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5 November 2026

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Women’s Reported Child Counts in Four East Asian Societies: Social Correlates and Change, 2006–2018

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Department of Sociology, Texas A&M University, College Station, TX 77843, USA
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Populations2026, 2(4), 24;https://doi.org/10.3390/populations2040024
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Abstract

East Asian societies have experienced profound changes in family formation, yet comparative evidence on the number of children reported by women across successive surveys remains limited. Using harmonized East Asian Social Survey data from 2006 to 2018, we examine cumulative reported child counts in China, Japan, South Korea, and Taiwan. We describe society–wave patterns, calculate survey-weighted age-standardized means for women aged 25–49, and estimate weighted Poisson models of demographic and socioeconomic correlates. The main analytical sample includes 12,786 women, with additional family-attitude measures available in the 2006 and 2016 surveys. Reported counts generally vary with age and education: women with higher education report fewer children than those in the lowest education category, conditional on observed characteristics. In China, the pronounced difference between unweighted all-age means in 2012 and 2015 is substantially attenuated when comparisons are restricted to ages 25–49 and age-standardized. A positive China-by-later-wave contrast is sensitive to the age range examined and does not identify an effect of fertility-policy reform. These findings highlight the importance of demographic composition and social stratification for understanding cumulative family-size patterns across East Asia.

1. Introduction

Across East Asia, declining fertility has unfolded alongside longer educational careers, delayed marriage, changes in women’s employment, and substantial shifts in expectations about family life. China, Japan, South Korea, and Taiwan share aspects of these transitions, but differ in the timing of family change, labor-market institutions, and the role of public policy. The extensive literature on low fertility has emphasized policy restrictions and incentives, rising opportunity costs of childbearing, and tensions between employment and domestic responsibilities [1,2,3,4,5,6,7,8,9]. Comparisons of women’s family-size histories can illuminate how these broader transformations intersect with life-course and socioeconomic differences.
Measures of childbearing require attention to the time frame they capture. A period fertility rate summarizes births occurring in a specified interval, whereas an adult respondent’s reported number of children reflects an accumulated family history up to the interview. The latter necessarily varies by age and may be related to cohort-specific schooling, marriage, and labor-market experiences. Although it is useful for comparing social patterns in family size, it should not be interpreted as the number of births in the survey year. This distinction is particularly relevant when repeated cross-sectional surveys span periods of rapid demographic change.
The East Asian Social Survey (EASS) offers a common framework for examining women in China, Japan, South Korea, and Taiwan over six waves between 2006 and 2018. Prior comparative EASS studies have explored family values, domestic labor, fertility preferences, and religious differences [10,11,12,13]. We extend this work by examining the total number of children women report, documenting its distribution across societies and waves and its associations with age, education, employment, marital status, and family attitudes. This outcome concerns children reported across a respondent’s family history, irrespective of whether they currently reside in the same household.
Our analysis addresses three questions. First, how do reported child counts differ among the four societies and across survey waves? Second, to what extent do survey-weighted comparisons standardized to a common age distribution differ from unadjusted comparisons of all adult women? Third, how are cumulative reported counts related to educational attainment, economic activity, and family-related characteristics? China’s transition to a universal two-child policy in 2016 provides historical context for one exploratory survey-period contrast, but the repeated cross-sections are not designed to identify births caused by this policy. The central contribution is a comparative account of family-size differences and their observed social correlates across contemporary East Asia.

2. Family Change in East Asia

2.1. Low Fertility and Changing Family Formation

The four societies share a history of rapid socioeconomic development and substantial declines in fertility, although the institutional circumstances of these declines vary. China pursued an extensive system of birth restrictions for several decades before introducing the universal two-child policy in 2016. Scholars have nevertheless emphasized the importance of socioeconomic change alongside the direct effects of population regulation in explaining China’s reproductive history [1,2,3,4,14,15]. Japan, South Korea, and Taiwan experienced sustained low fertility without an analogous nationwide one-child regime, demonstrating that restrictive policies are not a necessary condition for small family sizes [5,6,7,8,9].
Changes in marriage and partnership, educational aspirations, housing conditions, and work–family arrangements provide complementary explanations for smaller families. In East Asian settings where marriage remains closely connected to childbearing, later marriage can reduce time available for births or the number of children ultimately reported at a given age. Similarly, the expansion of higher education may delay parenthood and reshape desired family size. These processes unfold differently across societies and cohorts, so similarities in observed child counts need not reflect identical underlying pathways [5,6,7,8,9,16].

