IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v121y2023ics0264999323000147.html
   My bibliography  Save this article

Nonparametric estimates of price efficiency for the Greek infant milk market: Curing the curse of dimensionality with shannon entropy

Author

Listed:
  • Karagiannis, Roxani
  • Karagiannis, Giannis

Abstract

Despite the continuous promotion of breastfeeding by the World Health Organization and national health systems, the consumption of breast milk substitutes (BMS) is increasing. Several BMS brands in the market have varying prices and attributes (i.e., energy, protein, vitamins, minerals, etc.). Using data from Greece, we examine which of these are “value for money” choices and which are overpriced relative to their number of attributes. For this purpose, we estimate their price efficiency using data envelopment analysis, where the discrimination power hinges on the curse of dimensionality. To cope with this, we propose a novel use of Shannon's entropy for pre-aggregating vitamin and mineral items. Our empirical results indicate that most of the considered brands are overpriced relative to their number of attributes. This result is more severe for the infant formulae brands than the follow-on formulae, the growing-up milk brands, and brands with higher prices.

Suggested Citation

  • Karagiannis, Roxani & Karagiannis, Giannis, 2023. "Nonparametric estimates of price efficiency for the Greek infant milk market: Curing the curse of dimensionality with shannon entropy," Economic Modelling, Elsevier, vol. 121(C).
  • Handle: RePEc:eee:ecmode:v:121:y:2023:i:c:s0264999323000147
    DOI: 10.1016/j.econmod.2023.106202
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999323000147
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econmod.2023.106202?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. Phillip Fanchon, 2003. "Variable selection for dynamic measures of efficiency in the computer industry," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 9(3), pages 175-188, August.
    2. Marcos Lins & Luiz Novaes & Luiz Legey, 2005. "Real Estate Appraisal: A Double Perspective Data Envelopment Analysis Approach," Annals of Operations Research, Springer, vol. 138(1), pages 79-96, September.
    3. Ruben Chumpitaz & Kristiaan Kerstens & Nicholas Paparoidamis & Matthias Staat, 2010. "Hedonic price function estimation in economics and marketing: revisiting Lancaster’s issue of “noncombinable” goods," Annals of Operations Research, Springer, vol. 173(1), pages 145-161, January.
    4. Giannis Karagiannis, 2021. "DEA Models Without Inputs or Outputs: A Tour de Force," Springer Proceedings in Business and Economics, in: Christopher F. Parmeter & Robin C. Sickles (ed.), Advances in Efficiency and Productivity Analysis, pages 211-232, Springer.
    5. Wilson, Paul W., 2018. "Dimension reduction in nonparametric models of production," European Journal of Operational Research, Elsevier, vol. 267(1), pages 349-367.
    6. Adler, Nicole & Golany, Boaz, 2001. "Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an application to Western Europe," European Journal of Operational Research, Elsevier, vol. 132(2), pages 260-273, July.
    7. Charles, Vincent & Aparicio, Juan & Zhu, Joe, 2019. "The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 279(3), pages 929-940.
    8. Subhash C. Ray, 1991. "Resource-Use Efficiency in Public Schools: A Study of Connecticut Data," Management Science, INFORMS, vol. 37(12), pages 1620-1628, December.
    9. Cross, Robin & Färe, Rolf & Grosskopf, Shawna & Weber, William L., 2013. "Valuing Vineyards: A Directional Distance Function Approach," Journal of Wine Economics, Cambridge University Press, vol. 8(1), pages 69-82, May.
    10. Lee, Chia-Yen & Cai, Jia-Ying, 2020. "LASSO variable selection in data envelopment analysis with small datasets," Omega, Elsevier, vol. 91(C).
    11. Adler, Nicole & Berechman, Joseph, 2001. "Measuring airport quality from the airlines' viewpoint: an application of data envelopment analysis," Transport Policy, Elsevier, vol. 8(3), pages 171-181, July.
    12. Lovell, C. A. Knox & Pastor, Jesus T., 1999. "Radial DEA models without inputs or without outputs," European Journal of Operational Research, Elsevier, vol. 118(1), pages 46-51, October.
    13. Cinzia Daraio & Léopold Simar & Paul W. Wilson, 2018. "Central limit theorems for conditional efficiency measures and tests of the ‘separability’ condition in non‐parametric, two‐stage models of production," Econometrics Journal, Royal Economic Society, vol. 21(2), pages 170-191, June.
