Report of the Inter-Ministry Task Group

on
Redressing Growing Regional Imbalances

Planning Commission
January 2005
 Contents

Chapter Page No

1. Introduction 1

2. Measuring Regional Imbalances 6

3. Methods of Reducing Regional Imbalances 20

4. The Backward Districts Grant Fund 25

5. Redressal of Regional Imbalance through District

40
Budgeting

6. Preparation of Composite Village Plans 48

2
 CHAPTER I

1. INTRODUCTION

1.1.1. The sub-continental dimensions of India, with inherent differences in

geographical parameters and historical developments, have led to disparities in
the levels of development of different areas, owing to differences in resource
endowment, levels of infrastructure and socio-economic parameters.

1.1.2. From the very beginning, the national planning strategy incorporated the
locational concept in investment policies. Public sector investment was
promoted in less developed regions and incentives offered for the private
sector to consider relocating to such areas. The scope for such policies has
diminished considerably in the liberalized environment. Despite such policies,
disparities in development have persisted and increased.

1.1.3. Development of backward regions has been a major concern of planners in

India and several programmes have been initiated over the years to address the
special problems faced by various geographical regions. Prior to the Tenth
Plan, the issue of development of backward areas was approached as primarily
one of development of States and allocation of normal Plan assistance through
adoption of a formula for distribution of resources weighted in favour of less
developed States with Special Central Assistance for area programmes
focused on regions with specific problems hilly, tribal, border or drought
prone areas. Central sector and Centrally Sponsored Schemes run by major
departments and Ministries of the Central Government dealing with sectors in
the State and concurrent lists of the Constitution also focused on improving
developmental levels in backward regions, States, districts or blocks and built
these concerns into programme content and formulae applied for fund
allocation.

1.1.4. The Mid Term Appraisal of the Ninth Plan drew attention to wide disparities
among States and among districts within a State. Pockets of high poverty, low
growth and human development and poor governance were identified as key

3
 areas slowing down the growth and development of the country. Such
pockets also reflected the failure of existing policies and administrative
procedures.

1.1.5. The Tenth Plan marked a shift in approach with reference to earlier Plans.
Hitherto, the stand of the Planning Commission had been that planning and
development of an area and allocation of funds for this purpose were
primarily the responsibility of the State Government concerned, with the
Planning Commission supplementing such efforts through distribution of
Central Plan Assistance, promotion of Special Area Programmes and various
poverty alleviation and employment generation schemes. In the Tenth Plan, it
was decided that these areas should be targeted not in terms of additionality of
funds alone but also with regard to the use to which funds were put. The new
approach signaled the need for complete change in the ethos of governance
and scheme implementation with efficiency and flexibility as key words.
In this context, the Rashtriya Sam Vikas Yojana was introduced with special
plans for Bihar and the undivided Kalahandi- Bolangir-Koraput (KBK)
districts of Orissa and a Backward Districts Initiative currently covering 147
districts.

1.2 NATIONAL COMMON MINIMUM PROGRAMME AND REGIONAL

DEVELOPMENT

1.2.1 The National Common Minimum Programme has laid special emphasis on
redressing growing regional imbalances among as well as within States,
through fiscal, administrative, investment and other measures. The main
objective is to correct distortions in Plan allocations and Central Government
assistance through a Backward States Grant Fund that will be used to create
productive assets in the poorest and most backward areas of the country.

4
1.2.2 To operationalise the action programme for redressing growing regional
imbalances as per the National Common Minimum Programme, an Inter-
Ministry Task Group was set up on the subject (office order with composition
and terms of reference at Annex I)

5
 CHAPTER II

2. MEASURING REGIONAL IMBALANCES

Uneven regional development can affect a nation’s image, security and stability.
Noticeable divergence in economic conditions among different parts of the
same country leads to migration of capital and people, further exacerbating
existing inequalities. This can result in unplanned growth, affect the quality of
life and generate severe political tensions. It produces overcrowding, squalor
and slums in urban areas with adverse economic, social and political
consequences.

Strategies adopted to tackle regional imbalance must, however, be formulated

carefully with an eye on their incentive effects. When special dispensations are
offered to backward areas in the form of direct subsidies, tax concessions and
the like, care must be taken to ensure that they achieve desired outcomes and
do not send out wrong signals that discourage self reliance and performance.

Over almost a half- century of planned development in India, these issues have
occupied centre stage in the national debate. Several committees appointed by
the Planning Commission have examined and re-examined them.
Commissions appointed by State governments have also attempted to identify
the more backward areas within States to enable them to make targeted
interventions. A brief roundup of the approaches adopted by the most
important of these committees is given at Annexure II.

The concept of regional imbalance, originally developed in an environment in which

development was considered syno nymous with economic growth alone,
focused primarily on income deprivation assessed with reference to lack of
sustainable employment and low potential for agricultural and industrial
growth. Gradually, this has been extended to cover poor educational
parameters and, in recent reports, there is greater focus on demographic and
health status too. Various committees that have analysed the issue have

6
 covered the ground quite comprehensively as far as identification and
selection of appropriate parameters to measure regional disparities are
concerned. They are unanimously of the view that the district should be
treated as the appropriate unit for determining backwardness and taking
remedial action. Some of the committees, which have looked into the matter,
had specific sectoral objectives like selection of backward areas for locating
industries for example.

On the whole, these committees did not extend their analysis to the manner in which
Centrally sponsored schemes of key ministries and departments were also
targeting programs to backward regions and did not attempt to assess the
impact of existing formulae employed by the Finance Commission as well as
the Planning Commission to channel non Plan and Plan resources to low
income and less developed States. All committees have bemoaned the lack of
adequate up to date information that could be used both to identify
backwardness as well as monitor the effectiveness of remedial schemes. Most
reports are also silent about the time frame and manner in which programs
meant for redressing regional disparities should be dovetailed into normal
schemes when disparities are reduced through targeted interventions. Neither
has there been adequate focus on the incentive effects of special programs on
the better off States and districts as well as on the developmental strategies of
backward areas themselves.

The approach adopted by different committees till date has been to provide resources
or promote investment in identified backward areas through the State
machinery in a top-down manner. But structural and institutional deficiencies
in these districts which have kept them outside the mainstream of development
have reduced their absorptive capacity resulting in funds gravitating towards
more developed regions and affecting the realization of desired outcomes.
The preferred strategy of the present Task Group is different from these earlier
approaches. Our focus is on creating a Backward Districts Fund, integrated
with a district level budget/Plan developed from below through a system of
village plans based on the perceived needs and real capacities of these areas.

7
 This should ensure that resources provided are fully utilized within the
specified time frame to produce the expected results.

SELECTION OF APPROPRIATE PARAMETERS

Unequal resource endowments-physical, human, infrastructural and budgetary- lie at

the root of regional disparities, but they could as well be caused by historical
and cultural factors. Selection of relevant parameters is essential for
identifying and assessing the degree of regional imbalance within a nation or
State. A major constraint for analysts and governments is the availability of
reliable reasonably up to date information that can be used to measure
imbalances and monitor and evaluate the success of remedial schemes. The
second stage of the process of identifying backward areas is the manner in
which different parameters should be yoked together (as an index or by any
other method) to generate a composite measure of low levels of development.

