Introduction:
In contrast to the national target to achieve the replacement fertility of 2.1 children by 2010, NFHS-4 reported that a woman in India has an average of 2.2 children in her lifetime in 2015-16 which is declined from 2.7 in 2005-06. (1) The rates are below the replacement level in all south states, all north states except one Rajasthan (2.4), all west states except Dadra and Nagar Haveli (2.3). The rates are above the level (2.1) in all northeast states except two (Tripura - 1.7 and Sikkim - 1.2), all central states, and two east states - Bihar (3.4) and Jharkhand (2.6) (Table 1). In the same report, average fertility in rural areas is found to be 2.4 children to a woman, it is higher than in urban areas (1.8) in the country. In lowest wealth quintile category, the women have an average of 3.2 children, much more than women in highest wealth quintile with 1.5 children. The rates also decline in women with higher levels of education as the illiterate women have an average of 3.1 children compared with 1.7 children for women with at least 10 plus 2 level. Among scheduled tribes, the rates are higher as 2.5 children, scheduled caste with 2.3 children and that of other backward classes with 2.2 children.
Even after seven decades of independence, fertility rate and infant mortality rate have remained at high levels with regional variations in India. These are primarily due to abject poverty and widespread low education, especially among the females. Despite constant efforts made by the government, out of 36 states/ union territory 13 are still lagging behind the national target of replacement fertility. National Rural Health Mission (NRHM) (2005-12) also stressed that the improvement in 'quality of life' by emphasizing fertility not only at younger ages but also not continuing beyond 30 years of age and the long birth intervals. With a nation's 5th high rank of fertility rate (2.6), Manipur is facing the third birth transition, a serious demographic phenomenon with the replacement fertility level.
Among the factors influencing high fertility, female education is noted to be most important in many past studies. In India, a 10 percent increase in female literacy rate is associated with a 0.5 decline in total fertility rate. (2) They emphasized that for reducing fertility, it would be necessary to achieve for 80 percent female literacy. The high fertility is observed to be caused by uncontrolled pregnancies owing to unmet need of effective contraceptives which is witnessed in the previous studies. (3-5) A greater demand for sons than daughters is also seen when declining in fertility in the country. It is also observed a preference in United States for equal composition or so-called balanced family of one son and one daughter. (6) The balance norm in many European countries is supported. (7) But in Denmark, a mild girl preference in the families is highlighted with the balanced family norm. (8) However, in many developing countries including India, an association between sex of surviving children and reproductive intentions is witnessed. (9-11) Analyzing the data from Demographic and Health Survey (DHS) of fifty-seven countries, the strong preference of son found in Southern Asian Countries. (12)
| Table-1: Fertility rates (FR) of Indian States/Union Territory with replacement level |
Sl No. |
FR<2.1 |
FR>2.1 |
1 |
And & Nico Islands (1.44) |
Rajasthan (2.40) |
2 |
Andhra Pradesh (1.83) |
Chhattisgarh (2.23) |
3 |
Karnataka (1.80) |
Madhya Pradesh (2.32) |
4 |
Kerala (1.56) |
Arunachal Pradesh (2.10) |
5 |
Lakshadweep (1.82) |
Assam (2.21) |
6 |
Puducherry (1.70) |
Mizoram (2.27) |
7 |
Tamil Nadu (1.70) |
Dadra & N. Haveli (2.32) |
8 |
Telangana (1.78) |
Utter Pradesh (2.74) |
9 |
Maharashtra (1.87) |
Bihar (3.41) |
10 |
Goa (1.66) |
Jharkhand (2.55) |
11 |
Gujarat (2.03) |
Manipur (2.61) |
12 |
Daman & Diu (1.68) |
Meghalaya (3.04) |
13 |
Sikkim (1.17) |
Nagaland (2.74) |
14 |
Tripura (1.68) |
|
15 |
West Bengal (1.77) |
|
16 |
Odisha (2.03) |
|
17 |
Uttarakhand (2.07) |
|
18 |
Punjab (1.62) |
|
19 |
Jammu & Kashmir (2.01) |
|
20 |
Himachal Pradesh (1.88) |
|
21 |
Haryana (2.05) |
|
22 |
Delhi (1.78) |
|
23 |
Chandigarh (1.57) |
|
| Source: National Family Health Survey - 4 Reports (2017) |
In Nepal, human birth stopping after having a son is associated with use of contraceptives and last birth interval. (13) In policy point of view, issue of third birth is a serious demographic character as it obstructs the national goal of 2.1 fertility level. Low education and son preference might perhaps be the most causal factors to it. In India too, many couples have a strong son preference through religious, socio-cultural and socio-economic factors. Sons are more likely than daughters to support their parents in every family activity. (14) In case of intention, about 19 percent of couples want more sons than daughters while only 3 to 4 percent of them want more daughters than sons. (1) However, 25 percent of married women want more sons than daughters in 2015-16 which is declining from 31 percent in 2005-06 in Manipur. An empirical finding also highlighted the son preference and low education significantly influences the short inter-live birth intervals through its waiting time to conception which may lead high fertility rate in the State. (15,16)
The present study aims to investigate the differentials in completed fertility transited through past three patrilineal generations and also to explore the socio-demographic factors influencing the third birth transition as it is very far lagging behind the national goal of replacement fertility of 2.1 children according to National Population Policy (NPP-2000).
