The Difference Between the Actual and Ideal Number of Children Depending on Socioeconomic Status: An Analysis of National Fertility Survey Data in Japan
Medical Information Center, Kyushu University Hospital, Fukuoka 812-8582, Japan
Soc. Sci. 2025, 14(6), 330; https://doi.org/10.3390/socsci14060330
Submission received: 9 April 2025
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Revised: 22 May 2025
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Accepted: 23 May 2025
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Published: 26 May 2025
(This article belongs to the Section Family Studies)
Abstract
:This study aimed to investigate the association between socioeconomic status and the difference between the actual and ideal number of children. We used cross-sectional data from the National Fertility Survey for married couples conducted by the National Institute of Population and Social Security Research in 2015 and 2021. Combining files from both years, responses from a total of 12,632 couples were analyzed. A modified Poisson regression model was used to investigate associations between the outcomes and the socioeconomic characteristics of the studied couples. The two outcomes were having fewer children than ideal and having more children than ideal, and their proportions were 47.2% and 3.4%, respectively. Husbands and wives who were high school graduates were less inclined to have fewer children than ideal, and wives who were non-regular workers or unemployed were less inclined to have fewer children than ideal than wives who were regular workers. In addition, couples in the lowest household income group were significantly associated with a higher prevalence of having fewer children than ideal. These results showed being in the lower household income group was associated with a higher prevalence of having fewer children than ideal and suggested the need for support for low-income households.
1. Introduction
Japan is famous for its declining fertility rate in recent decades. The total fertility rate decreased from 1.76 in 1985 to 1.20 in 2023 (Ministry of Health, Labour and Welfare 2025). Moreover, the mean number of children born to married couples has exhibited a declining trend from 2.17 in 1977 to 2.01 in 2021 (National Institute of Population and Social Security Research 2023). Therefore, investigating the factors associated with the fertility rate or the mean number of children is crucial for measures aiming to address the declining fertility rate. Recent studies investigating the socioeconomic factors associated with the fertility rate or number of children have demonstrated the influence of income levels and educational attainment (Ghaznavi et al. 2022; Okui 2024b; Raymo and Shibata 2017). Likewise, the mean ideal number of children has shown a declining trend, reducing from 2.61 in 1977 to 2.25 in 2021 (National Institute of Population and Social Security Research 2023).
Some studies have investigated the factors associated with fertility intention or the ideal number of children in Japan and other countries and demonstrated associations with socioeconomic status such as income levels and educational attainment (Adewole et al. 2023; Ahinkorah et al. 2021; Chen et al. 2024; Gan and Wang 2023; Golovina et al. 2024; Ishi 2013; Kebede et al. 2022; Kim and Yi 2024; Zheng et al. 2016). Income level was negatively associated with the ideal number of children in a study conducted in Nigeria (Adewole et al. 2023), and the ideal number of children decreased with an increase in women’s educational attainment in China and sub-Saharan Africa (Kebede et al. 2022; Zheng et al. 2016). In addition, a study in Hong Kong showed that persons with higher educational attainments were associated with a lower level of fertility intentions among non-parents (Chen et al. 2024). Moreover, in Finland, men with higher education had a higher ideal number of children than men with basic education, and unemployed men had a higher ideal number of children than working men (Golovina et al. 2024). In Japan, a study investigating the factors associated with the ideal number of children found that among married couples, wives who were specialized and professional training college graduates were associated with a higher ideal number of children than wives who were high school graduates (Ishi 2013).
The actual and ideal numbers of children differ depending on socioeconomic features, and the difference in the numbers can also vary depending on socioeconomic features. A study conducted in Nigeria found that women with lower income or educational attainment were more inclined toward having fewer than the ideal number of children (Ibisomi et al. 2011). Meanwhile, a study in Japan investigating the factors associated with the difference between actual and ideal numbers of children found that age, the living environment, and owning a pet were associated with this difference (Matsuura 2008). However, the study used data collected from 2000 to 2003; hence, using recent survey data to investigate this difference is essential. It is hypothesized that couples with lower socioeconomic status tend to have fewer than their ideal number of children because, in Japan, the financial costs of child-rearing and education represent the most common reasons for which married couples do not have their ideal number of children (National Institute of Population and Social Security Research 2023). In addition, couples where the husband’s educational attainment is junior high school or high school tend to experience more unintended pregnancies in Japan (Okui 2024a). Thus, this factor may be related to having more than the ideal number of children. Socioeconomic status can be measured via multiple characteristics, including income, employment status, and educational attainment; it is important to clarify the associations between each of these socioeconomic characteristics and the differences between the actual and ideal numbers of children.
In this study, we investigated associations between socioeconomic status and the difference between the actual and ideal numbers of children in Japan using national survey data. Identifying factors associated with the difference between the actual and ideal numbers of children has several practical implications. For example, if a certain socioeconomic status is associated with having more than the ideal number of children, tailored family planning programs can be offered to the specific socioeconomic group. Meanwhile, if a certain socioeconomic status is associated with having fewer than the ideal number of children, policy measures for childbirth and child-rearing can be provided to the specific socioeconomic group.
2. Data and Methods
2.1. Data Source and Data Processing
Data from the National Fertility Survey for married couples, conducted by the National Institute of Population and Social Security Research in 2015 and 2021, were used in the analysis. These data were provided by the Institute based on the Statistics Act in Japan. The survey investigated the fertility-related characteristics of married couples in Japan and covered couples in which the wife was aged below 55 years. More detailed information on the survey is available from previous studies using the data (Ghaznavi et al. 2022; Iba et al. 2021; Okui 2024a). Stratified cluster sampling was used to extract the target districts of the Comprehensive Survey of Living Conditions in Japan, which is another national survey conducted by the government. Subsequently, the target districts of the National Fertility Survey were re-extracted through random sampling from the extracted districts (National Institute of Population and Social Security Research 2023).