The concept of regional disparities can be understood in terms of unequal resource

endowments, uneven human development, inadequate infrastructure and poor
budgetary resources.

Unequal resource endowments

Natural resources are distributed unevenly across the country giving some areas a
natural advantage over others in terms of the scope for higher incomes,
employment possibilities and more sustainable livelihoods. Relevant
indicators used to measure resource availability relate mainly to agricultural
and mineral resources. Availability cannot, however, be the sole criterion;
level of exploitation and ease and costs of exploitation are equally important.
The principal natural resources which contribute to economic growth are the
following:

• Water resources, either through ample rainfall or accessible supply from both
surface and ground water sources, enable the cultivation of high income

8
 yielding food and cash crops, provide potable water for habitations and towns
and facilitate industrialization. On this basis, desert and chronically drought
affected areas were identified by the Planning Commission study group that
drafted the Fourth Five Year Plan (1966-71) and special schemes for such
regions formulated. Net irrigable area with reference to net area sown or the
population supported is a criterion that has been selected by many study
groups to identify regional backwardness. Chronically flood affected areas
and coastal areas affected by salinity too were considered as problem areas by
the Sivaraman committee appointed by the Planning Commission in its report
on backward areas of 1981. Availability of water for drinking and household
use has again been assessed using criteria like the average distance of safe
drinking water sources (through tap, well etc.) from homes.
• The extent of exploitation of water resources has to be taken into consideration
when, for historical or other reasons, investment has not been adequate for
utilizing irrigation potential. This has been assessed using criteria like
gross/net irrigated area with reference to gross/net sown area and area sown
more than once or cropping intensity. From the human development (health
and sanitation) point of view, easy availability of potable water is also a
critical indicator of regional disparity.
• Mineral deposits can provide States with revenue from royalties and increase
income and employment through mining and related industrial activity.
However, they should not only be exploitable, revenues derived should accrue
to State budgets with regular price adjustments. This factor has not generally
figured as a criterion for assessing regional disparities, given existing
constitutional and statutory provisions relating to revision of rates of major
minerals for royalty purposes.
• Forests or tree cover also constitute a valuable natural resource, but this must
be exploitable in terms of employment or cash incomes as is the case with
plantations or horticultural crops. For environmental reasons, in the context of
the inadequate forest cover of the country, regions with ample forest resources
have been constrained in exploiting them to raise revenues for development or
generating higher incomes. Hence, this has not been used as a criterion for
assessing regional imbalance.

9
 • The value of na tural resources stems from their exploitability and the scope for
increasing income and employment through use of resources. Sustainable
exploitation is the core issue and resources will have to be husbanded. Water
sources (both surface and ground water) must be conserved and mineral
deposits judiciously mined for resources to contribute income and
employment over a long period. The number of black and grey talukas is a
good indicator of overexploitation of ground water pointing to alarming
depletion of valuable resources. These issues must be kept in mind while
assessing the backwardness of regions within the country.

Uneven human development

The concept of human development was brought to the fore by the UNDP during the
nineties to offset the tendency of assessing development through the single
parameter of per capita income. The human development index (HDI) and
related gender development and gender empowerment indices, computed
annually since 1990, ranked countries on the basis of three variables- income
levels (measured by per capita GDP), educational status (measured by a
combination of adult literacy and enrolment at the primary, secondary and
tertiary levels) and health status (measured by life expectancy at birth). The
idea was adapted to Indian conditions in the National Human Development
Report brought out by the Planning Commission, while State governments
used the same approach to measure intra State regional disparities.
Human resources depend upon demographic trends and economic, basic health and
educational status. A plethora of parameters is available to assess different
elements of human development; whether they can be prioritized and
combined in the form of an index to rank States and districts is another issue.
It is necessary to distinguish between process and outcome variables; the
presence of the former does not necessarily ensure that desired outcomes are
actually realized.
1) Economic status of the population has been the basic parameter used to
measure imbalances at the global and national levels. Per capita income
figures on all lists of variables used to rank regions in terms of development.
This is supplemented with additional parameters focused specifically on low-

10
 income levels-like population below the poverty line, per capita consumption
etc. On the economic front, various proxies are used to capture poorer
economic status. A high population ratio with reference to cultivable land or
high incidence of unemployment or underemployment could be used to reflect
income deprivation. Greater dependence on agriculture is also a pointer to
lower growth potential. Hence, higher dependency of population on secondary
and tertiary activity or the proportion of persons dependent on industry, the
percentage of establishments using electricity or per capita annual
consumption of electricity etc. are used to focus on backward areas.
Within sectors themselves, dependency ratios can be generated say of the percentage
of workers engaged in agriculture (or specifically agricultural labourers) and
the level of industrial employment (differentiating if possible between
household industries, registered and unregistered units), while productivity
(and resultant incomes) can be assessed using the per capita gross value of
agricultural or ind ustrial output. Since urbanization is likely to increase
productivity and income, the ratio of urban to rural population is also a useful
indicator.
2) Health status is measured by a fair number of process and outcome variables
drawn from demographic and governmental sources.
Outcome variables
A clutch of parameters is used to assess growth rate of the population (the
crude birth and death rates and the total fertility rate, rate of growth of the
population, percentage of births of order three and above etc.), with falling
rates leading to stabilized population levels being treated as indicative of
improved quality of life. Life expectancy is a clear indicator of improved
health condition. The status of vulnerable groups is assessed through analysis
of differences between the genders in life expectancy and the sex ratio as well
as by indicators like infant, child and maternal mortality and the percentage of
girls marrying below the age of 18. Morbidity data is difficult to obtain today
although it could be the most comprehensive guide to the health status of a
region.
Process variables
Data relating to institutions (in terms of availability of hospitals and health
centres) and medical staff (availability of doctors and nurses), the adoption of

11
 family planning methods, immunization, institutional or safe deliveries etc.
indicate the existence or nonexistence of adequate facilities but this is no guide
to the quality or effectiveness of service delivery.
3) Educational status is focused mainly on the primary and secondary levels.
Here too process and outcome variables need to be differentiated.
i) Outcome variables
Literacy is the basic indicator of education with focus on the gender gap and
lower achievement levels of rural population or persons belonging to
disadvantaged regions and groups.
ii) Process variables
Gross and net enrolment ratios at the primary, elementary and secondary
school levels with special reference to disadvantaged groups and regions are
the most useful indicators. Success rates in school leaving examinations can
also be used where data is available. As in the case of health, it is again
necessary to look at institutions with reference to population as well as
accessibility (distance from habitations).

Inadequate infrastructure
Regions which are resource-rich can remain underdeveloped and backward
due to infrastructural inadequacies. This can affect human development
significantly by reducing access to economic centres and markets, schools and
educational institutions and medical facilities. Indicators used to assess
infrastructural adequacy relate to road length with reference to both area and
population, tele-density, availability of rail connectivity, post offices, mo tor
vehicles and bank branches as well as credit-deposit ratio, spread of
cooperative credit institutions and the like. The kind of housing available
(kachha or pucca) and access to the three basic amenities of water supply,
electricity and sanitation are also good indicators of the quality of life of the
population.

