Preliminary Demography of 2011 Population Census in

Aalok Ranjan Chaurasia Professor

‘Shyam’ Institute 82, Aradhana Nagar , MP-462003 India www.shyaminstitute.in

July 2011 ‘Shyam’ Institute Mudian Ka Kuan, Datia, MP-475661, India 91-752-2234522 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 or by any means including photocopying, recording or any information storage and retrieval system without permission in writing from MLC Foundation.

ISBN: 978-93-82411-02-4 Rs 800 In the memory of ‘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

1 Introduction

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/ 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 Preliminary Demography of India

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, 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 Preliminary Demography of India

Table 1.1 Reference date and census methodology in India Census Reference date Methodology 1881 17th February de facto (Synchronous) 1891 26th February de facto (Synchronous) 1901 1st March de facto (Synchronous) 1911 10th March de facto (Synchronous) 1921 18th March de facto (Synchronous) 1931 26th February de facto (Synchronous) 1941 1st March Extended de facto (Synchronous) 1951 1st March Extended de facto (Synchronous) 1961 1st March Extended de facto (Synchronous) 1971 1st April Extended de facto (Synchronous) 1981 1st March Extended de facto (Synchronous) 1991 1st March Extended de facto (Synchronous) 2001 1st March Extended de facto (Synchronous) 2011 1st March Extended de facto (Synchronous) 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 Preliminary Demography of India

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 Census The population census 2011 was the 14th since 1881 and the 7th in the independent India. The canvassing of the questionnaire of the 2011 census was done during the period 9th February 2011 through 28th February 2011 while the revision round was conducted during the period 1st March 2011 through 5th March 2011. An exception to this schedule was made in selected areas of the country which were snow bound during the month of February. In these areas, canvassing of the questionnaire was done during the period 11th September through 30th September 2010 while the revision round was conducted during 1st October through 5th October 2010. The count was then updated to the reference moment of 00:00 hours of 1st March 2011 (Government of India, 2011). 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 Preliminary Demography of India

< 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

If Pc denotes the count of the people at the upper level administrative unit and Pd denotes the count of the people at the lower level administrative units, then, it is obvious that 3 œ 0 Pc = Pd d c, (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 œ 0 Edc = Pd/Pc d c. (1.2) It is obvious that 3 0 Edc = 1 for all d c (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

the variable V for the upper level administrative unit c (Vd/Vc). 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 0 Idc(v) = log (Vd/Vc) for all d c. (1.4) where log represents the logarithm to the base 10. It is obvious that when Vd/Vc = 1, Idc(v) = 0; when Vd/Vc > 1, Idc(v) > 0 and when Vd/Vc < 1 Id(v) < 0. When Idc(v) > 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

Ddc(v) = (Pd/Pc )*log (Vd/Vc) œ 0 = Edc*Idc(v) d c. (1.5) and the the index of the distribution of the variable V for the upper level administrative unit c is then defined as 3 œ d0c. Dcd(v) = Ddc(v) (1.6)

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 Preliminary Demography of India 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 3 œ 0 Ps = Pd d s, and (1.7) 3 œ 0 and Pc = Pc s c. (1.8)

We now define the following indicators for relative extensiveness œ 0 Eds = Pd/Ps d s, and (1.9) œ 0 Esc = Pc/Pc s c. (1.10) Obviously 3 0 Eds = 1 for all d s, and (1.11) 3 0 Esc = 1 for all s c. (1.12)

We can also define the indicators of relative gravity or intensiveness of a demographic variable V in the following manner 0 Ids(v) = log (Vd/Vs) for all d s, and (1.13) 0 Isc(v) = log (Vs/Vc) for all s c. (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

Ddc(v) = (Pd/Pc )*log (Vd/Vc) œ 0 = Edc*Idc(v) d c. (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 œ 0 Dds(v) = Eds*Ids(v) d s, (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 œ 0 Dsc(v) = Esc*Isc(v) s c. (1.17) Finally, the index of distribution of the variable V for the middle level administrative unit s can be defined as 3 Dsd(v) = Dds(v). (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 3 Dcs(v) = Dsc(v). (1.19)

It should be clear that Dcd(v) Dcs(v), although the index Dcd(v) can be decomposed into indexes Dsd(v) and Dcs(v). In fact, it is easy to show that 3 3 Dcd(v) = Esc*Dsd(v) + Eds*Dcs(v). (1.20)

Equation (1.20) shows that the distributive index Dcd(v) 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 Preliminary Demography of India

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 2 Population 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 1st March 2011. 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 Preliminary Demography of India

Table 2.1 Population size and growth in India 1901-2011. Year Population Average (million) Decadal change in population annual growth rate (million) (per cent) (per cent) 1901 238.396 1911 252.093 13.697 5.75 0.56 1921 251.321 -0.772 -0.31 -0.03 1931 278.977 27.656 11.00 1.04 1941 318.661 39.684 14.22 1.33 1951 361.088 42.427 13.31 1.25 1961 439.235 78.147 21.64 1.96 1971 548.160 108.925 24.80 2.22 1981 683.329 135.169 24.66 2.20 1991 846.303 162.974 23.85 2.14 2001 1028.615 182.312 21.54 1.95 2011 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

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

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

17 Preliminary Demography of India 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 Population Size and Growth 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, , with a population of almost 200 million, continues to be the most populous state of India followed by and both of which have a population of more than 100 million. On the other hand, , 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 . 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, is the most populous one with a population of almost 17 million whereas , with a population of less than 0.61 million is the least populated one. In addition to Delhi, there are only two small states - and Kashmir and - 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, 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 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 growth 1991 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 15.93 Major States Uttar Pradesh 132.062 166.198 199.581 34.136 33.383 25.85 20.09 20.80 Maharashtra 78.937 96.879 112.373 17.942 15.494 22.73 15.99 16.29 Bihar 64.531 82.999 103.805 18.468 20.806 28.62 25.07 17.74 68.078 80.176 91.348 12.098 11.172 17.77 13.93 11.63 66.508 76.210 84.666 9.702 8.456 14.59 11.10 11.19 48.566 60.348 72.598 11.782 12.250 24.26 20.30 19.64 55.859 62.406 72.139 6.547 9.733 11.72 15.60 8.07 44.006 56.507 68.621 12.501 12.114 28.41 21.44 20.04 44.977 52.851 61.131 7.874 8.280 17.51 15.67 12.43 41.310 50.671 60.384 9.361 9.713 22.66 19.17 16.48 Orissa 31.660 36.805 41.947 5.145 5.142 16.25 13.97 10.72 29.099 31.841 33.388 2.742 1.547 9.42 4.86 8.55 21.844 26.946 32.966 5.102 6.020 23.36 22.34 16.80 22.414 26.656 31.169 4.242 4.513 18.93 16.93 14.68 Punjab 20.282 24.359 27.704 4.077 3.345 20.10 13.73 13.63 Chhattisgarh 17.615 20.834 25.540 3.219 4.706 18.27 22.59 16.44 Haryana 16.464 21.145 25.353 4.681 4.208 28.43 19.90 20.31 Small States Delhi 9.421 13.851 16.753 4.430 2.902 47.02 20.95 33.22 Jammu and Kashmir 7.719 10.144 12.549 2.425 2.405 31.42 23.71 15.52 Uttarakhand 7.051 8.489 10.117 1.438 1.628 20.39 19.18 17.12 5.171 6.078 6.857 0.907 0.779 17.54 12.82 11.77

20 Country/State Population (million) Population growth 1991 2001 2011 Absolute (million) Percent 1991-2001 2001-2011 1991-2001 2001-11 2001-11 (P) 2.757 3.199 3.671 0.442 0.472 16.03 14.75 13.03 1.775 2.319 2.964 0.544 0.645 30.65 27.81 13.03 1.837 2.294 2.722 0.457 0.428 24.88 18.66 13.02 1.210 1.990 1.981 0.780 -0.009 64.46 -0.45 13.01 1.170 1.348 1.458 0.178 0.110 15.21 8.16 31.12 0.865 1.098 1.383 0.233 0.285 26.94 25.96 13.03 0.690 0.889 1.091 0.199 0.202 28.84 22.72 12.99 Sikkim 0.406 0.541 0.608 0.135 0.067 33.25 12.38 13.16 Union Territories Puducherry 0.808 0.974 1.244 0.166 0.270 20.54 27.72 42.76 Chandigarh 0.642 0.901 1.055 0.259 0.154 40.34 17.09 59.67 Andaman and Nikobar 0.281 0.356 0.380 0.075 0.024 26.69 6.74 38.70 0.138 0.220 0.343 0.082 0.123 59.42 55.91 60.55 0.102 0.158 0.243 0.056 0.085 54.90 53.80 70.67 Lakshadweep 0.052 0.061 0.064 0.009 0.003 17.31 4.92 25.31 Source: Author’s calculations

21 Preliminary Demography of India

Figure 2.2 Population growth in states/Union Territories

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.

22 Population Size and Growth

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 1.478 Major States Uttar Pradesh 2.299 1.83 1.890 Maharashtra 2.048 1.484 1.509 Bihar 2.517 2.237 1.633 West Bengal 1.636 1.304 1.100 Andhra Pradesh 1.362 1.052 1.060 Madhya Pradesh 2.172 1.848 1.793 Tamil Nadu 1.108 1.449 0.776 Rajasthan 2.500 1.942 1.826 Karnataka 1.613 1.455 1.171 Gujarat 2.042 1.754 1.525 Orissa 1.506 1.308 1.018 Kerala 0.901 0.474 0.820 Jharkhand 2.099 2.017 1.553 Assam 1.733 1.564 1.370 Punjab 1.832 1.287 1.277 Chhattisgarh 1.678 2.037 1.522 Haryana 2.502 1.815 1.849 Small States Delhi 3.854 1.903 2.868 Jammu and Kashmir 2.732 2.128 1.443 Uttarakhand 1.856 1.754 1.581 Himachal Pradesh 1.616 1.205 1.112 Tripura 1.488 1.376 1.225 Meghalaya 2.673 2.455 1.225 Manipur 1.651 1.71 1.224 Nagaland 4.975 -0.048 1.223 Goa 1.414 0.785 2.709 Arunachal Pradesh 2.385 2.305 1.225 Mizoram 2.529 2.052 1.221 Sikkim 2.868 1.165 1.236 Union Territories Puducherry 1.872 2.447 3.560 Chandigarh 3.385 1.579 4.679 Andaman and Nikobar 2.370 0.647 3.272 Dadra and Nagar Haveli 4.686 4.414 4.734 Daman and Diu 4.389 4.288 5.345 Lakshadweep 1.539 0.604 2.256 Source: Author’s calculations

23 Preliminary Demography of India

Figure 2.3 Average annual population growth rate (per cent) during 2001-2011 in states and Union Territories

24 Population Size and Growth

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 Preliminary Demography of India

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

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

26 Population Size and Growth 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 Preliminary Demography of India

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 1206.535 1217.949 and the death rate Adjustment in the birth rate but no adjustment in 1211.666 1222.724 the death rate Adjustments as per Bhat (2002) 1218.587 1229.167 Source: Author’s calculations

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 R2=0.99 in case of the birth

28 Population Size and Growth 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 Preliminary Demography of India

Table 2.5 Enumerated and estimated population of states and Union Territories, 2011

Population 2011 Difference State Enumerated Estimated Absolute Per cent (Million) (Million) (Million) Uttar Pradesh 199.582 206.417 -6.835 -3.425 Rajasthan 68.621 70.473 -1.852 -2.699 Kerala 33.388 34.863 -1.475 -4.418 Madhya Pradesh 72.598 73.996 -1.399 -1.927 Andhra Pradesh 84.666 85.927 -1.261 -1.489 Assam 31.169 31.454 -0.285 -0.914 Nagaland 1.981 2.253 -0.273 -13.781 Bihar 103.805 103.965 -0.16 -0.154 Andaman and Nikobar 0.38 0.401 -0.021 -5.526 Sikkim 0.608 0.625 -0.018 -2.961 Lakshadweep 0.064 0.068 -0.004 -6.250 Himachal Pradesh 6.857 6.855 0.001 0.015 Goa 1.458 1.447 0.011 0.754 Chandigarh 1.055 1.017 0.038 3.602 Orissa 41.947 41.904 0.044 0.105 Daman and Diu 0.243 0.182 0.061 25.103 Dadra and Nagar Haveli 0.343 0.278 0.065 18.950 Haryana 25.353 25.281 0.072 0.284 Mizoram 1.091 1.010 0.081 7.424 Arunachal Pradesh 1.383 1.296 0.087 6.291 Tripura 3.671 3.544 0.127 3.460 Manipur 2.722 2.553 0.169 6.209 Puducherry 1.244 1.072 0.172 13.826 Meghalaya 2.964 2.768 0.196 6.613 Punjab 27.704 27.406 0.298 1.076 Chhattisgarh 25.54 25.213 0.327 1.280 Uttarakhand 10.117 9.766 0.351 3.469 Jharkhand 32.966 32.591 0.375 1.138 West Bengal 91.348 90.864 0.484 0.530 Karnataka 61.131 60.605 0.526 0.860 Gujarat 60.384 59.847 0.537 0.889 Delhi 16.753 15.934 0.82 4.895 Jammu and Kashmir 12.549 11.600 0.949 7.562 Maharashtra 112.373 109.480 2.893 2.574 Tamil Nadu 72.139 68.728 3.411 4.728 Source: Author’s calculations

30 Population Size and Growth

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 Preliminary Demography of India

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 Population Size and Growth

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 3 Andhra Pradesh 0 0 7 6 10 23 Arunachal Pradesh 16 0 0 0 0 16 Assam 12 14 1 0 0 27 Bihar 3 10 10 9 6 38 Chandigarh 0 1 0 0 0 1 Chhattisgarh 8 6 2 1 1 18 Dadra & Nagar Haveli 1 0 0 0 0 1 Daman and Diu 2 0 0 0 0 2 Delhi 3 1 4 1 0 9 Goa 2 0 0 0 0 2 Gujarat 4 7 10 2 3 26 Haryana 6 15 0 0 0 21 Himachal Pradesh 11 1 0 0 0 12 Jammu and Kashmir 18 4 0 0 0 22 Jharkhand 8 11 5 0 0 24 Karnataka 2 20 6 0 2 30 Kerala 1 4 4 4 1 14 Lakshadweep 1 0 0 0 0 1 Madhya Pradesh 10 31 8 1 0 50 Maharashtra 1 12 9 5 8 35 Manipur 9 0 0 0 0 9 Meghalaya 7 0 0 0 0 7 Mizoram 8 0 0 0 0 8 Nagaland 11 0 0 0 0 11 Orissa 10 14 5 1 0 30 Puducherry 4 0 0 0 0 4 Punjab 10 5 4 1 0 20 Rajasthan 2 17 9 4 1 33 Sikkim 4 0 0 0 0 4 Tamil Nadu 3 13 7 8 1 32 Tripura 3 1 0 0 0 4 Uttar Pradesh 2 22 14 19 14 71 Uttarakhand 10 3 0 0 0 13 West Bengal 0 2 2 5 10 19 India 195 214 107 67 57 640 30.5 33.4 16.7 10.5 8.9 100.0 Source: Author’s calculations

33 Preliminary Demography of India

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 District State/Union District Territory Territory Arunachal Pradesh Kurung Kumey Nagaland Longleng Puducherry Yanam Nagaland Kiphire Haryana Delhi New Delhi Daman & Diu Daman Nagaland Mokochung Dadra & Nagar Dadra and Nagar Andaman and Nicobars Haveli Haveli Uttar Pradesh Gautam Budh Nagar Delhi Central Arunachal Pradesh Upper Subansiri Nagaland Zunheboto Arunachal Pradesh Lower Subansiri Maharashtra Andhra Pradesh Rangareddy Himachal Pradesh Lahul & Spiti Karnataka 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 Jammu & Kashmir Anantnag Uttarakhand Arunachal Pradesh East Kameng Uttarakhand Garhwal Mizoram Mamit Karnataka Chikmanglur Jammu and Kashmir Ganderbal Andaman & Nicobar North and Middle Maharashtra Thane Islands Andaman Tamil Nadu Thiruvallur Source: Author’s calculations

34 Population Size and Growth

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

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.

35 Preliminary Demography of India

Figure 2.6 Distribution of districts across states by average annual population growth rate (per cent), 2001-2011

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.

36 Population Size and Growth

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 3 Andhra Pradesh 0 13 9 0 1 23 Arunachal Pradesh 0 4 6 1 5 16 Assam 0 4 20 3 0 27 Bihar 0 0 11 27 0 38 Chandigarh 0 0 1 0 0 1 Chhattisgarh 0 1 13 3 1 18 Dadra and Nagar Haveli 0 0 0 0 1 1 Daman and Diu 0 0 1 0 1 2 Delhi 2 0 4 3 0 9 Goa 0 2 0 0 0 2 Gujarat 0 4 17 4 1 26 Haryana 0 1 15 3 2 21 Himachal Pradesh 1 2 9 0 0 12 Jammu and Kashmir 0 1 8 11 2 22 Jharkhand 0 0 9 15 0 24 Karnataka 1 14 12 2 1 30 Kerala 2 11 1 0 0 14 Lakshadweep 0 1 0 0 0 1 Madhya Pradesh 0 0 34 16 0 50 Maharashtra 3 9 18 4 1 35 Manipur 0 0 6 3 0 9 Meghalaya 0 0 0 6 1 7 Mizoram 0 0 4 3 1 8 Nagaland 6 2 0 3 0 11 Orissa 0 2 27 1 0 30 Puducherry 0 0 2 1 1 4 Punjab 0 5 14 1 0 20 Rajasthan 0 1 18 14 0 33 Sikkim 0 1 3 0 0 4 Tamil Nadu 1 6 22 1 2 32 Tripura 0 0 3 1 0 4 Uttar Pradesh 0 1 48 20 2 71 Uttarakhand 2 5 2 4 0 13 West Bengal 1 1 16 1 0 19 India 21 91 354 151 23 640 3.3 14.2 55.3 23.7 3.6 100.0 Source: Population census 2011.

37 Preliminary Demography of India

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

38 Population Size and Growth

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 , 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 Preliminary Demography of India

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 3 Population 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 Preliminary Demography of India

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

Ddc(x) = (Pd/Pc )*log (Xd/Xc) œ 0 = Edc*Idc(x) d c. (3.1) Similarly, œ 0 Dds(x) = Eds*Ids(x) d s, and (3.2) œ 0 Dsc(x) = Esc*Isc(x) s c. (3.3) Finally, we define the index of distribution of the population in the country as a whole as 3 Dcd(x) = Ddc(x). (3.4) Similarly, we may also define 3 Dsd(x) = Dds(x), and (3.5) 3 Dcs(x) = Dsc(x). (3.6) It is now easy to show that 3 3 Dcd(x) = Esc*Dsd(x) + Eds*Dcs(x). (3.7)

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

as the ratio of the population to the area for the country as a whole, Dcd(x) = 0. Moreover, the index Dcd(x) is independent of the unit of measurement of the population or the area as it is based on ratios not absolute values.

The advantage of using the index Dcd(x) to measure the distribution of population across administrative units should be obvious. The index Dcd(x) is logically related to the index Dsd(x) and the index Dcs(x). 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 that Dcd(x) 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

same as the population density of the state/Union Territory as a whole, Dsd(x) = 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 of the country, Dcs(x) = 0. The index Dcd(x) 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

distribution of the population across the districts of the country, Dcd(x), is estimated to be around 195 according to the provisional figures of the 2011 population census. As discussed earlier, this

index is the weighted sum of the index of intensiveness, Idc(x), of all the 640 districts of the country with weights being equal to the ratio of the population of the district to the population

of the country or the index of extensiveness, Edc. The index of population distribution Dcd(x) 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, then the Dcd(x) = 0 irrespective of the distribution of the population of the country across the districts. The index Dcd(x) 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.

It is possible to decompose the index Dcd(x) 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 Preliminary Demography of India

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 state/Union Territory. This context is measured by the index Dsd(x) which is defined by equation (3.3). The second context, on the other hand, is how the population across states/Union

Territories is distributed within the country. This context is measured by the index Dcs(x) which is defined by equation (3.4). The two indexes can be combined to arrive at the index Dcd(x) according to the equation (3.7).

Values of the indexes Dsd(x) and Dcs(x) are given in table 3.1 along with the population density in each state/Union territory of the country and the index Esc which reflects the proportionate distribution of the population of the country across the states/Union Territories. and Isc(x). 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

population distribution in Uttar Pradesh is because of a very large value of the index Esc 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 Population Distribution

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.

By comparison, the index Dsc(x) has been found to be the lowest in Rajasthan followed by Madhya Pradesh, although Arunachal Pradesh which has the lowest population density. In fact,

the index Ecs is very low in Arunachal Pradesh as the population of the state accounts for just around 0.1 per cent of the population of the country whereas Ecs is comparatively large in Rajasthan and Madhya Pradesh. Clearly, the ranking of states/Union Territories of the country by the index Dsc(x) 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.

Table 3.1 also suggests that the index of population distribution, Dsc(x), 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 Preliminary Demography of India

Table 3.1 Indexes of population distribution in India and states/Union Territories, 2011

Country/State Population Esc(x) Dsd(x) Dsc(x) Dcd(x) density Andaman and Nikobar 47 0.314 0.020 -0.285 -0.266 Andhra Pradesh 308 69.960 6.264 -6.499 -0.235 Arunachal Pradesh 17 1.142 0.143 -1.558 -1.414 Assam 398 25.756 2.254 0.478 2.732 Bihar 1049 85.775 2.951 37.690 40.641 Chandigarh 9252 0.872 0.000 1.207 1.207 Chhattisgarh 189 21.104 1.556 -6.437 -4.881 Dadra and Nagar Haveli 698 0.283 0.000 0.074 0.074 Daman and Diu 2169 0.201 0.004 0.152 0.156 Delhi 11282 13.843 1.882 20.366 22.248 Goa 394 1.205 0.009 0.017 0.026 Gujarat 308 49.896 7.085 -4.596 2.489 Haryana 530 20.950 0.451 2.994 3.446 Himachal Pradesh 123 5.666 1.259 -2.780 -1.521 Jammu and Kashmir 125 10.369 4.805 -5.034 -0.229 Jharkhand 414 27.240 1.488 0.966 2.453 Karnataka 319 50.513 6.943 -3.923 3.019 Kerala 863 27.589 1.252 9.787 11.038 Lakshadweep 2013 0.053 0.000 0.038 0.038 Madhya Pradesh 235 59.988 2.478 -12.555 -10.077 Maharashtra 365 92.855 18.819 -1.739 17.079 Manipur 122 2.249 0.784 -1.114 -0.330 Meghalaya 133 2.449 0.130 -1.124 -0.994 Mizoram 52 0.902 0.050 -0.781 -0.731 Nagaland 120 1.637 0.145 -0.819 -0.674 Orissa 269 34.662 2.544 -5.235 -2.690 Puducherry 2529 1.028 0.036 0.845 0.881 Punjab 550 22.892 0.584 3.645 4.229 Rajasthan 200 56.703 5.855 -15.872 -10.018 Sikkim 86 0.502 0.151 -0.326 -0.175 Tamil Nadu 556 59.609 6.482 9.763 16.244 Tripura 350 3.033 0.152 -0.113 0.039 Uttar Pradesh 829 164.917 6.619 55.599 62.219 Uttarakhand 189 8.360 1.623 -2.550 -0.927 West Bengal 1024 75.482 7.503 32.377 39.880 India 102.657 92.230 194.978 Source: Author’s calculations Remarks: For the definition of the indexes, see text

46 Population Distribution

Figure 3.1 Population density (per square kilometre) in states, 2011

47 Preliminary Demography of India

Figure 3.2 Index of extensiveness of population distribution in states/Union Territories

48 Population Distribution

Figure 3.4 Index of population distribution in states/Union Territories, 2011

49 Preliminary Demography of India

On the other hand, the index of population distribution across districts within states/Union

Territories or the index Dsd(x) 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.

Combining the indexes Dsd(x) and Dcs(x), 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 distribution, Dcd(x) for the country. Table 3.1 gives the share of different states and Union Territories to the index of population distribution, Dcd(x) 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

Estimates of the index of the population distribution, Ddc(x), index of extensiveness Edc(x) and the index of intensiveness Idc(x) 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 Population Distribution

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, 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 Preliminary Demography of India

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 AN Islands 3 0 0 0 0 3 Andhra Pradesh 0 11 7 4 1 23 Arunachal Pradesh 16 0 0 0 0 16 Assam 2 3 8 7 7 27 Bihar 1 0 0 2 35 38 Chandigarh 0 0 0 0 1 1 Chhattisgarh 8 6 4 0 0 18 Dadra and Nagar Haveli 0 0 0 0 1 1 Daman and Diu 0 0 0 0 2 2 Delhi 0 0 0 0 9 9 Goa 0 0 1 1 0 2 Gujarat 2 10 3 6 5 26 Haryana 0 0 4 7 10 21 Himachal Pradesh 4 5 3 0 0 12 Jammu and Kashmir 4 6 4 3 5 22 Jharkhand 0 7 7 3 7 24 Karnataka 2 15 11 1 1 30 Kerala 0 1 1 1 11 14 Lakshadweep 0 0 0 0 1 1 Madhya Pradesh 3 39 5 1 2 50 Maharashtra 1 18 10 2 4 35 Manipur 5 0 0 1 3 9 Meghalaya 5 2 0 0 0 7 Mizoram 8 0 0 0 0 8 Nagaland 9 1 1 0 0 11 Orissa 6 15 1 2 6 30 Puducherry 0 0 0 0 4 4 Punjab 0 0 8 7 5 20 Rajasthan 6 14 9 3 1 33 Sikkim 2 2 0 0 0 4 Tamil Nadu 0 1 13 6 12 32 Tripura 1 1 1 1 0 4 Uttar Pradesh 0 3 5 4 59 71 Uttarakhand 7 3 0 2 1 13 West Bengal 0 0 0 3 16 19 India 95 163 106 67 209 640 14.8 25.5 16.6 10.5 32.7 100.0 Source: Author’s calculations

52 Population Distribution

Figure 3.5 Population density in districts of India, 2011

53 Preliminary Demography of India

The distribution of districts according to indexes Edc(x), Idc(x), and Ddc(x) is given in tables 3.3, 3.4 and 3.5 respectively. The ten districts of the country which have the highest value of Ddc(x) 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,

Mumbai and North-East Delhi - very high value of Ddc(x) is primarily due to very high values of the index of intensiveness in population distribution, Idc(x) whereas in Bangalore and North Twenty Four Parganas districts, the very high value of the index of population distribution is primarily due to very high value of the index of extensiveness, Edc(x), which is amongst the highest in the country.

On the other hand, the lowest value of the index Ddc(x) has been estimated in district Kuchchh in Gujarat. Other districts with lowest level of the index Ddc(x) are five districts of Rajasthan - Barmer, , 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

North Sikkim in Sikkim. However, the index of the extensiveness of population distribution, Edc in these districts is also amongst the lowest in the country so that the product of the two or the

index of population distribution, Ddc(x), is not the lowest in these districts. The index of intensiveness of the population distribution, Idc(x), 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

of population distribution, Ddc(x), on the other hand, takes into consideration both the concentration of the population, measured in terms of the index Idc(x) and the size of the population, measured in terms of the index of extensiveness, over which this concentration prevails.

54 Population Distribution

Table 3.3

Districts by the index Edc, 2011 State/Union Territory Edc < 0.5 0.5-1.5 1.5-2.5 2.5-3.5 $3.5 Total AN Islands 3 0 0 0 0 3 Andhra Pradesh 0 0 7 11 5 23 Arunachal Pradesh 16 0 0 0 0 16 Assam 2 22 3 0 0 27 Bihar 0 11 12 9 6 38 Chandigarh 0 1 0 0 0 1 Chhattisgarh 3 11 2 2 0 18 Dadra and Nagar Haveli 1 0 0 0 0 1 Daman and Diu 2 0 0 0 0 2 Delhi 2 2 4 1 0 9 Goa 0 2 0 0 0 2 Gujarat 3 8 10 3 2 26 Haryana 1 20 0 0 0 21 Himachal Pradesh 9 3 0 0 0 12 Jammu and Kashmir 14 8 0 0 0 22 Jharkhand 3 15 6 0 0 24 Karnataka 1 17 10 0 2 30 Kerala 0 4 5 5 0 14 Lakshadweep 1 0 0 0 0 1 Madhya Pradesh 1 37 11 1 0 50 Maharashtra 0 11 12 5 7 35 Manipur 9 0 0 0 0 9 Meghalaya 5 2 0 0 0 7 Mizoram 8 0 0 0 0 8 Nagaland 11 0 0 0 0 11 Orissa 5 18 6 1 0 30 Puducherry 3 1 0 0 0 4 Punjab 2 12 5 1 0 20 Rajasthan 0 16 12 4 1 33 Sikkim 4 0 0 0 0 4 Tamil Nadu 1 12 10 8 1 32 Tripura 1 3 0 0 0 4 Uttar Pradesh 0 19 20 21 11 71 Uttarakhand 6 6 1 0 0 13 West Bengal 0 1 4 4 10 19 India 117 262 140 76 45 640 18.3 40.9 21.9 11.9 7.0 100.0 Source: Author’s calculations

55 Preliminary Demography of India

Figure 3.6 Distribution of the index Edc across districts, 2011

56 Population Distribution

Table 3.4

Distribution of districts by the index Idc(x), 2011 State/Union Territory Idc(x) <-0.25 -0.25 to 0 0 to 0.25 0.25 to 0.50 $0.50 Total AN Islands 3 0 0 0 0 3 Andhra Pradesh 5 9 8 0 1 23 Arunachal Pradesh 16 0 0 0 0 16 Assam 3 5 15 3 1 27 Bihar 1 0 3 20 14 38 Chandigarh 0 0 0 0 1 1 Chhattisgarh 12 4 2 0 0 18 Dadra and Nagar Haveli 0 0 0 1 0 1 Daman and Diu 0 0 0 0 2 2 Delhi 0 0 0 0 9 9 Goa 0 1 1 0 0 2 Gujarat 6 9 8 2 1 26 Haryana 0 3 12 6 0 21 Himachal Pradesh 6 5 1 0 0 12 Jammu and Kashmir 7 7 3 5 0 22 Jharkhand 4 6 10 3 1 24 Karnataka 7 17 5 0 1 30 Kerala 0 1 4 6 3 14 Lakshadweep 0 0 0 0 1 1 Madhya Pradesh 25 19 4 2 0 50 Maharashtra 6 22 4 1 2 35 Manipur 5 0 2 2 0 9 Meghalaya 6 1 0 0 0 7 Mizoram 8 0 0 0 0 8 Nagaland 9 1 1 0 0 11 Orissa 12 9 7 2 0 30 Puducherry 0 0 0 0 4 4 Punjab 0 3 13 4 0 20 Rajasthan 14 13 5 0 1 33 Sikkim 3 1 0 0 0 4 Tamil Nadu 0 10 14 7 1 32 Tripura 1 1 2 0 0 4 Uttar Pradesh 0 6 11 42 12 71 Uttarakhand 9 1 2 1 0 13 West Bengal 0 0 5 8 6 19 India 168 154 142 115 61 640 26.3 24.1 22.2 18.0 9.5 100.0 Source: Author’s calculations

57 Preliminary Demography of India

Figure 3.7 Distribution of the index Idc(x)in districts of India, 2011

58 Population Distribution

Table 3.5

Distribution of districts by the index Ddc(x), 2011 State/Union Territory Ddc(x) <-0.5 -0.5 to 0 0 to 0.5 0.5-1.0 $1.0 Total AN Islands 0 3 0 0 0 3 Andhra Pradesh 8 6 8 0 1 23 Arunachal Pradesh 0 16 0 0 0 16 Assam 0 9 15 3 0 27 Bihar 0 1 8 11 18 38 Chandigarh 0 0 0 0 1 1 Chhattisgarh 2 14 2 0 0 18 Dadra and Nagar Haveli 0 0 1 0 0 1 Daman and Diu 0 0 2 0 0 2 Delhi 0 0 1 1 7 9 Goa 0 1 1 0 0 2 Gujarat 3 12 8 1 2 26 Haryana 0 4 16 1 0 21 Himachal Pradesh 0 11 1 0 0 12 Jammu and Kashmir 0 14 8 0 0 22 Jharkhand 0 10 13 0 1 24 Karnataka 1 24 4 0 1 30 Kerala 0 2 3 3 6 14 Lakshadweep 0 0 1 0 0 1 Madhya Pradesh 3 42 3 2 0 50 Maharashtra 5 23 3 0 4 35 Manipur 0 5 4 0 0 9 Meghalaya 0 7 0 0 0 7 Mizoram 0 8 0 0 0 8 Nagaland 0 10 1 0 0 11 Orissa 0 21 7 2 0 30 Puducherry 0 0 3 1 0 4 Punjab 0 3 14 2 1 20 Rajasthan 11 16 5 0 1 33 Sikkim 0 4 0 0 0 4 Tamil Nadu 0 11 11 7 3 32 Tripura 0 2 2 0 0 4 Uttar Pradesh 0 6 19 24 22 71 Uttarakhand 0 10 2 1 0 13 West Bengal 0 0 4 5 10 19 India 33 295 170 64 78 640 5.2 46.1 26.6 10.0 12.2 100.0 Source: Author’s calculations

59 Preliminary Demography of India

Figure 3.8 Index of population distribution in districts of India, 2011

60 Population Distribution

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 extensiveness of population, measured in terms of the index Edc(x) 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 Preliminary Demography of India

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, the index of the intensiveness of the population, measured in terms of the index Idc(x) has been estimated to be very low either in all or in majority of the districts.

