Competitiveness of Indian garment exports

While garment exports have registered impressive growth rates relative to the rest of manufactured exports from India, as we saw in an earlier section, India’s relative performance vis a vis its competing nations have not been too well. India, falls under ring 3 along with a few other Asian peripheral economies. In this section, based on existing studies and new computations, we seek to measure the competitiveness of India’s exports. With the withdrawal of quota and price restrictions from 2005, India, despite having unrestrained access to global markets, may face tougher competition from similar countries seeking to expand their market shares. Hence, it is imperative that measures are taken to meet the possible increase in competition.

Competitiveness, in existing studies, has been measured primarily by a comparison of market shares (Chatterji and Mohan; Exim Bank of India 1995; Ramaswamy and Gereffi 1998). Alternately, as an input measure, labour costs corrected for labour productivity can be used. However, given the high presence of production in the informal sector, data on labour use is insufficient to use. Further, this measure is also difficult to be used as a comparative measure given the impact of exchange rates on wage costs. Given the importance of non-price factors like quality in influencing the competitiveness of garments, unit value realisation may be a better indicator as a measure of competitiveness. This measure, once again, is problematic given the highly fragmented nature of the apparel market. Higher unit values may probably indicate a foothold in a different market segment rather than competition in a similar market. Nevertheless, higher unit values indicate an ability to upgrade, which would be a critical factor in sustaining or improving competitiveness over time. Lastly, given the importance of many non-price factors like quick response, quality of fabric and processing, no single indicator can reflect the extent of competitiveness of Indian garment exports. Consequently, in this section, we draw upon a multitude of indicators to understand this dimension of Indian apparel exports.

At the three digit level (Table A3.4 in Annex) garment categories, non-knit women’s outerwear, non-knit undergarments and knitted undergarments constitute the biggest shares and together account for more than 70 percent of exports from India. Even within these categories, specific items like women’s blouses and men’s shirts dominate the export basket.

When compared to the market shares of these and other product categories of Indian exports against few of its competitors (Table A3.5 in Annex), India’s competitive edge is quite

mixed. This comparison is confined to apparel exports to the USA, the single largest market for Indian exports.

As can be seen in Table A3.5 in Annex, China and Hong Kong, in terms of market shares, appear to pose the strongest competition. Together, they have a higher market share in the US than India has in thirteen out of the seventeen product categories listed in the table.

In fact, China alone has a higher market share than India has in ten of the product categories.

Further, in quite a few categories, other countries like Indonesia, Pakistan, Sri Lanka and Bangladesh too have higher market shares than India. However, by and large, there seems to be a specialisation among the competing countries with each holding higher market shares in a few specific categories. On the other hand, China has penetrated significantly in most of the product categories. This leads us to infer that a region-wise specialisation in specific niches may enable the countries to expand their shares without undermining that of other countries. However, the market shares may also be influenced by the quota restrictions that prevent countries from expanding their exports beyond a point. Hence, the unit values of these product categories exported across these countries are examined in Table 3.8.

Table 3.8 indicates that unit values of garments exported from Hong Kong are higher than that of most other countries indicating that they compete in a different, relatively upmarket segment as compared to the other countries. Thus, unit values may not indicate the level of competitiveness too accurately as even at the four digit level. Garments are a highly differentiated category, in terms of design and quality and hence, price. However, we do obtain a measure of competitiveness when we relate India’s unit values to that of the average for all competing countries. It appears that India has an above average unit value in two of the six product categories though Indonesia has a higher unit value in both these categories and China in all of them. Thus, China appears to offer the biggest source of competition to India in the post-MFA era.

