1 – Decomposing childlessness

4The literature on childlessness has extensively discussed the issue of voluntary versus involuntary. For example, Poston and Cruz (2016) explored the National Survey of Family Growth data on American women to discuss alternative methods for separating voluntarily from involuntarily childless women. They explain why none of these methods are perfect and point to the need for intensive personal interviews to identify the underlying factors of childlessness related to women’s reproductive health, personal aspirations, and entire marital history. To our knowledge, large datasets built on such interviews do not exist for India or any other country.

5The absence of accurate data for India makes it impossible not only to break down childlessness into its voluntary and involuntary components, but also—and more importantly—to evaluate their socioeconomic gradients. For this reason, we use the decomposition proposed by Baudin et al. (2015), who distinguish three kinds of childlessness: natural sterility, poverty-driven childlessness, and opportunity-driven childlessness.

6While natural sterility refers to the innate inability to give birth and is uniformly distributed in the population (Leridon, 2008), poverty-driven childlessness refers to women who fail to have children because of their poverty. One main cause of definitive childlessness among the poor in less developed countries lies in their higher degree of exposure to venereal diseases and malnutrition (Retel-Laurentin, 1974; McFalls, 1979; Romaniuk, 1980; Frank, 1983; Poston et al., 1985). Weiss et al. (2008) and Solomon et al. (2009) confirmed that married women belonging to socially disadvantaged groups in India are at higher risk of infection by chlamydia and gonorrhea (STDs that potentially cause infertility) and HIV. In addition to poverty, Madhivanan et al. (2009) documented an increased risk of adverse pregnancy outcomes caused by Trichomonas vaginalis among women exposed to sexual intercourse at an early age through marriage. Even if recent evidence regarding malnutrition and fecundity may seem less salient than in the past, Nanda (2009) at least documented a positive association between low fertility and stunting among women in parts of India. Notably inegalitarian in our current times, poor societies lack universal access to assisted reproduction techniques (ART), which reinforces the socioeconomic gradient of poverty-driven childlessness: the rich can afford expensive ART, whereas the poor are excluded from it. Allahbadia (2013) and Rasool and Akhtar (2018) and have documented poor people having low access to ART in India.

7Opportunity-driven childlessness refers to women who have delayed motherhood to the point where having children has become unfeasible or undesirable. [2] One main reason behind this postponement is the time cost of having children. India is similar to most societies for whom having children requires investing time in child-rearing instead of labor market activities—for women much more than for men. Renouncing labor market activities forms part of the opportunity cost of having children (Becker, 1981). Another dimension of this opportunity cost is added by the need to abandon other personal aspirations whose importance is assumed to be positively associated with education. This fact was documented by Ghosh (2015) in the case of Kolkata’s second demographic transition, while Surkyn and Lesthaeghe (2004) covered this in a more general discussion.

8Our decomposition ascribes no reasons for childlessness to each childless woman, an approach that fares no better than the classical decomposition into voluntary and involuntary childlessness. Nevertheless, we depart from the classical decomposition (for instance, into wanting or never wanting to have children) by abandoning attitudinal concepts toward childlessness and instead rely on the measurable socioeconomic and biological factors of childlessness. [3] This conceptual framework allows us to formulate a theory of childlessness in India, which we describe in the next section.

2 – A theory of childlessness for India

1 – DLHS data

22We use secondary data from all three rounds of the District-Level Household and Facility Survey ([DLHS] 1998–1999, 2004–2005, and 2007–2008). The DLHS was conducted by Mumbai’s International Institute for Population Studies, funded by the Ministry of Health and Family Welfare, and it provides cross-sectional micro-level data that covers all districts and states in the country. DLHS data guarantee a large sample size and wide coverage of the Indian population over a long time span, while additionally providing sufficient information on childlessness, marriage, fertility, and other sociodemographic characteristics for all respondent categories. [7] Because the profession has raised some issues about DLHS data quality and suggests that Demographic and Health Survey (DHS) data are superior to DLHS, we also use the DHS 4 data (2015–2016) to test the external validity of our findings in Section V.

23After deleting missing values and input errors, then grouping women into birth cohorts and selecting only age groups of 40 to 49 years old, [8] we arrive at a final sample size of 158,112 currently married women born between 1953 and 1968, among whom 4,725 are childless. [9] We then obtain a childlessness rate equal to 2.9% against the 6.69% measured by the Census of India. A large portion of this difference arises from the alternative samples considered because our subsample includes only currently married women, while the census considers all women, including those who have never married or are divorced, widowed, or separated. Childlessness rates rise to over 90% among never-married women in India; among ever-married women, they are much higher for non-currently than currently married women. Reassuringly, DLHS and DHS estimates deliver very comparable childlessness rates among women by marital status. Asymmetries in birth omissions may explain the remaining differences between census and survey data. These two issues are discussed in detail in Online Appendix A: https://doi.org/10.34847/nkl.ab72r44w.

