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Construction of Health Infrastructure Index in Haryana: An Econometric Approach.

INTRODUCTION

In the age of globalization, health is one of the human rights everywhere including Haryana--a prosperous State of Indian economy. Health which refers to the state of complete physical, mental, social and spiritual wellbeing and not merely an absence of disease; received prominent place in the construction of human development index since 1990. In this context, World Health Organization pointed out the dependency of health on the availability of better health infrastructure in terms of health institutions, medical staff, beds in medical institutions, and doctor--patient ratio, doctor--nurse ratio et cetera (Saikia and Bhattacharjee, 2011). Therefore, provision of health infrastructure, health services and related health issues are one of the prime areas of concern. Besides, role of health infrastructure in the improvement of health indicators namely life expectancy, crude birth and death rates, infant mortality rate, maternal mortality rate and eradication of all diseases, is recognized all over the world (Subba Lakshmi and Sahoo, 2013; Anand, 2014). Thus, policy makers consider the health infrastructural facilities as one of the most decisive dimension for the attainment of the social goal of 'Health for all' by the year 2020. Moreover, the health related targets of Millennium Development Goals (MDGs: 2000-2015) as well as of Sustainable Development Goals (SDGs: 2015-2030) makes the States careful towards the attainment of equity, efficiency and sufficiency in health infrastructure which can be possible by significant investments (Goel, 2011; Goel and Garg, 2016). An adequate health care infrastructure has many components such as physical facilities; laboratory, training, and other support facilities; reliable supplies of pharmaceuticals and other materials; trained staff and professional training systems; by which preventive, diagnostic, and curative care is provided (Kumar and Gupta, 2012; IZUMI Foundation, 2013).

Thus, there may be a number of indicators which form health infrastructure, and from practical stand point, it becomes difficult to identify common trends across separate indicators. For the same, economists realize on composite indices (CI) which prove helpful in analyzing those concepts which encompass multiple indicators. With the help of composite indices, the indicators defining a particular phenomenon are merged into a single score value so that clear, relevant and reliable messages can be obtained (OECD, 2008). Keeping the same in mind, the present paper is primarily concerned with constructing a composite index for health infrastructure in the State of Haryana. Specifically following objectives are worked upon:

(1) To describe health infrastructure in Haryana on its individual indicators across selected years.

(2) To construct composite index for health infrastructure, and discuss its overall methodology.

(3) To examine health infrastructure according to overall ranking based on index scores.

REVIEW OF LITERATURE

Analyzing health infrastructure, health services and health outcomes through construction of composite indices is very popular among economists and researchers all over the world. Therefore, there is no dearth of

literature and out of which some are reviewed as follows:

Kumari and Raman (2011) developed composite indices of education and health attainment during 1990-91 and 2007-08 for Uttar Pradesh by using Principal Component Analysis. They considered eight and thirteen indicators for the health and educational attainment respectively. They found the existence of wide disparity among Districts with respect to education and health. However, Districts having good educational attainment are found to be poor in health attainment while the Districts with well performance in health remained poor in education.

Saikia (2012) used Data Envelopment analysis (DEA) and Principal Component analysis (PCA) to analyze the regional disparity with regard to social sector in India including all States and one Union Tertiary as Delhi. On the basis of two techniques the various ranks are given to the States which explore the presence of regional inequalities in social sector development in India. However, no significant difference exists between the rankings given by DEA and PCA since the rank correlation coefficient between them is strong.

Subba Lakshmi and Sahoo (2013) constructed the health infrastructure index for the period 1980-2010 considering health inputs like number of hospitals and dispensaries, number ofbeds and number ofdoctors in government hospitals for the State of Andhra Pradesh by applying principal component analysis. Utilizing health infrastructure index, elasticity coefficients of health indicators with respect to health infrastructure were computed by using double log simple regression model. In order to calculate elasticity coefficients, they. The results revealed that the elasticity coefficients of health indicators like crude birth rate, crude death rate, infant mortality rate and life expectancy at birth with respect to health infrastructure are -37.966, -27.816, -30.598 and 10.282 respectively. Thus, public health facilities are crucial for meeting the basic health requirements of masses in the state.

Anand (2014) by using Principal Component Analysis computed the composite indices of health status and health services for two Indian States namely Uttar Pradesh and Bihar. The study found the existence of wide interdistrict and interregion health disparity in both States with lower disparity in Uttar Pradesh as compared to Bihar in terms of health status and relatively high disparity in health infrastructure.

Lyngdoh (2015) made an attempt to form a healthcare infrastructure index for the north eastern States for the years 2001 and 2011 with the help of Principal Component Analysis. With this health infrastructure index, the States were ranked. The study explored that Tripura attained first rank and Mizoram got second rank in health infrastructure index during 2001 as well as 2011; the index score of Arunachal Pradesh have improved in 2011 over 2001. But States of Assam and Meghalaya were found to be the poorest performers.

Motivating from this literature, the present research work is conducted to examine health infrastructure in Haryana through estimation of its composite index.

METHODOLOGY

This section highlights the collection of data, selection of time period and variables, model for composite index and statistical techniques as follows:

1). Data Collection, Time Period and Selection of Variables

For the present study, data on health infrastructure is collected from various issues of Statistical Abstract of Haryana published by Government of Haryana each year. According to the data availability, fourteen major indicators of health infrastructure are selected (table 1) with data for a period of 1991-92 to 2011-12 (table 2). These indicators are the variables for present research.

There may be other variables also which define health infrastructure, but because of unavailability of reliable data on other indicators, these are not considered in the paper. According to the latest Statistical Abstract of Haryana, health infrastructure in Haryana is a combination of infrastructure allopathic institutions as well as AYUSH (ayurvedic, unani, siddha, and homoeopathic) institutions which are operative in the State and providing their services to people. The variables can be noted from table 1.

