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An analytical study on investors’ awareness and perception towards the hedge funds in gujarat
An analytical study on investors’ awareness and perception towards the hedge funds in gujarat
An analytical study on investors’ awareness and perception towards the hedge funds in gujarat
An analytical study on investors’ awareness and perception towards the hedge funds in gujarat
An analytical study on investors’ awareness and perception towards the hedge funds in gujarat
An analytical study on investors’ awareness and perception towards the hedge funds in gujarat
An analytical study on investors’ awareness and perception towards the hedge funds in gujarat
An analytical study on investors’ awareness and perception towards the hedge funds in gujarat
An analytical study on investors’ awareness and perception towards the hedge funds in gujarat
An analytical study on investors’ awareness and perception towards the hedge funds in gujarat
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An analytical study on investors’ awareness and perception towards the hedge funds in gujarat

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  • 1. International Journal of Advanced JOURNAL OF ADVANCED RESEARCH (Print), INTERNATIONAL Research in Management (IJARM), ISSN 0976 – 6324ISSN 0976 – 6332 (Online), Volume 3, Issue 2, July-December (2012) IN MANAGEMENT (IJARM)ISSN 0976 - 6324 (Print)ISSN 0976 - 6332 (Online)Volume 3, Issue 2, July-December (2012), pp. 11-20 IJARM© IAEME: www.iaeme.com/ijarm.html ©IAEMEJournal Impact Factor (2012): 2.8021 (Calculated by GISI)www.jifactor.com HEALTHCARE MANAGEMENT STATUS OF INDIAN STATES - AN INTERSTATE COMPARISON OF THE PUBLIC SECTOR USING A MCDM APPROACH Ayan Chattopadhyay Senior Manager – Regional Trade Marketing (E), Videocon Mobiles Research Scholar, NSOU & Visiting Faculty, IISWBM (Affiliated to Calcutta University) Arpita Banerjee Chattopadhyay Lecturer, Budge Budge College (Affiliated to Calcutta University)ABSTRACTHealthcare in any state or country is of prime concern. It becomes extremely crucialwhen the population base is huge. In India, healthcare is a very critical issue since almostseventy percent of the huge population base lives in rural areas where education andawareness, per capita income and supply side factors of healthcare management likeavailable professionals in medicine, dentistry, nursing, pharmacy is still behind the globalstandards; in fact it is scarce in many parts of the country. To address and minimize thegap between the demand & supply side factors affecting quality healthcare facilities, bothcentral & state governments have adopted several measures. Private players in healthcareindustry have not reached to the remote areas and public healthcare services still remainthe mainstream healthcare providers. The researchers in the present work have made anattempt to find out the progress made by Indian states with respect to public sectorhealthcare management status. The paper ranks the Indian states amidst multipleparameters i.e. in a multi criteria decision making environment (MCDM) usingTechnique for Order Preference by Similarity to Ideal Solution (TOPSIS) as the academicframework. The paper concludes that States of South India are ahead of the rest of thecountry in terms of public healthcare management in India. 11
  • 2. International Journal of Advanced Research in Management (IJARM), ISSN 0976 – 6324 (Print),ISSN 0976 – 6332 (Online), Volume 3, Issue 2, July-December (2012)KEY WORDSHealthcare, MCDM (Multi Criteria Decision Making), TOPSIS (Technique for OrderPreference by Similarity to Ideal Solution), Shannon’s WeightINTRODUCTIONHealth care refers to the treatment and prevention of illness which is delivered byprofessionals in medicine, dentistry, nursing, pharmacy and allied health. The health careindustry incorporates several sectors that are dedicated to providing services and productswith the objective of improving the