Variability in Reproductive Choices: A Comprehensive Analysis of Women’s Working Status and Fertility Behavior in Pakistan

Abstract

The relationship between women’s working status and fertility behavior has been a topic of interest for researchers and policymakers. The societal shifts over time, particularly the increasing participation of women in the workforce, have transformed traditional roles. Women, once primarily perceived as caregivers, are now assuming roles of economic independence. This transformation prompts a re-evaluation of the traditional association between women’s working status and fertility behavior. This study aims to investigate the impact of women’s working status on fertility behavior using a multistage stratified sampling design. A total of 408 women aged 15 to 49 years were recruited from 2 strata: working and non-working women. The data were collected through face-to-face interviews using a structured questionnaire. Descriptive statistics, cluster analysis, and generalized additive models were used for in-depth analysis of the dataset. An examination of fertility patterns indicates that, on average, working women bear 2.90 live children, while their non-working counterparts have an average of 3.52 children. Stillbirth was reported in 13% of housewives and 15.1% of working women. However, further analysis revealed that the relationship between women’s employment status and fertility behavior varied depending on Social and Cultural Norms, Reproductive Rights, Workplace Policies, Economic Independence, Age, and Life Stage. Our findings suggest that promoting access to family-friendly policies and services, as well as challenging gender norms and cultural values, could help address the impact of women’s employment on fertility behavior.

• What do we already know about this topic?

• Women’s workforce participation has undergone a profound evolution in recent decades in Pakistan.

• How does your research contribute to the field?

• This study compares the fertility levels of working and non-working women in District Peshawar.

• What are your research’s implications toward theory, practice, or policy?

• Create specialized peer support groups and counseling services tailored to the unique needs of both working women and housewives.

Introduction

Women’s participation in the workforce has undergone a significant transformation over the past few decades. With more women joining the workforce, their role in society has shifted from being primarily caregivers to becoming economically independent. This change has led to a shift in attitudes toward family planning and fertility behavior. In recent years, many studies have explored the impact of women’s working status on their fertility behavior.^1,2

According to the United Nations Population Fund, the global fertility rate has decreased from 4.7 children per woman in the 1960s to 2.4 children per woman in 2019. This decline in fertility can be attributed to various factors such as improvements in education, access to family planning services, and changing societal norms. ³ However, the role of women’s working status in fertility behavior remains a topic of debate.

Numerous studies have explored the relationship between women’s employment and their fertility behavior. Some studies suggest that women who work outside the home have fewer children compared to women who do not work.^4,5 However, other studies argue that women’s employment has a positive impact on their fertility behavior, especially in countries where childcare facilities are available.^6,7

In this article, we aim to explore the impact of women’s working status on their fertility behavior. We reviewed the existing literature on this topic and analyze the factors that influence the relationship between women’s employment and fertility behavior. The findings of this study have significant implications for policymakers, employers, and families who wish to understand the relationship between women’s employment and fertility behavior.

Literature Review

The impact of women’s working status on fertility behavior has been a topic of interest for researchers and policymakers for many years. The relationship between these 2 factors is complex and multifaceted, and has been examined from various perspectives in the literature.

Several studies have reported that women’s employment status is negatively associated with fertility behavior, indicating that working women tend to have fewer children than non-working women.^8,9 One possible explanation for this is that working women have less time and energy to devote to childbearing and child-rearing, which can reduce their fertility rates. ¹⁰ Furthermore, women engaged in demanding occupations may encounter obstacles in accessing family-friendly policies and services, including parental leave and affordable childcare, thereby placing constraints on their reproductive choices. ¹¹

However, other studies have found that the relationship between women’s employment status and fertility behavior is more nuanced and varies depending on several factors, such as age, education, and socioeconomic status. ¹² For example, some studies have reported that the negative relationship between women’s employment status and fertility behavior is stronger for highly educated women than for less-educated women. ⁶ Similarly, women who have access to high-quality childcare facilities and flexible work arrangements may be more likely to combine work and family responsibilities and have more children than those who do not have these resources. ¹³