62 4 Age 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 Preliminary Demography of India

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 years to the population aged 7 years and above. If P denotes the total population, P6 the population aged 0-6 years and P7 the population aged 7 years, then the proportion of the population 0-6 years to the total population C is defined as

C = P6/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

A = P6/P7 (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 0 Idc(a) = log (Ad/Ac) for all d c, (4.3) 0 Ids(a) = log (Ad/As) for all d s, and (4.4) 0 Isc(a) = log (As/Ac) for all s c. (4.5) Then the distribution indexes of the age composition (D) in district d are defined as 0 Ddc(a) = Edc *Idc(a) for all d c, (4.6) 0 Dds(a) = Eds *Ids(a) for all d s, and (4.7) 0 Dsc(a) = Esc *Isc(a) for all s c (4.8) and the distribution indexes of the age composition at the country and the state/Union Territory level may then be defined as 3 0 Dcd(a) = Ddc(a) for all d c, (4.9) 3 0 Dcs(a) = Dsc(a) for all s c, and (4.10) 3 0 Dsd(a) = Dds(a) for all d s, (4.11) Finally, it is easy to see that 3 3 Dcd(a) = Eds(a)*Dcs(a) + Esc(a)* Dsd(a) (4.12)

The distributive index of age composition in districts d, Ddc(a), 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

deviation applies. Thus, the distributive index Ddc(a) 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

Territories of the country. The advantage of the index Ddc(a), 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 Preliminary Demography of India

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 1st March 2011. The corresponding number 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

Figure 4.1 Age composition of population in India

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.

67 Preliminary Demography of India

Table 4.1 Age composition of the population in India Age group Population Change (million) 2001 2011 Absolute Proportionate (million) (per cent) < 7 years 163.82 158.789 -5.031 -3.07 (15.92) (13.12) $7 years 864.79 1051.404 186.614 21.58 (84.08) (86.88) All 1028.61 1210.193 181.583 17.65 (100.00) (100.00)

Index A = P6/P7 0.189 0.151 Distributive index of age 10.593 composition Remarks: Figures in parentheses denote percentages. For the definition of the distribution index of age composition, see text. Source: Author’s calculations

The provisional figures of the 2011 population census also suggest that the distribution index of

age composition, Dcd(a) 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

of this index is accounted by the index Dcs(a) which reflects the distribution of the index of the age composition of the population (index A) across the states/Union Territories while the remaining 30 per cent is accounted by the index Dsd(a) 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

Figure 4.2 Age composition of population in India by sex

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.

69 Preliminary Demography of India

Table 4.2 Age composition of the population in India by sex, 2011 Age group Population Difference (million) Male Female Absolute Proportionate (million) (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

Index A = P6/P7 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:

Im - If = (am - af)*pf + (pm - pf)*af +(am - af)*(pm - pf) (4.13) where

Im = age composite index for males = M6/M7, If = age composition index for female = F6/F7

am = M6/((M7+F7)/2) af = F6/((M7+F7)/2) pm = ((M7+F7)/2)/M7 pf = ((M7+F7)/2)/F7 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 Preliminary Demography of India

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 population 7 years and above 2001 2011 Difference India 0.189 0.151 -0.038 Andaman and Nikobar 0.144 0.116 -0.028 Andhra Pradesh 0.154 0.114 -0.040 Arunachal Pradesh 0.231 0.172 -0.059 Assam 0.203 0.169 -0.034 Bihar 0.254 0.218 -0.036 Chandigarh 0.147 0.126 -0.021 Chhattisgarh 0.206 0.163 -0.043 Dadra and Nagar Haveli 0.223 0.168 -0.055 Daman and Diu 0.150 0.119 -0.031 Delhi 0.170 0.133 -0.037 Goa 0.121 0.106 -0.015 Gujarat 0.175 0.142 -0.033 Haryana 0.187 0.150 -0.037 Himachal Pradesh 0.150 0.125 -0.025 Jammu and Kashmir 0.172 0.191 0.019 Jharkhand 0.225 0.189 -0.036 Karnataka 0.157 0.126 -0.031 Kerala 0.135 0.111 -0.024 Lakshadweep 0.176 0.124 -0.052 Madhya Pradesh 0.218 0.170 -0.048 Maharashtra 0.164 0.129 -0.035 Manipur 0.155 0.149 -0.006 Meghalaya 0.253 0.231 -0.022 Mizoram 0.193 0.179 -0.014 Nagaland 0.170 0.169 -0.001 Orissa 0.170 0.136 -0.034 Puducherry 0.137 0.114 -0.023 Punjab 0.150 0.119 -0.031 Rajasthan 0.232 0.181 -0.051 Sikkim 0.169 0.112 -0.057 Tamil Nadu 0.131 0.106 -0.025 Tripura 0.158 0.138 -0.020 Uttar Pradesh 0.235 0.175 -0.060 Uttarakhand 0.191 0.151 -0.040 West Bengal 0.166 0.124 -0.042 Source: Author’s calculations

72 Age Composition

Figure 4.1 Distribution of index A across states/Union Territories in India, 2011

73 Preliminary Demography of India

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

of the country as a whole (the index Dsc(a)) and in relation to the index of the age composition of the districts within the state/Union Territory (the index Dds(a)). The index Dsc(a) 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 the index Dsc(a) 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.

Table 4.4 also gives the estimates of the distributive index Dsd(a) captures the distribution of the age composition index across the districts within the states/Union Territories. Combining the

index Dsc(a) with the index Dsd(a) according to equation (4.12) gives the state share to the index Dcd(a). 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.

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Table 4.4 Distributive indexes of age composition of population in states and Union Territories, 2011

Country/State Esc Dsc(a) Dcd(a) 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 Source: Author’s calculations

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Figure 4.2 Distributive 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 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.

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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 AN Islands 0 3 0 0 0 3 Andhra Pradesh 2 21 0 0 0 23 Arunachal Pradesh 0 4 8 4 0 16 Assam 0 8 16 3 0 27 Bihar 0 0 7 28 3 38 Chandigarh 0 1 0 0 0 1 Chhattisgarh 0 5 12 1 0 18 Dadra and Nagar Haveli 0 0 1 0 0 1 Daman and Diu 0 2 0 0 0 2 Delhi 1 7 1 0 0 9 Goa 0 2 0 0 0 2 Gujarat 0 18 6 2 0 26 Haryana 0 16 4 0 1 21 Himachal Pradesh 0 11 1 0 0 12 Jammu and Kashmir 1 4 6 9 2 22 Jharkhand 0 1 13 10 0 24 Karnataka 4 19 7 0 0 30 Kerala 7 6 1 0 0 14 Lakshadweep 0 1 0 0 0 1 Madhya Pradesh 0 11 33 5 1 50 Maharashtra 2 25 8 0 0 35 Manipur 0 7 2 0 0 9 Meghalaya 0 0 1 4 2 7 Mizoram 0 1 4 3 0 8 Nagaland 0 2 5 4 0 11 Orissa 0 19 10 1 0 30 Puducherry 0 4 0 0 0 4 Punjab 0 20 0 0 0 20 Rajasthan 0 2 23 8 0 33 Sikkim 0 4 0 0 0 4 Tamil Nadu 9 23 0 0 0 32 Tripura 0 2 2 0 0 4 Uttar Pradesh 0 4 59 8 0 71 Uttarakhand 0 7 6 0 0 13 West Bengal 2 13 4 0 0 19 India 28 273 240 90 9 640 4.4 42.7 37.5 14.1 1.4 100.0 Source: Author’s calculations

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Figure 4.3 Proportion of population aged 0-6 years to the population aged 7 years and above in districts of India, 2011

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Table 4.6

Distribution of districts by the index Ddc(a), 2011 State Dcd(a) < -0.05 -0.05 to -0.025 -0.025 to 0 0 to 0.025 $ 0.025 Total AN Islands 0 0 3 0 0 3 Andhra Pradesh 6 11 6 0 0 23 Arunachal Pradesh 0 0 6 10 0 16 Assam 0 0 9 17 1 27 Bihar 0 0 0 11 27 38 Chandigarh 0 0 1 0 0 1 Chhattisgarh 0 0 5 13 0 18 Dadra and Nagar Haveli 0 0 0 1 0 1 Daman and Diu 0 0 2 0 0 2 Delhi 0 0 8 1 0 9 Goa 0 0 2 0 0 2 Gujarat 0 3 15 6 2 26 Haryana 0 0 17 3 1 21 Himachal Pradesh 0 0 11 1 0 12 Jammu and Kashmir 0 0 5 17 0 22 Jharkhand 0 0 1 22 1 24 Karnataka 1 5 17 7 0 30 Kerala 2 7 4 1 0 14 Lakshadweep 0 0 1 0 0 1 Madhya Pradesh 0 0 11 39 0 50 Maharashtra 4 5 18 8 0 35 Manipur 0 0 7 2 0 9 Meghalaya 0 0 0 7 0 7 Mizoram 0 0 1 7 0 8 Nagaland 0 0 3 8 0 11 Orissa 0 2 17 11 0 30 Puducherry 0 0 4 0 0 4 Punjab 0 2 18 0 0 20 Rajasthan 0 0 3 25 5 33 Sikkim 0 0 4 0 0 4 Tamil Nadu 2 16 14 0 0 32 Tripura 0 0 2 2 0 4 Uttar Pradesh 0 2 2 56 11 71 Uttarakhand 0 0 7 6 0 13 West Bengal 5 4 6 4 0 19 India 20 57 230 285 48 640 3.1 8.9 35.9 44.5 7.5 100.0 Source: Author’s calculations

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Figure 4.4 Distributive index of age composition in districts of India, 2011

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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, , Mumbai, , 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 5 Sex 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 Preliminary Demography of India

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 0 Idc(s) = log (Sd/Sc) for all d c. (5.5) 0 Ids(s) = log (Sd/Ss) for all d s, and (5.6) 0 Isc(s) = log (Ss/Sc) for all s c. (5.7) Then the indexes of sex composition may be defined as

Ddc(s) = Edc*Idc(s) (5.8) Dds(s) = Eds*Ids(s) (5.9) Dsc(s) = Esc*Isc(s) (5.10) Finally, the indexes of sex composition for the country and the state/Union Territory are defined as 3 Dcd(s) = Ddc(s) (5.11) 3 Dsd(s) = Dds(s) (5.12) 3 Dcs(s) = Dsc(s). (5.13)

84 Sex Composition

Finally, it is easy to show that 3 3 Dcd(s) = Esc(s)*Isd(s) + Eds(s)*Ics(s) (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.

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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

Figure 5.1 Sex ratio (females per 1000 males) in India, 1901-2011

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.

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Table 5.1 Females per 1000 males in India, states and Union Territories, 2001-2011 Country/State/ Total population 0-6 years 7 years and above Union Territory 2011 2001 2011 2001 2011 2001 India 940 933 914 927 944 934 Andaman & Nicobar Islands 878 846 966 957 868 831 Andhra Pradesh 992 978 943 961 997 981 Arunachal Pradesh 920 893 960 964 913 878 Assam 954 935 957 965 953 929 Bihar 916 919 933 942 912 914 Chandigarh 818 777 867 845 812 767 Chhattisgarh 991 989 964 975 995 992 Dadra & Nagar Haveli 775 812 924 979 752 779 Daman & Diu 618 710 909 926 589 682 Delhi 866 821 866 868 866 813 Goa 968 961 920 938 973 964 Gujarat 918 920 886 883 923 927 Haryana 877 861 830 819 885 869 Himachal Pradesh 974 968 906 896 983 980 Jammu & Kashmir 883 892 859 941 887 884 Jharkhand 947 941 943 965 948 935 Karnataka 968 965 943 946 971 968 Kerala 1084 1058 959 960 1099 1072 Lakshadweep 946 948 908 959 951 946 Madhya Pradesh 930 919 912 932 933 916 Maharashtra 925 922 883 913 931 924 Manipur 987 974 934 957 995 977 Meghalaya 986 972 970 973 989 971 Mizoram 975 935 971 964 976 930 Nagaland 931 900 944 964 929 890 Orissa 978 972 934 953 985 976 Puducherry 1038 1001 965 967 1047 1006 Punjab 893 876 846 798 899 888 Rajasthan 926 921 883 909 935 923 Sikkim 889 875 944 963 883 861 Tamil Nadu 995 987 946 942 1000 993 Tripura 961 948 953 966 962 945 Uttar Pradesh 908 898 899 916 910 894 Uttarakhand 963 962 886 908 975 973 West Bengal 947 934 950 960 946 929 Source: Author’s calculations

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Figure 5.2 Females per 1000 males in states and Union Territories, 2011

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Figure 5.3 Females per 1000 males in population aged 0-6 years in states/Union Territories, 2011

90 Sex Composition

Figure 5.4 Females per 1000 males in population aged 7 years and above in states and Union Territories, 2011

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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.

The index of the sex composition of the population, Dcd(s) 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 males relative to the sex ratio at the national level. Since the index Dcd(s) is the sum of the index of sex composition, Ddc(s), in all the 640 districts of the country, this means that the sum of all negative values of the index Ddc(s) 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.

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Table 5.2

Index Ddc(s) in India and states/Union Territories, 2011 Country/State/ Population Population Population Union Territory All ages 0-6 years 7 years and above Andaman & Nicobar Islands -0.009 0.007 -0.012 Andhra Pradesh 1.617 0.951 1.659 Arunachal Pradesh -0.011 0.025 -0.017 Assam 0.157 0.526 0.104 Bihar -0.986 0.719 -1.296 Chandigarh -0.053 -0.020 -0.057 Chhattisgarh 0.482 0.482 0.484 Dadra & Nagar Haveli -0.024 0.001 -0.028 Daman & Diu -0.037 -0.001 -0.042 Delhi -0.492 -0.323 -0.518 Goa 0.015 0.003 0.016 Gujarat -0.521 -0.741 -0.496 Haryana -0.633 -0.916 -0.596 Himachal Pradesh 0.086 -0.024 0.097 Jammu & Kashmir -0.286 -0.293 -0.278 Jharkhand 0.084 0.345 0.046 Karnataka 0.644 0.705 0.622 Kerala 1.704 0.570 1.818 Lakshadweep 0.000 0.000 0.000 Madhya Pradesh -0.284 -0.074 -0.306 Maharashtra -0.641 -1.298 -0.565 Manipur 0.047 0.020 0.051 Meghalaya 0.050 0.062 0.050 Mizoram 0.014 0.024 0.013 Nagaland -0.007 0.025 -0.012 Orissa 0.600 0.275 0.639 Puducherry 0.044 0.024 0.046 Punjab -0.513 -0.762 -0.492 Rajasthan -0.365 -0.897 -0.257 Sikkim -0.012 0.007 -0.015 Tamil Nadu 1.466 0.908 1.493 Tripura 0.029 0.053 0.025 Uttar Pradesh -2.496 -1.246 -2.684 Uttarakhand 0.086 -0.114 0.115 West Bengal 0.219 1.222 0.071 India -0.024 0.247 -0.318 Source: Author’s calculations

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Figure 5.5 Index of sex composition in population of all ages in states and Union Territories, 2011

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Figure 5.6 Index of sex composition in population aged 0-6 years in states and Union Territories, 2011

96 Sex Composition

Figure 5.7 Index of sex composition in population aged 7 years and above in states and Union Territories, 2011

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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.

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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.

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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 AN Islands 1 1 1 0 0 3 Andhra Pradesh 0 0 1 11 11 23 Arunachal Pradesh 4 1 6 3 2 16 Assam 0 0 9 18 0 27 Bihar 0 11 25 1 1 38 Chandigarh 1 0 0 0 0 1 Chhattisgarh 0 0 0 11 7 18 Dadra and Nagar Haveli 1 0 0 0 0 1 Daman and Diu 1 0 0 0 1 2 Delhi 2 7 0 0 0 9 Goa 0 0 0 2 0 2 Gujarat 1 0 18 5 2 26 Haryana 0 19 2 0 0 21 Himachal Pradesh 1 1 4 3 3 12 Jammu and Kashmir 3 11 6 2 0 22 Jharkhand 0 0 13 10 1 24 Karnataka 0 0 2 23 5 30 Kerala 0 0 0 0 14 14 Lakshadweep 0 0 1 0 0 1 Madhya Pradesh 2 8 19 17 4 50 Maharashtra 1 2 21 9 2 35 Manipur 0 0 3 3 3 9 Meghalaya 0 0 1 4 2 7 Mizoram 0 0 3 4 1 8 Nagaland 0 1 6 4 0 11 Orissa 0 0 4 16 10 30 Puducherry 0 0 0 0 4 4 Punjab 0 15 3 2 0 20 Rajasthan 2 6 16 9 0 33 Sikkim 1 1 2 0 0 4 Tamil Nadu 0 0 1 16 15 32 Tripura 0 0 1 3 0 4 Uttar Pradesh 0 38 20 10 3 71 Uttarakhand 0 1 3 2 7 13 West Bengal 0 1 9 9 0 19 India 21 124 200 197 98 640 3.3 19.4 31.3 30.8 15.3 100.0 Source: Author’s calculations

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Figure 5.8 Females per 1000 males in districts, 2011

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Table 5.4 Distribution of districts by sex ratio in population aged 0-6 years State/Union Territory Number of females per 1000 males < 850 850-900 900-950 950-1000 $1000 Total AN Islands 0 0 0 3 0 3 Andhra Pradesh 0 0 15 8 0 23 Arunachal Pradesh 1 0 3 11 1 16 Assam 0 0 5 22 0 27 Bihar 0 2 29 7 0 38 Chandigarh 0 1 0 0 0 1 Chhattisgarh 0 0 2 15 1 18 Dadra and Nagar Haveli 0 0 1 0 0 1 Daman and Diu 0 0 2 0 0 2 Delhi 1 7 1 0 0 9 Goa 0 0 2 0 0 2 Gujarat 3 13 9 1 0 26 Haryana 18 2 1 0 0 21 Himachal Pradesh 0 5 4 2 1 12 Jammu and Kashmir 7 9 5 1 0 22 Jharkhand 0 0 12 12 0 24 Karnataka 0 0 19 11 0 30 Kerala 0 0 2 12 0 14 Lakshadweep 0 0 1 0 0 1 Madhya Pradesh 3 8 34 5 0 50 Maharashtra 7 14 13 1 0 35 Manipur 0 0 9 0 0 9 Meghalaya 0 0 0 7 0 7 Mizoram 0 0 2 6 0 8 Nagaland 0 2 3 6 0 11 Orissa 0 4 15 11 0 30 Puducherry 0 0 1 3 0 4 Punjab 11 9 0 0 0 20 Rajasthan 3 21 9 0 0 33 Sikkim 0 1 3 0 0 4 Tamil Nadu 0 2 12 18 0 32 Tripura 0 0 2 2 0 4 Uttar Pradesh 6 27 36 2 0 71 Uttarakhand 1 9 3 0 0 13 West Bengal 0 0 13 6 0 19 India 61 136 268 172 3 640 9.5 21.3 41.9 26.9 0.5 100.0 Source: Author’s calculations

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Figure 5.9 Females per 1000 males in population aged 0-6 years in districts, 2011

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Table 5.5 Distribution of districts by sex ratio in population aged 7 years and above State/Union Territory Number of females per 1000 males < 850 850-900 900-950 950-1000 $1000 Total AN Islands 1 1 1 0 0 3 Andhra Pradesh 0 0 1 9 13 23 Arunachal Pradesh 4 2 5 3 2 16 Assam 0 0 11 16 0 27 Bihar 0 15 20 2 1 38 Chandigarh 1 0 0 0 0 1 Chhattisgarh 0 0 0 8 10 18 Dadra and Nagar Haveli 1 0 0 0 0 1 Daman and Diu 1 0 0 0 1 2 Delhi 2 7 0 0 0 9 Goa 0 0 0 2 0 2 Gujarat 1 0 16 7 2 26 Haryana 0 16 5 0 0 21 Himachal Pradesh 1 1 4 3 3 12 Jammu and Kashmir 3 12 4 3 0 22 Jharkhand 0 0 12 9 3 24 Karnataka 0 0 2 23 5 30 Kerala 0 0 0 0 14 14 Lakshadweep 0 0 0 1 0 1 Madhya Pradesh 2 8 18 17 5 50 Maharashtra 1 2 17 12 3 35 Manipur 0 0 2 3 4 9 Meghalaya 0 0 2 3 2 7 Mizoram 0 0 3 4 1 8 Nagaland 0 1 6 4 0 11 Orissa 0 0 3 17 10 30 Puducherry 0 0 0 0 4 4 Punjab 0 12 6 2 0 20 Rajasthan 2 5 11 12 3 33 Sikkim 1 1 2 0 0 4 Tamil Nadu 0 0 1 13 18 32 Tripura 0 0 1 3 0 4 Uttar Pradesh 2 36 19 10 4 71 Uttarakhand 0 1 3 1 8 13 West Bengal 0 1 9 9 0 19 India 23 121 184 196 116 640 3.6 18.9 28.8 30.6 18.1 100.0 Source: Author’s calculations

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Figure 5.10 Females per 1000 males in population aged 7 years and above in districts, 2011

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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 AN Islands 0 0 3 0 0 3 Andhra Pradesh 0 0 0 3 20 23 Arunachal Pradesh 0 0 10 6 0 16 Assam 0 0 3 24 0 27 Bihar 1 8 25 2 2 38 Chandigarh 0 1 0 0 0 1 Chhattisgarh 0 0 0 16 2 18 Dadra and Nagar Haveli 0 0 1 0 0 1 Daman and Diu 0 0 1 1 0 2 Delhi 1 3 5 0 0 9 Goa 0 0 0 2 0 2 Gujarat 2 0 14 10 0 26 Haryana 0 2 19 0 0 21 Himachal Pradesh 0 0 5 7 0 12 Jammu and Kashmir 0 0 20 2 0 22 Jharkhand 0 0 7 17 0 24 Karnataka 1 0 0 26 3 30 Kerala 0 0 0 2 12 14 Lakshadweep 0 0 0 1 0 1 Madhya Pradesh 0 3 23 23 1 50 Maharashtra 4 0 13 16 2 35 Manipur 0 0 2 7 0 9 Meghalaya 0 0 0 7 0 7 Mizoram 0 0 1 7 0 8 Nagaland 0 0 7 4 0 11 Orissa 0 0 2 26 2 30 Puducherry 0 0 0 4 0 4 Punjab 0 2 16 2 0 20 Rajasthan 0 3 15 15 0 33 Sikkim 0 0 4 0 0 4 Tamil Nadu 0 0 0 21 11 32 Tripura 0 0 0 4 0 4 Uttar Pradesh 7 17 30 12 5 71 Uttarakhand 0 0 4 9 0 13 West Bengal 0 1 4 14 0 19 India 16 40 234 290 60 640 2.5 6.3 36.6 45.3 9.4 100.0 Source: Author’s calculations

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Figure 5.11 Index of sex composition in total population in districts of India, 2011

110 Sex Composition

Table 5.7 Index of sex composition in the population aged 0-6 years 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 AN Islands 0 0 0 3 0 3 Andhra Pradesh 0 0 1 18 4 23 Arunachal Pradesh 0 0 1 15 0 16 Assam 0 0 0 25 2 27 Bihar 0 0 4 26 8 38 Chandigarh 0 0 1 0 0 1 Chhattisgarh 0 0 0 15 3 18 Dadra and Nagar Haveli 0 0 0 1 0 1 Daman and Diu 0 0 1 1 0 2 Delhi 0 2 7 0 0 9 Goa 0 0 1 1 0 2 Gujarat 2 1 16 7 0 26 Haryana 0 6 15 0 0 21 Himachal Pradesh 0 0 6 6 0 12 Jammu and Kashmir 0 2 14 6 0 22 Jharkhand 0 0 1 23 0 24 Karnataka 0 0 0 29 1 30 Kerala 0 0 0 13 1 14 Lakshadweep 0 0 1 0 0 1 Madhya Pradesh 0 3 18 29 0 50 Maharashtra 5 5 13 12 0 35 Manipur 0 0 1 8 0 9 Meghalaya 0 0 0 7 0 7 Mizoram 0 0 0 8 0 8 Nagaland 0 0 2 9 0 11 Orissa 0 0 6 24 0 30 Puducherry 0 0 0 4 0 4 Punjab 0 5 15 0 0 20 Rajasthan 1 5 22 5 0 33 Sikkim 0 0 1 3 0 4 Tamil Nadu 0 0 5 25 2 32 Tripura 0 0 0 4 0 4 Uttar Pradesh 5 6 35 23 2 71 Uttarakhand 0 0 11 2 0 13 West Bengal 0 0 0 11 8 19 India 13 35 198 363 31 640 2.0 5.5 30.9 56.7 4.8 100.0 Source: Author’s calculations.

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Figure 5.12 Index of sex composition in population aged 0-6 years in districts of India, 2011

112 Sex Composition

Table 5.8 Index of sex composition in population aged 7 years and above 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 AN Islands 0 0 3 0 0 3 Andhra Pradesh 0 0 1 1 21 23 Arunachal Pradesh 0 0 10 6 0 16 Assam 0 0 6 21 0 27 Bihar 1 9 25 1 2 38 Chandigarh 0 1 0 0 0 1 Chhattisgarh 0 0 0 16 2 18 Dadra and Nagar Haveli 0 0 1 0 0 1 Daman and Diu 0 0 1 1 0 2 Delhi 2 3 4 0 0 9 Goa 0 0 0 2 0 2 Gujarat 1 1 13 11 0 26 Haryana 0 1 20 0 0 21 Himachal Pradesh 0 0 5 7 0 12 Jammu and Kashmir 0 0 19 3 0 22 Jharkhand 0 0 7 17 0 24 Karnataka 1 0 0 26 3 30 Kerala 0 0 0 2 12 14 Lakshadweep 0 0 0 1 0 1 Madhya Pradesh 0 3 23 23 1 50 Maharashtra 4 0 11 18 2 35 Manipur 0 0 2 7 0 9 Meghalaya 0 0 1 6 0 7 Mizoram 0 0 3 5 0 8 Nagaland 0 0 7 4 0 11 Orissa 0 0 2 26 2 30 Puducherry 0 0 0 4 0 4 Punjab 1 1 16 2 0 20 Rajasthan 0 3 13 17 0 33 Sikkim 0 0 4 0 0 4 Tamil Nadu 0 0 0 19 13 32 Tripura 0 0 1 3 0 4 Uttar Pradesh 9 20 26 11 5 71 Uttarakhand 0 0 5 8 0 13 West Bengal 0 1 6 12 0 19 India 19 43 235 280 63 640 3.0 6.7 36.7 43.8 9.8 100.0 Source: Author’s calculations

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Figure 5.13 Index of sex composition in population aged 7 years and above in districts of India, 2011

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 Preliminary Demography of India 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 6 Patterns 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 Preliminary Demography of India

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 Preliminary Demography of India

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

Pcj = Enumerated population of the state/Union Territory j,

Psj = 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 * * 3* * Mej = 2*( Pcj - Psj / Pcj - Psj ) (6.1) while the intensiveness of net migration for the state/Union Territory j is defined as

Mij = log (Pcj/Psj) (6.2) and the index of net migration for the state/Union Territory j is defined as

Mj = Mej * Mij (6.3) Finally, the index of net internal migration for the country as a whole is defined as 3 M = Mj for all j. (6.4)

Notice that when Pcj = Psj, Mj = 0 and the net migration for the state or Union Territory j is zero.

On the other hand when Pcj > Psj, Mj > 0 which indicates that there is net in-migration to the state/Union Territory j. Similarly, when Pcj < Psj, Mj < 0 which means that there is net out-

migration from the state/Union Territory j. Finally, the sum of Mj for all states and Union Territories gives an idea about the magnitude and direction of internal migration in the country.

Notice that M is the weighted sum of Mj 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 Inter-state Migration

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 Preliminary Demography of India

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 Inter-state Migration

Figure 6.1 Crude net migration rate in states and Union Territories, 2011

123 Preliminary Demography of India

Table 6.1 Inter-state migration in India based on 2011 population census Country/ Enumerated Estimated Adjusted Net (CNMR) state population population population migration (per 1000) 2011 2011 2011 (million) (million) (million) (million) India 1210.194 1211.682 1210.194 Uttar Pradesh 199.582 206.417 206.164 -6.582 -32.98 Kerala 33.388 34.863 34.820 -1.433 -42.91 Rajasthan 68.621 70.473 70.386 -1.765 -25.72 Nagaland 1.981 2.253 2.250 -0.270 -136.25 Madhya Pradesh 72.598 73.996 73.906 -1.308 -18.02 Andhra Pradesh 84.666 85.927 85.821 -1.156 -13.65 Assam 31.169 31.454 31.415 -0.246 -7.89 Andaman & Nicobar Islands 0.380 0.401 0.400 -0.020 -53.40 Sikkim 0.608 0.625 0.625 -0.017 -27.81 Lakshadweep 0.064 0.068 0.068 -0.004 -60.55 Bihar 103.805 103.965 103.837 -0.033 -0.32 Himachal Pradesh 6.857 6.855 6.847 0.010 1.39 Goa 1.458 1.447 1.445 0.012 8.45 Orissa 41.947 41.904 41.852 0.095 2.27 Haryana 25.353 25.281 25.250 0.103 4.07 Chandigarh 1.055 1.017 1.015 0.039 37.36 West Bengal 91.348 90.864 90.752 0.596 6.52 Punjab 27.704 27.406 27.372 0.332 11.99 Tripura 3.671 3.544 3.539 0.132 35.89 Chhattisgarh 25.540 25.213 25.182 0.358 14.03 Jharkhand 32.966 32.591 32.551 0.415 12.58 Arunachal Pradesh 1.383 1.296 1.294 0.088 63.94 Karnataka 61.131 60.605 60.530 0.601 9.82 Gujarat 60.384 59.847 59.773 0.610 10.11 Mizoram 1.091 1.010 1.009 0.083 75.62 Manipur 2.722 2.553 2.550 0.172 63.08 Uttarakhand 10.117 9.766 9.754 0.363 35.87 Dadra and Nagar Haveli 0.343 0.278 0.278 0.065 189.87 Meghalaya 2.964 2.768 2.765 0.199 67.15 Daman and Diu 0.243 0.182 0.182 0.061 252.09 Puducherry 1.244 1.072 1.071 0.173 139.29 Delhi 16.753 15.934 15.914 0.839 50.09 Jammu and Kashmir 12.549 11.600 11.586 0.963 76.75 Maharashtra 112.373 109.480 109.345 3.028 26.94 Tamil Nadu 72.139 68.728 68.644 3.495 48.45 Source: Author’s calculations

124 Inter-state Migration

Figure 6.2 Migration index in states and Union Territories, 2011

125 Preliminary Demography of India

Table 6.2 Migration index in India and states, 2011 Country/ Net migration Index of Index of Index of net state during extensiveness intensiveness migration

2001-2011 (Mej) (Mij) (Mj) (million) India -12.832 Uttar Pradesh -6.582 0.5129 -0.0141 -7.227 Kerala -1.433 0.1116 -0.0182 -2.037 Rajasthan -1.765 0.1375 -0.0110 -1.517 Nagaland -0.270 0.0210 -0.0555 -1.167 Madhya Pradesh -1.308 0.1019 -0.0078 -0.790 Andhra Pradesh -1.156 0.0900 -0.0059 -0.530 Assam -0.246 0.0192 -0.0034 -0.065 Andaman and Nicobar Islands -0.020 0.0016 -0.0226 -0.036 Sikkim -0.017 0.0013 -0.0119 -0.016 Lakshadweep -0.004 0.0003 -0.0255 -0.008 Bihar -0.033 0.0026 -0.0001 0.000 Himachal Pradesh 0.010 0.0007 0.0006 0.000 Goa 0.012 0.0010 0.0037 0.004 Orissa 0.095 0.0074 0.0010 0.007 Haryana 0.103 0.0080 0.0018 0.014 Chandigarh 0.039 0.0031 0.0165 0.051 West Bengal 0.596 0.0464 0.0028 0.132 Punjab 0.332 0.0259 0.0052 0.136 Tripura 0.132 0.0103 0.0159 0.163 Chhattisgarh 0.358 0.0279 0.0061 0.171 Jharkhand 0.415 0.0323 0.0055 0.178 Arunachal Pradesh 0.088 0.0069 0.0287 0.198 Karnataka 0.601 0.0468 0.0043 0.201 Gujarat 0.610 0.0476 0.0044 0.210 Mizoram 0.083 0.0064 0.0341 0.220 Manipur 0.172 0.0134 0.0283 0.379 Uttarakhand 0.363 0.0283 0.0159 0.449 Dadra and Nagar Haveli 0.065 0.0051 0.0914 0.464 Meghalaya 0.199 0.0155 0.0302 0.468 Daman and Diu 0.061 0.0048 0.1261 0.602 Puducherry 0.173 0.0135 0.0651 0.880 Delhi 0.839 0.0654 0.0223 1.459 Jammu and Kashmir 0.963 0.0751 0.0347 2.603 Maharashtra 3.028 0.2359 0.0119 2.798 Tamil Nadu 3.495 0.2724 0.0220 5.875 Source: Author’s calculations

126 Inter-state Migration

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 Preliminary Demography of India

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 7 Epilogue

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 Preliminary Demography of India

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 Preliminary Demography of India 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) 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.

