Import Export

3.6.3. Preference for Imports: Except where it is entirely unavoidable, Indian administrators (whether in the public or in the private sector) fear to try local designs. They consider that well-worn imported designs alone are risk free. As a result, Indian designs face a Catch-22 situation: They will not be accepted unless they have been in use, and they cannot be in use until they are tried out! So, except when foreign technology is not available at all (as in defence, atomic energy and space), Indian designs do not get a chance to prove their worth. Private industry too is no different. They want swadeshi only for manufacturing operations but not for technology. For several reasons, that is a bad bargain. First, there is an opportunity cost − the Indian design might be superior. Second, imported designs too bear considerable risk. Precisely because they are well worn, they are liable to be obsolete, and not saleable in competitive foreign markets. Third, the policy gives ample scope for foreign firms to form cartels and overcharge. Fourth, there is a strategic risk. Several times in the past, United States (and other Western countries) have denied valuable spares for equipment already sold and have even reneged on committed contracts. 3.6.5. Overcharging by Foreign Suppliers:Predatory MNCs often trade on India’s weakness for imports and attempt to destroy any Indian technology that can prove a threat to their own hegemony. Such predatory tactics are not rare; they are, in fact, quite common<span class="Footnote-0020Reference--Char"><sup>7. At the same time, MNCs are known to dump their goods at throwaway prices to under cut Indian technology with a view to kill competition. While it would be unfair to tar all foreign firms, it would be unwise not to pr4.2.2. Import Bias: In India, when an official’s work is evaluated, errors of commission and not errors of omission are questioned. There is no audit of opportunity costs − profits lost due to “risks not taken”. So, whenever an Indian comes up with a new idea, the cautious reaction is to ask, “Who has used it before?” and “If the idea is so good why is it nobody abroad has tried it out before?” As a corollary, officials search for current limitations, not look for future prospects. This culture breeds “Technology Checkers”, not “Technology Acceptors” as a matter of course. Table 4.2 brings out the difference between these technology acceptors and technology checkers. It is self-explanatory. 4.2.2. Import Bias: In India, when an official’s work is evaluated, errors of commission and not errors of omission are questioned. There is no audit of opportunity costs − profits lost due to “risks not taken”. So, whenever an Indian comes up with a new idea, the cautious reaction is to ask, “Who has used it before?” and “If the idea is so good why is it nobody abroad has tried it out before?” As a corollary, officials search for current limitations, not look for future prospects. This culture breeds “Technology Checkers”, not “Technology Acceptors” as a matter of course. Table 4.2 brings out the difference between these technology acceptors and technology checkers. It is self-explanatory. Table 4.2. A Comparison of Technology Checkers with Acceptors

Technology Checker Authorised to procure only that which is currently the best.

Technology Acceptor Will procure what will ultimately be the best.

Buys off the shelf products; does not support Tries out new designs; offers feedback to technology development. improve product performance. Has to live with it even if it proves unsatisfactory.

Gets products on trial; returns them cost free if unsatisfactory.

Pays full price up-front.

Pays only after the product is proved satisfactory.

Career in jeopardy if product proves unsatisfactory.

Career in jeopardy if an opportunity to induct a novel product is overlooked.

Cautious. Will take no risks and buy only proven products even if the technology is old.

Adventurous. Will experiment with new technology.

Will resist change

Will welcome change.

1.56 The adverse effect of the global financial crisis was also felt on the export sector, first, on account of the drying up of international financing and trade credit, followed by a fall in global demand. 1.57 During 2008-09, the growth in exports was robust till August 2008. However, in September 2008, export growth evinced a sharp dip and turned negative in October 2008 and remained negative till the end of the financial year. The continued decline in export growth was due to the recessionary trends in the developed markets where the demand had plummeted. For the year as a whole, the growth in merchandise exports during 2008-09 was 3.6 per cent in US dollar terms and 16.9 per cent in rupee fall in commodity prices due to the crisis was a sharp decline in headline inflation, as indicated by the WPI, which was 0.8 per cent at end-March 2009 on yearonyear basis for all commodities. However, there has been wide variation in the constituents of the Index, with WPI Food Index (combined) showing year-on-year inflation of 6.8 per cent at the same point of time, which has been a cause for concern. The average WPI inflation for 2008-09 was 8.4 per cent as against 4.7 per cent in 2007-08. There has

also been significant variation in inflation rate in terms of WPI and the Consumer Price Indices (CPIs). Inflation rate as per Consumer Price Index for Rural Labourers (CPI-RL) was 9.7 per cent and on CPI for Industrial Workers (CPI-IW) was 8 per cent as of end-March 2009. The average inflation on CPI-RL and CP IW for the year 2008-09 was 10.2 and 9.1

