Demographic Trends in India

Framework:

Introduction History of demography Date sources Basic demographic equation Demographic transition theory Basic data Basic measures of fertility Basic measures of mortality Nuptiality analysis

Introduction:

The term demography is derived from the Greek word ‘demos’ meaning a human being. The term seems to have been coined for the first time by a Belgian statistician Achille Guillard in 1855 in the article entitled ‘Elements ode statistique humanie, ou demographiue comparee’.

Demography is defined as ‘the scientific study of human populations, primarily with respect to their size, their structure and their development. (Van de Wall, 1982)

Thus it is a scientific study of human population in their aggregate with regards to their size, composition and structure, special distributions and developments and changes in these over time.

‘Formal Demography’ is the set of techniques by which data collection in Census, surveys and vital registration systems are described, summarized and manipulated. It is defined as ‘the treatment of qualitative relations among the demographic phenomena in abstraction from their association with other phenomena. (Van de Wall, 1982)

‘Demographic processes’ means phenomena of fertility of births, mortality or deaths and migration or movement of people from one location to other which contributes to changes in the size and structure of the population.

The variables of Age, Sex and Marital status are considered the three basic demographic variables.

History of formal demography

John Graunt is generally acclaimed as a founding father of demography as a field of emperical research. Interesting to note that he was not having formal school education. In 1662, from the records of baptisms and burials in London, Graunt noted the excess of male over female births, variation in mortality by age, and temporal and spatial variation in the cause of death; and even attempted the construction of a crude mortality table.

However, Edmund Halley (1693), constructed first emperical life table showing a stationary population by single years of age up to 84 years. John Sussmilch 1741-65 covered a variety of demographic topics including vital rates and life tables. The increased availability of vital data in 19th century enabled Willium Farr, england first Registrar General, to produce the decennial series of English life tables from 1841. The techniques of life table construction and related theory were further improved by treating mortality as a continuous function.

At the end of the 18th century, Thomas Malthus concluded that, if unchecked, populations would be subject to exponential growth. He is seen as the intellectual father of ideas of overpopulation and the limits to growth. Later more sophisticated and realistic models were presented by e.g. Benjamin Gompertz and Verhulst.

The analytical theory of the relationship between fertility and mortality on the one hand, and population age structure on the other, and is called the ‘stable population theory’ which was first implied by Swiss mathematician, Leonard Euler. The most outstanding work in the field of fertility analysis is by Kuczynski (1935) who compiled and analyzed data on reproduction for a number of countries. Through this work, the concepts of fertility and Net reproduction rate (NRR) became widely recognize.

A Trace of the History of Formal Demography –

Mortality or the study of deaths in a population has been the area of initial investigation for over two centuries. John Grunt is generally acclaimed as the founding father of demography as a field of empirical research (quoted in glass, 1950). In 1662, from the records of baptisms and burials in London, Grunt noted the excess of male over female births, variations in mortality by age, and temporal and spatial variations in the causes of death; and even attempted the construction of a crude mortality table.

However, Edmund Halley(1693), famous for the discovery of comet named after him, who constructed the first empirical life table showing a stationary population by single-years of age up to 84 years(UN, 1973). Johann Sussmilch during 1741-65 covered a variety of demographic topics including vital rates and life tables (UN, 1973)

The increased availability of vital registration data during the nineteenth century enabled William Faar, England’s first registrar general, to produce the decennial series of English life tables from 1841(UN, 1973). The technique of life table construction and related theory was further improved by treating mortality as a continuous function.

The most outstanding work in the field of fertility analysis is by Kuczyanski (1935) who compiled and analyzed data on reproduction for a number of countries. Through this work the concept of total fertility and net reproduction rate (NRR) became widely recognized.

In recent years, the concept of different age-patterns of mortality, prevalent in human populations, was first recognized and used by the United Nations (1955) in the development of model schedules and of mortality or model life tables.

Another noteworthy development in this subject was the concept of singulate mean age at marriage developed by John Hajnal (1953). It is the most widely cited indices of nuptiality. Subsequently, Coale and Demency (1966) have produced more life tables from actual experiences of developed countries.

Data and methods

There are two methods of data collection: direct and indirect. Direct data come from vital statistics registries that track all births and deaths as well as certain changes in legal status such as marriage, divorce, and migration (registration of place of residence). In developed countries with good registration systems (such as the United States and much of Europe), registry statistics are the best method for estimating the number of births and deaths.