Recent studies have also highlighted the importance of considering the gender norms and cultural values that shape women’s decisions regarding fertility behavior and employment. ¹⁴ In some societies, women who prioritize their careers over motherhood may face stigma and discrimination, which can discourage them from having children. ¹⁵ On the other hand, in societies where women’s economic empowerment and gender equality are promoted, women may be more likely to pursue both work and family goals, leading to higher fertility rates.^16,17

In conclusion, the relationship between women’s working status and fertility behavior is a complex and multifaceted issue that has been examined from various perspectives in the literature. While some studies have reported a negative association between these factors, others have found more nuanced relationships that depend on several individual, cultural, and contextual factors. Future research should continue to explore these factors and their interrelationships to inform policy and practice aimed at promoting both women’s employment and reproductive rights.

Aim and Objectives

The objectives of the study are as follows:

• To compare the fertility levels of working and non-working women in District Peshawar, Pakistan.

• To identify the social and cultural factors that influences the fertility behavior among working and non-working women.

Methodology

Sampling Design

To investigate the impact of women’s working status on fertility behavior, we used the 2-stage stratified sampling technique. The stratification was done based on the working status of women in the district Peshawar that is, working women and housewives. In the first stage of our sampling design, we employ a random selection process to draw a sample of 10 union councils. It’s important to note that District Peshawar is administratively organized into 4 towns, encompassing a total of 92 union councils. In the second stage, women were selected randomly from the 2 specified strata. The equal allocation technique was employed to divide the samples from the strata of working women and housewives. The relevant information regarding the women’s population and their working status was obtained from local government records and relevant organizations.

Data Enumerators

The data collection process involved trained enumerators who specialized in conducting face-to-face interviews. These enumerators were selected based on their proficiency in communication and interviewing skills, as well as their familiarity with the local context of District Peshawar, Pakistan. Enumerators were assigned specific geographic areas within District Peshawar to conduct interviews.

Interview Process

The structured questionnaire was administered to collect data on various variables, including fertility behavior and working status. Enumerators meticulously recorded the responses provided by participants, ensuring accuracy and completeness of the data. Structured face-to-face interviews were employed to gather detailed information on fertility behavior and working status. The use of face-to-face interviews allowed for a comprehensive understanding of the participants’ experiences, and any necessary clarifications were addressed immediately. To enhance data accuracy, validation $\mathrm{n}=\frac{\mathrm{N}}{1+{\mathrm{Ne}}^2}$ checks and quality assurance measures were implemented. Incomplete or inconsistent responses were clarified through follow-up communication with participants.

Informed Consent

Ethical guidelines were followed throughout the data collection process. Informed consent was obtained from each participant, clarifying the purpose of the study, the voluntary nature of participation, and the confidentiality of the collected information.

Sample Size

To estimate the required sample size for this study, we used the Taro Yamane formula ¹⁸ which takes into account the population size and the desired level of precision. The Taro Yamane formula for estimating the sample size is given by:

$$\mathrm{n}=\frac{\mathrm{N}}{1+{\mathrm{Ne}}^2}$$ (1)

where N is the population size, e is the desired level of precision, and n is the sample size. As per the local government authorities, the female population of Peshawar is recorded as 2 049 157.

$$n=\frac{2049157}{1+2049157\mathrm{*}{0.05}^2}\approx 400$$

Hence, a sample size of at least 400 women has been chosen to guarantee accurate results for the study. Subsequently, the sample is divided into 2 strata, working women and housewives, utilizing the equal allocation method.

Statistical Methods

Descriptive statistics

Descriptive statistics are a crucial tool for researchers and writers to summarize and present data in a clear and understandable manner. These statistics can be used to describe patterns and trends in the data, and to highlight patterns in variables. Overall, the use of descriptive statistics can help to provide a clear and accurate picture of the data, and can help readers to better understand the findings of the study. The mean, standard deviations, frequency, percentages, and graphs were used to summarize and presents the dataset.