The analysis used information on the sampled couples, including the birth months and years of the wives, the number of children born to the couple, their ideal number of children, whether or not they plan to have more children in the future, their educational attainments, their employment status, and their income levels. The ideal number of children in the questionnaire indicates a personal ideal, not a social ideal. The questionnaire was distributed to 7511 couples in 2015, and 9401 couples in 2021, with response rates of 87.8% in 2015 and 72.7% in 2021, and survey data were available on 6598 couples in 2015 and 6834 couples in 2021 (National Institute of Population and Social Security Research 2017; National Institute of Population and Social Security Research 2023). The data on 6213 couples in 2015 and 6419 couples in 2021 were used after excluding couples who did not provide information on the difference between the actual and ideal numbers of children. We pooled the 2015 and 2021 data into an integrated dataset for analysis.
Couples whose actual numbers of children were fewer than and more than the ideal numbers were defined as having fewer children than the ideal number of children (hereafter referred to as having fewer children than ideal) and more children than the ideal number of children (hereafter referred to as having more children than ideal), respectively, and were used as the study outcomes. Couples whose actual number of children was equal to their ideal number were considered as having the ideal number of children. Therefore, this study categorized couples into those having more children than ideal, those having the ideal number of children, and those having fewer children than ideal.
The ages of the wives were derived from their birth years and months and categorized as <30 years, 30–34 years, 35–39 years, 40–44 years, 45–49 years, and ≥50 years. In terms of employment status, wives and husbands were categorized into regular, non-regular, and self-employed workers and unemployed. Meanwhile, in terms of educational attainments, the respondents were categorized into “junior high school”, “high school”, “specialized or professional training college”, “technical college or junior college”, “university or more”, and “others.” The specific educational attainment of “others” was uncertain; this category included persons currently enrolled in school and those who selected the category “others” to answer the questionnaire item regarding educational attainment. Current enrollment in school could be related to the actual and ideal numbers of children. Hence, the “others” category was treated as one category of educational attainment. The respondents’ income indicates that they obtained it from their jobs, and incomes for unemployed persons were set at zero. Household income was calculated from the income of the wives and husbands following the method used in previous studies and categorized into quantiles (Iba et al. 2021; Okui 2024a). Specifically, the income of the wives and husbands was answered in JPY 1 million increments (i.e., JPY 0.01–0.99 million, JPY 1.00–1.99 million, …, JPY 10 million or more). We calculated household income using the midpoint values of the income groups for the wife and the husband. For example, if the income of the wife was “JPY 0.01–0.99 million” and that of the husband was “JPY 1.00–1.99 million”, the household income was calculated as JPY 2 million. If the income of the wife or the husband was “JPY 10 million or more”, the household income also became “JPY 10 million or more”.
2.2. Statistical Analysis
We tallied the number of all couples, those having an ideal number of children, those having more children than ideal, and those having fewer children than ideal by socioeconomic characteristics. The proportions of the outcomes and the mean values of the actual and ideal numbers of children were calculated by the socioeconomic characteristics of the couples. A modified Poisson regression model was used to investigate the associations between the outcomes and socioeconomic characteristics. Having more children than ideal and fewer children than ideal were used as the outcome variables, and the wife’s age group, the couple’s employment statuses and educational attainments, household income levels, and survey year were used as explanatory variables. The wife’s age group was included in the model because it influences socioeconomic status and the actual number of children and is, therefore, a confounder of associations between socioeconomic status and the outcomes. In addition, we conducted another regression analysis targeting couples whose wives were 40 years old or older to investigate the associations among couples who were less likely to increase their current number of children. Furthermore, a couple may have fewer children than ideal but have plans to increase the number of children in the future. That might not necessarily be a problem. Therefore, verifying the association among couples who were less likely to increase their current number of children was considered meaningful, and a regression analysis was also conducted targeting couples who had no plans to increase their number of children. We used an analysis method using a modified Poisson regression model for a multinomial outcome variable (Camey et al. 2014). Although a multinomial logit model is often used for a multinomial outcome variable, it calculates an odds ratio of each explanatory variable. Instead, by using a modified Poisson regression model, we can calculate a prevalence ratio of each explanatory variable, and the interpretation of the results becomes more intuitive. Specifically, couples having fewer children than ideal and those having the ideal number of children were treated as the reference category of the outcome variable in a regression analysis that used those having more children than ideal as the outcome variable. Likewise, couples having more children than ideal and those having the ideal number of children were treated as the reference category of the outcome variable in the regression analysis that used those having fewer children than ideal as the outcome variable. Prevalence ratios (PRs), robust variance, 95% confidence intervals (CIs), and p-values were calculated, and p < 0.05 was considered statistically significant. The complete-case analysis was conducted using regression analysis, and multiple imputation was used as a sensitivity analysis (van Buuren and Groothuis-Oudshoorn 2011). Couples with missing data were not included in the complete-case analysis, and missing data were imputed with the other categories in the multiple imputation analysis. In addition, the multicollinearity of the explanatory variables was assessed based on the generalized variance inflation factor (GVIF), and the values of GVIF^(1/(2 × Degree of freedom)) were checked.
All statistical analyses were performed using R4.1.3 (R Core Team 2025), and the car, lmtest, mice, and sandwich packages were employed (Fox and Weisberg 2019; van Buuren and Groothuis-Oudshoorn 2011; Zeileis and Hothorn 2002; Zeileis et al. 2020). The statistics in this study were created by the author using data provided by the National Institute of Population and Social Security Research and are not statistics published by the Institute.
