South Korea: Relationship Between Childbirth Benefits & Total Fertility Rate of Local Governments
South Korea's fertility crisis is not a monolithic challenge but a complex tapestry of regional variations. A comprehensive analysis of 229 local government units (Exploring the Relationship between Childbirth Benefits and the Total Fertility Rate of Local Governments Using Geographically Weighted Regression Analysis by 이영서 (Lee Yeongseo)) reveals striking disparities in fertility rates and their determinants across the nation's provinces and metropolitan areas. From Seoul's bustling streets to Jeolla's rural landscapes, each region tells a unique story of demographic change, policy effectiveness, and socioeconomic dynamics. As South Korea grapples with the world's lowest fertility rate of 0.78 in 2022, understanding these nuanced regional patterns becomes crucial for crafting targeted interventions and reversing the nation's demographic decline.
Local fertility patterns:
Hotspots (high fertility): Mostly non-urban areas (e.g., Yanggu County in Gangwon, Jinan County in North Jeolla)
Cold spots (low fertility): Primarily urban areas (e.g., most districts in Seoul, parts of Busan, Daegu, and Incheon)
Highest local rate: 1.87 (Yeonggwang County, South Jeolla)
Lowest local rate: 0.38 (Jung District, Busan)
Key local policies and their impacts:
Childbirth benefits:
Policy: Cash payments to families for having children
Average benefit: 1.07 million won nationally
Highest impact: Jeolla and Gangwon provinces
Average at effective areas: 1.27 million won
Payment methods: Mix of lump-sum and installments
Effectiveness: Positively correlated with fertility rates where significant
Notable: Effective even with varying amounts and payment methods
Childcare facilities:
Policy: Provision of childcare facilities
Measure: Number of facilities per 1,000 children
Surprising finding: More facilities linked to lower fertility rates in some areas
Affected regions: Northern Gyeonggi, Northern Gangwon, Jeolla, South Gyeongsang
Possible explanation: May indicate work-life balance issues rather than a supportive environment
Healthcare access:
Policy: Ensuring access to pediatric care
Measure: Number of pediatricians per 1,000 children and adolescents
Impact: Positive correlation with fertility in parts of Gangwon and North Gyeongsang
Key finding: Most significant areas had below-average pediatrician numbers
Implication: Increasing pediatrician numbers could boost fertility in these areas
Marriage age policies:
Focus: Women's average age at first marriage
Impact: Lower age correlated with higher fertility in Gyeonggi, Gangwon, parts of South Jeolla and South Gyeongsang
Exception: Parts of North Chungcheong and adjacent North Gyeongsang showed the opposite trend
Economic factors:
Measures: Financial independence ratio and homeownership
Finding: Negatively correlated with fertility in Jeolla provinces
Not significant: In global regression models
Implication: Complex relationship between local economic factors and fertility
Quality of life initiatives:
Measure: Satisfaction with the living environment
Impact: Positive correlation with fertility in specific areas
Affected regions: Parts of Gangwon (Donghae, Taebaek, Samcheok cities, Yeongwol county), North Chungcheong (Jecheon city, Danyang county), North Gyeongsang (Yeongju, Sangju, Mungyeong cities)
Population Density:
Finding: Lower population density is associated with higher fertility rates in 169 out of 229 local government units
Exceptions: Parts of Seoul and Gyeonggi province!
Implication: Housing and Decentralization Policies could impact fertility rates
Provincial Patterns
Jeolla Province:
North Jeolla:
Fertility hot spots: Jinan County (1.56), Imsil County (1.80)
Highly responsive to childbirth benefits
Economic factors:
Negative correlation between financial independence and fertility:
Financial independence in this context refers to a local government's ability to generate its own revenue without relying on central government transfers. A higher ratio typically indicates a stronger local economy.
The negative correlation suggests that as a local area becomes more financially independent, its fertility rate tends to decrease. This could be explained by several factors:
More financially independent areas may have higher living costs, making child-rearing more expensive.
Areas with higher financial independence may attract young professionals focused on career advancement rather than family formation.
Negative correlation between homeownership and fertility:
This finding indicates that fertility rates tend to decrease as homeownership rates increase in an area. This is contrary to what might be expected, as homeownership is often associated with stability and family formation. Possible explanations include:
Housing costs may be higher in areas with high homeownership, leaving less disposable income for child-rearing.
Homeowners might be more focused on property investment and career advancement to meet mortgage payments, potentially delaying childbearing.
The type of housing available (e.g., smaller apartments vs. family homes) might not be conducive to larger families.
