Preliminary Demography of 2011 Population Census in India Aalok

Preliminary Demography of2011 Population Census

in India

Aalok Ranjan ChaurasiaProfessor

‘Shyam’ Institute82, Aradhana NagarBhopal, MP-462003

Indiawww.shyaminstitute.in

July 2011

http://www.shyaminstitute.in

‘Shyam’ InstituteMudian Ka Kuan,Datia, MP-475661, India91-752-2234522

www.shyaminstitute.in

Preliminary Demography of 2011 Population Census in India

© 2011 Shyam Institute

All rights reserved. No part of the publication can be reproduced or transmitted in any form orby any means including photocopying, recording or any information storage and retrievalsystem without permission in writing from MLC Foundation.

ISBN: 978-93-82411-02-4

Rs 800

In the memoryof

‘Amma’ and ‘Daddy’

Contents

1 Introduction 1

2 Population Size and Growth 15

3 Population Distribution 41

4 Age Composition 63

5 Sex Composition 83

6 Inter-state Migration 117

7 Conclusions 129

References 133

Statistical Tables 135

Provisional Population Totals 220

1Introduction

India is the largest democracy and the second most populous country of the world. It accounted

for more than 17 per cent of the world’s population in 2010 according to the estimates prepared

by the United Nations (United Nations, 2011). This 17 per cent of the world population lives on

less than 2.5 per cent of the total land area of the planet Earth. The population of the world is

estimated to have increased at the rate of 1.22 per cent per year between 2000 and 2010, adding,

on average, about 79 million persons every year. India accounted for very close to 22 per cent of

this increase. India’s contribution to the increase in the world population has been the largest,

even larger than the contribution of China, the most populous country in the world today (United

Nations, 2011). The medium variant of population projections prepared by the Population

Division of the United Nations suggests that population of India is the most likely to increase to

1614 million by the year 2050. At that time, India will account for almost 19 per cent of the

projected world population of around 9150 million. This means that out of the projected 2854

million increase in the world population during the 50 years between 2000 and 2050, more than

571 million or almost 19 per cent will be confined to India alone. The population projections

prepared by the United Nations also suggest that by the year 2025, India is most likely to become

the most populous country of the world, surpassing China. Obviously, population stabilisation

in the world as a whole will depend, to a very significant extent, on the pace of population

transition in India in the years to come. According to the medium, most likely, variant of

population projections prepared by the United Nations, there is little possibility that Indian will

be able to achieve the goal of population stabilisation before the year 2060 and not around the

year 2045 as stipulated in the National Population Policy (Government of India, 2000).

1

Preliminary Demography of India

After the 2001 population census, Government of India has taken a number of key policy

initiatives that have relevance to the future population growth in the country. The first of these

initiatives was the National Population Policy which was announced in the year 2000 and which

aimed at achieving zero population growth in the country by the year 2045 through reducing

fertility to the replacement level by the year 2010 (Government of India, 2000). In the year 2005,

India launched the National Rural Health Mission which aimed at architectural corrections in the

public health care delivery system of the country so as to meet the health and family welfare

needs of the people, especially, people living in rural and remote areas (Government of India,

2005). At the same time, the process of economic reforms that started in 1990 continued with a

varying pace. A revival of economic reforms and better economic policies during the first decade

of the present century has accelerated the economic growth. Today, India is the second fastest

growing major economy of the world.

These facts explain the special interest with which the results of the 2011 population census in

India were awaited. Provisional results of the 2011 population census have now been released

(Government of India, 2011). These figures supply basic data about population size and growth,

population distribution and age and sex composition of the population along with the level of

literacy for the country as a whole as well as for its constituent states and Union Territories and

for districts within the states and Union Territories. The synergistic possibilities of analysing

these data in the context of planning and programming for population transition and social and

economic development in the country and its constituent administrative units are truly

remarkable. Such analysis can transform the data available through the population census into

estimates of selected indicators of demographic and development dynamics to facilitate evidence-

based population and development planning and programming right up to the district level. The

importance of the population census in population and development planning may be judged

from the fact that, at the district level, population census is the only source of data for analysing

the population scenario and social and economic development situation and setting up the

priorities for social and economic development programmes and activities.

This monograph analyses the provisional data of the 2011 population census to present a first

hand perspective of the prevailing demographic situation in India and highlights the challenges

faced by the country in the context of population transition. The analysis is primarily confined

to the spatial analysis - analysis across administrative units - of selected population related issues

for which data are available through the 2011 population census. There have also been efforts

to analyse the change in selected population related variables between 2001 and 2011 for which

data at two points of time are available. At the 2001 population census, there were 35 states and

2

Introduction

Union Territories and 595 districts in the country. There has been no change in the number of

states and Union Territories at the 2011 population census but the number of districts has been

increased to 640. Analysis of the trends in some aspects of the population situation at the district

level, therefore, is not possible at present as it requires redistribution of the data collected at the

2001 population census across the 640 districts in the country as they existed at the 2011

population census.

The monograph is divided into six chapters in addition to the present introduction and the

customary epilogue. Each chapter of the monograph focusses upon one dimension of the

population situation at the country, state and district level on the basis of the provisional figures

of the 2011 population census. Chapter two of the monograph analyses the size and growth of

the population at the national, state/Union Territory and district level while the third chapter deals

with the issue of the distribution of the population across administrative units - states/Union

Territories and districts in the country. The fourth chapter carries out a preliminary analysis of

the age composition of the population whereas chapter five is devoted to the analysis of the sex

composition of the population including the sex composition of the population aged 0-6 years.

The sixth chapter of the monograph attempts a preliminary analysis of inter-state movement of

population during the period 2000 through 2011 on the basis of the estimated and enumerated

population of the country. Finally, the epilogue of the monograph highlights some salient

findings of the 2011 population census in the context of population transition and social and

economic development in the country and in its constituent states/Union Territories and districts

within the states/Union Territories.

An integral feature of the monograph is to present selected population-related indicators for all

the 640 districts of the country as they existed at the time of the 2011 population census on the

basis of provisional data of the 2011 population census. Although, the Registrar General and

Census Commissioner of India has released provisional data for all the 640 districts of the

country on the basis of the 2011 population census, yet district level analysis of these data has

been carried out in a limited sense at the state/Union Territory level only and has been released

as state/Union Territory specific Paper 1 of the 2011 population census. The Registrar General

and Census Commissioner of India has not carried out district level analysis at the national level.

Moreover, a review of the district level analysis carried out by different states and Union

Territories of the country reveals that there has been little uniformity even in the limited analysis

that has been carried out at the district level in different states and Union Territories of the

country. This monograph presents district level analysis of the provisional data of the 2011

population census for all the 640 districts of the country.

3


History of the Population Census in India

The history of the population census in India dates back to ancient times. The 'Rig-Veda' reveals

that some kind of population count was maintained in the ancient India. The celebrated

'Arthashastra' by 'Kautilya' written in the 3rd Century BC prescribed the collection of population

statistics as a measure of state policy for taxation. It contained a detailed description of methods

of conducting population, economic and agricultural censuses. During the regime of the Mughal

king Akbar, the administrative report 'Ain-e-Akbari' included comprehensive data pertaining to

population, industry, wealth and many other characteristics. In the recent times, a systematic and

modern population census, in its present form, was conducted non synchronously between 1865

and 1872 in different parts of the country. This effort culminated in the population census of

1872 which is popularly labelled as the first population census in India. However, the first

synchronous census in India was held in 1881 which provided the most complete and continuous

demographic record for any comparable population. Since then, the population census is being

conducted after every ten years in the country. The unbroken series of the decennial population

census in India, now spanning more than a century, provides an extraordinary storehouse of

information for demographic analyses. The population census in India has collected information

on such aspects as population size and growth, population distribution across administrative

units, population structure, etc. The population census in India has also collected information

related to such issues as housing conditions, migration, social class and residence structure,

literacy, religion, physical deformities, sex, civil conditions, etc. Another focus area of the

population census in India has been the occupational classification. The 1881 census adopted 6

classes, 18 orders, 75 sub-orders and 480 groups of occupations, while the 1891 census adopted

a set of 478 occupations divided into 7 classes, 24 orders and 77 sub-orders which was improved

upon at the 1901 population census by 521 occupations divided into 8 classes, 24 orders and 79

sub-orders. The classification adopted at the 1901 population census also made an exhaustive

analysis of social class specific occupations.

An innovative feature of the decennial population count in India is that it has never been bounded

hand-and-foot to the tradition and has never taken shelter 'behind an official wall of infallibility'.

Rather, every population census in the country has broken new grounds without losing

comparability with the previous census. The population census in India has always paid a good

deal of attention to the contemporary situation and the requirements of the government while

trying to keep pace with advanced census quests. In short, it has never rested on its oars, but has

always been the most fruitful single source of information on population of the country and it’s

constituent political and administrative units - states and Union Territories, districts, sub-districts,

towns and villages.

4

Introduction

The first population census in the independent India was conducted in 1951. The report of the

1951 population census attempted, for the first time, analysis of the past changes in the size and

structure of the population and pointed out the implications of these changes to the level of living

of the people. The report also recommended a reduction in the birth rate for accelerating the

social and economic progress in the country. The 1951 population census also attempted, for the

first time in the history of the population census in India, an assessment of the accuracy of the

census count by carrying out the post-enumeration check.

Since 1951, information requirements of different government departments including the

Planning Commission and other agencies necessitated the expansion of the scope of the decennial

population census and the analysis of the data available through the census. A novel feature of

the 1961 population census was large number of ancillary studies relating to rural craft, fairs and

festivals and ethnographic surveys. At the 1971 population census, the census schedules were

further modified. New features of the 1971 population census included (i) data on current

fertility, (ii) internal migration, and (iii) revamping of economic questions. The main activity of

a person was ascertained according to the time the person spent as a worker producing goods and

services or as a non-worker. A new concept of 'standard urban Area' was also introduced at the

time of the 1971 population census.

The population census in India has not been a mere head count of the people. The data available

through the population census in the country have been analysed to present not only the

demographic but also the social, cultural and the economic profile of the country and its

constituent states, Union Territories and districts. The data available through the population

census have also been used for the formulation of development policies and planning and

programming of development activities and programmes. The data available from the population

census and have been widely used by national and international agencies, researchers and

scholars, journalists and philanthropists and even by the business community. The census data

have also been used for such purposes as delimitation of electoral constituencies and affirmative

action such as reservation. The data available through different population censuses have always

been analysed and interpreted in an interesting manner to highlight the demographic, social,

cultural and development diversity. These analyses and interpretations have always been products

of scholarship. A large number of experts have been associated with the analysis of the diverse

nature of the data available through the population census. These analyses have often been the

only authentic source of the social, cultural and economic conditions of the people and the

demographic dynamics, especially at the local level. The decennial population census is an

indispensable part of the statistical system in India.

5


Table 1.1

Reference date and census methodology in India

Census Reference date Methodology

1881 17 February de facto (Synchronous)th


1901 1 March de facto (Synchronous)st

1911 10 March de facto (Synchronous)th

1921 18 March de facto (Synchronous)th


1941 1 March Extended de facto (Synchronous)st



1971 1 April Extended de facto (Synchronous)st





Source: Government of India (2011).

The organisation of the decennial population census in India is governed by the Census Act of

1948. Till 1951, the organisation responsible for conducting the population census in the country

functioned like the phoenix which means that the organisation used to come into existence just

on the eve of the population census and was wounded up as soon as the census operations were

over, usually within two or three years of its creation. With the enactment of the Census Act in

1948, a permanent nucleus for conducting the population census at the national level was created

which made it possible to continue activities related to the population census even during the

inter-census period. Subsequently, permanent establishments have also been created at the state

level. However, at the district level, the phoenix approach continues to exist so that there is

hardly any capacity to analyse of the data collected during the population census at the local

(district) level. Lack of analytical capacity at the district level severely limits the use of census

data for local level planning and programming of development activities.

6

Introduction

The population census in India is conducted on the basis of extended de facto canvasser method.

In this method, data are collected from every individual by visiting the household and canvassing

the same questionnaire all over the country during a specific period. The count is then updated

to the reference date and time by conducting a revision round. In the revision round, any change

in the entries that arise on account of births, deaths and migration between the time of

enumerators, visit and the reference date/time is noted and the record is updated. This approach

is a modification of the synchronous de facto method that was used till 1931 wherein the census

count was conducted throughout the country on a single night. This method, was not only costly

but it also required mobilisation of an extremely large force of enumerators on the day of

enumeration. In a large and diverse country like India, mobilising millions of enumerators for

counting the people on one single night was found extremely challenging and so this method was

replaced by the current method in 1941.

The census operations in India are carried out in two phases. In the first phase, house listing is

done and a census of all households is carried out. The house list prepared during the listing

operation serves a sound frame for population count. On the other hand, the household census

is carried out to collect information about the purpose for which the household is used. In

addition, such information as material used in constructing the house and facilities available in

houses being used for residential purposes such as availability of drinking water, sanitation

facilities including availability of the latrine and availability of the electricity are collected. Since

1981, there has been an attempt to collect information about a specific set of household assets

available in the residential households and the use of banking facilities by household members.

This information has been used along with the information about household facilities to measure

and analyse the living conditions of the people.

Right since its inception, the population census in India has evolved as a descriptive statistical

system, conceived as a general instrument of measurement of change through decennial

operations, delineating demographic, social and economic features of India (Mitra, 1973). There

have been efforts to transform the population census in India into a professional and analytical

statistical system but these efforts could not succeed because of the strength of the original

incrustation. One reason probably and so obviously is that the population census was conceived

as an aid to the general administration system in the country and therefore has remained adjunct

to the normal administrative machinery at the district, state/Union Territory and national level.

The analysis of the huge data collected through the population census has generally been left to

individual researchers in such disciplines as demography, sociology, economics, etc. for analysis

and, therefore, utilisation of the census data remains, at best, limited.

7


A unique feature of the data available through the population census is that it is distributive in

nature in the sense that the count at the national level can be distributed across states and Union

Territories and the count at the state/Union Territory level can be distributed across the districts

within the state/Union Territory. This process can be extended right up to the village/municipal

ward level. Alternatively, the count at the village/municipal ward level can be added up to the

count at the district, state/Union Territory and the national level. An implication of this

distributive property of the census data is that it is possible to estimate the contribution of the

situation at the lower level administrative units to the situation at the upper level administrative

units. For example, it is possible to estimate how the sex composition of the population in a

village or municipal ward contributes to the sex composition of the population at the district level

or the age composition of the population in a state/Union Territory contributes to the age

composition at the national level. There has however been little attempt to analyse the census

data in this context. Instead, the analysis of the census data has been confined to estimating such

indicators as the population sex ratio or the population in a certain age group. In this approach,

it is not possible to explore how the population sex ratio in a state or Union Territory influences

the population sex ratio at the national level or the age composition of the population in a village

influences the age composition of the population of the district. The preoccupation with the

description of the census data has resulted in a gross neglect of the analysis of the census data

which is necessary through the perspective of development planning and programming.

The 2011 Population CensusThe population census 2011 was the 14 since 1881 and the 7 in the independent India. Theth th

canvassing of the questionnaire of the 2011 census was done during the period 9 February 2011th

through 28 February 2011 while the revision round was conducted during the period 1 Marchth st

2011 through 5 March 2011. An exception to this schedule was made in selected areas of theth

country which were snow bound during the month of February. In these areas, canvassing of the

questionnaire was done during the period 11 September through 30 September 2010 while theth th

revision round was conducted during 1 October through 5 October 2010. The count was thenst th

updated to the reference moment of 00:00 hours of 1 March 2011 (Government of India, 2011). st

Two schedules were canvassed during the 2001 population census - house listing schedule and

household scheduled. The house listing schedule collected the following information:

• Predominant material of floor, roof and wall of the house.

• The purpose for which the house is being used.

• If used wholly or partially as residence then total number of persons normally residing in the

household and the name of the head of the household and her/his sex and social class.

8

Introduction

• In case of residential households

< Ownership of the household

< Number of dwelling rooms

< Number of married couples living in the household

< Main source of drinking water

< Availability of drinking water source

< Main source of lighting

< Latrine within the premise

< Waste water disposal

< Bathing facility available within the premise

< Availability of kitchen

< Fuel used for cooking

< Radio/Transistor

< Television

< Computer/Laptop

< Telephone/Mobile phone

< Bicycle

< Scooter/Motorcycle/Moped

< Car/Jeep/Van

< Use of banking services by household members.

On the other hand, the household schedule collected the following information for each member

of the household:

< Name of the member of the household

< Relationship with the head of the household

< Sex

< Date of birth and age

< Current marital status

< If married, age at marriage

< Religion

< Social class (Scheduled Castes/Scheduled Tribes)

< Any disability

< Mother tongue

< Other languages known

< Literacy status

< Status of school attendance

< Highest level of education attained

9


< Work status during one year prior to the census

< Economic activity in which involved

< Occupation

< Birth place

< Place of last residence

< Reasons for movement

< Duration of stay in the present place of residence

< Total number of children currently surviving

< Total number of children ever born

< Live birth in the last year.

The provisional figures of the 2011 population census released by the Census Commissioner of

India include total count of the people of all ages by sex, total count of the people in the age

group 0-6 years and the total count of the people who were literate - able to read and write with

understanding. These data are available for the country as a whole, for its 35 states and Union

Territories and for its 640 districts. This information constitutes the basic data set for the present

monograph.

Methodology

The present monograph incorporates an alternative approach to the analysis of the census data

which is built upon the distributive or the additive property of census counts across the

administrative units. Since the provisional figures of the 2011 population census have been

provided up to the district level only, the approach attempts to analyse how the situation

prevailing at the district level contributes to the situation that prevails at the country level. This

is done by adopting a two-dimensional approach of the analysis. The first dimension of this

approach captures how the situation prevailing at the district is different from the situation that

prevails at the country level. This difference is a reflection of the intensity of the situation

prevailing in a district relative to the situation prevailing at the country level. The second

dimension, on the other hand, captures the extent to which the given situation prevails or the

extensiveness of the situation. A combination of intensity and extensiveness then gives an idea

about the distribution across administrative units. This approach takes into account the

distributive property of the census data and establishes the link between the situation at lower

level administrative units with the situation at the upper level administrative units. All

measurements in this approach are in relative terms - the situation in a district relative to the

situation in the country. The use of the relative measures ensures that the indicators used for the

analysis have additive and multiplicative properties.

10

Introduction

c dIf P denotes the count of the people at the upper level administrative unit and P denotes the

count of the people at the lower level administrative units, then, it is obvious that

c dP = 3P � d0c, (1.1)

The most simple and straightforward measure of the size or the extensiveness population in a

lower level administrative unit d in relation to other lower level administrative units may then

be defined as the proportion of the population in the lower level administrative unit to the

population in of the upper level administrative unit. In other words, a measure of the relative size

of the population or an index of the extensiveness of population in the administrative unit d may

be defined as

dc d cE = P /P � d0c. (1.2)

It is obvious that

dc3E = 1 for all d0c (1.3)

On the other hand, the relative gravity or intensity of a demographic variable V in a lower level

administrative unit d in relation to other lower level administrative units may be defined in terms

of the ratio of the value of the variable V for the lower level administrative unit d to the value of

d cthe variable V for the upper level administrative unit c (V /V ). The relative gravity or intensity

of the demographic variable V in a lower level administrative unit in relation to other lower level

administrative units may now be measured through the index of intensiveness which is defined

as

dc(v) d cI = log (V /V ) for all d0c. (1.4)

d c dc(v) where log represents the logarithm to the base 10. It is obvious that when V /V = 1, I = 0;

d c dc(v) d c d(v) dc(v)when V /V > 1, I > 0 and when V /V < 1 I < 0. When I > 0, the variable V is more

intense in the lower level administrative unit d as compared to the upper level administrative unit

c and vice versa.

Finally, the index of the distribution of the variable V in a lower level administrative unit d is

defined in relation to the upper level administrative unit c as

dc(v) d c d cD = (P /P )*log (V /V )

dc dc(v) = E *I � d0c. (1.5)

and the the index of the distribution of the variable V for the upper level administrative unit c is

then defined as

cd(v) dc(v)D = 3D (1.6)� d0c.

The distributive indexes defined by (1.5) and (1.6) take into account both the demographic

situation and size of the in a lower level administrative unit in relation to other lower level

11


administrative units and therefore may be regarded as the fuller-information measure of the

variability in the demographic phenomena across the lower level administrative units in relation

to the upper level administrative units in situation where lower level administrative units are fully

nested in the upper level administrative unit. An important feature of the index of distribution

defined by (1.6) is that it has the additive property as it is the sum of the index of distribution of

all lower level administrative units. Another property of the indexes defined by (1.5) and (1.6)

is that they weight to the relative size of the population. A lower level administrative unit have

a larger population than another lower level administrative unit will have larger impact on the

index of distribution of the upper level administrative unit even if the relative intensity of the

demographic phenomenon in the two lower level administrative units is the same and vice versa.

Conventional indicators of measuring and analysing the demographic situation, commonly used

in the description and preliminary analysis of the census data, do not have these additive and

multiplicative properties.

The above approach can be extended to a situation where there are more than two levels of

administrative units. For example, suppose that there are three levels of administrative units with

the lowest level administrative unit termed as d, middle level administrative unit termed as s, and

the upper level administrative unit termed as c. Also assume that d are nested in s and s are

nested in c. Then it is straightforward to note that

s dP = 3P � d0s, and (1.7)

c cand P = 3P � s0c. (1.8)

We now define the following indicators for relative extensiveness

ds d sE = P /P � d0s, and (1.9)

sc c cE = P /P � s0c. (1.10)

Obviously

ds3E = 1 for all d0s, and (1.11)

sc3E = 1 for all s0c. (1.12)

We can also define the indicators of relative gravity or intensiveness of a demographic variable

V in the following manner

ds(v) d sI = log (V /V ) for all d0s, and (1.13)

sc(v) s cI = log (V /V ) for all s0c. (1.14)

Then the index of distribution of variable V for the lowest level administrative unit d can be

defined in relation to the upper level administrative unit c as

12

Introduction

dc(v) d c d cD = (P /P )*log (V /V )

dc dc(v) = E *I � d0c. (1.15)

Similarly, we can also define the index of distribution of variable V for the lowest level

administrative unit d in relation to the middle level administrative unit s as

ds(v) ds ds(v)D = E *I � d0s, (1.16)

and the index of distribution of variable V for the middle level administrative unit s in relation

to the upper level administrative unit c as

sc(v) sc sc(v)D = E *I � s0c. (1.17)

Finally, the index of distribution of the variable V for the middle level administrative unit s can

be defined as

sd(v) ds(v)D = 3D . (1.18)

At the same time, we can also define the index of distribution of the variable V for the upper

level administrative unit c in relation to the middle level administrative units s as

cs(v) sc(v)D = 3D . (1.19)

cd(v) cs(v) cd(v)It should be clear that D � D , although the index D can be decomposed into indexes

sd(v) cs(v)D and D . In fact, it is easy to show that

cd(v) sc sd(v) ds cs(v)D = 3E *D + 3E *D . (1.20)

cd(v)Equation (1.20) shows that the distributive index D which measures how the variable V is

distributed across the administrative level d in relation to the demographic situation at the

administrative level c can be decomposed into how the variable V is distributed across the

administrative level d in relation to the situation at the administrative level s and how the variable

V is distributed across the administrative unit s in relation to the situation at the administrative

level c. If the upper level administrative units represents the country, middle level the state/Union

Territory and the lower level the district, then equation (1.20) makes it possible to analyse the

distribution of a demographic variable across the states and Union Territories in relation to the

situation prevailing in the state/Union Territory contributes to the distribution of a demographic

variables across the districts in relation to the situation at the country level. Similarly, equation

(1.20) also permits to assess how the distribution of a demographic variable across the

states/Union Territories in relation to the situation at the country level also contributes to the

distribution of the demographic variable across the districts in relation to the situation at the

country level. In this sense, the equation (1.20) decomposes the diversity in the distribution of

demographic variables into within states/Union Territories across district component and within

country across state/Union territory component.

13


Throughout the present monograph, we apply the above approach for the analysis of the

provisional data of the 2011 population census. In addition, we also calculate and present the

conventional indicators of the demographic situation like population density, population sex

ratio, etc.

Finally, a word about units of measurement. We measure all indicators of extensiveness per 1000

population whereas all indicators of intensiveness are measured in terms of absolute ratios so that

the indicators of distribution are presented in the unit of 1000 throughout this monograph.

14

2Population Size and Growth

The provisional figures released by the Registrar General and Census Commissioner of India

suggest that the population of India was 1,210,193,422 persons at 00:00 hours of 1 March 2011.st

This means that between 2001 and 2011, around 181.578 million people were added to the

population of the country enumerated at the 2001 population census. This also means that during

the 60 years between 1951 and 2011, more than 849 million people were added to the population

of the country enumerated at the 1951 population census. By comparison, between 1901 and

1951, the net addition to the population of the country was only around 122 million.

In terms of proportions, India’s population increased by 17.653 per cent in the ten-year period

since the 2001 population census. The corresponding increase during the period 1991-2001 was

21.353 per cent which suggests that population increase in the country has continued to slow

down after attaining the highest proportionate increase of 24.80 per cent during the period 1961-

71. The preliminary figures of the 2011 population census also suggest that the slow down in the

population increase in the country has gained momentum during the period 2001-2011. This is

a welcome finding of the 2011 population census. This slow down in population growth has

resulted in a decrease in the net addition to the population of the country decreased, although the

decrease has been marginal. This is for the first time that the net decadal increase in the

population has decreased in the country. During the period 1991-2001, the net addition to the

population of the country was around 182.312 million whereas, during the period 2001-2011, the

net addition to the population of the country was around 181.578 million (Table 2.1). As a result,

the average annual population growth rate in the country decreased from 1.935 per cent during

the period 1991-2001 to 1.626 per cent during the period 2001-2011.

15


Table 2.1

Population size and growth in India 1901-2011.

Year Population

(million) Decadal change in population

Average

annual growth

rate

(per cent)(million) (per cent)1901 238.3961911 252.093 13.697 5.75 0.561921 251.321 -0.772 -0.31 -0.031931 278.977 27.656 11.00 1.041941 318.661 39.684 14.22 1.331951 361.088 42.427 13.31 1.251961 439.235 78.147 21.64 1.961971 548.160 108.925 24.80 2.221981 683.329 135.169 24.66 2.201991 846.303 162.974 23.85 2.142001 1028.615 182.312 21.54 1.952011 1210.193 181.578 17.65 1.63

Source: Census reports

The decrease in the net addition to the population is perhaps the most remarkable feature of

population transition in India during the period 2001-2011. If the average annual population

growth rate in the country during the period 2001-2011 would have been the same as the average

annual population growth rate during the period 1991-2001, the population of the country would

have increased to around 1246.315 million by the year 2011and the net addition to the population

of the country during the period 2001-2011would have been almost 218 million - 56 million

more than the actual addition to the population during the period 2001-2011 as revealed through

provisional figures of the 2011 population census.

A notable feature of the provisional population figures of the 2011 population census is that they

are very close to the population projected by the Government of India for the period 2001-2011

on the basis of the results of the 2001 population census. Government of India had projected that

the population of the country will increase to 1,192,506 thousand by the year 2011 (Government

of India, 2006). Similarly, United Nations has projected that India’s population would increase

to more than 1214 million by the year 2010 (United Nations, 2011). The provisional population

figures of the 2011 population census suggest that the enumerated population of the country

exceeded the projected population by almost 18 million. During the period 1991-2001, the

enumerated population of the country exceeded the projected population by around 16 million

16

Population Size and Growth

whereas, the enumerated population exceeded the projected population by less than 9 million

during the period 1981-91 (Chaurasia and Gulati, 2008). According to the population projections

of the Government of India, the population of the country should have grown by around 1.48 per

cent per year during the period 2001-2011 which is lower than the actual average annual

population growth rate of almost 1.63 per cent during the period 2001-2011. In other words,

provisional figures of the 2011 population census suggest that the demographic transition in the

country during the period 2001-2011 has been slower than the projected one. Population

projections prepared by the Government of India are based on the assumption that the

replacement fertility will be achieved in the country by the year 2021 - not by the year 2010 as

aimed in the National Population Policy 2000 - and the total fertility rate will decline to 2.6 births

per woman of reproductive age by the year 2010. However, the average annual population growth

rate during the period 2001-2011 derived from the provisional figures of the 2011 population

census suggests that the decrease in fertility in the country has been slower than the projected one

Figure 2.1Population (million) growth in India 1901-2001

17


which means that the country will not be able to achieve replacement fertility even by the year

2021. In other words, there is only a distant possibility of achieving stable population by the year

2045 as stipulated in National Population Policy 2000. This is one of the disheartening findings

of the 2011 population census. If the actual population growth in the country would have

followed the projected path, the decrease in the net addition to the population would have been

even more substantial.

The outstanding feature of the population growth in India, however, is not the rate of growth but

the size of the population to which growth accrues. The net addition to the population of the

country during the period 2001-11 is almost the same as the population of Brazil in 2005. Brazil,

incidently, is the fifth most populous country of the world (United Nations, 2011). Between 1951

and 2001, more than 849 million people have been added to 361 million people enumerated at

the 1951 population census while almost 972 million people have been added to the population

of the country since 1901. Clearly, despite the moderately high population growth rate, India is

adding huge numbers year after year putting enormous pressure on its limited resources to meet

the survival and development needs of its people.

Population Size and Growth in States/Union Territories

Regional diversity in the growth of population in India is well known and this diversity has

persisted over time. Any discussion about the size and the growth of India’s population,

therefore, is incomplete without a discussion on differences in the size and the growth of the

population across the constituent states and Union Territories of the country. The provisional

results of the 2011 population census provide information on population size and growth for the

29 states and 6 Union Territories of the country. This information is summarised in table 2.2

which includes data on population for the year 2001 and 2011 and estimates of the indicators of

population growth - the proportionate increase in the population and average annual population

growth rate for the period 2001-11.

Since the size of the population of different states and Union Territories of the country varies

widely, population growth in different states and Union Territories of the country has contributed

differently to the growth of the population of the country as a whole. Because of the varying

population size, it is customary to group the states and Union Territories of the country into three

broad categories; major states (states with a population of at least 25 million at the 2011 census),

small states (states with a population of less than 25 million at the 2011 census), and Union

Territories. According to the 2011 population census, there were 17 states in the country with a

population of 25 million and more while the population of 12 states was less than 25 million. In

18


addition, there are 6 Union Territories in the country all of which had a population of less than

25 million. Provisional results of the 2011 population census suggest that the 17 major states of

the country account for almost 95 per cent of the population of the country while the 12 small

states accounted for only about 5 per cent of the country’s population. Union Territories, on the

other hand, account for less than 0.3 per cent of the population of the country. Trends and

patterns of population growth in India, therefore, are primarily determined by the trends and

patterns of the population growth in the 17 major states of the country. The contribution of small

states and Union Territories to the growth of the population of the country has always been

insignificant, although trends and patterns of population growth in small states and Union

Territories are themselves an important area of interest and analysis.

According to the provisional figures of the 2011 population census, Uttar Pradesh, with a

population of almost 200 million, continues to be the most populous state of India followed by

Maharashtra and Bihar both of which have a population of more than 100 million. On the other

hand, Haryana, with a population of around 25 million has the smallest population among the

major states of the country. Other major states with a population less than 30 million at the 2011

population census are Punjab and Chhattisgarh. The total population of the 17 major states was

almost 1145 million or 94.6 percent of the population of the country. Interestingly, this

proportion has decreased during 2001-2011, although the decrease has been marginal.

Among smaller states of the country, Delhi is the most populous one with a population of almost

17 million whereas Sikkim, with a population of less than 0.61 million is the least populated one.

In addition to Delhi, there are only two small states - Jammu and Kashmir and Uttarakhand -

which had a population of more than 10 million at the 2011 population census. The total

population of these 12 states was around 62 million. Unlike the major states of the country, the

proportion of the population of these states to the total population of the country has increased

during the period 2001-2011.

Finally, the six Union Territories of the country had a population of more than 3.3 million at the

2011 population census with the Union Territory of Puducherry having a population of more than

1.2 million being the most populous one. In addition to Puducherry, Chandigarh is the only other

Union Territory of the country with more than 1 million population. Rest of the Union Territories

had a population of less than 0.50 million with the Union Territory of Lakshadweep being the

smallest state/Union territory of the country in terms of population size. Like the smaller states

of the country, the proportion of the population of the Union Territories to the total population

of the country has also increased during the period 2001-2011.

19

Table 2.2

Population size and growth in India, states and Union Territories, 1991-2001

Country/State Population (million) Population growth1991 2001 2011 Absolute (million) Percent

1991-2001 2001-2011 1991-2001 2001-11 2001-11 (P)India 846.303 1028.737 1210.193 182.434 181.456 21.56 17.64 15.93Major States

Uttar Pradesh 132.062 166.198 199.581 34.136 33.383 25.85 20.09 20.80Maharashtra 78.937 96.879 112.373 17.942 15.494 22.73 15.99 16.29Bihar 64.531 82.999 103.805 18.468 20.806 28.62 25.07 17.74West Bengal 68.078 80.176 91.348 12.098 11.172 17.77 13.93 11.63Andhra Pradesh 66.508 76.210 84.666 9.702 8.456 14.59 11.10 11.19Madhya Pradesh 48.566 60.348 72.598 11.782 12.250 24.26 20.30 19.64Tamil Nadu 55.859 62.406 72.139 6.547 9.733 11.72 15.60 8.07Rajasthan 44.006 56.507 68.621 12.501 12.114 28.41 21.44 20.04Karnataka 44.977 52.851 61.131 7.874 8.280 17.51 15.67 12.43Gujarat 41.310 50.671 60.384 9.361 9.713 22.66 19.17 16.48Orissa 31.660 36.805 41.947 5.145 5.142 16.25 13.97 10.72Kerala 29.099 31.841 33.388 2.742 1.547 9.42 4.86 8.55Jharkhand 21.844 26.946 32.966 5.102 6.020 23.36 22.34 16.80Assam 22.414 26.656 31.169 4.242 4.513 18.93 16.93 14.68Punjab 20.282 24.359 27.704 4.077 3.345 20.10 13.73 13.63Chhattisgarh 17.615 20.834 25.540 3.219 4.706 18.27 22.59 16.44Haryana 16.464 21.145 25.353 4.681 4.208 28.43 19.90 20.31

Small StatesDelhi 9.421 13.851 16.753 4.430 2.902 47.02 20.95 33.22Jammu and Kashmir 7.719 10.144 12.549 2.425 2.405 31.42 23.71 15.52Uttarakhand 7.051 8.489 10.117 1.438 1.628 20.39 19.18 17.12Himachal Pradesh 5.171 6.078 6.857 0.907 0.779 17.54 12.82 11.77

20

Country/State Population (million) Population growth1991 2001 2011 Absolute (million) Percent

1991-2001 2001-2011 1991-2001 2001-11 2001-11 (P)Tripura 2.757 3.199 3.671 0.442 0.472 16.03 14.75 13.03Meghalaya 1.775 2.319 2.964 0.544 0.645 30.65 27.81 13.03Manipur 1.837 2.294 2.722 0.457 0.428 24.88 18.66 13.02Nagaland 1.210 1.990 1.981 0.780 -0.009 64.46 -0.45 13.01Goa 1.170 1.348 1.458 0.178 0.110 15.21 8.16 31.12Arunachal Pradesh 0.865 1.098 1.383 0.233 0.285 26.94 25.96 13.03Mizoram 0.690 0.889 1.091 0.199 0.202 28.84 22.72 12.99Sikkim 0.406 0.541 0.608 0.135 0.067 33.25 12.38 13.16

Union TerritoriesPuducherry 0.808 0.974 1.244 0.166 0.270 20.54 27.72 42.76Chandigarh 0.642 0.901 1.055 0.259 0.154 40.34 17.09 59.67Andaman and Nikobar 0.281 0.356 0.380 0.075 0.024 26.69 6.74 38.70Dadra and Nagar Haveli 0.138 0.220 0.343 0.082 0.123 59.42 55.91 60.55Daman and Diu 0.102 0.158 0.243 0.056 0.085 54.90 53.80 70.67Lakshadweep 0.052 0.061 0.064 0.009 0.003 17.31 4.92 25.31

Source: Author’s calculations

21


Among the major states of the country, the population growth has been the most rapid in Bihar

followed by Chhattisgarh and Jharkhand. These are the only three major states where the average

annual population growth rate of more than 2 per cent per year was estimated. These three states

constitute a geographical continuity. The average annual population growth rate has also been

more than 2 per cent in Jammu and Kashmir, Meghalaya, Manipur, Arunachal Pradesh and

Mizoram during the period under reference. Four of these five states are located in the north-

eastern part of the country. These states are small states and the rapid population growth in these

states has only a minor impact on the population growth in the country as a whole.

Figure 2.2Population growth in states/Union Territories

22


Table 2.3

Average annual population growth rate in India and states/Union Territories

Country/State Average annual growth rate (Per cent)1991-2001 2001-2011 2001-2011(P)

India 1.951 1.624 1.478Major States

Uttar Pradesh 2.299 1.83 1.890Maharashtra 2.048 1.484 1.509Bihar 2.517 2.237 1.633West Bengal 1.636 1.304 1.100Andhra Pradesh 1.362 1.052 1.060Madhya Pradesh 2.172 1.848 1.793Tamil Nadu 1.108 1.449 0.776Rajasthan 2.500 1.942 1.826Karnataka 1.613 1.455 1.171Gujarat 2.042 1.754 1.525Orissa 1.506 1.308 1.018Kerala 0.901 0.474 0.820Jharkhand 2.099 2.017 1.553Assam 1.733 1.564 1.370Punjab 1.832 1.287 1.277Chhattisgarh 1.678 2.037 1.522Haryana 2.502 1.815 1.849

Small StatesDelhi 3.854 1.903 2.868Jammu and Kashmir 2.732 2.128 1.443Uttarakhand 1.856 1.754 1.581Himachal Pradesh 1.616 1.205 1.112Tripura 1.488 1.376 1.225Meghalaya 2.673 2.455 1.225Manipur 1.651 1.71 1.224Nagaland 4.975 -0.048 1.223Goa 1.414 0.785 2.709Arunachal Pradesh 2.385 2.305 1.225Mizoram 2.529 2.052 1.221Sikkim 2.868 1.165 1.236

Union TerritoriesPuducherry 1.872 2.447 3.560Chandigarh 3.385 1.579 4.679Andaman and Nikobar 2.370 0.647 3.272Dadra and Nagar Haveli 4.686 4.414 4.734Daman and Diu 4.389 4.288 5.345Lakshadweep 1.539 0.604 2.256


23


Figure 2.3Average annual population growth rate (per cent) during 2001-2011

in states and Union Territories

24


Provisional results of the 2011 population census suggest that population growth has also been

quite rapid in Rajasthan, Madhya Pradesh, Uttar Pradesh and Haryana. In these states, population

increased at an average annual growth rate of more than 1.8 per cent per year during the period

under reference which is well above the population growth rate of the country as a whole. All

these states are the major states of the country and, along with Bihar, Chhattisgarh and

Jharkhand, these states accounted for more than 93 million of the 181 million or more than 50

per cent increase in the population of the country during the period 2001-2011.

On the other hand, Nagaland is the only state in the country which has recorded a negative

population growth during the period under reference. During the period 1991-2001, the

population of Nagaland increased by a whopping 64.5 million but, during 2001-2011, the

population of the state decreased by a small number. This appears to be a very conspicuous

finding of the provisional results of the 2011 population census. Moreover, there are only two

states - Kerala and Goa - and two Union Territories - Andaman and Nikobar and Lakshadweep -

where the average annual growth rate during 2001-2011 is estimated to be less than 1 per cent.

Another encouraging feature of the provisional results of the 2011 population census is that the

growth in population has slowed down in all but three states and Union Territories of the country

during the period 2001-2011 as compared to the period 1991-2001 (Table 2.3). The three states

where the average annual population growth rate appears to have increased during the period

2001-2011 compared to the period 1991-2001 are Tamil Nadu, Chhattisgarh and Manipur.

Among these states, Tamil Nadu recorded a very low growth rate during the period 1991-2001

whereas the growth rate in Chhattisgarh and Manipur was more than 2 per cent per year. It

appears that rapid population growth situation has continued in these two states during the period

2001-2011 also.

The situation is however not so encouraging when the population growth estimated on the basis

of provisional figures of the 2011 population census is compared with the projected population

growth based on the projected population for the year 2011. This comparison suggests that in 20

states and Union Territories of the country, the actual population growth has been faster than the

projected population growth rate with the difference being the largest in Tamil Nadu followed

by Bihar among the major states of the country (Table 2.3). In these states and Union Territories,

actual population transition during the period 2001-2011 has been slower than the projected one.

At the same time, in 9 out the 12 small states, the actual population growth rate based on the

provisional figures of the 2011 population census has been faster than the project one whereas

in all Union Territories of the country, the actual population growth during 2001-2011 has been

25


slower than the projected one. This comparison suggests that the pace of population transition

in the country during the period 2001-2011 has been slower than what was projected or expected.

Obviously, the population transition scenario in the country and in most of the states, as revealed

through the provisional figures of the 2011 population census, does not appear to be very

encouraging. It is obvious from the table 2.3 that the country has missed the projected target of

an average annual population growth rate for the period 2001-2011, set on the basis of the results

of the 2001 population census. This means that the country will take more time to achieve the

goal of population stabilisation.

There has been considerable variation in population growth rates across the states/Union

Territories with acceleration in some states and Union Territories during 2001-2011 as compared

to 1991-2001 and slowdown in others. This is shown in figure 2.4 which compares the average

population growth rate registered in 1991-2001 with the average population growth registered

in 2001-2011. Deviation from the 45-degree line indicates the extent of change in the average

annual population growth rate between 1991-2001 and 2001-2011. Most of the states fall very

close to the 45-degree line. The deviation from the line is marked in Andaman and Nikobar,

Sikkim, Chandigarh, Delhi and Nagaland and in Tamil Nadu, Chhattisgarh, Manipur and

Puducherry. In the first group of states and Union Territories, average annual population growth

Figure 2.4Average annual population growth rate 1991-2001 and 2001-2011

26


rate has slowed down during the period 2001-2011 as compared to the average annual growth

rate during 1991-2001 with the change in the average annual population growth rate being the

most typical in Nagaland. In the second group of states and Union Territories, it has accelerated.

In other states, the average annual population growth rate registered during the period 2001-2011

is very close to that predicted on the basis of the average annual population growth rate recorded

during the period 1991-2001. This suggests that, although, the population growth rate in the

states and Union Territories of the country has shown a decline on the basis of the provisional

results of the 2011 population census, this decline appears to be, at best, a normal pattern in most

of the states and Union Territories. There are only a few marked deviations.

Provisional results of the 2011 population census also suggest that more than 45 per cent increase

in the population of the country during the decade 2001-2011 has been confined to only five

states - Uttar Pradesh, Bihar, Madhya Pradesh, Rajasthan, Jharkhand and Chhattisgarh. These

states accounted for around 40 per cent of the population of the country at the 2001 population

census but very close to 50 per cent of the increase in the population of the country during the

period 2001-2011. As the result, these states now account for almost 42 per cent of the

population of the country which indicates that an increasing proportion of population of the

country is getting concentrated in these states. The contribution of these states to the total

increase in the population of the country as a whole during 2001-2011 has been larger than that

at the 2001population census. This contribution has also increased in Haryana, Delhi, Jammu and

Kashmir, Uttarakhand, Meghalaya, Manipur, Arunachal Pradesh, Puducherry, Mizoram, Dadra

and Nagar Haveli, and Daman and Diu which indicates an increase in the concentration of

population in these states/Union Territories. However, these states/Union Territories contribute

only a small proportion to the population of the country.

Alternative Estimates of Population Growth

It is possible to have alternative estimates of population growth in the country during the period

2001-2011 on the basis of the information about birth and death rates available through the

sample registration system (SRS) and on the assumption that net migration at the national level

is an insignificant proportion to the natural increase in the population. Using the population

enumerated at the 2001 population census and estimates of the birth rate and the death rate

available through the sample registration system, it is possible to estimate the increase in the

population for different years of the period 2001-2011 as a result of the difference in the birth rate

and the death rate. This annual increase in population provides an alternative estimate of the

population in 2011 under the assumption that net international migration in the country

constitutes an insignificant proportion of the natural increase.

27


There are two problems in the application of the above approach to arrive at the estimates of

population growth in the country during the period 2001-2011. The first problem is that the

estimates of the birth rate and the death rate from the sample registration system are available up

to the year 2009 only. The second problem is associated with the omission rate at the 2001

population census and under reporting of births and deaths in the sample registration system for

which adjustments need to be made.

Table 2.4

Alternative estimates of population (million) in India 2011.

Adjustments in SRS estimates Adjustment in the 2001 census count

due to omission

No adjustment Adjusted for the

omission rate

No adjustments in the estimates of the birth rate

and the death rate

1206.535 1217.949

Adjustment in the birth rate but no adjustment in

the death rate

1211.666 1222.724

Adjustments as per Bhat (2002) 1218.587 1229.167


As regards the omission rate at the 2001 population census, the post enumeration survey

conducted by the Registrar General of India has revealed a net omission rate of 23.3 per 1000

population (Government of India, 2006). This means that the population in 2001 needs to be

inflated by 2.33 per cent which means that India’s population in 2001 was around 1053 million

and not 1029 million. On the other hand, estimates of the birth rate and the death rate obtained

from the system are generally believed to be quite accurate. An investigation conducted in 1980-

81 suggested an omission rate of 3.1 per cent at all India level in case of births (Government of

India, 1983) which decreased to 1.8 per cent in 1985 (Government of India, 1988) whereas

another inquiry conducted in 1991 suggested that deaths in the system have marginally been over

reported (Swamy et al, 1992). On the other hand, Bhat (2002) has estimated that births in the

sample registration system are under reported by about 7 per cent while deaths by around 8-9 per

cent through a different approach.

