While national goal of fertility replacement level (2.1) to be achieved by 2010 as parameter of medium term objectives of India's National Population Policy (NPP-2000), NFHS-3 (IIPS, 2007) report indicates the high inter-state variation in fertility levels. Currently, a woman in India will have an average of 2.7 children in her lifetime. The rates are at or below the replacement level in 10 states say Andhra Pradesh, Delhi, Goa, Himachal Pradesh, Karnataka, Kerala, Maharashtra, Punjab, Sikkim, and Tamil Nadu. Some other states are closed to the replacement level. In contrast, fertility rates are highest in Bihar and Uttar Pradesh, where a woman would have about 4 children. In this report, fertility in rural areas is 3 children per woman, much higher than in urban areas where the replacement level has been achieved. Great differentials in fertility are due to wealth and education. At current fertility rates, women in lowest wealth quintile will have two children more than women in highest wealth quintile. Also, the rates are higher for women in disadvantaged groups with 3.1 children per woman among scheduled tribes, 2.9 among scheduled caste, and 2.8 among other backward classes, compared with women who are not in any of these groups (2.4). The total fertility rate for Muslims (3.1) is slightly higher than the rate for Hindu (2.7), but this difference has been cut in half since the fertility of Muslims fell more rapidly than the fertility of Hindus in seven years between NFHS-2 and NFHS-3. According to the last report, the median interval between births is also observed to be 31 months. Only 11% of births take place within 18 months of the last birth, and 28% occur within 24 months. More than 60% occur within three years.
Even after six decades of Indian Independence, fertility rate and infant mortality rate have remained at high levels in the country. These are primarily due to abject poverty and widespread illiteracy among people, especially among the females. Despite constant efforts made by the government, the country is still lacking behind the national target (2.1 by 2010). The reasons thereof have to be analyzed to the core for better understanding of the situation and to help the government in formulation of appropriate policies and modified approaches (Srivastava et al., 2005). National Rural Health Mission (NRHM: 2005-12) also stress the improvement in 'quality of life' by emphasizing fertility not only at young ages but also not continuing beyond 30 years of age as well as the birth intervals being reasonably long. While in India growth rates are no doubt relevant, given the complexity of the country, it would therefore be necessary to analyze indebt growth rate and population distribution at regional levels. If policy prescriptions have to be effective, one would have to consider population growth rates in terms of broad regions because of wide variations over the country. The socio economic development programmes relating to literacy improvement, involvement of women in work and improvement in the overall status of women are considered to be essential in the context of long term policy of fertility control. In this regard, the need for integrating family welfare programme with other developmental programmes, particularly in relation to community participation, must be recognized. Programme activities in this is very essential socio economic sectors can no longer be viewed in isolation since it is almost axiomatic that economic development does, in the long run, being about a fall in the fertility level. From family planning to family welfare and now reproductive and child health there have been significant transitions over this time, including rapid policy changes during the last decade. In one sense, these changes have meant the designing of a comprehensive programme in accordance. Frequent policy shifts have also led to uncertainty and confusion among programme personals on the other hand. Hence, the statistical estimates and projections are very important for planning and formulation of policies. Accordingly the total fertility rates of most of the states in India are still higher than replacement level of fertility and 2.7, the average number of children wanted by ever married women. Notwithstanding, the significant drop that had been achieved is encouraging. The level of fertility has an influence on population policies and programme for the country.
At this crucial juncture, the study of fertility becomes paramount importance for population control. It is well established that fertility is influenced by a series of factors like socio economic, demographic, social customs etc. No doubt, the problems of fertility reduction are very difficult and complex at least in one respect that is motivation. However, motivation on the part of couples favorable for high fertility is strongly linked with socio economic backwardness (Patnaik, 1985). Desire to attain parenthood on the part of couples is universal and natural, but to produce more children resulting from the sex preference emanating from social, religion and economic considerations is very unfortunate in the existing situation, social evils like dowry system encourages couples to go for more number of boys than girls which, in turn, is responsible for high fertility. Besides couple who are afraid of losing their children due to prevailing high infant mortality, favors a large number of children. For some sections of population, the children are considered to be their economic assets, because as soon as attain the age of ten or even less, they contribute to the family income and for such sections the fertility is naturally found to be high. On the contrary, there exists a large number of intermediate factors, which can be treated as retarding agent of fertility such as urbanization, better educational; attainment, better employment status and economic assets, late marriage, family planning adoption etc. It is therefore, stated that the study of fertility is virtually complex.
The objective of the proposed study is to investigate the effects of various factors on human fertility differential in Imphal West district of Manipur. Specifically it is -
a) to study the impacts on the differentials in human fertility with respect to various social, cultural, economic, behavioural and demographic factors in isolation,
b) to examine the relative importance of the elsewhere factors contributing to variability of fertility level,
c) to measure the acceptance and practice of contraceptive devices to achieve adequate family size or so called family welfare programme according to different factors.
1.3 Materials and Methods
The proposed work will be based on the following criteria:
Research Question: What are the effects of social-cultural and demographic characteristics of human fertility differential?
Type: Cross sectional.
Setting: Community base.
Study population: Imphal West district of Manipur (in which natural fertility population is assumed to be existed). It is one of the valley districts of Manipur where, natural fertility is assumed to be existed. The inhabitants of the study population are different communities like Meitei, Muslim, Christian, indigenous tribes (non-Christian) etc.
Study subject: Eligible women (ever married women during effective reproductive span).
Sample size: 765 eligible women. It is determined based on a pilot survey specially designed by NFHS and census figures.
Sampling: Multistage cluster sampling (with proportional allocation).
Response - Fertility (to be defined as the number of live birth ever born).
Predictor - Socio-economic, cultural, behavioural, regional, and demographic factors. These variables of interest are assumed to be directly or indirectly related with the number of live birth ever born.
Data: A set of primary information which is collected through teephnic interview method. It is also associated with NFHS and Census figures.
Tools: Pre-tested and semi structural schedule.
Analysis: A detail statistical analysis has been performed by utilizing some suitable statistical tools ' F, t, - tests etc. In this analysis, the univariate, bivariate, and multivariate techniques (preferably regression models) etc. are planned to be adopted. Dummy variable techniques will also be utilized to quantify the qualitative information.
1.4 Definition of fertility
The term fertility may be defined as the actual reproductive performance of an individual, a couple, a group, or a population. In order to study the level of human fertility on the basis of sample data, we have two commonly used approaches . They are first, using birth performance of women in a particular calendar year and second, the fertility history. In the first method, only the current birth performance of women in the sample is taken into account. In the latter method the history of fertility of women is considered for measuring fertility. In the present study, the second method is adopted to investigate the effects of various predictor variables of interest on fertility regulation of Manipuri women. This measure is the average number of children born or so called mean fertility. The mean fertility further constitutes observations which are treated as response corresponding to the elsewhere factors (predictors).
1.5 Significance of study
The study of human fertility is considerably complex. It consists of many social parameters in its determinants. Many studies have also shown that men's negative attitudes are often major reasons why their wives fail to practice FP even when the latter are motivated. Though male fertility regulating methods are available, female methods, both spacing as well as sterilization, are more widely accepted and practiced. Ensuring greater male participation is one of key components of the Program of Action (POA) at the ICPD, and its thrust area of the RCH program of the Government of India (UN, 1995). In spite of overcoming sixty years of Indian Independence and fifty-five years of plan interventions for population control, Manipur is still no where near a satisfactory solution. However, under scientific approach say proper determination of sample size, reliable data collection, detail statistical analysis on the variations on the fertility level with respect to various predictor variables have their high significance on birth control. The potentials of the findings will be immense value particularly in population planning and health policy through which an attempt may also be made to improve the economic status of people resulting into better way of life.
1.6 Chapter organization
The present report consists of six chapters. The first chapter deals with the general information, objectives, data source, method, definition study subject and significance of the study. In second chapter the review and development of related studies is carried out. The brief description of the study population is also mentioned in the second chapter. The third chapter talks about the research methodology followed for the dissertation.
In the fourth chapter, a detail statistical analysis is carried out on the basis of various explanatory variables, in isolation, utilizing classical statistical tests- t and F. The level of significance is examined with P-value, particularly at 0.05 and 0.01 levels.
Adopting a regression model, a multivariate analysis is performed in fourth chapter. In this analysis, dummy variable technique is adopted to quantify the effects of some categorical variables. It is computed the degree of explanation of the factors determining the total variation on fertility in the study population. The detail discussion and interpretation on the findings of the present study is also done in the fourth chapter. The last fifth chapter summarizes with scientific delineation the overall findings of the present investigations on the fertility regulation of study population. It also deliberates on the major findings and their importance from empirical points of view.
1.7 Limitations of the study
' The data used are most secondary so the result may not be so accurate
' It could have been better if the data collection method employed would have been personal interview method
Review Work and Study Population
2.1 Review and development of related work
The available literature is so vast that is not possible to present exhaustively in this small section. The purpose of this review is, however, to give a proper orientation and perspective to the present work. As a complex process, human fertility is influenced by a large number of factors such as demographic characteristics, couple's behaviours due desire number of children and various socio economic factors like place of residence, income, caste, religion, educational status, occupation etc.
The high fertility of rural population especially agricultural sector is noticed and drastic changed in fertility taken place in the developed nations through urbanization and industrialization (Rodriquez and John, 1981; Chaoze, 1991; Zheng, 1996). In US among Protestants, the lower class wants more children than middle and upper classes (Blake, 1967). Rodriguez and John (1981) reveals that in all Latin American and Caribbean countries, urban fertility is lower than rural fertility but it is less pronounced in Asia and Pacific. However, several studies highlight that urbanization without the concomitant increase in income, educational levels and contraceptive prevalence has no depressing effect at all on fertility (Feeny and Jingyuan, 1987; Pritchett, 1994; Ian and Angela, 1997). Meanwhile, Andrew and Akim (2000) in his studies on Tanzanian fertility observed that the pattern of fertility decline exhibits similarities to the patterns identified some years ago in Zimbawe and Kenya.
Sources: Rodriguez, G. and John, C., 1981, Socio-economic determinants of fertility in twenty countries: A multivariate analysis in World Fertility Survey Conference 1981 held at London, Record of Proceeding, Voorrbirg, Netherlands. International Statistical Institute. Pp. 337-343.
Andrew, H. and Akim, J. M., 2000, Recent trends in Tanzanian fertility. Population Studies, 54: 177-191
In India, urban fertility has tended to be lower than that of rural (Namboodiri, 1994; Choudhuri, 1997; Mukherjee, 2000; Swain, 2001). However, Kulkarni ey al. (1998) estimates of total marital fertility, wanted total marital fertility and unwanted total marital fertility by place of residence in the eight states viz. Uttar Pradesh, Bihar, Madhya Pradesh, Rajasthan, Himachal Pradesh, Punjab, Maharashtra and Kerala. Urban-rural differences in unwanted fertility are quite small in all eight states.
