Abstract
To eliminate poverty in India in the foreseeable future, the Indian economy needs to grow at the rate of about 8% per year, or higher, for the next two decades; manufacturing-sector growth must accelerate markedly.
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Notes
- 1.
To eliminate poverty in India and turn it into an upper-middle-income country by 2035, Mohan (2019) emphasises the need for stepping up the growth rate of the Indian economy in the next two decades. According to him, to reverse the deceleration in the economy’s growth rate since 2008–09 and raise the growth rate to 8–9% per annum, certain key macroeconomic tasks, such as sustained increase in savings and investment, need to be performed, and one crucial area of action is to accelerate growth in the manufacturing sector. The need for manufacturing-led growth in India in the coming years has been emphasised also by Ghose (2015, 2016) and Mitra (2015).
- 2.
To accelerate growth in the manufacturing sector in India, the greatest need in the next decade or two is to induce a major expansion in labour using export-oriented manufacturing, similar to what has been done by China and other East Asian countries over the past 30–40 years (Mohan 2019, p. 41).
- 3.
In the context of the “Make in India” programme, Veeramani and Dhir (2017) observe that two groups of industries have the potential for export growth and employment generation. The first group is traditional unskilled-labour-intensive manufactured products, such as textiles, clothing, footwear and toys. The other group consists of products with an internationally fragmented manufacturing process where the production activity is controlled mainly by multinational enterprises and their global production networks. Evidently, Veeramani and Dhir (2017) recognise the importance of the wearing apparel industry as a potential thrust area for future economic development. It should be added here that the focus of the “Make in India” programme is on 25 sectors, and one of these 25 sectors is textiles and clothing.
- 4.
To substantiate this assertion, some statistics and estimates may be given. According to the estimates presented in a paper of the Export–Import Bank of India (2016), total employment (direct plus indirect) supported by exports of readymade garments and miscellaneous textile products was about 8.2 million in 2012–13. The corresponding figures on employment generation supported by exports were only 0.09 million for petroleum products, 0.31 million for drugs and medicines and 0.05 million for organic and inorganic chemicals; these are important items of exports from India. In 2012–13, the share of machinery in total exports was 5.2%, and the employment supported by machinery exports was 1.5 million; the share of transport equipment in total exports was 3.4%, and the employment supported by exports was 1.14 million. By contrast, the share of readymade garments and miscellaneous textile products in total exports was 3.94% in 2012–13 and employment supported by exports was much higher at 8.2 million. It should be mentioned here that the potential for employment generation is higher also in the exports of gems and jewellery. According to the aforementioned estimates of the Export–Import Bank of India, the share of gems and jewellery in total exports in 2012–13 was 10.22%, and the total employment supported by exports of gems and jewellery in that year was about 4.9 million. It needs to be pointed out, however, that compared to readymade garments, gems and jewellery exports have greater dependence on imports. Thus, the domestic value-added content in gems and jewellery exports is smaller than that in exports of readymade garments. According to the estimates of Goldar et al. (2017), in the case of readymade garments, the domestic value-added content in exports in 2007–08 was 84%, and in the case of gems and jewellery it was 63%.
- 5.
Veeramani and Dhir (2016) examine the export performance of India in unskilled-labour-intensive manufactured products (which include textiles and wearing apparel) in a comparative perspective using a gravity model, which is estimated on the basis of data for the year 2011. They compare India with China and several other developing countries in South Asia and South-East Asia. Based on the results of their analysis, they conclude that India has been an underperformer. They hold the opinion that flexible labour market regulations and improved infrastructure facilities will help in enhancing the growth of labour-intensive manufacturing in India.
- 6.
The ASI provides data on the share in products and by-products directly exported by plants, hereafter referred to as the export share in production or export intensity. Such data are available only from 2008–09 onwards. Hence, the analysis based on plant-level data presented in Sect. 16.3 and that presented subsequently in the paper are confined to the period from 2008–09 onwards.
- 7.