We have estimated birth rate and death rate for 2009 and 2010 on the basis of linear regression

of birth and death rates obtained from the sample registration system on time for the period 2001

through 2008. The regression exercise provided a very good fit with R =0.99 in case of the birth2

28


rate and 0.85 in case of the death rate. We have also calculated the estimated population in the

year 2011 after making adjustments in the population of the country in 2001 for the estimated

net omission rate as well as for different estimates of under reporting in the birth rate and the

death rate available through the sample registration system.

Results of the estimation exercise are given in table 2.4. When no adjustment related to the

omission rate and under reporting of births and deaths in the sample registration system is made,

the estimated population for the year 2011 comes out to be marginally less than the enumerated

population of the 2011 population census. However, when adjustments in the birth rate and death

rate suggested by the Government of India are taken into consideration and when the population

enumerated at the 2001 population census is not adjusted for the net omission rate at the 2001

population census, the population of the country for the year 2011 is estimated to be 1211.7

million which is very close to the provisional population figures of the 2011 population census.

When adjustment for the net omission rate is made in the population enumerated at the 2001

population census, the population of the country is estimated to be more than 1222 million in the

year. Finally, when no adjustments are made in the birth rate, the 2011 population is estimated

to be 1207 million which suggests that there is some under reporting of births in the sample

registration system. It is obvious that when the net omission rate of the 2001 population census

and the under reporting of births and deaths in the sample registration system is taken into

account, there appears substantial under count at the 2011 population census. Finally, when the

estimates of under reporting of births and deaths in the sample registration system are taken into

consideration, the estimated population in the year 2011 is around 1229 million. Table 2.4

suggests that there is some under count of the population at the 2011 population census also,

although the magnitude of the under count does not appear to be substantial given the size of the

population of the country.

We have carried out a similar exercise for the states and Union Territories of the country.

Estimates of the birth rate and the death rate for the period 2001 through 2009 are available

through the sample registration system for 31 of the 35 states and Union Territories of the

country. The exceptions are Chhattisgarh, Jharkhand, Nagaland and Uttarakhand for which

annual estimates of the birth rate and the death rate are available for the period 2004 through

2009 only. We have estimated the birth rate and the death rate for those years of the period 2001-

2010 for which direct estimates of these rates are not available the sample registration system by

assuming a linear time trend in the two rates and then used the enumerated population at the 2001

population census to estimate the population in 2011. We have carried out this exercise for all

the 35 states and Union Territories of the country.

29


Table 2.5

Enumerated and estimated population of states and Union Territories, 2011

State

Population 2011 Difference

Enumerated

(Million)

Estimated

(Million)

Absolute

(Million)

Per cent

Uttar Pradesh 199.582 206.417 -6.835 -3.425Rajasthan 68.621 70.473 -1.852 -2.699Kerala 33.388 34.863 -1.475 -4.418Madhya Pradesh 72.598 73.996 -1.399 -1.927Andhra Pradesh 84.666 85.927 -1.261 -1.489Assam 31.169 31.454 -0.285 -0.914Nagaland 1.981 2.253 -0.273 -13.781Bihar 103.805 103.965 -0.16 -0.154Andaman and Nikobar 0.38 0.401 -0.021 -5.526Sikkim 0.608 0.625 -0.018 -2.961Lakshadweep 0.064 0.068 -0.004 -6.250Himachal Pradesh 6.857 6.855 0.001 0.015Goa 1.458 1.447 0.011 0.754Chandigarh 1.055 1.017 0.038 3.602Orissa 41.947 41.904 0.044 0.105Daman and Diu 0.243 0.182 0.061 25.103Dadra and Nagar Haveli 0.343 0.278 0.065 18.950Haryana 25.353 25.281 0.072 0.284Mizoram 1.091 1.010 0.081 7.424Arunachal Pradesh 1.383 1.296 0.087 6.291Tripura 3.671 3.544 0.127 3.460Manipur 2.722 2.553 0.169 6.209Puducherry 1.244 1.072 0.172 13.826Meghalaya 2.964 2.768 0.196 6.613Punjab 27.704 27.406 0.298 1.076Chhattisgarh 25.54 25.213 0.327 1.280Uttarakhand 10.117 9.766 0.351 3.469Jharkhand 32.966 32.591 0.375 1.138West Bengal 91.348 90.864 0.484 0.530Karnataka 61.131 60.605 0.526 0.860Gujarat 60.384 59.847 0.537 0.889Delhi 16.753 15.934 0.82 4.895Jammu and Kashmir 12.549 11.600 0.949 7.562Maharashtra 112.373 109.480 2.893 2.574Tamil Nadu 72.139 68.728 3.411 4.728


30


Results of the exercise are presented in table 2.5. In some states of the country, the estimated

population for the year 2011 has been found to be larger than the population enumerated at the

2011 population census while in others the estimated population is found to be less than the

enumerated population. Uttar Pradesh tops the list in terms of the difference between the

enumerated and estimated population for the year 2011. The enumerated population in Uttar

Pradesh has been found to be almost 7 million less than the estimated population in the year

2011. On the other hand, in Tamil Nadu, the enumerated population has been found to be almost

3.5 million more than the estimated population whereas in Maharashtra, the enumerated

population has been found to be almost 3 million more than the estimated population. In Dadra

and Nagar Haveli, Daman and Diu, and Puducherry, the difference between the enumerated and

the estimated population has been found to be very substantial. By contrast, in Bihar, Himachal

Pradesh, Goa and Orissa, the difference between the enumerated and the estimated population

has been found to be very small.

The difference between the enumerated population and the population estimated on the basis of

the annual estimates of the birth rate and the death rate derived from the sample registration

system in a state/Union Territory is a reflection of the movement of the population across the

states/Union territories of the country. In those states and Union territories where the enumerated

population is less than the estimated one, it appears that the population has moved out of the

state/Union Territory during the period 2001-2011. Similarly, in states/Union Territories where

the enumerated population is larger than the estimated one, it can be assumed that population has

moved into the state/Union Territory during this period. In this sense, it can be argued that there

has been movement of the people out of Uttar Pradesh, Rajasthan, Kerala and Madhya Pradesh

whereas in Tamil Nadu, Maharashtra, Jammu and Kashmir, Delhi, etc., there has been inward

movement of the people during the period 2001-2011. This assessment, of course, is based on

the assumption that the omission rate at the 2001 and the 2011 population census is almost the

same and the estimates of the birth rate and the death rate available through the sample

registration system reflect the prevailing levels of fertility and mortality in the country and in its

states and Union Territories. Another assumption associated with this assessment is that the net

international migration from the country is either zero or an insignificant proportion of the total

population of the country as has been the case here. In any case, a comparison of the enumerated

and the estimated population of the country and states/Union Territories suggests that inter-state

movement of the population in the country remains quite substantial for a host of factors and

conditions most of which are well known. More attention to this important aspect of the

population stock in the country and in its constituent states/Union Territories is discussed in

Chapter six of the monograph.

31


Population Size and Growth in Districts

The provisional data of the 2011 population census also provide the population count in 640

districts of the country as they existed at the time of the 2011 population census. The population

of the districts enumerated at the 2011 population census is given in table 2.A along with the

population at the 2001 population census, the proportionate increase in population during 2001-

2011 and the average annual population growth rate during this period. According to the

provisional figures of the 2011 population census, district Thane in Maharashtra is the most

populous district of the country with a population of more than 11 million. The only other district

having a population of more than 10 million at the 2011 population census is the Twenty Four

Parganas district in West Bengal. By contrast, district Dibang Valley in Arunachal Pradesh is the

least populated district in the country with a population of less than eight thousand.

In majority of the districts of the country, the population enumerated at the 2011 population

census ranges between 1-3 million. There are 195 districts where the enumerated population is

less than 1 million whereas in 57 districts, the population is enumerated to be 4 million and more

at the 2011 population census. There are however 21 districts in the country which can be termed

as very large districts in terms of the size of the population. In these districts, the population

enumerated at the 2011 population census was 5 million and more. Twelve out of these 21

districts are located in only two states of the country - West Bengal and Maharashtra.

Like the size of the population, the growth of the population during the period 2001-2011 has

also been found to vary widely across the districts of the country. The average annual population

growth rate has been found to be the most rapid in district Kurung Kumey of Arunachal Pradesh

where population increased at a rate of more than 7 per cent per year during the decade 2001-

2011 resulting in a proportionate increase of more than 110 per cent between 2001 and 2011. In

all there are 23 districts in the country where population growth has been the fastest in the

country during the period 2001-2011. In these districts, population increased at an average annual

rate of more than 3 per cent per year during the period under reference. On the other hand, in 21

districts of the country, population growth has been negative during the period under reference

with the most rapid decrease in the population recorded in district Longleng of Nagaland where

the population decreased at an average annual rate of more than 11 per cent per year leading to

a proportionate decrease of more than 68 per cent according to the provisional figures of the 2011

population census. In six out of eleven districts in Nagaland, population growth has been

negative during the period 2001 through 2011. As a result, Nagaland is the only state/Union

Territory in the country where population, instead of increasing, decreased during the period

2001-2011.

32


Table 2.6

Districts by population size, 2011

State Population (million)< 1 1-2 2-3 3-4 $4 Total

AN Islands 3 0 0 0 0 3Andhra Pradesh 0 0 7 6 10 23Arunachal Pradesh 16 0 0 0 0 16Assam 12 14 1 0 0 27Bihar 3 10 10 9 6 38Chandigarh 0 1 0 0 0 1Chhattisgarh 8 6 2 1 1 18Dadra & Nagar Haveli 1 0 0 0 0 1Daman and Diu 2 0 0 0 0 2Delhi 3 1 4 1 0 9Goa 2 0 0 0 0 2Gujarat 4 7 10 2 3 26Haryana 6 15 0 0 0 21Himachal Pradesh 11 1 0 0 0 12Jammu and Kashmir 18 4 0 0 0 22Jharkhand 8 11 5 0 0 24Karnataka 2 20 6 0 2 30Kerala 1 4 4 4 1 14Lakshadweep 1 0 0 0 0 1Madhya Pradesh 10 31 8 1 0 50Maharashtra 1 12 9 5 8 35Manipur 9 0 0 0 0 9Meghalaya 7 0 0 0 0 7Mizoram 8 0 0 0 0 8Nagaland 11 0 0 0 0 11Orissa 10 14 5 1 0 30Puducherry 4 0 0 0 0 4Punjab 10 5 4 1 0 20Rajasthan 2 17 9 4 1 33Sikkim 4 0 0 0 0 4Tamil Nadu 3 13 7 8 1 32Tripura 3 1 0 0 0 4Uttar Pradesh 2 22 14 19 14 71Uttarakhand 10 3 0 0 0 13West Bengal 0 2 2 5 10 19India 195 214 107 67 57 640

30.5 33.4 16.7 10.5 8.9 100.0


33


Table 2.7

Districts with the highest population growth rate

and districts with the negative population growth during 2001-2011

Districts with highest growth rate Districts with negative population growth

State/Union

Territory

District State/Union

Territory

District

Arunachal Pradesh Kurung Kumey Nagaland Longleng

Puducherry Yanam Nagaland Kiphire

Haryana Gurgaon Delhi New Delhi

Daman & Diu Daman Nagaland Mokochung

Dadra & Nagar

Haveli

Dadra and Nagar

Haveli

Andaman and

Nicobar Islands

Nicobars

Uttar Pradesh Gautam Budh Nagar Delhi Central

Arunachal Pradesh Upper Subansiri Nagaland Zunheboto

Arunachal Pradesh Lower Subansiri Maharashtra Mumbai

Andhra Pradesh Rangareddy Himachal Pradesh Lahul & Spiti

Karnataka Bangalore Maharashtra Ratnagiri

Arunachal Pradesh Papum Pare Nagaland Mon

Gujarat Surat Tamil Nadu The Nilgiris

Meghalaya South Garo Hills Kerala Pathanamthitta

Chhattisgarh Kabeerdham Maharashtra Sindhdurg

Uttar Pradesh Ghaziabad Nagaland Peren

Tamil Nadu Kancheepurum Kerala Idduki

Haryana Mewat West Bengal Kolkata

Jammu & Kashmir Anantnag Uttarakhand Almora

Arunachal Pradesh East Kameng Uttarakhand Garhwal

Mizoram Mamit Karnataka Chikmanglur

Jammu and Kashmir Ganderbal Andaman & Nicobar

Islands

North and Middle

AndamanMaharashtra Thane

Tamil Nadu Thiruvallur


34


In more than half of the districts of the country (55 per cent), average annual population growth

rate during the period 2001-2011 ranged between 1-2 per cent per year according to the

provisional figures of the 2011 population census whereas in 91 districts, the growth of the

population was on average less than 1 per cent per year. By comparison, there are 23 districts in

the country where the growth of population during the period 2001-2011 has been very rapid -

3 per cent per year and higher. Out of these 23 districts, 5 are located in Arunachal Pradesh. In

Haryana, Jammu and Kashmir, Tamil Nadu and Uttar Pradesh, population growth has been very

rapid in two districts each.

Figure 2.5Distribution of districts across states by size of the population (million), 2011

35


In all, in 174 or more than 27 per cent districts of the country, population growth has been rapid

during the period 2001-2011 as the average annual population growth rate has been 2 per cent

per year and more, on average, in these districts according to the provisional results of the 2011

population census. By contrast, in 112 or about 18 per cent districts of the country, population

growth has been either negative or very slow, less than 1 per cent per year, on average, during

this period. In Kerala, in all but one district, the average annual population growth rate has been

less than 1 per cent per year during 2001-2011.

Figure 2.6Distribution of districts across states by

average annual population growth rate (per cent), 2001-2011

36


Table 2.8

Districts by average annual population growth rate, 2001-2011

State Average annual population growth rate (per cent)< 0 0-1 1-2 2-3 $3 Total

AN Islands 2 0 1 0 0 3Andhra Pradesh 0 13 9 0 1 23Arunachal Pradesh 0 4 6 1 5 16Assam 0 4 20 3 0 27Bihar 0 0 11 27 0 38Chandigarh 0 0 1 0 0 1Chhattisgarh 0 1 13 3 1 18Dadra and Nagar Haveli 0 0 0 0 1 1Daman and Diu 0 0 1 0 1 2Delhi 2 0 4 3 0 9Goa 0 2 0 0 0 2Gujarat 0 4 17 4 1 26Haryana 0 1 15 3 2 21Himachal Pradesh 1 2 9 0 0 12Jammu and Kashmir 0 1 8 11 2 22Jharkhand 0 0 9 15 0 24Karnataka 1 14 12 2 1 30Kerala 2 11 1 0 0 14Lakshadweep 0 1 0 0 0 1Madhya Pradesh 0 0 34 16 0 50Maharashtra 3 9 18 4 1 35Manipur 0 0 6 3 0 9Meghalaya 0 0 0 6 1 7Mizoram 0 0 4 3 1 8Nagaland 6 2 0 3 0 11Orissa 0 2 27 1 0 30Puducherry 0 0 2 1 1 4Punjab 0 5 14 1 0 20Rajasthan 0 1 18 14 0 33Sikkim 0 1 3 0 0 4Tamil Nadu 1 6 22 1 2 32Tripura 0 0 3 1 0 4Uttar Pradesh 0 1 48 20 2 71Uttarakhand 2 5 2 4 0 13West Bengal 1 1 16 1 0 19India 21 91 354 151 23 640

3.3 14.2 55.3 23.7 3.6 100.0

Source: Population census 2011.

37


Figure 2.7Average annual population growth rate (per cent) in districts, 2011

38


The growth of population is influenced by both the natural increase in the population resulting

from the difference in the birth rate and the death rate and the net migration rate. Provisional

figures of the 2011 population census do not provide data necessary to estimate the birth rate and

the death rate as well as migration rate during the period 2001-2011 to analyse further the factors

responsible especially for very rapid population growth in some of the districts of the country and

negative population growth in other districts. However, some speculative analysis may still be

carried out on the basis of the provisional figures of the 2011 population census.

Among the 23 districts of the country where the average annual population growth rate has been

more than 3 per cent per year during the period 2001-2011, seven districts are either metropolitan

districts or districts adjoining metropolitan districts. These are Bangalore in Karnataka, Thane

in Maharashtra which is next to Mumbai, Rangareddy in Andhra Pradesh which is adjacent to

Hyderabad, Gurgaon in Haryana and Gautam Budh Nagar and Ghaziabad in Uttar Pradesh which

are adjacent to Delhi. In addition, district Surat in Gujarat is a very highly industrialised district

while district Mewat adjoins district Faridabad, a highly industrialised district in Haryana which

also adjoins Delhi. Except one or two exceptions, these districts are also highly industrialised and

hence urbanised districts. As such, it appears that the rapid population growth witnessed in these

districts during the period between 2001 and 2011 is largely due to heavy to very heavy in-

migration of the working age population to these districts in search of better employment and

livelihood opportunities. Being highly industrialised and urbanised, there is little possibility that

rapid population growth in these districts is the result of a rapid natural increase in population

resulting from the high birth rate and the high death rate. In-migration, on a large scale, to these

districts appears to be the primary reason for the observed rapid population growth in these

districts.

On the other hand, population growth has also been very rapid in seven districts in the north-

eastern region of the country. Out of these seven districts, five - East Kameng, Papum Pare,

Lower Subansiri, Upper Subansiri and Kurung Kumey - are in Arunachal Pradesh while the sixth

- Mamit - is in Mizoram and the seventh - South Garo Hills - is in Meghalaya. These districts are

amongst the least developed districts of the country. It appears, that rapid population growth

witnessed in these districts is largely due to a rapid natural increase in population resulting from

the high birth rate and the high death rate.

Other districts where rapid population growth has been recorded during the period 2001-2011 -

Thiruvallur and Kancheepuram in Tamil Nadu, Kabeerdham in Chhattisgarh, Dadra and Nagar

Haveli, Daman in the Union Territory of Daman and Diu, and Ganderbal and Anantnag in

39


Jammu and Kashmir - both natural increase conditions and the large scale in-migration may be

responsible for the rapid population growth that has been witnessed during the period 2001-2011.

At present very little is known about the reasons behind very rapid population growth in these

districts. Once detailed data from the 2011 population census are available, it would be possible

to analyse, in detail, the factors that have contributed to the rapid population growth in these

districts.

40

3Population Distribution

The administrative divisions of India - states, Union Territories and districts within states and

Union Territories - vary widely in terms of both the size of the population and the geographic

area. As such, the distribution of the population across administrative units is not even but is

dense in some administrative areas and sparse in others. This uneven distribution of population

across administrative divisions of the country is a result of a range of factors. First, the

administrative divisions - state or Union Territory or district or even a village - do not have the

same geographical area. Second, within an administrative unit, such environmental factors as

mountains and deserts, etc. affect the distribution of the population. Similarly, factors associated

with social and economic development processes like industrialisation and urbanisation as well

as factors like the productivity of the land, also influence the distribution of population across

the administrative units.

The most commonly used indicator of analysing the distribution of the population is the

population density which is defined as the number of inhabitants per unit area. If all

administrative units within a country have the same area, then variation in the population density

across administrative units is the same as the variation in population across administrative units.

When area varies across the administrative units, variation in population density reflects both,

variation in the size of the population and variation in the area across the administrative units.

Population density, therefore, is not a good indicator of population distribution as it is influenced

by both the population as well as area of the administrative unit. Another problem with the

population density as a measure of population distribution is that it does not have additive and

multiplicative properties.

41


Population density of a district d is nothing but the population of the district d with respect to the

area of the district d. If X denotes the population density, then following the approach outlined

in Chapter 1, we have the following measures of population distribution

dc(x) d c d cD = (P /P )*log (X /X )

dc dc(x) = E *I � d0c. (3.1)

Similarly,

ds(x) ds ds(x)D = E *I � d0s, and (3.2)

sc(x) sc sc(x)D = E *I � s0c. (3.3)

Finally, we define the index of distribution of the population in the country as a whole as

cd(x) dc(x)D = 3D . (3.4)

Similarly, we may also define

sd(x) ds(x)D = 3D , and (3.5)

cs(x) sc(x)D = 3D . (3.6)

It is now easy to show that

cd(x) sc sd(x) ds cs(x)D = 3E *D + 3E *D . (3.7)

It is obvious that if all the districts of the country have the same ratio of the population to the area

cd(x)as the ratio of the population to the area for the country as a whole, D = 0. Moreover, the

cd(x)index D is independent of the unit of measurement of the population or the area as it is based

on ratios not absolute values.

cd(x)The advantage of using the index D to measure the distribution of population across

cd(x) sd(x)administrative units should be obvious. The index D is logically related to the index D

cs(x)and the index D . Population density does not have this property as it does not take into

account the variability in population distribution within administrative units. It assumes that the

population is distributed uniformly across the area within the administrative unit. It is also clear

cd(x)that D can be decomposed into within state/Union Territory and between states/Union

Territories components. The within state/Union Territory component is determined by the

distribution of the population across districts within the states/Union Territories whereas the

second component is determined by the distribution of the population across the states/Union

Territories within the country. It is obvious that if the population density of a district d as is the

sd(x)same as the population density of the state/Union Territory as a whole, D = 0. Similarly, if

the population density of a state/Union Territory is the same as the population density of the

country as a whole is the same as the area of the state/Union Territory as proportion to the area

cs(x) cd(x)of the country, D = 0. The index D takes into account both the intensiveness and the

extensiveness of population at the district level.

42

Population Distribution

Population Distribution in India

For India as a whole, the population density is estimated to be 382 persons per square kilometre

according to the provisional figures of the 2011 population census. The corresponding figure at

the 2001 population census was 325 persons per square kilometre. Thus, there were, on average,

57 more persons inhabited in every square kilometre in the country at the 2011 population census

as compared to the corresponding number at the 2001 population census. India accounts for only

2.4 per cent of the world surface area of 135.79 million square kilometres whereas it supports and

sustains 17.5 per cent of the world population. Among the ten most populous countries of the

world, only Bangladesh has a population density higher than the population density in India

(Government of India, 2011). An increase in the density of the population implies an increase in

the pressure of the population on natural resources and environment through increased resources

demand and increase in the wastes generated. This is a cause of concern as it has implications

in the context of sustainable social and economic development.

On the other hand, the index of population distribution for the country as a whole, based on the

cd(x)distribution of the population across the districts of the country, D , is estimated to be around

195 according to the provisional figures of the 2011 population census. As discussed earlier, this

dc(x)index is the weighted sum of the index of intensiveness, I , of all the 640 districts of the

country with weights being equal to the ratio of the population of the district to the population

dc cd(x)of the country or the index of extensiveness, E . The index of population distribution D

reflects the variability in population distribution across the districts within the country. If all

districts have the same population density as the population density of the country as a whole,

cd(x)then the D = 0 irrespective of the distribution of the population of the country across the

cd(x)districts. The index D is a fuller measure of population distribution across the districts of the

country in the sense that it also takes into account the relative distribution of the population

across the districts.

cd(x)It is possible to decompose the index D in to two components - distribution of the population

across the districts within the state/Union territory or the within state component and distribution

of the population across the states/Union Territories within the country or between states/Union

Territory component - according to equation 3.7. This decomposition exercise suggests that

variation in population distribution between states/Union Territories account for about 53 per

cent of the total variation in population distribution across the country whereas variation in

population distribution across districts within states/Union Territories account for about 47 per

cent of the total variation. This the variability in population distribution across the districts in the

country is almost equally divided into within and between state/Union Territory components.

43


Population Distribution across States/Union Territories

The provisional figures of the 2011 population census suggest that the population density varies

widely across the states/Union Territories of the country. The National Capital Territory of Delhi

has the highest population density amongst the states/Union Territories of the country with

almost 11,300 persons living in 1 square kilometre of area, on average. On the other hand,

population density has been estimated to be the lowest in Arunachal Pradesh where only 17

persons were living in 1 square kilometre of area, on average, at the time of the 2011 population

census. Population density has also been found to be very high in the Union Territory of

Chandigarh (9252) and in Puducherry (2598), Daman and Diu (2169), Lakshadweep (2013) and

Dadra and Nagar Haveli (698) - all Union Territories with very small geographic area. Among

the major states of the country - states with a population of at least 25 million at the 2011

population census - population density has been found to be the highest in Bihar (1102) followed

by West Bengal (1029), Kerala (859) and Uttar Pradesh (828).

The distribution of the population at the state/Union Territory level can be analysed in two

context. The first context is how the population is distributed across districts within a

sd(x)state/Union Territory. This context is measured by the index D which is defined by equation

(3.3). The second context, on the other hand, is how the population across states/Union

cs(x)Territories is distributed within the country. This context is measured by the index D which

cd(x)is defined by equation (3.4). The two indexes can be combined to arrive at the index D

according to the equation (3.7).

sd(x) cs(x)Values of the indexes D and D are given in table 3.1 along with the population density

scin each state/Union territory of the country and the index E which reflects the proportionate

sc(x)distribution of the population of the country across the states/Union Territories. and I . With

reference to the country as a whole, the index of population distribution has been found to be the

highest in Uttar Pradesh followed by Bihar, West Bengal and Delhi. The highest index of

scpopulation distribution in Uttar Pradesh is because of a very large value of the index E as the

state accounted for almost 16.5 per cent of the population of the country at the 2011 population

census. Similarly, Bihar accounted for almost 9 per cent of the population of the country because

of which the state has the second highest index of population distribution in the country. By

contrast, a very high index of population distribution in the National Capital Territory of Delhi

is mainly because of a very high index of the intensiveness of population as it accounts for less

than 1.4 per cent of the population of the country at the 2011 population census. The population

density in the National Capital Territory of Delhi is the highest in the country - more than 11,200

persons per square kilometre which is more than 10 times the population density in Uttar

44


Pradesh. The exceptionally high population density in the National Capital Territory of Delhi is

however restricted to a very small population relative to the population of the country and

therefore the index of population distribution in Delhi is not the highest in the country. In Uttar

Pradesh, Bihar and West Bengal, on the other hand, population density is only moderately high,

yet the index of population distribution is amongst the highest in the country mainly because the

population of these states is very large relative to other states and Union Territories of the

country.

Population density has also been found to be high to very high in a number of Union Territories

of the country. However, the index of population distribution is not high in these Union

Territories because they have very low index of the extensiveness of population. These Union

Territories accounted for a very small proportion of the population of the country at the 2011

population census. As such, the concentration of the population or the intensiveness of

population in these Union Territories have a very small, almost insignificant impact on the index

of population distribution in the country as a whole.

sc(x)By comparison, the index D has been found to be the lowest in Rajasthan followed by

Madhya Pradesh, although Arunachal Pradesh which has the lowest population density. In fact,

csthe index E is very low in Arunachal Pradesh as the population of the state accounts for just

csaround 0.1 per cent of the population of the country whereas E is comparatively large in

Rajasthan and Madhya Pradesh. Clearly, the ranking of states/Union Territories of the country

sc(x)by the index D is different from the ranking by the population density. The reason is that the

index of population distribution incorporates the variation in population size across states/Union

Territories whereas population density does not incorporates the variation in the population size

across the states and Union Territories.

sc(x)Table 3.1 also suggests that the index of population distribution, D , is negative in 19

states/Union Territories which account for around 47 per cent of the population of the country.

A negative index of population distribution means that these states/Union Territories have a

lower population density than the national average. On the other hand, the index of population

distribution was positive in 16 states/Union Territories of the country which means that the

population density in these states/Union Territories is higher than the national average. These 16

states/Union Territories accounted for almost 53 per cent of the population of the country at the

2011 population census. As a result, the index of the population distribution is positive for the

country as a whole which means that majority of the population of the country is living in high

population density areas.

45


Table 3.1

Indexes of population distribution in India and states/Union Territories, 2011

Country/State Population

densitysc(x) sd(x) sc(x) cd(x)E D D D

Andaman and Nikobar 47 0.314 0.020 -0.285 -0.266Andhra Pradesh 308 69.960 6.264 -6.499 -0.235Arunachal Pradesh 17 1.142 0.143 -1.558 -1.414Assam 398 25.756 2.254 0.478 2.732Bihar 1049 85.775 2.951 37.690 40.641Chandigarh 9252 0.872 0.000 1.207 1.207Chhattisgarh 189 21.104 1.556 -6.437 -4.881Dadra and Nagar Haveli 698 0.283 0.000 0.074 0.074Daman and Diu 2169 0.201 0.004 0.152 0.156Delhi 11282 13.843 1.882 20.366 22.248Goa 394 1.205 0.009 0.017 0.026Gujarat 308 49.896 7.085 -4.596 2.489Haryana 530 20.950 0.451 2.994 3.446Himachal Pradesh 123 5.666 1.259 -2.780 -1.521Jammu and Kashmir 125 10.369 4.805 -5.034 -0.229Jharkhand 414 27.240 1.488 0.966 2.453Karnataka 319 50.513 6.943 -3.923 3.019Kerala 863 27.589 1.252 9.787 11.038Lakshadweep 2013 0.053 0.000 0.038 0.038Madhya Pradesh 235 59.988 2.478 -12.555 -10.077Maharashtra 365 92.855 18.819 -1.739 17.079Manipur 122 2.249 0.784 -1.114 -0.330Meghalaya 133 2.449 0.130 -1.124 -0.994Mizoram 52 0.902 0.050 -0.781 -0.731Nagaland 120 1.637 0.145 -0.819 -0.674Orissa 269 34.662 2.544 -5.235 -2.690Puducherry 2529 1.028 0.036 0.845 0.881Punjab 550 22.892 0.584 3.645 4.229Rajasthan 200 56.703 5.855 -15.872 -10.018Sikkim 86 0.502 0.151 -0.326 -0.175Tamil Nadu 556 59.609 6.482 9.763 16.244Tripura 350 3.033 0.152 -0.113 0.039Uttar Pradesh 829 164.917 6.619 55.599 62.219Uttarakhand 189 8.360 1.623 -2.550 -0.927West Bengal 1024 75.482 7.503 32.377 39.880India 102.657 92.230 194.978

Source: Author’s calculationsRemarks: For the definition of the indexes, see text

46


Figure 3.1Population density (per square kilometre) in states, 2011

47


Figure 3.2Index of extensiveness of population distribution in states/Union Territories

48


Figure 3.4Index of population distribution in states/Union Territories, 2011

49


On the other hand, the index of population distribution across districts within states/Union

sd(x)Territories or the index D has been found to be the highest in Maharashtra (18.819) followed

by Gujarat (7.085) and Karnataka (6.943) which shows that the variability in the distribution of

population across districts is the highest in these states. By contrast, this index has been found

to be zero in Chandigarh, Dadra and Nagar Haveli and Lakshadweep as there is only one district

in these Union Territories and hence no variability in the distribution of population across

districts. Among the major states, the index has also been found to be very low, very close to

zero, in Haryana (0.451), Punjab (0.584), Kerala (1.252) and Chhattisgarh (1.556). In these states,

variability in the population distribution across the constituent districts is the lowest in the

country.

sd(x) cs(x)Combining the indexes D and D , according to the equation (3.7) for each state and then

adding the values for all the states and Union Territories gives the index of population

cd(x)distribution, D for the country. Table 3.1 gives the share of different states and Union

cd(x)Territories to the index of population distribution, D for the country which reflects the

variability in the distribution of population across districts. It may be seen from the table, and as

already discussed, Uttar Pradesh accounts for the largest share of the index of population

distribution at the country level followed by Bihar, West Bengal and Delhi. Uttar Pradesh and

Bihar, alone, accounts for almost 53 per cent of the total variation in the distribution of the

population across the districts of the country. By comparison, the share of small states and Union

Territories to the index of population distribution for the country as a whole is very small mainly

because their population constitutes a small proportion of the population of the country.

Population Distribution across Districts

dc(x) dc(x)Estimates of the index of the population distribution, D , index of extensiveness E and

dc(x)the index of intensiveness I for each of the 640 districts of the country are presented in the

appendix table 3.A along with the estimates of population density and the share of the population

of the district to the population of the country for each district. In all, in around one third (209)

districts of the country, the population density has been estimated to be more than 600 persons

per square kilometre at the 2011 population census. More than half of these 209 districts, are

located in only five states of the country - Uttar Pradesh (59), Bihar (35), West Bengal (16),

Kerala (11) and Delhi (9). All the districts in the National Capital Territory of Delhi, 16 out of

19 districts in West Bengal, 35 out of 38 districts in Bihar and 11 out of 14 districts in Kerala had

a population density of more than 600 persons per square kilometre according to the provisional

figures of the 2011 population census. These states are the most densely population states of the

country.

50


The population density has been found to be the highest in district Mumbai of Maharashtra where

more than 45 thousand people were found to be living, on average, in one square kilometre

according to the provisional figures of the 2011 population census. In addition to district

Mumbai, there are 10 more districts in the country where a population density of more than 10

thousand persons per square kilometre has been estimated on the basis of the provisional data of

the 2011 population census. These districts are, in order of the population density, North East

district (43091) and North West district (28087) in Delhi, Chennai in Tamil Nadu (26903),

Kolkata (24252) Central district (23147) and West district (22603) in Delhi, Hyderabad (18480),

Mumbai Suburban (17477) in Maharashtra and North (14973) and South (10935 districts of

Delhi. Moreover, the population density has been found to be exceptionally high in South West

and New Delhi districts of the National Capital Territory of Delhi and in Chandigarh where the

population living in one square kilometre ranged from 5000 to 10000 according to the

provisional population figures of the 2011 population census. In the National Capital Territory

of Delhi, the population density has been found to be more than 5000 persons per square

kilometres in 8 out of 9 districts. Even in the ninth district (East district) also, the population

density has been found to be very close to 4000 persons per square kilometre.

On the other hand, in 95 districts of the country, the population density has been found to be less

than 150 persons per square kilometre. These districts include 38 districts where the population

density has been found to be less than 50 persons per square kilometre according to the 2011

population census. District Dibang Valley in Arunachal Pradesh has the distinction of having the

lowest population density of just 1 person per square kilometre in the country. In Anjaw, Tirup

Upper Siang Valley districts of Arunachal Pradesh and in district Lahul and Spiti in Himachal

Pradesh, the population density has been estimated to be less than 5 persons per square kilometre

according to the provisional figures of the 2011 population census.

Out of the 95 districts of the country where the population density is estimated to be the lowest

in the country on the basis of the provisional figures of the 2011 population census, 61 or almost

one third districts are located in eight states - Arunachal Pradesh (16), Mizoram (8), Andaman

and Nicobar Islands (3), Nagaland (9), Chhattisgarh (8), Uttarakhand (7), Manipur (5) and

Meghalaya (5). In Andaman and Nicobar Islands, Arunachal Pradesh and Mizoram, population

density has been estimated to be less than 150 persons per square kilometre in all the districts.

In Nagaland, population density has been found to be less than 150 persons per square kilometre

in 9 out of 11 districts while in Meghalaya, very low population density is estimated in 5 out of

7 districts. All these states are located in the north-eastern part of the country which is full of

forests and mountains.

51


Table 3.2

Districts by population density (per square kilometre), 2011

State/Union Territory Population density< 150 150-300 300-450 450-600 $ 600 Total


14.8 25.5 16.6 10.5 32.7 100.0


52


Figure 3.5Population density in districts of India, 2011

53


dc(x) dc(x) dc(x)The distribution of districts according to indexes E , I , and D is given in tables 3.3,

dc(x)3.4 and 3.5 respectively. The ten districts of the country which have the highest value of D

are, in order, Mumbai Suburban in Maharashtra, Bangalore in Karnataka, Chennai in Tamil

Nadu, North Twenty Four Parganas in West Bengal, Kolkata in West Bengal, North-West

District in Delhi, Hyderabad in Andhra Pradesh, Mumbai in Maharashtra, Thane in Maharashtra

and North-East District in Delhi. All these districts, except district North Twenty Four Parganas

in West Bengal, are metropolitan districts with almost cent per cent urban population. In seven

of these ten districts - Mumbai suburban, Chennai, Kolkata, North-West Delhi, Hyderabad,

dc(x)Mumbai and North-East Delhi - very high value of D is primarily due to very high values of

dc(x)the index of intensiveness in population distribution, I whereas in Bangalore and North

Twenty Four Parganas districts, the very high value of the index of population distribution is

dc(x)primarily due to very high value of the index of extensiveness, E , which is amongst the

highest in the country.

dc(x)On the other hand, the lowest value of the index D has been estimated in district Kuchchh

dc(x)in Gujarat. Other districts with lowest level of the index D are five districts of Rajasthan -

Barmer, Bikaner, Jodhpur in, Nagaur and Churu - three districts of Andhra Pradesh - Anantpur,

Mahbubnagar and Adilabad - and Surguja in Chhattisgarh. In these districts, interestingly, the

lowest index of population distribution is not because of the lowest levels of the index of

intensiveness of population distribution in the country but because of the fact that moderately low

levels of the index of intensiveness in population distribution in these districts are associated with

moderately high levels of the index of the extensiveness of population distribution so that the

product of the two is the lowest in the country. The index of the intensiveness of population

distribution has actually been found to be the lowest in six districts of Arunachal Pradesh -

Dibang Valley, Anjaw, Upper Siang, Upper Subansiri, West Kameng and West Siang - two

districts of Jammu and Kashmir - Leh and Kargil - and Lahul and Spiti in Himachal Pradesh and

dcNorth Sikkim in Sikkim. However, the index of the extensiveness of population distribution, E

in these districts is also amongst the lowest in the country so that the product of the two or the

dc(x)index of population distribution, D , is not the lowest in these districts. The index of

dc(x)intensiveness of the population distribution, I , is actually a surrogate of the concentration of

the population in the district relative to the area but it does not take into consideration the size

of the population over which this intensity or concentration of the population prevails. The index

dc(x)of population distribution, D , on the other hand, takes into consideration both the

dc(x)concentration of the population, measured in terms of the index I and the size of the

population, measured in terms of the index of extensiveness, over which this concentration

prevails.

54


Table 3.3

dcDistricts by the index E , 2011

dcState/Union Territory E< 0.5 0.5-1.5 1.5-2.5 2.5-3.5 $3.5 Total


18.3 40.9 21.9 11.9 7.0 100.0


55


Figure 3.6

dcDistribution of the index E across districts, 2011

56


Table 3.4

dc(x)Distribution of districts by the index I , 2011

dc(x)State/Union Territory I<-0.25 -0.25 to 0 0 to 0.25 0.25 to 0.50 $0.50 Total


26.3 24.1 22.2 18.0 9.5 100.0


57


Figure 3.7

dc(x)Distribution of the index I in districts of India, 2011

58


Table 3.5

dc(x)Distribution of districts by the index D , 2011

dc(x)State/Union Territory D<-0.5 -0.5 to 0 0 to 0.5 0.5-1.0 $1.0 Total


5.2 46.1 26.6 10.0 12.2 100.0


59


Figure 3.8Index of population distribution in districts of India, 2011

60


The inter-district variations in the index of population distribution is the result of both inter-

district variation in the index of extensiveness of population and inter-district variation in the

index of intensiveness of population. The index of extensiveness of population varies from a low

of 0.048 per 1000 population in district Dibang Valley of Arunachal Pradesh to a high of 66.467

per 1000 population in district Thane in Maharashtra. In 117 districts of the country, the

dc(x)extensiveness of population, measured in terms of the index E has been found to be very low

(Table 3.3). In these districts, population as proportion of the population of the country is

amongst the lowest in the country according to the 2011 population census. More than half of

these districts are located in six states of the country - Arunachal Pradesh (16), Jammu and

Kashmir (14), Nagaland (11), Manipur (9), Mizoram (8) and Uttarakhand (6). In all districts of

Arunachal Pradesh, Manipur, Mizoram and Nagaland, the index of extensiveness has been found

to be less than 0.50 per 1000 population at the 2001 population census. These four states

constitute the north-eastern border of the country. Besides these four states, the index of

extensiveness of population has also been found to be amongst the lowest in the country in all

the three districts of the Union Territory of Andaman and Nicobar islands. The index of the

extensiveness of population has also been found to be extremely low in the border districts of

Jammu and kashmir and Uttarakhand.

By contrast, there are 45 districts in the country where the extensiveness of population found to

be amongst the highest in the country. All these districts have an index of extensiveness of

population at least 3.50 per 1000 population. All but six of these districts are located in the just

five states of the country - Uttar Pradesh (11), West Bengal (10), Maharashtra (7), Bihar (6) and

Andhra Pradesh (5). In addition, in two districts of Gujarat and Karnataka and one district of

Rajasthan and Tamil Nadu, the index of extensiveness of population has been found to be among

the highest in the country. The population of these districts as proportion to the population of the

country is the largest among all districts of the country.

The index of intensiveness of population, on the other hand, varies from a low of -2.256 in

district Dibang Valley of Arunachal Pradesh to a high of 2.133 in district Mumbai in

Maharashtra. There are 61 districts where the index of intensiveness of the population has been

found to be amongst the highest in the country. In all these districts, the population is at least

three times the area of the district. Most of these districts are located in only four states - Bihar

(14), Uttar Pradesh (12), Delhi (9) and West Bengal (6). In these districts, there is a very heavy

concentration of the population. All districts of the National Capital Territory of Delhi have a

very high index of the intensiveness of population distribution as population density in all the 9

is more than 5000 persons per square kilometre.

61


On the other hand, in 168 districts of the country, the index of intensiveness of the population

has been found to be very low. In these districts, the population enumerated at the 2011

population census is only about 55 per cent or less of the geographical area of the district. More

than half of these districts are located in Madhya Pradesh (25), Arunachal Pradesh (16),

Rajasthan (12), Chhattisgarh (12), Orissa (12) and Uttarakhand (9). In Madhya Pradesh, the index

of the intensiveness of the population has been found to be very low in 25 of the 50 districts in

the state. In Andaman and Nicobar Islands, Mizoram, Meghalaya, Nagaland and Sikkim also,

dc(x)the index of the intensiveness of the population, measured in terms of the index I has been

estimated to be very low either in all or in majority of the districts.

62

4Age Composition

Age composition of the population is one of the basic demographic variables. It is intertwined

with all other demographic variables. It affects and is affected by the three determinants of

population growth - fertility, mortality and migration. The age composition of the population is

directly related to different stages of demographic transition. The early stage of demographic

transition is characterised by high birth rate and high death rate. The age structure of a population

at the early stage of demographic transition is typically young - a large proportion of population

is in the younger ages - so that the population pyramid is triangular in shape with a large base and

a thin top. As demographic transition advances, first the death rate and then the birth rate

decreases. A decrease in the death rate is normally associated with a decrease in the death rate

of the child population also so that a decrease in the death rate leads to an increase in the

proportion of the child population to the total population if the birth rate remains unchanged. In

the third stage of demographic transition, the birth rate starts decreasing which results in a

decrease in the number of births and the base of the population pyramid starts shrinking. The

continued decrease in the death rate and the birth rate results in continued shrinking of the base

of the population pyramid and its bulging in the middle ages. Further advancement in

demographic transition results in the upward movement of the bulge in the population pyramid

and it turns more and more rectangular in shape. When very low levels of the birth rate and the

death rate persist for a long period, the shape of the population pyramid again turns triangular but

with a very narrow base and a broad top which implies that most of the population is

concentrated in the old ages. An analysis of the age composition of the population, therefore,

provides an idea about the stage of the demographic transition.

63


The age composition of the population is also influenced by the patterns of migration. Although,

people of all ages and both sexes can migrate, yet, the available evidence suggests that, like

fertility and mortality, migration is also age-selective. In general, migration is quite common

among men of early working ages who migrate in search of either better livelihood opportunities

or for education and learning and has an impact on the age composition of the population.

The provisional figures of the 2011 population census do not provide information about the age

composition of the population according to the conventional quinquennial age groups. However,

these figures provide information about the population below 7 years of age for the combined

population as well as separately for males and females for all the 35 states and Union Territories

and for all the 640 districts of the country. This information provides an opportunity to have a

preliminary analysis of the age composition of the population. This analysis also provides the

first hand information about the stage of age structure transition in different states/Union

Territories of the country as well as in different districts of the country. It may be pointed out here

that population census is the only source of information about the age composition of the

population at the district level in the country.

Two indexes of the age composition of the population can be calculated on the basis of the

provisional figures of the 2011 population census. The first is the proportion of the population

aged 0-6 years to the total population while the second is the ratio of the population aged 0-6

6years to the population aged 7 years and above. If P denotes the total population, P the

7population aged 0-6 years and P the population aged 7 years, then the proportion of the

population 0-6 years to the total population C is defined as

6C = P /P (4.1)

whereas the age composition index (A) may be defined as the ratio of the population aged 0-6

years to the population aged 7 years, or

6 7A = P /P (4.2)

The index A is better than the index C in the sense that in the calculation of the index C, the

population aged 0-6 years appears in both numerator and denominator whereas the numerator and

the denominator in the calculation of the index A are mutually exclusive. The indexes C and A

however have the limitation that they do not have additive and multiplicative properties. As such,

it is not possible to analyse how prevailing level of the index C or A in a district contributes to

the index C or A of the state/Union Territory or the country as a whole. In other words, these

indexes do not take into account the distributive property of the data available through the

population census.

64

Age Composition

In order to take into account the distributive property of the data available through the population

census, we follow the approach outlined in chapter 1 and develop relative measures of age

composition on the basis of the index A of the age composition defined by the equation (4.2).

We define the deviations of the age composition index A, in district d from the age composition

index A for the country and for the state as

dc(a) d cI = log (A /A ) for all d0c, (4.3)

ds(a) d sI = log (A /A ) for all d0s, and (4.4)

sc(a) s cI = log (A /A ) for all s0c. (4.5)

Then the distribution indexes of the age composition (D) in district d are defined as

dc(a) = dc * dc(a)D E I for all d0c, (4.6)

ds(a) = ds * ds(a)D E I for all d0s, and (4.7)

sc(a) = sc * sc(a)D E I for all s0c (4.8)

and the distribution indexes of the age composition at the country and the state/Union Territory

level may then be defined as

cd(a) dc(a) D = 3D for all d0c, (4.9)

cs(a) sc(a) D = 3D for all s0c, and (4.10)

sd(a) ds(a) D = 3D for all d0s, (4.11)

Finally, it is easy to see that

cd(a) ds(a) cs(a) sc(a) sd(a)D = 3E *D + 3E * D (4.12)

dc(a)The distributive index of age composition in districts d, D , is a relative index of age

composition in district d relative to the age composition of the country as a whole. It tells the

extent to which the age composition index of the district d, the index A, deviates from the age

composition index of the country as a whole and to what proportion of the population, this

dc(a) deviation applies. Thus, the distributive index D takes into account both the extent of the

deviation of the age composition index and the extensiveness of this deviation which is measured

in terms of the proportionate distribution of the population across the districts and states/Union

dc(a)Territories of the country. The advantage of the index D , as discussed in Chapter 1, is that

it can be decomposed into two components. One component of the index is related to the

distribution of the age composition index A across the districts within the state/Union Territory

while the second component is related to the distribution of the index across the states/Union

Territories within the country. Conventional measures of age composition like the proportion of

population aged 0-6 years to the total population or the age composition index A - ratio of the

population aged 0-6 years to the population aged 7 years and above - or any other similar index

of age composition do not have these additive and distributive property which is inherent in the

census data.

