Abstract

Background

In recent years, due to the continuous increase of China鈥檚 aging population and the decline of birth rate, China 鈥榮 population base has declined sharply, and the fertility problem of young people has attracted much attention from scholars. Therefore, this study aims to comprehensively analyze the fertility intentions of young people of childbearing age and their influencing factors, explore the reasons for China鈥檚 low fertility rate, and provide a reasonable theoretical basis for the formulation of subsequent fertility-related policies.

Methods

A systematic literature search was conducted from January 2012 to September 2023 through the databases PubMed, Web of Science, Embase, CNKI, Wanfang, and VIP using keywords including fertility desire, fertility intention, fertility plan, fertility purpose, fertility motivation, youth, urban youth, school-age youth, childbearing age youth in Chinese; and fertility desire, fertility intention, fertility plan, fertility willingness, youth, young people, child-bearing youth, and school-age youth in English. Two reviewers independently screened studies, extracted data, and appraised the risk of bias. RevMan 5.2 was used for the Meta-analysis of all outcomes.

Results

This study systematically evaluated 18 cross-sectional surveys, which included a total of 43,427 participants, with a combined fertility intention of 11.85%. Subgroup analyses revealed that gender, age, education level, economic level, nature of the household, number of existing children, and gender of the first child were related to fertility intentions of young people of childbearing age. In terms of fertility attitudes, the expectations of contemporary young people of childbearing age have shifted from the traditional attitude of 鈥渆mphasizing sons over daughters鈥� to the attitude of " gender equality 鈥�.

Conclusion

The low fertility intentions of young people of childbearing age in China are constrained by a combination of economic, age, and level of education. The government may increase the fertility intention of young people by increasing the economic support for fertility, implementing the local education promotion related to 鈥減arenting鈥�, and actively implementing the early and active fertility of young people of childbearing age.

Introduction

The aging of the population has become a serious global public health issue. The proportion of people aged 65 or over will be about 10.5% in 2024, and will reach 16% by 2050. The main reason for this phenomenon is that human life expectancy is longer, while the birth rate has continued to decline. However, the annual birth rate in China has shown a seriously decreasing trend. Notably, the seventh population census report revealed that China鈥檚 total fertility rate has dropped from 5.59 in 1971 to 1.15 in 2021 [1]. In response to this phenomenon, China has successively introduced a series of fertility policies, including 鈥渢he two-child policy for couples where both parents are only children鈥� implemented in November 2011, the 鈥渋ndividual two-child鈥� policy implemented in 2013, the 鈥渃omprehensive two-child policy鈥� introduced in 2016, and the 鈥渢hree-child policy鈥� enacted in 2021. However, despite the introduction of these policies, the total fertility rate of China鈥檚 population has not shown substantial improvements [2]. Some studies have found that the fertility level of the population is determined by the process of fertility behavior and individual fertility intentions. The process of fertility behavior is affected by a combination of factors, mainly including individual and family factors, social and cultural factors, economic and policy factors, and technological progress [3]. Fertility desire refers to an individual鈥檚 expectation of having children, which is manifested in the time of willingness to have children, the wanted number of children, and the gender of the wanted children. Fertility desire can also be understood as people鈥檚 attitudes and perceptions of fertility behavior [4].

At present, scholars in China have conducted research on fertility intentions from different perspectives, including nationwide large-sample survey studies and local survey studies. In these studies, the research object was broader, involving women of childbearing age, only children, migrant population, high-income groups, urban youth, college students, ethnic minorities, and so on [5]. Research on fertility intention mainly analyzes the basic situation of fertility, such as the ideal number of children and gender preference. The data obtained by Zheng Zhenzhen and others from a survey in Jiangsu Province in 2011 revealed that the average ideal number of children among the survey respondents was 1.47, suggesting that the current fertility level of China鈥檚 childbearing age population was lower than the replacement level; moreover, among women who have not yet given birth, the ideal number of children was on the low side. This number was not affected by residential area, occupation, and, age, and education, among other factors [6]. Wang Lei compared the fertility intentions of rural and urban areas and found that male children were favored in some of China鈥檚 rural areas, while the urban population adopted a more gender-equal stance, which was attributed to rapid economic development and high modernization level [7].