Fertility differentials by religion are observed in many parts of the world. North America and Europe have found notable variations between Catholic and Non-Catholic, Christian and Jewish, and Christian and Muslim fertility. Generally, Catholics have higher fertility as compared to the Protestants, and the Muslim as compared with Christians. This is because in multi-religious countries, precepts and injunctions influence fertility desires and contraceptive practices (Keysar et al., 1992). The recent findings also show that though there had been large fertility differences among religious groups in the past, the differences have narrowed down in the post baby boom period especially between Catholic and Non-Catholic population in the United States, Canada and Australia (Mosher and Gerry, 1984; Victor, 2001).
Sources: Namboodiri, N. K., 1994, Study on India's fertility transition: Some Issues. Janasamkhya, 12(1, 2): 65-77.
Choudhury, R. K., 1997, Rural 'Urban devide: The most perturbing features of India's poor economic profile. Kurukshestra, XLVI (1,2): 31-35
Mukherjee, S., 2000, Syndrome of poverty and fertility. Yojana, 44(5): 8.32
Mosher, D. W. and Gerry, E. H., 1984, Religion and fertility: A Replication. Demography, 21(2): 185191
Keysar,A., Sabatello, E. F., Zieger, I., Sharkshall, R., Kupinsky, S., Zur, R. and Reritz, E., 1992, Fertility pattern in the Jewish Population of Israel. In: E. Peritz and M. Baras (eds.), Jewish Demography and Statistics, pp. 21-57. Hebrew University of Jerusalem
In India, the estimates of fertility indicated that major religious groups, Muslims have experienced the highest and Sikh the lowest. For the country as a whole, the Muslim fertility has always been higher than that of Hindus on the one hand and Christian fertility is substantially lower than Hindu fertility (Namboodiri, 1994; Kulkarni et al., 1998; Yadava et al., 2000). Meanwhile, Kulkarni et al. (1998) observed some considerable differences of fertility in eight states viz., Uttar Pradesh, Bihar, Madhya Pradesh, Rajasthan, Himachal Pradesh, Punjab, Maharashtra and Kerala. In the states with sizable Muslim population, both wanted and unwanted fertility are higher among Muslims than that of Hindu. Within the states having sizable other religious groups, wanted and unwanted fertility do not differ much between Hindu and women belonging in other religions. Schedule caste and schedule tribe women frequently have higher wanted fertility than other women in the same states, but no consistent pattern is found in the variation of wanted fertility by caste or tribe. In Manipur, based on empirical analysis Muslim fertility is also found higher significantly than that of Hindu and the lowest fertility is found on Christian (Narendra, 1984; Singh, 2002).
Many past findings have shown that there exists an inverse relationship between education and fertility (United Nations, 1983; Patnaik, 1985; Gertler and Molyneus, 1994; Kumar, 1997). Bourne and Walker (1991) emphasis that education provides women the potential improve their health and lives as well as those of their children and other family members.
Sources: Namboodiri, N. K., 1994, Study on India's fertility transition: Some Issues. Janasamkhya, 12(1, 2): 65-77 Kulkarni, S., Kim, C. M. and Minja, 1998, Wanted and unwanted fertility in selected states of India. National Family Health Survey Report No. 6. pp. 3-32, Indian Institute of Population Studies, Mumbai
Narendra, R. K., 1984, A statistical study of components of birth intervals in relation to Manipuri women. Ph. D. thesis (unpublished). Patna University, Patna
Vlassoff (1991) also found that education even more important than income and occupation in reducing fertility. Education also helps women to realize the advantage of having small family and adoption of various family planning practices (Pritchett, 1994). In this context, Harvinder (2000) observed that the relation between education and fertility is very complex. It is intricately associated with many social, economic and psychological factors and attitudes. Thus education depresses fertility by rising the age at marriage, strengthening the propensity to be in the labour force, fostering a favourable attitude towards small family norm and improving awareness and use of family planning methods.
The women participation in economic activities has been found to be an important factor among the socio economic characteristics on the level of fertility decline. With regard to the nature of relationship between women's working status and fertility, researchers have come out with conflicting results. Unlike education of women which is found to have a strong negative effect on fertility, the effects of women's work participation on fertility is not uniform seems to depend on the socio economic conditions. In such situation women's working status and their fertility performance may be poorly correlated or even positive. Some social scientists believe that the commonly observed relation between work force participation and fertility in a population is serious and there are other factors which may influence fertility levels among working and non working women. Thus different studies have up with diverse conclusions regarding the relations between female work participation and fertility.
Sources: Vlassoff, C., 1991, Progress and Antagonation: Change in an Indian village. Population Studies, 46: 195-212.
Harvinder, K., 2000, Impact of income and education on fertility. The Journal of Family Welfare, 46(1): 70-76.
Studies conducted in developed countries have, generally shown a negative association between women's employment and fertility , while in developing countries they have revealed conflicting results and inconclusive on the nature of relationship (Bhargava and Saxena, 1984). Past findings found the conventional inverse relationship between income and fertility owing to the various reasons ' children represent an economic burden impeding social mobility, location of couples with higher income in large cities, less family orientation of the economically developed classes and others (Bernhardt, 1972). Many studies in India have taken into account the role of income in relation to fertility and most of them have depicted that fertility decreases. Reddy (1984) found that income is directly related to favourable attitudes to family planning, irrespective of caste. However, a study did not reflect any regular gradations in various indices of fertility with respect to various income groups (Singh, 1989). On the other hand, Patnaik (1985) observed no inverse relationship between fertility and income. He emphasized that income goes up from the lowest conceivable limit, fertility also tends to rise progressively up to a certain level, indicating a marked variation of positive correlation between the two. However beyond that level of income, there appeared to be a tendency of fertility decline.
Sources: Bhargava, R. K. and Saxena, P. C., 1984, Female labour force participation and fertility in a Metropolitan City of India. International Institute for Population Science, Bombay (Mimeographed).
Bernhardt, E. M., 1972, Fertility and economic status: Some recent findings on differential in Sweden. Population Studies, 26(2): 175-179
Reddy, M., 1984, Socio economic and demographic factors and their influence on family planning behaviour among non adopters. Journal of Family Welfare, 30(4): 92-101
Some studies demonstrated a negative relationship between age at marriage and fertility (Singh et al., 1993). Haokim (1994) using both bivariate and multivariate analysis examined the effect of some demographic and socio economic factors on the level of fertility based on Pakistan data and found age at marriage of wife as one of the most important variables explaining fertility. Similarly, Richard and Rao (1995) have studied the influence of age at marriage and family planning on fertility based on the data of Vallore district in Tamil Nadu. They have found a large differential in fertility level over age at marriage compared to family planning acceptance. Recently, a rise in the average age at marriage has also a non trivial contribution to the decline in fertility (Andrew and Akim, 2000). In the World Fertility Survey (WFS, 1984), the question on desire family size was given importance, which was also adopted by many developing countries in the surveys. Most of the studies seek to explain the desired number of children in relation to their birth control practice and related socio economic and demographic factors. Among the demographic factors, sex preference, age at marriage and family size are selected as related factors of desired family size (Mahadevan, 1979). In the similar studies, it is found that socio economic status also influences the decision making process among couples which shape their attitudes with regard to the desired number of children (Basheer, 1990; Singh, 2002).
Sources: Singh, K. K., Suchindran, C. M., Singh, V. and Ramakumar, R, 1993, An analysis of birth intervals in India's Utter Pradesh and Kerala states. Journal of Biosocial Science, 25: 229-239
Haokim, A., 1994, Factors affecting fertility in Pakistan. The Pakistan Development Review, 33(4): 685-709
Mahadevan, K., 1979, Sociology of fertility: Determinants of fertility differentials in South India. Staling Publishers, New Delhi.
Basheer, A., 1990, Determinants of desired family size in Rural Bangladesh: A two stage analysis. Journal of Family Welfare, 36(1): 95-117
In one sense, India was the first country to establish the National Family Planning Programme way back in 1952 due to increasing concern over rapidly growing population. As a result of continuing efforts made by various government and non-government organizations, considerable progress has been made in reducing the birth rate during the last few decades. During the same period, the infant mortality has also substantially (Health Information of India, 1999). Today, the knowledge of family planning is almost universal, and almost half of the couples are using some means of contraception. However, the government supported family planning programme is dominated by voluntary sterilization, especially female sterilization, which accounts for 75% of all contraceptive use (NFHS, 2000). On the other hand, India is well into its demographic transition with reduced and declining fertility and mortality levels. In spite of this, the population growth has remained high and relatively constant during the last 40 years, at a rate of almost 2% per year (Health Information of India, 1999).
Despite efforts of the National Family Planning Pragramme to increase contraceptive prevalence, it remains at around 48%. The interactions among a complex set of demand and supply aspects to family planning results into contraceptive uses. Even the recent research does not draw clear conclusions as to which aspects are most effective (Robey et al., 1993; Scrimber, 1994).
Sources: Robey, B., Rutstein, S. O. and Morris, L., 1993, The fertility decline in developing countries. Scientific American, 269(6): 60-66.
Scribner, S., 1994, Policies affecting fertility and contraceptive use: An assessment of twelve Sub-Saharan countries. World Bank Discussion Paper No. 259, Africa Technical Department Series. World Bank, Washington, DC
Health Information of India, 1999, An Annual Publication of the Directorate General of Health Services. Government of India
The answer may vary from one region to another region. Hence, the data gathering and their utilization in this regard becomes commitment of a number of research organizations. Considering secondary as well as primary data, a number of publications based on micro/macro data analysis are available mainly dealing with differentials, regional variations and its determinants (Dwivedi, 1992; Prichett, 1994; Amin et al., 1996; Dwivedi et al., 2006). They have emphasized the needs of regional studies. Since it is not clear which factors are most effective at achieving the goal of
higher contraceptive prevalence especially in target states, there is need of its epidemiological understanding from time to time. the country.
