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Trends in and determinants of technical efficiency of software companies in India

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  • Sahoo, Bimal Kishore
  • Nauriyal, D.K.

Abstract

This paper attempts to discuss the trends in and determinants of technical efficiency of software companies in India during 1999–2008 by applying input-oriented DEA model. Based upon the PROWESS Database of CMIE, the efficiencies were estimated for the old and new companies and also for Indian, multinational and group companies. The estimations were made for a sample of 72 software companies, under VRS assumption, as dataset manifested large magnitude of differences owing to the presence of big and small companies in the sample. The sales revenue is taken as output variable, and employment, expenditure on computers and electronics equipments, operating expenditure, power, fuel, and water charges as the input variables. The results and analyses demonstrate that the mean overall technical efficiency of the software industry in India during 1999–2008 was low suggesting that software firms, on an average, were wasting 35% of their inputs. It was found that the number of companies operating on most productive scale size has declined during the period under reference. The results also suggest that Indian-owned companies were more efficient than the foreign-owned and group-owned companies. Contrary to the expectations, exports were not found to have exercised significant impact on the efficiency of Indian software industry.

Suggested Citation

  • Sahoo, Bimal Kishore & Nauriyal, D.K., 2014. "Trends in and determinants of technical efficiency of software companies in India," Journal of Policy Modeling, Elsevier, vol. 36(3), pages 539-561.
    • Handle: RePEc:eee:jpolmo:v:36:y:2014:i:3:p:539-561
    DOI: 10.1016/j.jpolmod.2013.12.001
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    as
    1. Robert J. Barro, 2013. "Inflation and Economic Growth," Annals of Economics and Finance, Society for AEF, vol. 14(1), pages 121-144, May.
    2. White, Steven & Liu, Xielin, 1998. "Organizational processes to meet new performance criteria: Chinese pharmaceutical firms in transition," Research Policy, Elsevier, vol. 27(4), pages 369-383, August.
    3. Yang, Ji-Chung, 2006. "The efficiency of SMEs in the global market: Measuring the Korean performance," Journal of Policy Modeling, Elsevier, vol. 28(8), pages 861-876, November.
    4. Anders Sørensen & Hans Christian Kongsted & Mats Marcusson, 2003. "R&D, public innovation policy, and productivity: The case of danish manufacturing," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 12(2), pages 163-178.
    5. Sofronis K. Clerides & Saul Lach & James R. Tybout, 1998. "Is Learning by Exporting Important? Micro-Dynamic Evidence from Colombia, Mexico, and Morocco," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(3), pages 903-947.
    6. Aw, B. -Y. & Hwang, A. R., 1995. "Productivity and the export market: A firm-level analysis," Journal of Development Economics, Elsevier, vol. 47(2), pages 313-332, August.
    7. Valdemar Smith & Erik Strøjer Madsen & Mogens Dilling-Hansen, 2002. "Do R&D Investments Affect Export Performance?," CIE Discussion Papers 2002-09, University of Copenhagen. Department of Economics. Centre for Industrial Economics.
    8. Avkiran, Necmi K., 2001. "Investigating technical and scale efficiencies of Australian Universities through data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 35(1), pages 57-80, March.
    9. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    10. 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.
    11. Richard E. Caves, 1992. "Industrial Efficiency in Six Nations," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262031930, April.
    12. 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.
    13. Jovanovic, Boyan, 1982. "Selection and the Evolution of Industry," Econometrica, Econometric Society, vol. 50(3), pages 649-670, May.
    14. 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.
    15. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, October.
    16. Weiss, Andrew W, 1980. "Job Queues and Layoffs in Labor Markets with Flexible Wages," Journal of Political Economy, University of Chicago Press, vol. 88(3), pages 526-538, June.
    17. Laure Latruffe & Kelvin Balcombe & Sophia Davidova & Katarzyna Zawalinska, 2004. "Determinants of technical efficiency of crop and livestock farms in Poland," Applied Economics, Taylor & Francis Journals, vol. 36(12), pages 1255-1263.
    18. Hassan, M. Kabir & Isik, Ihsan & Mamun, Abdullah, 2010. "Trade liberalization and industry performance in Bangladesh," Journal of Policy Modeling, Elsevier, vol. 32(3), pages 399-417, May.
    19. Uri, Noel D., 2001. "The effect of incentive regulation on productive efficiency in telecommunications," Journal of Policy Modeling, Elsevier, vol. 23(8), pages 825-846, November.
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    More about this item

    Keywords

    Software industry in India; Efficiency; DEA; Tobit Regression;
    All these keywords.

    JEL classification:

    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models

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