2.2. Education, Employment, and Family Size

Educational attainment is central to research on the changing social organization of childbearing. Women with more education often enter marriage and parenthood later, confront different opportunity costs, and participate in labor markets with varying capacities to accommodate caregiving. At the same time, educational expansion is itself cohort-dependent: the distribution of qualifications differs between women interviewed at the same age in 2006 and 2018. A negative education gradient in cumulative child counts is therefore consistent with several linked processes, including postponed childbearing and changes in completed family size, and cannot be assigned to one mechanism without birth histories [6,7].
Economic activity is similarly intertwined with family formation. Employment can make family expenses more affordable while also competing with time demands for childcare, particularly where responsibilities within the household remain unequally distributed. Conversely, having children may shape whether and how women remain employed. Cross-sectional employment differences thus describe the joint patterning of work and family histories rather than a one-directional effect of employment on childbearing. These reciprocal relationships are especially relevant to comparisons across East Asian labor markets [5,7,11,12].

2.3. Family Norms and Survey-Period Differences

Family expectations concerning the division of labor, intergenerational obligations, and the value placed on sons form a further dimension of East Asian family life. EASS research has documented variation in family attitudes and domestic labor across China, Japan, South Korea, and Taiwan [10,11,12,13]. Attitudes observed at interview may be related to lifetime family size, but they may also have been shaped by family experience. The available survey items are therefore valuable as indicators of social context rather than measures that isolate the causal influence of norms on fertility.
Prior EASS research has also used reported child counts to examine different aspects of fertility and family life. Bessey studies children born to women in their first marriage in Japan, South Korea, and Taiwan, while Tan examines children reported by married couples in four East Asian societies using the 2006 and 2016 Family Modules [13,17]. Those studies focus on marital childbearing and specific explanatory factors. Our broader sample includes women irrespective of marital status and spans six waves, allowing us to examine age- and education-related differences in total reported child counts across a longer period.
Comparative survey waves also capture different combinations of age, cohort, and historical period. In China, the shift in family-planning rules is an important backdrop to the later interviews, but a woman interviewed in 2016 or 2018 may report children born many years before that transition. The contemporaneous surveys from Japan, South Korea, and Taiwan help situate Chinese patterns regionally, although they do not constitute equivalent counterfactual populations for evaluating Chinese policy. Our comparisons focus on observed family-size differences over interview periods, taking these temporal and institutional differences into account.

3. Data and Methods

3.1. Data and Analytical Sample

We use harmonized EASS cross-sectional data for 2006, 2010, 2012, 2015, 2016, and 2018. The surveys integrate established social-survey programs in China, Japan, South Korea, and Taiwan, providing measures of respondents’ demographic characteristics, socioeconomic circumstances, and family experiences [18]. The pooled files include 55,615 respondents and 29,099 women across 23 society–wave samples; South Korea is absent from the available 2015 series. Survey implementation and sampling schemes differ across societies, making survey weights and sensitivity to sample composition important considerations.
The principal models examine women aged 25–49 who reported a child count and had a positive survey weight (N = 12,786). This age range captures much of the childbearing life course while reducing the contrast between younger respondents and those whose childbearing histories are largely complete. It does not imply that all women in the sample have completed childbearing. Covariate availability reduces the fully adjusted all-wave sample to 12,647 women. Family-attitude measures are examined separately using the 2006 and 2016 Family Modules; those models contain between 4551 and 4587 women, depending on the included items.

3.2. Dependent and Explanatory Variables

The dependent variable is the respondent’s total reported number of children, recorded as a nonnegative count. The EASS questionnaires distinguish this total from questions about whether a child is currently a household member. In the 2006 Family Module, the reported total agrees with the sum of sons and daughters, including deceased children, in 6917 comparable records. In the 2016 module, the corresponding components agree in 7694 of 7704 records. Some questionnaires also include adopted or stepchildren, and the treatment of deceased children differs in certain waves [19,20,21,22]. We consequently describe the outcome as total reported children, a measure closely related to cumulative childbearing but not a strictly identical count of biological children ever born in every survey.
Explanatory variables include age, represented by linear and quadratic terms; educational attainment, grouped into lower, middle, and higher categories; and current economic activity, distinguishing employment from other statuses. Models include indicators for society and survey wave. The Family Module permits additional adjustment for marital status and responses to questions concerning gender roles, divorce, filial obligations, and the importance of having a son. Because some questions apply only to particular marital groups, the common family-attitude specification uses six items available across the relevant respondent groups. These variables describe circumstances and orientations associated with reported family size, not necessarily antecedents to it.