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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/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Andaman and Nicobars Islands Nicobars 36819 42068 -12.48 -1.33 North & Middle Andaman 105539 105613 -0.07 -0.01 South Andaman 237586 208471 13.97 1.31 Andhra Pradesh Adilabad 2737738 2488003 10.04 0.96 Anantapur 4083315 3640478 12.16 1.15 Chittoor 4170468 3745875 11.33 1.07 East Godavari 5151549 2601797 98.00 6.83 4889230 4901420 -0.25 -0.02 Hyderabad 4010238 4465144 -10.19 -1.07 Karimnagar 3811738 3829753 -0.47 -0.05 Khammam 2798214 3491822 -19.86 -2.21 Krishna 4529009 2578927 75.62 5.63 Kurnool 4046601 4187841 -3.37 -0.34 Mahbubnagar 4042191 3529494 14.53 1.36 Medak 3031877 3513934 -13.72 -1.48 Nalgonda 3483648 2670097 30.47 2.66 Nizamabad 2552073 3247982 -21.43 -2.41 Prakasam 3392764 2668564 27.14 2.40 Rangareddy 5296396 2345685 125.79 8.14 Sri Potti Sriramulu Nellore 2966082 3059423 -3.05 -0.31 2699471 3575064 -24.49 -2.81 Visakhapatnam 4288113 2537593 68.98 5.25 Vizianagaram 2342868 3832336 -38.87 -4.92 3522644 2249254 56.61 4.49 West Godavari 3934782 3246004 21.22 1.92 Y.S.R. 2884524 3803517 -24.16 -2.77 Arunachal Pradesh Anjaw 21089 18536 13.77 1.29 Changlang 147951 125422 17.96 1.65 Dibang Valley 7948 7272 9.30 0.89 East Kameng 78413 57179 37.14 3.16 East Siang 99019 87397 13.30 1.25 Kurung Kumey 89717 42518 111.01 7.47 Lohit 145538 124991 16.44 1.52 Lower Dibang Valley 53986 50448 7.01 0.68 Lower Subansiri 82839 55726 48.65 3.96

135 Preliminary Demography of India

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Papum Pare 176385 122003 44.57 3.69 Tawang 49950 38924 28.33 2.49 Tirap 111997 100326 11.63 1.1 Upper Siang 35289 55346 -36.24 -4.50 Upper Subansiri 83205 33363 149.39 9.14 West Kameng 87013 74599 16.64 1.54 West Siang 112272 103918 8.04 0.77 Assam Baksa 953773 857947 11.17 1.06 Barpeta 1693190 1394755 21.40 1.94 Bongaigaon 732639 612665 19.58 1.79 Cachar 1736319 1444921 20.17 1.84 Chirang 481818 433061 11.26 1.07 Darrang 908090 759858 19.51 1.78 Dhemaji 688077 571944 20.30 1.85 Dhubri 1948632 1566396 24.40 2.18 Dibrugarh 1327748 1185072 12.04 1.14 Dima Hasao 213529 188079 13.53 1.27 1008959 822035 22.74 2.05 1058674 946279 11.88 1.12 Hailakandi 659260 542872 21.44 1.94 Jorhat 1091295 999221 9.21 0.88 Kamrup 1517202 1311698 15.67 1.46 Kamrup Metropolitan 1260419 1059578 18.95 1.74 Karbi Anglong 965280 813311 18.69 1.71 Karimganj 1217002 1007976 20.74 1.88 Kokrajhar 886999 843243 5.19 0.51 Lakhimpur 1040644 889010 17.06 1.57 Morigaon 957853 776256 23.39 2.10 Nagaon 2826006 2314629 22.09 2.00 Nalbari 769919 689053 11.74 1.11 Sivasagar 1150253 1051736 9.37 0.90 Sonitpur 1925975 1665125 15.67 1.46 Tinsukia 1316948 1150062 14.51 1.36 Udalguri 832769 758746 9.76 0.93 Bihar Araria 2806200 2158608 30.00 2.62 Arwal 699563 587826 19.01 1.74 2511243 2013055 24.75 2.21 Banka 2029339 1608773 26.14 2.32

136 Statistical Tables

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Begusarai 2954367 2349366 25.75 2.29 Bhagalpur 3032226 2423172 25.13 2.24 Bhojpur 2720155 2243144 21.27 1.93 1707643 1402396 21.77 1.97 3921971 3295789 19.00 1.74 Gaya 4379383 3473428 26.08 2.32 Gopalganj 2558037 2152638 18.83 1.73 Jamui 1756078 1398796 25.54 2.27 Jehanabad 1124176 926489 21.34 1.93 Kaimur (Bhabua) 1626900 1289074 26.21 2.33 Katihar 3068149 2392638 28.23 2.49 Khagaria 1657599 1280354 29.46 2.58 Kishanganj 1690948 1296348 30.44 2.66 1000717 802225 24.74 2.21 Madhepura 1994618 1526646 30.65 2.67 Madhubani 4476044 3575281 25.19 2.25 Munger 1359054 1137797 19.45 1.78 Muzaffarpur 4778610 3746714 27.54 2.43 Nalanda 2872523 2370528 21.18 1.92 Nawada 2216653 1809696 22.49 2.03 Pashchim Champaran 3922780 3043466 28.89 2.54 5772804 4718592 22.34 2.02 Purba Champaran 5082868 3939773 29.01 2.55 Purnia 3273127 2543942 28.66 2.52 Rohtas 2962593 2450748 20.89 1.90 Saharsa 1897102 1508182 25.79 2.29 4254782 3394793 25.33 2.26 Saran 3943098 3248701 21.37 1.94 Sheikhpura 634927 525502 20.82 1.89 Sheohar 656916 515961 27.32 2.42 Sitamarhi 3419622 2682720 27.47 2.43 Siwan 3318176 2714349 22.25 2.01 Supaul 2228397 1732578 28.62 2.52 Vaishali 3495249 2718421 28.58 2.51 Chandigarh Chandigarh 1054686 900635 17.10 1.58 Chhattisgarh Bastar 1411644 1198067 17.83 1.64 Bijapur 255180 234637 8.76 0.84 Bilaspur 2662077 1998355 33.21 2.87

137 Preliminary Demography of India

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Dakshin Bastar Dantewada 532791 476119 11.90 1.12 Dhamtari 799199 706591 13.11 1.23 Durg 3343079 2810436 18.95 1.74 Janjgir - Champa 1620632 1317431 23.01 2.07 Jashpur 852043 743160 14.65 1.37 Kabeerdham 822239 584552 40.66 3.41 Korba 1206563 1011823 19.25 1.76 Koriya 659039 586327 12.40 1.17 Mahasamund 1032275 860257 20.00 1.82 Narayanpur 140206 117337 19.49 1.78 Raigarh 1493627 1265529 18.02 1.66 4062160 3016930 34.65 2.97 Rajnandgaon 1537520 1283224 19.82 1.81 Surguja 2361329 1972094 19.74 1.80 Uttar Bastar Kanker 748593 650934 15.00 1.40 Dadra and Nagar Haveli Dadra & Nagar Haveli 342853 220490 55.50 4.41 Daman and Diu Daman 190855 113989 67.43 5.15 Diu 52056 44215 17.73 1.63 Delhi Central 578671 646385 -10.48 -1.11 East 1707725 1463583 16.68 1.54 New Delhi 133713 179112 -25.35 -2.92 North 883418 781525 13.04 1.23 North East 2240749 1768061 26.73 2.37 North West 3651261 2860869 27.63 2.44 South 2733752 2267023 20.59 1.87 South West 2292363 1755041 30.62 2.67 West 2531583 2128908 18.91 1.73 Goa North Goa 817761 758573 7.80 0.75 South Goa 639962 589095 8.63 0.83 Gujarat Ahmadabad 7208200 5893164 22.31 2.01 Amreli 1513614 1393918 8.59 0.82 Anand 2090276 1856872 12.57 1.18 Banas Kantha 3116045 2504244 24.43 2.19 Bharuch 1550822 1370656 13.14 1.23 Bhavnagar 2877961 2469630 16.53 1.53

138 Statistical Tables

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Dohad 2126558 1636433 29.95 2.62 1387478 1237168 12.15 1.15 Jamnagar 2159130 1904278 13.38 1.26 Junagadh 2742291 2448173 12.01 1.13 Kachchh 2090313 1583225 32.03 2.78 Kheda 2298934 2037894 12.81 1.21 Mahesana 2027727 1844856 9.91 0.95 Narmada 590379 514404 14.77 1.38 Navsari 1330711 1229463 8.24 0.79 Panch Mahals 2388267 2025277 17.92 1.65 Patan 1342746 1182709 13.53 1.27 Porbandar 586062 536835 9.17 0.88 Rajkot 3799770 3169881 19.87 1.81 Sabar Kantha 2427346 2082531 16.56 1.53 Surat 6079231 4275540 42.19 3.52 Surendranagar 1755873 1515148 15.89 1.47 Tapi 806489 719634 12.07 1.14 The Dangs 226769 186729 21.44 1.94 Vadodara 4157568 3641802 14.16 1.32 Valsad 1703068 1410553 20.74 1.88 Haryana Ambala 1136784 1014442 12.06 1.14 Bhiwani 1629109 1425043 14.32 1.34 Faridabad 1798954 1365430 31.75 2.76 Fatehabad 941522 806167 16.79 1.55 Gurgaon 1514085 870514 73.93 5.53 Hisar 1742815 1537145 13.38 1.26 Jhajhjhar 956907 880076 8.73 0.84 Jind 1332042 1189854 11.95 1.13 1072861 946169 13.39 1.26 Karnal 1506323 1274169 18.22 1.67 Kurukshetra 964231 825470 16.81 1.55 Mahendragarh 921680 812554 13.43 1.26 Mewat 1089406 789768 37.94 3.22 Palwal 1040493 829144 25.49 2.27 558890 468396 19.32 1.77 Panipat 1202811 967434 24.33 2.18 Rewari 896129 765334 17.09 1.58 Rohtak 1058683 940132 12.61 1.19 1295114 1116670 15.98 1.48

139 Preliminary Demography of India

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 1480080 1279129 15.71 1.46 Yamunanagar 1214162 1041663 16.56 1.53 Himachal Pradesh Bilaspur 382056 340885 12.08 1.14 Chamba 518844 460887 12.58 1.18 Hamirpur 454293 412700 10.08 0.96 Kangra 1507223 1339030 12.56 1.18 Kinnaur 84298 78334 7.61 0.73 Kullu 437474 381571 14.65 1.37 Lahul & Spiti 31528 33224 -5.10 -0.52 Mandi 999518 901344 10.89 1.03 813384 722502 12.58 1.18 Sirmaur 530164 458593 15.61 1.45 Solan 576670 500557 15.21 1.42 Una 521057 448273 16.24 1.50 Jammu and Kashmir Anantnag 1070144 778408 37.48 3.18 Badgam 735753 607181 21.18 1.92 Bandipore 385099 304886 26.31 2.34 Baramula 1015503 843892 20.34 1.85 Doda 409576 320256 27.89 2.46 Ganderbal 297003 217907 36.30 3.10 Jammu 1526406 1357077 12.48 1.18 Kargil 143388 119307 20.18 1.84 Kathua 615711 511455 20.38 1.86 Kishtwar 231037 190843 21.06 1.91 422786 394026 7.30 0.70 Kupwara 875564 650393 34.62 2.97 Leh(Ladakh) 147104 117232 25.48 2.27 570060 441275 29.18 2.56 Punch 476820 372613 27.97 2.47 Rajouri 619266 483284 28.14 2.48 Ramban 283313 214944 31.81 2.76 Reasi 314714 247694 27.06 2.39 Samba 318611 272539 16.90 1.56 Shupiyan 265960 211332 25.85 2.30 1269751 1027670 23.56 2.12 Udhampur 555357 459486 20.86 1.90 Jharkhand 2061918 1777669 15.99 1.48

140 Statistical Tables

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Chatra 1042304 808113 28.98 2.54 1491879 1165348 28.02 2.47 Dhanbad 2682662 2397160 11.91 1.13 Dumka 1321096 1106538 19.39 1.77 Garhwa 1322387 1035461 27.71 2.45 Giridih 2445203 1905402 28.33 2.49 1311382 1047932 25.14 2.24 Gumla 1025656 832445 23.21 2.09 1734005 1378930 25.75 2.29 Jamtara 790207 653064 21.00 1.91 Khunti 530299 434814 21.96 1.99 717169 540892 32.59 2.82 Latehar 725673 560885 29.38 2.58 Lohardaga 461738 364520 26.67 2.36 899200 701678 28.15 2.48 Palamu 1936319 1537493 25.94 2.31 Pashchimi Singhbhum 1501619 1233971 21.69 1.96 Purbi Singhbhum 2291032 1983062 15.53 1.44 Ramgarh 949159 839518 13.06 1.23 2912022 2350300 23.90 2.14 Sahibganj 1150038 927749 23.96 2.15 Saraikela-Kharsawan 1063458 848865 25.28 2.25 Simdega 599813 514331 16.62 1.54 Karnataka Bagalkot 1890826 1651954 14.46 1.35 Bangalore 9588910 6537299 46.68 3.83 Bangalore Rural 987257 850937 16.02 1.49 Belgaum 4778439 4214534 13.38 1.26 2532383 2027204 24.92 2.23 Bidar 1700018 1502314 13.16 1.24 Bijapur 2175102 1806863 20.38 1.85 Chamarajanagar 1020962 965449 5.75 0.56 Chikkaballapura 1254377 1149013 9.17 0.88 Chikmagalur 1137753 1140948 -0.28 -0.03 1660378 1517852 9.39 0.90 Dakshina 2083625 1897655 9.80 0.93 Davanagere 1946905 1790916 8.71 0.84 Dharwad 1846993 1604267 15.13 1.41 Gadag 1065235 971841 9.61 0.92 Gulbarga 2564892 2174743 17.94 1.65

141 Preliminary Demography of India

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Hassan 1776221 1721645 3.17 0.31 Haveri 1598506 1439058 11.08 1.05 Kodagu 554762 548563 1.13 0.11 Kolar 1540231 1387096 11.04 1.05 Koppal 1391292 1196090 16.32 1.51 Mandya 1808680 1763706 2.55 0.25 Mysore 2994744 2641101 13.39 1.26 Raichur 1924773 1669795 15.27 1.42 Ramanagara 1082739 1030591 5.06 0.49 1755512 1642507 6.88 0.67 2681449 2584778 3.74 0.37 Udupi 1177908 1112283 5.90 0.57 Uttara Kannada 1436847 1353601 6.15 0.60 Yadgir 1172985 956212 22.67 2.04 Kerala Alappuzha 2121943 2109160 0.61 0.06 Ernakulam 3279860 3105798 5.60 0.55 Idukki 1107453 1129221 -1.93 -0.19 Kannur 2525637 2408956 4.84 0.47 Kasaragod 1302600 1204078 8.18 0.79 Kollam 2629703 2585208 1.72 0.17 Kottayam 1979384 1953646 1.32 0.13 Kozhikode 3089543 2879131 7.31 0.71 4110956 3625471 13.39 1.26 Palakkad 2810892 2617482 7.39 0.71 Pathanamthitta 1195537 1234016 -3.12 -0.32 3307284 3234356 2.25 0.22 Thrissur 3110327 2974232 4.58 0.45 Wayanad 816558 780619 4.60 0.45 Lakshadweep Lakshadweep 64429 60650 6.23 0.60 Madhya Pradesh Alirajpur 728677 610282 19.40 1.77 Anuppur 749521 667427 12.30 1.16 Ashoknagar 844979 689216 22.60 2.04 Balaghat 1701156 1497496 13.60 1.28 1385659 1086791 27.50 2.43 Betul 1575247 1395259 12.90 1.21 Bhind 1703562 1427965 19.30 1.76 Bhopal 2368145 1842914 28.50 2.51

142 Statistical Tables

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 756993 635061 19.20 1.76 Chhatarpur 1762857 1475194 19.50 1.78 Chhindwara 2090306 1849828 13.00 1.22 Damoh 1263703 1083793 16.60 1.54 Datia 786375 664168 18.40 1.69 1563107 1308039 19.50 1.78 2184672 1740775 25.50 2.27 Dindori 704218 580559 21.30 1.93 East Nimar 1309443 1078619 21.40 1.94 Guna 1240938 977887 26.90 2.38 Gwalior 2030543 1632269 24.40 2.18 Harda 570302 474461 20.20 1.84 Hoshangabad 1240975 1083821 14.50 1.35 Indore 3272335 2465965 32.70 2.83 Jabalpur 2460714 2150974 14.40 1.35 Jhabua 1024091 784143 30.60 2.67 Katni 1291684 1063990 21.40 1.94 Mandla 1053522 894331 17.80 1.64 Mandsaur 1339832 1183597 13.20 1.24 Morena 1965137 1592494 23.40 2.10 Narsimhapur 1092141 958018 14.00 1.31 Neemuch 825958 725798 13.80 1.29 Panna 1016028 856685 18.60 1.71 Raisen 1331699 1124746 18.40 1.69 Rajgarh 1546541 1254291 23.30 2.09 Ratlam 1454483 1215107 19.70 1.80 Rewa 2363744 1973075 19.80 1.81 Sagar 2378295 2022360 17.60 1.62 2228619 1869647 19.20 1.76 Sehore 1311008 1079019 21.50 1.95 Seoni 1378876 1166562 18.20 1.67 Shahdol 1064989 907919 17.30 1.60 Shajapur 1512353 1290404 17.20 1.59 Sheopur 687952 559311 23.00 2.07 Shivpuri 1725818 1406535 22.70 2.05 Sidhi 1126515 910683 23.70 2.13 Singrauli 1178132 920416 28.00 2.47 1444920 1203097 20.10 1.83 Ujjain 1986597 1711109 16.10 1.49 643579 516102 24.70 2.21

143 Preliminary Demography of India

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Vidisha 1458212 1215177 20.00 1.82 West Nimar 1872413 1524766 22.80 2.05 Maharashtra Ahmadnagar 4543083 4040642 12.43 1.17 1818617 1630239 11.56 1.09 Amravati 2887826 2607160 10.77 1.02 Aurangabad 3695928 2897013 27.58 2.44 1198810 1136146 5.52 0.54 Bid 2585962 2161250 19.65 1.79 Buldana 2588039 2232480 15.93 1.48 Chandrapur 2194262 2071101 5.95 0.58 Dhule 2048781 1707947 19.96 1.82 Gadchiroli 1071795 970294 10.46 0.99 Gondiya 1322331 1200707 10.13 0.96 Hingoli 1178973 987160 19.43 1.78 Jalgaon 4224442 3682690 14.71 1.37 Jalna 1958483 1612980 21.42 1.94 Kolhapur 3874015 3523162 9.96 0.95 Latur 2455543 2080285 18.04 1.66 Mumbai 3145966 3338031 -5.75 -0.59 Mumbai Suburban 9332481 8640419 8.01 0.77 Nagpur 4653171 4067637 14.39 1.34 Nanded 3356566 2876259 16.70 1.54 Nandurbar 1646177 1311709 25.50 2.27 Nashik 6109052 4993796 22.33 2.02 Osmanabad 1660311 1486586 11.69 1.11 Parbhani 1835982 1527715 20.18 1.84 Pune 9426959 7232555 30.34 2.65 Raigarh 2635394 2207929 19.36 1.77 Ratnagiri 1612672 1696777 -4.96 -0.51 Sangli 2820575 2583524 9.18 0.88 Satara 3003922 2808994 6.94 0.67 Sindhudurg 848868 868825 -2.30 -0.23 4315527 3849543 12.10 1.14 Thane 11054131 8131849 35.94 3.07 Wardha 1296157 1236736 4.80 0.47 Washim 1196714 1020216 17.30 1.60 2775457 2458271 12.90 1.21 Manipur Bishnupur 240363 208368 15.36 1.43

144 Statistical Tables

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Chandel 144028 118327 21.72 1.97 Churachandpur 271274 227905 19.03 1.74 East 452661 394876 14.63 1.37 Imphal West 514683 444382 15.82 1.47 Senapati 354972 156513 126.80 8.19 Tamenglong 140143 111499 25.69 2.29 Thoubal 420517 364140 15.48 1.44 Ukhrul 183115 140778 30.07 2.63 Meghalaya East Garo Hills 317618 250582 26.75 2.37 East Khasi Hills 824059 660923 24.68 2.21 Jaintia Hills 392852 299108 31.34 2.73 Ribhoi 258380 192790 34.02 2.93 South Garo Hills 142574 100980 41.19 3.45 West Garo Hills 642923 518390 24.02 2.15 West Khasi Hills 385601 296049 30.25 2.64 Mizoram 404054 325676 24.07 2.16 Champhai 125370 108392 15.66 1.46 Kolasib 83054 65960 25.92 2.30 Lawngtlai 117444 73620 59.53 4.67 Lunglei 154094 137223 12.29 1.16 Mamit 85757 62785 36.59 3.12 Saiha 56366 61056 -7.68 -0.80 Serchhip 64875 53861 20.45 1.86 Nagaland Dimapur 379769 309024 22.89 2.06 Kiphire 74033 106136 -30.25 -3.60 270063 213259 26.64 2.36 Longleng 50593 158300 -68.04 -11.41 Mokokchung 193171 232085 -16.77 -1.84 Mon 250671 260652 -3.83 -0.39 Peren 94954 96825 -1.93 -0.20 Phek 163294 148195 10.19 0.97 Tuensang 196801 150365 30.88 2.69 Wokha 166239 161223 3.11 0.31 Zunheboto 141014 153955 -8.41 -0.88 Orissa Anugul 1271703 1140003 11.55 1.09 Balangir 1648574 1337194 23.29 2.09

145 Preliminary Demography of India

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Baleshwar 2317419 2024508 14.47 1.35 Bargarh 1478833 1346336 9.84 0.94 Baudh 439917 373372 17.82 1.64 Bhadrak 1506522 1333749 12.95 1.22 Cuttack 2618708 2341094 11.86 1.12 Debagarh 312164 274108 13.88 1.30 Dhenkanal 1192948 1066878 11.82 1.12 Gajapati 575880 518837 10.99 1.04 Ganjam 3520151 3160635 11.37 1.08 Jagatsinghapur 1136604 1057629 7.47 0.72 Jajapur 1826275 1624341 12.43 1.17 Jharsuguda 579499 509716 13.69 1.28 Kalahandi 1573054 1335494 17.79 1.64 Kandhamal 731952 648201 12.92 1.22 1439891 1302005 10.59 1.01 Kendujhar 1802777 1561990 15.42 1.43 Khordha 2246341 1877395 19.65 1.79 Koraput 1376934 1180637 16.63 1.54 Malkangiri 612727 504198 21.53 1.95 Mayurbhanj 2513895 2223456 13.06 1.23 Nabarangapur 1218762 1025766 18.81 1.72 Nayagarh 962215 864516 11.30 1.07 Nuapada 606490 530690 14.28 1.34 1697983 1502682 13.00 1.22 Rayagada 961959 831109 15.74 1.46 Sambalpur 1044410 935613 11.63 1.10 Subarnapur 652107 541835 20.35 1.85 Sundargarh 2080664 1830673 13.66 1.28 Puducherry Karaikal 200314 170791 17.29 1.59 Mahe 41934 36828 13.86 1.30 Puducherry 946600 735332 28.73 2.53 Yanam 55616 31394 77.15 5.72 Punjab Amritsar 2490891 2156989 15.48 1.44 Barnala 596294 526948 13.16 1.24 Bathinda 1388859 1183317 17.37 1.60 Faridkot 618008 550907 12.18 1.15 Fatehgarh Sahib 599814 538481 11.39 1.08 2026831 1746064 16.08 1.49

146 Statistical Tables

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Gurdaspur 2299026 2103409 9.30 0.89 Hoshiarpur 1582793 1481322 6.85 0.66 Jalandhar 2181753 1962714 11.16 1.06 817668 754515 8.37 0.80 3487882 3032941 15.00 1.40 Mansa 768808 688773 11.62 1.10 Moga 992289 894760 10.90 1.03 Muktsar 902702 777521 16.10 1.49 Patiala 1892282 1584826 19.40 1.77 Rupnagar 683349 628829 8.67 0.83 Sahibzada Ajit Singh Nagar 986147 746968 32.02 2.78 Sangrur 1654408 1473204 12.30 1.16 Shahid Bhagat Singh Nagar 614362 587456 4.58 0.45 Tarn Taran 1120070 939026 19.28 1.76 Rajasthan Ajmer 2584913 2178420 18.66 1.71 Alwar 3671999 2991445 22.75 2.05 Banswara 1798194 1420599 26.58 2.36 Baran 1223921 1021466 19.82 1.81 Barmer 2604453 1964883 32.55 2.82 Bharatpur 2549121 2099943 21.39 1.94 Bhilwara 2410459 2021010 19.27 1.76 Bikaner 2367745 1902109 24.48 2.19 Bundi 1113725 962597 15.70 1.46 Chittaurgarh 1544392 1330340 16.09 1.49 Churu 2041172 1696030 20.35 1.85 Dausa 1637226 1323011 23.75 2.13 Dhaulpur 1207293 983298 22.78 2.05 1388906 1107669 25.39 2.26 Ganganagar 1969520 1789497 10.06 0.96 1779650 1517955 17.24 1.59 6663971 5250942 26.91 2.38 Jaisalmer 672008 508250 32.22 2.79 Jalor 1830151 1448936 26.31 2.34 Jhalawar 1411327 1180335 19.57 1.79 Jhunjhunun 2139658 1913655 11.81 1.12 Jodhpur 3685681 2886429 27.69 2.44 Karauli 1458459 1205936 20.94 1.90 Kota 1950491 1568675 24.34 2.18 Nagaur 3309234 2775039 19.25 1.76

147 Preliminary Demography of India

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Pali 2038533 1820281 11.99 1.13 Pratapgarh 868231 706798 22.84 2.06 Rajsamand 1158283 982512 17.89 1.65 Sawai Madhopur 1338114 1117050 19.79 1.81 Sikar 2677737 2287882 17.04 1.57 Sirohi 1037185 851128 21.86 1.98 Tonk 1421711 1211720 17.33 1.60 3067549 2481234 23.63 2.12 Sikkim East District 281293 245040 14.79 1.38 North District 43354 41030 5.66 0.55 South District 146742 131525 11.57 1.09 West District 136299 123256 10.58 1.01 Tamil Nadu Ariyalur 752481 695524 8.19 0.79 Chennai 4681087 4343645 7.77 0.75 Coimbatore 3472578 2916620 19.06 1.74 Cuddalore 2600880 2285395 13.80 1.29 Dharmapuri 1502900 1295182 16.04 1.49 Dindigul 2161367 1923014 12.39 1.17 Erode 2259608 2016582 12.05 1.14 Kancheepuram 3990897 2877468 38.69 3.27 Kanniyakumari 1863174 1676034 11.17 1.06 Karur 1076588 935686 15.06 1.40 Krishnagiri 1883731 1561118 20.67 1.88 Madurai 3041038 2578201 17.95 1.65 Nagapattinam 1614069 1488839 8.41 0.81 Namakkal 1721179 1493462 15.25 1.42 Perambalur 564511 493646 14.36 1.34 Pudukkottai 1618725 1459601 10.90 1.03 Ramanathapuram 1337560 1187604 12.63 1.19 Salem 3480008 3016346 15.37 1.43 Sivaganga 1341250 1155356 16.09 1.49 Thanjavur 2402781 2216138 8.42 0.81 The Nilgiris 735071 762141 -3.55 -0.36 Theni 1243684 1093950 13.69 1.28 Thiruvallur 3725697 2754756 35.25 3.02 Thiruvarur 1268094 1169474 8.43 0.81 Thoothukkudi 1738376 1592769 9.14 0.87 Tiruchirappalli 2713858 2418366 12.22 1.15

148 Statistical Tables

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Tirunelveli 3072880 2703492 13.66 1.28 Tiruppur 2471222 1920154 28.70 2.52 Tiruvannamalai 2468965 2186125 12.94 1.22 Vellore 3928106 3477317 12.96 1.22 Viluppuram 3463284 2960373 16.99 1.57 Virudhunagar 1943309 1751301 10.96 1.04 Tripura Dhalai 377988 307868 22.78 2.05 North Tripura 693281 590913 17.32 1.60 South Tripura 875144 767440 14.03 1.31 West Tripura 1724619 1532982 12.50 1.18 Uttar Pradesh Agra 4380793 3620436 21.00 1.91 Aligarh 3673849 2992286 22.78 2.05 Allahabad 5959798 4936105 20.74 1.88 Ambedkar Nagar 2398709 2026876 18.35 1.68 Auraiya 1372287 1179993 16.30 1.51 Azamgarh 4616509 3939916 17.17 1.58 1302156 1163991 11.87 1.12 3478257 2381072 46.08 3.79 3223642 2761620 16.73 1.55 2149066 1682350 27.74 2.45 Banda 1799541 1537334 17.06 1.57 Bara Banki 3257983 2673581 21.86 1.98 4465344 3618589 23.40 2.10 Basti 2461056 2084814 18.05 1.66 Bijnor 3683896 3131619 17.64 1.62 Budaun 3712738 3069426 20.96 1.90 Bulandshahr 3498507 2913122 20.09 1.83 Chandauli 1952713 1643251 18.83 1.73 Chitrakoot 990626 766225 29.29 2.57 Deoria 3098637 2712650 14.23 1.33 Etah 1761152 1531703 14.98 1.40 Etawah 1579160 1338871 17.95 1.65 Faizabad 2468371 2088928 18.16 1.67 Farrukhabad 1887577 1570408 20.20 1.84 Fatehpur 2632684 2308384 14.05 1.31 Firozabad 2496761 2052958 21.62 1.96 Gautam Buddha Nagar 1674714 1202030 39.32 3.32 Ghaziabad 4661452 3290586 41.66 3.48

149 Preliminary Demography of India

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Ghazipur 3622727 3037582 19.26 1.76 Gonda 3431386 2765586 24.07 2.16 4436275 3769456 17.69 1.63 Hamirpur 1104021 1043724 5.78 0.56 4091380 3398306 20.39 1.86 Jalaun 1670718 1454452 14.87 1.39 Jaunpur 4476072 3911679 14.43 1.35 2000755 1744931 14.66 1.37 Jyotiba Phule Nagar 1838771 1499068 22.66 2.04 Kannauj 1658005 1388923 19.37 1.77 Kanpur Dehat 1795092 1563336 14.82 1.38 Kanpur Nagar 4572951 4167999 9.72 0.93 Kanshiram Nagar 1438156 1228668 17.05 1.57 Kaushambi 1596909 1293154 23.49 2.11 Kheri 4013634 3207232 25.14 2.24 3560830 2893196 23.08 2.08 Lalitpur 1218002 977734 24.57 2.2 4588455 3647834 25.79 2.29 Mahamaya Nagar 1565678 1336031 17.19 1.59 Mahoba 876055 708447 23.66 2.12 Mahrajganj 2665292 2173878 22.61 2.04 Mainpuri 1847194 1596718 15.69 1.46 2541894 2074516 22.53 2.03 Mau 2205170 1853997 18.94 1.73 Meerut 3447405 2997361 15.01 1.40 Mirzapur 2494533 2116042 17.89 1.65 Moradabad 4773138 3810983 25.25 2.25 Muzaffarnagar 4138605 3543362 16.80 1.55 Pilibhit 2037225 1645183 23.83 2.14 Pratapgarh 3173752 2731174 16.20 1.50 Rae Bareli 3404004 2872335 18.51 1.70 Rampur 2335398 1923739 21.40 1.94 3464228 2896863 19.59 1.79 Sant Kabir Nagar 1714300 1420226 20.71 1.88 Sant Ravidas Nagar (Bhadohi) 1554203 1353705 14.81 1.38 Shahjahanpur 3002376 2547855 17.84 1.64 Shrawasti 1114615 1176391 -5.25 -0.54 Siddharthnagar 2553526 2040085 25.17 2.24 Sitapur 4474446 3619661 23.62 2.12 Sonbhadra 1862612 1463519 27.27 2.41

150 Statistical Tables

State/Union Territory/ Total population Proportionate Average District increase annual growth rate 2011 2001 2001-2011 2001-2011 Sultanpur 3790922 3214832 17.92 1.65 Unnao 3110595 2700324 15.19 1.41 3682194 3138671 17.32 1.60 Uttarakhand Almora 621927 630567 -1.37 -0.14 Bageshwar 259840 249462 4.16 0.41 Chamoli 391114 370359 5.60 0.55 Champawat 259315 224542 15.49 1.44 1698560 1282143 32.48 2.81 Garhwal 686527 697078 -1.51 -0.15 Hardwar 1927029 1447187 33.16 2.86 955128 762909 25.20 2.25 Pithoragarh 485993 462289 5.13 0.50 Rudraprayag 236857 227439 4.14 0.41 Tehri Garhwal 616409 604747 1.93 0.19 Udham Singh Nagar 1648367 1235614 33.40 2.88 Uttarkashi 329686 295013 11.75 1.11 West Bengal Bankura 3596292 3192695 12.64 1.19 Barddhaman 7723663 6895514 12.01 1.13 Birbhum 3502387 3015422 16.15 1.50 Dakshin Dinajpur 1670931 1503178 11.16 1.06 Darjiling 1842034 1609172 14.47 1.35 Haora 4841638 4273099 13.31 1.25 Hugli 5520389 5041976 9.49 0.91 Jalpaiguri 3869675 3401173 13.77 1.29 Koch Bihar 2822780 2479155 13.86 1.30 Kolkata 4486679 4572876 -1.88 -0.19 Maldah 3997970 3290468 21.50 1.95 7102430 5866569 21.07 1.91 Nadia 5168488 4604827 12.24 1.15 North Twenty Four Parganas 10082852 8934286 12.86 1.21 Paschim Medinipur 5943300 5193411 14.44 1.35 Purba Medinipur 5094238 4417377 15.32 1.43 Puruliya 2927965 2536516 15.43 1.44 South Twenty Four Parganas 8153176 6906689 18.05 1.66 Uttar Dinajpur 3000849 2441794 22.90 2.06