3. Results
Table 1 shows the numbers and proportions of couples having an ideal number of children, more children than ideal, and fewer children than ideal by socioeconomic characteristics. The proportions of couples having the ideal number of children, those having more children than ideal, and those having fewer children than ideal were 49.4%, 3.4%, and 47.2%, respectively. In terms of the wives’ and husbands’ educational attainment, the proportion of having more children than ideal was the highest for junior high school graduates, and the proportion of having the ideal number of children was the highest among high school graduates.
Meanwhile, in terms of employment status, the proportion of having fewer children than ideal among the wives was the highest for regular workers, and that among the husbands was the highest for unemployed persons. In terms of household income, the proportion of having fewer children than ideal was the highest for the lowest household income group, and this proportion decreased with an increase in household income level. Furthermore, the proportion of having fewer children than ideal decreased from 2015 to 2021, and the average age of wives in the study population increased from 39.3 years in 2015 to 42.4 years in 2021.
Table S1 shows the numbers and proportions of couples having an ideal number of children, more children than ideal, and fewer children than ideal by socioeconomic characteristics among couples who had no plans to increase the number of children. Among these couples, the proportion of couples having fewer children than ideal was smaller than that of all couples. The overall trends for couples who had no plans to increase the number of children were relatively similar to those of all couples, while in terms of the wife’s employment status, the proportion of couples having fewer children than ideal was the highest for self-employed workers among the employment status of the wives. In contrast to couples who had no plans to increase the number of children, there were 2551 couples who planned to increase the number of children. Among those couples, only 0.1% were couples having more children than ideal, with 97.6% having fewer children than ideal.
Table S2 shows the proportion of couples having fewer children than ideal by each combination of the couples’ educational attainments. There was no clear trend in the proportion of having fewer children than ideal among wives and husbands depending on the educational attainment of their partner.
Table S3 shows the proportions of couples having fewer children than ideal by each combination of the couples’ employment status. Among wives whose husbands were regular workers, non-regular workers, and unemployed persons were less likely to have fewer children than ideal compared with regular workers.
Table 2 shows the average actual and ideal number of children by socioeconomic characteristics. The differences in the ideal number of children across each socioeconomic characteristic tended to be smaller than the differences in the actual number of children. With respect to educational attainment, the mean values of the actual and ideal numbers of children were the lowest in university graduates for wives, whereas they were the lowest in “others” for husbands. The difference between the mean values of the actual and ideal number of children was the lowest for high school graduates. The mean values of actual and ideal numbers of children were the highest for self-employed workers and lowest for unemployed persons among the husbands’ employment status. The mean value of the actual number of children increased with increases in household income levels. However, the mean value of the ideal number of children decreased slightly as household income levels increased.
Table 3 shows the results of the regression analysis among all couples. The maximum value of GVIF^(1/(2 × Degree of freedom)) was 1.09, and the degree of multicollinearity was not high. A PR above 1 indicates a higher prevalence of the outcome in that category compared with the reference category, whereas a PR below 1 indicates a lower prevalence. For example, the adjusted PR of having more children than ideal for wives who were junior high school graduates was 2.62, indicating that junior high school graduates increased the prevalence of having more children than ideal by 2.62 times compared with university graduates, which was the reference category. Among the wives, junior high school graduates and specialized or professional technical college graduates were significantly associated with a higher prevalence of having more children than ideal, with adjusted PRs of 2.62 (95%CI: 1.53, 4.50) and 1.51 (95%CI: 1.05, 2.17), respectively. Meanwhile, among husbands, junior high school and high school graduates were significantly associated with a higher prevalence of having more children than ideal, with adjusted PRs of 2.59 (95%CI: 1.72, 3.88) and 1.42 (95%CI: 1.08, 1.87), respectively. In addition, wives and husbands who were high school graduates were significantly associated with a lower prevalence of having fewer children than ideal, with adjusted PRs of 0.89 (95%CI: 0.84, 0.94) and 0.90 (95%CI: 0.86, 0.95), respectively; likewise, wives who were technical college or junior college graduates were associated with a lower prevalence, with an adjusted PR of 0.93 (95%CI: 0.88, 0.99). The results showed that university graduates exhibited the lowest PR for having more children than ideal, whereas high school graduates recorded the lowest PR for having fewer children than ideal. Further, wives who were non-regular workers and unemployed were significantly associated with a lower prevalence of having fewer children than ideal, with adjusted PRs of 0.82 (95%CI: 0.78, 0.86) and 0.87 (95%CI: 0.82, 0.91), respectively. Finally, the lowest and second lowest household income groups were significantly associated with a higher prevalence of having fewer children than ideal, with adjusted PRs of 1.17 (95%CI: 1.09, 1.25) and 1.09 (95%CI: 1.02, 1.15).
Table S4 shows the results of the regression analysis among all couples using multiple imputation. The sensitivity analysis using imputed data yielded generally similar estimates as the complete-case analysis. In contrast, wives who were high school graduates were significantly associated with a higher prevalence of having more children than ideal compared with those who were university graduates.
Table 4 shows the results of the regression analysis among couples whose wives were 40 years old or older. In contrast to the result of the analysis using all couples, husbands who were high school graduates were not significantly associated with a higher prevalence of having more children than ideal, and wives who were high school graduates and technical college or junior college graduates were not significantly associated with a lower prevalence of having fewer children than ideal. In addition, wives who were unemployed were not significantly associated with a lower prevalence of having fewer children than ideal.
Table S5 shows the results of the regression analysis among couples whose wives were 40 years old or older using multiple imputation. The results were similar to those of the complete-case analysis.
Table S6 shows the results of the regression analysis among couples who had no plans to increase their number of children. In contrast to the result of the analysis using all couples, wives who were self-employed workers were significantly associated with a lower prevalence of having more children than ideal. In addition, wives who were high school graduates and technical college or junior college graduates were not significantly associated with a lower prevalence of having fewer children than ideal.
