Development of Hilly and Mountainous areas: A New Dimension Explored And Its Implications

ABSTRACT

It is known that the geographical area calculations by the Surveyor General of India are based on the plane table assumptions. In the case of mountainous areas, therefore, this appreciation of the geographical area does not capture the area of the slopes. In this manner, the area estimates ignore the fact that the actual developmental infrastructure is to be placed on the actual area (mostly on the steep slopes) and not on the proverbial plane table area and that the disparities in the levels of availability of infrastructure and consequentially the actual levels of development are far greater than have so far been understood or appreciated. This paper attempts to bring out this fact with the help of a study conducted by the Centre for Geo-Informatics, Research and Training of the CSK Himachal Pradesh Agricultural University at the behest of the State Planning Department, Government of Himachal Pradesh and compares the heightened disparities as one moves from the usual concept of plane table area or the 2-dimensional approach to the 3-dimensional approach used to estimate the actual area of the slopes in the above mentioned study. The paper, thereafter attempts to suggest the urgency for adoption of this methodology for area estimation in the case of mountainous areas and establish a new concept of development towards reducing the actual disparities rather than the commonly perceived disparities and makes out a case for more preferential fiscal resource transfers to the mountainous States than has existed so far.

It was the early 1970s when I moved from Ludhiana to Shimla to join the Government of Himachal Pradesh. When I travelled to Shimla to join the State Government in the Bureau of Economics and Statistics, the journey from Kalka to Shimla was a true test of the grit since it not only involved rising high to about 7,000 feet above the mean sea level from about 2,000 feet but also involved a distance of about 86 kilometres of winding, narrow and serpentine single lane narrow road taking close to 5 hours in a public transport bus. After reaching Shimla, I told a colleague that Shimla was tucked up in the Himalayas far too away and it needed enormous effort to travel to this place. In response, I was told by him that the actual distance between Kalka and Shimla, as the crow flies, was only 16 miles (or about 26 kilometres) instead of the actual distance of 86 kilometres by road. Initially, I did not believe him but once I had a hard look at the map, I gradually gravitated to believe in what he had said. With this, the seed of a question about the actual surface area of the mountainous territories was sown in my mind.

The latitude for Kalka in Haryana is 30 degrees, 50 minutes and 17.19 seconds north whereas that of Shimla is 31 degrees, 6 minutes and 14.08 seconds north. In the latitudes of thirties in the north, one degree indicates a distance of 68.88 miles, one minute of 1.15 miles and one second of 101.02 feet. Therefore, the actual aerial distance between Kalka and Shimla works out to 18.338 miles or about 29.5 kilometres. Subsequently, when I got the opportunity of serving the State Government in the Planning Department in various capacities, I decided to vigorously pursue this curiosity to some logical conclusion.

In the inter-regnum, on various occasions, the issue of calculating the actual area of the mountain slopes was raised by me with the authorities responsible for determining the geographical area but without an avail. Appropriate technologies to determine the actual area of the slopes of a mountain had become known and were being tried out in several places with limited applications. Attempts were also made by the author to raise this matter with the successive Central Finance Commissions and the National Planning Commission that the important fact of the actual area being far more than the usual plan table area could only be ignored to the gross disadvantage of the mountainous areas without appropriate appreciation. Since area or the surface area was one of the most important denominators of determining the levels of development, especially in the mountainous areas, it was really putting these underdeveloped areas at a perennial disadvantage by ignoring this aspect or input into the developmental experiment. This was truer for more mountainous areas. Moreover, it needed to be appreciated that since the entire developmental infrastructure was to be laid on the actual surface area, the unit costs as well as the total costs could be very high and needed to be factored into the developmental matrix.

Presuming that the actual surface area in a mountainous terrain is far more than the plane table area as is presently understood, what can be the implications in terms of disparities of development, harshness and quality of life, per unit cost of putting up developmental infrastructure, relative costs of bringing about parity in the levels of development at an aggregate level and the relationship of all these ramifications to the question of poverty and incomes? If a mountain were a perfect conical structure, the ratio of its conical surface area to its basal area would be the ratio of the length of the slope to the radius of the base of the cone. We could appreciate it better with the help of the following diagram:

Area of the base of the cone = πr2

Surface area of the conical surface = πrh

Ratio of the conical surface area to

the basal area of the cone = πrh/πr2

=h/r

Where h=(r 2 + l2 )1/2

With this simple diagram, we demonstrate that the area of the conical surface of a mountain will be proportional to the length of the slope. Higher the mountain, longer will be the slope and thus the area of the slope keeps getting larger with the higher altitudes. Here we are presuming that the conical surface of the mountain is a perfect surface with no folds. If it has folds, it is implicit that the surface area will continue to become larger. This is a simple mathematical illustration to further the cause of the issue under discussion in this paper.