Dwivedi, S.N., Singh, R., Sahu, D. and Pandey, A., 2006, Epidemiological analysis of contraceptive use in India: An application of Hierarchical methods. Demography India, 35(1): 145-158
Pritchett, L. H., 1994, Desire fertility and the impact of population policies. Population and Development Review, 20: 1-55
2.2 Study population
The demographic situation in Manipur becomes uncertain with a large number of heterogeneous characters. It is one of the smallest states located in easternmost part of India. Located between longitude 93.20E and 94.20E and latitude 23.50N and 25.40N, it has an international border with Myanmar to the southeast. On the north, south, and west it is bounded by Nagaland, Mizoram and Assam respectively. The state is landlocked and it does not have any water ways to link with other states of India. Manipur is connected to Assam by train network of which only one railway sub station is available at Jiribam sub-division of Imphal East district. Construction of the railway between Jiribam and Imphal via Tupul (Senapati district) is now underway. Some flights ' Indian Airlines, Jetlight, Air Deccan, Indigo, King Fisher etc., operate between Imphal and other states of the country. Road communication is only means of communication available within the state. National Highway Nos. 39, 53 and 150 (under construction at present) are the roads connecting the state with the rest of the country. Geographical area of Manipur is 22,327 sq. km. which constitutes only 0.7% of the total land surface of India. It has a population of 27,21,756 comprising 13,69,764 males and 13,51,992 females according to provisional figure of 2011 census. This population is distributed in 9 districts consisting of 38 sub divisions. In case of age structure, 12.98% of the state's population comprises children in the age group 0-6 years. It is supporting about 0.22% of the total population of India and hence nearly every four hundred thirtieth person in the country is a Manipuri. Two thirds of its total land surface area is hills inhabited by one third of its population while two thirds of its population is concentrated on one third of its total area ' the Manipur valley.
According to 2001 census, Hindu is largest religious community, which constitutes 46% of population of the state, followed by Christians who account for 34% of the population. The Meitei is the major community speaking Manipuri language (one of the eight schedule languages in India) in the valley, though there are pockets of Muslim, Tribal and some other communities. The hill areas are populated with nearly 33 different colourful tribes (Directorate of Economics and Statistics, Govt. of Manipur, 2008). The tribes are Aimol, Anal. Angami, Chiru, Chothe, Gangte, Hmar, Kabui, Kacha Naga, Koirao, Koireng, Kom, Lamgang, Mizo, Lushai, Maram, Maring, Mao, Monsang, Moyon, Paite, Ralte, Sema, Simte, Suhte, Tangkhul, Thadou, Vaiphei, Zou, Poumei Naga, Tarao, Kharam and Kuki tribes. The Mao tribe is mostly concentrating in Senapati district, the Tangkhuls in the Ukhrul district, the Kabuis in the Tamenglong district, the Anals and Marings in the Chandel district and Thadous and Kukis in the Churachandpur district. The Scheduled tribe population was 7.41 lakh, consisting of 3.74 lakh males and 3.67 lakh females, as against 6.32 lakh in 1991 census. The distribution of the population in the nine districts of the state is very heterogeneous showing a heavy concentration of population in Imphal West district. In this district, it has 21% of the total population and the second and third positions with 18% and 16% go respectivey to Imphal East and Thoubal district respectively. The lowest figure with 4% of its population is recorded in Tamenglong district.
During the decade 2001-2011, the population of the state has registered a growth of 18.65% declining from 24.86% in 1991-2001. The state added 4,27,860 individuals during the last decade. This figure may be meager even in a big state.
The growth rate during 1901-1951 was more or less same as the corresponding all India rate, that is 12.1% for Manipur against 12.5% for all India. But from 1952 onwards the growth rate of the state has been alarming as it is much above the national level. It has declined from 23.86% in 1981-1991, 21.56% in 1991-2001 to 17.64% in 2001-2011 for all India figure. It shows that the state's growth rate is higher than that of the national level during the last census-decade. The average annual growth rate of rural population is 2.90% as against the rate of 1.39% of the urban population in Manipur (Directorate of Economics and Statistics, Govt. of Manipur, 2008).
The population density is lower than that of India figure that is 122 for Manipur as against 382 for all India according to 2011 census. One of the important features is that the average density of population in the valley comprising four districts viz., Imphal West (992), Imphal East (638), Thoubal (818) and Bishnupur (485) is above 13 times the average density in the five hill districts viz., Senapati (109), Chandel (43), Ukhrul (40), Churachandpur (59) and Tamenglong (32). It may be noted that Imphal West district is the only city above one lakh population. Most populous urban areas and towns are Imphal (M CI)-2,21,492, Thoubal (M CI)-41,174, Kakching (M CI)-28,724, Mayang Imphal (NP)-20,532 and Lilong (NP)-20,257 respectively.
Literacy rate of Manipur is 16th rank among the states/ UTs according to 2011 census. With 79.85% consisting of 86.49% for male and 73.17% for female, the sate's literacy rate is higher than that of all India figure that 74.04% consisting of 82.14% for male and 65.46% for female respectively. Immediately after Indian independence there was a remarkable improvement in female literacy in the state. The district's literacy rates are heterogeneous in nature. With 86.70%, Imphal West has highest rate followed by Churachandpur with 84.29%, Imphal East ' 82.81%, Ukhrul ' 81.87%, Thoubal ' 76.66%, Bishnupur ' 76.35, Senapati ' 75.00%, Chandel ' 70.85% and the lowest rate ' 70.40% is recorded in Tamenglong district. Thus, it may be observed that the literacy status of two hill districts viz., Tamenglong and Chandel is lagging behind the national level. In NFHS-3, only 73% of women and 92% of men of age group 15-49 are literate in Manipur. This is measured by considering the literate persons as the one who have either completed at least standard six or passed a simple literacy test conducted as part of the survey. About 22% of women, compared with only 6% of men of the age group 15-49, have never been to school. 32% of men have completed at least 12 years of education, but only 21% of women have attained that level of education. Nevertheless, the proportion of adult of the age group15-49 who have completed at least 12 years of education is higher in Manipur than in any other North-Eastern states. And the proportion of men who have completed at least 12 years of education is higher in the state than in any other states in India, except Delhi.
The proportion of urban population in Manipur has declined from 27.52% in 1991 to 25.11% in 2001. The corresponding figures for all India are 25.72% and 27.78% respectively. The state's figure moves lower than that of national one. Growing urbanization is a recent phenomenon in developing countries. Population scientists recognize that the increase in urban population has been attributed both natural growth (through births) and mobility from rural areas because of employment opportunities, attraction to better living conditions and availability of social services such as education, health, transport, entertainment etc. In this view, in-depth research is necessary to look into the causes of declining the state figures during last census decade 1991-2001. The birth and death rates are important components of population growth.
The national birth rate has declined niggardly from 39.9 in 1951 to an estimated 30.2 per 1000 population in 1990 and 20.3 in 1999 whereas the death rate has considerably declined from 27.4 in 1951 to an estimated 9.7 per 1000 in 1990 and 6.3 in 1999. Birth rate is declining in the state as it is 20.1 in 1991 and 18.2 in 2001 which is slightly less than its national figure. The death rate in the state is slightly declined from 5.4 in 1991 to 5.1 in 2001. At current account (2011 census), out of 987 the sex ratio for the children in the age group 0-6 years is 934 (13%) in Manipur. Sex ratio of the state has increased from 974 in 2001 to 987 in 2011 which is higher than the corresponding all India figure say 933 to 940. The child sex ratios for hill and valley areas are almost same as the average child sex ratio for valley is 938 as against 926 for hill. While none of the sex ratio of valley districts of the state is below 1000 with an average rate of 1014 while none of the hill districts has above 1000 with an average of 949.
In NFHS-3 reports, the median age at first marriage among the women is 17.2 years in India. Men get married more than six years later, at a median 23.4 in the country. In one sense, it is well established fact that the women's age at marriage has in fact, a great impact on fertility rate in a nation. Early marriage is a long established custom in India. However, the age at marriage has been rising over time in India. The Child Marriage Restrain Act. 1978 rises the legal age at marriage from 15 to 18 years for girls and from 19 to 21 years for boys. Fortunately, average age at marriage in India has already moved to 19 years for girls and 24 years for boys in the year 1991. In Manipur, marriage takes place at a relatively later age. In 1993 at the range of age 20-29 years, 69% are married. A few percentages that is, 2-4 remain unmarried for those who are above 34 years of their age. However, a recent research finding advocates the average age at marriage in Manipur is 20.4 years for women and 25.7 years for men (Singh, 2002). According to NFHS-3, median age at first marriage in the state is 22 years among women age 25-49 years and 25-27 years among men age 30-39 years. An account, 13% of women age 20-24 years got married before the legal minimum age of 18 years and 12% of men age 25-29 years got married before the legal minimum age of 21 years.
NFHS-3 reports reflect the fact that the majority of women in India marry during their teens. It is found that among young women of age group 15-19 years in India, 16% have already begun childbearing and it is much lower that is 7% in Manipur. Young women in rural areas (9%) are above twice as likely to have begun childbearing as young women in urban areas (4%) in the state. But in Jharkhan, West Bengal, and Bihar, at least one in four teenage women has begun childbearing. Based on the current fertility levels (NFHS-3), a woman in Manipur will have an average of 2.8 children in her life time which is slightly higher than that of all India figures (2.7). It is above replacement level (2.1). Although fertility increased by 0.3 children between NFHS-1 and NFHS-2, in the period between NFHS-2 and NFHS-3, fertility felt back to its level in NFHS-1. In case of rural-urban differential, fertility in rural areas (3.1) is almost three-fourths of a child higher than in urban areas where fertility is 2.4 children per women. Among births in the three years preceding the survey, 22% were of birth order four or higher. According to the reports, fertility in the state declines sharply with education and wealth. A large fertility differential is also observed with respect to caste and religion. The women belonging to the scheduled tribes have at least one child more than women in other caste sub-groups. A Christian woman has in her lifetime, 1.4 more children on an average, than a Hindu woman.
The infant mortality rate (IMR) in India is steadily decreasing according to the current NFHS-3 report. However, more than one in eighteen still die within the first year of life, and one in thirteen die before reaching age five. But, one in thirty four children in Manipur still die within the first year of life, and about one in twenty four die before reaching age five. Population scientists have rightly identified high IMR as one of the factors contributing to high population growth. It is true that unless couples are reasonably certain that two or three children born to them will survive to adulthood, they may not accept the idea of family planning. National figures shows that the crude death rate has declined by 42% from 16 per 1000 population in 1975 to a little over 9 in 1994, while the IMR has declined by about 48% from 140 per 1000. Although a recent research (Singh et al., 2007) reveals that IMR in Manipur during 2004-2005 is 44.1 per 1000 live births consisting of 29.63 for urban and 73.49 for rural areas. But, the state's IMR in five years preceding the survey has been steadily declining. The rate is currently estimated at 30 deaths before age of one year per 1000 births, down from the NFHS-2 estimate of 37 and the NFHS-1 estimate of 42. However, NFHS-3 reports indicated that IMR in the state is lower than in all other states of India, except Goa and Kerala.