Generalized additive model

To further explore the relationship between women’s employment status and fertility behavior, this study also employed a generalized additive model (GAM). GAMs are a powerful statistical tool that can be used to model complex relationships between variables in an article. GAMs are an extension of traditional linear models that allow for non-linear relationships between the response variable and the predictor variables. This makes GAMs particularly useful in situations where traditional linear models may not adequately capture the underlying complexity of the data.

In this study, we model the number of live children considering Duration of Marriage, Husband Age, Women Age, and Women Age at Marriage as covariates. The model can be written as:

$$y=\alpha +s\left(x1\right)+s\left(x2\right)+s\left(x3\right)+s\left(x4\right)+g$$ (2)

where y represents number of live children, $x_i$ represents Duration of Marriage, Husband Age, Women Age, and Women Age at Marriage respectively, g is the random error term, and s is the non-parametric smoothing term. The model is fitted separately for housewives and working women.

K-means clustering

Additionally, k-means clustering was utilized in this study to identify patterns of fertility behavior among working and non-working women. The k-means clustering is a commonly used technique for grouping data points into distinct clusters based on their similarity. The usual k -means clustering algorithm is used to identify subgroups of women with similar fertility behaviors, based on children deaths, still births, abortions, live children, and monthly income. The dendrogram is used to identify the optimal number of clusters in the data-set. The Gower distance function is used to compute the distances between data objects.

Validity and Reliability of Questionnaires

Ensuring the questionnaire’s reliability in this study was a critical factor aimed to enhance the validity of our findings. Several measures were implemented to uphold the questionnaire’s reliability. Firstly, rigorous pilot testing was conducted with a diverse group of participants to identify and rectify any ambiguities, redundancies, or potential sources of confusion in the questions. This iterative process helped refine the questionnaire for clarity and coherence.

To ensure validity of the data collected, we calculated the Cronbach’s alpha coefficient, which is a measure of the internal consistency of a set of questionnaire items. A high Cronbach’s alpha coefficient indicates that the items are highly correlated, and thus are measuring the same underlying construct. The Cronbach’s alpha coefficient was found to be .876, indicating a high degree of internal consistency of the questionnaire items. This suggests that the data collected in this study are reliable and valid, and can be used to draw meaningful conclusions about the relationship between women’s working status and fertility behavior.

Data analysis was conducted using the R statistical software. The R environment provides a comprehensive suite of tools for statistical computing and graphics, facilitating robust and reproducible analyses.

Results

Descriptive Statistics

The word cloud presented in Figure 1 depicts the distribution of professions within the dataset. The professions commonly pursued by women in Peshawar can be explained in the context of broader socio-economic and cultural trends in Pakistan. For instance, teaching is a popular profession among women in Peshawar and Pakistan as a whole, as it is seen as a socially acceptable and financially rewarding profession that allows women to contribute to the education and development of future generations. ¹⁹ Furthermore, over the years, the field of journalism in Pakistan has seen increasing participation and representation of women. Women have made significant strides in this profession, breaking barriers and stereotypes to pursue careers as journalists, reporters, editors, and media consultants. Similarly, nursing and medicine are also popular professions among women in Peshawar, reflecting the high demand for healthcare services in the region and the growing need for female healthcare workers to provide culturally sensitive and gender-appropriate care. Administration, social work, and journalism are other professions that are increasingly being pursued by women in Peshawar, reflecting the growing demand for female professionals in these fields, as well as the need for gender-sensitive policies and services that address the unique needs of women and girls in the region. ²⁰ In recent years, there has also been a growing trend of women pursuing careers in traditionally male-dominated fields, such as law, engineering, and computer science, reflecting the changing social norms and attitudes toward gender roles and women’s empowerment. ²¹ This trend has been supported by government initiatives to promote women’s education and employment, as well as by the increasing availability of scholarships, internships, and mentoring programs for women in these fields.

Figure 1.

Professions of working women in Peshawar.

