REGIONAL INEQUALITY OF SOCIAL SECTOR DEVELOPMENT IN INDIA

REGIONAL INEQUALITY OF SOCIAL SECTOR DEVELOPMENT IN INDIA Hemanta Saikia* Debraj Roy College, Golaghat, Assam-78562 (India); Email: hemantaassam@yahoo.com *Address for correspondence Biographical note Hemanta Saikia has a M.A. in Economics from Dibrugarh University and is currently working as Assistant Professor in the Department of Economics, Debraj Roy College, Golaghat, India. He submitted his Ph.D. thesis in Economics from Dibrugarh University. Abstract: The major objective of deliberate progress of a society is to guarantee human well-being through sustained improvement in people`s quality of life. Therefore, the development of human resources is crucial for sustained enlargement and productive employment. From this perspective, the North East India is a front line region of India and one of the most ethically and linguistically diverse regions in Asia, where each State of North Eastern Region (NER) has its distinct culture and traditions. Though the NER of India is rich in terms of human and natural resources, the region is backward in terms of socio-economic development and consequently wide disparity in terms of various indicators of social sector development. In the paper, an endeavour is made to measure the intra and inter-regional disparity in India using various statistical measures especially Data Envelopment analysis and Principal Component analysis. In the last part of the paper, an attempt is made to analyse the main factors influencing the social sector development in NER. JELClassification: J21, J24, R11 Keywords: Social sector, development, regional disparity, human development

I. INTRODUCTION The ultimate objective of planned economic development is to ensure human well-being through continuous improvement in the quality of life of the people. The development of human resources contributes to sustained growth and development. The process of economic development is assessed in terms of benefits and opportunities evenly distributed between individuals in the society. Therefore, human capital formation and human development are inter-linked with social sector development and improvement in this sector is supposed to bring equity and economic development. After the Millennium Development Summit in 2000, the Millennium Development Goals (MDG) became the most widely accepted yardstick of development efforts by governments. Equally, improvement in human capital requires higher investments in the social sector. The MDGs for the social sector aim at universal education, gender equality, reducing infant mortality rate and mortality to two-thirds. In this regard, India has the mature vibrant democracy and a robust pillar of the world economy, but there is prevailed a wide spread disparities in the levels of social-economic development between the different regions of the country. Larger states with high population pressure and vast natural resources are unable to accomplish high growth rate. While inequality in per capita national income in the country tends to enlarge, state-level pointers of human development demonstrate decreasing diffusion. In this paper, an endeavour has been made to measure the level of disparity of social sector development among the states of India, especially with reference to North Eastern Region of India The economic development is not a homogenous process rather it is an outcome of the shared effect of socio-economic as well as and human pointers of a country. From the days of classical economists to the present, there is a general belief that development is bound to be undemocratic because it does not take place in every ingredient of a nation simultaneously. Since all the countries or regions of a country are not unified on the part of both physical and human resources, so the discrepancy in the process of growth and development is widely visible in present globalised world, especially in developing countries. Growth Pole dynamics and inverted-u hypothesis sustain that regional inequalities within developing countries are increased in the initial stage of development which can be ultimately reduced through factor mobility. Neoclassical growth theory also highlights the mobility of supply side factors, particularly capital stock, technical change and labour, as the source for the eventual reduction of such disparities. On the other hand the opposing theories, in particular dependency and structural change theories postulate that regional inequality is an inevitable outcome of capital accumulation and profit maximization (Noorbakhsh, 2003). Thus, unbalanced development and regional disparities are ubiquitous phenomena both in developed and 74

