Social development ranking of districts of Pakistan.

Ghaus, A.F. Aisha ; Pasha, Hafiz A. ; Ghaus, Rafia 等

The paper has used eleven indicators relating to the education, health and water supply sectors to rank districts of Pakistan in terms of the level of social development. It also seeks to explain regional variation in the development of social infrastructure across districts. The paper demonstrates the importance of education indicators in determining the overall level of social development, especially in terms of female literacy and enrolment rates. Also, the ranking demonstrate a close correlation between levels of social and economic development spatially with Pakistan. Other important determinants of regional variations in the level of social development include the extent of urbanisation, the administrative development of the district (location of provincial headquarters), and the geographical/economic significance (indicated by the presence of the sea port). Overall, Punjab appears to have the highest level of social development followed by NWFP, Sindh and Balochistan. However, the results indicate substantial variation among districts within a province in the level of social development. Least developed districts within each province are identified as targets for special development allocations within SAP.

1. INTRODUCTION

International comparisons reveal the lack of correlation between the ranking of countries in terms of levels of economic and social development. Pakistan is an example of a developing country with relatively high per capita income but extremely poor social/human development indicators. The objectives of this paper are two fold: first, determine the extent of variation among districts in the level of social development and second to examine in the spatial context for Pakistan how strong the relationship is between levels of economic and social development and what explains regional differences in the level of social development. The former will help us in particular in identifying districts which have a low ranking within the country in terms of the level of social development. These districts can be targeted for special development allocations within the SAP to reduce the extent of regional disparity in terms of access to basic services like primary education, health, water supply, etc. If it emerges that the socially underdeveloped districts are also economically backward then the underlying reason may be the absence of a strong private sector or the absence of a local tax base or income affordability to finance the provision of these services. As case can then be made for transfer of resources to such regions.

Earlier research at the district level in Pakistan by Pasha, Malik and Jamal (1990) has, in fact, demonstrated that education and housing indicators are highly correlated with the overall level of development. Districts which have a relatively developed/underdeveloped education sector in terms of literacy and primary enrolment rates generally appear to have higher/lower ranking in terms of the composite level of development. Although it is difficult to come to any definitive conclusions about the direction of causality this finding tends to substantiate the view that regions of the country which have made greater progress are endowed with higher levels of human development.

The paper is organised as follows: Section 2 gives the choice of social development indicators. Section 3 gives the methodology for derivation of the composite indicator of social development while Section 4 gives the resultant ranking of districts, Section 5 presents the regression model and results of determinants of regional variations in the level of social development. Finally, in Section 6 are given the conclusions.

2. CHOICE OF INDICATORS

The choice of development indicators at the district level is governed by a number of considerations. First, an attempt has been made to achieve as wide a sectoral coverage as possible. As such indicators have been selected to highlight development of sectors like education, health, water supply. Second, two alternatives were available regarding the choice of indicators: we could concentrate on measuring the consequences of development or the level of development inputs. Greater reliance in this study is on the latter primarily because of the lack of district-wise data on the former. For example, if the output approach had been adopted to measure development of the education sector, the indicators used would have been, for example, school graduates as a percentage of the labour force both in stock and in flow. But since data is not available on this magnitude the alternative chosen is to quantify the level of inputs in the form of teachers, schools, hospitals, beds, etc. Therefore, while there may be some loss of precision in the quantification of the level of development, the results are perhaps more useful and operational in character from the planning view point.

The lack of data has not only constrained the approach to the construction of social development but it has limited the number of indicators. Nevertheless, it has been possible to identify 11 indicators relating to health, education and water supply. Diverse sources of data have been used for quantifying the indicators. Firstly, data has been taken from the last census of population, housing survey by the FBS and development statistics of the provincial governments. Secondly, relevant data has also been collected from other published documents of the Federal, Provincial governments and FBS.

Described below are the social indicators chosen in each sector.

Education

Both stock and flow measures have been defined for the education sector. The stock measure is the literacy rate by gender which indicates the level of literacy among the population aged ten years and above in a district which has been taken from district census report of 1981. Measures of flow of output from the education sector relate to enrolment rates at the primary and secondary level (male and female separately). Information regarding enrolments at different levels has been taken from development statistics of the province. The relevant school age going population in each district have been projected on the basis of intercensal growth rates for purposes of deriving the enrolment rates. However, the distribution of census population has been adjusted according to newly formed districts which has been reported in the publication, Administrative Units of Pakistan, a publication of the Population Census organisation.

Health

Three types of indicators of development of the health sector have been defined. The first relates to health personnel i.e. doctors and nurses per 10,000 population, second, to hospital and rural health centre beds per 1,000 population while the third to number of patients treated in relation to total population. The last indicator is essentially an output measure. However, as the information regarding the number of district-wise doctors and nurses for the year 1991-92 was not available for Punjab. Therefore, it has been estimated on the basis of extrapolation of figures given in Health Statistics, a publication of provincial governments.

Housing

Only one indicator has been used to measure the level of social development, that is, access to water supply. The particular indicator use is percentages of households with inside water connections. As the data on water supply was not available for the latest year, the analysis has been done on the information reported in the Housing Survey of 1989 carried out by the FBS.

Ninety-four districts (as of 1991-92) and eleven indicators have been included in the analysis. This includes 34 districts from Punjab, 15 from Sindh, 20 from NWFP and 25 from Balochistan. Out of the eleven indicators, 6 relate to education, 4 to health and 1 to water supply.

Three summary measures, the mean, variance and the coefficient of variation, have been calculated to describe and compare the distributions of the indicators (see Table 1). By doing so we derive the extent of regional variation in social development. It needs to be pointed out that the means of the various indicators do not correspond to the national values of these indicators. This is because they are simple averages and not averages weighted by the population or area of the district depending on the indicator.

3. METHODOLOGY OF MEASUREMENT

In the literature on regional development, a number of techniques have been used to reduce the dimensions of the complex multivariate problem associated with the construction of composite development indicator. The first is the Z-sum technique which sums for a particular district its Z-score on each indicator. The Z-score is the standardised score, which has zero mean and unit variance. The higher the Z-sum (1) the more developed the region.

The second technique computes the taxonomic distance [Khan and Iqbal (1982)], which is the Euclidean distance from the highest (standardised) values observed for different indicators. (2) The lower the taxonomic distance of a region or district, the more developed it is. Both the techniques have the problem of assigning equal importance to all development indicators. Further, the taxonomic distance technique is very sensitive to the presence of outliers.

The third and the most sophisticated method for indexing a multidimensional phenomenon is Factor Analysis (FA) technique [Adelman and Dalton (1971)]. This technique reduces the number of relationships by grouping together all those variables which are most highly correlated with each other into one factor or component. Thus the FA model can be described as follows:

[X.sub.i] = [a.sub.i1] [F.sub.1] + [a.sub.i12] [F.sub.2] ... + [a.sub.ij] [F.sub.j] ... (1) where,

[X.sub.i] is the ith indicator.

[a.sub.ij] is called the factor loading and represents the proportion of the variation in [X.sub.i] which is accounted for by the jth factor.

[summation][a.sub.ij] is called the communality and it is equivalent to the multiple regression coefficient in regression analysis.

[F.sub.j] represents jth factor or component.

Principal Components Analysis (PCA) produces components in descending order of importance, that is, the first component explains the maximum amount of variation in the data, and the last component the minimum. It is often found that the first few components, called principal components, account for a sizeable part of the variation and subsequent components contribute very little. Using factor loadings of these principal components, factor score for each region or unit is computed as follows:

[(FS).sub.kj] = [[summation].sub.k] [e.sub.ij] [Z.sub.i] ... (2)

where,

[FS.sub.kj] represents factor score of the kth region and the jth factor, [Z.sub.i] is the standardised value of the ith indicator,

[summation][e.sub.ij] is the factor loading of the jth factor and the ith indicator.

To compute weighted factor score (WFS), these individual factor scores are derived from the following equation:

[(WFS).sub.k] = [[summation].sub.k] [e.sub.j] [(FS).sub.kj] ... (3)

where ej is the eigen value of the factor j and depicts the proportion of variation in the data set explained by the factor j. This WFS is used as an index for ranking regions on the basis of the general characteristics of the variable-set.

In this study, PCA is preferred to explain the grouping of variables, with WFS being used to rank the district due to its more appealing characteristics. However, Z-sum technique is also used to observe the sensitiveness of the results with respect to the choice of technique for deriving the composite indicators. Pasha and Hasan (1982); Pasha et al. (1990) also used these two techniques.

Table 2 presents the loading of each indicator on different factors. In addition, it gives the eigen values of each factor. Four factors emerge from the principal components analysis. These factors are described below:

Factor 1

Five out of 11 indicators load highly on this factor. It is by far the most important factor and includes most of the indicators from the education sector. As such education can be interpreted the most important service capturing variation in the level of social development.

Factor 2

This factor includes three indicators. It essentially comprises of health and water supply and sanitation.

Factor 3

The two indicators in this factor also relate to health. It is essentially a continuation of Factor 2 and reflects the same underlying phenomena.

Factor 4

This factor includes only one indicator, primary boys enrolment rate. This indicator represents the most basic level of education and, therefore, variation in its magnitude is not strongly correlated with the overall level of social development.

4. RANKING OF DISTRICTS

The rank ordering of districts in 1990-91 is presented in Table 3. The table gives rankings generated by the principal components analysis (weighted factor score) and the Z-sum technique respectively. The correlation between the two rankings is 0.988. This indicates the robustness of the results which is also highlighted by the fact that except for Gujranwala, the top ten districts in WFS are also in the list of top 10 districts indicated by the Z-score.

Karachi and Rawalpindi are the most developed districts in Pakistan in terms of social indicators according to the WFS while in Z-score ranking Lahore and Quetta displace Karachi and Rawalpindi as the most developed districts. Besides these the list of top 10 districts include Chakwal, Jhelum, Gujrat, Faisalabad, and Sialkot. Gujranwala and Peshawar rank 10th in the WFS and z-score rankings respectively. These top ten districts account for almost 25 percent of the country's population. It may be noted that according to both the techniques most of the top districts are located in the province of Punjab with one each in the other three provinces. This tends to indicate that Punjab is ahead of the other provinces in terms of social development.

