Modeling the influence of age on the technical efficiency by sector and time periods

In today’s competitive economy, technological leadership and technical efficiency are key to the successful development of enterprises, countries and territories. This paper investigates the influence of factors on the technical efficiency of a business. Situations where technical efficiency is calculated by the DEA method, and its determinants are defined in regression models, including tobit regression models, have been considered. The determinants of technical efficiency identified by foreign researchers have been systematised. Modeling of the influence of the “Age” factor on the technical efficiency of enterprises in six leading sectors of Russia’s economy over the period 2015–2019 has been performed. It has been found that the “Age” factor has different effects on technical efficiency in different industry sectors. Particularly, in the food industry younger companies are more technically efficient, while mature companies are more technically efficient in the information technology sector. Accordingly, the directions and priorities for incentives should differ across sectors of the economy. In particular, the technological development of the food industry requires support for the generation processes of young enterprises and start-ups. In the information technology sector, the priority should be to support mature enterprises and the growth processes of young enterprises to maturity. 

1. All-Russian Classifier of Types of Economic Activity - ОК 029-2014 (КDЕS Ed. 2). (approved by the Order of the Rosstandart (Federal Agency on Technical Regulating and Metrology), No. 14-st, dated on January 31, 2014) (as amended, dated on July 10, 2018), Legal reference system “ConsultantPlus”. Available at: http://www.consultant.ru/document/cons_doc_LAW_163320/ (accessed 08.07.2021).

2. Adizes I. K. Managing corporate lifecycle, Translated from English by V. Kuzin, Moscow, Mann, Ferber i Ivanov, 2014, 512 p. (In Russian).

3. Kurakova N. G., Petrov A. N. National technological initiative: evaluation of perspectives of Russia’s technological leadership, The Economics of Science, 2015, vol. 1, no. 2, pp. 84–93. (In Russian).

4. Pototsky O. V., Orlov A. I. Organizational crises as stages of development at small and medium business companies, Russian Journal of Entrepreneurship, 2016, vol. 17, no. 11, pp. 1351–1360. (In Russian). http://doi.org/10.18334/rp.17.11.35314

5. Ryabova E. V. The impact of the regional factor on organization life cycle, Upravlenie ekonomicheskimi systemami: elektronnyi nauchnyi zhurnal, 2015, no. 8, pp. 1–19. (In Russian).

6. Spicyna L. Yu., Spicyn V. V., Horoshilcev M. I. Influence of age on technical efficiency: econometric modeling of dependence for engineering enterprises, Finansovyi bizness, 2021, vol. 2, no. 212, pp. 104–108. (In Russian).

7. Information Resource Spark Interfax. Available at: http://www.spark-interfax.ru (accessed 08.07.2021).

8. Salikhov M. There will be no breakthrough: why the Russian economy is slowing down, News of the Day in Russia and the World – RBC. Available at: https://www.rbc.ru/opinions/economics/22/05/2019/5ce4fcb99a7947fe30aec458 (accessed 08.07.2021).

9. Alsaleh M, Abdul-Rahim A. S., Mohd-Shahwahid H. O. Determinants of technical efficiency in the bioenergy industry in the EU28 region, Renewable and Sustainable Energy Reviews, 2017, vol. 78, pp. 1331–1349. http://doi.org/10.1016/j.rser.2017.04.049

10. Anokhin S. A., Spitsin V., Akerman E., Morgan T. Technological leadership and firm performance in Russian industries during crisis, Journal of Business Venturing Insights, 2021, vol. 15, art. e00223, pp. 1–11. http://doi.org/10.1016/j.jbvi.2021.e00223

11. Charnes A., Cooper W., Lewin A. Y., Seiford L. M. Data envelopment analysis: Theory, methodology and applications, Journal of the Operational Research Society, 1997, vol. 48, no. 3, pp. 332–333. http://doi.org/10.1038/sj.jors.2600342

12. Cheruiyot K. J. Determinants of technical efficiency in Kenyan manufacturing sector, African Development Review, 2017, vol 29, no. 1, pp. 44–55. http://doi.org/10.1111/1467-8268.12237

13. Fare R., Grosskopf S., Norris M., Zhang Zhongyang Z. Z. Productivity growth, technical progress, and efficiency change in industrialized countries, American Economic Review, 1994, vol. 84, no. 1, pp. 66–83.

14. Latif M., Abdullah M. F., Sieng L. W. Determinants factor of technical efficiency in machinery manufacturing industry in Malaysia, International Journal of Supply Chain Management, 2019, vol 8, no. 6, pp. 917–928. Available at: https://ojs.excelingtech.co.uk/index.php/IJSCM/article/view/4086 (accessed 08.07.2021).

15. Marquardt D. W. Comment: You should standardize the predictor variables in your regression models, Journal of the American Statistical Association, 1980, vol. 75, no. 369, pp. 87–91. https://doi.org/10.1080/01621459.1980.10477430

16. Mok V., Yeung G., Han Zh., Li Zh. Leverage, technical efficiency and profitability: an application of DEA to foreigninvested toy manufacturing firms in China, Journal of Contemporary China, 2007, vol. 16, no. 51, pp. 259–274. http://doi.org/10.1080/10670560701194509

17. RezitisA. N., Kalantzi M.A. Investigating technical efficiency and its determinants by data envelopment analysis:An application in the Greek food and beverages manufacturing industry, Agribusiness, 2015, vol. 32, no. 2, pp. 254–271. http://doi.org/10.1002/agr.21432

18. Sahoo B. K., Nauriyal D. K. Trends in and determinants of technical efficiency of software companies in India, Journal of Policy Modeling, 2014, vol. 36, no. 3, pp. 539–561. http://doi.org/10.1016/j.jpolmod.2013.12.001

19. Yekti A., Hadi D. D., Jamhari, J., Hartono S. Technical efficiency of melon farming in Kulon Progo: A stochastic frontier approach (SFA), International Journal of Computer Applications, 2015, vol. 132, no. 6, pp. 15–19. http://doi.org/10.5120/ijca2015907428