It was with this background that a study was farmed out by the State Planning Department to the Centre for Geo-informatics, Research and Training of the Chaudhary Sarvan Kumar Himachal Pradesh Agricultural University, Palampur in the year 2005 to develop a scientific calculation of the actual surface area of Himachal Pradesh by measuring the three dimensional area of the land surface of the State. Such a scientific study was extremely necessary to drive home the point that the entire area based perception of development of mountainous areas in general, and that of Himachal Pradesh, in particular, needed a different view and also exhibited the intra-state as well as inter-State disparities in a more profound and sensitive manner. It was a matter of sustained learning and great satisfaction for the author to have in-depth interaction with the research team and help sharpen the objectives of the study on the one hand, and to reach the most appropriate estimation of the surface area to translate the actual levels of development in the different districts of the State, on the other. When such scientific data was carefully looked at with reference to different districts, it came home that the districts with more dissected topography and higher altitudes were at a much greater disadvantage as compared to other districts which had less dissected topography and comparatively lower altitudes. In this paper, we shall go to look at these ramifications in real life situations and present a different picture of comparative development in its spatial dimension in Himachal Pradesh than is commonly perceived.

For the purposes of this study, we will take the actual plane table area of different districts, the surface area of different districts and certain physical attributes of development for comparison purposes. But before doing that, let us visit some technical aspects of the need for this study.

For firming up the strategies for sustainable development in the mountain areas, the development planners and administrators must factor in as accurately as possible the area on which the developmental exercise has to be carried out. In the case of the plain areas or States, estimation of the actual area is much simpler an exercise because the two dimensional measurements of the flat lands are easy to be taken into cognizance with fairly high precision. However, for the mountain areas, where severely dissected topography, undulating terrain and extremely variable altitudes make it extremely difficult to make fair estimates of the actual surface area, the entire developmental exercise appears to have been set at nought ab initio as the present procedures and developmental practice ignores this most important dimension. Therefore, it is necessary to use the GIS technology to make area estimation for the mountain States to make more sense of the developmental exercise.

Triangular Irregular Networks (TIN) approach using vector data sets and polygons for generating surface area in such mountainous situations in inherently more accurate than any other methodology presently known. For conducting this exercise of estimating the actual surface area of the State of Himachal Pradesh, three dimensional TIN models were created from the contour lines by the team of researchers of the University at Palampur. The contour lines were digitised from the Survey of India topographic sheets of the study area. The contours were utilised for the creation of the triangular irregular network surface. The TIN model represents the surface as a set of contiguous, non-overlapping triangles and within each triangle the surface is represented by a plane. Detailed estimates of the district-wise area and the technical details of the methodology can be seen in the report published by the State Planning Department, Government of Himachal Pradesh titled “Developing District-wise Surface Area of Himachal Pradesh” which has been brought out in collaboration with the Centre for Geo-Informatics, Research and Training of the CSK Himachal Pradesh University, Palampur, Kangra district. Based on this methodology, comparative data on the actual surface area estimates by two dimensional approach and the three dimensional approach vis-à-vis the area notified by the Surveyor General of India are presented in the following table:-

Table 1 : Comparative Area figures by different measurement concepts for Himachal Pradesh

(Square Kms.)