At the lower end of the distribution, seven out of ten districts are the same in both the rankings. According to WFS, Dera Bugti and Jalmagsi are the least developed districts while Kohistan and Nasirabad emerge as the lowest two districts in Z-score ranking. The other least developed districts according to both the rankings include Zhob, Khuzdar, Kalat, Kharan, Turbat, Bolan, Panjgur, Awaran and Killa Saifullah, all districts of Balochistan. Estimates are that about 5 percent of the national population resides in these districts. Nine of these districts are in Balochistan. This implies that Balochistan is least socially developed province in the country.

Table 3 also classifies the 94 districts according tO the level of development. Relatively developed districts are those in which the top quartile of population lives. Districts at the intermediate level are those in which the second and the third quartile lives while the relatively under developed districts account for the bottom 25 percent of the population.

According to Z-score ranking, the top quartile consists of 10 districts. All the provincial capitals are in this category. Besides, Faisalabad, Rawalpindi, Gujrat, Sailkot and Jhelum are districts with high rate of urbanisation and buoyant industrial activity. Except of one district each in Sindh, NWFP and Balochistan all the other districts in this quartile are from Punjab.

The second quartile of population, according to the WFS, resides in 20 districts. Here again we observe the dominance of Punjab, with eleven out of these districts belonging to this province, like Gujranwala, Toba Tek Singh, Sahiwal, and Multan. Out of the remaining districts, seven are from NWFP, including Haripur, Abbotabad, Nowshera, Kohat, Charsadda, D. I. Khan, and Tank. The relatively high enrolment rates at primary level alongwith access to water supply facilities are the prime reason for the relatively high ranking of districts in the province.

Nine each out of 25 districts in the third quartile are from NWFP and Punjab respectively while six are from Sindh. The last quartile which consists of 38 districts is dominated by Balochistan, with 22 districts belonging to this province, followed by Punjab with seven districts and Sindh with six districts.

The population shares of each province in each quartile are presented in Table 4. The share of Punjab in the top three quartiles is larger than its share in national population (excluding FATA etc.) implying that Punjab, by and large, has a high to intermediate level of social development. Sindh has a high share in the first and the fourth quartile, indicating the dualistic pattern of development in the province with Karachi representing one polar extreme. NWFP has an intermediate level of development while Balochistan is the most backward province in terms of social development in the country. It is, however, important to note that even the relatively developed provinces have pockets of low development like the districts in the south of Punjab. Alternatively, even a relatively backward province has some areas with high level of social development. The best example of this is Quetta district in Balochistan.

Table 5 gives the zero-order correlation matrix between different indicators. High correlation is observed between doctors and nurses, primary and secondary enrolments, literacy rates and enrolment rates. In particular, girls primary and secondary enrolment rates are strongly related to the male and female literacy rates. There also appears to be a degree of correlation between different sectors. Linkage exists between water supply and health services and education and health services, specifically health personnel. This correlation is a reflection of the spillover and externalities generated by different social services and highlights the presence of synergies between sectors. On the whole, in the profile of development, the key sector appears to be education, in particular, female primary and secondary enrolment rates.

5. DETERMINANTS OF SOCIAL DEVELOPMENT OF DISTRICTS

The key question that arises is what determines regional variations in the level of social development in Pakistan. From the above discussion it appears that provincial headquarters rank high in terms of development. Also, to the extent the provision of services is characterised by economics of scale and is more efficient and cost effective in larger cities, there may exist a high degree of correlation between urbanisation and regional social development. Moreover, regions with buoyant industrial bases and high level of economic development may have a high demand and a higher ability to pay for social services. Therefore, as recognised generally in international literature, there may exist a close link between urbanisation, industrialisation, economic development and social development in Pakistan also.

Besides, there appear to be substantial interprovincial differences in the level of social development in Pakistan. The previous section indicates that Punjab is further ahead of the other provinces in terms of social development. In addition, the presence of special features, like the existence of sea port, may also have an impact on the spatial ranking of district in terms of social development.

To analyse the determinants of social development in Pakistan we have developed the following regression model:

[SOCIAL.sub.i] = f([PU.sub.i], [PCVA.sub.i], [ROAD.sub.i], PHQ, DUM,
 + + + + +/-
PORT) ... (4)
 +

Where:

[SOCIAL.sub.i] = Weighted Factor Score of the 'ith' district.

[PU.sub.i] = Percentage of urban population in the 'ith' district.

[PCVA.sub.i] = Per capita industrial value-added of the 'ith' district.

[ROAD.sub.i] = Road network in the 'ith' district.

PHQ = Dummy for provincial headquarters.

DUM = Provincial dummies.

PORT = Dummy for Karachi port.

Results

The above model has been estimated for the 94 districts in Pakistan. The results are as follows:

[SOCIAL.sub.i] = -0.3157 + 1.3433 [PU.sub.i] + 4.26 x 10
 (-2.055) (3.686) (1.162)

-5 [PCVA.sub.i] + 2.6461 [ROAD.sub.i] + 1.0842 PHQ
 (4.414) (2.515)

-1.0084 BDUM - 0.8082 SDUM - 0.4378 NDUM + 2.2333 POR ... (5)
 (-6.743) (-5.201) (-3.030) (3.697)

Figures in brackets are t-statistics [[bar.R].sup.2] = 0.753 No. of Observations = 94.

Where:

BDUM = Dummy for Balochistan province.

SDUM = Dummy for Sindh province.

NDUM = Dummy for NWFP province.

Equation (5) indicates a high positive correlation between the level of social development of a strict and the extent of its urbanisation and economic development. The latter is proxied by road network. Pasha and Hasan (1982), and Pasha et al. (1990) highlight the close link between the level of economic development and road network in the context of Pakistan. As such, our results substantiate the existence of a close relationship between the level of social development, urbanisation and economic development.

The results, however, do not demonstrate a high positive correlation between industrialisation and social development. This is not surprising because according to Pasha et al. (1990), the process of industrialisation does not possess a high degree of correlation with the overall process of economic development also. This is in conflict with the perception that large-scale manufacturing generally acts as the leading sector stimulating economic growth. The small share of this sector in the national economy, limited employment creation and its dependence on imported material have reduced its linkages with the rest of the economy. Consequently, districts with higher manufacturing value-added are not necessarily the most economically and/or socially developed.

As expected, provincial capitals have a highly developed network of social infrastructure as does the port city of Karachi. The negative provincial dummies substantiate our earlier conclusion that Punjab is the most highly developed province in social indicators followed by NWFP, Sindh and Balochistan. As such, there are clear inter-provincial differences in regional development in Pakistan. This may reflect historical differences in the level of public allocations per capita to the social sectors.

6. CONCLUSIONS

The paper has used eleven indicators relating to the education, health and water supply sectors to rank districts of Pakistan in terms of the level of social development. It also seeks to explain regional variation in the development of social infrastructure across districts. The paper demonstrates the importance of education indicators in determining the overall level of social development, especially in terms of female literacy and enrolment rates. Also, the ranking demonstrate a close correlation between levels of social and economic development spatially with Pakistan. Other important determinants of regional variations in the level of social development include the extent of urbanisation, the administrative development of the districts (location of provincial headquarters), and the geographical/economic significance (indicated by the presence of the sea port). Overall, Punjab appears to have the highest level of social development followed by NWFP, Sindh and Balochistan. However, the results indicate substantial variation among districts within a province in the level of social development. Least developed districts within each province are identified as targets for special development allocations within SAP.

Comments

The study is focused on very important issues of development i.e. identification of most underdeveloped areas. The authors have used Social Development Indicators to rank the districts of Pakistan. The objective of the study is to highlight the variation in the development of social infrastructure across the country. The main focus has been placed on the education and health variables. It has been claimed that a close correlation exists between social and economic development. Punjab appears to have the highest level of social development followed by the NWFP, Sindh, and Balochistan. The least developed districts are also identified for special development allocation (through SAP) so that such underdeveloped areas could be brought into the mainstream.

The authors have picked an important topic for research, particularly focusing at district-level research which has so far been lacking. Therefore, the paper also opens up an avenue to carry out research at micro level. The research work pertaining to the ranking of districts is a valuable contribution which could be used for policy direction; particularly, the 9th Plan may focus on the underdeveloped areas, which are neglected. The 6th Plan had a special development programme for such areas but it was hardly implemented. Thus, the information contained is valuable and timely and could be used for development policy direction. The study may be improved and made more meaningful by including the following points.

(i) The authors have focused mainly on education and health variables. Other variables of social development are ignored. Even important variables pertaining to health and education are not analysed. For example, sanitation facilities in rural areas, rural roads, health units, informal education programmes, etc. Moreover, there is a need to split the information on rural-urban basis. Most of the facilities are provided in urban areas whether the whole district appears to be top/bottom ranking. Thus, there is a need to broaden the scope of variables and regional areas within districts.

(ii) Primary involvement does not provide a good picture of the situation. I would not include kachi class; and dropouts are so high that such enrolment may not convey the desired message. Thus, the quality of data is as important as the results of the study.

(iii) Table 4, which provides percentage share of province in population quartile by level of development. Such percentage figures are misleading for policy direction. It might be better if the absolute number of persons in each province (by quartile) were also provided and a conclusion drawn based upon both percentage and absolute figures. The objective of development policies may not be the number of districts but the maximum the number of people, who should be the target of development policy. Therefore, the cluster of population needs to be identified, not in percentage term. By doing so, it could provide a better guideline for policy-makers. By doing so, the claims and ranking may not be the same as argued by the authors.

In brief, the study is a significant contribution to the literature which provides the bases for a development policy. However, it could be more useful if the point cited above were incorporated.