developing economies. However, these evils are more acute and dangerous for developing economies India due to its devastating nature of backwardness. This unevenness were more intensified and was on the rise only after independence when the national Government started its planned development programmes through five year plans since 1951. In the early phases of India s planned economic development efforts were mainly confined to sectoral economic development with greater emphasis on raising the per capita income of the people. As a result, certain areas went ahead leaving others lagging behind and thereby creating disparity among the different states in India. The Indian economy as a whole also faced the problems of sectoral imbalance and widening gap between the rich and the poor especially with respect to the social sector development. II. THE LITERATURE Regional inequality is a foremost concern in a large number of countries. Growth polarisation and inverted-u hypothesis claim that regional inequalities within developing countries will be eventually reduced through factor mobility. There are a lot of studies already carried out with the aim of analysing the measures of regional disparity. Boris and Daniel (2003) used income inequality measures to changes in the ranking, size and number of regions into which a country is divided. The population weighted coefficient of variation and population-weighted Gini coefficient may be considered a more or less reliable inequality measures, when applied to small countries. According to the test results, only the population weighted coefficient of variation and populationweighted Gini coefficient may be considered as more or less reliable inequality measures, when applied to small countries. Dreze and Sen (1995) also pointed out that the diversities in economic and social development amongst the Indian states are remarkable. Ravallion and Datt (2002) in a cross-state study on poverty in 15 major states in India highlighted that various states have different competences for poverty reduction for a variety of reasons. They argued that a substantial difference of the elasticity of poverty index to non-farm output between the states with 5 the lowest elasticity, Bihar, and the state of Kerala is due to the difference in literacy rates between these states. The evidence points to the case of regional divergence rather than convergence within developing countries. Similarly Kshamanidhi (2002) attempted to re-examine the issue of convergence and economic growth by focusing on the differences in the steady state of 14 major states of India from 1976-77 to 2000-01, by employing dynamic fixed effects panel growth regression. According to it, once per capita investment, population growth rate and human capital along with state-specific effects are controlled for, there is evidence of conditional convergence at the rate of around 12 percent per five years span. These variables alone could explain around 93 75

percent variation in the growth rate of per capita real income across 14 major states from 1976-2000. This highlights the importance of policy activism to achieve balanced growth and regional convergence. Eamon (2002) explored data problems in examining trends in socio-economic mortality differentials in Ireland. According to him, an increase in the size of the residual socioeconomic group category between 1981 and 1991 means that any discussion of changes in mortality differentials over time is very suspect. Consequently, there is no point in speculating whether mortality differentials have become better, or worse, for certain occupational groups in recent years. Singh et al. (2003) argued that regional inequality in India has increased after the economic reforms in 1991. This concern is supported by various statistical analyses. In particular, human development indices do not show the same increase in regional inequality. Furthermore, looking at consumption and credit indicators for regions disaggregated below the state level also suggests that inequality trends may not be as bad as suggested by State Domestic Product data, although the greater strength of the economies of the western and southern states emerges in their results. According to Spiezia (2003) in recent years, regional development issues have returned on the policy agenda of many OECD countries. There are at least three reasons for this. First, higher integration driven by institutional processes and economic trends is eroding national borders and creating competition along regional lines in the world market. Second, the persistence of significant regional disparities challenges countries capacity to promote economic growth while ensuring social cohesion. Finally, economic growth appears increasingly driven by the higher productivity of firms and workers concentrated around a small number of regional poles. In line with him, Noorbakhsh (2003) stated that there is enough evidence to suggest that regional disparities within most developing countries are alarmingly high and probably increasing. His paper analyses regional disparities amongst major states in India to find out if they are on a convergence or further divergence course. It compares human development and poverty indices for various states in India and investigates if there has been any reduction in disparities over a decade. The analysis is extended to the evolution of disparities amongst the states with respect to a larger set of socio-economic indicators. Nayak (2008) remarked that in spite of India`s policy of liberalization and globalization since early eighties and higher growth rates, it has not been able to achieve much because of human development and welfare, both at national level and North East India level. Human development index is below 0.62 in India and much below in its North Eastern Region. Rural-urban disparity, gender disparity and uneven human development across the states in the region are quite significant. The disturbing trend of increasing gender disparity in Nagaland and escalating rural-urban gap, particularly in the States of Assam and Meghalaya is a matter of concern. 76

Since 1991, India has become a highly liberalized and globalised economy with great faith in the efficiency of the market mechanism. Larger states with higher population pressure and vast natural resources are unable to achieve high growth rate. At the same time, disparities in social development across the regions and intraregional disparities among the different segments of the society have been the major plank for adopting planning process in India. India's per capita income is $820, ranking 134 th, while its per capita income (PPP) of US$3,700 places India on the 8th position in the world. However, one of the crucial problems facing India's economy is the mounting regional discrepancies among different states and territories in terms of per capita income, poverty, availability of infrastructure and socio-economic development. Although income inequality in India is relatively small (Gini coefficient was 0.31 in year 2004-2005), it has been constantly increasing over times. Wealth distribution in India is uneven, with the top 10 percent of income groups earning 33 percent of the income. Despite significant economic progress, a quarter of the population earns less than the government-specified poverty threshold of $0.40/day (27.5 percent of the population was living below the poverty line in 2004 2005). Therefore, with the growing inequalities among people, it is obvious that there is also the mounting inequality of social sector development among the different regions of the country. Some regions are remaining backward and some are looking forward. In this context, the north east India with eight states: Assam, Arunachal Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura remains backward even after more than 50 years of Indian independence. At the same time, there are wide differences among the eight states in the North-Eastern Region with respect to their resource endowments, level of industrialization as well as infrastructural facilities. Despite of being rich in natural resources, the economic development in the North Eastern region has lagged behind the rest of the country. The economy of the region is still primarily agrarian and industry has not really taken off in spite of the potential in the form of vast unexploited resource base available in the region. The contribution of agriculture to state domestic income is much higher in this region, except for Meghalaya and Nagaland. The per capita income in the North Eastern region on an average is Rs. 12,918 as compared with the national average of Rs. 17,947 at current prices of 2001-02. Looking at the above significance, an attempt has been made in this paper to analyse the disparity in social sector development in India, in general and NER in particular. 77