151 Preliminary Demography of India

Table 3.A Indexes of population distribution in districts of India, 2011

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Andaman and Nicobar Islands Nicobars 0.03 -1.28 -0.039 20 North & Middle Andaman 0.087 -1.070 -0.093 32 South Andaman 0.196 -0.680 -0.133 80 Andhra Pradesh Adilabad 2.262 -0.351 -0.794 170 Anantapur 3.374 -0.252 -0.850 213 Chittoor 3.446 -0.141 -0.488 275 East Godavari 4.257 0.097 0.413 477 Guntur 4.040 0.051 0.208 429 Hyderabad 3.314 1.686 5.585 18480 Karimnagar 3.150 -0.073 -0.229 322 Khammam 2.312 -0.339 -0.784 175 Krishna 3.742 0.134 0.501 519 Kurnool 3.344 -0.221 -0.739 229 Mahbubnagar 3.340 -0.240 -0.802 219 Medak 2.505 -0.086 -0.216 313 Nalgonda 2.879 -0.193 -0.555 245 Nizamabad 2.451 0.016 0.040 195 Prakasam 2.109 -0.291 -0.613 426 Rangareddy 2.803 0.049 0.136 300 Sri Potti Sriramulu Nellore 4.376 -0.103 -0.452 396 Srikakulam 2.231 0.084 0.187 462 Visakhapatnam 3.543 0.003 0.012 384 Vizianagaram 1.936 -0.027 -0.052 358 Warangal 2.911 -0.143 -0.417 274 West Godavari 3.251 0.125 0.406 508 Y.S.R. 2.384 -0.307 -0.733 188 Arunachal Pradesh Anjaw 0.017 -2.104 -0.037 3 Changlang 0.122 -1.076 -0.132 32 Dibang Valley 0.007 -2.581 -0.017 1 East Kameng 0.065 -1.302 -0.084 19 East Siang 0.082 -1.150 -0.094 27 Kurung Kumey 0.074 -1.405 -0.104 15 Lohit 0.120 -1.134 -0.136 28 Lower Dibang Valley 0.045 -1.435 -0.064 14 Lower Subansiri 0.068 -1.201 -0.082 24 Papum Pare 0.146 -0.874 -0.127 51 Tawang 0.041 -1.219 -0.050 23

152 Statistical Tables

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Tirap 0.093 -0.909 -0.084 47 Upper Siang 0.029 -1.882 -0.055 5 Upper Subansiri 0.069 -1.502 -0.103 12 West Kameng 0.072 -1.502 -0.108 12 West Siang 0.093 -1.467 -0.136 13 Assam Baksa 0.788 0.095 0.075 475 Barpeta 1.399 0.220 0.307 632 Bongaigaon 0.605 0.047 0.029 425 Cachar 1.435 0.081 0.116 459 Chirang 0.398 -0.194 -0.077 244 Darrang 0.750 0.110 0.082 491 Dhemaji 0.569 -0.253 -0.144 213 Dhubri 1.610 0.487 0.785 1171 Dibrugarh 1.097 0.013 0.014 393 Dima Hasao 0.176 -0.938 -0.165 44 Goalpara 0.834 0.162 0.135 553 Golaghat 0.875 -0.101 -0.089 302 Hailakandi 0.545 0.115 0.063 497 Jorhat 0.902 0.002 0.002 383 Kamrup 1.254 0.058 0.073 436 Kamrup Metropolitan 1.042 0.722 0.752 2010 Karbi Anglong 0.798 -0.613 -0.489 93 Karimganj 1.006 0.247 0.248 673 Kokrajhar 0.733 -0.134 -0.098 280 Lakhimpur 0.860 0.079 0.068 457 Morigaon 0.791 0.210 0.166 618 Nagaon 2.335 0.271 0.632 711 Nalbari 0.636 0.301 0.192 763 Sivasagar 0.950 0.053 0.051 431 Sonitpur 1.591 -0.019 -0.030 365 Tinsukia 1.088 -0.041 -0.044 347 Udalguri 0.688 0.115 0.079 497 Bihar Araria 2.319 0.415 0.963 992 Arwal 0.578 -0.421 -0.243 145 Aurangabad 2.075 0.300 0.622 760 Banka 1.677 0.246 0.413 672 Begusarai 2.441 0.607 1.481 1541 Bhagalpur 2.506 0.491 1.230 1180 Bhojpur 2.248 0.460 1.034 1100 Buxar 1.411 0.441 0.622 1052

153 Preliminary Demography of India

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Darbhanga 3.241 0.655 2.122 1722 Gaya 3.619 0.363 1.314 880 Gopalganj 2.114 0.519 1.096 1258 Jamui 1.451 0.172 0.250 567 Jehanabad 0.929 0.274 0.255 716 Kaimur (Bhabua) 1.344 0.458 0.616 1095 Katihar 2.535 0.421 1.066 1004 Khagaria 1.370 0.112 0.153 493 Kishanganj 1.397 0.372 0.520 898 Lakhisarai 0.827 0.330 0.273 814 Madhepura 1.648 0.467 0.769 1116 Madhubani 3.699 0.526 1.944 1279 Munger 1.123 0.400 0.449 958 Muzaffarpur 3.949 0.597 2.356 1506 Nalanda 2.374 0.505 1.199 1220 Nawada 1.832 0.368 0.674 890 Pashchim Champaran 3.241 0.294 0.953 750 Patna 4.770 0.675 3.219 1803 Purba Champaran 4.200 0.526 2.210 1281 Purnia 2.705 0.425 1.149 1014 Rohtas 2.448 0.305 0.747 770 Saharsa 1.568 0.466 0.730 1115 Samastipur 3.516 0.585 2.055 1465 Saran 3.258 0.593 1.932 1493 Sheikhpura 0.525 0.383 0.201 922 Sheohar 0.543 0.590 0.320 1483 Sitamarhi 2.826 0.611 1.725 1555 Siwan 2.742 0.594 1.627 1495 Supaul 1.841 0.385 0.709 925 Vaishali 2.888 0.654 1.887 1717 Chandigarh Chandigarh 0.872 1.385 1.207 9252 Chhattisgarh Bastar 1.166 -0.435 -0.507 140 Bijapur 0.211 -0.990 -0.209 39 Bilaspur 2.200 -0.073 -0.161 322 Dakshin Bastar Dantewada 0.440 -0.810 -0.357 59 Dhamtari 0.660 -0.208 -0.138 236 Durg 2.762 0.011 0.030 391 Janjgir - Champa 1.339 0.043 0.058 421 Jashpur 0.704 -0.417 -0.293 146 Kabeerdham 0.679 -0.291 -0.198 195

154 Statistical Tables

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Korba 0.997 -0.319 -0.318 183 Koriya 0.545 -0.581 -0.317 100 Mahasamund 0.853 -0.247 -0.210 216 Narayanpur 0.116 -1.280 -0.148 20 Raigarh 1.234 -0.257 -0.317 211 Raipur 3.357 -0.090 -0.302 310 Rajnandgaon 1.270 -0.300 -0.381 191 Surguja 1.951 -0.405 -0.790 150 Uttar Bastar Kanker 0.619 -0.520 -0.322 115 Dadra and Nagar Haveli Dadra & Nagar Haveli 0.283 0.263 0.074 698 Daman and Diu Daman 0.158 0.842 0.133 2651 Diu 0.043 0.533 0.023 1301 Delhi Central 0.478 1.783 0.853 23147 East 1.411 1.008 1.422 3881 New Delhi 0.110 1.203 0.133 6078 North 0.730 1.594 1.164 14973 North East 1.852 2.053 3.802 43091 North West 3.017 1.867 5.634 28087 South 2.259 1.458 3.293 10935 South West 1.894 1.182 2.240 5803 West 2.092 1.773 3.709 22603 Goa North Goa 0.676 0.092 0.062 471 South Goa 0.529 -0.069 -0.036 326 Gujarat Ahmadabad 5.956 0.368 2.193 890 Amreli 1.251 -0.269 -0.337 205 Anand 1.727 0.271 0.468 711 Banas Kantha 2.575 -0.119 -0.306 290 Bharuch 1.281 -0.205 -0.262 238 Bhavnagar 2.378 -0.122 -0.290 288 Dohad 1.757 0.184 0.323 582 Gandhinagar 1.146 0.238 0.273 660 Jamnagar 1.784 -0.397 -0.707 153 Junagadh 2.266 -0.090 -0.204 310 Kachchh 1.727 -0.918 -1.586 46 Kheda 1.900 0.152 0.289 541 Mahesana 1.676 0.083 0.140 462 Narmada 0.488 -0.251 -0.122 214

155 Preliminary Demography of India

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Navsari 1.100 0.198 0.218 602 Panch Mahals 1.973 0.080 0.157 458 Patan 1.110 -0.212 -0.235 234 Porbandar 0.484 -0.175 -0.085 255 Rajkot 3.140 -0.051 -0.160 339 Sabar Kantha 2.006 -0.065 -0.131 328 Surat 5.023 0.557 2.800 1376 Surendranagar 1.451 -0.358 -0.520 167 Tapi 0.666 -0.185 -0.123 249 The Dangs 0.187 -0.471 -0.088 129 Vadodara 3.435 0.160 0.550 551 Valsad 1.407 0.168 0.236 561 Haryana Ambala 0.939 0.279 0.262 725 Bhiwani 1.346 -0.080 -0.108 317 Faridabad 1.487 0.351 0.521 855 Fatehabad 0.778 -0.004 -0.003 378 Gurgaon 1.251 0.158 0.198 549 Hisar 1.440 0.082 0.118 460 Jhajjar 0.791 0.128 0.101 512 Jind 1.101 0.106 0.117 487 Kaithal 0.887 0.002 0.002 383 Karnal 1.245 0.204 0.254 610 Kurukshetra 0.797 0.318 0.253 792 Mahendragarh 0.762 0.157 0.120 548 Mewat 0.900 0.209 0.188 617 Palwal 0.860 0.163 0.140 555 Panchkula 0.462 0.254 0.118 685 Panipat 0.994 0.402 0.400 962 Rewari 0.740 0.178 0.132 575 Rohtak 0.875 0.221 0.194 635 Sirsa 1.070 -0.100 -0.107 303 Sonipat 1.223 0.235 0.287 655 Yamunanagar 1.003 0.259 0.259 691 Himachal Pradesh Bilaspur 0.316 -0.066 -0.021 327 Chamba 0.429 -0.681 -0.292 79 Hamirpur 0.375 0.028 0.010 406 Kangra 1.245 -0.162 -0.202 263 Kinnaur 0.070 -1.462 -0.102 13 Kullu 0.361 -0.681 -0.246 79 Lahul & Spiti 0.026 -2.223 -0.058 2

156 Statistical Tables

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Mandi 0.826 -0.178 -0.147 253 Shimla 0.672 -0.381 -0.256 159 Sirmaur 0.438 -0.308 -0.135 188 Solan 0.477 -0.107 -0.051 298 Una 0.431 -0.052 -0.022 338 Jammu and Kashmir Anantnag 0.884 -0.007 -0.006 375 Badgam 0.608 0.149 0.090 537 Bandipore 0.318 0.467 0.149 1117 Baramula 0.839 -0.097 -0.081 305 Doda 0.338 -0.684 -0.231 79 Ganderbal 0.245 0.480 0.118 1151 Jammu 1.261 0.194 0.245 596 Kargil 0.118 -1.581 -0.187 10 Kathua 0.509 -0.216 -0.110 232 Kishtwar 0.191 -0.484 -0.092 125 Kulgam 0.349 0.385 0.134 925 Kupwara 0.723 -0.015 -0.011 368 Leh(Ladakh) 0.122 -2.104 -0.256 3 Pulwama 0.471 0.196 0.092 598 Punch 0.394 -0.126 -0.050 285 Rajouri 0.512 -0.210 -0.108 235 Ramban 0.234 -0.253 -0.059 213 Reasi 0.260 -0.316 -0.082 184 Samba 0.263 -0.079 -0.021 318 Shupiyan 0.220 0.349 0.077 852 Srinagar 1.049 0.266 0.279 703 Udhampur 0.459 -0.257 -0.118 211 Jharkhand Bokaro 1.704 0.274 0.466 716 Chatra 0.861 -0.142 -0.122 275 Deoghar 1.233 0.198 0.245 602 Dhanbad 2.217 0.527 1.169 1284 Dumka 1.092 -0.104 -0.114 300 Garhwa 1.093 -0.067 -0.073 327 Giridih 2.021 0.115 0.233 497 Godda 1.084 0.213 0.230 622 Gumla 0.848 -0.296 -0.251 193 Hazaribagh 1.433 0.024 0.035 403 Jamtara 0.653 0.061 0.040 439 Khunti 0.438 -0.249 -0.109 215 Kodarma 0.593 0.049 0.029 427

157 Preliminary Demography of India

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Latehar 0.600 -0.280 -0.168 200 Lohardaga 0.382 -0.090 -0.034 310 Pakur 0.743 0.116 0.086 498 Palamu 1.600 -0.000 -0.000 381 Pashchimi Singhbhum 1.241 -0.261 -0.324 209 Purbi Singhbhum 1.893 0.230 0.436 648 Ramgarh 0.784 0.254 0.199 684 Ranchi 2.406 0.165 0.396 557 Sahibganj 0.950 0.276 0.262 719 Saraikela-Kharsawan 0.879 0.010 0.009 390 Simdega 0.496 -0.377 -0.187 160 Karnataka Bagalkot 1.562 -0.122 -0.190 288 Bangalore 7.923 1.060 8.399 4378 Bangalore Rural 0.816 0.063 0.052 441 Belgaum 3.948 -0.030 -0.117 356 Bellary 2.093 -0.104 -0.218 300 Bidar 1.405 -0.087 -0.122 312 Bijapur 1.797 -0.265 -0.477 207 Chamarajanagar 0.844 -0.280 -0.236 200 Chikkaballapura 1.037 -0.107 -0.111 298 Chikmagalur 0.940 -0.383 -0.360 158 Chitradurga 1.372 -0.287 -0.393 197 Dakshina Kannada 1.722 0.079 0.136 457 Davanagere 1.609 -0.064 -0.103 329 Dharwad 1.526 0.056 0.086 434 Gadag 0.880 -0.221 -0.195 229 Gulbarga 2.119 -0.214 -0.453 233 Hassan 1.468 -0.165 -0.242 261 Haveri 1.321 -0.061 -0.081 331 Kodagu 0.458 -0.451 -0.207 135 Kolar 1.273 0.003 0.004 384 Koppal 1.150 -0.183 -0.211 250 Mandya 1.495 -0.019 -0.028 365 Mysore 2.475 0.059 0.147 437 Raichur 1.590 -0.223 -0.355 228 Ramanagara 0.895 -0.100 -0.089 303 Shimoga 1.451 -0.265 -0.385 207 Tumkur 2.216 -0.178 -0.395 253 Udupi 0.973 -0.098 -0.096 304 Uttara Kannada 1.187 -0.435 -0.517 140 Yadgir 0.969 -0.231 -0.224 224

158 Statistical Tables

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Kerala Alappuzha 1.753 0.595 1.043 1501 Ernakulam 2.710 0.465 1.259 1111 Idukki 0.915 -0.188 -0.172 247 Kannur 2.087 0.349 0.728 852 Kasaragod 1.076 0.234 0.252 654 Kollam 2.173 0.441 0.959 1053 Kottayam 1.636 0.372 0.609 898 Kozhikode 2.553 0.539 1.375 1318 Malappuram 3.397 0.483 1.639 1158 Palakkad 2.323 0.216 0.503 627 Pathanamthitta 0.988 0.105 0.104 486 Thiruvananthapuram 2.733 0.597 1.633 1509 Thrissur 2.570 0.430 1.105 1026 Wayanad 0.675 0.002 0.001 383 Lakshadweep Lakshadweep 0.053 0.723 0.038 2013 Madhya Pradesh Alirajpur 0.602 -0.221 -0.133 229 Anuppur 0.619 -0.280 -0.174 200 Ashoknagar 0.698 -0.324 -0.226 181 Balaghat 1.406 -0.316 -0.445 184 Barwani 1.145 -0.173 -0.198 256 Betul 1.302 -0.385 -0.502 157 Bhind 1.408 0.001 0.001 382 Bhopal 1.957 0.350 0.685 854 Burhanpur 0.626 -0.237 -0.148 221 Chhatarpur 1.457 -0.274 -0.399 203 Chhindwara 1.727 -0.333 -0.576 177 Damoh 1.044 -0.343 -0.358 173 Datia 0.650 -0.116 -0.075 292 Dewas 1.292 -0.233 -0.301 223 Dhar 1.805 -0.153 -0.276 268 Dindori 0.582 -0.608 -0.354 94 East Nimar 1.082 -0.331 -0.358 178 Guna 1.025 -0.293 -0.301 194 Gwalior 1.678 0.067 0.113 445 Harda 0.471 -0.348 -0.164 171 Hoshangabad 1.025 -0.314 -0.322 185 Indore 2.704 0.343 0.926 839 Jabalpur 2.033 0.093 0.189 472 Jhabua 0.846 -0.126 -0.107 285

159 Preliminary Demography of India

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Katni 1.067 -0.165 -0.176 261 Mandla 0.871 -0.321 -0.280 182 Mandsaur 1.107 -0.197 -0.219 242 Morena 1.624 0.014 0.023 394 Narsimhapur 0.902 -0.253 -0.228 213 Neemuch 0.683 -0.293 -0.200 194 Panna 0.840 -0.429 -0.360 142 Raisen 1.100 -0.385 -0.424 157 Rajgarh 1.278 -0.182 -0.232 251 Ratlam 1.202 -0.106 -0.127 299 Rewa 1.953 -0.008 -0.016 374 Sagar 1.965 -0.216 -0.424 232 Satna 1.842 -0.108 -0.200 297 Sehore 1.083 -0.282 -0.306 199 Seoni 1.139 -0.385 -0.439 157 Shahdol 0.880 -0.346 -0.304 172 Shajapur 1.250 -0.194 -0.242 244 Sheopur 0.568 -0.564 -0.321 104 Shivpuri 1.426 -0.356 -0.508 168 Sidhi 0.931 -0.216 -0.201 232 Singrauli 0.974 -0.263 -0.256 208 Tikamgarh 1.194 -0.125 -0.149 286 Ujjain 1.642 -0.068 -0.112 326 Umaria 0.532 -0.383 -0.203 158 Vidisha 1.205 -0.285 -0.343 198 West Nimar 1.547 -0.214 -0.331 233 Maharashtra Ahmadnagar 3.754 -0.156 -0.584 266 Akola 1.503 -0.056 -0.084 335 Amravati 2.386 -0.208 -0.497 236 Aurangabad 3.054 -0.018 -0.055 366 Bhandara 0.991 -0.092 -0.092 308 Bid 2.137 -0.198 -0.422 242 Buldana 2.139 -0.153 -0.328 268 Chandrapur 1.813 -0.298 -0.541 192 Dhule 1.693 -0.178 -0.301 253 Gadchiroli 0.886 -0.710 -0.629 74 Gondiya 1.093 -0.195 -0.213 243 Hingoli 0.974 -0.165 -0.161 260 Jalgaon 3.491 -0.026 -0.091 359 Jalna 1.618 -0.177 -0.286 254 Kolhapur 3.201 0.121 0.388 504

160 Statistical Tables

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Latur 2.029 -0.046 -0.093 343 Mumbai 2.600 2.078 5.401 45594 Mumbai Suburban 7.712 1.661 12.811 17477 Nagpur 3.845 0.091 0.351 470 Nanded 2.774 -0.078 -0.215 319 Nandurbar 1.360 -0.068 -0.093 326 Nashik 5.048 0.013 0.067 393 Osmanabad 1.372 -0.240 -0.329 219 Parbhani 1.517 -0.131 -0.199 282 Pune 7.790 0.199 1.549 603 Raigarh 2.178 -0.015 -0.032 368 Ratnagiri 1.333 -0.288 -0.384 196 Sangli 2.331 -0.064 -0.149 329 Satara 2.482 -0.124 -0.307 287 Sindhudurg 0.701 -0.369 -0.259 163 Solapur 3.566 -0.119 -0.425 290 Thane 9.134 0.482 4.402 1157 Wardha 1.071 -0.268 -0.288 205 Washim 0.989 -0.215 -0.213 232 Yavatmal 2.293 -0.271 -0.621 204 Manipur Bishnupur 0.199 0.104 0.021 485 Chandel 0.119 -0.943 -0.112 43 Churachandpur 0.224 -0.808 -0.181 59 Imphal East 0.374 0.223 0.084 638 Imphal West 0.425 0.415 0.177 992 Senapati 0.293 -0.545 -0.160 109 Tamenglong 0.116 -1.077 -0.125 32 Thoubal 0.347 0.332 0.115 818 Ukhrul 0.151 -0.976 -0.148 40 Meghalaya East Garo Hills 0.262 -0.495 -0.130 122 East Khasi Hills 0.681 -0.105 -0.071 299 Jaintia Hills 0.325 -0.569 -0.185 103 Ribhoi 0.214 -0.545 -0.116 109 South Garo Hills 0.118 -0.694 -0.082 77 West Garo Hills 0.531 -0.343 -0.182 173 West Khasi Hills 0.319 -0.715 -0.228 73 Mizoram Aizawl 0.334 -0.528 -0.176 113 Champhai 0.104 -0.984 -0.102 40 Kolasib 0.069 -0.804 -0.055 60

161 Preliminary Demography of India

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Lawngtlai 0.097 -0.913 -0.089 47 Lunglei 0.127 -1.054 -0.134 34 Mamit 0.071 -1.120 -0.079 29 Saiha 0.047 -0.981 -0.046 40 Serchhip 0.054 -0.923 -0.049 46 Nagaland Dimapur 0.314 0.032 0.010 410 Kiphire 0.061 -0.810 -0.050 59 Kohima 0.223 -0.167 -0.037 259 Longleng 0.042 -0.824 -0.034 57 Mokokchung 0.160 -0.503 -0.080 120 Mon 0.207 -0.434 -0.090 140 Peren 0.078 -0.965 -0.076 41 Phek 0.135 -0.675 -0.091 81 Tuensang 0.163 -0.525 -0.085 114 Wokha 0.137 -0.572 -0.079 102 Zunheboto 0.117 -0.531 -0.062 112 Orissa Anugul 1.051 -0.282 -0.297 199 Balangir 1.362 -0.182 -0.247 251 Baleshwar 1.915 0.203 0.390 609 Bargarh 1.222 -0.178 -0.218 253 Baudh 0.364 -0.429 -0.156 142 Bhadrak 1.245 0.198 0.246 601 Cuttack 2.164 0.242 0.524 666 Debagarh 0.258 -0.556 -0.143 106 Dhenkanal 0.986 -0.153 -0.151 268 Gajapati 0.476 -0.457 -0.218 133 Ganjam 2.909 0.051 0.149 429 Jagatsinghapur 0.939 0.252 0.237 681 Jajapur 1.509 0.218 0.329 630 Jharsuguda 0.479 -0.143 -0.069 274 Kalahandi 1.300 -0.282 -0.367 199 Kandhamal 0.605 -0.622 -0.376 91 Kendrapara 1.190 0.155 0.185 545 Kendujhar 1.490 -0.245 -0.365 217 Khordha 1.856 0.321 0.596 799 Koraput 1.138 -0.388 -0.442 156 Malkangiri 0.506 -0.556 -0.281 106 Mayurbhanj 2.077 -0.199 -0.414 241 Nabarangapur 1.007 -0.219 -0.221 230 Nayagarh 0.795 -0.188 -0.150 247

162 Statistical Tables

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Nuapada 0.501 -0.385 -0.193 157 Puri 1.403 0.107 0.150 488 Rayagada 0.795 -0.448 -0.356 136 Sambalpur 0.863 -0.383 -0.330 158 Subarnapur 0.539 -0.136 -0.073 279 Sundargarh 1.719 -0.251 -0.431 214 Puducherry Karaikal 0.166 0.516 0.085 1252 Mahe 0.035 1.087 0.038 4659 Puducherry 0.782 0.928 0.726 3231 Yanam 0.046 0.687 0.032 1854 Punjab Amritsar 2.058 0.388 0.799 932 Barnala 0.493 0.041 0.020 419 Bathinda 1.148 0.036 0.041 414 Faridkot 0.511 0.046 0.024 424 Fatehgarh Sahib 0.496 0.125 0.062 508 Firozpur 1.675 -0.001 -0.002 380 Gurdaspur 1.900 0.231 0.439 649 Hoshiarpur 1.308 0.087 0.114 466 Jalandhar 1.803 0.338 0.610 831 Kapurthala 0.676 0.119 0.080 501 Ludhiana 2.882 0.408 1.175 975 Mansa 0.635 -0.037 -0.024 350 Moga 0.820 0.066 0.054 444 Muktsar 0.746 -0.040 -0.030 348 Patiala 1.564 0.194 0.303 596 Rupnagar 0.565 0.107 0.061 488 Sahibzada Ajit Singh Nagar 0.815 0.338 0.275 830 Sangrur 1.367 0.071 0.097 449 Shahid Bhagat Singh Nagar 0.508 0.099 0.050 479 Tarn Taran 0.926 0.085 0.079 464 Rajasthan Ajmer 2.136 -0.097 -0.208 305 Alwar 3.034 0.060 0.183 438 Banswara 1.486 -0.029 -0.042 357 Baran 1.011 -0.336 -0.340 176 Barmer 2.152 -0.619 -1.331 92 Bharatpur 2.106 0.121 0.254 503 Bhilwara 1.992 -0.218 -0.435 231 Bikaner 1.957 -0.642 -1.256 87 Bundi 0.920 -0.279 -0.256 201

163 Preliminary Demography of India

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Chittaurgarh 1.276 -0.428 -0.546 142 Churu 1.687 -0.497 -0.839 121 Dausa 1.353 0.098 0.132 477 Dhaulpur 0.998 0.012 0.011 391 Dungarpur 1.148 -0.015 -0.017 368 Ganganagar 1.627 -0.189 -0.308 247 Hanumangarh 1.471 -0.433 -0.636 141 Jaipur 5.507 0.195 1.075 598 Jaisalmer 0.555 -1.338 -0.743 17 Jalor 1.512 -0.346 -0.523 172 Jhalawar 1.166 -0.225 -0.263 227 Jhunjhunun 1.768 -0.024 -0.042 361 Jodhpur 3.046 -0.374 -1.138 161 Karauli 1.205 -0.160 -0.193 264 Kota 1.612 -0.027 -0.044 358 Nagaur 2.734 -0.310 -0.847 187 Pali 1.684 -0.365 -0.615 165 Pratapgarh 0.717 0.550 0.395 1352 Rajsamand 0.957 -0.103 -0.099 301 Sawai Madhopur 1.106 -0.108 -0.119 297 Sikar 2.213 -0.042 -0.092 346 Sirohi 0.857 -0.276 -0.237 202 Tonk 1.175 -0.285 -0.335 198 Udaipur 2.535 -0.222 -0.564 228 Sikkim East District 0.232 -0.112 -0.026 295 North District 0.036 -1.570 -0.056 10 South District 0.121 -0.290 -0.035 196 West District 0.113 -0.513 -0.058 117 Tamil Nadu Ariyalur 0.622 0.007 0.004 387 Chennai 3.868 1.849 7.150 26903 Coimbatore 2.869 0.293 0.840 748 Cuddalore 2.149 0.265 0.570 702 Dharmapuri 1.242 -0.060 -0.075 332 Dindigul 1.786 -0.029 -0.051 357 Erode 1.867 0.018 0.033 397 Kancheepuram 3.298 0.386 1.273 927 Kanniyakumari 1.540 0.463 0.712 1106 Karur 0.890 -0.012 -0.011 371 Krishnagiri 1.557 -0.013 -0.020 370 Madurai 2.513 0.334 0.840 823

164 Statistical Tables

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Nagapattinam 1.334 0.244 0.325 668 Namakkal 1.422 0.123 0.175 506 Perambalur 0.466 -0.072 -0.034 323 Pudukkottai 1.338 -0.040 -0.053 348 Ramanathapuram 1.105 -0.076 -0.084 320 Salem 2.876 0.240 0.691 663 Sivaganga 1.108 -0.071 -0.078 324 Thanjavur 1.985 0.258 0.513 691 The Nilgiris 0.607 -0.122 -0.074 288 Theni 1.028 0.055 0.057 433 Thiruvallur 3.079 0.440 1.353 1049 Thiruvarur 1.048 0.146 0.152 533 Thoothukkudi 1.436 -0.004 -0.005 378 Tiruchirappalli 2.242 0.198 0.445 602 Tirunelveli 2.539 0.080 0.202 458 Tiruppur 2.042 0.096 0.197 476 Tiruvannamalai 2.040 0.020 0.040 399 Vellore 3.246 0.229 0.743 646 Viluppuram 2.862 0.102 0.291 482 Virudhunagar 1.606 0.076 0.122 454 Tripura Dhalai 0.312 -0.406 -0.127 150 North Tripura 0.573 -0.191 -0.109 246 South Tripura 0.723 0.028 0.020 407 West Tripura 1.425 0.179 0.255 575 Uttar Pradesh Agra 3.620 0.455 1.648 1088 Aligarh 3.036 0.410 1.245 980 Allahabad 4.925 0.460 2.264 1099 Ambedkar Nagar 1.982 0.424 0.840 1011 Auraiya 1.134 0.244 0.277 669 Azamgarh 3.815 0.456 1.741 1090 Baghpat 1.076 0.405 0.436 968 Bahraich 2.874 0.201 0.577 605 Ballia 2.664 0.453 1.206 1081 Balrampur 1.776 0.285 0.506 735 Banda 1.487 0.029 0.043 408 Bara Banki 2.692 0.349 0.940 852 Bareilly 3.690 0.454 1.674 1084 Basti 2.034 0.328 0.667 811 Bijnor 3.044 0.326 0.993 808 Budaun 3.068 0.275 0.844 718

165 Preliminary Demography of India

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Bulandshahr 2.891 0.392 1.134 941 Chandauli 1.614 0.302 0.488 765 Chitrakoot 0.819 -0.091 -0.074 309 Deoria 2.560 0.506 1.296 1222 Etah 1.455 0.278 0.405 724 Etawah 1.305 0.258 0.337 690 Faizabad 2.040 0.370 0.754 893 Farrukhabad 1.560 0.337 0.526 828 Fatehpur 2.175 0.221 0.481 634 Firozabad 2.063 0.443 0.914 1058 Gautam Buddha Nagar 1.384 0.539 0.746 1320 Ghaziabad 3.852 0.796 3.066 2383 Ghazipur 2.994 0.449 1.345 1073 Gonda 2.835 0.308 0.874 775 Gorakhpur 3.666 0.544 1.994 1334 Hamirpur 0.912 -0.174 -0.159 255 Hardoi 3.381 0.254 0.857 683 Jalaun 1.381 -0.018 -0.024 366 Jaunpur 3.699 0.464 1.714 1108 Jhansi 1.653 0.019 0.031 398 Jyotiba Phule Nagar 1.519 0.318 0.483 792 Kannauj 1.370 0.339 0.464 832 Kanpur Dehat 1.483 0.176 0.260 571 Kanpur Nagar 3.779 0.598 2.259 1510 Kanshiram Nagar 1.188 0.286 0.339 736 Kaushambi 1.320 0.358 0.472 869 Kheri 3.317 0.137 0.454 523 Kushinagar 2.942 0.507 1.491 1224 Lalitpur 1.006 -0.198 -0.199 242 Lucknow 3.792 0.678 2.569 1815 Mahamaya Nagar 1.294 0.370 0.479 894 Mahoba 0.724 -0.093 -0.067 308 Mahrajganj 2.202 0.375 0.826 904 Mainpuri 1.526 0.244 0.373 669 Mathura 2.100 0.301 0.632 763 Mau 1.822 0.528 0.963 1287 Meerut 2.849 0.555 1.580 1367 Mirzapur 2.061 0.160 0.331 552 Moradabad 3.944 0.536 2.112 1308 Muzaffarnagar 3.420 0.433 1.480 1033 Pilibhit 1.683 0.184 0.310 582 Pratapgarh 2.623 0.350 0.918 854

166 Statistical Tables

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density Rae Bareli 2.813 0.287 0.808 739 Rampur 1.930 0.413 0.797 987 Saharanpur 2.863 0.392 1.121 939 Sant Kabir Nagar 1.417 0.494 0.700 1189 Sant Ravidas Nagar 1.284 0.628 0.807 1619 (Bhadohi) Shahjahanpur 2.481 0.236 0.585 656 Shrawasti 0.921 0.414 0.382 990 Siddharthnagar 2.110 0.386 0.815 928 Sitapur 3.697 0.310 1.148 779 Sonbhadra 1.539 -0.143 -0.220 274 Sultanpur 3.132 0.351 1.098 855 Unnao 2.570 0.253 0.650 682 Varanasi 3.043 0.787 2.394 2333 Uttarakhand Almora 0.514 -0.277 -0.143 201 Bageshwar 0.215 -0.530 -0.114 112 Chamoli 0.323 -0.875 -0.283 51 Champawat 0.214 -0.418 -0.090 146 Dehradun 1.404 0.159 0.223 550 Garhwal 0.567 -0.480 -0.272 126 Hardwar 1.592 0.331 0.527 817 Nainital 0.789 -0.187 -0.148 248 Pithoragarh 0.402 -0.746 -0.300 68 Rudraprayag 0.196 -0.485 -0.095 125 Tehri Garhwal 0.509 -0.403 -0.205 151 Udham Singh Nagar 1.362 0.172 0.234 566 Uttarkashi 0.272 -0.964 -0.262 41 West Bengal Bankura 2.972 0.137 0.407 523 Barddhaman 6.382 0.460 2.936 1100 Birbhum 2.894 0.306 0.885 771 Dakshin Dinajpur 1.381 0.303 0.418 765 Darjiling 1.522 0.186 0.283 585 Haora 4.001 0.937 3.750 3300 Hugli 4.562 0.663 3.023 1753 Jalpaiguri 3.198 0.212 0.679 621 Koch Bihar 2.333 0.340 0.792 833 Kolkata 3.707 1.804 6.687 24252 Maldah 3.304 0.449 1.482 1071 Murshidabad 5.869 0.544 3.192 1334 Nadia 4.271 0.538 2.298 1316

