As I have shown in previous blogs, long-term (I use 1000 years as the “magic” number – see the December 17, 2013 blog) exponential growth (or decline) cannot continue unabated without serious consequences. Lately, I have emphasized this concept mainly with regards to population growth, but these arguments apply equally to economic growth. As long as we are stuck here, the planet will eventually impose its own limits. Over the last 60 years we have learned how to collectively reduce fertility rates, thereby controlling population growth rates. While fertility rates and growth rates are now declining almost everywhere, regardless of a country’s wealth, we have not seen them stabilize around replacement rates.
In his fabulous guest blog (January 14, 2014) Jim Foreit, wrote that “half of the countries worldwide now have sub-replacement fertility” and that “the downside to this trend is shrinking labor forces.” After mentioning some sporadic efforts to reverse this trend, he concluded his blog with the suggestion that, “A sub-replacement fertility world seems inevitable, with fewer productive adults supporting larger numbers of elderly.”
In this blog I’d like to examine some specific cases, using data from the World-Bank database from the last 20 years. Based on the population pyramids I showed in my last blog, one should expect a time delay between the reduction in fertility and the expected reduction in population growth, which determines the distribution of the workforce
The tables below show some of the countries whose fertility rates have dropped the furthest below replacement rate (2011). We see that they are not restricted to rich countries. I have selected 5 countries for a more detailed longitudinal (study of changes over time) analysis.
Total Fertility Rates (TFR) Below Replacement (2011)
| Country | TFR | Country | TFR |
| Armenia | 1.7 | Hungary | 1.2 |
| Albania | 1.7 | Italy | 1.4 |
| Austria | 1.4 | Japan | 1.4 |
| Belarus | 1.5 | South Korea | 1.2 |
| Bosnia-Herzegovina | 1.3 | Latvia | 1.3 |
| Canada | 1.6 | Lebanon | 1.5 |
| China | 1.7 | Macedonia | 1.4 |
| Croatia | 1.5 | Moldova | 1.5 |
| Cuba | 1.5 | Romania | 1.3 |
| Czech Republic | 1.4 | Serbia | 1.4 |
| Estonia | 1.5 | Slovakia | 1.5 |
| Germany | 1.4 | Spain | 1.4 |
| Greece | 1.4 | Thailand | 1.4 |
World TFR (2011) – 2.5
China
| 1995 | 2000 | 2005 | 2010 | |
| TFR | 1.7 | 1.5 | 1.6 | 1.7 |
| Population(Millions) | 1200 | 1260 | 1300 | 1340 |
| Population Growth (%) | 1.1 | 0.8 | 0.6 | 0.5 |
| GDP/Capita ($2005) | 778 | 1,122 | 1,731 | 2,869 |
| GDP/Capita Growth (%) | 9.7 | 7.5 | 10.6 | 9.9 |
| Age Dependence Ratio* | 53 | 48 | 39 | 36 |
| Share of Women Employed** | 39 | ——- | ——- | ——- |
| Total Labor Force (% population) | 56.4 | 57.5 | 58.4 | 57.8 |
Japan
| 1995 | 2000 | 2005 | 2010 | |
| TFR | 1.4 | 1.4 | 1.3 | 1.4 |
| Population(Millions) | 125 | 127 | 128 | 127 |
| Population Growth (%) | 0.4 | 0.2 | 0.0 | -0.1 |
| GDP/Capita ($2005) | 32,438 | 33,957 | 35,781 | 36,473 |
| GDP/Capita Growth (%) | 1.6 | 2.1 | 1.3 | 4.7 |
| Age Dependence Ratio* | 44 | 47 | 51 | 57 |
| Share of Women Employed** | 39 | 40 | 41 | 43 |
| Total Labor Force (% population | 53.5 | 53.2 | 52.0 | 52.3 |
Thailand
| 1995 | 2000 | 2005 | 2010 | |
| TFR | 1.9 | 1.7 | 1.5 | 1.4 |
| Population(Millions) | 59 | 62.3 | 65.6 | 67.4 |
| Population Growth (%) | 0.8 | 1.2 | 0.7 | 0.2 |
| GDP/Capita($2005) | 2,280 | 2,206 | 2,690 | 3,164 |
| GDP/Capita Growth (%) | 8.3 | 3.5 | 3.9 | 7.6 |
| Age Dependence Ratio* | 49 | 44 | 43 | 39 |
| Share of Women Employed** | 41 | 44 | 45 | 45 |
| Total Labor Force (% of population) | 53.7 | 55.2 | 56.9 | 57.6 |
Spain
| 1995 | 2000 | 2005 | 2010 | |
| TFR | 1.2 | 1.2 | 1.3 | 1.4 |
| Population(Millions) | 39.4 | 40.2 | 43.3 | 46 |
| Population Growth (%) | 0.2 | 0.8 | 1.6 | 0.4 |
| GDP/Capita ($2005) | 19,997 | 23,921 | 26,056 | 25,596 |
| GDP/Capita Growth (%) | 2.5 | 4.2 | 1.9 | -0.6 |
| Age Dependence Ratio* | 47 | 46 | 45 | 47 |
| Share of Women Employed** | 36 | 39 | 42 | 47 |
| Total Labor Force (% of population) | 42.1 | 45.0 | 48.5 | 50.4 |