Poor budgetary resources

The size and adequacy of revenue resources can severely constrain the
capacity of a government to provide basic amenities to citizens. The theory of
fiscal federalism focuses on the different kinds of measures usually used to

12
 tackle the twin problems of vertical imbalance between federal and
subnational governments (due to the gap between the resource raising capacity
and spending needs of subnational governments to fulfill their Constitutional
role) as well as of horizontal imbalance among different subnational
governments (due to differing capacities to provide a minimum level of
services to citizens). Even where resources are available, inadequate
exploitation or inability to exploit them for varying reasons to improve
budgetary receipts may lead to deficits in State budgets which have to be met
through transfers of different kinds, keeping in mind incentive effects on
resource raising efforts and the quantity and quality of subnational
expenditure.

DEVELOPING A COMPOSITE APPROACH TO IDENTIFYING

BACKWARDNESS

2.3.1 Indicators selected to reflect regional imbalance have to be brought together to

arrive at the list of areas or regions requiring targeted attention. A common
method used for the purpose is building up a composite index combining
selected variables. There are serious technical flaws in this approach, since
variables tend to overlap and affect each other. This can happen within sectors
(there is a wide choice of demographic and outcome parameters in the case of
health with varying nuances) as well as across sectors. In the latter case, for
example, income levels have an obvious effect on outlays on health and
education and the demand for and access to these facilities in any region. An
educated population is likely to be better nourished, healthier and more
conscious of public health and sanitation concerns, even with no improvement
in income levels as studies have demonstrated time and again.
A major problem with indexation is the ticklish question of assigning appropriate
weights to selected parameters. The UNDP has developed the Human
Development Index which assigns the same weightage to variables reflecting
income, health and educational status, although there is difference in the
emphasis given to the two variables used to measure educational status. The
committee appointed by the Ministry of Rural Areas and Employment (headed
by Dr. EAS Sarma) to identify the 100 most backward and poorest districts in

13
 the country (which gave its report in November 1997) deliberated extensively
on this issue and assigned different weights to various parameters.

A second difficulty in applying the chosen variables for selecting backward areas is
identification of the cutoff point for intervention. This may even have to be
done arbitrarily by indicating that the focus will be on say the hundred most
backward units and ranking them on the basis of the composite index.

Another approach adopted by the EAS Sarma committee in its 1997 report was to
vary the weightage given to the poverty ratio in the composite index of
backwardness and select districts which repeatedly figured among the districts
at the lowest rung. The implications and usefulness of all these approaches
will have to be considered to identify backwardness and target the proposed
interventions.

PROPOSED METHODOLOGY FOR SELECTING BACKWARD AREAS

After considering possible approaches to the issue of identifying backwardness, the

task force has adopted the following procedure for selecting criteria and
aggregating different components. The level at which the selection process is
to be done and interventions made has also been suggested. A major factor
influencing our decisions has been the continuing and ready availability of
data for assessing backwardness and the existence of an administrative setup
that can implement programs and be monitored effectively.

2.4.1.1 Selection of backwardness criteria

We have considered the four types of regional disparities cited above to arrive
at the most appropriate selection criteria for general backwardness.
1) Poor resource endowment acts as an inbuilt constraint to development. Given
the current constitutional position with regard to powers enjoyed by States to
levy royalties for mineral exploitation and the preponderant control exercised
by the Central government in respect of determining royalty rates and

14
 managing exploitation particularly for major minerals, States and areas rich in
mineral wealth have not been free to take decisions to raise substantial
resources for development by drawing on these reserves. A separate analysis
and set of suggestions have been generated by us regarding streamlining and
improving the current procedure for royalty revision. Nevertheless, we do not
consider that availability of mineral wealth substantially reflects the growth
potential of an area. This applies to forest and tree cover also. Environmental
concerns severely limit the scope for raising revenues by rapid exploitation of
forest wealth when existing levels of tree cover are considered vastly
inadequate for the country. As for water resources, we are confronted with
diverse scenario ranging from unexploited irrigation potential based on surface
and ground water to over exploited dark and grey areas where conservation is
the predominant concern. Backward regions in general have remained
backward largely on account of inadequate exploitation of resource potential
not due to the absence of resources themselves. Against this background, we
have not adopted resource availability as a determinant parameter to identify
backwardness.
2) Human development should be a primary concern of the State and deprivation
in income as well as basic health and educational facilities must be a prime
concern while identifying backwardness.
Ø On the income front, data relating to per capita income is available only at
the State level. This is subject to serious infirmities as GSDP is computed
on income originating not accrual basis, with inadequate procedures to
adjust data for flows across borders. Some States like Karnataka are
generating district level domestic product figures by applying the income
originating principle to the primary sector and using a wide range of
proxies to capture incomes originating in the secondary and tertiary
sectors. This requires greater refinement; district level data is also not
available across the country. Poverty estimates or estimates of per capita
consumption cannot be generated from NSS data at the district level unless
State samples are used to supplement NSSO data. For many States, these
are available only at the regional level since NSSO regions, which do data
collection and analysis sometimes extend across State borders. For these
reasons, it is suggested that appropriate proxies should be used to capture

15
 poor economic status. Given the existing position regarding data
availability, preponderance of agricultural labourers in the population,
the level of agricultural wages and output per agricultural worker
would reflect the dependence of a region on low income based primary
sector activity. These variables could be supplemented with data relating
to per capita credit and deposits to capture the level of monetization and
saving. We recommend that these five parameters should be used as
criteria for capturing economic status for assessing the backwardness of a
district.

Ø As far as basic health conditions are considered, process and outcome

variables may both have to be taken into account to assess levels of
deprivation across regions. Availability of regular reliable data is a key
driver in the choice of variables but other considerations must also be kept
in mind. Life expectancy at birth, which is a variable in UNDP’s HDI,
may not be very useful as it tends to fluctuate within a narrow range.
Similarly, although fall in the sex ratio is a serious concern that must be
tackled immediately, this calls for publicity campaigns focused on
attitudinal change not resource availability. We consider that the most
useful indicator to capture poor health and nutritional status is the child
mortality rate but data on this parameter is not available at the district
level. Data regarding infant mortality furnished by the SRS is not
generated at the district level, but we have made estimates to supplement
available information relating to 292 districts from the health survey. We
have similarly generated data to complete available statistics at the district
level for the crude death rate, which is a useful variable to capture health
status.

Ø In the case of process parameters, departmental data is available on several

variables linked to availability and use of medical institutions and
personnel as well as to the use of fertility control methods. After
considering them in depth, we believe that focus should be shifted from
population control to service delivery mechanisms with emphasis being
placed on the vulnerable category of women and children. In our view,

16
 full immunization and institutional delivery are the most appropriate
parameters to reflect these concerns and we recommend adoption of these
criteria to identify regions with poor health status.

