From this WHO paper. It has 254 pages and I haven’t read them all – neither should you, a lot of them are just pages of data. Anyway, some more stuff from the paper (click to view graphs and tables in full size):
“37 of the 40 countries with the lowest life expectancy are in Sub-Saharan Africa. HIV/AIDS is a major cause of the poor performance of many Africa countries in terms of health gains over the last decade or so. Overall, life expectancy in Sub-Saharan Africa has declined by 3-5 years in the 1990s due to increasing mortality from HIV/AIDS, with the estimated loss reaching 15-20 years in countries such as Botswana, Zimbabwe and Zambia.” [my emphasis] [...]
“Of the 10.5 million deaths below age 5 estimated to have occurred in 1999, 99% of them were in developing regions (3). The probability of child death (5qo) is typically less than 1% in industrialized countries classified into the A Regional Strata (and 0.5% in Japan), but rises to 300-350 per 1000 in Niger and Sierra Leone. Levels of child mortality well in excess of 10% (100 per 1000) are still common throughout Africa and in parts of Asia (Mongolia, Cambodia, Laos, Afghanistan, Bhutan, Myanmar, Bangladesh and Nepal).
However, perhaps the widest disparities in mortality occur at the adult ages 15-59 years. In some Southern African countries such as Zimbabwe, Zambia and Botswana, where HIV/AIDS is now a major public health problem, 70% or more of adults who survive to age 15 can be expected to die before age 60 on current mortality rates [in the late 80es, the number for Zimbabwe was 15-20%, see p.25 - US]. In several others (e.g. Malawi, Namibia and Uganda) the risk exceeds 60%. The dramatic increase in 45q15 in South Africa is also noteworthy, with estimated levels of 601 per 1000 and 533 per 1000 for males and females respectively in 1999. At the other extreme, 45q15 levels of 90-100 per 1000 are common in most developed countries for men, with risks as low as half this again for women. [...] HIV/AIDS was the cause of about 2.2 million deaths in Africa in 1999, making it by far the leading cause of death on the continent.”
There’s a lot of variation in mortality rates:
…and Africa is not the only region that’s doing badly: “The extraordinary risks of premature adult death among men in Eastern Europe is also clear from the Figure, (EUR C Region) with more than 1 in 3 who survive to age 15 in this Region likely to die before reaching age 60, at current risks compared with 10-12% in Western Europe, Japan and Australia.”
“Globally, some 56 million people are estimated to have died in 1999, 10.5 million below age five years. More males (29million) then females (27million) died, reflecting the systematically higher death rates for males at all ages in almost all countries. [...] Worldwide, deaths at ages 15-59 in 1999 amounted to an estimated 15.5 million, (9 million males, 6.5 million females), but with wide uncertainty. By any definition, these deaths (28% of the total over all ages) must be considered premature.”
The Danish life tables are at page 112 and I decided to post them below. The US life tables are at page 245. More fine-grained and newer US data are also available here.
Which variables are reported above? Well: “For each age, estimates of central death rates (nMx), the probability of dying (nqx), number of survivors (lx), and expectation of life (ex) are shown.” (p. 19) I didn’t have a clue what the ‘central death rate’ is but luckily one can look that kind of stuff up:
“For a given population or cohort, the central death rate at age x during a given period of 12 months is found by dividing the number of people who died during this period while aged x (that is, after they had reached the exact age x but before reached the exact age x+1) by the average number who were living in that age group during the period.”
Do remember when looking at numbers such as these that it’s not just about how long you live – how you die matters a great deal.
I decided to follow up on this post and have a closer look at the Danish numbers. In the post I’ve used data from Statistics Denmark’s public database (Statistikbanken). First, let’s just have a look at the raw numbers (from: ‘SKI107: Skilsmisser fordelt efter parternes bopæl, alder og ægteskabets varighed’):
The above graph displays the total number of divorces as a function of the length of marriage for the divorces that happened in Denmark during the year 2010. To take an example, 911 couples divorced after 3 years of marriage. Divorce risk as a function of marriage duration is pretty much (though not completely) monotonically decreasing over time (yes, I know it’s problematic to extrapolate from cross-sectional data like this, but let’s just pretend for a moment that this makes sense anyway…) after the first decade of marriage. When looking only at the first 10-15 years the distribution looks a bit bimodal. Actually, I can’t help remarking here more specifically that when it comes to the 7th year, the divorce risk is actually lower than it is for any other marriage duration in the 0-9 year span except for the first two years of marriage – i.e. the 7 year mark is a local minimum. There were 148 divorces at the 25-year mark, but only 93 divorced after 27 years of marriage. This is not to say that the risk of divorce at the 25-year mark is high – it’s almost twice as high for marriages that have lasted exactly 20 years (291) – but the risk doesn’t really tail off there, rather it does it a couple years later (in terms of marriage duration). The total number of divorces in 2010 was 14292, or about 39 each day of the year. I found it interesting that whereas people are much more likely to marry during the summer, there does not seem to be much systematic variation in the divorce rate over the course of the year – but you can judge yourself, here are the data from 2010 (‘BEV3C: Vielser og skilsmisser på måneder’):
['2010M01' = First month of 2010 (and so on)]
Back to the other data set, if we once again assume that the age/duration profile of divorcees/divorces do not change much over time so that we can extrapolate from the data we have, and you then decide to condition on a divorce actually happening during a marriage, what is then the likelihood that a marriage that will fail will end at year X? (To make this absolutely clear: This is not the probability that a marriage that has lasted X years will end in divorce during that year.)