Table S7 shows the results of the regression analysis among couples who had no plans to increase their number of children using multiple imputation. The results were similar to those of the complete-case analysis. In contrast, wives who were unemployed were not significantly associated with a lower prevalence of having fewer children than ideal.
4. Discussion
This study investigated the associations between socioeconomic status and the differences between the actual and ideal numbers of children among married couples in Japan. Socioeconomic status was found to be associated with the study outcomes, and it was shown that lower educational attainments were associated with having more children than ideal, while lower household incomes were associated with having fewer children than ideal. In addition, we conducted a subgroup analysis with couples whose wives were 40 years old or older and couples who had no plans to have more children. In these subgroups, the prevalence of having fewer children than ideal was smaller than in the overall study population, although the direction of the associations for each socioeconomic status was similar. Moreover, there were some differences in the results of the complete-case analysis and the multiple imputation analysis, and we mainly focused on the robust associations that were significant in both analyses.
Lower educational attainments of wives and husbands were associated with a higher prevalence of having more children than ideal, regardless of the analysis method employed. In other countries, lower educational attainment for women is often shown to be associated with unintended pregnancies or childbearing (Iseyemi et al. 2017; Musick et al. 2009). In Japan, husbands who were junior high school graduates were associated with a higher number of unintended pregnancies (Okui 2024a); the higher number of unintended pregnancies for those with lower educational attainment may be the reason for this association. Lower educational attainments have been shown to be associated with a lower prevalence of reliable or modern contraceptive use in Japan and other countries (Konishi and Tamaki 2016; Ali et al. 2022; Lakew et al. 2013). In addition, persons with lower educational attainments tend to marry and have children at younger ages in Japan (Okui 2024b; Raymo 2023), giving more opportunities for such couples to have additional children. This study also found that wives and husbands who were high school graduates were associated with a lower prevalence of having fewer children than ideal compared with those who were university graduates. In addition, high school graduates had the lowest likelihood of having fewer children than ideal. Couples who were high school graduates had a higher actual number of children compared with university graduates, but only a small difference was noted for the ideal number of children. As mentioned before, the ages of the wife and husband at the time of childbirth tended to be lower for couples with lower educational attainment than those with higher educational attainment (Okui 2024b), which may explain this result. As there was no significant association between wives who were high school graduates and the outcome among couples who had no plans to increase their number of children and among couples whose wives were 40 years old or older, it is considered that the difference in the proportion of couples who planned to increase their number of children or childbearing age depending on the wives’ educational attainments was a cause for the association shown among all couples. The incompatibility between childbearing and employment for highly educated women has been documented (Brinton and Oh 2019); this study’s findings suggest that a similar incompatibility between child-rearing and career pursuit may also exist for highly educated men in Japan. In addition, a possible explanation for why high school graduates were least likely to have fewer children than ideal is that while high school graduates do not delay childbearing compared with those with higher educational attainments, they also have more resources for childbearing compared with junior high school graduates.
This study found that wives who were non-regular workers or unemployed were associated with a lower prevalence of having fewer children than ideal compared with those who were regular workers in the analysis performed using all couples. Considering that the proportion of women leaving the workforce after bearing children remains high in Japan, having a higher number of children may have led wives to become non-regular workers or unemployed (National Institute of Population and Social Security Research 2023). In 2021, 37.4% of the mothers in Japan were unemployed at the time of childbirth (Ministry of Health, Labour and Welfare 2023), and the proportion of unemployed women increased as the birth order increased. In Japan, wives often become housewives (unemployed) after bearing children if their husband has sufficient income; however, this proportion has decreased over the years (National Institute of Population and Social Security Research 2023). Wives also often turn to non-regular employment after childbirth for child-rearing purposes, and this proportion has been increasing in recent years (National Institute of Population and Social Security Research 2023). In contrast, the employment status of men does not tend to change after childbirth in Japan. Therefore, the social characteristics of unemployed persons and non-regular workers differ between wives and husbands.
Moreover, couples in the lowest household income group were consistently associated with a higher prevalence of having fewer children than ideal compared with those in the highest income group, and the mean number of children increased with an increase in household income. Although a higher income level is associated with a higher number of children in the population, including married and unmarried persons in Japan (Ghaznavi et al. 2022), this study showed that the association also exists among married couples. The most common reason why married couples do not have their ideal number of children is the financial cost of child-rearing and education (National Institute of Population and Social Security Research 2023), and the results of this study align with this fact. In Japan, when comparing households of the same income level, educational expenses for each child became lower as the number of children became higher (Tsutsumi 1996). Therefore, a smaller number of children is needed to ensure sufficient money for each child’s educational expenses. In addition, in Finland, unemployment delays parenthood among men and women, and income has been suggested as a mediator for this association (Miettinen and Jalovaara 2020). Therefore, income may affect the age of having the first child.
This study also showed that the differences in the ideal number of children across each socioeconomic characteristic tended to be smaller than the difference in the actual number of children. In contrast to these findings, the mean ideal number of children differed considerably depending on socioeconomic characteristics in a previous study conducted in Nigeria (Adewole et al. 2023), and a study in China also showed that the distribution of the ideal number of children largely differed depending on the educational attainment of women (Zheng et al. 2016). In addition, a study in Finland showed that the ideal number of children differed depending on educational attainment and income (Golovina et al. 2024). The reasons for the smaller difference in the ideal number of children in Japan are uncertain. As one potential explanation, as the ideal number of children is a concept, it may be less influenced by socioeconomic status than the actual number of children.
We used data from surveys conducted in 2015 and 2021. The proportion of having fewer children than ideal decreased from 2015 to 2021 whereas that of having more children than ideal increased. The results indicate that the ideal and actual number of children decreased and increased, respectively, from 2015 to 2021. This result could be attributed to the increase in the wives’ ages from 2015 to 2021. In addition, the mean ideal number of children has shown a decreasing trend over recent decades in Japan (National Institute of Population and Social Security Research 2023).
