Regional Disparity, Infrastructure Development and Economic Growth ...

Research and Practice in Social Sciences Vol. 6, No. 2 (February 2011) 17-30

Patra, A & Acharya, A

Regional Disparity, Infrastructure Development and Economic Growth: An Inter-State Analysis Aditya Kumar Patra* and Arabinda Acharya**

Abstract

This write-up, an attempt made to examine the spatial disparities in infrastructural facilities across 16 major states of India and in turn analyses its impact on regional economic growth. Further, an attempt has been made to examine the effect of the former on the latter at an aggregate level considering state as a unit of analysis, using a simple multivariate method to compute a composite Infrastructure Development Index (IDI) by combining various infrastructural services available at the state level. The effect of different infrastructural variables on economic growth is observed using correlation matrix and path regression analysis. Empirical evidence suggests that there is a positive relationship between Infrastructure Development Index & Per Capita Net State Domestic Product and negative relationship between Infrastructure Development Index & Poverty. Hence, effort should be directed to create more infrastructure facilities at the state level to raise the state domestic product and reduce the level of poverty and unemployment of the people concerned.

Key Words: Infrastructure Development Index, Regional Disparity

(*Aditya Kumar Patra, Lecturer in Economics, Kalinga Mahavidyalaya, G.Udayagiri, Kandhamal, Orissa; E-mail: [email protected]. **Arabinda Acharya, Research Scholar, Gokhale Institute of Politics and Economics, and Demographer, UNFPA; [email protected])

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Introduction Infrastructure is the foundation for development of any country. Availability of adequate infrastructure facilities is an important pre-condition for sustainable economic and social development. Recognizing their importance in economic development these services are also referred to as „Social Overhead Capital‟ (Hirschman, 1985). The word infrastructure is defined in dictionary as “the underlying foundation or basic framework”. Infrastructure can be categorized as „Economic Infrastructure‟ and „Social Infrastructure‟. The former includes Transport and Communication, Irrigation, Energy, Banking and Insurance etc., whereas the latter includes sectors like Health, Education, Housing etc. Infrastructure encompasses activities that share technical features (such as economies of scale) and economic features (such as spillovers) from users to non-users (WDR, 1994). Regional disparities in economic development can be explained in terms of varying levels of infrastructural services available to people in different regions. Improvement in infrastructural services is essential for enhancing efficacy of the productive process and for raising productivity of any economic entity.

Attempt has been made in this write-up to examine the spatial disparities in infrastructural facilities across 16 major states in India and in turn analyses its impact on regional economic growth. Further attempt has also been made to examine the effect of the former on the latter at an aggregate level considering state as unit of analysis. The paper is organized as follows: Section II speaks of the background while Section III deals with data and methodology. Section IV discusses spatial disparity in income and infrastructure index. Section V concludes the paper.

Background The linkage between infrastructure and economic growth is not a one-dimensional one, rather multiple and complex. Infrastructure not only affects production and consumption directly but also creates many direct and indirect externalities and involves large flows of expenditure thereby creating additional income and employment. Infrastructure promotes growth and economic growth brings about changes in infrastructure.

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In the literature, there are several studies examining the relationship between different physical infrastructure services and per capita income / output. These studies suggest that infrastructure does contribute towards the growth of output, income and employment of the economy and ultimately the quality of life of the people in the concerned economy [Looney and Frederiksen 1981; Hardy and Hudson 1981; Aschauer 1989; Ebert et al 1991; Queiroz and Gautam 1992; Gramlich 1994; Cutanda and Paricio 1994; Esfahani and Ramirez 2003]. Studies also exist on the inter-state disparities on the level of economic development and infrastructure facilities, e.g., Rao (1977), Elhance and Lakshmanan (1988), Ghosh and De (1998, 2004), Sahoo and Saxena (1999) are only a few to name. Sarkar (1994) adopts principal components method to compute the infrastructure index. CMIE (1997) obtained infrastructure index as a weighted average of various components of infrastructure facilities. However, weights have been assigned in an arbitrary manner. 10th and 11th Finance Commissions have used the index of infrastructure as one of the criteria for devolution of funds to states. Bhatia (1999) constructed an index of rural infrastructure and his study revealed that the index of infrastructure significantly influences the per hectare yield of food grains in the state. Nagar and Basu (2002) computed the Infrastructure Development Index for 17 major Indian states through Principal Component Analysis

