DOI: https://doi.org/10.21203/rs.3.rs-2892267/v1
Background
As fertility rate continues to decline and negative population growth emerges, China has sequentially introduced encouraging fertility policies to raise fertility levels. The impact of fertility behaviors on women's health remains inconclusive. It is essential to explore further the correlation between fertility behaviors and the health status of 113 million migrant women of childbearing age in China.
Methods
Using a nationally representative dataset from the 2018 China Migrants Dynamic Survey (CMDS), we examined the effects of fertility behaviors on the self-rated health of migrant women of childbearing age. An ordinary least squares regression model with moderating effects was used for the empirical study, and robustness tests were conducted based on the ordered probit model and propensity score matching to address endogeneity.
Results
The empirical results indicated that a rise in the number of children born significantly reduces the self-rated health of migrant women of childbearing age. An increase in years of schooling and household income can significantly mitigate the negative impact of childbearing behaviors on the health of migrant women. The robustness of the above results was validated through alternative models and propensity score matching (PSM) methods. The heterogeneity analysis revealed that fertility behavior exerts a negative impact on the health status of migrant women with rural household registration and on the health status of inter-provincial and inter-city migrant women. Further investigation found that the occurrence of childbirth during migration and an increase in the number of girls significantly negatively impacted the health status of migrant women. In contrast, the increase in the number of boys did not show a significant effect. Improving the health of migrant women of childbearing age significantly positively impacted their future childbearing intentions.
Conclusions
Migrant women of childbearing age bear the dual burden of migration and childbirth. Our findings showed the rise in the number of children born and the occurrence of childbirth during migration posed greater challenges to the health status of female migrants, particularly among those with lower socioeconomic status. Government and community efforts for enhancing health among migrant women of childbearing age are recommended.
According to the 7th National Census data, China's total fertility rate was 1.30 in 2020, significantly lower than the replacement level of 2.1. China has experienced a rapid transition from high to ultra-low fertility levels in a relatively short time. A prolonged period of ultra-low fertility levels will challenge the long-term balanced development of population. Therefore, since 2013, the country has gradually loosened its restrictions on fertility policies. In May 2021, China introduced the three-child policy, which allows couples to have three children and improves supporting measures. The substantial relaxation of fertility restrictions indicates the government's determination to raise the low fertility levels.
An increase in the number of children born can have various impacts on individuals and families, an important one being the health status of the parents, particularly the mothers. Such impacts can result in substantial changes in the individual's socioeconomic activity and quality of life. Most academic studies indicate that increasing the number of children can negatively impact women's health [1, 2]. However, some studies suggest that the relationship is "U-shaped" or "J-shaped" [3–5], and some even find a positive effect [6]. The impact of fertility behavior on health remains inconsistent in academic research, and most of the above findings are based on data from developed countries. As a country in transition with rapid socioeconomic changes, China requires more detailed and in-depth research on related issues.
With the gradual advancement of urbanization in China, the number of migrants is on the rise. Based on data from the 7th National Census, the total number of migrants in China reached 376 million in 2020, representing more than one-quarter of the country's total population. Among the migrants, the number of women of childbearing age has reached 113 million, which accounts for 30.19% of the total migrant population; the trend of youth in population migration has become increasingly evident. The dualistic urban-rural economic structure has long hindered the migrants from enjoying the same benefits as the local household population, resulting in more challenging working and living environments and significant difficulties in accessing healthcare [7]. The migrants are significant contributors and participants in China’s economic and social development. However, they still face the harsh reality of marginalization and numerous disparities, particularly concerning healthcare [8]. Interruption theory posits that the separation of couples or challenges encountered during migration can result in disruptions to fertility behaviors [9]. Therefore, special attention should be given to the health status of enormous-scale migrant women of childbearing age, who are simultaneously on migration and at the age of childbearing. It is of great practical significance to study the impact of fertility behaviors on the health of migrant women of childbearing age.
Based on the above context, this study utilizes data from the 2018 China Migrants Dynamic Survey (CMDS) conducted by the National Health Commission of the People’s Republic of China to examine the relationship between fertility behaviors and the health status of migrant women of childbearing age. The paper aims to provide a health perspective in interpreting China's current low fertility levels and inform policy-making by relevant authorities, ultimately improving the overall fertility rates.