Materials and Methods:
Utilizing a cluster sampling technique, a cross sectional study of 1145 currently married women aged below 50 years was conducted in 2018 taking 31st December 2017 as reference date of the survey in rural areas of valley districts of Manipur. The tool of the survey was a pre-tested and semi-structural interview schedule. The rural area under study is treated as the valley areas covered by Gram Panchayats (Indian Panchayati Raj System) and the clusters are also identified with the list of villages as per Population of Manipur. (17) The logistic regression models with binary dummy variables (0, 1) were adopted to find the determinants of 3rd birth transition. Here, the term 'fertility' is defined to be the number of live births ever born to a mother and the 'third birth transition' is measured by the issue of third live birth (1 if 3rd birth occurred and 0, at most 2nd birth). For generation fertility only three generations of 1st, 2nd, and 3rd in patrilineal are considered. With the retrospective information, the inferences of the analysis are explored through SPSS vs 23.
The present study population is the rural valley areas of Manipur. It is the Indian easternmost state internationally bordering with Myanmar. Out of the total population of 28.6 lakh according to Census 2011, 57 percent live in the valley and the rest 43 percent in the hill districts. With more than 33 scheduled tribes, the border State has a unique feature of largest number of dialects with least number of populations particularly in hill region.
Results:
The current fertility, computed from the mother of age above 40 years is found to be high as 3.6±1.6 transited from 1st generation (husband's grandfather), 4.8±2.8; 2nd generation (husband's father), 4.9±2.3; and 3rd generation (husband), 5.6±1.9. The corresponding matrilineal fertility levels are retrospectively observed as 4.4±2.0, 4.9±2.7 and 5.5±1.9 respectively. With 6.1 children for Muslim women followed by that of Christian (4.2) and Hindu (3.5), the current fertility is highly significantly varied according to religion (F=14.76, P<0.01). But the variation in fertility for past three generations is statistically insignificant (P>0.05) with the categories of religion. Here, the fertility for Muslim women is visibly found to be continuously highest as the current fertility 6.1±3.0 which is transited from that of previous three generations of 6.9±2.0 (1st), 5.9±1.7 (2nd) and 6.2±1.4 (3rd) depicted in Table 2. Also currently observed low figure of 3.5 children is found in Hindu transited from the three generations 4.9±2.5, 4.8±2.3 and 5.5±2.0 respectively.