65


Age Composition of Population in India

According to the provisional figures of the 2011 population census, total population aged 0-6

years in India was 158.789 million at 00:00 hours of 1 March 2011. The corresponding numberst

at the 2001 population census was 163.82 million which suggests that population aged 0-6 years

in the country decreased marginally - by approximately 5 million or about 3 per cent - during the

ten-year period between 2001 and 2011. At the same time, the population aged 7 years and

above in the country increased by approximately 187 million - from around 864.79 million in

2001 to around 1051.40 million in 2011 or by almost 22 per cent during this period. As a result,

the age composition index, the index A, decreased from around 19 per cent in 2001 to around 15

per cent in 2011 in the country. This is another encouraging finding of the 2011 population

census. However, it may be pointed out that the decrease in the age composition index has largely

been the result of the increase in the population aged 7 years and above and not the result of the

decrease in the population aged 0-6 years.

A marginal decrease in the population aged 0-6 years may be attributed to the net effect of the

decrease in the number of live births as a result of the decrease in the birth rate and an increase

in the population aged 0-6 years as the result of the decrease in the death rate in this age group.

The provisional figures of the 2011 population census suggest that the decrease in the number

of live births in the country during the period 2001-2011 has largely been compensated by the

increase in the population of 0-6 years of age as the result of the decrease in the death rate in this

age group so that there has been only a marginal change in the population aged 0-6 years. On the

other hand, a relatively faster increase in the population aged 7 years and above may be attributed

to the decrease in the death rate in the population aged 7 years and above and to the momentum

effect, the effect of the high birth rate in the past on the growth of the population (Table 4.1). The

provisional figures of the 2011 population census do not provide any idea about mortality in the

population aged 7 years and above but the information available through the sample registration

system gives no indication about any accelerated decrease in mortality in the population aged 7

years and above in the country. It therefore appears that the increase in the population aged 7

years and above in the country during the period 2001-2011 has primarily been due to the

momentum effect - the broad base of the population pyramid moving upwards with time which

appears to have contributed to a rapid increase in the proportion of the population aged 7 years

and above during the period 2001-2011. In any case, it is apparent from the table 4.1 that the

population of the country continues to be young and the associated population pyramid remains

typically triangular in shape. At the same time, it is also apparent from the provisional figures of

the 2011 population census that there has been only a marginal change in the age composition

of the population.

66

Age Composition

The age composition index A may be perceived as a crude indicator of the age structure of the

population. It is obvious that the higher is this ratio, the younger is the age structure of the

population and the larger are the resources requirements for meeting the development needs of

children 0-6 years of age in terms of their survival, growth and development as well as in terms

of their productive utilisation and participation in the social and economic production system,

housing and shelter needs, etc. in the years to come when they enter into the productively active

life or the adulthood. On the other hand, a low value of the index A suggests that the proportion

of the population aged 7 years and above is relatively high. In this context, the age composition

index A reflects the implications of the age structure of the population on the social and

economic production system and on social and economic development processes. At the same

time, the index A also reflects the transition in the demographic processes as it is well known that

the age structure of the population is essentially a reflection of the transition in fertility and

mortality and the patterns of migration.

Figure 4.1Age composition of population in India

67


Table 4.1

Age composition of the population in India

Age group Population

(million)

Change

2001 2011 Absolute

(million)

Proportionate

(per cent)

< 7 years 163.82

(15.92)

158.789

(13.12)

-5.031 -3.07

$7 years 864.79

(84.08)

1051.404

(86.88)

186.614 21.58

All 1028.61

(100.00)

1210.193

(100.00)

181.583 17.65

6 7Index A = P /P 0.189 0.151

Distributive index of age

composition

10.593

Remarks: Figures in parentheses denote percentages. For the definition of the distribution indexof age composition, see text.


The provisional figures of the 2011 population census also suggest that the distribution index of

cd(a)age composition, D was around 10.593 per 1000 population in the year 2011 for the country

as a whole. This index reflects the distribution of the age composition of the population measured

in terms of the age composition index A across the districts of the country. Nearly 70 per cent

cs(a)of this index is accounted by the index D which reflects the distribution of the index of the

age composition of the population (index A) across the states/Union Territories while the

sd(a)remaining 30 per cent is accounted by the index D which reflects the distribution of the index

of the age composition of the population across districts within the states/Union Territories of

the country. This implies that the age structure of the population varies more across the

states/Union Territories of the country as compared to the variation in the age structure of the

population across districts within the same state/Union Territory. This is expected as the

states/Union Territories of the country are at different stages of demographic transition which has

a reflection in terms of the age composition of the population. However, within a state/Union

Territory, the variation in the age composition of the population, as measured by the index A,

appears to be relatively small.

68

Age Composition

The provisional figures of the 2011 population census also provide information about the

population aged 0-6 years by the sex of the child. This information suggests that male children

outnumbered female children by more than 7 million or by more than 8 per cent in the age group

0-6 years whereas male population outnumbered female population by more than 30 million or

by about 5 per cent in the age group 7 years and above. The male-female difference in the

population of children aged 0-6 years is influenced by the sex ratio at birth and differentials

mortality by sex whereas male-female difference in the population aged 7 years and above

depends upon the sex ratio at 7 years of age and differential mortality by sex in the age group 7

years and above. The sex ratio at birth is favourable to males so that there are more male births

than female births. If female mortality is higher than the male mortality in the age group 0-6

years, then there would be relatively lesser number of female survivors than male survivors in

the age group 0-6 years. As a result, males will outnumber females in the age group 0-6 years as

is the case in India.

Figure 4.2Age composition of population in India by sex

69


Table 4.2

Age composition of the population in India by sex, 2011

Age group Population

(million)

Difference

Male Female Absolute

(million)

Proportionate

(per cent)

< 7 years 82.952 75.837 -7.115 -8.58

$7 years 540.772 510.632 -30.14 -5.57

All 623.724 586.469 -37.255 -5.97

6 7Index A = P /P 0.153 0.149

Source: 2011 population census

The very fact that the age structure of the male population in the country is younger than that of

the female population is reflected through the age composition index A which is higher for males

as compared to females, although the gap is not very large. Table 4.2 suggests that for every 1000

males aged 7 years and above, there were more than 153 males aged 0-6 years in the country

according to the 2011 population census. The corresponding number for females was only 149.

A part of this difference is due to the difference in the proportion of males aged 0-6 years to total

males and proportion of females aged 0-6 years to total females. One way to remove this

structure effect is to calculate males aged 0-6 years and females aged 0-6 years as proportion to

the average of the male and female population aged 7 years and above and than calculate the

male-female difference. It is possible to separate the structure effect from the level effect by

decomposing the observed difference in the male and female age composition index in the

following manner:

m f m f f m f f m f m fI - I = (a - a )*p + (p - p )*a +(a - a )*(p - p ) (4.13)

where

m 6 7I = age composite index for males = M /M ,

f 6 7I = age composition index for female = F /F

m 6 7 7a = M /((M +F )/2)

f 6 7 7a = F /((M +F )/2)

m 7 7 7p = ((M +F )/2)/M

f 7 7 7p = ((M +F )/2)/F

and M stands for the enumerated male population while F stands for the enumerated female

population.

70

Age Composition

The first term on the right side of the equation (4.13) gives difference in the age composition

index when the effect of the male-female difference in the proportion of population 7 years and

above is removed or when the proportion of males and the proportion of females aged 0-6 years

are calculated on the basis of the average male and female population aged 7 years and above.

On the other hand, the second term on the right side of the equation (4.13) reflects the difference

in the age composition index accounted by the male-female difference in the proportion of the

population 7 years and above. Finally, the third term is an interaction term which reflects the

combined effect of the difference in the level and the difference in the structure effects.

Application of the decomposition formula (4.13) to India suggests that when the male and female

population aged 0-6 years is calculated as the ratio of the average male and female population

aged 7 years and above, the male-female difference in the proportion of the population aged 0-6

years is around 14 per 1000 population - 158 per 1000 in males compared to only 144 per 1000

in females. This difference is highly unfavourable to females and suggests that males aged 0-6

years substantially outnumber females aged 0-6 years in the country. A part of this difference

may be due to male-female difference in the number live births or the male-female difference in

the birth rate. At the same time, this difference may also be the result of male-female difference

in mortality in the age group 0-6 years. An understanding of the determinants of the observed

difference between the number of males aged 0-6 years and the number of females aged 0-6 years

requires estimates of male and female birth rate and male and female death rate in the age group

0-6 years which are currently not available through the provisional figures of the 2011 population

census.

Age Composition across States/Union Territories

Estimates of the index age composition (index A) for the states and Union Territories of the

country are given in table 4.3 for the year 2001 and 2011. The index has been found to be the

lowest in Goa and the highest in Meghalaya. The index has also been found to be very high in

Bihar, Jammu and Kashmir and Jharkhand but very low in Kerala, Andhra Pradesh and

Karnataka. The wide variation in the index A across the states/Union Territories of the country

suggests that the age structure of the population varies widely across the states/Union territories

of the country which is a reflection of the fact that different states/Union Territories of the

country are at different stages of demographic transition. Estimates of the index of the age

composition (index A), presented in table 4.3, suggest that the demographic transition in states

like Bihar, Jammu Kashmir and Jharkhand is yet to pick up the momentum whereas the transition

appears to be fairly advanced in states like Kerala, Andhra Pradesh and Karnataka and very

advanced in Goa.

71


Table 4.3

Index of age composition (index A) in states and Union Territories, 2001-2011

Country/State Population 0-6 years as proportion to the population7 years and above

2001 2011 DifferenceIndia 0.189 0.151 -0.038Andaman and Nikobar 0.144 0.116 -0.028Andhra Pradesh 0.154 0.114 -0.040Arunachal Pradesh 0.231 0.172 -0.059Assam 0.203 0.169 -0.034Bihar 0.254 0.218 -0.036Chandigarh 0.147 0.126 -0.021Chhattisgarh 0.206 0.163 -0.043Dadra and Nagar Haveli 0.223 0.168 -0.055Daman and Diu 0.150 0.119 -0.031Delhi 0.170 0.133 -0.037Goa 0.121 0.106 -0.015Gujarat 0.175 0.142 -0.033Haryana 0.187 0.150 -0.037Himachal Pradesh 0.150 0.125 -0.025Jammu and Kashmir 0.172 0.191 0.019Jharkhand 0.225 0.189 -0.036Karnataka 0.157 0.126 -0.031Kerala 0.135 0.111 -0.024Lakshadweep 0.176 0.124 -0.052Madhya Pradesh 0.218 0.170 -0.048Maharashtra 0.164 0.129 -0.035Manipur 0.155 0.149 -0.006Meghalaya 0.253 0.231 -0.022Mizoram 0.193 0.179 -0.014Nagaland 0.170 0.169 -0.001Orissa 0.170 0.136 -0.034Puducherry 0.137 0.114 -0.023Punjab 0.150 0.119 -0.031Rajasthan 0.232 0.181 -0.051Sikkim 0.169 0.112 -0.057Tamil Nadu 0.131 0.106 -0.025Tripura 0.158 0.138 -0.020Uttar Pradesh 0.235 0.175 -0.060Uttarakhand 0.191 0.151 -0.040West Bengal 0.166 0.124 -0.042


72

Age Composition

Figure 4.1Distribution of index A across

states/Union Territories in India, 2011

73


Table 4.3 also suggests that the index of age composition has decreased in all the states/Union

Territories of the country during the period 2001-2011 except the state of Jammu and Kashmir.

However, the magnitude of the decrease varies across the states/Union Territories. The decrease

in the index has been the most rapid in Uttar Pradesh followed by Arunachal Pradesh, Sikkim,

Lakshadweep and Dadra and Nagar Haveli in that order. By contrast, there has been little change

in the index in Nagaland, Manipur, Mizoram and Goa. Out of these four states, Goa has the

lowest index of age composition in the country. In the remaining three states, there is a need to

explore the reasons behind the stagnation in the transition in the age structure of the population

as revealed through the provisional results of the 2011 population census. Similarly, there is also

a need to explore the reasons for the reversal of the transition in the age structure of the

population In Jammu and Kashmir where the index A increased from around 17 per cent in 2001

to more than 19 per cent in 2011.

Table 4.4 gives the estimates of distributive indexes of the age composition across the

states/Union Territories of the country. The distributive index of the age composition for a

state/Union Territory can be estimated in two ways - in relation to the index of age composition

sc(a)of the country as a whole (the index D ) and in relation to the index of the age composition

ds(a) sc(a)of the districts within the state/Union Territory (the index D ). The index D has been

found to vary from a high of 13.68 in Bihar to a low of -9.24 in Tamil Nadu. In Bihar, the index

of age composition has been found to be more than 1.44 times the corresponding index for the

country as a whole. At the same time, Bihar accounted for more than 8.5 per cent of the

population of the country. By contrast, the index of age composition in Tamil Nadu was less than

0.70 times the index at the national which implies that the proportion of the population aged 0-6

years to the total population in the state is the lowest in the country. Tamil Nadu accounted for

around 6 per cent of the population of the country at the 2011 population census. Besides Bihar,

Uttar Pradesh is the only other state/Union Territory in the country where a very high value of

sc(a)the index D has been estimated. On the other hand, at the other extreme of the scale of the

distributive index of the age composition are Andhra Pradesh, West Bengal and Maharashtra. All

the three states accounts for a large proportion of the population of the country.

sd(a)Table 4.4 also gives the estimates of the distributive index D captures the distribution of the

age composition index across the districts within the states/Union Territories. Combining the

sc(a) sd(a)index D with the index D according to equation (4.12) gives the state share to the index

cd(a)D . This exercise suggests that Bihar and Uttar Pradesh account for the largest share of the

distribution of the index of the age composition across the districts of the country irrespective

of the direction of the index.

74

Age Composition

Table 4.4

Distributive indexes of age composition of population in states and Union Territories, 2011

sc sc(a) cd(a)Country/State E D D

Andaman and Nikobar 0.314 0.000 -0.036

Andhra Pradesh 69.960 -0.128 -8.758

Arunachal Pradesh 1.143 -0.005 0.059

Assam 25.756 -0.151 1.122

Bihar 85.775 -0.096 13.585

Chandigarh 0.872 0.000 -0.069

Chhattisgarh 21.104 -0.030 0.682

Dadra and Nagar Haveli 0.283 0.000 0.013

Daman and Diu 0.201 0.000 -0.021

Delhi 13.843 -0.012 -0.763

Goa 1.205 -0.001 -0.187

Gujarat 49.896 -0.224 -1.606

Haryana 20.950 -0.097 -0.188

Himachal Pradesh 5.666 -0.009 -0.467

Jammu and Kashmir 10.369 -0.119 0.928

Jharkhand 27.241 -0.099 2.548

Karnataka 50.513 -0.283 -4.202

Kerala 27.589 -0.135 -3.878

Lakshadweep 0.053 0.000 -0.005

Madhya Pradesh 59.988 -0.178 2.905

Maharashtra 92.855 -0.333 -6.660

Manipur 2.249 -0.006 -0.018

Meghalaya 2.449 -0.008 0.444

Mizoram 0.902 -0.003 0.063

Nagaland 1.637 -0.010 0.069

Orissa 34.662 -0.179 -1.710

Puducherry 1.028 0.000 -0.125

Punjab 22.892 -0.012 -2.399

Rajasthan 56.703 -0.132 4.294

Sikkim 0.502 0.000 -0.066

Tamil Nadu 59.609 -0.076 -9.320

Tripura 3.033 -0.009 -0.132

Uttar Pradesh 164.917 -0.361 10.201

Uttarakhand 8.360 -0.012 -0.007

West Bengal 75.482 -0.555 -6.890

India 10.593


75


Figure 4.2Distributive index of age composition in

Indian states/Union Territories, 2011

76

Age Composition

Age Composition across Districts

District level estimates of the index of age composition (index A) are given in table 4.A while

table 4.5 presents the distribution of districts according to this index across the states/Union

Territories of the country. In all but 127 districts of the country, the index A varies within the

narrow range of 0.100 to 0.200 which implies that in most of the districts of the country,

population aged 0-6 years are 10-20 per cent of the population 7 years and above. There are 29

districts where the index A is estimated to be less than 0.100 which implies that population aged

0-6 years in these districts is less than 10 per cent of the population 7 years and above. Most of

these districts are located in Karnataka (4), Kerala (7) and Tamil Nadu (9).

On the other hand, in 98 districts of the country, population aged 0-6 years is estimated to be

more than 20 per cent of the population 7 years and above. Most of these districts are located in

Bihar (31), Jammu and Kashmir (11), Jharkhand (10) Rajasthan (8), Uttar Pradesh (8),

Meghalaya (6) and Madhya Pradesh (6). Out of these 98 districts, there are 9 districts where the

ratio of the population aged 0-6 years to the population aged 7 years and above has been

estimated to be at least 25 per cent. These nine districts are Araria, Khagaria and Kishanganj in

Bihar, Badgam and Kupwara in Jammu and Kashmir, Jaintia Hills and West Khasi Hills in

Meghalaya, Mewat in Haryana, and Jhabua in Madhya Pradesh.

Table 4.A also presents estimates of the distributive index of the age composition for all the 640

districts of the country. The distribution of districts according to the distributive index of age

composition in different states/Union Territories is given in table 4.7. There are 20 districts in

the country where the distributive index of age composition has been found to be very low which

suggests that the ratio of the population aged 0-6 years to the population aged 7 years and above

(the index A) in these districts is well below the corresponding index at the national level.

District North Twenty Four Parganas in West Bengal has the distinction of having the lowest

distributive index of age composition in the country. The index A has been estimated to be 0.098

in this districts which is just around 65 per cent of the corresponding index at the national level.

At the same time, the total population of the district at the 2011 population census was more than

1 million so that the distributive index of age composition in the district is estimated to be the

highest in the country. In fact, the index A has been the lowest in district Kolkata of West Bengal

- just around 47 per cent of the index at the national level. However, population of district

Kolkata accounts for only about 0.37 per cent of the population of the country at the 2011

population so that the distributive index of age composition of the population in district Kolkata

is estimated to be the third lowest in the country - next to North Twenty Four Parganas in West

Bengal and Mumbai Suburban in Maharashtra.

77


Table 4.5

Distribution of districts by the index A, 2011

State Index A< 0.10 0.10-0.15 0.15-0.20 0.20-0.25 $ 0.25 Total


4.4 42.7 37.5 14.1 1.4 100.0


78

Age Composition

Figure 4.3Proportion of population aged 0-6 years to the population aged 7 years and above

in districts of India, 2011

79


Table 4.6

dc(a)Distribution of districts by the index D , 2011

cd(a)State D< -0.05 -0.05 to -0.025 -0.025 to 0 0 to 0.025 $ 0.025 Total


3.1 8.9 35.9 44.5 7.5 100.0


80

Age Composition

Figure 4.4Distributive index of age composition in

districts of India, 2011

81


Most of the districts in which the distributive index of age composition has been estimated to be

amongst the lowest in the country are located in Andhra Pradesh (6), West Bengal (5) and

Maharashtra (4). Other districts with the very low distributive index of age composition are

located in Kerala (2), Tamil Nadu (2) and Karnataka (1). Some of these districts are highly

urbanised districts - Mumbai Suburban, Kolkata, Bangalore, Chennai, Thane, Coimbatore,

Mumbai, Pune, etc. One reason for the very low distributive index of age composition in these

districts may be very high proportion of the population aged 7 years and above as a result of the

large scale in-migration of working age population. The proportion of population aged 0-6 years

may also be very low in these districts because of low fertility.

At the other extreme of the scale, there are 48 districts in the country where the distributive index

of age composition has been estimated to be very high - the highest in the country - with district

Purba Champaran in Bihar topping the list. The index A in this district has been estimated to be

0.243 which is around 60 per cent higher than the index at the national level. Out of these 48

districts, 27 are located in Bihar alone. In addition, 11 districts in Uttar Pradesh, 5 districts in

Rajasthan, 2 districts in Gujarat and 1 district each in Assam, Haryana and Jharkhand also have

a very high distributive index of age composition, well above the national average. The very high

distributive index of age composition in these districts reflects either high fertility or some

substantial out migration of the working age population.

In all, out of the 640 districts of the country which existed at the time of 2011 population census,

the distributive index of age composition has been found to be negative in 303 (48 per cent)

districts whereas the index has been positive in 333 (52 per cent) districts. In all the 38 districts

of Bihar and in all the 7 districts of Meghalaya, the distributive index of age composition has

been found to be positive. Other states where the distributive index of age composition has been

found to be negative in majority of districts are: Assam (18 out of 27 districts), Chhattisgarh (13

out of 18 districts), Jammu and Kashmir (17 out of 22 districts), Jharkhand (23 out of 24

districts), Madhya Pradesh (39 out of 50 districts), Nagaland (8 out of 11 districts), Rajasthan (30

out of 33 districts), and Uttar Pradesh (67 out of 71 districts). On the other hand, out of the 333

districts where the distributive index of age composition has been found to be positive, 260

districts (78 per cent) are located in these 11 states. All these states are located in the central or

in the north-eastern part of the country except the state of Jammu and Kashmir. In 10

states/Union Territories, there is no district where the distributive index of age composition has

been negative. These states and Union Territories are Andhra Pradesh, Goa, Punjab, Sikkim,

Tamil Nadu, Andaman and Nicobar Islands, Chandigarh, Daman and Diu, Lakshadweep and

Puducherry.

82

5Sex Composition

The personal characteristic of sex holds an important position in demographic studies for a

number of reasons. The sex composition of the population, in conjunction with the age structure,

is an important determining factor in population growth as it has relevance to all the three

components of growth. First, procreation is confined to females of a specific age group only.

Second, the risk of death varies by sex and age of the individual. Third, migration - in or out -

is always sex and age selective. As such, the sex composition of the population influences the

demographic transition process. In addition, separate data for males and females are important

for the evaluation and completeness of the census count. The sex composition of population also

affects social and economic relationship within a community thereby influencing social roles and

cultural patterns and affecting patterns of migration. Imbalances in the sex composition of the

population have implications to patterns of marriage which has relevance to fertility in a country

like India where marriage signals the beginning of the socially accepted sexually active

reproductive period.

Three measures are generally used to measure and analyse the sex composition of the population.

The first is the masculinity proportion which is defined as the proportion of the male population

to the total population. The second measure is the population sex ratio which is defined as the

ratio of male to female population. The third measure, on the other hand, is the excess (or deficit)

of males as proportion to the total population. The three measures are inter-related. If M denotes

the masculinity proportion, S denotes the sex ratio, and X denotes the excess of males as

proportion to the total population, then it is straightforward to show that

83


M = S/(1+S) (5.1)

S = M/(1-M) (5.2)

X = M - (1-M) (5.3)

and

X = (S-1)/(S+1) (5.4)

The three measures of the sex composition of the population described above have been defined

in reference to males. They can also be defined in terms of females. Thus the femininity

proportion is the ratio of the female population to the total population whereas the sex ratio is

the ratio of the female population to the male population which is sometimes termed as the

femininity ratio to distinguish it from the masculinity ratio. Similarly, the sex composition of the

population may also be measured in terms of the excess (or deficit) of females as proportion to

the total population. It may however be noted that the measures of sex composition based on

males as the reference are complementary to measures of sex composition based on females as

the reference and one can be obtained from the other.

The three measures of sex composition of the population described above are absolute measures.

They do not have the additive property which can link the sex composition of the population in

the lower level administrative units with the sex composition of the population in the upper level

administrative units. A more rational approach is to use the relative measure of the sex

composition in which the sex composition of the population in an administrative unit is

expressed in relation to the sex composition of the population in other administrative units. Thus,

in relation to the sex composition of the population of the country as a whole, the indexes of the

intensity of sex composition may be defined as

dc(s) d cI = log (S /S ) for all d0c. (5.5)

ds(s) d sI = log (S /S ) for all d0s, and (5.6)

sc(s) s cI = log (S /S ) for all s0c. (5.7)

Then the indexes of sex composition may be defined as

dc(s) dc dc(s)D = E *I (5.8)

ds(s) ds ds(s)D = E *I (5.9)

sc(s) sc sc(s)D = E *I (5.10)

Finally, the indexes of sex composition for the country and the state/Union Territory are defined

as

cd(s) dc(s)D = 3D (5.11)

sd(s) ds(s)D = 3D (5.12)

cs(s) sc(s)D = 3D . (5.13)

84

Sex Composition

Finally, it is easy to show that

cd(s) sc(s) sd(s) ds(s) cs(s)D = 3E *I + 3E *I (5.14)

which decomposes the index of sex composition at the country level into between states/Union

Territories and within state/Union Territory components of the sex composition of the

population.

In India, the sex composition of the population is traditionally measured in terms of the

femininity ratio or the ratio of the female population to the male population. United Nations and

most of the developed countries, on the other hand, use the masculinity ratio which is defined as

the ratio of the male population to the female population to measure the sex composition of the

population. Following the Indian tradition, we have also used, throughout this chapter, the

femininity ratio to measure the ratio of the population of the two sexes at the district as well as

at the state and country level. The femininity ratio is defined as the ratio of the female population

to the male population.

In the absence of migration or in the situation where the net migration is either zero or

insignificant to the natural increase in the population, the sex composition of the population is

determined by the sex ratio at birth and the differential mortality of the two sexes. It is well

known that the sex ratio at birth is favourable to males. The global average sex ratio at birth is

generally assumed to be around 105 male live births for every 100 female live births or around

952 female live births for every 1000 male live births. In India, however, the sex ratio at birth is

estimated to be around 112 male live births for every 100 live births or around 893 female live

births for every 1000 male live births. It has also been observed that the sex ratio at birth in India

varies widely across constituent states/Union Territories and districts. The sex ratio at birth in

India, measured in terms of the ratio of the male live births to female live births is estimated to

be the third highest in the world, next only to China and Armenia. The abnormally high ratio of

the male live births to the female live births in India is a major contributing factor to the

abnormally high ratio of male to female population in the country.

In addition to the sex composition of the live births, the sex composition of the population is also

influenced by differential mortality of the two sexes which varies by age. Since the mortality of

the two sexes varies by age in all populations, the ratio of the population of the two sexes always

deviates from the ratio of the live births of the two sexes or the sex ratio at birth. Moreover,

mortality of different sexes is affected by a host of social, cultural, environmental and health

related factors so that the population sex ratio is also influenced by the prevailing social, cultural

and health related factors.

85


Sex Composition of the Population in India

The provisional figures of 2011 population census provide the count of males and females in the

country as well as in its constituent states, Union Territories and districts for the total population

as well as for the population aged 0-6 years and population aged 7 years and above. This

information suggests that there were around 940 females for every 1000 males in the country at

the time of 2011 population census. The good sign is that the improvement in the population sex

ratio that was observed at the 2001 population census has also continued at the 2011 population

census (Figure 5.1). Although, the population sex ratio in India still continues to be well below

the global average of 984 females per 1000 males (Government of India 2011).

Historically, India has experienced a fall in the population sex ratio continuously for 90 years

between 1901 and 1991. At the 1901 population census, India had a population sex ratio of 972

females for every 1000 males. This number decreased to an all-time low of just 927 females for

every 1000 males at the 1991 population census. The decrease in the population sex ratio was

almost continuous throughout this period except during 1941-51 and 1971-81. The gain in the

population sex ratio in 1951 is generally attributed to the displacement of the population after the

partition of the country in 1947. On the other hand, the gain in the population sex ratio in 1981

has been attributed to some improvements in the situation of women in the country. In fact, it is

only after 1991 that the trend in the population sex ratio in the country appears to have been

reversed. Between 1991 and 2001, the population sex ratio in the country, measured in terms of

the number of females per 1000 males, improved by 6 points whereas this improvement was of

7 points during the period 2001-2011 according to the provisional figures of the 2011 population

census. This suggests that there has been a marginal acceleration in the rate of improvement in

the sex ratio of the population during the period 2001-11 as compared to the period 1991-2001.

This is one of the welcome features of the 2011 population census.

The provisional figures of the 2011 population census also permit calculating the sex ratio in the

population aged 0-6 years and the sex ratio in the population aged 7 years and above. Unlike the

trend in the sex ratio in the population all ages combined, the sex ratio in the population aged 0-6

years is decreasing right since 1961 and this decrease has continued during the period 2001-2011

also. This decrease in the sex ratio in the population aged 0-6 years may be because of the

decrease in the sex ratio at birth or because of the increase in mortality of female population aged

0-6 years compared to the male population or both. The provisional figures of the 2011

population census do not permit analysis of the factors responsible for the decrease in the sex

ratio in the population aged 0-6 years. There is a general apprehension that increasing prevalence

of sex selective abortions has distorted the sex ratio at birth making it more and more

86

Sex Composition

unfavourable to the female live births and this distortion appears to be the primary reason behind

the continued decrease in the sex ratio of population aged 0-6 years. There may also be a

possibility that either the decrease in the female mortality in the age group 0-6 years has been

slower than the decrease in the male mortality in the age group 0-6 years during the period under

reference or the decrease in the female mortality in the age group 0-6 years has stagnated. The

life tables prepared by the Government of India on the basis of the sample registration system do

indicate a slow down in the decrease and even a slight increase in female mortality in the

population below five years of age in the country in recent years (Chaurasia, 2010) which may

have some impact on the sex ratio in the population aged 0-6 years. In any case, a sex ratio of just

914 females aged 0-6 years for every 1000 males aged 0-6 years in the country estimated on the

basis of provisional figures of the 2011 population census is the lowest sex ratio ever recorded

in the country.

Figure 5.1Sex ratio (females per 1000 males) in India, 1901-2011

87


Table 5.1

Females per 1000 males in India, states and Union Territories, 2001-2011

Country/State/

Union Territory

Total population 0-6 years 7 years and above

2011 2001 2011 2001 2011 2001

India 940 933 914 927 944 934Andaman & Nicobar Islands 878 846 966 957 868 831Andhra Pradesh 992 978 943 961 997 981Arunachal Pradesh 920 893 960 964 913 878Assam 954 935 957 965 953 929Bihar 916 919 933 942 912 914Chandigarh 818 777 867 845 812 767Chhattisgarh 991 989 964 975 995 992Dadra & Nagar Haveli 775 812 924 979 752 779Daman & Diu 618 710 909 926 589 682Delhi 866 821 866 868 866 813Goa 968 961 920 938 973 964Gujarat 918 920 886 883 923 927Haryana 877 861 830 819 885 869Himachal Pradesh 974 968 906 896 983 980Jammu & Kashmir 883 892 859 941 887 884Jharkhand 947 941 943 965 948 935Karnataka 968 965 943 946 971 968Kerala 1084 1058 959 960 1099 1072Lakshadweep 946 948 908 959 951 946Madhya Pradesh 930 919 912 932 933 916Maharashtra 925 922 883 913 931 924Manipur 987 974 934 957 995 977Meghalaya 986 972 970 973 989 971Mizoram 975 935 971 964 976 930Nagaland 931 900 944 964 929 890Orissa 978 972 934 953 985 976Puducherry 1038 1001 965 967 1047 1006Punjab 893 876 846 798 899 888Rajasthan 926 921 883 909 935 923Sikkim 889 875 944 963 883 861Tamil Nadu 995 987 946 942 1000 993Tripura 961 948 953 966 962 945Uttar Pradesh 908 898 899 916 910 894Uttarakhand 963 962 886 908 975 973West Bengal 947 934 950 960 946 929


88

Sex Composition

Figure 5.2Females per 1000 males

in states and Union Territories, 2011

89


Figure 5.3Females per 1000 males in population aged 0-6 years

in states/Union Territories, 2011

90

Sex Composition

Figure 5.4Females per 1000 males in population aged 7 years and above


91


On the other hand the sex ratio in the population aged 7 years and above has shown an increasing

trend in the country during the period 2001-2011. At the 2011 population census, there were 934

females for every 1000 males in the country. This number has increased to 944 females for every

1000 males at the 2011 population census - an increase of 10 points for every 1000 males. This

increase in the sex ratio in the population aged 7 years and above suggests that there has been a

relatively faster reduction in the mortality of females aged 7 years and above in the country as

compared to the reduction in the mortality of males aged 7 years and above during the period

2001-2011.

cd(s)The index of the sex composition of the population, D for the country has been found to be

very close to zero in the total population but quite substantial in the population aged 0-6 years

and the population aged 7 years and above. The index is negative in the total population and in

population aged 7 years and above. A negative value of the index implies that males outnumber

females in majority of the population while a positive value implies that females outnumber

cd(s)males relative to the sex ratio at the national level. Since the index D is the sum of the index

dc(s)of sex composition, D , in all the 640 districts of the country, this means that the sum of all

dc(s)negative values of the index D is almost the same as the sum of all positive values of the

index in the total population but not in the population aged 0-6 years and the population aged 7

years and above. Variation in the index of sex composition across the districts of the country in

relation of the sex composition at the national level will be discussed at length in the following

sections.

Sex Ratio in States/Union Territories

Estimates of the sex ratio (females per 1000 males) for the states and Union Territories of India

are given in table 5.1 for the total population as well as separately for the population aged 0-6

years and the population aged 7 years and above for the years 2001 and 2011. Wide variation in

the sex ratio across the states and Union Territories of the country is very much evident from the

table in the three population groups. For the total population, the sex ratio has been found to vary

from just 618 females per 1000 males in Daman and Diu to 1084 females per 1000 males in

Kerala according to the provisional figures of the 2011 population census. In addition to Kerala,

Puducherry is the only other state/Union Territory in the country where there were more females

than males at the 2011 population census. On the other hand, there are nine states/Union

Territories in the country where the sex ratio of the population has been found to be extremely

low, less than 900 females per 1000 males. All these states, except Haryana, are either small

states or Union Territories which account for only a very small proportion of the population of

the country.

92

Sex Composition

As regards the sex ratio in the population aged 0-6 years, there is no state in the country where

there were more females aged 0-6 years than males aged 0-6 years at the 2011 population census.

The highest number of females for every 1000 males in the population aged 0-6 years has been

estimated in Mizoram where there were 971 females aged 0-6 years for every 1000 males aged

0-6 years according to the 2011 population census. In addition to Mizoram, there are nine

states/Union Territories where the sex ratio in the population aged 0-6 years has been found to

be at least 950 females for every 1000 males in the year 2011. Six of these ten states/Union

Territories are located in the north-eastern part of the country. By contrast, there are 10

states/Union Territories of the country, where the sex ratio in the population aged 0-6 years has

been estimated to be less than 900 females per 1000 males on the basis of the provisional data

available through the 2011 population census. The sex ratio in the population aged 0-6 years has

been found to be the lowest in Haryana where there were only 830 females aged 0-6 years for

every 1000 males aged 0-6 years at the 2011 population census. Other states/Union Territories

where the sex ratio in the population aged 0-6 years has been found to be less than 900 females

per 1000 males at the 2011 population census are Punjab (846), Jammu and Kashmir (859), Delhi

(866), Chandigarh (867), Maharashtra (883), Rajasthan (883), Gujarat (886), Uttarakhand (886)

and Uttar Pradesh (899).

Finally, the sex ratio in population aged 7 years and above has been found to be the highest in

Kerala (1099 females per 1000 males aged 7 years and above) followed by Puducherry. These

are the only two states in the country where there were more females aged 7 years and above than

males aged 7 years and above. Moreover, in Tamil Nadu, the number of females aged 7 years and

above were found to be almost the same as the number of males aged 7 years and above so that

the sex ratio in the population aged 7 years and above is very close to 1000 females aged 7 years

and above for every 1000 males aged 7 years and above. By comparison the sex ratio in

population aged 7 years and above has been found to be extremely low in the Union Territory of

Daman and Diu where there were only 589 females aged 7 years and above for every 1000 males

aged 7 years and above at the 2011 population census. In all, in nine states/Union Territories of

the country, the sex ratio in the population aged 7 years and above has been found to be less than

900 females for every 1000 males. All these states are either small states or Union Territories of

the country except the states of Haryana and Punjab. As such, the impact of the prevailing sex

ratio in these states/Union Territories to the sex ratio of the country is insignificant. In any case,

a sex ratio in the population aged 7 years and above in these states and Union Territories which

is extremely unfavourable to females requires an in-depth exploration of the factors and

conditions that may be responsible for the sex ratio in the population aged 7 years and above as

revealed through the 2011 population census.

93


Table 5.2

dc(s)Index D in India and states/Union Territories, 2011

Country/State/

Union Territory

Population

All ages

Population

0-6 years

Population

7 years and aboveAndaman & Nicobar Islands -0.009 0.007 -0.012Andhra Pradesh 1.617 0.951 1.659Arunachal Pradesh -0.011 0.025 -0.017Assam 0.157 0.526 0.104Bihar -0.986 0.719 -1.296Chandigarh -0.053 -0.020 -0.057Chhattisgarh 0.482 0.482 0.484Dadra & Nagar Haveli -0.024 0.001 -0.028Daman & Diu -0.037 -0.001 -0.042Delhi -0.492 -0.323 -0.518Goa 0.015 0.003 0.016Gujarat -0.521 -0.741 -0.496Haryana -0.633 -0.916 -0.596Himachal Pradesh 0.086 -0.024 0.097Jammu & Kashmir -0.286 -0.293 -0.278Jharkhand 0.084 0.345 0.046Karnataka 0.644 0.705 0.622Kerala 1.704 0.570 1.818Lakshadweep 0.000 0.000 0.000Madhya Pradesh -0.284 -0.074 -0.306Maharashtra -0.641 -1.298 -0.565Manipur 0.047 0.020 0.051Meghalaya 0.050 0.062 0.050Mizoram 0.014 0.024 0.013Nagaland -0.007 0.025 -0.012Orissa 0.600 0.275 0.639Puducherry 0.044 0.024 0.046Punjab -0.513 -0.762 -0.492Rajasthan -0.365 -0.897 -0.257Sikkim -0.012 0.007 -0.015Tamil Nadu 1.466 0.908 1.493Tripura 0.029 0.053 0.025Uttar Pradesh -2.496 -1.246 -2.684Uttarakhand 0.086 -0.114 0.115West Bengal 0.219 1.222 0.071

India -0.024 0.247 -0.318


94

Sex Composition

Figure 5.5Index of sex composition in population of all ages


95


Figure 5.6Index of sex composition in population aged 0-6 years


96

Sex Composition

Figure 5.7Index of sex composition in population aged 7 years and above


97


During the period 2001-2011, the population sex ratio turned more favourable to females and it

has increased in all but six states/Union Territories of the country. The six states/Union

Territories where the population sex ratio has decreased during the period under reference are

Bihar, Dadra and Nagar Haveli, Daman and Diu, Gujarat, Jammu and Kashmir, and

Lakshadweep. In Bihar, Gujarat and Lakshadweep, there has been only a marginal decrease in

the population sex ratio but the decrease has been very substantial in Daman and Diu where the

number of females per 1000 males decreased from 710 in 2001 to 618 in 2011. In Dadra and

Nagar Haveli also, the number of females per 1000 males decreased from 812 in 2001 to 775 in

2011. On the other hand, the increase in the population sex ratio was maximum in Delhi where

the number of females per 1000 males increased from 821 to 866 between 2001 and 2011. Other

states/Union Territories where there has been a substantial increase in the population sex ratio

are: Chandigarh, Mizoram, Puducherry, Andaman and Nicobar Islands, Arunachal Pradesh and

Kerala. Interestingly, in all major states of the country, except Kerala, there has not been any

significant change in the population sex ratio during the period under reference.

Unlike the population sex ratio, the sex ratio in the population aged 0-6 years decreased in all but

8 states/Union Territories of the country. The states/Union Territories where the sex ratio in the

population ages 0-6 years has improved during 2001 through 2011 are Andaman and Nicobar

Islands, Chandigarh, Gujarat, Haryana, Himachal Pradesh, Mizoram, Punjab and Tamil Nadu.

The gain in the sex ratio in the population aged 0-6 years has been the maximum in Punjab where

the number of females per 1000 males in the age group 0-6 years increased from 798 in 2001 to

846 in 2011. By contrast, in Haryana, the number of females per 1000 males in the age group 0-6

years recorded and the increase of only 11 points during this period. On the other hand, the

decrease in the sex ratio in the population aged 0-6 years has been very sharp in Jammu and

Kashmir and Dadra and Nagar Haveli. In Jammu and Kashmir, the sex ratio in the population

aged 0-6 years decreased from 941 in 2001 to 859 in 2011. This very rapid decrease in the sex

ratio in the population aged 0-6 years appears to be largely responsible for the decrease in the sex

ratio of the total population.

Finally, the sex ratio in the population aged 7 years and above increased in all but only four

states/Union Territories of the country. In Bihar, Dadra and Nagar Haveli, Daman and Diu and

Gujarat, the sex ratio has decreased between 2001 and 2011. In Dadra and Nagar Haveli and

Daman and Diu, the decrease in the sex ratio in the population aged 7 years and above has been

quite substantial but the decrease has been only marginal in Bihar and Gujarat. In Daman and

Diu, the sex ratio in the population aged 7 years and above decrease by a whopping 92 points -

from 682 females to 590 during the ten years between 2001 through 2011.

98

Sex Composition

Table 5.2 presents estimates of the index of sex composition for the states/Union Territories of

the country in the three population groups - total population, population aged 0-6 years and

population aged 7 years and above. For the total population, the index has been found to be

negative in 17 states/Union Territories which means that the ratio of females to males in these

states/Union Territories is smaller than that of the country as a whole. In the Union Territory of

Puducherry, the value of the index has been found to be zero whereas in the remaining

states/Union Territories, the index is positive which implies that the ratio of females to males in

these states/Union Territories is higher than that of the country as a whole. In the population aged

0-6 years, on the other hand, the index has been found to be negative in only 13 states/Union

Territories whereas in case of population aged 7 years and above, the index is negative again in

17 states/Union Territories.

For the total population, the index of sex composition varies from the highest in Kerala to the

lowest in Uttar Pradesh. In addition to Kerala, the index of sex composition has also been found

to be very high in Andhra Pradesh and Tamil Nadu. By comparison, the index has been found

to be exceptionally low in Bihar, Haryana and Maharashtra in addition to Uttar Pradesh. On the

other hand, the index of sex composition in the population aged 0-6 years was the highest in

West Bengal followed by Bihar whereas it was the lowest in Uttar Pradesh again. Other

states/Union Territories where the index of sex composition for the population aged 0-6 years

has been found to be very low at the 2011 population census are Maharashtra, Rajasthan and

Haryana. Similarly, the index of sex composition in the population aged 7 years and above has

been found to be the highest in Kerala followed by Tamil Nadu and Andhra Pradesh but was the

lowest in Uttar Pradesh followed by Bihar.

The states/Union Territories of the country can be grouped into four categories on the basis of

the index of sex composition in different population groups. The first category comprises of 16

states/Union Territories where the index is positive in all the three age groups. The second

category comprises of 10 states/Union Territories where the index is negative in all the three age

groups. The third category comprises of 7 states/Union Territories where the index is negative

in population of all ages and in population aged 7 years and above but positive in the population

aged 0-6 years. Finally, the fourth category comprises of only two states/Union Territories where

the index is positive in population of all ages and population aged 7 years and above but negative

in the population aged 0-6 years. Geographic continuity is very much apparent in the distribution

of the states/Union Territories according to these four categories. The index of sex composition

of the population is positive in all the southern and in most of the eastern states/Union Territories

of the country.

99


Sex Composition in Districts

Among the districts of the country, the sex ratio varies widely in the total population as well as

in the population aged 0-6 years and the population aged 7 years and above. As regards the sex

ratio in the total population, there are 10 districts in the country where the number of females per

1000 males was enumerated to be more than 1100 at the 2011 population census. District Mahe

in Puducherry tops the districts of the country in terms of the most favourable sex ratio to females

as there were 1176 females for every 1000 males in the district at the 2011 population census.

Other districts where there were at least 1100 women for every 1000 men are Almora

(Uttarakhand), Kannur (Kerala), Pathanamthitta (Kerala), Ratnagiri (Maharashtra), Rudraprayag

(Uttarakhand), Kollam (Kerala), Thrissur (Kerala), Garhwal (Uttarakhand) and Alappuzha

(Kerala). On the other hand, district Daman in the Union Territory of Daman and Diu has the

lowest sex ratio in the country. There were only 533 females for every 1000 males in this district

according to the provisional figures of 2011 population census. Besides district Daman, district

Leh in Jammu and Kashmir is the only other district in the country with a sex ratio of less than

600 females for every 1000 males. In addition, in seven districts of the country, number of

females per 1000 males has been found to be less than 800 at the 2011 population census. These

districts are Tawang and West Kameng in Arunachal Pradesh, North District in Sikkim, Kargil

in Jammu and Kashmir, Dadra and Nagar Haveli, Nicobars in Andaman and Nicobar Islands and

Surat in Gujarat.

Table 5.3 gives the distribution of districts in different states/Union Territories by the level of the

sex ratio. In all, there are 145 districts in the country where number of females per 1000 males

have been enumerated to be less than 900. Most of these districts are located in Uttar Pradesh

(38), Haryana (19), Punjab (15), Jammu and Kashmir (14), Bihar (11) and Madhya Pradesh (10).

On the other hand, There are 98 districts where females out numbered males at the 2011

population census. Most of these districts are located in Tamil Nadu (15), Kerala (14), Andhra

Pradesh (11) and Orissa (10). In all the 14 districts of Kerala, females outnumbered males at the

2011 population census. Among the major states of the country, there was no district in five

states - Assam, Haryana, Punjab, Rajasthan and West Bengal - where females out numbered

males at the 2011 population census.

