����������������
Regional Trade Openness Index and Income Disparity A New Methodology and the Indian Experiment
This study tries to look at how “open” Indian states are with respect to international trade and then tries to characterise the relationship between regional disparity and openness. The major objective is to develop an openness index at the regional/state level. The methodology developed here is not only applicable to the Indian case but also for many countries where state-level trade data are not available. The paper tries to devise a proxy which ranks states over time in terms of their exposure to trade. It is observed that the relative income of a region is closely related to the extent of openness and that such a relationship gets stronger over time. There is evidence that openness is strongly correlated with rising income disparity across regions.
SUGATA MARJIT, SAIBAL KAR, DIBYENDU S MAITI
T
This study tries to look at how “open” Indian states are with respect to international trade and then tries to characterise regional disparity in light of this “openness”. The major purpose of this paper is to develop a workable openness index at the regional or state level. International trade data are not easily available at the sub-national level. This is particularly a problem for countries that are large in size and have diverse heterogeneous regions. The methodology developed here is not only applicable to the Indian case but should be useful for many such countries where state-level trade data are not available. We try to devise a proxy, which allows us to rank states over time in terms of their exposure to trade.
For geographically large developing countries having disparate regions, it is essential to understand whether trade has an equalising impact or not. Unfortunately, there are not many papers that deal with intra-national inequality as dictated by the volume and nature of international trade. Available work on the European Union where countries are treated as regions is not as problematic as the one we deal with, since there trade data are readily available for each nation. The closest paper related to our work and dealing with the EU is by Egger, Huber and Pfaffermayr (2005), which extends the empirical literature on the effects of trade liberalisation on regional disparities within a country. Studying the central and eastern European countries, they find significant convergence of real wages in only Poland and Bulgaria. Furthermore, countries with faster growing export openness in the period 1991-98 experienced larger increases in their regional disparities. Despite the apparent similarity with the issue in the present study, it should be noted that this paper does not use intra-national trade data, which consequently allows substantial differences in both the idea and approach we develop here. Our work essentially focuses on the case where the sub-national database is invoked to shed light on the state of regional disparity within the country in question. Thus, we use the sub-national openness index to find some relationship with interstate variations in income, both at levels and as percentage changes. While we do not intend to do any causality analysis, we are building up a case for such an analysis by looking at the correlation between an analytically constructed index and state-level income measures.
With this as a backdrop, we proceed as follows. Section I provides a literature survey. Section II discusses two theoretical papers, which provide the backbone to the statistical work undertaken in this paper. Section III provides the data, methodology and the index. Section IV reflects on the relationship between the index and inter-regional income disparity. Section V concludes.
I Review of Literature
First of all it is necessary to note that there is no literature that we know of which deals with the sub-national data on openness. However, one may have a brief discussion of how the literature on trade and growth has evolved focusing on the relative disparity among nations. Again this is not directly related to the problem in hand. But some information should help in putting things in a proper perspective.
The other task is to discuss various openness indices available so far. Again all these indices presume that explicit data on exports and imports are available to the researcher. If such information is not available to start with, what kind of proxies one can use is not discussed in the existing literature. Again this does not relate directly to what we set out to achieve.
The literature on the relationship between openness and economic performance mainly focuses on the impact of trade orientation on productivity and this relationship has long been a subject of intense debate amongst economists. Grossman and Helpman (1991) show that whether or not a country grows more from openness to trade depends on a number of factors, including its comparative advantage vis-à-vis the rest of the world. Buffie (1992) contends that whether an export boom acts as an engine of growth depends on the structural characteristics of the economy.
Levine and Renelt (1992) note that increasing openness raises long-run growth only when openness provides greater access to investment goods. Batra (1992), Batra and Slottje (1993), and Leamer (1998) go further by suggesting that free trade can be the primary source of economic downturn as trade liberalisation and openness may make imports more attractive than domestic production, and hence the domestic economy may suffer a loss.
The benefits of trade openness have received an enormous amount of interest since the times of David Ricardo, and have been addressed in many seminal studies by, for example, Scitovsky (1954), Keesing (1967), Bhagwati (1978), Krueger (1978), Liu et al (1997) etc, which broadly argue that openness exposes countries to the most advanced new ideas and methods of production dictated by international competitive behaviour, and thus it enhances efficiency. There are also a number of contributions that highlight the positive impact that trade openness can impart on economic growth of a country, such as Romer (1986, 1992), Lucas (1988), Barro and Sala-I-Martin (1995), etc.
However, all these studies on the impact of openness on economic performance deal with how a country, as a whole, benefits from international trade. How regions within a country get affected when the country engages in international trade, has found scant attention in trade literature. The Hecksher-Ohlin model predicted that with introduction of international trade, there would be a shift in factor employment in different industries, which will ultimately lead to factor price equalisation across countries. The same thing can be foreseen at the regional level. Without considering factor price equalisation here, looking at the first part, it is quite possible that as a state engages more in international trade its factors of production will shift from the import competing sector where their returns are lower and enter more into the export sector. This results in greater development of those states, which can attune their production structure to international demands.
It is not a drastic conjecture that different regions will be affected in different ways as a country opens up to trade or embarks on a trade liberalisation process. Thus, their interregional income differences can be explained through such openness to trade. The main aim of this exercise is to bridge the methodological gap in the existing literature to measure how open a particular region/state within a country would be as far as international trade in goods is concerned and how this openness can be employed to understand the character of regional disparity in income. The pioneering work trying to link economic geography with international trade is found in Krugman (1991) where he builds up an economic geography model. Elizondo and Krugman (1992) later use this model to demonstrate that the protectionary economic policies adopted by Mexico have led to the growth of large metropolises in the country. A consequence of the Elizondo and Krugman (1992) argument is that liberal trade policies should disperse economic activities, across locations and thus reduce regional disparity within a country. The reason is that liberal trade policies break the influence of the home market and activities should disperse. For example, the North American Free Trade Agreement (NAFTA) involving the US, Mexico and Canada have resulted in the shifting of economic activities from Mexico City towards border towns near the US. More discussions on this are available in Krugman (1995) and Fujita, Krugman and Venables (1999).
Greater equality across Europe in productivity and income has been one of the central goals of the European Community since the early days of European economic integration. And for a long time this was achieved. If one looks at the country level, it appears to be a tendency towards long-run convergence in productivity and income levels in the EU. However, this tendency covers important differences across regions of the same country. In fact, for most countries, there is either little change in regional dispersion, or a tendency towards divergence [Cappelen-Fagerberg-Verspagen 1999].
On the other hand, one could also argue that if trade becomes really important, activities will get concentrated around ports, in case shipping is a significant means of commodity transportation. In that case, regional disparity may increase and will hamper overall regional development. Again, increase in trade should improve real income of the regions producing exportables and reduce the real income of the regions producing import competing goods. Gains from trade make sure that the overall welfare effect is positive. But nonetheless, income is redistributed from the import competing to the exporting regions [Marjit and Beladi 2005]. Again there is a chance of an increase in regional disparity.
There are a number of rich studies on regional disparity in the Indian subcontinent, using the existing measurement of regional convergence or divergence, albeit these studies do not bring in the connection between trade openness and regional disparity. Nevertheless, a brief account of these studies may be useful to reflect on the larger issue of regional disparity and to further emphasise the purpose of the paper at hand. The unavailability of any study that investigates the connection between trade openness and regional disparity at a country level has left a void in the general topic, which the present study intends to cover.
A study of 20 Indian states over the period 1960-90 by Dholakia (1994) finds a tendency of convergence of long-term state domestic product (SDP) growth rates. A revised study by Dholakia (2003) concludes that regional disparity in terms of human development has been decreasing but the regional disparity of income has been almost constant over the past two decades. Marjit and Mitra (1996) study the issue of regional convergence in 24 Indian states (over the period 1961-62 to 1989-90). On the basis of real per capita net state domestic product (PCNSDP), they find no evidence in favour of convergence of PCNSDP among Indian states. Subsequently, Ghosh, Marjit and Neogi (1997) and Kurian (2000) find the same indications towards regional divergence across states over time. Dasgupta, Maiti, Mukherjee, Sarkar and Chakravorty (2000) also report a clear tendency of divergence in terms of per capita SDP for Indian states, although they find convergence of sectoral shares of SDP. A study by Krishna (2004) shows that while in the 1980s all states improved their growth performance relative to the previous two decades, the performance in the 1990s is quite uneven. Provinces that could take advantage of the reforms of the 1990s, which allowed much scope in policy making at the state level, seem to have performed better. In a recent paper Lall and Chakravorty (2006) observe spatial inequality of industrialisation in India due to cost saving for individual firms. Moreover, private industry seeks promising locations whereas state industries traditionally attach much less importance to the ideal location factor. Thus, the special pattern of industrialisation that emerged lately is predominantly led by investments mainly by the private sector.