The census is the other common direct method of collecting demographic data. A census is usually conducted by a national government and attempts to enumerate every person in a country. However, in contrast to vital statistics data, which are typically collected continuously and summarized on an annual basis, censuses typically, occur only every 10 years or so, and thus are not usually the best source of data on births and deaths. Analyses are conducted after a census to estimate how much over or undercounting took place. Censuses do more than just count people. They typically collect information about families or households, as well as about such individual characteristics as age, sex, marital status, literacy/education, employment status and occupation, and geographical location. They may also collect data on migration (or place of birth or of previous residence), language, religion, nationality (or ethnicity or race), and citizenship. In countries in which the vital registration system may be incomplete, the censuses are also used as a direct source of information about fertility and mortality; for example the censuses of the People's Republic of China gather information on births and deaths that occurred in the 18 months immediately preceding the census.

Indirect methods of collecting data are required in countries where full data are not available, such as is the case in much of the developing world. One of these techniques is the sister method, where survey researchers ask women how many of their sisters have died or had children and at what age. With these surveys, researchers can then indirectly estimate birth or death rates for the entire population. Other indirect methods include asking people about siblings, parents, and children.

Basic demographic equation:

The most basic equation that expresses the change in population over time in a simple form is known as the basic demographic equation or balancing equation. It is decomposition of the population change into its components. It is represented as follows-

P2= P1+ B-D+I-EWhere as P1 and P2 = population at two different points of timeB= birthsD= DeathsI= immigrantsE= emigrantsThe events contributing to the equation comes into two categories: B-D gives the natural increase, while I-E stands for net migration. So, it is re-expressed as P2=P1+NI+NMWhere as NI= natural increaseNM= Net migration

Demographic transition theory:Though it is not a part of formal demography, but is has become the central preoccupation of modern demography, principally because of the light it claims to shed on current rapid population growth in developing countries. It emphasizes the strong inter relationship between population structure, fertility and mortality. In general, it is a model description, which is attempting to explain the observed long term trends in fertility and mortality. The demographic transition theory can be broadly classified into three parts:

1. A description of changes in fertility and mortality2. The construction of theoretical causal models explaining these changes with

modernization or development and 3. Prediction for future changes, especially in the developing world.

Demographic data sources in India:Over time, there has been a gradual shift from priority of growth of the economy to the distribution of income, reduction in the incidence of poverty and employment generation. It has also been recognized that demographic variables play an important role in the development process. Therefore, any serious development strategy must integrate population policy as one of the means to achieve the ultimate goal. Under decentralized planning in India wherein political power is being devolved, in view of 73rd and 74th amendments of the constitution, to local self government (Panchayat Raj Institutions), there is greater need of population data at smaller units of administration, for example district block etc. In India data from various sources, available at different levels of administration are being used for development planning. Here we discuss following data sources:• National censuses• Civil registration system

• Sample registration System• Demographic Health Surveys, such as NFHS, DLHS-RCH.

National CensusesIndia has a record of unbroken census since 1881. The census of 1881 was the first step forward towards a modern synchronous and comprehensive operation in which much effort was made not only to ensure complete coverage but also on the classification of data by demographic, economic and social characteristics. Since then the census provides a population database at every 10 year interval required for 6 development planning. Latest census was carried out in 2001. Some of the data like population size by sex, population in the 0-6 age group, literacy status, etc. are made available at the village level. However, data available up to district level are:

(1) Age-Sex Distribution(2) Marital Status Data(3) Education Data(4) Religion of head of household and SCs/STs Data(5) Occupational Pattern(6) Industrial Classification of the Labor Force(7) Fertility (births during last one year and Children Ever Born (CEB))(8) Infant and Childhood Mortality indirectly is estimated using CEB and Children Surviving (CS) Data(9) In-migration Status (in-out both are available at state level)(10) Various Household and Community Amenities (Health facilities post office, bank, school, electricity, sanitation etc.)(11) Housing Condition Information

Civil Registration SystemAccording to the United Nations, civil registration is defined as the continuous permanent and compulsory recording of the occurrence of vital events, like, live births, deaths, foetal deaths, marriages, divorces as well as annulments, judicial separation, adoptions, legitimations and recognitions. Unlike other statistical systems, civil registration is performed under a law, decree or regulation so as to provide a legal basis to the records and certificates made from the system, which has got several uses in the personal life of individual citizens. Moreover, the information collectedthrough the registration process provides very useful and important vital statistics also on a continuous basis at the national level starting from the smallest administrative unit. In fact, obtaining detailed vital statistics on regular basis is one of the major functions of the Civil Registration System (CRS) in several countries of the world. Vital records obtained under CRS have got administrative uses in designing and implementing public health programmes and carrying out social, demographic and historical research.