The within state, inter-district variability in the population sex ratio appears to be the highest in

Madhya Pradesh. In two districts of the state, there were less than 850 females for every 1000

males at the 2011 population census whereas in 4 districts females outnumbered males. Other

states where inter-district variability in the population sex ratio is quite substantial are Arunachal

Pradesh, Himachal Pradesh and Maharashtra.

100

Sex Composition

As regards inter-district variations in the sex ratio in the population aged 0-6 years is concerned,

the highest sex ratio has been estimated in district Lahul and Spiti in Himachal Pradesh where

there were 1013 females aged 0-6 years for every 1000 males aged 0-6 years at the 2011

population census. On the other hand, the sex ratio was the lowest in Jhajhjhar district in

Rajasthan where there were only 774 females aged 0-6 years for every 1000 males aged 0-6

years. In all, there are only three districts in the country where females outnumber males in the

age group 0-6 years. These districts are Dakshin Bastar (Dantewada) in Chhattisgarh, Tawang

in Arunachal Pradesh and Lahul and Spiti in Himachal Pradesh. On the other hand in 61 districts

of the country, there were less than 850 females 0-6 years for every 1000 males aged 0-6 years

at the 2011 population census. Most of these districts are located in Haryana (18), Punjab (11),

Jammu and Kashmir (7), Maharashtra (7) and Uttar Pradesh (6). In Haryana, the sex ratio in the

population aged 0-6 years has been found to be extremely unfavourable to females in 18 of the

21 districts. There has been some improvement in the sex ratio in the population aged 0-6 years

in Haryana and Punjab in recent years but still the sex ratio in the population aged 0-6 years

remains heavily unfavourable to females.

Compared to the sex ratio in the population aged 0-6 years, the sex ratio in the population aged

7 years and above appears to be more favourable to females. The highest sex ratio in this age

group is estimated to be in district Mahe of Puducherry where there were 1206 females aged 7

years and above for every 1000 males aged 7 years and above. In all, there were 116 districts in

the country where there were more females aged 7 years and above than males aged 7 years and

above at the 2011 population census. Most of these districts are located in Andhra Pradesh (13),

Chhattisgarh (10), Kerala (14), Orissa (10), Tamil Nadu (18) and Uttarakhand (10). In all the

districts of Kerala, females aged 7 years and above outnumbered males 7 years and above at the

2011 population census.

On the other side, there are 144 districts in the country where the sex ratio in the population aged

7 years and above remains highly unfavourable to females. In these districts, there were less than

900 females aged 7 years and above for every 1000 males aged 7 years and above. These districts

are mainly located in Uttar Pradesh (38), Haryana (16), Bihar (15), Jammu and Kashmir (15)

Punjab (12) and Madhya Pradesh (10). In 23 districts, the sex ratio in the population aged 7 years

and above has been found to be even less than 850 females for every 1000 males. These 23

districts include four districts of Arunachal Pradesh, three districts of Jammu and Kashmir and

two districts each in Uttar Pradesh, Rajasthan, Madhya Pradesh and Delhi. In Gujarat, Himachal

Pradesh and Maharashtra also, the sex ratio in the population aged 7 years and above has been

found to be less than 850 females for every 1000 males in one district each.

101


Table 5.3

Distribution of districts by sex ratio in total population, 2011

State/Union Territory Number of females per 1000 males< 850 850-900 900-950 950-1000 $1000 Total


3.3 19.4 31.3 30.8 15.3 100.0


102

Sex Composition

Figure 5.8Females per 1000 males in districts, 2011

103


Table 5.4

Distribution of districts by sex ratio in population aged 0-6 years



9.5 21.3 41.9 26.9 0.5 100.0


104

Sex Composition

Figure 5.9Females per 1000 males in population aged 0-6 years in districts, 2011

105


Table 5.5

Distribution of districts by sex ratio in population aged 7 years and above



3.6 18.9 28.8 30.6 18.1 100.0


106

Sex Composition

Figure 5.10Females per 1000 males in population aged 7 years and above in districts, 2011

107


Table 5.B presents the estimates of the index of sex composition for the districts of the country

as derived from the provisional figures of the 2011 population census while table 5.6 presents

the distribution of districts by states/Union Territories according to the index of sex composition

in the total population. The index of sex composition as defined by the equation (5.11) is the

weighted sum of the index of the intensity of sex composition at the district level where the

intensity of the sex composition is defined as the ratio of the female population to the male

population in the district to the corresponding ratio at the national level and weight is the index

of extensiveness of population in the district. When the the ratio of the female population to the

male population in a district is the same as the ratio of the female to male population in the

country as a whole, the index of the intensity of the sex composition is zero. The larger or smaller

is the ratio of female to male population from the corresponding ratio at the national level, the

higher or the lower is the index of the intensiveness of the sex composition in the district relative

to the sex composition for the country as a whole.

The analysis based on the provisional figures of the 2011 population census suggests that in 291

districts of the country, the index of sex composition has been found to be negative which means

that the ratio of the female population to the male population or the sex ratio in these districts is

lower than the national average. In addition, the sex ratio in the population has been estimated

to be very low in 57 districts of the country. Most of the districts with very low sex ratio of the

total population are located in Uttar Pradesh (25) and Bihar (9). In four districts of the National

Capital Territory of Delhi also, the index of sex composition has been found to be very low

according to provisional figures of the 2011 population census which means that the population

sex ratio in these districts is substantially lower than the corresponding ratio in the country as a

whole.

On the other hand, the index of the sex composition has been estimated to be higher than the

national average in 349 districts of the country and very high in 60 districts. This means that the

sex ratio in these districts is higher than the sex ratio at the national level. Most of the districts

with very high index of sex composition are located in only three states/Union Territories of the

country - Andhra Pradesh (20), Kerala (12) and Tamil Nadu (11). All the three states are located

in the southern part of the country. The situation is interesting in Uttar Pradesh where the index

of sex composition has been found to be very low in 25 districts and, at the same time, very high

in 5 districts according to the provisional figures of the 2011 population census. Districts with

a very low index of sex composition are located in the eastern part of the state whereas districts

with a very high index of sex composition are located in the western part of the state (Figure

5.11).

108

Sex Composition

Table 5.6

Index of sex composition in the total population

State/Union Territory Index of sex composition< -0.10 -0.10 to -0.05 -0.05 to 0 0 to 0.05 $0.05 Total


2.5 6.3 36.6 45.3 9.4 100.0


109


Figure 5.11Index of sex composition in total population


110

Sex Composition

Table 5.7

Index of sex composition in the population aged 0-6 years



2.0 5.5 30.9 56.7 4.8 100.0

Source: Author’s calculations.

111


Figure 5.12Index of sex composition in population aged 0-6 years


112

Sex Composition

Table 5.8

Index of sex composition in population aged 7 years and above



3.0 6.7 36.7 43.8 9.8 100.0


113


Figure 5.13Index of sex composition in population aged 7 years and above


114

Sex Composition

The distribution of the districts of the country according to the index of sex composition in the

population aged 0-6 years is given in table 5.7. According to the provisional figures of the 2011

population census, the index of sex composition has been found to be very low in 48 districts of

the country most which are located in Uttar Pradesh (11), Maharashtra (10), Haryana (6),

Rajasthan (6) and Punjab (5). In these districts, the sex ratio in the population aged 0-6 years is

substantially lower than the corresponding ratio in the country as a whole. In these districts, there

is a significant deficit of female children aged 0-6 years compared to the male children 0-6 years

of age, compared to the situation at the national level. On the other hand, the index of sex

composition has been found to be very high in 31 districts most of which are located in Bihar (8),

West Bengal (8) Andhra Pradesh (4) and Chhattisgarh (3). This means that the sex ratio of

population aged 0-6 years in these districts is significantly higher than the corresponding ratio

at the national level which indicates towards some substantial deficit of male children 0-6 years

of age compared to the situation at the national level.

Lastly, table 5.8 gives the distribution of the districts across the states/Union Territories of the

country according to the index of sex composition in the population aged 7 years and above.

This index has been calculated by taking the proportionate distribution of the population aged

7 years and above as weights. It may be seen from the table that the index of sex composition in

population aged 7 years and above was very low in 60 districts and very high in 65 districts

according to the 2011 population census. Most of the districts with a very low index of sex

composition in population aged 7 years and above are located in Uttar Pradesh (28), Bihar (10),

Delhi and Maharashtra (4 each) and Madhya Pradesh and Rajasthan (3 each). On the other hand,

most of the districts with a very high index of sex composition in the population aged 7 years and

above are located in Andhra Pradesh (21), Tamil Nadu (15), Kerala (12) and Uttar Pradesh (5).

An important factor affecting the sex composition of the population in the states/Union

Territories as well as in the districts of the country is the inter-state and inter-district migration

of the population in addition to sex ratio at birth and sex differentials in mortality. It is well

known that migration for any cause is always age and sex selective. This age and sex selectivity

implies that migration influences not only the sex ratio in the population of all ages combined

but also the sex ratio in the population of different age groups. As such, variations in the sex ratio

of the total population as well as the sex ratio in different age groups need to be analysed in the

context of variations in the sex ratio at birth, sex differentials in mortality in different age groups

and age and sex patterns of migration across the districts and state/Union Territories. The

provisional figures of the 2011 population census do not provide data which make it possible to

estimate the sex ratio at birth as well as mortality by sex and age and the age and sex structure

115


of inter-state and inter-district migration. As such, it is not possible to explore the factors which

are responsible for the observed inter-state and inter-district variability in the sex ratio in the

population as a whole and in population of different age groups as revealed through the

provisional figures of the 2011 population census. It is however expected that once detailed data

of the 2011 population census are available, it will be possible to entangle the mystery of the sex

ratio of the population in the country.

116

6Patterns of Inter-state Migration

Migration in India is not new. Historical accounts show that people have moved in search of

work, in response to environmental shocks and stresses, to escape religious persecution and

political conflict. However improved communication and transport networks, conflicts over

natural resources and new economic opportunities have created unprecedented levels of mobility.

In recent years, an important reason behind the movement of the people in India has been unequal

development across states and Union Territories of the country. Available evidence suggests that

Delhi and the states of Gujarat and Maharashtra are top destinations for the inter-state movement

of the labour force.

Estimates of inter-state migration in India are derived mainly from two sources - population

census and the National Sample Survey. Both the population census and the National Sample

Survey use the birth place and enumeration place data to estimate the migrant population in every

state and Union Territory of the country. According to the population census 2001, around 30 per

cent of the country’s population or around 307 million people were migrants. Of these, nearly

a third had migrated during the period 1991-2001. The 2001 population census also revealed that

there were 65.4 million female migrants and 32.8 million male migrants in the country and

majority (42.4 million) of the females migrated because of marriage whereas majority of males

(12.3 million) migrated for work and employment. The 2001 population census also suggested

that the inter-state migration in the country had grown by more than 53 per cent. Total number

of inter-state migrants was 42.3 million and Uttar Pradesh (-2.6 million) and Bihar (-1.7 million)

were the two states with the largest net out-migration.

117


At the 2011 population census, the following questions related to migration were asked from

each individual:

1. Place of birth of the individual if the individual is not born at the place of enumeration. On

the basis of the answer to this question, it is possible to classify the enumerated population

into two groups:

1) migrants, defined as persons who are enumerated in a place different from the place

where they were born, and

2) non-migrants, defined as persons who are enumerated at the place where they were born.

2. For all migrant - persons who were born at a place other than the place of enumeration, the

following questions were asked:

a. Place of the last residence and whether the place of last residence was rural or urban.

b. Reasons for migration from the place of last residence to the place of enumeration.

Reasons included, 1) work or employment, 2) business, 3) education, 4) marriage, 5)

moved after birth, 6) moved with household and 7) other reasons.

c. Duration of stay in the place of enumeration since migration.

The birth-place enumeration-place data available through the population census can be used to

estimate life time migrants. At the same time, these data at two population censuses provide a

way of estimating the balance of inter-census migration. These data also help to analyse the net

balance into two components - net migration among persons from outside the administrative area

and net migration among persons born inside the administrative area. Answers of the above

questions can be analysed to estimate lifetime migrants. However, the provisional figures of the

2011 population census do not provide data related to the movement of the population and

reasons behind movement. It is however, possible to make a preliminary assessment of net

migratory flows across the states and Union Territories of the country during the period 2001-

2011 using the indirect approach which is also termed as the vital statistics method (Shryock and

Siegel, 1976; United Nations, 1970). This approach is based on the concept that the population

increase between two points of time in any administrative area is the result of the natural increase

in the population (excess of live births over deaths during the period) and the net migratory

movement. If an estimate of the natural increase in the population of an administrative area

during the period under reference is available, then the difference between the enumerated and

the expected population gives an estimate of the net increase in the population due to migration.

Thus, if the estimates of the birth rate and the death rate for an administrative area are available

for different years of the period under reference, estimates of the natural increase in the

population can be made and the difference between the enumerated population and the estimated

population provides an idea about the net migratory flow.

118

Inter-state Migration

Using the aforesaid approach, we have made an attempt to estimate net migratory flows across

the states and Union Territories of the country during the period 2001-2011on the basis of the

provisional figures or the 2011 population census, population of states and Union Territories at

the 2001 population census and annual estimates of birth rate and death rate for India and for

each of its 35 states and Union Territories available through the sample registration system. The

following steps were involved in the estimation of the net migratory flows for each of the 35

states and Union Territories of the country:

Step 1. Using the final population totals of the 2001 population census as the base and

estimates of the birth rate and the death rate for different years of the period 2001 through 2010

available through the sample registration system, the expected population for the country and for

each of its 35 states and Union Territories for the year 2011 was estimated. Estimates of birth rate

and death rate for country and for its constituent states and Union Territories were however

available up to the year 2009 only. Linear regression analysis was used to estimate the birth rate

and the death rate for the year 2010.

Step 2. The estimated population so obtained was then compared with the provisional

population of the country and the provisional population of each of the 35 states and Union

Territories as revealed through the 2011 population census. It was assumed that the net

international migration from the country relative to its population is very small, almost

negligible. It was also assumed that the net omission rate in the 2001 and the 2011 population

census is more or less the same. In the 2001 population census, a net omission rate of 23.3 per

1000 population was estimated on the basis of the post enumeration survey (Government of India

2008).

Step 3. Using the difference between the enumerated and the estimated population, the crude

net migration rate (CNMR) was calculated for each state and Union Territory of the country for

the period 2001-2011. The crude net migration rate is defined as the ratio of the net migration

(in or out) in a state/Union Territory divided by the enumerated population of that state/Union

Territory and is presented as a multiple of 1000.

Like the population density, the crude net migration rate is also not a good indicator of migratory

flow across administrative or geographical units as it does not have multiplicative and additive

properties. Moreover, the crude net migration rate defined above does not give any idea about

the extent - magnitude as well as direction - of the internal migration in an administrative or

geographical unit. For example, the crude net migration rate can be calculated at the state/Union

territory level but the crude net migration rates estimated for different states and Union territories

of the country provide no idea about internal migration in the country as a whole. At the country

level, the crude net migration rate can be calculated in the international context only.

119


One way to develop an alternative index of internal migration is to follow the approach used in

developing the index of population distribution across administrative or geographical areas. This

approach essentially combines a measure of extensiveness and a measure of intensiveness to

arrive at an index which takes into consideration both the extensiveness and intensiveness of the

distribution across administrative or geographical units. In the context of net migration across

states and Union territories, we develop an index of the extensiveness and an index of the

intensiveness of the movement of the population across states and Union Territories of the

country. To this end, let

cjP = Enumerated population of the state/Union Territory j,

sjP = Population of the state/Union Territory j estimated on the basis of annual

estimates of the birth rate and the death rate.

The extensiveness of net migration for the state/Union Territory j is now defined as

ej cj sj cj sjM = 2*(*P - P */3*P - P *) (6.1)

while the intensiveness of net migration for the state/Union Territory j is defined as

ij cj sjM = log (P /P ) (6.2)

and the index of net migration for the state/Union Territory j is defined as

j ej ijM = M * M (6.3)

Finally, the index of net internal migration for the country as a whole is defined as

jM = 3M for all j. (6.4)

cj sj jNotice that when P = P , M = 0 and the net migration for the state or Union Territory j is zero.

cj sj jOn the other hand when P > P , M > 0 which indicates that there is net in-migration to the

cj sj jstate/Union Territory j. Similarly, when P < P , M < 0 which means that there is net out-

jmigration from the state/Union Territory j. Finally, the sum of M for all states and Union

Territories gives an idea about the magnitude and direction of internal migration in the country.

jNotice that M is the weighted sum of M with weights equal to the index of the extensiveness of

inter-state migration.

Inter-state Migratory Flows in India

According to the provisional results of the 2011 population census, the enumerated population

of India was 1210.194 million whereas the estimated population based on the 2001 population

and the birth rate and the death rate for different years of the period 2001-2011 available through

the sample registration system was 1211.682 million (Table 6.1). This means that, at the country

level, the difference between the enumerated and the estimated population was only around 1.482

million or just 0.12 per cent which may be assumed to be negligible. In other words, the

population of the country was almost closed to the international migration during the period

120


2001-2011. In order to make sure that the enumerated and the estimated population in the year

2011 are the same, we have adjusted the estimated population on a pro-rata basis. It is obvious

that this adjustment is very small.

We have carried out the similar exercise for all the states and Union Territories of the country.

This exercise has revealed that there has been net out-migration from 11 states/Union Territories

of the country while there has been net in-migration in the remaining states/Union territories

during the period 2001-2011. The total net out-migration from the 11 states/Union Territories

of the country during the 10-year period between 2001 and 2011was more than 12.83 million.

This was also the net in-migration in the 24 states and Union Territories of the country during

the same period. The 11 states and Union Territories where there was net out-migration during

the period 2001-2011 are, in order, Uttar Pradesh (6.58 million), Rajasthan (1.77 million), Kerala

(1.43 million), Madhya Pradesh (1.31 million), Andhra Pradesh (1.16 million), Nagaland (0.27

million), Assam (0.25 million). In addition, there was net out migration in Andaman and Nicobar

Islands, Sikkim, Lakshadweep and Bihar also but the quantum of net out migration was very

small. In Bihar, the net out migration during the period 2001-2011 is estimated to be

insignificant. This is in quite contrast to the situation during the period 1991-2001 when a very

substantial out migration from Bihar was observed. There are indications that there have been

significant return migration in Bihar during the period 2001-2011.

On the other hand, states and Union Territories where the in-migration was substantial during

the period 2001-2011 are, in order, Tamil Nadu (3.50 million), Maharashtra (3.03 million,

Jammu and Kashmir (0.96 million), Delhi (0.84 million), Karnataka (0.61 million), Gujarat (0.60

million and West Bengal (0.60 million). Some substantial net in-migration has also been

estimated in the newly created states of Jharkhand (0.42 million), Chhattisgarh (0.36 million) and

Uttarakhand (0.17 million). There has also been some very heavy in-migration to the Union

Territories of Dadra and Nagar Haveli (0.36 million) and Daman and Diu (0.20 million). These

patterns of inter-state migration revealed through the provisional figures of the 2011 population

census are on the expected lines. The migration streams revealed through the present analysis are

traditional migration streams. It appears that there has been little change in these migratory

streams during the period 2001-2011.

Table 6.1 also gives estimates of the crude net migration rate and the index of migration for the

states and Union Territories of the country. The crude net in-migration rate has been estimated

to be around 250 per 1000 population in the Union Territory of Daman and Diu which confirms

that there has been very substantial in-migration to this Union Territory during the period 2001-

121


2011. Other states/Union Territories where the crude net in-migration was substantial during this

period are Dadra and Nagar Haveli (190), Puducherry (139), Jammu and Kashmir (76), Mizoram

(76), Arunachal Pradesh (64) and Manipur (63). Daman and Diu, Dadra and Nagar Haveli and

Puducherry are small Union Territories and even a small number of in-migrants to these Union

Territories lead to high net in-migration rates. At the same time, substantially high crude net in-

migration rates in Jammu and Kashmir, Mizoram, Arunachal Pradesh and Manipur need further

analysis. Incidentally, the heavy net in-migration reported in Arunachal Pradesh, Manipur and

Mizoram is associated with a heavy net out-migration from Nagaland.

On the other hand, the crude net out-migration rate has been found to be around 136 per 1000

population in Nagaland meaning a very heavy net out migration from the state and which is the

lowest in the country. The crude net migration rate has also been found to be quite substantial

in Kerala. In Uttar Pradesh, the crude net migration rate has been found to be around 33 per 1000

population despite very heavy out migration from the state.

Regional patterns in inter-state migration are also clear from the table 6.1. In the north western

states of Jammu and Kashmir, Punjab, Uttarakhand and Delhi, there are clear indications of

substantial in-migration. The same is true for Manipur, Mizoram, Meghalaya and Tripura in the

north-east, Maharashtra, Daman and Diu and Dadra and Nagar Haveli in the west and Tamil

Nadu and Puducherry in the south. On the other hand, there has been some substantial out-

migration from Madhya Pradesh, Rajasthan and Uttar Pradesh, all located in central India. These

three states are also amongst the least developed states of the country. The out-migration from

these states appears to be the out migration of the labour force in search of better livelihood and

employment opportunities. A similar situation appears to prevail in Andhra Pradesh where the

labour force appears to have moved in large numbers to Tamil Nadu and Maharashtra in search

of better livelihood opportunities. There has also been substantial out migration from Kerala

during the period under reference. Kerala has traditionally been an out migration state and this

tradition appears to have continued even today.

Out migration from Bihar during the period 2001-2011 has been estimated to be to be very small,

almost negligible. This trend is in quite contrast to the trend during the period 1991-2001 when

a very heavy out migration from the state was reported on the basis of the results of the 2001

population census. There are indications that there have been significant return migration to the

state, especially after the change in the political government. In any case, the preliminary results

of the 2011 population census suggest further investigation and analysis of in- and out-migration

from Bihar during the period 2001-2011.

122


Figure 6.1Crude net migration rate in states and Union Territories, 2011

123


Table 6.1

Inter-state migration in India based on 2011 population census

Country/state

Enumeratedpopulation

2011(million)

Estimatedpopulation

2011(million)

Adjustedpopulation

2011(million)

Netmigration(million)

(CNMR)(per 1000)

India 1210.194 1211.682 1210.194Uttar Pradesh 199.582 206.417 206.164 -6.582 -32.98Kerala 33.388 34.863 34.820 -1.433 -42.91Rajasthan 68.621 70.473 70.386 -1.765 -25.72Nagaland 1.981 2.253 2.250 -0.270 -136.25Madhya Pradesh 72.598 73.996 73.906 -1.308 -18.02Andhra Pradesh 84.666 85.927 85.821 -1.156 -13.65Assam 31.169 31.454 31.415 -0.246 -7.89Andaman & Nicobar Islands 0.380 0.401 0.400 -0.020 -53.40Sikkim 0.608 0.625 0.625 -0.017 -27.81Lakshadweep 0.064 0.068 0.068 -0.004 -60.55Bihar 103.805 103.965 103.837 -0.033 -0.32Himachal Pradesh 6.857 6.855 6.847 0.010 1.39Goa 1.458 1.447 1.445 0.012 8.45Orissa 41.947 41.904 41.852 0.095 2.27Haryana 25.353 25.281 25.250 0.103 4.07Chandigarh 1.055 1.017 1.015 0.039 37.36West Bengal 91.348 90.864 90.752 0.596 6.52Punjab 27.704 27.406 27.372 0.332 11.99Tripura 3.671 3.544 3.539 0.132 35.89Chhattisgarh 25.540 25.213 25.182 0.358 14.03Jharkhand 32.966 32.591 32.551 0.415 12.58Arunachal Pradesh 1.383 1.296 1.294 0.088 63.94Karnataka 61.131 60.605 60.530 0.601 9.82Gujarat 60.384 59.847 59.773 0.610 10.11Mizoram 1.091 1.010 1.009 0.083 75.62Manipur 2.722 2.553 2.550 0.172 63.08Uttarakhand 10.117 9.766 9.754 0.363 35.87Dadra and Nagar Haveli 0.343 0.278 0.278 0.065 189.87Meghalaya 2.964 2.768 2.765 0.199 67.15Daman and Diu 0.243 0.182 0.182 0.061 252.09Puducherry 1.244 1.072 1.071 0.173 139.29Delhi 16.753 15.934 15.914 0.839 50.09Jammu and Kashmir 12.549 11.600 11.586 0.963 76.75Maharashtra 112.373 109.480 109.345 3.028 26.94Tamil Nadu 72.139 68.728 68.644 3.495 48.45


124


Figure 6.2Migration index in states and Union Territories, 2011

125


Table 6.2

Migration index in India and states, 2011

Country/

state

Net migration

during

2001-2011

(million)

Index of

extensiveness

ej(M )

Index of

intensiveness

ij(M )

Index of net

migration

j(M )

India -12.832Uttar Pradesh -6.582 0.5129 -0.0141 -7.227Kerala -1.433 0.1116 -0.0182 -2.037Rajasthan -1.765 0.1375 -0.0110 -1.517Nagaland -0.270 0.0210 -0.0555 -1.167Madhya Pradesh -1.308 0.1019 -0.0078 -0.790Andhra Pradesh -1.156 0.0900 -0.0059 -0.530Assam -0.246 0.0192 -0.0034 -0.065Andaman and Nicobar Islands -0.020 0.0016 -0.0226 -0.036Sikkim -0.017 0.0013 -0.0119 -0.016Lakshadweep -0.004 0.0003 -0.0255 -0.008Bihar -0.033 0.0026 -0.0001 0.000Himachal Pradesh 0.010 0.0007 0.0006 0.000Goa 0.012 0.0010 0.0037 0.004Orissa 0.095 0.0074 0.0010 0.007Haryana 0.103 0.0080 0.0018 0.014Chandigarh 0.039 0.0031 0.0165 0.051West Bengal 0.596 0.0464 0.0028 0.132Punjab 0.332 0.0259 0.0052 0.136Tripura 0.132 0.0103 0.0159 0.163Chhattisgarh 0.358 0.0279 0.0061 0.171Jharkhand 0.415 0.0323 0.0055 0.178Arunachal Pradesh 0.088 0.0069 0.0287 0.198Karnataka 0.601 0.0468 0.0043 0.201Gujarat 0.610 0.0476 0.0044 0.210Mizoram 0.083 0.0064 0.0341 0.220Manipur 0.172 0.0134 0.0283 0.379Uttarakhand 0.363 0.0283 0.0159 0.449Dadra and Nagar Haveli 0.065 0.0051 0.0914 0.464Meghalaya 0.199 0.0155 0.0302 0.468Daman and Diu 0.061 0.0048 0.1261 0.602Puducherry 0.173 0.0135 0.0651 0.880Delhi 0.839 0.0654 0.0223 1.459Jammu and Kashmir 0.963 0.0751 0.0347 2.603Maharashtra 3.028 0.2359 0.0119 2.798Tamil Nadu 3.495 0.2724 0.0220 5.875


126


Table 6.2 presents estimates of the index of net migration for the states and Union Territories of

the country along with the index of extensiveness of migration and the index of the intensity of

the net migration defined above. The index of extensiveness of migration suggests that Uttar

Pradesh alone accounted for more than half of the total migratory flow in the country during the

period 2001-2011. At the same time, the index of the intensity of net migration in the state is

negative which means that the state has experienced net out migration during the period under

reference. Although, the index of the intensity of net migration in the state is low in comparison

to other states and Union Territories of the country, yet because of the very large share of the

migratory flow, the index of net migration for the state has been the lowest amongst all states and

Union Territories of the country (-7.2 per 1000 net migrants) indicating that the net out migration

from the state has been the largest in the country. Similarly, Tamil Nadu accounted for about 27

per cent of the net migrants in the country and the positive sign of the index of net migration

intensity suggests that the state has experienced substantial net in-migration during the period

under reference. Like Uttar Pradesh, Tamil Nadu also does not have a high index of the intensity

of net migration but the index of the extensiveness of het migration in the state is very high so

that the index of net migration is the highest in the country.

On the other hand, the index of the migration intensity has been found to be the highest in the

Union Territories of Daman and Diu and Dadra and Nagar Haveli. The Union Territory of

Daman and Diu, the net in-migration during the period 2001 through 2011 is estimated to be

more than 25 per cent of the population enumerated at the 2011 population census. In the Union

Territory of Dadra and Nagar Haveli, on the other hand, this proportion has been estimated to be

very close to 19 per cent. However, this high net in-migration intensity in these Union Territories

has been associated with a very low index of migration extensiveness so that the index of net

migration in these Union Territories is estimated to be very low. Obviously, the index of

migration calculated in table 6.2, takes into consideration both the severity or the intensity of net

migration and the extensiveness or the spread of the net migration across geographical or

administrative area. By contrast, the crude net migration rate takes into consideration only one

dimension of net migratory flow.

The approach adopted for the analysis of net migratory flows across states and Union territories

can also be applied to analyse net migratory flows across the districts. However, there is no other

source of estimating the birth rate and the death rate for the districts other than the population

census. One potential source of estimating district level birth and death rates in the country is the

civil registration system. However, there is serious under reporting of births and deaths under the

system despite the fact that registration of all births and deaths is compulsory under the Birth and

127


Death Registration Act of 1967. In the absence of district level estimates of birth and death rates,

assessment of inter-district migratory flows is not possible. The only way of estimating patterns

of migration at the district level in the country is the analysis of the birth-place, enumeration-

place data which is not yet available through the 2011 population census.

128

7Epilogue

Population census in India, essentially, remains a descriptive statistical system, originally evolved

more than 150 years ago, and confined to delineating demographic, social and economic features

of the population. It continues to be adjunct to the normal administrative machinery and is ridden

piggyback of the public administration system. It remains an undertaking that instructs and

informs the administrator either at the national level or at the local level and gives the

administrative system a feel of the features and textures of the population stock - the size and the

structure of the population. Because of this preoccupation with the description of the statistical

and demographic information, there has rarely been any serious attempt to develop an analytical

system based on the huge data collected through population census at every 10 years of interval

to support population and development planning and programming directed towards the

development needs of the people and to facilitate monitoring and assessment of the impact of

development programmes and activities. The lack of the analytical rigour remains perhaps the

weakest component of the population census in India. As a result, the huge data collected during

each census remains largely unanalysed, especially at the lower levels of public administration

system where these data are the only source for analysing population and its growth and

distribution as well as its various social, cultural and economic characteristics. In order to meet

the information needs of the decentralised development planning, it is imperative that the huge

data available through the population census on a regular basis are analysed in a systematic

manner so as to reflect the population and development situation right up to the grass roots level.

There is however little orientation to this direction within the population census system in the

country.

129


The routine, descriptive nature of the Indian population census is well reflected in the manner

the provisional results of the 2011 population census have been released. The Paper 1 of the 2011

population census released by the Registrar General and Census Commissioner of India for the

country as a whole. This paper contains the data collected during the 2011 population census for

the country as a whole and for its constituent states and Union Territories. On the other hand, for

the states and Union Territories of the country, state and Union Territory specific Paper 1 has

been released by the Census Commissioner of the specific state/ Union Territory. Interestingly,

no attempt has been made to present the data for the 640 districts of the country at one place and

in one publication. Even the papers presented by the Registrar General and Census Commissioner

of India and by Census Commissioner of different states/Union Territories present only a routine

description of the population situation in the country or in the specific state/Union Territory.

There have been little attempts to analyse even the preliminary results of the census in the context

of the population and in the context of social and economic development at the national, or

state/Union Territory or district level.

In this monograph, we have made an attempt to analyse the provisional figures of the 2011

population census in the context of information needs for decentralised district development

planning and programming as well as in the context of analysing the population and development

situation at the country, state/Union Territory and district level. In addition to estimating and

presenting conventional demographic indicators, we have also attempted to analyse spatial

patterns of the demographic situation across the 35 states/Union Territories and 640 districts of

the country as they existed at the time of the 2011 population census to present a national

perspective of demographic scenario of the country. It is expected that the present monograph

will serve as an important information support to decentralised district development planning in

the country.

The analysis of the provisional population data available through the 2001 population census

presented in the present monograph does not depict a very rosy picture of population transition

in the country and in its constituent states/Union Territories and districts. It appears that there has

been only a marginal change in the population scenario of the country during the 10-year period

between 2001-2011. There are unmistakable signs that population transition in India has

progressed and the average rate of population growth in the country has decrease at a faster pace

than before during the period 2001 through 2011. It also appears that, for the first time, the net

decadal addition to the population has decreased. Similarly, the decrease in the population aged

0-6 years indicates towards continued reduction in fertility in the country. However, the actual

growth of population between 2001 and 2011 has been faster than the population growth

130

Epilogue

projected by the Government of India on the basis of the results of the 2001 population census

and observed trends in fertility and mortality. Obviously, efforts to moderate the growth of the

population during 2001-2011 appear to have fallen short of the projected, most likely, path.

Provisional results of the 2011 population census also indicate that there is little possibility of

realising the expectations laid down in the National Population Policy 2000 and there is little

probability that the country will be able to reach stable population by the year 2045. The

provisional figures of the 2001 population census do not provide any indication that the country

will be able to achieve the cherished goal of population stabilisation by the year 2045 or even by

the year 2050 as enshrined in the National Population Policy 2000 until and unless serious efforts

are made to reinvigorate population stabilisation efforts in the country.

The provisional figures of the 2011 population census permit only a crude analysis of the age and

sex structure of the population. Even this analysis does not provide any solace as far as the

demographic transition in the country is concerned. There is every evidence to suggest that the

population of the country remains young and there appears very little transition in the age and

sex structure of the population. The analysis also depicts some extreme patterns of age and sex

composition of the population across the districts of the country that need further investigation

and analysis. At the same time, the provisional figures of the 2011 population census suggest a

rapid increase in the population aged 7 years and above which has implications for social and

economic development planning. Moreover, the inter-district variability in the demographic

situation revealed through the present analysis justifies the current emphasis on a decentralised

approach to population and development planning for meeting the development needs of the

people. The district level indicators of the age and sex structure of the population are expected

to be useful for planning, monitoring and evaluating population and development related

programmes at the districts level.

Another important observation of the analysis of the provisional figures of the 2011 population

census presented in this monograph is that out-migration from states like Uttar Pradesh,

Rajasthan, Madhya Pradesh and Andhra Pradesh continues unabated. Very little is currently

known about the demographic, social and economic characteristics of the migrant population in

the country as the detailed data about patterns of migration and demographic, social and

economic characteristics of the migrant population are not yet available through the 2011

population census. It is however generally believed that most of this out migration from the states

like Uttar Pradesh, Madhya Pradesh and Rajasthan is the distress migration of unskilled and

semi-skilled labour force in search of better livelihood opportunities as these three states remain

amongst the least developed states of the country. This distress migration has important

131


implications to social and economic development processes at both the place of origin and at the

place of the destination. There are indications to suggest that the population of the country is

increasingly getting concentrated in selected pockets. Implications of such migratory flows need

to be explored in depth.

The provisional results of the 2011 population census do not provide the information necessary

to analyse the determinants of population growth - fertility, mortality and migration - and social

and economic characteristics of the population. Once detailed information is available through

the 2011 population census and from other sources, it would be possible to carry out a detailed

analysis of factors that have contributed to the population growth revealed through the 2011

population census. It will also be possible to analyse the contribution of population momentum

to the future population growth as more and more of the future population growth in India will

be the result of the momentum built in the age structure of the population. Evidence available

from the sample registration system and from other sources suggests that more and more states

and Union Territories in the country will be reaching replacement fertility in the years to come

and population momentum will therefore be primarily driving the future population growth in

the country. As of now, the provisional results of the 2011 population census present a mixed

scenario - good signs but bad omens.

132

References

Bhat PN Mari (2002) Completeness of India’s sample registration system: An assessment using

general growth balance method. Population Studies 56(2): 119-134.

Chaurasia Aalok Ranjan (2010) Mortality transition in India: 1985-2005. Asian Population

Studies 6(1): 47-68.

Chaurasia Alok Ranjan, Gulati SC (2008) India: The State of Population 2007. New Delhi,

National Population Commission and Oxford University Press.

Government of India (1983) Report on intensive enquiry conducted in a sub-sample of SRS units

(1980-81). New Delhi, Registrar General. Occasional Paper No. 2 of 1983.

Government of India (1988) Report on intensive enquiry conducted in a sub-sample of SRS

Units. New Delhi, Registrar General. Occasional Paper No. 1 of 1988.

Government of India (2000) National Population Policy 2000. New Delhi, Ministry of Health

and Family Welfare.

Government of India (2005) National Rural Health Mission. New Delhi, Ministry of Health and

Family Welfare.

Government of India (2006) Census of India 2001. Population Projections for India and States

2001-2026. Report of the Technical Group of Population Projections. New Delhi, Ministry

of Health and Family Welfare. National Commission on Population.

Government of India (2006) Census of India 2001. Report of the Post Enumeration Survey. New

Delhi, Office of the Registrar General and Census Commissioner.

Government of India (2011) Census of India 2011. Provisional Population Totals. Paper 1 of

2011. India, Series 1. New Delhi, Registrar General and Census Commissioner of India.

133


Mitra A (1973) The census of India: Past and future. In A Bose, DB Gupta, G Raichaudhuri

(Eds) Population Statistics in India. Data Base of Indian Economy, Volume III. New Delhi,

Vikas Publishing House.

Shryock HS, Siegel J’S (1976) The Methods and Materials of Demography. New York,

Academic Press.

Swamy VS, Saxena AK, Palmore JA, Mishra V, Rele JR, Luther NY (1992) Evaluation of the

Sample Registration System using indirect estimates of fertility and mortality. New Delhi,

Office of the Registrar General and Census Commission of India. Occasional Paper 3 of

1992.

United Nations (1970) Manuals on Methods of Estimation. Manual VI: Methods of Measuring

Internal Migration. New York, Department of Economic and Social Affairs. Population

Studies No. 47.

United Nations (2011) World Population Prospects. 2011 Revision. New York, Department of

Economic and Social Affairs. Population Division.

134

Statistical Tables

Table 2.A

Population size and growth in districts of India

State/Union Territory/

District

Total population Proportionate

increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Andaman and Nicobars IslandsNicobars 36819 42068 -12.48 -1.33North & Middle Andaman 105539 105613 -0.07 -0.01South Andaman 237586 208471 13.97 1.31

Andhra PradeshAdilabad 2737738 2488003 10.04 0.96Anantapur 4083315 3640478 12.16 1.15Chittoor 4170468 3745875 11.33 1.07East Godavari 5151549 2601797 98.00 6.83Guntur 4889230 4901420 -0.25 -0.02Hyderabad 4010238 4465144 -10.19 -1.07Karimnagar 3811738 3829753 -0.47 -0.05Khammam 2798214 3491822 -19.86 -2.21Krishna 4529009 2578927 75.62 5.63Kurnool 4046601 4187841 -3.37 -0.34Mahbubnagar 4042191 3529494 14.53 1.36Medak 3031877 3513934 -13.72 -1.48Nalgonda 3483648 2670097 30.47 2.66Nizamabad 2552073 3247982 -21.43 -2.41Prakasam 3392764 2668564 27.14 2.40Rangareddy 5296396 2345685 125.79 8.14Sri Potti Sriramulu Nellore 2966082 3059423 -3.05 -0.31Srikakulam 2699471 3575064 -24.49 -2.81Visakhapatnam 4288113 2537593 68.98 5.25Vizianagaram 2342868 3832336 -38.87 -4.92Warangal 3522644 2249254 56.61 4.49West Godavari 3934782 3246004 21.22 1.92Y.S.R. 2884524 3803517 -24.16 -2.77

Arunachal PradeshAnjaw 21089 18536 13.77 1.29Changlang 147951 125422 17.96 1.65Dibang Valley 7948 7272 9.30 0.89East Kameng 78413 57179 37.14 3.16East Siang 99019 87397 13.30 1.25Kurung Kumey 89717 42518 111.01 7.47Lohit 145538 124991 16.44 1.52Lower Dibang Valley 53986 50448 7.01 0.68Lower Subansiri 82839 55726 48.65 3.96

135



District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Papum Pare 176385 122003 44.57 3.69Tawang 49950 38924 28.33 2.49Tirap 111997 100326 11.63 1.1Upper Siang 35289 55346 -36.24 -4.50Upper Subansiri 83205 33363 149.39 9.14West Kameng 87013 74599 16.64 1.54West Siang 112272 103918 8.04 0.77

AssamBaksa 953773 857947 11.17 1.06Barpeta 1693190 1394755 21.40 1.94Bongaigaon 732639 612665 19.58 1.79Cachar 1736319 1444921 20.17 1.84Chirang 481818 433061 11.26 1.07Darrang 908090 759858 19.51 1.78Dhemaji 688077 571944 20.30 1.85Dhubri 1948632 1566396 24.40 2.18Dibrugarh 1327748 1185072 12.04 1.14Dima Hasao 213529 188079 13.53 1.27Goalpara 1008959 822035 22.74 2.05Golaghat 1058674 946279 11.88 1.12Hailakandi 659260 542872 21.44 1.94Jorhat 1091295 999221 9.21 0.88Kamrup 1517202 1311698 15.67 1.46Kamrup Metropolitan 1260419 1059578 18.95 1.74Karbi Anglong 965280 813311 18.69 1.71Karimganj 1217002 1007976 20.74 1.88Kokrajhar 886999 843243 5.19 0.51Lakhimpur 1040644 889010 17.06 1.57Morigaon 957853 776256 23.39 2.10Nagaon 2826006 2314629 22.09 2.00Nalbari 769919 689053 11.74 1.11Sivasagar 1150253 1051736 9.37 0.90Sonitpur 1925975 1665125 15.67 1.46Tinsukia 1316948 1150062 14.51 1.36Udalguri 832769 758746 9.76 0.93

BiharAraria 2806200 2158608 30.00 2.62Arwal 699563 587826 19.01 1.74Aurangabad 2511243 2013055 24.75 2.21Banka 2029339 1608773 26.14 2.32

136

Statistical Tables


District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Begusarai 2954367 2349366 25.75 2.29Bhagalpur 3032226 2423172 25.13 2.24Bhojpur 2720155 2243144 21.27 1.93Buxar 1707643 1402396 21.77 1.97Darbhanga 3921971 3295789 19.00 1.74Gaya 4379383 3473428 26.08 2.32Gopalganj 2558037 2152638 18.83 1.73Jamui 1756078 1398796 25.54 2.27Jehanabad 1124176 926489 21.34 1.93Kaimur (Bhabua) 1626900 1289074 26.21 2.33Katihar 3068149 2392638 28.23 2.49Khagaria 1657599 1280354 29.46 2.58Kishanganj 1690948 1296348 30.44 2.66Lakhisarai 1000717 802225 24.74 2.21Madhepura 1994618 1526646 30.65 2.67Madhubani 4476044 3575281 25.19 2.25Munger 1359054 1137797 19.45 1.78Muzaffarpur 4778610 3746714 27.54 2.43Nalanda 2872523 2370528 21.18 1.92Nawada 2216653 1809696 22.49 2.03Pashchim Champaran 3922780 3043466 28.89 2.54Patna 5772804 4718592 22.34 2.02Purba Champaran 5082868 3939773 29.01 2.55Purnia 3273127 2543942 28.66 2.52Rohtas 2962593 2450748 20.89 1.90Saharsa 1897102 1508182 25.79 2.29Samastipur 4254782 3394793 25.33 2.26Saran 3943098 3248701 21.37 1.94Sheikhpura 634927 525502 20.82 1.89Sheohar 656916 515961 27.32 2.42Sitamarhi 3419622 2682720 27.47 2.43Siwan 3318176 2714349 22.25 2.01Supaul 2228397 1732578 28.62 2.52Vaishali 3495249 2718421 28.58 2.51ChandigarhChandigarh 1054686 900635 17.10 1.58

ChhattisgarhBastar 1411644 1198067 17.83 1.64Bijapur 255180 234637 8.76 0.84Bilaspur 2662077 1998355 33.21 2.87

137



District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Dakshin Bastar Dantewada 532791 476119 11.90 1.12Dhamtari 799199 706591 13.11 1.23Durg 3343079 2810436 18.95 1.74Janjgir - Champa 1620632 1317431 23.01 2.07Jashpur 852043 743160 14.65 1.37Kabeerdham 822239 584552 40.66 3.41Korba 1206563 1011823 19.25 1.76Koriya 659039 586327 12.40 1.17Mahasamund 1032275 860257 20.00 1.82Narayanpur 140206 117337 19.49 1.78Raigarh 1493627 1265529 18.02 1.66Raipur 4062160 3016930 34.65 2.97Rajnandgaon 1537520 1283224 19.82 1.81Surguja 2361329 1972094 19.74 1.80Uttar Bastar Kanker 748593 650934 15.00 1.40

Dadra and Nagar HaveliDadra & Nagar Haveli 342853 220490 55.50 4.41

Daman and DiuDaman 190855 113989 67.43 5.15Diu 52056 44215 17.73 1.63

DelhiCentral 578671 646385 -10.48 -1.11East 1707725 1463583 16.68 1.54New Delhi 133713 179112 -25.35 -2.92North 883418 781525 13.04 1.23North East 2240749 1768061 26.73 2.37North West 3651261 2860869 27.63 2.44South 2733752 2267023 20.59 1.87South West 2292363 1755041 30.62 2.67West 2531583 2128908 18.91 1.73

GoaNorth Goa 817761 758573 7.80 0.75South Goa 639962 589095 8.63 0.83

GujaratAhmadabad 7208200 5893164 22.31 2.01Amreli 1513614 1393918 8.59 0.82Anand 2090276 1856872 12.57 1.18Banas Kantha 3116045 2504244 24.43 2.19Bharuch 1550822 1370656 13.14 1.23Bhavnagar 2877961 2469630 16.53 1.53

