IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v270y2018i1p302-313.html
   My bibliography  Save this article

Evaluating treatment effects using data envelopment analysis on matched samples: An analysis of electronic information sharing and firm performance

Author

Listed:
  • Bogetoft, Peter
  • Kromann, Lene

Abstract

An intuitively obvious approach to evaluating the effects of a new business model is to compare the performance of firms using the business model (the treatment group) with the performance of a similar group of firms that do not use the business model (the control group). Data Envelopment Analysis (DEA) can be a powerful tool in such comparisons because it allows us to estimate changes in average performance as well as in frontier performance. In this paper, we suggest using matching together with DEA as a way to ensure sub‐sample homogeneity. The advantages of using a matched sample compared to a random sample of non-treated firms to remove sample size bias is documented using a simulation study. A real-world application is also provided. In the application, we study how information sharing has impacted the performance of Danish manufacturing firms. We match firms that use electronic information sharing to their “twin” firms that do not on the basis of firm characteristics. Before matching, there is a considerable difference in performance between the two groups. However, after matching, we can conclude that approximately 50% of the difference is the result of selection bias.

Suggested Citation

  • Bogetoft, Peter & Kromann, Lene, 2018. "Evaluating treatment effects using data envelopment analysis on matched samples: An analysis of electronic information sharing and firm performance," European Journal of Operational Research, Elsevier, vol. 270(1), pages 302-313.
    • Handle: RePEc:eee:ejores:v:270:y:2018:i:1:p:302-313
    DOI: 10.1016/j.ejor.2018.03.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221718302224
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2018.03.013?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. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
    2. GIJBELS, Irène & MAMMEN, Enno & PARK, Byeong U. & SIMAR, Léopold, 1997. "On estimation of monotone and concave frontier functions," LIDAM Discussion Papers CORE 1997031, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Kneip, Alois & Park, Byeong U. & Simar, Léopold, 1998. "A Note On The Convergence Of Nonparametric Dea Estimators For Production Efficiency Scores," Econometric Theory, Cambridge University Press, vol. 14(6), pages 783-793, December.
    4. Prajogo, Daniel & Olhager, Jan, 2012. "Supply chain integration and performance: The effects of long-term relationships, information technology and sharing, and logistics integration," International Journal of Production Economics, Elsevier, vol. 135(1), pages 514-522.
    5. Dimitrios Giokas & Nicolaos Eriotis & Ioannis Dokas, 2015. "Efficiency and productivity of the food and beverage listed firms in the pre-recession and recessionary periods in Greece," Applied Economics, Taylor & Francis Journals, vol. 47(19), pages 1927-1941, April.
    6. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    7. Yun Zhang & Robert Bartels, 1998. "The Effect of Sample Size on the Mean Efficiency in DEA with an Application to Electricity Distribution in Australia, Sweden and New Zealand," Journal of Productivity Analysis, Springer, vol. 9(3), pages 187-204, March.
    8. F Pedraja-Chaparro & J Salinas-Jiménez & P Smith, 1999. "On the quality of the data envelopment analysis model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(6), pages 636-644, June.
    9. Nicholas Bloom & Raffaella Sadun & John Van Reenen, 2012. "Americans Do IT Better: US Multinationals and the Productivity Miracle," American Economic Review, American Economic Association, vol. 102(1), pages 167-201, February.
    10. Dominique Deprins & Léopold Simar & Henry Tulkens, 2006. "Measuring Labor-Efficiency in Post Offices," Springer Books, in: Parkash Chander & Jacques Drèze & C. Knox Lovell & Jack Mintz (ed.), Public goods, environmental externalities and fiscal competition, chapter 0, pages 285-309, Springer.
    11. 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.
    12. Erik Brynjolfsson & Lorin M. Hitt, 2000. "Beyond Computation: Information Technology, Organizational Transformation and Business Performance," Journal of Economic Perspectives, American Economic Association, vol. 14(4), pages 23-48, Fall.
    13. Barber, Brad M. & Lyon, John D., 1996. "Detecting abnormal operating performance: The empirical power and specification of test statistics," Journal of Financial Economics, Elsevier, vol. 41(3), pages 359-399, July.
    14. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    15. Chen, Yao & Iqbal Ali, Agha, 2004. "DEA Malmquist productivity measure: New insights with an application to computer industry," European Journal of Operational Research, Elsevier, vol. 159(1), pages 239-249, November.
    16. Peter Bogetoft & Lars Otto, 2011. "Benchmarking with DEA, SFA, and R," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7961-2, December.
    17. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, November.
    18. 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)

    Citations

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


    Cited by:

    1. Weltin, Meike & Hüttel, Silke, 2019. "Farm eco-efficiency: Can sustainable intensification make the difference?," FORLand Working Papers 10 (2019), Humboldt University Berlin, DFG Research Unit 2569 FORLand "Agricultural Land Markets – Efficiency and Regulation".
    2. Meike Weltin & Silke Hüttel, 2023. "Sustainable Intensification Farming as an Enabler for Farm Eco-Efficiency?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(1), pages 315-342, January.
    3. Massimo Finocchiaro Castroa & Calogero Guccio & Ilde Rizzo, 2023. "How 'one-size-fits-all' public works contract does it better? An assessment of infrastructure provision in Italy," Papers 2304.10776, arXiv.org.
    4. Wei Xu & Yuchen Pan & Wenting Chen & Hongyong Fu, 2019. "Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine," Energies, MDPI, vol. 12(12), pages 1-20, June.
    5. Mattsson, Pontus, 2019. "The impact of labour subsidies on total factor productivity and profit per employee," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 325-341.
    6. Per J. Agrell & Pontus Mattsson & Jonas Månsson, 2020. "Impacts on efficiency of merging the Swedish district courts," Annals of Operations Research, Springer, vol. 288(2), pages 653-679, May.

    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. Zelenyuk, Valentin, 2020. "Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data," European Journal of Operational Research, Elsevier, vol. 282(1), pages 172-187.
    2. 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.
    3. Cordero, José Manuel & Prior, Diego & Simancas Rodríguez, Rosa, 2013. "A comparison of public and private schools in Spain using robust nonparametric frontier methods," MPRA Paper 51375, University Library of Munich, Germany.
    4. de Borger, Bruno & Kerstens, Kristiaan & Staat, Matthias, 2008. "Transit costs and cost efficiency: Bootstrapping non-parametric frontiers," Research in Transportation Economics, Elsevier, vol. 23(1), pages 53-64, January.
    5. Hanson, Torbjørn, 2016. "Efficiency and productivity in the operational units of the armed forces: A Norwegian example," International Journal of Production Economics, Elsevier, vol. 179(C), pages 12-23.
    6. Barros, Carlos Pestana & Peypoch, Nicolas, 2008. "Technical efficiency of thermoelectric power plants," Energy Economics, Elsevier, vol. 30(6), pages 3118-3127, November.
    7. Rafael Benítez & Vicente Coll-Serrano & Vicente J. Bolós, 2021. "deaR-Shiny: An Interactive Web App for Data Envelopment Analysis," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    8. Kohl, Sebastian & Brunner, Jens O., 2020. "Benchmarking the benchmarks – Comparing the accuracy of Data Envelopment Analysis models in constant returns to scale settings," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1042-1057.
    9. Sickles, Robin C. & Song, Wonho & Zelenyuk, Valentin, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," Working Papers 18-008, Rice University, Department of Economics.
    10. 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.
    11. Dakpo, Hervé K & Jeanneaux, Philippe & Latruffe, Laure, 2014. "Inclusion of undesirable outputs in production technology modeling: The case of greenhouse gas emissions in French meat sheep farming," Working Papers 207806, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
    12. Alois Kneip & Léopold Simar & Paul W. Wilson, 2016. "Testing Hypotheses in Nonparametric Models of Production," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 435-456, July.
    13. Adler, Nicole & Yazhemsky, Ekaterina, 2010. "Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction," European Journal of Operational Research, Elsevier, vol. 202(1), pages 273-284, April.
    14. Emrouznejad, Ali & De Witte, Kristof, 2010. "COOPER-framework: A unified process for non-parametric projects," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1573-1586, December.
    15. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    16. Halkos, George & Tzeremes, Nickolaos, 2010. "Measuring the effect of virtual mergers on banks’ efficiency levels:A non parametric analysis," MPRA Paper 23696, University Library of Munich, Germany.
    17. Calogero Guccio & Marco Ferdinando Martorana & Luisa Monaco, 2016. "Evaluating the impact of the Bologna Process on the efficiency convergence of Italian universities: a non-parametric frontier approach," Journal of Productivity Analysis, Springer, vol. 45(3), pages 275-298, June.
    18. Yu, Wantao & Ramanathan, Ramakrishnan, 2009. "An assessment of operational efficiency of retail firms in China," Journal of Retailing and Consumer Services, Elsevier, vol. 16(2), pages 109-122.
    19. Carlucci, Fabio & Corcione, Carlo & Mazzocchi, Paolo & Trincone, Barbara, 2021. "The role of logistics in promoting Italian agribusiness: The Belt and Road Initiative case study," Land Use Policy, Elsevier, vol. 108(C).
    20. Thieme, Claudio & Prior, Diego & Tortosa-Ausina, Emili, 2013. "A multilevel decomposition of school performance using robust nonparametric frontier techniques," Economics of Education Review, Elsevier, vol. 32(C), pages 104-121.

    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:ejores:v:270:y:2018:i:1:p:302-313. 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/eor .

    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.