Export Subsidies in India's textile sector

As mentioned in an earlier post, countries follow export-oriented or import-substituting industrialisation. India has followed a mix of both over the years and has been more towards the former in recent years, due to tariff reduction commitments as a result of multi-lateral trade negotiations amidst other free trade agreements, etc. The main policy of export incentivisation is just subsidizing exports. This is prominent in key sectors such as textiles. However, World Trade Organization (WTO) recommends all members to phase out their export subsidies. 

Now our policy-makers face a dilemma. Should we just remove them all abiding by WTO commitments or keep protecting the exporters? While this may render the export-oriented industries susceptible to tighter competition in their import markets, productivity improvements could help offset such disadvantages. More specifically, the money saved by the government by cutting subsidies can be used to increase productivity by better infrastructure, etc. A paper I wrote with Vasundhara Rungta, which is forthcoming in the journal Margin, explores the interaction between these two different aspects to evaluate the economy-wide impact of the export subsidy reforms and productivity improvements in Indian textile and clothing sector. 

Our analysis stands on various policy simulations applying the general equilibrium model of the Global Trade Analysis Project. The welfare impacts of the removal of the Indian textile and clothing subsidies in millions of US dollars shows that India is expected to encounter a loss of about 71.5 million US dollars, while the other Asian countries may gain about 218 million US dollars. In a different scenario, we simulate the impact of a complete phase out of subsidies provided to the textile and clothing industry of India and a simultaneous increase in total factor productivity growth to 3.5 %. This leads to a net positive welfare change! We conclude that merely removing subsidies is not enough as the policy makers often worry. Investments in total factor productivity should come about simultaneously, probably by employing the surplus funds from saved subsidy payments into areas like Research and Development (R&D) and infrastructure, in enhancing total factor productivity. This conclusion may be qualitatively generalised for any sector in the world which is examined for export subsidy reforms, but similar economy-wide studies are recommended for specific cases.

China's rise and its effect on labor in developed countries

Policy-makers in the developed countries often worry about the perceived damage to the labor market caused by the rise of China and India. In this new paper, forthcoming in Economic Modelling, it is shown that such an impression can be visible when we look at the aggregated data of workers, but the reality is that a major section of workers in the developed countries get benefited by the rise of China. Unskilled workers, as an aggregate entity lose in terms of wages and employment, but 54% of their categories actually gain, while the loss is not high for the remaining 46% as well!

Here is an extract from this paper's summary:

This paper examines the impacts of growth in China’s economy and trade on the skill premium of labor in developed countries. We utilize a unique global dataset that disaggregates workers by occupations to identify impacts across labor categories with different skill sets, complementing the widely used GTAP Data Base in the CGE framework offered by the GTAP model. To study the impacts of China’s fast-paced growth, we model the counterfactual, i.e., what if China grew and opened at a more modest rate; we then compare this baseline with China’s actual growth. Results indicate that a strong rise in manufacturing exports from China to the US impacts output and employment in the US. The US shifts its production away from light manufacturing sectors to more service-oriented sectors that also tend to engage higher skilled labor. There is a small decrease in the real wages of unskilled labor and a rise in the real wages of skilled labor. Interestingly, not all categories of unskilled labor lose, rather those that are more directly linked with manufacturing sectors are impacted; unskilled ‘service and shop workers’ and the unskilled ‘agricultural workers, machine operators, assemblers, craft workers, and others’ observe a small decline in real wages, while the impact on unskilled ‘clerks’ is insignificant. For all categories of skilled workers, there is an increase in real wages primarily driven by the shift in production to services and high-skilled labor intensive categories, resulting in the rising skill premium. Hence disaggregating the labor data provides greater depth on the understanding of the differential impacts on domestic workers resulting from trade, and thereby to guide policy how these differential impacts can be smoothed through redistribution of benefits. Consistent with other study findings, there is a positive impact on overall growth and welfare in the US, EU and Australasia.  

Understanding Policy Impacts and Supply Chains for Business Intelligence

Today's world is full of enormously inter-linked countries and even businesses. Therefore, business planning decisions should take into account the linkages between different business segments as well as countries. Government policies also affect businesses not only in the home country but also elsewhere. However, typically business analysts who work on forecasts and planning do not account for changes in terms of policies and supply chains in their countries and elsewhere. 