Chapter II Banking techniques for financing import-export operations Typical financial and banking techniques, designed for promoting international commercial transactions are very important in the whole of the international economic relations. Credit/financing products that companies can employ for their import-export activities can be classified as follows: - Depending on the period of time: short term (with a duration not longer than a year, except for loans for products with a long production cycle), medium termI(loans which are to be reimbursed between 1 and 5 years) and long term (loans granted for a duration of over 5 years); - Depending on the type of lending: operations recorded in balance sheet accounts (comprising of actually having the funds put at the company’s disposal) and operations external to the balance sheet (comprising of commiting to put funds at the company’s disposal, e.g.: granting endorsements, issuing scrisori de garantie bancara etc); - Depending on the mechanism of granting and carrying on: conventional/classical (creditul furnizor, creditul cumpărător, linia de credit etc) and unconventional/alternative (discounting, factoring, forfeiting, leasing etc). Short and very short term loan is employed when exporting low valued goods and with short production cycles. Viewed as a whole, short term loans for financing exports can be grouped as follows: - Loans granted for producing export goods, also called loans for prefinancing exports, are meant to satisfy the current or exceptional financing needs of

exporters (loans for financing temporary expenses and stocks, credite de trezorerie for goods with a long production cycle and loans typical for prefinancing exports – advance in current account, creditele în cont curent sau creditele în descoperit de cont, specialized pre-financing loans); - Loans granted to cover current activity needs on the duration between delivering goods and collecting its value, called export loans (advance payment for products’ papers, advance payment based on yielding debts, loans guaranteed by an insurance policy). Banking financing on medium and long term for import-export refers to loans whose reimbursement period is between 1 and 5 years and to loans over 5 years (long term), meant for the use of those companies who need to perform complex or extremely valuable import-export activities (creditul furnizor, creditul cumpărător), investments (linia de credit), different forms of industrial cooperation etc. Big Export Import Nation in Asia India might not be the rich nation, but it has a broad range of merchandise when it comes to export import trade. You will have no troubles in locating an overseas shipping firm to go the distance for you, and it would not cost you anything to submit obligation free queries with most of them. Prior to assigning your preferred international shipping firm with the responsibility of transporting your products to India, here are few common essentials and facts that you should be aware of. India supports global trade and welcomes people to import items into the country. The items must meet up a definite level of standards with regards to customs rules, successfully passing through labeling and quarantine requests. Packaging: Your international shipping firm can do this for you. This will be an additional expenditure, but consider of it as peace of mind that the job has been done appropriately. Tag your packages or shipment clearly with all forwarding particulars, terms of the contents and clear information of your destination and shipping agent. Customs quarantine and procedures: Some personal belongings and house hold goods being transported into the nation might be subject to a quarantine inspection. Products of particular interest mainly related with vegetation or earth. Although it might not stop you back at all, you should permit for up to a 14 day wait until the

customs officers are sure that your items are fit for way in. Few products are banned such as items made from animals on the rare species list. It is significant to verify these bans with either your global shipping firm or the Indian customs representatives Taxes, Duty as well as documentation mandatory, vary according to what you are transporting into the country. Some goods are duty free. Ports of entry: There are a various ports in India having vast shoreline spanning 7600 kilometers forming one of the biggest peninsulas on the earth. It is serviced by 12 main ports and 185 notified minor and transitional ports. Different ports deal with diverse merchandise and commodities. Kolkata (Port of Kolkata) West Bengal Paradip (Port of Paradip) Orissa Visakhapatnam (Port of Visakhapatnam) - Andhra Pradesh Chennai (Chennai Port) - Tamil Nadu Tuticorin (Tuticorin Port) - Tamil Nadu Cochin (Port of Kochi) - Kerala New Mangalore Port (Port of Mangalore) Karnataka Mormugao (Port of Mormugao) Goa Mumbai (Mumbai Harbour) Maharashtra J.N.P.T. (Jawaharlal Nehru Port) Maharashtra Ennore (Ennore Port) Chennai Kandla (Kandla Port) Gujarat When uncertain, get in touch with your shipping agent as they are in regular contact with the appropriate people and should be proficient to respond any queries you might have.

A component of the project is a paper which provides detailed comparisons of the Indian and Bangladesh statistics of bilateral trade47. One purpose of this study was to check whether there were any major discrepancies as to the general level of, and trends in, the total trade. Secondly, by making detailed comparisons, the object was to throw some light on the scale and scope of overinvoicing, underinvoicing, and similar practices, the likely products involved, and more broadly the potential scale of “technical smuggling”. There are a number of well known problems that have to be allowed for in making comparisons of this kind, before much can be inferred on these two questions. They include in particular: a. Freight and insurance which increase cif values above fob values b. Time lags between the fob stage in the exporting country and the cif stage in the importing country48 c. Differences in the reporting periods of the statistics d. Differing valuation practices at the Customs services

In the case of the India-Bangladesh trade, a priori, differences attributable to (a) and (b) should be minor relative to trade with countries outside the South Asia region, certainly for the land border trade49, and to a lesser extent for the sea trade, given the proximity of the Indian and Bangladesh ports. As regards (c), the Indian published trade statistics are for its April-March fiscal year, whereas the Bangladesh statistics are for its July-June fiscal year. As a spot check on how important this period difference might be, the Bangladesh import data over three years was reassembled into two Indian fiscal years, 1999/2000 and 2000/2001 for detailed comparison with Indian export data for the same periods. But (d) was a problem in 1999/2000 and before, in that during this period Bangladesh Customs was still using a predetermined list of “tariff values” as the base for Customs and other import duties on number of products, and these values, not invoice values, were entering the import statistics.50 However, except for petroleum it was decided that this was probably not on its own a major source of discrepancies between the Indian and the Bangladesh data, on the grounds that the committee that was deciding on the tariff values was looking at international prices each three months and was fixing tariff values that would not have differed very greatly from normal cif prices in international trade51. 47