If you instead look at the cumulative distribution function, it looks like this:
I cut it off after 20 years – more than 85% of all divorces are accounted for by then and adding more numbers seemed counterproductive because it made it harder to see what was going on to the left of the graph – where the most important stuff’s going on – in detail. More than half of the marriages that ended in divorce in 2010 were marriages between partners who had been together for 9 years or less. 73% of them were between partners who’d been together for 15 years or less. Almost one fourth of them (24%) had only lasted 4 years or less.
Before I started out this post I thought it would be the last one in the series, but at the end of the day I decided to wait with the crime data until later. This part will mostly deal with public expenditures and stuff like that. Here’s a link to the previous post in the series.
*While non-Western immigrants make out 6% of the population at the age of 16-64, they make up 10% of all people in Denmark who derive their main income from government transfers (…’are provided for by the government’ is perhaps a more ‘direct’ translation. The Danish term used in the report is: ‘er på offentlig forsørgelse’). In this framework, the concept of government transfers includes various direct income transfer programs like unemployment benefits (kontanthjælp, dagpenge), and early retirement programmes (efterløn, førtidspension), as well as governmentally subsidized employment programs (ansættelse med løntilskud, fleksjob). People working for the government are not included. (p.87-88) The ‘% of X who are provided for by the government’-measure is not the ratio of people in the sample who have received the various transfers included in the measure over the course of a year, it is rather based on a sum of all the people who have over various points in time during the year been receiving these transfers. If you have a group of one hundred people and twelve of them each received a transfer for one month during that year, that would translate to 1% of that population being provided for by the government; it’s a rough measure of the amount of ‘full-time recipients’ and should be interpreted as such. For people who receive early retirement transfers from the government the overlap between the total number of recipients over the course of a year and the number of ‘full-time recipients’ is naturally much larger than it is when it comes to transfers like unemployment benefits. (pp.87,104)
*In Denmark, two of the main social assistance programs for people who are in the workforce are ‘kontanthjælp’ and ‘dagpenge’. Kontanthjælp is the basic income support system for people without any kind of supplemental job insurance, and you can only receive it when you’ve basically depleted your assets – if you have liquid assets worth more than ~$2.000 (Danish link), you do not have the right to receive this transfer. In this context, a car you might need to drive to work is considered a liquid asset. Dagpenge is a more generous job insurance scheme subsidized by the government; the transfer payments are higher and they are completely independent of personal wealth. Approximately one in 4 (24%) of all people who receive kontanthjælp are non-Western immigrants. (p.87) 7% of all non-Western immigrants at the age of 16-64 receive kontanthjælp, whereas the corresponding number for people of Danish origin is 1,5%. (p.91)
*As the employment rates of non-Western immigrants are lower than the employment rates of people of Danish origin, it makes sense that they are also more likely to be provided for by the government. 38% of non-Western immigrants are provided for by the government, whereas the corresponding numbers for people of Danish origin and Western immigrants are 24% and 16%. (p.87)
*More than half of Lebanese-, Iraqi-, and Somali immigrants are provided for by the government. And more than half of all women from Lebanon, Somalia, Jugoslavia, Iraq and Turkey are provided for by the government. (p.87)
*Middle aged immigrants in particular have much lower employment rates than people of Danish origin at the same age, and they are thus much more likely to be provided for by the government. 60% of male non-Western immigrants at the age of 50-59 and 61% of female non-Western immigrants at the age of 50-59 are provided for by the government. The corresponding numbers for males and females of Danish origin are 23% and 26%. (p.87)
*The country of origin is an important variable when considering the likelihood that an individual immigrant is provided for by the government. 20,7% of all males of Danish origin at the age of 16-64 were provided for by the government in 2010. For Western immigrants combined it was 13,9% of males at the age of 16-64 who were provided for by the government, and for non-Western immigrants combined it was 36,7% of males at the age of 16-64 who were provided for by the government. Some more detailed numbers for male Western and non-Western immigrant populations – first the Western countries: Sweden (19,3%), Germany (18,6%), Great Britain (18,0%), Iceland (16,8%), Italy (15,7%), Norway (14,9%), Poland (12,9%), USA (11,0%), Netherlands (10,1%), France (8,8%), Romania (8,0%), and Lithuania (3,3%). The corresponding numbers for non-Western countries: Lebanon (57,8%), Iraq (51,5%), Somalia (50,1%), Bosnia-Hercegovina (45,6%), Ex Yugoslavia (44,4%), Iran (44,1%), Morocco (41,7%), Sri Lanka (37,3%), Turkey (37,0%), Afghanistan (35,1%), Vietnam (31,4%), Pakistan (29,5%), Russia (20,4%), Thailand (16,5%), Philippines (14,8%), India (9,7%), China (7,8%), and Ukraine (2%). (p.94)
*The female numbers are generally higher. I shall have to make a small digression here before I deal with those numbers: When the Danish Welfare Commission (Velfærdskommissionen) analyzed the distributionary features of the the Danish welfare system considering the gender variable, they found (Danish link) that females were on average net benefactors and males on average net contributors over an entire life span – a newborn male could, given current policies at the time the report was made, expect to pay in 0,8 million kroner ($150k) more than he’d receive over his lifespan, whereas a newborn female at that time could expect to receive 2,4 million kroner ($435k) more from the government than she’d contribute in taxes ect. Danes who are interested can read chapter 3 of this report – unfortunately I do not think an English version of that report exists. It’s likely that the relative contribution rates have changed somewhat by now, but it would surprise me a lot if they are much different now, as most of the reasons for these distributional consequenses of the welfare system have not changed much.
