4.1 Effect of Gender on Maternal Time Input
We begin by investigating the impact of child gender on maternal input. Considering the ordinal nature of the outcome variable, the theoretical model can be expressed as follows:
![](data:image/png;base64,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)
Female is an indicator variable that takes a value of 1 if the child is a girl. To account for the systematic differences in household where girls and boys live, we include for predetermined characteristics (χ), such as child characteristics (e.g., age in months and birth order), parental characteristics (e.g., mother’s age, mother’s education), and household characteristics (e.g., indicators for caste and rural location, household size and household wealth index).
The identifying assumption is that the child’s gender at birth is random and exogenous, implying that families with girls and boys have similar gender preference. Any differences in maternal input can thus be attributed to the child’s gender. Since the gender variable (Female) is binary, the coefficient β captures the average difference in time investment between girls and boys, given the control variables.
The data collection process for the MICS-2014 survey spanned five months, during which mother-child interactions occurred in different months. Since that Nepalese women play a central role in agricultural and resource management (Goodrich et al., 2017), so the survey period can influence mother-child interaction. For example, mothers tend to be busier during the planting period (June-July) compared to the growing period (February-March). As the MICS-2014 collects information on mother-child interactions three days prior to the survey date, we include the survey month fixed effect to control for the seasonal characteristics that may directly affect mother-child interaction.
4.2 Effect Maternal Time Input on Child outcomes
Theoretical frameworks suggest that skill formation is a cumulative process, wherein current and past inputs, along with child endowment, interact to produce cognitive and non-cognitive outcomes. For instance, children who receive early human capital investments may benefit more from later investments (dynamic complementarities), or human capital in earlier periods may also influence human capital in later periods (self-productivity). Moreover, mothers may engage in reinforcing or compensatory investment behaviors, where inputs are influenced by the initial child endowment.
Since the MICS-2014 dataset does not provide information on past inputs or human capital level, we employ the contemporaneous specification method, as described by Todd & Wolpin (2007).[8] In contemporaneous specification method, current inputs are assumed to generate current skills, based on two assumptions: first, inputs do not accumulate over time, and second, inputs are not influenced by child endowment or initial human capital.
To estimate the effect of maternal input on child outcomes, we utilize the following contemporaneous linear model.
![](data:image/png;base64,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)
The outcome for the child, (і) , is a function of the contemporaneous input (i.e., maternal time investment) and a set of children, maternal, and household characteristics (Χ). The residual includes the effects of lagged inputs and human capital, child endowment, and measurement error. It also captures unobserved maternal or child characteristics. For instance, mothers may invest equally in both genders, but the quality of time investment may vary depending on the child’s gender. To obtain an unbiased estimate of (α1) the omitted factors are assumed to be orthogonal to maternal time inputs.
3.5 Results
This section documents our main results: the effect of gender on maternal time input and impact of maternal input on child outcomes. We also provide robustness checks and address selection issues.
3.5.1 Effect of Gender on Maternal Time Input
Table 3 presents the estimates of maternal time input by gender using an ordered probit model. In the first column, the outcome variable is the sum of all maternal inputs. In columns 2 and 3, total input is disaggregated into educational activities and structural activities. Across all three models, mothers invest equally in boys and girls; that is, the null hypothesis that the coefficient of Female is equal to zero is not rejected.
[Table 3 here]
Our results challenge the notion that mothers in Nepal prefer sons over daughters, as indicated by numerous studies (e.g., Hatlebakk, 2017; Koolwal, 2007; Rai et al., 2014). We argue that preferences are less binding for maternal time investment among young children, a type of input that is not regularly observed. Consequently, absence of son preference observed in mothers when they have greater agency. The son preference may not be an intrinsic preference. If mothers do discriminate based on a child’s sex, it is likely to occur later in life when social and cultural needs differ significantly. Thus, the son preference observed in mothers may be the result of social and cultural constraints that become more apparent later in life.
3.5.1.1 Selection Issues:
To estimate the relationship between gender and maternal time input, the underlying assumption is that the child’s gender at birth is exogenous and random. However, this assumption is valid only if there is no sex-selective abortion and no gender-specific stopping rules.
In a country with a strong son-preference culture and lower-income, factors like sex-selective abortion and gender-specific stopping rules can be unfavorable to the female children, resulting in a sample biased towards a higher number of male children compared to the natural rate. Regarding sex-selective abortion, the Nepal Census of 2011 reports a sex ratio at birth of 1.07, which falls within the biologically normal range of 103-107 (Self, 2015). Additionally, criminalized sex-selective abortion complicates the availability and use of abortion, particularly for low income and rural households, which constitute the majority of our sample population. Therefore, while sex-selective abortions may have a weak impact on our estimates, they are unlikely to significantly influence our findings.