In summary, the fertility intentions of people of childbearing age are affected by factors at different levels. Although the government has made adjustments to the pressure of fertility costs and increased fertility subsidies, there are still large diffecences in the economic need between rural areas and unban areas. Therefore, which cannot be summarized by a single study. Secondly, the urgency of policy adjustment: China鈥檚 fertility policy has undergone a transition from 鈥渙ne child鈥� to 鈥渢hree children鈥�, which requires us to re-evaluate and understand the fertility desire of young people of childbearing age, so that the policy can respond more effectively to social needs. Therefore, we use a combination of subject words and free words to determine the childbearing age population after careful pre-search checks, a systematic review and analysis of the factors influencing the fertility intentions of young people of childbearing age in China is required to provide a basis for the formulation of relevant policies [8].

Materials and methods

This study complied with the Program for Reporting of Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The protocol of this systematic review was registered in the International Prospective Systematic Review Registry (registration number: CRD42024553110).

Literature search and strategies

A systematic literature search was conducted from January 2012 to September 2023 through the databases PubMed, Web of Science, Embase, CNKI, Wanfang, and VIP. Among them, the Chinese keywords included: fertility desire, fertility intention, fertility plan, fertility purpose, fertility motivation, youth, urban youth, school-age youth, childbearing age youth; the English keywords included: fertility desire, fertility intention, fertility plan, fertility willingness, youth, young people, child-bearing youth, and school-age youth. In addition, we used combinations of subject words and free words, determined after careful pre-search inspection. Specific search strategies are shown in the Table 1. A total of 977 articles were retrieved, among which 18 were finally included in the analysis.

Table 1 Search strategy

Inclusion and exclusion criteria

Inclusion Criteria: (1) Original cross-sectional study; (2) Participants were the Chinese population of childbearing age; (3) The study reported the presence or absence of fertility intentions, the number of children, and preferences for having children and related factors affecting childbearing.

Exclusion criteria: Studies, such as reviews, editorials, opinion papers, or letters to the editor, were excluded. Reproductive disorders, immune system disorders, and sexually transmitted diseases were also excluded. The articles ultimately included in this paper were selected independently by two reviewers after evaluating the titles, abstracts, and full text of all articles retrieved. Any disagreements regarding article selection were resolved by discussion with a third researcher.

Data extraction

Primary data were extracted from the included articles according to a standardized scale (Cochrane Effective Practice and Organization of Care Review Group data collection checklist), mainly including authors and year of publication, region, type of study and sample size, measurement tools used, age, and mean number of children desired. The data extraction was performed by one of the authors, which was reviewed by the other two authors independently. Disagreements were resolved by group discussion.

Quality assessment

Two reviewers independently assessed the quality of each eligible article by using the Guidelines for Strengthening Observational Studies in Epidemiology (University of Bern, 2009). The guidelines included 22 items to assess the quality of the cross-sectional and case-control articles. Each item was scored 1 if the study met the criteria of the guideline, otherwise, a score of 0 was given. The score of each item was added to obtain the final quality score, with a maximum score of 22 (The specific criteria in Table 2). Studies with a total score of 鈮モ��17 were categorized as high quality, those with a total score of 11鈥�16 were categorized as moderate quality, and those with a total score of 鈮も��10 were categorized as low quality. The quality assessment of the 18 included quantitative studies did not identify any low-quality studies. Among them, 9 were high-quality studies and 9 were moderate-quality studies.

Table 2 STROBE guidelines

Statistical analysis

In this study, a meta-analysis was performed to explore the relationship between factors and fertility intentions among young people of childbearing age. The meta-analysis was conducted to determine the significance of the combined Odds ratio(OR) by Z-test. Heterogeneity was estimated by the Q statistic and assessed by the I² statistic. If inter-study heterogeneity was low (I²鈥夆墹鈥�50%, P鈥夆墺鈥�0.1), it was considered that there was no significant heterogeneity among the studies, a fixed-effects model was used; otherwise (I²鈥�&驳迟;鈥�50%, P<0.1), it was considered that there was significant heterogeneity among the studies, a random-effects model was used. The included literature was eliminated one by one, and the sensitivity analysis of the combined detection rate was performed. Visual inspection of funnel plots and statistical assessment with Eggers鈥� regression were used to evaluate publication bias.