*Either way, as mentioned above when it comes to the females the numbers are generally higher for all groups. Of the females of Danish origin at the age of 16-64, 26,3% of them were supported by the government in 2010. For female immigrants from Western countries, the corresponding number was 18,9% and for non-Western female immigrants the number was 39,1%. Below some country-specific data – first Western countries: Sweden (24,3%), Poland (24,0%), Norway (23,5%), Great Britain (21,0%), Iceland (20,8%), Germany (18,7%), Romania (15,4%), Netherlands (14,2%), USA (12,4%), France (11,6%), Lithuania (11,5%), and Italy (11,3%). Non-Western countries: Lebanon (66,2%), Somalia (55,6%) Ex Yugoslavia (54,9%), Iraq (53,6%), Turkey (51,3%), Bosnia-Herzegovina (49,9%), Morocco (49,4%), Pakistan (45,1%), Iran (42,8%), Afghanistan (41,7%), Sri Lanka (41,6%), Vietnam (39,2%), Thailand (23,0%), Russia (20,9%), India (18,6%), China (13,9%), Ukraine (12,5%), and Philippines (11,7%). (p.95)
*The report doesn’t talk about the data much, but when analyzing the numbers above there are a couple of observations worth making here. The first is that the Swedish numbers are problematic to compare with the rest of the Western countries – it is quite likely that part of the reason why the Swedish numbers are high is that many of the ‘Swedish immigrants’ Denmark receive are in reality immigrants from non-Western countries who have used Sweden as a stepping-stone to enter Denmark, because Swedish immigration laws are much more lax than are the Danish, and it is much easier to enter Denmark via Sweden than, say, via Somalia. One other thing to note here is that the non-Western countries with high dependency rates are almost exclusively countries with large muslim populations. The non-Western immigrants from Thailand, China, Russia, India, and Ukraine in fact all ‘do better’, some of them much better, than people of Danish origin – and most of these populations are perfectly comparable to the immigrant populations from Western countries.
*Calculating net contribution rates is beyond the scope of a report like this, but I thought it would be worth including a few numbers from the publications of the Danish Welfare Commission (Velfærdskommissionen, also mentioned above). The short version is this (pp.121-122):
The graphs display the calculated net contribution to the government finances of males (the first one) and females (the second one) depending on age given the policies that were in effect at that point in time. The calculations are based on the Danish DREAM model.
Green = Danish origin.
Dark blue = immigrants from ‘developed countries’ (direct translation: ‘more developed countries’).
Turquoise = descendants of immigrants from -ll-.
Red = immigrants from ‘lesser-developed countries’.
Grey = descendants of -ll-.
They calculate in the report (p.123) that when looking at the financial net contributions to the government over the lifespan of an individual the estimated net present value (…NPV) of a male immigrant from a lesser-developed country is -0,28 mio. kroner ($50k), whereas the NPV of a female immigrant from a lesser-developed country is -4,4 mio. kroner ($800k). The NPV of a new-born male descendant of an immigrant from a lesser developed country is -0,17 mio. kroner ($30.000), and the NPV of a new-born female descendant of an immigrant from a lesser-developed country is -3,13 mio. kroner ($570k). The NPVs of immigrants from more-developed countries are 3,04 mio. kroner/$553k (males) and -0,65 mio. kroner/-$118k (females). The estimates are from 2004 and they are sensitive to changes in policy, but not that sensitive.
*Off topic, but I thought I should mention it anyway: The Florida Birth Defects Registry in 1999 estimated the lifetime costs for a child with Down Syndrome to be nearly $500,000. A Danish estimate would be much higher, but note that this cost estimate is significantly lower than the cost estimate of an average female immigrant from a lesser-developed country. In the 90es it was despite this not uncommon in Denmark to see political arguments to the effect that we needed to import immigrants from the Third World in order to save the Danish welfare state from economic ruin in the long run.
*Anyway, they remark in the Welfare Commission report that:
‘The negative contributions pr. person for immigrants and descendants from lesser-developed countries have a significant effect on the total future public-sector budget-balance problem, because both these groups are growing fast. In 2003 these two groups made up 4,7 % of the population, whereas they in 2040 are expected to make up 11,8% of the population, if the present (low) level of immigration is unchanged.’
(“De negative bidrag pr. person for indvandrere og efterkommere fra mindre udviklede lande har en betydelig effekt på det samlede fremtidige finansieringsproblem for den offentlige sektor, fordi begge disse grupper vokser med betydelig hast. I 2003 udgjorde de to grupper tilsammen 4,7 pct. af befolkningen, mens de i 2040 forventes at udgøre 11,8 pct. af befolkningen, hvis den nuværende (lave) indvandring fastholdes.” – p.125)
*As mentioned before, the overlap between the number of people who are in fact full-time recipients of a given public transfer payment and the number of people who have received a certain type of transfer payment only during a short time period over the course of the year depends on the nature of the transfer. A way to measure the average duration people receive a certain type of transfer is to divide the number of calculated full-time recipients with the number of people who have at some point during the year received the transfer. Immigrants from non-Western countries who receive temporary transfers on average receive those transfers for a longer period of time than do people of Danish origin or immigrants from Western countries and this is particularly the case when it comes to kontanthjælp: Non-Western immigrants who receive kontanthjælp on average receive it for 52% of the year, whereas the corresponding number for people of Danish origin is 40% – which is again significantly higher than the number for Western immigrants, which is 31-32% (judging from the graph on page 104; no numbers are given in the text).