Sample Registration System (SRS)Data on fertility and mortality from the census are still not very reliable and they are also available once in ten years. In the absence of reliable data from the civil registration system (CRS), the Government of India in late 1960s, initiated the Sample Registration System that is based on a Dual

Recording System. In this, regular registration of births and death is carried out in the sample areas that keep changing every 10 years or so. It was done in 1983, 1993 and 2003. The Sample registration System (SRS) provides reliable annual data on fertility and mortality at the state andnational levels for rural and urban areas separately. In the Sample Registration System, there is a continuous enumeration of births and deaths in a sample of villages/urban blocks by a resident part-time enumerator and then an independent six monthly retrospective survey by a full time supervisor. The data obtained through these two sources are matched and unmatched and partially matched events are re-verified in the field to get the correct number of events.Broadly, the following information are published annually with a time lag of about two years for major states separately for rural and urban areas.

(1) Age-Sex distribution(2) Marital Status(3) Fertility Indicators a: Age specific fertility rate b: Age specific marital fertility rate c: CBR, GFR and GMFR d: Total fertility rate (TFR) and Total marital fertility rate (TMFR) e: Birth order distribution and birth interval data(4) Mortality Indicators a: Crude death rate b: Age specific death rate c: IMR (Neo-natal, Post neo-natal, Peri-natal, Still births) d: SRS based life tables are separately publishedSRS also publishes the distribution of live births by type of medical attention received by the mother at the time of delivery. This is an annual publication with 2-3 year time lag. However, summary indicators are made available with a shorter time lag.

National Family Health SurveyThe International Institute for Population Sciences (IIPS), Mumbai has been involved since 1992 to create a database in India. IIPS is the Nodal agency for a nation wide survey under the banner of National Family Health Survey (NFHS) that was carried out first in 1992-93 (NFHS-1) and then again in 1998-99 (NFHS-2). This project is funded by the United States Agency for International Development (USAID) with additional funds for nutrition data in NFHS-2 from UNICEF. The technical assistance is provided by ORC Macro, Maryland, USA and the East-West Centre, Hawaii, USA.

The Primary objective of the National Family Health Survey (NFHS) has been to provide detailed data on two most important components of population growth and determinants. It also gives data on various aspects of family welfare program. At national and state levels data on the following topics are available:

• Fertility• Nuptiality• Family planning use and demand• Unwanted fertility• Utilization of RCH services• Breast feeding and weaning practices• Child nutrition and health• Infant and Child Mortality• Knowledge of HIV/AIDS(only in 13 states in NFHS-1 and all the states in NFHS-2)• Reproductive HealthThe data collected in the NFHS are also comparable with those of the Demographic and Health Survey (DHS) conducted in many other countries.

District Level Household Survey-RCHThe rapid household survey under the Reproductive and Child Health project aimed at providing data on key indicators of reproductive and child health at the district level. The first round of RHS was carried out during 1998-99 that provided information for 504 districts in India. About 1000 households were covered in each of the districts. From the data gathered under the RCH project it is possible to provide indirect estimates of fertility, say, the total fertility rate and the infant and child mortality rate.

The estimation of TFR can be obtained from the birth order statistics while that of IMR can be derived from the Children Ever Born and Children Surviving. Though the direct estimates are available, they are subject to fluctuation due to small sample size. The second round has been completed in 593 districts as per the 2001 census. The scope of this round was widened to cover birth history that provides fertility and mortality estimates. This data has been used to provide estimates in this report. The survey also collected data on anemia (Hb) among children below age 72 months, adolescents 15-19 and pregnant women.

Basic data 1. Age structure of population

For demographic purpose, age is defined as the number of full years lived by individuals. It is the age at last birthday that is generally used. In developing countries with high prevalence of illiteracy, age is one of the most difficult of personal characteristics to be ascertained by direct enquiries. The age of population can increase or decrease with respect to time. Clearly the age structure of the population at particular point of time is the result of past demographic process of births and deaths. The change in the number of births first affects the child age group. After many years this effect can be seen in older age groups as well. The effect of changes in deaths is generally to produce fewer variations than births in developed and developing nations. This is because deaths affect

most age groups uniformly but birth affects only the younger age groups. Thus the age structures of population is primarily dependent on births to a great extent, and deaths and migration to a lesser extent.

Population momentum:In a number of developing countries, the birth rates are very high and as a consequence the proportion of young children is high, because of high fertility in the past. Hence, even under rapid declines in fertility in the future, the number of births might keep on increasing because of rise in number of women in the reproductive age group in the future. This is simply because, there is an increasing number and proportion of potential parents who will enter the childbearing ages, who are already born and thus population will continue to grow in size. This aspect is generally known as population momentum.

