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Technology Import and Manufacturing Productivity in India: Firm Level Analysis

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Abstract

The paper examines the impact of import of technology on productivity of the organised manufacturing sector of India using firm level panel data for the period of 1995–2010. The estimation of an augmented Cobb-Douglas production function using the Levinsohn and Petrin econometric technique suggests that both embodied as well as disembodied forms of technology import have a positive and significant effect on aggregate manufacturing productivity. However, the sectoral estimation results based on technology intensive classification of firms reveal that the embodied technology purchases have a relatively more significant and positive impact across sectors, whereas the disembodied technology imports have positive effect on the productivity of medium technology intensive sectors.

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Notes

  1. Comin and Hobijn (2011) argue that countries that performed well in the post-World War II period did so because they were able to adopt new technology quickly.

  2. See Lall (2000b) for an extensive discussion of technological learning and capability among various manufacturing firms in developing countries.

  3. As discussed in Harris (2005), the bias can be disregarded only when the factors determining output and productivity have zero correlation. However, since both these are firm specific, the correlation between them is likely to be high.

  4. Previous empirical studies on technology import and productivity of Indian manufacturing by Basant and Fikkert (1996), Hasan (2002) and Parameswaran (2009) has also used similar estimation technique. This will allow us to have a comprehensive comparison with other studies.

  5. Although the Cobb-Douglas functional form is considered too restrictive, it is the most widely used specification in firm level studies on productivity (Hall and Mairesse 1995, O’Mahony and Vecchi 2009). Some of the alternative but more complicated specifications such as Translog do not yield substantially different results (Griliches 1998). See Parameswaran (2009) and Fernandes (2007) for similar estimation procedure.

  6. State variables are fixed factors, which are affected by the distribution of ω it conditional on information set available at t-1 period and past values of ω it . In the case of free variable factors, the input choices by the firm depend upon the current values of ω it (Olley and Pakes 1996).

  7. For a comprehensive survey, see Van (2012) and references therein.

  8. For detailed outline on Semiparametric methods, see Yatchew (2003).

  9. Bootstrapping is a nonparametric approach for assessing the distribution of a statistic based on random resampling (Guan 2003). For a lucid exposition of bootstrapping method in economics, see Mackinnon (2006).

  10. This process uses sampling with replacement and with equal probability from the sets of firm observations in the original sample (Horowitz 2001).

  11. The NIC 2008 is based on International Standard Industrial Classification (ISIC) rev4.

  12. Based on the selection criteria, we excluded three 2-digit sectors from the analysis sample. These are NIC 11 (manufacture of beverages), NIC 31 (Manufacture of furniture) and NIC 32 (other manufacturing). We dropped 387 firms under these product groups.

  13. This is our benchmark sample which we denote as unbalanced panel dataset A. Additionally, for robustness analysis, we also selected firms that reported sales data for at least 10 years during the entire period of reference (unbalanced panel dataset B) and all those firms that reported sales figures during the entire period (balanced panel dataset C). The dataset B consists of 3206 firms and the dataset C has 1442 firms.

  14. The latest OECD classification scheme categorises industries according to ISIC rev3.1, whereas our classification is based on ISIC rev4 (which is equivalent to NIC 2008). Therefore, we established correspondence between ISIC rev3.1 and ISIC rev4 at the aggregate level. See Table 4 in the appendix for the details.

  15. Several studies have employed similar procedures for constructing output variable at the firm level (See for instance, Srivastava (1996).

  16. In the absence of employment data at the firm level, several studies have used a similar procedure. For instance, see Srivastava (1996), Kambhampati (2003), Dougherty et al. (2009), and Kim and Saravanakumar (2012) among others.

  17. In PROWESS, the payments made to labour contractors are included in the wage bill of the firm but the workers employed through the contractors are not included in the payroll of the firm. This makes the number of workers as reported therein an inappropriate measure of labour input (Keshari 2013).

  18. In PROWESS, the foreign technology purchases are reported separately under firms’ total forex spending. This is available in its total foreign exchange transactions section.

  19. Parameswaran (2009) have also employed similar procedure.

  20. Hasan (2002), Kathuria (2001) and Parameswaran (2009) have used the same procedure.

  21. The annual expenditure on disembodied technology import consist of payment to foreigners for technical assistance and consulting, lump sum and royalty payment for the purchase of technology through licensing agreement between Indian and foreign firms. This information is recorded as expenses on royalties, technical expertise fees in PROWESS.

  22. The rationale behind choosing US R&D deflator is that US firms constitute the largest provider of technological assistance to Indian firms. Basant and Fikkert (1996), Hasan (2002) and Parameswaran (2009) have used similar assumption.

  23. See Marschak and Andrews (1944), Olley and Pakes (1996), Levinsohn and Petrin (2003) and Ackerberg et al. (2007) for a detailed exposition of these arguments.

  24. This is true irrespective of the dataset used for the study (see Table 7 in the appendix).

  25. Previous firm level studies on India have also reported a somewhat low impact of technology import variables. See for instance, Hasan (2002) and Parameswaran (2009).

  26. Additionally, we also checked our results with an alternative classification of technology intensity. We use Lall (2000b) methodology and re-classified firms into Resource Based (RB), Low Technology (LT), Medium Technology (MT) and High Technology (HT). We find that embodied technology is positive and significant for RB, MT and HT sectors while disembodied technology is positive and significant for only MT sector. The results are largely similar to what we have found in our benchmark estimates.

  27. Several studies have noted the importance of export orientation and productivity of firms (See Wagner 2007 for literature review). The export intensity (exi) is calculated by taking the share of exports of individual firm in their total sales. Another important determinant of productivity is capital intensity and is measured as the log of capital-labour ratio (ci).

  28. We have also estimated the model using alternative datasets including unbalanced dataset B and balanced panel dataset of C. The results are broadly similar.

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Acknowledgments

I am thankful to Prof. Alokesh Barua, Prof. K.L Krishna and Prof. Prema Chandra Athukorala for the extensive comments and suggestions to the earlier version of the paper. I thank Prof. Parameswaran for his valuable help and suggestions. I like to thank two anonymous referees of this journal for providing constructive comments and suggestions leading to substantial improvement of the paper. However, I am solely responsible for any errors and omissions that may remain.

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  1. Indian Institute of Foreign Trade, Research Division, Qutab Institutional Area, New Delhi, India, 110016

    R. Rijesh

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Appendix

Appendix

Table 4 Classification of Industries according to Technological Intensity
Full size table
Table 5 Growth Performance of Key Variables: Outputs and Inputs
Full size table
Table 6 Summary Statistics
Full size table
Table 7 LP Estimates of Production Function for Indian Manufacturing Sector (1995–2010) using Alternative Datasets, Dependent variable: lnq
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Rijesh, R. Technology Import and Manufacturing Productivity in India: Firm Level Analysis. J Ind Compet Trade 15, 411–434 (2015). https://doi.org/10.1007/s10842-015-0193-9

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