Ø Educational status can be measured using process and outcome variables.

Literacy is the basic indicator; to focus on the gender gap, we suggest that
the female literacy rate may be taken as the appropriate indicator. As for
process parameters, the appropriate indicators should be the net enrolment
ratio at the elementary school level for vulnerable groups like girls and
Scheduled Castes and Tribes. Unfortunately, however, departmental data
is not readily available although it should be possible to collect it from
States to enable us to apply this parameter. We have been constrained to
use the less satisfactory variable of the gross enrolment ratio and
supplement it with the availability of secondary schooling facilities
with reference to targeted population.

3) Availability of physical infrastructure acts as a major developmental

constraint. We have considered variables relating to road, power, drinking
water, banking services and teledensity looking at availability of data at the
district level. Data regarding road length to area at the district level should be
collected from States by the department for use as a relevant indicator of
regional imbalance. In its absence, we have used the percentage of
households without electricity and of rural households with drinking
water sources at a distance greater than 500 metres as well as the
percentage of households not availing of bank services as indicators of poor
infrastructure. At present, however, data regarding teledensity is not available
at the district level.

4) A robust budget and capacity to raise revenue using tax and non tax handles
are essential when investment has to be done in public goods. States and
areas, which are resource rich and even enjoy reasonable levels of human
development, may be hamstrung by the absence of effective administrative
machinery and adequate experienced personne l. This is the case for example
over much of the northeast, which continues to depend on Central transfers to

17
 meet the costs of daily governance partially because of the lack of systems,
trained staff, procedures and administrative traditions. Such concerns drive
the devolution formulae applied by the Finance Commission for non Plan
equalization grants as well as the Gadgil formula approved by the National
Development Council for general purpose Central assistance for State Plans,
under which 30% of total Central assistance is earmarked for special category
States. The parameters chosen by us to identify backwardness applied
uniformly across the country have brought to light districts within special
category States that require focused attention. We do not, therefore,
recommend the adoption of any additional parameter linked to budgetary
capacity.

An overall proxy for regional backwardness that we would like to adopt

to capture likely low levels of human development is the percentage of
Scheduled Caste and Scheduled Tribe population

2.4.1.2 Selection of the unit for identifying backwardness

Over a half century of developmental experience, we have gradually moved from

tackling regional imbalances at the State level to micro interventions targeted
at districts within States. Today, there are even demands that disparities must
be looked at from the sub-district or block level. Selection of the appropriate
unit for measuring and removing regional inequalities will depend on the level
at which reliable information is available on a regular basis and administrative
arrangements can be effectively put in place to successfully implement
equalization policies and programs.
We have looked at this issue from a pragmatic perspective. Studies made at the State
level and evaluations of major Central ministries dealing with key social
sectors have brought to light vast divergences within districts and pockets of
severe deprivation. An ambitious attempt has been made, for example, in the
recent comprehensive report of the committee on regional backwardness set
up by the Government of Karnataka to rank blocks within the State and pick
up the most deprived for focused attention. Nevertheless, given the present
stage of data availability, it is not feasible to move directly to the block level

18
 for tackling regional imbalance. Eventually, the planning process should
begin at the village level, with district level plans aggregated on the basis of
block and village plans. This can be achieved only when the statistical system
at lower levels achieves the required degree of sophistication and when
administrative reform and capacity building proceed apace. We believe
strongly that it is essential to improve the statistical system to generate block
wise information and proceed as quickly as possible to program
implementation focused at this unit. We have made later on several
suggestions on this issue. Even at the district level, we have already noted the
serious gaps in basic outcome monitoring data. Despite this, we have
generated as far as feasible maps of the developmental status of the country by
zeroing in on districts.
We have ranked districts on the basis of the above 17 chosen parameters relating to
income deprivation, health and educational status and infrastructural
inadequacy, summed up their ranks on the different parameters and arranged
them on the basis of their combined ranking to focus on relative deprivation
levels. We believe that districts ranked lowest on the combined ranking list
must be considered the most backward in the country.

19
 CHAPTER III

3. METHODS OF REDUCING REGIONAL IMBALANCES

Criteria to identify backwardness play a major role in determining the size of funds
transferred to States on different counts. At the sectoral level as well as in
general transfers, indicators of backwardness are being used for directing
public investment towards deprived regions. This is done under Centrally
sponsored schemes, in nonPlan transfers recommended by the Finance
Commission as well as assistance extended by the Planning Commission.
Present mechanisms are considered below:

CENTRALLY SPONSORED SCHEMES

3.2.1 Regional imbalances have been taken into consideration while drafting major
programs in core areas like poverty alleviation and social development.
5 Centrally sponsored schemes in the Rural Development Ministry itself
account for one third of the total CSS outlay of around Rs. 36000 crore in
2004-05 and adopt various indicators of backwardness so that funds can flow
into the most needy areas. These are briefly indicated below:
• SGSY (Department of Rural Development)-Rs. 1000 crore. Funds are
allocated in relation to the incidence of poverty in States but absorption
capacity and special requirements are considered in the course of the year.
• Sampoorna Grameen Rozgar Yojana (Department of Rural Development)-
Rs. 5100 crore. Funds are allocated to States on the basis of the proportion of
rural poor. At the district level, allocation is on the index of backwardness
using the proportion of rural SC/ST population and inverse of per capita
production of agricultural workers (with equal weightage).
• Indira Awaas Yojana (Department of Rural Development)-Rs. 2500 crore.
Funds are allocated to States giving equal weightage to poverty ratio and
housing shortage based on figures of the last census. Proportions of rural
SC/ST population and housing shortage within district to totals are the criteria
adopted for inter-district allocation within a State.
• Accelerated Rural Water Supply Program (Department of Drinking Water
Supply, Ministry of Rural Development)-Rs. 2900 crore. Funds are allocated
on the basis of rural population (weightage 40%), States under DPAP, DDP,
hill area development and special category hill States in terms of rural areas
(35% weightage), not covered / partially covered villages (at 2:1 ratio)-
10%/5% weightage, quality affected villages (40:40:15:5)-10% weightage and
overall water resource availability (unirrigated over irrigated area)
• Drought Prone Areas and Desert Development Programmes of the Department
of Land Resources of the Ministry of Rural Areas and Employment (Rs. 300
and 215 crore respectively) apply to blocks selected as drought prone on the
basis of the Hanumantha Rao committee report in 1994.
The entire gamut of Central sector and Centrally sponsored schemes is being
reviewed in great detail in connection with the specified objective of the
National Common Minimum Program of rationalizing them and transferring
as many as possible to States. An ongoing process of rationalization is already
under way through a subcommittee of the National Development Council and
the issue will also be commented upon during the mid term appraisal of the
Tenth Plan. Centrally sponsored schemes have specific sectoral objectives
and targe ts and the Backward States Grant Fund can be used to supplement
them through a comprehensive macro approach cutting across sectors and
meeting intersectoral requirements. While we are not commenting upon the
criteria adopted in many Centrally Sponsored Schemes, we hope that the
suitability of these parameters and the data on which they are based will be
examined indepth in the midterm appraisal exercise.