167 Preliminary Demography of India

State/Union Territory/ Edc(x) Idc(x) Ddc(x) Population District density North Twenty Four Parganas 8.332 0.810 6.750 2462 Paschim Medinipur 4.911 0.202 0.993 607 Purba Medinipur 4.209 0.446 1.877 1065 Puruliya 2.419 0.089 0.215 468 South Twenty Four Parganas 6.737 0.332 2.237 819 Uttar 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 Index C Index A Dds(a) Ddc(a) District Andaman and Nicobar Islands Nicobars 0.115 0.130 4.668 -0.002 North & Middle Andaman 0.110 0.124 8.075 -0.007 South Andaman 0.099 0.110 -13.430 -0.027 Andhra Pradesh Adilabad 0.108 0.121 0.892 -0.217 Anantapur 0.105 0.117 0.559 -0.377 Chittoor 0.101 0.113 -0.144 -0.435 East Godavari 0.096 0.106 -1.926 -0.660 Guntur 0.095 0.105 -1.892 -0.631 Hyderabad 0.105 0.117 0.561 -0.369 Karimnagar 0.085 0.093 -4.021 -0.670 Khammam 0.096 0.106 -1.042 -0.358 Krishna 0.090 0.099 -3.279 -0.691 Kurnool 0.118 0.134 3.365 -0.177 Mahbubnagar 0.124 0.142 4.576 -0.092 Medak 0.115 0.130 2.082 -0.163 Nalgonda 0.102 0.113 -0.038 -0.358 Nizamabad 0.105 0.117 0.425 -0.230 Prakasam 0.106 0.119 0.777 -0.291 Rangareddy 0.112 0.127 2.932 -0.335 Sri Potti Sriramulu Nellore 0.097 0.107 -0.883 -0.364 Srikakulam 0.098 0.109 -0.578 -0.316 Visakhapatnam 0.100 0.111 -0.480 -0.471 Vizianagaram 0.099 0.109 -0.463 -0.271 Warangal 0.092 0.101 -2.060 -0.503 West Godavari 0.092 0.102 -2.230 -0.557 Y.S.R. 0.109 0.122 1.034 -0.222 Arunachal Pradesh Anjaw 0.161 0.192 0.718 0.002 Changlang 0.172 0.208 8.879 0.017 Dibang Valley 0.139 0.161 -0.158 0.000 East Kameng 0.179 0.217 5.778 0.010 East Siang 0.122 0.139 -6.508 -0.003 Kurung Kumey 0.173 0.209 5.582 0.011 Lohit 0.162 0.194 5.448 0.013 Lower Dibang Valley 0.143 0.167 -0.515 0.002 Lower Subansiri 0.121 0.137 -5.869 -0.003 Papum Pare 0.134 0.155 -5.706 0.002 Tawang 0.113 0.127 -4.741 -0.003

169 Preliminary Demography of India

State/Union Territory Index C Index A Dds(a) Ddc(a) District Tirap 0.172 0.208 6.788 0.013 Upper Siang 0.131 0.151 -1.441 -0.000 Upper Subansiri 0.136 0.157 -2.305 0.001 West Kameng 0.131 0.151 -3.566 -0.000 West Siang 0.123 0.141 -7.022 -0.003 Assam Baksa 0.123 0.140 -2.485 -0.025 Barpeta 0.166 0.199 3.771 0.166 Bongaigaon 0.155 0.184 0.843 0.052 Cachar 0.142 0.166 -0.508 0.058 Chirang 0.146 0.170 0.049 0.021 Darrang 0.165 0.197 1.940 0.087 Dhemaji 0.145 0.169 0.012 0.028 Dhubri 0.184 0.226 7.820 0.281 Dibrugarh 0.117 0.132 -4.585 -0.064 Dima Hasao 0.149 0.175 0.095 0.011 Goalpara 0.164 0.197 2.107 0.095 Golaghat 0.121 0.138 -3.007 -0.034 Hailakandi 0.166 0.199 1.500 0.066 Jorhat 0.108 0.121 -5.141 -0.088 Kamrup 0.129 0.147 -2.910 -0.013 Kamrup Metropolitan 0.096 0.106 -8.264 -0.161 Karbi Anglong 0.190 0.235 4.433 0.154 Karimganj 0.167 0.200 2.870 0.124 Kokrajhar 0.149 0.175 0.388 0.046 Lakhimpur 0.145 0.170 0.029 0.043 Morigaon 0.166 0.199 2.174 0.095 Nagaon 0.158 0.188 4.040 0.219 Nalbari 0.118 0.133 -2.556 -0.034 Sivasagar 0.116 0.132 -4.019 -0.057 Sonitpur 0.139 0.161 -1.320 0.045 Tinsukia 0.133 0.153 -1.816 0.007 Udalguri 0.131 0.151 -1.321 -0.000 Bihar Araria 0.201 0.252 1.681 0.514 Arwal 0.177 0.215 -0.044 0.088 Aurangabad 0.174 0.211 -0.330 0.303 Banka 0.179 0.218 -0.021 0.266 Begusarai 0.180 0.220 0.100 0.398 Bhagalpur 0.176 0.213 -0.301 0.374 Bhojpur 0.162 0.193 -1.364 0.241 Buxar 0.168 0.202 -0.546 0.178

170 Statistical Tables

State/Union Territory Index C Index A Dds(a) Ddc(a) District Darbhanga 0.179 0.218 -0.031 0.514 Gaya 0.174 0.211 -0.617 0.524 Gopalganj 0.171 0.206 -0.606 0.285 Jamui 0.178 0.217 -0.026 0.229 Jehanabad 0.173 0.208 -0.211 0.130 Kaimur (Bhabua) 0.179 0.219 0.016 0.216 Katihar 0.196 0.244 1.443 0.528 Khagaria 0.209 0.265 1.348 0.334 Kishanganj 0.202 0.253 1.065 0.314 Lakhisarai 0.182 0.223 0.087 0.139 Madhepura 0.199 0.249 1.103 0.357 Madhubani 0.174 0.211 -0.630 0.536 Munger 0.163 0.194 -0.658 0.123 Muzaffarpur 0.171 0.206 -1.093 0.536 Nalanda 0.174 0.211 -0.379 0.346 Nawada 0.166 0.199 -0.868 0.218 Pashchim Champaran 0.192 0.238 1.418 0.639 Patna 0.157 0.186 -3.828 0.432 Purba Champaran 0.195 0.243 2.302 0.867 Purnia 0.197 0.245 1.595 0.568 Rohtas 0.166 0.200 -1.092 0.297 Saharsa 0.199 0.248 1.035 0.339 Samastipur 0.184 0.226 0.635 0.615 Saran 0.167 0.200 -1.421 0.398 Sheikhpura 0.186 0.229 0.128 0.095 Sheohar 0.190 0.235 0.204 0.104 Sitamarhi 0.188 0.232 0.885 0.527 Siwan 0.161 0.191 -1.816 0.282 Supaul 0.190 0.235 0.709 0.354 Vaishali 0.169 0.204 -0.991 0.376 Chandigarh Chandigarh 0.112 0.126 0.000 -0.069 Chhattisgarh Bastar 0.151 0.178 2.014 0.082 Bijapur 0.157 0.186 0.560 0.019 Bilaspur 0.151 0.177 3.713 0.153 Dakshin Bastar Dantewada 0.143 0.168 0.235 0.020 Dhamtari 0.126 0.144 -1.708 -0.014 Durg 0.126 0.144 -7.076 -0.056 Janjgir - Champa 0.135 0.156 -1.185 0.020 Jashpur 0.141 0.164 0.072 0.025 Kabeerdham 0.171 0.207 3.290 0.092

171 Preliminary Demography of India

State/Union Territory Index C Index A Dds(a) Ddc(a) District Korba 0.140 0.162 -0.124 0.031 Koriya 0.141 0.165 0.108 0.021 Mahasamund 0.127 0.146 -1.979 -0.013 Narayanpur 0.163 0.195 0.418 0.013 Raigarh 0.128 0.147 -2.677 -0.015 Raipur 0.140 0.163 -0.083 0.112 Rajnandgaon 0.134 0.155 -1.348 0.014 Surguja 0.157 0.187 5.443 0.181 Uttar Bastar Kanker 0.130 0.150 -1.101 -0.002 Dadra and Nagar Haveli Dadra & Nagar Haveli 0.143 0.168 0.000 0.013 Daman and Diu Daman 0.102 0.114 -15.038 -0.019 Diu 0.122 0.139 13.936 -0.002 Delhi Central 0.104 0.117 -2.019 -0.054 East 0.111 0.125 -2.905 -0.117 New Delhi 0.086 0.095 -1.191 -0.022 North 0.114 0.129 -0.766 -0.050 North East 0.132 0.152 7.755 0.007 North West 0.121 0.138 3.384 -0.117 South 0.118 0.134 0.429 -0.117 South West 0.115 0.129 -1.720 -0.127 West 0.112 0.126 -3.854 -0.167 Goa North Goa 0.092 0.101 -11.001 -0.118 South Goa 0.101 0.112 10.563 -0.069 Gujarat Ahmadabad 0.111 0.125 -6.422 -0.485 Amreli 0.111 0.125 -1.326 -0.101 Anand 0.117 0.132 -1.072 -0.101 Banas Kantha 0.160 0.191 6.642 0.260 Bharuch 0.110 0.124 -1.526 -0.112 Bhavnagar 0.128 0.147 0.801 -0.026 Dohad 0.189 0.234 7.656 0.333 Gandhinagar 0.115 0.130 -0.877 -0.076 Jamnagar 0.118 0.133 -0.941 -0.096 Junagadh 0.110 0.123 -2.714 -0.198 Kachchh 0.148 0.174 3.110 0.107 Kheda 0.121 0.137 -0.537 -0.079 Mahesana 0.112 0.126 -1.654 -0.129 Narmada 0.127 0.146 0.128 -0.007

172 Statistical Tables

State/Union Territory Index C Index A Dds(a) Ddc(a) District Navsari 0.097 0.108 -2.614 -0.161 Panch Mahals 0.146 0.171 3.241 0.107 Patan 0.134 0.154 0.817 0.010 Porbandar 0.109 0.122 -0.624 -0.045 Rajkot 0.112 0.126 -3.291 -0.251 Sabar Kantha 0.139 0.161 2.276 0.058 Surat 0.117 0.132 -2.965 -0.287 Surendranagar 0.133 0.154 1.044 0.012 Tapi 0.105 0.117 -1.105 -0.074 The Dangs 0.174 0.210 0.643 0.027 Vadodara 0.114 0.129 -2.847 -0.237 Valsad 0.121 0.138 -0.338 -0.056 Haryana Ambala 0.109 0.122 -3.974 -0.087 Bhiwani 0.126 0.145 -0.901 -0.025 Faridabad 0.132 0.152 0.606 0.006 Fatehabad 0.126 0.144 -0.617 -0.016 Gurgaon 0.131 0.150 0.132 -0.003 Hisar 0.121 0.138 -2.416 -0.057 Jhajjar 0.121 0.138 -1.295 -0.031 Jind 0.124 0.141 -1.343 -0.033 Kaithal 0.126 0.144 -0.677 -0.018 Karnal 0.129 0.148 -0.232 -0.010 Kurukshetra 0.120 0.136 -1.589 -0.037 Mahendragarh 0.119 0.135 -1.564 -0.036 Mewat 0.223 0.287 12.195 0.252 Panchkula 0.117 0.132 -1.192 -0.027 Palwal 0.165 0.198 4.972 0.100 Panipat 0.137 0.159 1.233 0.022 Rewari 0.125 0.143 -0.674 -0.017 Rohtak 0.119 0.134 -1.923 -0.044 Sirsa 0.119 0.134 -2.356 -0.054 Sonipat 0.127 0.145 -0.698 -0.020 Yamunanagar 0.118 0.134 -2.326 -0.053 Himachal Pradesh Bilaspur 0.109 0.122 -0.615 -0.029 Chamba 0.134 0.154 6.851 0.004 Hamirpur 0.105 0.117 -1.906 -0.041 Kangra 0.107 0.119 -4.596 -0.127 Kinnaur 0.095 0.105 -0.964 -0.011 Kullu 0.114 0.129 0.824 -0.025 Lahul & Spiti 0.095 0.105 -0.356 -0.004

173 Preliminary Demography of India

State/Union Territory Index C Index A Dds(a) Ddc(a) District Mandi 0.110 0.124 -0.895 -0.072 Shimla 0.099 0.110 -6.618 -0.092 Sirmaur 0.128 0.147 5.350 -0.005 Solan 0.115 0.130 1.327 -0.031 Una 0.112 0.126 0.096 -0.034 Jammu and Kashmir Anantnag 0.193 0.239 8.367 0.176 Badgam 0.207 0.261 7.999 0.144 Bandipore 0.157 0.186 -0.342 0.029 Baramula 0.159 0.190 -0.182 0.083 Doda 0.173 0.210 1.365 0.048 Ganderbal 0.170 0.205 0.756 0.033 Jammu 0.105 0.117 -25.776 -0.140 Kargil 0.142 0.166 -0.687 0.005 Kathua 0.130 0.150 -5.147 -0.002 Kishtwar 0.169 0.204 0.539 0.025 Kulgam 0.166 0.200 0.674 0.042 Kupwara 0.225 0.290 12.753 0.205 Leh(Ladakh) 0.080 0.087 -3.972 -0.029 Pulwama 0.171 0.207 1.602 0.064 Punch 0.176 0.214 1.928 0.060 Rajouri 0.191 0.237 4.643 0.100 Ramban 0.193 0.240 2.241 0.047 Reasi 0.177 0.216 1.341 0.040 Samba 0.119 0.135 -3.760 -0.012 Shupiyan 0.151 0.178 -0.605 0.016 Srinagar 0.123 0.140 -13.570 -0.035 Udhampur 0.149 0.175 -1.658 0.029 Jharkhand Bokaro 0.138 0.160 -4.514 0.043 Chatra 0.181 0.221 2.153 0.142 Deoghar 0.176 0.214 2.446 0.186 Dhanbad 0.137 0.159 -6.157 0.048 Dumka 0.161 0.192 0.296 0.114 Garhwa 0.177 0.215 2.220 0.167 Giridih 0.184 0.226 5.759 0.353 Godda 0.179 0.218 2.485 0.173 Gumla 0.164 0.196 0.515 0.096 Hazaribagh 0.158 0.187 -0.204 0.134 Jamtara 0.163 0.194 0.285 0.071 Khunti 0.157 0.186 -0.092 0.040 Kodarma 0.179 0.218 1.366 0.095

174 Statistical Tables

State/Union Territory Index C Index A Dds(a) Ddc(a) District Latehar 0.183 0.224 1.624 0.102 Lohardaga 0.164 0.196 0.226 0.043 Pakur 0.195 0.242 2.948 0.152 Palamu 0.163 0.195 0.865 0.179 Pashchimi Singhbhum 0.169 0.204 1.487 0.161 Purbi Singhbhum 0.125 0.143 -8.437 -0.046 Ramgarh 0.138 0.160 -2.110 0.019 Ranchi 0.133 0.154 -7.897 0.019 Sahibganj 0.188 0.232 3.101 0.177 Saraikela-Kharsawan 0.144 0.169 -1.583 0.042 Simdega 0.152 0.180 -0.401 0.037 Karnataka Bagalkot 0.140 0.162 3.352 0.048 Bangalore 0.103 0.115 -6.433 -0.940 Bangalore Rural 0.103 0.115 -0.643 -0.096 Belgaum 0.127 0.145 4.708 -0.069 Bellary 0.135 0.156 3.801 0.030 Bidar 0.128 0.146 1.768 -0.020 Bijapur 0.140 0.162 3.859 0.055 Chamarajanagar 0.093 0.102 -1.520 -0.142 Chikkaballapura 0.099 0.110 -1.200 -0.141 Chikmagalur 0.089 0.097 -2.118 -0.180 Chitradurga 0.107 0.120 -0.613 -0.137 Dakshina Kannada 0.097 0.108 -2.353 -0.252 Davanagere 0.106 0.119 -0.833 -0.167 Dharwad 0.114 0.128 0.217 -0.107 Gadag 0.119 0.136 0.541 -0.041 Gulbarga 0.137 0.159 4.211 0.048 Hassan 0.088 0.096 -3.463 -0.289 Haveri 0.117 0.133 0.593 -0.073 Kodagu 0.095 0.105 -0.730 -0.072 Kolar 0.105 0.117 -0.797 -0.139 Koppal 0.140 0.162 2.473 0.036 Mandya 0.090 0.098 -3.199 -0.278 Mysore 0.095 0.106 -3.818 -0.385 Raichur 0.142 0.165 3.659 0.061 Ramanagara 0.094 0.104 -1.531 -0.147 Shimoga 0.101 0.112 -1.493 -0.188 Tumkur 0.094 0.104 -3.728 -0.360 Udupi 0.085 0.093 -2.530 -0.203 Uttara Kannada 0.102 0.113 -1.092 -0.147 Yadgir 0.158 0.188 3.319 0.092

175 Preliminary Demography of India

State/Union Territory Index C Index A Dds(a) Ddc(a) District Kerala Alappuzha 0.088 0.096 -3.857 -0.344 Ernakulam 0.088 0.097 -5.678 -0.524 Idukki 0.090 0.099 -1.528 -0.166 Kannur 0.105 0.117 1.979 -0.229 Kasaragod 0.115 0.129 2.680 -0.072 Kollam 0.091 0.100 -3.573 -0.393 Kottayam 0.085 0.093 -4.415 -0.344 Kozhikode 0.105 0.117 2.283 -0.283 Malappuram 0.134 0.155 18.217 0.042 Palakkad 0.103 0.114 1.241 -0.281 Pathanamthitta 0.077 0.083 -4.473 -0.257 Thiruvananthapuram 0.088 0.096 -5.894 -0.533 Thrissur 0.093 0.102 -3.047 -0.433 Wayanad 0.110 0.123 1.176 -0.059 Lakshadweep Lakshadweep 0.110 0.124 0.000 -0.005 Madhya Pradesh Alirajpur 0.198 0.246 1.614 0.128 Anuppur 0.137 0.159 -0.291 0.014 Ashoknagar 0.162 0.193 0.641 0.074 Balaghat 0.122 0.138 -2.093 -0.053 Barwani 0.188 0.232 2.584 0.214 Betul 0.132 0.152 -1.084 0.002 Bhind 0.142 0.165 -0.290 0.055 Bhopal 0.124 0.141 -2.614 -0.056 Burhanpur 0.159 0.189 0.471 0.060 Chhatarpur 0.158 0.188 1.077 0.139 Chhindwara 0.128 0.147 -1.847 -0.022 Damoh 0.148 0.174 0.175 0.064 Datia 0.139 0.161 -0.258 0.018 Dewas 0.143 0.167 -0.187 0.055 Dhar 0.160 0.190 1.474 0.181 Dindori 0.151 0.179 0.206 0.042 East Nimar 0.155 0.184 0.608 0.092 Guna 0.162 0.194 0.981 0.112 Gwalior 0.125 0.143 -2.102 -0.040 Harda 0.144 0.168 -0.033 0.022 Hoshangabad 0.130 0.150 -0.953 -0.004 Indore 0.125 0.142 -3.488 -0.070 Jabalpur 0.117 0.132 -3.695 -0.117 Jhabua 0.203 0.255 2.478 0.192

176 Statistical Tables

State/Union Territory Index C Index A Dds(a) Ddc(a) District Katni 0.146 0.171 0.035 0.057 Mandla 0.137 0.159 -0.408 0.020 Mandsaur 0.130 0.149 -1.053 -0.006 Morena 0.152 0.180 0.655 0.123 Narsimhapur 0.128 0.146 -0.982 -0.013 Neemuch 0.128 0.147 -0.735 -0.009 Panna 0.158 0.188 0.616 0.080 Raisen 0.153 0.181 0.485 0.086 Rajgarh 0.146 0.171 0.046 0.068 Ratlam 0.146 0.171 0.032 0.064 Rewa 0.144 0.168 -0.131 0.092 Sagar 0.148 0.173 0.275 0.117 Satna 0.144 0.169 -0.096 0.089 Sehore 0.149 0.175 0.206 0.068 Seoni 0.128 0.146 -1.228 -0.015 Shahdol 0.145 0.169 -0.038 0.043 Shajapur 0.141 0.164 -0.324 0.045 Sheopur 0.168 0.202 0.708 0.072 Shivpuri 0.163 0.194 1.373 0.156 Sidhi 0.168 0.201 1.137 0.116 Singrauli 0.173 0.210 1.480 0.139 Tikamgarh 0.155 0.183 0.639 0.100 Ujjain 0.133 0.154 -1.202 0.012 Umaria 0.155 0.183 0.279 0.044 Vidisha 0.158 0.188 0.876 0.114 West Nimar 0.157 0.186 1.020 0.141 Maharashtra Ahmadnagar 0.118 0.134 0.673 -0.193 Akola 0.113 0.128 -0.072 -0.109 Amravati 0.104 0.116 -1.209 -0.275 Aurangabad 0.140 0.163 3.292 0.098 Bhandara 0.103 0.114 -0.566 -0.120 Bid 0.133 0.153 1.730 0.015 Buldana 0.125 0.143 1.044 -0.049 Chandrapur 0.102 0.114 -1.084 -0.224 Dhule 0.128 0.146 0.988 -0.024 Gadchiroli 0.107 0.120 -0.292 -0.087 Gondiya 0.103 0.115 -0.602 -0.130 Hingoli 0.137 0.158 0.928 0.020 Jalgaon 0.122 0.138 1.144 -0.132 Jalna 0.144 0.168 1.987 0.074 Kolhapur 0.102 0.114 -1.917 -0.396

177 Preliminary Demography of India

State/Union Territory Index C Index A Dds(a) Ddc(a) District Latur 0.125 0.143 0.989 -0.046 Mumbai 0.083 0.091 -4.261 -0.573 Mumbai Suburban 0.094 0.104 -7.898 -1.259 Nagpur 0.104 0.116 -2.001 -0.448 Nanded 0.132 0.153 2.172 0.013 Nandurbar 0.140 0.163 1.501 0.047 Nashik 0.132 0.152 3.830 0.012 Osmanabad 0.120 0.137 0.361 -0.060 Parbhani 0.137 0.159 1.478 0.034 Pune 0.113 0.128 -0.406 -0.568 Raigarh 0.110 0.124 -0.422 -0.188 Ratnagiri 0.093 0.102 -1.458 -0.226 Sangli 0.105 0.117 -1.088 -0.260 Satara 0.102 0.114 -1.433 -0.302 Sindhudurg 0.081 0.088 -1.258 -0.165 Solapur 0.120 0.137 0.983 -0.152 Thane 0.114 0.128 -0.261 -0.647 Wardha 0.096 0.106 -0.974 -0.163 Washim 0.123 0.141 0.393 -0.031 Yavatmal 0.115 0.131 0.118 -0.145 Manipur Bishnupur 0.124 0.142 -1.964 -0.006 Chandel 0.115 0.129 -3.272 -0.008 Churachandpur 0.127 0.146 -1.021 -0.004 Imphal East 0.134 0.155 2.803 0.004 Imphal West 0.113 0.128 -12.814 -0.031 Senapati 0.128 0.147 -0.891 -0.004 Tamenglong 0.129 0.148 -0.165 -0.001 Thoubal 0.159 0.189 16.024 0.034 Ukhrul 0.125 0.143 -1.163 -0.003 Meghalaya East Garo Hills 0.180 0.219 -2.441 0.042 East Khasi Hills 0.163 0.195 -20.435 0.075 Jaintia Hills 0.221 0.283 11.752 0.089 Ribhoi 0.200 0.249 2.906 0.046 South Garo Hills 0.192 0.238 0.633 0.023 West Garo Hills 0.174 0.211 -8.355 0.077 West Khasi Hills 0.225 0.290 12.849 0.090 Mizoram Aizawl 0.129 0.149 -29.641 -0.002 Champhai 0.176 0.214 8.863 0.016 Kolasib 0.153 0.181 0.310 0.005

178 Statistical Tables

State/Union Territory Index C Index A Dds(a) Ddc(a) District Lawngtlai 0.186 0.228 11.319 0.017 Lunglei 0.153 0.181 0.659 0.010 Mamit 0.173 0.209 5.293 0.010 Saiha 0.162 0.194 1.810 0.005 Serchhip 0.140 0.163 -2.433 0.002 Nagaland Dimapur 0.131 0.150 -9.696 -0.001 Kiphire 0.194 0.240 5.725 0.012 Kohima 0.134 0.155 -5.197 0.002 Longleng 0.175 0.212 2.525 0.006 Mokokchung 0.104 0.116 -15.956 -0.018 Mon 0.158 0.187 5.720 0.019 Peren 0.160 0.191 2.567 0.008 Phek 0.169 0.203 6.588 0.017 Tuensang 0.177 0.216 10.610 0.025 Wokha 0.118 0.134 -8.345 -0.007 Zunheboto 0.143 0.166 -0.464 0.005 Orissa Anugul 0.115 0.129 -0.697 -0.071 Balangir 0.126 0.144 0.871 -0.030 Baleshwar 0.118 0.134 -0.371 -0.097 Bargarh 0.106 0.118 -2.210 -0.131 Baudh 0.134 0.155 0.587 0.004 Bhadrak 0.117 0.133 -0.402 -0.069 Cuttack 0.096 0.106 -6.821 -0.332 Debagarh 0.124 0.141 0.111 -0.008 Dhenkanal 0.111 0.125 -1.070 -0.081 Gajapati 0.144 0.168 1.237 0.022 Ganjam 0.113 0.127 -2.481 -0.214 Jagatsinghapur 0.091 0.100 -3.631 -0.167 Jajapur 0.114 0.128 -1.198 -0.108 Jharsuguda 0.107 0.119 -0.798 -0.049 Kalahandi 0.136 0.158 2.346 0.024 Kandhamal 0.145 0.170 1.670 0.031 Kendrapara 0.107 0.119 -2.002 -0.122 Kendujhar 0.141 0.164 3.386 0.052 Khordha 0.099 0.110 -5.046 -0.257 Koraput 0.157 0.186 4.386 0.102 Malkangiri 0.172 0.208 2.685 0.071 Mayurbhanj 0.134 0.155 3.358 0.025 Nabarangapur 0.166 0.199 4.735 0.120 Nayagarh 0.105 0.118 -1.470 -0.086

179 Preliminary Demography of India

State/Union Territory Index C Index A Dds(a) Ddc(a) District Nuapada 0.140 0.163 1.108 0.016 Puri 0.097 0.107 -4.239 -0.209 Rayagada 0.147 0.172 2.307 0.045 Sambalpur 0.108 0.121 -1.274 -0.082 Subarnapur 0.117 0.133 -0.173 -0.030 Sundargarh 0.120 0.136 -0.074 -0.078 Puducherry Karaikal 0.108 0.121 3.816 -0.016 Mahe 0.109 0.123 1.061 -0.003 Puducherry 0.101 0.112 -6.245 -0.101 Yanam 0.108 0.121 1.177 -0.004 Punjab Amritsar 0.107 0.120 0.351 -0.207 Barnala 0.106 0.118 -0.053 -0.053 Bathinda 0.105 0.117 -0.345 -0.128 Faridkot 0.108 0.121 0.173 -0.049 Fatehgarh Sahib 0.101 0.113 -0.493 -0.063 Firozpur 0.119 0.135 4.100 -0.081 Gurdaspur 0.105 0.117 -0.525 -0.210 Hoshiarpur 0.103 0.114 -0.954 -0.158 Jalandhar 0.098 0.108 -3.115 -0.259 Kapurthala 0.101 0.112 -0.702 -0.087 Ludhiana 0.104 0.116 -1.208 -0.328 Mansa 0.106 0.119 -0.027 -0.067 Moga 0.103 0.115 -0.465 -0.096 Muktsar 0.113 0.127 0.993 -0.055 Patiala 0.108 0.121 0.653 -0.148 Rupnagar 0.102 0.113 -0.499 -0.070 Sahibzada Ajit Singh Nagar 0.111 0.125 0.739 -0.068 Sangrur 0.106 0.118 -0.094 -0.145 Shahid Bhagat Singh Nagar 0.099 0.109 -0.804 -0.071 Tarn Taran 0.116 0.131 1.738 -0.057 Rajasthan Ajmer 0.145 0.170 -1.047 0.107 Alwar 0.158 0.188 0.880 0.287 Banswara 0.179 0.218 2.108 0.235 Baran 0.147 0.172 -0.391 0.057 Barmer 0.192 0.237 4.479 0.422 Bharatpur 0.169 0.203 1.903 0.272 Bhilwara 0.148 0.173 -0.633 0.120 Bikaner 0.167 0.200 1.506 0.238 Bundi 0.142 0.165 -0.625 0.036

180 Statistical Tables

State/Union Territory Index C Index A Dds(a) Ddc(a) District Chittaurgarh 0.136 0.157 -1.388 0.021 Churu 0.154 0.182 0.067 0.135 Dausa 0.157 0.186 0.298 0.122 Dhaulpur 0.179 0.217 1.409 0.158 Dungarpur 0.173 0.208 1.254 0.161 Ganganagar 0.128 0.147 -2.579 -0.019 Hanumangarh 0.131 0.151 -2.056 -0.002 Jaipur 0.137 0.159 -5.403 0.123 Jaisalmer 0.194 0.241 1.219 0.112 Jalor 0.171 0.207 1.567 0.207 Jhalawar 0.145 0.169 -0.595 0.057 Jhunjhunun 0.133 0.154 -2.177 0.015 Jodhpur 0.161 0.192 1.374 0.316 Karauli 0.164 0.196 0.767 0.138 Kota 0.127 0.146 -2.631 -0.023 Nagaur 0.151 0.177 -0.394 0.191 Pali 0.144 0.168 -0.955 0.077 Pratapgarh 0.171 0.207 0.738 0.098 Rajsamand 0.150 0.177 -0.166 0.065 Sawai Madhopur 0.149 0.174 -0.300 0.069 Sikar 0.140 0.163 -1.729 0.075 Sirohi 0.166 0.198 0.611 0.102 Tonk 0.141 0.165 -0.841 0.044 Udaipur 0.163 0.194 1.403 0.277 Sikkim East District 0.094 0.104 -13.821 -0.037 North District 0.103 0.115 0.950 -0.004 South District 0.103 0.114 2.516 -0.015 West District 0.110 0.123 9.562 -0.010 Tamil Nadu Ariyalur 0.102 0.114 0.328 -0.077 Chennai 0.089 0.098 -2.071 -0.723 Coimbatore 0.085 0.093 -2.663 -0.604 Cuddalore 0.100 0.111 0.818 -0.284 Dharmapuri 0.108 0.121 1.219 -0.120 Dindigul 0.093 0.102 -0.462 -0.305 Erode 0.080 0.087 -2.618 -0.446 Kancheepuram 0.099 0.110 1.014 -0.451 Kanniyakumari 0.087 0.095 -1.171 -0.309 Karur 0.092 0.101 -0.278 -0.154 Krishnagiri 0.108 0.121 1.561 -0.148 Madurai 0.094 0.104 -0.249 -0.404

181 Preliminary Demography of India

State/Union Territory Index C Index A Dds(a) Ddc(a) District Nagapattinam 0.096 0.106 0.019 -0.206 Namakkal 0.082 0.089 -1.808 -0.328 Perambalur 0.099 0.110 0.137 -0.064 Pudukkottai 0.105 0.117 1.013 -0.147 Ramanathapuram 0.095 0.105 -0.027 -0.173 Salem 0.093 0.102 -0.671 -0.486 Sivaganga 0.095 0.105 -0.036 -0.174 Thanjavur 0.093 0.103 -0.404 -0.332 The Nilgiris 0.084 0.092 -0.636 -0.132 Theni 0.089 0.098 -0.571 -0.193 Thiruvallur 0.099 0.110 0.942 -0.421 Thiruvarur 0.091 0.100 -0.444 -0.189 Thoothukkudi 0.098 0.109 0.300 -0.205 Tiruchirappalli 0.093 0.103 -0.404 -0.372 Tirunelveli 0.098 0.109 0.522 -0.363 Tiruppur 0.090 0.098 -1.047 -0.379 Tiruvannamalai 0.104 0.116 1.364 -0.235 Vellore 0.104 0.115 2.101 -0.378 Viluppuram 0.109 0.123 3.116 -0.258 Virudhunagar 0.094 0.104 -0.177 -0.260 Tripura Dhalai 0.144 0.168 8.970 0.015 North Tripura 0.139 0.161 12.915 0.016 South Tripura 0.124 0.142 3.239 -0.019 West Tripura 0.107 0.120 -28.088 -0.143 Uttar Pradesh Agra 0.146 0.171 -0.235 0.193 Aligarh 0.151 0.178 0.140 0.217 Allahabad 0.140 0.162 -0.967 0.156 Ambedkar Nagar 0.135 0.156 -0.585 0.031 Auraiya 0.141 0.165 -0.183 0.042 Azamgarh 0.147 0.173 -0.118 0.225 Baghpat 0.145 0.170 -0.085 0.055 Bahraich 0.183 0.224 1.851 0.489 Ballia 0.139 0.162 -0.553 0.079 Balrampur 0.179 0.218 1.037 0.285 Banda 0.161 0.192 0.361 0.155 Bara Banki 0.155 0.183 0.321 0.225 Bareilly 0.150 0.176 0.078 0.249 Basti 0.151 0.178 0.098 0.146 Bijnor 0.149 0.175 0.010 0.197 Budaun 0.174 0.211 1.522 0.447