III. RELEVANT FEATURES SOCIAL SECTOR OF NER, 1991-2006 The demographic feature of the NER represents that area wise Arunachal Pradesh is the largest state in the region followed by Assam and Sikkim is the smallest state in terms of the area. But if we look at the population size Assam is largely populated state in NER. On the other hand, decadal growth rate of population reflects (1991-2001) that Nagaland has the highest decadal growth rate followed by Sikkim. Thus, there is a wide variation about the demographic features in NER. Table 1. Salient features of NER State Area in Sq. Km. Population (Person) Decadal Growth Rate (1991-2001) Density per sq. km. Arunachal Pradesh 83743 1097968 27.00 13 Assam 78438 26655528 18.92 340 Manipur 22327 216786 24.86** 103 Meghalaya 22429 2318822 30.65 103 Mizoram 22081 888573 28.82 42 Nagaland 16579 1990036 64.53 120 Sikkim 7096 540851 33.06 76 Tripura 10486 3199203 16.03 305 Total NER 263179 38857769 21.61 148 All India 3287263 1028610328 21.54 313 ** Excludes Mao Maram, Paomata & Purul sub-division of Senapati Dist. Source: Basic Statistics of NER 2006. The NER is marked by uneven spatial distribution of population among the constituent states, the primary reason being that the plains and valleys offer more congenial conditions for absorption of population, rather than the hills and difficult terrains. Apart from Assam and Tripura, tribes having unique social and cultural practices mostly inhabit the NE states. Overall, tribal people account for over 30 per cent of the total population of this region. However, in Arunachal Pradesh, Manipur, Meghalaya, Mizoram and Nagaland, scheduled tribes comprise more than 60 per cent of the population what gives these states a predominantly tribal character. The overall geographical land-to-man ratio for the NE region (0.67 hectares/person) is much higher than the National average (0.32 hectares/person). Among the NE State, the geographical land-to-man ratio is highest in Arunachal Pradesh (7.63 hectares/person) and lowest in Assam (0.29 hectares/person). The overall land to men ratio in NER is quite favourable because of lower density of population. The average size of operational holding is however quite small considering the fact that the majority of area is hilly. This has also led to about 78 per cent of the farmers in the region fit 78

into the small and marginal category. The high incidence of shifting cultivation, paucity of land actually available for cultivation, pre-dominance of small and marginal farmers besides small holdings are some of the obstacles in raising agricultural production in the region. In this specific context, the agricultural scene of the region has not been very conducive to adoption of improved agricultural technologies, such as use of high yielding varieties, chemical fertilizers, modern implements etc. Equally, the issue of access to employment, education and training opportunities for women who have been working at home is also of concern from a gender equality perspective (Russell, 2004). Sex ratio is one of the important parameter to measure the health status of women in a place right from the early child age. The sex ratio is the ratio of males to females in a population. In Europe, for example, there are approximately 105 women per 100 men, resulting in a sex ratio of 1050 per 1000 male. The number varies significantly around the world often due to the inequalities between men and women. This gender bias can begin before birth and impact the length of women's lives. Table 2. Sex Ratio in North East India States 1991 2001 Improvement Sikkim 878 875-3 Arunachal Pradesh 859 901 42 Nagaland 886 909 23 Manipur 958 978 20 Mizoram 921 938 17 Tripura 945 950 5 Meghalaya 955 975 20 Assam 923 932 9 Bihar 907 921 14 Kerala 1036 1058 22 India 927 933 6 NER 916 932 16 Source: Census of India, 2001 The data reflect that the sex ratio is improving in NER faster by comparison with India`s average, while states like Sikkim, Tripura and Assam register slower growth of sex ratio; infect negative in Sikkim, which was highest in Arunachal Pradesh among the states of NER. On the other hand, the infant mortality is defined as the number of infant deaths (one year of age or 79

