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Structural Change and Inter-sectoral Linkages

This article analyses the trend in sectoral shares in state domestic product and inter-sectoral linkages in north-east India for the period 1981 to 2007. The causality test reveals that there exists bidirectional causality among the sectoral output of north-eastern states, at least in the short run. In the long run, there exists a unidirectional causality running from the agricultural sector and industrial sector to services sector.

NOTES

Structural Change and Inter-sectoral Linkages

The Case of North-east India

Avijit Debnath, Niranjan Roy

a gricultural sector stimulates the industrial sector output to the extent of 0.5%. That study, however, claimed that the consumption linkages are much more powerful than the production linkages between sectors. The paper by Bhattacharya and Mitra (1997) provided empirical evidence in support of a positive linkage among the broad sec-

This article analyses the trend in sectoral shares in state domestic product and inter-sectoral linkages in north-east India for the period 1981 to 2007. The causality test reveals that there exists bidirectional causality among the sectoral output of north-eastern states, at least in the short run. In the long run, there exists a unidirectional causality running from the agricultural sector and industrial sector to services sector.

Avijit Debnath (debnath_avi@yahoo.com) and Niranjan Roy (roy_niranjan@yahoo.com) are with the department of economics, Assam University, Silchar.

1 Introduction

W
hether agriculture is more important for growth acceleration or industry should be considered as the engine of growth and how the tertiary sector is linked up with the other two sectors has always been the subject of research. This article is an attempt to analyse the trend in sectoral shares in state domestic product (SDP) and inter-sectoral linkages in north-east India (NEI)1 – a region which is isolated from mainland India not only in terms of geographic consideration, but also in terms of academic discussion. This region, barring Assam in a few cases, hardly finds any place in most regional level or state level studies that explore the dynamics of economic growth in India. Understanding i nter-sectoral linkages could shed important insights on the transition process, and such information should assist policymakers identify the optimal policies to accelerate economic growth in the region.

Section 2 deals with the empirical l iterature on sectoral interlinkage in I ndia. Section 3 is devoted to the discussion of the methodology followed and data used in the present study. Section 4 presents the results. Finally, in Section 5, we summarise the study and offer concluding remarks.

2 Interlinkage among Sectors: A Snapshot of Indian Economy

A number of researchers have studied sectoral interlinkages in the Indian economy. In one of the earliest studies on the subject, Rangarajan (1982) found a strong d egree of association between the agricultural and industrial sectors. In particular, it has been observed that an addition of 1% growth in the

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tors. It established that many services activities are significantly associated with the agricultural and industrial sectors and this helps in overall employment generation.

Hansda (2001) in a study analyses the inter-sectoral linkages as emanating from the input-output transactions tables for 1993-94, both at the aggregated level of 10 constructed national accounts categories and the most disaggregated level of 115 a ctivities. His study reveals that while services and agriculture do not seem to share much interdependence, industry is observed to be the most servicesintensive. Sustained services-growth, Hansda (2001) claimed, requires a growing industry too. Banga and Goldar (2004) in order to assess the contribution of the services sector to industrial growth estimated a capital, labour, energy, materials and services (KLEMS) production function for the Indian manufacturing sector for the period 1980-81 to 1999-2000. Empirically, it has been found that the contribution of services to output growth increased substantially to 2.07 percentage points per annum during the 1990s from a meagre 0.06 percentage points per annum during the 1980s. Sastry et al (2003) in a study maintained that due to modernisation of agriculture the dependence of agriculture on the industry for inputs has grown. As for the services sector, this study shows a movement of production linkages from the late 1960s to the early 1990s moderately in favour of agriculture, and sharply in favour of the services sector. On demand linkages, the study asserts that a fall in agricultural income reduces the demand for agricultural machinery and other industrial products, resulting in a fall of aggregate demand and vice versa. Further, a fall in aggregate demand

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e ither in agriculture or services sector is likely to cause serious production constraints in the industrial sector, thereby affecting both demand and p roduction linkages.

