Screening for breast cancer

“Main results

Eight eligible trials were identified.We excluded a biased trial and included 600,000 women in the analyses. Three trials with adequate randomisation did not show a significant reduction in breast cancer mortality at 13 years (relative risk (RR) 0.90, 95% confidence interval (CI) 0.79 to 1.02); four trials with suboptimal randomisation showed a significant reduction in breast cancer mortality with an RR of 0.75 (95% CI 0.67 to 0.83). The RR for all seven trials combined was 0.81 (95% CI 0.74 to 0.87).

We found that breast cancer mortality was an unreliable outcome that was biased in favour of screening, mainly because of differential misclassification of cause of death. The trials with adequate randomisation did not find an effect of screening on cancer mortality, including breast cancer, after 10 years (RR 1.02, 95% CI 0.95 to 1.10) or on all-cause mortality after 13 years (RR 0.99, 95% CI 0.95 to 1.03).

Numbers of lumpectomies and mastectomies were significantly larger in the screened groups (RR 1.31, 95% CI 1.22 to 1.42) for the two adequately randomised trials that measured this outcome; the use of radiotherapy was similarly increased.

Authors’ conclusions
Screening is likely to reduce breast cancer mortality. As the effect was lowest in the adequately randomised trials, a reasonable estimate is a 15% reduction corresponding to an absolute risk reduction of 0.05%. Screening led to 30% overdiagnosis and overtreatment, or an absolute risk increase of 0.5%. This means that for every 2000 women invited for screening throughout 10 years, one will have her life prolonged and 10 healthy women, who would not have been diagnosed if there had not been screening, will be treated unnecessarily. Furthermore, more than 200 women will experience important psychological distress for many months because of false positive findings. It is thus not clear whether screening does more good than harm.”

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From this review by Gøtzsche and Nielsen from The Nordic Cochrane Centre. Here’s a relatively recent press release from Cochrane (in Danish). Here’s a related article published a few days ago. By now, it seems that Gøtzsche thinks it is quite clear whether screening does more good than harm:

“I believe the time has come to realise that breast cancer screening programmes can no longer be justified,” Gøtzsche said.”

Maybe there’s a way to modify the current screening programmes somewhat so that they include mainly/only relatively high-risk subpopulations – but identifying just who the high-risk individuals are is never easy, which is part of why screening programmes like these are undertaken in the first place. Either way, if the results reported above are ‘in the right ballpark’ a serious cost/benefit analysis should in my mind lead to a rejection of the current programme(s).

Data on Danish immigrants, 2011 (3)

The third post in the series, here are the first two posts. This part will deal with education and I must admit that it’s less data-heavy than the previous two posts, in part because I felt it was necessary to spend some time explaining how the Danish education system actually works here (and in part because I feel there’s a limit as to how much time I can justify spending on posts like these). I’ll do another post on crime later on, so this is not the last post in the series. Anyway, here goes:

*In 2010, 44% of male descendants of non-Western immigrants and 61% of female descendants of non-Western immigrants in Denmark at the age of 30 had finished an education leading to a vocational/professional qualification (see below for some notes on terminology). The corresponding numbers for people of Danish origin at the age of 30 were 73% and 79%. The education level of non-Western female descendants has increased over time; in 2004 the number was 44%. (p.65)

*It was a bit harder to translate stuff from this section than the rest because the Danish education system is a bit different from that of e.g. the US, creating a few problems related to terminology. The terminology I’ve used in this section when I was in doubt follows this source. So, which educations are in fact included in the ‘education leading to a …’ (abbreviated ELVQs in the following) measure above and which are not? ELVQs include (Danish link) various technical educations (electrician, carpenter,…), further education leading to a degree (BA, MA, PhD) as well as various other educations (office education, teaching, nursing,…). A high school degree is not included in the set, nor is a grundskoleuddannelse (see below), and if you’re a college drop-out who have not obtained a degree you’re also not included in the set of people with an ELVQ. The idea is of course that if you have an ELVQ, you have finished an education that has given you some specific skills that are useful in terms of finding and retaining employment. I decided this would also be as good a place as any to add a bit more background info about the Danish education system you might need to make sense of the numbers in the report – it’s not in there, so no page references. In Denmark the lowest attainable ‘formal education level’ (i.e. disregarding drop-outs before that point) you can have is completion of the 9th grade (grundskoleuddannelse). The graduation exam is called ‘Folkeskolens afgangsprøve’. Technically it’s a little complicated as to where exactly to put high school in terms of grades, because some people finish 9th grade and then go to high school directly (I did) whereas others take 10th grade first at the same place they took 1st-9th grade before they go to high school. The coursework in Danish high schools is the same for people who went to 10th grade before going to HS and for people who didn’t, and HS classes are a mix of both types of students. I’m not completely sure if you’re required to take 10th grade before you can enroll in a vocational(/technical) education like carpentry, but I think some of them do demand that you have 10th grade before you can start, or at least that you have taken some of the specific courses (Danish, maths). Adult immigrants without an education can take a ‘basic adult education’ which is supposed to confer the same skills as a traditional grundskoleuddannelse (in a shorter amount of time) – after they have that they can move on to a vocational education or secondary education.

*A Danish ELVQ perhaps needless to say significantly increases employment opportunities. For 30-39 year old male non-Western immigrants who had only a grundskoleuddannelse/basic adult education, the employment rate was 58% in 2010 (females: 45%, p.79). For those with a vocational education, the employment rate was 76% (females: 78%). For those with a medium-cycle higher education (‘mellemlang videregående uddannelse’), the employment rate was 82% (females: 84%). For those with a long cycle higher education (MA or equivalent/higher), the employment rate was 79% (females: 77%). (p.65 unless otherwise specified)

*When you look at the descendants of non-Western immigrants at the age of 30 years, 41% of males and 25% of females have only a grundskoleuddannelse. The corresponding numbers for males and females of Danish origin are 18% and 13%. 22% of male- and 30% of female descendants of non-Western immigrants have a vocational education at the age of 30; the corresponding numbers for people of Danish origin are 40% and 30%. When it comes to medium-cycle higher education, the numbers for non-Western descendants are 6% and 15%; the corresponding numbers of people of Danish origin are 10% and 24%. 10% of male descendants and 8% of female descendants of non-Western immigrants at the age of 30 have a long cycle higher education; 13% of males of Danish origin and 15% of females of Danish origin at that age have one. As mentioned above there’s generally a pronounced gender difference when it comes to the education of non-Western descendants, as 61% of female descendants and 44% of male descendants at the age of 30 have a ELVQ. (p.67)

*I’ll add a couple of cautious remarks here regarding how to interpret the numbers above, cautious remarks which are not included in the report (so no page references): a) There’s probably a significant power issue here when considering forecasting based on these numbers, because the number of non-Western descendants in this age group (30-years-old) is quite low – n=558 (males) and n=559 (females). b) In terms of forecasting, heterogeneity might also be an issue. It matters if you’re looking at descendants born before or after 1983-84, because the composition of new immigrants changed at that point (in the medium run, so did the composition of immigrants in Denmark as a whole). I already talked a bit about related matters in the comment section here. Non-Westerns who came before, say, 1980 mostly came here to work; on the other hand the number of non-Westerns with fugitive status or family reunification status increased dramatically after 1983 due to policy changes implemented at that point. Another dimension along which heterogeneity is relevant is the change in the country profile of descendants, change which is not only driven by a change in the immigration patterns but also related to fertility differences across subpopulations; the total fertility rate of Somali immigrants is almost twice that of Turkish immigrants (86% higher, p.26) and these differences aren’t new. It should perhaps be made clear here that even if the change in the composition of non-Western descendants in the past might have had adverse effects on some human capital measures (SES of parents, IQ…) of the descendant group ‘as a whole’, it’s far from certain that this will lead to lower educational outcomes of the group in the future – for example, political commitment to improve educational outcomes of these groups might more than make up for the other effects. From 2004 to 2011 the educational outcomes of non-Western descendants improved, but there were only 72 non-Western descendants altogether in 2004 so it’s hard to draw strong conclusions from this as we once again run into the power issue.

*One way to try to draw inferences about the future educational profiles is to look at the educational profile of descendants currently aged 20-30 years old and compare them with the historical educational profiles of the 1980-generation (the current 30-year-olds). This is done below, the first graph contains data for the current 20-30 year-olds, the second contains data for the current 30-year-olds, green = females, blue = males – the lower ones are for non-Westerns, the graphs show how big a percentage of the group had obtained an ELVQ at any given age between 20 and 30. For example, 40% of non-Western males have an ELVQ at the age of 28 (and this was also the case for the 1980-generation):

*Part of the reason why I’ve focused mostly on descendants is that it is very hard to figure out the education levels of (first-generation) immigrants, because the data the authors made use of includes only educations which are completed at Danish educational institutions. In other words, both an Italian nuclear physicist educated in Rome and a poor Sudanese woman without a primary school education will have an ‘unknown’ education level (uoplyst) in these data sets, making it harder to pinpoint just exactly what is going on. A big majority of immigrants do not have a Danish education – 77% of Western and 69% of non-Western immigrants do not have a Danish education. (p.80) However, it seems relatively clear that at least when dealing with non-Western immigrants, an ‘unknown’ education level probably most often translates to a ‘low education level’ – the employment rate of non-Western female immigrants with an unknown education level is just 33% (p80).