Scholars have conducted numerous theoretical and empirical studies on the relationship between fertility behaviors and parental health. Measuring health based on different indicators has led to varying results, making it difficult to draw a uniform conclusion. Evolutionary theory posits that mothers transfer resources and energy to their offspring through procreation instead of maintaining their own organism. Disposable Soma Theory (DST) proposes that organisms must allocate more resources to maintaining somatic cell integrity to live longer. However, childbirth consumes a larger proportion of resources, resulting in fewer resources for somatic cell maintenance. If women decide to have more children, their capacity to repair and maintain somatic cells is reduced, leading to the accumulation of somatic cell damage, which in turn results in the deterioration of maternal health, an increased risk of disease, accelerated aging, and a reduced lifespan. Therefore, there exists a trade-off between childbearing and longevity [10]. Read et al. discovered that parents with four or more children reported lower levels of self-rated health compared to parents with two children through the British Household Panel Survey [2]. Yang et al. investigated the impact of the number of children on depression in the later life of older Chinese adults. They found a significant worsening effect of the increasing number of children on depression levels among older adults, particularly those with three or more children [11]. Hu et al. also found that age at first childbirth, the childbearing period and the number of children born significantly increased parental depression [12]. The study by Sharami et al. found that women with a higher number of children were more likely to have higher total and somatic symptoms than others [13].
Additionally, some scholars have identified a "U" or "J" curve relationship between the number of children born and parents' health status. The health status of parents who have no children or more than three children is significantly lower compared to those who have one or two children [3–5]. Nevertheless, there are scholars who have reached entirely opposite conclusions by utilizing different data and health measures. McArdle et al. discovered that having more children was related to a linear increase in life expectancy for both fathers and mothers, with each additional child increasing the father's life expectancy by 0.23 years and the mother's life expectancy by 0.32 years [6]. Yang et al. discovered a notable positive correlation between the number of children (especially the number of girls) and parental life expectancy, signifying a 0.42-year increase in life expectancy for each additional child and a 0.56-year increase for each extra girl [14].
After reviewing the literature, we have found inconsistent conclusion regarding the impact of fertility behaviors on parental health. Moreover, most of the studies have focused on the health of older parents. As China has gradually relaxed its fertility policy, changes in the number of children born mainly affect the health status of women of childbearing age. Therefore, it is of practical significance to focus on women of childbearing age as the study subject [15]. Based on the above study, we make hypothesis 1:
There is a negative correlation between the number of children born and the self-rated health status of migrant women of childbearing age.
Numerous previous investigations have been conducted on the determinants affecting the health of migrants. From an individual perspective, various factors such as age, marital status, and the household registration type can exert an impact on the health status of the migrants. Some scholars have also analyzed the factors that influence the health of migrants from a migrant perspective, such as distance, duration, and type of migration [16].
Socioeconomic factors constitute significant aspects that affect the health of migrants. It would be improper to discuss socioeconomic factors without addressing the socioeconomic status index proposed by Duncan, who merged two variables, education, and income, into a single indicator [17]. This indicator became a crucial metric used by sociologists to compare the socioeconomic status of various populations. Despite its widespread use, the validity of the SES index has been questioned by some scholars. In fact, subsequent research has demonstrated that the explanatory power of the SES index is not particularly strong [18]. Therefore, this paper uses the two variables of education and income to represent socioeconomic status directly.
The level of education can have a significant impact on the health status of individuals. According to the resource amplification theory, the health benefits of education follow a "Matthew effect," whereby the strong become stronger, and the weak become weaker [19]. Several studies have analyzed the effect of income on health, both from a theoretical and empirical perspective. One of these hypotheses is the absolute income hypothesis, which suggests that population health improves with average income [20]. Empirical studies have consistently shown the positive effect of income on an individual's health; Ettner employed a two-stage instrumental variables method to establish a strong positive association between income and both health and health literacy [21]. Therefore, we propose hypothesis 2:
An improvement in socioeconomic status can significantly mitigate the negative impact of fertility behaviors on the health of migrant women of childbearing age.
The influence of population migration on fertility has been of interest to scholars. Concerning the study of population migration and fertility conducted in China, a large amount of research has been undertaken by scholars both in China and abroad. Most investigations have concluded that migration can reduce fertility levels [22, 23]. The “disruption hypothesis” suggests that the process of migration is actually a disruption of an individual's prior way of life; as the migrant population enters a novel social environment, their ideology and patterns of behavior will be profoundly influenced. Furthermore, commencing a new life in an unfamiliar setting also engenders a state of tension, fatigue, and unease for them; the dual pressures of physiological and psychological stress make them have no time or unwilling to have children during this phase, thereby interfering with women's fertility behavior [24].