Now, in order to see how openness affects regional disparity, we first measure “openness”. Although the term openness is widely used in the related literature on international economics and economic growth, there is no consensus on how to measure it. In the existing empirical studies, various measures have been attempted. These include, trade dependency ratios and the rate of export growth [Balassa 1982], the trade orientation indexes which are defined as the distance between actual trade and the trade predicted by the “true” model in the absence of distortion [Leamer 1988; Wolf 1993], the World Bank’s outward orientation index which classifies countries into four categories according to their perceived degree of openness [World Bank 1987], the composite openness index which is based on such traderelated indicators as tariffs, quotas coverage, black market premia, social organisation and the existence of export marketing boards [Sachs and Warner 1995], and the Heritage Foundation index of trade policy which classifies countries into five categories according to the level of tariffs and other perceived distortions [Johnson and Sheehy 1996] [cited in Liu, Liu and Wei 2001]. However, all these indices use data at the national level. To find trade openness at the state or regional level, there is need to construct a regional openness index, where a substantial amount of ingenuity is required in order to make it sensible and practicable.
II Theoretical Background
Our statistical methodology rests on a simple theoretical and rather conventional idea drawn from a variant of Ricardian and Heckscher-Ohlin-Samuelson frameworks. At the very outset we must mention that we are talking about a case where only the nation engages in trade with the rest of the world as a sovereign entity and the regions trade via the nation. So it is not the case that West Bengal and Punjab are directly trading with the US. This is very different when two countries within EU trade with the rest of the world. Punjab may have huge agricultural resources, but India as a whole may import agricultural goods. If Punjab was a separate nation it could just export agricultural products and import industrial goods. If industrial prices increase in the rest of the world, India as a whole has a terms of trade gain, but Punjab is likely to lose. Thus the nation’s interest and the state’s interest do not necessarily converge and that is likely to be the case if regions are quite heterogeneous. Our approach is essentially as follows.
India’s overall factor endowments, among other things, will determine India’s pattern of trade and those states whose endowments match well with the national characteristics will have their production roughly matching with the national basket and therefore will have a similar trade pattern. But it does not say anything about what would have happened if each state could directly trade with the rest of the world. Also one must remember that the production patterns across states are very much conditioned by active government policies and therefore actual trade may not reflect the nature driven comparative advantage of regions. We are not into suggesting what the states should export or should import. Given the national trade and production patterns how much of it is replicated at a particular state level. With this as the background we now turn towards theoretical predictions.1
As regions open up for trade, the exporting regions should gain and import competing regions should lose. Therefore, if initially, the exporting regions were relatively well off, trade is going to increase inter-regional disparity. Trade does not necessarily lead to unequal outcomes at the regional level if the import competing regions were rich to start with.
Trade also reallocates resources towards the export sectors and therefore those regions, which were on the borderline of being identified as the import competing region, should switch first to being an exporting region. Eventually, there will be more states which will emerge as exporters. With full mobility of factors across states, it is difficult to predict interstate variations of income, except if there is some specific factor such as land. However, the initial distribution of income is very important for determining whether trade leads to further disparity. This is extensively discussed in terms of a continuum Ricardian model in Marjit and Beladi (2005). We look at some predictions of this model in our subsequent analyses.
In a multi-commodity Heckscher-Ohlin structure the interpretation of the theorem related to the pattern of trade can yield interesting results. For example, one may observe a country to export both relatively capital-intensive as well as labour-intensive goods depending on the relative endowment position of the trading partner. Thus, issues such as the Leontieff paradox become inconsequential. A general interpretation of the neoclassical trade model in that set up is provided in Jones, Beladi and Marjit (1999). In a multicommodity setting one could suggest that the production bundle of the country should be consistent with the endowment bundle. In other words, countries which are relatively capital abundant will produce greater volume of capital-intensive goods. Such consistency can accommodate the fact that India will produce more capital-intensive goods than, for example, Ghana but less compared to the US. As an offshoot of this argument we proceed as follows.
Since we do not have export-import data for each region, we argue that if a state’s production bundle matches closely with the national export bundle, i e, the state produces more of prominent exportables, the state is likely to be export-oriented. The regional production bundle in this case matches with the national trade bundle, which should match the national endowment vector.
III Data, Methodology and Results
For a specific state, the level of output (including industrial and agricultural) has been linked to all-India trade figures to get an approximate indicator of how open it is. If most of the production is concentrated in the items, which at the all-India level contribute largely to export value, then it is reasonable to conclude that a particular state is attuned to exports. For example, agro-based output like food, beverage, tobacco and textiles have traditionally been prime export earners of India. If a state has a very high production share of these items, it can be inferred that this state is contributing more to exports than others. Correspondingly, if a state has high production value of import substitutes then it must be relying less on imports and hence is not so “open”. For example, machinery and equipments figure largely in India’s non-oil imports. If a state is producing much of this then its import of the same is likely to be less than other states and hence it is considered to be less open in this study. Thus, in our analysis for a state to be open requires consistency of its production structure with the trade pattern of the country, i e, more important commodities in the state’s production basket would be the exportable, and/or less important contributors would be the major import-competing goods. After calculating how open a state is to trade, it is compared with its per capita net state domestic product to find the link between openness and regional income disparity.2
Before going to construct the openness index, it should be mentioned that frequent changes in the classification of industries and product group create a lot of problem to get a consistent panel data. Although we try to tally the classification for the entire period at the 3-digit classification of industry and product groups, we are unable to cope with change, if any, below the 3-digit level. So, this is the major limitation of the study. For the analysis, the first step involves the finding the gross value added (GVA) of each industry (at the 2-digit level of National Industrial Classification) for 15 major Indian states from 1980-81 to 2002-03. We ignore small states, because 15 states are sufficient to explain 70 to 80 per cent production share for each goods. We take only the manufacturing goods based on NIC reclassification of industries in 1998 and that requires transformation of industrial classification in order of NIC 1998. Since Indian states depend to a large extent on agriculture, it is also added to the agriculture related industry, i e, NIC 15-16. Share of value added contributed by each industrial group for all the states for all these years is calculated. These data are collected from the Annual Survey of Industries (ASI), various issues. For a particular state the share of value added by an industrial group is calculated by the following formula:
k kk
k GVAit (= NVAit + DPit )
it
s = ,t = 1980 − 81,..,2002 − 03, (1)
34−35
TVAitk (=∑GVAitk )
i=15−16
where sk = production share of ith industry in kth state at time
it period t;
GVAk = gross value added of ith industry in kth state at time
it period t;
NVAk = net value added of industry producing in kth state
it at time period t;
DPk = Depreciation of industry producing in kth state at
it time period t;
TVAk = Total of all gross value added of industries3 15-16
it to 34-35. The second step is to find out how these goods fared with the export profile of India for each year under consideration. Since export data classification is different from NIC, we take trade data and then club or disaggregate certain portions to make it tally with the new industrial groups under consideration. The way trade data is classified in the Directorate General of Commercial Intelligence and Statistics (DGCI&S) publications, it is easier to tally it with the ASI data at hand, compared to other sources. So, we have used trade data from DGCI&S publications.
Both NIC and trade classification of DGCI&S have undergone changes in the study period under consideration. We regroup all industrial data as per NIC 1998 classification (Table A). From April 1987, DGCI&S data classification has been changed to the Harmonised System of trade classification (i e, HS). Thus, to tally trade classification with NIC we construct different groupings prior to 1987 (Table B). For the purpose of this study, we have taken export data in such a fashion so as to include the agricultural exports in food, beverages and tobacco in Table B.
After collecting trade data and classifying them in this way, we calculated the share of the products under consideration in total exports of India in the following manner:
X
x = ,
it it (2)
Xt where xit is share of ith industry in total exports in the tth period; Xit is the export value of the ith industry in the tth period; Xt is the total export value of India in the tth period.
Similar to the export share, the import share is derived in the following manner: M
m =
it it (3)
Mt mit = import share of ith industry to total import in India at tth period; Mit = import of ith industry at tth period; Mt = total import in India at tth period.
The export shares (xit) and the imports shares (mit) are represented in Table C and Table D respectively.
Third, for a particular year the export and import shares of the goods at the all India level and the gross value added shares of the same at the state level are calculated. The next step is to correlate xitwith skit and mitwith skit. These correlation coefficients will clarify how the production structure of the states are in tune with the export and import structures of India. We calculate this for the entire period under consideration. Thus, over the 23-year period and for 15 major states in India we arrive at correlation coefficients between their production share and the export-import profile at the national level separately. These correlation coefficients are now ranked such that, Rk , Rk ∈ (1,2,...15) where
mtxtRk and Rk provide rank of the correlation between import and
mtxtexport shares respectively, with production shares of state k at the tth period. We assign the rank of 1 to the state with highest correlation and the rank of 15 to the state with lowest correlation. For example, in 2002-03 Gujarat shows the highest correlation in exports and is ranked 1 (implying it has the highest export performance), whereas Bihar shows the lowest correlation in exports (implying it is least export-oriented) and is given the rank of 15. These ranks can be seen as indicative of export and import performances of the states over the years (Tables 1 and 2).
The final stage of the analysis involves finding a trade openness index. This index is constructed using Rkxt (the export performance rank) and the inverse of Rkmt (the import competing per
~
formance rank) which is denoted by k . In the case of imports,
Rmtinverse ranking is followed, which represents the inverse rank of the correlation coefficient between mit and skit. Thus, in case of imports those states are ranked higher which import higher or contribute less to import substituting production, e g, in 2002-03 Gujarat shows the highest import correlation (implying it is the most import competing state for that year), and is given a rank of 15, whereas Orissa has the lowest correlation coefficient value and is ranked 1 (implying it was the least import competing state) (Table 3).
