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India’s Intra-industry Trade: Implication on Vertical Specialization, Environment and Employment

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Abstract

India’s IIT has grown in importance after the adoption of trade liberalization measures in 1991. This chapter tries to assess India’s IIT by considering different aspects, like vertical specialization associated with IIT, the impact of IIT on environment and employment. The analysis focuses on India’s bilateral IIT with the USA, EU (27) and China during 2001–02 to 2015–16. The study finds that share of IIT in total trade between India and its trade partners is increasing. India’s IIT is dominated by the products which are differentiated vertically (VIIT). This might be an indication of vertical specialization and the country’s increasing participation in global production network. Results also reveal low-quality VIIT dominates in trade with the USA and EU (27). In trade with China, both low-quality and high-quality VIITs are found to be equally important, though the share of low-quality varieties is rising. Regarding environmental impact of IIT, values of PTOT are greater than one in most of the cases implying India being a pollution haven. In trade with China, pollution intensity of export component of IIT has shown an upward trend over the study period. Regarding employment generation, IIT has a positive impact on the labour market, particularly in trade with EU (27) and China. As India is found to be specializing mostly in the varieties of lower quality which are unskilled labour-intensive, promotion of IIT could lead to an inclusive growth in an unskilled labour-surplus economy like India.

A preliminary version of the paper was presented at the 23rd International Input–Output conference held at Mexico during 22–26 June 2015.

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Notes

  1. 1.

    Balassa (1966) defined IIT as the inter-country exchange of commodities belonging to the same industry, observing a less disruptive effect in factor income distribution in adjustment to changes in intra-industry trade vs. adjustment to inter-industry trade.

  2. 2.

    α can be also assumed as 0.25. However, both Abd-el-Rahman (1991) and Greenway et al. (1994, 1995) showed that changing the range from 15 to 25% has impact on the result.

  3. 3.

    If \({\text{UV}}_{q}^{e} /{\text{UV}}_{q}^{m}\) < 0.85, it is considered as a low-quality export, and if \({\text{UV}}_{q}^{e} /{\text{UV}}_{q}^{m}\) > 1.15 it is considered as a high-quality export.

  4. 4.

    This is primarily due to limitation of data. Labour data are sourced from ImpactECON (2016) data set where data are given for 2011 only. Between the 2 years 2001–02 and 2015–16 considered in this study, the latter year is nearer to 2011. So, we have calculated the labour content only for 2015–16.

  5. 5.

    In Table 3 Textiles are included in the top five sectors, whereas both textiles and miscellaneous manufacturing would be included if top seven sectors are considered.

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Authors and Affiliations

  1. Department of Economics, Ananda Chandra College, Jalpaiguri, 735101, India

    Paramita Dasgupta

  2. Gokhale Institute of Politics and Economics, Pune, 4110004, India

    Kakali Mukhopadhyay

  3. Department of Natural Resource Sciences, Agricultural Economics Program, McGill University, Macdonald Campus, 21,111 Lakeshore Road, SteAnne de Bellevue, Montreal, QC, H9X3V9, Canada

    Kakali Mukhopadhyay

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  1. Paramita Dasgupta
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Correspondence to Paramita Dasgupta .

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  1. Gokhale Institute of Politics and Economics, Pune, India

    Kakali Mukhopadhyay

Appendix: Data Sources

Appendix: Data Sources

The data used in this paper are obtained from following sources:

  1. 1.

    Input–output tables:

    For India, the USA, EU (27) and China, the IO tables are taken from GTAP. For the year 2001–02 and 2015–16, we have used GTAP version 6 (Dimaranan 2006) with base year 2001 and GTAP version 9 (Narayanan et al. 2015) with base year 2011, respectively. Since version 9 with base year 2011 is the latest data set provided by the GTAP, we have to use this version to do our calculations for the year 2015–16.

  2. 2.

    Data on bilateral trade:

    Trade data for 2001–02 and 2015–16 are obtained from UN Comtrade, United Nations (available at http://comtrade.un.org/). The data applied in this study are categorized by the Standard International Trade Classification (SITC, Rev 3) System at the five-digit level. First, we have estimated the shares of IIT at the five-digit level using Grubel–Lloyd index and then the shares are aggregated for 18 sectors. The shares of IIT (and inter-industry trade) in total trade are then applied to the GTAP trade data aggregated for the 18 sectors. Concordances to assist in mapping data to the GTAP sectors are available in the GTAP Technical Paper (Aguiar et al. 2016).

  3. 3.

    Data on CO2 emission:

    Total CO2 emission in India, the USA, EU (27) and China at the sectoral level is obtained from GTAP version 6 (Lee 2002) and GTAP version 9 (Narayanan et al. 2015). Total sectoral output required to estimate the pollution coefficients is also collected from the same source.

  4. 4.

    Data on labour:

    Requirements of various categories of labour at the sectoral level are sourced from ImpactECON (2016), GTAP. Sectoral output data are taken from GTAP to calculate the labour coefficients for different categories of labour.

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Dasgupta, P., Mukhopadhyay, K. (2018). India’s Intra-industry Trade: Implication on Vertical Specialization, Environment and Employment. In: Mukhopadhyay, K. (eds) Applications of the Input-Output Framework. Springer Proceedings in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-13-1507-7_12

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