M. Aslam Chaudhary

Department of Economics, Quaid-i-Azam University, Islamabad.

Table A-1
Nation-wise Ranking of Districts in Social Indicators

 Doctors/ Nurse/
 Population Populationt
S No [1000pop] [1000pop]

 1 Lahore 15.70 Lahore 13.64
 2 Rawalpindi 7.08 Rawalpindi 4.00
 3 Bhawalpur 5.48 Sailkot 3.56
 4 Multan 5.18 Quetta 3.51
 5 Faisalabad 5.15 Multan 3.14
 6 Nawabshah 5.04 Gujramwala 2.90
 7 Quetta 4.86 Bhawalpur 2.90
 8 Hyderabad 4.49 Shaiwal 2.63
 9 Slaiwal 4.33 Faisalabad 2.41
 10 Jhelum 4.12 Jhelum 2.33
 11 Sailkot 4.10 Attock 2.00
 12 Mainwalai 3.84 Sheikhupura 1.88
 13 Thatta 3.73 Jahng 1.80
 14 Khushab 3.61 Mainwalai 1.80
 15 Attock 3.55 Gujrat 1.79
 16 Larkana 3.40 Nawshera 1.75
 17 Gujramwala 3.34 Peshawar 1.75
 18 Peshawar 3.19 Khushab 1.69
 19 Gujrat 3.16 Rahim Yar Khan 1.62
 20 Sargodha 3.12 Sargodha 1.46
 21 Shikarpur 3.08 T.T. Singh 1.30
 22 RahrrYUKhoo 3.06 Bhakkar 1.24
 23 Rahim Yar Khan 3.02 Rajanpur 1.21
 24 Badin 2.82 Chakwal 1.08
 25 T T. Singh 2.77 D.G. Khan 1.05
 26 Chitral 2.74 Kasur 0.98
 27 Bhakkar 2.65 Layyah 0.97
 28 Karachi 2.45 Narowal 0.96
 29 Sukkar 2.32 Larkana 0.93
 30 Malakand 2.19 Bahawalnagar 0.87
 31 Sheikhupura 2.17 Muzaffarghar 0.81
 32 Jhang 2.08 Harupur 0.80
 33 Khairpur 2.06 Abbottabad 0.80
 34 Bannu 2.06 Vehari 0.71
 35 Lakki 2.06 Okara 0.70
 36 Dadu 2.04 Khanewal 0.69
 37 Rajanpur 2.00 Hyderaabad 0.68
 38 Haripur 1.92 Lakki 0.63
 39 Abbottabad 1.92 Bannu 0.63
 40 Ziarat 1.91 Nawabshah 0.54
 41 Sibi 1.91 Lodhran 0.53
 42 Chakwal 1.91 Tank 0.52
 43 Sanghar 1.88 D.I.Khan 0.52
 44 Nausher Feroze 1.81 Karachi 0.50
 45 D.G. Khan 1.73 Hafizabad 0.47
 46 Tank 1.73 Pakpattan 0.45
 47 D.I. Khan 1.73 Malakand 0.33
 48 Gawader 1.71 Mandi Beha Uddin 0.29
 49 Bahawalnagar 1.65 Kahat 0.28
 50 Lyyah 1.59 Sibi 0.22
 51 Chagai 1.53 Khauipur 0.19
 52 Jacobabad 1.34 Sukkar 0.18
 53 Muzaffarghar 1.33 Mirpurkhas 0.18
 54 Kahat 1.17 Charasadda 0.13
 55 Vehari 1.17 Dir 0.10
 56 Karak 1.16 Sshikarpur 0.10
 57 Lasbela 1.16 Manshera 0.09
 58 Okara 1.14 Sanghar 0.08
 59 Khanewal 1.14 Badin 0.08
 60 Kasur 1.11 Thatta 0.05
 61 Narowal 1.10 Dadu 0.04
 62 Jhalmagsi 0.94 Pishin 0.04
 63 Pishin 0.94 Loralai 0.04
 64 Kohlu 0.90 Swabi 0.03
 65 Lodhran 0.87 Jacobabad 0.02
 66 Charsasdda 0.79 Panjgur 0.02
 67 Pakpattan 0.75 Turbat 0.02
 68 Bolan 0.72 Zhob 0.02
 69 Kharan 0.72 Kalat 0.02
 70 Loralai 0.70 Naushero Feroze 0.01
 71 Manshera 0.70 Tharparkar 0.01
 72 Awaran 0.66 Swat 0.01
 73 Khuzdar 0.65 Jhalmagsi 0.00
 74 Swat 0.65 Awaran 0.00
 75 Kalat 0.57 Jaffarabad 0.00
 76 Jaffarabad 0.54 Bolan 0.00
 77 Mardan 0.50 Gawader 0.00
 78 Dir 0.46 Kharan 0.00
 79 Dera Bugti 0.46 Lasbela 0.00
 80 Tharparkar 0.45 Mastung 0.00
 81 Swabi 0.45 Khuzdar 0.00
 82 Hafizabad 0.40 Chitral 0.00
 83 Mandi Baha Uddin 0.38 Buner 0.00
 84 Killa Saifullaha 0.37 Chagai 0.00
 85 Turbat 0.36 Karak 0.00
 86 Zhob 0.32 Kohistan 0.00
 87 Nasirabad 0.30 Mardan 0.00
 88 Panjgur 0.29 Killa Saifullaha 0.00
 89 Kohistan 0.05 Kohlu 0.00
 90 Musa Khail 0.00 Dera Bueti 0.00
 91 Barkhan 0.00 Nasirabad 0.00
 92 Buner 0.00 Musa Khail 0.00
 93 Nawshera 0.00 Barkhan 0.00
 94 Mastung 0.00 Ziarat 0.00

 Mean 2.03 Mean 0.87
 Variance 4.25 Variance 2.69

 Number of
 Patients Primary
 Treated/ Enrollment
S No Population Rate-Boys

 1 Quetta 1.76 Larkana 1.37
 2 Charsadda 1.52 Quetta 1.29
 3 Peshawar 1.52 Sibi 1.27
 4 Nawhera 1.52 T.T. Singh 1.19
 5 Lahore 1.47 Faisalabad 1.19
 6 Rahim Yar Khan 1.39 Jhelum 1.18
 7 Kahat 1.38 Karak 1.14
 8 Rawalpindi 1.06 Haripur 1.11
 9 Chitral 0.98 Abbottabad 1.08
 10 Buner 0.97 Swat 1.07
 11 Swat 0.97 Gujrat 1.07
 12 Lakki 0.85 Chakwal 1.03
 13 Bannu 0.85 Rawalpindi 1.02
 14 Bhawalpur 0.75 Narowal 1.02
 15 Tank 0.71 Kahat 0.99
 16 D.I. Khan 0.71 Jacobabad 0.99
 17 Faisalabad 0.70 Barkhan 0.97
 18 Sibi 0.69 Mandi Baha Uddin 0.96
 19 Gawader 0.62 Buner 0.95
 20 Karak 0.61 Chitral 0.95
 21 Jhelum 0.61 Khairpur 0.94
 22 Ziarat 0.61 Naushero Feroze 0.94
 23 Hyderabad 0.56 Shikarpur 0.94
 24 Chagai 0.55 Shaiwal 0.93
 25 Sailkot 0.52 Attock 0.92
 26 Larkana 0.51 Malakand 0.91
 27 Swabi 0.51 Sargodha 0.91
 28 Mardan 0.51 Khushab 0.91
 29 Mirpukhas 0.47 Chasadda 0.91
 30 Manshera 0.45 Mirpurkhas 0.89
 31 Gujranwala 0.43 Jhalmagsi 0.85
 32 Mainwalai 0.42 Nawshera 0.84
 33 Lasbla 0.42 Sanghar 0.83
 34 Thatta 0.42 Dera Bugti 0.83
 35 Dir 0.41 Bannu 0.83
 36 Karachi 0.40 Sukkar 0.83
 37 Sukkar 0.39 D.I. Khan 0.82
 38 Shaiwal 0.37 Peshawar 0.82
 39 Abbottabad 0.36 Kohlu 0.82
 40 Haripur 0.36 Sailkot 0.81
 41 Naushero Feroze 0.35 Swabi 0.81
 42 Jhalmagsi 0.34 Lakki 0.80
 43 Pishin 0.34 Bhakkar 0.78
 44 Kohlu 0.33 Gujeanwala 0.78
 45 Multan 0.31 Pishin 0.77
 46 Sargodha 0.30 Mainwalai 0.76
 47 Attock 0.30 Khanewal 0.75
 48 Bahawalnagar 0.30 Sheikhupura 0.74
 49 Badin 0.29 Dir 0.73
 50 Nawabshah 0.28 Hafizabad 0.72
 51 Khushab 0.27 Tank 0.72
 52 Gujrat 0.27 Jhang 0.71
 53 Shikarpur 0.27 Okara 0.71
 54 Bolan 0.26 Ziarat 0.71
 55 Kharar 0.26 Mardan 0.71
 56 D.G. Khan 0.26 Lasbela 0.70
 57 Dadu 0.25 Dadu 0.70
 58 Khairpur 0.25 Nawabshah 0.70
 59 Sanghar 0.24 Vehari 0.69
 60 T.T. Singh 0.24 Multan 0.66
 61 Chakwal 0.23 Karachi 0.66
 62 Jaffarabad 0.20 Kasur 0.66
 63 Jacobabad 0.18 Lahore 0.65
 64 Vehari 0.17 Chagai 0.65
 65 Dera Bugti 0.17 Lohran 0.62
 66 Barkkar 0.17 Thatta 0.62
 67 Barkhan 0.16 Badin 0.62
 68 Musa Khail 0.16 Mastung 0.61
 69 Loralai 0.16 Layyah 0.58
 70 Muzaffargahr 0.16 Gawader 0.58
 71 Sheikhupura 0.16 Manshera 0.57
 72 Khanewal 0.14 Bahawalnagar 0.54
 73 Rajanpur 0.14 D.G. Khan 0.53
 74 Kasur 0.14 Bhawalpur 0.51
 75 Killa Saifullaha 0.14 Rahim Yar Khan 0.50
 76 Layyah 0.13 Jaffarabad 0.47
 77 Turbat 0.13 Hyderabad 0.47
 78 Tharparkar 0.13 Bolan 0.46
 79 Jhang 0.13 Muzaffarghar 0.44
 80 Okara 0.13 Rajanpur 0.36
 81 Zhob 0.12 Loralai 0.36
 82 Mandi Baha Uddin 0.11 Kharan 0.35
 83 Narowal 0.11 Pakpattan 0.32
 84 Nasirabad 0.11 Khuzdar 0.32
 85 Panjgur n to Awaran 0.32
 86 Mastung 0.10 Killa Saifullaha 0.30
 87 Kalat 0.10 Narirabad 0.27
 88 Pakpattan 0.09 Zhob 0.25
 89 Hafizabad 0.08 Tharparkar 0.21
 90 Khuzdar 0.08 Kalat 0.19
 91 Awaran 0.08 Musa Khail 0.18
 92 Lodhran 0.06 Turbat 0.16
 93 Kohistan 0.04 Panjgur 0.11
 94 Malakand 0.00 Kohistan 0.04