A more serious problem than these is that the Bangladesh NBR trade database does not record “back to back L/C” imports i.e. imports of duty free intermediate inputs used by Bangladesh bonded warehouse exporters, and so these are recorded in the Indian export statistics but omitted altogether from the corresponding Bangladesh NBR import statistics. As India is an important supplier of these inputs-mainly textile yarns and fabrics for Bangladesh’s garment exporters-this is the source of large discrepancies between the Indian export statistics and Bangladesh’s import statistics, both in the aggregate and when disaggregated.

Fortunately payments under back-to-back LCs are recorded by Bangladesh Bank, although they are not disaggregated below 2-digit HS level and are not available before 1998/99. Even though the timing of these payments differs from Customs clearance times (which are the basis for the NBR import data), adding the totals to NBR’s total import data, gives a very approximate correspondence between the general level of the two sets of data for the years 1998/99 to 2002/03. Both statistics also indicate similar year to year changes during this period (Table 5.1 and Fig 5.1). However there is a major discrepancy in 2003/04, when the recorded total Bangladesh imports are only about 75% of the value of recorded Indian exports. More generally, in four of the six years, recorded Bangladesh imports are less than recorded Indian exports, and over the six years total recorded Bangladesh imports are $492 million (7.6%) less than total recorded Indian exports. Since the Indian exports are recorded fob and the Bangladesh imports are or should be recorded cif, these differences are the opposite of expectations, even though the overall freight and insurance cost may not be very great owing to the trade that goes by the land border crossings. It is also unlikely that these discrepancies can be explained by timing differences, since the Bangladesh statistical year (July 1-June 30) lags the Indian statistical year (April 1-March 31) by three months and would should pick up most shipments from India that

arrive in Bangladesh after the end of the Indian statistical year. Although data recording deficiencies and statistical errors may conceivably explain some of these differences, they are consistent with many reports of illegal practices at the Bangladesh Customs, especially at the Petrapole-Benapole land crossing, and with the large scale discrepancies between the Indian and Bangladesh data at product level, discussed below. 0

0

As regards the much smaller reverse trade from Bangladesh to India, the correspondence between Bangladesh’s aggregate export statistics and the Indian import statistics is fairly close. Table 5.2 and Fig 5.2 show comparisons for the 13 years 1991/92 to 2003/04. For these comparisons there is no problem of unrecorded imports, since duty free imports for exporters are recorded by Indian Customs and included in India’s import statistics, just as any other import. Differences between the statistics in individual years are substantial, including some years in which recorded imports in India are less than Bangladesh’s exports, but for this trade the fiscal year difference between Bangladesh and India increases the likelihood that this 01020304050607080901001992199319941995199619971998199920002001200220032004Bangladesh exports to IndiaIndian imports from Bangladesh$US millionFig 5.2 Comparison of Bangladesh export statistics and

Indian import statistics 1991/92-2003/04

34

will happen, and timing differences of a few large shipments could create large proportionate discrepancies owing to the low volume of the total trade. Over the whole period, the difference between aggregate Indian imports and aggregate Bangladesh exports has the expected positive sign, and the positive margins (3.4% between 1992 and 1998, 2.2% between 1999 and 2004) correspond to the expectation that cost of freight and insurance is low. This does not mean there was no undervaluation or misclassification to avoid import duties occurring on the Indian side, only that it was not so egregious as to show up in the aggregate import statistics. As well as comparing aggregates, the consultant study also compared the Indian export and

For this synthesis report, a brief further analysis was undertaken of the data for the 50 products (defined as HS 6-d level) with the largest absolute discrepancies in 2000/01. Textile products and a petroleum product were removed leaving 41 products altogether which accounted for about half the total recorded Indian exports (net of textile export Bangladesh in that year. The results are striking: 41 major non-textile products exported from India to Bangladesh in 200 (Indian fiscal year): Differences between Indian and Bangladesh trade statistics No of products (HS 6-digit) Value fob in Indian Value cif in Difference $US trade statistics $US Bangladesh trade million million statistics $US million All products 41 355 238 -117 Bangladesh value < Indian value 21 286 82 -203 Bangladesh value>Indian value 20 69 156 +87

Difference as Indian fob va

-33% -71% +126%

The following examples give a better feel for what may lie behind these aggregates. Other examples are given in the consultant study. For nearly all the products there were similar differences in the same direction during 1999/2000 Value fob in Indian trade statistics $US million

Value cif in Bangladesh trade statistics $US million

Products with Bdesh value< Indian value 070310 Onions & shallots 100630 Milled rice 270119 Other coal (bituminous) 879600 Chassis fitted with engines (for buses, cars, three wheelers and trucks) Product with Bdesh value> Indian value 401120 New tyres used on buses/lorries rim size>15 841182 Gas turbines 845522 Rolling millscold 870210 Motor vehicles, diesel engine public transport type (=buses?)