NON-PLAN TRANSFERS

3.3.1 The problem of horizontal imbalance among States in fulfilling their

responsibilities has been recognized by the Indian Constitution and provision
made to equalize budgetary capacity through the agency of the Finance
Commission which is appointed as an objective, expert body every five years
to recommend tax shares and transfers from the Centre to the States so that
there is horizontal equity among the citizens of different States. Finance

21
 Commissions have adopted different criteria relating to backwardness to
compute entitlements of States and equalize their budgetary capacities.
Criteria for determining backwardness used by the Eleventh Finance
Commission for distributing tax shares are population, per capita income, area
and index of infrastructure. It has also recommended grants in aid to selected
States, which are left with budge tary gaps after tax devolutions. Since
distribution of resources on the non Plan side for equalizing capacities of
States is being done by a statutory body set up under the Constitution, we do
not propose to comment on the criteria used for this process.

3.4 ALLOCATION OF NORMAL CENTRAL ASSISTANCE FOR STATE

PLANS

3.4.1 The Planning Commission applies the Gadgil formula to assist States to fund
their Annual Plans. The details of the formula are given in Annexure -III.
The criteria that specifically provide for backwardness in the formula are
population and per capita income although these are also allied to variables
that measure the performance of States in different areas including tax effort.
The National Development Council has approved the formula and determined
the criteria. Modification would require achievement of a consensus among
Chief Ministers of different States.

3.5 RSVY

3.5.1 Under the Rashtriya Sam Vikas Yojana which is a new initiative launched in
the Tenth Plan, 147 districts are covered – 115 backward districts and 32
districts affected by left wing extremism. Identification of backward districts
within a State was done on the basis of an index of backwardness using three
parameters with equal weights: the value of output per agricultural worker, the
agricultural wage rate and the percentage of SC and ST population in the
district. From the list of backward districts so identified, State capitals,
districts with urban agglomeration of one million plus and districts, which had
major cities of States, were excluded. The number of districts to be covered in
a State was decided on the basis of the incidence of poverty in the case of non-

22
 special category States and on the basis of population in the case of special
category States with the rider that each State would get at least one district.
Districts affected by left wing extremism have been identified by the Ministry
of Home Affairs on the basis of different criteria such as intensity of left wing
extremist violence, the presence of armed dalams, the spread of active front
organizations of these groups, the extent of proactive measures initiated by the
police and administration, etc. Rs. 15 crores of annual allocation are proposed
for each selected district for a programme to be completed in three years time
to redress regional imbalance District level plans are to be prepared and
approved by an empowered committee chaired by Secretary, Planning
Commission and these are to be implemented and closely monitored. We will
be considering this programme and its future in the context of the proposed
Backward Districts Initiative.

3.6 NATIONAL COMMISSION ON POPULATION

3.6.1 The National Commission on Population has worked out a composite index to
rank 569 districts of the country using the following variables, which it found
relevant to explain the fertility rate of population:
Decadal population growth rate
Percentage of births of order 3 and above (instead of total fertility rate)
Percentage of current users of family planning methods
Percentage of girls marrying below 18 years of age
Sex ratio
Percentage of women receiving skilled attention during deliveries
Percentage of children getting fully immunized
Female literacy
Percentage of villages not covered with pucca roads (estimated)
Percentage coverage of safe drinking water and sanitation (estimated)
Percentage of births registered (estimated)
Percentage of deaths registered (estimated)

3.6.2 One hundred and thirty three districts were identified on the basis of these
variables. A conference was held with the District Magistrates / Collectors of

23
these districts and they were asked to submit District Action Plans. Additional
Central Assistance was given to 67 districts on this basis in 2000-2001 and
2001-02 and the programme has since been discontinued. Fresh initiatives are
separately being considered in the health sector now.

24
 CHAPTER IV

4. THE BACKWARD DISTRICTS GRANT FUND

4.1.1 Over and above existing mechanisms, we believe that there is a strong case for
setting up a Backward Districts Grant Fund, which is a key component of the
National Common Minimum Program and which has been announced by the
Finance Minister in the current year’s budget speech. For optimal results and
effective targeting, this should be operated as a Backward Districts (rather
than a Backward States) Fund to ensure that there is focus on less developed
parts within States, even those that are otherwise considered developed. To
enable the Fund to realize its outcomes at the ground level, it should be
operated throughout the remaining period of the Tenth Plan as well as in the
Eleventh Plan period. Two years after operation, that is at the beginning of the
Eleventh Plan, the working of the Fund should be reviewed and corrections
necessary put into place.

4.1.2 Specific recommendations regarding the coverage and time frame for the
program, modalities of implementation, monitoring and closure are given
below.
4.2 COVERAGE AND TIME FRAME OF THE BACKWARD DISTRICTS
GRANT FUND

4.2.1 The Fund should cover the 115 most backward districts (map 1) identified on
the basis of the methodology given by us in Chapter II excluding Naxalite
affected districts. To this list should be added the 55 districts (map 2)
considered to be Naxalite affected, in which disaffection with low
developmental levels has driven people to violent forms of protest and
opposition. The Backward Districts Grant Fund will replace the current
RSVY and Backward Districts Initiative but action has already been initiated
in selected districts under these two schemes to implement developmental
plans over a three year time frame (of which one year would be completed in
2004-05 for all districts, with districts in which the project was piloted being

25
 further ahead in implementation). To avoid disruption in these programs,
these plans will be completed as originally targeted, but districts now covered
under RSVY, which do not qualify as most backward under the criteria chosen
by us, will not be eligible for further assistance from the Backward Districts
Grant Fund after the close of the Tenth Plan. Forty-six RSVY districts will
not qualify for assistance from the Backward Districts Grant Fund after the
Tenth Plan period is over. During the remaining two years of the Tenth Plan,
however, in all 216 districts will be covered but the number will come down to
170 (map 3) in two years time. Districts affected by Naxalism will also
require specific assistance related to connectivity, livelihood support
programs, land record reforms and governa nce issues which cause public
disaffection and induce citizens to turn towards violent redressal measures.
Map 4 & 5 depict State wise affected districts.
4.2.2 We have suggested the multi-sectoral parameters to be adopted for identifying
backward districts. Choice of variables has been made in a pragmatic manner
keeping in mind ready availability of data and ease of monitoring. In our
view, half the funding under the program should be distributed equally to the
identified districts and the remaining amount allocated on the basis of
population. After two years of operation at the close of the Tenth Plan, the
distribution formula should be modified. One third of the funding should be
distributed equally among the backward districts, a further third on the basis of