Chi-square test was used to analysis the significance of difference of the categorical variables, and all statistical analyses were performed using SPSS.26.0 and RevMan V.5.2. A two-tailed P value鈥�<鈥�0.05 was considered statistically significant.

Results

Search results

In this study, 977 publications were obtained from the databases (PubMed 8, Embase 7, Web of Science 529, CNKI 203, VIP 110, Wan fang 111), and 171 duplicates were removed. Subsequently, title and abstract screening were performed, and 675 studies were excluded as they were irrelevant. Then, the full texts of 131 studies were scrutinized, of which 113 were excluded for various reasons. Finally, 18 studies were included in the systematic evaluation. The literature selection process is shown in Fig. 1.

Fig. 1

PRISMA flow chart showing the study selection process

General information

The characteristics of the included studies are shown in Table 3. Cross-sectional studies were included, and all surveys were conducted between 2012 and 2023. The sample sizes of the included studies ranged from 136 to 26,110 young people of childbearing age; the data mainly originated from the coastal regions of Zhejiang, Shanghai, Jiangsu, and Guangdong, and a few central regions of China, such as Hubei and Hunan, and most of the measurements were either self-reported questionnaires or interviews. The 18 studies concluded that young people of childbearing age intended to have an average number of children of 1.54, which is a relatively low number.

Table 3 Characteristics of included studies

According STROBE guidelines, the quality assessment of 18 studies was determed. The studies did not identify any low-quality studies. Of these, 9 were considered high quality, while nine studies were rated moderate. The distribution of scores is listed in Table 4.

Table 4 Quality evaluation results of included studies

Meta-analysis of key findings

This study focused on the three dimensions of fertility intentions (willingness to have another child, number of wanted children, and gender of wanted children). The results indicated that the reproduction willingness of young people of childbearing age was constrained by age, education, economy, and policy benefits. Those who were willing to have children also maintained an attitude of equality between men and women, believing that one boy and one girl was the best outcome. A total of 11 out of 18 studies specified the overall reproduction willingness of the study population. However, one of the single studies found that people of childbearing age who already had children showed a significant variation in their willingness to have another child, ranging between 5.20 and 60.44%. The reproduction intention of youth of childbearing age working in the education sector was higher than that of other groups.

Fertility intention

Concerning fertility intentions, the meta-analysis was based on 10 cross-sectional surveys [13, 15, 16, 18,19,20, 22,23,24,25]. All variables were dichotomized according to the characteristics of the included literature. Gender [male vs. female, OR(95%CI): 1.41(1.23, 1.61), P<0.001], age [young (under 30 years old) vs. old (over 30 years old), OR(95%CI): 1.97(1.51, 2.57)P<0.001], registered permanent residence [ural vs. urban, OR(95%CI): 1.38(1.26, 1.51), P<0.001], education [high school and below vs. university and above, OR(95%CI): 1.35(1.02, 1.78), P=0.03], gender of first child [male vs. female, OR(95%CI): 0.45(0.25, 0.81), P = 0.03], and the number of existing children [2 and above vs. 2, OR(95%CI): 2.11(1.00, 1.44), P = 0.05] were associated with the willingness of young people of childbearing age to have children. Specifically, males were more likely to want children compared to females; younger age groups (under 30 years old) were more likely to want children than older age groups (over 30 years old); rural populations were more likely to want to have children compared to urban populations. In addition, participants with a high school education or less were more likely to want children compared to those with a university education or above; families with one boy were more likely to want children compared to those with one girl; families with two or more children compared to those with two or more children were more likely to want children. Families with two or more children were more likely to have children compared to those with fewer than two children.

Meanwhile, no statistically significant difference was found between the only child status [yes vs. no, OR(95%CI): 1.29(0.43, 3.85), P=0.65] and income [lower middle (鈮も��60,000) vs. above middle (>鈥�60,000), OR(95%CI): 0.77(0.43, 1.40), P = 0.40], which were not directly associated with the willingness of young people of childbearing age to have children (Fig. 2).