Now, we assign a weight of half to each of these ranks (in import competitive (rank 3 in Table 3). Its openness index, thus, Tables 1 and 3) in order to construct the openness index. is 1/2 * (2) + 1/2 * (3) = 2.5. Since its export performance rank is ~ high and inverse import competing performance rank is low (the
k kk
Ot = 
way we have assigned ranks), this implies that it is involved
We then rank the openness index giving lowest rank to the with more exportable production and less import substitute prohighest score, e g, Tamil Nadu has second highest export duction. Hence this state is more open. For 2002-03, Tamil Nadu performance in 2002-03 (rank 2) and it is also not much has the lowest value of openness index among other states and
Table 1: Ranks of Correlation Coefficients between Export Share and Gross Value Added Share of Industries in Various States
1980-81 1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91
Andhra Pradesh 7.5 8 6 4.5 6.5 5.5 4 8 9 9 10 Assam 11.5 12.5 10 11 9 13 14 13 11.5 13 13 Bihar 15 15 15 15 15 15 15 15 15 1515 Gujarat 1.5 1 121 1 2 12 22 Haryana 7.5 4 10 9 6.5 12 12.5 11.5 11.5 12 12 Karnataka 5 8 10 7.5 10 8 6.5 10 8 11 8.5 Kerala 1010.5 4 3 3 7 10 9 77.5 7 Madhya Pradesh 11.5 14 13 12 13 10 5 11.5 13 10 11 Maharashtra 1310.5 3 1 2 4 11 3 3 3 3 Orissa 14 12.5 14 13 12 11 12.5 14 14 14 14 Punjab 7.5 5.5 5 10 11 5.5 6.5 4 4 44.5 Rajasthan 4 3 765 3 3 56 54.5 Tamil Nadu 1.5 2 24.5 4 2 1 21 11 Uttar Pradesh 7.5 5.5 8 7.5 8 9 9 7 10 7.5 8.5 West Bengal 3 8 12 14 14 14 8 6 5 66
1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-2000 2000-01 2001-02 2002-03
Andhra Pradesh 12 9 9 12 9.5 12 11 10 11 12 10 10 Assam 13 13 14 14 14 13 13.5 14 14 13.5 15 12 Bihar 15 15 15 15 15 15 15 13 13 13.5 1415 Gujarat 222223.52 2 2 211 Haryana 11 11.5 11 10.5 12 10 13.5 12 12 10 12.5 14 Karnataka 910 67 7 8 5 6 87.5 44 Kerala 1011.5 12 99.5 119.5 11 9 9 99 Madhya Pradesh 7.5 8 88 8 7 4 86.5 486 Maharashtra 3 4 33 5 3.5 3 3 4 333 Orissa 14 14 13 13 13 14 12 15 15 15 12.5 13 Punjab 465543.5 6 5 3 668 Rajasthan 534433.5 8 4 5 555 Tamil Nadu 11111 11 1 1 122 Uttar Pradesh 7.5 7 10 10.5 11 9 9.5 9 10 11 11 11 West Bengal 6 5 76 6 6 7 76.5 7.577
Note:The state with the highest correlation is assigned rank 1 and vice versa.
Table 2: Ranks of Correlation Coefficients between Import Share and Gross Value Added Share of Industries in Various States
1980-81 1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91
Andhra Pradesh 7 9.5 9 4 6 6.5 6 11 9 13 12 Assam 15 11 13 14 12.5 9.5 13 15 14 5 4 Bihar 139.55.5 12 9 14 83.5 4 32 Gujarat 22121121143 Haryana 9 5 5.5 6.5 5 6.5 5 5 10 8.511 Karnataka 5.5 8 7 6.5 11 5 4 7 7 8.5 9.5 Kerala 43.5 4 5 4 49.5 65.5 25 Madhya Pradesh 8 6.5 12 10.5 7.5 12 7 9 5.5 10 13 Maharashtra 1 1 212 2 1 22 11 Orissa 12 15 15 15 15 15 14 13 8 69.5 Punjab 5.5 6.5 8 10.5 12.5 9.5 15 14 13 14 15 Rajasthan 14 14 14 13 10 11 11.5 12 15 15 14 Tamil Nadu 33.5 3 3 3 3 33.5 3 76 Uttar Pradesh 10.5 13 11 8.5 7.5 8 11.5 8 11 11 7 West Bengal 10.5 12 10 8.5 14 13 9.5 10 12 12 8
1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-2000 2000-01 2001-02 2002-03
Andhra Pradesh 9.5 9 10 9 9 11 9 7.5 8.5 8 6 6 Assam 34355 44 10 4 473 Bihar 5 5 5 46.5 7.5 10 12 6 6108 Gujarat 21111 12 2 1 211 Haryana 11 13 915 13 9 8 3 11 812 11 Karnataka 81011 76.5 5 5 4 10 5 45 Kerala 43410 47.53 5 3 334 Madhya Pradesh 15 12 12 11 10.5 12 11 11 7 10.5 8 7 Maharashtra 12222 21 1 2 122 Orissa 12 11 13 12 12 10 13 15 15 15 1515 Punjab 13 15 15 13.5 14 14 15 13.5 12 12 14 14 Rajasthan 14 14 14 13.5 15 15 14 13.5 13 10.5 12 10 Tamil Nadu 6 6 63 3 3 7 6 513 513 Uttar Pradesh 7 8 76 8 6 6 7.58.5 899 West Bengal 9.5 7 8 8 10.5 13 12 9 14 14 12 12
Note:As in Table 1.
Economic and Political Weekly March 3, 2007 it is ranked 1. Similarly, a state having highest value of openness Figure 1: Trend of Coefficient of Variation of PCNSDP across
Major States in India, 1980-81 to 2002-03
index is given the lowest rank of 15. In 2002-03, Assam is the least open state as per this specification. The values of openness
index are given in Table 4 and the ranks following from them,
in Table 5.Note that lower value of index implies greater openness.
It should be useful at this point to briefly discuss the properties
0 5 10 15 20 25 30 35 40 45 CV (per cent)of the indices that we construct. First, the index is rudimentary
and yet novel, and may be subject to future refinement either
with the same data or with applications to other countries. Second,
we have used 0.5 as the weights for export production and import production at each state level. This is once again amenable to alteration, where exact weights may be assigned for each state. Let us provide an example. Suppose (and in reality, it is so) India produces and exports a large amount of tea and that Assam and West Bengal are the prime locations where tea is grown. Thus, the method we develop next argues that these states have a high share of exportable production (if the case may be so) and should rank high in terms of their export potential. However, it is also possible that, the volume of the export products is only a small part of the total production bundle in the state, which also produces large shares of importables (say, soda ash). Given the production shares and the appropriate weights we then measure the level of openness for these states, which in turn offers the
1980-811982-831984-851986-871988-89
1990-911992-931994-951996-971998-99
2000-012002-03
Year
Figure 2: Correlation Coefficients and Trend Line betweenExport Performance and PCNSDP Ranks across States(1980-81 to 2002-03)
0.8
0.7
0.6
| R2 = 0.3676 |
|---|
weighted rank. In this case, the weights will be different from
0.5. Even if this alters the overall ranking a bit and hence the
0.5
subsequent correlation coefficients, the methodology of index construction shall not vary. Also, individual rankings in terms of export orientation and import competition are not altered. It 0.4 is only a matter of how one combines them. Third, the reconstruction of the index with differing weights may also be 0.3 useful to construct a panel where many other issues can be looked into in further detail.
0.2
IV Relationship between Openness and0.1 Inter-regional Income Disparity
0
Attempts are made in this section to relate the openness at the state level with the income pattern over time and it is worked out in three different ways. Before doing that, however, we
1980-81
1985-86
1990-91
1995-96
2000-01
2002-03
Year
observe the income disparity among the 15 major states over time in terms of the coefficients of variation in per capita net state domestic product. If one looks at the income variation among the major states in India, it reveals an increasing trend over time as between 1980 and 2003. Figure 1 shows that the coefficient of variation (standard deviation divided by mean) in income among the major states has increased from 31.09 per cent in 198081 to 38.16 per cent in 2002-03. In other words, the regional disparity has increased by about 25 per cent during the given period. This encourages us to examine if there is any relationship between inter-regional income disparity and trade openness for the Indian states between 1980-81 and 2002-03.
Relation between Openness and PCNSDPof the States
At first, the states are ranked according to their PCNSDP such that Rkt, ∈ (k = 1,2,...,15) . We get the data on PCNSDP of these states from the Central Statistical Organisation (CSO). However, this data is divided into two series. The old series is based on 1980-81 prices, whereas the new series is based on 1993-94 prices. To make the two compatible, the old series has been converted to 1993-94 prices and subsequently, we ranked the states according to their PCNSDP from 1980-81 to 2002-03. We rank states having a higher PCNSDP with higher ranks, for, e g, in 2002-03 Maharashtra had the highest PCNSDP (Rs 15,466) among the 15 states. So, it is ranked first. For the same year, Bihar has the lowest PCNSDP (Rs 4,448) and is given the last rank of 15. These ranks for all the years are shown in Table 6.
These two sets of ranks (presented in Tables 1 and 3) are ultimately correlated and presented in a scatter-plot in Figure 2 to find out the dynamics of export-led regional development. This figure clearly shows that the trend for the correlation coefficients is increasing over time, which directly implies that the inter-regional disparity as explained by export performance of the states is on the rise. We also find that the values of the correlation coefficients are higher after the reform period than before it.