Nonetheless, the "catch-up theory" holds the opposite perspective, arguing that assumes that the fertility rates in the place of origin for the migrants are higher. The fertility intentions of the migrants also remain at a high level. After a period of adjustment to life in the migrant destination, they may exhibit compensatory behavior, known as "catch-up behavior," to make up for the fertility decline caused by the disruption, leading to an increase in their fertility level accordingly [25]. Goldstein's study concluded that the decline in fertility due to interruption is only temporary and does not affect women's completed fertility level [26].
In fact, there are two different perspectives on the number of children born to migrants. However, both the “disruption theory” and the “catch-up theory” emphasize that migrant populations face greater challenges in their fertility behavior than non-migrant populations. It is difficult for the migrants to access the opportunities, benefits, social security and services in the labor market and social life of the inflow areas that the local registered population has, whose primary requirements for social services and security still a considerable extent rely on the rural areas where they originate from, so the migration experience has produced a dissipative effect on the health of migrants [27]. Meanwhile, disparities in utilization of basic public health services exist between rural-to-urban migrants and their local counterparts in China [28]. Based on the preceding content, we propose hypothesis 3:
The occurrence of childbearing during migration significantly reduces the health status of women of childbearing age.
This paper utilizes data from the 2018 China Migrants Dynamic Survey (CMDS) of the National Health Commission of the People’s Republic of China. This survey data is an annual large-scale nationwide sample survey of the migrants conducted by the National Health Commission since 2009, covering 31 provinces, cities and autonomous regions, and the Xinjiang Production and Construction Corps in the inflow areas where the migrants is concentrated, employing a stratified, multi-stage, probability proportional to size (PPS) sampling method. Since the study subject of this paper is women of childbearing age between 15 and 49 years old, the final sample size obtained was 65725.
The dependent variable in this paper is the self-rated health of migrant women of childbearing age; we constructed this variable by analyzing the responses to the "How is your health status?" question from the CMDS survey questionnaire. The answers include healthy, basically healthy, unhealthy, but able to take care of oneself, and not able to take care of oneself, with the options being allocated in the sequence 4=healthy, 3=basically healthy, 2=unhealthy, but able to take care of oneself, and 1=not able to take care of oneself.
The key explanatory variable in this paper is fertility behavior, which is measured by the total number of births. In the CMDS, it was obtained by the question, "How many children have you had?" A response of 0 indicates no children; the maximum number of children reported was 7, representing a continuous variable.
The moderating variables in this study are the socioeconomic status of the participants. Years of education and household income were used as proxy variables to measure socioeconomic status, years of education were converted based on the level of education attained[1], and the household income was logarithmically transformed.
Control variables include three levels of dimensions: personal characteristics, health characteristics, and migration characteristics. Personal characteristics variables include age, ethnicity, household registration, and marital status; health characteristics variables comprise whether an individual has medical insurance, whether an individual has received reproductive health education, whether an individual has established a resident health record, and whether an individual has contracted with a local doctor; migration characteristics variables encompass the distance of migration, the reason for migration, and the area of migration.
This manuscript employs moderated ordinary least squares (OLS) regression analysis by reference to Wen et al. as follows [29]:
In Eq. (1), Y denotes self-rated health, β0 is a constant term, βi is the coefficients of the variables, X1 is a fertility behavior, M represents a socioeconomic status variable, controls represent control variables, and ε is the random interference term.
Table 1 indicates that the overall health status of migrant women of childbearing years is relatively favorable. More than 97% of these women perceive themselves to be in a healthy or basically healthy state, with a relatively small proportion of women reporting as unhealthy or cannot take care of themselves. This suggests that the health state of women of childbearing age plays a significant role in the decision-making process for migration. As the number of children increases, the proportion of unhealthy women of childbearing age gradually rises, while the proportion of healthy or basically healthy women of childbearing age gradually declines. Moreover, the self-rated health of women of childbearing age is found to be negatively associated with the number of children born preliminary.
| Number of children born | Not able to take care of oneself | Unhealthy, but able to take care of oneself | Basically Healthy | Healthy |
|---|---|---|---|---|
| 0 | 0.05 | 0.65 | 7.03 | 92.27 |
| 1 | 0.03 | 1.02 | 9.48 | 89.48 |
| 2 | 0.02 | 1.57 | 10.62 | 87.79 |
| 3 and above | 0.07 | 2.99 | 13.47 | 83.47 |
| Data source: 2018 CMDS data | ||||
Table 2 presents the means or frequencies of the main variables, with descriptive statistical analyses grouped based on whether childbearing occurred during this migration. The results indicate that the health status of migrant women of childbearing age is relatively good, with a mean value of 3.87. Furthermore, the average number of births is 1.43, significantly lower than the replacement level of 2.1. Regarding personal characteristics, the average number of years of education is 10.20 years; The proportion of women aged 25–34 is 47.61%, accounting for the largest share, followed by 35–44 years old, 45–49 years old, and 15–24 years old, respectively; 91% of women are Han Chinese, 83% are in agricultural household registration, and 96% are in marital status.