26
Map 1

27
Map 2

28
Map 3

29
 Map 4
Most Backward Districts Identified including
Extremist Affected Districts

BIHAR JHARKHAND

Sahibganj
Pashchim Champaran
Godda

Purba Champaran
Pakaur *
Sitamarhi
Sheohar * Kodarma *
Gopalganj Madhubani
Kishanganj Giridih Deoghar
Supaul * Araria Dumka
Chatra *
Siwan Muzaffarpur Darbhanga Garhwa *
Palamu Hazaribag
Saran Purnia
Samastipur SaharsaMadhepura Dhanbad
Vaishali
Bokaro *
Katihar
Khagaria
Begusarai
Buxar * Bhojpur Patna Lohardaga
Bhagalpur
Munger
Nalanda Ranchi
Jehanabad Sheikhpura * Lakhisarai *

Kaimur (Bhabua) * Rohtas

Jamui * Banka *
Nawada Gumla
Aurangabad
Gaya

Pashchimi Sing Purbi Singhbhu

MADHYA PRADESH RAJASTHAN

Morena Bhind

Gwalior
Ganganagar
Sheopur * Datia Hanumangarh*

Shivpuri

Bikaner Churu
Tikamgarh
Jhunjhunun
Neemuch * Chhatarpur Rewa
Guna
Satna Alwar
Sikar
Panna Jaisalmer
Mandsaur
Nagaur Bharatpur
Sidhi
Jodhpur Jaipur Dausa *
Rajgarh Vidisha Dhaulpur
Sagar
Damoh Katni * Karauli *
Ratlam Shajapur
Umaria * Ajmer
Ujjain Bhopal TonkSawai Madhopur
Shahdol Barmer
Jabalpur Pali
Raisen
Sehore Bhilwara Bundi
Narsimhapur Rajsamand *
Jalor
Jhabua Indore Dewas
Dindori * Kota
Dhar Hoshangabad Sirohi Baran *
Mandla Chittaurgarh
Chittaurgarh
Harda * Seoni Udaipur Jhalawar
Chhindwara
West Nimar
Barwani * Betul Balaghat
East Nimar Dungarpur
Banswara

UTTAR PRADESH CHHATTISGARH

Saharanpur
Koriya *
Surguja
Muzaffarnagar
Bijnor

Baghpat *Meerut Jashpur *

Jyotiba Phule
Ghaziabad Korba *
MoradabadRampur
Bilaspur
Pilibhit
Gautam Buddha Bulandshahr Bareilly
Kawardha * Raigarh

Budaun Janjgir - Champa*

Kheri
Aligarh Shahjahanpur
Raipur
Bahraich
Mathura Hathras * Etah
Shrawasti *
Farrukhabad Sitapur Balrampur * Durg Mahasamund *
Hardoi Siddharthnagar Rajnandgaon
Firozabad Mainpuri Maharajganj
Agra Gonda
Kannauj *
Barabanki Sant Kabir Nag
Basti Kushinagar * Dhamtari *
Etawah Lucknow
Auraiya * Unnao Faizabad Gorakhpur
Kanpur Dehat Ambedkar Nagar Deoria Kanker *
Kanpur Nagar Sultanpur
Rae Bareli
Jalaun
Azamgarh Mau
Fatehpur Pratapgarh Ballia
Hamirpur Jaunpur Baster
Ghazipur
Jhansi Sant Ravidas N
Banda Kaushambi *
Mahoba * Varanasi
Allahabad
Chitrakoot *
Chandauli *
Mirzapur
Dantewada*
Lalitpur

Sonbhadra

LEGEND
Most Backward Districts other than Extremist Affected
Extremist Affected

Other

30
 Map 5
Most Backward Districts Identified including
Extremist Affected Districts

ANDHRA PRADESH ORISSA

Adilabad Sundargarh

Mayurbhanj
Jharsuguda *
Nizamabad Srikakulam
Karimnagar Vizianagaram
Kendujhar Baleshwar
Sambalpur
Debagarh *
Medak Warangal Visakhapatnam Bargarh *
Bhadrak *
Khammam Anugul *
Hyderabad Sonapur * Jajapur *
Dhenkanal
Rangareddi
Baudh *
East Godavari
Nalgonda Balangir Kendrapara *
West Godavari Nuapada * Cuttack

Jagatsinghapur *
Mahbubnagar Krishna Nayagarh *
Guntur Kandhamal Khordha *
Puri
Kalahandi
Ganjam
Nabarangapur *
Kurnool Prakasam
Rayagada *
Gajapati *

Koraput
Anantapur Cuddapah Nellore

Malkangiri *

Chittoor

MAHARASHTRA NORTH-EASTERN STATES

Upper Siang *
Dibang Valley
Nandurbar *
Upper SubansiriWest Siang
Lohit
East Siang
Dhule Amravati Lower Subansiri
Nagpur Gondiya * DhemajiTinsukiaChanglang
Jalgaon
Bhandara North Tawang Dibrugarh
Akola West Kameng Papum Pare *
Wardha Lakhimpur
Buldana East Kameng
West East Sibsagar Tirap
Nashik South Jorhat Mon
Washim * Sonitpur
Golaghat
Aurangabad Yavatmal Chandrapur NalbariDarrang Mokokchung
Kokrajhar Wokha
Thane Barpeta Nagaon Karbi Anglong Tuensang
Jalna Gadchiroli Bongaigaon Zunheboto
Hingoli * KamrupMarigaon
DhubriGoalpara Dimapur *Phek
Ri Bhoi *
Mumbai (Suburban) * Ahmadnagar Parbhani East Garo Hills North Cachar HillsKohima
Mumbai
Bid Nanded West Garo Hills West Khasi HillsJaintia Hills Senapati
East Khasi Hills Ukhrul
Raigarh Pune South Garo Hills * Tamenglong
HailakandiCachar Bishnupur
Thoubal
Latur Karimganj
Osmanabad Churachandpur Chandel
North TripuraKolasib *
West Tripura
Satara Solapur Dhalai * Mamit * Aizawl
Champhai *
Ratnagiri
South Tripura Serchhip *
Sangli
Lunglei

Kolhapur Lawngtlai Saiha *

Sindhudurg

GUJARAT WEST BENGAL

Darjiling
Jalpaiguri

Banas Kantha
Koch Bihar

Patan *
Kachchh
Mahesana Sabar Kantha
Uttar Dinajpur

Gandhinagar DakshinDinajp

Maldah
Surendranagar Kheda Dohad *
Ahmadabad Panch Mahals

Rajkot
Anand *
Jamnagar
Vadodara Murshidabad

Porbandar * Birbhum
Bharuch
Narmada *
Amreli Bhavnagar
Nadia
Junagadh Barddhaman

Surat Puruliya
Bankura

Hugli
Navsari *
The Dangs
North Twenty F
HaoraKolkata
Valsad
Medinipur South Twenty