 Mean 0.43 Mean 0.73
 Variance 0.15 Variance 0.08

 Primary Sec. Enrollment
 Enrollment Rate-Boys
S No Rate-Girls

 1 Jhelum 1.04 Rawalpindi 0.94
 2 Chakwal 0.95 Jhelum 0.83
 3 Gujrat 0.94 Naushero Feroze 0.75
 4 Quetta 0.94 Chakwal 0.74
 5 Rawalpindi 0.92 Khairpur 0.74
 6 Shaiwal 0.92 Lahore 0.64
 7 Narowal 0.88 Gujrat 0.60
 8 T.T. Singh 0.86 Attock 0.58
 9 Sailkol 0.78 Sailkot 0.52
 10 Attock 0.75 Gujranwala 0.49
 11 Mandi Baha, Uddin 0.72 Karachi 0.49
 12 Faisalabad 0.68 Quetta 0.48
 13 Sargodha 0.64 Narowal 0.48
 14 Lahore 0.63 Haripur 0.47
 15 Gujranwala 0.62 Karak 0.47
 16 Karachi 0.61 Mainwalai 0.47
 17 Haripur 0.56 Mandi Baha, Uddin 0.43
 18 Hafizabad 0.53 Multan 0.43
 19 Khushab 0.50 T.T. Singh 0.43
 20 Mainwalai 0.50 Rahim Yar Khan 0.42
 21 Abbottobad 0.50 Faisalabad 0.42
 22 Karak 0.46 Abbottobad 0.42
 23 Malakand 0.45 Chitral 0.42
 24 Sheikhupura 0.44 Buner 0.41
 25 Multan 0.42 Kasur 0.40
 26 Jhang 0.41 Sibi 0.40
 27 Vehari 0.41 Tank 0.39
 28 Bhakkar 0.39 Swabi 0.39
 29 Okara 0.38 Khusbab Q.38
 30 Nawshera 0.38 Swat 0.37
 31 Khanewal 0.38 D.I. Khan 0.37
 32 Swabi 0.38 Nawshera 0.37
 33 Bahawalnagar 0.31 Mardan 0.37
 34 Kahat 0.37 Malakand 0.36
 35 Layyah 0.36 Charsadda 0.36
 36 D.I. Khan 0.35 Sheikhupura 0.35
 37 Mardan 0.35 Sargodha 0.33
 38 Sibi 0.35 Layyah 0.33
 39 Kasur 0.34 Khanewal 0.32
 40 Tank 0.33 Kahat 0.31
 41 D.G. Khan 0.33 Shaiwal 0.31
 42 Bhawawalpur 0.32 Lakki 0.30
 43 Peshawar 0.32 Peshawar 0.29
 44 Chitral 0.31 Bahawalnagar 0.29
 45 Rahim Yar Khan 0.31 Bhawawalpur 0.29
 46 Swat 0.30 Bhakkar 0.28
 47 Hyderabad 0.29 Jhang 0.27
 48 Manshera 0.28 Ziarat 0.25
 49 Bannu 0.27 Bannu 0.25
 50 Buner 0.27 Vehari 0.24
 51 Charsadda 0.26 D.G. Khan 0.24
 52 Lodhran 0.26 Nawabshah 0.24
 53 Mirpurkhas 0.25 Dir 0.23
 54 Sukkar 0.23 Hyderabad 0.23
 55 Muzaffarghar 0.23 Mirpurkhas 0.23
 56 Khairpur 0.22 Okara 0.23
 57 Larkana 0.22 Sukkar 0.22
 58 Ziararat 0.20 Muzaffarghar 0.21
 59 Gawadar 0.19 Larkana 0.21
 60 Dir 0.18 Dadu 0.21
 61 Thatta 0.18 Hafizabad 0.20
 62 Chagai 0.18 Chagai 0.18
 63 Lasbela 0.17 Barkhan 0.18
 64 Nawabshah 0.17 Kohlu 0.17
 65 Rajanpur 0.16 Jaffarabad 0.17
 66 Lakki 0.16 Shikarpur 0.16
 67 Dadu 0.16 Lasbela 0.16
 68 Mastung 0.16 Manshera 0.16
 69 Pakpattan 0.14 Shikarpur 0.15
 70 Shikarpur 0.14 Lodhran 0.15
 71 Pishin 0.13 Rajanpur 0.14
 72 Turbat 0.11 Gawader 0.14
 73 Naushero Feroze 0.11 Pakpattan 0.13
 74 Bakhan 0.10 Dera Bugti 0.13
 75 Sanghar 0.10 Pishin 0.13
 76 Kharan 0.10 Mastung 0.13
 77 Killa Saifullaha 0.10 Thatta 0.12
 78 Badin 0.09 Jacobabad 0.12
 79 Kohlu 0.09 Kharan 0.11
 80 Panjgur 0.09 Badin 0.10
 81 Jaffarabad 0.09 Khuzdar 0.10
 82 Jacobabad 0.09 Jhalmagsi 0.10
 83 Khuzdar 0.07 Tharparkar 0.09
 84 Loralai 0.07 Awaran 0.09
 85 Awaran 0.06 Bolan 0.07
 86 Jhalmagsi 0.06 Loralai 0.06
 87 Zhob 0.05 Narirabad 0.06
 88 Narirabad 0.05 Killa Saifullaha 0.05
 89 Bolan 0.04 Turbat 0.03
 90 Musa khail 0.04 Zhob 0.03
 91 Tharparkar 0.03 Musa Khail 0.03
 92 Dera Bugti 0.03 Kalat 0.03
 93 Kalat 0.03 Panjgur 0.03
 94 Kohistan 0.01 Kohistan 0.00

 Mean 0.33 Mean 0.29
 Variance 0.07 Variance 0.04

 Sec. Enrollment Literarcy Ratio
S No Rate-Girls Male-1981

 1 Karachi 0.39 Rawalpindi 60.77
 2 Faisalabad 0.39 Karachi 60.00
 3 Lahore 0.35 Chakwal 54.63
 4 Rawalpindi 0.32 Lahore 54.58
 5 Quetta 0.31 Jhelum 53.39
 6 Chakwal 0.31 Quetta 46.30
 7 Jhelum 0.29 Gujrat 42.78
 8 Gujranwala 0.28 Abbottobad 41.64
 9 T.T. Singh 0.28 Faisalabad 41.61
 10 Gujrat 0.26 T.T. Singh 41.61
 11 Mandi Baha, Uddin 0.25 Sailkot 40.85
 12 Sailkot 0.25 Gujranwala 40.11
 13 Narowal 0.20 Sukkar 38.11
 14 Shaiwal 0.19 Attock 37.49
 15 Multan 0.18 Hyderabad 36.97
 16 Sheikhupura 0.19 Khanewal 36.51
 17 Khanewal 0.18 Sargodha 36.28
 18 Attock 0.17 Shikarpur 35.87
 19 Rahim Yar Khan 0.17 Karak 35.64
 20 Sargodha 0.17 Narowal 34.72
 21 Okara 0.15 Okara 34.70
 22 Mirpurkhas 0.14 Khairpur 34.23
 23 Bahawalnagar 0.14 Mainwalai 33.66
 24 Haripur 0.14 Mandi Baha, Uddin 33.40
 25 Hyderabad 0.14 Larkana 32.63
 26 Layyah 0.13 Multan 32.61
 27 D.I. Khan 0.13 Dadu 32.48
 28 Shikarpur 0.12 Kahat 32.09
 29 Vehari 0.12 Peshawar 31.27
 30 Malakand 0.12 Sheikhupura 30.91
 31 Peshawar 0.12 Shaiwal 30.48
 32 Kasur 0.12 Jhang 30.04
 33 Mainwalai 0.12 Khushab 30.04
 34 Lodhran 0.11 Hafizabad 28.60
 35 Bhawalpur 0.11 Layyah 28.58
 36 Tank 0.11 Sanghar 28.46
 37 Khushab 0.10 Bahawalnagar 28.33
 38 Larkana 0.10 Rahim Yar Khan 28.03
 39 Jhang 0.10 Vehari 27.89
 40 Hafizabad 0.09 Bhakkar 27.50
 41 Nawshera 0.09 Bhawalpur 27.44
 42 Abbottabad 0.09 D.I. Khan 27.24
 43 Karak 0.08 Bannu 27.14
 44 Chitral 0.08 Kasur 26.85
 45 Swabi 0.08 Thatta 26.60
 46 Sukkar 0.08 Haripur 26.47
 47 Muzaffarghar 0.08 Malakand 26.40
 48 Mardan 0.08 Mardan 26.08
 49 Kahat 0.08 Muzaffaghar 25.81
 50 Naushero Feroze 0.08 lakki 25.75
 51 Nawabshah 0.07 Pakpattan 24.99
 52 D.G. Khan 0.07 Chitral 24.12
 53 Charsasdda 0.07 Tharparkar 23.66
 54 Sibi 0.07 D.G. Khan 23.20
 55 Dadu 0.06 Lodhran 23.06
 56 Khairpur 0.06 Naushero Feroze 23.01
 57 Sanghar 0.06 Nawabshah 23.01
 58 Thatta 0.06 Badin 21.84
 59 Pakpattan 0.06 Nawshera 21.79
 60 Ziarat 0.06 Charsasdda 21.79
 61 Swat 0.05 Manshera 20.92
 62 Bhakkar 0.05 Tank 18.43
 63 Bunner 0.04 Sibi 17.60
 64 Bannu 0.04 Jacobabad 17.45
 65 Manshera 0.04 Dir 16.93
 66 Rajanpur 0.03 Mirpurkhas 16.40
 67 Lasbela 0.03 Swabi 15.95
 68 Badin 0.03 Rajanpur 15.52
 69 Chagai 0.02 Swat 15.08
 70 Mastung 0.02 Pishin 15.00
 71 Jacobabad 0.02 Ziarat 11.50
 72 Dir 0.02 Kalat 10.60
 73 Pishin 0.02 Lasbela 10.30
 74 Panjgur 0.02 Turbat 9.50
 75 Barkhan 0.01 Chagai 9.00
 76 Lakki 0.01 Zhob 9.00
 77 Jaffarabad 0.01 Buner 8.73
 78 Loralai 0.01 Loralai 8.70
 79 Tharparkar 0.01 Nasirabad 8.10
 80 Khuzdar 0.01 Bolan 8.10
 81 Gawader 0.01 Khuzdar 7.00
 82 Zhob 0.01 Mastung 6.20
 83 Killa Saifullaha 0.01 Killa Saifullaha 5.90
 84 Kharan 0.01 Kohlu 5.90
 85 Kohlu 0.01 Panjgur 5.80
 86 Awaran 0.01 Gawader 5.80
 87 Turbat 0.01 Barkhan 5.50
 88 Jhalmagsi 0.00 Musa Khail 5.50
 89 Kalat 0.00 Jhalmagsi 4.80
 90 Bolan 0.00 Awaran 4.70
 91 Nasirabad 0.00 Kharan 4.20
 92 Musa Khail 0.00 Awaran 4.20
 93 Dera Bugti 0.00 Kohistan 1.87
 94 Kohistan 0.00 Dera Bugti 0.00