Among the health characteristics, it was found that 94% of women have at least one kind of medical insurance. Additionally, 53% of women have received reproductive health education within their communities or work units. Furthermore, 31% of women have established resident health records, and only 14% of women have contracted with a local doctor.
In terms of migrant characteristics, the largest share of the migration distance is inter-provincial migration, which is 48.60%, followed by inter-city within provinces and inter- county within cities. Concerning reasons for migration, 77.68% of women of childbearing age migrated for work and business, accounting for the largest proportion, followed by migration for marriage and other reasons. As for the areas of migration, 45.12% of women of childbearing age migrated to the eastern region of China, whereas 29.19% have migrated to the western region and 19.69% to the central region, and the least 5.99% to the northeast region.
The sample was classified based on whether or not childbearing occurred during the migration. The results indicate that women of childbearing age who gave birth during the migration have higher mean values for health status, the number of children born, years of education, and household income compared to those who did not give birth during this migration; moreover, the percentage of women who were married, received health education, established health records, and contracted with local doctors are also higher; in contrast, the percentage of Han Chinese and agricultural households are lower.
Compared to the women who did not conduct fertility behavior during migration, the proportion of women who conducted fertility behavior during migration is larger in the younger age group (15–44 years old) and lower in the senior age group (45–49 years old); the proportion of inter-city within the province is higher, while the inter-provincial migration and inter-county within the cities are lower among the migration distance; the percentage of individuals migrating for marriage and other reasons is more significant, while the proportion of that migrating for work and business is relatively more minor; the proportion of those who migrate to the eastern and western regions is relatively high, and those who migrate to the central and northeastern regions are relatively low.
| Variables | Definition | Total | Birth migration | No birth migration |
|---|---|---|---|---|
| Dependent variable | ||||
| Self-rated health | 1 = Not able to take care of oneself; 2 = Unhealthy, but able to take care of oneself; 3 = Basically healthy; 4 = Healthy | 3.87 | 3.89 | 3.86 |
| Independent variable | ||||
| Fertility behavior | Number of children born | 1.43 | 1.56 | 1.32 |
| Moderating Variables | ||||
| Education level | Years of education | 10.20 | 10.95 | 9.58 |
| Household income | Logarithmic processing | 8.82 | 8.88 | 8.78 |
| Personal characteristics | ||||
| Age | 15–24 years | 7.43% | 9.70% | 5.54% |
| 25–34 years | 47.61% | 59.43% | 37.82% | |
| 35–44 years | 32.31% | 26.63% | 37.02% | |
| 45–49 years | 12.65% | 4.24% | 19.61% | |
| Ethnicity | 1 = Han Chinese; 0 = Minority | 0.91 | 0.90 | 0.92 |
| Hukou | 1 = Agricultural; 0 = Non-agricultural | 0.83 | 0.81 | 0.85 |
| Marital Status | 1 = In marriage; 0 = Not in marriage | 0.96 | 0.98 | 0.95 |
| Health characteristics | |||||
|---|---|---|---|---|---|
| Medical insurance | 1 = With insurance; 0 = Without insurance | 0.94 | 0.93 | 0.94 | |
| Health education | 1 = Received; 0 = Not received | 0.53 | 0.56 | 0.50 | |
| Health record | 1 = Established; 0 = Not established | 0.31 | 0.33 | 0.29 | |
| Local doctor | 1 = Contracted; 0 = Not contracted | 0.14 | 0.15 | 0.12 | |
| Migrant characteristics | |||||
| Migration distance | Inter-provincial | 48.60% | 47.95% | 49.14% | |
| Inter-city | 33.54% | 34.55% | 32.71% | ||
| Inter-county | 17.85% | 17.51% | 18.14% | ||
| Reasons for migration | Work and business | 77.68% | 70.25% | 83.83% | |
| Marriage | 20.24% | 27.55% | 14.19% | ||
| Other reasons | 2.08% | 2.21% | 1.98% | ||
| Migration areas | Eastern region | 45.12% | 46.8% | 43.73% | |
| Central region | 19.69% | 18.81% | 20.42% | ||
| Western region | 29.19% | 29.30% | 29.10% | ||
| Northeast region | 5.99% | 5.09% | 6.75% | ||
[Table 2 about here]
Table 3 shows the results of the baseline regression analyses. The first model incorporated the moderating variable of years of education, the second model incorporated the moderating variable of household income, and the third model included both moderating variables. The results reveal that the increased number of children born significantly decreases the health status of migrant women of childbearing age. Models 1 to 3 are all at the 1% significance level, thereby hypothesis 1 is confirmed.