2). Composite Index and Statistical Technique

Composite index is the aggregation of selected variables into one score. There are a number of techniques for aggregation with or without weights. But, mostly, aggregations are done by using weights. Mathematically, Composite Index = [N summation over (i=1)][w.sub.i][v.sub.i]/[[sigma].sub.i]

Where, [w.sub.i]= weight of respective variable; [v.sub.i]= variable; [[sigma].sub.i] = standard deviation of variable ([v.sub.i]) and N = number of variables.

In order to make the variable with unit variance, this variable is divided by its standard deviation (Fernando et al. 2012). However, from the point of view of index construction, the selection of measurement units and estimation of weights is much cumbersome.

In the present research work, Principal Component Analysis (PCA) is the foremost econometric technique used to arrive at the weights. IBM SPSS Statistics (Version 20) is utilized to apply the technique. Other major calculations are completed manually as well as by using Microsoft Excel (2013). A number of studies have applied Principal Component Analysis (PCA) for the computation of weights in index construction. In this context, OECD (2008), Nicoletti et al. (2000), Sharpe and Andrews (2012), Fernando et al. (2012), Singh and Gupta (2013), Anand (2014), Daka and Fandamu (2016) are few to mention. The procedures described by these researchers is the basis of statistical methods applied in the present paper.

ANALYSES AND INTERPRETATIONS

This section is mainly divided into three sub-sections and describes the analysis of data completed with suitable statistical techniques to arrive at the objectives.

1). Health Infrastructure in Haryana across Individual Indicators

In order to achieve the first objective table 2 provides a snapshot of collected data and descriptive statistics. It is found that since 1991-92, some of the indicators including numbers of PHCs, CHCs, SCs, doctors, ayurvedic and homoeopathic institutions and their dispensers/compounders have increased as they possess positive compound annual growth rate (CAGR). While the indicators including hospitals, dispensaries, nurses, other staff, beds, unani institutions and vaidyas/ hakims has faced negative CAGR in their number.

However, CHCs have experienced highest CAGR of 2.429 per cent in their number but this indicator also have largest coefficient of variation (CV) of 15.773 per cent thereby revealing high dispersions in the CHCs' number in the State during 1991-92 to 2011-12. Moreover, number of nurses has increased from 4045 in 1991-92 to 4078 in 2011-12 yet their CAGR is -0.200 per cent. Meanwhile, the number of PHCs have just 0.401 per cent CAGR but they possess least variations (CV= 2.824%) over the years.

Also, it is a serious cause of concern that among seven indicators having negative CAGR, hospitals, dispensaries, other staff, beds and unani instituions are found to be lesser in number during the year 2011-12 than their average. Another point worthy to be mentioned is that indicators including PHCs, CHCs, SCs, and homoeopathic institutions are found to attain maximum number in 2011-12 among twenty one year period whereas dispensaries and unani institutions stood at their minimum number for the same year.

However, in case of medical institutions sub centers are found highest in number (X=2354) with followed by ayurvedic institutions (X =439) and PHCs (X=407). Similarly, with regard to medical staff, the average number of doctors, nurses and other staff are observed as 1562, 3796 and 8194 while for vaidyas/hakims and dispensers/compounders average number is 428 and 406 respectively. In totality, number of allopathic medical institutions and their staff are much higher than ayurvedic, unani and homoeopathic institutions and their medical staff.

Next, this data is put into further analysis in order to attain second objective of getting a composite index score by combining all these indicators or variables. Thus, the data as revealed in table 2 is an input for further analysis in next section.

2). Composite Index Construction for Health Infrastructure

In order to prepare composite index of health infrastructure, step by step procedure is described in following sub-sections:

2.1). Normalization of Indicators of Health Infrastructure

As a preliminary step of constructing an index, normalization is required when the data is obtained in different measurement units to convert it into identical measures. Present data on indicators of health infrastructure have the same unit of measurement, as the entries in all the cells (table 2) denote the numbers. But, then also to convert that data into a specified range [here a range of 0 to 1], the given data is normalized by using a 'Linear Scaling Technique' also called 'Min-Max Normalization' (OECD, 2008; Sharpe and Andrews, 2012). Under this technique, minimum value of a variable in its data series is subtracted from the particular value for which normalization is undertaken and the resultant is divided by the range (difference between maximum and minimum value of this particular variable) of its data series. In this way, the normalized data comes up within a limit of 0 to 1.

Mathematically, Normalized Value = Indicator value-Minimum value/Maximum value-Minimum value

To elaborate fully, suppose, indicator or variable 'Hos' (Hospitals) is to be normalized for the year 1991-92 when they were 79. In table 2, in data series of 'Hos', minimum value is 60 and the maximum value is 80. Now, for normalization, subtract minimum value (60) from indicator value (79) and obtain resultant 19, which has to be divided by the subtraction of maximum (80) and minimum value (60) that is 20. The normalized value is 0.950 for an indicator value of 79. The same process is applied to all the values for all indicators or variables and results are shown in table 3 which are used as an input for Principal Component Analysis.

2.2). Principal Component Analysis for Composite Index

Principal component analysis of the normalized data is performed with the help of SPSS (version 20) and resultant output is shown under following headings:

* Correlation Matrix, Kaiser-Meyer-Olkin (KMO) Measure and Bartlett's Test

To start the analysis, it is necessary to test that whether present data is adequate for principal component analysis (PCA). In this regard, Correlation matrix (showing scores of correlation among variables), Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett's Test of Sphericity are computed by SPSS system (Constantin, 2014).

Since, the PCA method depends on the correlations between sets of variables. Therefore, it becomes imperative to examine the correlation among variables undertaken. More clearly, the correlation scores clarify that whether the PCA will be meaningful or not. The correlations between individual variables has to be higher than 0.30 for the analysis to provide significant results. However, low scores of some of the correlations do not create problem. But if, most of the correlations score near about zero, then method lose its usefulness (Mooi and Sarstedt, 2011; Hoque, 2014). The correlation matrix which is one of the output of PCA, are presented in table 4.