    14. Caporaletti, L. E. & Dulá, J. H. & Womer, N. K., 1999. "Performance evaluation based on multiple attributes with nonparametric frontiers," Omega, Elsevier, vol. 27(6), pages 637-645, December.
    15. Daraio, Cinzia & Simar, Leopold & Wilson, Paul, 2018. "Central limit theorems for conditional efficiency measures and tests of the ‘separability’ condition in non-parametric, two-stage models of production," LIDAM Reprints ISBA 2018023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    16. Sharma, Mithun J. & Yu, Song Jin, 2015. "Stepwise regression data envelopment analysis for variable reduction," Applied Mathematics and Computation, Elsevier, vol. 253(C), pages 126-134.
    17. Gupta, Pola & Ratchford, Brian T., 1992. "Estimating the efficiency of consumer choices of new automobiles," Journal of Economic Psychology, Elsevier, vol. 13(3), pages 375-397, September.
    18. John Ruggiero, 2005. "Impact Assessment Of Input Omission On Dea," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 4(03), pages 359-368.
    19. Jesús T. Pastor & JosÉ L. Ruiz & Inmaculada Sirvent, 2002. "A Statistical Test for Nested Radial Dea Models," Operations Research, INFORMS, vol. 50(4), pages 728-735, August.
    20. González, Eduardo & Cárcaba, Ana & Ventura, Juan, 2015. "How car dealers adjust prices to reach the product efficiency frontier in the Spanish automobile market," Omega, Elsevier, vol. 51(C), pages 38-48.
    21. Léopold Simar & Paul Wilson, 2011. "Two-stage DEA: caveat emptor," Journal of Productivity Analysis, Springer, vol. 36(2), pages 205-218, October.
    22. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    23. Giannis Karagiannis, 2015. "On structural and average technical efficiency," Journal of Productivity Analysis, Springer, vol. 43(3), pages 259-267, June.
    24. Xiu, Changbai & Klein, K.K., 2010. "Melamine in milk products in China: Examining the factors that led to deliberate use of the contaminant," Food Policy, Elsevier, vol. 35(5), pages 463-470, October.
    25. Ruben Chumpitaz & Kristiaan Kerstens & Nicholas Paparoidamis & Matthias Staat, 2010. "Comparing Efficiency Across Markets: An Extension and Critique of the Zhang and Bartels (1998) Methodology," Working Papers 2010-ECO-01, IESEG School of Management.
    26. Greenaway-McGrevy, Ryan & Sorensen, Kade, 2021. "A Time-Varying Hedonic Approach to quantifying the effects of loss aversion on house prices," Economic Modelling, Elsevier, vol. 99(C).
    27. N Adler & B Golany, 2002. "Including principal component weights to improve discrimination in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(9), pages 985-991, September.
    28. Reis, Hugo J. & Santos Silva, J.M.C., 2006. "Hedonic prices indexes for new passenger cars in Portugal (1997-2001)," Economic Modelling, Elsevier, vol. 23(6), pages 890-908, December.
    29. Jesús T. Pastor & José L. Ruiz, 2007. "Variables With Negative Values In Dea," Springer Books, in: Joe Zhu & Wade D. Cook (ed.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, chapter 0, pages 63-84, Springer.
    30. Rajiv D. Banker & Ram Natarajan, 2008. "Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis," Operations Research, INFORMS, vol. 56(1), pages 48-58, February.
    31. McDonald, John, 2009. "Using least squares and tobit in second stage DEA efficiency analyses," European Journal of Operational Research, Elsevier, vol. 197(2), pages 792-798, September.
    32. Jenkins, Larry & Anderson, Murray, 2003. "A multivariate statistical approach to reducing the number of variables in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 147(1), pages 51-61, May.
    33. Fedderke, Johannes W. & Li, Kaini, 2020. "Art in Africa: Hedonic price analysis of the South African fine art auction market, 2009–2014," Economic Modelling, Elsevier, vol. 84(C), pages 88-101.
    34. Kamakura, Wagner A & Ratchford, Brian T & Agrawal, Jagdish, 1988. "Measuring Market Efficiency and Welfare Loss," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(3), pages 289-302, December.
    35. Hoff, Ayoe, 2007. "Second stage DEA: Comparison of approaches for modelling the DEA score," European Journal of Operational Research, Elsevier, vol. 181(1), pages 425-435, August.
    36. Färe, Rolf & Karagiannis, Giannis, 2017. "The denominator rule for share-weighting aggregation," European Journal of Operational Research, Elsevier, vol. 260(3), pages 1175-1180.
    37. Hjorth-Andersen, Chr, 1984. "The Concept of Quality and the Efficiency of Markets for Consumer Products," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 11(2), pages 708-718, September.