The study involved a sample of 200 housewives and 208 employed women. Table 1 presents descriptive statistics for working and non-working women across various variables. The mean age for both working and non-working women is quite similar, with working women having a mean age of 34.88 years and non-working women having a mean age of 34.91 years. Housewives have a slightly lower mean husband age of 36.57 years compared to working women who have a mean husband age of 39.61 years. On average, working women get married at a later age, with a mean age at marriage of 25.76 years. This could be because they may prioritize their careers and delay marriage until they feel more established in their jobs or have achieved certain personal goals. In contrast, non-working women have a lower mean age at marriage, averaging at 21.91 years. This may suggest that non-working women tend to marry at a younger age, possibly due to different life circumstances or cultural factors.

Table 1.

Descriptive Statistics for Working and Non-Working Women.

variable	Working		Non-working
	Mean	Std. dev.	Mean	Std. dev.
Age	34.88	8.18	34.91	9.65
Husband Age	36.57	9.21	39.61	9.61
Age at Marriage	25.76	4.90	21.91	4.30
Live children	2.90	1.45	3.52	2.13
First gap	1.91	1.00	1.44	0.56
Second gap	1.83	0.81	1.57	0.57
Third gap	2.07	0.82	1.56	0.57

Working women, on average, have fewer live children (with a mean of 2.90) than non-working women (with a mean of 3.52), indicating that non-working women typically have a higher average number of children. The average time interval between the first and second child born (referred to as the First gap) is slightly longer for working women, standing at 1.91, as opposed to non-working women, who have an average of 1.44. Working women also exhibit a slightly lengthier mean duration between the second and third child (referred to as the Second gap) at 1.83, in contrast to non-working women, who have an average of 1.57. Furthermore, when considering the mean duration between the third and fourth child (termed the Third gap), working women again have a slightly higher average duration of 2.07, as compared to non-working women, who have a mean duration of 1.56.

The observation that the average number of children born to working women is lower than that of non-working women aligns with findings from prior research on this subject. For example, several studies are being conducted that found that women’s labor force participation was negatively associated with their fertility rates in several countries, including the United States, Canada, and Europe.^{22 -24} Similarly, a meta-analysis of 60 studies by Adsera and Menendez ²⁵ found a negative relationship between women’s labor force participation and fertility rates across countries and regions. These findings suggest that women’s employment may have a dampening effect on their fertility behavior, possibly due to factors such as the opportunity cost of having children while working, the lack of family-friendly policies and services, and gender norms that favor men’s careers over women’s reproductive roles.

Figure 2 illustrate the factors toward family environment and awareness regarding family planning. A slightly higher percentage of housewives in the study sample reported marrying within their own caste. In some cases, marrying within the same caste may lead to greater adherence to traditional family values and expectations, which can influence fertility behavior. Couples may feel more obligated to fulfill societal expectations of having children and may face social pressure to conceive and raise a larger family. This could result in higher fertility rates among couples who marry within their caste. ²⁶ Additionally, marrying within the same caste can foster a sense of social cohesion and support within the community. There may be shared values, beliefs, and practices related to family planning and reproductive health, which can impact fertility decisions. This can include cultural norms that prioritize early and frequent childbearing or discourage the use of modern contraceptive methods.^27,28 Exposure to different cultural perspectives in inter-caste marriages leads to greater awareness of family planning methods and a desire for smaller family sizes in working women.

Figure 2.

Trends of relationship dynamics and reproductive choices in working and non-working women.

Figure 3 illustrates the proportion of stillbirths in housewives and working women. The proportion of stillbirths is slightly higher in working women as compare to the housewives. The research exploring the connection between maternal employment and the risk of stillbirth revealed that women engaged in physically demanding occupations faced a higher risk of stillbirth compared to those who did not work. This elevated risk was attributed to the physical strain and occupational hazards associated with certain types of work. Nonetheless, it is important to consider that factors such as access to healthcare, maternal education, and socioeconomic status may have more substantial influence in addressing the issue of stillbirth. ²⁹

Figure 3.

Still births among housewives and working women.