 Mean 0.10 Mean 24.52
 Variance 0.01 Variance 190.70

 % of HH With
 Literarcy Ratio Inside Piped
S No Female-1981 Water 1987.00

 1 Karachi 48.84 Quetta 71.40
 2 Lahore 40.95 Karachi 66.80
 3 Rawalpindi 31.26 Lahore 63.10
 4 Haripur 26.60 Peshawar 49.16
 5 Chakwal 25.66 Hyderabad 34.50
 6 Jhelum 24.73 Kohlu 33.20
 7 Quetta 23.20 Loralai 32.76
 8 Nawabsheh 23.01 Barkhan 32.00
 9 Naushero Feroze 23.01 Kahat 31.90
 10 Charsasdda 21.79 Malakand 31.85
 11 Nawhera 21.79 Musa Khail 31.32
 12 Faisalabad 20.68 Rawalpindi 31.00
 13 Sailkot 20.56 Nawshera 30.10
 14 Gujranwala 20.52 Bannu 28.70
 15 Hyderabad 19.81 Lakki 27.75
 16 T.T. Singh 18.67 Abbottabad 27.38
 17 Gujrat 18.67 Chitral 27.32
 18 Mandi Baha, Uddin 18.67 Sibi 27.24
 19 Tank 18.43 Swat 27.16
 20 Mirpurkhas 16.40 Charsadda 26.76
 21 Swabi 15.95 Tank 25.96
 22 Narowal 15.47 D.I. Khan 25.17
 23 Okara 13.74 Chakwal 25.10
 24 Khanewal 13.65 Buner 25.00
 25 Multan 12.88 Haripur 24.76
 26 Sukkar 12.81 Sargodha 24.10
 27 Sargodha 12.77 Sailkot 23.50
 28 Sheikhupura 12.54 Gujrat 22.23
 29 Bhawalpur 12.21 Karak 21.90
 30 Shaiwal 11.68 Pishin 21.60
 31 Ziarat 11.50 Multan 21.44
 32 Attock 11.07 Mirpurkhas 20.34
 33 Abbottabad 10.90 Gujranwala 20.32
 34 Peshawar 10.86 Bahawalnagar 20.10
 35 Rahim Yar Khan 10.65 Faisalabad 19.80
 36 Larkana 9.93 Dir 18.90
 37 Hafizabad 9.70 Attock 17.80
 38 Bahawalnagar 9.60 Shaiwal 17.56
 39 Kasur 9.47 Pakpattan 17.08
 40 Jhang 9.30 D.G. Khan 16.00
 41 Khushab 9.30 Manshera 14.83
 42 Vehari 9.19 Sukkar 14.70
 43 Chagai 9.00 Jhelum 14.40
 44 Buner 8.73 Norowal 13.99
 45 Shikarpur 8.73 Kohistan 13.50
 46 Dadu 8.61 Nawabshah 13.50
 47 Sanghar 8.46 Mastung 13.11
 48 D.G. Khan 8.12 Mandi Baha Uddin 12.97
 49 Mainwalai 8.03 Hafizabad 12.72
 50 D.I. Khan 8.01 Mainwalai 12.10
 51 Tharparkar 7.89 Okara 11.50
 52 Pakpattan 7.80 Naushero Feroze 10.43
 53 Layyah 7.78 Rajanpur 10.40
 54 Thatta 7.72 T.T. Singh 10.10
 55 Khairpur 7.05 Lodhran 10.00
 56 Badin 6.79 Kalat 9.41
 57 Bhakkar 6.61 Bolan 9.39
 58 Manshera 6.52 Sheikhupura 9.30
 59 Kahat 6.36 Khushab 9.30
 60 Muzaffargahr 6.33 Mardan 9.08
 61 Mastung 6.20 Badin 8.60
 62 Killa Saifullaha 5.90 Dadu 8.30
 63 Gawader 5.80 Bhawalpur 8.30
 64 Panjgur 5.80 Khanewal 8.20
 65 Barkhan 5.50 Gawader 8.00
 66 Musa Khail 5.50 Thatta 7.90
 67 Malakand 5.41 Sanghar 7.80
 68 Lodhran 5.34 Jhalmagsi 7.68
 69 Rajanpur 5.32 Swabi 7.48
 70 Mardan 5.10 Chagai 7.10
 71 Jhatmagsi 4.80 Pangur 7.10
 72 Jaffarabad 4.70 Vehari 7.00
 73 Sibi 4.40 Tharparkar 6.91
 74 Kharan 4.40 Kluzdar 6.80
 75 Awaran 4.20 Rahim Yar Khan 6.60
 76 Karak 3.57 Lasbela 6.50
 77 Bannu 3.42 Ziarat 5.61
 78 Jacobabad 3.18 Larkana 5.60
 79 Chitral 2.93 Bhakkar 5.30
 80 Dir 2.77 Turbat 4.90
 81 Zhob 2.00 Jacobabad 4.80
 82 Lakki 1.80 Khairpur 4.40
 83 Pishin 1.80 Awaran 4.10
 84 Swat 1.73 Nasirabad 3.87
 85 Loralai 1.60 Jhang 3.80
 86 Lasbela 1.40 Kasur 3.80
 87 Kalat 1.10 Muzaffarghar 3.70
 88 Bolan 1.10 Kharan 2.40
 89 Turbat 0.80 Killa Saifullaha 2.30
 90 Nasirabad 0.80 Layyah 2.10
 91 Kohistan 0.73 Jaffarabad 2.04
 92 Khuzdar 0.70 Dera Bugti 1.80
 93 Kohlu 0.60 Zhob 0.78
 94 Dera Bugti 0.00 Shikarpur 0.70

