The Region, Regional Development Theories and Methods of Measuring Regional Disparities
2.4 Methods of Measuring Regional Disparities
(b) Mobile production factors (such as various kinds of labours and new investments) Potentiality factors for capital can be subdivided into, (a) material and immaterial capital and (b) private and public capital. Infrastructure capital is essentially public capital which may be either material or immaterial. These potentiality factors determine the regional development potential, although the impacts of these factors may differ, depending on their mobility, individuality, non-substitutability, polyvalence and non-exclusiveness. By means of these five characteristics the regional potentiality factors may be distinguished, directly or indirectly, from other productive resources. The emphasis of the Regional Development Potential theory on public capital is extremely important for regional infrastructure policy, because, such a policy may be an effective tool in coping with the problem of spatial disparities.
ordering method of Mitra and Nath ignores the magnitude of variation between any two regions with respect to any one variable113.
Rao114 used the multiple factor analysis and attempted with the help of 6 variables at studying regional disparities in India. Rao found that the most important cause of inter-state disparities in growth of crop output during 1953-54 to 1963-64 was the differences in the growth of irrigation. The inter regional disparities as well as the gap between the rich and poor farmers had widened.
Pal115 applied the method of principal component analysis to study regional disparities in India. This method was also followed by Rao. Prof. Pal found that agricultural labour productivity in rupees per person, agricultural income per acre of cropped area in rupees and percentage of irrigated area to total gross area sown are the main factors responsible for the inter districts disparities in agricultural development in India. Prof. Pal then ranked the 325 districts of the country on the basis of the aforesaid index and thereby established the existence and the extent of regional disparity in the level of agricultural development in the national context.
Iyengar, Nanjappa and Sudarshan116 made use of component dynamic index of development and measured inter-district differentials in Karnataka‘s development. They assigned weights to the indicators inversely proportional to the corresponding co-efficient of variation. They used 21 indicators to find out the index of level of development and ranked them accordingly. On the basis of the level of indices, all districts of Karnataka had been divided into five categories such as, very developed, developed, developing, backward and very backward.
Dadibhavi117 made use of the principal component analysis to find out inter-taluka disparity and backwardness in Karnataka. The inter-taluka variations in respect of agricultural development were not found to be high.
113. S.K.Rao, A Note on Measuring Economic Distances Between Regions in India, p. 796.
114. Ibid.
115. M.N.Pal, Regional Disparities in the levels of Development in India, 1965.
116.N.S.Iyengar, M.B.Nanjappa and P.Sudarshan, A Note on Inter-District Differences in the Karnataka‘s Development, pp.79-83.
Sundaram and Rao118 observed that specific regional policies are needed to guide deliberate action to bring about a more even economic and social development in different parts of the economy.
Sampath119 using a technique similar to the co-efficient of variation observed that agricultural sector significantly increases the inequality. According to author there existed wide inter- state inequality in India at the beginning of the planning era and it declined steadily until 1964-65, since then up to 1970-71, it increased steadily. Throughout the period 1951-71 non- agricultural sectors did not contribute to the increase in the inter-state inequality. In contrast to this, the growth policy of the agricultural sector during 1961-71 was such that it contributed significantly to the increase in the inter-state inequalities.
Mishra, Mahanti and Pathak120 in their study on agricultural development and planning in the context of micro-level planning with particular reference to East Champaran district (Bihar) found agricultural development distinctly unbalanced. They further observed the growth of agriculture to be positively related with area, yield rate and interaction of the two. The component-cropping pattern was found needing planning for both increasing output and decreasing disparities.
Prakash and Rajan121 observed that agricultural productivity per hectare and per capita were widely diffused while the agricultural inputs like iron, ploughs, tractors, fertilizers, HYV seeds, irrigation, etc. were highly concentrated. They made use of the co-efficient of concentration analysis of variance and intra-class correlations to indicate regional inequalities.
Shrivastava122 made use of the composite index to measure regional disparities in agricultural development in Madhya Pradesh. The author found a high degree of concentration of both inputs and outputs in those districts, which are highly and fairly developed.
118.K.V.Sundaram and P.Rao, Regional Imbalances in India, pp. 61-80.
119.R.K.Sampath, Inter-State Inequalities in Income in India,1977.
120.S.K.Mishra, T.K.Mahanti and C.R.Pathak, Micro-Level Planning for Agricultural Development, pp. 24-31
121.P.Rajan and Sri Prakash, Regional Inequalities in Rural Development in Madhya Pradesh, pp.1-14
122.S.L.Shrivastava, Regional Disparities in Agricultural Development in Madhya Pradesh, pp.55-60.
Mandal123 found improved farming among dominating factors that affect regional imbalances. The area under HYV and distribution of energized pump sets were found to be significantly influencing imbalances. Imbalances in respect of the agricultural productivity and area sown were not found to be significant.
Shaban and Bhole59 attempted to measure the inter-state differentials in rural development in India by using seventeen indicators of development. They used Principal Component Analysis, Cluster Analysis and various statistical methods to measure the same. In order to find out the rural development disparities at the aggregate level for the year 1991-92, Weighted Average Component Score (WACS) were calculated. The study finds that inter- state disparities in rural development are very high. At the aggregate level of development Punjab is the most developed State followed by Haryana, Kerala and Karnataka while the least developed State is Bihar followed by Orissa, Uttar Pradesh and Assam. As the WACS method is found suitable for our study and is used here for our analysis.
So far, the studies on regional (spatial) variation in the level of agricultural development were reviewed only, a brief review of the literature on temporal variation in the level of agricultural development points out the temporal variation of weather.
In the western study, James L. Stalling‘s124 studied most analytically the effect of weather on the annual yield of major crops in the U.S.A by fitting a linear regression model, where the residual was the weather-effect. Griliches125, using the same method showed that during 1911-58, the temporal variation of weather played a very important role in the total variation of crop output in the U.S.A.
123.S.K.Mandal, Regional Disparities and Imbalances in India‘s Planned Economic Development, pp. 103-106
59. Shaban Abdul and Bhole L.M, Regional Disparities in Rural Development in India., pp. 103-117.
124 . James.L. Stallings, Indexes of the Influence of Weather on Agricultural Output, 1960.
125 . Zvi. Griliches, Estimates of the Aggregate Agricultural Production Function from Cross-Section Data,
In Indian study, for the first time Mann126 and later on by Krishna and Rao127 studied the influence of the monsoon and other climatic variation from year to year on the agricultural production.
R.W.Cummings and S.K.Ray128 estimated the contribution of weather and technology to the 1967-68 and 1968-69 food grains production in India. This study was thus an attempt to identify the weather effect on agricultural production in India.