The correlation matrix is a rectangular arrangement of numbers showing the correlation coefficients between one variable and every other variables. It is evident from table 4 that the all elements on principal diagonal are 1 since correlation coefficient between a variable with itself is always unity. Below this principal diagonal, the some correlation coefficients are positive and some are negative thereby implying that some of the variables move in same direction with other variables, some varies oppositely with others. Further, correlations between indicator variables are found to be of high, moderate and low degree which are significant either at 1 per cent, or 5 per cent, or 10 per cent level of significance. However, some indicator variables show insignificant correlation with other variables, for example, doctors, nurses and vaidyas/hakims have insignificant correlation with most of the variables. This pattern suggests some variables may flow together and some others may go differently. Thus, possibly certain components or latent variables or factors can be obtained for these variables.

Next output, Kaiser-Meyer-Olkin (KMO) statistic, a measure of the strength of relationship among variables, indicates whether the correlations between variables can be explained by other variables in the dataset. Its value varies between 0 and 1. The data is considered suitable for PCA if KMO statistic is equal or higher than 0.50. In the present context, the value of KMO (0.460) is approximately equal to 0.5 therefore; data is suitable for the application of PCA.

Moreover, the Bartlett's test is another indicator of judging that whether original variables are sufficiently correlated. It is used to test the null hypothesis that the correlation matrix is an identity matrix that is, in which diagonal elements are 1 and others are 0. This implies that all variables are perfectly correlated with themselves but uncorrelated with others. Alternatively, correlation matrix is not identity matrix thereby implying that there are some degree of correlation between variables. To test the null hypothesis that all correlation coefficients are zero or not significant, chi-square statistic is computed under Bartlett test. The value of approximate chi-square statistic is found to be 399.705 with 91 degrees of freedom, which is significant at 0.000 level of significance which is under the accepted range of level of significance (p-value) 0.05. On this basis, null hypothesis that is variables are uncorrelated or correlation matrix is an identity matrix is rejected and alternative hypothesis is accepted which means original variables are correlated which is compulsory for the adequacy of PCA.

Thus, significant scores of correlation coefficients and results of KMO and Bartlett's test show that principal component analysis is preferable.

* Decision for Number of Components

After passing the above tests, next step is to identify the number of factors or principal components or latent variables that can represent all originally undertaken variables. For the same, eigenvalue-one criterion (Kaiser's criterion or latent root criterion), scree plot and total amount of variance extracted are the methods that can be used. But, present study adopts eigenvalue-one criterion according to the standard practice of decision as mentioned by OECD (2008) and Sharpe and Andrews (2012). Under, eigenvalue-one criterion, those factors or principal components or latent variables are selected which possess eigenvalues larger than 1, individual variance explained more than 10 per cent; and cumulative contribution to overall variance more than 60 per cent. In table 5, initial and rotated eigenvalues are presented as another output of PCA.

It is cleared from initial eigenvalues in the table that the number of components is equal to number of variables selected in the study and every component has an eigenvalue showing the variance extracted by itself. But, only first three components are possessing the eigenvalue greater than 1 (7.610, 1.889 and 1.752) and thus, fourteen indicators are reduced to these three set of components as shown in table under column extraction sums of squared loadings. For further clarification about retained components, rotation has been applied. It is found that there exist three components with eigenvalues 7.047, 2.254 and 1.951 which are some different from initial eigenvalues but their summation is same with initial eigenvalues of first three components. After rotation, these components individually explain 50.334 per cent, 16.099 per cent and 13.937 per cent of variance (well above suggested 10 per cent) but cumulatively explain 80.370 per cent of variance which are notably above the suggested criterion of 60 per cent. With this analysis, three components are retained which are able to represent fourteen variables selected for study.

* Component Matrix and Loadings

Table 6 is a triad for component loadings, squared component loadings and squared loadings scaled to unity sum. Component loadings are the correlations between variables and the latent components. These are used to examine which component is formed with which of the variables. This information is provided by PCA in its output named initial or un-rotated component loading matrix.

In this matrix, sometimes, it becomes difficult to recognize that which variable should be included in which component because various variables load moderately on each component. To overcome this problem, varimax rotation has been applied; as a result of which each original variable tends to be associated with one (or a small number) of retained components, and each component represents only a small number of variables (Abdi and Williams, 2010). However for the sake of simplicity, the rotated component loading matrix is shown in table 6In order to check which indicator variable load on which component, a criterion of component loadings greater than 0.5 is employed (Hair et al., 2010). It can be seen from the left side of table that first component is formed by Hos (hospitals), PHCs, Dis (dispensaries), CHCs, SCs, Beds and AIs (ayurvedic institutions), UIs (unani institutions) and HIs (homoeopathic institutions) since the component loadings of these variables are high on the first component among three components. Component two is a formation of variables Nur (nurses), Va/Ha (vaidyas/hakims) and Dis/Com (dispensers/compounders). On the same notions, component three is a conglomerate of variable namely Doc (doctors) and OS (other staff).

Now, at the middle of table there are squared component loadings (obtained by squaring the component loadings) which explain the amount of variation of the indicator variables that the latent components explain. Below the matrix of 'squared component loadings' explained variance of three components are displayed. This is attained by adding the square component loadings of components one, two and three respectively. Actually, these are the three eigenvalues, obtained after rotation. Now, total variance is the addition of three values of explained variance [7.047+2.254+1.951]. Explained variance when divided by total variance gives 'component weight'.

Third part of table 'squared loadings scaled to unity sum' is attained by dividing squared loadings in each component by the explained variance of respective component, and the values obtained are entitled as 'domain weight' for all original variables rests under various components. Now, the 'component weight' and 'domain weight' are used in table 7 to arrive at the final weights.

In above table 7, column 'domain weight' shows the weight of the original variable in the component in which it falls and is obtained by squared loadings scaled to unity sum shown in third part of table 6. Similarly, each variable has that component weight in which this variable lies and on this basis column of 'component weight' is prepared. For example, variable 'Hos' fell under first component whose weight is 0.626 as calculated in table 6.