Lower educational attainments of wives and husbands were consistently associated with having more children than ideal, suggesting that preventive measures against unintended pregnancy may be needed for these persons. Specifically, increased education about family planning methods, such as modern contraceptive use and enhancing access to them, are needed for persons with lower educational attainments to prevent unintended pregnancies and avoid having more children than ideal. In addition, those persons may tend to experience a higher burden of child-rearing; therefore, the reasons for this association require further study. Meanwhile, regularly employed wives tended to have fewer children than ideal; therefore, it is crucial to pursue a society where childbearing does not hinder women’s careers or work continuation. Moreover, lower income levels were associated with having fewer children than ideal, even when other socioeconomic characteristics were adjusted in the analysis. Policies that reduce the financial barriers of child-rearing would be effective in this regard. Specifically, administrative measures such as providing financial support for childcare and free education could contribute to reducing the prevalence of having fewer children than ideal for households with lower incomes. Finally, similar analyses in other countries are needed because fertility rates differ across countries; therefore, the relationships between socioeconomic characteristics and the difference in actual and ideal number of children may also differ.
Nevertheless, this study had a limitation that should be acknowledged. Because it was a cross-sectional study, it could not establish a causal association between socioeconomic status and the difference between the actual and ideal numbers of children. Specifically, it is possible that the actual and ideal numbers of children affect couples’ socioeconomic status. For example, a wife’s employment status can be changed based on the number of children, and having a child at a younger age can influence the wife’s educational attainment. In addition, there were non-responses in the survey, while data on sampling weight were not available.
5. Conclusions
This study found that husbands and wives who were junior high school graduates were associated with a higher prevalence of having more children than ideal. Meanwhile, wives who were non-regular workers or unemployed were associated with a lower prevalence of having fewer children than ideal. Finally, the lowest household income group was associated with a higher prevalence of having fewer children than ideal. These findings highlight the need for tailored social policies to help couples achieve their ideal number of children, particularly by supporting the financial difficulties of low-income households and improving knowledge and access to family planning for couples with lower educational attainment. In addition, it is also important to pursue a society where regular female workers can continue their careers even after childbearing.
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/socsci14060330/s1, Table S1: Numbers and proportions of couples having an ideal number of children, more children than ideal, and fewer children than ideal by socioeconomic characteristics among couples who had no plans to increase the number of children; Table S2: Proportion of having fewer children than ideal by each combination of educational attainments of the couples; Table S3: Proportion of having fewer children than ideal by each combination of employment statuses of the couples; Table S4: Results of the regression analysis among all couples using multiple imputation; Table S5: Results of the regression analysis among couples in which age of the wife was 40 years old or older using multiple imputation. Table S6: Results of the regression analysis among couples who had no plans to increase the number of children. Table S7: Results of the regression analysis among couples who had no plans to increase the number of children using multiple imputation.
Funding
This research was funded by JSPS KAKENHI, grant number JP22K17372.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and was approved by the Kyushu University Institutional Review Board for Clinical Research (No. 22221-09 and 3 October 2024).
Informed Consent Statement
Patient consent was waived because we used the official statistics data that were provided from the National Institute of Population and Social Security Research in Japan on the basis of the Statistics Act in Japan.
Data Availability Statement
The data that support the findings of this study are available from the National Institute of Population and Social Security Research in Japan, while the data are not publicly available. Data are however available from the National Institute of Population and Social Security Research if the Institute permits use of the data.
