Thailand’s Demographic Crisis: An ASEAN & Provincial Perspective
A new research paper (An Investigation of Provincial Birth Rate in Thailand by Sunti Tirapat) reveals stark differences in birth rates across Thailand's provinces and ASEAN countries. The study used panel data from 77 Thai provinces from 2012 to 2021 and ASEAN country-level data from 2007 to 2021.
Thailand's birth rate is now the second lowest in ASEAN after Singapore, with potentially severe economic consequences. Interestingly, the results paint a more complex picture of how different factors affect fertility rates in a South Asian context.
ASEAN Comparison
Income effect:
Higher GDP correlates with lower birth rates across ASEAN.
For every 1% increase in GDP per capita, birth rates decrease by approximately 3.45%.
This suggests that countries tend to have fewer children as they become wealthier, a trend observed globally.
Governance matters:
Political stability, government effectiveness, and voice/accountability positively affect birth rates.
Political stability shows the strongest relationship, with a 0.124% increase in birth rate for every 1-point increase in the stability index (99% confidence level).
Government effectiveness is associated with a 0.129% increase in birth rate per 1-point increase (95% confidence level).
Voice and accountability correlate with a 0.166% increase in birth rate per 1-point increase (99% confidence level).
Thailand lags behind Singapore in these metrics, potentially hampering efforts to address the demographic challenge.
This suggests that improving governance could be critical in addressing low birth rates.
Health expenditure:
Shows a positive but statistically insignificant relationship with birth rates.
While not statistically significant, the positive trend suggests that better healthcare might contribute to higher birth rates, possibly due to improved maternal and child health outcomes.
Key findings from Thai provincial data (2012-2021)
Income effect:
Higher provincial GDP per capita correlates with lower birth rates.
For every 1% increase in provincial GDP per capita, birth rates decrease by approximately 0.727%.
This suggests that economic development alone may not solve the low birth rate problem and might even exacerbate it.
Aging impact:
Provinces with larger elderly populations see lower birth rates.
Thailand's overall 65+ population: 19.80% (2022)
For every 1% increase in the proportion of the population aged 65+, birth rates decrease by about 43.1%.
This creates a feedback loop: fewer births lead to more aging, further depressing birth rates.
Female workforce:
Higher female labor force participation is linked to lower birth rates.
For every 1% increase in female labor force participation, birth rates decrease by approximately 49.4%.
This indicates potential conflicts between career advancement and family planning.
It points to the need for better work-life balance policies and childcare support.
Mental health factor:
Provinces with higher suicide rates show lower birth rates.
For every 1-point increase in the suicide rate (per 100,000 population), birth rates decrease by about 0.096%.
This suggests that overall quality of life and mental well-being influence family planning decisions.
It underscores the importance of mental health services in demographic policy.
Industrial boost:
More factories per capita correlate with higher birth rates.
For every additional factory per 1,000 population, birth rates increase by about 5.56%.
This likely reflects better job prospects and economic stability in industrialized areas, contradicting the income effect.
Industrial development policies could indirectly support population growth.
Urban advantage:
Higher population density is associated with higher birth rates.
For every increase of 1 person per square kilometer, birth rates increase by about 0.001%.
While the effect seems small, it's statistically significant and could be substantial in highly populated areas.
This contradicts some international findings but aligns with studies from Russia.
It may indicate better access to services, healthcare, and economic opportunities in urban areas.
Non-significant factors:
Poverty rates and number of temples per capita showed no significant relationship with birth rates despite poverty being often associated with higher birth rates.
The lack of significance for temples per capita suggests religious factors may not significantly affect birth rate differences across Thai provinces.
Between the lines
The factory and population density findings suggest job opportunities and urban amenities may encourage family formation.
However, this contrasts with the negative income effect, highlighting the complexity of the issue.
The interplay between urbanization, economic development, and birth rates needs further exploration.
What the paper’s author recommends
Improving social conditions in provinces with high suicide rates.
Promoting job opportunities across provinces.
Developing urban planning policies that support families, like workplace childcare facilities.
Addressing work-life balance through flexible work arrangements and parental leave policies.
Tailoring policies to local contexts, recognizing provincial differences.
Improving governance and political stability at the national level.
Investing in further research, including primary data collection at the individual level.
A few things we note
Interestingly, political stability and competence correlate with higher fertility rates in Southeast Asia, which is worth looking into. This study showcases (at least in the Thai or even Southeast Asian contexts) that mental health, economic stability, population density, and better government services are correlated with higher birth rates but not GDP growth. This is especially interesting as more recent studies show an ever-increasing correlation between people’s income growth and higher fertility rates, especially in countries like Japan.