health of individuals. This industry consists ofplayers from public sector (Government) as well as private sector. The delivery ofmodern health care depends on an expanding group of trained professionals comingtogether as an interdisciplinary team in both the sectors. The rate of growth of the healthcare industry in India is moving ahead neck to neck with the software industry of thecountry and the health care industry in India is reckoned to be the engine of the economyin the years to come. Indian population mostly resides in the rural areas (~70%) and it thepublic healthcare system that primarily offers healthcare need solutions in those areas.India in case of health care facilities still lakes the adequate supply, especially in the ruralareas. In fact there is huge gap between demand and supply at all the levels of society.Still there are many urban areas where one can hardly find any multi specialty hospital.Researches indicate that there are many constraints in healthcare system in India of whichthe absence of health insurance for the unorganized sector and the adverse resourceallocation for the rural sector stand out significantly in case of public healthcare system.Various state governments and the centre have adopted comprehensive agenda of healthsector reforms and health care management systems to improve the services and alsonarrow the demand supply gap. The present study aims to evaluate the healthcaremanagement status in Indian states.REVIEW OF LITERATUREAmlan Majumder (2005) in his work on “Economics of Health Care: A Study ofHealth Services in Cooch Behar and Jalpaiguri Districts” draws attention to theeconomic side of the health care services. The study applies econometric tools toinvestigate facts empirically in the rural and urban areas of Cooch Behar and Jalpaiguridistricts of North Bengal. Demographic factors like age, and family size has been foundto be important determinants of utilisation of care from modern source. Negativerelationship between education and utilisation of a care has been found out. Demand forpublic health facilities is also very high among rural mass. So, privatization or plan ofleasing out the primary health care system to private operators is not justified. Utilisationof health facilities by rural people is associated with low reported quality of care. In hisanother work on “Demand for Healthcare in India”, Amlan Majumder (2006)highlights the need for different types of health care which is changing very rapidlyamong Indian population in the phase of transition. The present study tries to investigatein Indian context whether the demand for public health facilities has decreased among all 12
  • 3. International Journal of Advanced Research in Management (IJARM), ISSN 0976 – 6324 (Print),ISSN 0976 – 6332 (Online), Volume 3, Issue 2, July-December (2012)sections of population for the easy availability of private sources of care or whetherpublic health care is perceived inferior to the private ones. The research highlights thatpublic health care, in Indian context, is an inferior commodity. Moreover, acceptability ofit is concentrated among some religious or some ethnic minorities who generally occupylower stratum in the local hierarchy. Among the factors in the supply side, availability ofdrugs played positively towards utilisation of public health facilities.J.K. Satia and Ramesh Bhat (1999) in their paper “Progress and challenges of healthsector: A balance sheet” highlights that considerable progress has been made inimproving the health status of the population over the last half-century in India. Despitethis impressive progress, many challenges remain. The life expectancy is still 4 yearsbelow world average. So is under five mortality (12 per 1000 per year) higher than globalaverage. New disease patterns and non-communicable diseases are also emerging asmajor challenges. The paper makes an attempt to explain the tardy progress in the healthsector. The programme management by public