Rkmt and Rkt and are correlated and the correlation coefficients are presented in the scatter plot in Figure 3. We find that the correlations are positive and the linear trend attached to it is downward sloping, i e, with time the correlations are becoming weaker. Studying Figures 2 and 3 it can be said that more export plotted in a scatter diagram (Figure 4). Figure 4 shows that the oriented and less import competing states gain in terms of income linear trend attached to the final correlation coefficients is a rising over time. one. This implies that with time the extent of regional income
At last to link states’ openness to trade with their PCNSDP is found to have a gradually strong positive relationship with the we correlate Okt with Rkt. The final correlation coefficients are trade openness of that region.
Table 3: Inverse Ranks of Correlation Coefficients between Import Share and GVA Share of Industries in Various States
1980-81 1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91
AP 96.5 712 10 9.5 10 5 7 34 Assam 1 5 3 23.5 6.5 3 1 2 1112 Bihar 3 6.5 10.5 4 7 2 8 12.5 12 13 14 Gujarat 14 14 15 14 15 15 14 15 15 1213 Haryana 7 11 10.5 9.5 11 9.5 11 11 6 7.5 5 Karnataka 10.5 8 9 9.5 5 11 12 9 9 7.5 6.5 Kerala 12 12.5 12 11 12 12 6.5 10 10.5 14 11 MP 8 9.5 45.5 8.5 4 9 710.5 63 Maharashtra 15 15 14 15 14 14 15 14 14 15 15 Orissa 4 1 111 1 2 3810 6.5 Punjab 10.5 9.5 85.5 3.5 6.5 1 2 3 21 Rajasthan 2 2 236 54.5 41 12 TN 13 12.5 13 13 13 13 13 12.5 13 910 UP 5.5 3 57.5 8.5 84.5 8 5 59 WB 5.5 4 67.5 2 36.5 64 48
1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-2000 2000-01 2001-02 2002-03
AP 6.5 7 67 7 5 7 8.5 7.5 81010 Assam 13 12 13 11 11 12 12 6 12 12 913 Bihar 11 11 11129.5 8.5 6 4 10 10 68 Gujarat 14 15 15 15 15 15 14 14 15 14 1515 Haryana 53713 78 13 5 845 Karnataka 8 6 5 9 9.5 11 11 12 6 11 12 11 Kerala 12 13 12 6 12 8.5 13 11 13 13 1312 MP 14455.5 45 5 95.5 89 Maharashtra 15 14 14 14 14 14 15 15 14 15 14 14 Orissa 45344 63 1 1 111 Punjab 3112.5 2 212.5 4 422 Rajasthan 2 2 22.5 1 1 2 2.5 3 5.5 46 TN 1010101313 13 9 10 11 3113 UP 9 8 910 8 1010 8.57.5 877 WB 6.5 9885.5 34 7 2 244
Note:As in Table 1.
Table 4: Yearly Openness Index Values of Indian States
1980-81 1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91
AP 8.25 7.25 6.5 8.25 8.25 7.5 7 6.5 8 6 7 Assam 6.25 8.75 6.5 6.5 6.25 9.75 8.5 7 6.75 12 12.5 Bihar 9 10.75 12.75 9.5 11 8.5 11.5 13.75 13.5 14 14.5 Gujarat 7.75 7.5 8 8 8 8 8 8 8.5 77.5 Haryana 7.25 7.5 10.25 9.25 8.75 10.75 11.75 11.25 8.75 9.75 8.5 Karnataka 7.75 8 9.5 8.5 7.5 9.5 9.25 9.5 8.5 9.25 7.5 Kerala 11 11.5 8 7 7.5 9.5 8.25 9.5 8.75 10.75 9 MP 9.75 11.75 8.5 8.75 10.75 7 7 9.25 11.75 8 7 Maharashtra 14 12.75 8.5 8 8 9 13 8.5 8.5 9 9 Orissa 9 6.75 7.5 7 6.5 6 7.25 8.5 11 12 10.25 Punjab 9 7.5 6.5 7.75 7.25 6 3.75 3 3.5 3 2.75 Rajasthan 3 2.5 4.5 4.5 5.5 4 3.75 4.5 3.5 3 3.25 TN 7.25 7.25 7.5 8.75 8.5 7.5 7 7.25 7 5 5.5 UP 6.5 4.25 6.5 7.5 8.25 8.5 6.75 7.5 7.5 6.25 8.75 WB 4.25 6 910.75 8 8.5 7.25 6 4.5 5 7
1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-2000 2000-01 2001-02 2002-03
AP 9.25 8 7.5 9.5 8.25 8.5 9 9.25 9.25 10 10 10 Assam 13 12.5 13.5 12.5 12.5 12.5 12.75 10 13 12.75 12 12.5 Bihar 13 13 13 13.5 12.25 11.75 10.5 8.5 11.5 11.75 10 11.5 Gujarat 8 8.5 8.5 8.5 8.5 9.25 8 8 8.5 8 8 8 Haryana 8 7.25 9 5.75 7.5 8.5 10.75 12.5 8.5 9 8.25 9.5 Karnataka 8.5 8 5.5 8 8.25 9.5 8 9 7 9.25 8 7.5 Kerala 11 12.25 12 7.5 10.75 9.75 11.25 11 11 11 11 10.5 MP 4.25 6 6 6.5 6.75 5.5 4.5 6.5 7.75 4.75 8 7.5 Maharashtra 9 9 8.5 8.5 9.5 8.75 9 9 9 9 8.5 8.5 Orissa 9 9.5 8 8.5 8.5 10 7.5 8 8 86.75 7 Punjab 3.5 3.5 3 3.75 3 2.75 3.5 3.75 3.5 5 4 5 Rajasthan 3.5 2.5 3 3.25 2 2.25 5 3.25 4 5.25 4.5 5.5 TN 5.55.5 5.5 7 7 7 5 5.5 6 26.52.5 UP 8.25 7.5 9.5 10.25 9.5 9.5 9.75 8.75 8.75 9.5 9 9 WB 6.25 7 7.5 7 5.75 4.5 5.5 7 4.25 4.75 5.5 5.5
Relation between Openness and Dispersionof PCNSDP
In order to examine the connection between trade openness and regional disparity in another way, first we look at the dispersion of the income calculating trend of coefficient of variation across states and try to correlate dispersion of the state with its openness. We have derived indices of “regional disparity”, viz, relative mean deviation in PCNSDP (σkt), to find out relative position of k-th state with respect to mean income at t-th period. Higher the value of relative mean deviation in PCNSDP (σkt with sign), the state should be considered richer on average. It should be noted that the sign of σkt will take positive of k-th state if PCNSDP is higher than the mean otherwise negative. Then we have ranked each state on the basis of the principle the “state with highest value for σk receives rank 1
t
and vice versa” and it is reported in Table 7.
k
(x)
− x *100
σkt = t , k = 1,...,15, t = 1980-81,...,2002-03
x xkt = PCNSDP for kth state in tth period.
(5)
x = = Mean of the PCNSDP among states in a given period.
Correlation coefficient between rank of states based on σkt and rank of states based on the openness index is calculated (Rct) and presented in Figure 5. The figure shows a positive relationship between the rank of states based on σst and the rank of states based on openness index. This suggests that a state, which is being more “open”, is also becoming more “wealthy” compared to the other states.
Relation between Openness and PCNSDP Growthof States
The growth rate of PCNSDP (γkt ) is defined by the percentage change of PCNSDP with respect to the pervious year.
kk
(− x )
x *100
k tt−1
γ= k , k = 1,...,15, t=1980-81,...,2002-03 (6)
t
x
t−1 xkt = PCNSDP for kth state in tth period.
Table 6: Coefficient of Variation of PCNSDP across Major States in India
Year Coefficient of Variation of PCNSDP
1980-81 1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03
31.09
31.45
32.44
30.66
32.28
34.64
34.72
35.05
34.43
36.03
35.99
36.37
38.65
35.81
35.37
36.82
37.32
36.88
37.15
37.07
36.66
36.36
38.16
Source:Based on Data in Handbook of Statistics on Indian Economy, 2003-04, RBI.