Reexamining the Case for Marriage: Union Formation and Changes in Well-being

“This article addresses open questions about the nature and meaning of the positive association between marriage and well-being, namely, the extent to which it is causal, shared with cohabitation, and stable over time. We relied on data from the National Survey of Families and Households (N = 2,737) and a modeling approach that controls for fixed differences between individuals by relating union transitions to changes in well-being. This study is unique in examining the persistence of changes in wellbeing as marriages and cohabitations progress (and potentially dissolve) over time. The effects of marriage and cohabitation are found to be similar across a range of measures tapping psychological well-being, health, and social ties. Where there are statistically significant differences, marriage is not always more advantageous. Overall, differences tend to be small and appear to dissipate over time, even when the greater instability of cohabitation is taken into account. [...]

Examined across a range of outcomes, we found the similarities between marriage and cohabitation to be more striking than the differences: Entering into any union improved psychological well-being and reduced contact with parents and friends. Direct marriage and marriage preceded by cohabitation were statistically indistinguishable in all outcomes examined, providing no evidence that premarital cohabitation has negative consequences for wellbeing or ties to family and friends. When union dissolutions were excluded from the analysis, there were no statistically significant differences between the married and cohabiting for depression, relationships with parents, contact with parents, or time with friends. [...] The married fared better in health than cohabitors, but the opposite was true of happiness and self-esteem. [...]

We found no evidence that marriage and cohabitation provide benefits over being single in the realm of social ties; indeed, entering into a union reduced contact with parents and social evenings with friends. In some ways, of course, it is not surprising that forming a coresidential relationship reduces time with others, as partners spend time together that cannot be spent elsewhere. These findings do not, however, support arguments in the literature that marriage expands social circles and does so to a greater extent than cohabitation (e.g., Nock, 1995). Our results are more consistent with Sarkisian and Gerstel’s (2008) assessment of marriage as a ‘‘greedy’’ institution — and suggest the same of cohabitation. [...] We found no change over time in the effects of marriage and cohabitation on ties with family and friends, suggesting that these ties do not rebound in the years following marriage or cohabitation.”

With as many as half of all marriages ending in divorce or separation (Goldstein, 1999; Raley & Bumpass, 2003), marriage is as likely to be temporary as it is to be a lifetime relationship.”

Here’s the link.

An update

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[3] 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[1] (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.

Stuff

i. “Jesus said to his disciples: “Things that cause people to stumble are bound to come, but woe to anyone through whom they come. It would be better for them to be thrown into the sea with a millstone tied around their neck than to cause one of these little ones to stumble.” (Luke 17, quote found here).

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ii. Dimetrodon. Image from the article:

“a predatory synapsid genus that flourished during the Permian period, living between 280–265 million years ago (during the Artinskian to Capitanian stages).

As a synapsid it was more closely related to mammals than to true reptiles such as lizards and snakes. It is classified as a pelycosaur. Fossils of Dimetrodon have been found in North America and Europe. Dimetrodon had a sail on its back, which is known to have been used for regulating body temperature. [...]

Dimetrodon has two types of teeth, shearing teeth and sharp canine teeth. Its name, in fact, means “two-measures of teeth”. Dimetrodon was one of the first animals with differentiated teeth and the teeth were suitable for killing animals then tearing them to pieces. [...]

The spines of Dimetrodon have grooves on the base that were presumably ingested by blood vessels and thus ensured good bloodflow through the skin of the sail. The theory is that Dimetrodon was active in the early morning when the sun rose. The sail would be pointed towards the sun and would absorb heat allowing rapid warming. This allowed Dimetrodon to hunt at a time when other animals were not sufficiently warmed up and were slow. The sail increased body surface area by 50%. According to calculations by Bramwell Fellgett, it took a 200 kg (440 lb) Dimetrodon approximately one and a half hours for its body temperature to go from 26 to 32 °C (79 to 90 °F) [13] A study by Haack concluded that warming was slower than previously thought and that the process probably took four hours.[14] In order to cool its body in the hot midday sun, Dimetrodon turned its sail away from the sun, causing the heat to drain. The rapid warming using the sail give Dimetrodon an edge over larger animals, weighing over 55 kg. Smaller animals had higher body surface-to-mass ratio, making them hotter than Dimetrodon. The prey of Dimetrodon would therefore have been mostly large animals like Diadectes, Eryops and Ophiacodon. The changing climate during the Permian period, when the temperature increased, is a possible reason for the extinction of Dimetrodon since the sail meant no benefit over other animals and was rather a disadvantage due to its fragility.”

Even though in most Western cultures there seem to be quite a bit of focus on dinosaurs and the Mesozoic and a lot less focus on what came before that, it’s worth remembering that there was a lot of stuff going on before life ever got to the dinosaurs.

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iii. A quote:

“My model of this situation is less sanguine than others here, though Yvain and Tetronian hinted at it: it’s identity politics. Humans very naturally associate themselves with many different groups, some of them arbitrarily defined, and often without any conscious thought. Religion, favorite sports teams, the street/neighborhood/city/state/country you live in, and many other things can be the focal point of these groups. The more you associate with one of these groups, the more its part of your identity – i.e. how you see yourself. If you associate with one of these groups particularly strongly, any action which appears to make a rival group look better will personally offend you and elicit a response.”

In general, on a related note I think that the likelihood that an argument will escalate (conflict level will increase) is increasing in n in most naturally occuring settings. When two people argue nobody else is watching – which means that there are nobody else there to impress/defend. The more people are watching, the more people will witness a status loss or a status gain resulting from the argument. Also, once several people are involved coalitions will start to form naturally and you’ll start to not only be defending yourself but also feel that you have a duty (due to implicit community norms ect.) to defend the tribe. Gender also matters; in my (admittedly limited) experience, a male with a female partner arguing with another male will argue ‘more strongly’ for X if the partner is present than if she is not (unless the female makes clear that she considers the argument irrelevant; if she does and the male picks up on that signal, he’ll be likely to ‘fold’ whether or not he ‘was winning’ (…which of course he was)). Also, males are probably likely to a) be more aggresive (conflict-prone) if there are women present, and b) be more -ll- if the gender ratio is skewed ‘against them’ (# males >> # females) and less likely to be -ll- if the gender ratio is skewed ‘in their favour’ (# males << # females).

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iv. Square/Cube Law. (see also wikipedia)

“When an object undergoes a proportional increase in size, its new volume is proportional to the cube of the multiplier and its new surface area is proportional to the square of the multiplier.

For example, if you double the size (measured by edge length) of a cube, its surface area is quadrupled, and its volume is increased by eight times.

The point of this law is that with living beings, muscle strength is (more or less) a function of surface area, but weight is a function of volume. And Newton’s famous Second Law (the “force = mass * acceleration” one) means that if you double a critter’s size, you end up with four times the muscle power moving eight times the mass, so instead of having the same relative agility as the original, the double-sized creature actually has only half.

This applies to flyers as well: Double the size, and you get four times the wingpower attempting to keep eight times the weight airborne, so the creature’s ability to fly has actually been cut by half.”

Part of why dung beetles can roll up to 50 times their own weight (/and why we can’t).

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v. “The ancient Greeks and Romans used torture for interrogation. Until the 2nd century AD, torture was used only on slaves (with a few exceptions). After this point it began to be extended to all members of the lower classes. A slave’s testimony was admissible only if extracted by torture, on the assumption that slaves could not be trusted to reveal the truth voluntarily.[12]” (wikipedia)

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vi. Swaziland

“Swaziland, officially the Kingdom of Swaziland (Umbuso weSwatini), and sometimes called Ngwane or Swatini, is a landlocked country in Southern Africa, bordered to the north, south and west by South Africa, and to the east by Mozambique. The nation, as well as its people, are named after the 19th century king Mswati II.

Swaziland is a small country, no more than 200 kilometres (120 mi) north to south and 130 kilometres (81 mi) east to west. [...]

Some 75% of the population are employed in subsistence farming, and 60% of the population live on less than the equivalent of US$1.25 per day. [...]

Swaziland’s economic growth and societal integrity is highly endangered by its disastrous HIV epidemic, to an extent where the United Nations Development Program has written that if it continues unabated, the “longer term existence of Swaziland as a country will be seriously threatened.”[5] The infection rate in the country is unprecedented and the highest in the world at 26.1% of adults[6] and over 50% of adults in their 20s.[5] [...]

…Swaziland has the highest HIV infection rate in the world [...] and also the lowest life expectancy at 32 years, which is 6 years lower than the next lowest average of Angola. From another perspective, the last available World Health Organization data in 2002 shows that 64% of all deaths in the country were caused by HIV/AIDS.[11] [...]

In 2004, Swaziland acknowledged for the first time that it suffered an AIDS crisis, with 38.8% of tested pregnant women infected with HIV [...] Life expectancy has fallen from 61 years in 2000 to 32 years in 2009.[15]“

Having fun (؟)

Click to view full size.

Curriculum is 500+ pages. Not all of it is that bad, but on the other hand some of it is significantly worse (the above was some of the first stuff we went through). No examination aids allowed at the exam. At the current point in time I estimate that I’m more likely to fail than I am likely to pass.

Everyone has a price, but there’s a limit? What will people (not) do for money?

“Questions: Would most people you know kill their favorite pet for $1 million? What about you?
Answers: Most people: Yes (23%) No (72%);
Yourself: Yes (11%) No (83%).”