i. I wrote about the exam/hospital stuff ect. on the twitter, I will not comment much more on that stuff here – go there for more info, I posted quite a few tweets about it (scroll down a bit and start from the bottom…). If you have questions/remarks related to that stuff, you can post them here though, I don’t mind. Anyway, right now I’m just glad it didn’t go any worse than it did, it was a very scary experience – I had enough of those kinds of episodes in my youth to consider the ‘found dead-in-bed from hypoglycemia’ one of the most likely scenarios when considering the question how I’d eventually die and the ‘severe hypoglycemia while sleeping’-fear has always been one of my biggest fears. I had an episode a few years back that required hospitalization as well, but that wasn’t sleep-related. I’ve not experienced anything like this in almost a decade. My room-mate will probably never see me completely ‘the same way’ again.
ii. Yesterday evening I started reading one of my christmas presents, Mistakes were made (but not by me). It’s pretty good, but I don’t think there’s a lot of new stuff in there to someone who’s read lesswrong and that kind of stuff for a while (at least not judging from the first 50 pages). I still like it though.
iii. Some data:
(From the website of the University of Leicester, direct link here). Most of Russia is pretty empty, the average population density is just 8,4 people/sq km – but regular readers of this blog will know that such average numbers can be quite misleading. 78% of the total population of Russia (110 million) live in the European part of Russia – and about 75% of Russia’s territory lies within Asia. The population (/40 million/) density of Siberia is 2.5 persons per km². Another way to put it – Siberia is (significantly) larger than Europe but the population of that area is about the same as Poland; the population of that enormous area is smaller than the population of countries such as Germany, France, UK, Italy, Spain or Ukraine.
But Russia’s not the only big country with a low population density – actually, a lot of places on Earth are very empty, compared to the places where most humans live. Canada’s population is a bit smaller than Siberia’s (34,7 mil), and if you add the two, their combined population size is smaller than that of Germany – despite the fact that they cover roughly 23 million square kilometers, more than 15% of the total land area of Earth. Incidentally, just like it’s a bit problematic to consider ‘the population density of Russia’, the same problems arise when you take a closer look at Canada. Northern Canada (Yukon, Northwest Territories, and Nunavut) makes up roughly 40% of the total area of Canada but it has a total population of little more than 100.000 people.
If you add Antarctica (14 million sq km) to Canada and Siberia we’re at 37 million square kilometres, or roughly one-fourth of the total land area of Earth. Add Australia to the list as well and you’re at maybe 44,5 million square km, about 30% of the total land area – and we’ve still not yet reached 100 million people combined. Remember that there are more than 7 billion people to account for – we’re clearly looking the wrong places. For fun, you can add Greenland, Mongolia, Namibia, Mauritania and others to the list yourself. There are a lot of relatively empty places on Earth and the empty areas are not small by any means. Here’s one way to look at ‘the big picture‘ (but again, averages can be deceiving):
One thing to remember here is that it isn’t just countries with low total populations that contain large empty areas – countries with huge populations often contain likewise huge areas with very low population densities. It’s easy to forget that a big total population combined with a big total area doesn’t mean that the country/area is not subject to large regional variations all the same; actually there are a few reasons why it seems quite obvious to me that the default hypothesis should rather be that d(var(population density))/d(total land area) should be positive. China is the country with the largest population on Earth, but the Tibet Autonomous Region has a population density comparable to Siberia (2,2/km2) and that area covers more than a million square kilometres. Another example would be Alaska in the US. Or consider Egypt:
(Wikipedia). “The great majority of its over 81 million people live near the banks of the Nile River, in an area of about 40,000 square kilometers”. “Nearly 100% of the country’s 80,810,912 (2011 est.) people live in three major regions of the country: Cairo and Alexandria and elsewhere along the banks of the Nile; throughout the Nile delta, which fans out north of Cairo; and along the Suez Canal.” (link) The country has millions and millions of people, but actually most of it is almost completely empty because people just can’t live there.
I decided to start out with this:
…in order to illustrate that you could probably write a not too dissimilar post about other countries as well. Also, it’s a nice image. Image credit: Wikipedia. “Description: Sex ratio total population. Pink = Female higher than male, Green = Equal, Blue = Male higher than female.”
This post will only deal with China. Here’s some related stuff about India.
So anyway, I was skimming a few world bank working papers and I found this one (pdf), which I decided to cover in a bit of detail here. It’s called China’s Marriage Market and Upcoming Challenges for Elderly Men and it’s written by Monica Das Gupta, Avraham Ebenstein & Ethan Jennings Sharygin. Some stuff from the paper:
“The Chinese census in 2005 reflected a staggering sex ratio at birth of 119, implying that each year there are roughly 1 million more boys born than girls.3 For cohorts born between 1985 and 2005, we estimate that there are 27 million more men than women4, implying a large number of men will fail to marry. [...]
We demonstrate two key facts regarding the Chinese marriage market using historical census microdata from 1990 and 2000. First, economic status is a crucial predictor of marital probability for men in China. We use years of education as the closest proxy for status, and document that while there is almost universal marriage for highly educated men, lower rates of marriage prevail among men of lower education. By contrast, the marriage market for women cleared: women across the educational distribution enjoy nearly universal marriage, and are able to engage in hypergamy, choosing spouses of higher status and income. Second, since many women migrate for the purpose of marriage, it seems very likely that in the coming decades the collapse of marital prospects for men will occur in poor areas of the country with low educational attainment. [...]