Bathla (2003) carried out a comprehensive econometric analysis of the intersectoral linkages in the Indian economy for the period 1950-51 to 2000-01. This study does not find any significant relationship between the primary and secondary sectors, while the primary sector was found to have a unidirectional causation with the “trade, hotels, restaurants, communication services” and “financing, insurance, real estate and business services” sectors. Further, the secondary sector was found to have a bidirectional causality both with “trade, hotels, restaurants, communication” and “financing-insurance-real estate and business” services. Under the cointegration framework, strong evidence of existence of long-run equilibrium relationship was found among the primary, secondary and the specialised services sectors.

All these studies have made useful contributions to understand the associations between different sectors in the economy. However, there is a significant gap in the literature because the intersectoral linkage studies were mainly concentrated on the national economy. The studies at the state or region level have not received comparable attention. Since India is a nation characterised by heterogeneous regions, what is true for the nation may not be true for a subregion. In an attempt to fill this gap in the literature, this study focuses on the intersectoral linkages that characterise the economic dynamics of north-east India.

3 Methodology and Data Sources

The trend in the sectoral share of SDP has been analysed by estimating the following simple regression equation:

y = Į+ȕT …(1)

where, y stands for the share of the ith sector in SDP, T stands for time trend, Į and ȕ are the coefficients of the model. A significant positive value of coefficient of time, ȕ, for a particular sector indicates a positive trend of that sector, while a significant negative value of the coefficient would mean a negative trend.

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In order to test for possible linkages among variables, the standard econometric tool of the Granger causality test (Granger 1969) has been generally used in the literature. This test states that if past values of a variable (y) significantly contribute to forecast the f uture value of another variable (x) then y is said to Granger cause x. Conversely, if past values of x statistically improve the prediction of y, then we can conclude that x Granger causes y (ibid). This paper also uses the tool of the Granger causality test to examine intersectoral linkage.

Data used in this paper are collected from the Handbook of Statistics on Indian Economy by the Reserve Bank of India (RBI 2009). All data are annual figures covering the 1980-81 – 2006-07 p eriod and variables that are measured are at constant 1999-2000 prices.2 The SDP data have been classified into three parts: agricultural SDP, industrial SDP and SDP originating from the services sector. The agricultural sector consists of agriculture and allied activities, fishery and forestry. The industrial sector includes mining and quarrying, manufacturing, construction, and electricity, gas and water supply. The services sector comprises the rest of the sub-sectors. Seven states of the north-eastern region (NER), viz, Assam, Arunachal Pradesh, Meghalaya, Nagaland, Manipur, Sikkim and Tripura have been selected for the study. Mizoram has been excluded from the study due to lack of database for the related variables.

4 Empirical Analysis and Results

4.1 Growth of SDP Components

Before we analyse the trend in sectoral shares and interlinkage among the major sectors of the north-east states, let us have a broad idea about the growth pattern of SDP components in these economies. To calculate the growth of SDP components in different states, the simple one period growth rate formula3 has been used. The annual average growth rate for the entire period has been calculated by dividing the sum of the annual growth rate by 27 (total number of years in the study).

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As one would expect, there is a considerable variation in the performance of individual states in respect of sectoral growth. It is seen from Table 1 that during the entire study period four of the north-east states, viz, Arunachal Pradesh, Meghalaya, Nagaland and Sikkim have grown at a rate higher than all-India standards in respect of agricultural SDP. Within the NER, Nagaland has been diagnosed as the top-growing state followed by Sikkim, Arunachal Pradesh, Meghalaya, Tripura, Manipur and Assam. In respect of the industrial sector, all of the north-east states except Assam have grown at a rate higher than the all-India standards. Within NER, Nagaland has been identified as the top-growing state in terms of income-generation from the industrial sector followed by Arunachal Pradesh and Tripura, whereas Assam secured the bottom place in this list. The growth pattern of services sector SDP is in Table 1: Growth Pattern of SDP by Major Sectors from 1981-2007 (At constant 1999-2000 prices,

in % per annum)

States Agricultural Industry Services
Arunachal Pradesh 4.73 11.59 7.52
Assam 1.72 5.17 5.56
Manipur 3.02 7.32 4.15
Meghalaya 3.41 7.72 5.81
Nagaland 8.61 13.60 5.27
Sikkim 5.31 8.57 10.40
Tripura 3.04 11.08 8.24
North-east region (NER) 2.31 6.12 5.82
All India 3.22 6.43 8.31

Source: Calculated by authors.

the final column of Table 1. Here, three of the north-east states, viz, Arunachal Pradesh, Sikkim and Tripura have grown at a rate higher than the all-India standards during the study period. Within the NER, Sikkim has been identified as the top-growing state in terms of incomegeneration from services, followed by Tripura and Arunachal Pradesh, whereas Assam secured the bottom place in this list. This analysis shows that though Assam is the “king” of the NER in terms of her contribution to SDP of NER,4 performance in respect of sectoral growth is among the poorest in the region.