A recent Vanity Fair poll, via Robin Hanson (whom I no longer read on a regular basis, but still visit once in a while). Hanson claims that you’d take the million. The survey and the responses made me start thinking about what people will actually do for money, what they won’t and which variables impact that decision process. Some general remarks:

i. Financial vulnerability/poverty lowers ‘your price’ and increases the choice set of stuff you’d do to get money.

ii. ‘Status effects’ matter – Hanson of course covers this. A few remarks: People usually know what ‘the right answer’ to these types of questions is supposed to be, and the more costly it seems to ‘do the right thing’, the higher the status value of professing that specific belief. It’s a bit like when dealing with religious tribes; the more crazy the idea is, the more credible the signal. This observation also in my mind leads to a related hypothesis: To make it more costly (in terms of time, effort, money) to ‘do the right thing’ in the hypothetical does not necessarily make it any less likely that people will ‘take the money’ – actually it can have the opposite effect, because the value of the signal goes up as well; perhaps the value of the signal increases even faster than the hypothetical costs, especially above a certain threshold where people decide that their choices will have no real-world consequenses. Paradoxically, by making one of the options so attractive as to be borderline absurd you can end up making sure that a lot of people will give you the opposite answer – i.e. ‘the perceived right answer’.

iii. Framing effects matter. Framing effects persist when people deal with real money in real-world settings, rather than hypothetical questions with no real-world consequences, but people usually act more rationally when they have more ‘skin in the game’. This, I think, lends support to the hypotheses that people will both a) treat the two scenarios – i. the hypothetical case, ii. the actual situation – as completely different in their minds given aforementioned threshold effects, and b) be more subject to framing effects (i.e. be less ‘rational’) in the hypothetical case. Unless you show up with a million dollars and an axe to kill the dog, the people you ask will only ever deal with the first scenario and those answers will not give much insight into what people would actually do if you came around with a check and an axe.

iv. Related to i., but still worth mentioning: There are likely threshold effects at work when dealing with choice set limitation. Poor people will be more likely to do some act X for a given amount of money Y than rich people will – but maybe it’s also the case that given some income level Z, some options simply go off the table altogether, given any price. Would a parent of three kill all their children for X dollars? This is probably where stuff like Maslow’s hierachy of needs and similar stuff from psychology come into play. Money is a claim on ressources. Still, people probably underestimate how important such claims on ressources can become.

v. Related to the last part of iv. above, correspondence bias probably play a role here when it comes to how people answer and how the hypothetical choice set limitation looks like. If correspondence bias is important, it’s probably safe to say that people who’ve answered the question as if they considered it (subconsciously, perhaps) a test of their support of the tribe/allegiance/trust will be unlikely to accept the idea that they’d act perhaps even radically differently in the real-world-scenario.

vi. “The report titled “The Big Payoff: Educational Attainment and Synthetic Estimates of Work-Life Earnings” [...] reveals that over an adult’s working life, high school graduates can expect, on average, to earn $1.2 million; those with a bachelor’s degree, $2.1 million; and people with a master’s degree, $2.5 million.

Persons with doctoral degrees earn an average of $3.4 million during their working life, while those with professional degrees do best at $4.4 million.” (link)

A third way to frame the question: You’re an average Joe with a master’s degree. You’re 25 and currently expect to work another 40 years on the labour market before you retire. If you choose to kill your dog today, you get 16 years of income tomorrow. You’d be able to retire at the age of 49, instead of at the age of 65 (this is disregarding discounting, compounded interest ect.; so the ‘subjective true value’ of that money will likely be even higher than that). Next, repeat the question using the high school grad numbers. A million dollars is a lot of money and it can buy you a lot of stuff.

I assume most readers of this blog would assume that they’d take the money in a real-world setting (though it’s impossible to be sure ‘unless [someone] show[s] up with a million dollars and an axe to kill the dog…’). If you think you wouldn’t take the money in the real-world scenario, please comment below!

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Appendix (added after swissecon’s comment):

A factor I didn’t include above is the ‘love of the pet’ variable. This one is a double-edged sword as well because there are relevant tradeoffs here too: The longer you’ve had the pet, the greater attachment you’ll feel towards it (ceteris paribus), but also the less time the pet has left of its life. All pets die, and if you’ve had your dog for a decade even though you love it very much you’ll know that it probably doesn’t have a lot of years left. The pet’s life has to end in a few years anyway. Lots of people who have pets that they love end the life of the pet before nature would by paying a vet to kill the pet, to ease the suffering of the pet. I’m not saying it’s an easy decision to make, I know it’s not, but lots of people do it all the time. How hard would it be to push that decision, say, 2 years ahead and get paid a million dollars to do it? 3 years? These aren’t questions I just bring up to make people uncomfortable – the point is that questions like these will be perfectly natural to ask yourself if the guy was actually standing in your yard with that 1 million dollar check and an axe. And it’s because of questions like those that I think people are lying to themselves if they claim that they’re relatively certain they would never kill the pet.

There are cases where the love will be very strong, like an 80-year-old with a 13 year old cat. But the combination of advanced age of both the pet and the pet-owner is not exactly the default situation when dealing with pets and pet-owners. Another important factor at play in that situation is also that an 80-year-old will have a lot less use of the money, because a lot of spending options available to young people are no longer available to her or him.

Wikipedia articles of interest

1. External ballistics.

“External ballistics is the part of the science of ballistics that deals with the behaviour of a non-powered projectile in flight. External ballistics is frequently associated with firearms, and deals with the behaviour of the bullet after it exits the barrel and before it hits the target.”

2. Propaganda in the Soviet Union.

“The main Soviet censorship body, Glavlit, employed 70,000 full-time staff not only to eliminate any undesirable printed materials, but also “to ensure that the correct ideological spin was put on every published item”.”

And Glavlit wasn’t even the only censorship body in the Soviet Union. Also:

“CIA estimated in 1980s that the budget of Soviet propaganda abroad was between 3.5-4.0 billion dollars.” [...] “Propaganda abroad was partly conducted by Soviet intelligence agencies. GRU alone spent more than $1 billion for propaganda and peace movements against Vietnam War”

3. Concussion (this is a ‘good article’).

4. Crypsis. “In ecology, crypsis is the ability of an organism to avoid observation or detection by other organisms. It may be either a predation strategy or an antipredator adaptation, and methods include camouflage, nocturnality, subterranean lifestyle, transparency,[2] and mimicry.”

The article has this awesome image (click to view in higher res.):

The frog you’re looking for is just to the left of the top end of the vertical stick. Can you see it? I couldn’t. Go here for an image up close where you can see the frog highlighted.

5. Ascomycota.

“The Ascomycota are a Division/Phylum of the kingdom Fungi, and subkingdom Dikarya. Its members are commonly known as the Sac fungi. They are the largest phylum of Fungi, with over 64,000 species.[2] The defining feature of this fungal group is the “ascus” (from Greek: ἀσκός (askos), meaning “sac” or “wineskin”), a microscopic sexual structure in which nonmotile spores, called ascospores, are formed. [...]

The ascomycetes are a monophyletic group, i.e., all of its members trace back to one common ancestor. This group is of particular relevance to humans as sources for medicinally important compounds, such as antibiotics and for making bread, alcoholic beverages, and cheese, but also as pathogens of humans and plants. Familiar examples of sac fungi include morels, truffles, brewer’s yeast and baker’s yeast, Dead Man’s Fingers, and cup fungi. The fungal symbionts in the majority of lichens (loosely termed “ascolichens”) such as Cladonia belong to the Ascomycota. There are many plant-pathogenic ascomycetes, including apple scab, rice blast, the ergot fungi, black knot, and the powdery mildews. Several species of ascomycetes are biological model organisms in laboratory research. Most famously Neurospora crassa, several species of yeasts, and Aspergillus species are used in many genetics and cell biology studies. Penicillium species on cheeses and those producing antibiotics for treating bacterial infectious diseases are examples of taxa that belong to the Ascomycota.”

The article has lots of additional links if you want to know more.

6. Cameroon. (this is a featured article)

Query

So, assume you’re me. Assume you’re feeling miserable and you want to do something about that by doing some activity that you think have a high likelihood of getting you into a better mood or engage you so that you don’t think about these things. What do you do?

I’ve been in this situation quite a few times over the last few months. Stuff that no longer seem to work quite as well as it used to: Watching some funny stuff on tv (like Scrubs, Monty Python, Yes Minister,…); reading a Pratchett novel (also because I can’t keep doing that – there are only so many books); taking a TV-tropes-walk; watching live chess games online; Chopin.

None of these activities involve other people, and I know very well that one of the main reasons why they don’t work very well is precisely that a big part of the reason why I feel miserable is that I’m lonely. But I don’t want to engage strangers when I feel like that, it takes too much effort.

I spend quite a bit of time these days thinking about how to make sure I don’t spend all my time thinking about stuff I should not be thinking about, and what it’s okay for me to be thinking about and what’s not. Some people might say I tend to overthink stuff (I was told that I do as late as earlier today, and that conversation did not have anything to do with these thoughts). So assuming at least a few of you guys aren’t too dissimilar from me to relate to this problem: how do you guys control what you’re thinking about, and how do you manage when you can’t?

On a related note: Assume for a second that you have the day off tomorrow and you could do whatever you like. You’d like to do something ‘different’, something you don’t normally do. What would you be doing? I don’t have the day off anytime soon – if everything goes well, I’ll do pretty much nothing but study (/reasonably) advanced mathematics for the next two-three weeks – but I ‘always do what I always do’ when I have days like those, and I probably shouldn’t.

I’m in a situation right now where the result of one exam will make the difference between having a BA + the first semester of the MA, or be kicked out of the university without any degree and possibly also without the hope of ever obtaining one. It’s not very much fun to be in this situation. Don’t expect serious blogging for the next weeks. I’ve done some work on a third post in the immigrant-data-series, but I probably won’t post that until late January.

Oh, one last thing: This might be as good a place as any for new readers to introduce themselves.

Random stuff from the net, links, wikipedia…

1. RAND: Living Well at the End of Life (via Razib Khan). Here’s a link to one of the sources, a book which deals with some of the same questions: Approaching Death: Improving Care at the End of Life. Looks interesting, don’t have time to read it at the moment.