Table 5: Yearly Openness Index Ranks of Indian States
1980-81 1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91
AP 95.5 3.5 910.55.5 5 4 7 55 Assam 3 11 3.5 2 2 14 11 5 413.5 14 Bihar 11 12 151415 9 13 15 15 15 15 Gujarat 7.5 8 8.57.5 8 7 9 8 9 77.5 Haryana 5.5 8 14 13 13 15 14 14 11.5 11 9 Karnataka 7.5 10 13 10 5.5 12.5 12 12.5 9 10 7.5 Kerala 14 13 8.5 3.5 5.5 12.5 10 12.5 11.5 12 11.5 MP 13 14 10.511.5 14 4 5 11 14 85 Maharashtra 15 15 10.5 7.5 8 11 15 9.5 9 9 11.5 Orissa 11 4 6.5 3.5 3 2.5 7.5 9.5 13 13.5 13 Punjab 11 8 3.5 6 4 2.5 1.5 11.5 1.51 Rajasthan 1 1 1 1 1 1 1.5 21.5 1.52 TN 5.5 5.5 6.511.5 12 5.5 5 6 5 3.5 3 UP 4 23.5510.5 9 3 76 610 WB 2 31215 8 97.5 3 33.5 5
1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-2000 2000-01 2001-02 2002-03
AP 12 8.5 6.5 12 7.5 6.5 9.5 12 12 12 12.5 12 Assam 14.5 14 15 14 15 15 15 13 15 15 15 15 Bihar 14.5 15 14 15 14 14 12 8 14 14 12.5 14 Gujarat 6.5 10 9.5 10 9.5 9 7.5 6.5 8.5 6.5 7 8 Haryana 6.5 6 11 3 6 6.5 13 15 8.5 8.5 911 Karnataka 9 8.5 3.5 8 7.5 10.5 7.5 10.5 5 10 7 6.5 Kerala 13 13 13 7 13 12 14 14 13 13 14 13 MP 34544 42 4 62.5 76.5 Maharashtra 10.5 11 9.5 10 11.5 8 9.5 10.5 11 8.5 10 9 Orissa 10.5 12 810 9.5 13 6 6.5 7 6.5 5 5 Punjab 1.5 21.52 2 2 1 2 1 412 Rajasthan 1.5 11.5 1 1 13.5 1 2 5 23.5 TN 4 33.55.5 5 53.5 3 4 141 UP 8 7 12 1311.5 10.5 11 9 10 11 1110 WB 5 56.55.5 3 3 5 5 32.5 33.5
Note:The state with lowest openness index value is assigned rank 1 and vice versa.
Figure 3: Correlation Coefficients and Trend Line betweenFigure 5: Correlation Coefficient between OpennessImport Competing Performance and PCNSDP Ranks acrossIndex Rank and Rank of Relative Mean Deviation Based on States (1980-81 to 2002-03) PCNSDP (1980-82 to 2002-03)
0.700
0.600
R2 = 0.2314 -0.2 1980-811982-831984-851986-871988-891990-911992-931994-951996-971998-992000-012002-03
Year
=R2 = 0.3964 -0.100 -0.200 -0.300 -0.400
0.2
0
Correlation Coefficient
0.500
0.400
0.300
0.200
0.100
0.000
1980-81
1982-83
1984-851986-871988-89
1990-91
1992-93
1994-95
1996-97
1998-99
2000-01
2002-03
Year
Figure 6: Correlation Coefficient between Rank of PCNSDPGrowth and Rank of Openness Index (1981-82 to 2002-03) Figure 4: Correlation Coefficients and Trend Line betweenOpenness Index and PCNSDP Ranks across States
Correlation Coefficient 0.800 0.600 0.400 0.200 0.000 -0.200 -0.400 -0.600 R2 = 0.025(1980-81 to 2002-03)
R2 = 0.4462 -0.11980-811982-831984-851986-871988-891990-911992-931994-951996-971998-992000-012002-03 0.3
0.2
0.1
0
-0.2 -0.3 -0.4
1981-82
1983-84
1985-86
1987-88
1989-90
1991-92
1993-94
1995-96
1997-98
1999-2000
2001-02
Year
We have calculated the value of γ kt for each state, for each year and then ranked each state on the basis of γkt using the same principle and reported in Table 8. As the definition suggests, γk
t can be considered a measure of a wealthy state compared to the other states in the tth period. The correlation coefficient between the rank of states based on γkt and rank of state Openness Index is also calculated and represented in Figure 6. The same conclusion can be drawn from Figure 6, which portrays a positive relationship, although trend of this correlation do not show sharp rising trend. Therefore, all three measures of inter-regional income disparity of states are found to be gradually more correlated with the openness of the states over the years. We would like to emphasise the fact that this analysis focuses only on the connection between exportable/importable production and PCNSDP ranks, the ranks of relative mean deviation and ranks of growth of states while controlling for policy changes like trade promotion schemes, foreign exchange regime changes, etc, which have been introduced in the reform period. This might have influenced trade performance and PCNSDP. Nevertheless, the analysis provides ample support in favour of the initial hypothesis that the increase in regional disparity in
Year
Indian states has some correlation with trade openness over the years.
V Concluding Remarks
Our objective has been to devise an openness index and develop a ranking of the Indian states according to their exposure to international trade. This is a proxy measure since trade data are not available at the regional level. We established that states with relatively high levels of income are also those with greater exposure to trade and such a relationship has grown stronger over time. This amounts to the suggestion that if we pick a relatively affluent state now, the chances that it is fairly open are higher than what would be in the early 1980s. We do not establish any causal link between regional prosperity and trade, which is an important future research agenda. In the process, we reconfirm the general theoretical intuition that exporting states are getting richer over the years and the import competing states are falling behind. Also a state generates higher PCNSDP by switching its production from import competing sectors to the export sectors.
One caveat is warranted at the final stage. Our openness is
Table 7: Ranks of States according to PCNSDP
| 1980-81 | 1981-82 | 1982-83 | 1983-84 | 1984-85 | 1985-86 | 1986-87 | 1987-88 | 1988-89 | 1989-90 | 1990-91 | ||
| Andhra Pradesh | 11 | 7 | 8 | 11 | 10 | 11 | 10 | 9 | 7 | 6 | 7 | |
| Assam | 10 | 10 | 9 | 9.5 | 9 | 7 | 9 | 11 | 12 | 12 | 12 | |
| Bihar | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | |
| Gujarat | 4 | 4 | 4 | 3 | 3 | 4 | 4 | 4 | 3 | 4 | 4 | |
| Haryana | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | |
| Karnataka | 9 | 8 | 10 | 6 | 6 | 9 | 6 | 6 | 6 | 7 | 9 | |
| Kerala | 5 | 6 | 5 | 9.5 | 7 | 6 | 7 | 8 | 9 | 8 | 6 | |
| Madhya Pradesh | 7 | 9 | 7 | 7 | 11 | 10 | 12 | 10 | 11 | 11 | 10 | |
| Maharashtra | 3 | 3 | 3 | 4 | 4 | 3 | 3 | 3 | 4 | 3 | 3 | |
| Orissa | 13 | 13 | 14 | 13 | 14 | 13 | 13 | 14 | 13 | 13 | 14 | |
| Punjab | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Rajasthan | 12 | 12 | 12 | 8 | 12 | 12 | 11 | 12 | 8 | 9 | 8 | |
| Tamil Nadu | 6 | 5 | 6 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | |
| Uttar Pradesh | 14 | 14 | 13 | 14 | 13 | 14 | 14 | 13 | 14 | 14 | 13 | |
| West Bengal | 8 | 11 | 11 | 12 | 8 | 8 | 8 | 7 | 10 | 10 | 11 | |
| 1991-92 | 1992-93 | 1993-94 | 1994-95 | 1995-96 | 1996-97 | 1997-98 | 1998-99 | 1999-2000 | 2000-01 | 2001-02 | 2002-03 | |
| Andhra Pradesh | 7 | 9 | 8 | 8 | 8 | 8 | 10 | 8 | 8 | 8 | 8 | 9 |
| Assam | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 |
| Bihar | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 |
| Gujarat | 5 | 4 | 4 | 4 | 3 | 3 | 3 | 3 | 4 | 5 | 4 | 4 |
| Haryana | 2 | 3 | 3 | 3 | 4 | 4 | 4 | 4 | 3 | 3 | 3 | 3 |
| Karnataka | 6 | 6 | 7 | 7 | 7 | 6 | 6 | 6 | 6 | 6 | 6 | 6 |
| Kerala | 8 | 7 | 6 | 6 | 6 | 7 | 7 | 7 | 7 | 7 | 7 | 7 |
| Madhya Pradesh | 11 | 11 | 10 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 |
| Maharashtra | 3 | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 2 | 1 |
| Orissa | 14 | 14 | 14 | 14 | 14 | 14 | 14 | 13 | 13 | 14 | 13 | 13 |
| Punjab | 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 |
| Rajasthan | 10 | 8 | 11 | 9 | 10 | 10 | 8 | 10 | 10 | 10 | 10 | 10 |
| Tamil Nadu | 4 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 |
| Uttar Pradesh | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 14 | 14 | 13 | 14 | 14 |
| West Bengal | 9 | 10 | 9 | 10 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 8 |
| Note: | The state with highest PCNSDP is assigned rank 1 and vice versa. | |||||||||||
| Source: Based on Handbook of Statistics on Indian Economy, 2003-04. | ||||||||||||
| Table 8: Rank of States on Relative Mean Deviation of States’ Income | ||||||||||||
| 1980-81 | 1981-82 | 1982-83 | 1983-84 | 1984-85 | 1985-86 | 1986-87 | 1987-88 | 1988-89 | 1989-90 | 1990-91 | 1991-92 | |
| Andhra Pradesh | 9 | 8 | 7 | 7 | 8 | 8 | 8 | 8 | 8 | 8 | 7 | 8 |
| Assam | 12 | 10 | 10 | 10 | 10 | 9 | 10 | 10 | 14 | 14 | 13 | 12 |
| Bihar | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 |
| Gujarat | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 5 | 4 | 4 | 4 | 4 |
| Haryana | 3 | 3 | 2 | 3 | 3 | 2 | 2 | 3 | 2 | 3 | 2 | 2 |
| Karnataka | 6 | 7 | 6 | 6 | 7 | 7 | 6 | 6 | 7 | 7 | 8 | 7 |
| Kerala | 7 | 9 | 9 | 13 | 9 | 10 | 9 | 9 | 11 | 10 | 10 | 9 |
| Madhya Pradesh | 10 | 11 | 11 | 11 | 13 | 12 | 14 | 11 | 13 | 13 | 11 | 13 |
| Maharashtra | 2 | 2 | 3 | 2 | 2 | 3 | 3 | 2 | 3 | 2 | 3 | 3 |
| Orissa | 11 | 12 | 14 | 12 | 14 | 11 | 11 | 13 | 10 | 11 | 14 | 14 |
| Punjab | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Rajasthan | 14 | 13 | 13 | 9 | 11 | 14 | 12 | 14 | 9 | 9 | 9 | 10 |
| Tamil Nadu | 8 | 6 | 8 | 8 | 6 | 6 | 7 | 7 | 6 | 5 | 5 | 5 |
| Uttar Pradesh | 13 | 14 | 12 | 14 | 12 | 13 | 13 | 12 | 12 | 12 | 12 | 11 |
| West Bengal | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 6 | 6 | 6 |
| 1992-93 | 1993-94 | 1994-95 | 1995-96 | 1996-97 | 1997-98 | 1998-99 | 1999-2000 | 2000-01 | 2001-02 | 2002-03 | ||
| Andhra Pradesh | 8 | 8 | 8 | 8 | 8 | 10 | 8 | 8 | 8 | 8 | 9 | |
| Assam | 13 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | |
| Bihar | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | 15 | |
| Gujarat | 4 | 4 | 4 | 3 | 3 | 3 | 3 | 4 | 5 | 4 | 4 | |
| Haryana | 3 | 3 | 3 | 4 | 4 | 4 | 4 | 3 | 3 | 3 | 3 | |
| Karnataka | 7 | 7 | 7 | 7 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | |
| Kerala | 10 | 6 | 6 | 6 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | |
| Madhya Pradesh | 11 | 10 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | |
| Maharashtra | 2 | 2 | 2 | 1 | 2 | 1 | 2 | 1 | 2 | 2 | 1 | |
| Orissa | 14 | 14 | 14 | 14 | 14 | 14 | 13 | 13 | 14 | 13 | 13 | |
| Punjab | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | |
| Rajasthan | 9 | 11 | 9 | 10 | 10 | 8 | 10 | 10 | 10 | 10 | 10 | |
| Tamil Nadu | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 5 | |
| Uttar Pradesh | 12 | 13 | 13 | 13 | 13 | 13 | 14 | 14 | 13 | 14 | 14 | |
| West Bengal | 6 | 9 | 10 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 8 |
Note: The state with highest value of relative mean deviation is assigned rank 1 and vice versa. Source: Handbook of Statistics on Indian Economy, 2003-04.