sector, allocation of public resources tohealth sector, centre-state roles and financing of programmes, private sector role,contribution and role of NGOs, public-private partnerships in health have been analysed.The paper suggests that key challenge in the next century is the leadership challenge andreforms in the health sector require several measures. Firstly, it requires policy andprogramme emphasis that ensures access to quality primary health care for all. Secondly,there is a need for inclusive political dialogue and decision making which will involvecommunity groups representing voices of the poor, local private sector and thegovernment in operationalizing the new vision of health sector. Thirdly, the social capitalin the sector needs to be built up which will promote trust, cooperation and other normsthat enable health markets to function effectively.Dileep Mavalankar (1998) in his paper on “Need and Challenges of ManagementEducation in Primary Health Care System in India” points out that Primary HealthCare (PHC) system in India is very large and consumes large amount of resources. Thepaper argues that given the lack of training of doctors in management it is imperative thatthe doctors who are put in charge of the PHC system receive reasonable skills andtraining in management so that the resources spent on the PHC system can be utilizedwell. It is also observed that most management training is very divorced from the day-to-day realities of the working of the PHC system and the kind of challenges they face. Thepaper also argues that there is a need for developing a separate health management cadrein India who will be trained in public health and health management to take up leadershiprole in PHC system in future. Finally the paper argues that substantial efforts will beneeded in preparing doctors for the management posts in the PHC system.Research studies conducted on Indian healthcare system and its management reveals thatmost of the works have been conducted on specific healthcare issues and problems, manyof them restricting to select geographical areas. Though public healthcare and its 13
  • 4. International Journal of Advanced Research in Management (IJARM), ISSN 0976 – 6324 (Print),ISSN 0976 – 6332 (Online), Volume 3, Issue 2, July-December (2012)management in Indian States have drawn attention but relative progress made by themhas not been found in the substantial number of literatures that were reviewed. The samehas thus been identified as the gap in the present research study.OBJECTIVETo rank and compare the relative position of Indian States basis their healthcaremanagement status using TOPSIS, a Multi Criteria Decision Making approach.METHODOLOGYEvaluating the relative position of Indian states basis their healthcare management statusinvolves finding out the state ranks against a set of chosen parameters. State ranks can beevaluated using additive rule that involves ranking each state against individualparameters considered and then adding them to arrive at the total rank score. The lowerthe value of the total rank score, higher is the overall ranking for that state. This methodhas a major limitation in considering equal weightage of all parameters since in reality allparameters cannot have equal importance. This limitation is overcome by incorporatingrelative weight of the parameters in the overall rank determination when studied amidstin a multi criteria decision making environment (MCDM). Within the MCDM approach,data of input parameters are first classified as positive or negative. A parameter isconsidered as positive if increase in its value enhances or improves the healthcare status,otherwise negative. The absolute values of the parameters are then subjected to statisticalnormalization to annul the effect of disparate units followed by weight determinationusing Shannon’s method before finally applying the MCDM approach for rankdetermination. Within this study, 30 input parameters (indicator variables) have