The results paint a grim picture for China’s ability to care for these men under the current policy structure of social assistance and social insurance programs that are primarily locally funded (Wang 2006, World Bank 2009). We estimate that in the absence of major redistribution of education and employment opportunities across China, the marriage squeeze will be in China’s poorer regions with large minority populations.7 Thus it will not necessarily be the more prosperous eastern regions of China with the most skewed sex ratio at birth that will experience high marriage failure rates among men. Rather, the poorer provinces ─ with more balanced sex ratios at birth ─ will bear a disproportionate share of the social and economic burden of China’s unmarried and childless men.”
How big is the difference in marriage rates between the successful males and the not quite so successful males, I hear you ask? Well, the paper states that: “over 98% of college graduates successfully marry by age 35 whereas the proportion is under 90% for men with less than a primary education.” One way to look at those numbers is that ‘that’s actually not that big of a difference’ – it’s around 9 out of 10 or more in both cases, right? But who are we actually comparing again? – another way to look at that is that males with less than a primary education are more than 5 times as likely to not succesfully marry by age 35. To me, that sounds like a huge difference, and it’s expected to get even worse over time: “over 10 percent of men with less than primary school education aged 30+ in 2030 are projected never to marry, and this figure increases to almost half in 2050″. Of course one might argue that economic growth increases mobility (so that even poor men might be able to move to find females willing to marry them) and ‘historical data are historical data’ which perhaps shouldn’t be given as much weight, given how much Chinese society has changed over the past decades. But rural China is still very poor and it isn’t growing very much compared to the rest – many of the people who have not left already for the urban provinces are people who can’t afford to, and they can’t really afford to save either so there’s not in my mind any compelling reason to think they will be able to afford to move in the future. Incidentally, it’s not really that hard to set up a model where you have decreased mobility over time even though the poor group has a positive net savings rate. Property prices are functions of local economic conditions, and if an area experiences significant income growth whereas another area does not and the people living in the poorer area are neither able to save enough money over time to at least keep up with the income growth of the richer area nor can afford to move there in the short run, the relative property price differential and the costs of moving will go up over time, even though the poor single guy might have a significant positive net savings rate. A very simplified model illustrating this could go along these lines:
Average income of ‘poor area’ residents: 10.
Average income of ‘rich area’ residents: 100.
Poor area income growth rate: 0%.
Rich area income growth rate: 10%
I shall assume that income growth rates and housing price growth rates are identical. In reality, housing prices are probably growing faster than income for the relevant demographic in the rich area and slower than income in the poor area. Let’s say the poor guy saves 20% of his income/year, i.e. 2 mu (‘monetary units’)/period. Say he invests that money in the rich area, earning 10%/year. After 10 years, he’ll have saved ~35 mu. How much will a house in the rich area that used to cost 100 mu cost after 10 years? 259. At the beginning, the poor guy was 98 mu short of being able to buy a house in the rich area – after ten years he’s now more than 200 mu short, even though he had a very high savings rate given his income and even though he earned a quite nice return on investment during that period. The property price differential was 90 mu to begin with, it’s 249 mu after 10 years. Maybe the effect sizes won’t be as large as assumed in the paper, but some of the dynamics described in the paper will probably play out to some degree.
Some more numbers and stuff related to these remarks from the paper:
“Poverty in China is heavily concentrated in the rural areas. Different measures of poverty all paint the same picture: while nearly 30 percent of the rural population was poor in 2005, this applied to only 5 percent or less of the urban population [...] The vast majority of the poor in 2003 lived in rural areas, and poverty is most heavily concentrated in the northwestern and southwestern regions [...] Both rural and urban incomes have continued to grow, but the rural-urban gap has continued to widen [...]
Significant proportions of urban workers are covered by formal social insurance programs: in 2007, around half of workers had pension coverage, 45 percent had Basic Medical Insurance, and 40 percent had unemployment insurance and work injury insurance [...] The rural pension system (funded mainly by personal contributions and collective subsidies) covered only about 10-11% of the rural labor force (World Bank 2009: Table 6.65), and coverage of the farm-based elderly population appeared to be particularly limited. Beneficiaries were highly concentrated in a few (mostly wealthy) provinces. [...]
Since men who are not as educated, healthy, and able to earn well tend to fail to attract a bride, they are likely to be heavily represented among those who are unable to save adequately for their old age, or labor heavily into their old age. They are the most vulnerable to income and illness shocks, since they cannot smooth fluctuations in household income by pooling earnings from spouses or children. Unmarried individuals are also more likely to be living without family to serve as caregivers (Table 5). For example, in the 2000 census, 65% of those aged 65-80 who had ever-married were co-residing with younger kin, compared with only 20% of those never-married. Moreover, levels of co-residence have dropped sharply in recent decades (Table 5), and this trend can be expected to continue. The men who fail to marry are among the least likely to be able to save for their old age, to work in their old age, and to have access to old age support from family members.”
Last, a few tables (click to view full size):
Wu Bao, Di Bao and Tekun Hu are various social assistance programs: “The Te Kun program provides cash assistance to very poor and incapacitated residents of less-developed areas, at the discretion of the local officials. The Wu Bao program, dating from the 1950s, sought to ensure that no section of the population remained destitute.11 In 2006, the State Council issued regulations that shift financing responsibility for wubao from village reserves to local fiscal budgets (World Bank 2008:79-80). The Di Bao program, also known as the Minimum Living Standard Scheme, provides subsidies and in-kind transfers to those living below a certain poverty line.”
More than 45 % of the total income of Chinese urban residents above the age of 60 comes from pensions; the number for rural residents in the same age group is about one-tenth of that, 4.6 %. Also take note of the family support numbers.