Data & Methodology Data Infrastructure can be measured either in terms of investment towards a particular service or in terms of physical quantity of the services available to the end users. In this study we employ the following 10 indicators of physical infrastructure services / facilities with their respective weights (calculated as per the methodology given below) to construct the infrastructure index. 1. Percentage of village electrified:

0.06820

2. Per capita consumption of electricity:

0.00334

3. Length of road per 1000 sq. km. of area:

0.00116

4. Length of railway route per 1000 sq. km. of area:

0.08849

5. Vehicle density per sq. km. of land area:

0.04521

6. Percentage of village connected by road:

0.03709

7. Number of post office per lakh of population:

0.27140

8. Bank per lakh of population:

0.44582 19



9. Number of mobile consumers in lakh:

0.01380

10. Registered motor vehicles per 1000 persons:

0.02549

The required data is collected from various sources such as: Statistical Abstract India 2006, Central Statistical Organisation; Banking Statistics Quarterly, March 2007 and Center for Monitoring Indian Economy (CMIE), Infrastructure, May 2006. The study is confined to 16 major states of Indiai for the year 2002-03.

Methodology Different kinds of infrastructural services combined together affect the per capita income and output of the economy. They are mutually interdependent. Hence, it is not appropriate to take one of the services and analyse its effect on growth of the economy. There is need to compute a „Composite Index of Infrastructure‟ by integrating various components in a suitable manner. The preceding description shows that there is no unanimity regarding the methodologies used to compute the infrastructure development index. Here an attempt is made to devise a method quite analogous to the one proposed by Morris and Liser (1977) and used by Mukherjee (1980), Iyengar and Sudarshan (1982). Under this procedure infrastructure development index is computed as a weighted average of various components of infrastructure services from a multivariate data set where the weights vary inversely to the variation of the componentsii. The detailed methodology runs as follow: Let X ij represent the value of the ith infrastructural development indicator in jth state, (i = 1, 2, 3, ……., 10; j = 1, 2, 3, ………, 16). Let us write Yij 

Where, Min

X ij  Min j X ij

…………………. (1)

Max j X ij  Min j X ij j

X

However, if X

ij

ij

and Max

j

X

ij

are the minimum and maximum of X

ij

respectively.

is negatively associated with the status of infrastructural development,

equation (1) can be written as: Yij 

Max j X ij  X ij Max j X ij  Min j X ij

…………………. (2)

Obviously, the scaled values, Y ij, vary from zero to oneiii. From the matrix of scaled values, Y = {(Y ij)}, we may construct infrastructure development index of different states as: 20

Research and Practice in Social Sciences Vol. 6, No. 2 (February 2011) 17-30 Y j  W1Y1 j  W2Y2 j  W3Y3 j  ........ WmYmj

Where, the weights W

i

Patra, A & Acharya, A ……………. (3)

vary inversely as the variation in the respective indicator of

infrastructure services subject to the condition: 0  Wi  1 and W1  W2  W3  ........  Wm  1

Such that, Wi 

K Variance Yi

…………………………. (4)

m 1 Where, K    i 1 Variance Yi 

1

  ……………… (5)  

The overall state index of infrastructural development, Yj, also varies from zero to one. The choice of weights in this manner ensures that large variation in any one of the indicators will not unduly dominate the contribution of the rest and distort the inter-state comparison.

The effect of infrastructure variables on net state domestic product, poverty and unemployment can be studied by examining the zero order correlation coefficient and path analysis. In the zero order correlation the study tries to comprehend the overall relationship. Through path diagram we are able to establish the pattern of (causal) relationships among sets of observable and unobservable variables. Keeping this in view, an attempt has been made in this paper to estimate zero order correlation coefficient between infrastructure variables, PCNSDP, poverty and unemployment and an effort has also been made to portray their relations in a path diagram.