Table 2 demonstrates the Chi-square test of association between the working status of women and various fertility related factors. It was found that the duration of marriage till the first child born is significantly associated with the working status of the women. Many studies have found that women who are employed tend to delay having their first child compared to non-working women. This trend has been observed in both developed and developing countries. ³⁰ The availability of job flexibility and supportive work environments can also influence the timing of the first child’s birth. Women who have access to family-friendly policies, such as flexible work hours and paid parental leave, may find it easier to balance work and family responsibilities. ³¹ Higher levels of education and career aspirations are often associated with delayed childbearing among working women. Education and career opportunities may lead women to prioritize personal and professional goals before starting a family. ³² Cultural and societal norms regarding women’s roles in the family and workforce may influence their decisions about when to start a family. Societal expectations may differ across countries and cultures, affecting the timing of childbearing. ⁸ Similarly, the duration since the last child was born is also associated with the working status of women. Working women may tend to delay having their last child and opt for longer birth intervals between their children. This decision is often related to the desire to balance work and family responsibilities, and career aspirations. ³³ Working women, especially those in formal employment, may have better access to family planning resources and healthcare, which can influence their reproductive decisions and family size. ³⁴ Women in formal employment might have better access to antenatal care (ANC) services due to employer-provided benefits, maternity leave, and health insurance coverage. This can positively impact their utilization of ANC. ³⁵ Working women who are well-informed about the importance of ANC and maternal health may actively seek out services despite their employment status. ³⁶ Informally employed or self-employed women might experience challenges accessing ANC due to financial constraints, lack of maternity benefits, and absence of paid leave. ³⁷ The association between working status and the methods of family planning could be related to access to healthcare and information. Working individuals may have more access to healthcare facilities and family planning information due to employer-provided health benefits or greater financial resources. The socioeconomic factors, including employment status, significantly influenced the choice of contraceptive methods. Working women in rural India were more likely to opt for modern contraceptive methods due to increased access and knowledge. ³⁸ The highly significant association between working status and the adoption of family planning may be due to various factors. Working women may have more autonomy and decision-making power regarding family planning, as they may contribute to the household’s financial resources. This could lead to a higher likelihood of adopting family planning methods. Working women have more say in family planning choices, leading to higher adoption rates. ³⁹

Table 2.

Chi-square Test of Association Between Working Status and Various Factors.

S. No.	Variables	Chi-Square	P-values
1	Duration of marriage till first child born	36.179	.000**
2	Duration since the last child born	72.075	.000**
3	Heard of family planning	20.233	.007**
4	Methods of family planning	18.218	.009**
5	Visit the family planning center	9.082	.014*
6	Adoption of family planning	178.115	.000**

^*Significant at 5%. **Significant at 1%.

Cluster Profiling

Figure 4 presents the dendrogram, a visual representation commonly used in cluster analysis to determine the optimal number of clusters for grouping data points based on some proximity measure. Each leaf node represents an individual data point, and the branches represent the merging of clusters as the algorithm progresses. The height at which branches merge indicates the level of similarity between clusters or data points. In this specific dendrogram, it appears that the data has been organized into 2 distinct clusters. This division suggests that the data points have been categorized into 2 separate groups that exhibit similar characteristics or patterns.

Figure 4.

Dendrogram for optimal number of clusters in data.

Figure 5 represents the cluster solution of data-set. The red dot on the graph symbolizes the mean of the overall data for the respective variable, providing a reference point for the entire dataset. Conversely, the bar on the graph represents the mean within the cluster for the same variables, offering insights into the central tendency specific to each identified cluster. This visual representation facilitates a comparative understanding of how each cluster’s mean for the variables compares to the overall dataset mean. Cluster-1 represents households facing health challenges and potentially lower socioeconomic conditions, contributing to higher child deaths and stillbirths. The lower incidence of abortions suggests that these households might have limited access to reproductive healthcare or family planning services. Cluster-2 seems to represent households with higher socioeconomic status, given the higher average income and abortion rates. The higher abortion rate might be associated with better access to reproductive healthcare and family planning services, allowing families in this cluster to make choices aligned with their circumstances.

Figure 5.

Cluster solution of data-set.