Difference $US million

Difference as % of In fob value

8.3

3.1

-5.2

-63

65.1 29.9

23.3 3.3

-41.8 -26.6

-64 -89

26.5

0.01

-26.5

-100

6.3

13.5

+7.3

+115

nil 1.4

24.0 12.0

+24.0 +10.6

+ +757

0.8

10.0

+9.2

+1150

References [1] Ahmed, Sadiq (2007) “India’s Long-term Growth Experience: Lessons and Prospects”. [2] Besedeš T. and T.J. Prusa (2006), “Ins, Outs, and the Duration Trade”, Canadian Journal of Economics, Vol. 39 (1), 266–295. [3] Centre for Monitoring Indian Economy (2006), India Trades Database [4] Cantwell, John. (1995), “The Globalization of Technology: What Remains of the Product Cycle Model?”, Cambridge Journal of Economics, vol. 19, 155-174. [5] Deardorff, Alan. V. (1984), “Testing Trade Theories and Predicting Trade Flows”, Chapter 10 in Handbook of International Economics, Vol I, Edited by R W Jones and P B Kenen, Elsevier Science Publishers B V [6] Feenstra, R.C., and A. K. Rose (2000), Putting Things in Order: Patterns of Trade Dynamics and Growth, The Review of Economics and Statistics, 82(3), 369-82. [7] Gagnon, J. E. and A. K. Rose. (1995), “Dynamic Persistence of Industry Trade Balances” How Pervasive is the Product Cycle?”, Oxford Economic Papers, vol. 47, 229248

[8] Grossman, G. M. and E. Helpman (1991), "Quality Ladders and Product Cycles," The Quarterly Journal of Economics, Vol. 106(2), 557-86. [9] Hall, R. E. and C. I. Jones (1999), “Why Do Some Countries Produce So Much More Output Per Worker Than Others?” The Quarterly Journal of Economics, Vol. 114 (1) 83116 [10] Kendall, M. and J. Dickinson (1990), Rank Correlation Methods, fifth edition, London [11] Krugman, P. (1979), "A Model of Innovation, Technology Transfer, and the World Distribution of Income," Journal of Political Economy, Vol. 87(2), 253-66. [12] Vernon, R. (1966), International Investment and International Trade in the Product Cycle”, The Quarterly Journal of Economics, Vol. 80(2), 190-207 [13] Xiang C. (2007) , “Product Cycles in U.S. Imports Data”, Purdue University

2. Methodology Our starting point is the open, static input-output model (see e.g. Miller and Blair, 1985, for an introduction), which is expressed as3 2 For

a rare exception, see Eskeland and Harrison (2003) who have examined - using data on US foreign direct investments - whether multinationals tend to flock to pollution havens in developing countries. 3 Input-output techniques have been widely applied to determine the role of international trade in environmental damage. See, for example, Fieleke (1974); Wright (1974); Antweiler (1996); Proops et al.

4 z = Az + y (1) where z is the k ﾗ1vector of gross output in each of the k commodities, A is the k ﾗk matrix of input coefficients, and y is the k ﾗ1 vector of final demands (including private and government consumption and investments, gross exports and inventory changes). The input coefficients ij a are obtained as ij ij j a = d / z , where ij d denotes the domestic intermediate deliveries in million rupees (mrs) of commodity i to industry j. So, the input coefficient ij a thus indicates the input in mrs of commodity i per mrs of output of commodity j. Assuming fixed input coefficients, for any exogenously specified final demand vector y ~ , the solution of the input-output model in (1) is given by ~z = (I − A)− 1~y = Ly~ , where L = (I − A)− 1 denotes the Leontief inverse. Its typical element ij l denotes the output of commodity i (in mrs), required per mrs of final demand for commodity j. The vector z ~ gives the domestic gross outputs that need to be produced to satisfy the final demands y ~ . The next step is to calculate how much input of fossil fuels is required to produce z~ , and therefore is required (directly and indirectly) to satisfy final demands y ~ . The fossil fuels are given by commodities 1 (coal and lignite) and 2 (crude petroleum and natural gas) – which shall be termed coal and oil for short. It is assumed that all the coal and oil are combusted whenever they are used as an intermediate input, generating CO2, SO2 and NOx emissions. It should be noted that it is not necessary that if, for example, the

industry that produces fertilizers uses inputs of crude oil, the actual combustion of crude oil takes place in that industry. The assumption is that somewhere in the entire production process that leads to a certain final demand (e.g. for fertilizers), all directly and indirectly required coal and oil is combusted. (1999); Lenzen (2001); Machado et al. (2001); Munksgaard and Pedersen (2001); and Kraines and Yoshida (2004).