182 Statistical Tables

State/Union Territory Index C Index A Dds(a) Ddc(a) District Bulandshahr 0.154 0.182 0.280 0.231 Chandauli 0.156 0.185 0.225 0.140 Chitrakoot 0.173 0.209 0.386 0.116 Deoria 0.144 0.168 -0.284 0.117 Etah 0.158 0.187 0.257 0.136 Etawah 0.139 0.162 -0.265 0.040 Faizabad 0.141 0.164 -0.362 0.071 Farrukhabad 0.155 0.184 0.196 0.132 Fatehpur 0.143 0.167 -0.264 0.096 Firozabad 0.148 0.174 -0.034 0.127 Gautam Buddha Nagar 0.146 0.172 -0.073 0.077 Ghaziabad 0.142 0.166 -0.542 0.157 Ghazipur 0.149 0.175 -0.019 0.189 Gonda 0.159 0.189 0.582 0.278 Gorakhpur 0.134 0.155 -1.170 0.042 Hamirpur 0.135 0.155 -0.284 0.012 Hardoi 0.162 0.193 0.885 0.362 Jalaun 0.131 0.151 -0.533 0.001 Jaunpur 0.144 0.168 -0.413 0.169 Jhansi 0.125 0.142 -0.903 -0.043 Jyotiba Phule Nagar 0.158 0.188 0.292 0.145 Kannauj 0.152 0.179 0.078 0.101 Kanpur Dehat 0.136 0.157 -0.418 0.026 Kanpur Nagar 0.106 0.119 -3.880 -0.398 Kanshiram Nagar 0.170 0.205 0.498 0.158 Kaushambi 0.165 0.198 0.421 0.154 Kheri 0.161 0.191 0.775 0.340 Kushinagar 0.155 0.183 0.356 0.247 Lalitpur 0.169 0.204 0.401 0.131 Lucknow 0.114 0.128 -3.099 -0.268 Mahamaya Nagar 0.154 0.181 0.121 0.103 Mahoba 0.142 0.166 -0.101 0.030 Mahrajganj 0.151 0.178 0.087 0.155 Mainpuri 0.149 0.175 0.008 0.099 Mathura 0.156 0.185 0.307 0.185 Mau 0.149 0.174 -0.017 0.114 Meerut 0.142 0.165 -0.442 0.110 Mirzapur 0.157 0.187 0.347 0.189 Moradabad 0.160 0.190 0.867 0.396 Muzaffarnagar 0.152 0.180 0.236 0.258 Pilibhit 0.146 0.171 -0.110 0.090 Pratapgarh 0.135 0.156 -0.807 0.035

183 Preliminary Demography of India

State/Union Territory Index C Index A Dds(a) Ddc(a) District Rae Bareli 0.135 0.157 -0.824 0.044 Rampur 0.159 0.188 0.375 0.185 Saharanpur 0.146 0.171 -0.186 0.153 Sant Kabir Nagar 0.159 0.189 0.280 0.137 Sant Ravidas Nagar 0.157 0.186 0.210 0.117 Shahjahanpur 0.163 0.194 0.685 0.272 Shrawasti 0.182 0.222 0.579 0.155 Siddharthnagar 0.182 0.223 1.349 0.358 Sitapur 0.164 0.196 1.093 0.417 Sonbhadra 0.166 0.199 0.517 0.184 Sultanpur 0.142 0.166 -0.443 0.128 Unnao 0.134 0.155 -0.828 0.028 Varanasi 0.130 0.149 -1.271 -0.015 Uttarakhand Almora 0.125 0.143 -1.420 -0.012 Bageshwar 0.133 0.154 0.194 0.002 Chamoli 0.130 0.149 -0.235 -0.002 Champawat 0.141 0.164 0.902 0.008 Dehradun 0.116 0.131 -10.648 -0.088 Garhwal 0.120 0.136 -3.162 -0.026 Hardwar 0.148 0.173 11.179 0.094 Nainital 0.128 0.147 -1.239 -0.010 Pithoragarh 0.129 0.149 -0.350 -0.003 Rudraprayag 0.128 0.146 -0.343 -0.003 Tehri Garhwal 0.134 0.154 0.544 0.005 Udham Singh Nagar 0.136 0.157 2.573 0.022 Uttarkashi 0.136 0.158 0.626 0.005 West Bengal Bankura 0.113 0.127 0.349 -0.223 Barddhaman 0.102 0.114 -3.325 -0.787 Birbhum 0.124 0.141 2.091 -0.085 Dakshin Dinajpur 0.107 0.120 -0.324 -0.140 Darjiling 0.098 0.108 -1.211 -0.219 Haora 0.103 0.115 -1.921 -0.481 Hugli 0.091 0.101 -5.587 -0.805 Jalpaiguri 0.115 0.130 0.790 -0.209 Koch Bihar 0.118 0.133 0.934 -0.125 Kolkata 0.067 0.072 -11.777 -1.200 Maldah 0.148 0.173 6.278 0.197 Murshidabad 0.138 0.160 8.476 0.147 Nadia 0.098 0.109 -3.372 -0.613 North 24 Parganas 0.090 0.098 -11.309 -1.553

184 Statistical Tables

State/Union Territory Index C Index A Dds(a) Ddc(a) District Paschim Medinipur 0.112 0.126 0.250 -0.393 Purba Medinipur 0.111 0.125 0.087 -0.347 Puruliya 0.134 0.155 3.078 0.029 South 24 Parganas 0.120 0.136 3.441 -0.306 Uttar Dinajpur 0.157 0.186 5.706 0.223 Source: Author’s calculations

185 Preliminary Demography of India

Table 5.A Sex ratio in districts of India, 2011 State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Andaman and Nicorbar Islands Nicobars 778 961 757 North & Middle Andaman 925 977 919 South Andaman 874 961 865 Andhra Pradesh Adilabad 1003 942 1010 Anantapur 977 927 984 Chittoor 1002 931 1010 East Godavari 1005 969 1009 Guntur 1003 948 1009 Hyderabad 943 938 943 Karimnagar 1009 937 1016 Khammam 1010 958 1016 Krishna 997 953 1001 Kurnool 984 937 990 Mahbubnagar 975 932 982 Medak 989 954 994 Nalgonda 982 921 989 Nizamabad 1038 946 1049 Prakasam 981 932 987 Rangareddy 955 947 956 Sri Potti Sriramulu Nellore 986 945 991 Srikakulam 1014 953 1021 Visakhapatnam 1003 961 1008 Vizianagaram 1016 955 1023 Warangal 994 912 1003 West Godavari 1004 970 1008 Y.S.R. 984 919 992 Arunachal Pradesh Anjaw 805 954 779 Changlang 914 954 906 Dibang Valley 808 831 804 East Kameng 1012 970 1021 East Siang 962 984 959 Kurung Kumey 1029 978 1040 Lohit 901 954 892 Lower Dibang Valley 919 945 915 Lower Subansiri 975 969 976 Papum Pare 950 963 948 Tawang 701 1005 669

186 Statistical Tables

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Tirap 931 950 927 Upper Siang 891 968 880 Upper Subansiri 982 968 985 West Kameng 755 965 728 West Siang 916 928 915 Assam Baksa 967 962 968 Barpeta 951 955 950 Bongaigaon 961 965 960 Cachar 958 955 959 Chirang 969 958 971 Darrang 923 941 920 Dhemaji 949 945 950 Dhubri 952 965 949 Dibrugarh 952 957 952 Dima Hasao 931 956 927 Goalpara 962 954 964 Golaghat 961 961 961 Hailakandi 946 948 946 Jorhat 956 963 955 Kamrup 946 962 944 Kamrup Metropolitan 922 994 915 Karbi Anglong 956 916 966 Karimganj 961 958 961 Kokrajhar 958 951 959 Lakhimpur 965 958 967 Morigaon 974 950 978 Nagaon 962 958 963 Nalbari 945 963 943 Sivasagar 951 957 951 Sonitpur 946 958 944 Tinsukia 948 971 945 Udalguri 966 965 966 Bihar Araria 921 954 913 Arwal 927 941 925 Aurangabad 916 945 910 Banka 907 939 900 Begusarai 894 911 890 Bhagalpur 879 934 867 Bhojpur 900 915 897 Buxar 922 925 922

187 Preliminary Demography of India

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Darbhanga 910 928 907 Gaya 932 959 926 Gopalganj 1015 945 1030 Jamui 921 956 913 Jehanabad 918 918 918 Kaimur (Bhabua) 919 939 915 Katihar 916 956 907 Khagaria 883 912 876 Kishanganj 946 966 941 Lakhisarai 900 915 897 Madhepura 914 923 911 Madhubani 925 931 924 Munger 879 925 870 Muzaffarpur 898 917 894 Nalanda 921 929 919 Nawada 936 985 926 Pashchim Champaran 906 950 896 Patna 892 899 891 Purba Champaran 901 923 896 Purnia 930 953 925 Rohtas 914 925 912 Saharsa 906 928 900 Samastipur 909 941 902 Saran 949 922 954 Sheikhpura 926 940 923 Sheohar 890 925 882 Sitamarhi 899 932 892 Siwan 984 934 994 Supaul 925 942 921 Vaishali 892 894 892 Chandigarh Chandigarh 818 867 812 Chhattisgarh Bastar 1024 991 1030 Bijapur 982 978 982 Bilaspur 972 957 975 Dakshin Bastar Dantewada 1022 1005 1024 Dhamtari 1012 969 1018 Durg 988 958 993 Janjgir - Champa 986 945 992 Jashpur 1004 974 1010 Kabeerdham 997 973 1003

188 Statistical Tables

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Korba 971 964 972 Koriya 971 968 972 Mahasamund 1018 960 1027 Narayanpur 998 975 1002 Raigarh 993 943 1001 Raipur 983 965 986 Rajnandgaon 1017 976 1024 Surguja 976 955 980 Uttar Bastar Kanker 1007 975 1012 Dadra and Nagar Haveli Dadra & Nagar Haveli 775 924 752 Daman and Diu Daman 533 905 500 Diu 1030 923 1046 Delhi Central 892 902 890 East 883 870 885 New Delhi 811 884 804 North 871 872 870 North East 886 875 887 North West 862 863 862 South 859 878 857 South West 836 836 836 West 876 867 877 Goa North Goa 959 911 964 South Goa 980 930 986 Gujarat Ahmadabad 903 859 909 Amreli 964 879 975 Anand 921 877 927 Banas Kantha 936 890 946 Bharuch 924 914 926 Bhavnagar 931 885 938 Dohad 986 937 998 Gandhinagar 920 847 930 Jamnagar 938 898 943 Junagadh 952 904 959 Kachchh 907 913 905 Kheda 937 887 944 Mahesana 925 845 936 Narmada 960 937 963

189 Preliminary Demography of India

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Navsari 961 921 966 Panch Mahals 945 923 949 Patan 935 884 943 Porbandar 947 894 954 Rajkot 924 854 933 Sabar Kantha 950 899 959 Surat 788 836 782 Surendranagar 929 889 935 Tapi 1004 944 1012 The Dangs 1007 963 1017 Vadodara 934 894 939 Valsad 926 926 926 Haryana Ambala 882 807 892 Bhiwani 884 831 892 Faridabad 871 842 875 Fatehabad 903 845 911 Gurgaon 853 826 857 Hisar 871 849 874 Jhajjar 861 774 873 Jind 870 835 875 Kaithal 880 821 889 Karnal 886 820 896 Kurukshetra 889 817 900 Mahendragarh 894 778 911 Mewat 906 903 907 Panchkula 870 850 872 Palwal 879 862 882 Panipat 861 833 865 Rewari 898 784 915 Rohtak 868 807 877 Sirsa 896 852 901 Sonipat 853 790 862 Yamunanagar 877 825 884 Himachal Pradesh Bilaspur 981 893 993 Chamba 989 950 995 Hamirpur 1096 881 1124 Kangra 1013 873 1032 Kinnaur 818 953 805 Kullu 950 962 949 Lahul & Spiti 916 1013 906

190 Statistical Tables

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Mandi 1012 913 1025 Shimla 916 922 915 Sirmaur 915 931 913 Solan 884 899 882 Una 977 870 991 Jammu and Kashmir Anantnag 937 831 964 Badgam 883 832 897 Bandipore 911 893 914 Baramula 873 866 874 Doda 922 932 920 Ganderbal 869 863 870 Jammu 871 795 881 Kargil 775 978 745 Kathua 877 836 884 Kishtwar 917 922 916 Kulgam 951 882 966 Kupwara 843 854 840 Leh(Ladakh) 583 944 558 Pulwama 913 836 930 Punch 890 895 889 Rajouri 863 837 869 Ramban 901 931 894 Reasi 891 921 885 Samba 886 787 900 Shupiyan 951 883 964 Srinagar 879 869 881 Udhampur 863 887 859 Jharkhand Bokaro 916 912 916 Chatra 951 963 949 Deoghar 921 939 917 Dhanbad 908 917 907 Dumka 974 957 977 Garhwa 933 958 928 Giridih 943 934 945 Godda 933 953 928 Gumla 993 955 1000 Hazaribagh 946 924 950 Jamtara 959 948 961 Khunti 994 951 1002 Kodarma 949 944 950

191 Preliminary Demography of India

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Latehar 964 964 964 Lohardaga 985 961 990 Pakur 985 965 989 Palamu 929 947 925 Pashchimi Singhbhum 1004 980 1009 Purbi Singhbhum 949 922 953 Ramgarh 921 926 920 Ranchi 950 937 952 Sahibganj 948 955 946 Saraikela-Kharsawan 958 937 961 Simdega 1000 975 1005 Karnataka Bagalkot 984 929 994 Bangalore 908 941 904 Bangalore Rural 945 947 945 Belgaum 969 931 974 Bellary 978 954 982 Bidar 952 935 955 Bijapur 954 930 958 Chamarajanagar 989 942 994 Chikkaballapura 968 945 970 Chikmagalur 1005 963 1009 Chitradurga 969 933 973 Dakshina Kannada 1018 946 1026 Davanagere 967 931 972 Dharwad 967 942 970 Gadag 978 944 983 Gulbarga 962 935 967 Hassan 1005 964 1009 Haveri 951 945 952 Kodagu 1019 977 1024 Kolar 976 955 979 Koppal 983 953 988 Mandya 989 934 994 Mysore 982 956 984 Raichur 992 949 999 Ramanagara 976 960 977 Shimoga 995 960 999 Tumkur 979 952 982 Udupi 1093 955 1106 Uttara Kannada 975 947 979 Yadgir 984 942 993

192 Statistical Tables

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Kerala Alappuzha 1100 947 1116 Ernakulam 1028 954 1035 Idukki 1006 958 1011 Kannur 1133 962 1155 Kasaragod 1079 960 1095 Kollam 1113 960 1129 Kottayam 1040 957 1048 Kozhikode 1097 963 1114 Malappuram 1096 960 1119 Palakkad 1067 962 1079 Pathanamthitta 1129 964 1144 Thiruvananthapuram 1088 967 1100 Thrissur 1109 948 1127 Wayanad 1035 960 1044 Lakshadweep Lakshadweep 946 908 951 Madhya Pradesh Alirajpur 1009 971 1018 Anuppur 975 943 980 Ashoknagar 900 914 898 Balaghat 1021 961 1029 Barwani 981 940 990 Betul 970 949 973 Bhind 838 835 838 Bhopal 911 916 910 Burhanpur 951 921 957 Chhatarpur 884 894 882 Chhindwara 966 950 968 Damoh 913 931 910 Datia 875 852 879 Dewas 941 907 947 Dhar 961 913 970 Dindori 1004 970 1011 East Nimar 944 931 947 Guna 910 901 912 Gwalior 862 832 866 Harda 932 921 934 Hoshangabad 912 911 912 Indore 924 892 929 Jabalpur 925 916 926 Jhabua 989 934 1004

193 Preliminary Demography of India

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Katni 948 934 951 Mandla 1005 965 1011 Mandsaur 966 921 973 Morena 839 825 842 Narsimhapur 917 900 920 Neemuch 959 918 965 Panna 907 910 906 Raisen 899 927 895 Rajgarh 955 916 962 Ratlam 973 931 980 Rewa 930 883 938 Sagar 896 925 891 Satna 927 907 930 Sehore 918 906 920 Seoni 984 954 989 Shahdol 968 946 971 Shajapur 939 913 944 Sheopur 902 888 905 Shivpuri 877 889 875 Sidhi 952 910 961 Singrauli 916 921 915 Tikamgarh 901 886 904 Ujjain 954 919 960 Umaria 953 946 954 Vidisha 897 922 892 West Nimar 963 931 970 Maharashtra Ahmadnagar 934 839 948 Akola 942 900 948 Amravati 947 927 950 Aurangabad 917 848 928 Bhandara 984 939 989 Bid 912 801 930 Buldana 928 842 941 Chandrapur 959 945 960 Dhule 941 876 951 Gadchiroli 975 956 977 Gondiya 996 944 1002 Hingoli 935 868 946 Jalgaon 922 829 936 Jalna 929 847 944 Kolhapur 953 845 966

194 Statistical Tables

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Latur 924 872 932 Mumbai 838 874 835 Mumbai Suburban 857 910 852 Nagpur 948 926 951 Nanded 937 897 944 Nandurbar 972 932 978 Nashik 931 882 938 Osmanabad 920 853 930 Parbhani 940 866 953 Pune 910 873 915 Raigarh 955 924 959 Ratnagiri 1123 940 1143 Sangli 964 862 977 Satara 986 881 999 Sindhudurg 1037 910 1049 Solapur 932 872 941 Thane 880 918 875 Wardha 946 916 950 Washim 926 859 936 Yavatmal 947 915 951 Manipur Bishnupur 1000 919 1012 Chandel 932 919 934 Churachandpur 969 945 973 Imphal East 1011 932 1023 Imphal West 1029 943 1041 Senapati 939 912 943 Tamenglong 953 941 955 Thoubal 1006 948 1017 Ukhrul 948 921 952 Meghalaya East Garo Hills 968 975 967 East Khasi Hills 1008 961 1018 Jaintia Hills 1008 969 1019 Ribhoi 951 956 950 South Garo Hills 944 973 938 West Garo Hills 979 980 979 West Khasi Hills 981 975 983 Mizoram Aizawl 1009 984 1013 Champhai 981 976 982 Kolasib 956 987 951

195 Preliminary Demography of India

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Lawngtlai 945 965 941 Lunglei 944 965 941 Mamit 924 979 913 Saiha 978 937 987 Serchhip 976 926 985 Nagaland Dimapur 916 968 909 Kiphire 961 955 962 Kohima 927 978 920 Longleng 903 882 907 Mokokchung 927 954 924 Mon 898 900 898 Peren 917 940 913 Phek 951 915 959 Tuensang 930 935 929 Wokha 969 970 969 Zunheboto 981 955 986 Orissa Anugul 942 884 950 Balangir 983 951 988 Baleshwar 957 941 959 Bargarh 976 946 980 Baudh 991 975 993 Bhadrak 981 931 988 Cuttack 955 913 960 Debagarh 976 917 984 Dhenkanal 947 870 957 Gajapati 1042 964 1055 Ganjam 981 899 992 Jagatsinghapur 967 929 971 Jajapur 972 921 979 Jharsuguda 951 938 953 Kalahandi 1003 947 1013 Kandhamal 1037 960 1050 Kendrapara 1006 921 1017 Kendujhar 987 957 992 Khordha 925 910 927 Koraput 1031 970 1043 Malkangiri 1016 979 1024 Mayurbhanj 1005 952 1014 Nabarangapur 1018 988 1024 Nayagarh 916 851 924

196 Statistical Tables

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Nuapada 1020 971 1028 Puri 963 924 967 Rayagada 1048 955 1065 Sambalpur 973 931 978 Subarnapur 959 947 961 Sundargarh 971 937 975 Puducherry Karaikal 1048 963 1059 Mahe 1176 959 1206 Puducherry 1031 969 1038 Yanam 1039 917 1055 Punjab Amritsar 884 824 892 Barnala 876 847 879 Bathinda 865 854 866 Faridkot 889 851 894 Fatehgarh Sahib 871 843 874 Firozpur 893 846 899 Gurdaspur 895 824 904 Hoshiarpur 962 859 974 Jalandhar 913 874 917 Kapurthala 912 872 917 Ludhiana 869 865 869 Mansa 880 831 886 Moga 893 863 896 Muktsar 895 830 904 Patiala 888 835 895 Rupnagar 913 866 918 Sahibzada Ajit Singh Nagar 878 842 883 Sangrur 883 835 889 Shahid Bhagat Singh Nagar 954 879 963 Tarn Taran 898 819 909 Rajasthan Ajmer 950 893 960 Alwar 894 861 900 Banswara 979 925 991 Baran 926 902 930 Barmer 900 899 901 Bharatpur 877 863 880 Bhilwara 969 916 978 Bikaner 903 902 904 Bundi 922 886 928

197 Preliminary Demography of India

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Chittaurgarh 970 903 981 Churu 938 896 946 Dausa 904 859 913 Dhaulpur 845 854 843 Dungarpur 990 916 1006 Ganganagar 887 854 892 Hanumangarh 906 869 912 Jaipur 909 859 917 Jaisalmer 849 868 845 Jalor 951 891 964 Jhalawar 945 905 952 Jhunjhunun 950 831 969 Jodhpur 915 890 920 Karauli 858 844 861 Kota 906 889 909 Nagaur 948 888 959 Pali 987 895 1004 Pratapgarh 982 926 995 Rajsamand 988 891 1006 Sawai Madhopur 894 865 899 Sikar 944 841 962 Sirohi 938 890 948 Tonk 949 882 961 Udaipur 958 920 965 Sikkim East District 872 946 865 North District 769 897 755 South District 914 948 910 West District 941 950 940 Tamil Nadu Ariyalur 1016 892 1031 Chennai 986 964 988 Coimbatore 1001 963 1005 Cuddalore 984 895 994 Dharmapuri 946 911 950 Dindigul 998 942 1004 Erode 992 956 996 Kancheepuram 985 967 987 Kanniyakumari 1010 961 1015 Karur 1015 946 1022 Krishnagiri 956 924 960 Madurai 990 939 995

198 Statistical Tables

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Nagapattinam 1025 961 1032 Namakkal 986 913 993 Perambalur 1006 913 1017 Pudukkottai 1015 959 1022 Ramanathapuram 977 967 978 Salem 954 917 958 Sivaganga 1000 961 1004 Thanjavur 1031 957 1039 The Nilgiris 1041 982 1046 Theni 990 937 995 Thiruvallur 983 954 987 Thiruvarur 1020 962 1027 Thoothukkudi 1024 970 1030 Tiruchirappalli 1013 952 1020 Tirunelveli 1024 964 1030 Tiruppur 988 951 992 Tiruvannamalai 993 932 1001 Vellore 1004 944 1012 Viluppuram 985 938 991 Virudhunagar 1009 962 1014 Tripura Dhalai 945 972 941 North Tripura 967 971 966 South Tripura 957 947 959 West Tripura 964 942 967 Uttar Pradesh Agra 859 835 864 Aligarh 876 871 877 Allahabad 902 902 902 Ambedkar Nagar 976 929 983 Auraiya 864 895 859 Azamgarh 1017 916 1035 Baghpat 858 837 862 Bahraich 891 933 882 Ballia 933 897 939 Balrampur 922 968 913 Banda 863 898 856 Bara Banki 908 930 903 Bareilly 883 900 880 Basti 959 922 966 Bijnor 913 870 921 Budaun 859 902 850

199 Preliminary Demography of India

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Bulandshahr 892 844 902 Chandauli 913 976 902 Chitrakoot 879 907 874 Deoria 1013 921 1029 Etah 863 878 861 Etawah 867 870 866 Faizabad 961 927 967 Farrukhabad 874 884 872 Fatehpur 900 905 899 Firozabad 867 879 865 Gautam Buddha Nagar 852 845 853 Ghaziabad 878 850 883 Ghazipur 951 907 959 Gonda 922 924 921 Gorakhpur 944 905 950 Hamirpur 860 885 856 Hardoi 856 863 855 Jalaun 865 880 863 Jaunpur 1018 916 1037 Jhansi 885 859 889 Jyotiba Phule Nagar 907 898 908 Kannauj 879 897 875 Kanpur Dehat 862 896 856 Kanpur Nagar 852 870 850 Kanshiram Nagar 879 888 877 Kaushambi 905 926 901 Kheri 887 926 880 Kushinagar 955 917 962 Lalitpur 905 914 903 Lucknow 906 913 905 Mahamaya Nagar 870 862 871 Mahoba 880 897 877 Mahrajganj 938 924 940 Mainpuri 876 878 875 Mathura 858 871 855 Mau 978 924 988 Meerut 885 850 891 Mirzapur 900 902 900 Moradabad 903 909 902 Muzaffarnagar 886 858 891 Pilibhit 889 909 885 Pratapgarh 994 915 1007

200 Statistical Tables

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Rae Bareli 941 929 943 Rampur 905 919 902 Saharanpur 887 883 888 Sant Kabir Nagar 969 940 975 Sant Ravidas Nagar (Bhadohi) 950 898 960 Shahjahanpur 865 902 858 Shrawasti 875 923 865 Siddharthnagar 970 922 981 Sitapur 879 921 872 Sonbhadra 913 920 912 Sultanpur 978 921 988 Unnao 901 913 899 Varanasi 909 896 911 Uttarakhand Almora 1142 921 1177 Bageshwar 1093 901 1127 Chamoli 1021 889 1042 Champawat 981 870 1001 Dehradun 902 890 903 Garhwal 1103 899 1134 Hardwar 879 869 881 Nainital 933 891 939 Pithoragarh 1021 812 1057 Rudraprayag 1120 899 1156 Tehri Garhwal 1078 888 1111 Udham Singh Nagar 919 896 923 Uttarkashi 959 915 966 West Bengal Bankura 954 943 955 Barddhaman 943 947 942 Birbhum 956 952 956 Dakshin Dinajpur 954 948 955 Darjiling 971 943 974 Haora 935 964 931 Hugli 958 946 959 Jalpaiguri 954 949 955 Koch Bihar 942 948 941 Kolkata 899 930 897 Maldah 939 945 938 Murshidabad 957 963 956 Nadia 947 955 946 North Twenty Four Parganas 949 947 950

201 Preliminary Demography of India

State/Union Territory/ Females per 1000 males District All ages 0-6 years 7 years and above Paschim Medinipur 960 952 961 Purba Medinipur 936 938 936 Puruliya 955 947 956 South Twenty Four Parganas 949 953 949 Uttar Dinajpur 936 946 934 Source: Author’s calculations

202 Statistical Tables

Table 5.B Index of sex composition in districts of India, 2011 State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Andaman and Nicorbar Islands Nicobars -0.025 0.006 -0.030 North & Middle Andaman -0.006 0.021 -0.011 South Andaman -0.063 0.032 -0.078 Andhra Pradesh Adilabad 0.632 0.238 0.684 Anantapur 0.568 0.163 0.615 Chittoor 0.947 0.211 1.041 East Godavari 0.467 0.042 0.523 Guntur 1.230 0.785 1.273 Hyderabad 1.131 0.458 1.209 Karimnagar 0.036 0.298 -0.018 Khammam 0.969 0.217 1.059 Krishna 0.721 0.343 0.765 Kurnool 0.946 0.464 0.994 Mahbubnagar 0.653 0.323 0.696 Medak 0.532 0.26 0.570 Nalgonda 0.552 0.409 0.567 Nizamabad 0.537 0.075 0.593 Prakasam 0.509 0.263 0.532 Rangareddy 0.906 0.254 0.996 Sri Potti Sriramulu Nellore 0.515 0.189 0.553 Srikakulam 0.302 0.579 0.247 Visakhapatnam 0.730 0.298 0.784 Vizianagaram 0.993 0.582 1.038 Warangal 0.654 0.278 0.701 West Godavari 0.708 -0.020 0.799 Y.S.R. 0.930 0.591 0.961 Arunachal Pradesh Anjaw -0.012 0.004 -0.014 Changlang -0.015 0.029 -0.021 Dibang Valley -0.004 -0.003 -0.005 East Kameng 0.021 0.023 0.021 East Siang 0.008 0.024 0.006 Kurung Kumey 0.029 0.029 0.029 Lohit -0.022 0.027 -0.029 Lower Dibang Valley -0.004 0.007 -0.006 Lower Subansiri 0.011 0.016 0.010 Papum Pare 0.007 0.034 0.003 Tawang -0.053 0.015 -0.063

203 Preliminary Demography of India

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Tirap -0.004 0.021 -0.007 Upper Siang -0.007 0.007 -0.009 Upper Subansiri 0.013 0.018 0.012 West Kameng -0.068 0.017 -0.081 West Siang -0.010 0.006 -0.013 Assam Baksa 0.097 0.165 0.086 Barpeta 0.068 0.334 0.036 Bongaigaon 0.058 0.170 0.043 Cachar 0.119 0.298 0.094 Chirang 0.052 0.090 0.048 Darrang -0.059 0.117 -0.082 Dhemaji 0.023 0.089 0.014 Dhubri 0.086 0.525 0.033 Dibrugarh 0.060 0.195 0.037 Dima Hasao -0.007 0.039 -0.014 Goalpara 0.084 0.195 0.072 Golaghat 0.082 0.175 0.066 Hailakandi 0.015 0.107 0.004 Jorhat 0.065 0.168 0.046 Kamrup 0.034 0.270 -0.002 Kamrup Metropolitan -0.087 0.275 -0.147 Karbi Anglong 0.058 0.009 0.073 Karimganj 0.094 0.262 0.074 Kokrajhar 0.060 0.143 0.050 Lakhimpur 0.098 0.192 0.086 Morigaon 0.120 0.169 0.117 Nagaon 0.233 0.576 0.191 Nalbari 0.014 0.128 -0.004 Sivasagar 0.049 0.168 0.028 Sonitpur 0.039 0.345 -0.005 Tinsukia 0.040 0.290 0.002 Udalguri 0.080 0.160 0.068 Bihar Araria -0.210 0.655 -0.314 Arwal -0.034 0.097 -0.050 Aurangabad -0.239 0.393 -0.319 Banka -0.265 0.264 -0.332 Begusarai -0.540 -0.049 -0.595 Bhagalpur -0.737 0.310 -0.878 Bhojpur -0.428 0.004 -0.482 Buxar -0.119 0.090 -0.142

204 Statistical Tables

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Darbhanga -0.456 0.285 -0.543 Gaya -0.140 0.996 -0.287 Gopalganj 0.699 0.393 0.759 Jamui -0.134 0.379 -0.200 Jehanabad -0.098 0.024 -0.110 Kaimur (Bhabua) -0.134 0.213 -0.175 Katihar -0.285 0.742 -0.414 Khagaria -0.370 -0.025 -0.405 Kishanganj 0.038 0.517 -0.018 Lakhisarai -0.157 0.005 -0.174 Madhepura -0.206 0.109 -0.236 Madhubani -0.260 0.397 -0.333 Munger -0.329 0.068 -0.383 Muzaffarpur -0.786 0.059 -0.888 Nalanda -0.217 0.224 -0.267 Nawada -0.038 0.750 -0.147 Pashchim Champaran -0.516 0.797 -0.683 Patna -1.091 -0.412 -1.174 Purba Champaran -0.782 0.253 -0.894 Purnia -0.128 0.724 -0.228 Rohtas -0.301 0.160 -0.357 Saharsa -0.255 0.152 -0.300 Samastipur -0.511 0.614 -0.651 Saran 0.126 0.145 0.142 Sheikhpura -0.034 0.089 -0.048 Sheohar -0.130 0.040 -0.151 Sitamarhi -0.546 0.333 -0.653 Siwan 0.546 0.315 0.594 Supaul -0.134 0.346 -0.189 Vaishali -0.656 -0.360 -0.683 Chandigarh Chandigarh -0.529 -0.172 -0.586 Chhattisgarh Bastar 0.433 0.468 0.432 Bijapur 0.040 0.074 0.035 Bilaspur 0.317 0.5 0.296 Dakshin Bastar Dantewada 0.158 0.197 0.153 Dhamtari 0.210 0.161 0.217 Durg 0.596 0.541 0.602 Janjgir - Champa 0.276 0.200 0.288 Jashpur 0.202 0.207 0.202 Kabeerdham 0.174 0.239 0.169

205 Preliminary Demography of India

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Korba 0.139 0.246 0.125 Koriya 0.077 0.146 0.067 Mahasamund 0.295 0.175 0.313 Narayanpur 0.030 0.040 0.029 Raigarh 0.292 0.161 0.312 Raipur 0.643 0.840 0.618 Rajnandgaon 0.434 0.367 0.445 Surguja 0.314 0.448 0.302 Uttar Bastar Kanker 0.184 0.173 0.186 Dadra and Nagar Haveli Dadra & Nagar Haveli -0.238 0.014 -0.276 Daman and Diu Daman -0.388 -0.006 -0.450 Diu 0.017 0.002 0.019 Delhi Central -0.111 -0.023 -0.126 East -0.382 -0.260 -0.405 New Delhi -0.071 -0.011 -0.081 North -0.244 -0.130 -0.263 North East -0.481 -0.357 -0.500 North West -1.135 -0.704 -1.205 South -0.883 -0.361 -0.967 South West -0.969 -0.639 -1.024 West -0.646 -0.407 -0.689 Goa North Goa 0.056 -0.008 0.062 South Goa 0.096 0.030 0.103 Gujarat Ahmadabad -1.035 -1.369 -1.004 Amreli 0.136 -0.180 0.179 Anand -0.157 -0.275 -0.144 Banas Kantha -0.045 -0.371 0.016 Bharuch -0.096 -0.002 -0.114 Bhavnagar -0.103 -0.324 -0.071 Dohad 0.361 0.270 0.392 Gandhinagar -0.106 -0.331 -0.075 Jamnagar -0.022 -0.125 -0.011 Junagadh 0.127 -0.096 0.153 Kachchh -0.273 -0.008 -0.309 Kheda -0.032 -0.228 -0.006 Mahesana -0.119 -0.492 -0.068 Narmada 0.043 0.050 0.042

