This year, South Korea’s fertility rate is expected to drop below 1.00.
This is quite a remarkable statistic. Firstly, an average fertility rate of less than 1.00 mathematically establishes that there are many women of childbearing age in Korea will not have any children. Secondly, as recently as fifty years ago, the fertility rate was almost at 5.00, and even higher prior to that. The decline has been dramatic —80% in 50 years, compared to a global average decline of around 50%.
Why has this happened?
While it is not yet the country with the lowest fertility rate (that honour falls to Singapore), in the context of such a massive decline it is natural to wonder why this is occurring. Most experts agree that the cause of this decline has been a combination of economic, sociological and cultural factors, primarily the following:
- Costs in Korea are have been growing rapidly. In Seoul for example, where 20% of South Korea’s population live, apartment prices have been increasing by about 5% per year.
- These costs, combined with a stubbornly high youth unemployment rate, has led to a massive growth of adults living with parents — a group that has been christened the ‘kangaroo tribe’. The proportion of households with at least one unmarried offspring over the age of 25 ballooned from 9% to 25% between 1985 and 2010.
- Workplace attitudes to pregnancy in Korea discourage women from having children, with many reports of women being made to feel guilty for taking maternity leave and having their jobs threatened.
Is it bad to have a low fertility rate?
Yes it is. Low fertility rates lead to economic decline. This argument can be counter-intuitive, since the poorest countries tend to have the highest birth rates. However, if we pause and think for a moment, it becomes abundantly clear what the problems are.
A low fertility rate means that the population will decline over time. According to the OECD: “Assuming no net migration and unchanged mortality, a total fertility rate of 2.1 children per woman ensures a broadly stable population.” Korea’s population growth has declined from 3.2% in 1960 to 0.4% in 2016. Assuming stable productivity, the consequence of low population growth is low economic growth.
A low fertility rate causes age imbalance, which is amplified by people living longer. With a greater proportion of aged people in the population, this causes an increase in costs associated with healthcare and social care, and imbalances in pension structures. Put simply, a smaller number of the young will need to fund a larger proportion of the old.
What does the data say?
To reassure myself, I did a quick analysis to determine the strength of the link between fertility rates and economic growth.
My first stop was the wonderful OECD data portal where I downloaded a csv file of fertility rates for all available countries between 2006 and 2016, and a csv file of Real GDP projections for 2015 to 2030.
I used the R programming language to do some very simple calculations and create a chart showing me the relationship between fertility rates and Real GDP growth. First, I loaded both data sets and used the
dplyr package to calculate the CAGR (compound annual growth rate) in Real GDP between 2016 and 2030:
gdp <- read.csv("GDP.csv")
fertility <- read.csv("fertility.csv")
gdp_by_country <- gdp %>% dplyr::group_by(LOCATION) %>% dplyr::summarise(cagr = (tail(Value, n = 1)/head(Value, n = 1))^(1/15) - 1)
This gave me a list of 49 countries and their Real GDP CAGR. Next I calculated the average fertility rate between 2006 and 2016 for the countries in the fertility data set, and joined this to the GDP dataset so that I had both the real GDP stats and the fertility stats by country.
fertility_by_country <- fertility %>% dplyr::group_by(LOCATION) %>% dplyr::summarise(avg_rate = mean(Value))
full_data <- dplyr::inner_join(gdp_by_country, fertility_by_country)
Not all the countries matched, so I was left with a total of 46 countries in my final set. Now I would like to plot GDP growth against fertility rates, and determine the relationship. It’s very easy to calculate a linear model in R, which I saved for the purpose of plotting later:
fit <- lm(cagr~avg_rate, data = full_data)
Now I will plot a scatter plot in my favourite graphing package
plotly and overlay the linear model onto it:
# plot basic scatter
plotly::plot_ly(data = full_data, x = ~avg_rate, y = ~cagr,
type = 'scatter', name = "OECD countries") %>%
# add country labels to markers
plotly::add_annotations(data = full_data, x = ~avg_rate,
y = ~cagr,
text = ~LOCATION,
showarrow = FALSE, xanchor = 'right',
yanchor = 'center') %>%
# add linear model
plotly::add_lines(y = ~fitted(fit), name = "Linear Model") %>%
# label axes and title
plotly::layout(xaxis = list(title = "Average fertility rate 2006-16"),
yaxis = list(title = "Real GDP CAGR 2015-30"),
title = "Fertility Rate vs Real GDP Growth for OECD countries")
This confirms a relationship between recent fertility rate and future economic growth. It also shows Korea has the lowest recent fertility rate of all the countries in the sample, though its economic growth is not expected to be quite as depressing as the fertility figures might lead one to believe.