2. Sex compositionSex of the individuals holds an important place in demography. Data on sex is required for planning purpose, particularly health services. The common measure which is used to represent the sex composition of a population is the sex ratio. It is simply the number of males per thousand females in a population.Sex ratio=Pm*Pf*1000Pm=number of males in a population at a specified timePf= number of females in a population at a specified timeThe level of sex ratio in a population as a whole is affected by the sex ratio at birth, sex differentials in mortality and migration.In India, contrary to international definitions, the sex ratio is defined as the number of females per thousand males. So, Sex ratio=number of females/number of males *1000

3. Age ratioThe age ratio for an age group is normally taken as equal to the number of persons in that age group divided by the number obtained from adding one-half of the number in the preceding and following age groups. Thus, the age ratio for a five-year age group is defined as: Age ratio=5Pa/0.5 (5Pa-5 + 5Pa+5)*100

4. Dependency ratioAge data are most commonly tabulated and published in five yrs age group. For inference based on economic activity and social problems, the age groups generally chosen are 0-14, 15-64, 65yrs. The first group being considered as children, second group as economically productive group and last is old age group. One frequently used measure to assess the extent of economic dependence in a population is the ratio of economically

active to economically inactive persons in a population. This ratio is called as dependency ratio and given by formula:Dependency ratio=P0-14 + P65*100/P15-64P0-14=population under age 15P65=population aged 65 and aboveP15-64=population between ages 15-64

Basic measures of fertility1. Child women ratio

This is the simplest measure generally used in the fertility analysis and is defined as:CWR= 5P0/ 35W15 *1000Where as, 5P0= number of children under 5 yrs of age in a particular time35W15= number of women in the age group 15-49 (reproductive span) in a particular timeBasically, it is the crude measure and if fertility is high the ratio will be high and if fertility is low the ratio is low.

2. Crude birth rateThis is the most widely used measure of period fertility. It is defined as:CBR= number of births during a year/population at mid-year *1000This measure is considered as a good measure of overall changes due to birth in a population and is simple in concept and measurement. This rate is called as crude as it includes all ages and both sexes in the denominator. As we know that only women can bear the child and that too within certain age limits. Thus, in computing the CBR we do not explicitly consider the population at risk of having those births. The measure is greatly affected by the age-sex composition and other characteristic of population. Hence, any comparison between of two populations with this measure can be misleading, because they may vary widely in their age sex composition.

3. General fertility rate As CBR includes entire population in the denominator. A more meaningful measure will be to use only women of the reproductive age group in the denominator. The rate thus obtained is called as GFR and is computed as:GFR= number of births during a year/mid yr female population aged 15-49*1000As far as the risk of conception is concerned, the denominator used in calculation of this measure is not homogeneous group. It uses a wide range of age groups in the denominator consisting of women of different fecundity status (physiological capacity to bear the children). Thus, it might be again misleading rate. To overcome this, it is necessary to consider fertility rates for each age or age group.

4. Age specific rateThe denominator of GFR uses all the women in reproductive ages and in the ASFR both the numerator and denominator pertain to births and number of women in a specific age or age group. The ASFR is defined as:ASFR at age X= births to women aged X in a year/mid yr female population aged X*1000ORASFR at age X, X+n= births to women aged X to (X+n) in a year/mid yr female population aged X to (X+n)*1000Generally, five year age groups of women are used for calculating the rate. The ASFRs are found to be useful in fertility analysis since there are wide variations in the fertility and fecundity by age. For the calculation of this measure, it is necessary to have birth classified by the age of the mother as well as women by same age or age group. The rate increases to a maximum between ages 20-29 and then decreases slowly and to reach to zero by age 50. The model value of the rate depends to a certain extent on the age at marriage of woman. Since, ASFR generally produce seven values by 5 yrs age groups, it is vary difficult to use this measure for comparison purposes.

5. Total fertility rateThe sum of the ASFRs over different ages 15 to 49 or 15-44 is known as TFR. Thus,TFR= sum of ASFRIt is the most widely used measure of fertility by demographers. The TFR is generally expressed as number per woman. This measure can be thought of as the number of children a woman would have of she survived to age 50 and throughout her reproductive life span she is subjected to a fertility schedule described by the ASFR. The importance of TFR is that is it a single figure and is independent of age structure. However, the computation of TFR needs lots of information, such as the birth by age of the mother and age of the women. If we want to express TFR per thousand women then, it is divided by 1000.