beenchosen in the present study which according to the researcher is the most important onesthat influence the healthcare management status. The 30 indicator variables chosen areshown in Exhibit 1. Sl # INDICATOR VARIABLES Sl # INDICATOR VARIABLES 1 Fertility Rate 16 Primary Health Centres (per 1 lac population) 2 Vaccination Coverage (%) 17 Hospital Beds (per 1 lac population) 3 HIV awareness (males%) 18 Rev. Exp. On Health (In Mn per 1 lac pop.) 4 HIV awareness (females%) 19 Cap. Exp. On Health (In Mn per 1 lac pop.) 5 Low BMI Males (%) 20 Health Exp. As a % of Tot. Exp. 6 Low BMI Females (%) 21 Rev. Exp. On Family Welfare (In Mn per 1 lac pop.) 7 Life Expectancy at Birth 22 Exp. On Medical Services (In Mn per 1 lac pop.) 8 Birth Rate (per 1000 population) 23 Exp. On Public Health (In Mn per 1 lac pop.) 9 Infant Mortality Rate (per 1000 live births) 24 Rev. Exp. On Med. Edu, Training & Research (In Mn per 1 lac pop.) 10 Institutional Births 25 Severe Anemia amongst pregnant women (%) 11 Birth Attended by trained Practiciners 26 Severe Anemia amongst adolescent girls (%) 12 Doctors (per 1 lac population) 27 % of Children as under nourished by weight (0-71 mths) 13 Nurses (per 1 lac population) 28 % of Children having iron deficiency - anemic (0-71 mths) 14 Hospitals (per 1 lac population) 29 Female per 1000 Male 15 Dispenseries (per 1 lac population) 30 Maternal Mortality Ratio Exhibit 1. List of Indicator Variables 14
  • 5. International Journal of Advanced Research in Management (IJARM), ISSN 0976 – 6324 (Print),ISSN 0976 – 6332 (Online), Volume 3, Issue 2, July-December (2012)THE MCDM APPROACHIn a MCDM environment, there are a number of alternatives to be assessed on the basisof their preference order. Many MCDM techniques available among which the techniquefor order preference by similarity to ideal solution (TOPSIS) proposed by Yoon (1980),Hwang and Yoon (1981) is a very effective one. The basic principle in this method is thatthe best alternative should have the shortest distance from the ideal alternative.The MCDM environment: Suppose there are all together K alternatives to be assessedand the best alternative is to be selected. Let the alternatives be denoted by S1, ………SK.there are also N criteria identified to assess the alternatives, which are denoted by C1,….CN. The k-th alternative’s value on the n-th criteria is obtained as xkn, and the same iswritten as: Sk = (xk1, ……., xkN), 1,……,K, and Cn = (x1n, ……, xkn), n = 1, ……,N.The ideal solution: It is feasible to compare each alternative with an “ideal alternative”to solve the assessment or decision making problem. TOPSIS adopts an intuitiveapproach to the construction of the best and worst alternative and calls them the ideal andthe negative-ideal alternatives or solutions. The ideal alternative S+, is formed by takingall the best values attained on each criterion by some alternatives, and can be denoted by: S+ = (x+1, ….., x+N) = [min {xk1}, …., min {xkM}, max {xkm + 1},……., max {xkN}].and the negative-ideal alternative S-, comprises of all the worst criterion values attainedby some alternatives, and is denoted by S- = (x-1, ….., x-N) = [max {xk1}, …., max {xkM}, min {xkm + 1},……., min {xkN}].The TOPSIS Procedure: With the above notation and explanation, the TOPSISprocedure for assessing the ranking can be described as follows: 1. Firstly we normalize the n-th criterion vector Cn into TCn: TCn = C n / || C n ||= ( x1n / || C n ||,....., xkn / || C n ||) ≡ (t1n ,......,t kn ), n = 1,...., N , Kwhere ||Cn|| = ∑ (x k =1 kn ) 2 is the Euclidean length or norm of Cn, so the new criterionvectors have the same unit length and are thus unit free and directly comparable. Underthe new criterion values, the k-th alternative, Sk, and the ideal and negative idealsolutions S+ and S- , are transformed to TSk, TS+ and TS-, respectively: TSk = (tk1,…..,tkN) = (xk1/||C1,…., xkN/||C1||), k=1,….,K, TS+= (t+1,….., t+N) = (x+1/||C1||,…..,x+N /||CN||, TS- = (t -1,….., t - N) = (x -1/||C1||,…..,x – N /||CN||, 2. Next the distances of Sk and x+ as the weighted Euclidean distance of TSk from TS+ are defined: 15