3.3. Descriptive and Multivariable Analysis

We first describe the mean number of children reported by women of all adult ages in each society and survey wave. To make comparisons less sensitive to differences in respondent age distributions, we then restrict the sample to ages 25–49, apply the available survey weights, and directly standardize each society–wave mean to the pooled weighted distribution of exact ages 25–49. Reporting both sets of estimates illustrates how the substantive interpretation of survey-wave differences depends on the population and adjustment used. These standardized counts should not be interpreted as period fertility rates.
For multivariable analysis, we estimate weighted Poisson generalized linear models with a log link, reporting log coefficients and exponentiated count ratios conditional on observed characteristics. The baseline specification includes age, society, and wave; subsequent models add education and economic activity. We also report an exploratory interaction between China and later survey waves (2016 and 2018) relative to earlier interviews, alongside separate society and wave indicators. Family Module models include marital status and progressively richer sets of family attitudes. Additional models use identical complete-case samples and vary age restrictions and included waves to assess the stability of reported associations.
The available survey weights enter descriptive summaries and regression estimating equations. Because the harmonized extracts do not contain the original primary sampling unit and stratum identifiers, the regression standard errors use a finite-sample-adjusted sandwich estimator clustered by society–wave (23 clusters in the all-wave analyses and eight in the Family Module). This adjustment accommodates within-cell dependence but does not account for correlations across repeated waves within the same society. With only four societies, conventional significance tests are particularly uncertain for between-society and society-by-period comparisons. We report conventional significance indicators for consistency with social-science practice but place greater weight on the direction, magnitude, and stability of estimates than on their nominal thresholds.

4. Results

4.1. Variation Across Societies and Waves

Table 1 presents mean reported child counts among all adult women. At the beginning of the observation period, Taiwan recorded the highest mean (2.112 in 2006), followed by South Korea (1.882), Japan (1.804), and China (1.528). Differences among the societies were not constant over time. Japan’s mean declined from 1.804 in 2006 to 1.647 in 2018, and Taiwan’s from 2.112 to 1.833, while the Korean series varied more substantially across the observed waves. Because these values average over women at different stages of the life course, part of the variation may reflect differences in the ages and family histories represented in each survey.
Table 1. Mean number of children reported by women, by society and EASS wave, all adult ages.
China shows a particularly pronounced fluctuation, with a mean of 0.952 in 2012 and 1.892 in 2015. The unusually low 2012 mean distinguishes that wave from the surrounding interviews and cautions against interpreting the raw series as a smooth trajectory of childbearing. We therefore also compare women within a narrower age range using standardized means.

4.2. Age-Standardized Comparisons

The weighted and age-standardized results for women aged 25–49 reveal a different comparative picture (Table 2). In 2006, estimated means were fairly close across the four societies, ranging from 1.521 in Japan to 1.652 in Taiwan. By 2018, standardized means were 1.511 in China, 1.154 in Japan, 1.330 in South Korea, and 1.192 in Taiwan. The results are consistent with smaller accumulated family sizes among women in the selected ages in several societies, although repeated cross-sections alone cannot separate changes in childbearing behavior from shifting cohort composition and reporting practices. The society-specific trajectories are also presented in Supplementary Figure S1.
Table 2. Survey-weighted, age-standardized mean number of children reported by women aged 25–49.
Age restriction, survey weighting, and standardization also greatly reduce the contrast between China’s 2012 and 2015 observations. The standardized means are 1.353 and 1.373, respectively, rather than the much more widely separated all-age unweighted means in Table 1. This comparison shows that the apparent size of the survey-wave difference depends heavily on the population and weighting approach. It does not establish that the two waves are fully equivalent in survey design or reporting rules, but it argues against treating the raw contrast as evidence of an abrupt change in fertility behavior.

4.3. Education and Economic Activity

Table 3 presents the weighted Poisson estimates for women aged 25–49. The age terms are consistent with higher cumulative child counts over much of this age range, with a diminishing increase at older ages. Educational attainment exhibits a clear negative gradient. In the specification that adjusts for age, society, survey wave, and economic activity (Model 3), the log coefficient for higher education relative to the lowest group is −0.501, corresponding to an expected reported count about 39% lower (exp[−0.501] = 0.606). The corresponding middle-education coefficient is −0.168, or an expected count approximately 15% lower. The education pattern changes little when the China-by-period interaction is added.
Table 3. Weighted Poisson models of reported child counts among women aged 25–49 (log coefficients).
Respondents outside the employed category have somewhat higher expected counts in the adjusted models (Model 3 coefficient 0.130; count ratio about 1.14). Current employment and parenthood are likely to influence each other, so this association should not be read as evidence that leaving employment raises fertility. Rather, the patterns suggest that education and labor-market position are closely intertwined with cumulative family-size histories in the region. The substantial education gradient is also consistent with research linking educational expansion to changes in the timing and social organization of family formation [6,7].