 

Let's take the example of cotton subsidies in the US. Although this is more debated in the US in the context of the debt ceiling issue than anywhere else, any decision on it has far-reaching implications on textile industries in India and other countries. Similar things may be said of what happens to policies influencing resources in resource-rich countries.

Therefore, we need a simple framework that marries data methods employed in economic policy modeling with day-to-day business intelligence. This framework could employ publicly available rich information on linkages between countries and business segments/sectors to evaluate the market changes that can arise from major global and local policies, such as the GTAP framework. This has immense potential to facilitate a scientific approach to handle future uncertainties involved in the business intelligence.

Trade Policy for the Indian Auto Industry

We all want all our industries to flourish despite the increasing global competition. How do we do this? Well, there are two ways. With little protection of the domestic industry, it may learn how to outperform its competitors from abroad in the domestic market. This may probably ensure a structural development of competitiveness in the industry eventually, helping the domestic industry grow up as a global player. This is the crux of export-oriented industrialization. This model had been followed in the past by the countries like South Korea, with great success.

Another way, which has been conventionally popular among the Indian trade policy-makers, is to protect the domestic industry heavily by imposing huge tariffs on imports. Thus, the domestic market is dominated by products from domestic industries as the imports are rendered too expensive. In a country where imports are dominant, this policy would tend to substitute the imports with domestic production. This is what we economists call import-substitution, which is usually implemented when there is an 'infant-industry' condition, whereby the domestic industry is too uncompetitive to face global players.

On the face of it, 'import-substitution' looks like a sensible thing to do: imagine how many Chinese motorcycles would flood the Indian market if we reduce the protection for Indian motorcycle industry. Roughly, our tariffs on Chinese motorcycles are about 50%, while they may be about 50% cheaper than Indian motorcycles. But, reducing the tariffs gradually would force the Indian manufacturers to somehow reduce the costs by increased efficiency, better technologies, research and development, reduced wastage, etc. This is is not a mundane statement, but is a well-researched conclusion arrived at using field-surveys and econometric analysis in an ICRIER study sponsored by the Govt of India.

A simple evidence exists in the trade policy structure of Indian auto industry. Auto-components sector, which primarily produces all types of parts required for the industry, has tariffs close to 8%, which has been systematically reduced over the years and this sector has expanded phenomenally, especially in terms of exports. Automobiles and motorcycles segment that has enjoyed immense protection has not grown so much in terms of exports, on the other hand.

This raises the important question of how to deal with diversity in the tariffs within an industry like automotives in India. Often while conducting research on trade policy for framing research-backed arguments in the trade negotiations such as WTO, one has two different alternative approaches to adopt. Firstly, as most of the consultancy organizations and governmental policy researchers do, one can consider all the hundreds of sub-sectors in the auto industry and do some impact-analysis. This approach misses several important points, such as the inter-linkages in the economy and overall welfare implications. For example, such analyses can seldom deal with the fact that auto industry depends on steel and plastics and they can seldom quantify how many dollars will Indian public at large lose by cutting some tariffs.

A second approach, adopted by academicians and some governmental researchers, is the Input-Output based approach or the Computable General Equilibrium approach. Here, one has the opportunity to consider inter-sectoral and international linkages and compute the approximate welfare measures of the tariff policies. These models are understandably complex and data-demanding, so they typically deal with aggregate data. For example, GTAP Data Base, which is prominently used in such simulations, gives Motor Vehicles and Equipments as a sector, which consists of the dozens of sub-sectors within the auto industry.

So both approaches have their shortcomings. We developed a model that would marry both approaches so that we can get the best of both worlds to analyze the issues in the trade policy of Indian auto industry. While our conclusions pointed out the better performance of this model, one of the few lessons for Indian trade policy makers here was this: slowly but steadily, reduce the tariffs in the automobiles/motorcycles. Steadily - because the customer stands to gain from the lower prices and the producer may become more cost-competitive in due course. Slowly - because drastic reduction of tariffs will perhaps wipe out the Indian industry due to the flooding of imports from countries like China. Further the extent of reduction should be specific to the sub-sectors, based on their competitiveness, size, etc. - something to investigate in future. Is'nt it amazing to see such simple and practical policy measures emerging from extremely complicated models and data?