Economic and Political Weekly March 3, 2007 related to export items, which command a significant share of total exports, and not those which are outgrowing others but remain less significant in terms of the overall share. This does not allow us to look at, for example, software-related exports. Also it is impossible to find a constant state-wise data set over time. One could make a separate ranking based on the “growth” in export, which we have not attempted here.
m Email: smarjit@hotmail.com saibal@cssscal.org
Notes
[This paper is based on a project funded by the ENRECA and the RBI Endowment at the Centre for Studies in Social Sciences, Calcutta. We are indebted to the seminar participants at ISEC Bangalore, Ronald Jones, Murray Kemp, Cheng Hsiao and particularly to Jyotsna Jalan and an anonymousreferee of this journal for insightful comments on an earlier draft. We are also thankful to Sejuti Jha and Chaitali Sinha for research assistance. The usual disclaimer applies.]
1 We are greatly indebted to the anonymous referee for encouraging such a discussion. Also, note that, perfect factor mobility implies that all states are identical and hence there should be no difference in their production or trade patterns. However, we do not use this as an assumption and instead argue that states, as the case is for all practical purposes in India, are different, with no further implications for what would be the ideal statespecific trade pattern with the rest of the world if they were trading independently. Tapalova (2005) further shows that trade liberalisation is responsible for increased incidence and depth in poverty in many districts of India, mainly because the factors were extremely limited in mobility across regions and states in the country. 2 See Annexure for detailed description of the data and classifications. 3 The value of agricultural output for each state is added to the industrial
group 15-16 (food, beverages and tobacco industry), as it is the agriculture related industry. We get agricultural value added data from the Central Statistical Organisation website/publication.
References
Balassa, B (1982): Development Strategies in Semi-Industrial Countries, Oxford University Press, Oxford.
Barro, R J and X Sala-I-Martin (1995): Economic Growth, McGraw-Hill, New York.
Batra, R (1992): ‘The Fallacy of Free Trade’, Review of International Economics, 1, 19-31.
Batra, R and D J Slottje (1993): ‘Trade Policy and Poverty in the United States: Theory and Evidence, 1974-1990’, Review of International Economics, 1, 189-208.
Buffie, E (1992): ‘On the Condition for Export-led Growth’, Canadian Journal of Economics, Vol 25, pp 211-25.
Annexure
Table A: NIC 1987 and 1998 Classifications
Industry 1987 Code 1998 Code
Food, beverages and tobacco 20-22 15-16 Textiles 23-26 17-18 Wood 27 20 Paper 28 21-22 Leather 29 19 Chemical 30 24 Rubber plastics and petroleum 31 23,25 Non-metal 32 26 Basic-metals 33 27 Metal products 34 28 Machinery and equipment 35-36 29-33,36 Transport 37-38 34-35
Source:Summary Statistics, ASI (1997, 1998).
Table 9: Rank of States on Growth Rate of PCNSDP
1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91 1991-92
Andhra Pradesh 1 10 10 12 6 13 1 5 3 95 Assam 3 4 910 514 915 510 7 Bihar 812 5 211 313 814 412 Gujarat 5 13 31114 5 15 213 1215 Haryana 10 313 7 110 11 312 310 Karnataka 7 9 6 515 1 411 7132 Kerala 14 715 6 712 10 9 2 69 Madhya Pradesh 11 5 8 14 3 15 2 12 11 2 13 Maharashtra 9 2 14 412 4 3 6 8 14 1 Orissa 1315 213 2 912 4 615 3 Punjab 4 812 3 4 8 613 111 6 Rajasthan 6 11 11513 2 14 115 114 Tamil Nadu 214 7 1 811 510 4 58 Uttar Pradesh 12 1 11 9 10 6 8 7 10 711 West Bengal 15 6 489 7 714 9 84
1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03
Andhra Pradesh 14 1 9 2 7 14 2 10 3 710 Assam 11 10 14 13 14 10 15 12 9 10 6 Bihar 1512 415 115 911 1151 Gujarat 1 14 111 2 11 5 1414 32 Haryana 12 9 814 312 11 6 6 85 Karnataka 9 410 4 6 5 1 8 214 7 Kerala 4 2 6613 8 4 2711 3 Madhya Pradesh 5 3 15 3 10 6 7 1 15 113 Maharashtra 3 13 5 811 7 3 15 8 615 Orissa 13 7 11 515 1 13 512 212 Punjab 7 813 9 8 9 8 910129 Rajasthan 2 15 210 4 2 12 1313 514 Tamil Nadu 6 5 3 712 310 4 413 8 Uttar Pradesh 10 11 12 12 5 13 14 7 11 9 11 West Bengal 8 6 719 4 6 35 44
Note: The state with highest growth rate is assigned rank 1 and vice versa. Source: Handbook of Statistics on Indian Economy, 2003-04.
Economic and Political Weekly March 3, 2007
Table B: DGCI&S Trade Classifications Tallied with ASI Data
ASI NIC Code DGCI&S (1980-81 to 1987-88) DGCI&S (1987-88 to 2002-03) Rs lakh Rs thousand Rs lakh
Food, beverages and tobacco 15-16 Section (0+1+4) Chapter 1-24Textiles 17-18 Division (26+65+84) Chapter 50-63 Wood 20 Division (24+63) Chapter 44-46 Paper 21-22 Division (25+64+892) Chapter 47-49 Leather 19 Division 61 Chapter 41-43 Chemical 24 Section 5-Division 58 Chapter 28-38Rubber, plastics and petroleum 23,25 Section 3+ Division (23+58+62) Chapter 27+ Chapter 39-40Non-metal 26 Division 66 Chapter 68-70 Base-metals 27 Division (67+68) Chapter 72-81 Metal products 28 Division 69 Chapter 82-83Machinery and equipments 29-33,36 Section 7+ Division (87+88)- Division 78 Chapter 84-85 + Chapter 90-92Transport 34-35 Division 78 Chapter 86-89
Note: All data in this analysis has been converted to Rs lakh before further analysis.