younger) per 1000 live births. The general infant mortality rate of India is 30.15 deaths/1,000 live births in 2009 as against the female infant mortality rate of 25.17 deaths/1,000 live births and ranked 72 among the countries of the world. In case of NER, the infant mortality rate seems to vary from state to state. The infant mortality rate of NER is lower than India`s average of 58 in 2004, which declined to 30.15 in further decades. Among the states, Assam has the highest IMR and Manipur has the lowest IMR. Education plays the major role for a country`s economic and human resource development, that is why it is used as a part of the Human development Index i.e. Knowledge, as measured by the adult literacy rate and the combined primary, secondary and tertiary gross enrolment ratio. It is not the present, but also the future of economic development of a country that depends on education level. Literacy is also the key terms which are closely connected to the empowerment of the people. Table 3. Literacy Rate in NER India / States / 1991 2001 Union Territories Males Females Males Females Sikkim 75.11 59.05 76.73 61.46 Arunachal Pradesh 58.09 37.56 64.07 44.24 Nagaland 67.73 57.87 71.77 61.92 Manipur 74.50 55.88 77.87 59.70 Mizoram 84.38 76.17 90.69 86.13 Tripura 78.89 61.05 81.47 65.41 Meghalaya 59.90 54.02 66.14 60.41 Assam 67.02 52.25 71.93 56.03 India 71.18 46.58 75.85 54.16 NER 70.7025 56.73125 75.46 61.91 Source: Census of India, 2001 According to the last census of 2001, the county`s percentage of female literacy is 54.16 percent. The literacy rate in the country has increased from 18.33 percent in 1951 to 65.38 percent as per 2001 census. The female literacy rate has also increased from 8.86 per cent in 1951 to 54.16 per cent. The 2001 figure of male and female literacy reflects that the entire female literacy rate is subordinated in all the states of NER. Mizoram has the highest female literacy rate among the states of the NER and Arunachal Pradesh has the lowest literacy rate. IV. METHODOLOGY AND DATA This paper is based on mainly secondary data. Secondary data is collected from the publications of various organizations viz. Statistical Department, Govt publications, Census of India, Directorate of Economics and Statistics, Research Publications of individual and institutional 80

NSSO, CSO, NATIONAL FAMILY HEALTH SURVEY (NFHS)-I, II, III etc. The census dates are mainly of 2001 and other data sets are available for the year 2005-06. Collected data was processed, tabulated, and then analysed using statistical and econometrics tools. Since single variable seldom reflects regional disparity or level of development among the state, for which composite index method is widely used to determine regional disparity. In order to measure the levels of disparity of the social sector development of states, eight major variables are taken into consideration for preparing a ranking table. For ranking and analyzing the efficiency of the state, Data envelopment analysis (DEA) was carried out. Data envelopment analysis (DEA) is a nonparametric method in operations research and economics for the estimation of production frontiers. It is used to empirically measure productive efficiency of decision-making units (or DMUs). Non-parametric approaches have the benefit of not assuming a particular functional form/shape for the frontier; however, they do not provide a general relationship relating output and input. The main variables taken into consideration for calculating the DEA are: Input variable 1) Per capita average expenditure in social sector from 2001-06 (Lakh) [Input-1] 2) Number of school per lakh population. [Input-2] 3) Access of safe drinking water facility (Percentage) [Input-3] Output Variable 1) Literacy (percentage) [Output-1] 2) Sex Ratio (number of women per thousand male populations) [Output-2] 3) Infant Survival Rate (number per thousand) [Output-3] 4) Children (0-3 age group) without malnutrition (Percentage) [Output-4] 5) Population above poverty line (Percentage) [Output-5] After calculating DEA, we ranked the states according to the efficiency score provide by EMS based DEA. In some cases where rank value of two or more states had the same value for a single variable, the problem of ordering the rank value was overcome by giving a rank value equal to the average of the successive ranks given to them and then adjusted the rank for the next state accordingly. It arbitrarily considered 5 top states as high category, the following 10 as medium and the last 10 as low category states. For conducting DEA, an output oriented measure is being used which quantifies the necessary output expansion holding the inputs constant. Radial based measure was used for measuring the distance, in order to identify the necessary improvements when all relevant factors are improved by the same factor equi-proportionally. 81