    38. Chumpitaz, Ruben & Kerstens, Kristiaan & Paparoidamis, Nicholas & Staat, Matthias, 2010. "Comparing efficiency across markets: An extension and critique of the methodology," European Journal of Operational Research, Elsevier, vol. 205(3), pages 719-728, September.
    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. Raul Moragues & Juan Aparicio & Miriam Esteve, 2023. "Ranking the Importance of Variables in a Nonparametric Frontier Analysis Using Unsupervised Machine Learning Techniques," Mathematics, MDPI, vol. 11(11), pages 1-24, June.
    2. Toloo, Mehdi & Tone, Kaoru & Izadikhah, Mohammad, 2023. "Selecting slacks-based data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1302-1318.
    3. Villanueva-Cantillo, Jeyms & Munoz-Marquez, Manuel, 2021. "Methodology for calculating critical values of relevance measures in variable selection methods in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 290(2), pages 657-670.
    4. Esteve, Miriam & Aparicio, Juan & Rodriguez-Sala, Jesus J. & Zhu, Joe, 2023. "Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull," European Journal of Operational Research, Elsevier, vol. 304(2), pages 729-744.
    5. Jamal Ouenniche & Skarleth Carrales, 2018. "Assessing efficiency profiles of UK commercial banks: a DEA analysis with regression-based feedback," Annals of Operations Research, Springer, vol. 266(1), pages 551-587, July.
    6. Imad Bou-Hamad & Abdel Latef Anouze & Ibrahim H. Osman, 2022. "A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information," Annals of Operations Research, Springer, vol. 308(1), pages 63-92, January.
    7. Nataraja, Niranjan R. & Johnson, Andrew L., 2011. "Guidelines for using variable selection techniques in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 215(3), pages 662-669, December.
    8. Peyrache, Antonio & Rose, Christiern & Sicilia, Gabriela, 2020. "Variable selection in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 282(2), pages 644-659.
    9. Amir Moradi-Motlagh & Ali Emrouznejad, 2022. "The origins and development of statistical approaches in non-parametric frontier models: a survey of the first two decades of scholarly literature (1998–2020)," Annals of Operations Research, Springer, vol. 318(1), pages 713-741, November.
    10. Yongjun Li & Xiao Shi & Min Yang & Liang Liang, 2017. "Variable selection in data envelopment analysis via Akaike’s information criteria," Annals of Operations Research, Springer, vol. 253(1), pages 453-476, June.
    11. Anna Łozowicka & Bartłomiej Lach, 2022. "CI-DEA: A Way to Improve the Discriminatory Power of DEA—Using the Example of the Efficiency Assessment of the Digitalization in the Life of the Generation 50+," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    12. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    13. Daniel Santín & Gabriela Sicilia, 2015. "Measuring the efficiency of public schools in Uruguay: main drivers and policy implications," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 24(1), pages 1-28, December.
    14. Roxani Karagiannis, 2015. "A system-of-equations two-stage DEA approach for explaining capacity utilization and technical efficiency," Annals of Operations Research, Springer, vol. 227(1), pages 25-43, April.
    15. Calogero Guccio & Marco Ferdinando Martorana & Isidoro Mazza & Giacomo Pignataro & Ilde Rizzo, 2022. "Is innovation in ICT valuable for the efficiency of Italian museums?," European Planning Studies, Taylor & Francis Journals, vol. 30(9), pages 1695-1716, September.
    16. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
    17. Qiwei Xie & Yuanyuan Li & Lizheng Wang & Chao Liu, 2018. "Improving discrimination in data envelopment analysis without losing information based on Renyi’s entropy," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(4), pages 1053-1068, December.
    18. 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.
    19. Charles, Vincent & Aparicio, Juan & Zhu, Joe, 2019. "The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 279(3), pages 929-940.
    20. López-Torres, Laura & Johnes, Jill & Elliott, Caroline & Polo, Cristina, 2021. "The effects of competition and collaboration on efficiency in the UK independent school sector," Economic Modelling, Elsevier, vol. 96(C), pages 40-53.

    More about this item

    Keywords

    Infant milk products; Price efficiency; DEA; Curse of dimensionality; Shannon's entropy;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco

    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:eee:ecmode:v:121:y:2023:i:c:s0264999323000147. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30411 .

    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.