**1. Introduction**

In 2001, South Korea recorded a total fertility rate of 1.30, becoming the lowest-low fertility society, with this social phenomenon persisting to this day—the total fertility rate was 0.98 in 2018, 0.92 in 2019, and is expected to drop to 0.86 by 2021 [1]. In response, the governmen<sup>t</sup> implemented several policies to address these low fertility issues, such as the "Third Master Plan of Low Fertility Aged Society" and "Housing Special-Provision Policy". However, most of these policies focused on multi-child families with three or more children, while fertility support for households with less than three children has been excluded.

According to the 2015 Newlyweds Panel Analysis of Housing Conditions by the Ministry of Land, Infrastructure and Transport, 16.2% of newlyweds currently do not have children, with more than 74.9% of households delaying their fertility plans due to difficulties in their careers, the burden of parenting, and economic circumstances [2]. Moreover, the average number of children of newlyweds is 1.16. Although the birth of a first child after marriage is most common for newlyweds, considering that newlyweds (defined as within five years of marriage) are the population group having children, it is necessary to implement targeted residential environment and housing policies to increase the fertility rate of newlyweds [3].

Recently, the residential environment has experienced rapid changes due to alterations in the social environment. The residential patterns of housing type, housing expenses,

**Citation:** Jeon, S.; Lee, M.; Kim, S. Factors Influencing Fertility Intentions of Newlyweds in South Korea: Focus on Demographics, Socioeconomics, Housing Situation, Residential Satisfaction, and Housing Expectation. *Sustainability* **2021**, *13*, 1534. https://doi.org/10.3390/su 13031534

Academic Editor: Colin A. Jones

Received: 13 January 2021 Accepted: 27 January 2021 Published: 1 February 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

housing tenure type, and residential period have had a significant influence on marriage and fertility [4,5]. Moreover, socio-cultural problems such as insufficient childcare support, parenting expenses, and marriage delays have been suggested as the cause of lowest-low fertility. Among these causes, housing issues are a critical factor directly contributing to the low fertility problem in South Korea.

Previous studies analyzed social, economic, and residential behavioral impacts on the fertility intention of married women to prepare measures counteracting low birthrates. Chun examined the cause of low fertility and the current state of housing policies related to fertility support and emphasized a necessity of the compatible residential setting for work and childcare, housing provision to support childcare and housework, social interest, and a shifted perception in the direction of residential policy for revitalizing the fertility rate [5]. In addition, Chun proposed that residential policies are the basis for supporting childbirth by empirically identifying the effects of housing policies on fertility support and the influences of residential charges on the birth rate [5]. Jeong critically reviewed the contents and problems of counter plans against the low fertility and highlighted the importance of political promotion to create an advantageous setting for childbirth via the reduction of childcare responsibility, the expansion of public childcare services, and the initiation of parental leaves [6]. Seo suggested age, parental value, the burden of costs, and career responsibility as the principal factors influencing the fertility intention of married women, i.e., the number of planned children [7]. Moreover, Seo underlined the significance of selective approaches across the number of born children, in contrast to a comprehensive approach, in order to deal with the issue of low fertility, as the beneficial capacity and child value were found to vary when women with a child plan to have additional children [7]. Jeong noticed the need for policies to alleviate the burden of delivering and parenting a child and the necessity of plans to promote the family support system to aid parenting, as suggested by the fertility age for the first child, educational levels, health status, and marriage satisfaction as the major influencing factors for the second childbirth [8]. Kang highlighted the necessity to create an overall environment that can reduce education and child-rearing costs, rather than temporary support, on the basis of a survey revealing that factors for the intention of the subsequent fertility were age, academic background, income, family make-up, and the number and gender of children [9].

