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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Sociol.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Sociology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Sociol.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2297-7775</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="doi">10.3389/fsoc.2026.1617367</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Kindergarten development and fertility intention: an empirical study from China</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Jingyi</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn0001"><sup>&#x2020;</sup></xref>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Gao</surname>
<given-names>Lifei</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Guojun</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="author-notes" rid="fn0001"><sup>&#x2020;</sup></xref>
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<aff id="aff1"><label>1</label><institution>School of Modern Service Management, Shandong Youth University of Political Science</institution>, <state>Shandong</state>, <country country="cn">China</country></aff>
<aff id="aff2"><label>2</label><institution>School of Economics, Beijing Technology and Business University</institution>, <city>Beijing</city>, <country country="cn">China</country></aff>
<aff id="aff3"><label>3</label><institution>School of Insurance and Economics, University of International Business and Economics</institution>, <city>Beijing</city>, <country country="cn">China</country></aff>
<author-notes>
<corresp id="c001"><label>&#x002A;</label>Correspondence: Lifei Gao, <email xlink:href="mailto:lifeigao@btbu.edu.cn">lifeigao@btbu.edu.cn</email></corresp>
<fn fn-type="other" id="fn0001">
<label>&#x2020;</label><p>ORCID: Jingyi Wang, <uri xlink:href="https://orcid.org/0000-0002-5924-5964">orcid.org/0000-0002-5924-5964</uri>; Lifei Gao, <uri xlink:href="https://orcid.org/0000-0001-5671-9805">orcid.org/0000-0001-5671-9805</uri>; Guojun Wang, <uri xlink:href="https://orcid.org/0000-0002-0236-7047">orcid.org/0000-0002-0236-7047</uri></p>
</fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-02-16">
<day>16</day>
<month>02</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2026</year>
</pub-date>
<volume>11</volume>
<elocation-id>1617367</elocation-id>
<history>
<date date-type="received">
<day>24</day>
<month>04</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>06</day>
<month>01</month>
<year>2026</year>
</date>
<date date-type="accepted">
<day>22</day>
<month>01</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2026 Wang, Gao and Wang.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Wang, Gao and Wang</copyright-holder>
<license>
<ali:license_ref start_date="2026-02-16">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<sec id="sec1001">
<title>Introduction</title>
<p>Faced with the inevitable trend of low fertility, how to build a more sustainable society that enables people to have the number of children they desire is becoming a global problem.</p>
</sec>
<sec id="sec1002">
<title>Methods</title>
<p>Based on data from the China General Social Survey and China Education Statistics, we use Poisson regression, ordered logit regression, bootstrap analysis and other methods to explore the relationship between kindergarten development and fertility intention.</p>
</sec>
<sec id="sec1003">
<title>Results</title>
<p>There is a positive correlation between developing kindergartens and fertility intention. The robustness tests prove the reliability of the research results. Developing kindergartens improves fertility intention by supporting career aspirations and increasing leisure accessibility. Women with a higher education level exhibit a stronger association with the development of kindergartens.</p>
</sec>
<sec id="sec1004">
<title>Discussion</title>
<p>Although the empirical evidence is based on fertility intentions rather than realized fertility outcomes, the observed evidence may offer potential directions for optimizing demographic structures and fostering sustainable socioeconomic development. In the future, countries with low fertility should take measures to increase the supply of high-quality kindergartens and relieve childcare pressure to effectively solve the problem of low fertility.</p>
</sec>
</abstract>
<kwd-group>
<kwd>career aspiration</kwd>
<kwd>fertility intention</kwd>
<kwd>kindergarten development</kwd>
<kwd>leisure accessibility</kwd>
<kwd>work&#x2013;family conflict</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the &#x201C;BTBU Digital Business Platform Project by BMEC&#x201D;, which is led by the corresponding author Lifei Gao, and the &#x201C;Research on the Development Path of China&#x2019;s Silver Economy in the Digital Intelligence Era (XXPY25074)&#x201D;, which is led by  the first author Jingyi Wang.</funding-statement>
</funding-group>
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<fig-count count="3"/>
<table-count count="8"/>
<equation-count count="2"/>
<ref-count count="66"/>
<page-count count="12"/>
<word-count count="9231"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Sociology of Families</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec1">
<label>1</label>
<title>Introduction</title>
<p>The total fertility rate (TFR) is a measure of the level of fertility over time. Population stability can be preserved above a TFR of 2.1 (<xref ref-type="bibr" rid="ref54">United Nations Population Fund, 2023</xref>). Affected by rising female educational attainment, advancing urbanization, greater accessibility to contraception, reproductive health services, and reduced child mortality, fertility continue to decline (<xref ref-type="bibr" rid="ref19">Goujon, 2025</xref>). According to the World Bank, many nations have already seen a decline in overall fertility rates below 2.1 by 2022 (<xref ref-type="bibr" rid="ref62">World Bank, 2022</xref>). The overall fertility rate in Europe in 2022 was below 2.0, and the United States has remained at 1.7. Korea has the world&#x2019;s lowest fertility rate, with an average of 0.8 children per woman, and Japan had an overall fertility rate of 1.3. Longitudinally, most developed western countries were already facing a relatively severe population crisis by the middle or end of the last century. In 2000, the number of countries which had a TFR below the replacement level of 2.1 in the world was around 70. In 2021, this number increased to nearly 110, of which nearly 40 countries and territories have experienced negative or zero population growth (<xref ref-type="bibr" rid="ref63">Wu, 2020</xref>). The population decline and aging caused by low fertility present serious challenges to public finances, social security, pension systems, and health care, directly affecting a country&#x2019;s socioeconomic development and national security (<xref ref-type="bibr" rid="ref24">Harper, 2014</xref>).</p>
<p>To address the issue of declining fertility rates, governments worldwide have explored a range of family policies aimed at encouraging childbirth, which can be broadly categorized into three types: economic incentives, public service support, and cultural interventions (<xref ref-type="bibr" rid="ref16">Gauthier, 2007</xref>). Economic incentives are the most direct approaches to promoting fertility, typically taking the form of cash subsidies, tax reductions, or housing support (<xref ref-type="bibr" rid="ref65">Zhang et al., 2023</xref>). These measures have been shown to be more effective among low-income families, while their impact on high-income households tends to be weaker (<xref ref-type="bibr" rid="ref32">Kalwij, 2010</xref>). Cultural intervention policies are designed to enhance fertility intentions by shifting societal norms. However, the effects of these policies are typically slow to materialize and difficult to quantify, as they often depend on the long-term construction of social consensus (<xref ref-type="bibr" rid="ref39">Mcdonald, 2006</xref>). Public service support policies primarily include childcare services, parental leave systems, and educational subsidies. These measures have proven effective in increasing fertility intentions (<xref ref-type="bibr" rid="ref36">Luci-Greulich and Th&#x00E9;venon, 2013</xref>).</p>
<p>While well-designed family support policies can moderately raise fertility rates or prevent them from declining further, such impacts are usually modest and far from restoring fertility to the replacement level (<xref ref-type="bibr" rid="ref17">Gauthier and Gietel-Basten, 2025</xref>). According to the theory of demographic transition, low fertility is an inevitable outcome of socioeconomic development. At present, most regions of the world have undergone demographic transition, and more regions will experience fertility decline driven by educational advancement, urbanization, and improved child survival rates in the future. Given the inevitability of low fertility rates, the focus of policies should shift from efforts to raise fertility to a specific level toward building a fairer, more sustainable, and more caring society&#x2014;one that enables people to have the number of children they desire [<xref ref-type="bibr" rid="ref55">United Nations Population Fund (UNFPA), 2025</xref>]. As a key component of such a society, the enhancement of both the accessibility and quality of childcare services is strongly positively associated with the overall fertility trend (<xref ref-type="bibr" rid="ref5">Bergsvik et al., 2021</xref>). Accordingly, this study integrates individual-level data from the Chinese General Social Survey (CGSS) database with provincial-level indicators from the China Education Database, to investigate the relationship between regional kindergarten development and individual fertility intentions (expected number of children), with a focus on elucidating the underlying mechanisms. The regional kindergarten development is a comprehensive concept in this study, including the number of kindergartens as well as the staffing of teachers and allocation of facilities.</p>