Italy
| 1995 | 2000 | 2005 | 2010 | |
| TFR | 1.2 | 1.3 | 1.3 | 1.4 |
| Population(Millions) | 56.8 | 56.9 | 58.6 | 60.5 |
| Population Growth (%) | 0.0 | 0.0 | 0.7 | 0.2 |
| GDP/Capita ($2005) | 26,464 | 29,872 | 30,479 | 29,163 |
| GDP/Capita Growth (%) | 2.9 | 3.6 | 0.2 | 1.2 |
| Age Dependence Ratio* | 46 | 48 | 51 | 52 |
| Share of Women Employed** | 37 | 40 | 43 | 44 |
| Total Labor Force (% of population) | 40.1 | 40.9 | 42.1 | 41.5 |
* Age dependency ratio is the ratio of dependents—(people older than 64 or younger than 15) –to the working-age population–those ages 15-64.
** Share of women employed in non-agricultural sector as % of total non-agricultural sector.
Among these selected countries, in spite of the fact that these countries have crossed the replacement fertility rate for almost a full generation, this has only translated to negative population growth very recently in one country (Spain). The rest are still growing (including zero growth), albeit at a very slow pace. The only consistent trend, not surprisingly, can be seen in China. The situation in China is somewhat unique and I will try to analyze it in more detail in one of the future blogs. The time period that these tables cover includes the recent global-wide recession. The data for labor force availability (% of population) show an actual increase except for Japan, which shows a small decrease. The age dependency ratio (ratio of the non-working population ages to the working population ages) is sharply decreasing in China and Thailand, increasing in Japan and Italy, and staying approximately constant in Spain. The share of women employed is increasing in four of the countries (data were not available for China). All the data show that the impact of crossing the fertility replacement rate has yet to crystallize into a decipherable trend – in spite of the passing of almost a generation.
Obviously, the empirical evidence of the impacts of crossing the replacement fertility rates is not yet available. However, over the relatively long period of time that we are discussing, it should be clear that we cannot let exponential decline go unchecked.
The next blog will focus on some of the science involved in trying to stabilize the system.
Thanks for your comment.
This particular posting was focused on countries that have crossed the fertility replacement rate (FTR) in a significant way. The crossing is a relatively recent phenomena and data (at least in the World Bank) were not available for more than the last 20 years. The search for trends here was not designed for the purpose of being able to extrapolate the future but, instead, try to learn from these cases the magnitude of the impact, especially labor force availability. This issue is not restricted to climate change and not even to the more general issue of global environmental impact of any kind. As I have showed before, exponential decline cannot be allowed to continue in the long term and we have to learn how to stabilize such a decline. The best place to learn is from our own history. The data show that the time is still too short to draw conclusions. The emphasis on population decline will continue in the next few blogs.
Micha
Professor, often, a trend depends upon the time used in measuring a factor. Your tables go back 15 year but I suspect that if they were expanded to 1950 or 1800, there would be a more defined increase and the 15 years of decline would nearly disappear. According to a quote from a UN researcher on climate change, a trend must be 30 or 40 years. I think the argument that we are outgrowing our planet is almost as old as those (including the USGS) that report the total planet wide reserves of crude oil will be depleted in a few decades.
Speaking of climate change, if most people think global warming is real, then why are so many complaining about all the low temperature and high snowfall records that are being broken in January? I would think they would welcome the frigid blasts as a sign of good times. I am just grousing.