Likewise, all variables have domain weight as well as component weight. As a next step, the 'domain weight' and 'component weight' is multiplied (as shown under column 'Weight Score' in table 7) and the resultant is divided by summation of multiplication values to arrive at weight scores. In this way, final weights of each variable are obtained and displayed in the last column under the heading 'Resulting Weight'. The summation of weights in this column is definitely come out to be 1, and now these weights are ready to be used for a weighed aggregation of variables for getting a composite index.

* Composite Index

To prepare composite index, firstly, each indicator or variable (normalized) for various years is multiplied by its weight (computed in table 7) and is divided by its standard deviation (obtained from normalized data). In this way all variables will be credited with weighted scores for various years which has to be summated to obtain composite index for every year.

To keep the scores of composite index ranging between 0 and 1, it is necessary to normalize the composite index by subtracting minimum value in the series from each value and then divide the resultant by the difference of maximum and minimum value. In this way, composite index of health infrastructure is constructed and presented in table 9.

3) Ranking of Health Infrastructure in Haryana

To achieve the third objective, ranks are given to the State of Haryana for health infra/structure in various years as per the calculated index scores. It is cleared from the table 9 that score of health infrastructure index have improved from 0.168 in the year 1991-92 to 0.527 in 1994-95 and consequently, the rank is also upgraded from 19th to 8th for the same period. But, in the next year the value of index is declined to 0.361 which attain 15th rank. But, in subsequent three years (1996-97, 1997-98 and 1998-99) the rank of health infrastructure become better to 13th, 10th and 6th with index values 0.392, 0.480 and 0.574 respectively.

Unfortunately, in 1999-00, the index value once again falls and reaches to 0.251 with 18th ranking. But, this situation changes in 2002-03 when index score climbs to 0.573 with rank 7th which is second best over previous years. However, this trend is found to continue in 2003-04 with index score 0.764 attaining 3rd rank and in 2004-05 with 1.000 index score receiving 1st rank. Thus, in 200405, health infrastructure in the State of Haryana is at best status in comparison with other years. Meanwhile, the situation deviated once again when index score drops to 0.325 in 2005-06 and thus, in one year the rank slips to 16th from 1st. In the next three years the index score increases to 0.526, 0.574 and 0.629 respectively and consequently ranks also hiked. It is noteworthy that the years 1998-99 and 2007-08 have same index score (0.574) and rank (6th) for health infrastructure status in the State.

However, the worst condition is seen in the year 2009-10, when the value of index become zero. It does not implies the nil availability of health infrastructure in 2009-10; actually, the index score for this year shows least availability of health infrastructural facilities in comparison with other years. It can also be identified from first column of 'Composite index' in table 9 highlighting 1.284 score (without normalization) which is lowest for the year 2009-10. In the next two years, index score is increased to 0.617 with 5th rank in 2010-11 and 0.837 in 2011-12 with rank 2nd. Thus, from the point of view of health infrastructure beginning year 1991-92 was not so encouraging because of 19th rank; but then conditions seemed to be improving in between with certain fluctuations and up-downs. On the whole, health infrastructure index score is found to stood between 0 and 0.5 for eleven years; between 0.5 and 0.6 for two years; 0.6 and above for eight years.

Now, the findings are summarized with certain policy implications and future research directions in the next section.

CONCLUSION AND POLICY IMPLICATIONS

In nut shell, the present study is a humble attempt to examine the status of health infrastructure via constructing its index for State of Haryana. For which, firstly, the relevant indicators of health infrastructure are analyzed individually by their raw data as well as by computing descriptive statistics. The analysis reveals that health infrastructure' indicators namely numbers of PHCs, CHCs, SCs, doctors, ayurvedic and homoeopathic institutions and their dispensers/compounders have increased as their CAGR is positive. While, numbers of hospitals, dispensaries, nurses, other staff, beds, unani institutions and vaidyas/hakims have experienced negative CAGR. Besides, CHCs have achieved highest rate of growth in their number but also possess much dispersions. Meanwhile, numbers of PHCs, CHCs, SCs, and homoeopathic institutions are at their maximum number in 2011-12 (latest year of data) whereas dispensaries and unani institutions are found to experience their minimum number for the same year.

Next, summated index of all indicators of health infrastructure is prepared by applying principal component analysis (PCA) in various steps while explaining its detailed methodology. First of all, available data is normalized on which PCA is applied; consequently correlation matrix, KMO measure and Bartlett's test result are attempted which are found to favor the application of PCA. Secondly, three principal or latent components capable of representing all original variables are identified on the basis of their eigenvalues produced by PCA. Next, final output of PCA in terms of matrix of component loadings is attained to compute final weights for all variables (indicators of health infrastructure). By utilizing these weights, composite index of health infrastructure is constructed.

The scores of health infrastructure index reveal that 2004-05 is the best year for Haryana's health infrastructural status due to first rank. This rank is achieved in thirteenth year from 1991-92. Between 1991-92 and 2011-12, up and downs in index scores as well as in ranks are felt and thus continuous yearly improvement in ranks are not seen. On the whole, in health infrastructure index, Haryana State have experienced average and above score (0.6 and above) for eight years while for eleven years the score remained 0.5 and lesser, among time period of twenty one years. However, a very embarrassing situation is found during 200910 with lowest rank for which health infrastructure index scores zero implying the least availability of health infrastructural facilities for that year in comparison with other years. Thus, one cannot deny the fact that variations exist in the availability of overall health facilities over the years in the State of Haryana. Since, the State have attained second rank in health infrastructure index during the year 2011-12, therefore, there is a hope for further promotion of the status of health infrastructural facilities in coming years.

The present research implicated that the status of health infrastructure needs to be improved in the State of Haryana. The negative growth rate of certain indicators of health infrastructure is a serious cause of concern which require immediate attention from Government. Moreover, less than average score of health infrastructure index for many years also highlights the poor status of health infrastructure in the State. It may be due to lack of adequate investments in health infrastructure. Thus, there is emergence for sufficient amounts of public expenditure that must be incurred on health infrastructure. Besides, growing demand for healthcare services calls for enhancement in budgetary allocations on health sector every year. However, economically backward sections of society prominently depend on the public sector health care facilities; therefore, it becomes the primary duty of the State to ensure accessibility and affordability of health services to all citizens in general and vulnerable sections in particular. Last but not least, to develop health sector according to modern world, the corrupt practices among health service providers and financial leakages by Government officials must also be checked via adopting good governance at all levels.