6. Gross reproductive rateGRR is another summary measure of period fertility rate. This rate is essentially a TFR, with the modification that it is computed only for female births. GRR is the average number of daughters that would be born to a woman during her life time if she passed through the childbearing ages experiencing the average age-specific fertility pattern of a given period. If the data on births by sex are available for each group in the childbearing age group, the ASFRs may be computed for female children and the GRR can be computed by summing up these ASFRs, multiplying the sum by 5, and finally dividing the product by 1000. The data on births by sex is often not available. In this case, GRR is

related to the TFR in the following manner, assuming that the sex ratio at birth do not vary with the age of the mother.GRR=TFR*(1/1+S)S=sex ratio at birth

Basic measures of mortality1. Crude death rate

A simple and direct measure of deaths in a population is the CDR. This rate relates the number of deaths during a year to the mid-year population of that year. The denominator uses the total population with varying risk to death and hence CDR dose not measure the risk of dying for a person in the population in the probabilistic sense. This measure is generally a poor indicator of mortality as it does not take age structure into account. The risk of dying is generally high in the infants and childhood ages, declines drastically thereafter up to age 20 and then rise slowly but steadily increasing sharply at ages above 50 yrs. This type of curve is known as reverse J-shaped curve is characteristics of age pattern of mortality in all population. Many developing countries show low CDR than developed countries as they have high fertility rate.

2. Age specific death rateThe CDR is the weighted average of the age specific rate, the weight being the population size at each age. The death rate may vary not only by age but differently for two sexes. The age-specific rates for males and females, separately, are the commonly used specific rates. The ASDR for the age group X to X+n is defined as:nMx= nDx/nPx *1000nDx= number of deaths between x and x+n in the yrnPx= number of person ages between x and x+n at the middle of the yr

Neonatal mortality rate (NMR):The risk of dying in the first yr of life is not uniform in the interval. The risk is maximum in first few weeks and causes are also different than the deaths that occurs later on. Because of this reasons, the IMR is divided into two parts, by dividing the first yr of life into two periods based on the intensity of risk of mortality. The first 4 weeks or a month is found to have a very high risk and the number of infant deaths during this period os termed as neonatal mortality rate and given by formula:NMR=deaths of babies less than 4 weeks old/number of live births during the same yr*1000

Post neonatal mortality rate (PNMR):The number of infant deaths after 4 weeks is termed as post neonatal mortality and given by the formula:

PNMR= deaths of babies aged 4-52 weeks during a yr/number of live births during the same yr*1000

Cause specific death rate (CSDR):For the control of disease in any population it is important to know the cause of death. Thus cause specific measures of mortality are useful in understanding the relative contributions of different causes to the overall mortality rates in the population and resources to be mobilized for controlling the specific disease. The most commonly used measure is the cause specific death rate (CSDR) and given by formula:CSDR= Dc/P*1000Where as, DC=number of deaths in a yr due to a particular cause “c”P=mid yr total populationCSDR can be computed by age and sex too. Another measure, which expresses the number of deaths due to a particular cause as a proportion of all deaths is called cause-specific death ratio and is also commonly used in the mortality analysis.

Infant mortality rate (IMR):It is defined as the number of deaths under 1 yr during a specified period divided by the number of live births in the same period and usually expressed per thousand births.IMR= No. of deaths under 1 yr in a yr/No. of live births in the same yr*1000

Maternal mortality ratio (MMR):It is a widely used type of cause specific mortality rate representing approximately the risk of dying as a result of complication of pregnancy, childbirth and puerperium. This ratio is generally defined as the number of deaths due to puerperal cause per 100000 live births.

MMR=Dp/B*100000Where as, Dp=deaths due to puerperal causes (deaths occur to mother with in 42 days of delivery due to cause associated with pregnancy and child birth during a yrB=number of live births in the same yr

If the denominator uses the number of married women, then the measure is called a maternal mortality rate, Maternal mortality rate=Dp*100000/number of married women aged 15-49 yrs

Nuptiality analysis1. Crude marriage rate

It is the most common and simplest measure. If M is the number of marriage among residents in an area during a year, and P is the mid yr population in the area during the same year, then the CMR is defined as:CMR=M/P*1000This measure also suffers from the usual defects of a crude rate. The numerator of the rate uses the number of marriages. However, marriage involves two persons and if we use the number of persons getting married during the yr, the rate becomes twice as large. Further, the entire population used can not be considered as the population exposed to the risk of marriage, as the very young and the person who are already married do not belong to the exposed category.

2. General marriage rate (GMR)The denominator of the CMR is not wholly the population exposed to the risk of marriage. A more refined measure can be calculated by taking the person of marriageable ages as the denominator. The rate thus obtained is called General marriage rate (GMR) and given by formula:GMR=M*1000/P15+Where as, M=total number of marriage in a yrP15+= population aged 15 and above Further refinement can be done by taking account of unmarried persons in the marriageable ages and sex composition of the population and given by:GMR=M*1000/Unmarried persons aged 15+ yrs

3. Age specific marriage rateThe GMR partially controls the age distribution of the population, but the internal variations in the age composition within the population of those marriageable are not taken into these rates. It should also be noted that since generally husbands tend to be older than wives, the age specific rate will be different for the sexes. Hence, it is advisable to have age specific marriage rate and given by formula:ASMR=nMx*1000/nPxnMx=number of marriages in a yr between ages x and x+nnPx=number of persons in the age group (x, x+n)