Sl.No. Districts Area according to SGI Area according to 2-D calculation Area according to 3-D calculation Per cent increase in area
1. Bilaspur 1167 1160 1327 13.7
2. Chamba 6528 6480 11675 78.8
3. Hamirpur 1118 1111 1147 2.6
4. Kangra 5739 5567 7088 23.5
5. Kinnaur 6401 6242 11762 83.8
6. Kullu 5503 5495 9694 76.2
7. Lahaul-Spiti 13835 14002 22893 65.5
8. Mandi 3950 3960 5403 36.8
9. Shimla 5131 5084 7888 55.2
10. Sirmaur 2825 2864 3654 29.3
11. Solan 1936 1839 2285 18.0
12. Una 1540 1538 1569 1.9
Total H.P. 55673 55342 86385 55.2

Even though the above effort may have some methodological or calculation inaccuracies and certain members of scientific community may find minor technical flaws with the methodology, it certainly indicates that the actual surface area of the mountainous terrain can be and is certainly far more than the usual plane table approach followed so far and throws up new challenges for a closer appreciation of the developmental aspirations of the mountain areas. On the face of it, there appear to be no shortcomings in terms of technological aspects followed for the above estimation. It is important to underline that the area according to the Surveyor General of India and the 2-dimensional approach is almost the same.

Given this outcome of the study, we understand that the actual surface area of Himachal Pradesh after we factor in the area of the slopes of the mountains is 55.2 per cent more than what is conventionally known to us. What are the overall and district-wise implications of the above findings? We analyse the available data on development in some selected sectors to drive home the point of real versus the commonly perceived or traditionally known disparities.

Let us first of all look at the scatter of the habitations from the conventional view and from the three dimensional aspect of the surface area. The relevant data is presented in the table given below:-

Table 2 : Average area per habitation in square kilometres

Sl.No. Districts Area according to SGI Area according to 3-D calculation No. of villages 2001 census Area per village in sq. kms.
SGI area 3-D area
1. Bilaspur 1167 1327 965 1.209 1.375
2. Chamba 6528 11675 1118 5.839 10.443
3. Hamirpur 1118 1147 1635 0.684 0.701
4. Kangra 5739 7088 3619 1.586 1.959
5. Kinnaur 6401 11762 234 27.355 50.265
6. Kullu 5503 9694 172 31.994 56.360
7. L-Spiti 13835 22893 287 48.206 79.767
8. Mandi 3950 5403 2833 1.394 1.907
9. Shimla 5131 7888 2520 2.036 3.130
10. Sirmaur 2825 3654 966 2.924 3.782
11. Solan 1936 2285 2388 0.811 0.957
12. Una 1540 1569 758 2.031 2.070
13. HP 55673 86385 17495 3.182 4.938

Note: The number of villages is the number of inhabited villages

A quick look at the above data throws up interesting aspect of the theme of this study. By the conventional area approach, the range of the area per habitation is from 0.684 square kilometres to 48.206 square kilometres in Hamirpur and Lahaul-Spiti districts, respectively. Assuming that all habitations are evenly dispersed on the area, this implies that the average distance between two habitations in case of Hamirpur is 0.61 kilometres as against the corresponding figure of 5.12 kilometres based on the conventional appreciation of the geographical area. Opposed to this, with the 3-dimensional approach to the surface area, the mean distance for habitations in Hamirpur comes to 0.62 kilometres and that for Lahaul-Spiti comes to 6.60 kilometres. In this manner, the ratio for the average distance between the least and the farthest spaced habitations by conventional approach is 1:8.39, whereas the same ratio by the 3-dimesional appreciation of the area becomes 1:10.64. These ratios open up a question. The formidability of the developmental exercise in terms of implementation is at least 30 per cent higher in case of the approach by the 3-dimensional area as compared to the plane table approach.

From the pure perspective of the dispersal of the habitations, one could look at the situation on the basis of density of population by the two area approaches. The data in this behalf is presented in the following table:

Table 3 : Density of population (persons per sq. kilometer of area)

Sl.No. Districts Area according to SGI Area according to 3-D calculation Population by 2001 census Density
SGI area 3-D area
1. Bilaspur 1167 1327 340885 292 257
2. Chamba 6528 11675 460887 71 39
3. Hamirpur 1118 1147 412700 369 360
4. Kangra 5739 7088 1339030 233 189
5. Kinnaur 6401 11762 78334 12 7
6. Kullu 5503 9694 381571 69 39
7. L-Spiti 13835 22893 33224 2 1
8. Mandi 3950 5403 901344 228 167
9. Shimla 5131 7888 722502 141 92
10. Sirmaur 2825 3654 458593 162 125
11. Solan 1936 2285 500557 258 219
12. Una 1540 1569 448273 291 286
13. HP 55673 86385 6077900 109 70

The data on density of population again indicates that the districts which have high altitudes and also have deeply dissected topography see a drastic decline in the number depicting the density of population per square kilometre by the 3-dimensional area approach whereas the districts which have much lower altitudes and are more or less plain do not witness a drastic fall in the density of population even after the 3-dimensional area approach is used to denominate the total population. Sparseness of population increases with the use of 3-dimensional area measurement in the case of high altitude districts with dissected topographical relief and therefore, renders the task of development more difficult in such areas.