Table 3 provides a comprehensive profiling of clusters based on the working status of women, offering insights into the distinct characteristics and patterns within each cluster. In Cluster 1, there are a total of 163 individuals classified as housewives, and 123 individuals classified as working women. In Cluster 2, there are a total of 45 individuals classified as housewives, and 69 individuals classified as working women. This information is useful for understanding the distribution of working status within each cluster. This suggests that mostly the working women suffer through the abortions, while housewives faces still births and children deaths. Households with higher monthly income (which might be associated with Cluster 2) are more likely to have better access to healthcare facilities. This improved access could contribute to addressing the issues of stillbirths and child deaths, as these households can potentially avail better medical care and support. Similarly, households with higher socio-economic conditions (such as those in Cluster 2) may prioritize their careers. This could lead them to delay childbearing, resulting in lower fertility rates. Career-focused individuals might choose to have fewer children to focus on their professional aspirations. Furthermore, another aspect is that women in households with higher socio-economic status might opt for abortions at early stages. This might be due to various reasons, including family planning choices, health considerations, and the availability of medical facilities for safe abortions.

Table 3.

Profiling of Clusters According to the Working Status of the Women.

Cluster	Working-status
	Housewives	Working women
1	163	123
2	45	69

GAM for Housewives

The GAM model is used to explore the relationship between the number of live children a woman has and several predictor variables. The model uses smoothing functions to capture potentially non-linear relationships between the predictor variables and the outcome. Table 4 demonstrates the GAM for predicting the live number of children for housewives. The adjusted R-squared suggests that around 81.5% of the variability in the outcome is explained by the predictors. Furthermore, Deviance explained provides additional information about the goodness-of-fit of the model. It suggests that approximately 75.2% of the variability in the outcome is explained by the model. GCV (Generalized Cross-Validation) represents the Generalized Cross-Validation score, which is a measure of the model’s predictive accuracy. A lower GCV score indicates better predictive performance. The smooth term representing the relationship between the duration of marriage and the outcome variable is statistically significant. The P-value is extremely small (1.96e−06), suggesting a strong evidence of a non-linear relationship. The smooth term representing the relationship between the husband’s age and the outcome variable is statistically significant. The P-value of .00209 suggests that there is evidence of a non-linear relationship between the husband’s age and the outcome. The smooth term representing the relationship between the age at marriage and the outcome variable is not statistically significant. The P-value of .55323 suggests that there is insufficient evidence of a significant non-linear relationship. The smooth term representing the relationship between the woman’s age and the outcome variable is not statistically significant. The P-value of .42220 suggests that there is limited evidence of a significant non-linear relationship.

Table 4.

GAM for Predicting Number of Live Children for Housewives.

Parametric coefficients	Estimate	Std. error	t	Pr(>|t|)
(Intercept)	3.5028	0.1108	31.61	2e−16**
Approximate significance of smooth terms:
	edf	Ref.df	F	P-value
s(Duration of m marriage)	3.958	4.901	7.958	1.96e−06**
s(Husband age)	7.686	8.539	3.399	.00209**
s(Age at marriage)	1.000	1.000	0.353	.55323
s(Women age)	1.000	1.000	0.647	.42220
R-sq.(adj) = .815 Deviance explained = 75.2% GCV = 2.4181

^*Significant at 5%. **Significant at 1%.

Figure 6 represents the GAM for housewives. The model (figure in the top-left corner) suggested that there is often a positive association between longer marital duration and higher fertility in housewives. Early years of marriage provide couples with more time to plan and raise children, potentially leading to larger family sizes. This positive relationship between marital duration and fertility is particularly evident in societies where early marriage is prevalent. In the initial years of marriage, couples might delay childbearing due to educational

Figure 6.

GAM model for housewives.