| Table-2: Fertility transition through three generation (Gen) |
Variable |
Gen-4 (n=401; Age>40) |
Gen-3 (n=1145) |
Gen-2 (n=1145) |
Gen-1 (n=607; 315p; 292m) |
All |
pPatrilineal |
3.45±1.61 |
5.57±1.88 |
4.93±2.30 |
4.82±2.83 |
mMatrilineal |
5.49±1.94 |
4.93±2.71 |
4.37±1.98 |
Religion |
Hindu |
3.47±1.54 (79.4) |
F=14.8; P<0.01 |
5.46±2.01 (85.1) |
F=1.95; P>0.05 |
4.83±2.33 (85.1) |
F=1.79; P>0.05 |
4.91±2.54 (81.1) |
F=2.12; P>0.05 |
Meetei |
3.87±1.23 (11.8) |
5.33±1.89 (12.2) |
5.17±2.37 (12.2) |
4.75±1.88 (11.5) |
Muslim |
6.09±3.03 (4.3) |
6.21±1.35(1.4) |
5.85±1.67 (1.4) |
6.94±1.97(3.8) |
Christian & others |
4.23±1.76 (4.5) |
6.01±2.32(1.3) |
4.79±2.56 (1.3) |
4.54±2.17(3.6) |
| Figures in parenthesis indicates percentage |
To examine the factors responsible for third birth transition in current fertility dynamics in the population, a multiple logistic regression is carried out with binary characters in response variable that 1 when at least 3rd birth occurred and 0 for at most 2nd birth occurred. In this analysis, 3rd birth transition is assumed to be serious demographic event which is against the national target of 2.1 children. The inferences are interpreted in terms of odds ratios (OR=eb) and P-value of test statistics (Wald). With binary variable technique (0, 1), the independent or so called explanatory variables considered here are religion (1 if case and 0 otherwise), type of family (1 if nuclear and 0 otherwise), educational level (as ordinal: illiterate = 0, literate but under matriculate = 1, matriculate = 2, 10 plus 2 level = 3, graduate and above = 4), employment status (1 if employed with good regular income and 0, otherwise that is unemployed in any government, semi-government, private sectors etc.), fertility of past generations (discrete), marriage age or so called age at marriage (continuous), desire number of son (discrete), and contraceptive use (1 if effective use and 0 otherwise) during the period of third birth occur. In this analysis, 6 out of 13 factors are identified as significant causal factors in the regression models shown in Table 3. With little varied significance levels and odds ratios (OR), the inferences are observed in both adjusted and stepwise models. In the last 6th model, the significant factors found are 1st generation fertility (P<0.05, OR=1.17), 2nd generation fertility (P<0.05, OR=0.87), education of wife (P<0.01, OR=0.87), marriage age of wife (P<0.01, OR=0.87), use of effective contraceptives (P<0.05, OR=0.23) and of husband's employment (P<0.05, OR=1.88) which are manifested in Table 4.
| Table-3: Odds Ratio of variables on 3rd birth transition |
Variables |
b |
OR (95%CI) |
P-value |
Religion (Hindu) |
0.013 |
1.013 (0.502, 2.042) |
>0.05 |
Religion (Islam) |
-0.129 |
0.879 (0.118, 6.554) |
>0.05 |
Fertility of generation-1 |
0.023 |
1.024 (0.919, 1.140) |
>0.05 |
Fertility of generation-2 |
-0.138 |
0.871 (0.782, 0.970) |
<0.05 |
Fertility of generation-3 |
0.171 |
1.186 (1.041, 1.352) |
<0.05 |
Education of husband |
0.025 |
1.025 (0.943, 1.115) |
>0.05 |
Education of wife |
-0.143 |
0.867 (0.816, 0.921) |
<0.01 |
Marriage age of wife |
-0.122 |
0.885 (0.815, 0.960) |
<0.01 |
Marriage age of husband |
-0.025 |
0.975 (0.905, 1.051) |
>0.05 |
Couple's desired no. of son |
0.154 |
1.167 (0.859, 1.586) |
>0.05 |
Employment of husband |
0.570 |
1.769 (1.047, 2.989) |
<0.05 |
Employment of wife |
-0.333 |
0.717 (0.191, 2.697) |
>0.05 |
Use of contraceptives |
-1.525 |
0.218 (0.068, 0.697) |
<0.05 |
Constant |
4.448 |
85.442 |
<0.01 |
Discussion:
Six variables that 2nd generation fertility, 3rd generation fertility, education, age at marriage, contraceptive use and husband's employment fit the last logistic regression model. When controlled the joint effects of five other variables, education is found as highly influential factor (P<0.01) in the transition of 3rd birth in the population. In this study population, Muslim women have high fertility associated with low educated, low income and early marriage. Most of previous findings highlighted that Muslim religious doctrine does not prohibit voluntary birth control and institutional pressures to have many sons are strong. This view is in agreement with the empirical findings of a study conducted in Manipur. (16,18) It is also observed here that effective contraceptive uses can significantly reduce the current fertility level (P<0.05). The reproductive desire and behaviours are found to be significantly influenced by sex of surviving children in most of the developing countries. (9,11) In the present findings, set of covariates like women's education, age at marriage and use of effective contraceptives are negatively as well as significantly associated with the third birth transition irrespective of the other factors under investigation. While controlled the joint effects of women's age at marriage, education and husband's employment and contraceptive use, the 3rd generation's fertility positively influences the phenomenon of third birth and that of 2nd generation negatively influence thereon. It may also be observed that the current fertility level could be reduced significantly by the influence of the fertility of 2nd generation in the population.