2. Fatal familial insomnia. “Fatal familial insomnia (FFI) is a very rare autosomal dominant inherited prion disease of the brain. It is almost always caused by a mutation to the protein PrPC, but can also develop spontaneously in patients with a non-inherited mutation variant called sporadic Fatal Insomnia (sFI). FFI is an incurable disease, involving progressively worsening insomnia, which leads to hallucinations, delirium, and confusional states like that of dementia.[1] The average survival span for patients diagnosed with FFI after the onset of symptoms is 18 months.”

Sleep’s important.

3. False consensus effect.

“In psychology, the false consensus effect is a cognitive bias whereby a person tends to overestimate how much other people agree with him or her. There is a tendency for people to assume that their own opinions, beliefs, preferences, values and habits are ‘normal’ and that others also think the same way that they do.[1] This cognitive bias tends to lead to the perception of a consensus that does not exist, a ‘false consensus’. This false consensus is significant because it increases self-esteem. The need to be “normal” and fit in with other people is underlined by a desire to conform and be liked by others in a social environment.

Within the realm of personality psychology, the false consensus effect does not have significant effects. This is because the false consensus effect relies heavily on the social environment and how a person interprets this environment. Instead of looking at situational attributions, personality psychology evaluates a person with dispositional attributions, making the false consensus effect relatively irrelevant in that domain. Therefore, a person’s personality potentially could affect the degree that the person relies on false consensus effect, but not the existence of such a trait.

The false consensus effect is not necessarily restricted to cases where people believe that their values are shared by the majority. The false consensus effect is also evidenced when people overestimate the extent of their particular belief is correlated with the belief of others. Thus, fundamentalists do not necessarily believe that the majority of people share their views, but their estimates of the number of people who share their point of view will tend to exceed the actual number.

This bias is especially prevalent in group settings where one thinks the collective opinion of their own group matches that of the larger population. Since the members of a group reach a consensus and rarely encounter those who dispute it, they tend to believe that everybody thinks the same way.

Additionally, when confronted with evidence that a consensus does not exist, people often assume that those who do not agree with them are defective in some way.[2] There is no single cause for this cognitive bias; the availability heuristic and self-serving bias have been suggested as at least partial underlying factors.

The false consensus effect can be contrasted with pluralistic ignorance, an error in which people privately disapprove but publicly support what seems to be the majority view (regarding a norm or belief), when the majority in fact shares their (private) disapproval. While the false consensus effect leads people to wrongly believe that they agree with the majority (when the majority, in fact, openly disagrees with them), the pluralistic ignorance effect leads people to wrongly believe that they disagree with the majority (when the majority, in fact, covertly agrees with them).”

4. Malthus, An Essay on the Principle of Population. Salman Khan recently made a video on the subject, here’s wikipedia.

5. Marital Rape License (warning, tvtropes link).

“Only a few decades ago, it was legal for a man to rape his wife. Sweden was the first country to explicitly criminalize it in 1965, and it has only been illegal in all fifty US states since 1993. Fifty-three countries around the world still don’t consider it a crime.

In some old patriarchal systems, a woman belonged first to her father (or closest living male relative if the father was dead) and then to her husband. Once married — and in some systems she could be married off without her consent to some old man she despised or had never met — her husband had a legal and “moral” right to her body whether she liked it or not. It gets even creepier when the bride is underage.”

We tend to take a lot of stuff for granted. Another reason why you should read Nothing To Envy.

6. Schema (psychology)

“A schema (pl. schemata or schemas), in psychology and cognitive science, describes any of several concepts including:

*An organized pattern of thought or behavior.
*A structured cluster of pre-conceived ideas.
*A mental structure that represents some aspect of the world.
*A specific knowledge structure or cognitive representation of the self.
*A mental framework centering on a specific theme, that helps us to organize social information.
*Structures that organize our knowledge and assumptions about something and are used for interpreting and processing information.

A schema for oneself is called a “self schema”. Schemata for other people are called “person schemata”. Schemata for roles or occupations are called “role schemata”, and schemata for events or situations are called “event schemata” (or scripts).

Schemata influence our attention, as we are more likely to notice things that fit into our schema. If something contradicts our schema, it may be encoded or interpreted as an exception or as unique. Thus, schemata are prone to distortion. They influence what we look for in a situation. They have a tendency to remain unchanged, even in the face of contradictory information. We are inclined to place people who do not fit our schema in a “special” or “different” category, rather than to consider the possibility that our schema may be faulty. As a result of schemata, we might act in such a way that actually causes our expectations to come true.”

7. Koch Snowflake Fractal (a structure with infinite perimeter but a finite area). Couldn’t remember if I’ve already blogged this at one point, but no harm done in case I have:

The persona

“Every person develops a persona, and that persona persists because inconsistencies in your personal narrative get rewritten, redacted and misinterpreted. If you are like most people, you have high self-esteem and tend to believe you are above average in just about every way. It keeps you going, keeps your head above water, so when the source of your own behavior is mysterious you will confabulate a story which paints you in a positive light. If you are on the other end of the self-esteem spectrum and tend to see yourself as undeserving and unworthy, you will rewrite nebulous behavior as the result of attitudes consistent with the persona of an incompetent person, deviant, or whatever flavor of loser you believe yourself to be. Successes will make you uncomfortable so you will dismiss them as flukes. If people are nice to you, you will assume they have ulterior motives or are mistaken. Whether you love or hate your persona, you protect the self with which you’ve become comfortable. When you observe your own behavior, or feel the gaze of an outsider, you manipulate the facts so they match your expectations.”

I quoted it here not that long ago, I try to remember this these days.

Tomorrow’s the first of the two exams I have this semester. If I fail, I guess I’ll have to rename the blog. Last time I was in this situation I more or less decided the night before the exam that I’d jump out in front of a bus after taking a week’s worth of insulin if I failed. I don’t really know today if I’d have followed through with it or not – I didn’t fail, despite the not-exactly-ideal circumstances.

My perspective has changed since then, I feel differently about these things now – I can see myself failing tomorrow (or later this month) and still be alive in 10 years. I’ve gotten quite a bit of help from a student advisory organization. Regular readers will know that thoughts like those mentioned above are not just about the All-Important Exams. If I’d died, I’d not exactly have been some straight-A student who killed himself because he got an F – I’d have been a depressed, immensely lonely loser who hated his life and wanted it to end a couple of decades earlier than it otherwise would have. In case you were in doubt, most of the time I really do hate my persona even if it’s actually a bit different today. The hate isn’t all bad; it motivates change.

Blogging will also be light in January if I pass tomorrow’s exam. I can’t really commit to not waste my time doing stupid waste-of-time stuff, but I can commit to not waste time writing in detail about my waste of time. I’ll try to update the twitter regularly.

The Gauss Christmath Special

It’s that time of the year again:

…

Update: Here are a few other videos. If you’re sick of christmas songs, you can have those playing in the background while you’re busy making a christmas cake (or whatever) instead:

Part 4, The Foundation of Rome:

(His youtube channel has a lot more)

Data on Danish immigrants, 2011 (2)

Thanks for the feedback.

And just a remark in case you were in doubt (most people probably weren’t, but just in case) – yes, I know very well that it doesn’t make all that much sense to report population estimates on a population of millions of people 40 years into the future down to almost fractions of a person without even including error bars (like the 6.139.618 population estimate for 2050. 618 you say? Not 617?). But the report doesn’t include error bars and I don’t feel comfortable rounding these numbers – so I decided from the start to just report the numbers they give and work with those; there are all sorts of problems related to doing anything else. So anyway, here’s some more stuff from the report:

*According to Statistics Denmark’s latest model estimates, the number of non-Western immigrants in the population will grow with 39% from 2011 to 2050, so that there will be 358.000 non-Western immigrants in Denmark. Today the number is 258.000. The corresponding increase in the number of Western immigrants is estimated at 47%. (p.48)
*The number of descendants of Western immigrants is expected to increase significantly during the period, so that by 2050 the number will be 4,4 times higher than it is today. The number of descendants of non-Western immigrants is likewise expected to increase over time, by a factor of 2,2. Despite these differences in growth rates, the number of non-Western descendants is still expected to turn out to be a little more than 3 times as high as the number of Western descendants by 2050. (p.48) This is because the current number of descendants of non-Western immigrants living in Denmark is much higher (115.597) than the current number of descendants of Western immigrants (18.016) living in Denmark. (p.49)
*The ratio of the Danish population categorized as people ‘of Danish origin’ is expected to decrease over time from 89,9% in 2011 to 84,7% in 2050. (p.48)
*The total Danish population (people of Danish origin, immigrants and descendants combined) is expected to grow by 578.990 people from 2011 to 2050, from 5.560.628 people in 2011 to 6.139.618 people in 2050. The subset of Western immigrants living in Denmark is expected to increase by 79.876 over that time period, from 170.758 to 250 634. The number of descendants of Western immigrants is expected to grow by 61.477, from 18.016 to 79.493. The part of the total Danish population growth from 2011 to 2050 which can be explained by Western immigrants and their descendants is thus equal to 141.353, which is roughly one-fourth of the total estimated population growth (24,4%) (p.49). The subset of non-Western immigrants living in Denmark is expected to increase by 99.413 from 2011 to 2050, from 258.146 to 357.559. The number of descendants of non-Western immigrants is expected to grow by 135.221, from 115 597 to 250 818.
The part of the total Danish population growth from 2011 to 2050 which can be explained by non-Western immigrants and their descendants is thus equal to 234.634, or 40,5% of the total estimated population growth. The part of the population growth over the period explained by people of Danish origin is 203.003, or 35,1% of the total population growth – despite the fact that this group makes up ~85-90% of the population over the entire time period in question. (all numbers from Tabel 1.18, p.49. They didn’t actually report these specific growth component percentages in the report, but it doesn’t take much work to calculate them from the data provided and I thought they’d be interesting to have a look at.)
*When looking at age groups, a few developments are noteworthy. In 2050, people of Danish origin are expected to make out 80,7 % of people at the ages of 40-64, vs. 91,4% today. The employment level of this age group is relatively high, compared to other age groups, which is part of what makes this development interesting – non-Western immigrants in general have much lower levels of employment than do people of Danish origin; more on that stuff below. Another factor perhaps worth noting is that the percentage of immigrants from non-Western countries above 64 years old is expected to increase from 1,2% today to 7,9% in 2050 (the expected growth of Western immigrants in that age group is much smaller – from 2,5% to 3,5%). (p.49)