Table 3.8 US imports from selected countries by MFA categories, 1996 (unit values)

Category description Average India Bangladesh Pakistan Indonesia Hong China Kong

Cotton men’s knit shirts 8.35 10.35 8.92 9.65 16.00 20.83 16.42 Cotton men’s non-knit shirts 4.13 4.35 3.05 2.73 4.75 5.67 4.69 Cotton women’s non-knit shirts 5.57 4.42 3.86 3.74 5.63 7.71 7.85

Cotton other manufacturers 0.75 0.57 0.42 0.52 0.57 1.29 1.07

Cotton men’s trousers 5.19 4.16 4.21 3.76 5.59 6.70 5.99

Cotton women’s trousers 4.82 4.76 3.81 2.76 5.77 6.40 6.13

Note: Average for all countries

Source: Ramaswami and Gereffi (1998, 127)

As a step towards further refining the measures of competitiveness, we next calculate the revealed comparative advantage (RCA) in some of the product categories of Indian garment exports and compare them with that of China and Indonesia. It is well known that

the comparative advantage of a country is influenced by a number of factors, which may be broadly classified as price and non price factors. It is however, difficult to obtain information on these factors across products and countries. For example, sufficient information to make inter-country cost comparisons is not available. Thus Balassa (1965) suggested that it is sufficient to provide information on Revealed Comparative Advantage. The RCA, a well known measure, is simply a ratio of the industry A’s export share to total merchandise export from that country to the export share of the world exports of A to total world exports.

The RCA is thus a ratio of two shares and is expressed as:

RCA= (India’s export of product A/India’s total merchandise export)/(World export of product A/World’s total merchandise exports).

It has definite advantages over use of market share as an indicator. The simple market share is very sensitive to the size of the country. To illustrate, China obviously will have larger share in the world exports as compared to say, Nepal. Such a large market share need not be related to comparative advantage per se as the larger share may be reflecting the larger size of China. But, RCA is a standardised measure and using this measure it is possible to find that Nepal records a comparative advantage despite its low share in the world market. To be explicit, one cannot say anything about comparative advantage on the basis of simple shares. But, if RCA is greater than 1, one can make a definite statement that the country has a comparative advantage. Similarly, if the RCA is less than 1, one can make a definite statement that the country has a comparative disadvantage.

To add, unit values are generally not used as a measure of comparative advantage.

Rather, it is used as a measure of quality of the product. This measure as an indicator of quality also is not free from flaws. It is very sensitive to the level of aggregation used. At higher levels of aggregation, it is not an accurate measure as the units of measurement may vary at specific product level. Thus, differences in unit value need not capture quality.

Rather, it may arise as a result of the particular aggregation followed.

The use of ‘revealed comparative advantage’ offers other advantages as well. Its basic thrust is to `measure’ the patterns of comparative advantage as are revealed by the observed trade flows. The Hecksher-Ohlin-Samuelson theory tries to explain trade flows in terms of factor intensities and factor endowments. In other theories of comparative advantage, there are propositions about the relationship between some other determinants of trade flows and the actual trade flows. For example, such determinants include technology gap (as in technology gap theory), economies of scale (Dreze, 1960), and domestic demand (Linder, 1961) etc. In the approach of RCA, there is an explicit recognition to the effect that the observed pattern of comparative advantage is the result of multiplicity of factors, which encompass all the standard theories of comparative advantage.

The main advantage of the RCA measure over simple share and unit value is clear from the above discussion. That is, the RCA measure is very much derived from theory, whereas the uses of simple shares and unit values do not have any theoretical rationale. For calculation

of the RCA, we use ‘India Trades’, an electronic database from the Centre for Monitoring Indian Economy (CMIE), as the source. This database contains detailed information on India’s trade as well as world trade. While information on India’s trade is sourced from the Directorate General of Commercial Intelligence and Statistics (DGCI&S)), that on World trade is sourced from the Statistics Department of the United Nations (UN).

Earlier studies like that by Chatterji and Mohan (1993) and Ramaswamy and Gereffi (1998) too use the UN data. They are however, based on SITC Rev-2, wherein SITC 84 represents garments. As per an understanding with the UN, all individual countries are now supposed to adopt a new commodity classification system called Harmonised Commodity Description and Coding System. In fact, decision in this regard was taken long back, but many countries are yet to adopt the new commodity classification system. Thus, the UN in its published sources has been reporting the data on the basis of the earlier classification system (SITC-Rev 2). However, the data in India Trades is based on the Harmonised System, wherein 61 and 62 represent garments¹⁶. One limitation of the world trade data in India Trades is that it is available for only one year (1995). The CMIE does not give any explicit reason why the data is confined to only 1995. It is possibly due to the new commodity classification system followed. Thus, while data for the years prior to 1995 are based on the earlier classification, that for 1995 and beyond are based on the new system.

