farming: a Comparison Analysis through the Italian FADN," MPRA Paper 21786, University Library of Munich, Germany.
    11. Andrew Johnson & Timo Kuosmanen, 2011. "One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient, root-n consistent StoNEZD method," Journal of Productivity Analysis, Springer, vol. 36(2), pages 219-230, October.
    12. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    13. Daniel Tyteca, 1998. "Sustainability Indicators at the Firm Level," Journal of Industrial Ecology, Yale University, vol. 2(4), pages 61-77, October.
    14. Laure Latruffe, 2010. "Competitiveness, Productivity and Efficiency in the Agricultural and Agri-Food Sectors," OECD Food, Agriculture and Fisheries Papers 30, OECD Publishing.
    15. Pang Du & Christopher F. Parmeter & Jeffrey S. Racine, 2012. "Nonparametric Kernel Regression with Multiple Predictors and Multiple Shape Constraints," Department of Economics Working Papers 2012-08, McMaster University.
    16. Hung-jen Wang & Peter Schmidt, 2002. "One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels," Journal of Productivity Analysis, Springer, vol. 18(2), pages 129-144, September.
    17. Konstantinos Giannakas & Kien C. Tran & Vangelis Tzouvelekas, 2003. "On the choice of functional form in stochastic frontier modeling," Empirical Economics, Springer, vol. 28(1), pages 75-100, January.
    18. Figge, Frank & Hahn, Tobias, 2009. "Not measuring sustainable value at all: A response to Kuosmanen and Kuosmanen," Ecological Economics, Elsevier, vol. 69(2), pages 244-249, December.
    19. 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.
    20. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    21. 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.
    22. Timo Kuosmanen & Mika Kortelainen, 2012. "Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints," Journal of Productivity Analysis, Springer, vol. 38(1), pages 11-28, August.
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    Cited by:

    1. Maria Raimondo & Francesco Caracciolo & Concetta Nazzaro & Giuseppe Marotta, 2021. "Organic Farming Increases the Technical Efficiency of Olive Farms in Italy," Agriculture, MDPI, vol. 11(3), pages 1-15, March.
    2. Cristina Polo & Julián Ramajo & Alejandro Ricci‐Risquete, 2021. "A stochastic semi‐non‐parametric analysis of regional efficiency in the European Union," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 7-24, February.
    3. Federico Belotti & Giancarlo Ferrara, 2019. "Imposing monotonicity in stochastic frontier models: an iterative nonlinear least squares procedure," CEIS Research Paper 462, Tor Vergata University, CEIS, revised 29 Jan 2021.
    4. Ferrara, Giancarlo & Bucci, Valeria & Campagna, Arianna, 2023. "Audit, presumptive taxation and efficiency: An integrated approach for tax compliance analysis," Economic Systems, Elsevier, vol. 47(3).
    5. Jose M. Cordero & Cristina Polo & Daniel Santín, 2020. "Assessment of new methods for incorporating contextual variables into efficiency measures: a Monte Carlo simulation," Operational Research, Springer, vol. 20(4), pages 2245-2265, December.
    6. Mohammed, Sadick & Abdulai, Awudu, 2021. "Extension Participation and Improved Technology Adoption: Impact on Efficiency and Welfare of Farmers in Ghana," 2021 Conference, August 17-31, 2021, Virtual 315362, International Association of Agricultural Economists.
    7. Fabio Pammolli & Francesco Porcelli & Francesco Vidoli & Guido Borà, 2014. "La spesa sanitaria delle Regioni in Italia - Saniregio 3," Working Papers CERM 02-2014, Competitività, Regole, Mercati (CERM).
    8. Ferrara, Giancarlo & Vidoli, Francesco, 2017. "Semiparametric stochastic frontier models: A generalized additive model approach," European Journal of Operational Research, Elsevier, vol. 258(2), pages 761-777.
    9. ferrara, giancarlo & campagna, arianna & bucci, valeria & atella, vincenzo, 2021. "Presumptive taxation and firms’ efficiency: an integrated approach for tax compliance analysis," MPRA Paper 111516, University Library of Munich, Germany.
    10. Ferrara, Giancarlo & Vidoli, Francesco & Canello, Jacopo & Campagna, Arianna, 2013. "Labour-use Efficiency in the Italian Machinery Industry: a Non-parametric Stochastic Frontier Perspective," MPRA Paper 94359, University Library of Munich, Germany.
    11. Fabio Pammolli & Francesco Porcelli & Francesco Vidoli & Guido Borà, 2015. "La spesa sanitaria delle Regioni in Italia - Saniregio 2015," Working Papers CERM 01-2015, Competitività, Regole, Mercati (CERM), revised 04 Jan 2016.

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    More about this item

    Keywords

    Stochastic frontier; Generalized additive model; StoNEZD; Agriculture; Efficiency; Sustainable value; Q12; C14; Q57;
    All these keywords.

    JEL classification:

    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics

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