138

Statistical Tables


District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Dohad 2126558 1636433 29.95 2.62Gandhinagar 1387478 1237168 12.15 1.15Jamnagar 2159130 1904278 13.38 1.26Junagadh 2742291 2448173 12.01 1.13Kachchh 2090313 1583225 32.03 2.78Kheda 2298934 2037894 12.81 1.21Mahesana 2027727 1844856 9.91 0.95Narmada 590379 514404 14.77 1.38Navsari 1330711 1229463 8.24 0.79Panch Mahals 2388267 2025277 17.92 1.65Patan 1342746 1182709 13.53 1.27Porbandar 586062 536835 9.17 0.88Rajkot 3799770 3169881 19.87 1.81Sabar Kantha 2427346 2082531 16.56 1.53Surat 6079231 4275540 42.19 3.52Surendranagar 1755873 1515148 15.89 1.47Tapi 806489 719634 12.07 1.14The Dangs 226769 186729 21.44 1.94Vadodara 4157568 3641802 14.16 1.32Valsad 1703068 1410553 20.74 1.88

HaryanaAmbala 1136784 1014442 12.06 1.14Bhiwani 1629109 1425043 14.32 1.34Faridabad 1798954 1365430 31.75 2.76Fatehabad 941522 806167 16.79 1.55Gurgaon 1514085 870514 73.93 5.53Hisar 1742815 1537145 13.38 1.26Jhajhjhar 956907 880076 8.73 0.84Jind 1332042 1189854 11.95 1.13Kaithal 1072861 946169 13.39 1.26Karnal 1506323 1274169 18.22 1.67Kurukshetra 964231 825470 16.81 1.55Mahendragarh 921680 812554 13.43 1.26Mewat 1089406 789768 37.94 3.22Palwal 1040493 829144 25.49 2.27Panchkula 558890 468396 19.32 1.77Panipat 1202811 967434 24.33 2.18Rewari 896129 765334 17.09 1.58Rohtak 1058683 940132 12.61 1.19Sirsa 1295114 1116670 15.98 1.48

139



District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Sonipat 1480080 1279129 15.71 1.46Yamunanagar 1214162 1041663 16.56 1.53

Himachal PradeshBilaspur 382056 340885 12.08 1.14Chamba 518844 460887 12.58 1.18Hamirpur 454293 412700 10.08 0.96Kangra 1507223 1339030 12.56 1.18Kinnaur 84298 78334 7.61 0.73Kullu 437474 381571 14.65 1.37Lahul & Spiti 31528 33224 -5.10 -0.52Mandi 999518 901344 10.89 1.03Shimla 813384 722502 12.58 1.18Sirmaur 530164 458593 15.61 1.45Solan 576670 500557 15.21 1.42Una 521057 448273 16.24 1.50Jammu and KashmirAnantnag 1070144 778408 37.48 3.18Badgam 735753 607181 21.18 1.92Bandipore 385099 304886 26.31 2.34Baramula 1015503 843892 20.34 1.85Doda 409576 320256 27.89 2.46Ganderbal 297003 217907 36.30 3.10Jammu 1526406 1357077 12.48 1.18Kargil 143388 119307 20.18 1.84Kathua 615711 511455 20.38 1.86Kishtwar 231037 190843 21.06 1.91Kulgam 422786 394026 7.30 0.70Kupwara 875564 650393 34.62 2.97Leh(Ladakh) 147104 117232 25.48 2.27Pulwama 570060 441275 29.18 2.56Punch 476820 372613 27.97 2.47Rajouri 619266 483284 28.14 2.48Ramban 283313 214944 31.81 2.76Reasi 314714 247694 27.06 2.39Samba 318611 272539 16.90 1.56Shupiyan 265960 211332 25.85 2.30Srinagar 1269751 1027670 23.56 2.12Udhampur 555357 459486 20.86 1.90

JharkhandBokaro 2061918 1777669 15.99 1.48

140

Statistical Tables


District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Chatra 1042304 808113 28.98 2.54Deoghar 1491879 1165348 28.02 2.47Dhanbad 2682662 2397160 11.91 1.13Dumka 1321096 1106538 19.39 1.77Garhwa 1322387 1035461 27.71 2.45Giridih 2445203 1905402 28.33 2.49Godda 1311382 1047932 25.14 2.24Gumla 1025656 832445 23.21 2.09Hazaribagh 1734005 1378930 25.75 2.29Jamtara 790207 653064 21.00 1.91Khunti 530299 434814 21.96 1.99Kodarma 717169 540892 32.59 2.82Latehar 725673 560885 29.38 2.58Lohardaga 461738 364520 26.67 2.36Pakur 899200 701678 28.15 2.48Palamu 1936319 1537493 25.94 2.31Pashchimi Singhbhum 1501619 1233971 21.69 1.96Purbi Singhbhum 2291032 1983062 15.53 1.44Ramgarh 949159 839518 13.06 1.23Ranchi 2912022 2350300 23.90 2.14Sahibganj 1150038 927749 23.96 2.15Saraikela-Kharsawan 1063458 848865 25.28 2.25Simdega 599813 514331 16.62 1.54

KarnatakaBagalkot 1890826 1651954 14.46 1.35Bangalore 9588910 6537299 46.68 3.83Bangalore Rural 987257 850937 16.02 1.49Belgaum 4778439 4214534 13.38 1.26Bellary 2532383 2027204 24.92 2.23Bidar 1700018 1502314 13.16 1.24Bijapur 2175102 1806863 20.38 1.85Chamarajanagar 1020962 965449 5.75 0.56Chikkaballapura 1254377 1149013 9.17 0.88Chikmagalur 1137753 1140948 -0.28 -0.03Chitradurga 1660378 1517852 9.39 0.90Dakshina Kannada 2083625 1897655 9.80 0.93Davanagere 1946905 1790916 8.71 0.84Dharwad 1846993 1604267 15.13 1.41Gadag 1065235 971841 9.61 0.92Gulbarga 2564892 2174743 17.94 1.65

141



District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Hassan 1776221 1721645 3.17 0.31Haveri 1598506 1439058 11.08 1.05Kodagu 554762 548563 1.13 0.11Kolar 1540231 1387096 11.04 1.05Koppal 1391292 1196090 16.32 1.51Mandya 1808680 1763706 2.55 0.25Mysore 2994744 2641101 13.39 1.26Raichur 1924773 1669795 15.27 1.42Ramanagara 1082739 1030591 5.06 0.49Shimoga 1755512 1642507 6.88 0.67Tumkur 2681449 2584778 3.74 0.37Udupi 1177908 1112283 5.90 0.57Uttara Kannada 1436847 1353601 6.15 0.60Yadgir 1172985 956212 22.67 2.04

KeralaAlappuzha 2121943 2109160 0.61 0.06Ernakulam 3279860 3105798 5.60 0.55Idukki 1107453 1129221 -1.93 -0.19Kannur 2525637 2408956 4.84 0.47Kasaragod 1302600 1204078 8.18 0.79Kollam 2629703 2585208 1.72 0.17Kottayam 1979384 1953646 1.32 0.13Kozhikode 3089543 2879131 7.31 0.71Malappuram 4110956 3625471 13.39 1.26Palakkad 2810892 2617482 7.39 0.71Pathanamthitta 1195537 1234016 -3.12 -0.32Thiruvananthapuram 3307284 3234356 2.25 0.22Thrissur 3110327 2974232 4.58 0.45Wayanad 816558 780619 4.60 0.45

LakshadweepLakshadweep 64429 60650 6.23 0.60

Madhya PradeshAlirajpur 728677 610282 19.40 1.77Anuppur 749521 667427 12.30 1.16Ashoknagar 844979 689216 22.60 2.04Balaghat 1701156 1497496 13.60 1.28Barwani 1385659 1086791 27.50 2.43Betul 1575247 1395259 12.90 1.21Bhind 1703562 1427965 19.30 1.76Bhopal 2368145 1842914 28.50 2.51

142

Statistical Tables


District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Burhanpur 756993 635061 19.20 1.76Chhatarpur 1762857 1475194 19.50 1.78Chhindwara 2090306 1849828 13.00 1.22Damoh 1263703 1083793 16.60 1.54Datia 786375 664168 18.40 1.69Dewas 1563107 1308039 19.50 1.78Dhar 2184672 1740775 25.50 2.27Dindori 704218 580559 21.30 1.93East Nimar 1309443 1078619 21.40 1.94Guna 1240938 977887 26.90 2.38Gwalior 2030543 1632269 24.40 2.18Harda 570302 474461 20.20 1.84Hoshangabad 1240975 1083821 14.50 1.35Indore 3272335 2465965 32.70 2.83Jabalpur 2460714 2150974 14.40 1.35Jhabua 1024091 784143 30.60 2.67Katni 1291684 1063990 21.40 1.94Mandla 1053522 894331 17.80 1.64Mandsaur 1339832 1183597 13.20 1.24Morena 1965137 1592494 23.40 2.10Narsimhapur 1092141 958018 14.00 1.31Neemuch 825958 725798 13.80 1.29Panna 1016028 856685 18.60 1.71Raisen 1331699 1124746 18.40 1.69Rajgarh 1546541 1254291 23.30 2.09Ratlam 1454483 1215107 19.70 1.80Rewa 2363744 1973075 19.80 1.81Sagar 2378295 2022360 17.60 1.62Satna 2228619 1869647 19.20 1.76Sehore 1311008 1079019 21.50 1.95Seoni 1378876 1166562 18.20 1.67Shahdol 1064989 907919 17.30 1.60Shajapur 1512353 1290404 17.20 1.59Sheopur 687952 559311 23.00 2.07Shivpuri 1725818 1406535 22.70 2.05Sidhi 1126515 910683 23.70 2.13Singrauli 1178132 920416 28.00 2.47Tikamgarh 1444920 1203097 20.10 1.83Ujjain 1986597 1711109 16.10 1.49Umaria 643579 516102 24.70 2.21

143



District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Vidisha 1458212 1215177 20.00 1.82West Nimar 1872413 1524766 22.80 2.05

MaharashtraAhmadnagar 4543083 4040642 12.43 1.17Akola 1818617 1630239 11.56 1.09Amravati 2887826 2607160 10.77 1.02Aurangabad 3695928 2897013 27.58 2.44Bhandara 1198810 1136146 5.52 0.54Bid 2585962 2161250 19.65 1.79Buldana 2588039 2232480 15.93 1.48Chandrapur 2194262 2071101 5.95 0.58Dhule 2048781 1707947 19.96 1.82Gadchiroli 1071795 970294 10.46 0.99Gondiya 1322331 1200707 10.13 0.96Hingoli 1178973 987160 19.43 1.78Jalgaon 4224442 3682690 14.71 1.37Jalna 1958483 1612980 21.42 1.94Kolhapur 3874015 3523162 9.96 0.95Latur 2455543 2080285 18.04 1.66Mumbai 3145966 3338031 -5.75 -0.59Mumbai Suburban 9332481 8640419 8.01 0.77Nagpur 4653171 4067637 14.39 1.34Nanded 3356566 2876259 16.70 1.54Nandurbar 1646177 1311709 25.50 2.27Nashik 6109052 4993796 22.33 2.02Osmanabad 1660311 1486586 11.69 1.11Parbhani 1835982 1527715 20.18 1.84Pune 9426959 7232555 30.34 2.65Raigarh 2635394 2207929 19.36 1.77Ratnagiri 1612672 1696777 -4.96 -0.51Sangli 2820575 2583524 9.18 0.88Satara 3003922 2808994 6.94 0.67Sindhudurg 848868 868825 -2.30 -0.23Solapur 4315527 3849543 12.10 1.14Thane 11054131 8131849 35.94 3.07Wardha 1296157 1236736 4.80 0.47Washim 1196714 1020216 17.30 1.60Yavatmal 2775457 2458271 12.90 1.21

ManipurBishnupur 240363 208368 15.36 1.43

144

Statistical Tables


District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Chandel 144028 118327 21.72 1.97Churachandpur 271274 227905 19.03 1.74Imphal East 452661 394876 14.63 1.37Imphal West 514683 444382 15.82 1.47Senapati 354972 156513 126.80 8.19Tamenglong 140143 111499 25.69 2.29Thoubal 420517 364140 15.48 1.44Ukhrul 183115 140778 30.07 2.63

MeghalayaEast Garo Hills 317618 250582 26.75 2.37East Khasi Hills 824059 660923 24.68 2.21Jaintia Hills 392852 299108 31.34 2.73Ribhoi 258380 192790 34.02 2.93South Garo Hills 142574 100980 41.19 3.45West Garo Hills 642923 518390 24.02 2.15West Khasi Hills 385601 296049 30.25 2.64

MizoramAizawl 404054 325676 24.07 2.16Champhai 125370 108392 15.66 1.46Kolasib 83054 65960 25.92 2.30Lawngtlai 117444 73620 59.53 4.67Lunglei 154094 137223 12.29 1.16Mamit 85757 62785 36.59 3.12Saiha 56366 61056 -7.68 -0.80Serchhip 64875 53861 20.45 1.86

NagalandDimapur 379769 309024 22.89 2.06Kiphire 74033 106136 -30.25 -3.60Kohima 270063 213259 26.64 2.36Longleng 50593 158300 -68.04 -11.41Mokokchung 193171 232085 -16.77 -1.84Mon 250671 260652 -3.83 -0.39Peren 94954 96825 -1.93 -0.20Phek 163294 148195 10.19 0.97Tuensang 196801 150365 30.88 2.69Wokha 166239 161223 3.11 0.31Zunheboto 141014 153955 -8.41 -0.88

OrissaAnugul 1271703 1140003 11.55 1.09Balangir 1648574 1337194 23.29 2.09

145



District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Baleshwar 2317419 2024508 14.47 1.35Bargarh 1478833 1346336 9.84 0.94Baudh 439917 373372 17.82 1.64Bhadrak 1506522 1333749 12.95 1.22Cuttack 2618708 2341094 11.86 1.12Debagarh 312164 274108 13.88 1.30Dhenkanal 1192948 1066878 11.82 1.12Gajapati 575880 518837 10.99 1.04Ganjam 3520151 3160635 11.37 1.08Jagatsinghapur 1136604 1057629 7.47 0.72Jajapur 1826275 1624341 12.43 1.17Jharsuguda 579499 509716 13.69 1.28Kalahandi 1573054 1335494 17.79 1.64Kandhamal 731952 648201 12.92 1.22Kendrapara 1439891 1302005 10.59 1.01Kendujhar 1802777 1561990 15.42 1.43Khordha 2246341 1877395 19.65 1.79Koraput 1376934 1180637 16.63 1.54Malkangiri 612727 504198 21.53 1.95Mayurbhanj 2513895 2223456 13.06 1.23Nabarangapur 1218762 1025766 18.81 1.72Nayagarh 962215 864516 11.30 1.07Nuapada 606490 530690 14.28 1.34Puri 1697983 1502682 13.00 1.22Rayagada 961959 831109 15.74 1.46Sambalpur 1044410 935613 11.63 1.10Subarnapur 652107 541835 20.35 1.85Sundargarh 2080664 1830673 13.66 1.28

PuducherryKaraikal 200314 170791 17.29 1.59Mahe 41934 36828 13.86 1.30Puducherry 946600 735332 28.73 2.53Yanam 55616 31394 77.15 5.72

PunjabAmritsar 2490891 2156989 15.48 1.44Barnala 596294 526948 13.16 1.24Bathinda 1388859 1183317 17.37 1.60Faridkot 618008 550907 12.18 1.15Fatehgarh Sahib 599814 538481 11.39 1.08Firozpur 2026831 1746064 16.08 1.49

146

Statistical Tables


District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Gurdaspur 2299026 2103409 9.30 0.89Hoshiarpur 1582793 1481322 6.85 0.66Jalandhar 2181753 1962714 11.16 1.06Kapurthala 817668 754515 8.37 0.80Ludhiana 3487882 3032941 15.00 1.40Mansa 768808 688773 11.62 1.10Moga 992289 894760 10.90 1.03Muktsar 902702 777521 16.10 1.49Patiala 1892282 1584826 19.40 1.77Rupnagar 683349 628829 8.67 0.83Sahibzada Ajit Singh Nagar 986147 746968 32.02 2.78Sangrur 1654408 1473204 12.30 1.16Shahid Bhagat Singh Nagar 614362 587456 4.58 0.45Tarn Taran 1120070 939026 19.28 1.76

RajasthanAjmer 2584913 2178420 18.66 1.71Alwar 3671999 2991445 22.75 2.05Banswara 1798194 1420599 26.58 2.36Baran 1223921 1021466 19.82 1.81Barmer 2604453 1964883 32.55 2.82Bharatpur 2549121 2099943 21.39 1.94Bhilwara 2410459 2021010 19.27 1.76Bikaner 2367745 1902109 24.48 2.19Bundi 1113725 962597 15.70 1.46Chittaurgarh 1544392 1330340 16.09 1.49Churu 2041172 1696030 20.35 1.85Dausa 1637226 1323011 23.75 2.13Dhaulpur 1207293 983298 22.78 2.05Dungarpur 1388906 1107669 25.39 2.26Ganganagar 1969520 1789497 10.06 0.96Hanumangarh 1779650 1517955 17.24 1.59Jaipur 6663971 5250942 26.91 2.38Jaisalmer 672008 508250 32.22 2.79Jalor 1830151 1448936 26.31 2.34Jhalawar 1411327 1180335 19.57 1.79Jhunjhunun 2139658 1913655 11.81 1.12Jodhpur 3685681 2886429 27.69 2.44Karauli 1458459 1205936 20.94 1.90Kota 1950491 1568675 24.34 2.18Nagaur 3309234 2775039 19.25 1.76

147



District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Pali 2038533 1820281 11.99 1.13Pratapgarh 868231 706798 22.84 2.06Rajsamand 1158283 982512 17.89 1.65Sawai Madhopur 1338114 1117050 19.79 1.81Sikar 2677737 2287882 17.04 1.57Sirohi 1037185 851128 21.86 1.98Tonk 1421711 1211720 17.33 1.60Udaipur 3067549 2481234 23.63 2.12

SikkimEast District 281293 245040 14.79 1.38North District 43354 41030 5.66 0.55South District 146742 131525 11.57 1.09West District 136299 123256 10.58 1.01

Tamil NaduAriyalur 752481 695524 8.19 0.79Chennai 4681087 4343645 7.77 0.75Coimbatore 3472578 2916620 19.06 1.74Cuddalore 2600880 2285395 13.80 1.29Dharmapuri 1502900 1295182 16.04 1.49Dindigul 2161367 1923014 12.39 1.17Erode 2259608 2016582 12.05 1.14Kancheepuram 3990897 2877468 38.69 3.27Kanniyakumari 1863174 1676034 11.17 1.06Karur 1076588 935686 15.06 1.40Krishnagiri 1883731 1561118 20.67 1.88Madurai 3041038 2578201 17.95 1.65Nagapattinam 1614069 1488839 8.41 0.81Namakkal 1721179 1493462 15.25 1.42Perambalur 564511 493646 14.36 1.34Pudukkottai 1618725 1459601 10.90 1.03Ramanathapuram 1337560 1187604 12.63 1.19Salem 3480008 3016346 15.37 1.43Sivaganga 1341250 1155356 16.09 1.49Thanjavur 2402781 2216138 8.42 0.81The Nilgiris 735071 762141 -3.55 -0.36Theni 1243684 1093950 13.69 1.28Thiruvallur 3725697 2754756 35.25 3.02Thiruvarur 1268094 1169474 8.43 0.81Thoothukkudi 1738376 1592769 9.14 0.87Tiruchirappalli 2713858 2418366 12.22 1.15

148

Statistical Tables


District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Tirunelveli 3072880 2703492 13.66 1.28Tiruppur 2471222 1920154 28.70 2.52Tiruvannamalai 2468965 2186125 12.94 1.22Vellore 3928106 3477317 12.96 1.22Viluppuram 3463284 2960373 16.99 1.57Virudhunagar 1943309 1751301 10.96 1.04

TripuraDhalai 377988 307868 22.78 2.05North Tripura 693281 590913 17.32 1.60South Tripura 875144 767440 14.03 1.31West Tripura 1724619 1532982 12.50 1.18

Uttar PradeshAgra 4380793 3620436 21.00 1.91Aligarh 3673849 2992286 22.78 2.05Allahabad 5959798 4936105 20.74 1.88Ambedkar Nagar 2398709 2026876 18.35 1.68Auraiya 1372287 1179993 16.30 1.51Azamgarh 4616509 3939916 17.17 1.58Baghpat 1302156 1163991 11.87 1.12Bahraich 3478257 2381072 46.08 3.79Ballia 3223642 2761620 16.73 1.55Balrampur 2149066 1682350 27.74 2.45Banda 1799541 1537334 17.06 1.57Bara Banki 3257983 2673581 21.86 1.98Bareilly 4465344 3618589 23.40 2.10Basti 2461056 2084814 18.05 1.66Bijnor 3683896 3131619 17.64 1.62Budaun 3712738 3069426 20.96 1.90Bulandshahr 3498507 2913122 20.09 1.83Chandauli 1952713 1643251 18.83 1.73Chitrakoot 990626 766225 29.29 2.57Deoria 3098637 2712650 14.23 1.33Etah 1761152 1531703 14.98 1.40Etawah 1579160 1338871 17.95 1.65Faizabad 2468371 2088928 18.16 1.67Farrukhabad 1887577 1570408 20.20 1.84Fatehpur 2632684 2308384 14.05 1.31Firozabad 2496761 2052958 21.62 1.96Gautam Buddha Nagar 1674714 1202030 39.32 3.32Ghaziabad 4661452 3290586 41.66 3.48

149



District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Ghazipur 3622727 3037582 19.26 1.76Gonda 3431386 2765586 24.07 2.16Gorakhpur 4436275 3769456 17.69 1.63Hamirpur 1104021 1043724 5.78 0.56Hardoi 4091380 3398306 20.39 1.86Jalaun 1670718 1454452 14.87 1.39Jaunpur 4476072 3911679 14.43 1.35Jhansi 2000755 1744931 14.66 1.37Jyotiba Phule Nagar 1838771 1499068 22.66 2.04Kannauj 1658005 1388923 19.37 1.77Kanpur Dehat 1795092 1563336 14.82 1.38Kanpur Nagar 4572951 4167999 9.72 0.93Kanshiram Nagar 1438156 1228668 17.05 1.57Kaushambi 1596909 1293154 23.49 2.11Kheri 4013634 3207232 25.14 2.24Kushinagar 3560830 2893196 23.08 2.08Lalitpur 1218002 977734 24.57 2.2Lucknow 4588455 3647834 25.79 2.29Mahamaya Nagar 1565678 1336031 17.19 1.59Mahoba 876055 708447 23.66 2.12Mahrajganj 2665292 2173878 22.61 2.04Mainpuri 1847194 1596718 15.69 1.46Mathura 2541894 2074516 22.53 2.03Mau 2205170 1853997 18.94 1.73Meerut 3447405 2997361 15.01 1.40Mirzapur 2494533 2116042 17.89 1.65Moradabad 4773138 3810983 25.25 2.25Muzaffarnagar 4138605 3543362 16.80 1.55Pilibhit 2037225 1645183 23.83 2.14Pratapgarh 3173752 2731174 16.20 1.50Rae Bareli 3404004 2872335 18.51 1.70Rampur 2335398 1923739 21.40 1.94Saharanpur 3464228 2896863 19.59 1.79Sant Kabir Nagar 1714300 1420226 20.71 1.88Sant Ravidas Nagar (Bhadohi) 1554203 1353705 14.81 1.38Shahjahanpur 3002376 2547855 17.84 1.64Shrawasti 1114615 1176391 -5.25 -0.54Siddharthnagar 2553526 2040085 25.17 2.24Sitapur 4474446 3619661 23.62 2.12Sonbhadra 1862612 1463519 27.27 2.41

150

Statistical Tables


District


increase

Average

annual

growth rate2011 2001 2001-2011 2001-2011

Sultanpur 3790922 3214832 17.92 1.65Unnao 3110595 2700324 15.19 1.41Varanasi 3682194 3138671 17.32 1.60

UttarakhandAlmora 621927 630567 -1.37 -0.14Bageshwar 259840 249462 4.16 0.41Chamoli 391114 370359 5.60 0.55Champawat 259315 224542 15.49 1.44Dehradun 1698560 1282143 32.48 2.81Garhwal 686527 697078 -1.51 -0.15Hardwar 1927029 1447187 33.16 2.86Nainital 955128 762909 25.20 2.25Pithoragarh 485993 462289 5.13 0.50Rudraprayag 236857 227439 4.14 0.41Tehri Garhwal 616409 604747 1.93 0.19Udham Singh Nagar 1648367 1235614 33.40 2.88Uttarkashi 329686 295013 11.75 1.11

West BengalBankura 3596292 3192695 12.64 1.19Barddhaman 7723663 6895514 12.01 1.13Birbhum 3502387 3015422 16.15 1.50Dakshin Dinajpur 1670931 1503178 11.16 1.06Darjiling 1842034 1609172 14.47 1.35Haora 4841638 4273099 13.31 1.25Hugli 5520389 5041976 9.49 0.91Jalpaiguri 3869675 3401173 13.77 1.29Koch Bihar 2822780 2479155 13.86 1.30Kolkata 4486679 4572876 -1.88 -0.19Maldah 3997970 3290468 21.50 1.95Murshidabad 7102430 5866569 21.07 1.91Nadia 5168488 4604827 12.24 1.15North Twenty Four Parganas 10082852 8934286 12.86 1.21Paschim Medinipur 5943300 5193411 14.44 1.35Purba Medinipur 5094238 4417377 15.32 1.43Puruliya 2927965 2536516 15.43 1.44South Twenty Four Parganas 8153176 6906689 18.05 1.66Uttar Dinajpur 3000849 2441794 22.90 2.06

151


Table 3.A

Indexes of population distribution in districts of India, 2011


Districtdc(x)E dc(x) dc(x)I D Population

densityAndaman and Nicobar Islands

Nicobars 0.03 -1.28 -0.039 20North & Middle Andaman 0.087 -1.070 -0.093 32South Andaman 0.196 -0.680 -0.133 80

Andhra PradeshAdilabad 2.262 -0.351 -0.794 170Anantapur 3.374 -0.252 -0.850 213Chittoor 3.446 -0.141 -0.488 275East Godavari 4.257 0.097 0.413 477Guntur 4.040 0.051 0.208 429Hyderabad 3.314 1.686 5.585 18480Karimnagar 3.150 -0.073 -0.229 322Khammam 2.312 -0.339 -0.784 175Krishna 3.742 0.134 0.501 519Kurnool 3.344 -0.221 -0.739 229Mahbubnagar 3.340 -0.240 -0.802 219Medak 2.505 -0.086 -0.216 313Nalgonda 2.879 -0.193 -0.555 245Nizamabad 2.451 0.016 0.040 195Prakasam 2.109 -0.291 -0.613 426Rangareddy 2.803 0.049 0.136 300Sri Potti Sriramulu Nellore 4.376 -0.103 -0.452 396Srikakulam 2.231 0.084 0.187 462Visakhapatnam 3.543 0.003 0.012 384Vizianagaram 1.936 -0.027 -0.052 358Warangal 2.911 -0.143 -0.417 274West Godavari 3.251 0.125 0.406 508Y.S.R. 2.384 -0.307 -0.733 188

Arunachal PradeshAnjaw 0.017 -2.104 -0.037 3Changlang 0.122 -1.076 -0.132 32Dibang Valley 0.007 -2.581 -0.017 1East Kameng 0.065 -1.302 -0.084 19East Siang 0.082 -1.150 -0.094 27Kurung Kumey 0.074 -1.405 -0.104 15Lohit 0.120 -1.134 -0.136 28Lower Dibang Valley 0.045 -1.435 -0.064 14Lower Subansiri 0.068 -1.201 -0.082 24Papum Pare 0.146 -0.874 -0.127 51Tawang 0.041 -1.219 -0.050 23

152

Statistical Tables


Districtdc(x) dc(x) dc(x)E I D Population

densityTirap 0.093 -0.909 -0.084 47Upper Siang 0.029 -1.882 -0.055 5Upper Subansiri 0.069 -1.502 -0.103 12West Kameng 0.072 -1.502 -0.108 12West Siang 0.093 -1.467 -0.136 13

AssamBaksa 0.788 0.095 0.075 475Barpeta 1.399 0.220 0.307 632Bongaigaon 0.605 0.047 0.029 425Cachar 1.435 0.081 0.116 459Chirang 0.398 -0.194 -0.077 244Darrang 0.750 0.110 0.082 491Dhemaji 0.569 -0.253 -0.144 213Dhubri 1.610 0.487 0.785 1171Dibrugarh 1.097 0.013 0.014 393Dima Hasao 0.176 -0.938 -0.165 44Goalpara 0.834 0.162 0.135 553Golaghat 0.875 -0.101 -0.089 302Hailakandi 0.545 0.115 0.063 497Jorhat 0.902 0.002 0.002 383Kamrup 1.254 0.058 0.073 436Kamrup Metropolitan 1.042 0.722 0.752 2010Karbi Anglong 0.798 -0.613 -0.489 93Karimganj 1.006 0.247 0.248 673Kokrajhar 0.733 -0.134 -0.098 280Lakhimpur 0.860 0.079 0.068 457Morigaon 0.791 0.210 0.166 618Nagaon 2.335 0.271 0.632 711Nalbari 0.636 0.301 0.192 763Sivasagar 0.950 0.053 0.051 431Sonitpur 1.591 -0.019 -0.030 365Tinsukia 1.088 -0.041 -0.044 347Udalguri 0.688 0.115 0.079 497

BiharAraria 2.319 0.415 0.963 992Arwal 0.578 -0.421 -0.243 145Aurangabad 2.075 0.300 0.622 760Banka 1.677 0.246 0.413 672Begusarai 2.441 0.607 1.481 1541Bhagalpur 2.506 0.491 1.230 1180Bhojpur 2.248 0.460 1.034 1100Buxar 1.411 0.441 0.622 1052

153




densityDarbhanga 3.241 0.655 2.122 1722Gaya 3.619 0.363 1.314 880Gopalganj 2.114 0.519 1.096 1258Jamui 1.451 0.172 0.250 567Jehanabad 0.929 0.274 0.255 716Kaimur (Bhabua) 1.344 0.458 0.616 1095Katihar 2.535 0.421 1.066 1004Khagaria 1.370 0.112 0.153 493Kishanganj 1.397 0.372 0.520 898Lakhisarai 0.827 0.330 0.273 814Madhepura 1.648 0.467 0.769 1116Madhubani 3.699 0.526 1.944 1279Munger 1.123 0.400 0.449 958Muzaffarpur 3.949 0.597 2.356 1506Nalanda 2.374 0.505 1.199 1220Nawada 1.832 0.368 0.674 890Pashchim Champaran 3.241 0.294 0.953 750Patna 4.770 0.675 3.219 1803Purba Champaran 4.200 0.526 2.210 1281Purnia 2.705 0.425 1.149 1014Rohtas 2.448 0.305 0.747 770Saharsa 1.568 0.466 0.730 1115Samastipur 3.516 0.585 2.055 1465Saran 3.258 0.593 1.932 1493Sheikhpura 0.525 0.383 0.201 922Sheohar 0.543 0.590 0.320 1483Sitamarhi 2.826 0.611 1.725 1555Siwan 2.742 0.594 1.627 1495Supaul 1.841 0.385 0.709 925Vaishali 2.888 0.654 1.887 1717

ChandigarhChandigarh 0.872 1.385 1.207 9252

ChhattisgarhBastar 1.166 -0.435 -0.507 140Bijapur 0.211 -0.990 -0.209 39Bilaspur 2.200 -0.073 -0.161 322Dakshin Bastar Dantewada 0.440 -0.810 -0.357 59Dhamtari 0.660 -0.208 -0.138 236Durg 2.762 0.011 0.030 391Janjgir - Champa 1.339 0.043 0.058 421Jashpur 0.704 -0.417 -0.293 146Kabeerdham 0.679 -0.291 -0.198 195

154

Statistical Tables



densityKorba 0.997 -0.319 -0.318 183Koriya 0.545 -0.581 -0.317 100Mahasamund 0.853 -0.247 -0.210 216Narayanpur 0.116 -1.280 -0.148 20Raigarh 1.234 -0.257 -0.317 211Raipur 3.357 -0.090 -0.302 310Rajnandgaon 1.270 -0.300 -0.381 191Surguja 1.951 -0.405 -0.790 150Uttar Bastar Kanker 0.619 -0.520 -0.322 115

Dadra and Nagar HaveliDadra & Nagar Haveli 0.283 0.263 0.074 698

Daman and DiuDaman 0.158 0.842 0.133 2651Diu 0.043 0.533 0.023 1301

DelhiCentral 0.478 1.783 0.853 23147East 1.411 1.008 1.422 3881New Delhi 0.110 1.203 0.133 6078North 0.730 1.594 1.164 14973North East 1.852 2.053 3.802 43091North West 3.017 1.867 5.634 28087South 2.259 1.458 3.293 10935South West 1.894 1.182 2.240 5803West 2.092 1.773 3.709 22603

GoaNorth Goa 0.676 0.092 0.062 471South Goa 0.529 -0.069 -0.036 326

GujaratAhmadabad 5.956 0.368 2.193 890Amreli 1.251 -0.269 -0.337 205Anand 1.727 0.271 0.468 711Banas Kantha 2.575 -0.119 -0.306 290Bharuch 1.281 -0.205 -0.262 238Bhavnagar 2.378 -0.122 -0.290 288Dohad 1.757 0.184 0.323 582Gandhinagar 1.146 0.238 0.273 660Jamnagar 1.784 -0.397 -0.707 153Junagadh 2.266 -0.090 -0.204 310Kachchh 1.727 -0.918 -1.586 46Kheda 1.900 0.152 0.289 541Mahesana 1.676 0.083 0.140 462Narmada 0.488 -0.251 -0.122 214

155




densityNavsari 1.100 0.198 0.218 602Panch Mahals 1.973 0.080 0.157 458Patan 1.110 -0.212 -0.235 234Porbandar 0.484 -0.175 -0.085 255Rajkot 3.140 -0.051 -0.160 339Sabar Kantha 2.006 -0.065 -0.131 328Surat 5.023 0.557 2.800 1376Surendranagar 1.451 -0.358 -0.520 167Tapi 0.666 -0.185 -0.123 249The Dangs 0.187 -0.471 -0.088 129Vadodara 3.435 0.160 0.550 551Valsad 1.407 0.168 0.236 561

HaryanaAmbala 0.939 0.279 0.262 725Bhiwani 1.346 -0.080 -0.108 317Faridabad 1.487 0.351 0.521 855Fatehabad 0.778 -0.004 -0.003 378Gurgaon 1.251 0.158 0.198 549Hisar 1.440 0.082 0.118 460Jhajjar 0.791 0.128 0.101 512Jind 1.101 0.106 0.117 487Kaithal 0.887 0.002 0.002 383Karnal 1.245 0.204 0.254 610Kurukshetra 0.797 0.318 0.253 792Mahendragarh 0.762 0.157 0.120 548Mewat 0.900 0.209 0.188 617Palwal 0.860 0.163 0.140 555Panchkula 0.462 0.254 0.118 685Panipat 0.994 0.402 0.400 962Rewari 0.740 0.178 0.132 575Rohtak 0.875 0.221 0.194 635Sirsa 1.070 -0.100 -0.107 303Sonipat 1.223 0.235 0.287 655Yamunanagar 1.003 0.259 0.259 691

Himachal PradeshBilaspur 0.316 -0.066 -0.021 327Chamba 0.429 -0.681 -0.292 79Hamirpur 0.375 0.028 0.010 406Kangra 1.245 -0.162 -0.202 263Kinnaur 0.070 -1.462 -0.102 13Kullu 0.361 -0.681 -0.246 79Lahul & Spiti 0.026 -2.223 -0.058 2

156

Statistical Tables



densityMandi 0.826 -0.178 -0.147 253Shimla 0.672 -0.381 -0.256 159Sirmaur 0.438 -0.308 -0.135 188Solan 0.477 -0.107 -0.051 298Una 0.431 -0.052 -0.022 338

Jammu and KashmirAnantnag 0.884 -0.007 -0.006 375Badgam 0.608 0.149 0.090 537Bandipore 0.318 0.467 0.149 1117Baramula 0.839 -0.097 -0.081 305Doda 0.338 -0.684 -0.231 79Ganderbal 0.245 0.480 0.118 1151Jammu 1.261 0.194 0.245 596Kargil 0.118 -1.581 -0.187 10Kathua 0.509 -0.216 -0.110 232Kishtwar 0.191 -0.484 -0.092 125Kulgam 0.349 0.385 0.134 925Kupwara 0.723 -0.015 -0.011 368Leh(Ladakh) 0.122 -2.104 -0.256 3Pulwama 0.471 0.196 0.092 598Punch 0.394 -0.126 -0.050 285Rajouri 0.512 -0.210 -0.108 235Ramban 0.234 -0.253 -0.059 213Reasi 0.260 -0.316 -0.082 184Samba 0.263 -0.079 -0.021 318Shupiyan 0.220 0.349 0.077 852Srinagar 1.049 0.266 0.279 703Udhampur 0.459 -0.257 -0.118 211

JharkhandBokaro 1.704 0.274 0.466 716Chatra 0.861 -0.142 -0.122 275Deoghar 1.233 0.198 0.245 602Dhanbad 2.217 0.527 1.169 1284Dumka 1.092 -0.104 -0.114 300Garhwa 1.093 -0.067 -0.073 327Giridih 2.021 0.115 0.233 497Godda 1.084 0.213 0.230 622Gumla 0.848 -0.296 -0.251 193Hazaribagh 1.433 0.024 0.035 403Jamtara 0.653 0.061 0.040 439Khunti 0.438 -0.249 -0.109 215Kodarma 0.593 0.049 0.029 427

157




densityLatehar 0.600 -0.280 -0.168 200Lohardaga 0.382 -0.090 -0.034 310Pakur 0.743 0.116 0.086 498Palamu 1.600 -0.000 -0.000 381Pashchimi Singhbhum 1.241 -0.261 -0.324 209Purbi Singhbhum 1.893 0.230 0.436 648Ramgarh 0.784 0.254 0.199 684Ranchi 2.406 0.165 0.396 557Sahibganj 0.950 0.276 0.262 719Saraikela-Kharsawan 0.879 0.010 0.009 390Simdega 0.496 -0.377 -0.187 160

KarnatakaBagalkot 1.562 -0.122 -0.190 288Bangalore 7.923 1.060 8.399 4378Bangalore Rural 0.816 0.063 0.052 441Belgaum 3.948 -0.030 -0.117 356Bellary 2.093 -0.104 -0.218 300Bidar 1.405 -0.087 -0.122 312Bijapur 1.797 -0.265 -0.477 207Chamarajanagar 0.844 -0.280 -0.236 200Chikkaballapura 1.037 -0.107 -0.111 298Chikmagalur 0.940 -0.383 -0.360 158Chitradurga 1.372 -0.287 -0.393 197Dakshina Kannada 1.722 0.079 0.136 457Davanagere 1.609 -0.064 -0.103 329Dharwad 1.526 0.056 0.086 434Gadag 0.880 -0.221 -0.195 229Gulbarga 2.119 -0.214 -0.453 233Hassan 1.468 -0.165 -0.242 261Haveri 1.321 -0.061 -0.081 331Kodagu 0.458 -0.451 -0.207 135Kolar 1.273 0.003 0.004 384Koppal 1.150 -0.183 -0.211 250Mandya 1.495 -0.019 -0.028 365Mysore 2.475 0.059 0.147 437Raichur 1.590 -0.223 -0.355 228Ramanagara 0.895 -0.100 -0.089 303Shimoga 1.451 -0.265 -0.385 207Tumkur 2.216 -0.178 -0.395 253Udupi 0.973 -0.098 -0.096 304Uttara Kannada 1.187 -0.435 -0.517 140Yadgir 0.969 -0.231 -0.224 224

158

Statistical Tables



densityKerala

Alappuzha 1.753 0.595 1.043 1501Ernakulam 2.710 0.465 1.259 1111Idukki 0.915 -0.188 -0.172 247Kannur 2.087 0.349 0.728 852Kasaragod 1.076 0.234 0.252 654Kollam 2.173 0.441 0.959 1053Kottayam 1.636 0.372 0.609 898Kozhikode 2.553 0.539 1.375 1318Malappuram 3.397 0.483 1.639 1158Palakkad 2.323 0.216 0.503 627Pathanamthitta 0.988 0.105 0.104 486Thiruvananthapuram 2.733 0.597 1.633 1509Thrissur 2.570 0.430 1.105 1026Wayanad 0.675 0.002 0.001 383

LakshadweepLakshadweep 0.053 0.723 0.038 2013

Madhya PradeshAlirajpur 0.602 -0.221 -0.133 229Anuppur 0.619 -0.280 -0.174 200Ashoknagar 0.698 -0.324 -0.226 181Balaghat 1.406 -0.316 -0.445 184Barwani 1.145 -0.173 -0.198 256Betul 1.302 -0.385 -0.502 157Bhind 1.408 0.001 0.001 382Bhopal 1.957 0.350 0.685 854Burhanpur 0.626 -0.237 -0.148 221Chhatarpur 1.457 -0.274 -0.399 203Chhindwara 1.727 -0.333 -0.576 177Damoh 1.044 -0.343 -0.358 173Datia 0.650 -0.116 -0.075 292Dewas 1.292 -0.233 -0.301 223Dhar 1.805 -0.153 -0.276 268Dindori 0.582 -0.608 -0.354 94East Nimar 1.082 -0.331 -0.358 178Guna 1.025 -0.293 -0.301 194Gwalior 1.678 0.067 0.113 445Harda 0.471 -0.348 -0.164 171Hoshangabad 1.025 -0.314 -0.322 185Indore 2.704 0.343 0.926 839Jabalpur 2.033 0.093 0.189 472Jhabua 0.846 -0.126 -0.107 285

159




densityKatni 1.067 -0.165 -0.176 261Mandla 0.871 -0.321 -0.280 182Mandsaur 1.107 -0.197 -0.219 242Morena 1.624 0.014 0.023 394Narsimhapur 0.902 -0.253 -0.228 213Neemuch 0.683 -0.293 -0.200 194Panna 0.840 -0.429 -0.360 142Raisen 1.100 -0.385 -0.424 157Rajgarh 1.278 -0.182 -0.232 251Ratlam 1.202 -0.106 -0.127 299Rewa 1.953 -0.008 -0.016 374Sagar 1.965 -0.216 -0.424 232Satna 1.842 -0.108 -0.200 297Sehore 1.083 -0.282 -0.306 199Seoni 1.139 -0.385 -0.439 157Shahdol 0.880 -0.346 -0.304 172Shajapur 1.250 -0.194 -0.242 244Sheopur 0.568 -0.564 -0.321 104Shivpuri 1.426 -0.356 -0.508 168Sidhi 0.931 -0.216 -0.201 232Singrauli 0.974 -0.263 -0.256 208Tikamgarh 1.194 -0.125 -0.149 286Ujjain 1.642 -0.068 -0.112 326Umaria 0.532 -0.383 -0.203 158Vidisha 1.205 -0.285 -0.343 198West Nimar 1.547 -0.214 -0.331 233

MaharashtraAhmadnagar 3.754 -0.156 -0.584 266Akola 1.503 -0.056 -0.084 335Amravati 2.386 -0.208 -0.497 236Aurangabad 3.054 -0.018 -0.055 366Bhandara 0.991 -0.092 -0.092 308Bid 2.137 -0.198 -0.422 242Buldana 2.139 -0.153 -0.328 268Chandrapur 1.813 -0.298 -0.541 192Dhule 1.693 -0.178 -0.301 253Gadchiroli 0.886 -0.710 -0.629 74Gondiya 1.093 -0.195 -0.213 243Hingoli 0.974 -0.165 -0.161 260Jalgaon 3.491 -0.026 -0.091 359Jalna 1.618 -0.177 -0.286 254Kolhapur 3.201 0.121 0.388 504

160

Statistical Tables



densityLatur 2.029 -0.046 -0.093 343Mumbai 2.600 2.078 5.401 45594Mumbai Suburban 7.712 1.661 12.811 17477Nagpur 3.845 0.091 0.351 470Nanded 2.774 -0.078 -0.215 319Nandurbar 1.360 -0.068 -0.093 326Nashik 5.048 0.013 0.067 393Osmanabad 1.372 -0.240 -0.329 219Parbhani 1.517 -0.131 -0.199 282Pune 7.790 0.199 1.549 603Raigarh 2.178 -0.015 -0.032 368Ratnagiri 1.333 -0.288 -0.384 196Sangli 2.331 -0.064 -0.149 329Satara 2.482 -0.124 -0.307 287Sindhudurg 0.701 -0.369 -0.259 163Solapur 3.566 -0.119 -0.425 290Thane 9.134 0.482 4.402 1157Wardha 1.071 -0.268 -0.288 205Washim 0.989 -0.215 -0.213 232Yavatmal 2.293 -0.271 -0.621 204

ManipurBishnupur 0.199 0.104 0.021 485Chandel 0.119 -0.943 -0.112 43Churachandpur 0.224 -0.808 -0.181 59Imphal East 0.374 0.223 0.084 638Imphal West 0.425 0.415 0.177 992Senapati 0.293 -0.545 -0.160 109Tamenglong 0.116 -1.077 -0.125 32Thoubal 0.347 0.332 0.115 818Ukhrul 0.151 -0.976 -0.148 40

MeghalayaEast Garo Hills 0.262 -0.495 -0.130 122East Khasi Hills 0.681 -0.105 -0.071 299Jaintia Hills 0.325 -0.569 -0.185 103Ribhoi 0.214 -0.545 -0.116 109South Garo Hills 0.118 -0.694 -0.082 77West Garo Hills 0.531 -0.343 -0.182 173West Khasi Hills 0.319 -0.715 -0.228 73

MizoramAizawl 0.334 -0.528 -0.176 113Champhai 0.104 -0.984 -0.102 40Kolasib 0.069 -0.804 -0.055 60

161




densityLawngtlai 0.097 -0.913 -0.089 47Lunglei 0.127 -1.054 -0.134 34Mamit 0.071 -1.120 -0.079 29Saiha 0.047 -0.981 -0.046 40Serchhip 0.054 -0.923 -0.049 46