Models 1 to 3 indicate that the interaction term between the socioeconomic status variables (years of education and household income) and the number of children born positively affects the self-rated health of migrant women of childbearing age; this suggests that the socioeconomic status of migrant women positively moderates their health status and that as their socioeconomic status increases, the negative impact of having more children on their health is significantly reduced. Thus, hypothesis 2 is verified.
Regarding control variables, among the personal characteristics, it is found that the health status of women in the other age groups is poorer compared to women in the 15–24 age group; Han Chinese, agricultural households, and married women show better health statuses. Among the health characteristics variables, it is observed that possessing medical insurance, receiving reproductive health education, establishing health records, and contracting with a local doctor can significantly improve the health status of migrant women of childbearing age. Among the variables of migration characteristics, compared to inter-provincial women, the health status of inter-city and inter-county women is poorer; women who migrate for marriage and other reasons are less healthy than those who migrate for work and business. Compared to women who migrate to eastern China, women who migrate to the western and northeastern regions have poorer health status, and women who migrate to the central region have better health status.
[Table 3 about here]
| Variables | Model 1 N = 51286 | Model 2 N = 51286 | Model 3 N = 51286 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Estimate | Std.error | Estimate | Std.error | Estimate | Std.error | ||||
| Fertility behavior | -0.034*** | 0.009 | -0.133*** | 0.042 | -0.120*** | 0.041 | |||
| Years of education | 0.004*** | 0.001 | 0.003** | 0.001 | |||||
| Fertility×education | 0.003*** | 0.001 | 0.003*** | 0.001 | |||||
| Household income | 0.024*** | 0.007 | 0.024*** | 0.007 | |||||
| Fertility×income | 0.014*** | 0.005 | 0.010** | 0.005 | |||||
| Age group | |||||||||
| 15–24 years | |||||||||
| 25–34 years | -0.029*** | 0.005 | -0.025*** | 0.005 | -0.029*** | 0.005 | |||
| 35–44 years | -0.091*** | 0.006 | -0.095*** | 0.006 | -0.090*** | 0.006 | |||
| 45–49 years | -0.194*** | 0.008 | -0.208*** | 0.008 | -0.191*** | 0.008 | |||
| Ethnicity | 0.032*** | 0.007 | 0.037*** | 0.007 | 0.029*** | 0.007 | |||
| Hukou | 0.018*** | 0.005 | 0.005 | 0.004 | 0.021*** | 0.005 | |||
| Marital status | 0.030*** | 0.010 | 0.017* | 0.010 | 0.016 | 0.010 | |||
| Medical insurance | 0.013* | 0.007 | 0.013* | 0.007 | 0.010 | 0.007 | |||
| Health education | 0.027*** | 0.003 | 0.030*** | 0.003 | 0.027*** | 0.003 | |||
| Health record | 0.019*** | 0.004 | 0.021*** | 0.004 | 0.019*** | 0.004 | |||
| Local doctor | 0.019*** | 0.005 | 0.020*** | 0.005 | 0.020*** | 0.005 | |||
| Migrant distance | |||||||||
| inter-provincial | |||||||||
| inter-city | -0.008* | 0.004 | -0.004 | 0.004 | -0.007 | 0.004 | |||
| Inter-county | -0.015*** | 0.005 | -0.007 | 0.005 | -0.010* | 0.005 | |||
| Migration reasons | |||||||||
| Work and business | |||||||||
| Marriage | -0.047*** | 0.005 | -0.042*** | 0.005 | -0.043*** | 0.005 | |||
| Other reasons | -0.074*** | 0.015 | -0.069*** | 0.015 | -0.071*** | 0.015 | |||
| Migration areas | |||||||||
| Eastern region | |||||||||
| Central region | 0.006 | 0.011 | 0.019* | 0.011 | 0.020* | 0.011 | |||
| Western region | -0.052*** | 0.016 | -0.045*** | 0.016 | -0.034** | 0.016 | |||
| Northeast region | -0.106*** | 0.018 | -0.092*** | 0.018 | -0.084*** | 0.018 | |||
| Provincial control | Yes | Yes | Yes | ||||||
| Constant term | 3.839*** | 0.021 | 3.677*** | 0.068 | 3.639*** | 0.06 |
|---|---|---|---|---|---|---|
| R2_a | 0.055 | 0.056 | 0.059 | |||
| Note: *P < 0.10; **P < 0.05; ***P < 0.01 | ||||||
As the dependent variable is an ordinal variable, the regression model is replaced with an ordered probit (Oprobit) model for robustness testing. Models 4 and 5 contain the moderating variables of years of schooling and household income, respectively. Model 6 includes the two moderating variables. The findings in Table 4 demonstrate that the key explanatory variable remains significantly negative, similar to the results in Table 3; this implies that as the number of children born increases, the health status of migrant women of childbearing age significantly declines.