<p>Compared to prior studies, this research makes three significant contributions. First, to our knowledge, it provides the first empirical evidence linking comprehensive kindergarten development to fertility intentions. Unlike previous research that has focused on isolated factors such as cash subsidies or childcare quantity, this study offers a more holistic perspective, thereby complementing existing research on the determinants of fertility behavior. Second, it documents two key pathways&#x2014;career aspiration and leisure accessibility&#x2014;that help explain the correspondence between kindergarten development and fertility decisions. Third, the results offer actionable policy insights. Specifically, expanding kindergarten infrastructure and optimizing early childcare services emerge as viable strategies to address low fertility intentions. These findings provide critical theoretical and practical guidance for China to solve its low-fertility problem and achieve sustainable socioeconomic development.</p>
<p>The remainder of this paper is organized as follows. Section 2 introduces the decline in China&#x2019;s fertility rate and the policy responses. Section 3 reviews the relevant literature and formulates theoretical hypotheses. Section 4 describes the data and variables. Section 5 introduces the methodology. The results are presented in Section 6. The final section discusses the results and provides conclusions.</p>
</sec>
<sec id="sec2">
<label>2</label>
<title>Background: fertility decline and policy responses in China</title>
<p>As the largest developing economy, China is also faced with the issue of declining fertility rates. China&#x2019;s TFR was approximately 6 in 1970, but it had declined significantly to around 1 by 2023, significantly below the replacement level of 2.1. The rapid decline in China&#x2019;s fertility rate can be attributed to historical factors. In the early years of the People&#x2019;s Republic of China, high fertility intentions among the population, coupled with relatively lenient government policies on childbirth, led to rapid population growth (<xref ref-type="bibr" rid="ref40">National Bureau of Statistics of China, 2024</xref>). In response to the challenges posed by overpopulation, the central government implemented the family planning policy in the 1970s, advocating for one child per couple. This policy effectively controlled population growth, with scholars estimating that it prevented the birth of 264&#x2013;320 million people between 1972 and 2000 (<xref ref-type="bibr" rid="ref58">Wan et al., 2020</xref>).</p>
<p>Since the beginning of the 21st century, improvements in living standards and advancements in medical technology have led to increased life expectancy. However, the decline in fertility rates has directly contributed to the deepening of China&#x2019;s population aging. Recognizing the severity of the population structure imbalance, the government began adjusting its family planning policies. In 2011, the two-child policy was introduced, and in 2021, the policy was further relaxed to allow couples to have three children. Nevertheless, due to the long-tail effects of the family planning policy and the high costs of child-rearing, the relaxation of birth restrictions has had limited effect in increasing fertility. <xref ref-type="fig" rid="fig1">Figure 1</xref> illustrates that China&#x2019;s birth rate has been declining steadily since 2017, reaching a record low in 2023 since 1949. The birth rate has fallen significantly below the death rate, resulting in a natural population growth rate of &#x2212;1.48&#x2030;. With the intensification of population aging, if fertility rates remain persistently low, it will pose significant challenges to the sustainable development of the economy and society. Enhancing fertility rates is a crucial strategy for addressing the population crisis. Specifically, effectively increasing individuals&#x2019; fertility intentions represents a key leverage point for improving national fertility rates.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Changes in China&#x2019;s birth, death, and natural growth rates, 1978&#x2013;2023 (&#x2030;). Data source: National Bureau of Statistics of the People&#x2019;s Republic of China.</p>
</caption>
<graphic xlink:href="fsoc-11-1617367-g001.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Line graph showing birth rate, mortality rate, and natural growth rate from 1978 to 2023. Birth rate starts around 18, peaking near 1983 and declining sharply by 2023. Mortality rate remains steady around 5. Natural growth rate fluctuates, dropping below zero by 2023.</alt-text>
</graphic>
</fig>
<p>In response to national fertility policies, China has prioritized the development of childcare services. During 2016&#x2013;2017, the government introduced policy documents to guide and incentivize social organizations to establish non-profit women&#x2019;s and children&#x2019;s hospitals, affordable nurseries, and kindergartens. Qualified kindergartens were further urged to provide public early education guidance for children aged 0&#x2013;3 to parents and communities (<xref ref-type="bibr" rid="ref50">The Central Committee of the Communist Party of China, &#x0026; The State Council of the People&#x2019;s Republic of China, 2015</xref>). In 2021, aligning with the three-child policy, China has promulgated policy documents that explicitly identify the establishment of an inclusive childcare service system as a key measure to enhance fertility support (<xref ref-type="bibr" rid="ref51">The Central Committee of the Communist Party of China, &#x0026; The State Council of the People&#x2019;s Republic of China, 2021</xref>). By 2024, the State Council has released a report on childcare services, which highlights persistent challenges, including insufficient supply, uneven service quality, and inadequate policy support (<xref ref-type="bibr" rid="ref52">The State Council of the People&#x2019;s Republic of China, 2024</xref>). The report emphasized the urgent need to accelerate the expansion of childcare infrastructure and foster a socially supportive environment for childbearing to address these gaps.</p>
</sec>
<sec id="sec3">
<label>3</label>
<title>Literature review and theoretical hypothesis</title>
<sec id="sec4">
<label>3.1</label>
<title>Childcare services and fertility intentions</title>
<p>Childcare for infants and young children has become a critical factor influencing family fertility intentions (<xref ref-type="bibr" rid="ref59">Wang et al., 2019</xref>; <xref ref-type="bibr" rid="ref26">Hong and Zhu, 2022</xref>). In China, inter-generational care plays a significant role in the childcare system, effectively alleviating parenting pressures for couples and enhancing fertility intentions to some extent (<xref ref-type="bibr" rid="ref21">Gu et al., 2021</xref>). However, with demographic shifts and evolving societal attitudes, a noticeable trend toward the &#x201C;de-familialization&#x201D; of childcare has emerged, as an increasing number of young parents seek external childcare support. Survey results indicate that accessible childcare services rank as the top policy demand among individuals of childbearing age (<xref ref-type="bibr" rid="ref64">Yang et al., 2021</xref>). Families urgently require affordable, state-supported childcare services to assist in child-rearing (<xref ref-type="bibr" rid="ref26">Hong and Zhu, 2022</xref>). A study on the fertility intentions of women of childbearing age in Shanghai specifically highlights that the positive impact of grand-parental care on fertility intentions is significantly weaker than that of formal childcare services (<xref ref-type="bibr" rid="ref53">Tian et al., 2020</xref>). Therefore, expanding the provision of public childcare services and offering high-quality social childcare support for infants and young children represent the most effective policies for boosting fertility intentions, particularly for second-child births.</p>
<p>Kindergartens are specialized institutions providing childcare services for young children. The development of these institutions is examined for its potential to enhance fertility intentions. A study conducted quasi-natural experiments to investigate the impact of kindergarten supply expansion and public childcare services on fertility intentions. Their findings reveal that policies promoting public kindergartens have a positive causal effect on family size, while public childcare services increase residents&#x2019; intentions to have a second child by approximately 16.2&#x2013;20.5% (<xref ref-type="bibr" rid="ref30">Jiang, 2021</xref>; <xref ref-type="bibr" rid="ref11">Chen et al., 2023</xref>).</p>
<p>The positive impact of increased childcare services on fertility rates has been widely supported in developed countries. In Norway, a 20-percentage-point increase in childcare provision would result in no more than 0.05 additional children per woman in completed cohort fertility (<xref ref-type="bibr" rid="ref33">Kravdal, 1996</xref>). It is also a Norwegian study that finds an increase in childcare coverage from 0 to 60% is associated with an average increase of 0.5&#x2013;0.7 children among women up to 35&#x202F;years old (<xref ref-type="bibr" rid="ref43">Rindfuss and Choe, 2016</xref>). A major childcare reform in Germany resulting in a 10-percentage-point increase in public childcare coverage led to an additional 1.2 births per 1,000 women (<xref ref-type="bibr" rid="ref3">Bauernschuster et al., 2016</xref>). Similarly, a positive effect of expanded childcare services on first birth odds also be observed in Belgium (<xref ref-type="bibr" rid="ref61">Wood and Neels, 2019</xref>).</p>
<p>Based on the above analysis, the first hypothesis is proposed:</p>
<disp-quote>
<p><italic>H1</italic>: The development of kindergartens can enhance individuals' fertility intentions.</p>
</disp-quote>
</sec>
<sec id="sec5">
<label>3.2</label>
<title>Work&#x2013;family conflict and fertility intentions</title>
<p>Work and family demands are inherently incompatible in terms of time, energy, and behavioral norms (<xref ref-type="bibr" rid="ref31">Kahn et al., 1964</xref>). Work-related pressures can spill over into the family domain, and conversely, family-related demands can also affect work performance (<xref ref-type="bibr" rid="ref14">Crouter, 1984</xref>). Following industrialization, significant changes in the nature and organization of work have exacerbated work&#x2013;family conflicts (<xref ref-type="bibr" rid="ref9">Brewster and Rindfuss, 2000</xref>). Women, in particular, have been disproportionately affected by these challenges (<xref ref-type="bibr" rid="ref12">Connelly, 1992</xref>).</p>