LIMITATIONS AND FUTURE RESEARCH DIRECTIONS

Generally, researchers face certain problems while conducting their research which are called the limitations of their study. In the present context, first limitation is that some important indicators of health infrastructure including number of ambulances, blood banks, and stock of medicines, are not undertaken due to the unavailability of their data. Secondly, health infrastructure index is constructed by employing an econometric technique namely Principal Component Analysis which itself has certain shortcomings including requirement of large number of variables or indicators, adequate sample size and variables must be correlated (at least moderately) with each other.

With regard to future research directions, the present study will be directive for future researches as it explains construction of health infrastructure index in step by step procedure. Besides, the present research can be extended by including data for more years and more indicators. Furthermore, the index for health outcomes by taking data on its indicators can also be constructed for the state of Haryana.

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M. M. Goel

RGNIYD, Govt. of India, Sriperumbudur (Tamil Nadu)

Ishu Garg

Department of Economics, Kurukshetra University, Kurukshetra (Haryana)

DOI: 10.15415/jtmge.2018.91005
Table 1: Health Infrastructure Indicators

Infrastructure  Broad         Indicators                   Abbreviations
                Ingredients                                  used in
                                                             Analysis

                Medical        1. Hospitals                 1. Hos
                Institutions   2. Primary Health            2. PHCs
                                  Centers
                               3. Dispensaries              3. Dis
                               4. Community Health          4. CHCs
                                  Centers
Allopathic                     5. Sub Centers               5. SCs
Institutions    Medical        6. Doctors                   6. Doc
                Staff          7. Nurses                    7. Nur
                               8. Other Staff               8. OS
                Others         9. Beds                      9. Beds
                Medical       10. Ayurvedic Institutions   10. AIs
AYUSH           Institutions  11. Unani Institutions       11. UIs
Institutions                  12. Homeopathic              12. HIs
                                  Institutions
                Medical       13. Vaidyas/ Hakims          13. Va/Ha
                Staff         14. Dispensers/Compounders   14. Dis/Com

Source: Researchers' Compilation

Table 2: Indicators of Health Infrastructure in Haryana during 1991-92
to 2011-12
(all figures are in numbers)

Years     Infrastructure in Allopathic Institutions
             Medical Institutions
          Hospitals   PHCs     Dispen   CHCs    SCs
                               saries
          (Hos)                (Dis)

1991-92   79          395      232      51      2293
1992-93   78          395      232      59      2299
1993-94   79          395      232      59      2299
1994-95   79          398      234      60      2299
1995-96   79          398      232      63      2299
1996-97   79          398      234      63      2299
1997-98   80          401      231      64      2299
1998-99   80          401      231      64      2299
1999-00   78          402      231      64      2299
2000-01   78          402      229      64      2299
2001-02   79          402      229      64      2299
2002-03   79          403      229      64      2299
2003-04   79          408      228      65      2299
2004-05   79          408      228      72      2433
2005-06   60          409      193      81      2433
2006-07   61          409      193      84      2433
2007-08   67          416      193      82      2433
2008-09   68          420      193      84      2433
2009-10   69          429      193      85      2465
2010-11   69          429      193      86      2465
2011-12   69          431      193      86      2465
Min.      60          395      193      51      2293
Max.      80          431      234      86      2465
Range     20           36       41      35       172
Mean      75          407      218      70      2354
S.D.       6.560       11.493   18.357  11.041    73.548
CV (%)     8.747        2.824    8.421  15.773     3.124
CGR(%)    -1.094        0.401   -1.193   2.429     0.401

Years      Infrastructure in Allopathic Institutions
                  Medical Staff
           Doctors     Nurses      Other       Beds
            (Doc)       (Nur)      Staff
                                   (OS)

1991-92     1381        4045        7729       10681
1992-93     1399        4013        7941       11182
1993-94     1492        4032        8369       11382
1994-95     1541        3962        8510       11308
1995-96     1519        3551        8650       11328
1996-97     1500        3748        8272       11328
1997-98     1586        3639        9138       11416
1998-99     1595        3847        9334       11440
1999-00     1415        3649        8224       10878
2000-01     1610        3746        8803       10878
2001-02     1605        3582        8548       10944
2002-03     1770        3724        8595       11044
2003-04     1837        3966        8714       11082
2004-05     1828        3719        8389       11082
2005-06     1839        3403        8242        9584
2006-07     1548        3998        8590        9614
2007-08     1441        4088        7449        9866
2008-09     1554        3806        8154        9986
2009-10     1205        3138        5504       10006
2010-11     1538        3991        7408       10006
2011-12     1593        4078        7514       10028
Min.        1205        3138        5504        9584
Max.        1839        4088        9334       11440
Range        634         950        3830        1856
Mean        1562        3796        8194       10717
S.D.         159.382     247.282     800.432     651.474
CV (%)        10.204       6.514       9.769       6.079
CGR(%)         0.300      -0.200      -0.797      -0.797

Years               Infrastructure in AYUSH Institutions
             Medical Institutions   Medical Staff
            Ayurvedic   Unani    Homoeopathic   Vaidyas    Dispensers
           (AIs)        (UIs)    (HIs)          /Hakims    /Compounders
                                                (Va/Ha)    (Dis/Com)

1991-92       393         20       20             451        426
1992-93       404         19       20             455        427
1993-94       404         19       20             455        427
1994-95       406         21       20             451        412
1995-96       407         21       20             418        398
1996-97       411         21       20             443        371
1997-98       413         21       20             386        387
1998-99       421         21       20             371        385
1999-00       423         21       20             384        379
2000-01       433         21       20             416        365
2001-02       434         20       20             404        370
2002-03       446         20       20             442        360
2003-04       468         20       20             414        355
2004-05       470         20       20             461        410
2005-06       483         19       20             454        379
2006-07       477         19       20             419        482
2007-08       477         19       21             450        457
2008-09       481         19       22             434        411
2009-10       437         20       20             427        406
2010-11       462         17       20             402        459
2011-12       468         17       23             440        452
Min.          393         17       20             371        355
Max.          483         21       23             461        482
Range          90          4        3              90        127
Mean          439         20       20             428        406
S.D.           30.490      1.221    0.784          26.536     35.987
CV (%)          6.945      6.105    3.920           6.200      8.864
CGR(%)          1.005     -0.598    0.300          -0.049      0.401

Source: Data is collected from Statistical Abstracts of Haryana
(Various Issues); Descriptive statistics is computed by researchers.