*The employment rate [beskæftigelsesfrekvens] of males of Danish origin was 75,1% in 2010. The employment rate of females of Danish origin was 73,0% in 2010. The employment rate of male Western immigrants was 62,8% and the employment rate of female Western immigrants was 57,4%. The employment rate of non-Western male immigrants was 53,9% in 2010. The employment rate of non-Western female immigrants was 44,6% in 2010. (p.54)
*The employment rate of non-Western male descendants was 55% in 2010, and the employment rate of female non-Western descendants was 56%. (p.51)
*The employment rate differences between people of Danish origin and non-Western immigrants are particularly pronounced in the age group of 50-59 year olds: Whereas the employment rate of that age group was 79% for females of Danish origin, the corresponding number for non-Western female immigrants was 38%. (p.51)
*The employment rate difference between males of Danish origin and male immigrants of non-Western origin was 21 percentage points in 2010, whereas the employment rate difference between females of Danish origin and female immigrants of non-Western origin was 28 percentage points in 2010.(p.51)
*In 1996 the difference in the employment rates of males of Danish origin and those of male immigrants of non-Western origin was 40 percentage points. The corresponding difference in the employment rates of females of Danish origin and those of female immigrants of non-Western origin was 44 percentage points in 1996. 1996 was two years before the first election where immigration policy became a major factor (though in terms of formation of the government, it did not decide the election – that didn’t happen until 2001).
*The 2008-report contained a nice illustration of how the employment rate differences between non-Western immigrants and Danes vary with age and I decided to include it in this post – you can find it at page 65. The numbers are from 2007. Dark-blue = males, light-blue = females, the y-axis is the employment rate difference between people of Danish origin and non-Western immigrants measured in percentage points, the x-axis is age:

So, to take an example, the employment rate of non-Western female immigrants at the age of 40 was approximately 35 percentage points lower than the employment rate of females of Danish origin at the age of 40 in 2007.

*From 1996 to 2008 the employment rate of non-Western immigrants increased significantly. The male employment rate increased from 40% to 63%, the female employment rate increased from 26% to 50%. Here are two graphs from the report (p.53), click on them to view them in a higher resolution – the first one is on male data, the second is on female data:

Explanation:
“Indv., vestlige lande” = Immigrants from Western countries
“Indv., ikke-vestlige lande” = Immigrants from non-Western countries
“Dansk oprindelse” = Danish origin
“Eftk., vestlige lande” = Descendants, Western countries
“Eftk., ikke-vestlige lande” = Descendants, non-Western countries.
In both cases, the y-axis is the employment rate.

*Country of origin is a very important variable – not all Western countries are the same, nor are all non-Western countries the same. The employment rate of immigrants from the Netherlands is the same as that of people of Danish origin – 74%. The Polish immigrants have an employment rate of 66%, and so do the British. These employment numbers are much higher than those of the immigrants from the US, where the employment rate is just 49%. (p.57) However, ‘many young Western immigrants come to Denmark to study, they’re often only here for a short while and return home after they’ve finished their coursework here’ (paraphrasing some of the relevant remarks on p.76). The authors don’t go into any details about the US immigrants in the report, but I think it’s safe to say that they are more likely to be university students than are immigrants from, say, Poland – the lower employment rates probably shouldn’t be all that surprising. Employment rates on their own don’t care about differences in labor force participation rates.
*Non-Western immigrants generally have lower employment rates, and it’s also among these countries of origin that we find the subpopulations with the lowest employment numbers. The bottom three are Iraq (36% employed), Lebanon (35% employed) and Somalia (31% employed). Less than one in four of female Lebanese immigrants in Denmark are employed. But worth noticing here is also that some of the non-Western countries do quite well: 67% of Ukrainians are employed, and so are 63% of the immigrants from Thailand. (p.57)

A table from the report (p.57), click on it to view it full size:

As I know a lot of terms might cause problems I decided to add an explanation. It was either that, translate everything and make my own table or report some more of the numbers in the text – I decided you should have the data but I didn’t want to spend a lot of time reconstructing that table. You can probably figure out a lot of the stuff I’ve translated below on your own, but in my experience it’s very nice to not have to be the least bit in doubt when reading tables like these. If you have questions, ask:
Title: Employment rates of 16-64 year olds. 2010.
“Antal personer”: Number of people. (antal = number)
“Beskæftigelsesfrekvens”: Employment rate.
“Mænd” = Males.
“Kvinder” = Females.
“I alt” = Total/combined.

“Indvandrere, vestlige lande”: Immigrants, Western countries
Nederlandene = The Netherlands
Storbritannien = Great Britain
Polen = Poland
Rumænien = Romania
Sverige = Sweden
Tyskland = Germany
Litauen = Lithuania
Norge = Norway
Island = Iceland
Italien = Italy
Frankrig = France

“Indvandrere, ikke-vestlige lande” = Immigrants, non-Western countries
Kina = China
Tyrkiet = Turkey
Rusland = Russia
Indien = India
Jugoslavien = Jugoslavia
Marokko = Morocco
Filippinerne = The Philippines
Irak = Iraq
Libanon = Lebanon

The specific 30 countries were chosen because those were the 30 countries of origin with the highest amounts of 16-64 year olds.

…

The employment rate is somewhat dependent on how long people have lived here, so the authors also decided to split up the data using that variable. Again, click to view it full size:

From page 60. Additional explanation:
Title (roughly): ‘Employment rates of 16-64-year old immigrants – distributed based on the amount of time spent in Denmark. 2010.’
‘Opholdstid’ = Time spent in Denmark.
‘Under 3 år’ = Less than 3 years.
’3-6 år’ = 3-6 years. [I think you get the picture...]
‘Over 15 år’ = More than 15 years.

*Do note when interpreting the employment numbers of e.g. Filipino women that people who are employed as au-pairs are not counted as employed. (p.59)

*’Even when taking into account differences in the amounts of time spent in the country, there are still big differences. Immigrants from Iraq, Lebanon and Somalia who have been in Denmark for at least a decade have employment rates between 30% and 41%. Immigrants from the Philippines, China and Thailand who’ve been in Denmark for at least a decade have employment rates between 67% and 75%.’ (p.59)

—

I’ll post at least one more post on this subject. I will probably add the posts together into one single post when I’m done.

Data on Danish immigrants, 2011 (1?)

The central Danish statistical office, Statistics Denmark, has just published a report with a lot of data on Danish immigrants, Immigrants in Denmark, 2011. I thought some of the non-Danes reading along might appreciate a post in English on this subject.

At the site, they’ve given no indications that they’re planning to translate this, so I don’t think an English version of this material is coming up anytime soon. My translation of the stuff is better than what you’d get from google translate, but do remember that I’m not exactly a professional translator. I’ve decided to page-source every bit of data for this reason, so that it’s easier to go have a look for yourself if you’re in doubt. It was most convenient for me to page-source the pdf version pages, not the official page numbers at the top of each page in the report. Don’t think of the statements below as direct quotations from the report – I’ve frequently had to reformulate the expressions used in the report. If something’s unclear, please ask away. Anyway, let’s start:

*10,1 % of the Danish population are immigrants or descendants of immigrants. (p.13)
*Immigrants make up 7,7% and descendants make up 2,4%. (p.13) [A small note here: The report only explicitly mentions the 10,1% and the 7,7%, not the 2,4% - but I think it's safe to assume that this is a simple subtraction problem and that it makes good sense to post that number as well just for completeness.]
*60,2% of all immigrants are from non-Western countries. (p.13)
*66% of all immigrants and descendants are from non-Western countries. (p.25)
*The number of non-Western immigrants has almost sextupled since 1980. (p.14)
*From 1980 to 2011, the number of non-Western descendants has increased from 7.653 to 115.597. (p.15)
*The number of descendants of Western immigrants grew by 70% from 1980 to 2011. (p.15)

*The immigrants living in Denmark come from more than 200 countries. (p.15)
*The distribution is asymmetric. Immigrants from the top 12 countries (in terms of number of immigrants living in Denmark) make up 50% of all immigrants. (p.15)
*Turkey is at the top of the list with 32 479 immigrants living in Denmark. (p.15)
*5 out of the top 12 countries are Western countries (Germany, Poland, Norway, Sweden, GB). 7 are Non-western countries (Turkey, Iraq, Bosnia and Herzegovina, Iran, Lebanon, Pakistan, ex-Jugoslavia). (p.16)
*There’s significant variation in the age distribution of immigrants from different countries. When looking at the top twelve, 20% of the Western immigrants in that group are 60 years old or older, whereas only 10% of the non-Western immigrants in the top-twelve are 60 years old or older. (p.16)
*As to the Poles, they’re an interesting case because they’re quite different from the rest of the Western immigrants. They’re the third largest immigrant population (26 580) in Denmark – the number of Polish born people living in Denmark is higher than the number of immigrants from Sweden and Great Britain combined – and more than half of the Poles (53%) are between 20 and 40. 68% of the Polish immigrants are between 20 and 49 years old. 10 % of them are 60 years or older. (p.16)

*When looking at the descendant populations living in Denmark, 11 out of the top 12 countries are non-Western countries. More than one in five (21%) of all descendants living in Denmark are descendants of Turkish immigrants. Lebanon and Pakistan are next on the list, with 9% and 7% respectively. (p.17)
*Most descendants are quite young. 41% of them are below the age of 10, and only 10% have reached the age of 30.