Conflicts of Interest
The author declares no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| CI | Confidence intervals |
| GVIF | Generalized variance inflation factor |
| PR | Prevalence ratio |
| SD | Standard deviation |
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Table 1.
Numbers and proportions of couples having an ideal number of children, more children than ideal, and fewer children than ideal by socioeconomic characteristics.
Table 1.
Numbers and proportions of couples having an ideal number of children, more children than ideal, and fewer children than ideal by socioeconomic characteristics.
| Characteristics | All Couples | Having Ideal Number of Children | Having More Children than Ideal | Having Fewer Children than Ideal | |||
|---|---|---|---|---|---|---|---|
| Number (%) | Number (%) | Row Percentage (%) * | Number (%) | Row Percentage (%) * | Number (%) | Row Percentage (%) * | |
| Total | 12,632 (100.0) | 6239 (100.0) | 49.4 | 431 (100.0) | 3.4 | 5962 (100.0) | 47.2 |
| Age group of wife | |||||||
| Under 30 years | 933 (7.4) | 150 (2.4) | 16.1 | 9 (2.1) | 1.0 | 774 (13.0) | 83.0 |
| 30–34 years | 1683 (13.3) | 566 (9.1) | 33.6 | 27 (6.3) | 1.6 | 1090 (18.3) | 64.8 |
| 35–39 years | 2457 (19.5) | 1198 (19.2) | 48.8 | 87 (20.2) | 3.5 | 1172 (19.7) | 47.7 |
| 40–44 years | 3165 (25.1) | 1735 (27.8) | 54.8 | 140 (32.5) | 4.4 | 1290 (21.6) | 40.8 |
| 45–49 years | 3044 (24.1) | 1780 (28.5) | 58.5 | 116 (26.9) | 3.8 | 1148 (19.3) | 37.7 |
| 50 years or older | 1350 (10.7) | 810 (13.0) | 60.0 | 52 (12.1) | 3.9 | 488 (8.2) | 36.1 |
| Educational attainment of wife | |||||||
| Junior high school | 285 (2.3) | 123 (2.0) | 43.2 | 23 (5.3) | 8.1 | 139 (2.3) | 48.8 |
| High school | 4000 (31.7) | 2129 (34.1) | 53.2 | 157 (36.4) | 3.9 | 1714 (28.7) | 42.9 |
| Specialized or professional training college | 2340 (18.5) | 1083 (17.4) | 46.3 | 90 (20.9) | 3.8 | 1167 (19.6) | 49.9 |
| Technical college or junior college | 2603 (20.6) | 1366 (21.9) | 52.5 | 83 (19.3) | 3.2 | 1154 (19.4) | 44.3 |
| University or more | 3050 (24.1) | 1372 (22.0) | 45.0 | 61 (14.2) | 2.0 | 1617 (27.1) | 53.0 |
| Others | 49 (0.4) | 17 (0.3) | 34.7 | 3 (0.7) | 6.1 | 29 (0.5) | 59.2 |
| Missing | 305 (2.4) | 149 (2.4) | 48.9 | 14 (3.2) | 4.6 | 142 (2.4) | 46.6 |
| Educational attainment of husband | |||||||
| Junior high school | 553 (4.4) | 260 (4.2) | 47.0 | 37 (8.6) | 6.7 | 256 (4.3) | 46.3 |
| High school | 4175 (33.1) | 2191 (35.1) | 52.5 | 170 (39.4) | 4.1 | 1814 (30.4) | 43.4 |
| Specialized or professional training college | 1751 (13.9) | 836 (13.4) | 47.7 | 59 (13.7) | 3.4 | 856 (14.4) | 48.9 |
| Technical college or junior college | 448 (3.5) | 222 (3.6) | 49.6 | 12 (2.8) | 2.7 | 214 (3.6) | 47.8 |
| University or more | 5256 (41.6) | 2512 (40.3) | 47.8 | 132 (30.6) | 2.5 | 2612 (43.8) | 49.7 |
| Others | 41 (0.3) | 18 (0.3) | 43.9 | 1 (0.2) | 2.4 | 22 (0.4) | 53.7 |
| Missing | 408 (3.2) | 200 (3.2) | 49.0 | 20 (4.6) | 4.9 | 188 (3.2) | 46.1 |
| Employment status of wife | |||||||
| Regular worker | 3426 (27.1) | 1502 (24.1) | 43.8 | 103 (23.9) | 3.0 | 1821 (30.5) | 53.2 |
| Non-regular worker | 4898 (38.8) | 2668 (42.8) | 54.5 | 191 (44.3) | 3.9 | 2039 (34.2) | 41.6 |
| Self-employed worker | 773 (6.1) | 409 (6.6) | 52.9 | 18 (4.2) | 2.3 | 346 (5.8) | 44.8 |
| Unemployed person | 3258 (25.8) | 1527 (24.5) | 46.9 | 98 (22.7) | 3.0 | 1633 (27.4) | 50.1 |
| Missing | 277 (2.2) | 133 (2.1) | 48.0 | 21 (4.9) | 7.6 | 123 (2.1) | 44.4 |
| Employment status of husband | |||||||
| Regular worker | 10,223 (80.9) | 5061 (81.1) | 49.5 | 334 (77.5) | 3.3 | 4828 (81.0) | 47.2 |
| Non-regular worker | 447 (3.5) | 206 (3.3) | 46.1 | 13 (3.0) | 2.9 | 228 (3.8) | 51.0 |
| Self-employed worker | 1349 (10.7) | 691 (11.1) | 51.2 | 53 (12.3) | 3.9 | 605 (10.1) | 44.8 |
| Unemployed person | 162 (1.3) | 72 (1.2) | 44.4 | 6 (1.4) | 3.7 | 84 (1.4) | 51.9 |
| Missing | 451 (3.6) | 209 (3.3) | 46.3 | 25 (5.8) | 5.5 | 217 (3.6) | 48.1 |
| Household income | |||||||
| Quantile 1(Lowest) | 2424 (19.2) | 1011 (16.2) | 41.7 | 88 (20.4) | 3.6 | 1325 (22.2) | 54.7 |
| Quantile 2 | 3040 (24.1) | 1502 (24.1) | 49.4 | 89 (20.6) | 2.9 | 1449 (24.3) | 47.7 |
| Quantile 3 | 2421 (19.2) | 1243 (19.9) | 51.3 | 86 (20.0) | 3.6 | 1092 (18.3) | 45.1 |
| Quantile 4 (Highest) | 3274 (25.9) | 1732 (27.8) | 52.9 | 101 (23.4) | 3.1 | 1441 (24.2) | 44.0 |
| Missing | 1473 (11.7) | 751 (12.0) | 51.0 | 67 (15.5) | 4.5 | 655 (11.0) | 44.5 |
| Survey year | |||||||
| 2015 | 6213 (49.2) | 2838 (45.5) | 45.7 | 194 (45.0) | 3.1 | 3181 (53.4) | 51.2 |
| 2021 | 6419 (50.8) | 3401 (54.5) | 53.0 | 237 (55.0) | 3.7 | 2781 (46.6) | 43.3 |
* For each category, the proportion represents the percentage of couples in that category who are in the specified situation.
Table 2.