Fig. 2

Forest plot showing intention to have children (yes vs no). A Gender. B Age. C Registered permanent residence. D Education. E Gender of first child. F The number of existing children. G The only child status. H Income

Number of children

Regarding the number of children, the meta-analysis was based on 9 studies that included 4552 participants [5, 9,10,11,12,13, 17, 19, 21]. The ideal number of children (鈮モ��2 vs.鈥�<鈥�2) for people of childbearing age showed no statistical significance in terms of gender [male vs. female, OR(95%CI): 1.07(0.94, 1.23), P=0.29], education [high school and below vs. university and above, OR(95%CI): 1.13(0.46, 2.79), P = 0.78], marital status [married vs. unmarried, OR(95%CI): 1.18(0.62, 2.23), P = 0.61], registered permanent residence [rural vs. urban, OR(95%CI): 0.97(0.82, 1.15), P = 0.73], and only child [yes vs. no, OR(95%CI): 1.02(0.70, 1.49), P = 093] (Fig. 3).

Fig. 3

Forest plot showing number of children born (鈮�2 vs <2). A Gender. B Education. C Marital status. D Registered permanent residence. E The only child status

Gender preferences

Child gender preference (preference for male vs. preference for female) was assessed based on eight meta-analyses [5, 9, 11, 13,14,15, 17, 21], including a total of 954 participants. The fertility preference of young people of childbearing age was correlated with gender [male vs. female, OR(95%CI): 1.30(1.00, 1.68), P=0.05], income [lower-middle (<鈥�60,00) vs. above-middle (>鈥�60,000), OR(95%CI): 1.63(1.08, 2.45), P = 0.02], and the gender of the first child [male vs. female, OR(95%CI): 0.02(0.00, 0.09), P<0.001]. No significant correlation was observed with registered permanent residence [rural vs. urban, OR(95%CI): 1.05(0.72, 1.55), P = 0.97], education [high school and below vs. university and above, OR(95%CI): 1.05(0.70, 1.58), P = 0.80], and only child [yes vs. no, OR(95%CI): 1.05(0.73, 1.50), P = 0.80] ( Fig. 4).

Fig. 4

Forest plot showing gender preferences (preference for male vs preference for female). A Gender. B Income. C Gender of first child. D Registered permanent residence. E Education. F The only child status

Sensitivity test and publication bias

When discussing the impact of household registration type on fertility intention, we excluded the literature of Xue [24] [ before exclusion : random effect model, OR (95% CI) : 1.12 (0.82, 1.54) ; after exclusion : fixed effect model, OR (95% CI) : 1.38 (1.26, 1.51)]. Other heterogeneity was not found.

Funnel plot and Eggers鈥� regression were used to analyze the influencing factors of more included literatures(n鈥夆墺鈥�6). As illustrated through Fig. 5, the funnel plot appeared symmetrical, indicating an absence of significant publication bias in the studies we included in our meta-analysis. This initial visual evaluation was supported by the quantitative evidence from Eggers鈥� regression. And the Eggers鈥� regression produced a p-value of 0.2522 and 0.9545, exceeding the customary significance level of 0.05, thereby providing no statistical indication of publication bias. Other outcome indicators involve less literature research, so funnel plot analysis is not carried out.

Fig. 5

Funnel plot assessing publication bias ( a The impact of gender on fertility gender preferences ; b The impact of gender on expected number of children)

Fertility policy

In terms of fertility policy, all three studies showed that a minority of young people of childbearing age were not aware of fertility policies. Combined, the three studies revealed a large difference between the proportions of participants who answered 鈥渒now very much鈥� and those who answered 鈥渒now a little鈥� about fertility policies(Chi-Square test, P鈥夆墹鈥�0.000)(罢补产濒别 5). These findings suggested that the degree of knowledge about fertility policies may be a factor directly affecting the fertility intentions of young people of childbearing age.