  • 6. International Journal of Advanced Research in Management (IJARM), ISSN 0976 – 6324 (Print),ISSN 0976 – 6332 (Online), Volume 3, Issue 2, July-December (2012) N Nd ( S k , S + ) =|| w • (TS k − TS + ) ||= ∑[Wn (t kn − t +1 ]2 = n =1 ∑[W ( x n =1 n kn − x+ n / || C n || 2 N= ∑ [W n ( x kn − min {x jn }) || C n ||] 2 + j ∑ n = M +1 [Wn ( x kn − max{x jn }) / || C n ||] 2 j k = 1,…..,K,where “ • ” is vector product operator and w is an N-dimensional weight vector whoseelements represent the relative importance of the N criteria. Similarly, the distance of Skfrom S- is defined as the weighted Euclidean distance of TSk from TS- and the same is N Nrepresented as: d ( S k , S − ) =|| w • (TS k − TS − ) ||= ∑ [W (t n =1 n kn − t −n ] 2 ∑ [W n =1 n ( x kn − x − n / || C n ||) 2 M N= ∑[Wn ( xkn − max{x jn }) || Cn ||] 2 + n =1 j ∑ n = M +1 [Wn ( x kn − min{x jn } / || C n ||] 2 k = 1,……,K, j 3. Finally the K alternatives are ranked according to the preference order by their relative closeness to the ideal alternative S+ which for the k-th alternative is defined as: r(Sk, S+) = d(Sk, S+)/[d(Sk, S+) + d(Sk, S-)], k = 1,…..,K The assessment criterion of TOPSIS is that the smaller the value of r(Sk, S+) which ranges between 0 and 1, the more preferred is the alternative Sk.Choice of weights: A reasonably good approach to obtain internal importance weights isto use the entropy concept. It is a criterion for the amount of information (or uncertainty)represented by a discrete probability distribution, p1, …..pk and this measure of kinformation was given by Shannon and Weaver (1947) as E ( p1 ,...., p k ) = −φk ∑ pk1n( pk ) k =1where φ k=1/1n(K) is a positive constant which guarantees that 0 ≤ E(p1,……,pk) ≤ 1. itis noted that the larger the E(p1,……,pk) value, the smaller the variations among the pk’sand that 0 entropy means maximum information and 1 minimum information. For the n-th criterion vector Cn in an MCDM environment, let Xn = x1n + …+ xKn be the total valueof the criterion. If we view the normalized values pkn = xkn / Xn for k = 1, ….,K as the“probability distribution” of Cn on the K alternatives, the entropy of Cn may be defined K Kas: E(Cn) = - ø k ∑ p k 1n( p k ) = φk ∑ ( xkn / X n )1b( xkn / X n ), n = 1,......N , and define the k =1 k =1 Nweights as wn = (1 − E (C n )) / ∑ (1 − E (C j )), n = 1,...., N . j =1FINDINGS & ANALYSISThe values of 30 indicator variables have been initially plotted for each state as shownbelow. To annul the effect of the varying units of indicator variables, StatisticalNormalization was done followed by weight determination using Shannon’s Method. Thedistance from Normalized Ideal and Negative Ideal is calculated before finallycalculating the rank of Indian states. 16
  • 7. International Journal of Advanced Research in Management (IJARM), ISSN 0976 – 6324 (Print),ISSN 0976 – 6332 (Online), Volume 3, Issue 2, July-December (2012) Infant HIV Low BMI Life Birth Rate Mortality Vaccination HIV awareness Low BMI Institutional Fertility Rate awareness Females Expectancy (per 1000 Rate (per Coverage (%) (females%) Males (%) Births (males%) (%) at Birth population) 1000 live births) C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 Negative Positive Positive Positive Negative Negative Positive Negative Negative Positive ANDHRA PRADESH 1.8 74 93 74 24.8 30.8 64.4 21.7 66 43 ARUNACHAL PRADESH 3 28 75 66 13.6 15.5 67 23.1 44 26 ASSAM 2.4 32 75 53 33.4 36.5 58.9 27.9 76 21 BIHAR 4 33 70 35 28.7 43 61.6 32.8 67 15.8 CHATTISH GARH 2.6 47 67 41 31.8 41 58 29.2 32 16 DELHI 2.6 69 80 57 28.1 33 63.5 20.3 32 49 GOA 1.5 79 92 83 116.8 20.5 64 11.7 44 93 GUJRAT 2.4 56 80 49 28.2 32.3 64.1 26.8 64 36.3 HARYANA 2.7 65 87 60 26.8 27.8 66.2 26.9 67 24.8 HIMACHAL PRADESH 1.9 67 92 79 19.8 24.3 67 22.1 60 24.3 JAMMU & K 2.4 67 88 61 19.9 21.3 64 19.6 50 54 JHARKHAND 3.3 35 53 29 33.4 42.6 64 28.8 66 19 KARNATAKA 