4. Total marriage rate

Like TFR, it is possible to derive a total Marriage rate (TMR) from the ASMR. The value of TMR indicates the total number of marriages in a synthetic cohort passing through life together. Thus, TMR= ∑ (ASMR)The ASMR can be calculated separately for male and females. Instead of the number of persons in the age group (x, x+n), the number of unmarried persons can also be used in the denominator. As remarriages are possible and hence it is possible to re-enter the married state. Thus it is important to have an order-specific marriage rate. The generally used measure is the age-specific first marriage rate (ASFMR) is given by:ASFMR=nM1x*1000/nP0xWhere as, nM1x= number of first marriages in a yr in the age groupnP0x=number of single or unmarried persons in the same age group and eligible for marriage for the first timeThe ASFME can be used to construct a Nuptiality table similar to life table.

5. Singulate mean age at marriage (SMAM)In most populations of developing countries, information on age at marriage of persons getting married in a year or a period of time are often not available, and if available, grossly incomplete and poor in quality. In such situations the method adopted by Hajnal (1953) has been used to estimate the average age at marriage provided data on person remaining unmarried at each age are available. The measure obtained by the method is called Singulate mean age at marriage (SMAM). This methodology generally ignores the effect of migration and mortality. SMAM=d+ (d-D)S(D) +∑ S(x)/1-S(D)

POPULATION: Trends and Projections

India’s status in World Population: India Ranks second most populous country in the world after china. Three countries in SEAR, India (17.56%), Indonesia (3.48%) and Bangladesh (2.38%) are among the ten most populous countries of the world.

As per Census 2001, India’s population was 1029 million (1,028,610,328). Every year 16 million people are added to the current population making additional demand for resources like clothing, housing, food, education, health etc. At present, India accounts for only 2.4% of worlds land surface area, supports about as much as 17.5% of world’s population. And now Census 2011 shows increase of population to more than 1210 million (1, 21, 01, 93422)

The State and District: Maharashtra been reported the population of 11,23,72,972 as per 2011 census which constituted 9.42% of the country’s population and second most populous state after Uttar Pradesh while Sikkim is least populous state in India.

Density of population of the country is 382/sq. Km in 2011 which is steadily increasing over the years. Maharashtra has reported an analogous density of 365/sq. Km.

2011 census has reported the population of Wardha district of 12,96, 157 which was 1.27% of the population of Maharashtra while area wise the district covers about 2%(6310 sq. Km.) of the area of the state.

Age Distribution Of Population The SRS 2006 reports of male population to be 1% more than females in 1-14 years age group (11.8 %) while 0.6% less than female population in above 60 years age group. The proportion of population below 14 is showing a decline (31.1%) while proportion of elderly in the country is increasing (11.1%). This trend is to continue in the time to come.

The projected population and age distribution of population

Dependency Ratio-Societal dependency ratio or total dependency ratio is the ratio of combined age groups 0-14 years plus 65 years and above to the 15-65 years age group. Age structure of population suggested 31.1% population in 0-14 yrs age group and 5.3% population above 65 years age group thus suggesting of the total dependency ratio of 0.57

Sex Distribution Of Population - A fundamental indicator of gender inequality in India is a preference for sons so strong that it is manifested as limiting the birth and survival of girls. The 2001 census data for India revealed a sharp decline in the sex ratio for the population age 0-6, from 945 females in 1991 to 927 females per 1,000 males. Census 2011 shows a degree of hope by indicating a rise to 940 females per 1000 males.

Comparison of Age and sex distribution of the population of two most populous countries of the world by population pyramids

Levels and trends in the sex ratio at birth In India, the sex ratio at birth calculated for all births in the five complete calendar-year periods before each of the three National Family Health Surveys not only shows a downward trend in the sex ratio at birth in the six years between 1987-91 and 1993-97, but also shows an acceleration in the decline in the sex ratio between 1993-97 and 2000-04. This trend is continued in 2011 census also which indicates fall upto 914 females to 1000 males.

The sex ratio of births that died in each of the five calendar-year periods increased from 991 females per 1,000 males in the period 1987-91 to 1,045 females in the period from 2000-04.In

the same time period, the sex ratio of surviving births declined from 936 to 910. These data suggest that not only are fewer than expected females being born, but females are increasingly over-represented in births that die.