[I used to comment on this fact back when I did political discussions, because it is often overlooked or simply ignored in discussions about what might be termed the demographic potential of descendant populations. We have no idea how many children descendants will end up having, and it makes no sense to try to draw strong conclusions out of sample from the data sets that are available now. Putting the above numbers in context, the average age of women having their first child in Denmark was 29,1 years in 2010 (Statistikbanken, FOD11). I also urge people to remember here that the growth rate of population segment X in a population doesn't just depend on the total fertility rate differential, but also on age of birth differentials. If women from population segment X get children at the age of 30 and women from population segment Y get children at the age of 20, population segment Y will grow faster than population segment X, even if every single woman in the two population segments have the same amount of children. This remark is relevant because non-Western immigrants as a rule get children at a lower age than ethnic Danes. Females of Danish origin get on average 0,21 children during the period of their lives where they are 20-24 years old. For all non-Western female immigrants, the corresponding average number is 0,35. For Lebanese women, the number is 0,72. (pp. 27-28)]

*Western descendants are much older than non-Western descendants, on average. [worsening the data problems mentioned above. Especially if you mix up the Westerns and non-Westerns - does it make sense to extrapolate birth rates of Turkish descendants in 2015 from the historical birth rates of descendants of Norwegian women?] One third of Western immigrants are above the age of 30, only 6% of non-Western immigrants are that old. (p.18)
*Descendants from Turkey, Pakistan, Jugoslavia or Morocco make up 77% of all 30+ year old descendants from non-Western countries. (p.18)
*The total fertility rate of Somali immigrants in Denmark is 3,937. (p.26)
*In the period 2006-2010, there were an average of 64.056 living births pr. year. In the same period, there were an average of 5.860 (9,1%) children born every year of non-Western immigrants and an average of 2.310 (3,6%) children every year born of Western immigrants. The average annual number of children of descendants over the time period was just 961. (p.26)

*The report has some stats on family patterns. When it comes to male immigrants from Western countries who are classified as being in a relationship, in 59% of the cases the partner is of Danish origin and in 37% of the cases the partner is an immigrant from a Western country. When it comes to the female immigrants from a Western country, 63% of the partners are of Danish origin and in one-third of the cases it’s a Western immigrant. The pattern is different when it comes to immigrants from non-Western countries. For male immigrants from non-Western countries, 13% have partners of Danish origin and 80% have partners from a non-Western country. For female immigrants from non-Western countries, 28% have partners of Danish origin and 68% have partners of non-Western origin. Interestingly, when it comes to descendants Western immigrants are more likely to have a partner of Danish origin than are first generation immigrants (83% and 85% for males and females respectively), whereas this pattern is actually reversed for females from non-Western countries, where descendants are less likely to have a Danish partner than are first generation immigrants (19% of females who are descendants of immigrants from non-Western countries with a partner have a partner of Danish origin, whereas the corresponding number for the first generation non-Western female immigrants is 28%.) 3 out of 5 non-Western descendants who are in a relationship are in a relationship with a non-Western immigrant and 18% of them have a partner who’s also a descendant of immigrants from a non-Western country. (all numbers above from Tabel 1.9, p.32)
*When it comes to the non-Western females who find Danish male partners, few of these women come from the major immigrant countries. Of the 19.981 female non-Western immigrants with a partner of Danish origin, females from Thailand, Philippines, Russia, China, Brazil and Ukraine make up 11.644 of them – 58%. (p.33)
*Females from Thailand and Philippines alone make up 39% of the non-Western females who have partners of Danish origin. (p.34)
*When it comes to females from Turkey, Pakistan and Iraq, only 2% of them have a partner of Danish origin. (p.34)
*97% of female Turkish immigrants with a partner have a partner of Turkish origin. 94% of Pakistani females in a relationship have a partner of Pakistani origin. (p.35)
*88% of Turkish descendants in a relationship have a partner of Turkish origin. (p.37)

*Today the country from which Denmark receives the largest number of immigrants is Poland. Denmark received 3850 Polish immigrants in 2010. (p.38)
*(not direct citation but paraphrasing…)’Immigrants from Western countries like USA, Spain and Italy rarely come to Denmark to live here permanently and a large share of them leave Denmark again.’ – ‘This is not the case for non-Western immigrants.’ (p.40) Some data: 77% of the Poles who came to Denmark in 2002 had left the country by January 1st, 2011. 88% of the immigrants from the US who came in 2002 had left Denmark by 2011. On the other hand, only 9 percent of Iraqis who came in 2002 had left by 2011. 24% of the Turks who arrived in 2002 had left by 2011. (all numbers from table, p.39) [the 9% number is interesting also because during that time period, Denmark actually had various policies (Danish links) in place where Iraqis who decided to leave Denmark could get a one-time cash prize for doing so.]

This post dealt with roughly the first 40 pages of the report. The report has 153 pages. So there’s a lot of stuff to cover – there’s also data on education, crime, employment, ect. I might write another post or two on this subject if people liked this one.

Major related hint: If you’d like me to write another post on this, tell me, either by using the rating system or by commenting. If I don’t get positive feedback, I probably won’t do any more work on this – it adds a not insignificant time component to not being able to just quote directly from the report because the stuff needs to be translated as well.

But what if it doesn’t get better?

So, imagine you’re the friend of some guy stuck at some low-paying dead-end job. A few times every year the guy is close to having a nervous breakdown; bashing his head into the wall late at night, unable to sleep for extended periods of time, restless, depressed – all of it related to work-stuff that clusters around specific times of the year. He seriously considers the option of taking his own life if the current project fails and he gets fired. Even though the guy knows that the project has to be finished by some specific date a long time ahead, he still repeats the same pattern every year of not doing enough work on a day-to-day basis over the time period up to the project deadline, and he ends up working way too much over short periods of time up to the deadline. If he doesn’t get fired, the only satisfaction he gets is the warm feeling in his stomach that he gets from knowing that he’s allowed to continue doing what he does and that he will not have to repeat the experience he just went through until in 6 months time or so. Although he also knows that if he gets fired, his total future income stream will be reduced significantly, a lot of jobs will suddenly be impossible for him to get and future job security will decrease significantly. He used to, a very long time ago, have this idea about what it would mean to be succesful, ideas about what would be significant accomplishments, what to strive for. He still sometimes is reminded of such things, those ideals he let go of, discarded, when he is interacting with his family. His measure of success is different from what it used to be and different from what most people would consider an appealing measure; it is the absense of complete and total failure. He keeps telling himself that this shouldn’t be what he’s aiming for; that other people seem to have no problem aiming higher than that and still achieve their goals; that when it doesn’t actually look all that hard maybe it’s worth considering. But then he reminds himself that achieving those goals is a lot harder than it looks.

Most people would probably advise the guy to lighten up a bit, stop taking work so seriously (but he doesn’t really, that’s part of the problem), get better at time management, find another job, find a girlfriend or some other distraction, …? What do I know – there are Plenty Of Options. The current situation does not seem ideal, but the alternatives aren’t great and they have huge and irreversible consequences. Some people might bring up the idea that perhaps the job is not the underlying issue here.

Well, maybe it isn’t. I really don’t know anymore. But if it isn’t, is that a good thing or a bad thing? I’m not even sure about the answer to that one. Maybe even if I actually get through this, I’ll just end up the same way some place else a few years down the road. I don’t want that, and I’ve repeatedly told myself that if I actuallly get to finish my education, the worst would be behind me. Lately I’ve been questioning this assumption. Maybe it just doesn’t get better, even if ‘everything goes well’. A small part of, but certainly not the main reason, why I haven’t slept particularly well lately. I feel trapped, but escaping by getting fat and just ignoring the hamster wheel doesn’t feel like something that would constitute an improvement.

There’s a project deadline coming up in two weeks or so.

Cost-Effectiveness of Interventions to Prevent and Control Diabetes Mellitus: A Systematic Review

Here’s the link. If you have any interest in this subject, you should probably read all of it.

“We conducted a systematic review of literature on the CE of diabetes interventions recommended by the American Diabetes Association (ADA) and published between January 1985 and May 2008. We categorized the strength of evidence about the CE of an intervention as strong, supportive, or uncertain. CEs were classified as cost saving (more health benefit at a lower cost), very cost-effective (≤$25,000 per life year gained [LYG] or quality-adjusted life year [QALY]), cost-effective ($25,001 to $50,000 per LYG or QALY), marginally cost-effective ($50,001 to $100,000 per LYG or QALY), or not costeffective (>$100,000 per LYG or QALY). The CE classification of an intervention was reported separately by country setting (U.S. or other developed countries) if CE varied by where the intervention was implemented. Costs were measured in 2007 U.S. dollars.