Average actual and ideal numbers of children by socioeconomic characteristics.
| Characteristics | Actual Number of Children | Ideal Number of Children |
|---|---|---|
| Mean (SD) | Mean (SD) | |
| Total | 1.64 (0.99) | 2.25 (0.86) |
| Age group of wife | ||
| Under 30 years | 0.96 (0.89) | 2.37 (0.79) |
| 30–34 years | 1.44 (0.98) | 2.37 (0.79) |
| 35–39 years | 1.75 (0.97) | 2.32 (0.81) |
| 40–44 years | 1.75 (0.97) | 2.22 (0.87) |
| 45–49 years | 1.74 (0.94) | 2.19 (0.89) |
| 50 years or older | 1.69 (0.99) | 2.12 (0.96) |
| Educational attainment of wife | ||
| Junior high school | 1.60 (1.15) | 2.27 (1.21) |
| High school | 1.71 (1.00) | 2.24 (0.90) |
| Specialized or professional training college | 1.67 (1.01) | 2.31 (0.83) |
| Technical college or junior college | 1.69 (0.94) | 2.25 (0.82) |
| University or more | 1.49 (0.95) | 2.21 (0.81) |
| Others | 1.51 (1.00) | 2.35 (0.95) |
| Missing | 1.68 (1.06) | 2.22 (0.96) |
| Educational attainment of husband | ||
| Junior high school | 1.68 (1.07) | 2.27 (0.93) |
| High school | 1.74 (1.00) | 2.29 (0.88) |
| Specialized or professional training college | 1.64 (1.00) | 2.26 (0.89) |
| Technical college or junior college | 1.60 (0.90) | 2.22 (0.84) |
| University or more | 1.56 (0.96) | 2.22 (0.81) |
| Others | 1.41 (1.00) | 2.10 (0.92) |
| Missing | 1.69 (1.06) | 2.26 (1.03) |
| Employment status of wife | ||
| Regular worker | 1.51 (1.01) | 2.26 (0.84) |
| Non-regular worker | 1.74 (0.96) | 2.25 (0.85) |
| Self-employed worker | 1.78 (1.01) | 2.36 (0.92) |
| Unemployed person | 1.59 (0.97) | 2.22 (0.87) |
| Missing | 1.78 (0.98) | 2.28 (0.90) |
| Employment status of husband | ||
| Regular worker | 1.65 (0.97) | 2.26 (0.83) |
| Non-regular worker | 1.36 (1.09) | 2.11 (1.05) |
| Self-employed worker | 1.71 (1.04) | 2.29 (0.90) |
| Unemployed person | 1.19 (1.10) | 1.89 (1.13) |
| Missing | 1.68 (1.02) | 2.28 (1.03) |
| Household income | ||
| Quantile 1(Lowest) | 1.54 (1.03) | 2.26 (0.90) |
| Quantile 2 | 1.64 (0.99) | 2.26 (0.86) |
| Quantile 3 | 1.65 (0.97) | 2.24 (0.84) |
| Quantile 4 (Highest) | 1.67 (0.96) | 2.22 (0.82) |
| Missing | 1.74 (0.98) | 2.30 (0.89) |
| Survey year | ||
| 2015 | 1.62 (0.98) | 2.30 (0.84) |
| 2021 | 1.66 (0.99) | 2.20 (0.88) |
SD, standard deviation.
Table 3.
Results of the regression analysis among all couples.
| Having More Children than Ideal | Having Fewer Children than Ideal | |||
|---|---|---|---|---|
| Characteristics | Adjusted PR (95%CI) | p-Value | Adjusted PR (95%CI) | p-Value |
| Age group of wife | ||||
| Under 30 years | 0.20 (0.09, 0.42) | <0.001 | 1.94 (1.84, 2.06) | <0.001 |
| 30–34 years | 0.38 (0.24, 0.60) | <0.001 | 1.52 (1.44, 1.62) | <0.001 |
| 35–39 years | 0.76 (0.57, 1.03) | 0.080 | 1.14 (1.07, 1.21) | <0.001 |
| 40–44 years | Reference | Reference | ||
| 45–49 years | 0.82 (0.62, 1.08) | 0.164 | 0.95 (0.89, 1.02) | 0.129 |
| 50 years or older | 0.74 (0.50, 1.08) | 0.116 | 0.99 (0.90, 1.09) | 0.874 |
| Educational attainment of wife | ||||
| Junior high school | 2.62 (1.53, 4.50) | <0.001 | 0.98 (0.85, 1.12) | 0.737 |
| High school | 1.37 (0.97, 1.92) | 0.072 | 0.89 (0.84, 0.94) | <0.001 |
| Specialized or professional training college | 1.51 (1.05, 2.17) | 0.025 | 1.00 (0.94, 1.05) | 0.887 |
| Technical college or junior college | 1.23 (0.85, 1.78) | 0.267 | 0.93 (0.88, 0.99) | 0.020 |
| University or more | Reference | Reference | ||
| Others | 1.08 (0.14, 8.20) | 0.940 | 1.19 (0.95, 1.50) | 0.129 |
| Educational attainment of husband | ||||
| Junior high school | 2.59 (1.72, 3.88) | <0.001 | 0.93 (0.84, 1.03) | 0.184 |
| High school | 1.42 (1.08, 1.87) | 0.012 | 0.90 (0.86, 0.95) | <0.001 |
| Specialized or professional training college | 1.17 (0.82, 1.67) | 0.387 | 0.98 (0.92, 1.04) | 0.427 |
| Technical college or junior college | 1.09 (0.58, 2.04) | 0.791 | 1.00 (0.90, 1.11) | 0.995 |
| University or more | Reference | Reference | ||
| Others | 1.20 (0.16, 9.15) | 0.859 | 0.93 (0.69, 1.26) | 0.648 |
| Employment status of wife | ||||
| Regular worker | Reference | Reference | ||
| Non-regular worker | 1.16 (0.87, 1.54) | 0.325 | 0.82 (0.78, 0.86) | <0.001 |
| Self-employed worker | 0.58 (0.32, 1.07) | 0.080 | 0.92 (0.83, 1.01) | 0.094 |
| Unemployed person | 1.03 (0.73, 1.43) | 0.884 | 0.87 (0.82, 0.91) | <0.001 |
| Employment status of husband | ||||
| Regular worker | Reference | Reference | ||
| Non-regular worker | 0.64 (0.34, 1.19) | 0.159 | 1.07 (0.98, 1.18) | 0.144 |
| Self-employed worker | 0.99 (0.70, 1.40) | 0.976 | 1.00 (0.93, 1.08) | 0.918 |
| Unemployed person | 0.64 (0.23, 1.74) | 0.380 | 1.04 (0.89, 1.23) | 0.597 |
| Household income | ||||
| Quantile 1(Lowest) | 1.13 (0.80, 1.60) | 0.491 | 1.17 (1.09, 1.25) | <0.001 |
| Quantile 2 | 0.81 (0.59, 1.13) | 0.213 | 1.09 (1.02, 1.15) | 0.006 |
| Quantile 3 | 1.01 (0.74, 1.37) | 0.959 | 1.04 (0.98, 1.10) | 0.189 |
| Quantile 4 (Highest) | Reference | Reference | ||
| Survey year | ||||
| 2015 | Reference | Reference | ||
| 2021 | 1.23 (0.98, 1.55) | 0.073 | 0.88 (0.84, 0.91) | <0.001 |
PR, prevalence ratio; CI, confidence interval.