 Mean 10.50 Mean 16.77
 Variance 76.57 Variance 187.31

 Total Hospital
 Beds\Population
S No [1000pop] WFS

 1 Quetta 3.94 Karachi 26.01
 2 Sibi 2.34 Rawalpindi 16.90
 3 Peshawar 1.85 Chakwal 16.24
 4 Lahore 1.81 Lahore 15.86
 5 Haripur 1.23 Jhelum 13.85
 6 Abbottobad 1.23 Quetta 11.47
 7 Bannu 1.10 Gujrat 10.67
 8 Lakki 1.10 Faisalbad 10.26
 9 Jhalmagsi 1.10 Sailkot 9.51
 10 Nawabshah 1.03 Gujranwala 9.02
 11 Rawalpindi 0.95 T.T. Singh 8.72
 12 Tank 0.94 Mandi Baha Uddin 7.88
 13 D.I. Khan 0.94 Narowal 7.44
 14 Ziarat 0.93 Haripur 6.31
 15 Kohlu 0.89 Attock 5.12
 16 Bhakku 0.84 Sargodha 5.06
 17 Larkana 0.79 Hyderabad 4.86
 18 Hyderabad 0.79 Shaiwal 4.38
 19 Bhawalpur 0.73 Nawshera 4.04
 20 Malakand 0.71 Khanewal 3.53
 21 Kahat 0.68 Multan 3.32
 22 Multan 0.65 Naushero Feroze 3.30
 23 Manshera 0.64 Okara 2.84
 24 Swat 0.62 Sheikhupura 2.74
 25 Shiwal 0.61 Aboottabad 2.73
 26 Jhelum 0.61 Charsadda 2.33
 27 Mandi Baha Uddin 0.60 Tank 2.20
 28 Gujrat 0.60 Bahawalnagar 2.13
 29 Chitral 0.60 Malakand 1.61
 30 Faisalabad 0.54 Peshawar 1.31
 31 Sailkot 0.54 Mirpurkhas 1.04
 32 Mainwalai 0.53 Mainwalai 1.02
 33 Attock 0.50 Hafizabad 0.89
 34 Charsadda 0.49 Karak 0.76
 35 Gujranwala 0.48 Sukkar 0.64
 36 Hafizabad 0.48 D.I. Khan 0.64
 37 Rahim Yar Khan 0.45 Swabi 0.54
 38 Mardan 0.44 Vehari 0.32
 39 Khushab 0.44 Rahim Yar Khan 0.29
 40 Sargodha 0.41 Khushab 0.24
 41 Dadu 0.38 Kasur 0.22
 42 Chagai 0.38 Kahat 0.15
 43 Thatta 0.38 Khairpur -0.20
 44 Karak 0.37 Nawabshah -0.20
 45 Rajanpur 0.36 Layyah -0.23
 46 T.T. Singh 0.36 Jhang -0.63
 47 Nawshera 0.34 D.G. Khan -0.96
 48 Chakwal 0.34 Buner -1.30
 49 Jhang 0.33 Bhawalpur -1.43
 50 Karachi 0.33 Pakpattan -1.45
 51 Mirpurkhas 0.31 Chitral -1.51
 52 Sheikhupura 0.29 Mardan -1.56
 53 Shikarpur 0.28 Lodharan -1.77
 54 D.G. Khan 0.27 Dadu -2.13
 55 Norowal 0.26 Shikarpur -2.25
 56 Layyah 0.26 Muzaffarghar -2.56
 57 Dir 0.25 Bannu -2.99
 58 Sanghar 0.21 Larkana -3.02
 59 Pishin 0.25 Sanghar -3.13
 60 Sukkar 0.24 Bhakkar -3.16
 61 Loralai 0.24 Manshera -3.17
 62 Muzaffargahr 0.23 Swat -3.18
 63 Khahpur 0.23 Barkhan -3.64
 64 Bahawalnagw 0.22 Thatta -3.78
 65 Khairpur 0.21 Tharparkar -3.93
 66 Zhob 0.20 Musa Khail -3.97
 67 Khuzdar 0.20 Dir -4.12
 68 Okara 0.18 Sibi -4.31
 69 Badin 0.17 Ziarat -4.38
 70 Gawader 0.17 Lakki -4.45
 71 Killa Saifullaha 0.16 Loralai -4.60
 72 Vehari 0.16 Rajanpur -4.76
 73 Swabi 0.16 Mastung -4.77
 74 Khanewal 0.16 Badin -4.85
 75 Kharan 0.15 Pishin -5.09
 76 Penjgur 0.15 Chagai -5.17
 77 Kal. 0.15 Panjgur -6.04
 78 Naushero Feroze 0.15 Kohlu -6.04
 79 Lodhran 0.14 Gawader -6.32
 80 Barkhan 0.14 Lasbela -6.54
 81 Jacobabad 0.13 Jacobabad -6.78
 82 Jaffarabad 0.12 Killa Saifullaha -6.78
 83 Bolan 0.12 Jaffarabad -6.86
 84 Pakpattan 0.11 Awarari -7.12
 85 Mastung 0.11 Kalat -7.13
 86 Turbat 0.10 Turbat -7.21
 87 Dera Bugti 0.10 Kharan -7.26
 88 Musa Khail 0.09 Kohistan -7.37
 89 Awaran 0.09 Khuzdar -7.43
 90 Lasbela 0.07 Bolan -7.52
 91 Nasirabad 0.07 Nasirabad -7.77
 92 Tharparkar 0.05 Zhob -7.84
 93 Kohistan 1.00 Jhalmagsi -8.77
 94 Buner 0.00 Dera Bugti -9.47

 Mean 0.50 Mean 0.00
 Variance 0.30 Variance 42.35

 Total Hospital
 Beds\Population
S No [1000pop] WFS

 1 Quetta 3.94 Karachi 26.01
 2 Sibi 2.34 Rawalpindi 16.90
 3 Peshawar 1.85 Chakwal 16.24
 4 Lahore 1.81 Lahore 15.86
 5 Haripur 1.23 Jhelum 13.85
 6 Abbottobad 1.23 Quetta 11.47
 7 Bannu 1.10 Gujrat 10.67
 8 Lakki 1.10 Faisalbad 10.26
 9 Jhalmagsi 1.10 Sailkot 9.51
 10 Nawabshah 1.03 Gujranwala 9.02
 11 Rawalpindi 0.95 T.T. Singh 8.72
 12 Tank 0.94 Mandi Baha Uddin 7.88
 13 D.I. Khan 0.94 Narowal 7.44
 14 Ziarat 0.93 Haripur 6.31
 15 Kohlu 0.89 Attock 5.12
 16 Bhakku 0.84 Sargodha 5.06
 17 Larkana 0.79 Hyderabad 4.86
 18 Hyderabad 0.79 Shaiwal 4.38
 19 Bhawalpur 0.73 Nawshera 4.04
 20 Malakand 0.71 Khanewal 3.53
 21 Kahat 0.68 Multan 3.32
 22 Multan 0.65 Naushero Feroze 3.30
 23 Manshera 0.64 Okara 2.84
 24 Swat 0.62 Sheikhupura 2.74
 25 Shiwal 0.61 Aboottabad 2.73
 26 Jhelum 0.61 Charsadda 2.33
 27 Mandi Baha Uddin 0.60 Tank 2.20
 28 Gujrat 0.60 Bahawalnagar 2.13
 29 Chitral 0.60 Malakand 1.61
 30 Faisalabad 0.54 Peshawar 1.31
 31 Sailkot 0.54 Mirpurkhas 1.04
 32 Mainwalai 0.53 Mainwalai 1.02
 33 Attock 0.50 Hafizabad 0.89
 34 Charsadda 0.49 Karak 0.76
 35 Gujranwala 0.48 Sukkar 0.64
 36 Hafizabad 0.48 D.I. Khan 0.64
 37 Rahim Yar Khan 0.45 Swabi 0.54
 38 Mardan 0.44 Vehari 0.32
 39 Khushab 0.44 Rahim Yar Khan 0.29
 40 Sargodha 0.41 Khushab 0.24
 41 Dadu 0.38 Kasur 0.22
 42 Chagai 0.38 Kahat 0.15
 43 Thatta 0.38 Khairpur -0.20
 44 Karak 0.37 Nawabshah -0.20
 45 Rajanpur 0.36 Layyah -0.23
 46 T.T. Singh 0.36 Jhang -0.63
 47 Nawshera 0.34 D.G. Khan -0.96
 48 Chakwal 0.34 Buner -1.30
 49 Jhang 0.33 Bhawalpur -1.43
 50 Karachi 0.33 Pakpattan -1.45
 51 Mirpurkhas 0.31 Chitral -1.51
 52 Sheikhupura 0.29 Mardan -1.56
 53 Shikarpur 0.28 Lodharan -1.77
 54 D.G. Khan 0.27 Dadu -2.13
 55 Norowal 0.26 Shikarpur -2.25
 56 Layyah 0.26 Muzaffarghar -2.56
 57 Dir 0.25 Bannu -2.99
 58 Sanghar 0.21 Larkana -3.02
 59 Pishin 0.25 Sanghar -3.13
 60 Sukkar 0.24 Bhakkar -3.16
 61 Loralai 0.24 Manshera -3.17
 62 Muzaffargahr 0.23 Swat -3.18
 63 Khahpur 0.23 Barkhan -3.64
 64 Bahawalnagw 0.22 Thatta -3.78
 65 Khairpur 0.21 Tharparkar -3.93
 66 Zhob 0.20 Musa Khail -3.97
 67 Khuzdar 0.20 Dir -4.12
 68 Okara 0.18 Sibi -4.31
 69 Badin 0.17 Ziarat -4.38
 70 Gawader 0.17 Lakki -4.45
 71 Killa Saifullaha 0.16 Loralai -4.60
 72 Vehari 0.16 Rajanpur -4.76
 73 Swabi 0.16 Mastung -4.77
 74 Khanewal 0.16 Badin -4.85
 75 Kharan 0.15 Pishin -5.09
 76 Penjgur 0.15 Chagai -5.17
 77 Kal. 0.15 Panjgur -6.04
 78 Naushero Feroze 0.15 Kohlu -6.04
 79 Lodhran 0.14 Gawader -6.32
 80 Barkhan 0.14 Lasbela -6.54
 81 Jacobabad 0.13 Jacobabad -6.78
 82 Jaffarabad 0.12 Killa Saifullaha -6.78
 83 Bolan 0.12 Jaffarabad -6.86
 84 Pakpattan 0.11 Awarari -7.12
 85 Mastung 0.11 Kalat -7.13
 86 Turbat 0.10 Turbat -7.21
 87 Dera Bugti 0.10 Kharan -7.26
 88 Musa Khail 0.09 Kohistan -7.37
 89 Awaran 0.09 Khuzdar -7.43
 90 Lasbela 0.07 Bolan -7.52
 91 Nasirabad 0.07 Nasirabad -7.77
 92 Tharparkar 0.05 Zhob -7.84
 93 Kohistan 1.00 Jhalmagsi -8.77
 94 Buner 0.00 Dera Bugti -9.47