These studies have crucial implications not only from an academic perspective but also from a political perspective, for what kind of and how much housing will affect marriage or fertility. However, housing itself has an impact, i.e., via residential patterns, rather than influencing marriage and fertility. Park et al. analyzed the relationship between housing and fertility [10]. Describing housing stability and parenting affinity as positive influencing factors on the fertility intention, Park states that facilities related to childbirth and parenting should meet the aspect of life convenience and increase the overall birth rate because these factors directly impact the fertility rate. Lee highlighted that the impact of housing on marriage and fertility differs depending on house ownership, housing type, residential period, housing purchase cost, and housing size, and that, thereby, differentiated housing policies should be approached by the types of housing [11]. Mulder and Billari investigated the association between fertility rates and homeownership regimes in Western countries [12]. They analyzed four types of homeownership regimes in terms of the owner-occupied housing and mortgage accessibility, and argued that countries with homeownership regimes with a high share of owners and low access to mortgages have the lowest fertility rates [12]. Masanja et al. studied factors driving the decrease in fertility rates in urban and rural areas in Tanzania [13]. The authors suggested the need for appropriate governmen<sup>t</sup> policies and programs to match social changes that affect the fertility rate, such as small family sizes, improving the level of children's education, and a change in the social status of women. Vignoli et al. investigated the relationship between the fertility rate and the economic situation [14]. They found that the psychological stability of residents in the current residential environment is an important factor in fertility intentions. In addition, they argued that social and economic policies should be supported to increase the fertility

rate because employment security and economic conditions are related to a sense of stability in the residential environment. Ság<sup>i</sup> and Lentner studied Hungarian pro-birth policies and reported a policy gap in housing subsidies [15]. The authors evaluated family policy interventions such as housing support, tax allowances, and other child-raising benefits and concluded that an optimal mix of family policy incentives could maintain sustainable level of birth rate levels, but not necessarily increase them.

Previous studies exploring the factors influencing fertility intention and childbirth planning have focused on demographic factors such as wife's age, income, education level, and family composition [8,9]; socioeconomic factors such as income and childcare expenses [5–7,13]; and housing factors such as housing type, size of housing, housing satisfaction, and housing costs [4,10–12,14,15]. In addition, the studies explored parental leave, employment stability, childcare-friendly environment, social awareness trends, and economic activities of women [5,6,10,13]. Table 1 summarizes the outcomes of prior studies on fertility and its effects on demographics, socioeconomics, and housing. Most of these previous studies analyzed influencing factors on the fertility intention on the basis of ordinary families, although the main subjects of pregnancy planning and childbirth.

**Table 1.** Previous studies on fertility and the influences of demographics, socioeconomics, and housing.


To overcome the limitations of previous studies, we first based our study on the influences commonly emphasized in fertility theories and previous studies. Available variables were extracted; classified into the characteristics of demographic, economic, and residential environments; and used for analysis. Second, factors affecting the fertility intention and plans of newlyweds, the main subjects of counter plans against low fertility, not ordinary households, were considered on the basis of national survey data. Third, to deal with low fertility issues, it is more important to selectively approach the number of born children rather than to comprehensively approach the fertility intention. On the basis of these assumptions, we identified whether there are differentiated characteristics between the influences on the period of the initial childbirth planning and the affecting factors on the plans for the first and second child.

The remainder of this study is structured as follows. Section 2 presents the research method, the selected model for empirical analysis, selected variables and the data analysis, and a descriptive analysis of the variables. In Section 3, on the basis of the influences revealed in prior fertility theories and studies, we extract and analyze the characteristics of demographics, socioeconomics, housing situation, residential environment, and housing expectation as derived from the Newlyweds Panel Analysis of Housing Conditions dataset. In Section 4, the significance of this study and future research directions are highlighted.

#### **2. Materials and Methods**

Publicly available microdata from the 2015 Newlyweds Panel Analysis of Housing Conditions released by the Ministry of Land, Infrastructure and Transport (https://mdis. kostat.go.kr) was used in this study [1,2]. In total, data on 2702 first-married couples within 5 years of marriage were selected for the analysis, whose marriages were reported from 1 January 2010 to 31 December 2014. Factors, such as the characteristics of demographics, socioeconomics, housing situation, residential satisfaction, and housing expectation were extracted on the basis of influences revealed in prior fertility theories and studies [3–6,14].

The demographic characteristics were categorized by the age of the wife, the duration of the marriage, and the residential region (metropolitan/non-metropolitan). The Republic of Korea is divided into 5 districts as follows: Seoul area (Seoul Metropolitan City), Gyeongin area (Incheon, Gyeonggi-do, Gangwon-do), Chungcheong area (Daejeon, Sejong Special Self-Governing City, Chungcheongnam-do, Chungcheongbuk-do), Jeolla area (Gwangju, Jeollabuk-do, Jeollanam-do, Jeju-do), and Gyeongsang area (Daegu, Ulsan, Busan, Gyeongsangbuk-do, Gyeongsangnam-do). The metropolitan region includes Seoul city, Incheon city, and Gyeonggi province. The wife's age value was generated by converting the date of birth. Economic characteristics were defined as income, mortgage (monthly expenses), and dual-income status. Pre-tax gross annual salary statements were used for income, and mortgages were applied to the analysis on the basis of mortgage statement as an average monthly expense.