Table 4.
Results of the regression analysis among couples whose wives were 40 years old or older.
| Having More Children than Ideal | Having Fewer Children than Ideal | |||
|---|---|---|---|---|
| Characteristics | Adjusted PR (95%CI) | p-Value | Adjusted PR (95%CI) | p-Value |
| Age group of wife | ||||
| 40–44 years | Reference | Reference | ||
| 45–49 years | 0.83 (0.62, 1.09) | 0.180 | 0.95 (0.89, 1.02) | 0.185 |
| 50 years or older | 0.75 (0.50, 1.11) | 0.150 | 1.03 (0.93, 1.14) | 0.572 |
| Educational attainment of wife | ||||
| Junior high school | 2.15 (1.07, 4.29) | 0.030 | 1.07 (0.85, 1.34) | 0.576 |
| High school | 1.35 (0.89, 2.03) | 0.157 | 0.96 (0.87, 1.06) | 0.389 |
| Specialized or professional training college | 1.56 (1.01, 2.42) | 0.046 | 1.01 (0.91, 1.12) | 0.788 |
| Technical college or junior college | 1.25 (0.81, 1.92) | 0.310 | 0.98 (0.89, 1.07) | 0.646 |
| University or more | Reference | Reference | ||
| Others | 0.00 (0.00, 0.00) | <0.001 | 0.98 (0.52, 1.84) | 0.940 |
| Educational attainment of husband | ||||
| Junior high school | 2.16 (1.34, 3.48) | 0.002 | 1.00 (0.85, 1.17) | 0.983 |
| High school | 1.28 (0.93, 1.76) | 0.127 | 0.91 (0.83, 0.98) | 0.019 |
| Specialized or professional training college | 1.13 (0.76, 1.68) | 0.558 | 1.01 (0.92, 1.12) | 0.775 |
| Technical college or junior college | 0.61 (0.24, 1.52) | 0.289 | 1.08 (0.92, 1.26) | 0.342 |
| University or more | Reference | Reference | ||
| Others | 1.54 (0.20, 12.09) | 0.682 | 0.83 (0.44, 1.55) | 0.551 |
| Employment status of wife | ||||
| Regular worker | Reference | Reference | ||
| Non-regular worker | 0.89 (0.64, 1.23) | 0.471 | 0.84 (0.78, 0.91) | <0.001 |
| Self-employed worker | 0.56 (0.30, 1.05) | 0.071 | 0.97 (0.84, 1.12) | 0.714 |
| Unemployed person | 0.87 (0.59, 1.28) | 0.474 | 0.92 (0.84, 1.01) | 0.090 |
| Employment status of husband | ||||
| Regular worker | Reference | Reference | ||
| Non-regular worker | 0.78 (0.38, 1.57) | 0.481 | 1.04 (0.89, 1.21) | 0.644 |
| Self-employed worker | 1.18 (0.81, 1.73) | 0.378 | 0.96 (0.86, 1.07) | 0.473 |
| Unemployed person | 0.63 (0.20, 2.02) | 0.436 | 1.07 (0.86, 1.33) | 0.537 |
| Household income | ||||
| Quantile 1(Lowest) | 1.02 (0.67, 1.56) | 0.908 | 1.24 (1.11, 1.37) | <0.001 |
| Quantile 2 | 0.92 (0.63, 1.33) | 0.654 | 1.13 (1.03, 1.23) | 0.008 |
| Quantile 3 | 1.02 (0.73, 1.44) | 0.897 | 1.05 (0.96, 1.15) | 0.250 |
| Quantile 4 (Highest) | Reference | Reference | ||
| Survey year | ||||
| 2015 | Reference | Reference | ||
| 2021 | 1.22 (0.93, 1.61) | 0.151 | 0.83 (0.78, 0.89) | <0.001 |
PR, prevalence ratio; CI, confidence interval.
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Okui, T. The Difference Between the Actual and Ideal Number of Children Depending on Socioeconomic Status: An Analysis of National Fertility Survey Data in Japan. Soc. Sci. 2025, 14, 330. https://doi.org/10.3390/socsci14060330
AMA Style
Okui T. The Difference Between the Actual and Ideal Number of Children Depending on Socioeconomic Status: An Analysis of National Fertility Survey Data in Japan. Social Sciences. 2025; 14(6):330. https://doi.org/10.3390/socsci14060330
Chicago/Turabian StyleOkui, Tasuku. 2025. "The Difference Between the Actual and Ideal Number of Children Depending on Socioeconomic Status: An Analysis of National Fertility Survey Data in Japan" Social Sciences 14, no. 6: 330. https://doi.org/10.3390/socsci14060330
APA StyleOkui, T. (2025). The Difference Between the Actual and Ideal Number of Children Depending on Socioeconomic Status: An Analysis of National Fertility Survey Data in Japan. Social Sciences, 14(6), 330. https://doi.org/10.3390/socsci14060330
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