RESULTS— Fifty-six studies from 20 countries met the inclusion criteria. A large majority of the ADA recommended interventions are cost-effective. We found strong evidence to classify the following interventions as cost saving or very cost-effective: (I) Cost saving — 1) ACE inhibitor (ACEI) therapy for intensive hypertension control compared with standard hypertension control; 2) ACEI or angiotensin receptor blocker (ARB) therapy to prevent end-stage renal disease (ESRD) compared with no ACEI or ARB treatment; 3) early irbesartan therapy (at the microalbuminuria stage) to prevent ESRD compared with later treatment (at the macroalbuminuria stage); 4) comprehensive foot care to prevent ulcers compared with usual care; 5) multi-component interventions for diabetic risk factor control and early detection of complications compared with conventional insulin therapy for persons with type 1 diabetes; and 6) multi-component interventions for diabetic risk factor control and early detection of complications compared with standard glycemic control for persons with type 2 diabetes. (II) Very cost-effective — 1) intensive lifestyle interventions to prevent type 2 diabetes among persons with impaired glucose tolerance compared with standard lifestyle recommendations; 2) universal opportunistic screening for undiagnosed type 2 diabetes in African Americans between 45 and 54 years old; 3) intensive glycemic control as implemented in the UK Prospective Diabetes Study in persons with newly diagnosed type 2 diabetes compared with conventional glycemic control; 4) statin therapy for secondary prevention of cardiovascular disease compared with no statin therapy; 5) counseling and treatment for smoking cessation compared with no counseling and treatment; 6) annual screening for diabetic retinopathy and ensuing treatment in persons with type 1 diabetes compared with no screening; 7) annual screening for diabetic retinopathy and ensuing treatment in persons with type 2 diabetes compared with no screening; and 8 ) immediate vitrectomy to treat diabetic retinopathy compared with deferred vitrectomy.

CONCLUSIONS — Many interventions intended to prevent/control diabetes are cost saving or very cost-effective and supported by strong evidence. Policy makers should consider giving these interventions a higher priority.”

…

In health cost-effectiveness analyses, it’s quite common to find measures/interventions that are cost-effective but do not actually save ‘you’ money, because the effectiveness variable is some sort of (/weighted) effect/$ measure. There’s also always the problem with figuring out what’s the relevant alternative course of (in-?)action. But what I found very, very interesting here is that one of these interventions were actually cost-saving when compared to doing nothing:

“The six cost-saving interventions with strong evidence were [...] 2) ACEI or ARB therapy to prevent ESRD for type 2 diabetes compared with no ACEI or ARB therapy”

This is very interesting. Of course it’s also noteworthy that quite a few other treatment options would actually be cost saving if implemented on a larger scale than is currently the case. I should probably also comment on some of the stuff that’s not worth the money:

“The four interventions with strong evidence of not being cost-effective were 1) one-time universal opportunistic screening for undiagnosed type 2 diabetes among those aged 45 years and older compared with no screening; 2) universal
screening for type 2 diabetes compared with targeted screening; 3) intensive glycemic control in the U.S. setting for patients diagnosed with diabetes at older ages (55–94 years of age) compared with usual care; and 4) annual screening for retinopathy compared with screening every two years. All these studies were for type 2 diabetes.”

Some sort of universal opportunistic screening program is one of those things that might sound like a good idea, but probably isn’t. It’s just too damn expensive. Btw., when applying age-targeting approaches it’s not at all clear where to place the cut-off point:

“Current evidence is uncertain on how the CE of screening for undiagnosed type 2 diabetes would change with the age of
those screened. Two studies evaluated the CE of screening for undiagnosed type 2 diabetes; one study reported that costeffectiveness ratios (CERs) increased with initial screening age (16) while the other reported that they decreased with screening age (35).”

A History of Chinese Civilization

…by Jacques Gernet.

From one of the amazon reviews: “If you compare this book to its obvious competitors (e.g. Valerie Hansen’s Open Empire, Schirokauer’s Brief History of Chinese Civilization), you have to be amazed at the relatively low list price–especially considering that the publisher, Cambridge University Press, is not famous for selling cheap books. If you can buy only one textbook history of China, this one is worth considering for that reason alone.”

Cambridge University Press is also not famous for selling crappy books, and combine that observation with the remarks above and you have a big part of the reason why I bought it. Judging from what I’ve read so far it’s a good book with a reasonable amount of details, all things considered (there’s a lot of ground to cover here…). Some stuff from the book:

i. “The cart with a pole and two horses harnessed with a neck-yoke gave way at the time of the Warring States to the cart with two shafts. And it seems that at the same time the neck-yoke – which was to remain for a very long time the only method of harnessing known in the rest of the world – was replaced by the breast harness. This new device, and also the horse-collar, which was to appear between the fifth and ninth centuries A.D., were important pieces of progress in the field of animal traction. By freeing the horses from the pressure of the yoke, which tended to choke them, they made driving easier and rendered it possible to pull heavier loads. One single horse would suffice where formerly two or sometimes even four were required. It is noteworthy that the casting of iron and more rational methods of harnessing, attested in the Chinese world at the time of the Warring States, both appear in Europe at the end of the Middle Ages.”

ii. “Although the Han founder considerably softened the extreme harshness of the penal laws of the first empire, the political and administrative organization that Liu Pang put in place differed little from that of the Ch’in. We find the same division of the territory into commanderies (chün) and prefectures (hsien), and the same tripartite division of functions both in the capital and in the provinces: civil affairs, military affairs, and inspection and supervision of the administration. In short the same ‘Legalist’ empire was perpetuated not only in the territories directly dependent on the central power, but also in the ‘fiefs’ (feng-kuo) granted first to the founder’s companions-in-arms and later to relatives of the imperial family. Its power was based on the direct control of the peoples and individuals by the state. This implies recourse to accurate censuses, and in fact those which have been preserved from the Han period are reckoned to be among the most precise in history. Every subject was liable to a personal tax payable in coin (this tax was levied even on children of tender years), to annual stints of forced labour and to military service. In addition, the Legalist system of rewards and punishments [...] made it possible to classify the whole population in the continuous hierachy of the twenty-four degrees of dignity (chüeh). [...] The use of the passport, the antecendents of which go back to the age of the Warring States, is well attested in the Han period [206 BCE – 220 CE], as is the use of police dogs.”

iii. “the hold of the central power was firmest where the settlement was most recent; in the long-settled regions the imperial administration had to come to terms with the great families. [...] This sheds light on one of the main reasons for transfers of population: it was in the state’s interest to move influential families, to shift them from their surroundings, in order to rob them of all power. Similarly, it was also in the state’s interest to extend the areas of land clearance and colonization, for it is easier to keep in hand a population consisting of displaced persons – convicts, freedmen, soldiers, and bankrupt peasants. [...] a big effort was made to colonize the north-western regions, and the number of people settled there in the reign of the emperor Wu Ti [141-87] may be estimated at two million. A few figures will suffice to bring home the scale of these transfers of population. In 127, 100,000 peasants were settled in Shuo-fang [...] in 102, 180,000 soldier-farmers went off to people the Chiu-ch’üan and Chang-yeh commanderies; and in 120,after big floods in western Shantung, 700,000 victims of the disaster were transferred to Shensi. These transfers of population were numerous enough to affect the distribution of the population in North China…”

iv. “Like the Ch’in, the first Han rulers pursued a policy of undertaking big public works, the majority of which were strategic or economic in character. In 192 and 190 B.C. peasants and their womenfolk from the valley of the Wei were conscripted for the construction of the walls of the new capital, Ch’ang-an. In each of these two years there were nearly 150,000 people at work. [...] In 132 B.C. 100,000 soldiers were drafted to repair a breach in the dykes of the Yellow Rver. [...] Besides ramparts and forts, canals and roads were also built. These reinforced the hold of the central power on the regions but also corresponded to economic needs. In 129 B.C. ninety miles of canal were dug between Honan and Shensi to connect the basin of the Wei with the Yellow River; 95 B.C. saw the opening of a canal some sixty miles long linking the course of the Wei to that of the Chiang further north. But innumerable irrigation works were carried out in the whole of North China during the reigns of Wu Ti and his immediate successors.”

v. “It would be simplistic to see in the Great Walls a sharp divide between the world of the nomadic cattle-raisers and that of the Chinese farmers and townspeople. The northern frontiers of the Chinese world formed a zone where the opposing modes of life of the farmer and the herdsman mingled and combined. Down the centuries sometimes the pasturages would advance and the cultivated land shrink, sometimes the arid lands would be conquered and developed by the sedentary peoples. Just as certain tribes of herdsmen changed over to agriculture, so some Han adopted the nomads’ mode of life. [... an example:] A defeat incurred by the Chinese armies in 201-200 [B.C.] caused a general retreat south of the Great Walls which lasted until about 135 [B.C.]. [...] The organization of the Han armies and defence system on the northern frontier is fairly well known to us thanks to the discovery of a substantial number of manuscript texts on wood and bamboo and to the excavations carried out on the Chinese limes in the Han period since the beginning of the century. [...] About 10,000 in number, they consist of reports, communiqués, inventories, soldiers’ letters, fragments of legal texts, and so on. [...] The dates mentioned in them run from about 100 B.C. to A.D. 100. [...]