People often note that it’s a bad idea to compare small European countries with a country that is so big that it is comparable in size to the continent that the small country is a part of. I’ll go into a bit more detail about the differences in this post.
So, in a comment I left over at MR I noted that:
‘The United States is 3 times as big as EU-15 used to be, and EU-15 included pretty much all of the countries in Western Europe that people from the US like to compare to their own country (Italy, Germany, Spain, France, UK, Sweden…)’
Here’s the map:
It’s not ‘completely true’, but it’s very close – the area of EU-15 was 3,367,154 km^2 (link). The area of the United States is 9.83 million km^2.
Some more random numbers, I used wikipedia’s numbers and I couldn’t be bothered to add links because it would have taken forever and nobody would follow them anyway – you can look it up if something sounds really wrong. Texas: 696,200 km^2. France: 674,843 km^2. (Metropolitan France – i.e. ‘France-France (+Corsica)’: 551,695 km^2). Spain: 504,030 km^2. California: 423,970 km^2. Germany: 357,021 km^2. Denmark: 43,075 km^2. Netherlands: 41,543 km^2.
The red bit in the picture below is larger than any country in Europe which is not Russia (or another way to visualize it: That bit is actually significantly larger than the Iberian Peninsula in the map above). Maybe the scales aren’t completely similar, but they’re actually not really that far off:
If you take a trip in Europe from Venezia, Italy to Amsterdam, Netherlands, you’ll travel ~1200-1300 kilometers depending on the route. The lenght and width of Texas are both in the neighbourhood of ~1,250 km.
Now, Arizona is another southern US state with an area of 295,254 km^2 and a population of 6,4 million people. The Netherlands’ population is estimated at 16.85 million. If you combine the populations of Netherlands (16,85), Denmark (5,5) and Belgium (11 mill), those 33 million people are distributed over an area of ~115.000 km^2. The (smaller) combined populations of Texas (25,1) and Arizona (6,4) have roughly a million square kilometers to deal with.
Does it make better sense to compare Texas with France? And those small countries with, say, the state of New York? It probably would. But it’s really hard to find good matches here, in particular due to the problem with population density differences. If you do find areas that match on this metric, odds are they don’t exactly match on other key metrics. The population density of the United States as a whole is 33,7/km^2. If you scale that up by a factor of ten, you get to the third most densely populated state, Massachusetts (324.1 /km^2). The population density of Massachusetts is somewhat lower than both Belgium’s (354.7/km^2) and Netherlands’ (403/km^2). The population density of Germany (229/km^2) is comparable to that of Maryland (229.7/km^2), which is in the US top five – Germany is almost 7 times as densely populated as ‘the US as a whole’. The population density of Great Britain is 277/km^2, comparable to Connecticut’s (285.0/km^2) – the state of Connecticut is btw. #4 on the US list. Italy is at 201.2/km^2, between Delaware and Maryland – it would be on the top 6 if it was a US state. Americans like to use the expression ‘France and Germany’, but at least in terms of population density, there’s a huge difference between these two countries that I’m not sure they’re aware of: The population density of France is much lower (116/km^2) than that of Germany, and rather more comparable to that of Spain (93/km^2). All US states outside the top ten have population densities well below 100/km^2, so note that even though Spain and France are relatively sparcely populated in a Western European context, France would be well within the top 10 and Spain just outside top 10 if the two countries were US states. The average population density of the entire European Union, including a lot of Eastern European countries most Americans couldn’t find on a map, is about the same as that of France, 116.2/km^2; 3.5 times as high as the US average.
The population density of Iceland is 3.1/km^2. As mentioned, the US average is 33.7/km^2 and Belgium’s density is 354.7/km^2. Remember these magnitudes. And yes, I know that the US population density is not homogenous and that a lot of it is almost empty. The population density of Europe isn’t homogenous either – to take an example, approximately one eighth of the German population – 10 million people – live in the very small Rhine-Ruhr metropolitan region (7,110 square kilometers, or less than 2% of the area). A fifth (12+ mill) of the French population live in the Paris metropolitan area. On the other hand, the population density of Norway, which even though she is a bit of an outlier is still very much a part of Western Europe, is 12,5/km^2, comparable on that metric to, say, Nevada (9.02/km^2) in the US.
If you look at differences in the US internally, when it comes to the 10 most densely populated states the one that is situated the most to the west of these is Ohio (the state border of which is still within 500 km of the Atlantic Ocean). Here’s a map:
Remember here that these numbers are people/sq mile, so to compare the numbers there with the rest of the numbers in this post you need to divide by ~2,6 or so. I found this comparable map of Europe convenient both because it gives density limits in sq. miles and because it’s a lot more fine grained than just data on the national level:
Last of all: Languages! Here’s the European map:
Let’s just say that a map of the US would look different. Yeah, a lot has been written about the Spanish/English-thing going on in the US. Well, intranational language barriers and -linguistic diversity aren’t exactly unknown phenomena in Europe either, despite the small size of the countries involved. A thing worth remembering here is also that in many of the bilingual regions of Europe highlighted here, English is the third language. If you’re a US tourist visiting some European bilingual region and you’re annoyed people don’t speak much English, ask yourself how many areas of the US you can think of where people can hold conversations in, say, English, Spanish and French.
Update: To the many visitors who followed Razib Khan’s link or the brownpundits link and have never seen this blog before – welcome! If you liked the post, take a look around – I’ve been blogging for 5+ years and it’s not unlikely that I’ve written other stuff that might be of interest. For instance, did you know that 90 percent of the human population lives on the Northern Hemisphere? I didn’t, before I wrote this.