<p>Work&#x2013;family conflict is a significant factor contributing to low fertility intentions. Childbearing requires women to reallocate a portion of their time and energy from work to childcare. This reallocation often subjects them to severe time pressures in their professional lives and significantly reduces their fertility intentions (<xref ref-type="bibr" rid="ref4">Begall and Mills, 2011</xref>). In more extreme cases, some women may forgo opportunities to re-enter the workforce due to childcare responsibilities. This inability to achieve self-fulfillment through their careers can further exacerbate the negative impact on their fertility intentions (<xref ref-type="bibr" rid="ref60">Wesolowski, 2015</xref>). This issue is not limited to women. Men also experience tensions between career aspirations and childbearing, as they often perceive child-rearing as a potential disruption and source of pressure in their professional lives (<xref ref-type="bibr" rid="ref35">Langdridge et al., 2005</xref>; <xref ref-type="bibr" rid="ref47">Schytt et al., 2014</xref>). Additionally, reduced accessibility to formal early childhood education has been shown to decrease labor force participation rates and working hours (<xref ref-type="bibr" rid="ref15">Doiron and Kalb, 2005</xref>; <xref ref-type="bibr" rid="ref57">Viitanen, 2005</xref>). This, in turn, makes it difficult for individuals to achieve their career goals and exerts a negative influence on fertility intentions (<xref ref-type="bibr" rid="ref60">Wesolowski, 2015</xref>).</p>
<p>Insufficient leisure time is another manifestation of work&#x2013;family conflict. A primary source of work&#x2013;family conflict lies in the allocation of time, where inadequate leisure time directly reflects this tension (<xref ref-type="bibr" rid="ref20">Greenhaus and Beutell, 1985</xref>). Both the quantity and quality of leisure time can influence fertility decisions (<xref ref-type="bibr" rid="ref18">Gimenez-Nadal and Sevilla, 2012</xref>). Moreover, the quality of leisure time, including its predictability and degree of autonomy, has a more significant impact on fertility intentions than its quantity (<xref ref-type="bibr" rid="ref13">Craig and Mullan, 2011</xref>). Empirical studies have demonstrated that an increase of just 1 h of leisure time per week can significantly enhance fertility intentions (<xref ref-type="bibr" rid="ref18">Gimenez-Nadal and Sevilla, 2012</xref>).</p>
<p>Career aspiration and leisure accessibility often compete with child-rearing responsibilities, which is a phenomenon observed in both women and men (<xref ref-type="bibr" rid="ref22">Hakim, 2003</xref>; <xref ref-type="bibr" rid="ref37">Martinez Mendiola and Cortina, 2024</xref>). Individuals can mitigate work&#x2013;family conflicts arising from childbearing by seeking external support (<xref ref-type="bibr" rid="ref25">Hobfoll, 1989</xref>). High-quality kindergartens typically provide superior childcare services, thereby alleviating the time and energy demands on parents. By entrusting their children to these institutions, parents are afforded greater opportunities to pursue career goals and engage in leisure activities, which in turn enhances their fertility intentions.</p>
<p>Based on the above analysis, the following hypotheses are proposed:</p>
<disp-quote>
<p><italic>H2a</italic>: The development of kindergartens enhances fertility intentions by supporting parents' career aspirations.</p>
</disp-quote>
<disp-quote>
<p><italic>H2b</italic>: The development of kindergartens enhances fertility intentions by increasing parents' leisure accessibility.</p>
</disp-quote>
</sec>
<sec id="sec6">
<label>3.3</label>
<title>Theoretical framework: theory of planned behavior</title>
<p>In the Theory of Planned Behavior, a behavioral intention can find expression in behavior, which is influenced by the attitude toward the behavior, subjective norm and perceived behavioral control (<xref ref-type="bibr" rid="ref1">Ajzen, 1991</xref>). The more favorable the attitude and subjective norm with respect to a behavior, and the greater the perceived behavioral control, the stronger should be an individual&#x2019;s intention to perform the behavior under consideration. If a person can decide whether to perform the behavior, and intends to do so, he or she should be able to successfully translate that intention into action.</p>
<p>Building on the Theory of Planned Behavior (<xref ref-type="bibr" rid="ref1">Ajzen, 1991</xref>), fertility intentions can capture the motivational factors that influence fertility behavior, which can be conceptualized as the result of three sets of determinants: (i) attitudes towards having (additional) children, (ii) subjective norms regarding appropriate family size, and (iii) perceived behavioral control, that is, the perceived ability to realize childbearing plans given time, financial and institutional constraints. In this framework, kindergarten development is expected to influence fertility intentions primarily through perceived behavioral control. By reducing time-intensive childcare demands and improving the compatibility between employment, leisure and parenting, high-quality kindergartens increase parents&#x2019; perceived ability to have (additional) children without sacrificing their career aspirations or leisure needs. At the same time, visible public investment in early childhood education may also foster more positive attitudes toward childbearing and contribute to a normative climate more supportive of larger family sizes. Thus, the Theory of Planned Behavior provides a coherent micro-foundation for our hypotheses: we expect higher levels of kindergarten development to be associated with stronger fertility intentions overall (H1), and to operate in particular through the mechanisms of career aspiration and leisure accessibility (H2a, H2b). <xref ref-type="fig" rid="fig2">Figure 2</xref> illustrates the theoretical framework of this paper based on the Theory of Planned Behavior.</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Theoretical framework based on the theory of planned behavior.</p>
</caption>
<graphic xlink:href="fsoc-11-1617367-g002.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Flowchart illustrating factors affecting fertility behavior. Arrows connect boxes labeled: Kindergarten Development, Attitude toward the behavior, Subjective norm, Perceived behavioral control, Fertility Intention, and Fertility Behavior. Perceived behavioral control includes Career aspiration and Leisure accessibility. Solid and dashed arrows indicate relationships between elements.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec id="sec7">
<label>4</label>
<title>Data and variables</title>
<sec id="sec8">
<label>4.1</label>
<title>Data source</title>
<p>The data utilized in this study are sourced from the Chinese General Social Survey (CGSS) database, the China Family Panel Studies (CFPS) database and the China Education Database. Both the CGSS and the CFPS have surveyed respondents&#x2019; fertility intentions. However, the CGSS conducts such surveys in each survey year, while the CFPS only included this survey in 2018. To ensure the credibility of the research results, we use the CGSS for the baseline regression and the CFPS for the robustness test. China Education Database provides detailed information on the development of kindergartens in each province every year, including the number of kindergartens, kindergarten staff, and operational conditions. To make full use of the individual samples and explore the impact of kindergarten development on individual fertility intention, we match the data from the CGSS for the years 2012, 2013, 2015, 2017 and 2018 with provincial-level kindergarten indicators from China Education Database for the corresponding years. Considering that the fertility intentions among individuals outside the childbearing age range make a limited contribution to the increase in social fertility, we restrict our sample to individuals aged 16&#x2013;49. After removing missing values and outliers, we construct a database comprising 20,434 samples.</p>
</sec>
<sec id="sec9">
<label>4.2</label>
<title>Variables</title>
<sec id="sec10">
<label>4.2.1</label>
<title>Dependent variables</title>
<p>The dependent variable is people&#x2019;s fertility intention. Fertility intentions can be categorized into three levels: the ideal, expected, and intended number of children. These levels reflect varying degrees of alignment with actual fertility behavior. The ideal number of children reflects individuals&#x2019; attitudes toward childbearing and is not closely related to actual fertility behavior (<xref ref-type="bibr" rid="ref63">Wu, 2020</xref>). The expected number represents the number of children individuals would like to have, incorporating their intrinsic needs. The intended number, which is most closely related to actual fertility behavior (<xref ref-type="bibr" rid="ref45">Ryder and Westoff, 2017</xref>), reflects the number of children individuals plan to have, taking into account both realistic conditions and intrinsic needs. The CGSS questionnaire includes question related to fertility intentions: If there are no policy restrictions, how many children would you like to have? The question investigates individuals&#x2019; expected number of children, which reflects social demand for fertility (<xref ref-type="bibr" rid="ref38">McClelland, 1983</xref>) and can be a useful measure of fertility intention.</p>
</sec>
<sec id="sec11">
<label>4.2.2</label>
<title>Independent variables</title>
<p>The independent variable in this study is kindergarten development, which is represented by multiple indicators in the China Education Database. To make the study more concise, we use principal component analysis (PCA) to reduce the dimensionality of these indicators and consolidate several basic indicators into a single comprehensive variable reflecting the overall level of kindergarten development in each province. Based on the classifications in the China Education Database, five basic indicators are used: the number of kindergartens, faculty levels, indoor areas, outdoor areas, and teaching resources. The indicator composition is detailed in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S1</xref>.</p>