4.2 Structural Changes in the State Economies

In order to analyse structural changes in the state economies of NER, the SDP data is divided into three major sectors, viz, the

NOTES

Table 2: The Estimated OLS Result of Changing Share of Sectors

Dependant Variable Agriculture Industry Services

States Equn1 Equn2 Equn3

Arunachal -0.007*** -0.001** 0.005*** Pradesh (-24.65) (-3.21) (18.69)

Assam -0.007*** -0.001** 0.005*** (-24.66) (-3.21) (18.69)

Manipur -0.005*** 0.003 0.002*** (-6.31) (1.07) (3.42)

Meghalaya -0.007*** 0.005*** 0.003*** (-10.03) (4.52) (4.66)

Nagaland 0.002* 0.004** -0.013***

(1.91) (5.02) (-16.14)

Sikkim -0.012*** 0.001* 0.010*** (-18.05) (1.80) (11.14)

Tripura -0.014*** 0.005*** 0.007*** (-38.71) (5.54) (8.73)

(*), (**) and (***) indicate significant at 10%, 5% and 1% level. Student's tests are in parentheses.

agricultural, industrial and services sector and then the output of each of these sectors has been regressed on time using Equation (1). The values of time coefficients for the different sectors in each of the north-east states are presented in Table 2.

From Table 2, we observe that the coefficients of time are highly significant in almost all the regressions, suggesting the presence of significant trends in the sectoral share. The coefficient of time is negative for the agricultural sector in all the states, except in Nagaland, and it is statistically significant. On the contrary, the coefficient of time for services is positive, except in Nagaland, and it is also statistically highly significant. This implies that the share of the primary sector has fallen sharply in almost all the states and that of the tertiary sector has risen, except for Nagaland where the primary sector’s share has increased and the tertiary sector’s share has fallen over the study period. The coefficient of time for the industrial sector turns out to be positive for five states. For Arunachal Pradesh and Assam, the sign has been found to be linkages among the major sectors of the state economies. For this purpose, the standard econometric tool of the Granger causality test (Granger 1969) has been used. One important precondition for conducting the Granger causality test is to examine the time series properties of the variables in study. Because if the vector auto regressive equation used to conduct the Granger causality test is estimated with data that are non-stationary,5 the results would not be reliable. To be specific, the t-statistics of the estimated coefficients will be unreliable since the underlying time series would have theoretically infinite variance (Granger 1986). To investigate stationary property of time series, the test for a single unit root has been conducted using Phillips-Perron (PP) panel unit root tests. We chose this method because the PP-test has greater power than the Augmented Dickey and Fuller (ADF) test (Banerjee et al 1993). Another advantage of the PP tests over the ADF test is that the PP tests are r obust to general forms of heteroskedasticity in the error term u (Phillips

t

and Perron 1988). Besides, unlike the ADF technique, the user does not have to specify a lag length for the test regression in the PP technique. Table 3 presents the results of PP panel unit root tests with lag length chosen by downward search (t-test on the longest lag). The null hypothesis of a unit root is not rejected for any of the three variables in levels. However, each of the series is found to be stationary in fist difference. Therefore, all the variables are integrated of order one.

Table 3: Panel Unit Root Test Results (Phillips-Perron) Series Level@ First Difference#

AGRit 3.91 58.77***
SRVit 0.06 79.48 ***
INDit 0.88 79.29***

and Wu (1999). Table 4 shows the results of panel co-integration tests under the null hypothesis of no co-integration.

Table 4: Johansen Fisher Panel Co-integration Test

Hypothesised No of Maximum Eigenvalue P-Value Co-integrating Equation(s): H0

None (r = 0) 70.49

At most 1 ( r ”1) 34.64 0.01

‘r’ indicates the number of co-integrating vectors.