206 Statistical Tables

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Navsari 0.107 0.026 0.113 Panch Mahals 0.044 0.095 0.041 Patan -0.029 -0.165 -0.008 Porbandar 0.016 -0.038 0.022 Rajkot -0.241 -0.789 -0.168 Sabar Kantha 0.094 -0.159 0.135 Surat -3.850 -1.737 -4.182 Surendranagar -0.076 -0.178 -0.060 Tapi 0.190 0.074 0.205 The Dangs 0.056 0.056 0.057 Vadodara -0.107 -0.290 -0.089 Valsad -0.091 0.073 -0.118 Haryana Ambala -0.261 -0.422 -0.241 Bhiwani -0.359 -0.535 -0.334 Faridabad -0.495 -0.535 -0.488 Fatehabad -0.138 -0.256 -0.121 Gurgaon -0.532 -0.547 -0.530 Hisar -0.479 -0.429 -0.489 Jhajjar -0.304 -0.530 -0.272 Jind -0.370 -0.409 -0.366 Kaithal -0.254 -0.398 -0.233 Karnal -0.324 -0.578 -0.286 Kurukshetra -0.193 -0.356 -0.170 Mahendragarh -0.166 -0.485 -0.119 Mewat -0.144 -0.081 -0.140 Panchkula -0.251 -0.274 -0.243 Palwal -0.156 -0.129 -0.162 Panipat -0.380 -0.418 -0.374 Rewari -0.150 -0.470 -0.102 Rohtak -0.303 -0.427 -0.287 Sirsa -0.227 -0.294 -0.219 Sonipat -0.520 -0.751 -0.487 Yamunanagar -0.303 -0.403 -0.290 Himachal Pradesh Bilaspur 0.059 -0.027 0.070 Chamba 0.094 0.073 0.098 Hamirpur 0.250 -0.048 0.293 Kangra 0.406 -0.203 0.493 Kinnaur -0.042 0.009 -0.050 Kullu 0.017 0.070 0.008 Lahul & Spiti -0.003 0.008 -0.005

207 Preliminary Demography of India

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Mandi 0.264 -0.006 0.301 Shimla -0.076 0.020 -0.094 Sirmaur -0.051 0.033 -0.064 Solan -0.129 -0.031 -0.145 Una 0.072 -0.078 0.093 Jammu and Kashmir Anantnag -0.012 -0.535 0.075 Badgam -0.165 -0.392 -0.124 Bandipore -0.044 -0.039 -0.043 Baramula -0.270 -0.238 -0.272 Doda -0.029 0.037 -0.036 Ganderbal -0.084 -0.079 -0.083 Jammu -0.418 -0.611 -0.394 Kargil -0.100 0.037 -0.120 Kathua -0.153 -0.195 -0.147 Kishtwar -0.020 0.009 -0.024 Kulgam 0.018 -0.068 0.033 Kupwara -0.344 -0.367 -0.329 Leh(Ladakh) -0.252 0.01 -0.294 Pulwama -0.060 -0.238 -0.030 Punch -0.093 -0.049 -0.097 Rajouri -0.191 -0.285 -0.172 Ramban -0.043 0.027 -0.052 Reasi -0.060 0.011 -0.069 Samba -0.068 -0.156 -0.055 Shupiyan 0.011 -0.038 0.019 Srinagar -0.306 -0.217 -0.321 Udhampur -0.171 -0.069 -0.185 Jharkhand Bokaro -0.195 -0.020 -0.220 Chatra 0.043 0.266 0.016 Deoghar -0.113 0.196 -0.150 Dhanbad -0.334 0.028 -0.386 Dumka 0.167 0.267 0.157 Garhwa -0.035 0.297 -0.077 Giridih 0.023 0.267 0.004 Godda -0.038 0.265 -0.075 Gumla 0.199 0.199 0.204 Hazaribagh 0.036 0.078 0.036 Jamtara 0.055 0.127 0.047 Khunti 0.106 0.090 0.110 Kodarma 0.024 0.113 0.015

208 Statistical Tables

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Latehar 0.064 0.191 0.050 Lohardaga 0.078 0.103 0.076 Pakur 0.148 0.261 0.139 Palamu -0.085 0.301 -0.135 Pashchimi Singhbhum 0.353 0.484 0.341 Purbi Singhbhum 0.073 0.062 0.073 Ramgarh -0.070 0.045 -0.086 Ranchi 0.107 0.262 0.085 Sahibganj 0.033 0.257 0.009 Saraikela-Kharsawan 0.070 0.105 0.066 Simdega 0.133 0.160 0.13 Karnataka Bagalkot 0.31 0.112 0.342 Bangalore -1.200 0.780 -1.535 Bangalore Rural 0.019 0.098 0.003 Belgaum 0.512 0.299 0.542 Bellary 0.356 0.396 0.351 Bidar 0.077 0.131 0.067 Bijapur 0.116 0.145 0.114 Chamarajanagar 0.184 0.077 0.195 Chikkaballapura 0.129 0.112 0.126 Chikmagalur 0.271 0.143 0.284 Chitradurga 0.177 0.099 0.184 Dakshina Kannada 0.593 0.187 0.645 Davanagere 0.198 0.101 0.207 Dharwad 0.184 0.173 0.181 Gadag 0.151 0.111 0.156 Gulbarga 0.214 0.221 0.214 Hassan 0.426 0.228 0.445 Haveri 0.066 0.171 0.047 Kodagu 0.161 0.095 0.168 Kolar 0.207 0.192 0.204 Koppal 0.224 0.218 0.226 Mandya 0.327 0.094 0.352 Mysore 0.463 0.350 0.466 Raichur 0.366 0.279 0.382 Ramanagara 0.143 0.137 0.139 Shimoga 0.358 0.235 0.370 Tumkur 0.391 0.280 0.395 Udupi 0.635 0.120 0.705 Uttara Kannada 0.188 0.141 0.190 Yadgir 0.193 0.154 0.203

209 Preliminary Demography of India

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Kerala Alappuzha 1.198 0.178 1.340 Ernakulam 1.045 0.337 1.134 Idukki 0.270 0.128 0.286 Kannur 1.691 0.372 1.882 Kasaragod 0.642 0.201 0.706 Kollam 1.588 0.316 1.766 Kottayam 0.718 0.213 0.782 Kozhikode 1.713 0.460 1.892 Malappuram 2.266 0.746 2.497 Palakkad 1.273 0.405 1.393 Pathanamthitta 0.784 0.133 0.873 Thiruvananthapuram 1.728 0.445 1.903 Thrissur 1.844 0.286 2.064 Wayanad 0.280 0.120 0.302 Lakshadweep Lakshadweep 0.001 -0.001 0.002 Madhya Pradesh Alirajpur 0.184 0.239 0.182 Anuppur 0.098 0.086 0.100 Ashoknagar -0.132 -0.002 -0.148 Balaghat 0.502 0.281 0.533 Barwani 0.209 0.201 0.221 Betul 0.174 0.212 0.169 Bhind -0.705 -0.602 -0.718 Bhopal -0.271 0.013 -0.316 Burhanpur 0.030 0.022 0.034 Chhatarpur -0.393 -0.172 -0.420 Chhindwara 0.201 0.280 0.189 Damoh -0.132 0.090 -0.163 Datia -0.203 -0.211 -0.202 Dewas 0.006 -0.046 0.016 Dhar 0.168 -0.010 0.203 Dindori 0.167 0.172 0.168 East Nimar 0.020 0.101 0.012 Guna -0.144 -0.079 -0.149 Gwalior -0.635 -0.658 -0.633 Harda -0.018 0.016 -0.023 Hoshangabad -0.135 -0.016 -0.153 Indore -0.200 -0.279 -0.191 Jabalpur -0.147 0.018 -0.176 Jhabua 0.186 0.123 0.206

210 Statistical Tables

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Katni 0.038 0.108 0.030 Mandla 0.251 0.214 0.258 Mandsaur 0.131 0.036 0.145 Morena -0.800 -0.837 -0.789 Narsimhapur -0.097 -0.058 -0.103 Neemuch 0.058 0.010 0.065 Panna -0.132 -0.019 -0.146 Raisen -0.212 0.075 -0.252 Rajgarh 0.087 0.014 0.101 Ratlam 0.176 0.104 0.190 Rewa -0.096 -0.326 -0.057 Sagar -0.410 0.108 -0.483 Satna -0.117 -0.071 -0.120 Sehore -0.115 -0.047 -0.123 Seoni 0.226 0.205 0.229 Shahdol 0.110 0.145 0.107 Shajapur -0.006 -0.010 -0.004 Sheopur -0.102 -0.091 -0.101 Shivpuri -0.431 -0.215 -0.456 Sidhi 0.050 -0.024 0.067 Singrauli -0.110 0.038 -0.126 Tikamgarh -0.218 -0.191 -0.218 Ujjain 0.107 0.036 0.118 Umaria 0.031 0.094 0.024 Vidisha -0.248 0.055 -0.289 West Nimar 0.164 0.147 0.173 Maharashtra Ahmadnagar -0.105 -1.267 0.062 Akola 0.015 -0.086 0.026 Amravati 0.079 0.115 0.063 Aurangabad -0.335 -1.056 -0.222 Bhandara 0.194 0.090 0.205 Bid -0.283 -1.249 -0.137 Buldana -0.119 -0.731 -0.029 Chandrapur 0.152 0.203 0.136 Dhule 0.004 -0.306 0.049 Gadchiroli 0.138 0.141 0.134 Gondiya 0.273 0.120 0.291 Hingoli -0.025 -0.230 0.007 Jalgaon -0.296 -1.375 -0.139 Jalna -0.082 -0.591 -0.003 Kolhapur 0.192 -0.848 0.333

211 Preliminary Demography of India

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Latur -0.154 -0.396 -0.119 Mumbai -1.300 -0.320 -1.469 Mumbai Suburban -3.100 -0.109 -3.598 Nagpur 0.139 0.162 0.118 Nanded -0.038 -0.226 -0.009 Nandurbar 0.195 0.123 0.208 Nashik -0.226 -0.788 -0.140 Osmanabad -0.129 -0.381 -0.093 Parbhani 0.001 -0.374 0.059 Pune -1.118 -1.358 -1.105 Raigarh 0.146 0.086 0.148 Ratnagiri 1.027 0.112 1.157 Sangli 0.255 -0.479 0.355 Satara 0.512 -0.316 0.626 Sindhudurg 0.299 -0.008 0.339 Solapur -0.138 -0.679 -0.062 Thane -2.621 0.148 -3.065 Wardha 0.030 0.006 0.028 Washim -0.064 -0.250 -0.037 Yavatmal 0.070 0.006 0.074 Manipur Bishnupur 0.053 0.004 0.060 Chandel -0.004 0.002 -0.006 Churachandpur 0.030 0.031 0.029 Imphal East 0.117 0.032 0.130 Imphal West 0.167 0.050 0.184 Senapati -0.002 -0.003 -0.002 Tamenglong 0.007 0.014 0.006 Thoubal 0.101 0.067 0.108 Ukhrul 0.005 0.004 0.005 Meghalaya East Garo Hills 0.033 0.101 0.025 East Khasi Hills 0.206 0.182 0.213 Jaintia Hills 0.098 0.139 0.097 Ribhoi 0.010 0.063 0.005 South Garo Hills 0.002 0.047 -0.003 West Garo Hills 0.093 0.212 0.079 West Khasi Hills 0.059 0.152 0.050 Mizoram Aizawl 0.103 0.105 0.103 Champhai 0.019 0.039 0.017 Kolasib 0.005 0.026 0.002

212 Statistical Tables

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Lawngtlai 0.002 0.032 -0.002 Lunglei 0.002 0.035 -0.002 Mamit -0.005 0.028 -0.010 Saiha 0.008 0.006 0.009 Serchhip 0.009 0.003 0.010 Nagaland Dimapur -0.035 0.078 -0.052 Kiphire 0.006 0.017 0.005 Kohima -0.013 0.067 -0.025 Longleng -0.007 -0.009 -0.007 Mokokchung -0.010 0.023 -0.015 Mon -0.041 -0.017 -0.044 Peren -0.009 0.012 -0.011 Phek 0.007 0.001 0.009 Tuensang -0.008 0.022 -0.011 Wokha 0.018 0.032 0.016 Zunheboto 0.022 0.024 0.022 Orissa Anugul 0.008 -0.132 0.026 Balangir 0.263 0.222 0.268 Baleshwar 0.144 0.214 0.129 Bargarh 0.199 0.147 0.202 Baudh 0.082 0.103 0.079 Bhadrak 0.228 0.086 0.246 Cuttack 0.151 -0.006 0.162 Debagarh 0.041 0.003 0.047 Dhenkanal 0.032 -0.179 0.061 Gajapati 0.212 0.121 0.227 Ganjam 0.530 -0.186 0.630 Jagatsinghapur 0.116 0.046 0.121 Jajapur 0.218 0.039 0.241 Jharsuguda 0.024 0.043 0.019 Kalahandi 0.367 0.205 0.392 Kandhamal 0.256 0.143 0.275 Kendrapara 0.351 0.032 0.394 Kendujhar 0.316 0.317 0.318 Khordha -0.132 -0.026 -0.158 Koraput 0.457 0.352 0.477 Malkangiri 0.171 0.199 0.170 Mayurbhanj 0.603 0.378 0.638 Nabarangapur 0.346 0.427 0.339 Nayagarh -0.090 -0.200 -0.077

213 Preliminary Demography of India

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Nuapada 0.176 0.140 0.182 Puri 0.142 0.047 0.149 Rayagada 0.375 0.170 0.408 Sambalpur 0.127 0.055 0.135 Subarnapur 0.046 0.073 0.041 Sundargarh 0.239 0.171 0.246 Puducherry Karaikal 0.078 0.030 0.085 Mahe 0.034 0.006 0.038 Puducherry 0.312 0.153 0.332 Yanam 0.020 0.000 0.023 Punjab Amritsar -0.551 -0.757 -0.528 Barnala -0.151 -0.131 -0.157 Bathinda -0.418 -0.271 -0.446 Faridkot -0.124 -0.131 -0.125 Fatehgarh Sahib -0.165 -0.135 -0.172 Firozpur -0.377 -0.509 -0.360 Gurdaspur -0.404 -0.689 -0.369 Hoshiarpur 0.127 -0.276 0.182 Jalandhar -0.231 -0.264 -0.236 Kapurthala -0.090 -0.108 -0.090 Ludhiana -0.987 -0.555 -1.065 Mansa -0.182 -0.211 -0.181 Moga -0.185 -0.162 -0.192 Muktsar -0.159 -0.270 -0.144 Patiala 0.032 -0.066 0.044 Rupnagar -0.386 -0.508 -0.374 Sahibzada Ajit Singh Nagar -0.073 -0.104 -0.071 Sangrur -0.373 -0.434 -0.370 Shahid Bhagat Singh Nagar -0.241 -0.245 -0.243 Tarn Taran -0.186 -0.392 -0.158 Rajasthan Ajmer 0.091 -0.243 0.146 Alwar -0.667 -0.947 -0.612 Banswara 0.259 0.101 0.294 Baran -0.067 -0.063 -0.066 Barmer -0.405 -0.227 -0.411 Bharatpur -0.635 -0.680 -0.615 Bhilwara 0.256 0.020 0.297 Bikaner -0.339 -0.146 -0.357 Bundi -0.077 -0.134 -0.067

214 Statistical Tables

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Chittaurgarh 0.171 -0.073 0.209 Churu -0.020 -0.171 0.009 Dausa -0.230 -0.436 -0.194 Dhaulpur -0.463 -0.402 -0.464 Dungarpur 0.255 0.010 0.300 Ganganagar -0.412 -0.469 -0.404 Hanumangarh -0.236 -0.321 -0.224 Jaipur -0.808 -1.572 -0.688 Jaisalmer -0.245 -0.185 -0.248 Jalor 0.077 -0.224 0.132 Jhalawar 0.025 -0.054 0.039 Jhunjhunun 0.077 -0.742 0.201 Jodhpur -0.356 -0.432 -0.330 Karauli -0.479 -0.526 -0.466 Kota -0.257 -0.190 -0.268 Nagaur 0.098 -0.401 0.181 Pali 0.355 -0.174 0.439 Pratapgarh 0.137 0.053 0.154 Rajsamand 0.205 -0.122 0.258 Sawai Madhopur -0.243 -0.299 -0.232 Sikar 0.043 -0.854 0.181 Sirohi -0.008 -0.126 0.015 Tonk 0.048 -0.199 0.087 Udaipur 0.204 0.084 0.235 Sikkim East District -0.076 0.025 -0.093 North District -0.031 -0.002 -0.036 South District -0.015 0.015 -0.020 West District 0.000 0.016 -0.002 Tamil Nadu Ariyalur 0.208 -0.052 0.245 Chennai 0.789 0.609 0.790 Coimbatore 0.781 0.420 0.814 Cuddalore 0.421 -0.152 0.497 Dharmapuri 0.030 -0.016 0.032 Dindigul 0.460 0.165 0.493 Erode 0.436 0.221 0.454 Kancheepuram 0.669 0.603 0.662 Kanniyakumari 0.481 0.221 0.509 Karur 0.294 0.093 0.319 Krishnagiri 0.111 0.061 0.112 Madurai 0.560 0.212 0.598

215 Preliminary Demography of India

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Nagapattinam 0.498 0.209 0.534 Namakkal 0.292 -0.005 0.326 Perambalur 0.137 -0.002 0.155 Pudukkottai 0.444 0.220 0.472 Ramanathapuram 0.184 0.196 0.175 Salem 0.187 0.025 0.193 Sivaganga 0.297 0.173 0.309 Thanjavur 0.793 0.283 0.858 The Nilgiris 0.268 0.121 0.286 Theni 0.231 0.075 0.247 Thiruvallur 0.598 0.435 0.607 Thiruvarur 0.373 0.160 0.398 Thoothukkudi 0.532 0.276 0.563 Tiruchirappalli 0.730 0.280 0.784 Tirunelveli 0.935 0.433 0.998 Tiruppur 0.440 0.238 0.457 Tiruvannamalai 0.485 0.135 0.529 Vellore 0.931 0.361 1.003 Viluppuram 0.576 0.269 0.612 Virudhunagar 0.490 0.253 0.516 Tripura Dhalai 0.007 0.091 -0.005 North Tripura 0.070 0.159 0.057 South Tripura 0.056 0.106 0.048 West Tripura 0.157 0.151 0.152 Uttar Pradesh Agra -1.415 -1.593 -1.380 Aligarh -0.936 -0.739 -0.956 Allahabad -0.889 -0.300 -0.972 Ambedkar Nagar 0.317 0.140 0.345 Auraiya -0.416 -0.115 -0.459 Azamgarh 1.292 0.029 1.493 Baghpat -0.426 -0.459 -0.419 Bahraich -0.666 0.353 -0.796 Ballia -0.088 -0.231 -0.063 Balrampur -0.149 0.600 -0.248 Banda -0.556 -0.141 -0.612 Bara Banki -0.414 0.241 -0.503 Bareilly -1.008 -0.289 -1.105 Basti 0.176 0.083 0.197 Bijnor -0.390 -0.749 -0.327 Budaun -1.205 -0.237 -1.331

216 Statistical Tables

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Bulandshahr -0.655 -1.182 -0.565 Chandauli -0.207 0.541 -0.313 Chitrakoot -0.238 -0.039 -0.263 Deoria 0.824 0.084 0.941 Etah -0.539 -0.306 -0.569 Etawah -0.461 -0.297 -0.484 Faizabad 0.197 0.132 0.210 Farrukhabad -0.498 -0.270 -0.527 Fatehpur -0.413 -0.110 -0.454 Firozabad -0.724 -0.402 -0.767 Gautam Buddha Nagar -0.596 -0.529 -0.602 Ghaziabad -1.141 -1.326 -1.107 Ghazipur 0.151 -0.118 0.200 Gonda -0.246 0.155 -0.294 Gorakhpur 0.067 -0.160 0.103 Hamirpur -0.354 -0.134 -0.386 Hardoi -1.377 -1.040 -1.411 Jalaun -0.439 -0.387 -0.442 Jaunpur -0.500 -0.228 -0.541 Jhansi 1.282 0.030 1.479 Jyotiba Phule Nagar -0.434 -0.423 -0.437 Kannauj -0.239 -0.138 -0.248 Kanpur Dehat -0.403 -0.130 -0.440 Kanpur Nagar -0.563 -0.138 -0.626 Kanshiram Nagar -1.617 -0.663 -1.776 Kaushambi -0.350 -0.195 -0.366 Kheri -0.217 0.096 -0.256 Kushinagar -0.837 0.228 -0.982 Lalitpur 0.201 0.039 0.237 Lucknow -0.167 -0.002 -0.186 Mahamaya Nagar -0.619 -0.023 -0.720 Mahoba -0.024 0.114 -0.038 Mahrajganj -0.208 -0.066 -0.228 Mainpuri -0.473 -0.304 -0.494 Mathura -0.840 -0.526 -0.880 Mau 0.311 0.093 0.349 Meerut -0.754 -0.976 -0.716 Mirzapur -0.391 -0.139 -0.420 Moradabad -0.694 -0.112 -0.764 Muzaffarnagar -0.885 -1.089 -0.843 Pilibhit -0.411 -0.045 -0.462 Pratapgarh 0.635 0.010 0.731

217 Preliminary Demography of India

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Rae Bareli 0.015 0.199 -0.011 Rampur -0.324 0.049 -0.372 Saharanpur -0.724 -0.483 -0.753 Sant Kabir Nagar 0.187 0.209 0.190 Sant Ravidas Nagar (Bhadohi) 0.055 -0.119 0.087 Shahjahanpur -0.904 -0.182 -1.000 Shrawasti -0.286 0.051 -0.329 Siddharthnagar 0.288 0.111 0.332 Sitapur -1.073 0.137 -1.236 Sonbhadra -0.194 0.057 -0.223 Sultanpur 0.539 0.110 0.609 Unnao -0.476 -0.012 -0.545 Varanasi -0.444 -0.261 -0.472 Uttarakhand Almora 0.433 0.016 0.496 Bageshwar 0.141 -0.014 0.164 Chamoli 0.115 -0.038 0.138 Champawat 0.040 -0.049 0.053 Dehradun -0.256 -0.146 -0.276 Garhwal 0.394 -0.038 0.458 Hardwar -0.464 -0.399 -0.470 Nainital -0.027 -0.087 -0.018 Pithoragarh 0.144 -0.203 0.196 Rudraprayag 0.148 -0.014 0.173 Tehri Garhwal 0.303 -0.067 0.359 Udham Singh Nagar -0.134 -0.124 -0.135 Uttarkashi 0.023 0.001 0.026 West Bengal Bankura 0.187 0.345 0.154 Barddhaman 0.077 0.756 -0.055 Birbhum 0.202 0.484 0.156 Dakshin Dinajpur 0.087 0.177 0.068 Darjiling 0.209 0.153 0.210 Haora -0.102 0.717 -0.244 Hugli 0.374 0.478 0.330 Jalpaiguri 0.206 0.449 0.161 Koch Bihar 0.018 0.331 -0.034 Kolkata -0.723 0.140 -0.892 Maldah -0.015 0.526 -0.088 Murshidabad 0.444 1.395 0.307 Nadia 0.126 0.600 0.031 North Twenty Four Parganas 0.352 0.881 0.216

218 Statistical Tables

State/Union Territory/ Index of sex composition District All ages 0-6 years 7 years and above Paschim Medinipur 0.438 0.728 0.379 Purba Medinipur -0.080 0.401 -0.166 Puruliya 0.164 0.376 0.133 South Twenty Four Parganas 0.278 1.100 0.141 Uttar Dinajpur -0.052 0.435 -0.115 Source: Author’s calculations

219 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female India 1210193422 623724248 586469174 158789287 82952135 75837152 Jammu & Kashmir 12548926 6665561 5883365 2008642 1080662 927980 Kupwara 875564 475126 400438 196983 106247 90736 Badgam 735753 390705 345048 152241 83100 69141 Leh(Ladakh) 147104 92907 54197 11816 6079 5737 Kargil 143388 80791 62597 20407 10319 10088 Punch 476820 252240 224580 84112 44390 39722 Rajouri 619266 332424 286842 118514 64503 54011 Kathua 615711 327953 287758 80157 43648 36509 Baramula 1015503 542171 473332 161841 86711 75130 Bandipore 385099 201531 183568 60325 31868 28457 Srinagar 1269751 675667 594084 155875 83408 72467 Ganderbal 297003 158900 138103 50551 27127 23424 Pulwama 570060 297988 272072 97642 53176 44466 Shupiyan 265960 136302 129658 40271 21381 18890 Anantnag 1070144 552404 517740 206338 112661 93677 Kulgam 422786 216672 206114 70331 37364 32967 Doda 409576 213091 196485 71038 36772 34266 Ramban 283313 149032 134281 54745 28354 26391 Kishtwar 231037 120496 110541 39124 20357 18767 Udhampur 555357 298094 257263 82638 43801 38837 Reasi 314714 166392 148322 55805 29051 26754 Jammu 1526406 815727 710679 159868 89067 70801 Samba 318611 168948 149663 38020 21278 16742 Himachal Pradesh 6856509 3473892 3382617 763864 400681 363183 Chamba 518844 260848 257996 69409 35591 33818

220 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Kangra 1507223 748559 758664 160865 85888 74977 Lahul & Spiti 31528 16455 15073 2994 1487 1507 Kullu 437474 224320 213154 50041 25504 24537 Mandi 999518 496787 502731 109963 57496 52467 Hamirpur 454293 216742 237551 47708 25357 22351 Una 521057 263541 257516 58200 31117 27083 Bilaspur 382056 192827 189229 41612 21983 19629 Solan 576670 306162 270508 66349 34948 31401 Sirmaur 530164 276801 253363 67958 35202 32756 Shimla 813384 424486 388898 80778 42018 38760 Kinnaur 84298 46364 37934 7987 4090 3897 Punjab 27704236 14634819 13069417 2941570 1593262 1348308 Gurdaspur 2299026 1212995 1086031 240945 132133 108812 Kapurthala 817668 427659 390009 82657 44160 38497 Jalandhar 2181753 1140536 1041217 213460 113916 99544 Hoshiarpur 1582793 806921 775872 162368 87333 75035 Shahid Bhagat Singh Nagar 614362 314415 299947 60523 32217 28306 Fatehgarh Sahib 599814 320603 279211 60761 32972 27789 Ludhiana 3487882 1866203 1621679 363086 194734 168352 Moga 992289 524289 468000 102574 55059 47515 Firozpur 2026831 1070812 956019 241319 130701 110618 Muktsar 902702 476300 426402 102028 55759 46269 Faridkot 618008 327121 290887 66675 36022 30653 Bathinda 1388859 744875 643984 145391 78420 66971 Mansa 768808 408921 359887 81466 44481 36985 Patiala 1892282 1002112 890170 204905 111667 93238

221 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Amritsar 2490891 1322088 1168803 266608 146158 120450 Tarn Taran 1120070 590239 529831 129863 71400 58463 Rupnagar 683349 357265 326084 69593 37302 32291 Sahibzada Ajit Singh Nagar 986147 524989 461158 109263 59311 49952 Sangrur 1654408 878628 775780 175095 95418 79677 Barnala 596294 317848 278446 62990 34099 28891 Chandigarh 1054686 580282 474404 117953 63187 54766 Chandigarh 1054686 580282 474404 117953 63187 54766 Uttarakhand 10116752 5154178 4962574 1328844 704769 624075 Uttarkashi 329686 168335 161351 44995 23494 21501 Chamoli 391114 193572 197542 50753 26861 23892 Rudraprayag 236857 111747 125110 30212 15910 14302 Tehri Garhwal 616409 296604 319805 82422 43667 38755 Dehradun 1698560 893222 805338 196298 103874 92424 Garhwal 686527 326406 360121 82099 43233 38866 Pithoragarh 485993 240427 245566 62911 34710 28201 Bageshwar 259840 124121 135719 34650 18232 16418 Almora 621927 290414 331513 77991 40601 37390 Champawat 259315 130881 128434 36531 19532 16999 Nainital 955128 494115 461013 122199 64626 57573 Udham Singh Nagar 1648367 858906 789461 223445 117856 105589 Hardwar 1927029 1025428 901601 284338 152173 132165 Haryana 25353081 13505130 11847951 3297724 1802047 1495677 Panchkula 558890 298919 259971 65180 35224 29956 Ambala 1136784 604044 532740 123534 68365 55169 Yamunanagar 1214162 646801 567361 143189 78471 64718

222 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Kurukshetra 964231 510370 453861 115291 63462 51829 Kaithal 1072861 570595 502266 135136 74217 60919 Karnal 1506323 798840 707483 194403 106809 87594 Panipat 1202811 646324 556487 164778 89881 74897 Sonipat 1480080 798948 681132 187955 105001 82954 Jind 1332042 712254 619788 164579 89702 74877 Fatehabad 941522 494834 446688 118446 64203 54243 Sirsa 1295114 683242 611872 153495 82862 70633 Hisar 1742815 931535 811280 211204 114238 96966 Bhiwani 1629109 864616 764493 206023 112491 93532 Rohtak 1058683 566708 491975 125490 69433 56057 Jhajjar 956907 514303 442604 116160 65485 50675 Mahendragarh 921680 486553 435127 109928 61827 48101 Rewari 896129 472254 423875 112184 62874 49310 Gurgaon 1514085 817274 696811 197816 108312 89504 Mewat 1089406 571480 517926 243206 127786 115420 Faridabad 1798954 961532 837422 238028 129216 108812 Palwal 1040493 553704 486789 171699 92188 79511 Delhi 16753235 8976410 7776825 1970510 1055735 914775 North West 3651261 1960677 1690584 443195 237941 205254 North 883418 472260 411158 100879 53888 46991 North East 2240749 1188307 1052442 296224 157999 138225 East 1707725 906721 801004 189519 101371 88148 New Delhi 133713 73846 59867 11549 6131 5418 Central 578671 305926 272745 60385 31752 28633 West 2531583 1349685 1181898 282678 151379 131299

223 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female South West 2292363 1248700 1043663 262815 143112 119703 South 2733752 1470288 1263464 323266 172162 151104 Rajasthan 68621012 35620086 33000926 10504916 5580212 4924704 Ganganagar 1969520 1043730 925790 252376 136111 116265 Hanumangarh 1779650 933660 845990 232933 124606 108327 Bikaner 2367745 1243916 1123829 394396 207364 187032 Churu 2041172 1053375 987797 313852 165521 148331 Jhunjhunun 2139658 1097390 1042268 285395 155842 129553 Alwar 3671999 1938929 1733070 580388 311819 268569 Bharatpur 2549121 1357896 1191225 430833 231265 199568 Dhaulpur 1207293 654344 552949 215567 116276 99291 Karauli 1458459 784943 673516 239449 129872 109577 Sawai Madhopur 1338114 706558 631556 198777 106564 92213 Dausa 1637226 859821 777405 256802 138121 118681 Jaipur 6663971 3490787 3173184 914327 491960 422367 Sikar 2677737 1377120 1300617 375752 204065 171687 Nagaur 3309234 1698760 1610474 498585 264118 234467 Jodhpur 3685681 1924326 1761355 592959 313704 279255 Jaisalmer 672008 363346 308662 130400 69809 60591 Barmer 2604453 1370494 1233959 499328 262925 236403 Jalor 1830151 937918 892233 313808 165979 147829 Sirohi 1037185 535115 502070 171699 90849 80850 Pali 2038533 1025895 1012638 293002 154656 138346 Ajmer 2584913 1325911 1259002 374745 197987 176758 Tonk 1421711 729390 692321 200963 106799 94164 Bundi 1113725 579385 534340 158088 83809 74279

224 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Bhilwara 2410459 1224483 1185976 356230 185917 170313 Rajsamand 1158283 582670 575613 173944 91977 81967 Dungarpur 1388906 698069 690837 239608 125077 114531 Banswara 1798194 908755 889439 321288 166923 154365 Chittaurgarh 1544392 784054 760338 209376 110047 99329 Kota 1950491 1023153 927338 248585 131595 116990 Baran 1223921 635495 588426 179496 94348 85148 Jhalawar 1411327 725667 685660 204140 107132 97008 Udaipur 3067549 1566781 1500768 499072 259948 239124 Pratapgarh 868231 437950 430281 148753 77227 71526 Uttar Pradesh 199581477 104596415 94985062 29728235 15653175 14075060 Saharanpur 3464228 1835740 1628488 505263 268356 236907 Muzaffarnagar 4138605 2194540 1944065 630329 339201 291128 Bijnor 3683896 1925787 1758109 549305 293785 255520 Moradabad 4773138 2508299 2264839 763000 399613 363387 Rampur 2335398 1226175 1109223 370259 192981 177278 Jyotiba Phule Nagar 1838771 964319 874452 291320 153448 137872 Meerut 3447405 1829192 1618213 488271 263961 224310 Baghpat 1302156 700724 601432 189088 102953 86135 Ghaziabad 4661452 2481803 2179649 663367 358621 304746 Gautam Buddha Nagar 1674714 904505 770209 245232 132925 112307 Bulandshahr 3498507 1848643 1649864 537624 291617 246007 Aligarh 3673849 1958536 1715313 555429 296894 258535 Mahamaya Nagar 1565678 837446 728232 240376 129098 111278 Mathura 2541894 1368445 1173449 396853 212111 184742 Agra 4380793 2356104 2024689 638983 348298 290685