<p>The 15 basic indicators have a Kaiser&#x2013;Meyer&#x2013;Olkin value of 0.877 and a Bartlett significance of 0.000, indicating they are highly correlated with each other and satisfy the conditions of PCA. Using a cumulative variance contribution of over 85% (<xref ref-type="bibr" rid="ref66">Zhou et al., 2008</xref>), we extract three principal components, respectively, representing the facilities level, the faculty level and the number of kindergartens (see <xref ref-type="supplementary-material" rid="SM1">Supplementary Tables S2, S3</xref>). The final kindergarten variables are derived based on the relative weights of the variance contributions of each principal component to the cumulative variance of the extracted principal components.</p>
</sec>
<sec id="sec12">
<label>4.2.3</label>
<title>Control variables</title>
<p>According to previous studies, factors influencing fertility intentions can be categorized into individual, family, and social characteristics. Accordingly, sex (<xref ref-type="bibr" rid="ref48">Seccombe, 1991</xref>), age (<xref ref-type="bibr" rid="ref6">Berninger et al., 2011</xref>), self-rated-health (<xref ref-type="bibr" rid="ref7">Boivin et al., 2018</xref>), education (<xref ref-type="bibr" rid="ref49">Testa, 2014</xref>), household income and number of properties (<xref ref-type="bibr" rid="ref23">Hanappi et al., 2017</xref>) are selected as control variables to account for individual characteristics. Marital status (<xref ref-type="bibr" rid="ref42">Rijkin and Liefbroer, 2009</xref>; <xref ref-type="bibr" rid="ref56">Vignoli et al., 2013</xref>) is selected as a control variable to account for family characteristics. Social insurance participation is selected as a control variable to account for social characteristics. Some scholars argue that social security can alleviate the financial pressure in old age, thereby reducing the desire to have children (<xref ref-type="bibr" rid="ref8">Boldrin et al., 2015</xref>). In contrast, others suggest that the burden of social security contributions crowds out current consumption, thereby increasing fertility intentions (<xref ref-type="bibr" rid="ref29">Huang and Xu, 2019</xref>). To account for national-specific conditions, ethnicity and household registration status are included as additional control variables. Specifically, individuals of Han ethnicity tend to have lower fertility intentions compared to other ethnic groups, and fertility rates are generally lower in urban areas (<xref ref-type="bibr" rid="ref10">Chen and Shi, 2002</xref>).</p>
</sec>
<sec id="sec13">
<label>4.2.4</label>
<title>Mediating variables</title>
<sec id="sec14">
<label>4.2.4.1</label>
<title>Career aspiration</title>
<p>The difficulty in realizing career aspirations is a significant factor negatively impacting fertility intentions (<xref ref-type="bibr" rid="ref60">Wesolowski, 2015</xref>). People with higher socioeconomic status are more likely to actively explore career directions and persist in their goals, thereby maximizing the realization of their career aspirations (<xref ref-type="bibr" rid="ref28">Hu et al., 2022</xref>). Since there is a significant positive relationship between socioeconomic status and career aspirations (<xref ref-type="bibr" rid="ref27">Howard et al., 2011</xref>), this study employs the question, &#x201C;Which social stratum do you believe you belong to?&#x201D; to measure respondents&#x2019; self-socioeconomic status, serving as a proxy for career aspiration. Higher level of self-socioeconomic status indicates that an individual&#x2019;s career aspirations are relatively well satisfied.</p>
</sec>
<sec id="sec15">
<label>4.2.4.2</label>
<title>Leisure accessibility</title>
<p>When childcare support is unavailable, individuals who prioritize leisure tend to exhibit lower fertility intentions (<xref ref-type="bibr" rid="ref2">Arranz Becker and Lois, 2013</xref>). This study employs the question (i.e., &#x201C;How often have you socialized/relaxed/studied in the past year?&#x201D;) to measure respondents&#x2019; leisure engagement, which is used as a proxy for leisure accessibility. Frequent engagement in social activities, relaxation, and study suggests higher levels of leisure accessibility.</p>
<p><xref ref-type="table" rid="tab1">Table 1</xref> is the definition of variables.</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Definition of variables.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="left" valign="top">Indicator</th>
<th align="left" valign="top">Definition</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Dependent variable</td>
<td align="left" valign="top">Child</td>
<td align="left" valign="top">Expected number of children</td>
</tr>
<tr>
<td align="left" valign="top">Independent variable</td>
<td align="left" valign="top">Kindergarten</td>
<td align="left" valign="top">Kindergarten development level</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="11">Control variable</td>
<td align="left" valign="top">Gender</td>
<td align="left" valign="top">Male&#x202F;=&#x202F;1; female&#x202F;=&#x202F;0</td>
</tr>
<tr>
<td align="left" valign="top">Age</td>
<td align="left" valign="top">Respondents&#x2019; age at the time of the survey</td>
</tr>
<tr>
<td align="left" valign="top">Household registration</td>
<td align="left" valign="top">Agricultural&#x202F;=&#x202F;1; non-agricultural&#x202F;=&#x202F;0</td>
</tr>
<tr>
<td align="left" valign="top">Ethnicity</td>
<td align="left" valign="top">Han&#x202F;=&#x202F;1; minority&#x202F;=&#x202F;0</td>
</tr>
<tr>
<td align="left" valign="top">Marital status</td>
<td align="left" valign="top">Married&#x202F;=&#x202F;1; unmarried&#x202F;=&#x202F;0</td>
</tr>
<tr>
<td align="left" valign="top">Education</td>
<td align="left" valign="top">Integer variables from 1 to 14; a high value indicates a high level of education</td>
</tr>
<tr>
<td align="left" valign="top">Income</td>
<td align="left" valign="top">Logarithm of household income</td>
</tr>
<tr>
<td align="left" valign="top">House</td>
<td align="left" valign="top">Number of properties</td>
</tr>
<tr>
<td align="left" valign="top">Self-rated-health</td>
<td align="left" valign="top">Integer variables from 1 to 5; a high value indicates good health</td>
</tr>
<tr>
<td align="left" valign="top">Basic medical insurance</td>
<td align="left" valign="top">Yes&#x202F;=&#x202F;1; No&#x202F;=&#x202F;0</td>
</tr>
<tr>
<td align="left" valign="top">Basic retirement insurance</td>
<td align="left" valign="top">Yes&#x202F;=&#x202F;1; No&#x202F;=&#x202F;0</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="2">Mediating variable</td>
<td align="left" valign="top">Career aspiration</td>
<td align="left" valign="top">Integer variables from 1 to 10; a high value indicates a high degree of self-fulfillment</td>
</tr>
<tr>
<td align="left" valign="top">Leisure accessibility</td>
<td align="left" valign="top">Integer variables from 3 to 15; a high value indicates more leisure engagement</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
</sec>
</sec>
<sec sec-type="methods" id="sec16">
<label>5</label>
<title>Methodology</title>
<sec id="sec17">
<label>5.1</label>
<title>Poisson regression</title>
<p>The dependent variable is fertility intention, expressed by expected number of children, which are typically count variables. After testing, there is no overdispersion issue in the data. Therefore, the poisson regression model is used.</p>
<disp-formula id="E1">
<mml:math id="M1">
<mml:mi mathvariant="normal">E</mml:mi>
<mml:mo stretchy="true">(</mml:mo>
<mml:mtext mathvariant="italic">chil</mml:mtext>
<mml:msub>
<mml:mi>d</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mo>&#x2223;</mml:mo>
<mml:mtext mathvariant="italic">kindergarte</mml:mtext>
<mml:msub>
<mml:mi>n</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mo>,</mml:mo>
<mml:msub>
<mml:mi>X</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mo stretchy="true">)</mml:mo>
<mml:mo>=</mml:mo>
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>0</mml:mn>
</mml:msub>
<mml:mo>+</mml:mo>
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
<mml:mtext mathvariant="italic">kindergarte</mml:mtext>
<mml:msub>
<mml:mi>n</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mo>+</mml:mo>
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
<mml:msub>
<mml:mi>X</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mo>+</mml:mo>
<mml:msub>
<mml:mi>&#x03B5;</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
</mml:math>
<label>(1)</label>
</disp-formula>
<p><xref ref-type="disp-formula" rid="E1">Equation 1</xref> is the expression for poisson regression, where <inline-formula>
<mml:math id="M2">
<mml:mtext mathvariant="italic">chil</mml:mtext>
<mml:msub>
<mml:mi>d</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
</mml:math>
</inline-formula> denotes the number of children that individual i expects to have. <inline-formula>
<mml:math id="M3">
<mml:mtext mathvariant="italic">kindergarte</mml:mtext>
<mml:msub>
<mml:mi>n</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
</mml:math>
</inline-formula> indicates the development of kindergartens in each province. <inline-formula>
<mml:math id="M4">
<mml:msub>
<mml:mi>X</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
</mml:math>
</inline-formula> indicates a range of control variables, including sex, age, household registration, ethnicity, marital status, education, income, house, self-rated-health, social security participation. We also control for the impact of time.<inline-formula>
<mml:math id="M5">
<mml:mspace width="0.25em"/>
<mml:msub>
<mml:mi>&#x03B5;</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
</mml:math>
</inline-formula> is the residual term of the model. Standard errors are clustered on the provincial level.</p>
</sec>
<sec id="sec18">
<label>5.2</label>
<title>Ordered logit regression</title>
<p>In addition, we use ordered logit regression to ensure the robustness of the results.</p>
<disp-formula id="E2">
<mml:math id="M6">
<mml:mtable displaystyle="true">
<mml:mtr>
<mml:mtd>
<mml:mtext mathvariant="italic">logit</mml:mtext>
<mml:mo stretchy="true">(</mml:mo>
<mml:mi>P</mml:mi>