The results indicate that the null hypothesis of the zero co-integrating vector is rejected using the 99% critical value. This implies that the variables are co-integ rated with at least one co-integrating vector. Given the evidence of co-integration,6 the long-run relationship among the variables can be expressed as:

SRV = 1531.54+1.12IND+1.11AGR …(2)

In the co-integration Equation (2), the positive sign of the coefficient of agr indicates that if output of agricultural sector were to increase by one unit, output of services sector would increase by

1.11 units. The positive sign of the coefficient of INDindicates that if output of industrial sector were to increase by one unit, output of services sector would increase by 1.12 units.

The long-run relationship among the variables merely shows the degree of association and not interlinkage. That is, from co-integration Equation (2) we can say what would happen to output of ith sector if output of jth sector increases, but we cannot say whether it is output of ith sector that causes the output of jth sector to change, or the other way around. In order to examine the direction of linkage, we have to conduct Granger causality tests among the variables. But for a VAR first-differences system with co-integrated variables the Granger causality test must

negative. For Manipur, the coefficient though positive but not significant. These results indicate that the share of the industrial sector has remained mostly stable or increased (marginally) in almost all states, except in Arunachal Pradesh and Assam where the share of the industrial sector has fallen.

4.3 Interlinkages

Having observed the pattern of structural change among the north-east economies of India, we should now explore the

*Reject null hypothesis of non-stationary at 1% significant level. @ indicates with drift and trend, and # indicates with drift only. The residual spectrum has been estimated by the Bartlet kernel method with Newey-West bandwidth selection. AGR-agricultural SDP, SRV-services sector SDP, and IND – industrial sector SDP.

Since all the series are integrated of the same order – integration of order 1 (I (1))

– the series can be further tested for the existence of long-run relationships among them using the co-integration technique. We apply the Johansen-type panel cointegration test as developed by Maddala

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be conducted in a vector error correction model (VECM) setting (Greene 2008). Thus, to analyse in details the long-run adjustments between sector shares we propose following dynamic panel vector error correction models:7

n n ǻAGRi,t =Į1+ Ȉȕ1,lǻAGRi,t–l+ȈȖ1,lǻSRVi,t–l

l=1 l=1 n

Ȉį1,l ...(3)

ǻINDi,t–l + ȜECTt–l + İLW

l=1 n n ǻSRVi,t =Į2+ Ȉȕ2,lǻAGRi,t–l+ȈȖ2,lǻSRVi,t–l

l=1 l=1 n

Ȉį2,l ...(4)

ǻINDi,t–l + ȜECTt–l + İLW

l=1

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NOTES
Table 5: Granger Causality Test Results under Panel VECM

ǻINDi,t =Į3+ Ȉȕ3,lǻAGRi,t–l+ȈȖ3,lǻSRVi,t–l

l=1 l=1

Dependent Explanatory Variables R2

n

Variables p ǻAGRǻSRVǻIND CoefficientofECT

it–1 it–1 it–1

Ȉį3,l ...(5)

ǻINDi,t–l + ȜECTt–l + İLW (1) (2) (3) (4) (5) (6)

l=1

F – Statistics

where index i refers to the state (i = 1,..., ǻAGRit 21.33*** 33.00*** -0.01 (-0.64) 0.51

7), t to the time period (t = 1,..., T) and l ǻSRVit 21.92** 11.94* -0.14*** (7.20) 0.75

to the lag.8 İLW, İLW and İLW are supposed to be white-noise errors. Ȝ1,Ȝ2and Ȝ3are coefficients for the error-correction terms. These coefficients are expected to capture the adjustments of ǻAGRit, ǻSRVit, and ǻINDit towards long-run equilibrium. In our case, Equation (3) is used to test causation from services sector SDP, and industrial SDP to agricultural SDP. If all the Ȗ1,l = , change in SRV does not Granger cause change in AGR. And if all the į1,l = then it implies that change in IND does not Granger cause change in AGR. Similarly, Equation (4) is used to test causality from the industrial sector SDP, and agricultural SDP to services sector SDP. Change in AGR does not Granger cause change in SRV, if all the ȕ2,l= , and if all the į2,l= then it will imply that change in IND does not Granger cause change in SRV. Finally, if all the ȕ3,l and all the Ȗ3,l are equal to zero in Equation (5), then neither change in AGR nor change in SRV would Granger cause change in IND.