LEGEND
Most Backward Districts other than Extremist Affected
Extremist Affected

Other

31
 List of 170 Districts identified under Backwardness including 55
Extremist Affected Districts (State -wise)
No. State Name District Name
1 ANDHRA PRADESH Adilabad
2 ANDHRA PRADESH Karimnagar
3 ANDHRA PRADESH Khammam
4 ANDHRA PRADESH Mahbubnagar
5 ANDHRA PRADESH Medak
6 ANDHRA PRADESH Nalgonda
7 ANDHRA PRADESH Nizamabad
8 ANDHRA PRADESH Warangal
9 ASSAM Barpeta
10 ASSAM Cachar
11 ASSAM Dhemaji
12 ASSAM Goalpara
13 ASSAM Hailakandi
14 ASSAM Karbi Anglong
15 ASSAM Kokrajhar
16 ASSAM Marigaon
17 BIHAR Araria
18 BIHAR Aurangabad
19 BIHAR Banka *
20 BIHAR Begusarai
21 BIHAR Bhagalpur
22 BIHAR Bhojpur
23 BIHAR Buxar *
24 BIHAR Darbhanga
25 BIHAR Gaya
26 BIHAR Gopalganj
27 BIHAR Jamui *
28 BIHAR Jehanabad
29 BIHAR Kaimur (Bhabua
30 BIHAR Katihar
31 BIHAR Khagaria
32 BIHAR Kishanganj
33 BIHAR Lakhisarai *
34 BIHAR Madhepura
35 BIHAR Madhubani
36 BIHAR Munger
37 BIHAR Muzaffarpur
38 BIHAR Nalanda
39 BIHAR Nawada
40 BIHAR Pashchim Champ
41 BIHAR Patna
42 BIHAR Purba Champara
43 BIHAR Purnia
44 BIHAR Rohtas

32
 List of 170 Districts identified under Backwardness including 55
Extremist Affected Districts (State -wise)
No. State Name District Name
45 BIHAR Saharsa
46 BIHAR Samastipur
47 BIHAR Saran
48 BIHAR Sheikhpura *
49 BIHAR Sheohar *
50 BIHAR Sitamarhi
51 BIHAR Supaul *
52 BIHAR Vaishali
53 CHHATTISGARH Baster
54 CHHATTISGARH Dantewada*
55 CHHATTISGARH Jashpur *
56 CHHATTISGARH Kanker *
57 CHHATTISGARH Kawardha *
58 CHHATTISGARH Korba *
59 CHHATTISGARH Mahasamund *
60 CHHATTISGARH Rajnandgaon
61 CHHATTISGARH Surguja
62 GUJARAT Dohad *
63 JHARKHAND Bokaro *
64 JHARKHAND Chatra *
65 JHARKHAND Deoghar
66 JHARKHAND Dhanbad
67 JHARKHAND Dumka
68 JHARKHAND Garhwa *
69 JHARKHAND Giridih
70 JHARKHAND Godda
71 JHARKHAND Gumla
72 JHARKHAND Hazaribag
73 JHARKHAND Kodarma *
74 JHARKHAND Lohardaga
75 JHARKHAND Pakaur *
76 JHARKHAND Palamu
77 JHARKHAND Pashchimi Sing
78 JHARKHAND Ranchi
79 JHARKHAND Sahibganj
80 MADHYA PRADESH Balaghat
81 MADHYA PRADESH Barwani *
82 MADHYA PRADESH Chhatarpur
83 MADHYA PRADESH Damoh
84 MADHYA PRADESH Dindori *
85 MADHYA PRADESH Guna
86 MADHYA PRADESH Jhabua
87 MADHYA PRADESH Katni *
88 MADHYA PRADESH Mandla

33
 List of 170 Districts identified under Backwardness including 55
Extremist Affected Districts (State -wise)
No. State Name District Name
89 MADHYA PRADESH Panna
90 MADHYA PRADESH Rajgarh
91 MADHYA PRADESH Rewa
92 MADHYA PRADESH Seoni
93 MADHYA PRADESH Shahdol
94 MADHYA PRADESH Sheopur *
95 MADHYA PRADESH Shivpuri
96 MADHYA PRADESH Sidhi
97 MADHYA PRADESH Tikamgarh
98 MADHYA PRADESH Umaria *
99 MADHYA PRADESH West Nimar
100 MAHARASHTRA Bhandara
101 MAHARASHTRA Chandrapur
102 MAHARASHTRA Gadchiroli
103 MAHARASHTRA Gondiya *
104 MANIPUR Chandel
105 MANIPUR Churachandpur
106 MEGHALAYA Ri Bhoi *
107 MEGHALAYA South Garo Hil
108 MEGHALAYA West Garo Hill
109 NAGALAND Mon
110 NAGALAND Tuensang
111 NAGALAND Wokha
112 ORISSA Balangir
113 ORISSA Baudh *
114 ORISSA Debagarh *
115 ORISSA Gajapati *
116 ORISSA Ganjam
117 ORISSA Kalahandi
118 ORISSA Kandhamal
119 ORISSA Kendujhar
120 ORISSA Koraput
121 ORISSA Malkangiri *
122 ORISSA Mayurbhanj
123 ORISSA Nabarangapur
124 ORISSA Nuapada *
125 ORISSA Rayagada *
126 ORISSA Sonapur *
127 RAJASTHAN Banswara
128 RAJASTHAN Barmer
129 RAJASTHAN Chittaurgarh
130 RAJASTHAN Dungarpur
131 RAJASTHAN Jaisalmer
132 RAJASTHAN Jalor

34
 List of 170 Districts identified under Backwardness including 55
Extremist Affected Districts (State -wise)
No. State Name District Name
133 RAJASTHAN Karauli *
134 RAJASTHAN Sawai Madhopur
135 RAJASTHAN Tonk
136 UTTAR PRADESH Ambedkar Nagar
137 UTTAR PRADESH Bahraich
138 UTTAR PRADESH Balrampur *
139 UTTAR PRADESH Banda
140 UTTAR PRADESH Barabanki
141 UTTAR PRADESH Basti
142 UTTAR PRADESH Budaun
143 UTTAR PRADESH Chandauli *
144 UTTAR PRADESH Chitrakoot *
145 UTTAR PRADESH Etah
146 UTTAR PRADESH Farrukhabad
147 UTTAR PRADESH Fatehpur
148 UTTAR PRADESH Gonda
149 UTTAR PRADESH Hamirpur
150 UTTAR PRADESH Hardoi
151 UTTAR PRADESH Jalaun
152 UTTAR PRADESH Kaushambi *
153 UTTAR PRADESH Kheri
154 UTTAR PRADESH Kushinagar *
155 UTTAR PRADESH Lalitpur
156 UTTAR PRADESH Maharajganj
157 UTTAR PRADESH Mahoba *
158 UTTAR PRADESH Mirzapur
159 UTTAR PRADESH Rae Bareli
160 UTTAR PRADESH Sant Kabir Nag
161 UTTAR PRADESH Shrawasti *
162 UTTAR PRADESH Siddharthnagar
163 UTTAR PRADESH Sitapur
164 UTTAR PRADESH Sonbhadra
165 UTTAR PRADESH Unnao
166 WEST BENGAL Bankura
167 WEST BENGAL Dakshin Dinajp
168 WEST BENGAL Medinipur
169 WEST BENGAL Puruliya
170 WEST BENGAL Uttar Dinajpur

35
population and another one third on the basis of performance against predetermined
targets. This is expected to act as an incentive for realizing the desired outcomes and
reward States and districts that put in maximum effort. Releases made to backward
districts will be treated as non- lapsable so that they can be utilized as and when
absorptive capacity is created.