2 – Fertility and childlessness patterns in India

24The literature has extensively documented a negative relationship between a mother’s fertility and income, and thus a positive relationship between the degree of poverty and fertility (Birdsall et al., 2001). Therefore, if childlessness results mainly from poverty or infertility, either no correlation or a positive one should exist between childlessness rates and the average completed fertility of women in Indian states. Only sterility matters if no correlation exists, while, if positive, poorer states should have both higher childlessness and higher fertility. Using state-level data (Figure 1A), we show that the correlation between childlessness rates and the average completed fertility of women is negative. This contradicts the idea that childlessness in India is due only to infertility or poverty, and it indicates the possible existence of opportunity-driven childlessness. However, this does not mean that childlessness is only opportunity driven in India; outliers like the states of Haryana and Nagaland indicate that both childlessness and the average completed fertility of women can be high, which is also indicative of a potentially high prevalence of poverty-driven childlessness. [10]

Figure 1 - Correlations between childlessness rates and (a) average completed fertility and (b) educational attainment

Figure 1 - Correlations between childlessness rates and (a) average completed fertility and (b) educational attainment

Notes: Women born between 1939 and 1968. All states are included, but union territories are excluded.

3 – The education gradient

25In our subsample, 29% of women never went to school, 24.8% received primary education, 38.6% secondary education, [11] and less than 10% have at least some years of postsecondary education. Figure 1B indicates that childlessness at the country level exhibits a J-shaped relationship with years of education, as childlessness increases after 9–11 years of schooling. This becomes more salient when focusing on the youngest cohorts.

26In a cross-state perspective, childlessness rates exhibit only a weak linear correlation of 0.09 with average education when considering all states. However, this correlation becomes clearly positive and equals 0.35 when we exclude states representing less than 1% of the DLHS sample (Chandigarh, Uttarakhand, Delhi, Sikkim, Tripura, and Goa), which we do because they exhibit relatively extreme percentages and create outliers because of their small size. This suggests that states with high levels of education (presumably the most developed ones) also have higher childlessness, while those with low education levels have lower childlessness rates. A similar pattern can be seen at the district level, where childlessness is higher among highly educated (graduate and above) than less educated women in all districts of India (see Online Appendix Figure C1). Though the existence of opportunity-driven childlessness can be expected from the above descriptive findings, it remains unclear whether the effect of education is robust to other socioeconomic effects among childless women. The next section dispels this doubt using multivariate regression models.

1 – Methodology

28We use logistic regression models to study the determinants of a woman’s probability of ending her reproductive life childless, for which we use information about completed fertility to build the dichotomous variable “childlessness.” It takes a value of 1 if the respondent has no children and 0 otherwise. From our theoretical hypothesis (illustrated in Appendix Figure A.1), it appears that the relationship between education and causes of childlessness may be confounded by at least the caste system, spatial diversity, and cohort. Following the methodology of Wunsch (2007), we must control for these elements when estimating the relationship between education and childlessness.

29We consider two kinds of fixed effects: cohort and state. Eight cohorts were compiled for the study; the oldest women were born in 1953–1954 and the youngest in 1967–1968. The state fixed effects in the models consider all 35 states and union territories in India. [12]

30To take into account potential confounding factors and control variables, we have introduced independent variables in a stepwise manner. In the first step, we group the woman’s education level into four categories: no education, primary, secondary, and higher. In the second step, we do the same for the husband’s education. Although not made explicit in our causal diagram, we control for the variable “minor marriage,” which takes a value of 1 if the woman married before age 18 (the legal marriageable age in India). This allows us to control for prolonged exposure to the risk of pregnancy without introducing confounding variables (see Section III.2 for details). In the third step, we add cultural variables for religious affiliation (Hindu, Muslim, Christian, Sikh, Buddhist, or other) and caste category (Scheduled Caste, Scheduled Tribe, Other Backward Class, General, or Other). Instead of using state fixed effects in the fourth step, we add the variable “state development level” to categorize states into the most, least, and intermediately developed, based on their average years of schooling among women (averages are computed from the 2011 census). States where women average more than 7.4 years are categorized as developed, those where they average less than 5.1 years as least developed, and all others as intermediate states.