2.1 55 85 66 25.5 31.4 65.3 19.4 57 49 KERALA 1.9 75 99 95 11.9 12.5 74 17.9 14 97.1 MADHYA PRADESH 3.1 54 68 45 36.3 40.1 58 31.2 88 16.4 MAHARASTRA 2.1 59 87 82 24.9 32.6 67.2 20.9 48 48.6 MANIPUR 2.8 59 99 99 12.2 13.9 66 18.3 23 43 MEGHALAYA 3.8 33 63 57 8 13.7 63 28.5 58 30 MIZORAM 2.9 72 96 94 6 15.3 71 19.2 23 65 NAGALAND 3.7 21 91 81 10.8 15.9 63.5 12.2 16 12 ORISSA 2.4 52 73 62 32.1 40.5 59.6 24.3 96 14.1 PUNJAB 2 60 92 70 12 13.5 69.4 21.5 52 12.8 RAJASTAN 3.2 27 52 37 33.8 33.6 62 31.2 79 8.1 SIKKIM 2 70 89 75 7.2 9.6 59 21.8 49 49 TAMIL NADU 1.8 81 98 94 18.5 23.5 66.2 19.2 51 64.7 TRIPURA 2.2 50 89 73 38.3 35.1 65 16.5 41 49 UTTAR PRADESH 3.8 23 74 40 32.7 34.1 61 32.8 83 8 UTTARANCHAL 2.6 60 90 79 21.8 25.7 60 24.6 83 36 WEST BENGAL 2.3 64 74 50 31.6 37.7 64.9 20.6 51 35.8 Exhibit 2. Indicator Variables Primary Birth Hospital Rev. Exp. Cap. Exp. Health Doctors (per Nurses (per Hospitals Dispenseries Health Attended by Beds (per 1 On Health On Health Exp. As a 1 lac 1 lac (per 1 lac (per 1 lac Centres trained lac (In Mn per (In Mn per % of Tot. population) population) population) population) (per 1 lac Practiciners population) 1 lac pop.) 1 lac pop.) Exp. population) C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive ANDHRA PRADESH 27.7 73.29 133.42 5.45 0.23 2.52 121.31 19.37 0.70 3.53 ARUNACHAL PRADESH 28.9 45.6 62.4 23.86 1 7.51 225.52 48.52 3.65 4.45 ASSAM 16.2 53.72 33.29 1.01 1.22 2.64 47.66 20.95 0.93 3.06 BIHAR 19.8 38.65 10.65 0.4 0.51 2.97 35.16 6.75 0.08 3.24 CHATTISH GARH 29 31.2 61.4 0.16 0.16 3.57 69.3 12.84 1.96 3.74 DELHI 18 152.31 166.72 4.04 2.08 0.85 89.63 58.95 1.73 2.78 GOA 5.8 127.85 166.08 8.1 2.32 3.49 69.77 75.89 3.18 3.27 GUJRAT 38.4 63.67 137.59 4.99 14.32 3.17 143.49 14.82 0.31 3.05 HARYANA 68 5.03 63.41 0.37 0.61 2.68 32.23 15.46 0.59 2.59 HIMACHAL PRADESH 26.6 62.22 96.81 1.33 2.83 5.54 104.9 42.71 8.38 5.08 JAMMU & K 28 29.6 49.3 0.42 3.97 4.4 20.56 35.79 4.03 4.78 JHARKHAND 31 36.9 61.6 0.42 0.54 2.89 36.2 10.83 1.33 3.65 KARNATAKA 26.2 109.29 146.36 0.55 1.51 4.83 75.01 17.42 0.70 3.49 KERALA 1.8 91.87 185.65 13.92 0.17 4.03 308.17 23.57 0.90 4.71 MADHYA PRADESH 22.3 29.75 142.95 0.16 0.17 3.73 63.76 12.77 0.54 3.39 MAHARASTRA 20.6 79.97 106.3 3.56 6.04 3.19 107.1 16.60 0.47 3.51 MANIPUR 19 59.4 88.9 0.78 1.73 3.67 71.38 31.96 2.81 3.72 MEGHALAYA 18.9 61.2 87.7 0.3 0.78 4.55 53.6 31.02 4.65 5.23 MIZORAM 12.5 55.55 164.91 1.24 1.5 13.01 116.1 53.19 0.16 3.96 NAGALAND 19.8 58.4 89.5 0.85 1.76 2.62 55.48 36.28 23.12 4.68 ORISSA 24.1 38.27 105.26 0.74 3.42 4.35 33.32 15.40 1.53 3.90 PUNJAB 86.1 129.66 152.45 0.9 5.96 3.02 83.26 27.94 0.94 3.10 RAJASTAN 26.4 34.87 44.79 0.2 0.47 3.86 31.05 16.38 0.35 3.94 SIKKIM 13.5 56.3 67.8 0.37 30.11 4.97 147.92 89.91 3.42 2.56 TAMIL NADU 21.6 102.26 166.95 0.65 0.82 4.09 78.61 19.16 1.26 4.20 TRIPURA 12.3 11.52 15.5 0.84 19.54 2.2 55.4 26.31 6.51 3.79 UTTAR PRADESH 42.3 0.11 0.04 0.05 0.13 0.01 3.92 11.06 0.89 4.49 UTTARANCHAL 41.6 59.89 78.4 0.04 0.12 0.01 3.74 25.11 6.52 4.34 WEST BENGAL 13.9 61.75 53.94 0.51 0.26 2.2 68.68 2.92 1.04 0.93 Exhibit 3. Indicator Variables 17
  • 8. International Journal of Advanced Research in Management (IJARM), ISSN 0976 – 6324 (Print),ISSN 0976 – 6332 (Online), Volume 3, Issue 2, July-December (2012) Rev. Exp. Exp. On Exp. On Rev. Exp. Severe Severe % of % of Children On Family Medical Public On Med. Anemia Anemia Children as having iron Female Maternal Welfare (In Services (In Health (In Edu, amongst amongst under deficiency - per 1000 Mortality Mn per 1 lac Mn per 1 Mn per 1 Training & pregnant adolescent nourished anemic (0-71 Male Ratio pop.) lac pop.) lac pop.) Research women girls (%) by weight (0- mths) C21 C22 C23 (In Mn per C24 (%) C25 C26 71 C27 mths) C28 C29 C30 Positive Positive Positive Positive Negative Negative Negative Negative Positive Negative ANDHRA PRADESH 3.22 7.94 1.74 0.69 2.1 23.6 42.3 38.7 978 154 ARUNACHAL PRADESH 1.56 28.07 5.07 0.84 7.8 40.3 32.2 42.9 901 480 ASSAM 2.24 5.15 