Moreover, the prevalence of a male-biased fertility-stopping rule implies that if a male child is born, the family may decide to stop having more children, whereas if a female child is born, the family is more likely to continue having more children. This leads to female living in larger households with more siblings as gender is correlated with family size. As family size increases, the resources per capita decrease, which includes maternal time input. The birth order does not significantly differ between boys and girls. Therefore, the male-biased fertility-stopping rule is unlikely to have a significant impact on our finding.
To address sex-selective abortion and gender-specific stopping rules, we estimate the gender effect on a sample of firstborn and second-born children in Nepal. Previous studies suggests that sex-selective abortion and gender-specific stopping rules are less influential among earlier births (e.g., Bharadwaj et al., 2013). Firstborn children are generally assumed to be more random in terms of gender assignment (e.g., Baker & Milligan, 2016; Choi & Hwang, 2015).[9]
Given the higher fertility rate in Nepal and the sample size, we limit our analysis to first and second born children. The results presented in Table A.2 are consistent with the main findings from Table 3, indicating that mothers invest equally in both sexes. Additionally following the common practice in the literature of restricting the sample to the firstborn (e.g., Baker & Milligan, 2016), Table A.3 in the Appendix reports the effect of gender on maternal input among the firstborn children. Once again, the results are in line with the findings in Table 3.
Finally, we argue that under-five mortality does not create a sample selection issues due to a higher proportion of female mortality. The common assumption is that female children should experience higher under-five mortality due to lower investment in health and nutrition. However, under-five mortality for male children is greater compared to female children (29.9 versus 34.3 per 1000 live births) (World Bank, 2018). Therefore, the selection issues arising from under-five mortality does not contaminate our estimates.
3.5.1.2 Heterogeneity
Our results could be driven by maternal characteristics, particularly those of rural and less educated mothers. Educated mothers, for example, tend to spend more time in childcare as they have fewer children (Guryan et al., 2008), while rural mothers may allocate more time to their sons (Barcellos et al., 2014). In Tables A.4 and A.5, we report the results for rural households, and mothers with less than or equal to five years of formal schooling. These findings are consistent with the main results, indicating that neither rural mothers nor less educated mothers differentiate their treatment between girls and boys.
3.5.2 Effect of Maternal Time Investment on Child Outcomes
Table 4 presents the impact of maternal time investment on child outcomes. In the first column, the child’s outcome is a binary variable equal to one if children are developmentally on track in at least three of the four domains as described by UNICEF. The results indicate that maternal time input increases the likelihood of a child being developmentally on track. In column 2, we use the composite score of the ECD Index as a measure of child development. Using linear regression, we find a strong positive relationship between child outcomes and maternal inputs. In columns 3 and 4, we disaggregate the child outcomes into cognitive and behavioral outcomes, both showing that an increase in the mother’s input improves the probability of better outcomes. Overall, these results provide strong evidence that maternal time input is an important determinant of child outcomes.
[Table 4 here]
We further provide evidence that the type of activities, whether structural or educational, plays a role in skill formation. In line with previous (e.g., Fiorini & Keane, 2014; Hsin & Felfe, 2014), we find that maternal cognitive inputs only impact cognitive outcomes and not behavioral outcomes. Additionally, structural input influences behavioral outcome but not cognitive outcomes (Table 5). These findings highlight the significance of the type of investment in shaping various skill formations.
[Table 5 here]
Finally, in Figures 1, 2, and 3, we illustrate the predicted probabilities of child outcomes based on maternal and child characteristics. Employing the model from Table 4, Column 1, Figure 1 shows the predicted probabilities of a child’s outcome by age and gender. It indicates that a child’s outcome increases with age but at a decreasing rate, implying that maternal investment is more crucial when children are younger.
[Figure 1 here]
Figure 2 demonstrates that the predicted probabilities increase with a mother’s years of schooling, indicating that children born to well-educated mothers tend to have better outcomes than those born to less educated mothers.
[Figure 2 here]
Figure 3 categorizes the mother’s time investment into low, medium, and high categories. Then, we estimate the predicted probabilities of a mother’s time input by the child’s age. The results indicate that compared to mothers who invest less time, the impact of maternal input on cognitive outcomes decreases for both sexes as the child ages, but it increases for mothers who invest more time. Interestingly, for high-investing mothers, the difference in impact between boys and girls gradually diminishes with the child’s age.