The findings of recent studies have shown that spacing of births have a significant impact on the reduction in fertility rates. A recent empirical finding (Singh, 2002) submits the average time interval between the last two live-births in this state is 33.43 months which contributes the average fertility say 4 live-births. It also marks a terrible stage of the NPP-2000 medium goals for replacement level (2.1). According to NFHS-3 report, the median interval between births in Manipur is found to be 35 months. A proportion of 20% of non-first order births take place within 24 months of the previous birth, including 7% that take place within 18 months. About 52% of births occur within three years. This report also shows that waiting at least three years between children reduces the risk of infant mortality. Couple Protection Rate (CPR) is an indicator of the prevalence of contraceptive practice in the community. It is the percentage of eligible couples effectively protected against child birth by one or other approved methods of family planning. The CPR in Manipur went up from 10.1% in 1981 to above 26.6% in 1991. This progress is slow as compared to the national figure that is from about 22.7% in 1981 to about 44.1% in 1991. In Manipur, the rate was dramatically decreased to 23.7% in 1994, as against the corresponding all India figure, 45.5%. Again it reached 39.03% during 1999-2000 by all methods based on urban population of Imphal.
The knowledge of contraception is almost universal in India according to NFHS-3. It is not exception in Manipur too. However, knowledge of some temporary methods including injectables and female condoms is lower than that of others. The three methods ' the pill, the IUD, and the condoms are known to nearly all (93-95%) currently married women; the condom and pill are also known to nearly all men (94-99%), but the IUD is known to fewer men (85%). The two traditional methods, rhythm and withdrawal are also known to a majority of women and men; however, withdrawal is known by a higher proportion of women and men than the rhythm method. A higher proportion of men reports knowledge of each of the different contraceptive methods than women, with the exception of the IUD. It may be noted that knowledge of sterilization among women has been high since NFHS-1, but knowledge of all temporary contraceptive methods has increased considerably since then. Among currently married women, 93% now know about the pill which is only 78% in NFHS-1 and 82% in NFHS-2. Similarly only 60% of women knew about the condom in NFHS-1; this proportion has risen steadily to 95% in NFHS-3. Only 44% of men agree that women who use contraception may become promiscuous and 58% of men incorrectly believe that women who are breastfeeding cannot become pregnant. Over 83% of men know that condom protects against conception most of the time. In the mean time, 12% of currently married women have unmet need for family planning, declining from 24% in NFHS-2 and 22% in NFHS-1. Currently, 80% of the demand for family planning in the state is being met, increased from 62% in NFHS-2.
Rural-urban variation in contraceptive use is found to be high in Manipur. It is 55% in urban areas, higher than 46% in rural areas. The contraceptive prevalence rate among currently married women is 49% which is increased from 39% in NFHS-2 and 35% in NFHS-1. Unlike most other states in the country, the 25% of currently married women are using traditional method in the state. It is even marginally higher than that of using modern method (24%). The most common traditional method reported is withdrawal, which is being used by 17% of women. Only 8% of eligible women used the next most common methods of female sterilization and the rhythm, 5% of women used pill and IUD and that of only 4% used condom. The rate of female sterilization has declined by 6% in NFHS-3 from its level in NFHS-2 (14%). According to NFHS-3 reports, the contraceptive use is found to vary according to various socio-demographic factors like religion, caste, education, age, desire number of sons etc.
The data for the study has been collected from both primary and secondary sources. The primary data has been collected through telephonic interview with intensive interaction with people of Imphal East.
The secondary data was collected from some concerned department like planning commission,NIC,statistics department,etc
The research design for the present study was basically exploratory in nature. The study started with exploratory research design in order to have a deeper insight of the development in the handloom cluster .
The secondary data were collected from the publication of the state government, organization, involved in studying the fertility of women.
Study Region ' The area where the study was carried out include main District of Manipur i.e, Imphal West. Imphal west is the most populated area in Manipur.In Imphal west natural fertility is assumed to be existed.The inhabitants of the study population are different communities like Meitei,Muslim,Christain,etc.
The data are interpreted with the help of various analytical and statistical tools like pie chart,bar graphs ,p,f test,chi square test etc.
Bivariate Analysis on Fertility Regulation
4.1 Socio-demographic determinants of fertility
The human fertility is used to express the actual reproductive performance of a woman or a group of women. The actual measure of fertility in a community is provided by actual number of live births to a group of women during a given a period of time. However, human fertility is directly or indirectly affected and influenced by a large number of socio economic, demographic and cultural factors such as caste, type of family, religion, place of residence, income, educational level, employment status, occupation, sex preference, lactation, infant mortality, age at menarche, age at marriage, present age of women, etc. Moreover, fertility is highly susceptible to human control both by deliberate action and by effects of behaviour not consciously be directed to this purpose. In many communities, couples are typically conditioned to desire a given number of boy(s) and girl(s) depending on a number of socio economic, demographic, cultural and other factors. In fact, human reproduction is basically a complex physiological process but child bearing ion any society is performed in many socio demographic and cultural setups of the society.
Many past findings observed that fertility may vary from place to place, society to society, community to community and within a society or community itself. In industrialized countries the fertility rate is low while in agricultural and developing countries it is comparatively high. The pattern of fertility variation in different countries is heterogeneous because of their paramount constituents of socio economic, demographic, ecological and cultural factors. According to Patnaik (1985), in European and other developed countries where individualism dominates the mode of living, education is perfect, economic condition is sound, standard of living is high and people are exposed to media of communication such conditions and values prevail there to account for low birth rate and control fertility. On the other hand, in a developing country like ours where society is primarily agricultural, traditional and joint family oriented, people are living below poverty line and educational status is comparatively low, and therefore there is always prevalence of high fertility. In this regard, the study population is not an exception. Manipur society is almost agricultural and traditional. It is having little effect of deliberate birth control on the pattern of fertility differentials as in the case of other traditional societies. Thus the investigation of fertility variation in the study population with respect to socio economic and demographic characters may be fascinating and interesting area of human research to a considerable extent.
4.2 Place of residence and fertility
Place of residence in developing societies usually determines people's life styles, their perceptions of the world and of others; their economic, social, cultural and political activities. Urban/rural differences transcend in importance many other social and economic characteristics; and that residents of Mumbai, Kolkata or Banglore may have more in common with each other than their own rural neighbours (Pandey et al., 2004). The study of fertility as influenced by place of residence in India has assumed a special significance in recent years in view of the problem emanating from rapid population growth. The concept of urban residence as it is understood in the West is different in context of Manipuri society. Even if the couples have urban residence, they often continue to have link with rural areas by way of economic interest there.
Mean and S.D. of fertility according to socio economic factors
Factors No. of cases (%) Mean S. D. Test & P-values
Urban 337 (44.05) 3.57 1.76
Rural 428 (55.95) 4.22 2.20 t = 4.42, P< 0.01
Joint 343 (44.84) 3.74 2.12
Nuclear 422 (55.16) 4.09 1.90 t = 2.41, P< 0.05
General 641 (83.79) 3.88 1.99 t(G,S.T)=1.40, P>0.05
S. T. 68 (8.89) 3.53 1.59 t(G,S.C)=3.91, P<0.01
S.C. 56 (7.32) 4.93 2.31 t(S.C,S.T)=4.09, P<0.01
F = 9.40, P< 0.01
Hindu (H) 510 (66.67) 3.82 1.94 t(H,M)=1.65, P>0.05
Meiteiism (M) 151 (19.74) 4.12 2.04 t(H,I)=5.04, P<0.01
Islam (I) 37 (4.83) 5.53 2.57 t(H,CH)=1.17, P>0.05
Christian & others (CH) 67 (8.76) 3.53 1.60 t(M,I)=3.55, P<0.01
t(I, CH)=4.84, P<0.01
F= 10.02, P< 0.01
Educational level of husband:
Illiterate 42 (5.49) 4.79 1.94
Literate & < VI 90 (11.76) 4.71 2.13
Above VI & < X 202 (26.41) 3.81 1.86
X passed & < XII 136 (13.20) 4.16 2.22
XII & < Graduate 101 (13.20) 3.48 1.75
Graduate & above 194 (25.36) 3.33 1.31 F = 7.40, P< 0.01
Educational level of wife:
Illiterate 227 (29.67) 4.87 2.05
Literate & < VI 110 (14.38) 3.85 1.89
Above VI & < X 164 (21.44) 3.93 2.06
X passed & < XII 116 (15.16) 3.26 1.49
XII & < Graduate 54 (7.06) 3.20 1.51
Graduate & above 94 (12.29) 2.77 0.96 F =19.12, P< 0.01
Family income (in '000 Rs.):
Below 1 62 (8.11) 3.87 1.65
1 ' 2 252 (32.94) 4.03 2.02
2 ' 3 198 (25.88) 4.18 2.19
3 ' 4 110 (14.38) 3.85 1.97
4 ' 5 41 (5.36) 3.79 2.36 r = - 0.12,
5 & above 102 (13.33) 3.30 1.47 t = 3.34, P< 0.01
Employment status of husband:
Employed 320 (41.83) 3.82 1.90
Unemployed 445 (58.17) 4.00 1.98 t = 2.08, P< 0.05
Employment status of wife:
Employed 84 (10.98) 3.10 1.47
Unemployed 681 (89.02) 4.03 2.03 t = 4.06, P< 0.01
Occupation of husband:
Professional 39 (5.10) 3.76 1.49
Teacher 60 (7.84) 3.53 1.88
Office worker 118 (15.42) 3.87 1.71
Business 123 (15.08) 3.32 1.97
Cultivator 195 (25.49) 4.20 1.80
Others 230 (30.07) 3.91 1.89 F = 3.87, P< 0.01
Total: 765 (100.00) 3.93 1.97
Graph showing residence as one factor(Urban and rural areas)
Interpretation- From the above graph it can be interpreted that place of residence also plays an important role in determining the fertility level.
Graph showing type of family as one factor (joint family and nuclear family)
In recent studies, institution of joint family or so called extended family is considered to be one of the important factors influencing high rate of fertility in India. In the study population, 55.16% of the total eligible couples belong to nuclear family and the rest, 44.84% to joint family (table-1).
Graph showing correlation between fertility and caste:
. It is generally considered as a stratification variable in studies of differential fertility for various reasons
Table-1 also highlights the variations in the mean fertility according to three different castes in the study population. The schedule caste has highest mean ' 4.98, followed by general ' 3.88 and the smallest one 3.53 is existed for schedule tribe. In this analysis, no significant difference in the mean fertility is found between
Dependence on the occupation of husband
Interpretation- From table-1, the variation of the mean fertility according to employment status of both spouses can be examined. In this analysis, employment status is divided into two categories viz., Employed and Unemployed. Here the employed refers only to those persons who are service holders either in government or in semi-government or in even private sector having a regular income. All the persons other than the employed ones may be treated as unemployed. It is observed that the mean fertility among employed husbands is found to be 3.82 ( 1.90) against that of unemployed 4.00 ( 1.98.) The corresponding figures for wives are 3.10 ( 1.47) and 4.03 ( 2.03) respectively. In this analysis, the mean fertility varies significantly with employment status of husband (t = 2.08, P< 0.05).