Whenever one talks about the development in the mountainous areas, the core element of all variants of strategies is the availability of roads. And since the habitations tend to be sparsely scattered, the density of roads per unit of area is considered one of the most important indicators of development. Roads are called the very life-lines in the hills and mountainous areas and therefore, denominating the available road length with the actual area rather than the plane table area can explain the disparities inter-State as well as intra-State. The data on the road density per hundred square kilometres of area district-wise in Himachal Pradesh is presented in the following table:

Table 4 : Average road length per 100 square kilometers of area

Sl.No. Districts Area according to SGI Area according to 3-D calculation Road length in Kms. 2007-08 Road length per 100 sq. kms. Of
SGI area 3-D area
1. Bilaspur 1167 1327 1439 123.30 108.44
2. Chamba 6528 11675 3009 46.09 25.77
3. Hamirpur 1118 1147 1665 148.93 145.16
4. Kangra 5739 7088 5140 89.56 72.52
5. Kinnaur 6401 11762 978 15.27 8.31
6. Kullu 5503 9694 1512 27.46 15.60
7. L-Spiti 13835 22893 1172 8.47 5.12
8. Mandi 3950 5403 4966 125.72 91.91
9. Shimla 5131 7888 4672 91.05 59.23
10. Sirmaur 2825 3654 2809 99.43 76.87
11. Solan 1936 2285 2540 131.19 111.16
12. Una 1540 1569 1610 104.55 102.61
13. HP 55673 86385 31512 56.60 36.48

Since Lahaul-Spiti district is a typical case with nearly 25 per cent of the State’s area, we may like to ignore it for the comparative purposes or for analysing the impact of the area increase by the 3-dimensional approach. Districts like Hamirpur and Una where the difference in area by the two approaches is non-significant, do not witness any reduction of consequence in the road density. On the other hand, for districts like Chamba, Kinnaur, Kullu and Shimla which have a vast difference in the area measurement by the two approaches, the density of roads gets reduced to about half the level when the road length is denominated by the 3-dimensional area. This drastic reduction in the level of this crucial indicator leads to heightening the inter-district disparities in the levels of development. The ratio of the minima and maxima of the road density by the area based on Surveyor General of India’s assessment is 1:17.58 whereas the same by the area based on the 3-dimensional approach becomes 1:28.35. Therefore, the inter-district disparities are seen to be more pronounced when we assess the development index of the road density based on the actual surface area of the mountain slopes. One of the latent disabilities which the numbers or numerical indicators can not capture relates to the physical difficulty of the task of road construction. Road construction in the high mountainous areas is not only arduous due to the altitudinal aspect; slope and geological strata encountered, but is also severely constrained by the extremities of climate and very short working season. All these factors compound the disparity for the already under-privileged district by rendering the task of catching up far more formidable than what could be managed by mere adequacy of resource flows.

From the important aspect of road density which is the central input for development in the mountainous areas, we now come to look at the two most important social inputs into development and how these get impacted by the enormous increase in the actual surface area through the 3-dimensional approach. These are the scatter of the educational and health institutions. The following text deals with the data on these sectors.

Table 5 : Average area served per educational institution

Sl.No. Districts Area according to SGI Area according to 3-D calculation Total schools 2007-08 Area served per school
SGI area 3-D area
1. Bilaspur 1167 1327 846 1.379 1.569
2. Chamba 6528 11675 1526 4.278 7.651
3. Hamirpur 1118 1147 862 1.297 1.331
4. Kangra 5739 7088 2583 2.222 2.744
5. Kinnaur 6401 11762 271 23.620 43.402
6. Kullu 5503 9694 977 5.632 9.922
7. L-Spiti 13835 22893 267 51.816 85.742
8. Mandi 3950 5403 2418 1.633 2.342
9. Shimla 5131 7888 2307 2.224 3.419
10. Sirmaur 2825 3654 1325 2.132 2.758
11. Solan 1936 2285 1074 1.803 2.128
12. Una 1540 1569 764 2.015 2.054
13. HP 55673 86385 15220 3.658 5.676