Childcare: Negative correlation between childcare facilities and fertility
It's important to note that these correlations don't imply causation. They likely reflect complex socioeconomic dynamics specific to Jeolla Province. For instance, areas with higher homeownership might also have aging populations, naturally leading to lower fertility rates.
South Jeolla:
Highest fertility rate: Yeonggwang County (1.87)
Other high rates: Sinan County (1.50), Haenam County (1.36)
Women's marriage age: Negatively correlated with fertility
Childbirth benefits: Strong positive impact on fertility rates
Economic factors: Similar negative correlations as North Jeolla
Gangwon Province:
Fertility hot spots: Yanggu County (1.52), Inje County (1.47), Cheorwon County (1.39)
Highly responsive to childbirth benefits
Healthcare:
Positive correlation between pediatrician numbers and fertility in some areas (e.g., Taebaek City)
Most significant areas had below-average pediatrician numbers
Quality of life: Initiatives could boost fertility in specific cities (Donghae, Taebaek, Samcheok, Yeongwol)
Women's marriage age: Negatively correlated with fertility
Childcare: Negative correlation between facilities and fertility in northern regions
Gyeonggi Province:
Mixed results on population density impact
Women's marriage age: Negatively correlated with fertility
Childcare: Negative correlation between facilities and fertility in northern regions
Outliers:
Gwacheon and Yangju cities showed higher fertility rates than surrounding areas
Economic factors: Less significant impact compared to other provinces
North Gyeongsang Province:
Fertility hot spots: Uiseong County (1.38), Cheongsong County (1.39)
Healthcare:
Positive correlation between pediatrician numbers and fertility in some areas (Andong, Cheongsong, Yeongyang, Yeongdeok)
Most significant areas had below-average pediatrician numbers
Quality of life: Initiatives could boost fertility in specific cities (Yeongju, Sangju, Mungyeong)
Unique pattern: Parts adjacent to North Chungcheong show a positive correlation between later marriage age and fertility
Childcare: Less significant impact compared to other provinces
South Gyeongsang Province:
Women's marriage age: Negatively correlated with fertility
Childcare: Negative correlation between facilities and fertility
Economic factors: Less significant impact compared to Jeolla provinces
Quality of life: Less impact compared to North Gyeongsang
Chungcheong Provinces:
North Chungcheong:
Unique pattern: Later marriage age positively correlated with fertility in some areas (Boeun and Okcheon counties)
Quality of life: Improvements could boost fertility in some areas (e.g., Jecheon city, Danyang county)
Childcare and healthcare: Less significant impact compared to other provinces
South Chungcheong:
Less distinct patterns compared to other provinces
Moderate responsiveness to childbirth benefits
Economic and social factors: Mixed impacts across different areas
Seoul Metropolitan Area:
Consistently low fertility rates across most districts
The highest population density is strongly negatively correlated with fertility, yet higher density in the city is correlated with higher fertility
Women's marriage age: Strongly negatively correlated with fertility
Economic factors: Less significant impact on fertility compared to rural areas
Childbirth benefits: Less effective compared to rural provinces
Busan Metropolitan City:
Contains some of the lowest fertility rates nationally (e.g., Jung District: 0.38)
Similar patterns to Seoul, with high population density negatively impacting fertility
Economic and social factors: Similar impacts to Seoul
Other Metropolitan Cities (Daegu, Incheon, Gwangju, Daejeon, Ulsan):
Generally follow urban patterns with low fertility rates
Population density: Strong negative correlation with fertility
Childbirth benefits: Less effective compared to rural areas
Some variations:
Daegu: Dalseong County shows higher fertility than surrounding areas
Incheon: Some outer districts show slightly higher fertility rates
Bottomline
South Korea's fertility landscape defies simple explanations, showcasing a intricate interplay of urban-rural divides, provincial characteristics, and policy impacts. Rural areas, particularly in Jeolla and Gangwon provinces, emerge as relative success stories with higher fertility rates and greater responsiveness to policies like childbirth benefits. In contrast, urban centers, led by Seoul and Busan, face persistent challenges of low fertility despite varied policy approaches. Still, individuals in those regions face dramatically higher costs than other regions.
The effectiveness of interventions—from healthcare access to economic incentives—varies dramatically across regions, underscoring the need for tailored, localized strategies. As South Korea confronts its demographic challenges, the key to success lies in two things.
Recognizing and leveraging these regional differences, moving away from one-size-fits-all approaches to nuanced, data-driven policies that address each area's unique needs and dynamics.
Understanding what causes responsiveness to family policies in the first place, and see if we can adjust the responsiveness in different regions.