An Evaluation of the Determinants of Total Factor Productivity Growth in Indian Information Technology Industry: An Application of DEA-based Malmquist Index


  • Prosenjit Das



information technology industry, data envelopment analysis, Malmquist productivity index, random-effects model, total factor productivity, catch-up, frontier-shift, India


Aim: India has emerged as one of the most favoured destinations in the global Information Technology (IT) outsourcing market. On the other hand, the IT industry has been playing an instrumental role in transforming India’s image from a low income-backward nation to a knowledge-based economy.  Furthermore, the role of IT industry has been pivotal in putting India on a higher growth path. In addition, India’s IT industry has been showing robust performance in revenue earning, particularly in export revenue. However, the performance of this industry is likely affected by some recent global phenomena, such as 2008’s subprime crisis originated in the US, uncertainties in changes in H1-B visa rules, Britain’s exit from the EU, automation etc. There are other factors, like exchange rate volatility, emerging competition from other low-cost outsourcing destination countries, are also posing threat to India’s IT-outsourcing business. Against this backdrop, it is crucial to analyse the sustainability of performance of Indian IT industry. Thus, the present study aims at assessing the performance of Indian IT industry and evaluating the determinants of performance thereafter.

Design / Research methods: To realize the objectives of the study, firm level data has been collected from the Centre for Monitoring Indian Economy (CMIE) Prowess database. For empirical analysis, we have applied a two-stage method. In the first-stage, we have used Data Envelopment Analysis (DEA) based Malmquist Productivity Index (MPI) to evaluate the Total Factor Productivity Growth (TFPG) of Indian IT industry during the period from 2004-05 to 2014-15. For this purpose, a balanced panel consists of 70 IT firms has been considered. Further, the TFPG has been decomposed into three components, viz. Catch-up, frontier-shift, and scale efficiency change (SEC). Consequently, in the second-stage, three random-effects panel regression models are considered to investigate the determinants of TFPG, catch-up, and frontier-shift separately. 

Conclusions / findings: During the study period, the average TFP and frontier-shift has been improved. On the other hand, catch up effect is found to have declined. The variables, such as export intensity, salaries and wages intensity have positive and statistically significant impact on the catch-up and frontier-shift. Export intensity has positive impact on TFPG. Age of the firms has positive impact on catch-up and TFPG. Salaries and wages intensity has positive impact on TFPG. On an average, the firms which spent on research and Development (R&D) have experienced improvement in TFPG and frontier-shift. The public limited firms performed better than their private counterparts in terms of catch-up, frontier-shift, and TFPG. The non-group firms have performed better than the group firms in case of catch-up. On the other hand, on an average, the firms exhibiting decreasing Returns to Scale (DRS) are found to have registered deterioration in catch-up and TFPG with respect to the benchmark firms which are exhibiting Constant Returns to Scale (CRS). The firms exhibiting Increasing Returns to Scale (IRS) have shown improvement in catch-up and TFPG over the benchmark CRS firms. The impact of the US subprime crisis has been negative on catch-up, frontier-shift, and TFPG. The firms, which have spent on royalty, have experienced improvement in catch-up and TFPG. 

Originality / value of the article: So far in our knowledge, not so many studies of this kind have been done in the arena of empirical research pertains to the IT industry, especially in a developing country like India. Moreover, we have not found any study that covers the span of the dataset considered in the present study. In addition to this, the present study has employed a random-effects panel model to accommodate a number of time-invariant dummy variables which would not be possible in case of a fixed-effects panel model incorporated by some previous studies of this genre.

Implications of the research: The identification of the determinants of TFPG and its components would help the stakeholders and policy makers of the IT industry to formulate appropriate policies which could mitigate the risks faced by the industry on one hand, and stimulate the forces that would enhance the growth of this industry on the other. For instance, to mitigate future risks, Indian IT industry should reduce its dependence on the US and UK markets. Besides, it should explore new markets in the EU, and other emerging economies where opportunities are plenty. To maintain India’s robust global position in the long run, Government of India should play the key role in providing world class infrastructure and telecommunication facilities to its IT industry. In addition to this, Government needs to rationalise and simplify the existing Indian labour law to facilitate the business of IT industry. Various stakeholders along with the Government should put necessary efforts to develop the domestic IT market as there exists ample of opportunities in future.


Keywords: information technology industry, data envelopment analysis, Malmquist productivity index, random-effects model, total factor productivity, catch-up, frontier-shift, India.


JEL: C23, C61, L86, O47