Look-out duty, patrols, and training occupied a considerable part of the time of the troops serving in the first line of defence. Each post was in permanent contact with neighbouring posts and with the rear, thanks to a system of signals: red and blue flags, smoke by day and fires by night, rendered more easily visible by long pivoting poles rather like Egyptian shadoofs. This system of signals, which made possible, thanks to a fairly complex code, the swift transmission of relatively precise information about troop movements and attacks, is mentioned in the texts as early as 166 B.C. All messages sent and received were recorded in writing. The head of each post was obliged by a very formalistic administrative routine to write a large number of letters and to keep extensive records which deal not only with military activities but also with victualling and the weapons kept in magazine – bows, arrows, crossbows, and catapults.”

vi. “one of the most frequent practices in the Han age was that of sending hostages (chih) to the imperial court. As a token of their loyalty, princes of central Asian kingdoms and leaders of tribal confederations would offer their own sons, who were lavishly entertained in the capital at the emperor’s expense, received a Chinese education and were often appointed to posts in the imperial guards or in the domestic administration of the palace. Having been converted to the mode of life and culture of the Chinese, when they returned to their own countries they acted as agents of Han influence. Besides forming a guarantee against the breaking of alliances, the hostage system also provided a means of interfering more easily in the dynastic affairs of the countries allied with China.”

vii. “The progress in iron metallurgy continued under the Han. One has to wait until the sixth century A.D. to find a description of an open hearth process, the ancestor of the Modern Siemens-Martin process, but the Chinese could produce steel as early as the second century A.D. by heating and working together irons with different carbon contents. [...] The reign of Wang Mang (9-23) saw the appearance of the water mill. [...] We have written evidence of wheelbarrow in Szechwan in the third century A.D., but figurative representations of it go back to the first and second centuries. [...]

“When the state monopoly in iron and salt was instituted in 117 B.C., forty-eight foundries were established by the government, each of which employed a labour force of some hundreds to a thousand workers. [...] Outside the two big sectors of salt and iron, where in any case the state monopoly was strictly enforced for less than a century, private and public enterprises existed side by side. The same is true of silk weaving. [...] One of the social peculiarities of the Han period as a whole was in fact the existence of very rich families who combined agricultural enterprises [...] with industrial undertakings (cloth mills, foundries, lacquer factories) and commercial businesses, and who had at their disposal a very large labour force. [...] The Cho family, one of the richest in Ch’eng-tu, owned huge expanses of cultivated land, fish-ponds, and game parks. It possessed ironworks and steelworks in which it employed 800 slave workers and grew rich through the iron trade…”

Random stuff

1. It’s been a very long time since the last time I had a blogging pause for more than a week – I hate that this had to happen so soon after Razib’s links, because that’s just the wrong impression to give to these potential new readers. Look at the monthly archieves – I’ve posted more than 200 posts this year so far. This is an active blog, dammit, with frequent updates!

But I’ve been very busy. Real life stuff.

2. Let’s say you’re a nudist. Say you’re invited to a party at a friend’s house. Most of your friends are ‘normal people’. Most normal people will probably prefer not to invite you if you insist on showing up naked at the party, and quite a few of them will probably make such an invitation conditional on you wearing clothes at the party.

Next, say you think the earth is a 6000 years old creation of an invisible sky fairy and that the person inviting you will Burn In Hell because he hasn’t ‘seen the light’. Say that in order to save his children you like to try to convince them, when you meet, that if they do not ‘sign up’ and go to an official Indoctrination Center every week, they (and their parents) will suffer unspeakable horrors for all eternity.

Now ask yourself what are the ‘standard’, as in ‘considered normal’, magnitudes of response to these two types of behaviours/behavioural patterns. If the religious nutter and the nudist both live in the same neighbourhood, is one of them more likely to be considered the ‘black sheep’ by the rest of the community than the other?

3. I’ve been following the live commentary of the London Games here. Here’s the main site. Comp. analysis is usually available at Polgar’s blog. ‘I’ve been busy’ = this is pretty much the only outlet I’ve allowed myself over the last days, aside perhaps from the piano. The commentators – Trent, King, ‘who was that third guy?’ – are doing a wonderful job and usually the guy who has a rest day joins the party and gives his views about a few of the games being played; so that’s live analysis by the world elite provided for free online. There’s also an extensive library available of the previous day’s coverage, so that you can see the game commentary later on if you don’t have the option of watching it live. (Little voice screaming in a very high-pitched voice inside my head: “Nerd, geek!”).

4. Some wikipedia articles of interest (from my bookmarks, haven’t spent much time at wikipedia of late): Hayflick limit. Paracetamol (“Paracetamol toxicity is the foremost cause of acute liver failure in the Western world, and accounts for most drug overdoses in the United States, the United Kingdom, Australia and New Zealand.”). [Related: After reading this, I sent a link to this study to my big brother - it's a new result so it's still uncertain if there's 'anything to it', but I didn't think I should keep it to myself given their situation (my brother's GF is pregnant). My brother knows how to read a study too.] Red imported fire ant. Here’s a related link to Ed Yong’s post on the subject – don’t miss the awesome rafting video in that post! And here’s an article about the Kargil War (which very few people living in Europe has probably ever heard about).

‘Publish or perish’ and bias

Do Pressures to Publish Increase Scientists’ Bias? An Empirical Support from US States Data, by Daniele Fanelli (link). Abstract:

“The growing competition and “publish or perish” culture in academia might conflict with the objectivity and integrity of research, because it forces scientists to produce “publishable” results at all costs. Papers are less likely to be published and to be cited if they report “negative” results (results that fail to support the tested hypothesis). Therefore, if publication pressures increase scientific bias, the frequency of “positive” results in the literature should be higher in the more competitive and “productive” academic environments. This study verified this hypothesis by measuring the frequency of positive results in a large random sample of papers with a corresponding author based in the US. Across all disciplines, papers were more likely to support a tested hypothesis if their corresponding authors were working in states that, according to NSF data, produced more academic papers per capita. The size of this effect increased when controlling for state’s per capita R&D expenditure and for study characteristics that previous research showed to correlate with the frequency of positive results, including discipline and methodology. Although the confounding effect of institutions’ prestige could not be excluded (researchers in the more productive universities could be the most clever and successful in their experiments), these results support the hypothesis that competitive academic environments increase not only scientists’ productivity but also their bias. The same phenomenon might be observed in other countries where academic competition and pressures to publish are high.”

Figure 2:

An important bit on ‘”negative” results’ from the paper:

“Words like “positive”, “significant”, “negative” or “null” are common scientific jargon, but are obviously misleading, because all results are equally relevant to science, as long as they have been produced by sound logic and methods [11,12]. Yet, literature surveys and meta-analyses have extensively documented an excess of positive and/or statistically significant results in fields and subfields of, for example, biomedicine [13], biology [14], ecology and evolution [15], psychology [16], economics [17], sociology [18].
Many factors contribute to this publication bias against negative results, which is rooted in the psychology and sociology of science. Like all human beings, scientists are confirmationbiased (i.e. tend to select information that supports their hypotheses about the world) [19,20,21], and they are far from indifferent to the outcome of their own research: positive results make them happy and negative ones disappointed [22]. This bias is likely to be reinforced by a positive feedback from the scientific community. Since papers reporting positive results attract more interest and are cited more often, journal editors and peer reviewers might tend to favour them, which will further increase the desirability of a positive outcome to researchers, particularly if their careers are evaluated by counting the number of papers listed in their CVs and the impact factor of the journals they are published in.
Confronted with a “negative” result, therefore, a scientist might be tempted to either not spend time publishing it (what is often called the “file-drawer effect”, because negative papers are imagined to lie in scientists’ drawers) or to turn it somehow into a positive result. This can be done by re-formulating the hypothesis (sometimes referred to as HARKing: Hypothesizing After the Results are Known [23]), by selecting the results to be published [24], by tweaking data or analyses to “improve” the outcome, or by willingly and consciously falsifying them [25]. Data fabrication and falsification are probably rare, but other questionable research practices might be relatively common [26].”

E-mails from an A**hole

Yesterday I came across this website. Unlike 99.9 % of the stuff I post here, I’m actually not 100 percent sure if the link is NSFW – it probably depends on where you work but if you want to be sure don’t click the link. Not science, not a place to learn stuff – so if that’s what you’re here for; come back later, there’ll be plenty of that stuff later. I was arguing a long time with myself about whether I should link to this or not.

Some of them reminded me of the letters in Eric Ericson’s Brev till Utlandet which I’ve previously blogged here, though I’m not sure Ericson would like that comparison (the blogger is on a whole different level of hyperbole, rudeness, obnoxiousness ect.).

I thought the best of them were really, really funny and I decided to post a couple of examples:

“From Alex Mcgob to ***********@***********.org
Hey! I am interested in renting your place, it sounds awesome! I can pay straight cash every month. Just don’t ask where it comes from.

A little bit about myself, I am 22, and love having fun! I saw you are avid movie watchers, which is great because I have a large collection of [*]. I don’t really like cleaning, so I will expect people to clean after me. I have 5 german shepherds, but don’t worry, they are cool. I have a habit of eating any food I find, but I’ll try to restock the fridge with tap water at least once a week. I love playing the bagpipes, and I usually play them every night for a few hours.

Now I just wanted to let you know, I am a bit of an alcoholic. I drink every night until I black out and am often loud and obnoxious. I don’t have a car because I am currently sorting out my 3rd DUI, so is it cool if I borrow a car if I need to run to the liquor store or something? I’ll make sure I put some gas in it.

Some people have complained that I don’t shower, but my minor odor is nothing compared to the amount of money you will be saving on water.

I look forward to hearing from you!

Thanks,

Alex”

[* word erased to avoid getting my blog caught in word filters used to block sites in workplace environments - it starts with a p, the next letter is an o...]

…

“This was in response to an ad for a guy looking for a parking pass to the Eagles/Giants game last season at Giant’s Stadium. I don’t think he actually looked at the parking pass I sent him. If he did try to use it, he’s a retard.
Timmy Tucker to ****************@***********.org
Hi there! I have season parking passes to the game and would be willing to give up my parking pass for this one because I am taking a cab to the game. I will sell it for $25.

I scanned a picture of it here if you are interested:

Please let me know!

Go Eagles! Fuck the Cowboys!

- Tim

MATTHEW *************** to Me

Hey that sounds great! Do you think that maybe you could get me one for my friend too? He is going to the Carolina Arizona game and if you could get one for him, I would gladly give you 60 for the pair.”