<mml:mo stretchy="true">(</mml:mo>
<mml:mtext mathvariant="italic">chil</mml:mtext>
<mml:msub>
<mml:mi>d</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mo>&#x2264;</mml:mo>
<mml:mi>i</mml:mi>
<mml:mo>&#x2223;</mml:mo>
<mml:mtext mathvariant="italic">kindergarte</mml:mtext>
<mml:msub>
<mml:mi>n</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mo>,</mml:mo>
<mml:msub>
<mml:mi>X</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mo stretchy="true">)</mml:mo>
<mml:mo stretchy="true">)</mml:mo>
<mml:mo>=</mml:mo>
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>0</mml:mn>
</mml:msub>
<mml:mo>+</mml:mo>
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
<mml:mtext mathvariant="italic">kindergarte</mml:mtext>
<mml:msub>
<mml:mi>n</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
</mml:mtd>
</mml:mtr>
<mml:mtr>
<mml:mtd>
<mml:mo>+</mml:mo>
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
<mml:msub>
<mml:mi>X</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mo>+</mml:mo>
<mml:msub>
<mml:mi>&#x03B5;</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
</mml:mtd>
</mml:mtr>
</mml:mtable>
</mml:math>
<label>(2)</label>
</disp-formula>
<p><xref ref-type="disp-formula" rid="E2">Equation 2</xref> is the expression for ordered logit regression. The meaning of each variable is consistent with that in the Poisson Regression.</p>
</sec>
<sec id="sec19">
<label>5.3</label>
<title>Bootstrap analysis</title>
<p>We utilize the Bootstrap method for mediating association analysis to explore the pathways that help explain the relationship between kindergarten development and fertility intentions. This statistical approach generates confidence intervals through repeated sampling procedures, does not rely on strict normality assumptions, and is applied to evaluate the statistical significance of the indirect linkage between an independent variable and a dependent variable that operates through a mediating variable.</p>
</sec>
</sec>
<sec sec-type="results" id="sec20">
<label>6</label>
<title>Results</title>
<sec id="sec21">
<label>6.1</label>
<title>Descriptive statistics</title>
<p>The kindergarten variable constructed by PCA is a continuous variable with a mean of 0. Provinces with a value greater than 0 are considered to have higher levels of kindergarten development compared to the national average, while those with a value less than 0 are considered to have lower levels. Based on this criterion, the sample is divided into two groups: a low kindergarten development group and a high kindergarten development group. <xref ref-type="table" rid="tab2">Table 2</xref> presents the distribution of fertility intentions across different subsamples.</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>The distribution of fertility intentions across different subsamples.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Variable</th>
<th align="center" valign="top" rowspan="2">
<italic>N</italic>
</th>
<th align="center" valign="top" colspan="3">Child</th>
</tr>
<tr>
<th align="center" valign="top">Mean</th>
<th align="center" valign="top">Min</th>
<th align="center" valign="top">Max</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" colspan="5">Kindergarten</td>
</tr>
<tr>
<td align="left" valign="middle">High level</td>
<td align="center" valign="middle">9,201</td>
<td align="center" valign="middle">1.99</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle">Low level</td>
<td align="center" valign="middle">11,233</td>
<td align="center" valign="middle">1.83</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="5">Gender</td>
</tr>
<tr>
<td align="left" valign="middle">Male</td>
<td align="center" valign="middle">9,616</td>
<td align="center" valign="middle">1.92</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle">Female</td>
<td align="center" valign="middle">10,818</td>
<td align="center" valign="middle">1.89</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">12</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="5">Age</td>
</tr>
<tr>
<td align="left" valign="middle">16&#x2013;20</td>
<td align="center" valign="middle">800</td>
<td align="center" valign="middle">1.73</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">10</td>
</tr>
<tr>
<td align="left" valign="middle">20&#x2013;30</td>
<td align="center" valign="middle">4,949</td>
<td align="center" valign="middle">1.85</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle">30&#x2013;40</td>
<td align="center" valign="middle">6,768</td>
<td align="center" valign="middle">1.89</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle">40&#x2013;49</td>
<td align="center" valign="middle">7,917</td>
<td align="center" valign="middle">1.96</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">12</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="5">Household registration</td>
</tr>
<tr>
<td align="left" valign="middle">Non-agricultural</td>
<td align="center" valign="middle">7,392</td>
<td align="center" valign="middle">1.79</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">11</td>
</tr>
<tr>
<td align="left" valign="middle">Agricultural</td>
<td align="center" valign="middle">13,042</td>
<td align="center" valign="middle">1.97</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="5">Nation</td>
</tr>
<tr>
<td align="left" valign="middle">Han</td>
<td align="center" valign="middle">18,733</td>
<td align="center" valign="middle">1.89</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle">Minority</td>
<td align="center" valign="middle">1701</td>
<td align="center" valign="middle">2.09</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">10</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="5">Marital status</td>
</tr>
<tr>
<td align="left" valign="middle">Married</td>
<td align="center" valign="middle">16,268</td>
<td align="center" valign="middle">1.94</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle">Unmarried</td>
<td align="center" valign="middle">4,166</td>
<td align="center" valign="middle">1.76</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="5">Education</td>
</tr>
<tr>
<td align="left" valign="middle">Junior High School</td>
<td align="center" valign="middle">10,883</td>
<td align="center" valign="middle">1.98</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle">Senior High School</td>
<td align="center" valign="middle">2,758</td>
<td align="center" valign="middle">1.85</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">11</td>
</tr>
<tr>
<td align="left" valign="middle">College</td>
<td align="center" valign="middle">6,453</td>
<td align="center" valign="middle">1.80</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">10</td>
</tr>
<tr>
<td align="left" valign="middle">Graduate student</td>
<td align="center" valign="middle">340</td>
<td align="center" valign="middle">1.84</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">6</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="5">Income</td>
</tr>
<tr>
<td align="left" valign="middle">High level</td>
<td align="center" valign="middle">10,796</td>
<td align="center" valign="middle">1.84</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">11</td>
</tr>
<tr>
<td align="left" valign="middle">Low level</td>
<td align="center" valign="middle">9,638</td>
<td align="center" valign="middle">1.97</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="5">House</td>
</tr>
<tr>
<td align="left" valign="middle">0</td>
<td align="center" valign="middle">1807</td>
<td align="center" valign="middle">1.80</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">10</td>
</tr>
<tr>
<td align="left" valign="middle">1</td>
<td align="center" valign="middle">15,426</td>
<td align="center" valign="middle">1.91</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle">2 or more</td>
<td align="center" valign="middle">3,201</td>
<td align="center" valign="middle">1.93</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">11</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="5">Self-rated-health</td>
</tr>
<tr>
<td align="left" valign="middle">1</td>
<td align="center" valign="middle">296</td>
<td align="center" valign="middle">2.03</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">10</td>
</tr>
<tr>
<td align="left" valign="middle">2</td>
<td align="center" valign="middle">1,494</td>
<td align="center" valign="middle">2.01</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle">3</td>
<td align="center" valign="middle">3,675</td>
<td align="center" valign="middle">1.92</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">10</td>
</tr>
<tr>
<td align="left" valign="middle">4</td>
<td align="center" valign="middle">8,592</td>
<td align="center" valign="middle">1.87</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">12</td>
</tr>
<tr>
<td align="left" valign="middle">5</td>
<td align="center" valign="middle">6,377</td>
<td align="center" valign="middle">1.90</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="5">Basic medical insurance</td>
</tr>
<tr>
<td align="left" valign="middle">Yes</td>
<td align="center" valign="middle">18,368</td>
<td align="center" valign="middle">1.91</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle">No</td>
<td align="center" valign="middle">2066</td>
<td align="center" valign="middle">1.83</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">11</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="5">Basic retirement insurance</td>
</tr>
<tr>
<td align="left" valign="middle">Yes</td>
<td align="center" valign="middle">12,710</td>
<td align="center" valign="middle">1.90</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">12</td>
</tr>
<tr>
<td align="left" valign="middle">No</td>
<td align="center" valign="middle">7,724</td>
<td align="center" valign="middle">1.91</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
<tr>
<td align="left" valign="middle">Total</td>
<td align="center" valign="middle">20,434</td>
<td align="center" valign="middle">1.90</td>
<td align="center" valign="middle">0</td>