When we analyse the school infrastructure availability based on the area according to the Surveyor General of India (the plane table approach), we find that the area served per school ranges from 1.297 square kilometres in Hamirpur to 51.816 square kilometres in the case of Lahaul-Spiti. On the other hand, when we look at the area served per school by the 3-dimensional approach (actual surface area of the slopes), the range becomes from 1.331 square kilometres for Hamirpur to 85.742 square kilometres for Lahaul-Spiti. This clearly means that the backward districts appear to further slide down the scale when we compare the density of educational infrastructure based on the 3-dimensional area measurement approach. Same analysis and conclusions hold true in the case of the availability of health infrastructure. The comparative data on the area served per health institution by the two area measurement approaches is presented in the following table:

Table 6 : Average area served per health institution

Sl.No. Districts Area according to SGI Area according to 3-D calculation Total health instns. 2006-07 Area served per institution
SGI area 3-D area
1. Bilaspur 1167 1327 220 5.304 6.032
2. Chamba 6528 11675 324 20.148 36.034
3. Hamirpur 1118 1147 255 4.384 4.498
4. Kangra 5739 7088 768 7.473 9.229
5. Kinnaur 6401 11762 87 73.575 135.195
6. Kullu 5503 9694 191 28.811 50.754
7. L-Spiti 13835 22893 75 184.467 305.240
8. Mandi 3950 5403 551 7.715 9.806
9. Shimla 5131 7888 512 10.021 15.406
10. Sirmaur 2825 3654 273 10.348 13.384
11. Solan 1936 2285 302 6.411 7.566
12. Una 1540 1569 230 6.696 6.822
13. HP 55673 86385 3788 14.697 22.805

Banks are another developmental infrastructure playing a key role in the programmes relating to poverty amelioration as also for promoting the development of farm based economies in the mountainous areas. Since the area of Himachal Pradesh increases by about 55.2 per cent and that for Chamba, Kinnaur, Kullu and Lahaul-Spiti by 78.8 per cent, 83.8 per cent, 76.2 per cent and 65.5 per cent, respectively; these districts are bound to suffer from greater developmental lag when we visualise the picture through the area assessment by the 3-dimensional approach. The data on area served per scheduled commercial bank by the two area approaches is compared in the following table:

Table 7 : Average area served per scheduled commercial bank

Sl.No. Districts Area according to SGI Area according to 3-D calculation Total banks Dec. 2006 Area served per institution
SGI area 3-D area
1. Bilaspur 1167 1327 46 25.370 28.848
2. Chamba 6528 11675 53 123.170 220.283
3. Hamirpur 1118 1147 58 19.276 19.776
4. Kangra 5739 7088 157 36.554 45.146
5. Kinnaur 6401 11762 19 336.895 619.053
6. Kullu 5503 9694 51 107.902 190.078
7. L-Spiti 13835 22893 9 1537.222 2543.667
8. Mandi 3950 5403 104 37.981 51.952
9. Shimla 5131 7888 137 37.453 57.577
10. Sirmaur 2825 3654 49 57.653 74.571
11. Solan 1936 2285 91 21.275 25.110
12. Una 1540 1569 56 27.500 28.018
13. HP 55673 86385 830 67.076 104.078

As for the inter-State comparisons, just one illustration would be sufficient to drive home the point. It is common knowledge that the surface area of the States of Punjab and Haryana by the conventional methodology and that by the 3-dimensional approach would not be much different from each other because both the States are plain area. Let us take the data for the total number of allopathic health institutions up to the level of Community Health centres in the three States of Punjab, Haryana and Himachal Pradesh and reduce it to the indicator of area served per institution for the comparison purposes with reference to the question of worsening of the disparities. The data in this regard is contained in the following table:

Table 8 : Inter-State comparison of health infrastructure availability

Sr.No. Item Punjab Haryana Himachal Pradesh
1. Health Institutions 3612 2939 2593
2. Geographic area SGI data sq. kms. 50362 44212 55673
3. Area by 3-D approach sq.kms. 50362* 44212* 86385**
4. Area per Institution in sq. kms. 13.94 15.04 21.47(SGI based)

33.31

(3-D based)

Note : * :Area for Punjab and Haryana by the 3-D approach has been assumed to be the same as the plane table area.