1.08 0.58 0.4 0.2 12.6 23.6 932 312 BIHAR 0.85 1.93 0.54 0.53 2.2 27.6 54.6 46.6 921 371 CHATTISH GARH 0.45 3.94 0.51 2.13 5.1 48.3 47.4 55.5 990 379 DELHI 11.30 18.23 8.23 6.72 1.3 28.7 35.3 48 821 101 GOA 2.13 36.71 3.23 4.29 0 10.8 30 24.9 960 62 GUJRAT 2.20 7.05 1.92 0.83 5.1 39 46 51.7 921 160 HARYANA 1.34 8.25 1.97 2.32 3.3 40.2 35.6 54.1 861 186 HIMACHAL PRADESH 4.68 28.93 4.22 7.31 4 31 36.4 47.7 970 196 JAMMU & K 1.51 17.96 3.68 2.04 2.6 10.1 20.3 27.9 900 196 JHARKHAND 2.39 6.66 0.72 0.09 1.3 24.2 52.2 40.9 941 371 KARNATAKA 2.24 8.83 0.66 1.61 0.9 14.8 44.8 34 964 213 KERALA 2.76 13.30 1.51 2.24 0 2.2 35.8 10.2 1,058 95 MADHYA PRADESH 1.51 8.27 1.97 0.73 3.4 33.2 55.4 50.2 920 335 MAHARASTRA 1.58 5.42 4.28 1.12 1.8 29.4 47.7 50.2 922 130 MANIPUR 2.80 10.44 4.49 1.38 1.2 9.4 34.9 34.9 978 401 MEGHALAYA 2.50 14.07 2.60 0.56 1.5 0.7 15.2 24.1 975 404 MIZORAM 4.54 20.26 4.18 1.15 1.1 21 21.4 30.5 938 398 NAGALAND 4.27 27.96 1.78 0.13 4 21.4 9.7 39.4 909 396 ORISSA 1.81 6.29 1.42 0.71 3.8 27.2 42.8 40.9 972 303 PUNJAB 1.62 15.67 1.54 2.11 2.9 33.9 40 50.2 874 192 RAJASTAN 2.12 9.14 1.04 0.98 3.3 21.9 58.1 39.7 922 388 SIKKIM 7.78 60.30 4.16 0.14 0.8 19.3 30.2 42.7 875 212 TAMIL NADU 2.62 11.10 2.42 1.29 1.9 17.7 38.3 30.6 986 111 TRIPURA 5.01 11.08 1.71 0.47 1 8.5 29.7 17.8 950 407 UTTAR PRADESH 3.32 0.35 0.89 0.39 3.4 28.8 55.3 47.1 898 440 UTTARANCHAL 26.40 1.32 1.04 1.06 3.2 28.6 52.6 36.6 964 517 WEST BENGAL 2.03 9.43 1.51 0.89 3.7 18 44.9 30.7 934 141 Exhibit 4. Indicator VariablesThe relative weights of all the chosen indicator variables has been calculated usingShannon’s Weight determination method and the same is shown in Exhibit 5. No. ofHospitals, No. of Dispensaries, Capital Expenditure on Health, Revenue Expenditure onMedical Training, Revenue Expenditure on Family Welfare, Expenditure on MedicalServices, No. of Primary Health Centres, Low BMI of male & females, Anemia amongstpregnant women have been found to be the 10 most important indicator variablesaffecting the healthcare management status of public sector in Indian states. Shannons ShannonsSl # INDICATOR VARIABLES Sl # INDICATOR VARIABLES Weight (%) Weight (%) 1 Fertility Rate 0.41 16 Primary Health Centres (per 1 lac population) 4.52 2 Vaccination Coverage (%) 0.76 17 Hospital Beds (per 1 lac population) 3.45 3 HIV awareness (males%) 0.17 18 Rev. Exp. On Health (In Mn per 1 lac pop.) 2.92 4 HIV awareness (females%) 0.59 19 Cap. Exp. On Health (In Mn per 1 lac pop.) 9.10 5 Low BMI Males (%) 4.50 20 Health Exp. As a % of Tot. Exp. 0.42 6 Low BMI Females (%) 3.94 21 Rev. Exp. On Family Welfare (In Mn per 1 lac pop.) 5.56 7 Life Expectancy at Birth 0.02 22 Exp. On Medical Services (In Mn per 1 lac pop.) 4.30 8 Birth Rate (per 1000 population) 1.92 23 Exp. On Public Health (In Mn per 1 lac pop.) 3.00 9 Infant Mortality Rate (per 1000 live births) 2.67 24 Rev. Exp. On Med. Edu, Training & Research (In Mn per 1 lac pop.) 5.82 10 Institutional Births 2.45 25 Severe Anemia amongst pregnant women (%) 3.26 11 Birth Attended by trained Practiciners 2.31 26 Severe Anemia amongst adolescent girls (%) 2.30 12 Doctors (per 1 lac population) 2.47 27 % of Children as under nourished by weight (0-71 mths) 0.87 13 Nurses (per 1 lac population) 2.31 28 % of Children having iron deficiency - anemic (0-71 mths) 0.63 14 Hospitals (per 1 lac population) 13.60 29 Female per 1000 Male 0.02 15 Dispenseries (per 1 lac population) 14.12 30 Maternal Mortality Ratio 1.55 Exhibit 5. Shannon’s Weight of Indicator variables 18