Indian Textile Sector: Fiscal Structure and Changes in Commodity-mix

Generally, it is expected that changes in fiscal structure have a direct impact on prices of commodities, which gets translated into changes in demand and hence a change in the commodity-mix. A close look at fiscal structure and consumption behaviour of textiles in India helps us understand the complexities involved in this economic phenomenon and the policy lessons arising from them. Textile sector may broadly be classified as natural/conventional fibres (NF) and man-made/unconventional fibres (MMF). Decades ago, the excise and customs duties relevant for the MMF were set much higher than NF and this trend has been continuing till today, supposedly to protect the declining conventional sectors. However, the recent Indian budgets have been reducing the gap gradually. Excise duties for MMF are still quite high: 8-16%. This analysis will point out what more is needed in this direction and why.

Textile consumption contributing to about 6-7% of an average Indian’s consumption basket (calculations based on NSS 60^th Round, 2005-06), by itself is vital for enhancing the economy, in addition to the fact that textile sector accounts for a major part of employment, GDP and exports of India. Of late, textile sector is becoming demand-constrained domestically, though the external constraint is far less than the MFA quota regime. Given these factors, enhancing domestic textile consumption, as a whole, is an important policy outcome.

The basic premise behind high excise duties for MMF is that without them, low prices of MMF products might destruct the NF market, owing to substitution. However, an empirical analysis using a huge reliable household-level survey data on Indian textile purchases from 1994 to 2003, proves that demand for MMF is more elastic to its own price and also that MMFs and NFs do not any more substitute each other, as seen from their negligible cross-price elasticities, which reflect the extent to which the demand of NF falls with a fall in price of MMF. So, a fiscal-measure-induced fall in MMF prices will lead to a greater expansion in MMF demand than the one in NF prices. What this means to a policy-maker is: If you decrease the excise/customs duties for MMFs, you are not harming NFs because they are not substituted and in fact, you are facilitating an expansion of the textile consumption, which is in the interest of the entire textile industry and economy as a whole. This has been one step that is almost never missed by any Finance Minister of India in the Annual Budget. Perhaps this is a testimony to sound research-backed policy-making in India?

Changing Demography and Indian Steel Industry

India is the seventh largest steel producer in the world, producing 42.64 million metric tons of finished carbon steel in 2005-06. Its domestic consumption stood at over 38 million tons in 2005-06, holding sixth position in the world. Indian steel exports are mainly to the USA, EU and South East and East Asia. Indian steel production has been growing for the past 15 years at a rate of 7% per annum and is projected to grow at a faster pace. India exported over 5 million tons of steel and imported nearly 4.7 million tons in 2005-06.

In the meantime, various demographic changes are taking place in India. Rural population in India in 1991 is thrice that in 1901, while urban population in 1991 is nine times that in 1901. About 400 million Indians comprise the working population in 2001 as against 314 million in 1991. At the same time, number of enterprises and employment have grown more rapidly in rural areas, as inferred from Economic Census, 2005. India has the highest youth population (Aged 15-35 years) in the world. People aged between 15 and 59, who have the potential to contribute to the economic activity, comprise around 60% of Indian population and this is likely to increase in future. Further, the middle and higher income-classes have been expanding in India in the recent years and are expected to retain the momentum, thanks to increased economic activities in the country.

The number of people living in urban areas has risen to 27.8% in 2001 from 25.7% in 1991. Urban population in India is 285 Million, which is close to the US’ total population of 300 Million people. Further, the urban sector contributes 50-60% to GDP of India. Moreover, number of towns and cities in India has increased from 3891 in 1981 to 4378 in 2001. Number of migrants from rural to urban India has grown from 229 million in 1991 to 307 million in 2001.

All these trends point towards the fact that the country is at the threshold of a huge urbanization and consumption spree. This may lead to massive surge in steel demand along with urban construction and associated change in the lifestyle of the population. Facing such a steel demand surge, India has a recoverable reserve of 13460 million tons of iron-ore. However, reduced iron ore exports from India have grown from a mere 2 million Kgs (Rs. 16.6 Million) in 200-01 to 20 million Kgs (Rs 233.7 Million) in 2005-06, despite fluctuations over years, necessitating an import of 2.48 million Kgs of pig iron. On the other hand, iron-ore mining leases might not be given to the new steel cpacities in many Indian states. Further, tribal people need to relocated for the expansion of minig activities in the Eastern states. All these socio-economic and demographic aspects affect both supply and demand of Indian steel industry. This needs to be scrutinized further by both the industry and the government for any policy or strategic decision.