<td align="center" valign="middle">21</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>As shown in <xref ref-type="table" rid="tab2">Table 2</xref>, the expected number of children for the entire sample lies between that of the low and high kindergarten development groups. Specifically, the high kindergarten development group exhibits significantly higher fertility intentions compared to the low kindergarten development group, with the number of expected children being significantly higher in the high kindergarten development group. Based on these preliminary findings, we can tentatively infer that higher levels of kindergarten development may be associated with increased fertility intentions. However, given that fertility intentions are shaped by a multitude of factors, further empirical analysis is required to confirm whether there is a significant relationship between kindergarten development and fertility intentions.</p>
</sec>
<sec id="sec22">
<label>6.2</label>
<title>Baseline regression results</title>
<p><xref ref-type="table" rid="tab3">Table 3</xref> shows the specific impact of kindergartens on fertility intention. Columns (1)&#x2013;(2) present the relationship between kindergarten development and the expected number of children derived from poisson regression. Columns (3)&#x2013;(4) show the results obtained through ordered logit regression. The results indicate that enhancing kindergarten development is significantly associated with increased fertility intentions. Poisson regression indicates that each one-point increase in the total kindergarten score is associated with a 9% increase in the expected number of children. Ordered logit regression indicates that for each one-point increase in the quality level of kindergartens, the odds of having a higher level of fertility intention are 1.9 times the original.</p>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Kindergarten development and fertility intention.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Variables</th>
<th align="center" valign="top" colspan="4">Child</th>
</tr>
<tr>
<th align="center" valign="top">(1)</th>
<th align="center" valign="top">(2)</th>
<th align="center" valign="top">(3)</th>
<th align="center" valign="top">(4)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Kindergarten</td>
<td align="center" valign="middle">1.061&#x002A;&#x002A;&#x002A; (3.01)</td>
<td align="center" valign="middle">1.095&#x002A;&#x002A;&#x002A; (3.74)</td>
<td align="center" valign="middle">1.503&#x002A;&#x002A;&#x002A; (3.44)</td>
<td align="center" valign="middle">1.889&#x002A;&#x002A;&#x002A; (4.11)</td>
</tr>
<tr>
<td align="left" valign="middle">Control</td>
<td align="center" valign="middle">No</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="middle">No</td>
<td align="center" valign="middle">Yes</td>
</tr>
<tr>
<td align="left" valign="middle">Provincial cluster</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="top">Yes</td>
<td align="center" valign="top">Yes</td>
<td align="center" valign="top">Yes</td>
</tr>
<tr>
<td align="left" valign="middle">Obs</td>
<td align="center" valign="middle">20,434</td>
<td align="center" valign="middle">20,434</td>
<td align="center" valign="middle">20,434</td>
<td align="center" valign="middle">20,434</td>
</tr>
<tr>
<td align="left" valign="middle">R-squared</td>
<td align="center" valign="middle">0.001</td>
<td align="center" valign="middle">0.005</td>
<td align="center" valign="middle">0.009</td>
<td align="center" valign="middle">0.034</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>T</italic> statistic value in parentheses. &#x002A;&#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.01, &#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05, &#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.1.</p>
<p>Columns (1)&#x2013;(2) are incidence rate ratios of Poisson regression; columns (3)&#x2013;(4) are odds ratios of ordered logit regression.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec23">
<label>6.3</label>
<title>Endogeneity tests</title>
<p>To solve potential endogeneity problems arising from reverse causality, omitted variables, and measurement error, we employ instrumental variables to mitigate the impact of these problems on the estimation results.</p>
<p>We adopt the frequency of the term &#x201C;kindergarten&#x201D; mentioned in provincial educational development plans, which are released every 5 years, as an instrumental variable for kindergarten development. Theoretically, the frequency of the term &#x201C;kindergarten&#x201D; mentioned in provincial plans is indicative of the priority given to kindergarten development. A higher frequency of mentions indicates greater attention to and better implementation of kindergarten development within the province. Moreover, how kindergartens are referred to in policy documents does not directly influence individuals&#x2019; fertility intentions. This characteristic satisfies the criteria for selecting instrumental variables. To further mitigate the potential influence of contemporaneous policies on fertility intentions, we employ lagged mentions as instrumental variables. Specifically, the frequency of the term &#x201C;kindergarten&#x201D; mentioned in plans from 2016 to 2020 serves as the instrumental variable for the years 2012, 2013, and 2015, while mentions from 2021 to 2025 plans are used for the analysis of 2017 and 2018 (<xref ref-type="bibr" rid="ref41">Reed, 2015</xref>).</p>
<p><xref ref-type="table" rid="tab4">Table 4</xref> presents the regression results. As shown, when the frequency of kindergarten mentioned (kindergarten_f) is employed as the instrumental variable, the coefficients in the first-stage regression are significantly positive. The LM statistic of 8.38 and the Wald F statistic of 24.14 indicate that the instrumental variables are effective. The results of the second-stage regression show that there is still a significant positive relationship between kindergarten development and fertility intention after using the instrumental variable. This is consistent with the findings of the basic regression.</p>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Kindergarten development and fertility intention: IV regression.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Variables</th>
<th align="center" valign="top">First stage</th>
<th align="center" valign="top">Second stage</th>
</tr>
<tr>
<th align="center" valign="top">Kindergarten</th>
<th align="center" valign="top">Child</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Kindergarten</td>
<td/>
<td align="center" valign="middle">0.275&#x002A;&#x002A;&#x002A; (5.50)</td>
</tr>
<tr>
<td align="left" valign="top">Kindergarten-f</td>
<td align="center" valign="top">0.028&#x002A;&#x002A;&#x002A; (5.31)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Control</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="middle">Yes</td>
</tr>
<tr>
<td align="left" valign="middle">LM statistic</td>
<td align="center" valign="top" colspan="2">8.38</td>
</tr>
<tr>
<td align="left" valign="middle">Wald <italic>F</italic> statistic</td>
<td align="center" valign="top" colspan="2">24.14</td>
</tr>
<tr>
<td align="left" valign="middle">Obs</td>
<td align="center" valign="top" colspan="2">19,492</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>T</italic> statistic value in parentheses. &#x002A;&#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.01, &#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05, &#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.1.</p>
<p>The reduction in observations is attributable to the absence of educational development plans in some provinces.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec24">
<label>6.4</label>
<title>Robustness tests</title>
<sec id="sec25">
<label>6.4.1</label>
<title>Replace the database</title>
<p>The China Family Panel Studies (CFPS) 2018 also investigates individuals&#x2019; fertility intention. The indicator construction, variable selection, and empirical methodology are consistent with those used in the basic regression. <xref ref-type="table" rid="tab5">Table 5</xref> illustrates that an increase in the level of kindergarten development is associated with a significant rise in the expected number of children.</p>
<table-wrap position="float" id="tab5">
<label>Table 5</label>
<caption>
<p>Kindergarten development and fertility intention: Alternative the database.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Variables</th>
<th align="center" valign="top" colspan="4">Child</th>
</tr>
<tr>
<th align="center" valign="top">(1)</th>
<th align="center" valign="top">(2)</th>
<th align="center" valign="top">(3)</th>
<th align="center" valign="top">(4)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Kindergarten</td>
<td align="center" valign="middle">1.028&#x002A;&#x002A;&#x002A; (3.14)</td>
<td align="center" valign="middle">1.024&#x002A;&#x002A;&#x002A; (2.96)</td>
<td align="center" valign="middle">1.246&#x002A;&#x002A;&#x002A; (3.88)</td>
<td align="center" valign="middle">1.220&#x002A;&#x002A;&#x002A; (3.56)</td>
</tr>
<tr>
<td align="left" valign="middle">Control</td>
<td align="center" valign="middle">No</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="middle">No</td>
<td align="center" valign="middle">Yes</td>
</tr>
<tr>
<td align="left" valign="middle">Provincial cluster</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="top">Yes</td>
<td align="center" valign="top">Yes</td>
<td align="center" valign="top">Yes</td>
</tr>
<tr>
<td align="left" valign="middle">Obs</td>
<td align="center" valign="middle">12,638</td>
<td align="center" valign="top">12,638</td>
<td align="center" valign="top">12,638</td>
<td align="center" valign="top">12,638</td>
</tr>
<tr>
<td align="left" valign="middle">R-squared</td>
<td align="center" valign="middle">0.003</td>
<td align="center" valign="middle">0.008</td>
<td align="center" valign="middle">0.032</td>
<td align="center" valign="middle">0.075</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>T</italic> statistic value in parentheses. &#x002A;&#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.01, &#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05, &#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.1.</p>
<p>Columns (1)&#x2013;(2) are incidence rate ratios of Poisson regression; columns (3)&#x2013;(4) are odds ratios of ordered logit regression.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec26">
<label>6.4.2</label>
<title>Replace the dependent variable</title>