Table C: Shares of Export Commodities in Total Exports of India over the Years
Industrial Groups 1980-81 1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91
Food, beverages
and tobacco 0.2780 0.2787 0.2467 0.2350 0.2283 0.2504 0.2410 0.2165 0.1824 0.1769 0.1683 Textiles and clothing 0.1117 0.0919 0.0828 0.0262 0.0146 0.1088 0.1305 0.2649 0.2279 0.2399 0.2744 Wood 0.0028 0.0026 0.0018 0.0014 0.0013 0.0014 0.0015 0.0012 0.0010 0.0009 0.0008 Paper 0.0020 0.0029 0.0021 0.0020 0.0023 0.0024 0.0021 0.0021 0.0019 0.0019 0.0019 Leather 0.0502 0.0473 0.0409 0.0439 0.0534 0.0594 0.0587 0.0558 0.0525 0.0507 0.0539 Chemical 0.0347 0.0477 0.0393 0.0333 0.0407 0.0356 0.0382 0.0470 0.0681 0.0840 0.0787 Rubber, plastics
and petroleum 0.0084 0.0338 0.1467 0.1672 0.1619 0.0684 0.0413 0.0499 0.0340 0.0377 0.0413 Non-metal 0.0963 0.1039 0.1122 0.1280 0.1019 0.1334 0.1636 0.0031 0.0038 0.0038 0.0043 Base-metals 0.0127 0.0118 0.0087 0.0072 0.0080 0.0090 0.0065 0.0179 0.0285 0.0330 0.0342 Metal products 0.0277 0.0283 0.0223 0.0201 0.0170 0.0140 0.0132 0.0062 0.0070 0.0068 0.0067 Machinery and equipment 0.0562 0.0578 0.0509 0.0445 0.0446 0.0514 0.0548 0.0533 0.0589 0.0598 0.0570 Transport equipment 0.0292 0.0272 0.0208 0.0157 0.0162 0.0172 0.0162 0.0162 0.0180 0.0196 0.0222
1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03
Food, beverages
and tobacco 0.1798 0.1727 0.1775 0.1639 0.1970 0.1974 0.1888 0.1853 0.1567 0.1388 0.1396 0.1308 Textiles and clothing 0.2732 0.2760 0.2579 0.2731 0.2561 0.2742 0.2685 0.2721 0.2704 0.2597 0.2362 0.2249 Wood 0.0009 0.0007 0.0023 0.0017 0.0012 0.0013 0.0010 0.0007 0.0008 0.0008 0.0008 0.0009 Paper 0.0020 0.0025 0.0025 0.0038 0.0048 0.0043 0.0033 0.0038 0.0044 0.0054 0.0057 0.0061 Leather 0.0471 0.0491 0.0402 0.0429 0.0384 0.0326 0.0338 0.0346 0.0290 0.0314 0.0306 0.0252 Chemical 0.0861 0.0684 0.0709 0.0784 0.0774 0.0856 0.0944 0.0906 0.0955 0.0951 0.0971 0.1024 Rubber, plastics and
petroleum 0.0350 0.0464 0.0455 0.0454 0.0412 0.0376 0.0321 0.0232 0.0221 0.0672 0.0762 0.0804 Non-metal 0.0057 0.0071 0.0080 0.0099 0.0102 0.0098 0.0094 0.0091 0.0106 0.0118 0.0115 0.0118 Base-metals 0.0387 0.0519 0.0558 0.0481 0.0485 0.0513 0.0562 0.0469 0.0545 0.0602 0.0574 0.0734 Metal products 0.0069 0.0076 0.0077 0.0070 0.0073 0.0071 0.0069 0.0069 0.0081 0.0077 0.0079 0.0068 Machinery and equipment 0.0524 0.0454 0.0475 0.0502 0.0538 0.0613 0.0627 0.0581 0.0572 0.0684 0.0728 0.0683 Transport equipment 0.0277 0.0287 0.0266 0.0292 0.0291 0.0289 0.0266 0.0229 0.0221 0.0237 0.0233 0.0254
Table D: Share of Import Commodities in Total Imports of India over the Years
Industrial groups 1980-81 1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91
Food, beverages
and tobacco 0.0868 0.1014 0.0762 0.1163 0.1017 0.0840 0.0701 0.0864 0.0824 0.0367 0.0342 Textiles and clothing 0.0178 0.0258 0.0180 0.0245 0.0202 0.0210 0.0188 0.0203 0.0244 0.0219 0.0208 Wood 0.0007 0.0012 0.0004 0.0004 0.0005 0.0006 0.0066 0.0108 0.0129 0.0113 0.0106 Paper 0.0177 0.0226 0.0157 0.0183 0.0241 0.0272 0.0264 0.0267 0.0246 0.0215 0.0241 Leather 0.0001 0.0003 0.0002 0.0004 0.0006 0.0004 0.0008 0.0011 0.0018 0.0030 0.0045 Chemical 0.0959 0.0881 0.0596 0.0763 0.1289 0.1298 0.1096 0.0831 0.1098 0.1198 0.1087 Rubber, plastics
and petroleum 0.4354 0.4008 0.4188 0.3269 0.3394 0.2891 0.1808 0.2263 0.2081 0.2325 0.3071 Non-metal 0.0442 0.0376 0.0591 0.0807 0.0650 0.0611 0.0805 0.0046 0.0057 0.0048 0.0047 Base-metals 0.1060 0.1176 0.1061 0.0909 0.0790 0.0347 0.1032 0.1129 0.1257 0.1315 0.1096 Metal products 0.0071 0.0085 0.0010 0.0094 0.0082 0.0103 0.0104 0.0027 0.0025 0.0025 0.0025 Machinery and equipment 0.1215 0.1379 0.1515 0.1921 0.1719 0.1981 0.2979 0.1976 0.1781 0.1729 0.1599 Transport equipment 0.0376 0.0224 0.0448 0.0282 0.0215 0.0289 0.0400 0.0342 0.0267 0.0422 0.0387
1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03
Food, beverages
and tobacco 0.0287 0.0390 0.0327 0.0614 0.0439 0.0459 0.0516 0.0790 0.0655 0.0439 0.0548 0.0541 Textiles and clothing 0.0173 0.0221 0.0232 0.0329 0.0266 0.0200 0.0202 0.0201 0.0227 0.0232 0.0298 0.0267 Wood 0.0088 0.0091 0.0062 0.0079 0.0069 0.0070 0.0103 0.0091 0.0093 0.0098 0.0108 0.0068 Paper 0.0185 0.0179 0.0192 0.0180 0.0229 0.0208 0.0224 0.0203 0.0169 0.0182 0.0187 0.0161 Leather 0.0040 0.0039 0.0050 0.0044 0.0038 0.0037 0.0037 0.0036 0.0032 0.0040 0.0044 0.0035 Chemical 0.1341 0.1279 0.1151 0.1310 0.1366 0.1128 0.1222 0.1146 0.1075 0.0847 0.0946 0.0852 Rubber, plastics and
petroleum 0.3359 0.3217 0.2984 0.2679 0.2713 0.3213 0.2675 0.2136 0.3103 0.3658 0.3280 0.3408 Non-metal 0.0046 0.0041 0.0038 0.0047 0.0041 0.0033 0.0036 0.0040 0.0035 0.0037 0.0046 0.0040 Base-metals 0.0840 0.0835 0.0748 0.0899 0.0860 0.0849 0.0752 0.0563 0.0442 0.0405 0.0470 0.0406 Metal products 0.0027 0.0026 0.0028 0.0029 0.0027 0.0031 0.0033 0.0037 0.0043 0.0029 0.0027 0.0028 Machinery and equipment 0.1352 0.1408 0.1497 0.1681 0.1928 0.1651 0.1763 0.1630 0.1439 0.1565 0.1681 0.1886 Transport equipment 0.0192 0.0211 0.0545 0.0389 0.0302 0.0380 0.0254 0.0189 0.0230 0.0189 0.0224 0.0309
| 1980-81 | 1981-82 | 1982-83 | 1983-84 | 1984-85 | 1985-86 | 1986-87 | 1987-88 | 1988-89 | 1989-90 | 1990-91 1991-92 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Andhra Pradesh | -15.410 | -7.009 | -7.449 | -10.264 | -0.151 | -14.610 | -18.797 | -10.312 | -8.450 | -6.107 | -6.570 | -2.979 |
| Assam | -21.295 | -16.907 | -14.910 | -17.192 | -0.188 | -18.030 | -22.052 | -20.828 | -30.545 | -29.242 | -29.973 | -28.394 |
| Bihar | -43.791 | -43.874 | -44.635 | -43.499 | -0.397 | -41.698 | -38.433 | -43.372 | -44.379 | -47.946 | -45.711 | -49.762 |
| Gujarat | 18.916 | 23.513 | 18.901 | 31.985 | 0.287 | 18.667 | 23.459 | 7.108 | 31.464 | 23.325 | 19.780 | 8.250 |
| Haryana | 45.274 | 42.183 | 47.264 | 39.646 | 0.411 | 57.046 | 53.238 | 46.101 | 57.978 | 51.777 | 59.148 | 59.079 |
| Karnataka | -6.828 | -6.180 | -6.087 | -6.320 | -0.018 | -10.756 | -4.314 | -0.065 | -4.992 | -4.148 | -7.523 | 2.840 |
| Kerala | -7.564 | -12.936 | -12.068 | -20.798 | -0.173 | -18.193 | -21.184 | -20.073 | -22.476 | -20.473 | -17.682 | -16.982 |
| Madhya Pradesh | -16.759 | -19.396 | -17.811 | -19.615 | -0.255 | -23.513 | -28.670 | -21.314 | -26.559 | -28.962 | -23.079 | -30.076 |
| Maharashtra | 49.258 | 44.672 | 46.850 | 45.279 | 0.436 | 46.841 | 44.614 | 49.984 | 44.097 | 59.240 | 57.969 | 54.533 |
| Orissa | -19.456 | -23.545 | -29.477 | -20.741 | -0.261 | -21.721 | -22.106 | -26.383 | -22.044 | -20.753 | -37.275 | -30.440 |
| Punjab | 63.908 | 70.394 | 72.075 | 63.587 | 0.725 | 76.372 | 79.113 | 83.907 | 69.362 | 73.979 | 69.171 | 73.901 |
| Rajasthan | -25.095 | -23.841 | -24.443 | -14.094 | -0.226 | -27.367 | -22.540 | -30.159 | -13.974 | -19.960 | -11.922 | -20.210 |