5 Let us indicate the first two rows (corresponding to coal and oil) of the input coefficients matrix A by 1 a′ and 2 a′ , respectively.4 The jth element of the row vector 1 a′ then expresses the amount (in mrs) of domestically produced coal used as input for one mrs of output of commodity j. To find the input of coal that is actually combusted, we have to add the imported coal per mrs of output in industry j. Let this be denoted by the elements of the vector 1 b′ for coal and b′2 for oil. So, the vector 1 1 a′ + b′ gives the total amount of coal (in mrs) used as an input per mrs of output. Hence, the jth element of the vector (a b )L 1 1′ + ′ gives the input in mrs of coal (both domestically produced and imported) per mrs of final demand for product j. The inputs of the fossil fuels coal and oil (which are assumed to be combusted) in mrs have to be “converted” into the generation of emissions. The conversion factors have been estimated following the guidelines of the Intergovernmental Panel on Climate Change (IPCC). The amounts of coal and oil in mrs are “translated” first in million tons of oil equivalent (mtoe), which are then converted into million tons (mt) of emissions. For the empirical application in Section 4 we have used the input-output table for 1991/2 in current prices and the 1996/7 table in constant 1991/2 prices.5 In principle, the translation of mrs of coal into mtoe of coal should be the same over time, because the effects of price changes have been singled out. In the actual case, however, this does not hold exactly. It should be borne in mind, that the commodities in an input-output table are aggregates themselves, consisting of many sub-commodities for which no data are made available. The mix of sub-commodities within a single commodity may thus change over time. As a consequence, the mtoe/mrs ratio for a single commodity may differ across time, even if this ratio is constant for each sub-commodity. Recall that the first commodity covers coal and lignite and the second commodity includes crude petroleum and natural gas, so that changes in the mix do affect the translation. For coal (and lignite), for example, we find an mtoe/mrs ratio of 0.0026 in 1991/2. Next the mtoe of coal and lignite, for example, have to be converted into mt of CO2 emission. The carbon emission factor equals 0.55 mt of carbon per mtoe of coal and 4 We

adopt the usual convention that vectors are columns by definition, so that rows are obtained by transposition, indicated by a prime. 5 Because in India the statistical year starts on March 9, the tables are always indicated by two dates.

6 98% of the carbon is oxidized. The molecular weight of CO2 is 44 and that of C is 12, so that the molecular weight ratio equals 44/12 = 3.66 mt of CO2 per mt of C. Hence, the combustion of one mtoe of coal implies that 0.55 ﾗ0.98  ﾗ(44/12)  = 1.976 mt of CO2 are generated. Combining the two steps yields that the combustion of one mrs of coal generates 0.0026 ﾗ1.976  = 5.1376 ﾗ10  -3 mt of CO2. We denote this conversion factor by 1 c , where the subscript 1 indicates the combustion of coal. Subscript 2 is used for the

combustion of oil, and s and n are used for the generation of SO2 and NOx emissions (in mt), respectively. The conversion factors are given in Table 1

Let us assume that the world consists of two countries or regions, North and South. In the empirical application in Section 4, we use India for South and the rest of the world 6A

“hat” is used to indicate a diagonal matrix. For example, zˆ is the matrix with the elements of the vector z on its main diagonal and all other entries equal to zero.

7 (RoW) as North. In our examination of the pollution haven hypothesis, we will compare the current situation with the situation where trade has increased and calculate the effects in terms of CO2, SO2 and NOx emissions. It is assumed that both the imports and the exports are increased by the same amount of money, so that the balance of the current account remains unaffected. The vectors of changes in the exports and imports are denoted by ⊗e and ⊗m, respectively, and the indexes for North and South are N and S, respectively. So, N S ⊗e = ⊗ m indicates the changes in the exports of North and the imports of South, which are equal to each other in a two-country setting. Similarly, we have S N ⊗e = ⊗ m . Note that the total value of the changes in imports and exports is the same, so that the elements in the vectors have the same sum. That is, i S i i S i  (⊗e ) =  (⊗m ) . In Section 2, we have seen that the jth element of the vector [ (a b ) (a b )]L 1 1 1 2 2 2c ′ + ′ + c ′ + ′ indicates the emission of CO2 (in mt) that is required for the production of one mrs of final demand of commodity j, due to the combustion of coal and oil. Let us write this as N N ′ L for North and as S S ′ L for South. The increase in the exports of South implies that the emissions are raised by ( ) S S S ′ L ⊗e . The increase in imports by South implies that these goods are now no longer produced at home, which yields less emissions to the amount of ( ) S S S ′ L ⊗m . If we write S ⊗ for the extra emissions in South as caused by increased trade, we have S ⊗ = ( ) S S S S ′ L ⊗e − ⊗ m . If South is a developing country, the pollution haven hypothesis states that South is left worse off (in terms of pollution) by an increase in trade. That is, for South the total amount of emissions is larger than it was before, S ⊗ > 0. S⊗ being positive, however, is only one side of the story. Let N ⊗ = ( ) N N N N ′ L ⊗e − ⊗ m denote the extra emissions in North due to increased trade. In order for South to be a pollution haven, North must benefit (in environmental terms) from trade. That is, N ⊗ < 0. Because the exports of the one are the imports of the other, we may also write N ⊗ = − ′ (⊗ − ⊗ ) < 0 N N S S  L e m . At the world level, an increase of trade is beneficial if the total amount of extra emissions decreases. That is, if ⊗ S + N ⊗ =