In addition, the interaction term between the years of education and fertility behavior had a significant positive impact on self-rated health in both Model 4 and Model 6, indicating that the impact of fertility behavior on health status is positively moderated by years of education. Similarly, the interaction term between household income and fertility behavior in Model 5 has a significantly positive moderating effect on health status. However, the household income moderating effect was not significant in Model 6. In general, the robustness of the conclusions can be obtained through the method of replacement model.
| Variables | Model 4 N = 51286 | Model 5 N = 51286 | Model 6 N = 51286 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Estimate | Std.error | Estimate | Std.error | Estimate | Std.error | ||||
| Fertility behavior | -0.052* | 0.027 | -0.255** | 0.125 | -0.241* | 0.123 | |||
| Years of education | 0.026*** | 0.005 | 0.021*** | 0.005 | |||||
| Fertility×education | 0.006** | 0.003 | 0.005** | 0.003 | |||||
| Household income | 0.108*** | 0.024 | 0.094*** | 0.024 | |||||
| Fertility×income | 0.025* | 0.014 | 0.022 | 0.014 | |||||
| Control variables | Yes | Yes | Yes | ||||||
| Provincial control | Yes | Yes | Yes | ||||||
| Note: *P < 0.10; **P < 0.05; ***P < 0.01 | |||||||||
As fertility behavior is a decision made by women of childbearing age, it is a non-random event, and various other factors can influence fertility behavior. Therefore, the estimated coefficients may be biased due to the self-selection problem. To further ensure the robustness of the results, this study adopts the propensity score matching method (PSM) to deal with the endogeneity issue, which can effectively address the problems of omitted variables and sample self-selection bias. The fundamental concept of PSM is to construct a counterfactual framework and an approximate "randomized experiment" to eliminate significant bias resulting from the observable characteristics of the treatment and control groups and calculate the average treatment effect.
Given that the independent variable in this paper is continuous, we have categorized the samples whose number of children born exceeds the mean value of 1.43 as the treatment group, and those below 1.43 as the control group. We further examined the robustness of the results through four matching methods: K-nearest neighbor matching, radius matching, kernel matching, and intra-caliper K-nearest neighbor matching.
Table 5 presents the average treatment effects of fertility behaviors on the health of migrant women of childbearing age under different matching methods. We used the Bootstrap self-sampling methods to adjust the possible bias because of single matching. The results in Table 5 indicate that the average treatment effects obtained from various matching methods are significant at the 1% level. Migrant women with an actual number of children born above the mean value have a significantly worse self-rated health status of 0.014–0.017 compared to those born below the mean value. The ATT values are consistent across different matching methods, further supporting the robustness of the results.
| Matching method | Average treatment effect | Bootstrap std.error | T-value |
|---|---|---|---|
| K-Nearest neighbor matching | -0.017 | 0.005 | -3.51*** |
| Radius matching | -0.014 | 0.004 | -3.39*** |
| Kernel matching | -0.014 | 0.004 | -3.43*** |
| Intra-caliper k-nearest neighbor matching | -0.017 | 0.004 | -4.16*** |
| Note: K nearest neighbor matching set K = 4; radius matching set caliper value = 0.01; kernel function and bandwidth of kernel matching use their default values; intra-caliper K-nearest neighbor matching set K = 4, caliper value = 0.01; Bootstrap sampling number is 100 | |||
The analysis above reveals that an increase in the number of children born significantly reduces the self-rated health of migrant women of childbearing age. However, there are notable distinctions among the childbearing behaviors of different groups of women of childbearing age, leading to varying impacts on women's health. Tables 6 and 7 explore the heterogeneity in the impacts of fertility behavior on the health of migrant women of childbearing age with varying household registration and migration distance, respectively.