[Figure 3 here]
3.6 Discussion
Our main results indicate that, based on observable characteristics, Nepalese mothers do not discriminate in time investment between preschool boys and girls. Our finding is at odds with previous research in Nepal, which consistently demostrates differential parental investment by gender (e.g., Hatlebakk, 2017; Koolwal, 2007; Rai et al., 2014).
The obvious question arises: why would Nepalese mothers invest equally in preschool boys and girls? One possible explanation is a shift in maternal preferences, which often occurs with increase in per capita income or economic transitions toward the service industry (Jayachandran, 2015). Economically, such changes suggests that the relative productivity of females may have increased, leading to a higher return on investment in girls and incentivizing mothers to invest more in their daughters. However, this does not seem to be case in Nepal. The economy is still largely dominated by the agricultural sector, and per capital income remains relatively low at $787 (World Bank, 2018).
An alternative explanation for why mothers may spend equal time with boys and girls is that mother pass on their own socialization to their daughters, teaching them how to perform gender-specific roles and thus upholding societal expectation of a gendered division of labor. If this is the case, we would expect mothers to devote more time to structural activities compared to educational activities. However, our findings contradict this premise, as mothers are equally involved in both types of activities for either son or daughter.
Another possibility is that mothers are aware of the gender-specific needs of their children. Since fathers and grandparents may spend more time with boys compared to girls, mothers may invest less time, or have fewer hours, with sons compared to daughters. If mothers are concerned that their daughter do not receive enough time in educational activities, we would expect higher maternal investment in educational activities. However, as stated earlier, mothers were not more likely to spend additional time in educational activities based on the child’s gender.
Moreover, from a pragmatic standpoint, mothers should theoretically prefer boys over girls as they enjoy greater wellbeing after the birth of a son than a daughter (Jayachandran, 2015). If this were the case, mothers would invest less in girls compared to boys in types of activities. Again, this contradicts our findings. Finally, since preferences are not exogenous, markets, economic institutions, and social norms can shape a mother’s values, tastes, and personalities, including how they invest in young children. With changing social and legal circumstances, it is possible that Nepalese mothers may be treating boys and girls equally, at least among preschool children. Maternal preferences may not be solely based on intrinsic preference, but rather influenced by social and cultural constraints. Therefore, when these constraints are less binding (e.g., during preschool ages and in terms of time investment), mothers may invest equally in boys and girls. However, this does not imply that mothers will not treat their children differently in adulthood.
We also find that maternal time investment is a significant predictor of child outcomes. The predicted probabilities in Figures 1, 2 and 3 indicate that the effect of maternal investment decreases as the child ages. This finding can have critical policy implications, as mothers play a pivotal role in helping young girls catch up with young boys in later life outcomes. With changing socio-economic conditions, female labor force participation is likely to increase in the coming decade, potentially leading to a shift in childcare responsibilities from mothers to commercial care providers or other family members. However, as seen in previous studies where parents invest differently in education (e.g., sending boys to high-quality private schools compared to their girl siblings) (Khanal, 2018), similar investment trends may continue in childcare. In such a scenario, the gap between boys and girls could widen further.
3.7 Conclusion
In contrast to previous evidence indicating strong son-preferences in Nepal, our study finds no evidence that mothers differentially treat boys and girls, especially in terms of maternal time investment among preschool children. Additionally, we do not observe gender discrimination on educational and structural activities by mothers. We argue that since mothers have higher autonomy in time investment and cultural or economic incentives are less binding in childhood, maternal preferences may not stem from intrinsic preference for a particular gender, but rather from social and cultural constraints. This finding is particularly significant because maternal time input is an important determinant of both cognitive and behavioral outcomes.
Our study could be improved in several ways. Firstly, our data lacks information on the specific amount of time (in minutes and hours) mothers spends on childcare or the quality of mother-child interaction. Most research typically uses time-use surveys. Such surveys for developing countries are not readily available. Secondly, we could improve our estimates by incorporating lagged investment models to contemporaneous specification model for skill accumulation. Thirdly, our identification strategy relies on assuming that gender at birth is random, which is only plausible if there is no sex selective abortion. To address this concern, it would be appropriate to use a sample of firstborn children, as this is a common practice in studies aiming to account for potential sex-selective biases. Lastly, our study may be susceptible to survey biases. Specifically, when mothers are asked about their time investment in boys and girls, they may feel pressured to provide socially desirable answers, potentially leading to an overestimation of equal treatment between genders.