225 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Firozabad 2496761 1337141 1159620 369940 196925 173015 Mainpuri 1847194 984892 862302 275616 146750 128866 Budaun 3712738 1997242 1715496 647664 340501 307163 Bareilly 4465344 2371454 2093890 669681 352479 317202 Pilibhit 2037225 1078525 958700 297116 155624 141492 Shahjahanpur 3002376 1610182 1392194 488615 256917 231698 Kheri 4013634 2126782 1886852 644410 334562 309848 Sitapur 4474446 2380666 2093780 732695 381510 351185 Hardoi 4091380 2204264 1887116 662807 355722 307085 Unnao 3110595 1636295 1474300 417145 218024 199121 Lucknow 4588455 2407897 2180558 521815 272810 249005 Rae Bareli 3404004 1753344 1650660 460898 238963 221935 Farrukhabad 1887577 1007479 880098 292791 155414 137377 Kannauj 1658005 882546 775459 251533 132588 118945 Etawah 1579160 845893 733267 220220 117748 102472 Auraiya 1372287 736144 636143 193969 102380 91589 Kanpur Dehat 1795092 964284 830808 243919 128679 115240 Kanpur Nagar 4572951 2469114 2103837 484529 259156 225373 Jalaun 1670718 895804 774914 219378 116678 102700 Jhansi 2000755 1061310 939445 249154 134015 115139 Lalitpur 1218002 639392 578610 206018 107644 98374 Hamirpur 1104021 593576 510445 148557 78829 69728 Mahoba 876055 465937 410118 124719 65751 58968 Banda 1799541 966123 833418 289764 152656 137108 Chitrakoot 990626 527101 463525 171468 89927 81541 Fatehpur 2632684 1385556 1247128 377020 197960 179060

226 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Pratapgarh 3173752 1591480 1582272 427623 223300 204323 Kaushambi 1596909 838095 758814 263467 136764 126703 Allahabad 5959798 3133479 2826319 832870 437827 395043 Bara Banki 3257983 1707951 1550032 504272 261236 243036 Faizabad 2468371 1258455 1209916 347080 180112 166968 Ambedkar Nagar 2398709 1214225 1184484 324550 168270 156280 Sultanpur 3790922 1916297 1874625 539347 280754 258593 Bahraich 3478257 1838988 1639269 635383 328703 306680 Shrawasti 1114615 594318 520297 202667 105412 97255 Balrampur 2149066 1117984 1031082 385308 195808 189500 Gonda 3431386 1785629 1645757 545944 283786 262158 Siddharthnagar 2553526 1296046 1257480 465777 242313 223464 Basti 2461056 1256158 1204898 372315 193740 178575 Sant Kabir Nagar 1714300 870547 843753 272117 140251 131866 Mahrajganj 2665292 1375367 1289925 402081 209004 193077 Gorakhpur 4436275 2281763 2154512 595495 312549 282946 Kushinagar 3560830 1821242 1739588 551467 287733 263734 Deoria 3098637 1539608 1559029 445259 231842 213417 Azamgarh 4616509 2289336 2327173 680792 355385 325407 Mau 2205170 1114888 1090282 327500 170238 157262 Ballia 3223642 1667557 1556085 448844 236586 212258 Jaunpur 4476072 2217635 2258437 643020 335643 307377 Ghazipur 3622727 1856584 1766143 538527 282402 256125 Chandauli 1952713 1020789 931924 304229 153993 150236 Varanasi 3682194 1928641 1753553 478474 252332 226142 Sant Ravidas Nagar (Bhadohi) 1554203 797164 757039 244012 128559 115453

227 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Mirzapur 2494533 1312822 1181711 392230 206168 186062 Sonbhadra 1862612 973480 889132 308921 160859 148062 Etah 1761152 945157 815995 277672 147841 129831 Kanshiram Nagar 1438156 765529 672627 244852 129691 115161 Bihar 103804637 54185347 49619290 18582229 9615280 8966949 Pashchim Champaran 3922780 2057669 1865111 753429 386320 367109 Purba Champaran 5082868 2674037 2408831 993569 516736 476833 Sheohar 656916 347614 309302 124919 64892 60027 Sitamarhi 3419622 1800441 1619181 643851 333315 310536 Madhubani 4476044 2324984 2151060 779360 403516 375844 Supaul 2228397 1157815 1070582 424411 218560 205851 Araria 2806200 1460878 1345322 564131 288728 275403 Kishanganj 1690948 868845 822103 341943 173914 168029 Purnia 3273127 1695829 1577298 644083 329865 314218 Katihar 3068149 1601158 1466991 601745 307584 294161 Madhepura 1994618 1042373 952245 397468 206647 190821 Saharsa 1897102 995502 901600 377504 195819 181685 Darbhanga 3921971 2053043 1868928 700992 363597 337395 Muzaffarpur 4778610 2517500 2261110 817709 426633 391076 Gopalganj 2558037 1269677 1288360 437031 224717 212314 Siwan 3318176 1672121 1646055 532868 275500 257368 Saran 3943098 2023476 1919622 657316 342060 315256 Vaishali 3495249 1847058 1648191 591634 312354 279280 Samastipur 4254782 2228432 2026350 784203 404068 380135 Begusarai 2954367 1560203 1394164 532382 278564 253818 Khagaria 1657599 880065 777534 347048 181528 165520

228 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Bhagalpur 3032226 1614014 1418212 532307 275248 257059 Banka 2029339 1064307 965032 362548 186986 175562 Munger 1359054 723280 635774 221026 114841 106185 Lakhisarai 1000717 526651 474066 182234 95154 87080 Sheikhpura 634927 329593 305334 118228 60952 57276 Nalanda 2872523 1495577 1376946 501046 259703 241343 Patna 5772804 3051117 2721687 905708 476906 428802 Bhojpur 2720155 1431722 1288433 440847 230267 210580 Buxar 1707643 888356 819287 286969 149097 137872 Kaimur (Bhabua) 1626900 847784 779116 291785 150490 141295 Rohtas 2962593 1547856 1414737 493047 256108 236939 Aurangabad 2511243 1310867 1200376 438065 225256 212809 Gaya 4379383 2266865 2112518 762507 389247 373260 Nawada 2216653 1145123 1071530 367231 184990 182241 Jamui 1756078 914368 841710 313455 160287 153168 Jehanabad 1124176 586202 537974 193946 101103 92843 Arwal 699563 362945 336618 123684 63728 59956 Sikkim 607688 321661 286027 61077 31418 29659 North District 43354 24513 18841 4479 2361 2118 West District 136299 70225 66074 14957 7669 7288 South District 146742 76663 70079 15070 7737 7333 East District 281293 150260 131033 26571 13651 12920 Arunachal Pradesh 1382611 720232 662379 202759 103430 99329 Tawang 49950 29361 20589 5630 2808 2822 West Kameng 87013 49568 37445 11404 5803 5601 East Kameng 78413 38974 39439 13997 7106 6891

229 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Papum Pare 176385 90447 85938 23675 12060 11615 Upper Subansiri 83205 41974 41231 11312 5747 5565 West Siang 112272 58589 53683 13859 7187 6672 East Siang 99019 50467 48552 12115 6106 6009 Upper Siang 35289 18657 16632 4627 2351 2276 Changlang 147951 77289 70662 25478 13042 12436 Tirap 111997 57992 54005 19317 9904 9413 Lower Subansiri 82839 41935 40904 9991 5074 4917 Kurung Kumey 89717 44226 45491 15540 7856 7684 Dibang Valley 7948 4396 3552 1104 603 501 Lower Dibang Valley 53986 28127 25859 7714 3966 3748 Lohit 145538 76544 68994 23606 12082 11524 Anjaw 21089 11686 9403 3390 1735 1655 Nagaland 1980602 1025707 954895 285981 147111 138870 Mon 250671 132062 118609 39538 20808 18730 Mokokchung 193171 100229 92942 20046 10260 9786 Zunheboto 141014 71169 69845 20101 10283 9818 Wokha 166239 84429 81810 19673 9985 9688 Dimapur 379769 198163 181606 49595 25197 24398 Phek 163294 83684 79610 27538 14377 13161 Tuensang 196801 101977 94824 34931 18048 16883 Longleng 50593 26588 24005 8846 4700 4146 Kiphire 74033 37758 36275 14335 7331 7004 Kohima 270063 140118 129945 36157 18277 17880 Peren 94954 49530 45424 15221 7845 7376

230 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Manipur 2721756 1369764 1351992 353237 182684 170553 Senapati 354972 183081 171891 45442 23766 21676 Tamenglong 140143 71762 68381 18072 9310 8762 Churachandpur 271274 137748 133526 34490 17731 16759 Bishnupur 240363 120185 120178 29831 15543 14288 Thoubal 420517 209674 210843 66953 34365 32588 Imphal West 514683 253628 261055 58239 29972 28267 Imphal East 452661 225130 227531 60760 31451 29309 Ukhrul 183115 94013 89102 22954 11951 11003 Chandel 144028 74543 69485 16496 8595 7901 Mizoram 1091014 552339 538675 165536 83965 81571 Mamit 85757 44567 41190 14817 7487 7330 Kolasib 83054 42456 40598 12702 6394 6308 Aizawl 404054 201072 202982 52324 26375 25949 Champhai 125370 63299 62071 22068 11170 10898 Serchhip 64875 32824 32051 9082 4716 4366 Lunglei 154094 79252 74842 23594 12007 11587 Lawngtlai 117444 60379 57065 21795 11091 10704 Saiha 56366 28490 27876 9154 4725 4429 Tripura 3671032 1871867 1799165 444055 227354 216701 West Tripura 1724619 877930 846689 184656 95085 89571 South Tripura 875144 447124 428020 108805 55876 52929 Dhalai 377988 194342 183646 54416 27600 26816 North Tripura 693281 352471 340810 96178 48793 47385 Meghalaya 2964007 1492668 1471339 555822 282189 273633 West Garo Hills 642923 324900 318023 112115 56637 55478

231 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female East Garo Hills 317618 161372 156246 57064 28886 28178 South Garo Hills 142574 73322 69252 27401 13886 13515 West Khasi Hills 385601 194628 190973 86626 43867 42759 Ribhoi 258380 132445 125935 51547 26353 25194 East Khasi Hills 824059 410360 413699 134395 68548 65847 Jaintia Hills 392852 195641 197211 86674 44012 42662 Assam 31169272 15954927 15214345 4511307 2305088 2206219 Kokrajhar 886999 452965 434034 131865 67584 64281 Dhubri 1948632 998346 950286 358841 182662 176179 Goalpara 1008959 514162 494797 165762 84818 80944 Barpeta 1693190 867891 825299 280506 143487 137019 Morigaon 957853 485328 472525 159088 81567 77521 Nagaon 2826006 1440307 1385699 446238 227853 218385 Sonitpur 1925975 989919 936056 267238 136458 130780 Lakhimpur 1040644 529484 511160 150880 77064 73816 Dhemaji 688077 353043 335034 99692 51266 48426 Tinsukia 1316948 675986 640962 175038 88790 86248 Dibrugarh 1327748 680114 647634 154912 79146 75766 Sivasagar 1150253 589454 560799 133858 68392 65466 Jorhat 1091295 557944 533351 117515 59859 57656 Golaghat 1058674 539949 518725 128395 65472 62923 Karbi Anglong 965280 493482 471798 183862 95971 87891 Dima Hasao 213529 110566 102963 31758 16239 15519 Cachar 1736319 886616 849703 246826 126223 120603 Karimganj 1217002 620722 596280 203203 103760 99443 Hailakandi 659260 338766 320494 109537 56244 53293

232 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Bongaigaon 732639 373590 359049 113751 57874 55877 Chirang 481818 244675 237143 70177 35835 34342 Kamrup 1517202 779608 737594 194983 99397 95586 Kamrup Metropolitan 1260419 655630 604789 120500 60434 60066 Nalbari 769919 395804 374115 90593 46156 44437 Baksa 953773 484825 468948 117400 59823 57577 Darrang 908090 472134 435956 149626 77096 72530 Udalguri 832769 423617 409152 109263 55618 53645 West Bengal 91347736 46927389 44420347 10112599 5187264 4925335 Darjiling 1842034 934796 907238 180170 92728 87442 Jalpaiguri 3869675 1980068 1889607 445025 228381 216644 Koch Bihar 2822780 1453590 1369190 332355 170598 161757 Uttar Dinajpur 3000849 1550219 1450630 469971 241547 228424 Dakshin Dinajpur 1670931 855104 815827 178374 91564 86810 Maldah 3997970 2061593 1936377 590237 303540 286697 Murshidabad 7102430 3629595 3472835 979665 499040 480625 Birbhum 3502387 1791017 1711370 433186 221877 211309 Barddhaman 7723663 3975356 3748307 788582 405057 383525 Nadia 5168488 2655056 2513432 505995 258853 247142 North Twenty Four Parganas 10082852 5172138 4910714 902644 463502 439142 Hugli 5520389 2819100 2701289 504660 259277 245383 Bankura 3596292 1840504 1755788 405401 208632 196769 Puruliya 2927965 1497656 1430309 393562 202165 191397 Haora 4841638 2502453 2339185 497476 253337 244139 Kolkata 4486679 2362662 2124017 300052 155475 144577 South Twenty Four Parganas 8153176 4182758 3970418 976351 500011 476340

233 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Paschim Medinipur 5943300 3032630 2910670 663134 339781 323353 Purba Medinipur 5094238 2631094 2463144 565759 291899 273860 Jharkhand 32966238 16931688 16034550 5237582 2695921 2541661 Garhwa 1322387 683984 638403 233604 119325 114279 Chatra 1042304 534209 508095 188620 96108 92512 Kodarma 717169 367952 349217 128491 66097 62394 Giridih 2445203 1258607 1186596 450527 232924 217603 Deoghar 1491879 776741 715138 262903 135552 127351 Godda 1311382 678504 632878 234807 120246 114561 Sahibganj 1150038 590390 559648 216402 110706 105696 Pakur 899200 453101 446099 175356 89219 86137 Dhanbad 2682662 1405847 1276815 367402 191677 175725 Bokaro 2061918 1076270 985648 284353 148733 135620 Lohardaga 461738 232575 229163 75679 38592 37087 Purbi Singhbhum 2291032 1175696 1115336 286322 149006 137316 Palamu 1936319 1003876 932443 316511 162599 153912 Latehar 725673 369534 356139 132730 67593 65137 Hazaribagh 1734005 891179 842826 273427 142129 131298 Ramgarh 949159 494037 455122 130606 67816 62790 Dumka 1321096 669240 651856 212912 108786 104126 Jamtara 790207 403450 386757 128460 65950 62510 Ranchi 2912022 1493376 1418646 388052 200327 187725 Khunti 530299 265939 264360 83323 42707 40616 Gumla 1025656 514730 510926 168241 86072 82169 Simdega 599813 299905 299908 91297 46230 45067 Pashchimi Singhbhum 1501619 749314 752305 254046 128292 125754

234 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Saraikela-Kharsawan 1063458 543232 520226 153511 79235 74276 Orissa 41947358 21201678 20745680 5035650 2603208 2432442 Bargarh 1478833 748332 730501 156185 80246 75939 Jharsuguda 579499 297014 282485 61823 31907 29916 Sambalpur 1044410 529424 514986 112946 58505 54441 Debagarh 312164 158017 154147 38621 20149 18472 Sundargarh 2080664 1055723 1024941 249020 128529 120491 Kendujhar 1802777 907135 895642 253418 129494 123924 Mayurbhanj 2513895 1253633 1260262 337757 172992 164765 Baleshwar 2317419 1184371 1133048 274432 141412 133020 Bhadrak 1506522 760591 745931 176793 91577 85216 Kendrapara 1439891 717695 722196 153443 79869 73574 Jagatsinghapur 1136604 577699 558905 103517 53661 49856 Cuttack 2618708 1339153 1279555 251152 131259 119893 Jajapur 1826275 926058 900217 207310 107945 99365 Dhenkanal 1192948 612597 580351 132647 70927 61720 Anugul 1271703 654898 616805 145690 77311 68379 Nayagarh 962215 502194 460021 101337 54759 46578 Khordha 2246341 1166949 1079392 222275 116350 105925 Puri 1697983 865209 832774 164388 85444 78944 Ganjam 3520151 1777324 1742827 397920 209573 188347 Gajapati 575880 282041 293839 82777 42141 40636 Kandhamal 731952 359401 372551 106379 54266 52113 Baudh 439917 220993 218924 59094 29928 29166 Subarnapur 652107 332897 319210 76536 39314 37222 Balangir 1648574 831349 817225 206964 106090 100874

235 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Nuapada 606490 300307 306183 84893 43066 41827 Kalahandi 1573054 785179 787875 214111 109977 104134 Rayagada 961959 469672 492287 141167 72195 68972 Nabarangapur 1218762 604046 614716 201901 101577 100324 Koraput 1376934 677864 699070 215518 109376 106142 Malkangiri 612727 303913 308814 105636 53369 52267 Chhattisgarh 25540196 12827915 12712281 3584028 1824987 1759041 Koriya 659039 334336 324703 93249 47376 45873 Surguja 2361329 1195145 1166184 371891 190191 181700 Jashpur 852043 425085 426958 120079 60840 59239 Raigarh 1493627 749439 744188 191319 98473 92846 Korba 1206563 612158 594405 168437 85748 82689 Janjgir - Champa 1620632 816057 804575 219143 112656 106487 Bilaspur 2662077 1349928 1312149 400695 204757 195938 Kabeerdham 822239 411637 410602 140752 71348 69404 Rajnandgaon 1537520 762170 775350 206372 104461 101911 Durg 3343079 1681521 1661558 421141 215065 206076 Raipur 4062160 2048856 2013304 569447 289815 279632 Mahasamund 1032275 511475 520800 131380 67038 64342 Dhamtari 799199 397250 401949 100575 51073 49502 Uttar Bastar Kanker 748593 372987 375606 97479 49344 48135 Bastar 1411644 697359 714285 212819 106904 105915 Narayanpur 140206 70189 70017 22833 11561 11272 Dakshin Bastar Dantewada 532791 263562 269229 76450 38134 38316 Bijapur 255180 128761 126419 39967 20203 19764

236 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Madhya Pradesh 72597565 37612920 34984645 10548295 5516957 5031338 Sheopur 687952 361685 326267 115577 61204 54373 Morena 1965137 1068364 896773 299394 164016 135378 Bhind 1703562 926940 776622 241562 131671 109891 Gwalior 2030543 1090647 939896 254009 138681 115328 Datia 786375 419432 366943 109000 58863 50137 Shivpuri 1725818 919405 806413 280630 148565 132065 Tikamgarh 1444920 759891 685029 223570 118534 105036 Chhatarpur 1762857 935906 826951 279317 147484 131833 Panna 1016028 532866 483162 160884 84216 76668 Sagar 2378295 1254251 1124044 351306 182540 168766 Damoh 1263703 660478 603225 187275 97008 90267 Satna 2228619 1156734 1071885 321819 168769 153050 Rewa 2363744 1224918 1138826 340727 180971 159756 Umaria 643579 329527 314052 99457 51096 48361 Neemuch 825958 421640 404318 105548 55044 50504 Mandsaur 1339832 681439 658393 173814 90472 83342 Ratlam 1454483 737365 717118 212009 109801 102208 Ujjain 1986597 1016432 970165 264578 137889 126689 Shajapur 1512353 779900 732453 213107 111421 101686 Dewas 1563107 805212 757895 223252 117043 106209 Dhar 2184672 1114267 1070405 349262 182551 166711 Indore 3272335 1700483 1571852 407536 215446 192090 West Nimar 1872413 953617 918796 293913 152203 141710 Barwani 1385659 699578 686081 261103 134565 126538 Rajgarh 1546541 791038 755503 225662 117759 107903

237 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Vidisha 1458212 768799 689413 230714 120023 110691 Bhopal 2368145 1239378 1128767 293294 153101 140193 Sehore 1311008 683703 627305 194801 102194 92607 Raisen 1331699 701114 630585 203777 105772 98005 Betul 1575247 799721 775526 207282 106353 100929 Harda 570302 295208 275094 82183 42788 39395 Hoshangabad 1240975 648970 592005 161406 84463 76943 Katni 1291684 663064 628620 188415 97439 90976 Jabalpur 2460714 1278448 1182266 287433 149988 137445 Narsimhapur 1092141 569618 522523 139366 73333 66033 Dindori 704218 351344 352874 106665 54155 52510 Mandla 1053522 525495 528027 144799 73693 71106 Chhindwara 2090306 1063302 1027004 267351 137105 130246 Seoni 1378876 694916 683960 176170 90159 86011 Balaghat 1701156 841794 859362 206815 105479 101336 Guna 1240938 649591 591347 201630 106049 95581 Ashoknagar 844979 444651 400328 136680 71424 65256 Shahdol 1064989 541208 523781 153947 79100 74847 Anuppur 749521 379496 370025 103005 53025 49980 Sidhi 1126515 577091 549424 188733 98810 89923 Singrauli 1178132 614885 563247 204255 106355 97900 Jhabua 1024091 514830 509261 207931 107504 100427 Alirajpur 728677 362748 365929 143954 73024 70930 East Nimar 1309443 673491 635952 203237 105252 97985 Burhanpur 756993 388040 368953 120141 62557 57584

238 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Gujarat 60383628 31482282 28901346 7494176 3974286 3519890 Kachchh 2090313 1096343 993970 310192 162116 148076 Banas Kantha 3116045 1609148 1506897 498790 263953 234837 Patan 1342746 694062 648684 179392 95215 84177 Mahesana 2027727 1053337 974390 227701 123428 104273 Sabar Kantha 2427346 1244491 1182855 337374 177699 159675 Gandhinagar 1387478 722459 665019 159378 86276 73102 Ahmadabad 7208200 3787050 3421150 801967 431421 370546 Surendranagar 1755873 910266 845607 234196 123964 110232 Rajkot 3799770 1975131 1824639 424061 228713 195348 Jamnagar 2159130 1114360 1044770 254066 133861 120205 Porbandar 586062 300967 285095 63820 33687 30133 Junagadh 2742291 1404506 1337785 301395 158328 143067 Amreli 1513614 770651 742963 168715 89782 78933 Bhavnagar 2877961 1490465 1387496 369460 195965 173495 Anand 2090276 1088253 1002023 243653 129791 113862 Kheda 2298934 1187098 1111836 277300 146939 130361 Panch Mahals 2388267 1227805 1160462 348959 181428 167531 Dohad 2126558 1070843 1055715 402903 208014 194889 Vadodara 4157568 2150229 2007339 474479 250513 223966 Narmada 590379 301270 289109 75226 38844 36382 Bharuch 1550822 805945 744877 170565 89119 81446 The Dangs 226769 112976 113793 39387 20065 19322 Navsari 1330711 678423 652288 129530 67427 62103 Valsad 1703068 884064 819004 206309 107110 99199 Surat 6079231 3399742 2679489 710805 387131 323674

239 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Tapi 806489 402398 404091 84553 43497 41056 Daman & Diu 242911 150100 92811 25880 13556 12324 Diu 52056 25639 26417 6333 3294 3039 Daman 190855 124461 66394 19547 10262 9285 Dadra & Nagar Haveli 342853 193178 149675 49196 25575 23621 Dadra & Nagar Haveli 342853 193178 149675 49196 25575 23621 Maharashtra 112372972 58361397 54011575 12848375 6822262 6026113 Nandurbar 1646177 834866 811311 231268 119694 111574 Dhule 2048781 1055669 993112 261397 139345 122052 Jalgaon 4224442 2197835 2026607 513797 280915 232882 Buldana 2588039 1342152 1245887 324389 176116 148273 Akola 1818617 936226 882391 206053 108425 97628 Washim 1196714 621228 575486 147467 79318 68149 Amravati 2887826 1482845 1404981 299806 155572 144234 Wardha 1296157 665925 630232 124536 65005 59531 Nagpur 4653171 2388558 2264613 481814 250223 231591 Bhandara 1198810 604371 594439 122931 63398 59533 Gondiya 1322331 662524 659807 136116 70015 66101 Gadchiroli 1071795 542813 528982 115104 58842 56262 Chandrapur 2194262 1120316 1073946 223861 115090 108771 Yavatmal 2775457 1425593 1349864 320441 167346 153095 Nanded 3356566 1732567 1623999 444466 234249 210217 Hingoli 1178973 609386 569587 161086 86250 74836 Parbhani 1835982 946185 889797 251851 134971 116880 Jalna 1958483 1015116 943367 281495 152430 129065 Aurangabad 3695928 1928156 1767772 516791 279582 237209

240 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Nashik 6109052 3164261 2944791 805302 427878 377424 Thane 11054131 5879387 5174744 1257080 655354 601726 Mumbai Suburban 9332481 5025165 4307316 876917 459101 417816 Mumbai 3145966 1711650 1434316 262229 139906 122323 Raigarh 2635394 1348089 1287305 290439 150938 139501 Pune 9426959 4936362 4490597 1067261 569916 497345 Ahmadnagar 4543083 2348802 2194281 537346 292242 245104 Bid 2585962 1352468 1233494 344122 191115 153007 Latur 2455543 1276262 1179281 307726 164361 143365 Osmanabad 1660311 864674 795637 199509 107695 91814 Solapur 4315527 2233778 2081749 519781 277726 242055 Satara 3003922 1512524 1491398 307673 163605 144068 Ratnagiri 1612672 759703 852969 149486 77066 72420 Sindhudurg 848868 416695 432173 68637 35930 32707 Kolhapur 3874015 1983274 1890741 395143 214144 180999 Sangli 2820575 1435972 1384603 295055 158499 136556 Andhra Pradesh 84665533 42509881 42155652 8642686 4448330 4194356 Adilabad 2737738 1366964 1370774 295811 152362 143449 Nizamabad 2552073 1252191 1299882 268202 137788 130414 Karimnagar 3811738 1897068 1914670 322897 166698 156199 Medak 3031877 1524187 1507690 348721 178441 170280 Hyderabad 4010238 2064359 1945879 419500 216428 203072 Rangareddy 5296396 2708694 2587702 595352 305728 289624 Mahbubnagar 4042191 2046247 1995944 501878 259810 242068 Nalgonda 3483648 1758061 1725587 354940 184739 170201 Warangal 3522644 1766257 1756387 324410 169654 154756

241 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Khammam 2798214 1391936 1406278 267553 136637 130916 Srikakulam 2699471 1340430 1359041 265404 135929 129475 Vizianagaram 2342868 1161913 1180955 231021 118149 112872 Visakhapatnam 4288113 2140872 2147241 429234 218923 210311 East Godavari 5151549 2569419 2582130 492446 250086 242360 West Godavari 3934782 1963184 1971598 363536 184513 179023 Krishna 4529009 2268312 2260697 406927 208341 198586 Guntur 4889230 2441128 2448102 466285 239408 226877 Prakasam 3392764 1712735 1680029 360461 186581 173880 Sri Potti Sriramulu Nellore 2966082 1493254 1472828 287368 147719 139649 Y.S.R. 2884524 1454136 1430388 313455 163371 150084 Kurnool 4046601 2040101 2006500 477198 246345 230853 Anantapur 4083315 2064928 2018387 426922 221539 205383 Chittoor 4170468 2083505 2086963 423165 219141 204024 Karnataka 61130704 31057742 30072962 6855801 3527844 3327957 Belgaum 4778439 2427104 2351335 605524 313599 291925 Bagalkot 1890826 952902 937924 263781 136780 127001 Bijapur 2175102 1112953 1062149 303480 157212 146268 Bidar 1700018 870850 829168 216885 112103 104782 Raichur 1924773 966493 958280 272703 139917 132786 Koppal 1391292 701479 689813 194199 99460 94739 Gadag 1065235 538477 526758 127259 65464 61795 Dharwad 1846993 939127 907866 210194 108231 101963 Uttara Kannada 1436847 727424 709423 146457 75225 71232 Haveri 1598506 819295 779211 187754 96518 91236 Bellary 2532383 1280402 1251981 341804 174946 166858

242 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Chitradurga 1660378 843411 816967 177786 91973 85813 Davanagere 1946905 989602 957303 206935 107181 99754 Shimoga 1755512 879817 875695 176904 90271 86633 Udupi 1177908 562896 615012 100579 51448 49131 Chikmagalur 1137753 567483 570270 100791 51347 49444 Tumkur 2681449 1354770 1326679 252307 129253 123054 Bangalore 9588910 5025498 4563412 988482 509268 479214 Mandya 1808680 909441 899239 162147 83846 78301 Hassan 1776221 885807 890414 155579 79197 76382 Dakshina Kannada 2083625 1032577 1051048 202670 104169 98501 Kodagu 554762 274725 280037 52697 26661 26036 Mysore 2994744 1511206 1483538 285956 146192 139764 Chamarajanagar 1020962 513359 507603 94859 48854 46005 Gulbarga 2564892 1307061 1257831 352162 181955 170207 Yadgir 1172985 591104 581881 185727 95620 90107 Kolar 1540231 779401 760830 161877 82814 79063 Chikkaballapura 1254377 637504 616873 124719 64129 60590 Bangalore Rural 987257 507514 479743 102019 52400 49619 Ramanagara 1082739 548060 534679 101565 51811 49754 Goa 1457723 740711 717012 139495 72669 66826 North Goa 817761 417536 400225 75117 39316 35801 South Goa 639962 323175 316787 64378 33353 31025 Lakshadweep 64429 33106 31323 7088 3715 3373 Lakshadweep 64429 33106 31323 7088 3715 3373 Kerala 33387677 16021290 17366387 3322247 1695935 1626312 Kasaragod 1302600 626617 675983 149280 76149 73131

243 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Kannur 2525637 1184012 1341625 265276 135189 130087 Wayanad 816558 401314 415244 89720 45776 43944 Kozhikode 3089543 1473028 1616515 323511 164800 158711 Malappuram 4110956 1961014 2149942 552771 281958 270813 Palakkad 2810892 1360067 1450825 288366 146947 141419 Thrissur 3110327 1474665 1635662 289126 148428 140698 Ernakulam 3279860 1617602 1662258 289281 148047 141234 Idukki 1107453 551944 555509 100107 51132 48975 Kottayam 1979384 970140 1009244 168563 86113 82450 Alappuzha 2121943 1010252 1111691 186022 95556 90466 Pathanamthitta 1195537 561620 633917 91501 46582 44919 Kollam 2629703 1244815 1384888 238062 121481 116581 Thiruvananthapuram 3307284 1584200 1723084 290661 147777 142884 Tamil Nadu 72138958 36158871 35980087 6894821 3542351 3352470 Thiruvallur 3725697 1878559 1847138 369854 189244 180610 Chennai 4681087 2357633 2323454 418541 213084 205457 Kancheepuram 3990897 2010309 1980588 396254 201499 194755 Vellore 3928106 1959676 1968430 406705 209168 197537 Tiruvannamalai 2468965 1238688 1230277 256299 132664 123635 Viluppuram 3463284 1744832 1718452 378530 195294 183236 Salem 3480008 1780569 1699439 323102 168560 154542 Namakkal 1721179 866740 854439 140314 73345 66969 Erode 2259608 1134191 1125417 181188 92638 88550 The Nilgiris 735071 360170 374901 61644 31099 30545 Dindigul 2161367 1081934 1079433 200034 102989 97045 Karur 1076588 534392 542196 98980 50855 48125

244 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Tiruchirappalli 2713858 1347863 1365995 253633 129947 123686 Perambalur 564511 281436 283075 55950 29245 26705 Ariyalur 752481 373319 379162 76775 40579 36196 Cuddalore 2600880 1311151 1289729 260584 137513 123071 Nagapattinam 1614069 797214 816855 154543 78826 75717 Thiruvarur 1268094 627616 640478 114977 58602 56375 Thanjavur 2402781 1183112 1219669 223910 114386 109524 Pudukkottai 1618725 803337 815388 169886 86739 83147 Sivaganga 1341250 670597 670653 127682 65123 62559 Madurai 3041038 1528308 1512730 287101 148050 139051 Theni 1243684 624922 618762 110919 57258 53661 Virudhunagar 1943309 967437 975872 183214 93401 89813 Ramanathapuram 1337560 676574 660986 127447 64790 62657 Thoothukkudi 1738376 858919 879457 170507 86555 83952 Tirunelveli 3072880 1518595 1554285 301275 153437 147838 Kanniyakumari 1863174 926800 936374 161956 82586 79370 Dharmapuri 1502900 772490 730410 162118 84840 77278 Krishnagiri 1883731 963152 920579 203730 105872 97858 Coimbatore 3472578 1735362 1737216 295584 150580 145004 Tiruppur 2471222 1242974 1228248 221585 113583 108002 Puducherry 1244464 610485 633979 127610 64932 62678 Yanam 55616 27277 28339 6021 3141 2880 Puducherry 946600 466143 480457 95432 48459 46973 Mahe 41934 19269 22665 4588 2342 2246 Karaikal 200314 97796 102518 21569 10990 10579

245 Provisional population totals, 2011 Country/State/Union Territory Population Population (0-6 years) District Person Male Female Person Male Female Andaman & Nicobar Islands 379944 202330 177614 39497 20094 19403 Nicobars 36819 20705 16114 4225 2154 2071 North & Middle Andaman 105539 54821 50718 11647 5890 5757 South Andaman 237586 126804 110782 23625 12050 11575

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