V. RESULTS Social sector is also very important from a regional development perspective. States Karnataka, Kerala, West Bengal, Himachal Pradesh and Delhi are considered high category states in social sector as represented by DEA analysis while Andhra Pradesh, Mizoram, Goa, Gujarat, Haryana, Madhya Pradesh Maharashtra, Punjab, Rajasthan and Tamil Nadu fit the middle developed states category. The rest of the states fall within low developed states group. Sl. No. Table 4. Result of Data Envelopment Analysis of Social Sector Development in India DMU Score (%) Benchmarks {S} Input- 1{I} {S} Input- 2{I} {S} Input- 3{I} {S} Output- 1{O} {S} Output- 2{O} {S} Output- 3{O} {S} Output- 4{O} {S} Output- 5{O} 1 Andhra Pradesh 109.33 2 (0.39) 13 (0.38) 16 (0.15) 21 (0.25) 0 0 0 1.46 0 81.67 0.59 0 2 Bihar 78.84 5 3 Chhattisgarh 111.82 16 (0.84) 21 (0.46) 0 53.96 0 0 103.68 158.23 22.62 33.14 4 Goa 103.31 9 (0.71) 21 (0.36) 3513.03 0 0 0 52.52 22.37 3.01 5.42 5 Gujarat 102.75 6 (0.43) 9 (0.05) 17 (0.46) 21 (0.10) 0 0 0 0 30.41 20.65 7.71 0 6 Haryana 97.90 1 7 Jharkhand 106.43 2 (0.73) 17 (0.22) 21 (0.13) 613.09 0 0 0 0 15.61 8.93 8.61 8 Karnataka 112.94 9 (0.09) 17 (0.72) 21 (0.32) 0 0 0 0 9.53 8.62 3.07 7.34 9 Kerala 80.29 10 10 Madhya Pradesh 102.93 16 (1.13) 21 (0.03) 0 92.37 0 0 109.87 131.53 21.17 15.74 11 Maharashtra 107.06 9 (0.50) 17 (0.39) 21 (0.18) 0 0 0 0 47.08 7.07 5.85 14.76 12 Orissa 109.09 16 (1.21) 21 (0.02) 0 44.89 0 0 81.16 125.84 3.65 24.15 13 Punjab 86.24 5 14 Rajasthan 109.77 16 (1.06) 21 (0.15) 0 13.19 0 1.69 114.77 103.55 5.04 0 15 Tamil Nadu 103.97 2 (0.25) 9 (0.42) 13 (0.27) 21 (0.12) 181.64 0 0 0 0 29.77 0 7.08 16 Uttar Pradesh 115.06 8 17 West Bengal 115.33 8 18 Arunachal 81.22 2 (0.25) 21 (0.77) 2763.52 0 0 0.36 0 4.22 1.86 5.37 Pradesh 19 Assam 85.02 16 (0.64) 17 (0.15) 0 0 0 0 51.81 95.5 4.02 2.69 21 (0.30) 20 Himachal 79.73 16 (0.61) 17 (0.04) 0 0 0 0 369.34 327.06 20.69 21.1 Pradesh 21 (0.71) 21 Jammu and 158.86 20 Kashmir 22 Manipur 84.06 9 (0.27) 13 (0.04) 17 0 0 0 0 61.23 53.73 0 16.35 (0.03) 21 (0.74) 23 Meghalaya 82.41 16 (0.46) 21 (0.66) 0 116.62 0 0 69.88 115.32 17.13 11.83 24 Mizoram 84.56 9 (0.10) 21 (1.43) 1302.93 0 0 0 482.32 500.49 30.03 56.83 25 Nagaland 96.67 2 (0.27) 9 (0.06) 13 1658 0 0 0 0 65.94 0 10.59 (0.44) 21 (0.32) 26 Sikkim 82.01 9 (0.15) 13 (0.23) 21 5685.74 0 0 0 29.29 74.12 0 20.84 (0.71) 27 Tripura 90.76 9 (0.32) 17 (0.43) 21 1073.34 0 0 0 0 13.74 4.49 3.13 (0.26) 28 Delhi 66.59 0 82