That labour market rigidities may be constraining the growth performance of labour-intensive export-oriented industries in India is recognised by Veeramani and Dhir (2016). How labour market conditions make a difference to the performance of organised sector plants in textiles and wearing apparel industry in India is studied by Ahluwalia et al. (2018). They employ a difference-in-difference estimation strategy. There used to be quota restrictions on the exports of apparel and textile products by developing countries to the developed world; these restrictions were abolished in 2005. Ahluwalia et al. (2018) consider the impact of the abolition and find that it led to significant gains in employment and wages in textile- and apparel-producing plants in the organised sector of the industry in Indian states that had more flexible labour market regulations as compared to the states with inflexible labour market regulations.
- 8.
On the question whether the exchange rate channel is effective in supporting exports, views differ. Doubts have been expressed on whether substantial gains in India’s exports can accrue from exchange rate depreciation. Some studies find that a depreciation in the exchange rate does not positively affect India’s manufactured exports (see, e.g., Bhanumurthy and Sharma 2013), but several empirical studies find that the exchange rate significantly impacted India’s exports, depreciation having a favourable effect and appreciation having an unfavourable effect (see, e.g., Veeramani 2008; Cheung and Sengupta 2013; Kapur and Mohan 2014; Chinoy and Jain 2019). The need for a “realistic and competitive” exchange rate for India has been emphasised by Mohan (2019) as a requirement for attaining 8–9% annual growth rate in the Indian economy in the years ahead.
- 9.
Estimates of the total factor productivity (TFP) of the plants have been made for the period from 1998–99 to 2012–13. Data on export intensity are available from 2008–09 onwards. Hence, the analysis is confined to the period from 2008–09 to 2012–13.
- 10.
The value of material input directly imported by the plants is divided by total materials used (indigenously procured and imported).
- 11.
The value of manufacturing services purchased is normalised by dividing it by the value of fixed capital.
- 12.
Considering the distribution of plant-year observations (for wearing apparel industry) in terms of the real value of fixed capital, the 25th percentile is considered, and plants with real fixed capital stock below the 25th percentile are treated as small plants. Accordingly, a dummy variable is formed and used.
- 13.
The dummy variables for plants located in states with inflexible labour market regulations have been formed using the list of states with flexible, inflexible and neutral labour market regulations provided in and used by Ahluwalia et al. (2018).
- 14.
The gross value-added (GVA) has been deflated by the wholesale price index (WPI) for wearing apparel.
- 15.
A blanket deflation procedure has been used for deriving the estimates of fixed capital stock at constant prices rather than using the perpetual inventory method, which would have been more appropriate. The ASI-reported data on fixed capital stock (net fixed assets, closing figure for the year) for various plant-year observations have been deflated by the implicit deflator for gross fixed capital formation for manufacturing obtained from the National Accounts Statistics.
- 16.
Data on exports (in US$) have been taken from the WITS database (https://wits.worldbank.org/about_wits.html).
- 17.
Data on the REER (base 2007 = 100) have been taken from a data set on REER published by Bruegel (https://bruegel.org/publications/datasets/real-effective-exchange-rates-for-178-countries-a-new-database/). For details, see Darvas (2012a, b).
- 18.
Data on India’s exports of readymade garments have been taken from the Handbook of Statistics on Indian Economy, Reserve Bank of India, and data on world exports on wearing apparel have been taken from the WITS database. It should be pointed out that while data on India’s exports are presented for the financial year (April–March) the data on world exports are presented for the calendar year.
- 19.
The REER index with base 2007 (= 100)—the previously mentioned data set on REER published by Bruegel—is used. India’s REER has been divided by a weighted average of the REER indices for Bangladesh, Cambodia, China, Hong Kong, Indonesia, Malaysia, Pakistan, Sri Lanka, Thailand and Vietnam to get a measure of R_REER. The weights are the exports of clothing from these countries during 2012 to 2017.
- 20.
The series on value of output of wearing apparel industry (Code 18 of NIC 1998/2004) has been taken from the database prepared by the Economic and Political Weekly Research Foundation (EPWRF) using ASI data. It should be noted that production data relate only to the organised segment of the industry whereas data on exports cover both the organised and unorganised segments.
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Goldar, B., Parida, Y. (2021). Productivity, Competitiveness and Export Performance: A Plant-Level Analysis of India’s Wearing Apparel Industry. In: Bathla, S., Kannan, E. (eds) Agro and Food Processing Industry in India. India Studies in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-15-9468-7_16
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