The residential attributes were defined as housing ownership, rental status, and apartments or non-apartments. Residential satisfaction consisted of the satisfaction of the housing setting and environment of residential area. The housing setting satisfaction was based on the house location, housing condition, and managemen<sup>t</sup> cost. The satisfaction of the environment of residential area was surveyed on satisfaction with local safety, local markets, transportation, neighboring nature, and child-friendly environment. The value of residential environment was calculated by averaging the satisfaction of housing setting and environment of residential area. The newlyweds' expected years of house purchase was classified as follows: less than 1 year, 1 to 3 years, 3 to 5 years, 5 to 10 years, more than 10 years, impossible, and unknown. Further residential circumstances were applied for the analysis using statements of the anticipated period for housing purchase.

We set up model 1, model 2, and model 3 to analyze factors affecting the fertility intention of newlyweds. Model 1 analyzed whether newlyweds had the intention to plan their fertility in particular situations, regardless of whether they have a child. Via model 2, the factors influencing the fertility intention for the first child were identified, and model 3 was applied to analyze the factors influencing additional childbirth, on the basis of first-married couples with one child only.

We used a binomial logistic regression for our statistical analysis. Logistic regression analysis was developed to alleviate the challenge of calculation, which is a disadvantage of the Probit model, with logistic regression being a model of selected probability assuming that the probabilistic utility is an independent distribution with a Weibull distribution. The purpose and procedure of the analysis are similar to linear regression analysis but differ in that ostensible-typed variables are applied as dependent variables. Logistic regression analysis utilizes odds, a ratio between the probability of occurrence and the probability of non-occurrence. The corresponding formula is expressed as Odds = p/(1 − p). The

concept of odds cannot be used for the general regression analysis with values between 0 and 1.

The concept of odds has 2 problems: the first is that it does not have negative (-) values, and the second is that the relationship among probabilities reveals an asymmetry around 1. As a method to solve these problems, natural logs are assigned to the values of odds, which is called logit. On the condition of a given explanatory variable, if the S-shape of a logistic function with a maximum value of 1 and a minimum value of 0, which represents the probabilities that a particular choice occurs or not, is converted to logit, it appears linearly. The formula is expressed as follows:

$$\begin{array}{c} \text{Odds} = \frac{\text{P}}{\text{I} - \text{P}} = \exp\left[\alpha + \text{B}\_{1}\text{X}\_{1} + \text{B}\_{2}\text{X}\_{2} + \cdots + \text{B}\_{\text{P}}\text{X}\_{\text{P}}\right],\\ \ln\left(\frac{\text{P}}{\text{I} - \text{P}}\right) = \alpha + \text{B}\_{1}\text{X}\_{1} + \text{B}\_{2}\text{X}\_{2} + \cdots + \text{B}\_{\text{P}}\text{X}\_{\text{P}} \end{array}$$

The concept of the odds ratio is used in the interpretation of the logistic regression analysis. The odds ratio refers to a change as Xi increases by a unit, given that the explanatory variable is constant. The formula is expressed as follows:

$$\text{Odds Ratio} = \frac{\text{Exp}\left(\mathbf{a} + \mathbf{B}\_1 \mathbf{X}\_1 + \dots + \mathbf{B}\_l (\mathbf{X}\_l + 1) + \dots + \mathbf{B}\_{\mathsf{P}} \mathbf{X}\_{\mathsf{P}}\right)}{\text{Exp}\left(\mathbf{a} + \mathbf{B}\_1 \mathbf{X}\_1 + \dots + \mathbf{B}\_l \mathbf{X}\_l + \dots + \mathbf{B}\_{\mathsf{P}} \mathbf{X}\_{\mathsf{P}}\right)} = \text{Exp}(\mathbf{B}\_l).$$

If the odds ratio is less than 1, the explanatory variable Xi has a negative (-) impact on the dependent variables, and if the odds ratio is larger than 1, Xi has a positive (+) influence. All analyses were performed using IBM SPSS Statistics ver. 22.0 (IBM Corp., Armonk, NY, USA).