Table 5 Differential analysis of knowledge of fertility policy

Discussion

The fertility intentions of young people of childbearing age remain a complex issue influenced by many factors. The discussion primarily revolves around individuals鈥� fertility intentions, the number of children they want, and their fertility preferences [26]. These three dimensions are intertwined and are influenced by multiple aspects, including individual, family, society, and policy factors. This study analyzed 18 cross-sectional studies, comprising a total of 43,427 participants, to analyze the fertility intentions, attitudes toward childbearing, and factors influencing fertility intentions among young people of childbearing age. The results revealed low fertility intentions among young people of childbearing age in China, at only 11.85%, with gender, age, nature of registered permanent residence, level of education, the sex of the first child, and the number of existing children having a direct impact on fertility intentions. No direct correlation was observed between economic factors and whether or not the participants is the only child status.

Fertility intentions reflect an individual鈥檚 attitude and willingness to have children at different life stages [27]. This systematic evaluation was based on cross-sectional studies and revealed that the fertility intentions of young people of childbearing age were significantly associated with gender, age, registered permanent residence, education, gender of the first child, and the number of existing children. These findings were consistent with the study from Xiangze et al., which divided 11,031 respondents into infertile and childbearing groups and conducted binary logistic regression analysis, indicating that gender, age, and education had a significant effect on fertility intentions. The difference between the non-childbearing group and the 1鈥�2 children group in terms of willingness to have children was much lower than that of 1鈥�2 versus 鈮モ��3 children families [28]. This systematic evaluation found no direct correlation between fertility intentions and the only child status, suggesting that the one-child policy exerted a minor role in controlling the birth rate. However, some studies argued that the one-child policy had many negative consequences and had a significant impact on fertility decisions [29, 30]. However, studies have reported that China has entered a low fertility era [31] and may no longer need any birth-limiting policies. Despite the implementation of the 鈥渢wo-child policy鈥� and 鈥渢hree-child policy鈥�, the birth rate has not increased significantly. In these studies, those who were willing to have children believed that the joy of life, fulfillment of parent-child bonding, family happiness, natural childbearing age, urging by elders to have children, passing on the family name, and raising children to protect the elderly were the motives for having children. In contrast, those who were unwilling to have children reported that economic pressure, affecting career development, trouble in pregnancy and childbearing, lowering the quality of life, and physical reasons contributed to not wanting to have children. In addition, some studies have shown that economic pressure and the implementation of fertility policies in reproductive age groups are major factors affecting fertility intentions [32]. Among them, Chen Xiuhong and others conducted an in-depth study on the fertility intentions of women in the reproductive age group and found that greater economic pressure reduced their willingness to have children [33]. In further exploring the mechanism of the influence of economic factors on fertility intentions, Liu Qing et al. reported that family financial burdens also impacted young parents [34]. Furthermore, Zhang Hao et al. employed logistic regression model analysis to explore the main factors affecting second-child birth plans and the extent of their influence [35]. The group concluded that the monthly family income and material values were the main factors affecting second-child birth plans. Among the eight dimensions of fertility intention explored in this study, no simple direct correlation was found between economic factors and fertility intention of young people of childbearing age, which may be similar to the 鈥渃ost-effect鈥� recognized in the West. Overseas scholars mainly investigated fertility intentions from the perspective of economic theory. G. Becker, an American economist, reported that childbearing behavior is a kind of consumption behavior and is positively correlated with family. On the one hand, children are regarded as durable consumer goods; when people鈥檚 income increases, part of this income can be allocated to raising a child. On the other hand, in order to ensure the quality of raising children, people tend to choose to have fewer children but to raise them better. Therefore, the number of children born decreases as income increases. However, American sociologist R. Easterlin put forward a different viewpoint, arguing that changes in consumption preferences caused by changes in income can have a positive impact on childbearing. Thus, the influence of economic factors on fertility intentions has a complex correlation that deserves further in-depth exploration.