It may be observed from table-1 that the number of eligible mothers in the urban and the rural areas are 337 and 428 respectively. The mean fertility in urban area is found to be 3.57 (with S. D., 1.76) and that of rural area, 4.22 (with S. D., 2.20). As the overall average fertility of the study population is 3.93 (with S. D., 1.97), the average fertility is found to be significantly different with place of residence. It is evidenced by t-value, 4.42 which is highly significant at 0.01 probability level of significance.
4.3 Type of family and fertility
In recent studies, institution of joint family or so called extended family is considered to be one of the important factors influencing high rate of fertility in India. In Manipur, a considerable proportion of nuclear family, households have spent substantial period of time in joint families before formation of nuclear families of their own. In fact, nuclear families in proper sense or in Western sense are supposed to be quite negligible in Indian Societies and particularly in Manipur societies. In this study nuclear family refers to residentially nuclear families and it may be regarded as the family consisting of husband, wife and their own children only. All types of family other nuclear one may be considered as joint or extended family.
In the study population, 55.16% of the total eligible couples belong to nuclear family and the rest, 44.84% to joint family (table-1). The mean fertility measured in terms of average number of children ever born for joint family is found to be 3.74 (?? 2.12) and that of nuclear family, 4.09 (?? 1.90). This variation in mean fertility is significant at 0.05 significance level (t = 2.41, P< 0.05). It may be concluded that there is statistically difference between the average fertility of joint family and that of nuclear ones in Manipur.
4.4 Caste and fertility
The caste system is historically pivotal point in the stratification of Indian society in a hierarchical order. It is generally considered as a stratification variable in studies of differential fertility for various reasons. In few earlier studies conducted in India, attempts were made by various researchers to bring out the fertility variation on the basis of caste. In this regard, in his study conducted in U. P. (Banaras) Rele 1963) shown that the upper caste - Hindu, Brahmins and Kshetriya have lower fertility as compared to that of the Muslim. But, Agarawala (1970) and Wyor and Gardon (1971) reported that no difference in fertility among different caste groups. Patnaik (1985) observed the mean fertility of the upper caste is lower than that of the backward caste or schedule caste. The social stratification by caste system is not taken into consideration to be strict prejudice in Manipur. The Meities (original inhabitants of Manipur), the Brahmins, the Lois (schedule caste) and tribes (schedule tribe) adopted to live together in various parts of the state. Moreover in contrast to the caste prejudices in some other parts of India the schedule caste and schedule tribe are not untouchable sections of the population in the community. In other words, there is no practically untouchable community in the state, Manipur.
Table-1 also highlights the variations in the mean fertility according to three different castes in the study population. The schedule caste has highest mean ' 4.98, followed by general ' 3.88 and the smallest one 3.53 is existed for schedule tribe. In this analysis, no significant difference in the mean fertility is found between General and schedule caste (P> 0.05). General and schedule tribe has highly significant variations in their mean fertility (P< 0.01). Also such highly significance is observed between schedule caste and schedule tribe. Hence the variation in mean fertility in the study population is found to be highly significant among the three caste groups. It is evidenced by value of F (= 9.39, P< 0.01). Thus, caste has a non-trivial impact on fertility differentials in Manipur.
4.5 Religion and fertility
The religious diversity has contributed to definite fertility differences in India. A good number of studies conducted in India have shown that there exist high differences in fertility behaviour of the two major religious categories Viz., Hindu and Muslim (Nag, 1965; Lal, 1973; Patnaik, 1985; Balaiah et al., 2001; Narendra and Singh, 2003). In the present study, four religious categories are taken into account. They are Hinduism (H), Meiteism (M), Islam (I) and Christian and others (Ch). Meitei is the own religion of Meiteis predominantly reside mostly in the valley areas of Manipur. Being small in number and their homogeneity, the tribal religions in Manipur are clubbed into Christian category.
The findings indicate that the mean fertility for Islam is the highest say 5.53, followed by Meiteism, 4.12; Hinduism, 3.82; and Christian, 3.53. Moreover, the values of Fisher's t, employed to each pair of means for all sub religion categories, have been found to be statistically significant except the two pairs say between 'Hinduism and Meiteism' and 'Hinduism and Christianity'. The findings also generate the fact that the fertility of Islam (Muslim women highly influences the overall mean fertility with respect to religion. The high fertility for Muslim women may be due to the low-educational attainment, low employment status and poor economic condition in the study population. Further, the findings may be concluded that the variation in the mean fertility is highly significant with respect to religion groups in the population as evidenced by the value of variance ratio (F) ' 10.02 (P< 0.01) shown in table - 1.
4.6 Educational level and fertility
An inverse relationship appears to have existed between fertility and educational level both in United and countries in Europe but since the late nineteenth century. This relationship is now diminishing or even disappearing in some low fertility countries. In developing countries like India, mother's education has been considered to have a strong effect on mortality (which is related to fertility) of young children (Rao et al., 1997; Das and Dey, 2003). The educated mothers are more likely than non-literate mothers ensuring healthy environment, nutritious food and knowledge of better health care facilities for the children. In India, education may be a direct and powerful indicator of the women's status. The relationship between education and fertility is however complex. It is intricately associated with many social, economic and psychological factors and attitudes. Education depresses fertility by rising marriage age, strengthening the propensity to be in the labour force, fostering a favourable attitude towards small family size norm and improving awareness and use of family planning methods. Some recent studies have depicted clearly the impact of female education on reduction of fertility (Arora, 1990; Vashisht and Rana, 1991).
At the present analysis, it is observed a clear inverse relationship between educational level of both spouses and the mean fertility (table-1). It may be observed from this table that the mean fertility with respect to the educational level of husband is maximum of 4.79 ( 1.94) for illiterate group and is minimum of 3.3 ( 1.31) for those who are post graduate and above. The corresponding figures for wife are 4.87 ( 2.05) and 2.77 ( 0.96) respectively. It shows that the mean fertility declines as the educational level of couple increases. Comparing the patterns of the mean fertilities of two spouses it may further be observed that the education of wife plays a more significant role than that of the husband in reducing fertility level in the study population. The values of the variance ratios for both cases highlight that the variation in the mean fertility differentiated by the educational level of both spouses are statistically significantly. However, educational attainment of wife (F = 19.12, P< 0.01) has more impact on fertility regulation than that of husband (F = 7.40, P< 0.01) in the population.
4.7 Income and fertility
In India, many studies have taken into account the role of income in relation to fertility and most of them have depicted the fertility decrease as income increases. Reddy (1984) finds that income is directly related to favourable attitudes to family planning, irrespective of caste. However, Singh (1989) do not reflect any regular gradations in various indices of fertility with respect to various income groups. On the other hand, Patnaik (1985) observes no inverse relationship between fertility and income. He shows that as income goes up from the lowest conceivable limit, fertility also tends to rise progressively up to a certain level, indicating a marked variation of positive correlation between the two. However, beyond that level of income, there appears to be a tendency for fertility to decline. This tends to suggest that minimum economic propensity may be essential for decline in fertility. In this regard, Harvinder (2000) depicts that monthly family income is inversely related to mean fertility. He emphasizes that this relation leads to the conclusion that by raising economic status, the mean fertility can be brought down.
In this analysis (table-1), the mean fertility increases from 3.87 for those families with monthly income group of below Rs. 1000 to 4.03 and 4.18 for those families in the income group of Rs. 1000-Rs. 2000 and Rs. 2000-Rs. 3000. And it gradually declines from the notable mean viz., 3.84, 3.79 and 3.30 for the families in the high income groups say Rs. 3000-Rs. 4000, Rs. 4000-Rs. 5000 and above Rs. 5000 respectively. The value of correlation coefficient (r) on the observation shows that there is significant difference between the mean fertility and monthly family income (t = 3.34, P< 0.01). Thus the present finding supports the hypothesis that mean fertility bears an inverse relationship with family income. It indicates that the mean fertility is lower5 for families in the higher income groups and is higher for families in the lower income groups. It may be due to the fact that the couple of higher income groups are generally educated and more exposed to different media of communication, most of them probably come from professional groups and that they have varying opportunities right types of recreational activities. Besides, the less recreational opportunities and low level of education account for the prevalence of high fertility among the couples in low income groups.
4.8 Employment and fertility
Most of the studies observe that employment of women outside the home is found to be inversely related to fertility. Once the woman became economically independent by way of employment, it is just believed that the fertility of such employed working women may result in lower fertility than that of unemployed or non working women. The reason of low fertility among employed women may be attributed to have better education, good economic conditions, better standard of living and consequently late marriage.
From table-1, the variation of the mean fertility according to employment status of both spouses can be examined. In this analysis, employment status is divided into two categories viz., Employed and Unemployed. Here the employed refers only to those persons who are service holders either in government or in semi-government or in even private sector having a regular income. All the persons other than the employed ones may be treated as unemployed. It is observed that the mean fertility among employed husbands is found to be 3.82 ( 1.90) against that of unemployed 4.00 ( 1.98.) The corresponding figures for wives are 3.10 ( 1.47) and 4.03 ( 2.03) respectively. In this analysis, the mean fertility varies significantly with employment status of husband (t = 2.08, P< 0.05). However, a highly significant difference on mean fertility is observed according to the employment of wife. It is evidenced by the value of t, 4.06 (P< 0.01). Thus, it may statistically be concluded that the women employment has more impact on the reduction of fertility level than that of husband in the present analysis.
4.9 Occupation and fertility
The relations between socio-economic indicators and fertility level have largely focused on empirical aspects of occupational status of husband in the Indian Patriarchal society. If one wishes to know how fast a nation is progressing in its economic modernization, one has to look at the figures on occupation, at the percentage of population engaged in agriculture, industries and services etc. In this view, only occupation of husband is taken into account here, since a good number of wives are engaged in household works in the study population.
In this study, classification of husband according to their occupation is difficult owing to its large number. Here, it is broadly classified into six categories. They are professional (doctor, engineer, lawyer etc.), office worker (executor, administrator, managerial work etc.), teacher, cultivator (farmer, fisherman and related works), business, and others (grade-4 employee, dependent, labour, driver etc.). The distribution of mean fertility with respect to occupation of husband is manifested in table-2. All the means except category of cultivator are existed below the overall average (3.93 1.97). Thus it may be noted that the mean fertility of agricultural labours and related workers is higher than those of other categories of occupation of husband in the study population. The present analysis indicates that there is highly significant variation in mean fertility according to different occupations of husband under study. It is witnessed by value of F viz., 3.87 (P< 0.01).