Table 3: Normalized Indicators of Health Infrastructure

Years                Infrastructure in Allopathic Institutions
              Medical Institutions                 Medical Staff
          Hos     PHCs   Dis    CHCs   SCs    Doc    Nur     OS    Beds
          (Nm)    (Nm)   (Nm)   (Nm)   (Nm)   (Nm)   (Nm)   (Nm)   (Nm)

1991-92   0.950   0.000  0.951  0.000  0.000  0.278  0.955  0.581  0.591
1992-93   0.900   0.000  0.951  0.229  0.035  0.306  0.921  0.636  0.861
1993-94   0.950   0.000  0.951  0.229  0.035  0.453  0.941  0.748  0.969
1994-95   0.950   0.083  1.000  0.257  0.035  0.530  0.867  0.785  0.929
1995-96   0.950   0.083  0.951  0.343  0.035  0.495  0.435  0.821  0.940
1996-97   0.950   0.083  1.000  0.343  0.035  0.465  0.642  0.723  0.940
1997-98   1.000   0.167  0.927  0.371  0.035  0.601  0.527  0.949  0.987
1998-99   1.000   0.167  0.927  0.371  0.035  0.615  0.746  1.000  1.000
1999-00   0.900   0.194  0.927  0.371  0.035  0.331  0.538  0.710  0.697
2000-01   0.900   0.194  0.878  0.371  0.035  0.639  0.640  0.861  0.697
2001-02   0.950   0.194  0.878  0.371  0.035  0.631  0.467  0.795  0.733
2002-03   0.950   0.222  0.878  0.371  0.035  0.891  0.617  0.807  0.787
2003-04   0.950   0.361  0.854  0.400  0.035  0.997  0.872  0.838  0.807
2004-05   0.950   0.361  0.854  0.600  0.814  0.983  0.612  0.753  0.807
2005-06   0.000   0.389  0.000  0.857  0.814  1.000  0.279  0.715  0.000
2006-07   0.050   0.389  0.000  0.943  0.814  0.541  0.905  0.806  0.016
2007-08   0.350   0.583  0.000  0.886  0.814  0.372  1.000  0.508  0.152
2008-09   0.400   0.694  0.000  0.943  0.814  0.550  0.703  0.692  0.217
2009-10   0.450   0.944  0.000  0.971  1.000  0.000  0.000  0.000  0.227
2010-11   0.450   0.944  0.000  1.000  1.000  0.525  0.898  0.497  0.227
2011-12   0.450   1.000  0.000  1.000  1.000  0.612  0.989  0.525  0.239
Mean      0.733   0.336  0.616  0.535  0.357  0.563  0.693  0.702  0.611
S.D.      0.328   0.319  0.448  0.315  0.428  0.251  0.260  0.209  0.351

Years       Infrastructure in AYUSH Institutions
            Medical Institutions   Medical Staff
           AIs     UIs     HIs      Va      Dis
           (Nm)    (Nm)    (Nm)     /Ha     /Com
                                   (Nm)    (Nm)

1991-92    0.000   0.750   0.000   0.889   0.559
1992-93    0.122   0.500   0.000   0.933   0.567
1993-94    0.122   0.500   0.000   0.933   0.567
1994-95    0.144   1.000   0.000   0.889   0.449
1995-96    0.156   1.000   0.000   0.522   0.339
1996-97    0.200   1.000   0.000   0.800   0.126
1997-98    0.222   1.000   0.000   0.167   0.252
1998-99    0.311   1.000   0.000   0.000   0.236
1999-00    0.333   1.000   0.000   0.144   0.189
2000-01    0.444   1.000   0.000   0.500   0.079
2001-02    0.456   0.750   0.000   0.367   0.118
2002-03    0.589   0.750   0.000   0.789   0.039
2003-04    0.833   0.750   0.000   0.478   0.000
2004-05    0.856   0.750   0.000   1.000   0.433
2005-06    1.000   0.500   0.000   0.922   0.189
2006-07    0.933   0.500   0.000   0.533   1.000
2007-08    0.933   0.500   0.333   0.878   0.803
2008-09    0.978   0.500   0.667   0.700   0.441
2009-10    0.489   0.750   0.000   0.622   0.402
2010-11    0.767   0.000   0.000   0.344   0.819
2011-12    0.833   0.000   1.000   0.767   0.764
Mean       0.511   0.690   0.095   0.628   0.399
S.D.       0.339   0.305   0.261   0.295   0.283

Source: Researchers' Calculations of Normalization
Note: 'Nm' symbolizes that the shown variables have now been normalized

Table 4: Correlation Matrix, KMO and Bartlett's Test

                              Correlation Matrix
Health       Hos         PHCs        Dis         CHCs        SCs
Indicators