The VECM approach, besides showing the direction of Granger causality among the variables, enables one to distinguish between “short-run” and “long-run” Granger causality. The former is generally referred to as the Channel 1 source of causation and can be evaluated by testing whether the estimated coefficients on lagged values are jointly statistically significant. This can be done using the F test. For convenience, we interpret this short-run Granger causality as weak causality. On the other hand, longrun Granger causality is generally referred to as the Channel 2 source of causation and can be evaluated by testing whether the coefficient of the error-correction term in each equation [that is, Ȝ1 = ; Ȝ2 = ] is statistically different from zero by a t-test. The empirical results of causality through these channels are shown in Table 5. We report three causality tests relating to zero restriction of relevant variables in the VECM where the null hypothesis is that there is

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ǻINDit 51.94*** 21.79*** 0.01 (0.53) 0.54

Numbers in parentheses are t-statistics, the asterisks indicate the following levels of significance: * = 10%, ** = 5%, and *** = 1 %. The optimal lag-structure determined through AIC criterion is 4. Diagnostic tests (not reported) conducted for

residual autocorrelation are overall found to be satisfactory.
Source: Estimated by authors.
no Granger causality against the alter- INS to SEV is rejected at 1% level. The
native that there is Granger causality. estimated error correction coefficient
In Table 5 beginning with the short (-0.14) of Equation (4) indicates that the
run Granger causality, the F statistics annual adjustment of SRV will be 14%
suggest that there is a strong inter-secto of the deviation of SRVit–1 from its co
ral growth linkage in the states of north integrating value. That is if SRV is above
east India. Focusing first on the agricul its equilibrium value by one point in any
tural SD P equation, the change in SD P time, SRV falls by 0.14 points on average
share of services sector ('SRV) appears in the next year and vice versa.
to “Granger cause” a change in the SD P
share of the agricultural sector ('AGR) 5 Conclusions
at 1% significance level. The inclusion of Our findings point to a large degree of
past information on 'SRV improves the interdependence in sectoral growth. The
forecast for 'AGR. Nevertheless, from causality test reveals that there exists a
the services sector equation, it is found bidirectional causality among the sec
that 'SRV are driven by 'AGR. There toral output of north-eastern states at least
fore, we observe the presence of bidirec in the short run. In the long run, there
tional causality between agricultural exists a unidirectional causality running
and services sector output. Another at from the agricultural and industrial sec
tractive outcome is that 'AGR seems to tors to the services sector. The nature
Granger cause change in industrial SD P of relationship between services and
('IND), and also 'IND seems to Granger industry, and between services and agri
cause 'AGR. Thus, bidirectional cau culture are both positive. This study
sality existed between agricultural and certainly reveals that the income of state
industrial output as well. Further, the economies in north-east India largely
null hypothesis that 'SRV does not depends on the income-generating from
Granger cause 'IND is rejected at 1% the services sector, and the income growth
level. We can also reject the hypothesis of services sector, in turn, depends on
that 'IND does not Granger cause 'SRV growth of agriculture and industry. De
at 10% level of significance. This shows spite the continuous fall in the share of
bidirectional causality between ser- agricultural sector in SD P, this sector has
vices sector and industrial sector output. not lost its importance in overall economic
Thus, there exists bidirectional causal- growth of state economies in this region.
ity among the sectoral output of north- Similarly, the contribution of the indus
eastern states in the short run. trial sector though relatively poor, it has
Based on the t-statistics of the error a significant positive impact on services
correction terms in column 5, it follows sector income. Therefore, for fostering
that the error-correction terms in Equa rapid, sustained and broad-based growth
tions (3) and (5) are insignificant; this in north-east India, the agricultural and
suggests that 'AGR and 'IND do not industrial sector remain the key priority
react to the co-integrating errors. There for governmental policies.
fore, these variables are exogenous in the
long run. However, the error-correction No t e s
term in Equation (4) is highly significant. 1 The north-east region (NER) of India comprises
Therefore, the null hypothesis of no long-run causality from AGS to SRV and eight states – Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura – occupying 2,62,179 sq km
vol x l v i I n o 6 75