( )( ) S S N N S S ′ L −  ′ L ⊗e − ⊗ m < 0. Note that if technology is the same in both countries, we have S N ′ =  ′ and S N L = L . As a consequence, the extra emissions at the world level ( S ⊗ + N⊗ ) are zero. This is not surprising because at the world level it doesn’t matter whether the products are produced in North or in South. This also implies that the gains (in terms of extra pollution) of the one are the losses of the other, i.e. S ⊗ = N− ⊗ . In general, technologies will be different and we have four possible outcomes. First, S ⊗ < 0 and N ⊗ < 0. In this case, both countries benefit from increased trade. It occurs if both countries export the products in which they have a comparative advantage from the viewpoint of their technology (i.e. trade is in line with the Ricardian theory). Second, S ⊗ > 0 and N ⊗ > 0. Both countries clearly lose from trade. North (South) exports products for which the production is polluting at home but relatively clean in South (North). This case of anti-Ricardian behavior is unlikely to occur and both countries would gain by a complete trade reversal (i.e. if, instead of importing a product, exactly the same amount were exported, and vice versa). Third, S ⊗ < 0 and N ⊗ >0 , in which case South gains from extra trade, whereas North loses. It depends on the sum ( S⊗ + N⊗ ), whether there are gains (<0) or losses (>0) at the world level. The fourth case reflects the pollution haven hypothesis, where North gains at the cost of South. That

is, ⊗ S

> 0 and ⊗ N

<0

hypothesis The causal relationship between exports and economic growth pertains to a fundamental question in economics what factors determine economic growth? From the viewpoint of economic policy, this is an important issue because if exports cause growth [export led growth (ELG) hypothesis], export promotion through policies such as export subsidies or exchange rate depreciation will increase growth. The substance of the neoclassical arguments underlying the export led growth hypothesis is that competition in international markets promotes scale economies and increases efficiency by concentrating resources in sectors in which the country has a comparative advantage. These positive externalities promote economic growth [see, for instance, Bhagwati (1978), Balassa (1978), Krueger (1978), Feder (1982)]. The reverse side of this argument that economic growth promotes export growth relies on the notion that gains in productivity give rise to comparative advantages in certain sectors that lead naturally to export growth. Also, countries with high growth rates and relatively low absorption rates must necessarily export the excess output

India is an interesting case study of the export economic growth relationship. It is difficult from the Indian experience to a priori assess the nature of this causal relationship. The trade sector constitutes a small section of the Indian economy and this seems to indicate a minor role for exports in economic development. However, it is important to recognize that the size of the export sector in India does not by itself exclude the possibility of export-led growth. Little, et al. (1993; p. 118) in a discussion of the development experience of LDCs point out that "The relationship between export performance and growth does not arise merely because exports are part of GDP. Except for a handful of countries, the value of exports was not a very high proportion of GDP even in 1988.... In the main, it appears that rapid export growth relieves a country in balance of payments difficulties from having to compress imports by import restrictions or deflationary action. It permits a more liberal trade regime with all the benefits associated with the exploitation of comparative advantage..... It also makes a country more creditworthy, while relief from a dominating concern with debt and the balance of payments permits authorities to pursue economic reforms outside the field of trade and payments". Up to the 1960s, India had followed an import substitution policy. However, the failure of import substitution as a viable industrial policy and the rapid escalation of import bills and balance of payments deficits in the late 1960s forced India to shift to an export oriented strategy. Recent economic reforms in India have largely accentuated this export orientation. A large empirical literature has re-examined the ELG hypothesis with mixed findings [see, for instance, Jung and Marshall (1985), Chow (1987), Hsiao (1987), Kwan and Cotsomitis (1991), Ahmad and Kwan (1991), Marin (1992), Oxley (1993), Fung et aI. (1994), Shan and Sun (1998) and Moosa (1999)]. The ELG hypothesis, as it pertains to India, has been examined by Nandi and Biswas (1991), Sharma and Dhakal (1994), Mallick (1996), and more recently by Dhawan and Biswal (1999), Nidugala (2000), and Anwer and Sampath (2001). The empirical evidence offered by these papers is, however, mixed. Dhawan and Biswal (1999) examine the period between 1961-93 and find that growth in GDP causes growth in exports while causality from exports to GDP appears to be a short run phenomenon. Nidugala (2000) finds that exports had a crucial role in influencing GDP growth in the 1980s. Anwer and Sampath (2000) examine the export-economic growth nexus for a wide cross section of developing countries over the 1960-92 period. They find that exports and economic growth in India are cointegrated but do not find any strong evidence of causality from exports to economic growth or vice versa. In much of the ELG literature as it pertains to India, there is little recognition of the importance of issues such as stationarity and cointegration in empirical testing. Nandi and Biswas (1991) and Sharma and Dhakal (1994) offer some evidence of the ELG hypothesis for India but the empirical evidence offered by both papers is unreliable. Nandi and Biswas (1991) do not test for stationarity and conduct Sims causality tests on the levels of the income and export variables. Given that the levels of the income and export variables are usually non stationary, Nandi and Biswas' results are unreliable. Sharma and Dhakal (1994) conclude that the income and export series for India are non stationary using the Phillips Perron test. They test for causality but do not test for cointegration. However, the correct application of Granger tests requires the identification of a possible cointegrating relationship. If the levels of the export and income series have a unit root (i.e. the series are non stationary) and are cointegrated, the appropriate Granger causality tests must be