NagalandDimapur 0.314 0.032 0.010 410Kiphire 0.061 -0.810 -0.050 59Kohima 0.223 -0.167 -0.037 259Longleng 0.042 -0.824 -0.034 57Mokokchung 0.160 -0.503 -0.080 120Mon 0.207 -0.434 -0.090 140Peren 0.078 -0.965 -0.076 41Phek 0.135 -0.675 -0.091 81Tuensang 0.163 -0.525 -0.085 114Wokha 0.137 -0.572 -0.079 102Zunheboto 0.117 -0.531 -0.062 112

OrissaAnugul 1.051 -0.282 -0.297 199Balangir 1.362 -0.182 -0.247 251Baleshwar 1.915 0.203 0.390 609Bargarh 1.222 -0.178 -0.218 253Baudh 0.364 -0.429 -0.156 142Bhadrak 1.245 0.198 0.246 601Cuttack 2.164 0.242 0.524 666Debagarh 0.258 -0.556 -0.143 106Dhenkanal 0.986 -0.153 -0.151 268Gajapati 0.476 -0.457 -0.218 133Ganjam 2.909 0.051 0.149 429Jagatsinghapur 0.939 0.252 0.237 681Jajapur 1.509 0.218 0.329 630Jharsuguda 0.479 -0.143 -0.069 274Kalahandi 1.300 -0.282 -0.367 199Kandhamal 0.605 -0.622 -0.376 91Kendrapara 1.190 0.155 0.185 545Kendujhar 1.490 -0.245 -0.365 217Khordha 1.856 0.321 0.596 799Koraput 1.138 -0.388 -0.442 156Malkangiri 0.506 -0.556 -0.281 106Mayurbhanj 2.077 -0.199 -0.414 241Nabarangapur 1.007 -0.219 -0.221 230Nayagarh 0.795 -0.188 -0.150 247

162

Statistical Tables



densityNuapada 0.501 -0.385 -0.193 157Puri 1.403 0.107 0.150 488Rayagada 0.795 -0.448 -0.356 136Sambalpur 0.863 -0.383 -0.330 158Subarnapur 0.539 -0.136 -0.073 279Sundargarh 1.719 -0.251 -0.431 214

PuducherryKaraikal 0.166 0.516 0.085 1252Mahe 0.035 1.087 0.038 4659Puducherry 0.782 0.928 0.726 3231Yanam 0.046 0.687 0.032 1854

PunjabAmritsar 2.058 0.388 0.799 932Barnala 0.493 0.041 0.020 419Bathinda 1.148 0.036 0.041 414Faridkot 0.511 0.046 0.024 424Fatehgarh Sahib 0.496 0.125 0.062 508Firozpur 1.675 -0.001 -0.002 380Gurdaspur 1.900 0.231 0.439 649Hoshiarpur 1.308 0.087 0.114 466Jalandhar 1.803 0.338 0.610 831Kapurthala 0.676 0.119 0.080 501Ludhiana 2.882 0.408 1.175 975Mansa 0.635 -0.037 -0.024 350Moga 0.820 0.066 0.054 444Muktsar 0.746 -0.040 -0.030 348Patiala 1.564 0.194 0.303 596Rupnagar 0.565 0.107 0.061 488Sahibzada Ajit Singh Nagar 0.815 0.338 0.275 830Sangrur 1.367 0.071 0.097 449Shahid Bhagat Singh Nagar 0.508 0.099 0.050 479Tarn Taran 0.926 0.085 0.079 464

RajasthanAjmer 2.136 -0.097 -0.208 305Alwar 3.034 0.060 0.183 438Banswara 1.486 -0.029 -0.042 357Baran 1.011 -0.336 -0.340 176Barmer 2.152 -0.619 -1.331 92Bharatpur 2.106 0.121 0.254 503Bhilwara 1.992 -0.218 -0.435 231Bikaner 1.957 -0.642 -1.256 87Bundi 0.920 -0.279 -0.256 201

163




densityChittaurgarh 1.276 -0.428 -0.546 142Churu 1.687 -0.497 -0.839 121Dausa 1.353 0.098 0.132 477Dhaulpur 0.998 0.012 0.011 391Dungarpur 1.148 -0.015 -0.017 368Ganganagar 1.627 -0.189 -0.308 247Hanumangarh 1.471 -0.433 -0.636 141Jaipur 5.507 0.195 1.075 598Jaisalmer 0.555 -1.338 -0.743 17Jalor 1.512 -0.346 -0.523 172Jhalawar 1.166 -0.225 -0.263 227Jhunjhunun 1.768 -0.024 -0.042 361Jodhpur 3.046 -0.374 -1.138 161Karauli 1.205 -0.160 -0.193 264Kota 1.612 -0.027 -0.044 358Nagaur 2.734 -0.310 -0.847 187Pali 1.684 -0.365 -0.615 165Pratapgarh 0.717 0.550 0.395 1352Rajsamand 0.957 -0.103 -0.099 301Sawai Madhopur 1.106 -0.108 -0.119 297Sikar 2.213 -0.042 -0.092 346Sirohi 0.857 -0.276 -0.237 202Tonk 1.175 -0.285 -0.335 198Udaipur 2.535 -0.222 -0.564 228

SikkimEast District 0.232 -0.112 -0.026 295North District 0.036 -1.570 -0.056 10South District 0.121 -0.290 -0.035 196West District 0.113 -0.513 -0.058 117

Tamil NaduAriyalur 0.622 0.007 0.004 387Chennai 3.868 1.849 7.150 26903Coimbatore 2.869 0.293 0.840 748Cuddalore 2.149 0.265 0.570 702Dharmapuri 1.242 -0.060 -0.075 332Dindigul 1.786 -0.029 -0.051 357Erode 1.867 0.018 0.033 397Kancheepuram 3.298 0.386 1.273 927Kanniyakumari 1.540 0.463 0.712 1106Karur 0.890 -0.012 -0.011 371Krishnagiri 1.557 -0.013 -0.020 370Madurai 2.513 0.334 0.840 823

164

Statistical Tables



densityNagapattinam 1.334 0.244 0.325 668Namakkal 1.422 0.123 0.175 506Perambalur 0.466 -0.072 -0.034 323Pudukkottai 1.338 -0.040 -0.053 348Ramanathapuram 1.105 -0.076 -0.084 320Salem 2.876 0.240 0.691 663Sivaganga 1.108 -0.071 -0.078 324Thanjavur 1.985 0.258 0.513 691The Nilgiris 0.607 -0.122 -0.074 288Theni 1.028 0.055 0.057 433Thiruvallur 3.079 0.440 1.353 1049Thiruvarur 1.048 0.146 0.152 533Thoothukkudi 1.436 -0.004 -0.005 378Tiruchirappalli 2.242 0.198 0.445 602Tirunelveli 2.539 0.080 0.202 458Tiruppur 2.042 0.096 0.197 476Tiruvannamalai 2.040 0.020 0.040 399Vellore 3.246 0.229 0.743 646Viluppuram 2.862 0.102 0.291 482Virudhunagar 1.606 0.076 0.122 454

TripuraDhalai 0.312 -0.406 -0.127 150North Tripura 0.573 -0.191 -0.109 246South Tripura 0.723 0.028 0.020 407West Tripura 1.425 0.179 0.255 575

Uttar PradeshAgra 3.620 0.455 1.648 1088Aligarh 3.036 0.410 1.245 980Allahabad 4.925 0.460 2.264 1099Ambedkar Nagar 1.982 0.424 0.840 1011Auraiya 1.134 0.244 0.277 669Azamgarh 3.815 0.456 1.741 1090Baghpat 1.076 0.405 0.436 968Bahraich 2.874 0.201 0.577 605Ballia 2.664 0.453 1.206 1081Balrampur 1.776 0.285 0.506 735Banda 1.487 0.029 0.043 408Bara Banki 2.692 0.349 0.940 852Bareilly 3.690 0.454 1.674 1084Basti 2.034 0.328 0.667 811Bijnor 3.044 0.326 0.993 808Budaun 3.068 0.275 0.844 718

165




densityBulandshahr 2.891 0.392 1.134 941Chandauli 1.614 0.302 0.488 765Chitrakoot 0.819 -0.091 -0.074 309Deoria 2.560 0.506 1.296 1222Etah 1.455 0.278 0.405 724Etawah 1.305 0.258 0.337 690Faizabad 2.040 0.370 0.754 893Farrukhabad 1.560 0.337 0.526 828Fatehpur 2.175 0.221 0.481 634Firozabad 2.063 0.443 0.914 1058Gautam Buddha Nagar 1.384 0.539 0.746 1320Ghaziabad 3.852 0.796 3.066 2383Ghazipur 2.994 0.449 1.345 1073Gonda 2.835 0.308 0.874 775Gorakhpur 3.666 0.544 1.994 1334Hamirpur 0.912 -0.174 -0.159 255Hardoi 3.381 0.254 0.857 683Jalaun 1.381 -0.018 -0.024 366Jaunpur 3.699 0.464 1.714 1108Jhansi 1.653 0.019 0.031 398Jyotiba Phule Nagar 1.519 0.318 0.483 792Kannauj 1.370 0.339 0.464 832Kanpur Dehat 1.483 0.176 0.260 571Kanpur Nagar 3.779 0.598 2.259 1510Kanshiram Nagar 1.188 0.286 0.339 736Kaushambi 1.320 0.358 0.472 869Kheri 3.317 0.137 0.454 523Kushinagar 2.942 0.507 1.491 1224Lalitpur 1.006 -0.198 -0.199 242Lucknow 3.792 0.678 2.569 1815Mahamaya Nagar 1.294 0.370 0.479 894Mahoba 0.724 -0.093 -0.067 308Mahrajganj 2.202 0.375 0.826 904Mainpuri 1.526 0.244 0.373 669Mathura 2.100 0.301 0.632 763Mau 1.822 0.528 0.963 1287Meerut 2.849 0.555 1.580 1367Mirzapur 2.061 0.160 0.331 552Moradabad 3.944 0.536 2.112 1308Muzaffarnagar 3.420 0.433 1.480 1033Pilibhit 1.683 0.184 0.310 582Pratapgarh 2.623 0.350 0.918 854

166

Statistical Tables



densityRae Bareli 2.813 0.287 0.808 739Rampur 1.930 0.413 0.797 987Saharanpur 2.863 0.392 1.121 939Sant Kabir Nagar 1.417 0.494 0.700 1189Sant Ravidas Nagar

(Bhadohi)

1.284 0.628 0.807 1619

Shahjahanpur 2.481 0.236 0.585 656Shrawasti 0.921 0.414 0.382 990Siddharthnagar 2.110 0.386 0.815 928Sitapur 3.697 0.310 1.148 779Sonbhadra 1.539 -0.143 -0.220 274Sultanpur 3.132 0.351 1.098 855Unnao 2.570 0.253 0.650 682Varanasi 3.043 0.787 2.394 2333

UttarakhandAlmora 0.514 -0.277 -0.143 201Bageshwar 0.215 -0.530 -0.114 112Chamoli 0.323 -0.875 -0.283 51Champawat 0.214 -0.418 -0.090 146Dehradun 1.404 0.159 0.223 550Garhwal 0.567 -0.480 -0.272 126Hardwar 1.592 0.331 0.527 817Nainital 0.789 -0.187 -0.148 248Pithoragarh 0.402 -0.746 -0.300 68Rudraprayag 0.196 -0.485 -0.095 125Tehri Garhwal 0.509 -0.403 -0.205 151Udham Singh Nagar 1.362 0.172 0.234 566Uttarkashi 0.272 -0.964 -0.262 41

West BengalBankura 2.972 0.137 0.407 523Barddhaman 6.382 0.460 2.936 1100Birbhum 2.894 0.306 0.885 771Dakshin Dinajpur 1.381 0.303 0.418 765Darjiling 1.522 0.186 0.283 585Haora 4.001 0.937 3.750 3300Hugli 4.562 0.663 3.023 1753Jalpaiguri 3.198 0.212 0.679 621Koch Bihar 2.333 0.340 0.792 833Kolkata 3.707 1.804 6.687 24252Maldah 3.304 0.449 1.482 1071Murshidabad 5.869 0.544 3.192 1334Nadia 4.271 0.538 2.298 1316

167




densityNorth Twenty Four Parganas 8.332 0.810 6.750 2462Paschim Medinipur 4.911 0.202 0.993 607Purba Medinipur 4.209 0.446 1.877 1065Puruliya 2.419 0.089 0.215 468South Twenty Four Parganas 6.737 0.332 2.237 819Uttar Dinajpur 2.480 0.394 0.976 944

Source: Author’s calculations.

168

Statistical Tables

Table 4.A

Indicators of age composition of population in districts of India, 2011

State/Union Territory

Districtds(a) dc(a)Index C Index A D D

Andaman and Nicobar IslandsNicobars 0.115 0.130 4.668 -0.002North & Middle Andaman 0.110 0.124 8.075 -0.007South Andaman 0.099 0.110 -13.430 -0.027

Andhra PradeshAdilabad 0.108 0.121 0.892 -0.217Anantapur 0.105 0.117 0.559 -0.377Chittoor 0.101 0.113 -0.144 -0.435East Godavari 0.096 0.106 -1.926 -0.660Guntur 0.095 0.105 -1.892 -0.631Hyderabad 0.105 0.117 0.561 -0.369Karimnagar 0.085 0.093 -4.021 -0.670Khammam 0.096 0.106 -1.042 -0.358Krishna 0.090 0.099 -3.279 -0.691Kurnool 0.118 0.134 3.365 -0.177Mahbubnagar 0.124 0.142 4.576 -0.092Medak 0.115 0.130 2.082 -0.163Nalgonda 0.102 0.113 -0.038 -0.358Nizamabad 0.105 0.117 0.425 -0.230Prakasam 0.106 0.119 0.777 -0.291Rangareddy 0.112 0.127 2.932 -0.335Sri Potti Sriramulu Nellore 0.097 0.107 -0.883 -0.364Srikakulam 0.098 0.109 -0.578 -0.316Visakhapatnam 0.100 0.111 -0.480 -0.471Vizianagaram 0.099 0.109 -0.463 -0.271Warangal 0.092 0.101 -2.060 -0.503West Godavari 0.092 0.102 -2.230 -0.557Y.S.R. 0.109 0.122 1.034 -0.222

Arunachal PradeshAnjaw 0.161 0.192 0.718 0.002Changlang 0.172 0.208 8.879 0.017Dibang Valley 0.139 0.161 -0.158 0.000East Kameng 0.179 0.217 5.778 0.010East Siang 0.122 0.139 -6.508 -0.003Kurung Kumey 0.173 0.209 5.582 0.011Lohit 0.162 0.194 5.448 0.013Lower Dibang Valley 0.143 0.167 -0.515 0.002Lower Subansiri 0.121 0.137 -5.869 -0.003Papum Pare 0.134 0.155 -5.706 0.002Tawang 0.113 0.127 -4.741 -0.003

169




Tirap 0.172 0.208 6.788 0.013Upper Siang 0.131 0.151 -1.441 -0.000Upper Subansiri 0.136 0.157 -2.305 0.001West Kameng 0.131 0.151 -3.566 -0.000West Siang 0.123 0.141 -7.022 -0.003

AssamBaksa 0.123 0.140 -2.485 -0.025Barpeta 0.166 0.199 3.771 0.166Bongaigaon 0.155 0.184 0.843 0.052Cachar 0.142 0.166 -0.508 0.058Chirang 0.146 0.170 0.049 0.021Darrang 0.165 0.197 1.940 0.087Dhemaji 0.145 0.169 0.012 0.028Dhubri 0.184 0.226 7.820 0.281Dibrugarh 0.117 0.132 -4.585 -0.064Dima Hasao 0.149 0.175 0.095 0.011Goalpara 0.164 0.197 2.107 0.095Golaghat 0.121 0.138 -3.007 -0.034Hailakandi 0.166 0.199 1.500 0.066Jorhat 0.108 0.121 -5.141 -0.088Kamrup 0.129 0.147 -2.910 -0.013Kamrup Metropolitan 0.096 0.106 -8.264 -0.161Karbi Anglong 0.190 0.235 4.433 0.154Karimganj 0.167 0.200 2.870 0.124Kokrajhar 0.149 0.175 0.388 0.046Lakhimpur 0.145 0.170 0.029 0.043Morigaon 0.166 0.199 2.174 0.095Nagaon 0.158 0.188 4.040 0.219Nalbari 0.118 0.133 -2.556 -0.034Sivasagar 0.116 0.132 -4.019 -0.057Sonitpur 0.139 0.161 -1.320 0.045Tinsukia 0.133 0.153 -1.816 0.007Udalguri 0.131 0.151 -1.321 -0.000

BiharAraria 0.201 0.252 1.681 0.514Arwal 0.177 0.215 -0.044 0.088Aurangabad 0.174 0.211 -0.330 0.303Banka 0.179 0.218 -0.021 0.266Begusarai 0.180 0.220 0.100 0.398Bhagalpur 0.176 0.213 -0.301 0.374Bhojpur 0.162 0.193 -1.364 0.241Buxar 0.168 0.202 -0.546 0.178

170

Statistical Tables



Darbhanga 0.179 0.218 -0.031 0.514Gaya 0.174 0.211 -0.617 0.524Gopalganj 0.171 0.206 -0.606 0.285Jamui 0.178 0.217 -0.026 0.229Jehanabad 0.173 0.208 -0.211 0.130Kaimur (Bhabua) 0.179 0.219 0.016 0.216Katihar 0.196 0.244 1.443 0.528Khagaria 0.209 0.265 1.348 0.334Kishanganj 0.202 0.253 1.065 0.314Lakhisarai 0.182 0.223 0.087 0.139Madhepura 0.199 0.249 1.103 0.357Madhubani 0.174 0.211 -0.630 0.536Munger 0.163 0.194 -0.658 0.123Muzaffarpur 0.171 0.206 -1.093 0.536Nalanda 0.174 0.211 -0.379 0.346Nawada 0.166 0.199 -0.868 0.218Pashchim Champaran 0.192 0.238 1.418 0.639Patna 0.157 0.186 -3.828 0.432Purba Champaran 0.195 0.243 2.302 0.867Purnia 0.197 0.245 1.595 0.568Rohtas 0.166 0.200 -1.092 0.297Saharsa 0.199 0.248 1.035 0.339Samastipur 0.184 0.226 0.635 0.615Saran 0.167 0.200 -1.421 0.398Sheikhpura 0.186 0.229 0.128 0.095Sheohar 0.190 0.235 0.204 0.104Sitamarhi 0.188 0.232 0.885 0.527Siwan 0.161 0.191 -1.816 0.282Supaul 0.190 0.235 0.709 0.354Vaishali 0.169 0.204 -0.991 0.376

ChandigarhChandigarh 0.112 0.126 0.000 -0.069

ChhattisgarhBastar 0.151 0.178 2.014 0.082Bijapur 0.157 0.186 0.560 0.019Bilaspur 0.151 0.177 3.713 0.153Dakshin Bastar Dantewada 0.143 0.168 0.235 0.020Dhamtari 0.126 0.144 -1.708 -0.014Durg 0.126 0.144 -7.076 -0.056Janjgir - Champa 0.135 0.156 -1.185 0.020Jashpur 0.141 0.164 0.072 0.025Kabeerdham 0.171 0.207 3.290 0.092

171




Korba 0.140 0.162 -0.124 0.031Koriya 0.141 0.165 0.108 0.021Mahasamund 0.127 0.146 -1.979 -0.013Narayanpur 0.163 0.195 0.418 0.013Raigarh 0.128 0.147 -2.677 -0.015Raipur 0.140 0.163 -0.083 0.112Rajnandgaon 0.134 0.155 -1.348 0.014Surguja 0.157 0.187 5.443 0.181Uttar Bastar Kanker 0.130 0.150 -1.101 -0.002

Dadra and Nagar HaveliDadra & Nagar Haveli 0.143 0.168 0.000 0.013

Daman and DiuDaman 0.102 0.114 -15.038 -0.019Diu 0.122 0.139 13.936 -0.002

DelhiCentral 0.104 0.117 -2.019 -0.054East 0.111 0.125 -2.905 -0.117New Delhi 0.086 0.095 -1.191 -0.022North 0.114 0.129 -0.766 -0.050North East 0.132 0.152 7.755 0.007North West 0.121 0.138 3.384 -0.117South 0.118 0.134 0.429 -0.117South West 0.115 0.129 -1.720 -0.127West 0.112 0.126 -3.854 -0.167

GoaNorth Goa 0.092 0.101 -11.001 -0.118South Goa 0.101 0.112 10.563 -0.069

GujaratAhmadabad 0.111 0.125 -6.422 -0.485Amreli 0.111 0.125 -1.326 -0.101Anand 0.117 0.132 -1.072 -0.101Banas Kantha 0.160 0.191 6.642 0.260Bharuch 0.110 0.124 -1.526 -0.112Bhavnagar 0.128 0.147 0.801 -0.026Dohad 0.189 0.234 7.656 0.333Gandhinagar 0.115 0.130 -0.877 -0.076Jamnagar 0.118 0.133 -0.941 -0.096Junagadh 0.110 0.123 -2.714 -0.198Kachchh 0.148 0.174 3.110 0.107Kheda 0.121 0.137 -0.537 -0.079Mahesana 0.112 0.126 -1.654 -0.129Narmada 0.127 0.146 0.128 -0.007

172

Statistical Tables



Navsari 0.097 0.108 -2.614 -0.161Panch Mahals 0.146 0.171 3.241 0.107Patan 0.134 0.154 0.817 0.010Porbandar 0.109 0.122 -0.624 -0.045Rajkot 0.112 0.126 -3.291 -0.251Sabar Kantha 0.139 0.161 2.276 0.058Surat 0.117 0.132 -2.965 -0.287Surendranagar 0.133 0.154 1.044 0.012Tapi 0.105 0.117 -1.105 -0.074The Dangs 0.174 0.210 0.643 0.027Vadodara 0.114 0.129 -2.847 -0.237Valsad 0.121 0.138 -0.338 -0.056

HaryanaAmbala 0.109 0.122 -3.974 -0.087Bhiwani 0.126 0.145 -0.901 -0.025Faridabad 0.132 0.152 0.606 0.006Fatehabad 0.126 0.144 -0.617 -0.016Gurgaon 0.131 0.150 0.132 -0.003Hisar 0.121 0.138 -2.416 -0.057Jhajjar 0.121 0.138 -1.295 -0.031Jind 0.124 0.141 -1.343 -0.033Kaithal 0.126 0.144 -0.677 -0.018Karnal 0.129 0.148 -0.232 -0.010Kurukshetra 0.120 0.136 -1.589 -0.037Mahendragarh 0.119 0.135 -1.564 -0.036Mewat 0.223 0.287 12.195 0.252Panchkula 0.117 0.132 -1.192 -0.027Palwal 0.165 0.198 4.972 0.100Panipat 0.137 0.159 1.233 0.022Rewari 0.125 0.143 -0.674 -0.017Rohtak 0.119 0.134 -1.923 -0.044Sirsa 0.119 0.134 -2.356 -0.054Sonipat 0.127 0.145 -0.698 -0.020Yamunanagar 0.118 0.134 -2.326 -0.053

Himachal PradeshBilaspur 0.109 0.122 -0.615 -0.029Chamba 0.134 0.154 6.851 0.004Hamirpur 0.105 0.117 -1.906 -0.041Kangra 0.107 0.119 -4.596 -0.127Kinnaur 0.095 0.105 -0.964 -0.011Kullu 0.114 0.129 0.824 -0.025Lahul & Spiti 0.095 0.105 -0.356 -0.004

173




Mandi 0.110 0.124 -0.895 -0.072Shimla 0.099 0.110 -6.618 -0.092Sirmaur 0.128 0.147 5.350 -0.005Solan 0.115 0.130 1.327 -0.031Una 0.112 0.126 0.096 -0.034

Jammu and KashmirAnantnag 0.193 0.239 8.367 0.176Badgam 0.207 0.261 7.999 0.144Bandipore 0.157 0.186 -0.342 0.029Baramula 0.159 0.190 -0.182 0.083Doda 0.173 0.210 1.365 0.048Ganderbal 0.170 0.205 0.756 0.033Jammu 0.105 0.117 -25.776 -0.140Kargil 0.142 0.166 -0.687 0.005Kathua 0.130 0.150 -5.147 -0.002Kishtwar 0.169 0.204 0.539 0.025Kulgam 0.166 0.200 0.674 0.042Kupwara 0.225 0.290 12.753 0.205Leh(Ladakh) 0.080 0.087 -3.972 -0.029Pulwama 0.171 0.207 1.602 0.064Punch 0.176 0.214 1.928 0.060Rajouri 0.191 0.237 4.643 0.100Ramban 0.193 0.240 2.241 0.047Reasi 0.177 0.216 1.341 0.040Samba 0.119 0.135 -3.760 -0.012Shupiyan 0.151 0.178 -0.605 0.016Srinagar 0.123 0.140 -13.570 -0.035Udhampur 0.149 0.175 -1.658 0.029

JharkhandBokaro 0.138 0.160 -4.514 0.043Chatra 0.181 0.221 2.153 0.142Deoghar 0.176 0.214 2.446 0.186Dhanbad 0.137 0.159 -6.157 0.048Dumka 0.161 0.192 0.296 0.114Garhwa 0.177 0.215 2.220 0.167Giridih 0.184 0.226 5.759 0.353Godda 0.179 0.218 2.485 0.173Gumla 0.164 0.196 0.515 0.096Hazaribagh 0.158 0.187 -0.204 0.134Jamtara 0.163 0.194 0.285 0.071Khunti 0.157 0.186 -0.092 0.040Kodarma 0.179 0.218 1.366 0.095

174

Statistical Tables



Latehar 0.183 0.224 1.624 0.102Lohardaga 0.164 0.196 0.226 0.043Pakur 0.195 0.242 2.948 0.152Palamu 0.163 0.195 0.865 0.179Pashchimi Singhbhum 0.169 0.204 1.487 0.161Purbi Singhbhum 0.125 0.143 -8.437 -0.046Ramgarh 0.138 0.160 -2.110 0.019Ranchi 0.133 0.154 -7.897 0.019Sahibganj 0.188 0.232 3.101 0.177Saraikela-Kharsawan 0.144 0.169 -1.583 0.042Simdega 0.152 0.180 -0.401 0.037

KarnatakaBagalkot 0.140 0.162 3.352 0.048Bangalore 0.103 0.115 -6.433 -0.940Bangalore Rural 0.103 0.115 -0.643 -0.096Belgaum 0.127 0.145 4.708 -0.069Bellary 0.135 0.156 3.801 0.030Bidar 0.128 0.146 1.768 -0.020Bijapur 0.140 0.162 3.859 0.055Chamarajanagar 0.093 0.102 -1.520 -0.142Chikkaballapura 0.099 0.110 -1.200 -0.141Chikmagalur 0.089 0.097 -2.118 -0.180Chitradurga 0.107 0.120 -0.613 -0.137Dakshina Kannada 0.097 0.108 -2.353 -0.252Davanagere 0.106 0.119 -0.833 -0.167Dharwad 0.114 0.128 0.217 -0.107Gadag 0.119 0.136 0.541 -0.041Gulbarga 0.137 0.159 4.211 0.048Hassan 0.088 0.096 -3.463 -0.289Haveri 0.117 0.133 0.593 -0.073Kodagu 0.095 0.105 -0.730 -0.072Kolar 0.105 0.117 -0.797 -0.139Koppal 0.140 0.162 2.473 0.036Mandya 0.090 0.098 -3.199 -0.278Mysore 0.095 0.106 -3.818 -0.385Raichur 0.142 0.165 3.659 0.061Ramanagara 0.094 0.104 -1.531 -0.147Shimoga 0.101 0.112 -1.493 -0.188Tumkur 0.094 0.104 -3.728 -0.360Udupi 0.085 0.093 -2.530 -0.203Uttara Kannada 0.102 0.113 -1.092 -0.147Yadgir 0.158 0.188 3.319 0.092

175




KeralaAlappuzha 0.088 0.096 -3.857 -0.344Ernakulam 0.088 0.097 -5.678 -0.524Idukki 0.090 0.099 -1.528 -0.166Kannur 0.105 0.117 1.979 -0.229Kasaragod 0.115 0.129 2.680 -0.072Kollam 0.091 0.100 -3.573 -0.393Kottayam 0.085 0.093 -4.415 -0.344Kozhikode 0.105 0.117 2.283 -0.283Malappuram 0.134 0.155 18.217 0.042Palakkad 0.103 0.114 1.241 -0.281Pathanamthitta 0.077 0.083 -4.473 -0.257Thiruvananthapuram 0.088 0.096 -5.894 -0.533Thrissur 0.093 0.102 -3.047 -0.433Wayanad 0.110 0.123 1.176 -0.059

LakshadweepLakshadweep 0.110 0.124 0.000 -0.005

Madhya PradeshAlirajpur 0.198 0.246 1.614 0.128Anuppur 0.137 0.159 -0.291 0.014Ashoknagar 0.162 0.193 0.641 0.074Balaghat 0.122 0.138 -2.093 -0.053Barwani 0.188 0.232 2.584 0.214Betul 0.132 0.152 -1.084 0.002Bhind 0.142 0.165 -0.290 0.055Bhopal 0.124 0.141 -2.614 -0.056Burhanpur 0.159 0.189 0.471 0.060Chhatarpur 0.158 0.188 1.077 0.139Chhindwara 0.128 0.147 -1.847 -0.022Damoh 0.148 0.174 0.175 0.064Datia 0.139 0.161 -0.258 0.018Dewas 0.143 0.167 -0.187 0.055Dhar 0.160 0.190 1.474 0.181Dindori 0.151 0.179 0.206 0.042East Nimar 0.155 0.184 0.608 0.092Guna 0.162 0.194 0.981 0.112Gwalior 0.125 0.143 -2.102 -0.040Harda 0.144 0.168 -0.033 0.022Hoshangabad 0.130 0.150 -0.953 -0.004Indore 0.125 0.142 -3.488 -0.070Jabalpur 0.117 0.132 -3.695 -0.117Jhabua 0.203 0.255 2.478 0.192

176

Statistical Tables



Katni 0.146 0.171 0.035 0.057Mandla 0.137 0.159 -0.408 0.020Mandsaur 0.130 0.149 -1.053 -0.006Morena 0.152 0.180 0.655 0.123Narsimhapur 0.128 0.146 -0.982 -0.013Neemuch 0.128 0.147 -0.735 -0.009Panna 0.158 0.188 0.616 0.080Raisen 0.153 0.181 0.485 0.086Rajgarh 0.146 0.171 0.046 0.068Ratlam 0.146 0.171 0.032 0.064Rewa 0.144 0.168 -0.131 0.092Sagar 0.148 0.173 0.275 0.117Satna 0.144 0.169 -0.096 0.089Sehore 0.149 0.175 0.206 0.068Seoni 0.128 0.146 -1.228 -0.015Shahdol 0.145 0.169 -0.038 0.043Shajapur 0.141 0.164 -0.324 0.045Sheopur 0.168 0.202 0.708 0.072Shivpuri 0.163 0.194 1.373 0.156Sidhi 0.168 0.201 1.137 0.116Singrauli 0.173 0.210 1.480 0.139Tikamgarh 0.155 0.183 0.639 0.100Ujjain 0.133 0.154 -1.202 0.012Umaria 0.155 0.183 0.279 0.044Vidisha 0.158 0.188 0.876 0.114West Nimar 0.157 0.186 1.020 0.141

MaharashtraAhmadnagar 0.118 0.134 0.673 -0.193Akola 0.113 0.128 -0.072 -0.109Amravati 0.104 0.116 -1.209 -0.275Aurangabad 0.140 0.163 3.292 0.098Bhandara 0.103 0.114 -0.566 -0.120Bid 0.133 0.153 1.730 0.015Buldana 0.125 0.143 1.044 -0.049Chandrapur 0.102 0.114 -1.084 -0.224Dhule 0.128 0.146 0.988 -0.024Gadchiroli 0.107 0.120 -0.292 -0.087Gondiya 0.103 0.115 -0.602 -0.130Hingoli 0.137 0.158 0.928 0.020Jalgaon 0.122 0.138 1.144 -0.132Jalna 0.144 0.168 1.987 0.074Kolhapur 0.102 0.114 -1.917 -0.396

177




Latur 0.125 0.143 0.989 -0.046Mumbai 0.083 0.091 -4.261 -0.573Mumbai Suburban 0.094 0.104 -7.898 -1.259Nagpur 0.104 0.116 -2.001 -0.448Nanded 0.132 0.153 2.172 0.013Nandurbar 0.140 0.163 1.501 0.047Nashik 0.132 0.152 3.830 0.012Osmanabad 0.120 0.137 0.361 -0.060Parbhani 0.137 0.159 1.478 0.034Pune 0.113 0.128 -0.406 -0.568Raigarh 0.110 0.124 -0.422 -0.188Ratnagiri 0.093 0.102 -1.458 -0.226Sangli 0.105 0.117 -1.088 -0.260Satara 0.102 0.114 -1.433 -0.302Sindhudurg 0.081 0.088 -1.258 -0.165Solapur 0.120 0.137 0.983 -0.152Thane 0.114 0.128 -0.261 -0.647Wardha 0.096 0.106 -0.974 -0.163Washim 0.123 0.141 0.393 -0.031Yavatmal 0.115 0.131 0.118 -0.145

ManipurBishnupur 0.124 0.142 -1.964 -0.006Chandel 0.115 0.129 -3.272 -0.008Churachandpur 0.127 0.146 -1.021 -0.004Imphal East 0.134 0.155 2.803 0.004Imphal West 0.113 0.128 -12.814 -0.031Senapati 0.128 0.147 -0.891 -0.004Tamenglong 0.129 0.148 -0.165 -0.001Thoubal 0.159 0.189 16.024 0.034Ukhrul 0.125 0.143 -1.163 -0.003

MeghalayaEast Garo Hills 0.180 0.219 -2.441 0.042East Khasi Hills 0.163 0.195 -20.435 0.075Jaintia Hills 0.221 0.283 11.752 0.089Ribhoi 0.200 0.249 2.906 0.046South Garo Hills 0.192 0.238 0.633 0.023West Garo Hills 0.174 0.211 -8.355 0.077West Khasi Hills 0.225 0.290 12.849 0.090

MizoramAizawl 0.129 0.149 -29.641 -0.002Champhai 0.176 0.214 8.863 0.016Kolasib 0.153 0.181 0.310 0.005

178

Statistical Tables



Lawngtlai 0.186 0.228 11.319 0.017Lunglei 0.153 0.181 0.659 0.010Mamit 0.173 0.209 5.293 0.010Saiha 0.162 0.194 1.810 0.005Serchhip 0.140 0.163 -2.433 0.002

NagalandDimapur 0.131 0.150 -9.696 -0.001Kiphire 0.194 0.240 5.725 0.012Kohima 0.134 0.155 -5.197 0.002Longleng 0.175 0.212 2.525 0.006Mokokchung 0.104 0.116 -15.956 -0.018Mon 0.158 0.187 5.720 0.019Peren 0.160 0.191 2.567 0.008Phek 0.169 0.203 6.588 0.017Tuensang 0.177 0.216 10.610 0.025Wokha 0.118 0.134 -8.345 -0.007Zunheboto 0.143 0.166 -0.464 0.005

OrissaAnugul 0.115 0.129 -0.697 -0.071Balangir 0.126 0.144 0.871 -0.030Baleshwar 0.118 0.134 -0.371 -0.097Bargarh 0.106 0.118 -2.210 -0.131Baudh 0.134 0.155 0.587 0.004Bhadrak 0.117 0.133 -0.402 -0.069Cuttack 0.096 0.106 -6.821 -0.332Debagarh 0.124 0.141 0.111 -0.008Dhenkanal 0.111 0.125 -1.070 -0.081Gajapati 0.144 0.168 1.237 0.022Ganjam 0.113 0.127 -2.481 -0.214Jagatsinghapur 0.091 0.100 -3.631 -0.167Jajapur 0.114 0.128 -1.198 -0.108Jharsuguda 0.107 0.119 -0.798 -0.049Kalahandi 0.136 0.158 2.346 0.024Kandhamal 0.145 0.170 1.670 0.031Kendrapara 0.107 0.119 -2.002 -0.122Kendujhar 0.141 0.164 3.386 0.052Khordha 0.099 0.110 -5.046 -0.257Koraput 0.157 0.186 4.386 0.102Malkangiri 0.172 0.208 2.685 0.071Mayurbhanj 0.134 0.155 3.358 0.025Nabarangapur 0.166 0.199 4.735 0.120Nayagarh 0.105 0.118 -1.470 -0.086

179




Nuapada 0.140 0.163 1.108 0.016Puri 0.097 0.107 -4.239 -0.209Rayagada 0.147 0.172 2.307 0.045Sambalpur 0.108 0.121 -1.274 -0.082Subarnapur 0.117 0.133 -0.173 -0.030Sundargarh 0.120 0.136 -0.074 -0.078

PuducherryKaraikal 0.108 0.121 3.816 -0.016Mahe 0.109 0.123 1.061 -0.003Puducherry 0.101 0.112 -6.245 -0.101Yanam 0.108 0.121 1.177 -0.004

PunjabAmritsar 0.107 0.120 0.351 -0.207Barnala 0.106 0.118 -0.053 -0.053Bathinda 0.105 0.117 -0.345 -0.128Faridkot 0.108 0.121 0.173 -0.049Fatehgarh Sahib 0.101 0.113 -0.493 -0.063Firozpur 0.119 0.135 4.100 -0.081Gurdaspur 0.105 0.117 -0.525 -0.210Hoshiarpur 0.103 0.114 -0.954 -0.158Jalandhar 0.098 0.108 -3.115 -0.259Kapurthala 0.101 0.112 -0.702 -0.087Ludhiana 0.104 0.116 -1.208 -0.328Mansa 0.106 0.119 -0.027 -0.067Moga 0.103 0.115 -0.465 -0.096Muktsar 0.113 0.127 0.993 -0.055Patiala 0.108 0.121 0.653 -0.148Rupnagar 0.102 0.113 -0.499 -0.070Sahibzada Ajit Singh Nagar 0.111 0.125 0.739 -0.068Sangrur 0.106 0.118 -0.094 -0.145Shahid Bhagat Singh Nagar 0.099 0.109 -0.804 -0.071Tarn Taran 0.116 0.131 1.738 -0.057

RajasthanAjmer 0.145 0.170 -1.047 0.107Alwar 0.158 0.188 0.880 0.287Banswara 0.179 0.218 2.108 0.235Baran 0.147 0.172 -0.391 0.057Barmer 0.192 0.237 4.479 0.422Bharatpur 0.169 0.203 1.903 0.272Bhilwara 0.148 0.173 -0.633 0.120Bikaner 0.167 0.200 1.506 0.238Bundi 0.142 0.165 -0.625 0.036

180

Statistical Tables



Chittaurgarh 0.136 0.157 -1.388 0.021Churu 0.154 0.182 0.067 0.135Dausa 0.157 0.186 0.298 0.122Dhaulpur 0.179 0.217 1.409 0.158Dungarpur 0.173 0.208 1.254 0.161Ganganagar 0.128 0.147 -2.579 -0.019Hanumangarh 0.131 0.151 -2.056 -0.002Jaipur 0.137 0.159 -5.403 0.123Jaisalmer 0.194 0.241 1.219 0.112Jalor 0.171 0.207 1.567 0.207Jhalawar 0.145 0.169 -0.595 0.057Jhunjhunun 0.133 0.154 -2.177 0.015Jodhpur 0.161 0.192 1.374 0.316Karauli 0.164 0.196 0.767 0.138Kota 0.127 0.146 -2.631 -0.023Nagaur 0.151 0.177 -0.394 0.191Pali 0.144 0.168 -0.955 0.077Pratapgarh 0.171 0.207 0.738 0.098Rajsamand 0.150 0.177 -0.166 0.065Sawai Madhopur 0.149 0.174 -0.300 0.069Sikar 0.140 0.163 -1.729 0.075Sirohi 0.166 0.198 0.611 0.102Tonk 0.141 0.165 -0.841 0.044Udaipur 0.163 0.194 1.403 0.277

SikkimEast District 0.094 0.104 -13.821 -0.037North District 0.103 0.115 0.950 -0.004South District 0.103 0.114 2.516 -0.015West District 0.110 0.123 9.562 -0.010

Tamil NaduAriyalur 0.102 0.114 0.328 -0.077Chennai 0.089 0.098 -2.071 -0.723Coimbatore 0.085 0.093 -2.663 -0.604Cuddalore 0.100 0.111 0.818 -0.284Dharmapuri 0.108 0.121 1.219 -0.120Dindigul 0.093 0.102 -0.462 -0.305Erode 0.080 0.087 -2.618 -0.446Kancheepuram 0.099 0.110 1.014 -0.451Kanniyakumari 0.087 0.095 -1.171 -0.309Karur 0.092 0.101 -0.278 -0.154Krishnagiri 0.108 0.121 1.561 -0.148Madurai 0.094 0.104 -0.249 -0.404

181




Nagapattinam 0.096 0.106 0.019 -0.206Namakkal 0.082 0.089 -1.808 -0.328Perambalur 0.099 0.110 0.137 -0.064Pudukkottai 0.105 0.117 1.013 -0.147Ramanathapuram 0.095 0.105 -0.027 -0.173Salem 0.093 0.102 -0.671 -0.486Sivaganga 0.095 0.105 -0.036 -0.174Thanjavur 0.093 0.103 -0.404 -0.332The Nilgiris 0.084 0.092 -0.636 -0.132Theni 0.089 0.098 -0.571 -0.193Thiruvallur 0.099 0.110 0.942 -0.421Thiruvarur 0.091 0.100 -0.444 -0.189Thoothukkudi 0.098 0.109 0.300 -0.205Tiruchirappalli 0.093 0.103 -0.404 -0.372Tirunelveli 0.098 0.109 0.522 -0.363Tiruppur 0.090 0.098 -1.047 -0.379Tiruvannamalai 0.104 0.116 1.364 -0.235Vellore 0.104 0.115 2.101 -0.378Viluppuram 0.109 0.123 3.116 -0.258Virudhunagar 0.094 0.104 -0.177 -0.260

TripuraDhalai 0.144 0.168 8.970 0.015North Tripura 0.139 0.161 12.915 0.016South Tripura 0.124 0.142 3.239 -0.019West Tripura 0.107 0.120 -28.088 -0.143

Uttar PradeshAgra 0.146 0.171 -0.235 0.193Aligarh 0.151 0.178 0.140 0.217Allahabad 0.140 0.162 -0.967 0.156Ambedkar Nagar 0.135 0.156 -0.585 0.031Auraiya 0.141 0.165 -0.183 0.042Azamgarh 0.147 0.173 -0.118 0.225Baghpat 0.145 0.170 -0.085 0.055Bahraich 0.183 0.224 1.851 0.489Ballia 0.139 0.162 -0.553 0.079Balrampur 0.179 0.218 1.037 0.285Banda 0.161 0.192 0.361 0.155Bara Banki 0.155 0.183 0.321 0.225Bareilly 0.150 0.176 0.078 0.249Basti 0.151 0.178 0.098 0.146Bijnor 0.149 0.175 0.010 0.197Budaun 0.174 0.211 1.522 0.447

182

Statistical Tables



Bulandshahr 0.154 0.182 0.280 0.231Chandauli 0.156 0.185 0.225 0.140Chitrakoot 0.173 0.209 0.386 0.116Deoria 0.144 0.168 -0.284 0.117Etah 0.158 0.187 0.257 0.136Etawah 0.139 0.162 -0.265 0.040Faizabad 0.141 0.164 -0.362 0.071Farrukhabad 0.155 0.184 0.196 0.132Fatehpur 0.143 0.167 -0.264 0.096Firozabad 0.148 0.174 -0.034 0.127Gautam Buddha Nagar 0.146 0.172 -0.073 0.077Ghaziabad 0.142 0.166 -0.542 0.157Ghazipur 0.149 0.175 -0.019 0.189Gonda 0.159 0.189 0.582 0.278Gorakhpur 0.134 0.155 -1.170 0.042Hamirpur 0.135 0.155 -0.284 0.012Hardoi 0.162 0.193 0.885 0.362Jalaun 0.131 0.151 -0.533 0.001Jaunpur 0.144 0.168 -0.413 0.169Jhansi 0.125 0.142 -0.903 -0.043Jyotiba Phule Nagar 0.158 0.188 0.292 0.145Kannauj 0.152 0.179 0.078 0.101Kanpur Dehat 0.136 0.157 -0.418 0.026Kanpur Nagar 0.106 0.119 -3.880 -0.398Kanshiram Nagar 0.170 0.205 0.498 0.158Kaushambi 0.165 0.198 0.421 0.154Kheri 0.161 0.191 0.775 0.340Kushinagar 0.155 0.183 0.356 0.247Lalitpur 0.169 0.204 0.401 0.131Lucknow 0.114 0.128 -3.099 -0.268Mahamaya Nagar 0.154 0.181 0.121 0.103Mahoba 0.142 0.166 -0.101 0.030Mahrajganj 0.151 0.178 0.087 0.155Mainpuri 0.149 0.175 0.008 0.099Mathura 0.156 0.185 0.307 0.185Mau 0.149 0.174 -0.017 0.114Meerut 0.142 0.165 -0.442 0.110Mirzapur 0.157 0.187 0.347 0.189Moradabad 0.160 0.190 0.867 0.396Muzaffarnagar 0.152 0.180 0.236 0.258Pilibhit 0.146 0.171 -0.110 0.090Pratapgarh 0.135 0.156 -0.807 0.035

183




Rae Bareli 0.135 0.157 -0.824 0.044Rampur 0.159 0.188 0.375 0.185Saharanpur 0.146 0.171 -0.186 0.153Sant Kabir Nagar 0.159 0.189 0.280 0.137Sant Ravidas Nagar 0.157 0.186 0.210 0.117Shahjahanpur 0.163 0.194 0.685 0.272Shrawasti 0.182 0.222 0.579 0.155Siddharthnagar 0.182 0.223 1.349 0.358Sitapur 0.164 0.196 1.093 0.417Sonbhadra 0.166 0.199 0.517 0.184Sultanpur 0.142 0.166 -0.443 0.128Unnao 0.134 0.155 -0.828 0.028Varanasi 0.130 0.149 -1.271 -0.015