Trends in the sex ratios at birth of live births and births that have died in thefive complete calendar years preceding NFHS-1, NFHS-2, and NFHS-3, India

Sex Ratios at Birth of Last BirthsYet another way to examine gender preferences is to examine sex ratios at birth of last births. Table shows the total sex ratios, sex ratios of last births, and sex ratios of all other births for all births in the birth histories of women age 15-49 in NFHS-1, NFHS-2, and NFHS-3. In all three surveys, the sex ratio of last births has been much lower than that for all births and the disparity has been growing over time. In NFHS-1, the sex ratio of last births was 14% lower than the sex ratio of all other births; this disparity increased to19% in NFHS-2 and then to 26% in NFHS-3.

Trends in sex ratios at birth (females per 1,000 males) of last births,all other births, and all births, India

Life expectancy- Expectation of life at a given age is the average number of years which a person of that age may expect to live, according to the mortality pattern prevalent in that country.

Life expectancy has continued to increase globally over the years. Trends show that people are living longer, and they have a right to long life in good health, rather than that of pain and disability. Hence planning for prevention and control of old age diseases is felt.

Life expectancy at birth

Year Males Females1961 41.89 40.551971 46.40 44.701981 54.10 54.701991 59.70 60.902001 63.90 66.902011 67.43 69.24

Urban: Rural distribution of population - Like other developing countries, India has undergone rapid urbanization over the past fifty years. As per the 2001 Census, 28 percent of the population of India was living in urban areas. From 1951 to 2001, India's urban population grew almost fivefold, from around 62 million in 1951 to around 286 million in 2001. Any compact habitation of at least 300 people or about 60 – 70 households of poorly built congested tenements, in unhygienic environments, usually without adequate infrastructure and lacking in proper sanitary and drinking water facilities in these towns irrespective of the fact as to whether such areas have been notified or not as ‘slum’ by state/local government and union territory administration under any act, recognized or not, are legal or not, are declared slums under NUHM. In 2002, the United Nations operationally defined slums as communities characterized by insecure residential status, poor structural quality of housing, overcrowding, and inadequate access to safe water, sanitation, and other infrastructure (United Nations Human Settlements Program, 2003). According to the 2001 Census, 42.6 million people (14.8% of urban population) lived in slums in 8.2 million households and 640 towns spread across 26 states and Union Territories in India. The slum estimates did not include towns below 50,000 population, as well as a few towns and cities with a population of 50,000 or more where local bodies did not recognize any slum area (136 towns in all, including such large cities as Lucknow) and a few north-eastern states that did not have any urban centre with 50,000 or more population (Office of the Registrar General and Census Commissioner, 2005).

Growth of urban population in India

Literacy Rate –

Indian definition of literate is “a person aged 7 and above who can both read and write with understanding”. The graph shows literacy rate reported in population census over a period of fifty years. There has been a steady increase in the literacy rate of the country with 2001 census reporting of 65.38% literacy rate. However the national figures are misleading and there has been wide variation between the states as far as literacy is concerned. Kerala has the top literacy rate of more than 93.91% while states like Bihar and Jharkhand have literacy rate of only 63%. There has also been sex wise difference of literacy rate with 74.04 % literacy rate among males and 65.46 % among female population.

School attendance by age In the school year 2005-06, 71% of children age 6-17 attended school—77% in urban areas and 69% in rural areas. The sex

ratio of children 6-17 attending school in the 2005-06 school years is 889 girls per 1,000 boys.

Percentage of boys and girls attending school in the2005-06 school years, NFHS-3, India

Age-appropriate school attendance the net attendance rate (NAR) and the gross attendance rate (GAR) are measures of age-appropriate school attendance. These rates are defined separately for the primary and secondary levels of education as follows:For primary schoolNAR: Children age 6-10 years in classes 1-5 as a proportion of all children age 6-10 yearsGAR: All children in classes 1-5 as a proportion of all children age 6-10 yearsFor secondary schoolNAR: Children age 11-17 years in classes 6-12 as a proportion of children age 11-17 yearsGAR: All children in classes 6-12 as a proportion of all children age 11-17 years

Net attendance rate (NAR) for children age 6-17 for the school year 2005-06, NFHS-3, India

Gross attendance rate (GAR) for children age 6-17 for the school year 2005-06,NFHS-3, India

Age-appropriate primary level school attendance, at about 7 out of 10 children, is considerably lower than school attendance by children age 6-10 years (more than 8 out of 10). This suggests that a large number of children are not starting school at the recommended age.

The NAR at the secondary level also reveals large gender disparities in rural areas and a high dropout rate for girls as well as for boys.

Fertility-related Statistics –

The age-specific fertility rates show that in the three years preceding NFHS-3, there were 90 births per 1,000 women age 15-19 and 209 births per 1,000 women age 20-24, the highest of any age group. These age-specific fertility rates imply that by the time a woman in India passes through the period of youth, she will have 1.5 children on average.