Table 5. Rank of the Indian States States Score (%) Rank Andhra Pradesh 109.33 7 Bihar 78.84 27 Chhattisgarh 66.59 28 Goa 103.31 12 Gujarat 102.75 14 Haryana 97.90 15 Jharkhand 84.56 20 Karnataka 112.94 4 Kerala 115.06 3 Madhya Pradesh 102.93 13 Maharashtra 107.06 9 Orissa 86.24 18 Punjab 109.09 8 Rajasthan 103.97 11 Tamil Nadu 109.77 6 Uttar Pradesh 80.29 25 West Bengal 115.33 2 Arunachal Pradesh 81.22 24 Assam 85.02 19 Himachal Pradesh 158.86 1 Jammu and Kashmir 79.73 26 Manipur 84.06 21 Meghalaya 82.41 22 Mizoram 106.43 10 Nagaland 96.67 16 Sikkim 82.01 23 Tripura 90.76 17 Delhi 11.82 5 Source: Calculated from DEAP Analysis Inter sectoral variation in the levels of Social development are not uniform among the northeastern (NE) states. Except Mizoram, all states are in low category. Only Mizoram is able to hold 10 positions in social sector development. The Intra regional disparity reflects that Mizoram is at the top of the states of NER followed by Nagaland (16), Tripura (17), Assam (19), Manipur (21), Meghalaya (22), Sikkim (23) and Arunachal Pradesh (24). VI. ALTERNATIVE METHOD OF RANKING OF STATES: PRINCIPAL COMPONENT ANALYSIS In order to evaluate the raking of the social sector development, we used the principal component method to compare which is the best method of ranking. There are mainly two purposes to apply the principal component analysis on the data of Social Sector development: to determine the relative importance of the factors that support the social sector development and to use the factor scores of the principal components in ranking the States. The PCA does not establish weights 83

a priori. This analysis calculates the principal components that maximize the explanation of the variances. We have used only the output based 5 variables as used in DEA. Table 6. Correlation Matrix Correlation Matrix 01 02 03 04 05 Correlation 01 1.00-0.13 0.61 0.59 0.44 02-0.13 1.00 0.11-0.06-0.31 03 0.61 0.11 1.00 0.68 0.47 04 0.59-0.06 0.68 1.00 0.74 05 0.44-0.31 0.47 0.74 1.00 Sig. (1-tailed) 01 0.25 0.00 0.00 0.01 02 0.25 0.28 0.39 0.05 03 0.00 0.28 0.00 0.01 04 0.00 0.39 0.00 0.00 05 0.01 0.05 0.01 0.00 Determinant = 0.104 The cross-correlation of parameters can be seen in Table 1: four parameters are highly correlated among themselves (>0.50).The four factors namely 01, 03, 04 and 05 are strongly correlated and the positive definite (0.104) determinant also state that there is no multi-colinearity problem with the data. Since the Kaiser-Meyer-Olkin Measure of Sampling Adequacy is 0.678, which is considered as very good for data for applying the factor analysis. Table 7.KMO and Bartlett's Test Kaiser-Meyer-Olkin measure of sampling Adequacy 0.67 Bartlett's Test of Sphericity Approx. Chi-Square 55.44 Sig. 0.00 Principal Component1 explains about half (55.91 per cent ) of the data variance, Component 2 explains about 22 per cent and Component 3 explains about 11 percent the data variance. The initial Eigen values of the three components are at large explaining almost 90 percent of the cumulative variance. Comp onent 1 2 3 4 5 Table 8. Total Variance Explained Initial Eigen values Extraction Sums of Squared Rotation Sums of Squared Loadings Loadings Total % of Cumulative Total % of Cumulative Total % of Cumulati Variance % Variance % Variance ve % 2.80 55.92 55.92 2.80 55.92 55.92 2.74 54.85 54.85 1.13 22.68 78.60 1.13 22.68 78.60 1.19 23.75 78.60 0.58 11.50 90.09 0.31 6.26 96.35 0.18 3.65 100.00 84