4.4. Family Characteristics and Attitudes

The Family Module data allow a closer examination of marriage-related characteristics and family orientations in 2006 and 2016 (Table 4). The negative association between higher education and the reported number of children persists when marital status is included. The higher-education coefficient is −0.422 in the basic family model and −0.369 with six family-attitude measures, corresponding to conditional count ratios of approximately 0.66 and 0.69. Thus, the educational gradient is not confined to the larger all-wave sample or explained away by the additional observed family characteristics.
Table 4. Weighted Poisson models of reported child counts with family characteristics, EASS Family Modules 2006 and 2016.
The attitude models also show associations with responses about household gender roles and the importance of having a son. Their coefficients should be interpreted in relation to the original response scales, rather than as direct measures of changing fertility preferences. Because these attitudes were measured at the interview, they may reflect earlier childbearing experiences as well as views that preceded them. The substantive implication is that accumulated family size is patterned by educational and family circumstances; these models cannot distinguish the mechanisms linking values, partnership histories, and births.

4.5. The China-by-Period Contrast

A secondary comparison considers whether China’s association between survey period and reported child counts differs from the corresponding pattern in Japan, South Korea, and Taiwan. The adjusted China-by-later-wave coefficient is 0.122 (count ratio 1.130) for women aged 25–49 (Table 5). This represents a ratio of conditional expected reported counts across sets of surveyed women, not an estimate of additional births after the 2016 policy transition. The children counted in later interviews include births that occurred earlier, and policy eligibility is not observed at the level needed for causal analysis.
Table 5. Sensitivity of the China-by-later-wave association to survey-wave and age restrictions.
The contrast is sensitive to the observations included. Excluding 2012 reduces the coefficient to 0.098, while restricting the sample to ages 40–49 reduces it to 0.037. On identical complete-case samples, the family-model estimates are similar across specifications with and without the attitude variables, as shown in the analytical supplement. These comparisons indicate that relative survey-period differences coexist with substantial age and cohort heterogeneity. They warrant interpretation as regional descriptive patterns rather than evidence that China’s policy relaxation changed childbearing.

5. Discussion

Several findings emerge from the comparative analysis. First, differences in reported family size among East Asian women depend considerably on where respondents are in the life course. The survey-wave means for all adult women combine cohorts with markedly different educational and reproductive histories. China’s pronounced 2012–2015 fluctuation becomes far smaller when women of similar childbearing ages are compared using survey weights and a common age distribution. More broadly, this result shows why claims about changing family size in repeated cross-sections require careful attention to sample composition, even when the surveys use broadly comparable questions.
Second, education is consistently associated with fewer reported children, both in the broad 2006–2018 analysis and in the more detailed Family Module specifications. This pattern accords with scholarship connecting educational expansion to later family formation and the difficulty of coordinating intensive schooling, employment, and caregiving in East Asian societies [6,7]. It does not follow that schooling alone causes women to have fewer children. Educational attainment also marks generational change, family background, and opportunities that differ substantially among these four societies. The descriptive gradient is therefore best understood as one part of the social stratification of family-size histories.
Third, economic activity and family attitudes contribute to the observed patterning of reported child counts, but the links are unlikely to be one-way. Women’s employment may influence decisions about partnership and parenthood, while the experience of having children can reorganize work trajectories. Beliefs about gender roles, intergenerational obligations, and sons may be connected to reproductive expectations, yet those beliefs can also be reshaped by family experience. Previous EASS research has demonstrated the diversity of family orientations across the region [10,11,12,13]. The present findings add an outcome based on accumulated reported children, suggesting that social differences in family histories are visible alongside the attitudinal differences more frequently studied.
China offers an especially informative setting for considering the relation between demographic change and policy. Its history of birth restrictions distinguishes it from the three comparison societies, and the 2016 relaxation was an important institutional development. Nevertheless, the differences observed in cumulative child counts cannot be attributed to that event alone. Education, marriage, labor-market conditions, and preexisting cohort patterns were also changing, and many women interviewed in the later waves had already formed their families before the new policy took effect. The positive China-by-later-wave estimate, particularly its sensitivity to age restrictions, is more useful as a prompt for closer study of heterogeneous family-size histories than as a measure of policy effectiveness [2,3,4,14,15].
These findings matter for research on low fertility because they connect regional demographic change to differences among women rather than treating each society as a single fertility trajectory. Policies intended to support family formation operate in settings where educational careers, employment expectations, partnership timing, and care arrangements interact. Our analysis cannot determine which interventions would increase births; it does, however, reinforce the relevance of examining those social contexts when interpreting aggregate fertility trends. Cross-society comparisons are most informative when they preserve the distinction between accumulated family histories, current fertility behavior, and policy exposure.
The study has limitations that shape this interpretation. The child-count questions are not identical in every society and wave, particularly in their treatment of deceased and nonbiological children; reported totals therefore cannot be treated as perfectly harmonized biological parity. Repeated cross-sections do not reveal the timing of each child’s birth, and the harmonized data do not provide the full original sampling-design information needed for design-based variance estimation. The limited number of societies, especially in the two-wave Family Module analyses, further constrains formal comparisons. These features do not preclude descriptive analysis, but they favor attention to the consistency of substantive patterns rather than precise claims about policy effects or causal mechanisms.