 Mean 0.50 Mean 0.00
 Variance 0.30 Variance 42.35

S No Z-SUM

 1 Lahore 33.78
 2 Quetta 27.17
 3 Rawalpindi 21.76
 4 Jhelum 15.20
 5 Karachi 15.04
 6 Faisalabad 12.47
 7 Chakwal 11.69
 8 Sailkol 10.44
 9 Gujrat 10.27
 10 Peshawar 9.67
 11 Gujranwala 8.40
 12 T.T. Singh 7.67
 13 Haripur 7.17
 14 Shaiwal 6.82
 15 Attock 6.65
 16 Multan 5.72
 17 Abbottabad 5.53
 18 Sibi 5.29
 19 Nawshera 4.99
 20 Sargodha 4.79
 21 Narowal 4.51
 22 Mandi Baha Uddin 4.10
 23 Kahat 4.07
 24 Hyderabad 4.04
 25 Charsadda 3.88
 26 Rahim Yar Khan 3.36
 27 Mainwalai 3.33
 28 Bhawalpur 3.19
 29 Tank 2.84
 30 D.I. Khan 2.77
 31 Larkana 2.48
 32 Chitral 2.44
 33 Karak 2.27
 34 Khushab 2.25
 35 Bannu 1.58
 36 Nawabshah 1.56
 37 Naushero Feroze 1.36
 38 Malakand 1.34
 39 Sheikhupura 1.29
 40 Lakki 0.62
 41 Mirpurkhas 0.47
 42 Swat 0.47
 43 Khairpur 0.23
 44 Khanewal -0.17
 45 Sukkar -0.47
 46 Bahawalnagar -0.75
 47 Bhakkar -0.80
 48 Okara -0.95
 49 Jhang -1.10
 50 Buner -1.26
 51 Swabi -1.66
 52 Hafizabad -1.81
 53 Shikarpur -1.84
 54 Kasur -2.04
 55 Mardan -2.14
 56 Ziarat -2.24
 57 Layyah -2.33
 58 Vehari -2.33
 59 D.G. Khan -2.65
 60 Dadu -3.03
 61 Thatta -3.06
 62 Sanghar -3.79
 63 Manshera -3.81
 64 Kohlu -4.14
 65 Dir -4.78
 66 Lodhran -4.84
 67 Chagai -4.95
 68 Muzaffarghar -4.97
 69 Barkhah -5.26
 70 Badin -5.38
 71 Pishin -5.45
 72 Jhalmagsi -5.62
 73 Rajanpur -5.94
 74 Pakpattan -6.16
 75 Gawader -6.16
 76 Jacobabad -6.19
 77 Lasbela -6.77
 78 Loralai -7.78
 79 Mastung -7.96
 80 Tharparkar -8.92
 81 Jaffarabad -9.14
 82 Musa Khail -9.30
 83 Bolan -9.32
 84 Dera Bugti -9.46
 85 Kharan -9.63
 86 Khuzdar -10.17
 87 Killa Saifullaha -10.29
 88 Awaran -10.51
 89 Kalat -10.81
 90 Panjgur -10.83
 91 Zhob -11.06
 92 Turbat -11.08
 93 Nasirabad -11.20
 94 Kohistan -12.62

 Mean -0.00
 Variance 67.03

[ILLUSTRATION OMITTED]

Authors' Note: The authors would like to acknowledge the excellent research assistance of Naeem Ahmed

REFERENCES

Adelman, I., and G. Dalton (1971) A Factor Analysis of Modernisation in Village India. Economic Journal 81: 323.

Khan, M. H., and M. Iqbal (1982) Socio-economic Indicators in Rural Pakistan: Some Evidence. The Pakistan Development Review 21:3 217-230.

Pasha, Hafiz A., and Tariq Hasan (1982) Development Ranking of the Districts of Pakistan. Pakistan Journal of Applied Economics 1:2.

Pasha, Hafiz A., Salman Malik and Haroon Jamal (1990) The Changing Profile of Regional Development in Pakistan. Pakistan Journal of Applied Economics 9:1.

(1) The Z-sum can be computed as follows:

[(Z sum ).sub.j] = [[summation].sup.n.sub.i = 1] [Z.sub.ij]

where Zij = Xij-Xi / Si, n= numbers of indicators, Xi= mean value of the ith indicator, Si= Standard deviation of the ith indicator, Xij= value of the ith indicator in the jth district.

(2) The taxonomic distance can be derived as follows:

[(TD).sub.j] = [[[summation].sup.n.sub.i = 1] [([Z.sub.ij] - [Z.sup.*.sub.i]).sup.2]].sup.1/2]

where Zij = standardised (as described in the previous footnote) value of the ith indicator in the jth region, [Zi.sup.*] = highest standardised value of the ith indicator in all regions. The taxonomic distance is an Euclidean measure of the distance of a district from a hypothetical district which has the highest value for all the development indicators.

A. F. Aisha Ghaus is Deputy/Acting Managing Director, Social policy and Development Centre, Karachi. Hafiz A. Pasha is Deputy Chairman, The Planning Commission, Government of Pakistan, Islamabad. Rafia Ghaus is Economist at the Social Policy and Development Centre, Karachi.

Table 1
Summary Statistics of Indicators

 Coefficient
 of
Indicators Mean Variance Variation

Doctors per 1000 Population 2.03 4.25 0.98
Nurses per 1000 Population 0.87 2.69 0.53
Patients Treated per Population 0.43 0.15 1.10
Primary Enrolment--Boys (%) 73 0.08 2.55
Primary Enrolment--Girls (%) 33 0.07 1.29
Secondary Enrolment--Boys (%) 29 0.04 1.53
Secondary Enrolment--Girls (%) 10 0.01 1.05
Literacy Rate--Male (%) 24.52 190.70 1.78
Literacy Rate--Female (%) 10.50 76.57 1.20
Households with Access to Water (%) 16.77 187.31 1.23
Hospital Beds per 1000 Population 0.50 0.30 0.92

Table 2
Factor Loading Matrix

Variable Factor 1 Factor 2 Factor 3

Secondary Enrolment--Girls (%) 0.88133 0.14516 0.30654
Literacy Rate--Female (%) 0.82926 0.27829 0.24511
Literacy Rate--Male (%) 0.80951 0.11763 0.30996
Primary Enrolment--Girls (%) 0.79726 0.10043 0.20128
Secondary Enrolment--Boys (%) 0.71632 0.15801 0.20314
Households with Access to Water (%) 0.40003 0.84549 0.06185
Patients Treated per Population 0.07031 0.80268 0.21396
Hospital Beds per 1000 Population 0.0549 0.75256 0.3051
Doctors per 1000 Population 0.25292 0.24332 0.86359
Nurses per 1000 Population 0.37494 0.24848 0.85231
Primary Enrolment--Boys (%) 0.37031 0.26334 -0.03524
Eigenvalues 6.19901 1.49286 1.14938

Variable Factor 4 Communality

Secondary Enrolment--Girls (%) 0.1646 0.919
Literacy Rate--Female (%) -0.07239 0.847
Literacy Rate--Male (%) 0.27688 0.842
Primary Enrolment--Girls (%) 0.40248 0.848
Secondary Enrolment--Boys (%) 0.47522 0.805
Households with Access to Water (%) -0.10853 0.890
Patients Treated per Population 0.22458 0.745
Hospital Beds per 1000 Population 0.35518 0.789
Doctors per 1000 Population 0.0883 0.937
Nurses per 1000 Population -0.02692 0.929
Primary Enrolment--Boys (%) 0.8318 0.900
Eigenvalues 0.61098

Table 3
Districts-wise Ranking of Social Sector of Pakistan

District Province WFS

 Top Quartile

1 Karachi [ S ] 26.0147
2 Rawalpindi [ P ] 16.9032
3 Chakwal [ P ] 16.2396
4 Lahore [ P ] 15.8617
5 Jhelum [ P ] 13.8476
6 Quetta [ B ] 11.4693
7 Gujrat [ P ] 10.6669
8 Faisalabad [ P ] 10.2559
9 Sailkot [ P ] 9.5103
10 Gujranwala [ P ] 9.0223

 Second Quartile

11 T.T. Singh [ P ] 8.7161
12 M. Baha Uddin [ P ] 7.8838
13 Narowal [ P ] 7.4406
14 Haripur [ N ] 6.3132
15 Attock [ P ] 5.4162
16 Sargodha [ P ] 5.0561
17 Hyderabad [ S ] 4.8612
18 Shaiwal [ P ] 4.3784
19 Nawshera [ N ] 4.0355
20 Khanewal [ P ] 3.5312
21 Multan [ P ] 3.3155
22 Naushero F. [ S ] 3.3003
23 Okara [ P ] 2.8373
24 Sheikhupura [ P ] 2.7449
25 Abbottabad [ N ] 2.7280
26 Charsadda [ N ] 2.3308
27 Tank [ N ] 2.2013
28 Bahawalnagar [ P ] 2.1264
29 Malakand [ N ] 1.6083
30 Peshawar [ N ] 1.3097

 Third Quartile

31 Mirpurkhas [ S ] 1.0353
32 Mainwalai [ P ] 1.0231
33 Hafizabad [ P ] 0.8930
34 Karak [ N ] 0.7639
35 Sukkar [ S ] 0.6430
36 D.I. Khan [ N ] 0.6428
37 Swabi [ N ] 0.5445
38 Vehari [ P ] 0.3224
39 Rahim Yar
 Khan [ P ] 0.2881
40 Khushab [ P ] 0.2413
41 Kasur [ P ] 0.2153
42 Kohat [ N ] 0.1457
43 Khairpur [ S ] -0.1975
44 Nawabshah [ S ] -0.1986
45 Layyah [ P ] -0.2253
46 Jhang [ P ] -0.6348
47 D.G. Khan [ P ] -0.9605
48 Buner [ N ] -1.3008
49 Bhawalpur [ P ] -1.4317
50 Pakpattan [ P ] -1.4499
51 Chitra1 [ N ] -1.5092
52 Mardan [ N ] -1.5608
53 Lodhran [ P ] -1.7701
54 Dadu [ S ] -2.1306