GAM for Working Women

The GAM model is used to explore the relationship between the number of live children a woman has and several predictor variables. The model uses smoothing functions to capture potentially non-linear relationships between the predictor variables and the outcome. Table 5 demonstrates the GAM for predicting the live number of children for housewives. The adjusted R-squared of .838 suggests that around 83.8% of the variability in the outcome is explained by the predictors. Furthermore, Deviance explained provides additional information about the goodness-of-fit of the model. It suggests that approximately 71.8% of the variability in the outcome is explained by the model. GCV represents the Generalized Cross-Validation score, which is a measure of the model’s predictive accuracy. A lower GCV score indicates better predictive performance. The smooth term representing the relationship between the duration of marriage and the outcome variable is statistically significant. The P-value is extremely small (1.96e−06), suggesting a strong evidence of a non-linear relationship. The P-value of .11620 is larger than .05, suggesting that there is limited evidence to conclude a significant non-linear relationship. The smooth term representing the relationship between the age at marriage and the outcome variable is statistically significant. The P-value of .00753 suggests that there is evidence of a significant non-linear relationship. The smooth term representing the relationship between the woman’s age and the outcome variable is not statistically significant. The P-value of .71041 is larger than .05, suggesting that there is insufficient evidence of a significant non-linear relationship. Smooth term representing the relationship between the age at marriage and the outcome variable is statistically significant. The P-value of .00753 suggests that there is evidence of a significant non-linear relationship. The smooth term representing the relationship between the woman’s age and the outcome variable is not statistically significant. The P-value of .71041 is larger than .05, suggesting that there is insufficient evidence of a significant non-linear relationship.

Table 5.

GAM for Predicting Number of Live Children for Housewives.

Parametric coefficients	Estimate	Std. error	t	Pr(>|t|)
(Intercept)	2.866	0.100	28.65	2e−16**
Approximate significance of smooth terms:
	edf	Ref.df	F	P-value
s(Duration of marriage)	4.062	4.901	7.958	1.96e−06**
s(Husband age)	1.067	1.091	2.498	.11620
s(Age at marriage)	1.000	1.000	7.352	.00753**
s(Women age)	1.230	1.424	0.157	.71041
R-sq.(adj) = .838 Deviance explained = 71.8% GCV = 1.5782

^*Significant at 5%. **Significant at 1%.

Figure 7 represents the GAM for working women. There exists a nonlinear relationship between the duration of marriage and the number of children (shown by figure in top-left corner), with higher fertility in the initial years and a plateau or decline as the marriage continues. This phenomenon is known as the “duration-parity” relationship. The duration of marriage can influence family planning decisions. Couples may choose to space their children based on the duration of their marriage, aiming for a specific age gap between siblings. Results of the model (shown in bottom-left figure) indicates that delayed marriage (marrying at a later age) often leads to a lower number of children for women. Working women usually marry at a later age and have fewer childbearing years available to them, which naturally results in a reduced number of births. ⁴⁰

Figure 7.

GAM model for working women.

Discussion

The study encompassed a sample comprising 200 housewives and 208 employed women, offering a diverse representation of both non-working and working individuals. This balanced sampling strategy allows for a comprehensive exploration of the factors influencing fertility behavior across different employment statuses. The inclusion of both groups provides a nuanced understanding of the dynamics surrounding family planning choices and reproductive health. The observed disparity in the average number of children born to working women, notably lower than their non-working counterparts, resonates with the evolving landscape of women’s roles in contemporary society. The traditional perception of women primarily as caregivers has shifted, with women now actively engaging in roles of economic independence. However, the nuanced analysis of the relationship between women’s working status and fertility behavior reveals a multifaceted interplay influenced by diverse factors.

Our findings highlight the importance of considering Social and Cultural Norms, Reproductive Rights, Workplace Policies, Economic Independence, and Age and Life Stage dynamics in understanding the intricacies of this association. Social and cultural norms continue to exert a profound influence on family planning decisions, even as women take on more diverse roles. Reproductive rights emerge as a key determinant, with working women exhibiting distinctive patterns influenced by increased decision-making autonomy. Workplace policies play a crucial role in shaping the relationship, emphasizing the need for family-friendly initiatives to support the dual responsibilities of career and family.