4.3 MODALITIES OF IMPLEMENTATION

4.3.1 For convergence and flexibility, the district budget and the village composite
plan concepts should be used to maximize results using funds available from
all schemes and untied funds from other sources. PRI institutions should be
integrated with the system and key operational staff should be monitored by
them. Non-Plan budgetary support should be provided to cover salary
expenditure so that Plan funds are not diverted to this end. We have further
elaborated the manner in which district and village level budgets and plans
should be prepared in the later chapters. Other policy modifications required in
planning and governance are indicated below:
Regional, inter-district infrastructure or amount needed for area development
purposes should be built out of normal departmental funds. These needs
should be identified and concerned departments mandated to earmark a
percentage of their Plan funds for this purpose using a suitable administrative
system. This is desirable for ensuring accountability at the district and sub-
district levels.
Funds allocated for improving deprived areas will be utilized as planned only if
trained, competent personnel are posted to work in such regions. It is essential
that this aspect is considered in depth and incentive policies introduced so that
the most dedicated staff are motivated to devote two to three years of their
careers in backward regions, vacancies of key personnel avoided and support
services made available. A package of incentives should be introduced to
encourage the best personnel to work in backward areas with utmost devotion.
The special facilities at present being given by the Central governme nt for
persons serving in northeastern States should be extended to those working in
the most backward districts. Apart from housing and educational facilities (as
well as adequate security in Naxalite affected districts), a 25% special

36
 allowance should also be given to government employees working in
backward districts. Medical personnel could be attracted to these areas if they
are assured admission to postgraduate courses after serving three years.
Persons posted to backward districts must be retained for full three-year tenure
and relieved immediately after this is over. They should also be permitted to
retain official quarters in State headquarters during the posting period.
Finally, a successful tenure in backward districts should be treated as a
desirable qualification for future postings, promotions and career progression.
Continuous upgradation of skills must form part of the program content. At least 10%
of the total fund should be earmarked for a separate capacity building plan and
financing systems and information technology upgraded. The process of
setting up networked information and delivery systems should be put in place
from the initial stage itself side by side with other basic infrastructure and
improved administration.

4.4 MONITORING OUTCOMES

4.4.1 A major impediment in putting in place an effective selection mechanism for

backward districts and monitoring outcomes under schemes targeted to needy
areas has been the lack of useful, regular and updated information. We
believe that schemes and programs of all government levels cannot be
operationalised without giving the utmost priority to improvement of the
statistical system in key areas. The indicators on which data must be regularly
collected, analysed and made available for planners and implementing
departments are briefly indicated below along with the agency that could be
strengthened for performing this task:
Data relating to the incidence of poverty is available on a regional basis through
occasional (quinquennial) sample sur veys conducted by the NSSO and
through the head count run from time to time by the Rural Development
Department. Both should be strengthened. The NSS should be required to
obtain and analyse data on a State wise disaggregated basis by increasing
sample size. It should actively involve State agencies, train and guide them so
that State samples can be used along with Central tables and district wise
results obtained. The census of the Rural Development department should be

37
 operated on a two-stage basis, us ing some external indicators of income to
zero in on the most deprived households
GSDP data is not available regularly for all States in a reasonably reliable manner.
The CSO should be empowered and strengthened preferably by creating a
Statistical Commission and endowing it with powers to direct, guide and
control State Statistical departments so that comparable GSDP data for the
country is available with only a year’s delay. The recommendations of the
Rangarajan Committee in this regard should be implemented very early.
With regard to outcome variables relating to educational status, data regarding literacy
will have to be collected with greater regularity and frequency than at present
as we rely today only on decennial census data. This can be done either
through the NSS or through special surveys undertaken from time to time.
Health and demographic outcomes are best identified with regularity by strengthening
the Compulsory Registration System for Births and Deaths operating
throughout the country, not by depending on Sample Registration System data,
which is meant only to check the effectiveness of the main system. SRS
sampling is not large enough to generate district level data and this is a major
gap in our reporting system. The CRS should be strengthened, upgraded,
transferred for operation to the most effective agency (which may be the
medical rather than the revenue department) and computerized to obtain online
results for even the smallest administrative unit. Panchayat Raj institutions
should be actively involved in this process and they should be enabled to use
the data for planning purposes
Departmental data on education (dropouts, enrolments and availability of physical
facilities and teachers) and health (availability of medical personnel and
institutions and effectiveness of interventions relating to family welfare) is not
being collected and analysed through networking among States; this must be
strengthened.
Data relating to agricultural and industrial productivity as well as the spread of
infrastructure must also be collated accurately.

4.4.2 We believe that the districts selected under the Backward States’ Grant Fund
could be used as pilot areas for establishing the statistical system indicated by
us on priority basis so that outcomes can be monitored regularly. Information

38
 requirements and system to be built at the village level have been covered
extensively in a later chapter.

4.4.3 Audit and evaluation must be done on a concurrent basis using modern IT
tools. Field level feedback must be obtained through independent agencies
(experts, CAs etc.) and NGOs. The model followed in District Poverty
Initiatives Programme districts could be used. Local fund audit should be
reviewed by the CAG and strengthened with adequate training. The unit cost
of delivery should be specified and provision made for regional variations.
Outcome indicators should be enumerated for each backward district
mentioning the current benchmark and the targeted level.

39
 CHAPTER V

5. REDRESSAL OF REGIONAL IMBALANCE TRHOUGH DISTRICT

BUDGETING.

5.1.1 The strategy for tackling regional imbalance through the mechanism of the
Backward Districts Grant Fund has to be made operational through a process
of district budgeting so that plans formulated for development of backward
areas reflect realistically the perceived needs and aspirations of the population.
The Planning Commission has advocated the concept of district planning as an
integral part of the planning process ever since the first guidelines for district
planning were issued as early as 1969. These and subsequent attempts to
bring in effective decentralization as recommended by a number of
committees met with limited success. District NIC Centres were established
to maintain and provide district level data to develop a strong information
base. But the most dramatic development came in 1992 with the 73rd and
74th Constitutional Amendment Acts, which conferred constitutional status on
Panchayati Ra j Institutions by envisaging the establishment of a democratic
decentralized development process through peoples’ participation in decision-
making, implementation and delivery. To achieve these objectives, the
Constitution provides for devolution of powers and responsibilities to
Panchayats at appropriate levels. Twenty-nine subjects listed in the Eleventh
Schedule of the Constitution were also identified for devolution to the
Panchayati Raj Institutions.

5.1.2 Article 243 ZD indicates that committees for district planning must be set up
as follows, “There shall be constituted in every State at the District level a
District Planning Committee to consolidate the Plans prepared by the
Panchayats and the Municipalities in the district and to prepare a draft
development Plan for the district as a whole”. However, even a dozen years
after the coming into force of the amendments, panchayati raj institutions
(PRIs) have not been empowered and enabled to function in the manner
envisaged for them. Where the 29 subjects listed for transfer to PRIs in the

40