 Bottom Quartile

55 Shikarpur [ S ] -2.2492
56 Muzaffarghar [ P ] -2.5598
57 Bannu [ N ] -2.9875
58 Larkana [ S ] -3.0215
59 Sanghar [ S ] -3.1303
60 Bhakkar [ P ] -3.1602
61 Manshera [ N ] -3.1704
62 Swat [ N ] -3.1779
63 Barkhan [ B ] -3.6361
64 Thatta [ S ] -3.7789
65 Tharparkar [ S ] -3.9269
66 Musa Khail [ B ] -3.9667
67 Dir [ N ] -4.1152
68 Sibi [ B ] -4.3073
69 Ziarat [ B ] -4.3808
70 Lakki [ N ] -4.4524
71 Loralai [ B ] -4.6029
72 Rajanpur [ P ] -4.7602
73 Mastung [ B ] -4.7734
74 Badin [ S ] -4.8466
75 Pishin [ B ] -5.0904
76 Chagai [ B ] -5.1677
77 Panjgur [ B ] -6.0387
78 Kohlu [ B ] -6.0408
79 Gawader [ B ] -6.3226
80 Lasbela [ B ] -6.5395
81 Jacobabad [ S ] -6.5698
82 Killa Saifullaha [ B ] -6.7825
83 Jaffarabad [ B ] -6.8593
84 Awaran [ B ] -7.1243
85 Kalat [ B ] -7.1316
86 Turbat [ B ] -7.2116
87 Kharan [ B ] -7.2608
88 Kohistan [ N ] -7.3670
89 Khuzdar [ B ] -7.4268
90 Bolan [ B ] -7.5248
91 Nasirabad [ B ] -7.7698
92 Zhob [ B ] -7.8430
93 Jhalmagsi [ B ] -8.7686
94 Dera Bugti [ B ] -9.4706

District Province Z-Score

 Top Quartile

1 Lahore [ P ] 33.7790
2 Quetta [ B ] 27.1702
3 Rawalpindi [ P ] 21.7602
4 Jhelum [ P ] 15.1961
5 Karachi [ S ] 15.0423
6 Faisalabad [ P ] 12.4723
7 Chakwal [ P ] 11.6895
8 Sailkot [ P ] 10.4392
9 Gujrat [ P ] 10.2695
10 Peshawar [ N ] 9.6742

 Second Quartile

11 Gujranwala [ P ] 8.3997
12 T.T. Singh [ P ] 7.6672
13 Haripur [ N ] 7.1679
14 Shaiwal [ P ] 6.8214
15 Attock [ P ] 6.6496
16 Multan [ P ] 5.7214
17 Abbottabad [ N ] 5.5262
18 Sibi [ B ] 5.2867
19 Nawshera [ N ] 4.9870
20 Sargodha [ P ] 4.7876
21 Narowal [ P ] 4.5065
22 M. Baha Uddin [ P ] 4.1047
23 Kohat [ N 1 4.0671
24 Hyderabad [ S ] 4.0355
25 Charsadda [ N ] 3.8821
26 Rahim Yar
 Khan [ P ] 3.3607
27 Mainwalai [ P ] 3.3278
28 Bhawalpur [ P ] 3.1852
29 Tank [ N ] 2.8443
30 D.I. Khan [ N ] 2.7729
31 Larkana [ S ] 2.4750

 Third Quartile

32 Chitral [ N ] 2.4402
33 Karak [ N ] 2.2741
34 Khushab [ P ] 2.2490
35 Bannu [ N ] 1.5821
36 Nawabshah [ S ] 1.5610
37 Naushero F. [ S ] 1.3569
38 Malakand [ N ] 1.3358
39 Sheikhupura [ P ] 1.2868
40 Lakki [ N ] 0.6170
41 Mirpurkhas [ S ] 0.4713
42 Swat [ N ] 0.4668
43 Khairpur [ S ] 0.2289
44 Khanewal [ P ] -0.1656
45 Sukkar [ S ] -0.4690
46 Bahawalnagar [ P ] -0.7517
47 Bhakkar [ P ] -0.7959
48 Okara [ P ] -0.9460
49 Jhang [ P ] -1.1024
50 Buner [ N ] -1.2584
51 Swabi [ N ] -1.6588
52 Hafizabad [ P ] -1.8140
53 Shikarpur [ S ] -1.8390
54 Kasur [ P ] -2.0419
55 Mardan [ N ] -2.1385
56 Ziarat [ B ] -2.2424

 Bottom Quartile

57 Layyah [ P ] -2.3279
58 Vehari [ P ] -2.3336
59 D.G. Khan [ P ] -2.6532
60 Dadu [ S ] -3.0322
61 Thatta [ S ] -3.0647
62 Sanghar [ S ] -3.7943
63 Manshera [ N ] -3.8104
64 Kohlu [ B ] -4.1405
65 Dir [ N ] -4.7799
66 Lodhran [ P ] -4.8434
67 Chagai [ B ] -4.9470
68 Muzaffarghar [ P ] -4.9657
69 Barkhan [ B ] -5.2614
70 Badin [ S ] -5.3758
71 Pishin [ B ] -5.4476
72 Jhalmagsi [ B ] -5.6175
73 Rajanpur [ P ] -5.9379
74 Pakpattan [ P ] -6.1570
75 Gawader [ B ] -6.1616
76 Jacobabad [ S ] -6.1918
77 Lasbela [ B ] -6.7740
78 Loralai [ B ] -7.7837
79 Mastung [ B ] -7.9594
80 Tharparkar [ S ] -8.9178
81 Jaffarabad [ B ] -9.1419
82 Musa Khail [ B ] -9.2995
83 Bolan [ B ] -9.3237
84 Dera Bugti [ B ] -9.4643
85 Kharan [ B ] -9.6348
86 Khuzdar [ B ] -10.1718
87 Killa Saifullaha [ B ] -10.2935
88 Awaran [ B ] -10.5132
89 Kalat [ B ] -10.8131
90 Panjgur [ B ] -10.8265
91 Zhob [ B ] -11.0581
92 Turbat [ B ] -11.0819
93 Nasirabad [ B ] -11.1989
94 Kohistan [ N ] -12.6158

Table 4
Percentage Share of Provinces in Population Quartile,
by Level of Development

Quartile Punjab Sindh NWFP Balochistan Total

Top Quartile 61.1 31.5 5.6 1.8 100.0
Second Quartile 55.8 23.6 20.4 0.2 100.0
Third Quartile 55.8 23.6 20.4 0.2 100.0
Bottom Quartile 33.4 31.5 8.7 26.3 100.0

Overall Population
 Share 55.2 24.1 13.9 6.8 100.0

Table 5

Correlation Between Social Indicators

 Patients
 Doctors Nurses Treated
 per Thousand per Thousand per Thousand
Indicators Population Population Population

Doctors
 per Thousand
 Population 1.00000
Nurses
 per Thousand
 Population 0.88090 1.00000
Patients Treated
 per Thousand
 Population 0.40591 0.40132 1.00000
Primary Enrolment
 Rate
 (Boys) 1991-92 0.27303 0.16642 0.38080
Primary Enrolment
 Rate
 (Girls) 1991-92 0.47133 0.51504 0.24912
Secondary Enrolment
 Rate
 (Boys) 1991-92 0.49811 0.48307 0.38916
Secondary Enrolment
 Rate
 (Girls) 1991-92 0.61623 0.61686 0.28043
Literacy Rate
 (Male) 1981 0.61825 0.54877 0.28096
Literacy Rate
 (Female) 1981 0.58983 0.56980 0.34780
Percent of Household
 with Inside
 Piped Water 0.41134 0.44265 0.59216
Hospital Beds
 (Hospital + RHCs) 0.47711 0.42465 0.59562

 Primary Primary Secondary
 Enrolment Enrolment Enrolment
 Rate (Boys) Rate (Girls) Rate (Boys)
Indicators 1991-92 1991-92 1991-92

Doctors
 per Thousand
 Population
Nurses
 per Thousand
 Population
Patients Treated
 per Thousand
 Population
Primary Enrolment
 Rate
 (Boys) 1991-92 1.00000
Primary Enrolment
 Rate
 (Girls) 1991-92 0.59583 1.00000
Secondary Enrolment
 Rate
 (Boys) 1991-92 0.64211 0.77362 1.00000
Secondary Enrolment
 Rate
 (Girls) 1991-92 0.48885 0.86746 0.73185
Literacy Rate
 (Male) 1981 0.52766 0.79438 0.75025
Literacy Rate
 (Female) 1981 0.35702 0.64480 0.66845
Percent of Household
 with Inside
 Piped Water 0.31705 0.38558 0.37318
Hospital Beds
 (Hospital + RHCs) 0.45330 0.37197 0.33016

 Secondary Literacy Literacy
 Enrolment Ratio Ratio
 Rate (Girls) (Male) (Female)
Indicators 1991-92 1981 1981

Doctors
 per Thousand
 Population
Nurses
 per Thousand
 Population
Patients Treated
 per Thousand
 Population
Primary Enrolment
 Rate
 (Boys) 1991-92
Primary Enrolment
 Rate
 (Girls) 1991-92
Secondary Enrolment
 Rate
 (Boys) 1991-92
Secondary Enrolment
 Rate
 (Girls) 1991-92 1.00000
Literacy Rate
 (Male) 1981 0.85712 1.00000
Literacy Rate
 (Female) 1981 0.81675 0.72088 1.00000
Percent of Household
 with Inside
 Piped Water 0.46214 0.41752 0.51800
Hospital Beds
 (Hospital + RHCs) 0.33867 0.34572 0.30970

 Percent of Hospital
 Households Beds
 with Inside (Hospitals
Indicators Piped Water + RHCs)

Doctors
 per Thousand
 Population
Nurses
 per Thousand
 Population
Patients Treated
 per Thousand
 Population
Primary Enrolment
 Rate
 (Boys) 1991-92
Primary Enrolment
 Rate
 (Girls) 1991-92
Secondary Enrolment
 Rate
 (Boys) 1991-92
Secondary Enrolment
 Rate
 (Girls) 1991-92
Literacy Rate
 (Male) 1981
Literacy Rate
 (Female) 1981
Percent of Household
 with Inside
 Piped Water 1.00000
Hospital Beds
 (Hospital + RHCs) 0.62680 1.00000