As a result of China's dualistic economic institution, there exists a notable difference in the social resources and welfare accessible to women of childbearing age during the process of childbirth. Table 6 shows that an increase in the number of children significantly reduces the self-rated health of rural migrant women. However, this effect is insignificant for migrant women with non-agricultural household registration.
| Variables | Rural N = 42533 | Urban N = 8753 | ||
|---|---|---|---|---|
| Estimate | Std.error | Estimate | Std.error | |
| Fertility behavior | -0.127*** | 0.046 | -0.093 | 0.095 |
| Moderating variables | Yes | Yes | ||
| Control variables | Yes | Yes | ||
| Provincial control | Yes | Yes | ||
| Constant term | 3.676*** | 0.078 | 3.576*** | 0.128 |
| R2_a | 0.060 | 0.056 | ||
| Note: *P < 0.10, **P < 0.05, ***P < 0.01 | ||||
This paper further conducts group regression based on the migrant distance; the results show that further of the migration distance, the negative impact of fertility behavior on women's health is more serious. Specifically, an increase in the number of children born can significantly reduce the health status of women who migrate inter-provincially and inter-city. In contrast, there is no significant adverse effect of fertility behavior on the health of inter-county migrant women. This situation could be attributed to the notion that as a woman migrates farther away from her place of origin, she may receive less support from her family and encounter more incredible difficulty in obtaining reproductive-related benefits.
| Variables | Inter-provincial N = 24926 | Inter-city N = 17203 | Inter-County N = 9157 | |||
|---|---|---|---|---|---|---|
| Estimate | Std.error | Estimate | Std.error | Estimate | Std.error | |
| Fertility behavior | -0.129** | 0.053 | -0.161** | 0.082 | 0.032 | 0.096 |
| Moderating variables | Yes | Yes | Yes | |||
| Control variables | Yes | Yes | Yes | |||
| Provincial control | Yes | Yes | Yes | |||
| Constant term | 3.737*** | 0.083 | 3.634*** | 0.132 | 3.306*** | 0.176 |
| R2_a | 0.046 | 0.070 | 0.076 | |||
| Note: *P < 0.10, **P < 0.05, ***P < 0.01 | ||||||
The concept of fertility behavior includes more than one indicator of the number of children born. Next, we investigated the impacts of different fertility behaviors on the health of migrant women of childbearing age in four aspects: whether to give birth during this migration, the number of boys born, the number of girls born, and the gender structure of fertility (number of boys divided by the number of girls).
The findings presented in Table 8 demonstrate that giving birth during migration significantly weakens the self-rated health of women of childbearing age, indicating that migrant women would face additional challenges if they chose to give birth during migration because it is difficult for them to access the same welfare benefits that local women can more easily obtain. Ultimately, this fertility behavior hurts the health of migrant women of childbearing age. Hypothesis 3 is verified.
Furthermore, models 8 to 10 reveal that a rise in the number of female children considerably diminishes the self-rated health of mothers. Conversely, the increase in the number of male children and the proportion of boys in the gender structure does not negatively impact the health of migrant women of childbearing age; meanwhile, the corresponding coefficient values are positive. The potential reason for this phenomenon may be the persistence of the "son preference" among the migrants. With the birth of more boys in migrant families, mothers can receive better healthcare and nutrition support from the family. Furthermore, the birth of male children can provide mothers with greater psychological satisfaction, ultimately resulting in no significant negative impact on their self-rated health.
| Variables | Model 7 N = 51286 | Model 8 N = 47471 | Model 9 N = 47471 | Model 10 N = 27363 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Estimate | Std.error | Estimate | Std.error | Estimate | Std.error | Estimate | Std.error | |||||||
| Birth migration | -0.009** | 0.003 | ||||||||||||
| Number of boys | 0.002 | 0.003 | ||||||||||||
| Number of girls | -0.005** | 0.003 | ||||||||||||
| Fertility structure | 0.005 | 0.005 | ||||||||||||
| Control variables | Yes | Yes | Yes | Yes | ||||||||||
| Provincial control | Yes | Yes | Yes | Yes | ||||||||||
| Constant term | 3.457*** | 0.036 | 3.426*** | 0.038 | 3.430*** | 0.038 | 3.444*** | 0.050 | ||||||
| R2_a | 0.058 | 0.060 | 0.060 | 0.060 | ||||||||||
| Note: *P < 0.10, **P < 0.05, ***P < 0.01 | ||||||||||||||
Given the continued decline in China's fertility rate in recent years, coupled with the substantial size of the migrant population of childbearing age, increasing the fertility rate of the migrants has become a crucial factor in improving the overall fertility level. The subsequent content centers on the impact of the health status of migrant women of childbearing age on their future fertility intentions. The binary dependent variable is whether or not migrant women plan to give birth in the following two years. The key independent variable is the self-rated health status of migrant women of childbearing age. Table 9 employs Logit and Probit models to perform the empirical analysis. The results demonstrate that enhancement in health status significantly raises the probability of migrant women’s intention to have children within the next two years.