6. Conclusions

The EASS data reveal both shared and distinctive patterns in women’s reported family size across China, Japan, South Korea, and Taiwan. When comparisons account for women’s age, several apparent wave-to-wave differences become less pronounced, while a substantial negative association between educational attainment and cumulative reported child counts persists across model specifications. Economic activity and family circumstances also contribute to the social patterning of these counts.
Understanding East Asia’s family change therefore requires more than comparison of headline fertility statistics or national policy dates. The number of children women report reflects life-course experiences embedded in schooling, work, family relationships, and distinct institutional settings. Comparative surveys help describe these histories and identify important social gradients; linked birth histories and stronger exposure measures would be needed to explain how those gradients translate into fertility change over time.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/populations2040024/s1. Figure S1: Survey-weighted, directly age-standardized mean total reported children among women aged 25–49; Table S1: Child-count consistency check from 2006 Family Module; Table S2: Survey-wave sample characteristics and means for all adult women; Table S3: Restricted primary sample, ages 25–49; Table S4: Full coefficient estimates for M1_age_society_wave; Table S5: Full coefficient estimates for M2_add_education; Table S6: Full coefficient estimates for M3_add_employment; Table S7: Full coefficient estimates for M4_exploratory_china_period; Table S8: Full coefficient estimates for F1_family_age_education; Table S9: Full coefficient estimates for F2_family_gender_norms; Table S10: Full coefficient estimates for F3_family_six_attitudes; Table S11: Full coefficient estimates for C1_common_allwave; Table S12: Full coefficient estimates for C2_common_allwave; Table S13: Full coefficient estimates for C3_common_allwave; Table S14: Full coefficient estimates for C4_common_allwave; Table S15: Full coefficient estimates for CF1_family_common; Table S16: Full coefficient estimates for CF2_family_common; Table S17: Full coefficient estimates for CF3_family_common; Table S18: Full coefficient estimates for S1_exclude_2012; Table S19: Full coefficient estimates for S2_exclude_2015; Table S20: Full coefficient estimates for S3_age_40_49; Table S21: Full coefficient estimates for S4_age_25_54; Table S22: Full coefficient estimates for S5_2006_2016_only; Table S23: Age-specific child count diagnostics for China (CGSS).

Author Contributions

Conceptualization and methodology, N.-Y.C. and E.F.L.A.; data curation, N.-Y.C.; formal analysis, N.-Y.C. and E.F.L.A.; writing—original draft, N.-Y.C.; writing—review and editing, N.-Y.C. and E.F.L.A.; supervision, E.F.L.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Institutional Review Board approval was waived because this study uses publicly available secondary survey data (East Asian Social Survey) and does not involve direct human subjects research.

Data Availability Statement

The data presented in this study are openly available in East Asian Social Survey Data Archive (EASSDA)/East Asian Social Survey (EASS) at https://www.eassda.org/pages/access.php (accessed on 8 September 2026).

Acknowledgments

During preparation of this manuscript, ChatGPT (OpenAI, GPT-6) was used to assist with survey-documentation review, statistical programming and checks, and drafting and editing. The authors retain responsibility for the analyses, interpretations, and submitted text.

Conflicts of Interest

The authors declare no conflicts of interest.

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