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ICT Investment, Adoption of ISO 14000 Certification, and Export Performance in Indian Manufacturing Plants

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Abstract

An econometric analysis of the impact of ICT (information and communication technology) investment and adoption of ISO 14000 certification on export performance is undertaken using plant-level data for Indian manufacturing (organized segment) drawn from unit-level data from the Annual Survey of Industries. A panel dataset for 2008–09 to 2017–18 is used for the analysis. The estimates are based on data on about 150 thousand plants (factories), with about 440 thousand observations. The results of econometric analyses show a significant positive effect of ICT investment and adoption of ISO 14000 certification on the export intensity of manufacturing plants. Two other important determinants of the export performance of Indian manufacturing plants identified by the econometric analyses are (a) the extent of outsourcing of manufacturing activity and (b) the share of imported materials in total materials used, both having a positive impact on exports. Another finding is that increases in contract worker intensity lead to better export performance among plants belonging to low technology industries.

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Fig. 1

Source: Author’s computations based on unit-level data of ASI, 2008–09 to 2017–18

Fig. 2

Source: Author’s computations based on unit-level data of ASI, 2008–09 to 2017–18

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Data availability

The data used for the analysis in the paper are proprietary and cannot be shared.

Notes

  1. Das et al. (2016), Goldar and Mukherjee (2016), Majumder and Sawhney (2020) and Goldar and Parida (2021) have used the plant-level data of ASI to study the determinants of export performance. None of these studies considered the effects of ICT investment and ISO 14000 certification.

  2. A portion of manufacturing firms have multiple plants located in different regions of the country (Goldar 2022). Hence, if firm-level data are used, it is difficult to incorporate regional aspect into the analysis.

  3. See, for instance, Brynjolfsson and Hitt (2000) and Commander et al. (2011). The study undertaken by Commander and associates covered selected manufacturing industries in India and in Brazil. Other studies in which the effect of ICT on productivity has been examined for Indian manufacturing include Joseph and Abraham (2007), Kite (2012, 2013), Sharma and Singh (2013), Mitra et al. (2016), Goldar (2020) and Krishna et al. (2020). In these studies, a positive effect of ICT on productivity in Indian manufacturing was commonly found. Also see, in this context, Erumban and Das (2016, 2020) and Kite (2020).

  4. For some observations on how investments in ICT would contribute to enhanced export performance of firms, see Hagsten and Kotnik (2017) and Kotnik and Hagsten (2018), among others.

  5. See, in this context, Clarke (2008), Ricci and Trionfetti (2012), Sojoodi (2016), Hagsten and Kotnik (2017), Kotnik and Hagsten (2018), Fernandes et al. (2019), Cassetta et al. (2020), Singh and Maiti (2020), and Haini (2021).

  6. The list includes Kumar and Siddharthan (1994), Aggarwal (2002), Goldar and Kato (2009), Franco and Sasidharan (2010), Ranjan and Raychaudhuri (2011), Srinivasan and Archana (2011a), Haidar (2012), Bas (2013), Goldar (2013a, b), Pradhan and Das (2013a, b, 2016), Nagaraj (2014), Mukherjee (2015), Keshari (2016), Sahu and Narayanan (2016a), Thomas and Narayanan (2016), Kapoor et al. (2017), Padmaja and Sasidharan (2017), Goldar et al. (2017), Goldar et al. (2018), Banga and Banga (2020), Mohapatra (2021), and Padmaja and Sasidharan (2021).

  7. In a recent study, Goldar and Majumder (2022) find that exporting to a foreign market has a positive impact on the adoption of ISO 14000 among Indian manufacturing plants.

  8. National Industrial Classification, 2008, National Statistical Office, Ministry of Statistics and Programme Implementation, Government of India.

  9. For some discussion on the nature of panel data obtained from the unit-level datasets of ASI for different years, see Goldar (2020) and Majumder and Sawhney (2020), among others.

  10. The reported data on net fixed capital stock of different plants in different years during 2008–09 to 2017–18 show wide variation. In some cases, the reported capital stock is very low, which might be reflecting the fact that, in the balance sheet, the depreciated net value of the existing machinery in those units has declined over time to very low levels even though the machines remain in workable conditions and will fetch a much higher value in the market than the value shown in the balance sheet, or those units are carrying on production activity mostly on the basis of rented premises and rented machinery. To take care of these issues, a floor value of Rs. 5000 has been applied to data on net fixed capital stock, i.e., if the reported value is lower than Rs. 5000, then it has been raised to Rs. 5000. The medium of fixed capita stock in the total sample is about Rs 12 million.