Although urban fertility is lower than rural fertility, the urban age-specific fertility rates show that even in urban areas women have, on average, more than one child before they are 25years of age.

The Total fertility rate has dropped down from 3.39 in NFHS-1 to 2.68 as per NFHS-3.

Fertility during the youth years accounts for more than half (56%) of total fertility.

Indicator Of Fertility NFHS-2 NFHS-3

General Fertility Rate(GFR) 118.2 93.3General Marital Fertility Rate (GMFR) 153.3 127.2

Total Fertility Rate (TFR) 3.5 2.8Total Marital Fertility Rate (TMFR) 4.9 4.4

Gross Reproduction Rate(GRR) 1.7 1.3

Trends in the Contraceptive Prevalence Rate for Youth-

Twenty-eight percent of currently married women age 15-24 use a method of contraception.

The contraceptive prevalence rate among youth increases from 13 percent among teenagers to 33 percent among women age 20-24 years.

Contraceptive use has been increasing over the years among both younger and older youth and the increase has been particularly marked in the years between NFHS-2 and NFHS-3.

Couple protection Rate –

The couple protection rate (CPR) is usually expressed as the percentage of women in the age group of 15-49 years, protected from pregnancy/child birth in the year under consideration for a specific area. Efforts to improve availability and access to contraceptive care in India during the seventies and early eighties resulted in a steep rise in couple protection rates. However, there was no commensurate fall in the birth rate. Service reports on CPR and SRS estimates of CBR indicate that there has been a steady decline in the CBR during the nineties in spite of the fact that the rise in CPR during the nineties has been very slow. This may indicate that either there has been a reduction in over reporting of contraceptive acceptance or there has been improvement in the quality of services and appropriate contraceptives are being provided at appropriate time.

Couple protection rate and Crude birth rate

Indicators of nuptiality - NFHS 3 show that 80% women and 74% men in the age group of 15-49 years are

married. Divorce, separation and desertion is relatively uncommon in India: in the age group of

15-49, only 1.5% of women and 0.6% of men are currently divorced, separated, or deserted.

Widowhood is the more common form of marital disruption: 3% of women age 15-49 are widows and 1% of men in this age group are widowers.

Among ever-married women, only 2% have had more than one marriage; among ever-married men, by contrast, the proportion married more than once is almost three times as high, at 6%.

Marriage before age 15 continues to be common among women in India: even in the age-group 25-29, one in four women were married before age 15. This proportion has declined from its high of 33% among women age 40-49, but still continues to be significant.

Percentage of women and men age 25-49 first married by specific exact ages, according to current age, NFHS-3, India

The Singulate Mean age at marriage (SMIM) is a mean number of years lived in a single state as implied by a schedule of age specific percentages single.

Trends in SMIS reported for countries in SEAR

Indicators of Birth and death –

Estimates All India SRS Vital Annual Rates per 1000 Population (Rural, Urban and Combined)

Year Crude Birth Rate Crude Death Rate Infant Mortality Rate

R U T R U T R U T

1970 38.9 29.7 36.8 17.3 10.2 15.7 136 90 129

1975 36.7 28.5 35.2 17.3 10.2 15.9 151 84 140

1980 35.1 27.8 33.7 13.7 7.9 12.6 124 65 114

1985 34.3 28.1 32.9 13.0 7.8 11.8 107 59 97

1990 31.1 24.7 30.2 10.5 6.8 9.7 86 50 80

1995 30.0 22.7 28.3 9.8 6.6 9.0 80 48 74

2000 27.6 20.7 25.8 9.3 6.3 8.5 74 44 68

2005 25.6 19.1 23.8 8.1 6.0 7.6 64 40 58

2008 24.4 18.5 22.8 8.0 5.9 7.4 58 36 53

Maternal Mortality Ratio reported by several studies

References -

1. Basic demographic techniques and applications; K. Srinivasan.

2. Census of India2011, population totals. Cited from www.censusindia.net

3. A profile of youth in India; Ministry of Health and Family Welfare Government of India,

NFHS-3 India 2005-06.

4. Gender equity and women’s empowerment in India; Ministry of Health and Family Welfare

Government of India, NFHS-3 India 2005-06.

5. Government of India (2007), Sample Registration System Statistical report 2008, Report no. 4 of

2007.

6. Government of India (2004), Sample Registration System Statistical report 2006, Report no. 1 of

2006.

7. Demography and Family planning; Park’s Textbook of Preventive and social medicine 17 th and

20th edition.

8. Bose A. Population of India, 2001 Census results and Methodology. 1st edition. Delhi: B.R.

Publishing Corporation; 2001.

9. Bhende A, Kanitkar T, 2003 Population Studies. 16 th Edition. Mumbai: Himalaya Publications;

2003.