Extraction Method: Principal Component Analysis The component matrix and rotate component matrix with Varimax with Kaiser Normalization method are presented bellow. In the component matrix before and after the extraction of four parameters namely 01, 03 and 04 are highly correlated with factor 1. However after rotation, four variables are highly loaded in component one. Table 9. Rotated Component Matrix Component O4 0.91-0.10 O3 0.86 0.22 O1 0.79-0.08 O5 0.75-0.45 O2-0.01 0.96 Rotation Method: Varimax with Kaiser Normalization. Table 10.Component Score Coefficient Matrix Component 01 0.29 0.01 02 0.09 0.83 03 0.34 0.27 04 0.33 0.00 05 0.24-0.32 Rotation Method: Varimax with Kaiser Normalization component Scores Table 11. Rank of the States States Factor Score PCI Rank DEA Ranking National HDI Ranking (2001) Andhra Pradesh 1.81 16 7 10 Bihar 1.58 28 27 15 Chhattisgarh 1.52 23 28 NA Goa 1.31 2 12 NA Gujarat 0.86 18 14 6 Haryana 0.78 19 15 5 Jharkhand 0.74 26 20 NA Karnataka 0.61 17 4 7 Kerala 0.52 1 3 1 Madhya Pradesh 0.51 27 13 12 Maharashtra 0.48 13 9 4 Orissa 0.23 24 18 11 Punjab 0.22 9 8 2 Rajasthan 0.09 22 11 9 Tamil Nadu 0.06 8 6 3 Uttar Pradesh -0.10 25 25 13 West Bengal -0.16 15 2 8 Arunachal Pradesh -0.33 14 24 NA Assam -0.34 20 19 14 Himachal Pradesh -0.47 10 1 NA 85

J& K -0.51 12 26 NA Manipur -0.80 4 21 NA Meghalaya -1.09 21 22 NA Mizoram -1.29 3 10 NA Nagaland -1.32 7 16 NA Sikkim -1.40 5 23 NA Tripura -1.66 11 17 NA Delhi -1.83 6 5 NA Therefore, we can rank the states using two methods; one is DEA and another is Principal Component analysis (PCA). Now the question is which is the best method for measuring the social sector development. To answer the question, we evaluated the Pearson rank correlation method. First, we assessed the result of the rank correlation of DEA method and PCA method and we got a significant but moderate negative correlation. So, definitely one of the methods are not fitting the measurement goal. Table 12. Correlations in between DEA and PCA based Disparity Ranking DEA PCA Spearman's rho DEA Correlation Coefficient 1.00 -.443 * Sig. (2-tailed) -.02 N 28 28 PCA Correlation Coefficient -.44 * 1.00 Sig. (2-tailed).02 - N 28 28 *. Correlation is significant at the 0.05 level (2-tailed). Secondly, we have interlinked the National HDI Raking of 2001, with both measures (DEA and PCA) as with the development of social sector, human development will also take place. Because the national HDI of 2001 is available only for 15 states, we calculated the Pearson rank correlation in between the DEA ranking and Ranking of the national HDI, 2001 and obtained a very strong correlation (0.871), which is significant at even 0.01 level. Table 13. Correlations in between DEA and National HDI, 2001 DEA National HDI, 2001 Spearman's rho DEA Correlation Coefficient 1.00.87 ** Sig. (2-tailed)..000 N 15 15 National HDI, Correlation Coefficient.87 ** 1.00 2001 Sig. (2-tailed).00. N 15 15 **. Correlation is significant at the 0.01 level (2-tailed). On the other hand, the Pearson rank correlation in between the PCA ranking and Ranking of the national HDI, 2001 reflects moderate correlations (0.575), which are also significant at even 0.05 levels. Therefore, even though both methods can be used as a measure of social sector 86

development, DEA is somewhat a more consistent and stronger measure as compared to PCA based measure. Table 14. Correlations in between PCA and National HDI, 2001 PCA National HDI, 2001 Spearman's rho PCA Correlation 1.00.58 * Coefficient Sig. (2-tailed)..01 N 15 15 National HDI, Correlation.58 ** 1.00 2001 Coefficient Sig. (2-tailed).01. N 15 15 *. Correlation is significant at the 0.05 level (2-tailed). VII. REASONS BEHIND REGIONAL IMBALANCE IN SOCIAL SECTOR DEVELOPMENT Regional imbalance or disparities as existing in countries like India are mostly influenced by a variety of factors, ranging from historical, geographical, economic and even political factors. Historical, regional imbalance in India started from its British regime. The British rulers as well as industrialists started to develop only those earmarked regions of the country as per their own interest and trading activities. They preferred to develop places like West Bengal, Maharastra, Calcutta, and Madras etc. for their own interest. Therefore, these states were developing as compared to other. Geographical factors play an important role in supporting activities within a developing economy. The difficult terrain surrounded by hills, rivers and dense forest leads to increase in the cost of administration, cast of developmental projects, besides making mobilization of resources particularly difficult. Most of the Himalayan states of India, Himachal Pradesh, and North Eastern region are underdeveloped because of geographical factors. Therefore, these natural factors led to uneven growth of different regions of India. Moreover, due to location advantage, some regions get special advantages in terms of site selections for various projects. While determining the location of iron and steel projects or refineries or any heavy industrial project, some technical factors linked to location advantages are paid considerable attention. Economic overheads like transport and communication facilities, power, technology, banking etc. are considered very important for the development of particular regions. Due to adequacy such economic overheads, some regions are getting, special favour in respect of settlement of some development projects, increasing the development gap between states. Although balance growth has been accepted as one of the major objectives of economic planning in India 87