The number of children born is often influenced by factors such as policies and economic conditions. Most of the existing studies indicated that the ideal number of children in China far exceeds the actual number of children, suggesting a large difference. These findings indicated that the population鈥檚 fertility intentions were greater than their fertility behaviors, under the dual effects of restrictive fertility policies and socioeconomic development [36,37,38,39]. This study showed similar results, revealing no significant difference in the ideal number of children based on the five dimensions of gender, education level, marital status, registered permanent residence, and only-child status. These findings indicate the presence of a large difference between the actual number of children and the ideal number of children. Most people prefer to have two children (the ideal number of children), but the actual willingness to have more children is low and uncertain [40]. In contrast, Jia Zhike et al. analyzed the differences in fertility intentions and actual fertility behaviors of young urban couples by constructing a dichotomous logistic regression model, focusing on the quantitative dimension, and found a significant difference between the actual number of children and the ideal number of children of urban young people; the rational gender preference, whether or not to have children, and the type of occupation were the main factors influencing the number of children. However, no difference in gender, marital status, and registered permanent residence was observed [41], which is consistent with the results of this study. Sida Li conducted an empirical study on different samples using 2015 CGSS data and nested models using Ologit modeling and found that higher levels of education had a negative impact on the number of children [42]. In practice, previous studies have often used the 鈥渋deal number of children鈥� to determine fertility intentions, which is operationalized by asking questions such as 鈥淗ow many children do you think is the ideal number of children?鈥�. Nonetheless, this approach is closer to an expression of preference or desire than a commitment to reproductive behavior. Using the ideal number of children to predict actual childbearing behavior will undoubtedly widen the gap between intentions and behavior [43]. Western societies have different explanations for the low number of children. K. Dabis proposed the theory of demographic change and response, which attributes the rapid population growth at this stage to the reduction of infant mortality. Furthermore, the modernization theory attributes the decline in the number of children to two factors. On the one hand, people pursue value realization and quality of life due to the general increase in the level of education, and their concept of childbearing has become more rational; on the other hand, as more women are employed, they are able to independently choose a variety of opportunities for development and pursue the realization of their self-worth.

Fertility preference is a complex and multilayered social phenomenon, involving the family鈥檚 [44] preference for the sex of the child and the influence of education on fertility decisions. The study revealed that with the development of the social economy and the change of fertility concepts among young people of childbearing age in China, the traditional preference for boys has gradually weakened. Moreover, concepts such as 鈥済irl preference鈥� [45] and 鈥渕ale and female alike鈥� [46] have emerged, indicating that China鈥檚 concept of raising children to prevent old age has gradually shifted to commercial pension insurance, resulting in a reduction in the overall fertility intention of young people of childbearing age [39]. This study found that the gender of family members, income, and the gender of the child born were the main influencing factors determining fertility preference, which is consistent with the findings of Jenny Li et al. The team analyzed the factors influencing the fertility preferences of young urban couples of childbearing age through a logit model and found that, unlike the previous 鈥減reference for boys,鈥� a new 鈥減reference for girls鈥� has emerged in the urban youth group. In terms of gender expectations, the desire to have another girl is much stronger than the desire to have another boy. This girl preference was first seen among women in urban areas, middle socioeconomic status, and high education level groups [47]. This may also be due to the traditional concept of marriage, where the cost of raising a boy is much higher than that of a girl, which reduces the preference for having a boy to some extent. As for factors such as registered permanent residence, education, and only-child status, no statistically significant association was found with fertility preferences. In contrast, Gao Yang and Zou Li analyzed 845 urban and rural women of childbearing age and found that rural women had a significant preference for the gender of their children, which was inconsistent with our results [48]. Similarly, Zhou Zena analyzed the fertility preferences of urban and rural families from the perspective of fertility concepts by building a multivariate logistic regression model, revealing that the gender preference of the rural family population was stronger compared to the urban families [49]. This is consistent with the theory of the 鈥渟erial model鈥� of fertility intention [50], which shows that the fertility intention of young people of childbearing age in different regions of China changes dynamically. In some places, traditional fertility concepts are still expressed [51], such as 鈥渕ore children, more happiness鈥� and 鈥減assing on the family name to the next generation鈥�. Other people prefer modern fertility concepts such as 鈥渇emale independence鈥� and 鈥渇ewer children鈥�.