  11. Among the econometric studies on determinants of export performance in Indian manufacturing based on firm-level data, size of the firm has been measured by logarithm of deflated total assets in Goldar et al. (2018), Padmaja and Sasidharan (2021) and Sasidharan and Reddy (2021), and by logarithm of deflated sales in Padmaja and Sasidharan (2017).

  12. This variable has been winsorized at 1st and 99th percentile, which applies also to manufacturing services input intensity. This takes care of the unrealistically high value or unrealistically low value taken by these variables in the dataset, which if not corrected for will affect the model estimates.

  13. Firms might be selling their products to traders who are exporting those products. This component of exports known as indirect exports is not considered in the analysis. See Goldar (2022) on the relative magnitude of indirect exports.

  14. In the ASI unit-level data for 2008–09 and 2009–10, the recorded export share in production exceeds 100 percent in a small number of cases (10–15 cases in a sample of more than 36 thousand factories belonging to manufacturing with their status recorded as “open”). In these cases, the recorded export share has been capped at 100 percent for the purpose of the analysis.

  15. The population estimate of mean export intensity based on sample weights given in the datasets comes to about 4 percent which is less than the unweighted sample mean of this variable by more than one percentage point. The population estimate is expected to be relatively lower than unweighted sample mean of export intensity because of the sample design in the survey. While the census sector of ASI is completely enumerated, the sample sector which contains relatively smaller plants, much larger in number (say, three to four times) than the number of plants belonging to the census sector, are covered on the basis of probability sampling—the overall sampling fraction was 10.5% in the survey for 2018–19. This needs to be seen in conjunction with the fact that export intensity is relatively lower in relatively small sized plants.

  16. It should be noted that when sample weights are applied, the proportion of plants that participate in exports comes down to about seven percent. This is consistent with gap between the unweighted sample mean of export intensity and population estimate of mean export intensity presented in Table 2.

  17. See ISIC Rev.3 Technology Intensity Definition, document of the Economic Analysis and Statistics Division, Directorate of Science, Technology and Industry, OECD, dated 2011.

  18. It will be noticed that the number of observations in the estimates in Table 7 are substantially lower than that in Table 6. The reason lies in the fact that when some variables are taken with one year lag, an observation will be included in the estimate only if the previous year’s observation for the plant is available in the dataset. This does not occur for plants belonging to the sample sector of ASI (barring a small fraction). See footnote 15 in this connection.

  19. Das et al. (2016) have used manufacturing industries’ plant-level (panel) data of ASI for 4 years, 2008–09 to 2011–12. They find a positive relationship between plant size and export intensity. This is broadly consistent with the findings of this study. Goldar and Mukherjee (2016) have used ASI unit-level data for 2011–12 to study determinants of export intensity and have found a positive effect of plant size (measured in terms of logarithm of total net fixed assets) on export intensity. This is in line with the findings of this study. In the study of export participation based on ASI plant level data undertaken by Majumder and Sawhney (2020), a positive effect of plant size on exports is found. Again, this is broadly consistent with the findings of this study.

  20. The determinants of exports performance of firms in India have been studied by Pradhan and Das (2013a, b, 2016) who take into account the influence of the conditions prevailing in the states where the firms are located. How state level infrastructure and other state level factors impact export participation by manufacturing plants has been studied by Majumder and Sawhney (2020). The location aspect is brought into the analysis of export performance through the use of infrastructure variables in Srinivasan and Archana (2011b). The impact of operational efficiency of India’s ports on manufactured exports is discussed in Goldar and Paul (2019), which has a regional dimension.

  21. See in this context, Banga (2019), who emphasizes the need for a digitally-informed foreign trade policy in India in order to improve India’s trade competitiveness in the digital era. According to her, some important components of the required digital initiatives include: (1) building digit infrastructure for trade, (2) enhancing the digital content in exports, and (3) developing digital skills (e.g., designing using computer-aided design) especially in traditional export industries such as textiles, clothing and leather products.

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Appendix

Appendix

Tables 9, 10, 11 and 12.

Table 9 Determinants of export intensity in Indian manufacturing plants, regression results, random effects panel Tobit model, changed contract worker intensity variable.
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Table 10 Determinants of export intensity in Indian manufacturing plants, regression results, Tobit model estimated from pooled data with industry and state dummies.
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Table 11 Probit model explaining plants’ decision to adopt ISO 14000 certification.
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Table 12 Mean value of covariates, comparison between treated (plants having ISO 14000 certification, the first year in the study period) and non-treated plants.
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Goldar, B. ICT Investment, Adoption of ISO 14000 Certification, and Export Performance in Indian Manufacturing Plants. J. Quant. Econ. 21, 523–553 (2023). https://doi.org/10.1007/s40953-023-00350-1

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