4.10 Present age and fertility
Generally, the reproductive span of Indian women is between age 15-45 years and therefore the fertility rates for age groups below 15 years and 45 years and above can be ignored since the rates are very minimal. As such as the fertility is age dependent, the present age of female spouse has certainly a major impact towards the pattern of fertility differentials. Several studies conducted in many parts of the world have also shown that the present age of wife is a major index of fertility. According to Patnaik (1985) among several populations practicing little or no birth control, the mean fertility tends to increase with the rise of age of wife. This is more significant among wives who have low educational status.
The differentials in mean fertility according to different age groups of wife are shown in table-2. The study eligible women have been clubbed into five age groups namely, below 25 years, 25-30 years, 30-35 years 35-40 years and 40 above years according to their age. The minimum mean fertility, 1.74 ( 0.64) is existed at the age group below 25 years and it is gradually increased by 2.93 ( 1.56) at 25-30 years, 3.49 ( 1.55) at 30-35 years, and 4.26 ( 1.74) at 35-40 years. The maximum mean, 5.26 ( 2.19) is observed at age 40 years and above. That is to say that the completed fertility in the study population is 5.3. The positive correlation (r = 0.50) indicates that there is a strong linear relationship between fertility and the present age of wives in the population. It is highly significant (t = 15.94) at 0.01 significance level.
Mean and S.D. of fertility according to demographic factors
Factors No. of cases (%) Mean S. D. Test & P-values
Present age of wife (in years):
Below 25 51 (6.68) 1.74 0.64
25 ' 30 118 (15.42) 2.93 1.56
30 ' 35 201 (26.27) 3.49 1.55
35 ' 40 206 (26.93) 4.26 1.74 r = 0.50,
40 & above 189 (5.26) 5.26 2.19 t = 15.94, P< 0.01
Age at marriage of wife (in years):
Below 20 335 (43.79) 4.73 2.10
20 ' 25 269 (35.16) 3.46 1.56
25 ' 30 130 (16.99) 2.98 1.33 r = -0.37;
30 & above 31 (1.05) 2.73 1.41 t = 11.04, P< 0.01
Number of desire son:
1 51 (6.67) 2.13 1.00
2 573 (74.90) 3.58 1.60
3 & above 141 (18.48) 5.85 2.01
Number of living son:
0 74 (9.64) 2.64 1.55
1 224 (29.28) 2.93 1.62
2 288 (37.65) 3.96 1.58
3 119 (15.56) 5.11 1.67
4 37 (4.84) 5.41 1.64
5 & above 23 (3.00) 6.98 1.35
Number of living daughter:
0 117 (15.29) 2.29 1.38
1 245 (32.03) 3.08 1.51
2 205 (26.80) 4.10 1.44
3 112 (14.64) 4.96 1.31
4 53 (4.31) 6.07 1.71
5 & above 33 (4.31) 7.38 1.39
Total: 765 (100.00) 3.93 1.97
Sources- Census report
Graphs showing percentage of fertility by demographic factor(taking present age of wife and age at the time of marriage)
Interpretation-It shows the percentage of fertility by demographic factor comparing present age of wife and age at the time of marriage
Graphs showing percentage of fertility by demographic factor(no.of desired son,no.of living son,no.of living daughter)
Interpretation-The graph shows percentage of fertility by demographic factor no.of desired son,no.of living son,no.of living daughter and its comparison with the other.
4.11 Age at marriage and fertility
It is well established fact that marriage is social institution binding a man and woman to fulfill their sexual urge which has close relation to the process of procreation. In fact, all births in India occur within the institution of marriage and the proportion of illegitimate births is negligible. Low age at marriage has adverse consequences on population growth, maternal and child health and women's life, especially women's status. However, even if fertility approaches to replacement level in the year 2010 (the medium-term objective of National Population Policy 2000), absolute number of population will continue to increase for many years because of population momentum. Population momentum is an effect of high fertility in the recent past, which thus produced a large population base leading to an enormous number of young women, entering into reproductive age. One way of reducing the effect of population momentum is to rise the age at marriage.
Some recent findings have shown that children born to young mothers have a higher chance of mortality (Martin et al., 1983; Hobcrafts et al., 1984). Maternal mortality is also high among young mothers (Koenig et al., 1988). Early marriage and subsequent childbearing at an early age hamper biological and mental development of women (Frisancho et al., 1985). Due to early childbearing, biological growth of young women cannot occur in full potential because of maternal drainage during pregnancy and lactation (Naeye, 1981). Moreover, repeated pregnancies beginning in early age adversely affect women's health through maternal depletion syndrome (Jellife and Jellife, 1978). Early age at marriage negatively affects women's status. Husband and in-lows dominate over young wives on decision making about desired family size, use of contraceptives, and use of reproductive health services (Heaton, 1996). Given the culture of early marriage and early childbearing, its negative consequences on population momentum and on maternal and child mortality, family planning and reproductive health programme can reduce maternal and child mortality through appropriate and effective contraceptive methods supplied to young married or newly wed couples. However, recent past studies show that contraceptive use among young married women is low.
Keeping in this view, it is to investigate whether the age at marriage of wife influences the fertility pattern in the population. For analysis purpose, the eligible women have been divided into four groups according to their age at marriage. They are below 20 years, 20-25 years, 25-30 years and 30 years and above. Thus the mean fertility according to age at marriage of wife is distributed in table-3. The maximum mean (4.73 2.10) is found among the wives who married at the age below 20 years. It is followed (3.46 1.56) by those who married at 20-25 years of their age. The minimum mean fertility (2.73 1.41) is found in the marriage age group, 30 years and above. This variation in the fertility is highly as well as negatively correlated (r = -0.37) with age at marriage of wife. It is highly significant as evidenced by the value of Fisher's-t, 11.04 (P< 0.01) at one percent significance level.
4.12 Sex preference and fertility
Many studies recognize India as strongly patrilineal society and there are several stereotypes in the Indian societies. There are also several conceptions why Indian people prefer sons to daughter. Firstly, not only in India but also in the other developing countries where women are economically and socially dependent on men, sons are considered as security for the family (Rahman and DaVanzo, 1993). Secondly Indians believe that only a son can perform some of the religious rites upon the deaths of his parents and son is the principal heir of their father's property (Nath and Goswami, 1997).
The preference of sons has several implications, for the status of women and for the population control programmes in the country Kaur et al., 1987). The low status of women and a strong preference of male children are two patriarchal constraints in the northern states of India (Arokiasamy, 2002). Moore (1994) even remarks that Indian families would not accept family planning until they have two sons. Parents consider essential to have two sons, as insurance in the event that one dies. Indian couples, mostly living in traditional societies have several girls before a boy is born, thereby creating bias towards larger families (Talukdar, 1994). Nag et al. (1978) considered that Economic value of children is one of the important factors for persistence of high fertility. Sex selective abortions are happening in some parts of India (Sen, 2001). Vlassoff and Blassoff (1980) remarked that a direct linkage between reproductive motivation and old age security is difficult to demonstrate because of the long interval between fertility decisions and security received from sons in old age. On the other hand, labour force participation rate among the elderly in India is not less. According to 1991 census, as many as 43% of those aged sixty years and above were in the workforce (Rajan et al., 1999). Kanbargi (1985) also remarked that the question of the extent to which the elderly people are supported has been answered in the context of lack of empirical data; understandably the whole issue of son preference has been characterized by ethnocentric bias.
In this context, it is to investigate the relationship between son preference and the fertility differentials of the couples under study. The two factors firstly, the attitude of the couple on the desire number of son(s) and secondly, the demographic variable - number of living son(s) in the family are into account for analysis. The findings reveal that only 6.67% of the respondents with mean fertility 2.13 desire only one son; 74.90% of the respondents with mean fertility of 3.58 desire two sons and that of 18.48% with 5.85 desire at least three sons in their family (table-3). Thus the present findings clearly show that the mean fertility of couples is found to have a positive association with the respondent's attitude of desire number of sons. This variation is highly significant (P < 0.01).
From table-2, it may be observed that 9.67% of couples with no son have the mean fertility 2.64. The largest proportion like 37.65% of couples with two sons has recorded the fertility, 3.96 and that of the lowest, 3% with at least five sons have noted the fertility 6.98. On the other hand, the fertility of 2.29 is noted among the 15.29% of couple with no daughter. The highest fertility say 7.38 is manifested among 4.31% of couples having at least five daughters. Among the high fertility groups it is noted that the mean fertility 6.07 having four daughters is greater than that of 5.41 of couples having four sons. Similarly the highest mean 7.38 of couples having at least five daughters is greater than that of 6.98 of couples having five sons. This finding may be attributed as the sex of the new born baby cannot be preplanned by the parents they go on producing more until they get the desire number of children of the preferred sex. During the process, many undesirable daughters are added to the total number leading to the average fertility very high for those who have a strong desire for more sons.
Multivariate Analysis on Fertility Regulation
In various studies, many socio-demographic factor like residence, educational status, income, age at marriage, current age, lactation, women participation in gainful employment, practice of family planning methods etc. influenced people in controlling their fertility level. In this regard, a regression analysis has been carried out to the present data in order to study the effects of some important demographic, socio-economic and behavioural factors on fertility regulation in the study population.
In this analysis, the number of live birth ever born is assumed to be functionally related with the place of residence, type of family, religion, caste, educational level of couples, employment status of husband, income-expenditure difference, desire number of son, age at menarche, age at marriage of couples, current age of wife, open birth interval (time interval between date of marriage and 1st birth) and use of contraceptive device. These expected causal factors are treated to be the predictor variables of interest in the present analysis. To reduce multi-collinearity, the variables are included through scientific scanning and controlling process (Retherford, 1998).
In all there are seventeen predictor variables and one response variable (fertility). Some of these predictor variables are not quantitative and therefore a few at least cannot directly be treated as variables. Among these, income-expenditure difference, age at menarche, current age of wife, age at marriage, number of living children of the couple, number of family member, desire number of son, duration of open birth interval have their quantitative values and hence at present no difficulties of measurement. The educational level has been quantified by the number of completed years in schooling. The factors like place of residence, type of family, religion, caste, employment status and use of contraceptive devices are obviously qualitative in nature. Thus, some suitable dummy variables have been introduced in the present analysis.
5.2 Regression Model
The considered model is
y = a + 'bixi + e, i = 1, 2, 3, . . . , 16,
where 'y' is the predicted value of fertility,
'bi' represent the best fitting regression weights,
'a' is the value of y when all predictor variables (xi) are all zero with residual variable 'e'.