<p>A higher proportion of multiple-member households in a province indicates a greater prevalence of families with higher fertility intentions. Based on the China Statistical Yearbook, we use the proportion of households with four or more members, five or more members, six or more members in each province as proxies for the dependent variable in the basic regression analysis. <xref ref-type="table" rid="tab6">Table 6</xref> illustrates that an increase in the level of kindergarten development is significantly associated with a higher proportion of multiple-member households.</p>
<table-wrap position="float" id="tab6">
<label>Table 6</label>
<caption>
<p>Kindergarten development and fertility intention: alternative the dependent variable.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Variables</th>
<th align="center" valign="top" colspan="2">Household of 4 or more</th>
<th align="center" valign="top" colspan="2">Household of 5 or more</th>
<th align="center" valign="top" colspan="2">Household of 6 or more</th>
</tr>
<tr>
<th align="center" valign="top">(1)</th>
<th align="center" valign="top">(2)</th>
<th align="center" valign="top">(3)</th>
<th align="center" valign="top">(4)</th>
<th align="center" valign="top">(5)</th>
<th align="center" valign="top">(6)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Kindergarten</td>
<td align="center" valign="middle">0.068<sup>&#x002A;&#x002A;&#x002A;</sup> (4.24)</td>
<td align="center" valign="middle">0.095<sup>&#x002A;&#x002A;&#x002A;</sup> (4.50)</td>
<td align="center" valign="middle">0.040<sup>&#x002A;&#x002A;&#x002A;</sup> (4.50)</td>
<td align="center" valign="middle">0.054&#x002A;&#x002A;&#x002A; (4.37)</td>
<td align="center" valign="middle">0.025<sup>&#x002A;&#x002A;&#x002A;</sup> (4.68)</td>
<td align="center" valign="middle">0.032<sup>&#x002A;&#x002A;&#x002A;</sup> (4.19)</td>
</tr>
<tr>
<td align="left" valign="middle">Control</td>
<td align="center" valign="middle">No</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="middle">No</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="middle">No</td>
<td align="center" valign="middle">Yes</td>
</tr>
<tr>
<td align="left" valign="middle">Provincial cluster</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="middle">Yes</td>
</tr>
<tr>
<td align="left" valign="middle">Obs</td>
<td align="center" valign="middle">20,434</td>
<td align="center" valign="middle">20,434</td>
<td align="center" valign="middle">20,434</td>
<td align="center" valign="middle">20,434</td>
<td align="center" valign="middle">20,434</td>
<td align="center" valign="middle">20,434</td>
</tr>
<tr>
<td align="left" valign="middle">R-squared</td>
<td align="center" valign="middle">0.255</td>
<td align="center" valign="middle">0.470</td>
<td align="center" valign="middle">0.264</td>
<td align="center" valign="middle">0.448</td>
<td align="center" valign="middle">0.303</td>
<td align="center" valign="middle">0.436</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>T</italic> statistic value in parentheses. &#x002A;&#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.01, &#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05, &#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.1.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec27">
<label>6.4.3</label>
<title>Add policy control variables</title>
<p>The national fertility regulation policy may affect residents&#x2019; fertility intention (<xref ref-type="bibr" rid="ref34">Lan, 2021</xref>). To mitigate the potential impact of fertility policies on our study, we include dummy variables for the two-child and three-child policies in China as control variables. <xref ref-type="table" rid="tab7">Table 7</xref> illustrates that the regression results are consistent with those of the basic regression analysis.</p>
<table-wrap position="float" id="tab7">
<label>Table 7</label>
<caption>
<p>Kindergarten development and fertility intention: add policy control variables.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Variables</th>
<th align="center" valign="top" colspan="4">Child</th>
</tr>
<tr>
<th align="center" valign="top">(1)</th>
<th align="center" valign="top">(2)</th>
<th align="center" valign="top">(3)</th>
<th align="center" valign="top">(4)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Kindergarten</td>
<td align="center" valign="middle">1.095&#x002A;&#x002A;&#x002A; (3.74)</td>
<td align="center" valign="middle">1.095&#x002A;&#x002A;&#x002A; (3.74)</td>
<td align="center" valign="middle">1.889&#x002A;&#x002A;&#x002A; (4.11)</td>
<td align="center" valign="middle">1.889&#x002A;&#x002A;&#x002A; (4.11)</td>
</tr>
<tr>
<td align="left" valign="middle">Control</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="middle">Yes</td>
</tr>
<tr>
<td align="left" valign="middle">Provincial cluster</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="top">Yes</td>
<td align="center" valign="top">Yes</td>
<td align="center" valign="top">Yes</td>
</tr>
<tr>
<td align="left" valign="middle">Policy</td>
<td align="center" valign="middle">No</td>
<td align="center" valign="middle">Yes</td>
<td align="center" valign="middle">No</td>
<td align="center" valign="middle">Yes</td>
</tr>
<tr>
<td align="left" valign="middle">Obs</td>
<td align="center" valign="middle">20,434</td>
<td align="center" valign="middle">20,434</td>
<td align="center" valign="middle">20,434</td>
<td align="center" valign="middle">20,434</td>
</tr>
<tr>
<td align="left" valign="middle">R-squared</td>
<td align="center" valign="middle">0.005</td>
<td align="center" valign="middle">0.005</td>
<td align="center" valign="middle">0.034</td>
<td align="center" valign="middle">0.034</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>T</italic> statistic value in parentheses. &#x002A;&#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.01, &#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05, &#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.1.</p>
<p>Columns (1)&#x2013;(2) are incidence rate ratios of Poisson regression; columns (3)&#x2013;(4) are odds ratios of ordered logit regression.</p>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec id="sec28">
<label>6.5</label>
<title>Mechanism analysis</title>
<p>We employ levels of self-fulfillment and frequency of social activities, relaxation, and study as proxies for the career aspiration and leisure accessibility, respectively. Using Bootstrap analysis, the linkages between kindergarten development and fertility intentions are explored. <xref ref-type="fig" rid="fig3">Figure 3</xref> illustrates that career aspiration and leisure accessibility partially account for the association between kindergarten development and fertility intentions.</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Path diagram of the mechanism analysis for the association between kindergarten development level and fertility intention. &#x002A;&#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.01, &#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05, &#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.1.</p>
</caption>
<graphic xlink:href="fsoc-11-1617367-g003.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Flowchart showing the influence of kindergarten development on career aspiration and leisure accessibility, both leading to a child's outcome. Arrow values are 0.002 for career aspiration, 0.001 for leisure accessibility, and 0.17 between career aspiration and leisure accessibility.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec29">
<label>6.6</label>
<title>Heterogeneity analysis</title>
<p>Given that women generally face greater work&#x2013;family conflicts than men (<xref ref-type="bibr" rid="ref12">Connelly, 1992</xref>), this section focuses on women, with particular attention to the relationship between kindergarten development and the fertility intentions of women with different educational backgrounds and household registration statuses. Specifically, female samples are divided into four groups: urban women with low education, urban women with high education, rural women with high education, and rural women with low education. The association between kindergarten development and fertility intention is then examined across these groups. <xref ref-type="table" rid="tab8">Table 8</xref> presents the regression results.</p>
<table-wrap position="float" id="tab8">
<label>Table 8</label>
<caption>
<p>Kindergarten development and fertility intention: heterogeneity analysis.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Group</th>
<th align="center" valign="top" colspan="2">Child</th>
</tr>
<tr>
<th align="center" valign="top">(1)</th>
<th align="center" valign="top">(2)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">(1)</td>
<td align="center" valign="middle">0.986 (&#x2212;0.12)</td>
<td align="center" valign="middle">0.914 (&#x2212;0.09)</td>
</tr>
<tr>
<td align="left" valign="middle">(2)</td>
<td align="center" valign="middle">1.099&#x002A;&#x002A;&#x002A; (4.87)</td>
<td align="center" valign="middle">1.829&#x002A;&#x002A;&#x002A; (5.10)</td>
</tr>
<tr>
<td align="left" valign="middle">(3)</td>
<td align="center" valign="middle">1.101&#x002A;&#x002A;&#x002A; (3.30)</td>
<td align="center" valign="middle">2.008&#x002A;&#x002A;&#x002A; (3.39)</td>
</tr>
<tr>
<td align="left" valign="middle">(4)</td>
<td align="center" valign="middle">1.028 (0.68)</td>
<td align="center" valign="middle">1.228 (0.73)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>T</italic> statistic value in parentheses. &#x002A;&#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.01, &#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.05, &#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.1.</p>
<p>Columns (1) are incidence rate ratios of Poisson regression; columns (2) are odds ratios of ordered logit regression.</p>
<p>Groups (1)&#x2013;(4) are urban women with low education, urban women with high education, rural women with high education, and rural women with low education, respectively.</p>
</table-wrap-foot>
</table-wrap>
<p>The results indicate that improved kindergarten development is significantly positively associated with fertility intention among women with higher educational levels. Poisson regression results show that each one-point increase in the total kindergarten score corresponds with an approximate 10% increase in the expected number of children. Ordered logit regression results reveal that for each one-point increase in the quality level of kindergartens, the odds of reporting a higher level of fertility intention are approximately twice the baseline value. In contrast, for women with lower educational levels, the linkage between kindergarten quality and fertility intention is not statistically significant.</p>