UttarakhandAlmora 0.125 0.143 -1.420 -0.012Bageshwar 0.133 0.154 0.194 0.002Chamoli 0.130 0.149 -0.235 -0.002Champawat 0.141 0.164 0.902 0.008Dehradun 0.116 0.131 -10.648 -0.088Garhwal 0.120 0.136 -3.162 -0.026Hardwar 0.148 0.173 11.179 0.094Nainital 0.128 0.147 -1.239 -0.010Pithoragarh 0.129 0.149 -0.350 -0.003Rudraprayag 0.128 0.146 -0.343 -0.003Tehri Garhwal 0.134 0.154 0.544 0.005Udham Singh Nagar 0.136 0.157 2.573 0.022Uttarkashi 0.136 0.158 0.626 0.005

West BengalBankura 0.113 0.127 0.349 -0.223Barddhaman 0.102 0.114 -3.325 -0.787Birbhum 0.124 0.141 2.091 -0.085Dakshin Dinajpur 0.107 0.120 -0.324 -0.140Darjiling 0.098 0.108 -1.211 -0.219Haora 0.103 0.115 -1.921 -0.481Hugli 0.091 0.101 -5.587 -0.805Jalpaiguri 0.115 0.130 0.790 -0.209Koch Bihar 0.118 0.133 0.934 -0.125Kolkata 0.067 0.072 -11.777 -1.200Maldah 0.148 0.173 6.278 0.197Murshidabad 0.138 0.160 8.476 0.147Nadia 0.098 0.109 -3.372 -0.613North 24 Parganas 0.090 0.098 -11.309 -1.553

184

Statistical Tables



Paschim Medinipur 0.112 0.126 0.250 -0.393Purba Medinipur 0.111 0.125 0.087 -0.347Puruliya 0.134 0.155 3.078 0.029South 24 Parganas 0.120 0.136 3.441 -0.306Uttar Dinajpur 0.157 0.186 5.706 0.223


185


Table 5.A

Sex ratio in districts of India, 2011


District

Females per 1000 males

All ages 0-6 years 7 years and above

Andaman and Nicorbar IslandsNicobars 778 961 757North & Middle Andaman 925 977 919South Andaman 874 961 865

Andhra PradeshAdilabad 1003 942 1010Anantapur 977 927 984Chittoor 1002 931 1010East Godavari 1005 969 1009Guntur 1003 948 1009Hyderabad 943 938 943Karimnagar 1009 937 1016Khammam 1010 958 1016Krishna 997 953 1001Kurnool 984 937 990Mahbubnagar 975 932 982Medak 989 954 994Nalgonda 982 921 989Nizamabad 1038 946 1049Prakasam 981 932 987Rangareddy 955 947 956Sri Potti Sriramulu Nellore 986 945 991Srikakulam 1014 953 1021Visakhapatnam 1003 961 1008Vizianagaram 1016 955 1023Warangal 994 912 1003West Godavari 1004 970 1008Y.S.R. 984 919 992

Arunachal PradeshAnjaw 805 954 779Changlang 914 954 906Dibang Valley 808 831 804East Kameng 1012 970 1021East Siang 962 984 959Kurung Kumey 1029 978 1040Lohit 901 954 892Lower Dibang Valley 919 945 915Lower Subansiri 975 969 976Papum Pare 950 963 948Tawang 701 1005 669

186

Statistical Tables


District



Tirap 931 950 927Upper Siang 891 968 880Upper Subansiri 982 968 985West Kameng 755 965 728West Siang 916 928 915

AssamBaksa 967 962 968Barpeta 951 955 950Bongaigaon 961 965 960Cachar 958 955 959Chirang 969 958 971Darrang 923 941 920Dhemaji 949 945 950Dhubri 952 965 949Dibrugarh 952 957 952Dima Hasao 931 956 927Goalpara 962 954 964Golaghat 961 961 961Hailakandi 946 948 946Jorhat 956 963 955Kamrup 946 962 944Kamrup Metropolitan 922 994 915Karbi Anglong 956 916 966Karimganj 961 958 961Kokrajhar 958 951 959Lakhimpur 965 958 967Morigaon 974 950 978Nagaon 962 958 963Nalbari 945 963 943Sivasagar 951 957 951Sonitpur 946 958 944Tinsukia 948 971 945Udalguri 966 965 966

BiharAraria 921 954 913Arwal 927 941 925Aurangabad 916 945 910Banka 907 939 900Begusarai 894 911 890Bhagalpur 879 934 867Bhojpur 900 915 897Buxar 922 925 922

187



District



Darbhanga 910 928 907Gaya 932 959 926Gopalganj 1015 945 1030Jamui 921 956 913Jehanabad 918 918 918Kaimur (Bhabua) 919 939 915Katihar 916 956 907Khagaria 883 912 876Kishanganj 946 966 941Lakhisarai 900 915 897Madhepura 914 923 911Madhubani 925 931 924Munger 879 925 870Muzaffarpur 898 917 894Nalanda 921 929 919Nawada 936 985 926Pashchim Champaran 906 950 896Patna 892 899 891Purba Champaran 901 923 896Purnia 930 953 925Rohtas 914 925 912Saharsa 906 928 900Samastipur 909 941 902Saran 949 922 954Sheikhpura 926 940 923Sheohar 890 925 882Sitamarhi 899 932 892Siwan 984 934 994Supaul 925 942 921Vaishali 892 894 892

ChandigarhChandigarh 818 867 812

ChhattisgarhBastar 1024 991 1030Bijapur 982 978 982Bilaspur 972 957 975Dakshin Bastar Dantewada 1022 1005 1024Dhamtari 1012 969 1018Durg 988 958 993Janjgir - Champa 986 945 992Jashpur 1004 974 1010Kabeerdham 997 973 1003

188

Statistical Tables


District



Korba 971 964 972Koriya 971 968 972Mahasamund 1018 960 1027Narayanpur 998 975 1002Raigarh 993 943 1001Raipur 983 965 986Rajnandgaon 1017 976 1024Surguja 976 955 980Uttar Bastar Kanker 1007 975 1012

Dadra and Nagar HaveliDadra & Nagar Haveli 775 924 752

Daman and DiuDaman 533 905 500Diu 1030 923 1046

DelhiCentral 892 902 890East 883 870 885New Delhi 811 884 804North 871 872 870North East 886 875 887North West 862 863 862South 859 878 857South West 836 836 836West 876 867 877

GoaNorth Goa 959 911 964South Goa 980 930 986

GujaratAhmadabad 903 859 909Amreli 964 879 975Anand 921 877 927Banas Kantha 936 890 946Bharuch 924 914 926Bhavnagar 931 885 938Dohad 986 937 998Gandhinagar 920 847 930Jamnagar 938 898 943Junagadh 952 904 959Kachchh 907 913 905Kheda 937 887 944Mahesana 925 845 936Narmada 960 937 963

189



District



Navsari 961 921 966Panch Mahals 945 923 949Patan 935 884 943Porbandar 947 894 954Rajkot 924 854 933Sabar Kantha 950 899 959Surat 788 836 782Surendranagar 929 889 935Tapi 1004 944 1012The Dangs 1007 963 1017Vadodara 934 894 939Valsad 926 926 926

HaryanaAmbala 882 807 892Bhiwani 884 831 892Faridabad 871 842 875Fatehabad 903 845 911Gurgaon 853 826 857Hisar 871 849 874Jhajjar 861 774 873Jind 870 835 875Kaithal 880 821 889Karnal 886 820 896Kurukshetra 889 817 900Mahendragarh 894 778 911Mewat 906 903 907Panchkula 870 850 872Palwal 879 862 882Panipat 861 833 865Rewari 898 784 915Rohtak 868 807 877Sirsa 896 852 901Sonipat 853 790 862Yamunanagar 877 825 884

Himachal PradeshBilaspur 981 893 993Chamba 989 950 995Hamirpur 1096 881 1124Kangra 1013 873 1032Kinnaur 818 953 805Kullu 950 962 949Lahul & Spiti 916 1013 906

190

Statistical Tables


District



Mandi 1012 913 1025Shimla 916 922 915Sirmaur 915 931 913Solan 884 899 882Una 977 870 991

Jammu and KashmirAnantnag 937 831 964Badgam 883 832 897Bandipore 911 893 914Baramula 873 866 874Doda 922 932 920Ganderbal 869 863 870Jammu 871 795 881Kargil 775 978 745Kathua 877 836 884Kishtwar 917 922 916Kulgam 951 882 966Kupwara 843 854 840Leh(Ladakh) 583 944 558Pulwama 913 836 930Punch 890 895 889Rajouri 863 837 869Ramban 901 931 894Reasi 891 921 885Samba 886 787 900Shupiyan 951 883 964Srinagar 879 869 881Udhampur 863 887 859

JharkhandBokaro 916 912 916Chatra 951 963 949Deoghar 921 939 917Dhanbad 908 917 907Dumka 974 957 977Garhwa 933 958 928Giridih 943 934 945Godda 933 953 928Gumla 993 955 1000Hazaribagh 946 924 950Jamtara 959 948 961Khunti 994 951 1002Kodarma 949 944 950

191



District



Latehar 964 964 964Lohardaga 985 961 990Pakur 985 965 989Palamu 929 947 925Pashchimi Singhbhum 1004 980 1009Purbi Singhbhum 949 922 953Ramgarh 921 926 920Ranchi 950 937 952Sahibganj 948 955 946Saraikela-Kharsawan 958 937 961Simdega 1000 975 1005

KarnatakaBagalkot 984 929 994Bangalore 908 941 904Bangalore Rural 945 947 945Belgaum 969 931 974Bellary 978 954 982Bidar 952 935 955Bijapur 954 930 958Chamarajanagar 989 942 994Chikkaballapura 968 945 970Chikmagalur 1005 963 1009Chitradurga 969 933 973Dakshina Kannada 1018 946 1026Davanagere 967 931 972Dharwad 967 942 970Gadag 978 944 983Gulbarga 962 935 967Hassan 1005 964 1009Haveri 951 945 952Kodagu 1019 977 1024Kolar 976 955 979Koppal 983 953 988Mandya 989 934 994Mysore 982 956 984Raichur 992 949 999Ramanagara 976 960 977Shimoga 995 960 999Tumkur 979 952 982Udupi 1093 955 1106Uttara Kannada 975 947 979Yadgir 984 942 993

192

Statistical Tables


District



KeralaAlappuzha 1100 947 1116Ernakulam 1028 954 1035Idukki 1006 958 1011Kannur 1133 962 1155Kasaragod 1079 960 1095Kollam 1113 960 1129Kottayam 1040 957 1048Kozhikode 1097 963 1114Malappuram 1096 960 1119Palakkad 1067 962 1079Pathanamthitta 1129 964 1144Thiruvananthapuram 1088 967 1100Thrissur 1109 948 1127Wayanad 1035 960 1044

LakshadweepLakshadweep 946 908 951

Madhya PradeshAlirajpur 1009 971 1018Anuppur 975 943 980Ashoknagar 900 914 898Balaghat 1021 961 1029Barwani 981 940 990Betul 970 949 973Bhind 838 835 838Bhopal 911 916 910Burhanpur 951 921 957Chhatarpur 884 894 882Chhindwara 966 950 968Damoh 913 931 910Datia 875 852 879Dewas 941 907 947Dhar 961 913 970Dindori 1004 970 1011East Nimar 944 931 947Guna 910 901 912Gwalior 862 832 866Harda 932 921 934Hoshangabad 912 911 912Indore 924 892 929Jabalpur 925 916 926Jhabua 989 934 1004

193



District



Katni 948 934 951Mandla 1005 965 1011Mandsaur 966 921 973Morena 839 825 842Narsimhapur 917 900 920Neemuch 959 918 965Panna 907 910 906Raisen 899 927 895Rajgarh 955 916 962Ratlam 973 931 980Rewa 930 883 938Sagar 896 925 891Satna 927 907 930Sehore 918 906 920Seoni 984 954 989Shahdol 968 946 971Shajapur 939 913 944Sheopur 902 888 905Shivpuri 877 889 875Sidhi 952 910 961Singrauli 916 921 915Tikamgarh 901 886 904Ujjain 954 919 960Umaria 953 946 954Vidisha 897 922 892West Nimar 963 931 970

MaharashtraAhmadnagar 934 839 948Akola 942 900 948Amravati 947 927 950Aurangabad 917 848 928Bhandara 984 939 989Bid 912 801 930Buldana 928 842 941Chandrapur 959 945 960Dhule 941 876 951Gadchiroli 975 956 977Gondiya 996 944 1002Hingoli 935 868 946Jalgaon 922 829 936Jalna 929 847 944Kolhapur 953 845 966

194

Statistical Tables


District



Latur 924 872 932Mumbai 838 874 835Mumbai Suburban 857 910 852Nagpur 948 926 951Nanded 937 897 944Nandurbar 972 932 978Nashik 931 882 938Osmanabad 920 853 930Parbhani 940 866 953Pune 910 873 915Raigarh 955 924 959Ratnagiri 1123 940 1143Sangli 964 862 977Satara 986 881 999Sindhudurg 1037 910 1049Solapur 932 872 941Thane 880 918 875Wardha 946 916 950Washim 926 859 936Yavatmal 947 915 951

ManipurBishnupur 1000 919 1012Chandel 932 919 934Churachandpur 969 945 973Imphal East 1011 932 1023Imphal West 1029 943 1041Senapati 939 912 943Tamenglong 953 941 955Thoubal 1006 948 1017Ukhrul 948 921 952

MeghalayaEast Garo Hills 968 975 967East Khasi Hills 1008 961 1018Jaintia Hills 1008 969 1019Ribhoi 951 956 950South Garo Hills 944 973 938West Garo Hills 979 980 979West Khasi Hills 981 975 983

MizoramAizawl 1009 984 1013Champhai 981 976 982Kolasib 956 987 951

195



District



Lawngtlai 945 965 941Lunglei 944 965 941Mamit 924 979 913Saiha 978 937 987Serchhip 976 926 985

NagalandDimapur 916 968 909Kiphire 961 955 962Kohima 927 978 920Longleng 903 882 907Mokokchung 927 954 924Mon 898 900 898Peren 917 940 913Phek 951 915 959Tuensang 930 935 929Wokha 969 970 969Zunheboto 981 955 986

OrissaAnugul 942 884 950Balangir 983 951 988Baleshwar 957 941 959Bargarh 976 946 980Baudh 991 975 993Bhadrak 981 931 988Cuttack 955 913 960Debagarh 976 917 984Dhenkanal 947 870 957Gajapati 1042 964 1055Ganjam 981 899 992Jagatsinghapur 967 929 971Jajapur 972 921 979Jharsuguda 951 938 953Kalahandi 1003 947 1013Kandhamal 1037 960 1050Kendrapara 1006 921 1017Kendujhar 987 957 992Khordha 925 910 927Koraput 1031 970 1043Malkangiri 1016 979 1024Mayurbhanj 1005 952 1014Nabarangapur 1018 988 1024Nayagarh 916 851 924

196

Statistical Tables


District



Nuapada 1020 971 1028Puri 963 924 967Rayagada 1048 955 1065Sambalpur 973 931 978Subarnapur 959 947 961Sundargarh 971 937 975

PuducherryKaraikal 1048 963 1059Mahe 1176 959 1206Puducherry 1031 969 1038Yanam 1039 917 1055

PunjabAmritsar 884 824 892Barnala 876 847 879Bathinda 865 854 866Faridkot 889 851 894Fatehgarh Sahib 871 843 874Firozpur 893 846 899Gurdaspur 895 824 904Hoshiarpur 962 859 974Jalandhar 913 874 917Kapurthala 912 872 917Ludhiana 869 865 869Mansa 880 831 886Moga 893 863 896Muktsar 895 830 904Patiala 888 835 895Rupnagar 913 866 918Sahibzada Ajit Singh Nagar 878 842 883Sangrur 883 835 889Shahid Bhagat Singh Nagar 954 879 963Tarn Taran 898 819 909

RajasthanAjmer 950 893 960Alwar 894 861 900Banswara 979 925 991Baran 926 902 930Barmer 900 899 901Bharatpur 877 863 880Bhilwara 969 916 978Bikaner 903 902 904Bundi 922 886 928

197



District



Chittaurgarh 970 903 981Churu 938 896 946Dausa 904 859 913Dhaulpur 845 854 843Dungarpur 990 916 1006Ganganagar 887 854 892Hanumangarh 906 869 912Jaipur 909 859 917Jaisalmer 849 868 845Jalor 951 891 964Jhalawar 945 905 952Jhunjhunun 950 831 969Jodhpur 915 890 920Karauli 858 844 861Kota 906 889 909Nagaur 948 888 959Pali 987 895 1004Pratapgarh 982 926 995Rajsamand 988 891 1006Sawai Madhopur 894 865 899Sikar 944 841 962Sirohi 938 890 948Tonk 949 882 961Udaipur 958 920 965

SikkimEast District 872 946 865North District 769 897 755South District 914 948 910West District 941 950 940

Tamil NaduAriyalur 1016 892 1031Chennai 986 964 988Coimbatore 1001 963 1005Cuddalore 984 895 994Dharmapuri 946 911 950Dindigul 998 942 1004Erode 992 956 996Kancheepuram 985 967 987Kanniyakumari 1010 961 1015Karur 1015 946 1022Krishnagiri 956 924 960Madurai 990 939 995

198

Statistical Tables


District



Nagapattinam 1025 961 1032Namakkal 986 913 993Perambalur 1006 913 1017Pudukkottai 1015 959 1022Ramanathapuram 977 967 978Salem 954 917 958Sivaganga 1000 961 1004Thanjavur 1031 957 1039The Nilgiris 1041 982 1046Theni 990 937 995Thiruvallur 983 954 987Thiruvarur 1020 962 1027Thoothukkudi 1024 970 1030Tiruchirappalli 1013 952 1020Tirunelveli 1024 964 1030Tiruppur 988 951 992Tiruvannamalai 993 932 1001Vellore 1004 944 1012Viluppuram 985 938 991Virudhunagar 1009 962 1014

TripuraDhalai 945 972 941North Tripura 967 971 966South Tripura 957 947 959West Tripura 964 942 967

Uttar PradeshAgra 859 835 864Aligarh 876 871 877Allahabad 902 902 902Ambedkar Nagar 976 929 983Auraiya 864 895 859Azamgarh 1017 916 1035Baghpat 858 837 862Bahraich 891 933 882Ballia 933 897 939Balrampur 922 968 913Banda 863 898 856Bara Banki 908 930 903Bareilly 883 900 880Basti 959 922 966Bijnor 913 870 921Budaun 859 902 850

199



District



Bulandshahr 892 844 902Chandauli 913 976 902Chitrakoot 879 907 874Deoria 1013 921 1029Etah 863 878 861Etawah 867 870 866Faizabad 961 927 967Farrukhabad 874 884 872Fatehpur 900 905 899Firozabad 867 879 865Gautam Buddha Nagar 852 845 853Ghaziabad 878 850 883Ghazipur 951 907 959Gonda 922 924 921Gorakhpur 944 905 950Hamirpur 860 885 856Hardoi 856 863 855Jalaun 865 880 863Jaunpur 1018 916 1037Jhansi 885 859 889Jyotiba Phule Nagar 907 898 908Kannauj 879 897 875Kanpur Dehat 862 896 856Kanpur Nagar 852 870 850Kanshiram Nagar 879 888 877Kaushambi 905 926 901Kheri 887 926 880Kushinagar 955 917 962Lalitpur 905 914 903Lucknow 906 913 905Mahamaya Nagar 870 862 871Mahoba 880 897 877Mahrajganj 938 924 940Mainpuri 876 878 875Mathura 858 871 855Mau 978 924 988Meerut 885 850 891Mirzapur 900 902 900Moradabad 903 909 902Muzaffarnagar 886 858 891Pilibhit 889 909 885Pratapgarh 994 915 1007

200

Statistical Tables


District



Rae Bareli 941 929 943Rampur 905 919 902Saharanpur 887 883 888Sant Kabir Nagar 969 940 975Sant Ravidas Nagar (Bhadohi) 950 898 960Shahjahanpur 865 902 858Shrawasti 875 923 865Siddharthnagar 970 922 981Sitapur 879 921 872Sonbhadra 913 920 912Sultanpur 978 921 988Unnao 901 913 899Varanasi 909 896 911

UttarakhandAlmora 1142 921 1177Bageshwar 1093 901 1127Chamoli 1021 889 1042Champawat 981 870 1001Dehradun 902 890 903Garhwal 1103 899 1134Hardwar 879 869 881Nainital 933 891 939Pithoragarh 1021 812 1057Rudraprayag 1120 899 1156Tehri Garhwal 1078 888 1111Udham Singh Nagar 919 896 923Uttarkashi 959 915 966

West BengalBankura 954 943 955Barddhaman 943 947 942Birbhum 956 952 956Dakshin Dinajpur 954 948 955Darjiling 971 943 974Haora 935 964 931Hugli 958 946 959Jalpaiguri 954 949 955Koch Bihar 942 948 941Kolkata 899 930 897Maldah 939 945 938Murshidabad 957 963 956Nadia 947 955 946North Twenty Four Parganas 949 947 950

201



District



Paschim Medinipur 960 952 961Purba Medinipur 936 938 936Puruliya 955 947 956South Twenty Four Parganas 949 953 949Uttar Dinajpur 936 946 934


202

Statistical Tables

Table 5.B

Index of sex composition in districts of India, 2011


District

Index of sex composition


Andaman and Nicorbar IslandsNicobars -0.025 0.006 -0.030North & Middle Andaman -0.006 0.021 -0.011South Andaman -0.063 0.032 -0.078

Andhra PradeshAdilabad 0.632 0.238 0.684Anantapur 0.568 0.163 0.615Chittoor 0.947 0.211 1.041East Godavari 0.467 0.042 0.523Guntur 1.230 0.785 1.273Hyderabad 1.131 0.458 1.209Karimnagar 0.036 0.298 -0.018Khammam 0.969 0.217 1.059Krishna 0.721 0.343 0.765Kurnool 0.946 0.464 0.994Mahbubnagar 0.653 0.323 0.696Medak 0.532 0.26 0.570Nalgonda 0.552 0.409 0.567Nizamabad 0.537 0.075 0.593Prakasam 0.509 0.263 0.532Rangareddy 0.906 0.254 0.996Sri Potti Sriramulu Nellore 0.515 0.189 0.553Srikakulam 0.302 0.579 0.247Visakhapatnam 0.730 0.298 0.784Vizianagaram 0.993 0.582 1.038Warangal 0.654 0.278 0.701West Godavari 0.708 -0.020 0.799Y.S.R. 0.930 0.591 0.961

Arunachal PradeshAnjaw -0.012 0.004 -0.014Changlang -0.015 0.029 -0.021Dibang Valley -0.004 -0.003 -0.005East Kameng 0.021 0.023 0.021East Siang 0.008 0.024 0.006Kurung Kumey 0.029 0.029 0.029Lohit -0.022 0.027 -0.029Lower Dibang Valley -0.004 0.007 -0.006Lower Subansiri 0.011 0.016 0.010Papum Pare 0.007 0.034 0.003Tawang -0.053 0.015 -0.063

203



District



Tirap -0.004 0.021 -0.007Upper Siang -0.007 0.007 -0.009Upper Subansiri 0.013 0.018 0.012West Kameng -0.068 0.017 -0.081West Siang -0.010 0.006 -0.013

AssamBaksa 0.097 0.165 0.086Barpeta 0.068 0.334 0.036Bongaigaon 0.058 0.170 0.043Cachar 0.119 0.298 0.094Chirang 0.052 0.090 0.048Darrang -0.059 0.117 -0.082Dhemaji 0.023 0.089 0.014Dhubri 0.086 0.525 0.033Dibrugarh 0.060 0.195 0.037Dima Hasao -0.007 0.039 -0.014Goalpara 0.084 0.195 0.072Golaghat 0.082 0.175 0.066Hailakandi 0.015 0.107 0.004Jorhat 0.065 0.168 0.046Kamrup 0.034 0.270 -0.002Kamrup Metropolitan -0.087 0.275 -0.147Karbi Anglong 0.058 0.009 0.073Karimganj 0.094 0.262 0.074Kokrajhar 0.060 0.143 0.050Lakhimpur 0.098 0.192 0.086Morigaon 0.120 0.169 0.117Nagaon 0.233 0.576 0.191Nalbari 0.014 0.128 -0.004Sivasagar 0.049 0.168 0.028Sonitpur 0.039 0.345 -0.005Tinsukia 0.040 0.290 0.002Udalguri 0.080 0.160 0.068

BiharAraria -0.210 0.655 -0.314Arwal -0.034 0.097 -0.050Aurangabad -0.239 0.393 -0.319Banka -0.265 0.264 -0.332Begusarai -0.540 -0.049 -0.595Bhagalpur -0.737 0.310 -0.878Bhojpur -0.428 0.004 -0.482Buxar -0.119 0.090 -0.142

204

Statistical Tables


District



Darbhanga -0.456 0.285 -0.543Gaya -0.140 0.996 -0.287Gopalganj 0.699 0.393 0.759Jamui -0.134 0.379 -0.200Jehanabad -0.098 0.024 -0.110Kaimur (Bhabua) -0.134 0.213 -0.175Katihar -0.285 0.742 -0.414Khagaria -0.370 -0.025 -0.405Kishanganj 0.038 0.517 -0.018Lakhisarai -0.157 0.005 -0.174Madhepura -0.206 0.109 -0.236Madhubani -0.260 0.397 -0.333Munger -0.329 0.068 -0.383Muzaffarpur -0.786 0.059 -0.888Nalanda -0.217 0.224 -0.267Nawada -0.038 0.750 -0.147Pashchim Champaran -0.516 0.797 -0.683Patna -1.091 -0.412 -1.174Purba Champaran -0.782 0.253 -0.894Purnia -0.128 0.724 -0.228Rohtas -0.301 0.160 -0.357Saharsa -0.255 0.152 -0.300Samastipur -0.511 0.614 -0.651Saran 0.126 0.145 0.142Sheikhpura -0.034 0.089 -0.048Sheohar -0.130 0.040 -0.151Sitamarhi -0.546 0.333 -0.653Siwan 0.546 0.315 0.594Supaul -0.134 0.346 -0.189Vaishali -0.656 -0.360 -0.683

ChandigarhChandigarh -0.529 -0.172 -0.586

ChhattisgarhBastar 0.433 0.468 0.432Bijapur 0.040 0.074 0.035Bilaspur 0.317 0.5 0.296Dakshin Bastar Dantewada 0.158 0.197 0.153Dhamtari 0.210 0.161 0.217Durg 0.596 0.541 0.602Janjgir - Champa 0.276 0.200 0.288Jashpur 0.202 0.207 0.202Kabeerdham 0.174 0.239 0.169

205



District



Korba 0.139 0.246 0.125Koriya 0.077 0.146 0.067Mahasamund 0.295 0.175 0.313Narayanpur 0.030 0.040 0.029Raigarh 0.292 0.161 0.312Raipur 0.643 0.840 0.618Rajnandgaon 0.434 0.367 0.445Surguja 0.314 0.448 0.302Uttar Bastar Kanker 0.184 0.173 0.186

Dadra and Nagar HaveliDadra & Nagar Haveli -0.238 0.014 -0.276

Daman and DiuDaman -0.388 -0.006 -0.450Diu 0.017 0.002 0.019

DelhiCentral -0.111 -0.023 -0.126East -0.382 -0.260 -0.405New Delhi -0.071 -0.011 -0.081North -0.244 -0.130 -0.263North East -0.481 -0.357 -0.500North West -1.135 -0.704 -1.205South -0.883 -0.361 -0.967South West -0.969 -0.639 -1.024West -0.646 -0.407 -0.689

GoaNorth Goa 0.056 -0.008 0.062South Goa 0.096 0.030 0.103

GujaratAhmadabad -1.035 -1.369 -1.004Amreli 0.136 -0.180 0.179Anand -0.157 -0.275 -0.144Banas Kantha -0.045 -0.371 0.016Bharuch -0.096 -0.002 -0.114Bhavnagar -0.103 -0.324 -0.071Dohad 0.361 0.270 0.392Gandhinagar -0.106 -0.331 -0.075Jamnagar -0.022 -0.125 -0.011Junagadh 0.127 -0.096 0.153Kachchh -0.273 -0.008 -0.309Kheda -0.032 -0.228 -0.006Mahesana -0.119 -0.492 -0.068Narmada 0.043 0.050 0.042

206

Statistical Tables


District



Navsari 0.107 0.026 0.113Panch Mahals 0.044 0.095 0.041Patan -0.029 -0.165 -0.008Porbandar 0.016 -0.038 0.022Rajkot -0.241 -0.789 -0.168Sabar Kantha 0.094 -0.159 0.135Surat -3.850 -1.737 -4.182Surendranagar -0.076 -0.178 -0.060Tapi 0.190 0.074 0.205The Dangs 0.056 0.056 0.057Vadodara -0.107 -0.290 -0.089Valsad -0.091 0.073 -0.118

HaryanaAmbala -0.261 -0.422 -0.241Bhiwani -0.359 -0.535 -0.334Faridabad -0.495 -0.535 -0.488Fatehabad -0.138 -0.256 -0.121Gurgaon -0.532 -0.547 -0.530Hisar -0.479 -0.429 -0.489Jhajjar -0.304 -0.530 -0.272Jind -0.370 -0.409 -0.366Kaithal -0.254 -0.398 -0.233Karnal -0.324 -0.578 -0.286Kurukshetra -0.193 -0.356 -0.170Mahendragarh -0.166 -0.485 -0.119Mewat -0.144 -0.081 -0.140Panchkula -0.251 -0.274 -0.243Palwal -0.156 -0.129 -0.162Panipat -0.380 -0.418 -0.374Rewari -0.150 -0.470 -0.102Rohtak -0.303 -0.427 -0.287Sirsa -0.227 -0.294 -0.219Sonipat -0.520 -0.751 -0.487Yamunanagar -0.303 -0.403 -0.290

Himachal PradeshBilaspur 0.059 -0.027 0.070Chamba 0.094 0.073 0.098Hamirpur 0.250 -0.048 0.293Kangra 0.406 -0.203 0.493Kinnaur -0.042 0.009 -0.050Kullu 0.017 0.070 0.008Lahul & Spiti -0.003 0.008 -0.005

207



District



Mandi 0.264 -0.006 0.301Shimla -0.076 0.020 -0.094Sirmaur -0.051 0.033 -0.064Solan -0.129 -0.031 -0.145Una 0.072 -0.078 0.093

Jammu and KashmirAnantnag -0.012 -0.535 0.075Badgam -0.165 -0.392 -0.124Bandipore -0.044 -0.039 -0.043Baramula -0.270 -0.238 -0.272Doda -0.029 0.037 -0.036Ganderbal -0.084 -0.079 -0.083Jammu -0.418 -0.611 -0.394Kargil -0.100 0.037 -0.120Kathua -0.153 -0.195 -0.147Kishtwar -0.020 0.009 -0.024Kulgam 0.018 -0.068 0.033Kupwara -0.344 -0.367 -0.329Leh(Ladakh) -0.252 0.01 -0.294Pulwama -0.060 -0.238 -0.030Punch -0.093 -0.049 -0.097Rajouri -0.191 -0.285 -0.172Ramban -0.043 0.027 -0.052Reasi -0.060 0.011 -0.069Samba -0.068 -0.156 -0.055Shupiyan 0.011 -0.038 0.019Srinagar -0.306 -0.217 -0.321Udhampur -0.171 -0.069 -0.185

JharkhandBokaro -0.195 -0.020 -0.220Chatra 0.043 0.266 0.016Deoghar -0.113 0.196 -0.150Dhanbad -0.334 0.028 -0.386Dumka 0.167 0.267 0.157Garhwa -0.035 0.297 -0.077Giridih 0.023 0.267 0.004Godda -0.038 0.265 -0.075Gumla 0.199 0.199 0.204Hazaribagh 0.036 0.078 0.036Jamtara 0.055 0.127 0.047Khunti 0.106 0.090 0.110Kodarma 0.024 0.113 0.015

208

Statistical Tables


District



Latehar 0.064 0.191 0.050Lohardaga 0.078 0.103 0.076Pakur 0.148 0.261 0.139Palamu -0.085 0.301 -0.135Pashchimi Singhbhum 0.353 0.484 0.341Purbi Singhbhum 0.073 0.062 0.073Ramgarh -0.070 0.045 -0.086Ranchi 0.107 0.262 0.085Sahibganj 0.033 0.257 0.009Saraikela-Kharsawan 0.070 0.105 0.066Simdega 0.133 0.160 0.13

KarnatakaBagalkot 0.31 0.112 0.342Bangalore -1.200 0.780 -1.535Bangalore Rural 0.019 0.098 0.003Belgaum 0.512 0.299 0.542Bellary 0.356 0.396 0.351Bidar 0.077 0.131 0.067Bijapur 0.116 0.145 0.114Chamarajanagar 0.184 0.077 0.195Chikkaballapura 0.129 0.112 0.126Chikmagalur 0.271 0.143 0.284Chitradurga 0.177 0.099 0.184Dakshina Kannada 0.593 0.187 0.645Davanagere 0.198 0.101 0.207Dharwad 0.184 0.173 0.181Gadag 0.151 0.111 0.156Gulbarga 0.214 0.221 0.214Hassan 0.426 0.228 0.445Haveri 0.066 0.171 0.047Kodagu 0.161 0.095 0.168Kolar 0.207 0.192 0.204Koppal 0.224 0.218 0.226Mandya 0.327 0.094 0.352Mysore 0.463 0.350 0.466Raichur 0.366 0.279 0.382Ramanagara 0.143 0.137 0.139Shimoga 0.358 0.235 0.370Tumkur 0.391 0.280 0.395Udupi 0.635 0.120 0.705Uttara Kannada 0.188 0.141 0.190Yadgir 0.193 0.154 0.203

209



District



KeralaAlappuzha 1.198 0.178 1.340Ernakulam 1.045 0.337 1.134Idukki 0.270 0.128 0.286Kannur 1.691 0.372 1.882Kasaragod 0.642 0.201 0.706Kollam 1.588 0.316 1.766Kottayam 0.718 0.213 0.782Kozhikode 1.713 0.460 1.892Malappuram 2.266 0.746 2.497Palakkad 1.273 0.405 1.393Pathanamthitta 0.784 0.133 0.873Thiruvananthapuram 1.728 0.445 1.903Thrissur 1.844 0.286 2.064Wayanad 0.280 0.120 0.302

LakshadweepLakshadweep 0.001 -0.001 0.002

Madhya PradeshAlirajpur 0.184 0.239 0.182Anuppur 0.098 0.086 0.100Ashoknagar -0.132 -0.002 -0.148Balaghat 0.502 0.281 0.533Barwani 0.209 0.201 0.221Betul 0.174 0.212 0.169Bhind -0.705 -0.602 -0.718Bhopal -0.271 0.013 -0.316Burhanpur 0.030 0.022 0.034Chhatarpur -0.393 -0.172 -0.420Chhindwara 0.201 0.280 0.189Damoh -0.132 0.090 -0.163Datia -0.203 -0.211 -0.202Dewas 0.006 -0.046 0.016Dhar 0.168 -0.010 0.203Dindori 0.167 0.172 0.168East Nimar 0.020 0.101 0.012Guna -0.144 -0.079 -0.149Gwalior -0.635 -0.658 -0.633Harda -0.018 0.016 -0.023Hoshangabad -0.135 -0.016 -0.153Indore -0.200 -0.279 -0.191Jabalpur -0.147 0.018 -0.176Jhabua 0.186 0.123 0.206

210

Statistical Tables


District



Katni 0.038 0.108 0.030Mandla 0.251 0.214 0.258Mandsaur 0.131 0.036 0.145Morena -0.800 -0.837 -0.789Narsimhapur -0.097 -0.058 -0.103Neemuch 0.058 0.010 0.065Panna -0.132 -0.019 -0.146Raisen -0.212 0.075 -0.252Rajgarh 0.087 0.014 0.101Ratlam 0.176 0.104 0.190Rewa -0.096 -0.326 -0.057Sagar -0.410 0.108 -0.483Satna -0.117 -0.071 -0.120Sehore -0.115 -0.047 -0.123Seoni 0.226 0.205 0.229Shahdol 0.110 0.145 0.107Shajapur -0.006 -0.010 -0.004Sheopur -0.102 -0.091 -0.101Shivpuri -0.431 -0.215 -0.456Sidhi 0.050 -0.024 0.067Singrauli -0.110 0.038 -0.126Tikamgarh -0.218 -0.191 -0.218Ujjain 0.107 0.036 0.118Umaria 0.031 0.094 0.024Vidisha -0.248 0.055 -0.289West Nimar 0.164 0.147 0.173

MaharashtraAhmadnagar -0.105 -1.267 0.062Akola 0.015 -0.086 0.026Amravati 0.079 0.115 0.063Aurangabad -0.335 -1.056 -0.222Bhandara 0.194 0.090 0.205Bid -0.283 -1.249 -0.137Buldana -0.119 -0.731 -0.029Chandrapur 0.152 0.203 0.136Dhule 0.004 -0.306 0.049Gadchiroli 0.138 0.141 0.134Gondiya 0.273 0.120 0.291Hingoli -0.025 -0.230 0.007Jalgaon -0.296 -1.375 -0.139Jalna -0.082 -0.591 -0.003Kolhapur 0.192 -0.848 0.333

211



District



Latur -0.154 -0.396 -0.119Mumbai -1.300 -0.320 -1.469Mumbai Suburban -3.100 -0.109 -3.598Nagpur 0.139 0.162 0.118Nanded -0.038 -0.226 -0.009Nandurbar 0.195 0.123 0.208Nashik -0.226 -0.788 -0.140Osmanabad -0.129 -0.381 -0.093Parbhani 0.001 -0.374 0.059Pune -1.118 -1.358 -1.105Raigarh 0.146 0.086 0.148Ratnagiri 1.027 0.112 1.157Sangli 0.255 -0.479 0.355Satara 0.512 -0.316 0.626Sindhudurg 0.299 -0.008 0.339Solapur -0.138 -0.679 -0.062Thane -2.621 0.148 -3.065Wardha 0.030 0.006 0.028Washim -0.064 -0.250 -0.037Yavatmal 0.070 0.006 0.074

ManipurBishnupur 0.053 0.004 0.060Chandel -0.004 0.002 -0.006Churachandpur 0.030 0.031 0.029Imphal East 0.117 0.032 0.130Imphal West 0.167 0.050 0.184Senapati -0.002 -0.003 -0.002Tamenglong 0.007 0.014 0.006Thoubal 0.101 0.067 0.108Ukhrul 0.005 0.004 0.005

MeghalayaEast Garo Hills 0.033 0.101 0.025East Khasi Hills 0.206 0.182 0.213Jaintia Hills 0.098 0.139 0.097Ribhoi 0.010 0.063 0.005South Garo Hills 0.002 0.047 -0.003West Garo Hills 0.093 0.212 0.079West Khasi Hills 0.059 0.152 0.050

MizoramAizawl 0.103 0.105 0.103Champhai 0.019 0.039 0.017Kolasib 0.005 0.026 0.002

212

Statistical Tables


District



Lawngtlai 0.002 0.032 -0.002Lunglei 0.002 0.035 -0.002Mamit -0.005 0.028 -0.010Saiha 0.008 0.006 0.009Serchhip 0.009 0.003 0.010

NagalandDimapur -0.035 0.078 -0.052Kiphire 0.006 0.017 0.005Kohima -0.013 0.067 -0.025Longleng -0.007 -0.009 -0.007Mokokchung -0.010 0.023 -0.015Mon -0.041 -0.017 -0.044Peren -0.009 0.012 -0.011Phek 0.007 0.001 0.009Tuensang -0.008 0.022 -0.011Wokha 0.018 0.032 0.016Zunheboto 0.022 0.024 0.022

OrissaAnugul 0.008 -0.132 0.026Balangir 0.263 0.222 0.268Baleshwar 0.144 0.214 0.129Bargarh 0.199 0.147 0.202Baudh 0.082 0.103 0.079Bhadrak 0.228 0.086 0.246Cuttack 0.151 -0.006 0.162Debagarh 0.041 0.003 0.047Dhenkanal 0.032 -0.179 0.061Gajapati 0.212 0.121 0.227Ganjam 0.530 -0.186 0.630Jagatsinghapur 0.116 0.046 0.121Jajapur 0.218 0.039 0.241Jharsuguda 0.024 0.043 0.019Kalahandi 0.367 0.205 0.392Kandhamal 0.256 0.143 0.275Kendrapara 0.351 0.032 0.394Kendujhar 0.316 0.317 0.318Khordha -0.132 -0.026 -0.158Koraput 0.457 0.352 0.477Malkangiri 0.171 0.199 0.170Mayurbhanj 0.603 0.378 0.638Nabarangapur 0.346 0.427 0.339Nayagarh -0.090 -0.200 -0.077

213



District



Nuapada 0.176 0.140 0.182Puri 0.142 0.047 0.149Rayagada 0.375 0.170 0.408Sambalpur 0.127 0.055 0.135Subarnapur 0.046 0.073 0.041Sundargarh 0.239 0.171 0.246

PuducherryKaraikal 0.078 0.030 0.085Mahe 0.034 0.006 0.038Puducherry 0.312 0.153 0.332Yanam 0.020 0.000 0.023

PunjabAmritsar -0.551 -0.757 -0.528Barnala -0.151 -0.131 -0.157Bathinda -0.418 -0.271 -0.446Faridkot -0.124 -0.131 -0.125Fatehgarh Sahib -0.165 -0.135 -0.172Firozpur -0.377 -0.509 -0.360Gurdaspur -0.404 -0.689 -0.369Hoshiarpur 0.127 -0.276 0.182Jalandhar -0.231 -0.264 -0.236Kapurthala -0.090 -0.108 -0.090Ludhiana -0.987 -0.555 -1.065Mansa -0.182 -0.211 -0.181Moga -0.185 -0.162 -0.192Muktsar -0.159 -0.270 -0.144Patiala 0.032 -0.066 0.044Rupnagar -0.386 -0.508 -0.374Sahibzada Ajit Singh Nagar -0.073 -0.104 -0.071Sangrur -0.373 -0.434 -0.370Shahid Bhagat Singh Nagar -0.241 -0.245 -0.243Tarn Taran -0.186 -0.392 -0.158

RajasthanAjmer 0.091 -0.243 0.146Alwar -0.667 -0.947 -0.612Banswara 0.259 0.101 0.294Baran -0.067 -0.063 -0.066Barmer -0.405 -0.227 -0.411Bharatpur -0.635 -0.680 -0.615Bhilwara 0.256 0.020 0.297Bikaner -0.339 -0.146 -0.357Bundi -0.077 -0.134 -0.067

214

Statistical Tables


District



Chittaurgarh 0.171 -0.073 0.209Churu -0.020 -0.171 0.009Dausa -0.230 -0.436 -0.194Dhaulpur -0.463 -0.402 -0.464Dungarpur 0.255 0.010 0.300Ganganagar -0.412 -0.469 -0.404Hanumangarh -0.236 -0.321 -0.224Jaipur -0.808 -1.572 -0.688Jaisalmer -0.245 -0.185 -0.248Jalor 0.077 -0.224 0.132Jhalawar 0.025 -0.054 0.039Jhunjhunun 0.077 -0.742 0.201Jodhpur -0.356 -0.432 -0.330Karauli -0.479 -0.526 -0.466Kota -0.257 -0.190 -0.268Nagaur 0.098 -0.401 0.181Pali 0.355 -0.174 0.439Pratapgarh 0.137 0.053 0.154Rajsamand 0.205 -0.122 0.258Sawai Madhopur -0.243 -0.299 -0.232Sikar 0.043 -0.854 0.181Sirohi -0.008 -0.126 0.015Tonk 0.048 -0.199 0.087Udaipur 0.204 0.084 0.235

SikkimEast District -0.076 0.025 -0.093North District -0.031 -0.002 -0.036South District -0.015 0.015 -0.020West District 0.000 0.016 -0.002

Tamil NaduAriyalur 0.208 -0.052 0.245Chennai 0.789 0.609 0.790Coimbatore 0.781 0.420 0.814Cuddalore 0.421 -0.152 0.497Dharmapuri 0.030 -0.016 0.032Dindigul 0.460 0.165 0.493Erode 0.436 0.221 0.454Kancheepuram 0.669 0.603 0.662Kanniyakumari 0.481 0.221 0.509Karur 0.294 0.093 0.319Krishnagiri 0.111 0.061 0.112Madurai 0.560 0.212 0.598

215



District



Nagapattinam 0.498 0.209 0.534Namakkal 0.292 -0.005 0.326Perambalur 0.137 -0.002 0.155Pudukkottai 0.444 0.220 0.472Ramanathapuram 0.184 0.196 0.175Salem 0.187 0.025 0.193Sivaganga 0.297 0.173 0.309Thanjavur 0.793 0.283 0.858The Nilgiris 0.268 0.121 0.286Theni 0.231 0.075 0.247Thiruvallur 0.598 0.435 0.607Thiruvarur 0.373 0.160 0.398Thoothukkudi 0.532 0.276 0.563Tiruchirappalli 0.730 0.280 0.784Tirunelveli 0.935 0.433 0.998Tiruppur 0.440 0.238 0.457Tiruvannamalai 0.485 0.135 0.529Vellore 0.931 0.361 1.003Viluppuram 0.576 0.269 0.612Virudhunagar 0.490 0.253 0.516

TripuraDhalai 0.007 0.091 -0.005North Tripura 0.070 0.159 0.057South Tripura 0.056 0.106 0.048West Tripura 0.157 0.151 0.152

Uttar PradeshAgra -1.415 -1.593 -1.380Aligarh -0.936 -0.739 -0.956Allahabad -0.889 -0.300 -0.972Ambedkar Nagar 0.317 0.140 0.345Auraiya -0.416 -0.115 -0.459Azamgarh 1.292 0.029 1.493Baghpat -0.426 -0.459 -0.419Bahraich -0.666 0.353 -0.796Ballia -0.088 -0.231 -0.063Balrampur -0.149 0.600 -0.248Banda -0.556 -0.141 -0.612Bara Banki -0.414 0.241 -0.503Bareilly -1.008 -0.289 -1.105Basti 0.176 0.083 0.197Bijnor -0.390 -0.749 -0.327Budaun -1.205 -0.237 -1.331