The observed variations underscore the complexity inherent in the impact of women’s employment on fertility behavior, necessitating a tailored approach in intervention strategies. Our study proposes that promoting access to family-friendly policies and services, coupled with challenging gender norms and cultural values, could be instrumental in mitigating the perceived impact of women’s employment on fertility behavior.

Limitations

One limitation of this sample design is that it assumes that women are evenly distributed across the strata of working and non-working women. However, in reality, there may be variations in the distribution of women across these strata. Additionally, the sample may not be representative of the entire population, especially if there are regional or socioeconomic differences in the distribution of women across the strata. Finally, the data used for this study are likely self-reported by participants. Self-report data can be subject to recall bias or social desirability bias, where participants may not provide completely accurate information about sensitive topics such as family planning and working status.

Conclusion

In conclusion, this study examined the impact of women’s working status on fertility behavior, using a 2 stage stratified sampling design. The results showed that on average, working women had lower fertility rates compared to non-working women. However, further analysis revealed that the relationship between women’s employment status and fertility behavior varied depending on several factors. Firstly, working women tend to have a different pattern when it comes to the duration of key reproductive milestones, such as the time between marriage and the birth of the first child or the duration since the birth of the last child. These differences suggest that employment status can influence the timing of family planning decisions and childbearing. Secondly, working status is also linked to awareness and utilization of family planning services. Working women may have better access to healthcare facilities and family planning information, potentially leading to differences in the methods of family planning chosen. They may also face challenges in visiting family planning centers due to their work commitments. Most notably, the adoption of family planning methods exhibits a highly significant association with working status. Working women often enjoy greater autonomy and decision-making power in family planning decisions, influenced by their economic independence and contributions to the household.

These findings underscore the complex interplay between employment status and family planning choices. Understanding these associations is crucial for policymakers, healthcare providers, and researchers aiming to promote reproductive health and family planning. It is essential to recognize that individuals’ choices are influenced by a multitude of factors, including socioeconomic status, access to healthcare, cultural norms, and personal preferences. Consequently, tailored interventions and support systems should be developed to ensure that individuals, regardless of their working status, have access to comprehensive family planning information and services to make informed decisions about their reproductive health. Furthermore, further research and nuanced analyses are needed to explore these associations within specific cultural and regional contexts, as they may vary significantly. Ultimately, the goal should be to empower individuals to make choices that align with their unique circumstances and aspirations while promoting their overall well-being and reproductive health. Ultimately, sup- porting women’s right to work and to choose when and how many children to have is essential for promoting sustainable development and achieving gender equality.

Recommendations

Here are recommendations for tailored interventions and women’s empowerment based on the findings of the study:

• Gender-responsive family planning education: Develop and implement educational programs that specifically address family planning, reproductive health, and contraception. These programs should be tailored to the needs and preferences of working women and housewives, providing them with comprehensive information and options for family planning.

• Workplace support: Advocate for workplace policies that support women’s reproductive health and family planning decisions. These policies may include flexible work hours, maternity and paternity leave, and access to healthcare services or information through employee assistance programs.

• Peer support and counseling: Establish peer support groups or counseling services specifically for working women and housewives to address family planning concerns and provide a safe space for discussing related issues. Peer support can be highly effective in sharing experiences and information.

• Community awareness campaigns: Launch community-based awareness campaigns on family planning, targeting both working women and their communities. These campaigns can help reduce stigma and promote informed decision-making.

• Tailored healthcare services: Collaborate with healthcare providers to offer specialized family planning services that cater to the needs of working women and housewives, such as convenient appointment times and telemedicine options for consultations.

• Inclusive reproductive rights advocacy: Support advocacy efforts that emphasize the importance of reproductive rights and bodily autonomy. Engage with policymakers and organizations to ensure that women’s voices are heard in shaping policies related to family planning and healthcare.

• Address cultural and social norms: Work to challenge and change cultural and societal norms that may influence family planning decisions. Engage with community leaders and influencers to promote gender equality and reproductive health. Promote comprehensive sex education in schools and communities, emphasizing the importance of informed decision-making, consent, and responsible family planning.