As increasing the number of children born significantly negatively impact the health status of women of childbearing age, the negative health effect is one of the reasons that women forgo giving birth to a higher parity. This situation is manifestly incongruous with China's intention to elevate the national fertility levels.
The findings presented in Table 9 can enlighten Chinese policymakers. Specifically, the government ought to fully consider women regarding the negative health effect of fertility-related actions when it attempts to increase the overall fertility level. If migrant women of childbearing age could receive adequate support and assistance from their families and society during the phases of childbirth and child-rearing, and their health status is optimally protected, then it would significantly increase their desire to have more children in the future. This result presents novel insights for prospective policymakers who aim to raise fertility levels.
| Variables | Logit N = 50002 | Probit N = 50002 | |||
|---|---|---|---|---|---|
| Estimate | Std.error | Estimate | Std.error | ||
| Self-rated health | 0.106* | 0.059 | 0.058* | 0.031 | |
| Control variables | Yes | Yes | |||
| Provincial control | Yes | Yes | |||
| Constant term | -4.644*** | 0.690 | -2.806*** | 0.385 | |
| Note: *P < 0.10, **P < 0.05, ***P < 0.01 | |||||
Based on the data derived from the 2018 China Migrants Dynamic Survey (CMDS), this paper examines the influence of fertility behaviors on the self-rated health of migrant women of childbearing age and draws the following conclusions:
First, the finding suggests that there is a significant negative correlation between the number of children born and the self-rated health of migrant women of childbearing age; in addition, an elevation in the socioeconomic status can act as a positive moderator and mitigate the negative impact of fertility behavior on the health of migrant women.
Second, the results of the heterogeneity analysis indicate that fertility behavior has varying impacts on the health of different categories of migrant women. The number of children born negatively affects the health of rural hukou, inter-provincial, and inter-city migrant women. In contrast, it does not significantly affect the health of urban hukou and inter-county migrant women.
Third, further investigation into the effects of various fertility behaviors on the health of migrant women, we found that giving birth during migration and an increase in the number of girls had a significant negative impact on the self-rated health of migrant women. Conversely, an increase in the number of boys and the proportion of boys in the gender structure did not significantly affect maternal health. Moreover, improved self-rated health of migrant women of childbearing age significantly increased the probability of giving birth in the following two years.
Based on the results of the aforementioned study, there are three policy implications for the government:
Firstly, it is crucial to fully consider the negative effect of childbearing behavior on the health status of women of childbearing age, with the health loss resulting from high parity being one of the reasons leading to women's reluctance to give birth to more children. Migrant women of reproductive age, in particular, face the dual challenges of migration and childbirth. Therefore, their health status demands greater attention, as their fertility behavior during migration will induce more inconvenience and obstacles.
Second, regarding the socioeconomic status of migrant women, it is notable that improving the level of education and household income among migrant women can considerably mitigate the adverse impact of fertility behavior on their health status; meanwhile, the negative effect of fertility behavior on rural women’s health is comparatively more prominent. Thus, the health status of migrant women of childbearing age with lower socioeconomic status should receive more attention.
Third, with the declining fertility rate in China, improving the health status of migrant women of childbearing age and compensating for their health loss during childbirth is one of the strategies to raise future fertility levels. For instance, the government could introduce specific policies and regulations that offer targeted healthcare and financial aid to migrant women of childbearing age during childbirth. Such measures would not only alleviate migrant women’s anxieties concerning potential health loss associated with childbirth but would also potentially yield benefits in terms of increasing future fertility rates.
Acknowledgements
A special word of thank you goes to National Health Commission of the People’s Republic of China for giving permission to access 2018 CMDS dataset.
Authors’ contributions
XY designed the study, collected data and wrote the manuscript. LX analysis and interpretation of data. All authors read and approved the final manuscript.
Funding
Not applicable.
Availability of data and material
The datasets analyzed during the current study are available from the 2018 China Migrants Dynamic Survey of the National Health Commission of the People’s Republic of China. Application for use through research institution.
Ethical approval and consent to participate
This study was based on a secondary analysis of an existing dataset with all participant identifiers removed, and the data was publicly available. Informed consent was obtained from all participants before the questionnaire was administered. All methods were carried out in accordance with the relevant guidelines and regulations, and human subject protection is not an issue here. The survey protocol and instruments followed the Helsinki guidelines and were approved by the National Bureau of Statistics of China, a state organization.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1Northeast Asian Research Center, Jilin University, Changchun, Jilin Province, 130012, China
2Northeast Asian Studies College, Jilin University, Changchun, Jilin Province, 130012, China