** : Area for Himachal Pradesh is based on the study referred to above in this paper.

A cursory look at the data in the above table reveals that even on the basis of the area according to the Surveyor General of India, Himachal Pradesh lags behind the plain States of Punjab and Haryana in the physical availability of health institutions. But when one goes to compare the States by using the estimated 3-D area for Himachal Pradesh, the inter-State disparity widens considerably. It should also be remembered that the geographical area in Himachal Pradesh represents high altitudes and dissected topography accompanied by complex geological structures and therefore, a much larger area per institution for this State presents a greater disparity when the ease of transportation along with higher road densities for the plain States is also factored in.

The above analysis and the findings need to be understood and appreciated. The first and foremost fact is that the geographical area or the actual surface area of the mountainous territories is larger than the plain territories. The second fact is that the area tends to be larger for those territories which are manifested by extreme variations in altitudes and also by higher altitudes. The factor of increase of area is a function of altitudinal variations, dissected topography and variable relief. The third fact is that degree of difficulty of living and sustenance increases with higher altitudes. The fourth fact is that cost of development rises telescopically as the altitudes get higher and the pace of development is severely constrained by extremities of climate. The fifth and overarching fact is that most of the “more” mountainous areas in India are located in the Himalayas and these areas or States have a great responsibility for ecological conservation and improvement of forest cover so that the riparian States can live well, have sustained access to natural resources and contribute to the overall goal of national development.

One final comment requiring a specific mention is that all the Himalayan States are classified as “Special Category States” for the purposes of development. The national Planning Commission treats them with preference for meeting their developmental aspirations by “Lumpsum Allocation” system of central plan assistance and the assistance thus determined is passed on to these States on a special dispensation of 90 per cent grant and 10 per cent loan basis. The successive Finance Commissions have been treating these States with preference inasmuch as the fact that their revenue account deficits are largely met by “Gap Filling” revenue deficit grants and in addition, the cost norms for maintenance of physical infrastructure as also for putting up new infrastructure towards upgradation of the standards of administration are provisioned at a 30 per cent premium vis-à-vis the other non-hilly or non-mountainous States. The central fiscal transfers by way of central sector or centrally sponsored schemes are also effected on a more favourable basis than the other States. All these measures are integral parts of a fiscal transfer system which furthers equity in the federal context. However, practice of this “favourable” treatment for nearly four decades for plan purposes and for six decades for statutory Finance Commission transfers has not really achieved the desired equity. One of the important elements which could go a long way in the achievement of this equity is the realisation and usage of the actual surface area of these States for administering developmental dispensation. Now that the methodology for estimating the actual surface area of the mountainous slopes is available, it is high time that the practitioners of development start using it to deal with the disparities of development between the mountainous States and other “plain States” of India.

As to the question of more preferential system of fiscal transfers than being practiced at present, one could think of the central plan assistance to be transferred to these States on a cent per cent grant basis rather than the 90:10 dispensation. This would go a long way in reducing the indebtedness of these States to a certain extent. Similarly the assistance on account of the centrally sponsored schemes could also be considered to be transferred to these States entirely as grants. Since the tough topography, extremities of climate and the concomitant disabilities that come with these conditions are a common problem for these areas, instead of population based norms for several socio-economic services and infrastructure, the appropriate equalising equation could have the norms based on the actual area as explained in the paper. The Finance Commissions presently accept an allowance of 30 per cent in the cost norms for the hill areas. This could be considered to be raised to 60 per cent because the area alone increased by over 55 per cent for Himachal Pradesh. The additional 5 per cent allowance is being suggested to neutralise the higher physical constraints like slope, temperatures, etc.

As regards the policy implications for the individual States, it is high time that the State Governments recognise this fact and use it for more equitable allocation of resources to districts based on the actual area. The normative exercises could also follow the area basis rather than the population basis. More equitable process could involve the area as well as population basis in an appropriate weighting diagram to do more justice for dealing with the intra-State inter-district disparities.

* : D.K. Sharma is a former Principal Adviser and Secretary (Planning) to the Government of Himachal Pradesh and Adviser to ‘My Himachal’.

Devinder Kumar Sharma is a visiting professor and a statistician economist. For over three decades he was involved in budget formulation of Himachal Pradesh, is an authority on policy initiatives about hill development and has undertaken over 100 research studies. He lives in Shimla.

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