constructed either on levels or on the stationary differenced series with an error correction term derived from the cointegrating relationship. Our paper corrects problems in previous empirical studies on the ELG hypothesis in India by not only adjusting for issues such as stationarity, cointegration and error-correction but also applying these tests within the context of a more rigorous and sophisticated econometric framework. For instance, evidence of cointegration between exports and economic growth by itself implies the existence of at least uni-directional causality. Another important aspect of causality testing involves determining the optimal number of lags in the Granger regressions. These lags are usually chosen in an adhoe manner resulting in a mis-specification of the autoregressive process. Akaike's Final Prediction Error (FPE) criterion provides a robust method of determining the order of the bi-variate autoregressive process. Combining the FPE method with the Granger definition of causality provides an econometrically rigorous method of testing causality.

Sourcing from India India, an expanding middle class, a higher disposable incomes and proactive Government policies have underlined the explosive growth of the Indian industries. Over the last decade (since 1991), the market has opened up to global majors and today, the Indian industries rivals some of the countries in terms of potential growth and market size. India offers global industries an opportunity to buy quality products at an extremely competitive prices and in a healthy business climate. The economic policies, regulatory environment and the taxation system has become more of foreign-buyers-friendly. Particular attention is given to the industry-related logistical issues. This section will discuss about some of the useful guidelines for sourcing from India. The Basic Steps Involved in Sourcing For a firm or individual, before sourcing from India take into consideration the following few tips:

• • • • • • • • • • • • • •

Pick product which is to be sourced. Define your supplier and the product criteria. Search for relevant suppliers. Research the supplier's qualifications. Evaluate the samples received. Audit factories. Test the order. Choose supplier. Establish reliable quality control through dependable agent. Establish communications. Establish supply chains. Monitor patent protection. Enforce long-term cost reductions. Repeat the process as needed.

Why Source from India The question arises about why to source from India. What are the advantages to source from India and what is the infrastructure which will support for easy sourcing. The following briefing will answer all the related questions:

• • • • • • • • •

Among Top 10 economies of the world. Vast manufacturing base including aircraft, locomotives, ships, cars, power plants, capital machinery, chemicals, consumer electronics, textiles and food products. Top destination in the world for low-cost, high-value software services and R&D.; Largest concentration of English speaking educated workforce in Asia. The 2nd largest railway network in the world, together with vast coastline & sea ports. A free market society with advanced legal, financial systems and a free press/media. Business and cultural linkages with neighbouring countries Bangladesh, Sri Lanka, and Nepal provide a platform for sourcing from them as well. The country has an abundant, low-cost base of labour which has long-term sustainability and very high skill. Foreign companies are seen more as partners in building domestic manufacturing capabilities rather than a threat to Indian businesses.

Sourcing Agent To overcome with the problem of initial capital, management outlay, and the complex operational and regulatory environment, buyers can take the service of sourcing firms in the country. Some of the sourcing agents covers the entire process, from finding suppliers to transferring of the design specifications to setting up the supply chain and conducting quality control. These sourcing agents are good for small companies or for those companies which has limited management resources. These firms usually charge a commission between 3 to 12 percent. Other sourcing agents operate like a matchmaker. They gather providers of high-value manufacturers according to client specifications and offers order coordination, trade financing, and limited delivery services. These firms are useful for those companies who are comfortable at handling their own supply management and are willing to take a more hands-on approach.

Balance of payments

Cumulative Current Account Balance 1980-2008 based on theIMF data

Since independence, India's balance of payments on its current account has been negative. Since liberalisation in the 1990s (precipitated by a balance of payment crisis), India's exports have been consistently rising, covering 80.3% of its imports in 2002–03, up from 66.2% in 1990–91. India's growing oil import bill is seen as the main driver behind the large current account deficit.[105] In 2007-08, India imported 120.1 million tonnes of crude oil, more than 3/4th of the domestic demand, at a cost of $61.72 billion.[106]