5.3 Specification of variables
The measurements of the variables are as follows:
Response variable (Y): Number of live birth ever born.
Predictor variable (Xi):
1. Place of residence (RES, rural = 1, urban = 0)
2. Type of family (TYF, joint = 1, nuclear = 0)
3. Religion (HIN, Hindu = 1, others = 0)
Religion (ISL, Islam = 1, others = 0)
Religion (CHK, Christian = 1, others = 0)
4. Caste (GEN, General = 1, others = 0),
Caste (SC, Schedule Caste = 1, others = 0) &
Caste (ST, Schedule Tribe = 1, others = 0)
5. Educational level of husband (EDH) &
Educational level of wife (EDW)
6. Employment status of husband (EMH, employed having regular income = 1, others = 0)
7. Income-expenditure difference (IED)
8. Desire number of son by the couple (DNS)
9. Number of living son (NLS) &
Number of living daughter (NLD)
10. Number of family member (NFM)
11. Age at menarche (AME)
12. Current age of wife (CAW)
13. Age at marriage of husband (AMH) Age at marriage of wife (AMW)
14. Open birth interval (OBI, interval between marriage and 1st birth)
15. Lactation (LAC, duration of breast feeding of the last or last but one birth subject to availability of its complete duration of lactation)
16. Use of contraceptive device (UCD, Used = 1, otherwise = 0, defined during the course of fertility)
From Table 4, it is evident that the coefficients of only six variables out of twenty-three classified coefficients for sixteen variables say AMW, CAW, ISL, NLD, NLS, and UCD are highly significant at 0.01 level and that of only two variables say DNS and LAC are significant each at 0.05 level of significance. The value of the estimate for '' that is 'a' in the model is 0.765 and that of 't' is 0.950 which is not significantly different from zero at the 0.05 level. The value of R2 is found to be 0.918 and that of Durbin-Watson Statistic (d) is 2.042. It is evident that the total variation in the mean fertility has been explained 92% by the considered predictor variables in the study population after the problem of multi-collinearity be diagnosed and controlled.
Adjusted regression analysis
Variable Unstandardized Standardized t-value P-value
Coefficients (S.E) Coefficients ('')
Constant 0.765 (0.805) 0.950 0.343
RES 8.720E-03 (0.051) 0.002 0.172 0.864
TYF -2.5E-02 (0.056) -0.006 -0.442 0.659
HIN 0.102 (0.055) 0.025 1.861 0.063
ISL 0.423 (0.119) 0.047** 3.551 0.000
CHK -0.245 (0.574) -0.036 -0.427 0.670
GEN -0.376 (0.705) -0.072 -0.534 0.594
SC -0.0249 (0.708) -0.034 -0.351 0.726
ST 3.786E-02 (0.403) 0.006 0.094 0.925
EDH -5.7E-03 (0.007) -0.012 -0.822 0.411
EDW -2.9E-02 (0.015) -0.079 -1.942 0.052
EMH 1.504E-02 (0.047) 0.004 0.320 0.749
IED -2.8E-05 (0.000) -0.011 -0.815 0.415
DNS 0.153 (0.060) 0.042* 2.542 0.011
NLS 0.861 (0.024) 0.550** 35.241 0.000
NLD 0.879 (0.023) 0.638** 39.014 0.000
NFM 1.632E-02 (0.014) 0.022 1.208 0.227
AME -1.8E-02 (0.023) -0.009 -0.789 0.430
CAW 3.677E-02 (0.005) 0.129** 7.706 0.000
AMH -5.2E-03 (0.006) -0.013 -0.877 0.381
AMW -4.1E-02 (0.010) -0.091** -4.232 0.000
OBI -3.8E-03 (0.002) -0.020 -1.744 0.081 LAC -6.7E-03 (0.003) -0.028* -2.305 0.021
UCD -0.132 (0.046) -0.033** -2.850 0.004
R2=0.918; d = 2.042;
* indicates P< 0.05 & ** indicates P< 0.01
Sources- Statistics department of Manipur
For more information of predictor variables on the explanation of total variation in mean fertility the model is further analysed by using Stepwise regression method which is shown in the table-4. The analysis is carried out in ten steps say from Model 1 to Model 10. It reveals that only ten predictor variables have significant contribution on fertility regulation in the population under study. They are NLD, NLS, DSW, EDW, CAW, AMW, ISL, UCD, LAC and OBI in which eight variables are highly significant (P < 0.01) and only two variables say LAC and OBI are statistically significant at the 0.05 level of significance. The value of R2 in the Model 1 is 0.459 and finally it marks 0.916 in the last 10 with monotonically increasing fashion. To check the serial correlation in the residual of the observations, Durbin-Watson Statistic is also computed in the last model which is found to be 2.02. It is shown in the model summary (table 5). The ten predictor variables explain 92% of total variation in the mean fertility in the study population. In view of the above model specifications the latter one say Stepwise method improves the model from adjusted method in all respects. It is therefore revealing useful information.
Stepwise regression analysis
Model & P-
Variable B (S.E) ' t value''''''''''''''''
1 (Constant) 2.240 (0.084) 26.684 0.000
NLD 0.934 (0.037) 0.677** 25.440 0.000
2 (Constant) 8.062E-02(0.052) 1.548 0.122
NLD 1.038(0.016) 0.753** 65.082 0.000
NLS 1.045 (0.018) 0.668** 57.746 0.000
3 (Constant) -0.407 (0.089) -4.583 0.000
NLD 1.003 (0.016) 0.728** 61.330 0.000
NLS 0.987 (0.020) 0.631** 50.255 0.000
DNS 0.310 (0.046) 0.086** 6.685 0.000
4 (Constant) -0.128 (0.103) -1.239 0.216
NLD 0.990 (0.016) 0.718** 60.825 0.000
NLS 0.974 (0.019) 0.623** 50.028 0.000
DNS 0.270 (0.046) 0.075** 5.828 0.000
EDW -2.2E-02(0.004) -0.059** -5.098 0.000
5 (Constant) -0.557 (0.141) -3.939 0.000
NLD 0.954 (0.018) 0.692** 52.921 0.000
NLS 0.940 (0.021) 0.600** 45.109 0.000
DNS 0.272 (0.046) 0.075** 5.948 0.000
EDW -2.4E-02 (0.004) -0.065** -5.643 0.000
CAW 1.615E-02(0.004) 0.057** 4.380 0.000
6 (Constant) -6.6E-02(0.170) -0.389 0.697
NLD 0.914 (0.019) 0.663** 47.042 0.000
NLS 0.893 (0.023) 0.571** 39.676 0.000
DSW 0.252(0.045) 0.070** 5.578 0.000
EDW -1.7E-02(0.004) -0.045** -3.763 0.000
CAW 2.602E-02(0.004) 0.091** 6.311 0.000
AMW -3.3E-02(0.006) -0.072** -5.047 0.000
7 (Constant) -8.4E-02(0.169) -0.497 0.619
NLD 0.913(0.019) 0.663** 47.240 0.000
NLS 0.887(0.022) 0.567** 39.539 0.000
DSW 0.215(0.047) 0.060** 4.610 0.000
EDW -1.6E-02(0.004) -0.043** -3.599 0.000
CAW 2.803E-02(0.004) 0.098** 6.753 0.000
AMW -3.2E-02(0.006) -0.070** -4.936 0.000
ISL 0.320(0.103) 0.036** 3.090 0.002
8 (Constant) -4.4E-02(0.168) -0.260 0.795
NLD 0.903(0.020) 0.655** 46.266 0.000
NLS 0.889(0.022) 0.568** 39.812 0.000
DSW 0.199(0.047) 0.055** 4.269 0.000
EDW -1.5E-02(0.004) -0.040** -3.378 0.001
CAW 3.186E-02(0.004) 0.112** 7.393 0.000
AMW -3.6E-02(0.007) -0.079** -5.467 0.000
ISL 0.321(0.103) 0.036** 3.125 0.002
UCD -0.140(0.045) -0.035** -3.090 0.002
9 (Constant) 9.311E-02(0.179) 0.520 0.063
NLD 0.902(0.019) 0.655** 46.338 0.000
NLS 0.887(0.022) 0.567** 39.815 0.000
DSW 0.194(0.047) 0.054** 4.173 0.000
EDW -1.6E-02(0.004) -0.043** -3.599 0.000
CAW 3.284E-02(0.004) 0.115** 7.600 0.000
AMW -3.8E-02(0.007) -0.084** -5.773 0.000
ISL 0.303(0.103) 0.034** 2.948 0.003
UCD -0.138(0.045) -0.034** -3.052 0.002
LAC -5.8E-03(0.003) -0.024* -2.210 0.027
10 (Constant) 0.185(0.184) 1.005 0.315
NLD 0.896(0.020) 0.650** 45.658 0.000
NLS 0.881(0.022) 0.563** 39.382 0.000
DSW 0.190(0.046) 0.053** 4.080 0.000
EDW -1.7E-02(0.004) -0.046** -3.810 0.000
CAW 3.498E-02(0.004) 0.123** 7.895 0.000
AMW -4.0E-02(0.007) -0.088** -6.003 0.000
ISL 0.309(0.103) 0.035** 3.007 0.003
UCD -0.145(0.045) -0.036** -3.208 0.001
LAC -6.2E-03(0.003) -0.026* -2.347 0.019
OBI -4.5E-03(0.002) 0.023* -2.100 0.036
*indicates P < 0.05; ** indicates P < 0.01
Sources- Statistics department of Manipur
Model Change Statistics Change in
& R2 S.E. R2-Change F-Change d.f.1 d.f.2 P-value
1 0.459a 1.4126 0.440 647.216 1 763 0.000
2 0.899b 0.6096 0.440 334.654 1 762 0.000
3 0.905c 0.5929 0.006 44.690 1 761 0.000
4 0.908d 0.5834 0.003 25.988 1 760 0.000
5 0.910e 0.5765 0.002 19.183 1 759 0.000
6 0.913f 0.5674 0.003 25.473 1 758 0.000
7 0.914g 0.5643 0.001 9.550 1 757 0.002
8 0.915h 0.5611 0.001 9.547 1 756 0.002
9 0.916i 0.5597 0.001 4.882 1 755 0.027
10 0.916j 0.5584 0.000 4.408 1 754 0.036
The value of 'd' =2.020
a. Predictors: (Const.), NLD
b. Predictors: (Const.), NLD, NLS
c. Predictors: (Const.), NLD, NLS, DNS
d. Predictors: (Const.), NLD, NLS, DNS, EDW
e. Predictors: (Const.), NLD, NLS, DSW, EDW, CAW
f. Predictors: (Const.), NLD, NLS, DNS, EDW, CAW, AMW
g. Predictors: (Const.), NLD, NLS, DNS, EDW, CAW, AMW, ISL
h. Predictors: (Const.), NLD, NLS, DNS, EDW, CAW, AMW, ISL, UCD
i. Predictors: (Const.), NLD, NLS, DNS, EDW, CAW, AMW, ISL, UCD, LAC
j. Predictors: (Const.), NLD, NLS, DNS, EDW, CAW, AMW, ISL, UCD, LAC, OBI
Sources- Statistics department of Manipur
5.5 Results and Discussion
The coefficients of the number of living son (NLS) and daughter (NLD) are found to be highly significant for various reasons. The findings also show that the number of living daughter has more effective on fertility regulation than that of son owing to the fact that the couples are desirous of more number of sons than daughters. It is again witnessed by highly significant coefficient of DNS (0.563), desire number of son(s) by the couples. Thus it may be concluded that there are unavoidable problems of son preference causing high fertility in the population under study.