216

Statistical Tables


District



Bulandshahr -0.655 -1.182 -0.565Chandauli -0.207 0.541 -0.313Chitrakoot -0.238 -0.039 -0.263Deoria 0.824 0.084 0.941Etah -0.539 -0.306 -0.569Etawah -0.461 -0.297 -0.484Faizabad 0.197 0.132 0.210Farrukhabad -0.498 -0.270 -0.527Fatehpur -0.413 -0.110 -0.454Firozabad -0.724 -0.402 -0.767Gautam Buddha Nagar -0.596 -0.529 -0.602Ghaziabad -1.141 -1.326 -1.107Ghazipur 0.151 -0.118 0.200Gonda -0.246 0.155 -0.294Gorakhpur 0.067 -0.160 0.103Hamirpur -0.354 -0.134 -0.386Hardoi -1.377 -1.040 -1.411Jalaun -0.439 -0.387 -0.442Jaunpur -0.500 -0.228 -0.541Jhansi 1.282 0.030 1.479Jyotiba Phule Nagar -0.434 -0.423 -0.437Kannauj -0.239 -0.138 -0.248Kanpur Dehat -0.403 -0.130 -0.440Kanpur Nagar -0.563 -0.138 -0.626Kanshiram Nagar -1.617 -0.663 -1.776Kaushambi -0.350 -0.195 -0.366Kheri -0.217 0.096 -0.256Kushinagar -0.837 0.228 -0.982Lalitpur 0.201 0.039 0.237Lucknow -0.167 -0.002 -0.186Mahamaya Nagar -0.619 -0.023 -0.720Mahoba -0.024 0.114 -0.038Mahrajganj -0.208 -0.066 -0.228Mainpuri -0.473 -0.304 -0.494Mathura -0.840 -0.526 -0.880Mau 0.311 0.093 0.349Meerut -0.754 -0.976 -0.716Mirzapur -0.391 -0.139 -0.420Moradabad -0.694 -0.112 -0.764Muzaffarnagar -0.885 -1.089 -0.843Pilibhit -0.411 -0.045 -0.462Pratapgarh 0.635 0.010 0.731

217



District



Rae Bareli 0.015 0.199 -0.011Rampur -0.324 0.049 -0.372Saharanpur -0.724 -0.483 -0.753Sant Kabir Nagar 0.187 0.209 0.190Sant Ravidas Nagar (Bhadohi) 0.055 -0.119 0.087Shahjahanpur -0.904 -0.182 -1.000Shrawasti -0.286 0.051 -0.329Siddharthnagar 0.288 0.111 0.332Sitapur -1.073 0.137 -1.236Sonbhadra -0.194 0.057 -0.223Sultanpur 0.539 0.110 0.609Unnao -0.476 -0.012 -0.545Varanasi -0.444 -0.261 -0.472

UttarakhandAlmora 0.433 0.016 0.496Bageshwar 0.141 -0.014 0.164Chamoli 0.115 -0.038 0.138Champawat 0.040 -0.049 0.053Dehradun -0.256 -0.146 -0.276Garhwal 0.394 -0.038 0.458Hardwar -0.464 -0.399 -0.470Nainital -0.027 -0.087 -0.018Pithoragarh 0.144 -0.203 0.196Rudraprayag 0.148 -0.014 0.173Tehri Garhwal 0.303 -0.067 0.359Udham Singh Nagar -0.134 -0.124 -0.135Uttarkashi 0.023 0.001 0.026

West BengalBankura 0.187 0.345 0.154Barddhaman 0.077 0.756 -0.055Birbhum 0.202 0.484 0.156Dakshin Dinajpur 0.087 0.177 0.068Darjiling 0.209 0.153 0.210Haora -0.102 0.717 -0.244Hugli 0.374 0.478 0.330Jalpaiguri 0.206 0.449 0.161Koch Bihar 0.018 0.331 -0.034Kolkata -0.723 0.140 -0.892Maldah -0.015 0.526 -0.088Murshidabad 0.444 1.395 0.307Nadia 0.126 0.600 0.031North Twenty Four Parganas 0.352 0.881 0.216

218

Statistical Tables


District



Paschim Medinipur 0.438 0.728 0.379Purba Medinipur -0.080 0.401 -0.166Puruliya 0.164 0.376 0.133South Twenty Four Parganas 0.278 1.100 0.141Uttar Dinajpur -0.052 0.435 -0.115


219

Provisional population totals, 2011Country/State/Union Territory

District

Population Population (0-6 years)

Person Male Female Person Male Female

India 1210193422 623724248 586469174 158789287 82952135 75837152Jammu & Kashmir 12548926 6665561 5883365 2008642 1080662 927980

Kupwara 875564 475126 400438 196983 106247 90736Badgam 735753 390705 345048 152241 83100 69141Leh(Ladakh) 147104 92907 54197 11816 6079 5737Kargil 143388 80791 62597 20407 10319 10088Punch 476820 252240 224580 84112 44390 39722Rajouri 619266 332424 286842 118514 64503 54011Kathua 615711 327953 287758 80157 43648 36509Baramula 1015503 542171 473332 161841 86711 75130Bandipore 385099 201531 183568 60325 31868 28457Srinagar 1269751 675667 594084 155875 83408 72467Ganderbal 297003 158900 138103 50551 27127 23424Pulwama 570060 297988 272072 97642 53176 44466Shupiyan 265960 136302 129658 40271 21381 18890Anantnag 1070144 552404 517740 206338 112661 93677Kulgam 422786 216672 206114 70331 37364 32967Doda 409576 213091 196485 71038 36772 34266Ramban 283313 149032 134281 54745 28354 26391Kishtwar 231037 120496 110541 39124 20357 18767Udhampur 555357 298094 257263 82638 43801 38837Reasi 314714 166392 148322 55805 29051 26754Jammu 1526406 815727 710679 159868 89067 70801Samba 318611 168948 149663 38020 21278 16742

Himachal Pradesh 6856509 3473892 3382617 763864 400681 363183Chamba 518844 260848 257996 69409 35591 33818

220


District



Kangra 1507223 748559 758664 160865 85888 74977Lahul & Spiti 31528 16455 15073 2994 1487 1507Kullu 437474 224320 213154 50041 25504 24537Mandi 999518 496787 502731 109963 57496 52467Hamirpur 454293 216742 237551 47708 25357 22351Una 521057 263541 257516 58200 31117 27083Bilaspur 382056 192827 189229 41612 21983 19629Solan 576670 306162 270508 66349 34948 31401Sirmaur 530164 276801 253363 67958 35202 32756Shimla 813384 424486 388898 80778 42018 38760Kinnaur 84298 46364 37934 7987 4090 3897

Punjab 27704236 14634819 13069417 2941570 1593262 1348308Gurdaspur 2299026 1212995 1086031 240945 132133 108812Kapurthala 817668 427659 390009 82657 44160 38497Jalandhar 2181753 1140536 1041217 213460 113916 99544Hoshiarpur 1582793 806921 775872 162368 87333 75035Shahid Bhagat Singh Nagar 614362 314415 299947 60523 32217 28306Fatehgarh Sahib 599814 320603 279211 60761 32972 27789Ludhiana 3487882 1866203 1621679 363086 194734 168352Moga 992289 524289 468000 102574 55059 47515Firozpur 2026831 1070812 956019 241319 130701 110618Muktsar 902702 476300 426402 102028 55759 46269Faridkot 618008 327121 290887 66675 36022 30653Bathinda 1388859 744875 643984 145391 78420 66971Mansa 768808 408921 359887 81466 44481 36985Patiala 1892282 1002112 890170 204905 111667 93238

221


District



Amritsar 2490891 1322088 1168803 266608 146158 120450Tarn Taran 1120070 590239 529831 129863 71400 58463Rupnagar 683349 357265 326084 69593 37302 32291Sahibzada Ajit Singh Nagar 986147 524989 461158 109263 59311 49952Sangrur 1654408 878628 775780 175095 95418 79677Barnala 596294 317848 278446 62990 34099 28891

Chandigarh 1054686 580282 474404 117953 63187 54766Chandigarh 1054686 580282 474404 117953 63187 54766

Uttarakhand 10116752 5154178 4962574 1328844 704769 624075Uttarkashi 329686 168335 161351 44995 23494 21501Chamoli 391114 193572 197542 50753 26861 23892Rudraprayag 236857 111747 125110 30212 15910 14302Tehri Garhwal 616409 296604 319805 82422 43667 38755Dehradun 1698560 893222 805338 196298 103874 92424Garhwal 686527 326406 360121 82099 43233 38866Pithoragarh 485993 240427 245566 62911 34710 28201Bageshwar 259840 124121 135719 34650 18232 16418Almora 621927 290414 331513 77991 40601 37390Champawat 259315 130881 128434 36531 19532 16999Nainital 955128 494115 461013 122199 64626 57573Udham Singh Nagar 1648367 858906 789461 223445 117856 105589Hardwar 1927029 1025428 901601 284338 152173 132165

Haryana 25353081 13505130 11847951 3297724 1802047 1495677Panchkula 558890 298919 259971 65180 35224 29956Ambala 1136784 604044 532740 123534 68365 55169Yamunanagar 1214162 646801 567361 143189 78471 64718

222


District



Kurukshetra 964231 510370 453861 115291 63462 51829Kaithal 1072861 570595 502266 135136 74217 60919Karnal 1506323 798840 707483 194403 106809 87594Panipat 1202811 646324 556487 164778 89881 74897Sonipat 1480080 798948 681132 187955 105001 82954Jind 1332042 712254 619788 164579 89702 74877Fatehabad 941522 494834 446688 118446 64203 54243Sirsa 1295114 683242 611872 153495 82862 70633Hisar 1742815 931535 811280 211204 114238 96966Bhiwani 1629109 864616 764493 206023 112491 93532Rohtak 1058683 566708 491975 125490 69433 56057Jhajjar 956907 514303 442604 116160 65485 50675Mahendragarh 921680 486553 435127 109928 61827 48101Rewari 896129 472254 423875 112184 62874 49310Gurgaon 1514085 817274 696811 197816 108312 89504Mewat 1089406 571480 517926 243206 127786 115420Faridabad 1798954 961532 837422 238028 129216 108812Palwal 1040493 553704 486789 171699 92188 79511

Delhi 16753235 8976410 7776825 1970510 1055735 914775North West 3651261 1960677 1690584 443195 237941 205254North 883418 472260 411158 100879 53888 46991North East 2240749 1188307 1052442 296224 157999 138225East 1707725 906721 801004 189519 101371 88148New Delhi 133713 73846 59867 11549 6131 5418Central 578671 305926 272745 60385 31752 28633West 2531583 1349685 1181898 282678 151379 131299

223


District



South West 2292363 1248700 1043663 262815 143112 119703South 2733752 1470288 1263464 323266 172162 151104

Rajasthan 68621012 35620086 33000926 10504916 5580212 4924704Ganganagar 1969520 1043730 925790 252376 136111 116265Hanumangarh 1779650 933660 845990 232933 124606 108327Bikaner 2367745 1243916 1123829 394396 207364 187032Churu 2041172 1053375 987797 313852 165521 148331Jhunjhunun 2139658 1097390 1042268 285395 155842 129553Alwar 3671999 1938929 1733070 580388 311819 268569Bharatpur 2549121 1357896 1191225 430833 231265 199568Dhaulpur 1207293 654344 552949 215567 116276 99291Karauli 1458459 784943 673516 239449 129872 109577Sawai Madhopur 1338114 706558 631556 198777 106564 92213Dausa 1637226 859821 777405 256802 138121 118681Jaipur 6663971 3490787 3173184 914327 491960 422367Sikar 2677737 1377120 1300617 375752 204065 171687Nagaur 3309234 1698760 1610474 498585 264118 234467Jodhpur 3685681 1924326 1761355 592959 313704 279255Jaisalmer 672008 363346 308662 130400 69809 60591Barmer 2604453 1370494 1233959 499328 262925 236403Jalor 1830151 937918 892233 313808 165979 147829Sirohi 1037185 535115 502070 171699 90849 80850Pali 2038533 1025895 1012638 293002 154656 138346Ajmer 2584913 1325911 1259002 374745 197987 176758Tonk 1421711 729390 692321 200963 106799 94164Bundi 1113725 579385 534340 158088 83809 74279

224


District



Bhilwara 2410459 1224483 1185976 356230 185917 170313Rajsamand 1158283 582670 575613 173944 91977 81967Dungarpur 1388906 698069 690837 239608 125077 114531Banswara 1798194 908755 889439 321288 166923 154365Chittaurgarh 1544392 784054 760338 209376 110047 99329Kota 1950491 1023153 927338 248585 131595 116990Baran 1223921 635495 588426 179496 94348 85148Jhalawar 1411327 725667 685660 204140 107132 97008Udaipur 3067549 1566781 1500768 499072 259948 239124Pratapgarh 868231 437950 430281 148753 77227 71526

Uttar Pradesh 199581477 104596415 94985062 29728235 15653175 14075060Saharanpur 3464228 1835740 1628488 505263 268356 236907Muzaffarnagar 4138605 2194540 1944065 630329 339201 291128Bijnor 3683896 1925787 1758109 549305 293785 255520Moradabad 4773138 2508299 2264839 763000 399613 363387Rampur 2335398 1226175 1109223 370259 192981 177278Jyotiba Phule Nagar 1838771 964319 874452 291320 153448 137872Meerut 3447405 1829192 1618213 488271 263961 224310Baghpat 1302156 700724 601432 189088 102953 86135Ghaziabad 4661452 2481803 2179649 663367 358621 304746Gautam Buddha Nagar 1674714 904505 770209 245232 132925 112307Bulandshahr 3498507 1848643 1649864 537624 291617 246007Aligarh 3673849 1958536 1715313 555429 296894 258535Mahamaya Nagar 1565678 837446 728232 240376 129098 111278Mathura 2541894 1368445 1173449 396853 212111 184742Agra 4380793 2356104 2024689 638983 348298 290685

225


District



Firozabad 2496761 1337141 1159620 369940 196925 173015Mainpuri 1847194 984892 862302 275616 146750 128866Budaun 3712738 1997242 1715496 647664 340501 307163Bareilly 4465344 2371454 2093890 669681 352479 317202Pilibhit 2037225 1078525 958700 297116 155624 141492Shahjahanpur 3002376 1610182 1392194 488615 256917 231698Kheri 4013634 2126782 1886852 644410 334562 309848Sitapur 4474446 2380666 2093780 732695 381510 351185Hardoi 4091380 2204264 1887116 662807 355722 307085Unnao 3110595 1636295 1474300 417145 218024 199121Lucknow 4588455 2407897 2180558 521815 272810 249005Rae Bareli 3404004 1753344 1650660 460898 238963 221935Farrukhabad 1887577 1007479 880098 292791 155414 137377Kannauj 1658005 882546 775459 251533 132588 118945Etawah 1579160 845893 733267 220220 117748 102472Auraiya 1372287 736144 636143 193969 102380 91589Kanpur Dehat 1795092 964284 830808 243919 128679 115240Kanpur Nagar 4572951 2469114 2103837 484529 259156 225373Jalaun 1670718 895804 774914 219378 116678 102700Jhansi 2000755 1061310 939445 249154 134015 115139Lalitpur 1218002 639392 578610 206018 107644 98374Hamirpur 1104021 593576 510445 148557 78829 69728Mahoba 876055 465937 410118 124719 65751 58968Banda 1799541 966123 833418 289764 152656 137108Chitrakoot 990626 527101 463525 171468 89927 81541Fatehpur 2632684 1385556 1247128 377020 197960 179060

226


District



Pratapgarh 3173752 1591480 1582272 427623 223300 204323Kaushambi 1596909 838095 758814 263467 136764 126703Allahabad 5959798 3133479 2826319 832870 437827 395043Bara Banki 3257983 1707951 1550032 504272 261236 243036Faizabad 2468371 1258455 1209916 347080 180112 166968Ambedkar Nagar 2398709 1214225 1184484 324550 168270 156280Sultanpur 3790922 1916297 1874625 539347 280754 258593Bahraich 3478257 1838988 1639269 635383 328703 306680Shrawasti 1114615 594318 520297 202667 105412 97255Balrampur 2149066 1117984 1031082 385308 195808 189500Gonda 3431386 1785629 1645757 545944 283786 262158Siddharthnagar 2553526 1296046 1257480 465777 242313 223464Basti 2461056 1256158 1204898 372315 193740 178575Sant Kabir Nagar 1714300 870547 843753 272117 140251 131866Mahrajganj 2665292 1375367 1289925 402081 209004 193077Gorakhpur 4436275 2281763 2154512 595495 312549 282946Kushinagar 3560830 1821242 1739588 551467 287733 263734Deoria 3098637 1539608 1559029 445259 231842 213417Azamgarh 4616509 2289336 2327173 680792 355385 325407Mau 2205170 1114888 1090282 327500 170238 157262Ballia 3223642 1667557 1556085 448844 236586 212258Jaunpur 4476072 2217635 2258437 643020 335643 307377Ghazipur 3622727 1856584 1766143 538527 282402 256125Chandauli 1952713 1020789 931924 304229 153993 150236Varanasi 3682194 1928641 1753553 478474 252332 226142Sant Ravidas Nagar (Bhadohi) 1554203 797164 757039 244012 128559 115453

227


District



Mirzapur 2494533 1312822 1181711 392230 206168 186062Sonbhadra 1862612 973480 889132 308921 160859 148062Etah 1761152 945157 815995 277672 147841 129831Kanshiram Nagar 1438156 765529 672627 244852 129691 115161

Bihar 103804637 54185347 49619290 18582229 9615280 8966949Pashchim Champaran 3922780 2057669 1865111 753429 386320 367109Purba Champaran 5082868 2674037 2408831 993569 516736 476833Sheohar 656916 347614 309302 124919 64892 60027Sitamarhi 3419622 1800441 1619181 643851 333315 310536Madhubani 4476044 2324984 2151060 779360 403516 375844Supaul 2228397 1157815 1070582 424411 218560 205851Araria 2806200 1460878 1345322 564131 288728 275403Kishanganj 1690948 868845 822103 341943 173914 168029Purnia 3273127 1695829 1577298 644083 329865 314218Katihar 3068149 1601158 1466991 601745 307584 294161Madhepura 1994618 1042373 952245 397468 206647 190821Saharsa 1897102 995502 901600 377504 195819 181685Darbhanga 3921971 2053043 1868928 700992 363597 337395Muzaffarpur 4778610 2517500 2261110 817709 426633 391076Gopalganj 2558037 1269677 1288360 437031 224717 212314Siwan 3318176 1672121 1646055 532868 275500 257368Saran 3943098 2023476 1919622 657316 342060 315256Vaishali 3495249 1847058 1648191 591634 312354 279280Samastipur 4254782 2228432 2026350 784203 404068 380135Begusarai 2954367 1560203 1394164 532382 278564 253818Khagaria 1657599 880065 777534 347048 181528 165520

228


District



Bhagalpur 3032226 1614014 1418212 532307 275248 257059Banka 2029339 1064307 965032 362548 186986 175562Munger 1359054 723280 635774 221026 114841 106185Lakhisarai 1000717 526651 474066 182234 95154 87080Sheikhpura 634927 329593 305334 118228 60952 57276Nalanda 2872523 1495577 1376946 501046 259703 241343Patna 5772804 3051117 2721687 905708 476906 428802Bhojpur 2720155 1431722 1288433 440847 230267 210580Buxar 1707643 888356 819287 286969 149097 137872Kaimur (Bhabua) 1626900 847784 779116 291785 150490 141295Rohtas 2962593 1547856 1414737 493047 256108 236939Aurangabad 2511243 1310867 1200376 438065 225256 212809Gaya 4379383 2266865 2112518 762507 389247 373260Nawada 2216653 1145123 1071530 367231 184990 182241Jamui 1756078 914368 841710 313455 160287 153168Jehanabad 1124176 586202 537974 193946 101103 92843Arwal 699563 362945 336618 123684 63728 59956

Sikkim 607688 321661 286027 61077 31418 29659North District 43354 24513 18841 4479 2361 2118West District 136299 70225 66074 14957 7669 7288South District 146742 76663 70079 15070 7737 7333East District 281293 150260 131033 26571 13651 12920

Arunachal Pradesh 1382611 720232 662379 202759 103430 99329Tawang 49950 29361 20589 5630 2808 2822West Kameng 87013 49568 37445 11404 5803 5601East Kameng 78413 38974 39439 13997 7106 6891

229


District



Papum Pare 176385 90447 85938 23675 12060 11615Upper Subansiri 83205 41974 41231 11312 5747 5565West Siang 112272 58589 53683 13859 7187 6672East Siang 99019 50467 48552 12115 6106 6009Upper Siang 35289 18657 16632 4627 2351 2276Changlang 147951 77289 70662 25478 13042 12436Tirap 111997 57992 54005 19317 9904 9413Lower Subansiri 82839 41935 40904 9991 5074 4917Kurung Kumey 89717 44226 45491 15540 7856 7684Dibang Valley 7948 4396 3552 1104 603 501Lower Dibang Valley 53986 28127 25859 7714 3966 3748Lohit 145538 76544 68994 23606 12082 11524Anjaw 21089 11686 9403 3390 1735 1655

Nagaland 1980602 1025707 954895 285981 147111 138870Mon 250671 132062 118609 39538 20808 18730Mokokchung 193171 100229 92942 20046 10260 9786Zunheboto 141014 71169 69845 20101 10283 9818Wokha 166239 84429 81810 19673 9985 9688Dimapur 379769 198163 181606 49595 25197 24398Phek 163294 83684 79610 27538 14377 13161Tuensang 196801 101977 94824 34931 18048 16883Longleng 50593 26588 24005 8846 4700 4146Kiphire 74033 37758 36275 14335 7331 7004Kohima 270063 140118 129945 36157 18277 17880Peren 94954 49530 45424 15221 7845 7376

230


District



Manipur 2721756 1369764 1351992 353237 182684 170553Senapati 354972 183081 171891 45442 23766 21676Tamenglong 140143 71762 68381 18072 9310 8762Churachandpur 271274 137748 133526 34490 17731 16759Bishnupur 240363 120185 120178 29831 15543 14288Thoubal 420517 209674 210843 66953 34365 32588Imphal West 514683 253628 261055 58239 29972 28267Imphal East 452661 225130 227531 60760 31451 29309Ukhrul 183115 94013 89102 22954 11951 11003Chandel 144028 74543 69485 16496 8595 7901

Mizoram 1091014 552339 538675 165536 83965 81571Mamit 85757 44567 41190 14817 7487 7330Kolasib 83054 42456 40598 12702 6394 6308Aizawl 404054 201072 202982 52324 26375 25949Champhai 125370 63299 62071 22068 11170 10898Serchhip 64875 32824 32051 9082 4716 4366Lunglei 154094 79252 74842 23594 12007 11587Lawngtlai 117444 60379 57065 21795 11091 10704Saiha 56366 28490 27876 9154 4725 4429

Tripura 3671032 1871867 1799165 444055 227354 216701West Tripura 1724619 877930 846689 184656 95085 89571South Tripura 875144 447124 428020 108805 55876 52929Dhalai 377988 194342 183646 54416 27600 26816North Tripura 693281 352471 340810 96178 48793 47385

Meghalaya 2964007 1492668 1471339 555822 282189 273633West Garo Hills 642923 324900 318023 112115 56637 55478

231


District



East Garo Hills 317618 161372 156246 57064 28886 28178South Garo Hills 142574 73322 69252 27401 13886 13515West Khasi Hills 385601 194628 190973 86626 43867 42759Ribhoi 258380 132445 125935 51547 26353 25194East Khasi Hills 824059 410360 413699 134395 68548 65847Jaintia Hills 392852 195641 197211 86674 44012 42662

Assam 31169272 15954927 15214345 4511307 2305088 2206219Kokrajhar 886999 452965 434034 131865 67584 64281Dhubri 1948632 998346 950286 358841 182662 176179Goalpara 1008959 514162 494797 165762 84818 80944Barpeta 1693190 867891 825299 280506 143487 137019Morigaon 957853 485328 472525 159088 81567 77521Nagaon 2826006 1440307 1385699 446238 227853 218385Sonitpur 1925975 989919 936056 267238 136458 130780Lakhimpur 1040644 529484 511160 150880 77064 73816Dhemaji 688077 353043 335034 99692 51266 48426Tinsukia 1316948 675986 640962 175038 88790 86248Dibrugarh 1327748 680114 647634 154912 79146 75766Sivasagar 1150253 589454 560799 133858 68392 65466Jorhat 1091295 557944 533351 117515 59859 57656Golaghat 1058674 539949 518725 128395 65472 62923Karbi Anglong 965280 493482 471798 183862 95971 87891Dima Hasao 213529 110566 102963 31758 16239 15519Cachar 1736319 886616 849703 246826 126223 120603Karimganj 1217002 620722 596280 203203 103760 99443Hailakandi 659260 338766 320494 109537 56244 53293

232


District



Bongaigaon 732639 373590 359049 113751 57874 55877Chirang 481818 244675 237143 70177 35835 34342Kamrup 1517202 779608 737594 194983 99397 95586Kamrup Metropolitan 1260419 655630 604789 120500 60434 60066Nalbari 769919 395804 374115 90593 46156 44437Baksa 953773 484825 468948 117400 59823 57577Darrang 908090 472134 435956 149626 77096 72530Udalguri 832769 423617 409152 109263 55618 53645

West Bengal 91347736 46927389 44420347 10112599 5187264 4925335Darjiling 1842034 934796 907238 180170 92728 87442Jalpaiguri 3869675 1980068 1889607 445025 228381 216644Koch Bihar 2822780 1453590 1369190 332355 170598 161757Uttar Dinajpur 3000849 1550219 1450630 469971 241547 228424Dakshin Dinajpur 1670931 855104 815827 178374 91564 86810Maldah 3997970 2061593 1936377 590237 303540 286697Murshidabad 7102430 3629595 3472835 979665 499040 480625Birbhum 3502387 1791017 1711370 433186 221877 211309Barddhaman 7723663 3975356 3748307 788582 405057 383525Nadia 5168488 2655056 2513432 505995 258853 247142North Twenty Four Parganas 10082852 5172138 4910714 902644 463502 439142Hugli 5520389 2819100 2701289 504660 259277 245383Bankura 3596292 1840504 1755788 405401 208632 196769Puruliya 2927965 1497656 1430309 393562 202165 191397Haora 4841638 2502453 2339185 497476 253337 244139Kolkata 4486679 2362662 2124017 300052 155475 144577South Twenty Four Parganas 8153176 4182758 3970418 976351 500011 476340

233


District



Paschim Medinipur 5943300 3032630 2910670 663134 339781 323353Purba Medinipur 5094238 2631094 2463144 565759 291899 273860

Jharkhand 32966238 16931688 16034550 5237582 2695921 2541661Garhwa 1322387 683984 638403 233604 119325 114279Chatra 1042304 534209 508095 188620 96108 92512Kodarma 717169 367952 349217 128491 66097 62394Giridih 2445203 1258607 1186596 450527 232924 217603Deoghar 1491879 776741 715138 262903 135552 127351Godda 1311382 678504 632878 234807 120246 114561Sahibganj 1150038 590390 559648 216402 110706 105696Pakur 899200 453101 446099 175356 89219 86137Dhanbad 2682662 1405847 1276815 367402 191677 175725Bokaro 2061918 1076270 985648 284353 148733 135620Lohardaga 461738 232575 229163 75679 38592 37087Purbi Singhbhum 2291032 1175696 1115336 286322 149006 137316Palamu 1936319 1003876 932443 316511 162599 153912Latehar 725673 369534 356139 132730 67593 65137Hazaribagh 1734005 891179 842826 273427 142129 131298Ramgarh 949159 494037 455122 130606 67816 62790Dumka 1321096 669240 651856 212912 108786 104126Jamtara 790207 403450 386757 128460 65950 62510Ranchi 2912022 1493376 1418646 388052 200327 187725Khunti 530299 265939 264360 83323 42707 40616Gumla 1025656 514730 510926 168241 86072 82169Simdega 599813 299905 299908 91297 46230 45067Pashchimi Singhbhum 1501619 749314 752305 254046 128292 125754

234


District



Saraikela-Kharsawan 1063458 543232 520226 153511 79235 74276Orissa 41947358 21201678 20745680 5035650 2603208 2432442

Bargarh 1478833 748332 730501 156185 80246 75939Jharsuguda 579499 297014 282485 61823 31907 29916Sambalpur 1044410 529424 514986 112946 58505 54441Debagarh 312164 158017 154147 38621 20149 18472Sundargarh 2080664 1055723 1024941 249020 128529 120491Kendujhar 1802777 907135 895642 253418 129494 123924Mayurbhanj 2513895 1253633 1260262 337757 172992 164765Baleshwar 2317419 1184371 1133048 274432 141412 133020Bhadrak 1506522 760591 745931 176793 91577 85216Kendrapara 1439891 717695 722196 153443 79869 73574Jagatsinghapur 1136604 577699 558905 103517 53661 49856Cuttack 2618708 1339153 1279555 251152 131259 119893Jajapur 1826275 926058 900217 207310 107945 99365Dhenkanal 1192948 612597 580351 132647 70927 61720Anugul 1271703 654898 616805 145690 77311 68379Nayagarh 962215 502194 460021 101337 54759 46578Khordha 2246341 1166949 1079392 222275 116350 105925Puri 1697983 865209 832774 164388 85444 78944Ganjam 3520151 1777324 1742827 397920 209573 188347Gajapati 575880 282041 293839 82777 42141 40636Kandhamal 731952 359401 372551 106379 54266 52113Baudh 439917 220993 218924 59094 29928 29166Subarnapur 652107 332897 319210 76536 39314 37222Balangir 1648574 831349 817225 206964 106090 100874

235


District



Nuapada 606490 300307 306183 84893 43066 41827Kalahandi 1573054 785179 787875 214111 109977 104134Rayagada 961959 469672 492287 141167 72195 68972Nabarangapur 1218762 604046 614716 201901 101577 100324Koraput 1376934 677864 699070 215518 109376 106142Malkangiri 612727 303913 308814 105636 53369 52267

Chhattisgarh 25540196 12827915 12712281 3584028 1824987 1759041Koriya 659039 334336 324703 93249 47376 45873Surguja 2361329 1195145 1166184 371891 190191 181700Jashpur 852043 425085 426958 120079 60840 59239Raigarh 1493627 749439 744188 191319 98473 92846Korba 1206563 612158 594405 168437 85748 82689Janjgir - Champa 1620632 816057 804575 219143 112656 106487Bilaspur 2662077 1349928 1312149 400695 204757 195938Kabeerdham 822239 411637 410602 140752 71348 69404Rajnandgaon 1537520 762170 775350 206372 104461 101911Durg 3343079 1681521 1661558 421141 215065 206076Raipur 4062160 2048856 2013304 569447 289815 279632Mahasamund 1032275 511475 520800 131380 67038 64342Dhamtari 799199 397250 401949 100575 51073 49502Uttar Bastar Kanker 748593 372987 375606 97479 49344 48135Bastar 1411644 697359 714285 212819 106904 105915Narayanpur 140206 70189 70017 22833 11561 11272Dakshin Bastar Dantewada 532791 263562 269229 76450 38134 38316Bijapur 255180 128761 126419 39967 20203 19764

236


District



Madhya Pradesh 72597565 37612920 34984645 10548295 5516957 5031338Sheopur 687952 361685 326267 115577 61204 54373Morena 1965137 1068364 896773 299394 164016 135378Bhind 1703562 926940 776622 241562 131671 109891Gwalior 2030543 1090647 939896 254009 138681 115328Datia 786375 419432 366943 109000 58863 50137Shivpuri 1725818 919405 806413 280630 148565 132065Tikamgarh 1444920 759891 685029 223570 118534 105036Chhatarpur 1762857 935906 826951 279317 147484 131833Panna 1016028 532866 483162 160884 84216 76668Sagar 2378295 1254251 1124044 351306 182540 168766Damoh 1263703 660478 603225 187275 97008 90267Satna 2228619 1156734 1071885 321819 168769 153050Rewa 2363744 1224918 1138826 340727 180971 159756Umaria 643579 329527 314052 99457 51096 48361Neemuch 825958 421640 404318 105548 55044 50504Mandsaur 1339832 681439 658393 173814 90472 83342Ratlam 1454483 737365 717118 212009 109801 102208Ujjain 1986597 1016432 970165 264578 137889 126689Shajapur 1512353 779900 732453 213107 111421 101686Dewas 1563107 805212 757895 223252 117043 106209Dhar 2184672 1114267 1070405 349262 182551 166711Indore 3272335 1700483 1571852 407536 215446 192090West Nimar 1872413 953617 918796 293913 152203 141710Barwani 1385659 699578 686081 261103 134565 126538Rajgarh 1546541 791038 755503 225662 117759 107903

237


District



Vidisha 1458212 768799 689413 230714 120023 110691Bhopal 2368145 1239378 1128767 293294 153101 140193Sehore 1311008 683703 627305 194801 102194 92607Raisen 1331699 701114 630585 203777 105772 98005Betul 1575247 799721 775526 207282 106353 100929Harda 570302 295208 275094 82183 42788 39395Hoshangabad 1240975 648970 592005 161406 84463 76943Katni 1291684 663064 628620 188415 97439 90976Jabalpur 2460714 1278448 1182266 287433 149988 137445Narsimhapur 1092141 569618 522523 139366 73333 66033Dindori 704218 351344 352874 106665 54155 52510Mandla 1053522 525495 528027 144799 73693 71106Chhindwara 2090306 1063302 1027004 267351 137105 130246Seoni 1378876 694916 683960 176170 90159 86011Balaghat 1701156 841794 859362 206815 105479 101336Guna 1240938 649591 591347 201630 106049 95581Ashoknagar 844979 444651 400328 136680 71424 65256Shahdol 1064989 541208 523781 153947 79100 74847Anuppur 749521 379496 370025 103005 53025 49980Sidhi 1126515 577091 549424 188733 98810 89923Singrauli 1178132 614885 563247 204255 106355 97900Jhabua 1024091 514830 509261 207931 107504 100427Alirajpur 728677 362748 365929 143954 73024 70930East Nimar 1309443 673491 635952 203237 105252 97985Burhanpur 756993 388040 368953 120141 62557 57584

238


District



Gujarat 60383628 31482282 28901346 7494176 3974286 3519890Kachchh 2090313 1096343 993970 310192 162116 148076Banas Kantha 3116045 1609148 1506897 498790 263953 234837Patan 1342746 694062 648684 179392 95215 84177Mahesana 2027727 1053337 974390 227701 123428 104273Sabar Kantha 2427346 1244491 1182855 337374 177699 159675Gandhinagar 1387478 722459 665019 159378 86276 73102Ahmadabad 7208200 3787050 3421150 801967 431421 370546Surendranagar 1755873 910266 845607 234196 123964 110232Rajkot 3799770 1975131 1824639 424061 228713 195348Jamnagar 2159130 1114360 1044770 254066 133861 120205Porbandar 586062 300967 285095 63820 33687 30133Junagadh 2742291 1404506 1337785 301395 158328 143067Amreli 1513614 770651 742963 168715 89782 78933Bhavnagar 2877961 1490465 1387496 369460 195965 173495Anand 2090276 1088253 1002023 243653 129791 113862Kheda 2298934 1187098 1111836 277300 146939 130361Panch Mahals 2388267 1227805 1160462 348959 181428 167531Dohad 2126558 1070843 1055715 402903 208014 194889Vadodara 4157568 2150229 2007339 474479 250513 223966Narmada 590379 301270 289109 75226 38844 36382Bharuch 1550822 805945 744877 170565 89119 81446The Dangs 226769 112976 113793 39387 20065 19322Navsari 1330711 678423 652288 129530 67427 62103Valsad 1703068 884064 819004 206309 107110 99199Surat 6079231 3399742 2679489 710805 387131 323674

239


District



Tapi 806489 402398 404091 84553 43497 41056Daman & Diu 242911 150100 92811 25880 13556 12324

Diu 52056 25639 26417 6333 3294 3039Daman 190855 124461 66394 19547 10262 9285

Dadra & Nagar Haveli 342853 193178 149675 49196 25575 23621Dadra & Nagar Haveli 342853 193178 149675 49196 25575 23621

Maharashtra 112372972 58361397 54011575 12848375 6822262 6026113Nandurbar 1646177 834866 811311 231268 119694 111574Dhule 2048781 1055669 993112 261397 139345 122052Jalgaon 4224442 2197835 2026607 513797 280915 232882Buldana 2588039 1342152 1245887 324389 176116 148273Akola 1818617 936226 882391 206053 108425 97628Washim 1196714 621228 575486 147467 79318 68149Amravati 2887826 1482845 1404981 299806 155572 144234Wardha 1296157 665925 630232 124536 65005 59531Nagpur 4653171 2388558 2264613 481814 250223 231591Bhandara 1198810 604371 594439 122931 63398 59533Gondiya 1322331 662524 659807 136116 70015 66101Gadchiroli 1071795 542813 528982 115104 58842 56262Chandrapur 2194262 1120316 1073946 223861 115090 108771Yavatmal 2775457 1425593 1349864 320441 167346 153095Nanded 3356566 1732567 1623999 444466 234249 210217Hingoli 1178973 609386 569587 161086 86250 74836Parbhani 1835982 946185 889797 251851 134971 116880Jalna 1958483 1015116 943367 281495 152430 129065Aurangabad 3695928 1928156 1767772 516791 279582 237209

240


District



Nashik 6109052 3164261 2944791 805302 427878 377424Thane 11054131 5879387 5174744 1257080 655354 601726Mumbai Suburban 9332481 5025165 4307316 876917 459101 417816Mumbai 3145966 1711650 1434316 262229 139906 122323Raigarh 2635394 1348089 1287305 290439 150938 139501Pune 9426959 4936362 4490597 1067261 569916 497345Ahmadnagar 4543083 2348802 2194281 537346 292242 245104Bid 2585962 1352468 1233494 344122 191115 153007Latur 2455543 1276262 1179281 307726 164361 143365Osmanabad 1660311 864674 795637 199509 107695 91814Solapur 4315527 2233778 2081749 519781 277726 242055Satara 3003922 1512524 1491398 307673 163605 144068Ratnagiri 1612672 759703 852969 149486 77066 72420Sindhudurg 848868 416695 432173 68637 35930 32707Kolhapur 3874015 1983274 1890741 395143 214144 180999Sangli 2820575 1435972 1384603 295055 158499 136556

Andhra Pradesh 84665533 42509881 42155652 8642686 4448330 4194356Adilabad 2737738 1366964 1370774 295811 152362 143449Nizamabad 2552073 1252191 1299882 268202 137788 130414Karimnagar 3811738 1897068 1914670 322897 166698 156199Medak 3031877 1524187 1507690 348721 178441 170280Hyderabad 4010238 2064359 1945879 419500 216428 203072Rangareddy 5296396 2708694 2587702 595352 305728 289624Mahbubnagar 4042191 2046247 1995944 501878 259810 242068Nalgonda 3483648 1758061 1725587 354940 184739 170201Warangal 3522644 1766257 1756387 324410 169654 154756

241


District



Khammam 2798214 1391936 1406278 267553 136637 130916Srikakulam 2699471 1340430 1359041 265404 135929 129475Vizianagaram 2342868 1161913 1180955 231021 118149 112872Visakhapatnam 4288113 2140872 2147241 429234 218923 210311East Godavari 5151549 2569419 2582130 492446 250086 242360West Godavari 3934782 1963184 1971598 363536 184513 179023Krishna 4529009 2268312 2260697 406927 208341 198586Guntur 4889230 2441128 2448102 466285 239408 226877Prakasam 3392764 1712735 1680029 360461 186581 173880Sri Potti Sriramulu Nellore 2966082 1493254 1472828 287368 147719 139649Y.S.R. 2884524 1454136 1430388 313455 163371 150084Kurnool 4046601 2040101 2006500 477198 246345 230853Anantapur 4083315 2064928 2018387 426922 221539 205383Chittoor 4170468 2083505 2086963 423165 219141 204024

Karnataka 61130704 31057742 30072962 6855801 3527844 3327957Belgaum 4778439 2427104 2351335 605524 313599 291925Bagalkot 1890826 952902 937924 263781 136780 127001Bijapur 2175102 1112953 1062149 303480 157212 146268Bidar 1700018 870850 829168 216885 112103 104782Raichur 1924773 966493 958280 272703 139917 132786Koppal 1391292 701479 689813 194199 99460 94739Gadag 1065235 538477 526758 127259 65464 61795Dharwad 1846993 939127 907866 210194 108231 101963Uttara Kannada 1436847 727424 709423 146457 75225 71232Haveri 1598506 819295 779211 187754 96518 91236Bellary 2532383 1280402 1251981 341804 174946 166858

242


District



Chitradurga 1660378 843411 816967 177786 91973 85813Davanagere 1946905 989602 957303 206935 107181 99754Shimoga 1755512 879817 875695 176904 90271 86633Udupi 1177908 562896 615012 100579 51448 49131Chikmagalur 1137753 567483 570270 100791 51347 49444Tumkur 2681449 1354770 1326679 252307 129253 123054Bangalore 9588910 5025498 4563412 988482 509268 479214Mandya 1808680 909441 899239 162147 83846 78301Hassan 1776221 885807 890414 155579 79197 76382Dakshina Kannada 2083625 1032577 1051048 202670 104169 98501Kodagu 554762 274725 280037 52697 26661 26036Mysore 2994744 1511206 1483538 285956 146192 139764Chamarajanagar 1020962 513359 507603 94859 48854 46005Gulbarga 2564892 1307061 1257831 352162 181955 170207Yadgir 1172985 591104 581881 185727 95620 90107Kolar 1540231 779401 760830 161877 82814 79063Chikkaballapura 1254377 637504 616873 124719 64129 60590Bangalore Rural 987257 507514 479743 102019 52400 49619Ramanagara 1082739 548060 534679 101565 51811 49754

Goa 1457723 740711 717012 139495 72669 66826North Goa 817761 417536 400225 75117 39316 35801South Goa 639962 323175 316787 64378 33353 31025Lakshadweep 64429 33106 31323 7088 3715 3373Lakshadweep 64429 33106 31323 7088 3715 3373

Kerala 33387677 16021290 17366387 3322247 1695935 1626312Kasaragod 1302600 626617 675983 149280 76149 73131

243


District



Kannur 2525637 1184012 1341625 265276 135189 130087Wayanad 816558 401314 415244 89720 45776 43944Kozhikode 3089543 1473028 1616515 323511 164800 158711Malappuram 4110956 1961014 2149942 552771 281958 270813Palakkad 2810892 1360067 1450825 288366 146947 141419Thrissur 3110327 1474665 1635662 289126 148428 140698Ernakulam 3279860 1617602 1662258 289281 148047 141234Idukki 1107453 551944 555509 100107 51132 48975Kottayam 1979384 970140 1009244 168563 86113 82450Alappuzha 2121943 1010252 1111691 186022 95556 90466Pathanamthitta 1195537 561620 633917 91501 46582 44919Kollam 2629703 1244815 1384888 238062 121481 116581Thiruvananthapuram 3307284 1584200 1723084 290661 147777 142884

Tamil Nadu 72138958 36158871 35980087 6894821 3542351 3352470Thiruvallur 3725697 1878559 1847138 369854 189244 180610Chennai 4681087 2357633 2323454 418541 213084 205457Kancheepuram 3990897 2010309 1980588 396254 201499 194755Vellore 3928106 1959676 1968430 406705 209168 197537Tiruvannamalai 2468965 1238688 1230277 256299 132664 123635Viluppuram 3463284 1744832 1718452 378530 195294 183236Salem 3480008 1780569 1699439 323102 168560 154542Namakkal 1721179 866740 854439 140314 73345 66969Erode 2259608 1134191 1125417 181188 92638 88550The Nilgiris 735071 360170 374901 61644 31099 30545Dindigul 2161367 1081934 1079433 200034 102989 97045Karur 1076588 534392 542196 98980 50855 48125

244


District



Tiruchirappalli 2713858 1347863 1365995 253633 129947 123686Perambalur 564511 281436 283075 55950 29245 26705Ariyalur 752481 373319 379162 76775 40579 36196Cuddalore 2600880 1311151 1289729 260584 137513 123071Nagapattinam 1614069 797214 816855 154543 78826 75717Thiruvarur 1268094 627616 640478 114977 58602 56375Thanjavur 2402781 1183112 1219669 223910 114386 109524Pudukkottai 1618725 803337 815388 169886 86739 83147Sivaganga 1341250 670597 670653 127682 65123 62559Madurai 3041038 1528308 1512730 287101 148050 139051Theni 1243684 624922 618762 110919 57258 53661Virudhunagar 1943309 967437 975872 183214 93401 89813Ramanathapuram 1337560 676574 660986 127447 64790 62657Thoothukkudi 1738376 858919 879457 170507 86555 83952Tirunelveli 3072880 1518595 1554285 301275 153437 147838Kanniyakumari 1863174 926800 936374 161956 82586 79370Dharmapuri 1502900 772490 730410 162118 84840 77278Krishnagiri 1883731 963152 920579 203730 105872 97858Coimbatore 3472578 1735362 1737216 295584 150580 145004Tiruppur 2471222 1242974 1228248 221585 113583 108002

Puducherry 1244464 610485 633979 127610 64932 62678Yanam 55616 27277 28339 6021 3141 2880Puducherry 946600 466143 480457 95432 48459 46973Mahe 41934 19269 22665 4588 2342 2246Karaikal 200314 97796 102518 21569 10990 10579

245


District



Andaman & Nicobar Islands 379944 202330 177614 39497 20094 19403Nicobars 36819 20705 16114 4225 2154 2071North & Middle Andaman 105539 54821 50718 11647 5890 5757South Andaman 237586 126804 110782 23625 12050 11575

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Documents

Preliminary Demography of 2011 Population Census in India Aalok