Usually, in its published sources, the UN covers data on all major countries including South Asian countries of Bangladesh, SriLanka, and Pakistan. In India Trades however, these countries are not covered. Again, the CMIE has not given reasons for not covering these countries. Yet again, the likely reason could be that these countries have not yet started providing data on the basis of the Harmonised System. All the countries, included in the database are probably the ones, which actually started providing data on the basis of the Harmonised system.

To illustrate how RCA indices can be interpreted, India’s exports of clothing account for around 13 to 14 percent of merchandise exports and the world exports of clothing is a mere 3 percent of world merchandise exports. Then RCA is equal to 14/3 = 4.66. An RCA of unity would imply ‘normal’ export performance. An RCA of more than unity is usually taken as an indicator of comparative advantage and an RCA of less than unity imply comparative disadvantage. The calculated values for India and two of its primary competitors, China and Indonesia are given in Table A3.7 in Annex for product categories at the 4-digit level. It may be seen from the table that the three countries indeed record comparative advantages in most products. China has a comparative advantage in 32 out of 34 product categories. The similar figure for India is 25 while that for Indonesia is 29. Based on the above calculations, the products can be categorised in terms of which of the three countries have the highest comparative advantage (Table A3.8 in Annex).

16 Also, note that the DGCI&S has been following the Harmonised System since 1987.

Also, using the RCA, one can draw certain inferences on structural characteristics. For example, if R1 denotes the vector of RCAs for country 1 and R2 is the similar vector for country 2, then a rank correlation between the two vectors (R1 R2), indicates the similarity or dissimilarity in the patterns of RCA between the two countries. Here, we have undertaken such an exercise with regard to India, Indonesia and China (Table 3.9).

Table 3.9: Rank correlation coefficients of the RCA indices for pairs of countries (garments)

Pairs of countries Rank correlation Karl Pearson correlation

India and China 0.119 -0.095

India and Indonesia -0.050 -0.025

China and Indonesia 0.230 0.114

Note: None of the correlation is statistically significant.

Source: Calculated from ‘India Trades’, CMIE.

The small values of coefficients indicate that there are no similarities in the pattern of revealed comparative advantage between the pairs of countries. It may, however, be noted that the similarity in patterns of comparative advantage is relatively higher for China vs.

Indonesia as compared to other pairs of countries. This exercise once again indicates that the line of specialisation among the three countries may enable them to compete in the global market without eating into the market shares of other countries.

Another indicator of competitiveness is that of labour productivity. The Global Competitiveness Report 1999 gives a number of competitiveness indicators of countries in terms of labour. One such indicator is the wage adjusted for productivity differences. It is found that wage adjusted for productivity is one of the highest in India. While the rank of India is extremely low at 51 out of 59 countries, that of China and Indonesia is 5 and 45 respectively. In fact, wages are found to be very low for the country’s level of productivity in China. Further, China too ranks pretty favourably as compared to India in terms of flexible hiring and firing practices despite better educational levels. Though these indicators are only representative of the entire workforce and may not hold true for the garment sector, it is quite likely that some of the differences would favour China even within the garment sector. In fact, though the data on wage rates would indicate that Indian wage rates are not too different from other peripheral economies, it is found that the cost per standard minute in India is higher than that of Indonesia, Thailand and China (Majumdar 1996).

Its performance when placed against that of other peripheral economies is poor. Even in the leading categories, other peripheral economies have a bigger market share than India.

Various reasons have been cited for the relatively poor performance of the Indian garment sector in the world market. One, garment exports from India is largely confined to cotton garments and hence confined to only one segment of the world apparel market. Two, and more importantly, it is said that government policies have created distortions in the industrial

structure that prevent Indian producers from competing on equal terms with other low-income regions. In the next, section, the role of government policy in influencing the prospects for Indian garment exports is analyzed through a case study of Tiruppur Knitwear Industry.

5. Labour and Indian garment exports: a case study of Tiruppur knitwear