Result & Findings Table 1 reports the infrastructure Development Index (IDI) along with their rank iv of 16 Indian states for the year 2002-03 using the methodology analyzed in the earlier paragraph. The table shows that there is huge variation among the states in terms of spatial development. Punjab has the distinction of being at the top with index score 0.836 and highly developed state, while Uttar Pradesh remains at the lowest with index score of 0.179 (Chart 1). The difference between two index score of IDI of two extreme states in terms rank is 0.657. The mean index score of IDI for all the states is 0.468, standard deviation is 0.2097 and coefficient of variation is 44.66 per cent. This clearly shows that there is spatial difference 21



between states in respect of Index of Infrastructure. We may divide the states according to the value of IDI into two groups, viz., (i) developed states and (ii) backward states. The „developed state‟ category has IDI value higher than the average (IDI > 0.468) and the „backward state‟ category has lower than the average (IDI < 0.468). If we adopt Bordav rule for ranking of infrastructure services it is quite evident that there exists spatial disparity. A comparison of ranks as per the method given above and the Borda rule we observe that there is a close proximity of results. One can observe that the ranks under both these methods are very close, and the rank correlation coefficient is 0.853.

Linkage between IDI, PCNSDP & Poverty Now the question arises whether the states having good infrastructure facilities are better performing ones. To examine this I have taken two indicators: (1) per capita net state domestic product and (2) ratio of poverty. Table 2 depicts the per capita net state domestic product (PCNSDP) of different major states of India for the year 2002-03 at current prices and percentage of population below poverty line for those states in the year 2004-05vi, with their respective ranks in each category. Data shows that there is a negative relationship between PCNSDP and poverty; if one is high the other is low. The value of correlation coefficient is (-) 0.774. This is fully consistent with the commonsense knowledge.

Table 3 presents the status of states with regard to IDI, PCNSDP and proportion of people below poverty line. It can be examined from the table that the states having high IDI value signifies high PCNSDP and low percentage of people below poverty line and the converse is true for the low value of IDI. However, there are certain exceptions: (1) Rajasthan having low IDI value represents low PCNSDP and at the same time less percentage of people living below poverty line and (2) West Bengal has also low IDI value but PCNSDP is higher than the average (though marginally) consequently poverty is lower than the national average.

Hence, the empirical data is quite consistent with our conclusion that the better performing states in physical infrastructure services are well off in terms of per capita income and adoption of poverty alleviation programmes at the state level. The rank correlation coefficient between IDI and PCNSDP is 0.785 and between IDI and Poverty ratio is (-) 0.750.

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Table 4 presents the result of zero order correlation coefficient matrix. The result reveals that per capita net state domestic product has significant relationship between maximum infrastructure variables except the road and rail length. Further it is also observed that there is an array of positive and significant relationship between poverty reduction and growth of infrastructure facilities. However, the study is unable to establish any relationship between infrastructure and unemployment. The relationship between reduction in unemployment and growth of infrastructure facilities is quite dismal. This may be due to fact that, mere existence of physical infrastructure facilities does not ensure its service utility to the people of the region. Probably people of the concerned area are not able to reap the benefits with the proper utilization of the various infrastructural facilities.

In order to investigate how the infrastructure variables influence per capita net state domestic product, poverty and unemployment we use a multivariate technique known as „Path Analysis‟. The results of path analysis (path coefficients and R2 values) are shown in Table 5. Empirical evidence shows that infrastructure variables have high potentiality to influence the growth of per capita net state domestic product, which in turn able to reduce poverty and ultimately helps economic growth. The path diagram as to how each of the infrastructure variables influences PCNSDP, poverty and unemployment, directly and indirectly, through a proximate determinant relationship is depicted in Chart 2.