| Tamil Nadu | -8.177 | -2.801 | -9.581 | -10.883 | -0.013 | -2.396 | -4.802 | -0.928 | -4.560 | -2.329 | 1.457 | 3.204 |
| Uttar Pradesh | -21.662 | -24.375 | -20.417 | -23.164 | -0.240 | -25.358 | -23.950 | -22.716 | -23.917 | -25.697 | -25.075 | -26.030 |
| West Bengal | 8.680 | 0.103 | 1.788 | 6.073 | 0.062 | 4.716 | 6.426 | 9.050 | -1.005 | -2.702 | -2.715 | 3.067 |
| 1992-93 | 1993-94 | 1994-95 | 1995-96 | 1996-97 | 1997-98 | 1998-99 | 1999-00 | 2000-01 | 2001-02 | 2002-03 | ||
| Andhra Pradesh | -11.206 | -4.228 | -5.514 | -3.305 | -3.505 | -9.121 | -2.587 | -2.901 | 3.860 | 5.603 | 3.636 | |
| Assam | -32.196 | -26.195 | -29.702 | -30.992 | -34.344 | -35.693 | -39.660 | -40.528 | -39.456 | -39.618 | -39.372 | |
| Bihar | -55.712 | -60.780 | -59.490 | -67.317 | -62.168 | -65.605 | -65.803 | -66.270 | -60.483 | -64.623 | -60.549 | |
| Gujarat | 34.606 | 26.508 | 41.343 | 39.561 | 49.672 | 44.435 | 46.322 | 36.709 | 27.230 | 31.713 | 41.693 | |
| Haryana | 48.977 | 43.077 | 42.115 | 38.315 | 42.702 | 37.456 | 35.595 | 36.812 | 40.810 | 41.160 | 43.203 | |
| Karnataka | -0.798 | 1.222 | -0.784 | 0.253 | 1.890 | 4.471 | 12.381 | 12.180 | 21.230 | 14.632 | 14.990 | |
| Kerala | -15.866 | 2.513 | 4.350 | 4.806 | 1.856 | 0.732 | 2.474 | 4.634 | 7.069 | 6.599 | 10.994 | |
| Madhya Pradesh | -29.540 | -14.973 | -19.740 | -18.652 | -19.656 | -18.995 | -18.812 | -15.207 | -26.702 | -23.363 | -31.634 | |
| Maharashtra | 67.092 | 57.334 | 48.977 | 58.395 | 52.597 | 54.498 | 50.594 | 56.037 | 44.773 | 45.858 | 50.727 | |
| Orissa | -35.724 | -36.772 | -38.071 | -37.653 | -45.904 | -40.287 | -41.716 | -41.042 | -43.338 | -41.002 | -43.124 | |
| Punjab | 71.186 | 64.140 | 56.647 | 55.843 | 55.328 | 53.244 | 52.693 | 52.243 | 53.300 | 51.253 | 48.758 | |
| Rajasthan | -13.993 | -20.164 | -12.584 | -13.548 | -10.895 | -4.572 | -6.741 | -12.051 | -17.441 | -14.683 | -25.855 | |
| Tamil Nadu | 2.903 | 15.647 | 21.701 | 21.567 | 18.448 | 24.930 | 23.493 | 25.226 | 32.609 | 26.587 | 25.125 | |
| Uttar Pradesh | -29.670 | -34.577 | -36.172 | -37.030 | -35.330 | -38.778 | -42.132 | -41.658 | -43.256 | -43.391 | -45.327 | |
| West Bengal | -0.058 | -12.752 | -13.074 | -10.242 | -10.691 | -6.713 | -6.102 | -4.186 | -0.204 | 3.274 | 6.735 |
Cappelen A, J Fagerberg and B Verspagen (1999): ‘Lack of Regional Convergence’ in J Fagerberg, P Guerrieri and B Verspagen (eds), The Economic Challenge for Europe: Adapting to Innovation-based Growth, Edward Elgar, Aldershot.
Chenery, H and A Strout (1966): ‘Foreign Assistance and Economic Development’, American Economic Review, 56, 679-733.
Clark, B (1999): ‘NAFTA: The Effects on Mexico’, Social Science, 410 (November).
Dasgupta, D, P Maity, R Mukherjee, S Sarkar and S Chakroborty (2000): ‘Growth and Interstate Disparities in India’, Economic and Political Weekly, 35, 27, 2413-22.
Dholakia, R H (1994): ‘Spatial Dimension of Acceleration of Economic Growth in India’, Economic and Political Weekly, 29, 35, 2303-09.
– (2003): ‘Regional Disparity in Economic and Human Development in India’, Economic and Political Weekly, 37, 39, 4166-72.
Egger, P, P Huber and M Pfaffermayr (2005): ‘A Note on Export Openness and Regional Disparity in Central and Eastern Europe’, Annals of Regional Science, Vol 39, No 1, pp 63-72.
Elizondo, R and P Krugman (1992): ‘Trade Policy and the Third World Metropolis’, NBER Working Paper, No 4238.
Fujita, M, P Krugman and A Venables (1999): The Spatial Economy: Cities, Regions, and International Trade, MIT Press, Cambridge, MA.
Ghosh, B, S Marjit and C Neogi (1998): ‘Economic Growth, and Regional Divergence in India: 1960 to 1995’, Economic and Political Weekly, 27, 83-116.
Grossman, G and E Helpman (1991): Innovation and Growth in the Global Economy, MIT Press, Cambridge. Johnson, B and T Sheehy (eds) (1996): Index of Economic Freedom, The Heritage Foundation, Washington. Jones, R W, H Beladi and S Marjit (1999): ‘Three Faces of Factor Intensities’, Journal of International Economics, 48 (2) 413-420. Keesing, D B (1967): ‘Outward-Looking Policies and Economic Development’, Economic Journal, 77, 303-20. Krishna, K L (2004): ‘Patterns and Determinants of Economic Growth in Indian States’, Working Paper, No 144, ICRIER. Krueger, A O (1978): Foreign Trade Regimes and Economic Development: Liberalisation Attempts and Consequences, Ballinger, Cambrige, MA. Krugman, P (1991): ‘Increasing Returns and Economic Geography’,
Journal of Political Economy, 99, 3, 483-99.
– (1995): Development, Geography and Economic Theory, MIT Press. Kurian, N J (2000): ‘Widening Regional Disparities in India: Some Indicators’, Economic and Political Weekly, 35, 7, 538-50.
Lall, S V and S Chakravarty (2006): ‘Industrial Location and Special Inequality: Theory and Evidence’ in R Kanbur, A J Venbles and G Wan (eds), Special Disparities in Human Development: Perspective from Asia, United Nations University Press, Tokyo.
Leamer, E (1988): ‘Measures of Openness’ in R Baldwin (ed), Trade Policy and Empirical Analysis, University of Chicago Press, Chicago.
Levine, R and D Renelt (1992): ‘A Sensitivity Analysis of Cross-Country Growth Regression’, American Economic Review, 82, 942-63.
Liu, G S, X Liu and Y Wei (2001): ‘Openness and Efficiency of India and China Relative to the World Economy: A Comparative Study’, http:// www.brunel.ac.uk/329/efwps/02-18.pdf
Lucas, R E (1988): ‘On the Mechanics of Economic Development’, Journal of Monetary Economics, 22(11), 3-42. Marjit, S and S Mitra (1996): ‘Convergence in Regional Growth Rates, Indian Research Agenda’, Economic and Political Weekly, 31, 33, 2239- 42. Marjit, S and H Beladi (2005): ‘International Trade and Inter-regional Disparity’, City University of Hong Kong, mimeo. Romer, P M (1986): ‘Increasing Returns and Long-Run Growth’, Journal of Political Economy, 94(5), 1002-37.
– (1992): ‘Two Strategies for Economic Development: Using Ideas and Producing Ideas’, World Bank Annual Conference on Economic Development, World Bank, Washington DC.
Sachs, J and A Warner (1995): ‘Economic Reform and the Process of Global Integration’, Brookings Papers on Economic Activity, 1, 1-118. Scitovsky, T (1954): ‘Two Concepts of External Economies’, Journal of Political Economy, 62, 143-51. Tapalova, Petia (2005): ‘Trade Liberalisation, Poverty and Inequality: Evidence from Indian Districts’, NBER Working Paper No 11614. Voivodas, C (1973): ‘Exports, Foreign Capital Inflow and Economic Growth’, Journal of International Economics, 3, 337-49. Wolf, H (1993): ‘Trade Orientation: Measurement and Consequences’, Estudios de economia, 20(2), 52-72. World Bank (1987): World Development Report 1987, Oxford University Press, Oxford.