</sec>
</sec>
<sec sec-type="discussion" id="sec30">
<label>7</label>
<title>Discussion</title>
<p>This study analyzes the relationship between kindergarten development levels and fertility intentions using Poisson regression models based on five waves of CGSS data and the China Education Database. The results indicate that higher levels of kindergarten development are positively associated with increased fertility intentions. Each one-point increase in the total kindergarten score is associated with a 9% increase in the expected number of children. Hypothesis 1 is validated. Our findings are consistent with the previous research (<xref ref-type="bibr" rid="ref30">Jiang, 2021</xref>; <xref ref-type="bibr" rid="ref11">Chen et al., 2023</xref>). However, prior studies operated on markedly different time horizons, which range from short-term annual birth rates to long-term completed fertility, and are therefore not directly comparable in effect size or interpretation. And these studies primarily examined the relationship between kindergarten supply and fertility intentions from the perspectives of kindergarten quantity and availability, without focusing on the overall development level of kindergartens. It is worth noting that although the estimated percentage change in fertility intentions appears substantial, the corresponding absolute increase is relatively modest. This discrepancy stems from the low baseline value of expected number of children, which serves as the explained variable in our research. Therefore, while kindergarten development is statistically significantly associated with higher fertility intention, its practical relevance to the overall fertility level is limited and should not be overstated. Additionally, the potential directional nature of the relationship between fertility intentions and kindergarten development requires attention. For instance, regions with higher fertility intentions tend to have more children, which may correspond with greater investment in kindergarten construction in these regions. To address potential endogeneity issues, we employ instrumental variables, and the regression results remain consistent with the baseline findings. Robustness checks including sample adjustments, dataset replacements, and changes in dependent variables, as well as the consideration of policy implications, confirmed the stability of the baseline regression results.</p>
<p>In contexts with low-quality childcare services, the negative relationship between work-family imbalance-induced time pressures and fertility intentions becomes more pronounced (<xref ref-type="bibr" rid="ref4">Begall and Mills, 2011</xref>). This suggests that higher quality of childcare services is associated with a reduction in the negative linkage between work-family imbalance and fertility intentions. We utilize the Bootstrap method to examine the mechanisms that help explain the linkage between kindergarten development and fertility intentions. Our study provides preliminary evidence suggesting that career aspiration and leisure accessibility may act as potential mediating factors in the relationship linking kindergarten development to fertility intentions. In contexts with higher kindergarten development levels, we observe less parental involvement in time-intensive childcare, alongside greater time allocated to career goals and leisure activities among parents. Hypothesis 2a and Hypothesis 2b are also validated. It should be noted that data constraints have limited our options in selecting proxies for mediating variables. While self-socioeconomic status can reflect the degree of career aspiration attainment to a certain extent, it is more indicative of attitudinal dimensions and neglects the impact of behavioral engagement on the realization of occupational aspirations. Moreover, access to leisure is not confined to social interaction, learning, and rest alone. Although our findings cannot fully capture the pathways through which kindergarten development is associated with fertility intentions, they can to some extent illustrate the linkages underlying this relationship.</p>
<p>The association between kindergarten development and fertility intentions varies across different demographic groups, which reflects the documented linkage between kindergarten availability and the mitigation of work&#x2013;family conflicts, as well as corresponding trends in fertility intentions. Our findings indicate that compared to women with lower education levels, kindergarten development exhibits a stronger positive correspondence with fertility intentions among women with higher education levels. Compared to individuals with lower education levels, highly educated individuals often face more demanding and rapid lifestyles, making it challenging to balance household responsibilities and professional work (<xref ref-type="bibr" rid="ref46">Schieman and Glavin, 2011</xref>; <xref ref-type="bibr" rid="ref44">Rubenstein et al., 2022</xref>). High-quality kindergartens provide superior childcare services, a characteristic that corresponds with better work-family balance among parents. Therefore, these groups exhibit greater sensitivity to variations in kindergarten development levels.</p>
<p>The findings that high-quality kindergartens are associated with improved fertility intentions provide preliminary insights for addressing low-fertility challenges, though interpretations and applications of these insights should be cautious. This is because the empirical evidence is based on fertility intentions rather than realized fertility outcomes&#x2014;with the translation of intentions into actual fertility behavior still requiring further verification. Nevertheless, the observed evidence may offer potential directions for optimizing demographic structures and fostering sustainable socioeconomic development in low-fertility countries, especially those where parents face more severe work&#x2013;family conflicts. First, governments may warrant recognizing the potential role of kindergarten development in policies aimed at shaping fertility intentions and could consider proactive measures to expand the supply of kindergartens, enhance their accessibility, and thereby alleviate the childcare burden on families. Second, policymakers might focus on improving the quality of kindergarten services as a potential means to support fertility intentions. Feasible measures could include strengthening kindergarten teacher training, optimizing the allocation of educational resources, and upgrading supporting infrastructure. Finally, governments could explore encouraging and supporting eligible organizations and enterprises to establish on-site kindergartens and other welfare-oriented childcare facilities. Promoting social participation in the provision of kindergarten services may also help build a network of reliable, affordable, and accessible childcare resources.</p>
<p>There are still some limitations to this study. First, the price of kindergarten services, pedagogical quality, staff-child ratios, or parental satisfaction are important indicators for measuring the development of kindergartens. However, due to data constraints, we are unable to integrate these variables into the comprehensive kindergarten development index or investigate the characteristics of their linkage with fertility intentions. Second, the observed association between kindergarten development and fertility intentions may be partially moderated by family support, where reduced access to informal extended family childcare is associated with increased reliance on formal childcare provisions. Limited by data availability, we are unable to control for factors such as family support in the model. Third, with respect to the mechanisms that underpin the correspondence, the two indirect linkages identified in the study account for a relatively small share of the overall relationship, indicating limited explanatory power. Therefore, the specific pathways that characterize the association between kindergarten development and fertility intentions require further empirical investigation.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="sec31">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">Supplementary material</xref>, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec sec-type="author-contributions" id="sec32">
<title>Author contributions</title>
<p>JW: Methodology, Software, Writing &#x2013; review &#x0026; editing, Writing &#x2013; original draft. LG: Conceptualization, Supervision, Funding acquisition, Writing &#x2013; review &#x0026; editing. GW: Supervision, Writing &#x2013; review &#x0026; editing, Resources, Funding acquisition.</p>
</sec>
<sec sec-type="COI-statement" id="sec33">
<title>Conflict of interest</title>
<p>The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="sec34">
<title>Generative AI statement</title>
<p>The author(s) declared that Generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="sec35">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="sec36">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fsoc.2026.1617367/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fsoc.2026.1617367/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Table_1.DOCX" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
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<fn fn-type="custom" custom-type="edited-by" id="fn0002">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2219312/overview">Alessandra Minello</ext-link>, University of Padua, Italy</p>
</fn>
<fn fn-type="custom" custom-type="reviewed-by" id="fn0003">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2522099/overview">Kerstin Ruckdeschel</ext-link>, Federal Institute for Population Research, Germany</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2097910/overview">Nutteera Phakdeephirot</ext-link>, Rajamangala University of Technology Rattanakosin, Thailand</p>
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