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Infrastructure, ICT and Firms’ Productivity and Efficiency: An Application to the Indian Manufacturing

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Globalization of Indian Industries

Part of the book series: India Studies in Business and Economics ((ISBE))

Abstract

This paper highlights the role of infrastructure and information and communication technology (ICT) in the context of total factor productivity (TFP) and technical efficiency (TE) of the Indian manufacturing sector for the period 1994–2008. We use advanced estimation techniques to overcome problems of non-stationary, omitted variables, endogeneity and reverse causality by applying fully modified OLS, panel co-integration and system GMM. Estimation results suggest that the impact of infrastructure and ICT is rather strong. Interestingly, sectors exposed relatively more to foreign competition (e.g. Transport Equipment, Textile, Chemicals, Metal and Metal Products) are more sensitive to infrastructure deficiencies. This finding implies that improving infrastructure and ICT would benefit these sectors to a large extent, thus contributing to India’s competitiveness. This outcome is of particular importance in the context of infrastructure bottlenecks in India.

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Notes

  1. 1.

    Prowess (CMIE) classified the Indian manufacturing in eight two digit industries. The prowess follows an internal product classification that is based on the Harmonized System and national industry classification (NIC) schedules. There are a total of 1,886 products linked to 108 four-digit NIC industries across the 22 manufacturing sectors (two-digit NIC codes) in the database. For analysis, we have covered all available industries in the database. Furthermore, these eight groups of industries cover a sizeable part of the total organized industrial production in India.

  2. 2.

    We prefer gross value added as a measure of output in computing TFP, as it is widely used in the Indian manufacturing sector literature (Goldar 2004; Kumar 2006). There are many advantages of using gross value added over output. Firstly, it allows us a comparison between the firms that use different raw materials. Secondly, if gross output is used as a measure of output, it adds the necessity of including raw materials, which may obscure the role of labour and capital in the productivity growth (Kumar 2006).

  3. 3.

    The principal component analysis (PCA) method is a widely used aggregation technique because of the subjectivity attached to other ad hoc aggregation methods. PCA is designed to linearly transform a set of initial variables into a new set of uncorrelated components, which account for all of the variance in the original variables. Each component corresponds to a virtual axe on which the data are projected. The earlier component explains more of the variance of the series than do the later component. The number of components is proportional to the number of initial variables that are used in the PCA. Usually, only the first components are retained, because they explain most of the variance in the dataset. The proportion gives the explanatory power of each component. For more details on the aggregation method using principal component analysis (PCA), see Nagaraj et al. (2000) and Mitra et al. (2002).

  4. 4.

    We choose fixed effect (FE) model because the test statistic suggests that the OLS and Random Effect models are rejected. The fixed effect suggests that the firm specific group effects are strong. Other alternative methods of estimating productivity include growth accounting technique, but that is inferior to econometric estimation.

  5. 5.

    This methodology, initially used with firm-level data, has also been employed to estimate productivity at the aggregate level (see Kathuria et al. 2010). Our working hypothesis is that some industries operate more efficiently than others.

  6. 6.

    It is well established, in the related literature, that Research and Development (R&D) is an important determinant of productivity and export performance of firms. The pioneering study of Griliches (1979) has shown in the ‘R&D Capital Stock Model’ that this factor has a direct effect on the performance of firms. Empirical evidence reported by Lichtenberg and Siegal (1989) and Hall and Mairesse (1995) also provides strong support to Griliches’s view. To capture the R&D intensity, this study considers the ratio of R&D expenditure to industry’s total sales. This variable is expected to have a positive impact on industries’ productivity and efficiency.

  7. 7.

    Trade intensive firms benefit from technology transfers through exporting and importing output material and other inputs, which can potentially help firms to enhance their productivity (see Sachs and Warner 1995). In this study, Trade intensity is captured by the ratio of total export plus import to the value of total sales of the industry. It is expected to have a positive impact on industries’ performance.

  8. 8.

    Theoretically, because of economies of scale, a larger size and increasing output should have a positive influence on the productivity of industry. In our model, capital (K) is taken as a proxy of the size of the industry and it is expected to have a positive influence on productivity, as well as on efficiency.

  9. 9.

    We have applied ‘group-mean FMOLS’, because we have a small sample for the analysis. Pedroni (2000) has shown that the ‘group-FMOLS’ has relatively lower small sample distortions and more flexibility in terms of hypothesis testing than other three versions of FMOLS (see also Basher and Mohsin 2004).

  10. 10.

    We will see that it is not the case anymore for TE.

  11. 11.

    In Miscellaneous Manufacturing also, the variable is estimated to be statistically significant, however, the sign of the coefficient is negative.

  12. 12.

    It is noteworthy that Chemical, in which TFP and infrastructure are uncorrelated, is responsive to infrastructure in terms of TE.

  13. 13.

    Trade intensity is now a factor of efficiency in the Chemical and Textile industry, in addition to Non Metal and Metal sectors as in the case of TFP, with much smaller elasticities however.

  14. 14.

    Results regarding the other control variables are not found to be very different from the previous estimation.

  15. 15.

    The early findings by Aschauer (1989) and Munnell (1990) were widely criticized on three grounds. First, common trends in output and public infrastructure data are suspected to have led to spurious correlation. Second, it is argued that causation runs in the opposite direction, that is, from output to public capital. Final, it has also been observed that applying the OLS technique directly on non-stationary data of infrastructure and output, may be a reason of a large elasticity magnitude in these studies (see Aaron 1990; Tatom 1991; Garcia-Mila et al. 1996). Considering the FMOLS and Sys-GMM estimation in this study, it seems we have overcome these problems and therefore the probability of spurious finding is rather low.

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Author information

Authors and Affiliations

  1. Institute of Economic Growth (IEG), University of Delhi Enclave, Delhi, India

    Arup Mitra

  2. Indian Institute of Management (IIM), Lucknow, India

    Chandan Sharma

  3. CERDI–CNRS, Université d’Auvergne, Clermont Ferrand, France

    Marie-Ange Véganzonès-Varoudakis

Authors
  1. Arup Mitra
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  2. Chandan Sharma
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  3. Marie-Ange Véganzonès-Varoudakis
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Corresponding author

Correspondence to Arup Mitra .

Editor information

Editors and Affiliations

  1. Faculty of Economics and Business, KU Leuven Universit, Antwerpen, Belgium

    Filip De Beule

  2. Humanities and Social Sciences, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    K. Narayanan

Appendices

Appendix 1

See Tables 2.6 and 2.7.

Table 2.6 Estimated TFP of the Indian manufacturing industries, 1994–2008
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Table 2.7 Estimated TE of the Indian manufacturing industries, 1994–2008
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Appendix 2

See Tables 2.8, 2.9 and 2.10.

Table 2.8 Infrastructure and ICT variables: sources of data
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Table 2.9 Correlation between infrastructure variables
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Table 2.10 Relative infrastructure endowments in Indiaa
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Appendix 3

See Table 2.11.

Table 2.11 Summary statistics
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Appendix 4

See Tables 2.12 and 2.13.

Table 2.12 Test for panel unit root applying Im, pesaran and Shin W-statistics
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Table 2.13 Pedroni (1999) panel cointegration test results
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Mitra, A., Sharma, C., Véganzonès-Varoudakis, MA. (2016). Infrastructure, ICT and Firms’ Productivity and Efficiency: An Application to the Indian Manufacturing. In: De Beule, F., Narayanan, K. (eds) Globalization of Indian Industries. India Studies in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-10-0083-6_2

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