31Importantly, observations in our main regressions are not weighted with the sample weights offered by DLHS due to the difficulty of gathering information on how weights were computed in the first two waves. For this reason, we suspect that the comparability of data between waves cannot be guaranteed using weights. [13]

2 – The U-shaped effect of female education on childlessness

32Model 1 (Table 1) exhibits a U-shaped relationship between educational attainment and the probability of being childless at age 40+ after controlling for cohort and state fixed effects. This result still holds in Models 2 to 4 and confirms the main prediction of our theory. [14]

33Model 2 shows that the higher the husband’s education, the lower the probability of the woman remaining childless, with a significantly and strongly negative gradient. This means that the husband’s education clearly plays the role of insurance against poverty-driven childlessness among low-educated women, while it reduces the opportunity cost of having children for highly educated women.

34As discussed in Section I.2, fertility decisions are negotiated by both the wife and the husband. The negative effect of male education on female childlessness may be due to negotiation power reallocated in favor of men. Generally, men bear much lower child-rearing costs than women, making them less prone to not having children and so making them negotiate in favor of parenthood.

Table 1 - Determinants of the probability of ending reproductive life childless

Variables Model 1 Model 2 Model 3 Model 4 Woman’s education No education 1.361*** 1.219*** 1.214*** 1.123* Primary 0.945 0.972 0.970 0.944 Secondary education Ref. Ref. Ref. Ref. Higher 1.398*** 1.385*** 1.384*** 1.352*** Husband’s education No education Ref. Ref. Ref. Primary 0.728*** 0.741*** 0.778*** Secondary education 0.662*** 0.664*** 0.657*** Higher 0.539*** 0.542*** 0.543*** Minor marriage No Ref. Ref. Ref. Yes 0.537*** 0.539*** 0.582*** Religion Hindu Ref. Ref. Muslim 0.889* 0.812*** Christian 0.742*** 0.819*** Sikh 1.024 0.683*** Buddhist 0.801 0.772* Other 1.036 1.121 Caste General Ref. Ref. SC 1.047 1.083 ST 1.129* 0.981 OBC 0.981 0.891* Others 1.108 1.138 State development level Most developed Ref. Intermediate 0.872*** Least developed 0.760*** Fixed effects Cohort Yes Yes Yes Yes State Yes Yes Yes No Pseudo R2 0.012 0.021 0.021 0.012 BIC 33,585.197 33,365.756 33,454.349 33,374.480 Number of observations 158,112 158,112 158,112 158,112

Table 1 - Determinants of the probability of ending reproductive life childless

***p < .01. **p < .05. *p < .10.
Note: Odds ratios reported.

35For a childhood or teenage marriage, the greater exposure time to the risk of conception multiplies by 2 the chance of not being childless compared to a woman who marries later. Furthermore, being in a traditional arranged marriage may also increase the incentive or family pressure to conform to the traditional family system, which views having children as compulsory. In the DLHS data, the correlation between the respondent’s education and age at marriage is positive, but this does not prevent education from exerting a U-shaped influence on the probability of remaining childless.

36One may wonder why we have not divided age at marriage into more groups to capture late entry into marriage, as this could be important, considering that women who do not want to have children may also delay marriage as much as possible. We test this alternative in Online Appendix Table D1 and show that our results improve in terms of goodness of fit. Indeed, women who marry later are likelier to remain childless. That said, including this finer categorization for age of entry into marriage erases the U-shaped relationship between education and childlessness, only to replace it with a decreasing relationship, as having children and marrying late are both influenced by the economic opportunities offered to women. Thus, endogeneity issues arise when using age at marriage as a determinant of childlessness, making any result potentially spurious. This last criticism does not apply when considering only minor marriage because this kind of marriage is decided by the family and not the respondent herself. [15]

37The possibility remains that reverse causality exists between education and childlessness, as the women in these cohorts who did not want children may have focused on studying. However, even if the causality is reversed, both mechanisms nevertheless refer to opportunity-driven childlessness because not wanting children may lead a woman to avoid having them by focusing on education and job opportunities.

38In Model 3, caste has only a limited effect, as the probability of remaining childless is higher only among the STs when comparing them and all others to the general caste. This result may be initially surprising, but less so upon recalling that we control for state fixed effects. Indeed, discrimination against the lower castes has decreased over time and differs in space, which our fixed effects capture. [16]

39We find that both Christians and Muslims are less likely to remain childless than Hindus. As explained in Section I.2, which describes our theory, this could be due to pronatalist aspects of these religions and to their minority status in the country. The fourth model adds the state development level, which describes the state-specific average years of schooling among women. To avoid introducing multicollinearity, we eliminated the state fixed effect from that model. The finding is in line with the macro model of Poston and Trent (1982). At the micro level, education itself has an impact on the probability of being childless, and this effect is reinforced by a macro effect of development, proxied by average education at the state level.

40Compared to a woman with some years of secondary education, a woman having never gone to school is 1.21 times more likely to finish her fertile life childless, and a woman with some years of postsecondary education is 1.38 times more likely (Model 3). [17]