NOTES

and with a population of 39 million as per form plays an important role in the interpreta
the 2001 Census. The region is surrounded tion of the estimated coefficients, therefore, we
by Bangladesh, Bhutan, China (Tibet) and conducted a formal test (BOX-COX transforma-
Myanmar. tion test) to decide between those two specifi
2 The RBI publications on SDP and its sectoral cations. Our test shows that the model in liner
distribution do not provide data at uniform form is significantly better than the model in
constant price figures. In order to have a con double logarithmic form. Therefore, we specify
sistent set of data on SDP and its sectoral break the model in linear form. For detail on this
up at 1999-2000 constant prices, the usual pro issue see Asteriou (2006) p-176-177.
cedure of linking the indices by changing the 8 The lag lengths are determined so that İ1,i,t, İ2,i,t
base of constant prices is followed. and İ3,i,t are serially uncorrelated.
3 In order to calculate the growth rate of a
variable (Y) in period t over period t–1,
following simple method has been applied: References
W W W W W W W J ½½ § · ° ° u u® ¾ ® ¾¨ ¸° °¯ ¿ © ¹¯ ¿ Asteriou, D (2006): Applied Econometrics: A Modern Approach using Eviews and Microfit (New York:
Here gt–1,t is the percentage growth/change in Palgrave Macmillan).
4 5 variable Y from period t-1 to t. Assam contributes around 70% of total SDP of NER during the period 1981-2007. A time series is said to be stationary if its mean and variance are constant over time and the value of the covariance between the two-time periods depends only on the distance or gap between the two-time periods and not the ac- Banerjee, A, J Dolado, J H Galbraith and D F Hendry (1993): Co-Integration, Error-Correction, and the Econometric Analysis of Non-Stationary Data: Advanced Texts in Econometrics (Oxford: Oxford University Press). Banga, R and B N Goldar (2004): “Contribution of Services to Output Growth and Productivity in Indian Manufacturing: Pre- and Post-Reform”,
tual time at which the covariance is computed. ICRIER Working Papers, No 139, Indian Coun
6 Time series variables are said to be co-integrated if each of the series taken individually is non cil for Research on International Economic Relations, New Delhi.
stationary with integration of order one, i e, I (1), Bhattacharya, B B and A Mitra (1997): “Changing
while the linear combination of the series are Composition of Employment in Tertiary Sector:
stationary with integration of order zero, i e, I (). A Cross-Country Analysis”, Economic Politi
7 In literature, some authors specify this model cal Weekly, 21 March, Vol 32, 529-34.
in double logarithmic form, while others specify Bathla, S (2003): “Inter-sectoral Growth Linkages
in liner form. Since the choice of functional in India: Implications for Policy and Liberalised

Reforms”, Institute of Economic Growth Discussion Papers, No 77, Institute of Economic Growth, Delhi.

Granger, C W J (1969): “Investigating Causal Relations by Econometric Models and Cross Spectral Methods”, Econometrica, Vol 37, pp 428-38.

– (1986): “Development in the Study of Cointegrated Economic Variables”, Oxford Bulletin of Economics and Statistics, Vol 48, pp 213-28.

Greene, W H (2008): Econometric Analysis (New Delhi: Pearson Education).

Hansda, S (2001): “Sustainability of Services-led Growth: An Input-Output Analysis of the Indian Economy”, Reserve Bank of India Occasional P apers, Vol 22, pp 73-118.

Maddala, G S and S Wu (1999): “A Comparative Study of Unit Root Tests with Panel Data and New Simple Test”, Oxford Bulletin of Economics and Statistics, 61, pp 631-52.

Phillips, P C B and P Perron (1988): “Testing for a Unit Root in Time Series Regression”, Biometrika, Vol 75, pp 335-46.

Rangarajan, C (1982): “Agricultural Growth and Industrial Performance in India”, Research Report No 33, International Food Policy Research Institute, Washington DC.

RBI (2009): “Handbook of Statistics on Indian Economy”, Reserve Bank of India, viewed on 4 February 2010 (http://www.rbi.org.in).

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