Conclusion This research writ-up proposes a simple multivariate method to compute a composite Infrastructure Development Index (IDI) by combining various infrastructural services available at the state level. The weights attached to each component is calculated in such a manner that it will ensure that large variation in any one of the indicators will not unduly dominate the contribution of the rest and distort the inter-state comparison. The position of each state has been determined as per the value of IDI and PCNSDP on the one hand and IDI and Poverty ratio on the other. Empirical evidence suggests that there is a positive relationship between IDI & PCNSDP and negative relationship between IDI & Poverty. Therefore if the reform processes in the field of infrastructure facilities are to be accelerated with the reduction of regional disparities than the nation shall undoubtedly be able to achieve the avowed objective of „growth with justice‟. Hence, efforts should be directed to create more infrastructure facilities at the state level, to raise the state domestic product and reduce the level of poverty and ultimately the standard of living of the people concerned. 23


Patra, A & Acharya, A Reference

Aschauer.D.A., 1989. Public Investment and Productivity Growth in the Group of Seven, Economic Perspectives, Vol 13, No 5. Bhatia.M.S., 1999. Rural Infrastructure and Growth in Agriculture, Economic and Political Weekly, Vol 34, No 13. CMIE, Profile of States: March, 1997. CMIE, Mumbai Cutanda.A and J.Paricio, 1994. Infrastructure and Regional Economic Growth: The Spanish Case, Regional Studies, Vol 28, No 1. Ebert.R.W. and K.T.Duffy-Deno, 1991. Public Infrastructure and Regional Economic Development: A Simultaneous Equations Approach, Journal of Urban Economics, Vol 30. Elhance.A.P and T.R. Lakshamana, 1988. Infrastructure-Production System Dynamics in National and Regional Systems: An Economic Study of the Indian Economy, Regional Science and Urban Economies, Vol 18, No 2 Esfahania.H.S. and M.T.Ramyrezb, 2003. Institutions, Infrastructure and Economic Growth, Journal of Development Economics, Vol 70, No 2. Ghosh.B. and P.De, 1998. Role of Infrastructure in Regional Development: A Study of India over the Plan period, Economic and Political Weekly, Vol 33 No 47 and 48. Ghosh.B. and P.De, 2004. How do Different Categories of Infrastructure Affect Development? Evidence from Indian States, Economic and Political Weekly, October 16. Gramlich.E.M., 1994. Infrastructure Investment: A Review Essay, Journal of Economic Literature, Vol 32, No 3. Hardy.A and H.Hudson, 1981. The Role of the Telephone in Economic Development:An Empirical Analysis, OECD, Geneva Hirschman. A.O., 1985. The Strategy of Economic Development, (Yale University Press, New Haven). Iyengar.N.S. and P.Sudarshan, 1982. A Method of Classifying Regions from Multivariate Data, Economic and Political Weekly, December 18. Looney.R and P.Frederickson, 1981. The Regional Impact of Infrastructure Investment in Mexico, Regional Studies, Vol 15, No 4. Morris.M.D. and P.B.Liser, 1977. The PQIL: Measuring Progress in Meeting Human Needs, Overseas Development Council, Communique on Development Issues. Mukherjee.M., 1980. Physical Quality of Life Index, CMIE, Mumbai. Nagar.A.L. and S.R.Basu, 2002. Infrastructure Development Index: An Analysis for 17 Major Indian States (1990-91 to 1996-97), Journal of Combinatorics, Information & System Sciences, Vol 27, No 1-4. Querioz.C. and S. Gautam, 1992. Road Infrastructure and Economic Development: Some Diagnostics Indicators, Policy Research Working Paper 921, World Bank. Rao.H., 1977. Identification of Backward Regions and the Trends in Regional Disparities in India, Artha Vijana, Vol 9, No 2. 24



Sahoo.S and K.K.Saxena, 1999. Infrastructure and Economic Development: Some Empirical Evidence, The Indian Economic Journal, Vol 47, No 2. Sarkar.P.C., 1994. Regional Imbalancesin Indian Economy over Plan Periods, Economic and Political Weekly, Vol 29, No 11. World Development Report: Infrastructure for Development, 1994. (Oxford University Press, New York). Tables Table 1: Infrastructure Development Index (IDI) of 16 Major Indian States State Andhra Pradesh Bihar Chhatisgarh Gujurat Haryana Jharkhand Karnataka Kerala Madhya Pradesh Maharashtra Orissa Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal

IDI 0.595 0.184 0.346 0.570 0.508 0.239 0.687 0.787 0.381 0.478 0.361 0.836 0.397 0.685 0.179 0.260

Borda Rank 6 16 13 4 5 15 8 3 14 7 10 1 12 2 12 9

Rank 5 15 12 6 7 14 3 2 10 8 11 1 9 4 16 13

Source: Computed

Table 2: Per Capita Net State Domestic Product and Poverty of 16 Major Indian States State Andhra Pradesh Bihar Chhatisgarh Gujurat Haryana Jharkhand Karnatak Kerala Madhya Pradesh Maharashtra Orissa Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal Average

PCNSDP 19087 5606 12369 22624 26818 11139 19576 22776 11500 26858 10164 26395 12641 21740 9963 18494 17359.38

Rank 8 16 11 5 2 13 7 4 12 1 14 3 10 6 15 9

Poverty 15.8 41.4 40.9 16.8 14.0 40.3 25.0 15.0 38.3 30.7 46.4 8.4 22.1 22.5 32.8 24.7 27.2

Rank 13 2 3 12 15 4 8 14 5 7 1 16 11 10 6 9

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Table 3: Status of 16 Major Indian States with respect to IDI, PCNSDP & Poverty State

IDI

Andhra Pradesh Bihar Chhatisgarh Gujurat Haryana Jharkhand Karnatak Kerala Madhya Pradesh Maharashtra Orissa Punjab Rajasthan Tamil Nadu Uttar Pradesh West Bengal Source: Computed

Note:

Above Average *

Below Average * *

* * * * * * * * *  * * 

PCNSDP Above Below Average Average * * * * * * * * * * * *  * * 

Poverty Above Below Average Average * * * * * * * * * * * *  * * 

„*‟ Represents High IDI, High PCNSDP & Low Poverty „ „ Represents inconsistent relationship between IDI, PCNSDP & Poverty

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Table 4: Zero Order Correlation Matrix PVE PVE PCONE RLT RaLT VED VCRL PPL BANPL MOBL REM PCNSDP POV UNEM

1 0.601* 0.073 -0.255 0.412 0.548* 0.400 0.617* 0.021 0.720** 0.722** -0.615* 0.144

PCONE 1 -0.149 0.093 0.487 0.611* 0.156 0.475 0.006 0.877** 0.716** -0.582* -0.033

RLT

1 0.164 0.566* 0.221 -0.012 0.578* -0.003 0.103 0.229 -0.233 0.754**

RaLT

VED

1 0.583* -0.052 -0.651** 0.186 0.249 0.184 0.252 -0.425 0.237

1 0.432 -0.187 0.791** 0.172 0.753** 0.747** -0.742** 0.577*

VCRL

PPL

1 0.073 0.648** -0.081 0.531* 0.636** -0.596* 0.020

1 0.153 -0.210 0.175 -0.134 0.088 0.104

BANPL

1 0.068 0.704** 0.746** -0.748** 0.436

MOBL

1 0.147 0.301 -0.334 -0.063

REM

PCNSDP

1 0.819** -0.737** 0.207

POV

1 -0.797** 0.279

UNEM

1 -0.168

1

Source: Computed Table 5: Path Coefficients and R2 Values R2

Dependent variables

Predictors PVE

PCON E

RLT

RaL T

VED

VCRL

PPL

BANP L

MOBL

REM

POV

-0.448*

0.410*

-0.2

-0.76

0.56**

-0.37**

-0.06

-0.12

-0.15*

-0.74

UNEM

0.39*

0.84

0.29

-0.15

1.75

-0.43

0.39

-0.14

0.04*

-1.7

0.22**

PCNSDP

0.39*

0.91*

-0.38

-0.47

0.72

-0.17

-0.46*

0.53*

0.28**

-1.02

0.28

POV

UNEM 0.13**

0.87 0.79

-0.061

0.99

Source: Computed

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Notes for Table 4 & Table 5 *

Correlation is significant at the 0.05 level (2-tailed).

**

Correlation is significant at the 0.01 level (2-tailed). ( **0