Hos          1.000
PHCs         -.654 (*)   1.000
Dis           .938 (*)   -.859 (*)   1.000
CHCs         -.859 (*)    .905 (*)   -.951 (*)   1.000
SCs          -.827 (*)    .880 (*)   -.921 (*)    .945 (*)   1.000
Doc           .016 (ns)  -.070 (ns)   .089 (ns)   .025 (ns)  -.013 (ns)
Nur           .060 (ns)  -.092 (ns)   .044 (ns)  -.126 (ns)  -.076 (ns)
OS            .424 (**)  -.644 (*)    .569 (*)   -.495 (**)  -.600 (*)
Beds          .952 (*)   -.740 (*)    .949 (*)   -.852 (*)   -.849 (*)
AIs          -.732 (*)    .695 (*)   -.766 (*)    .825 (*)    .769 (*)
UIs           .620 (*)   -.672 (*)    .712 (*)   -.647 (*)   -.699 (*)
HIs          -.370 (**)   .591 (*)   -.526 (*)    .518 (*)    .493 (**)
Va/Ha        -.181 (ns)  -.035 (ns)  -.114 (ns)   .017 (ns)   .216 (ns)
Dis/Com      -.538 (*)    .409 (**)  -.568 (*)    .483 (**)   .593 (*)

                                Correlation Matrix
Health         Doc        Nur         OS           Beds        AIs
Indicators

Hos
PHCs
Dis
CHCs
SCs
Doc         1.000
Nur          .031 (ns)  1.000
OS           .595 (*)    .232 (ns)   1.000
Beds         .081 (ns)   .039 (ns)    .546 (*)    1.000
AIs          .477 (**)   .008 (ns)   -.154 (ns)   -.744 (*)   1.000
UIs         -.023 (ns)  -.401 (**)    .429 (**)    .657 (*)   -.547 (*)
HIs         -.017 (ns)   .298 (***)  -.228 (ns)   -.429 (**)   .438 (**)
Va/Ha        .052 (ns)   .205 (ns)   -.306 (***)  -.170 (ns)   .097 (ns)
Dis/Com     -.383 (**)   .524 (*)    -.386 (**)   -.531 (*)    .232 (ns)

                           Correlation Matrix
Health        UIs         HIs          Va/Ha      Dis/Com
Indicators

Hos
PHCs
Dis
CHCs
SCs
Doc
Nur
OS
Beds
AIs
UIs          1.000
HIs          -.552 (*)   1.000
Va/Ha        -.288 (ns)   .176 (ns)   1.000
Dis/Com      -.672 (*)    .357 (***)   .260 (ns)  1.000

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling   KMO Measure             .460
Adequacy
                                         Approx. Chi-Square   399.705
Bartlett's Test of Sphericity            df                    91
                                         Sig.                   0.000

Source: Researchers' Calculations
Note: (*) 1 per cent significance; (**) 5 per cent significance; (***)
10 per cent significance; (ns) not significant

Table 5: Eigenvalues (Initial and Rotated) and Components

Component       Initial Eigenvalues          Extraction Sums of Squared
                                                     Loadings
            Total   % of       Cumulative   Total   % of      Cumulative
                    Variance   %                    Variance  %

 1          7.610   54.359      54.359      7.610   54.359    54.359
 2          1.889   13.496      67.855      1.889   13.496    67.855
 3          1.752   12.515      80.370      1.752   12.515    80.370
 4           .998    7.129      87.499
 5           .758    5.415      92.914
 6           .418    2.983      95.897
 7           .247    1.768      97.665
 8           .192    1.375      99.040
 9           .054     .386      99.427
10           .045     .324      99.751
11           .015     .108      99.859
12           .012     .083      99.941
13           .007     .052      99.994
14           .001     .006     100.000

Component   Rotation Sums of Squared Loadings
             Total    % of      Cumulative %
                     Variance

 1           7.047   50.334      50.334
 2           2.254   16.099      66.433
 3           1.951   13.937      80.370
 4
 5
 6
 7
 8
 9
10
11
12
13
14

Source: Researchers' Calculations


Table 6: Rotated Component Loadings Matrices

Indicator         Component Loadings         Squared Component
Variables                                      Loadings
                     Component                 Component
               1         2       3      1       2       3

Hos          -.888    -.136    .079   0.788   0.018   0.006
PHCs          .891     .055   -.174   0.795   0.003   0.030
Dis          -.959    -.155    .161   0.920   0.024   0.026
CHCs          .975     .045   -.052   0.951   0.002   0.003
SCs           .937     .164   -.145   0.878   0.027   0.021
Doc           .106    -.081    .945   0.011   0.007   0.893
Nur          -.177     .900    .188   0.031   0.810   0.036
OS           -.504    -.027    .768   0.254   0.001   0.591
Beds         -.907    -.147    .154   0.822   0.022   0.024
AIs           .869     .063    .418   0.755   0.004   0.175
UIs          -.654    -.608    .045   0.428   0.370   0.002
HIs           .499     .463    .049   0.249   0.214   0.002
Va/Ha         .065     .490   -.092   0.004   0.240   0.008
Dis/Com       .400     .716   -.367   0.160   0.513   0.135
Explained    7.047    2.254   1.951   1.000   1.000   1.000
Variance
Total                11.252
Variance
Explained    0.626    0.200   0.173
Variance
/Total
Variance
(Component
Weight)

Indicator    Squared Scaled to Unity Sum
Variables          (Domain Weight)
                     Component
                 1       2       3

Hos            0.112   0.008   0.003
PHCs           0.113   0.001   0.016
Dis            0.131   0.011   0.013
CHCs           0.135   0.001   0.001
SCs            0.125   0.012   0.011
Doc            0.002   0.003   0.458
Nur            0.004   0.359   0.018
OS             0.036   0.000   0.303
Beds           0.117   0.010   0.012
AIs            0.107   0.002   0.090
UIs            0.061   0.164   0.001
HIs            0.035   0.095   0.001
Va/Ha          0.001   0.106   0.004
Dis/Com        0.023   0.228   0.069
Explained
Variance
Total
Variance
Explained
Variance
/Total
Variance
(Component
Weight)

Source: Researchers' Calculations
Note: Columns under 'Component Loadings' is the output of PCA.