In many societies as the couples are educated, eagerness to restrict the family size increases. The present findings observe the same view. But in comparing the effect of education of husband (EDH) with their wife (EDW) counterpart, it is evident that the wife's education plays more significant role in reducing fertility. Its effects include in delaying age at marriage, reduction in the desired family size, increase opportunities for personal advancement, awareness of social mobility and freedom from close familiarities of women outside the home and greater exposure to knowledge and favourable attitude towards family limitations. Thus, enhancement of education is supposed to result in non-familial aspiration and a greater understanding of the process and ways of controlling high fertility. This view is supported by Cochrane (1979); Casterline (1983); Yadava et al. (2000) and others.
The coefficient of AMW shows that the age at marriage of wife has negatively as well as highly significant impact on fertility regulation in the present analysis. This leads us to the conclusion that by rising age at marriage of wife, the mean fertility can be brought down. The higher age at marriage is observed among those who are belonging to the higher income group, which is shown in the previous section. And higher age at marriage shortens the reproductive span which, in turn, reduces the fertility rate. On the other hand, education depresses fertility by rising the age at marriage, strengthening the propensity to be in the labour force, fostering a favourable attitude towards small family norm and improving awareness and use of family planning methods. This view is incorporated with the findings of the studies conducted by Das and Dey (1998); Harvindar (2000); Sheela and Audinarayana (2000) and others.
The coefficient of CAW (0.123) in the last model suggests that the current age of wife is significantly and linearly related with the total number of children ever born in the population. One of the most important cultural factors influencing high fertility is found to be Islam (religion) which is indicated by highly positive significant coefficient of ISL (0.035) in the last model 10. In the present study, generally Muslims are low educated, having low income, taking early marriage and hence resulting high fertility. Mahadevan (1979) emphasizes that Muslim religious doctrine does not specially prohibit voluntary birth limitation, the institutional pressures to have many children, especially sons, are strong. This view is incorporated with the present findings.
In this stepwise regression analysis, it is to assess that the use of contraceptive device has a strong negative impact on fertility. It may be due to the fact that there in a strong relationship between the educational level of wife and the use of different contraceptive devices and hence reducing fertility level. In this context, Rajaretnam (2000) observes, in his study conducted in Goa and Kerala that the number of living children influences the women's choice of contraceptive methods and the relationship is not linear but curvilinear. This is evident from the significant and negative regression coefficient for the square term of the variable.
In countries with prolong breast-feeding, birth intervals are usually longer because of prolonged PPA (Perez et al, 1972; Howie and McNeily, 1982; Srinivasan et al., 1989; Kaushalendra et al. 1999). It again reduces the number of births. The same view has been observed in the present study. The coefficient of LAC ('0.026) in the last model indicates that the duration of breast feeding has a negative significant effect on fertility regulation. In the similar way, the open birth interval has also a negative significant impact on fertility differentials.
Thus in the present regression analysis, it may be concluded that there are ten important predictor variables after scientific scanning of other factors according to their degree of importance to explain the variability in the fertility of Manipuri women. They are number of living daughter, number of living son, desire number of son by the couples, educational level of wife, current age of wife, age at marriage of wife, Islam religion, use of contraceptive device, lactation and open birth interval. These variables of interest have explained 92% of total variation in the mean fertility in the study population.
The present study takes the initiative to evaluate the distributions of fertility of Manipuri women. It is observed that the nature and pattern of fertility extensively according to the characteristics of eligible couples in the study population. The characteristics under study are socio economic, demographic and behavioural factors. The socio economic factors consist of place of residence, type of family, caste, religion, income, educational level, employment status. The demographic factors deals herewith age at menarche, age at marriage, current age of wife, number of living children, open birth interval etc. and the behavioural factors covered under study are desire number of son, lactation and use of contraceptive devices. The findings arrive at in the present analysis highlight useful information on fertility regulation in Manipur. The findings may be useful not only in identifying the supporting and retarding factors of differential fertility but to explain fertility behaviour of Manipuri women. Further, it may be useful in isolating and identifying the stratum of couples which have high fertility potential. These information may also provide a proper guide line in implementing the Family Welfare (FW) programmes or Reproductive and Child Health (RCH) programmes, particularly for those sections of couples who have been identified as sensitive or so called target sector. Besides, the present study may be useful in providing basic information and ground work to the future researchers who are working in this area of human research.
Many studies conducted in many parts of the world observed that couples differentiated by various socio economic, demographic and behavioural factors have considerable variations in fertility levels. In view of the present findings, it may be inferred that the mean fertility varies significantly with place of residence (t = 4.42, P< 0.01). In the similar manner, the mean fertility varies significantly with various socio economic factors like type of family (t = 2.41, P< 0.05), caste (F = 9.39, P< 0.01), religion (F = 10.02, P< 0.01), family income (r = -0.12, t = 3.34, P <0.01), educational level of husband (F = 7.39, P< 0.01), educational level of wife (F = 19.12, P< 0.01), employment status of wife (t = 4.06, P< 0.01), occupation of husband (F = 3.87, P< 0.01). It may interestingly be observed that the educational level of wife has more impact on the variation of fertility than that of husband counterpart. Among the demographic factors, age at marriage of wife has highly significant impact on fertility regulation as evidenced by the test values (r = -0.37, t = 11.04, P< 0.01). The average fertility has also high significant variations according to current age of wife (r = 0.50, t = 15.94, P< 0.01).
A multiple regression analysis has been carried out to measure the degree of importance of various predictor variables on regulation of fertility. In this analysis, out of sixteen predictor variables eight factors are found to be statistically significant by utilizing adjusted coefficients. They are number of daughter (P< 0.01), number of son (P <0.01), current age of wife (P< 0.01), age at marriage (P< 0.01), Islamic religion (P < 0.01), desire number of son (P< 0.05), duration of breast feeding (P< 0.05) and use of contraceptive device (P< 0.05). It is also confirmed that number of daughter, number of son, current age of wife, Islamic religion and desire number son have positive impact in one hand and that of remaining three factors say age at marriage of wife, use of contraceptive and duration of breast feeding have negative impacts on the fertility regulation in the study population on the other. It may be worthwhile to note that educational level of wife has significant impact on fertility dynamics in univariate analysis. However, it is found insignificant (P> 0.05) in multivariate analysis. It may be thought to be caused due to the joint effect of various factors those are controlled at a constant level while extracting the degree of influence. In this regression analysis, the total variation in the average fertility has been explained 92% by the present sixteen predictor variables (R2 = 0.92).
To obtain the best set of covariates in the multivariate analysis, the stepwise method is adopted. Amongthe predictors only ten variables are found to be statistically significant. They have non-trivial impacts on the fertility regulation in the population. The significant variables are number of living daughter (P< 0.01), number of living son (P< 0.01), desire number of son (P< 0.01), educational level of wife (P< 0.01), current age of wife (P< 0.01), age at marriage of wife (P< 0.01), Islamic religion (P< 0.01), use of contraceptive device (P< 0.01), duration of breast feeding (P< 0.05) and open birth interval (P< 0.05). The variables other than these ten ones are excluded in stepwise manner in the analysis owing to their insignificant roles in the variation of mean fertility in the study population. The significant variables with adjusted effects of others are again found significant in the stepwise method in the same direction. But their levels of significance are somewhat different. Four factors like number of living daughter, number of living son, current age of wife and age at marriage of wife are having high significant impacts on fertility regulation each at 0.001 significance level in both methods. However, desire number of son which is significant at 0.05 level in the adjusted method is again found to be highly significant even at 0.001 level in later method. The two factors viz., use of contraceptive device and duration of beast feeding (lactation) exist in both methods at equal significance level of 0.01 and 0.05 respectively. On the contrary, the two factors like educational level of wife and open birth interval, found insignificant in adjusted method are notably found significant in the stepwise method even at 0.01 significance levels. This changing pattern of influence is so happen because of excluded variables in the stepwise method whose effects are considered in the former method. For instance, in the stepwise method the effect of educational level of wife on mean fertility is -0.05 (P< 0.01) keeping the effects of other nine variables controlled. In this manner, each value of the coefficients in the latter method may be interpreted on the basis of the ten considered variables only.
Thus in the last fitted model, the ten predictor variables explain 92% (R2 = 0.92) of the total variation in the mean fertility. In this method, sixteen variables are excluded step by step with respect to their degree of insignificant influences on the variation in average fertility. Thus the significant ten variable found in the stepwise regression analysis may be treated as the best set of covariates of fertility differentials in the study population.
On the basis of the findings in the present study it may be suggested that in order to achieve a significant reduction in the fertility level in the state, the Government of Manipur may try to formulate, execute and implement the measures viz., age at marriage be raised, couples be educated to maintain proper spacing between births, infant mortality be reduced, policies be made to increase the level of education especially for women and attempt also be made to improve the economic condition of people resulting into better way of life. In spite of so much emphasis given on the programmes of Family Planning, Family Welfare and Reproductive and Child Health, The state Manipur is still no where near a satisfactory solution thereof. For instance, the maximum number of couples adopt contraceptives particularly permanent methods achieving their desire number of children which is against the small family norms of India when couples are expected to adopt permanent methods while they have no more than two children.
Besides, there is a long way to go, to ensure the effective implementation of Reproductive and Child Health program in the state in order to achieve the basic standard for higher quality of family planning. The standard includes not only technical quality but informed consent, a range of contraception choice in early part of child bearing period, health services in addition to contraception and respectful as well as accurate communication between client and provider. And monitoring progress toward improved services will also require new indicators ' not merely of contraceptive use but of individual' ability who are to achieve their reproductive goals in a healthy manners.
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