I had an interesting discussion yesterday which touched briefly upon a few of these subjects, so I decided to take a closer look at the data just to make sure I wasn’t completely wrong about the stuff I thought I knew – and now I’m glad I did as I seem to have somehow picked up a mistaken idea about the land area of the Southern Hemisphere (I thought it was even smaller than it is). Now, if you asked a random guy he wouldn’t know most of these numbers or even the relevant neighbourhood. Somehow I feel like people should. So here we go, most of these numbers are pulled from wikipedia:
1. Asia covers 8.7 % of the Earth’s total surface area and hosts ~60 % of the world’s current human population. It covers 29.5 % of the land area of Earth.
1a. Africa covers 6 % of the Earth’s total surface area and hosts ~14-15 % of the world’s population. It covers 20.4% of the total land area.
1b. North America: 4.8 % of surface area, 8 % of population. 16.5 % of total land area.
1c. South America: 3.5 % of surface area, 6 % of population. 12.0 % of total land area.
1d. Antarctica: 2.7 % of surface area, 0 % of population. 9.2% of total land area.
1e. Europe: 2 % of surface area, 11.5 % of population. 6.8 % of total land area.
1f. Australia: 1.5 % of surface area, 0.5 % of population. 5.1 % of total land area.
2. Russia covers 17,075,400 square kilometres. Europe and Australia combined make out ~17,8 mio. square kilometres, a number which incidentally is about the same as South America. So if we for a moment disregard the fact that Russia already makes up 40 % of the total area of Europe, it’s large enough to almost cover the two smallest continents combined.
3. According to a 2010 census, the population of China was/is 1,339,724,852 – which is more than 19 % of the population of Earth. This is a higher population than that of any single continent which is not Asia. The population of China is significantly larger than the combined populations of South America (385,7 mio), North America (529 mio) and Australia (31,26). It’s larger than the combined populations of Europe and North America. Here’s a neat image comparing sizes and populations of the continents.
4. This source notes that: “In the Northern Hemisphere, the ratio of land to ocean is about 1 to 1.5. The ratio of land to ocean in the Southern Hemisphere is 1 to 4.” Translating those ratios into percentages of the hemispheres, it turns out that in the Northern Hemisphere 60 % of the area is made up of ocean and 40 % is covered by land, whereas only 20 % of the Southern Hemisphere is covered by land and 80 % is covered by ocean. Oceans cover roughly 70,8 % of the total area of earth and land masses cover 29,2 %, so these numbers are probably ok. Here’s an image from Wikipedia:
About 90 percent of the human population lives on the Northern Hemisphere – the combined human population of the entire Southern Hemisphere is smaller than the population of Europe.
4a. The Pacific Ocean covers a larger area than all land masses of Earth combined.
4b. The Atlantic Ocean covers as a very rough approximation the same area (106 mio. square kilometres) as the total land area of the Northern Hemisphere. It covers an area corresponding to more than 70 percent of the total land area of earth.
4c. The Indian Ocean covers 68,556,000 square kilometres, approximately the same area as Asia and North America combined.
4d. The average depth of the world oceans is about 3.8 kilometers (link).
5. I can’t copy the image, but go here for a really neat illustration of the surface elevation of the areas of Earth – I’m really annoyed I can’t copy this and put it in the post. Antarctica has by far the highest mean elevation of all continents. According to this source, the mean elevation of the continent is 2,286 m. Disregarding Antarctica (which can be considered somewhat an outlier because of the ice-thing), it seems that there’s a connection between the area of a continent and its mean elevation – i.e. the larger the area of the continent, the higher the elevation. Here’s a relevant paper.)
I spent a bit of time at Statistikbanken (Statbank Denmark) yesterday, below are some numbers from it that might be of interest. When you click the link you get to the front page of the site – now, if you look to the right there’s a small Union Jack which says ‘English’ if you hover over it. Click this and you get to the English version of the site. I don’t think all of the stuff at the Danish version of the site has been translated at the English link – but a lot of stuff has, so if you’re a foreigner curious about Denmark and the Danes, go take a look..
i. This part contains data from ‘KRHFU1: Befolkningens højeste fuldførte uddannelse (15-69 år) efter område, herkomst, uddannelse alder og køn’.
In 2010, when looking at the age segment of Danes who were 30-34 years old, 20494 Danish males and 22812 Danish females had as the highest achieved education level completed a ‘long-cycle higher education’ (I think this is the term they use in the English version of the data; in Danish it’s just ‘lang videregående uddannelse’. It corresponds to an education level above BA-level but below PhD-level, i.e. Master’s Degree or equivalent). Notice that more females than males at that age has completed this level of education. This is also true after you correct for the fact that there are more males than females in that age segment of the population; in total, there were 177078 males and 176291 females in that age segment of the Danish population. In terms of percentages of the total population in the specific age segment, 11,6 % of the males and 12,9 % of the females at the age of 30-34 had completed a long-cycle higher education in 2010 – the gender difference is about 10 percent.
Now, a funny thing happens when you compare these numbers to the age segment of Danes at the age of 65-69 (people who’ve just retired). In that sample, 9655 males and 3818 females have a long-cycle higher education – out of 146029
males and 152812 females. In that sample, 6,6 % of the males and just 2,5 % of the females have a long-cycle higher education – males in that age group are more than 2,5 times as likely to have a high education than females.