Table 7: Weighted Scores for Variables

Indicators   Domain       Component     Weight Score   Resulting Weight
             Weight (I)   Weight (II)   (I*II) III         III/0.856
                                        Total=0.856

Hos          0.112          0.626          0.070        0.082
PHCs         0.113          0.626          0.071        0.083
Dis          0.131          0.626          0.082        0.096
CHCs         0.135          0.626          0.085        0.099
SCs          0.125          0.626          0.078        0.091
Doc          0.458          0.173          0.079        0.093
Nur          0.359          0.200          0.072        0.084
OtSt         0.303          0.173          0.052        0.061
Beds         0.117          0.626          0.073        0.086
AyHos        0.107          0.626          0.067        0.078
UnHos        0.061          0.626          0.038        0.045
HoHos        0.035          0.626          0.022        0.026
Va/Ha        0.106          0.200          0.021        0.025
Dis/Com      0.228          0.200          0.046        0.053

Source: Researchers' Calculations

Table 8: Weighted Variables or Weighted Indicators of Health
Infrastructure

                    Infrastructure in Allopathic Institutions
Years             Medical Institutions               Medical Staff
           Hos     PHCs    Dis    CHCs    SCs    Doc     Nur     OtSt

1991-92   0.238   0.000   0.204   0.000   0.000  0.101   0.309   0.170
1992-93   0.225   0.000   0.204   0.072   0.007  0.111   0.298   0.186
1993-94   0.238   0.000   0.204   0.072   0.007  0.164   0.304   0.218
1994-95   0.238   0.022   0.214   0.081   0.007  0.192   0.280   0.229
1995-96   0.238   0.022   0.204   0.107   0.007  0.179   0.141   0.240
1996-97   0.238   0.022   0.214   0.107   0.007  0.169   0.207   0.211
1997-98   0.250   0.043   0.199   0.116   0.007  0.218   0.170   0.277
1998-99   0.250   0.043   0.199   0.116   0.007  0.223   0.241   0.292
1999-00   0.225   0.050   0.199   0.116   0.007  0.120   0.174   0.207
2000-01   0.225   0.050   0.188   0.116   0.007  0.232   0.207   0.251
2001-02   0.238   0.050   0.188   0.116   0.007  0.229   0.151   0.232
2002-03   0.238   0.058   0.188   0.116   0.007  0.323   0.199   0.236
2003-04   0.238   0.094   0.183   0.125   0.007  0.361   0.282   0.245
2004-05   0.238   0.094   0.183   0.188   0.173  0.356   0.198   0.220
2005-06   0.000   0.101   0.000   0.268   0.173  0.363   0.090   0.209
2006-07   0.013   0.101   0.000   0.295   0.173  0.196   0.292   0.235
2007-08   0.088   0.152   0.000   0.278   0.173  0.135   0.323   0.148
2008-09   0.100   0.181   0.000   0.295   0.173  0.199   0.227   0.202
2009-10   0.113   0.246   0.000   0.304   0.213  0.000   0.000   0.000
2010-11   0.113   0.246   0.000   0.313   0.213  0.190   0.290   0.145
2011-12   0.113   0.260   0.000   0.313   0.213  0.222   0.320   0.153

            Infrastructure in AYUSH Institutions
Years     Medical Institutions       Medical Staff
          Beds    AyHos   UnHos   HoHos   Va/Ha   Dis/Com

1991-92   0.145   0.000   0.111   0.000   0.075   0.105
1992-93   0.211   0.028   0.074   0.000   0.079   0.106
1993-94   0.237   0.028   0.074   0.000   0.079   0.106
1994-95   0.228   0.033   0.148   0.000   0.075   0.084
1995-96   0.230   0.036   0.148   0.000   0.044   0.063
1996-97   0.230   0.046   0.148   0.000   0.068   0.024
1997-98   0.242   0.051   0.148   0.000   0.014   0.047
1998-99   0.245   0.072   0.148   0.000   0.000   0.044
1999-00   0.171   0.077   0.148   0.000   0.012   0.035
2000-01   0.171   0.102   0.148   0.000   0.042   0.015
2001-02   0.180   0.105   0.111   0.000   0.031   0.022
2002-03   0.193   0.136   0.111   0.000   0.067   0.007
2003-04   0.198   0.192   0.111   0.000   0.041   0.000
2004-05   0.198   0.197   0.111   0.000   0.085   0.081
2005-06   0.000   0.230   0.074   0.000   0.078   0.035
2006-07   0.004   0.215   0.074   0.000   0.045   0.187
2007-08   0.037   0.215   0.074   0.033   0.074   0.150
2008-09   0.053   0.225   0.074   0.066   0.059   0.083
2009-10   0.056   0.113   0.111   0.000   0.053   0.075
2010-11   0.056   0.176   0.000   0.000   0.029   0.153
2011-12   0.059   0.192   0.000   0.100   0.065   0.143

Source: Authors Calculations
Note: Weighted variables are computed by multiplying each indicator by
its respective weight and then divide by indicator's Standard deviation.

Table 9: Composite Index of Health Infrastructure

             Composite Index            Normalized
Years     (Aggregation of Weighted   Composite Index   Ranking
              Indicators)

1991-92         1.458                    0.168         19th
1992-93         1.601                    0.305         17th
1993-94         1.731                    0.431         12th
1994-95         1.831                    0.527          8th
1995-96         1.659                    0.361         15th
1996-97         1.691                    0.392         13th
1997-98         1.782                    0.480         10th
1998-99         1.880                    0.574          6th
1999-00         1.541                    0.248         18th
2000-01         1.754                    0.453         11th
2001-02         1.660                    0.362         14th
2002-03         1.879                    0.573          7th
2003-04         2.077                    0.764          3rd
2004-05         2.322                    1.000          1st
2005-06         1.621                    0.325         16th
2006-07         1.830                    0.526          9th
2007-08         1.880                    0.574          6th
2008-09         1.937                    0.629          4th
2009-10         1.284                    0.000         20th
2010-11         1.924                    0.617          5th
2011-12         2.153                    0.837          2nd

Source: Researchers' Calculations based on previous table 8
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Author:Goel, M.M.; Garg, Ishu
Publication:Journal of Technology Management for Growing Economies (JTMGE)
Article Type:Report
Geographic Code:9INDI
Date:Apr 1, 2018
Words:8998
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