In addition, in line with the global trend of establishing service-oriented governments, the public service theory has been recognized by a growing number of experts and scholars [52]. The fertility intentions of the reproductive-age population are influenced by factors such as social management, social insurance, medical services, and public service systems such as women鈥檚 employment security [53]. Liang Chengcheng and Wang Peng explored the impact of public services on residents鈥� fertility intentions with the CGSS survey data in 2013 and 2015, respectively. The results indicated that among residents between the ages of 20 and 50, the continuous improvement of educational resources, social management, social security, low-income insurance, housing, labor and employment, and culture and sports all significantly contribute to the willingness to have a second child of the; to varying degrees, all of them increased the willingness to have a child [54]. However, a single study revealed that the difference between the number of young people of childbearing age who 鈥渒now a lot鈥� and those who 鈥渒now a little鈥� about the birth policy was significant, this suggests that fertility policies have a significant impact on the population鈥檚 fertility intentions. But, studies in different regions found that there were significant differences in the understanding of policies ( P鈥�<鈥�0.05 ). It shows that the fertility policy is closely related to the influencing factors of fertility intention, indicating that the improvement of fertility support policy is the key issue that needs to be notable in the future. When formulating fertility policies, the government needs to consider the differences in these regions and formulate differentiated fertility policies. As mentioned in Yu Jie鈥檚 study [14], raising the fertility rate requires corresponding public policies to promote and reduce the cost of parenting. At the same time, more supporting measures are introduced for fertility, so that ordinary families are willing to give birth, be able to afford and raise well, including incentives for families with multiple children, reduction of personal income tax for families with multiple children, and extension of maternity leave. Therefore, in the context of China鈥檚 recent adjustment of its progressive fertility policy [55], the creation of fertility-friendly cities has become a focal point on the government鈥檚 agenda [56]. Society needs to provide sufficient fertility support to provide a favorable environment for birth and education. Furthermore, the costs of childbearing should be shared by improving public service facilities and increasing social support to improve the fertility rate.

Limitations and recommendations

Limitations: (1) A high degree of heterogeneity was found in the subgroup analyses due to differences in the time and place of the study; (2) Considering the limited number of articles included, participants of childbearing age could not be further subgrouped. For example, the fertility intentions and attitudes of married people of childbearing age could not be compared to their unmarried counterparts; therefore, the difference in fertility intentions and attitudes of the fertile and infertile groups, of different genders and age groups, as well as the timing of and intervals between childbearing remain uncertain; (3) this study did not consider the family as a unit, and did not explore the effects of the interpersonal relationships and philosophies within the family on the fertility intentions of young people of childbearing age. Hence, the specific meaning of fertility intention could not be explored, requiring further research; (4) due to limited data, this paper does not explore the impact of macro factors, such as social welfare policies, on the fertility of young people of childbearing age.

Moreover, considering the vastness of China鈥檚 geography and the unevenness of its economic development, differences in fertility intentions of young people of childbearing age among different regions have not yet been fully elucidated. In addition to intuitive economic and family size factors, gender relations within the family and pressure from parents to have children are also areas that need further study. Finally, another perspective to be explored is the effects of the conflict between traditional Chinese fertility culture and modern fertility concepts on young people鈥檚 fertility concepts.

Recommendation: In order to increase the fertility intentions of young people of childbearing age, a number of policy measures are needed. First, the government can implement economic incentives, such as the provision of maternity allowances and childcare subsidies, to alleviate the costs of childbearing and childcare. Second, more flexible working arrangements, including support for flexible working hours and telecommuting, should be implemented to help parents better balance work and family responsibilities. In addition, childcare service facilities should be strengthened to assist parents in arranging for the care of their children. Comprehensive maternity medical protection should be provided, including prenatal check-ups, delivery services, and post-natal rehabilitation care. Furthermore, through appropriate housing policies, young couples should be helped to obtain suitable living conditions conducive to childbirth and child-rearing. Finally, social values that encourage childbearing should be established through education, publicity, and guidance, whereas the negative perceptions of childbearing should be eliminated, thereby creating a positive atmosphere for childbearing. These policy measures will increase the willingness of young people of childbearing age to have children and create a better environment and support system.

Conclusion

The low fertility intentions of young people of childbearing age in China are constrained by a combination of physical fitness, the cost of childbearing, and policy benefits. Studies have shown that gender, age, nature of registered permanent residence, educational level, gender of the first child, and the number of children they have significantly affect the fertility intentions of young people of childbearing age. Economic factors are the main influence on fertility preferences, while no significant effect has been found on the number of children.