| Table-4: Stepwise Odds Ratio of variables on 3rd birth transition |
Step |
Variable |
b |
OR(95% CI) |
P-value |
1 |
Education of wife |
-0.158 |
0.854 (0.814, 0.895) |
<0.01 |
Constant |
0.789 |
2.201 |
<0.01 |
2 |
Education of wife |
-0.123 |
0.884 (0.840, 0.930) |
<0.01 |
Marriage age of wife |
-0.134 |
0.875 (0.827, 0.925) |
<0.01 |
Constant |
3.539 |
34.427 |
<0.01 |
3 |
Education of wife |
-0.124 |
0.884 (0.839, 0.930) |
<0.01 |
Marriage age of wife |
-0.137 |
0.872 (0.824, 0.923) |
<0.01 |
Use of contraceptives |
-1.376 |
0.252 (0.083, 0.763) |
<0.05 |
Constant |
4.920 |
137.054 |
<0.01 |
4 |
Education of wife |
-0.138 |
0.871 (0.825, 0.919) |
<0.01 |
Marriage age of wife |
-0.134 |
0.875 (0.826, 0.926) |
<0.01 |
Employment of husband |
0.582 |
1.790 (1.091, 2.937) |
<0.05 |
Use of contraceptives |
-1.302 |
0.272 (0.090, 0.819) |
<0.05 |
Constant |
4.717 |
111.874 |
<0.01 |
5 |
Fertility of generation-2 |
-0.122 |
0.885 (0.797, 0.982) |
<0.05 |
Education of wife |
-0.142 |
0.867 (0.821, 0.916) |
<0.01 |
Marriage age of wife |
-0.134 |
0.874 (0.826, 0.926) |
<0.01 |
Employment of husband |
0.585 |
1.796 (1.089, 2.961) |
<0.05 |
Use of contraceptives |
-1.441 |
0.237 (0.076, 0.737) |
<0.05 |
Constant |
5.539 |
254.385 |
<0.01 |
6 |
Fertility of generation-3 |
0.158 |
1.171 (1.032, 1.329) |
<0.05 |
Fertility of generation-2 |
-0.138 |
0.871 (0.783, 0.969 |
<0.05 |
Education of wife |
-0.139 |
0.870 (0.823, 0.920) |
<0.01 |
Marriage age of wife |
-0.142 |
0.868 (0.819, 0.920) |
<0.01 |
Employment of husband |
0.631 |
1.879 (1.133, 3.116) |
<0.05 |
Use of contraceptives |
-1.466 |
0.231 (0.075, 0.714) |
<0.05 |
Constant |
4.906 |
135.083 |
<0.01 |
Conclusion:
In this study, the completed fertility for women of age above 40 years is found to be high as 3.5 children. The figure is of serious concern for demographers with national target of replacement fertility level of 2.1 children. Though variability in fertility in the population seems complex, lack of education is detected to be one of the major factors influencing high fertility in the population. It leads longer exposures during reproductive span in addition to the high-risk factors of contraceptives use and early age at marriage. Management of such causal factors at a level consistent with the national demographic goals is urgently needed in Manipur particularly in its rural areas.
Recommendations:
Based on the present findings, it must be recognized that rural, low educated women and lower income employment or so-called unemployment is significantly influencing early marriage and also the phenomenon of third birth transition. These indicate to the need for strong efforts to achieve the national target for replacement fertility of 2.1 children in the easternmost border State, Manipur. Besides, social development programmes must provide better education and job opportunities particularly for rural women and their families with feasible options other than early marriage so as to enhance their quality of life.
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