  • 9. International Journal of Advanced Research in Management (IJARM), ISSN 0976 – 6324 (Print),ISSN 0976 – 6332 (Online), Volume 3, Issue 2, July-December (2012) Rank Table Relative Closeness TOPSIS STATES Value RANK KERALA 0.30098236 1 SIKKIM 0.43986403 2 GUJRAT 0.48134232 3 DELHI 0.48945707 4 ARUNACHAL PRADESH 0.49493230 5 ANDHRA PRADESH 0.52015254 6 TAMIL NADU 0.52299724 7 MAHARASTRA 0.52525553 8 PUNJAB 0.52561855 9 GOA 0.53127624 10 HIMACHAL PRADESH 0.54751923 11 MIZORAM 0.56189787 12 KARNATAKA 0.57160608 13 WEST BENGAL 0.60203498 14 JAMMU & K 0.63580135 15 NAGALAND 0.63741721 16 MANIPUR 0.64319223 17 HARYANA 0.64680915 18 MEGHALAYA 0.66260120 19 MADHYA PRADESH 0.67244550 20 ORISSA 0.68935158 21 TRIPURA 0.69413424 22 ASSAM 0.69870290 23 CHATTISH GARH 0.69935484 24 JHARKHAND 0.73008429 25 UTTARANCHAL 0.73437144 26 BIHAR 0.73981427 27 RAJASTAN 0.74733990 28 UTTAR PRADESH 0.78774375 29 Exhibit 6. Rank of Indian StatesCONCLUSIONThe ensuing research study reveals that Kerala is the state with the best public healthcaremanagement status in India followed by Sikkim and Gujarat respectively. This indicatesthat in these states, the overall healthcare status is being managed better compared toother states. Looking at the top 10 developed states in India on public healthcaremanagement status, it is to be noted that 3 states are from South India, 3 from West India,2 from East India and 2 from North India. Again looking at the bottom 10 states, it isnoted that 5 are from East India and North East, 2 from Central India, 2 from North Indiaand 1 from West India. Looking at the Top 10 and Bottom 10 states, the researcheropines that public healthcare management status is positive and has progressed in stateswhere the impact of globalization has been high and public sector tends to compete withthe private sector, especially in South & West India.LIMITATIONS & DIRECTIONS FOR FUTURE RESEARCHThe present work includes 30 indicator variables which could be a limitation in the sensethat there is a scope to increase the same. This research work is based on secondary dataand incorporation of primary data could have led to a more real time analysis. Theresearch can be extended to other areas on social development like assessing the publiceducation status and crime status in Indian States. 19
  • 10. International Journal of Advanced Research in Management (IJARM), ISSN 0976 – 6324 (Print),ISSN 0976 – 6332 (Online), Volume 3, Issue 2, July-December (2012)REFERENCES • 11th Plan Targets – Health & Nutrition, Ministry of Health, GOI. • Census of India 2011, GOI. • Emerging Trends in Healthcare, 2011, ASSOCHAM – KPMG Report. • Estimates of Maternal Mortality Ratios in India and its States, Indian Council of Medical Research, 2003, Ministry of Health & Family Welfare, GOI. • Gujarat Institute of Development Research Report, Ahmedabad. (2005). Infrastructure and growth in a Regional context: Indian states since the 1980s. pp 21. • Hwang, C.L., & Yoon, K. (1997). Multiple Attribute Decision Making - Methods & Application. New York: Springer – Verlag. • India Social Development Report. (2008). Council for Social Development, New Delhi. pp 311. • J.K. Satia and Ramesh Bhat. (1999). “Progress and challenges of health sector: A balance sheet”. Working paper No. 99-10-08, Indian Institute of Management, Ahmedabad, 1-20 • Majumder, A. (2005). "Economics of Health Care: A Study of Health Services in Cooch Behar and Jalpaiguri Districts," Artha Beekshan, 14 (1): 52-66. • Majumder, A. (2006). "Demand for health care in India," Artha Beekshan, 15 (3): 48-63. • Mavalankar, D. (1998). “Need and Challenges of Management Education in Primary Health Care System in India”. Working paper No.98-11-05, Indian Institute of Management, Ahmedabad, 1-14 • National Health Profile Report, 2009, Ministry of Health, GOI. • Nutritional Status of Children and Prevalence of Anemia among children, adolescent girls and pregnant women, 2006, International Institute for Population Sciences (Deemed University) and Ministry of Health and Family Welfare, GOI. • Palanithurai, G. (2004). Panchayats and Communities in Family welfare. Social Welfare, 51(7), pp 22-30. • Pattanaik, A. & Badu Kanak, M. (2003). Population Explosion and Media. Indian Journal of Population Education, (20), pp 36-47. • SRS Bulletin, June 2011, Registrar General of India. • SRS Bulletin, October 2008, Registrar General of India. 20

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