Although India is still a net importer, since 1996–97 its overall balance of payments (i.e., including thecapital account balance) has been positive, largely on account of increased foreign direct investment and deposits from non-resident Indians; until this time, the overall balance was only occasionally positive on account of external assistance and commercial borrowings. As a result, India's foreign currency reserves stood at $285 billion in 2008, which could be used in infrastructural development of the country if used effectively. Due to the global late-2000s recession, both Indian exports and imports declined by 29.2% and 39.2% respectively in June 2009. [107] The steep decline was because countries hit hardest by the global recession, such as United States and members of the European Union, account for more than 60% of Indian exports.[108] However, since the decline in imports was much sharper compared to the decline in exports, India's trade deficit reduced to $252.5 billion.[107] India's reliance on external assistance and commercial borrowings has decreased since 1991–92, and since 2002–03, it has gradually been repaying these debts. Declining interest rates and reduced borrowings decreased India's debt service ratio to 4.5% in 2007.[109] In India,External Commercial Borrowings (ECBs) are being permitted by the Government for providing an additional source of funds to Indian corporates. The Ministry of Finance monitors and regulates these borrowings (ECBs) through ECB policy guidelines.[110] [edit]

Merchandise Trade Exports (i) The decline in exports which started since October 2008 continued during the first quarter of 2009-10. On a BoP basis, India’s merchandise exports recorded a decline of 21.0 per cent in Q1 of 2009-10 as against an increase of 43.0 per cent

in Q1 of 2008-09. (ii) As per the data released by the Directorate General of Commercial Intelligence and Statistics (DGCI&S), merchandise exports declined by 26.4 per cent in Q1 of 2009-10 as against a higher growth of 37.4 per cent in Q1 of 2008-09, reflecting fall in demand worldwide due to the global economic crisis.

INDIA’s cumulative value of exports for the period April- August, 2009 was US$ 64129 million (Rs.

311715 crore) as against US $ 92959 million (Rs. 391841 crore) registering a negative growth of 31 per cent in Dollar terms and 20.4 per cent in Rupee terms over the same period last year. Cumulative value of imports for the period April- August 2009 was US$ 102300 million (Rs. 497108 crore) as against US$ 153691 million (Rs. 648041 crore) registering a negative growth of 33.4 per cent in Dollar terms and 23.3 per cent in Rupee terms over the same period last year. Oil imports during April- August, 2009 were valued at US$ 28275 million which was 47.4 per cent lower than the oil imports of US $ 53742 million in the corresponding period last year. Non-oil imports during April- August, 2009 were valued at US$ 74024 million which was 25.9 per cent lower than the level of such imports valued at US$99949 million in April- August, 2008. The trade deficit for April- August, 2009 was estimated at US $38171 million which was lower than the deficit of US $ 60732 million during April-August, 2008.

EXPORTS & IMPORTS (April-August, FY 2009-10) In $ Million

In Rs Crore

2008-09

92959

391841

2009-10

64129

311715

Growth 2009-10/2008-2009 (percent)

-31.0

-20.4

2008-09

153691

648041

2009-10

102300

497108

Growth 2009-10/2008-2009 (percent)

-33.4

-23.3

2008-09

-60732

-256200

2009-10

-38171

-185393

Exports including re-exports

Imports

Trade Balance

Figures for 2008-09 and 2009-10 are provisional

The trade deficit for April- June, 2009 was estimated at $ 15504 million which was lower than the deficit at $ 28642 million during April- June, 2008. Source: Federal Ministry of Commerce, Government of India

Imports

(i) Import payments, on a BoP basis, also continued its declining trend. Imports declined by 19.6 per cent in Q1 of 2009-10 as against a positive growth of 42.9 per cent in Q1 of 2008-09. (ii) According to the data released by the DGCI&S, the decline in imports is mainly attributed to the sharp fall in oil import payments due to lower crude oil prices during Q1 of 2009-10 (US$ 63.9 per barrel in Q1 of 2009-10 as against US$ 119 per barrel in Q1 of 2008-09). POL imports recorded a sharp decline of 56.9 per cent during Q1 of 2009-10 as against a sharp increase of 74.2 per cent during Q1 of 2008-09. As per the data released by the Ministry of Petroleum & Natural Gas, Government of India, POL imports showed a decline of 45.1 per cent during Q1 of 2009-10 despite a quantity growth of 10 per cent mainly due to lower crude oil price. (iii) According to the DGCI&S data, out of the total decline in imports of US$ 26.7 billion in Q1 of 2009-10 over the corresponding previous quarter, oil imports declined by US$ 16.8 billion (share of 63.1 per cent in the decline in total imports during Q1 of 2009-10 as against 59.8 per cent share in total increase in imports during Q1 of 2008-09), while non-oil imports decreased by US$ 9.8 billion (share of 36.9 per cent in the decline in total imports during Q1 of 2009-10 as against 40.2 per cent share in total increase in imports during Q1 of 2008-09).

Inflows & Outflows from NRI Deposits and Local Withdrawals (In $ million) Inflows

Outflows

Local Withdrawals

2006-07 (R)

19914

15593

13208

2007-08 (PR)

29401

29222

18919

2008-09 (P)

37,089

32,799

20,617

2008-09 (Q1) (PR)

9063

8249

5157

2009-10 (Q1) (P)

11172

9354

5568

P: Preliminary, PR: Partially revised. R: revised SOURCE: Reserve Bank of India report India's Balance of Payments Developments during the First Quarter (April-June 2009) of 2009-10

Variation in Reserves