since the 2 nd five year plan onwards, figures prove that regional disparity has enlargedby allocating resources to support developed states such as Punjab, Gujarat, Maharastra etc. Due to such divergent trend, imbalance between the different states in India has been continuously widening, in spite of framing achievement of regional imbalance as one of the important objective of economic planning in the country. In India, green revolution has improved the agricultural sector to a considerable extent through the adopting of new agricultural strategy. However, despite the potential benefits of such new agricultural strategy, it has been marginalized to certain definite sectors keeping the others very untouched. The benefit of green revolution is restricted to states like Punjab Haryana and Uttar Pradesh etc., while other eastern states remain large behind. In order to attain regional development of all areas, a country must adopt a well-defined strategy for attaining such development. In order to achieve balanced regional development, the govt. should try to design effective schemes for removing various socio-economic problems like poverty, unemployment and poor standard of living. In this context as an under develop country, Indian govt. has to undertake a number of steps for reducing regional disparities. The govt. has designed its industrial and licensing policy to promote balanced regional development. Public sector enterprises are also dispersed in different regions of the country and special preference are being given in set up new public sector units in backward regions. From the very beginning of economic planning, Indian planners and the govt. have been giving attention towards attainment of balanced Regional development. Regional divides are another challenge for the 11 th Plan. Balanced regional development has been an important objective of planning and various instruments including fiscal incentives industrial policies and directly targeted programmes have been deployed to achieve it. Some policies, such as industrial licensing are no longer relevant in today s economic environment since investment can t be directed to particular location.in a competitive world investment must be allowed to flow to locations perceived to have an attractive investment climate and better infrastructural facilities. VIII. CONCLUSION Despite the simplistic methodology, it appears that the DEA is a good method of combining the component indexes and should be viewed, perhaps, with less scepticism. The rank of the countries based on both DEA and PCA reflects that there are no significant differences in between the two types of ranking since the rank correlation coefficient in between them is strong and it is 88

seemed to positive in two-tailed test in 1 per cent level of significance. Therefore, DEA even though it may seem a simple measure of social sector development, is more representative. From the above analysis even though the Govt of India has adopted several measures to reduce social sector development, further combined effort is needed from the Govt, the state and the people, who are the key actors in the process of planning efforts. At the same time a balance sectoral development process with sectoral linkage incentive can be a well strategy for future development of the Indian regions, especially the North Eastern region of India. Therefore, various regions can try to utilize its potential fully as an integral part of the country so, with the advancement of national economic; all rounds regional development is attained. Therefore, to reduce regional imbalance, it is essential to exploit the natural resource of backward regions, to work continuously in those directions where development is crucial and to achieve a selective and judicious dispersion of the available rescores to attain regional and imbalanced regional development. REFERENCES Boris, A., Portnovand, D. Felsenstein (2003), Measures of Regional Inequality for Small Countries, Regional Disparities in Small Countries, European Regional Science Association, ERSA pp. 5-36 Dreze, J. and Sen A.K. (1995), India: Economic Development and Social Opportunity, Oxford University Press. O Shea, E. (2002), Measuring Trends in Male Mortality by Socio-Economic Group in Ireland: A Note on the Quality of the Data, The Economic and Social Review, Vol. 33, No. 2, Summer/Autumn, pp. 247 257 Russell, H. and O Connell, P. J. (2004), Women Returning to Employment, Education and Training in Ireland: An Analysis of Transitions, The Economic and Social Review, Vol. 35, No. 1, Spring, pp. 1-25 Ravallion, M. and Datt, G. (2002), Is India's economic growth leaving the poor behind: Policy Research, Working Paper Series, 2846, The World Bank, 29 December. Noorbakhsh, F. (2003), Human Development and Regional Disparities in India, Centre for Development Studies, Department of Economics, University of Glasgow Kshamanidhi, A. (2004) Economic Growth and Convergence in India, working paper Series, 153, Institute for Social and Economic Change, 17 December 89

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