How does it look when you include the age groups in between those two? Like this:
More females than males get a long education today and it’s been that way for at least 10-15 years.
ii. This part contains data from ‘Folketal pr. 1. januar efter tid, alder og køn’ and ‘KM6: Befolkningen 1 januar efter kommune, køn, alder og folkekirkemedlemsskab’
(red: females, blue: males. The x-axis is age, the y-axis is the percentage of each age group who are members of Folkekirken)
So I took out the number of male and female members of Folkekirken at the ages of 1-80 and divided by the total number of Danes in the specific age-group – this gives a measure of how big a percentage of each age group is a member of Folkekirken (Danish National Church). It seems that there are some age cycles here. I did a quick logical test in Excel to get an overview of how the membership rate changes from age group to age group. At the ages of 1-15 years, membership grows ‘every year’ (2-year olds are more likely to be members than 1-year olds, ect.). At the age group of people 18-27 years old, membership drops ‘every year’. Between 30-43 it pretty much grows every year again, then it stabilizes around the new level. For people above the age of 55, it pretty much grows every year again. I decided to not include people above the age of 80 because nothing much of interest happens there; as should be clear from the graph this age segment has by far the highest membership rates and more than 9 out of 10 are members. Remember when interpreting the relatively low membership of children to the left of the graph and the membership growth of the 1-15 years old that part of this is probably because of the relatively higher fertility of Muslim immigrants (as opposed to fewer atheist children).
iii. This part contains data from ‘FAM55N: Husstande pr. 1. januar efter kommune/region, husstandstype og husstandsstørrelse’. Every time some econ blog posts something about the household income development over time (like this one) I also see a commenter asking: ‘but what about household size?’ What I very rarely see is a commenter linking to actual data on household size. This puzzles me every time, because at least in Denmark that kind of data actually isn’t all that hard to get your hands on. Here’s a quick run from Statistikbanken:
I omitted some of the classes because otherwise it quickly gets very messy and they don’t add much to the big picture anyway, this is why the numbers don’t quite add up to the total population – but the table does include far most Danes (the 2011 numbers include 4,92 million people, the 1986 numbers 4,42 million people). The number of single person households with one male or one female living alone has increased somewhat. If you wanted to do it completely right, you’d add all the omitted classes as well before making the calculation, but in terms of the people in the sample (which covers ~ 90% of all Danes) the percentage of people living in single person households went up from 16,2 % to 20,3 %. In terms of the percentage of all households that are single person households, the number is of course much higher. In 1986, 35,6 % of all households (in the sample) were single person households, in 2011 it was 41,5 %. The number has gone up, but less than I’d thought.
I found it interesting that the number of households with a married couple and 3-4 inhabitants altogether (the most likely constellation is a married couple plus 1 or 2 children) has decreased significantly and movement from ‘married couples’ to ‘other couples’ does not explain all of it. Is the driver an increase in the divorce rate or lower fertility rate? I don’t know.
The title of the paper, Inequalities in healthy life years in the 25 countries of the European Union in 2005: a cross-national meta-regression analysis, was too long for me to use as a post title.
“Background: Although life expectancy in the European Union (EU) is increasing, whether most of these extra years are spent in good health is unclear. This information would be crucial to both contain health-care costs and increase labour-force participation for older people. We investigated inequalities in life expectancies and healthy life years (HLYs) at 50 years of age for the 25 countries in the EU in 2005 and the potential for increasing the proportion of older people in the labour force.”
“Findings: In 2005, an average 50-year-old man in the 25 EU countries could expect to live until 67,3 years free of activity limitation, and a woman to 68,1 years. HLYs at 50 years for both men and women varied more between countries than did life expectancy (HLY range for men: from 9,1 years in Estonia to 23,6 years in Denmark; for women: from 10,4 years in Estonia to 24,1 years in Denmark). Gross domestic product and expenditure on elderly care were both positively associated with HLYs at 50 years in men and women (p<0,039 for both indicators and sexes); however, in men alone, long-term unemployment was negatively associated (p=0,023) and life-long learning positively associated (p=0,021) with HLYs at 50 years of age."
I did not know that Denmark did that well on this metric. The link has a lot more.
(click to view in a higher resolution)
During 2000–2030, the worldwide population aged >65 years is projected to increase by approximately 550 million to 973 million (3), increasing from 6.9% to 12.0% worldwide, from 15.5% to 24.3% in Europe, from 12.6% to 20.3% in North America, from 6.0% to 12.0% in Asia, and from 5.5% to 11.6% in Latin America and the Caribbean (2). [...] During 2000–2030, the number of persons in developing countries aged >65 years is projected to almost triple, from approximately 249 million in 2000 to an estimated 690 million in 2030 (3), and the developing countries’ share of the world’s population aged >65 years is projected to increase from 59% to 71% (2). However, migration patterns could influence these projections.
The report isn’t new, so maybe the data look a little bit different now, but hardly all that different. A few years ago, this subject was discussed reasonably often here in DK, but now I can’t even remember the last time I’ve heard a politician talk about this particular ‘problem’.
In case you’re wondering, this is a problem that really isn’t going anywhere – in fact, with current policies continued, the problem (a stagnating labor force supporting an increasingly larger group of old people who can no longer support themselves and who have for most of their lives counted on the government to support them when they reached old age, meaning that a lot of them have saved far too little to support themselves in old age) will only grow over time, given that people can expect to live longer and longer (in Denmark, appr. 1 more year/decade) as time goes by.
If the retirement age were to be adjusted so that it would roughly match the increasing longevity of the population (1 month/year or so), this problem would be much easier to solve in the long run. The more time passes, the less likely the enactment of such an adjustment model will become.
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