Data on Danish immigrants, 2011 (4)

Before I started out this post I thought it would be the last one in the series, but at the end of the day I decided to wait with the crime data until later. This part will mostly deal with public expenditures and stuff like that. Here’s a link to the previous post in the series.

*While non-Western immigrants make out 6% of the population at the age of 16-64, they make up 10% of all people in Denmark who derive their main income from government transfers (…’are provided for by the government’ is perhaps a more ‘direct’ translation. The Danish term used in the report is: ‘er på offentlig forsørgelse’). In this framework, the concept of government transfers includes various direct income transfer programs like unemployment benefits (kontanthjælp, dagpenge), and early retirement programmes (efterløn, førtidspension), as well as governmentally subsidized employment programs (ansættelse med løntilskud, fleksjob). People working for the government are not included. (p.87-88) The ‘% of X who are provided for by the government’-measure is not the ratio of people in the sample who have received the various transfers included in the measure over the course of a year, it is rather based on a sum of all the people who have over various points in time during the year been receiving these transfers. If you have a group of one hundred people and twelve of them each received a transfer for one month during that year, that would translate to 1% of that population being provided for by the government; it’s a rough measure of the amount of ‘full-time recipients’ and should be interpreted as such. For people who receive early retirement transfers from the government the overlap between the total number of recipients over the course of a year and the number of ‘full-time recipients’ is naturally much larger than it is when it comes to transfers like unemployment benefits. (pp.87,104)

*In Denmark, two of the main social assistance programs for people who are in the workforce are ‘kontanthjælp’ and ‘dagpenge’. Kontanthjælp is the basic income support system for people without any kind of supplemental job insurance, and you can only receive it when you’ve basically depleted your assets – if you have liquid assets worth more than ~$2.000 (Danish link), you do not have the right to receive this transfer. In this context, a car you might need to drive to work is considered a liquid asset. Dagpenge is a more generous job insurance scheme subsidized by the government; the transfer payments are higher and they are completely independent of personal wealth. Approximately one in 4 (24%) of all people who receive kontanthjælp are non-Western immigrants. (p.87) 7% of all non-Western immigrants at the age of 16-64 receive kontanthjælp, whereas the corresponding number for people of Danish origin is 1,5%. (p.91)

*As the employment rates of non-Western immigrants are lower than the employment rates of people of Danish origin, it makes sense that they are also more likely to be provided for by the government. 38% of non-Western immigrants are provided for by the government, whereas the corresponding numbers for people of Danish origin and Western immigrants are 24% and 16%. (p.87)

*More than half of Lebanese-, Iraqi-, and Somali immigrants are provided for by the government. And more than half of all women from Lebanon, Somalia, Jugoslavia, Iraq and Turkey are provided for by the government. (p.87)

*Middle aged immigrants in particular have much lower employment rates than people of Danish origin at the same age, and they are thus much more likely to be provided for by the government. 60% of male non-Western immigrants at the age of 50-59 and 61% of female non-Western immigrants at the age of 50-59 are provided for by the government. The corresponding numbers for males and females of Danish origin are 23% and 26%. (p.87)

*The country of origin is an important variable when considering the likelihood that an individual immigrant is provided for by the government. 20,7% of all males of Danish origin at the age of 16-64 were provided for by the government in 2010. For Western immigrants combined it was 13,9% of males at the age of 16-64 who were provided for by the government, and for non-Western immigrants combined it was 36,7% of males at the age of 16-64 who were provided for by the government. Some more detailed numbers for male Western and non-Western immigrant populations – first the Western countries: Sweden (19,3%), Germany (18,6%), Great Britain (18,0%), Iceland (16,8%), Italy (15,7%), Norway (14,9%), Poland (12,9%), USA (11,0%), Netherlands (10,1%), France (8,8%), Romania (8,0%), and Lithuania (3,3%). The corresponding numbers for non-Western countries: Lebanon (57,8%), Iraq (51,5%), Somalia (50,1%), Bosnia-Hercegovina (45,6%), Ex Yugoslavia (44,4%), Iran (44,1%), Morocco (41,7%), Sri Lanka (37,3%), Turkey (37,0%), Afghanistan (35,1%), Vietnam (31,4%), Pakistan (29,5%), Russia (20,4%), Thailand (16,5%), Philippines (14,8%), India (9,7%), China (7,8%), and Ukraine (2%). (p.94)

*The female numbers are generally higher. I shall have to make a small digression here before I deal with those numbers: When the Danish Welfare Commission (Velfærdskommissionen) analyzed the distributionary features of the the Danish welfare system considering the gender variable, they found (Danish link) that females were on average net benefactors and males on average net contributors over an entire life span – a newborn male could, given current policies at the time the report was made, expect to pay in 0,8 million kroner ($150k) more than he’d receive over his lifespan, whereas a newborn female at that time could expect to receive 2,4 million kroner ($435k) more from the government than she’d contribute in taxes ect. Danes who are interested can read chapter 3 of this report – unfortunately I do not think an English version of that report exists. It’s likely that the relative contribution rates have changed somewhat by now, but it would surprise me a lot if they are much different now, as most of the reasons for these distributional consequenses of the welfare system have not changed much.

*Either way, as mentioned above when it comes to the females the numbers are generally higher for all groups. Of the females of Danish origin at the age of 16-64, 26,3% of them were supported by the government in 2010. For female immigrants from Western countries, the corresponding number was 18,9% and for non-Western female immigrants the number was 39,1%. Below some country-specific data – first Western countries: Sweden (24,3%), Poland (24,0%), Norway (23,5%), Great Britain (21,0%), Iceland (20,8%), Germany (18,7%), Romania (15,4%), Netherlands (14,2%), USA (12,4%), France (11,6%), Lithuania (11,5%), and Italy (11,3%). Non-Western countries: Lebanon (66,2%), Somalia (55,6%) Ex Yugoslavia (54,9%), Iraq (53,6%), Turkey (51,3%), Bosnia-Herzegovina (49,9%), Morocco (49,4%), Pakistan (45,1%), Iran (42,8%), Afghanistan (41,7%), Sri Lanka (41,6%), Vietnam (39,2%), Thailand (23,0%), Russia (20,9%), India (18,6%), China (13,9%), Ukraine (12,5%), and Philippines (11,7%). (p.95)

*The report doesn’t talk about the data much, but when analyzing the numbers above there are a couple of observations worth making here. The first is that the Swedish numbers are problematic to compare with the rest of the Western countries – it is quite likely that part of the reason why the Swedish numbers are high is that many of the ‘Swedish immigrants’ Denmark receive are in reality immigrants from non-Western countries who have used Sweden as a stepping-stone to enter Denmark, because Swedish immigration laws are much more lax than are the Danish, and it is much easier to enter Denmark via Sweden than, say, via Somalia. One other thing to note here is that the non-Western countries with high dependency rates are almost exclusively countries with large muslim populations. The non-Western immigrants from Thailand, China, Russia, India, and Ukraine in fact all ‘do better’, some of them much better, than people of Danish origin – and most of these populations are perfectly comparable to the immigrant populations from Western countries.

*Calculating net contribution rates is beyond the scope of a report like this, but I thought it would be worth including a few numbers from the publications of the Danish Welfare Commission (Velfærdskommissionen, also mentioned above). The short version is this (pp.121-122):

The graphs display the calculated net contribution to the government finances of males (the first one) and females (the second one) depending on age given the policies that were in effect at that point in time. The calculations are based on the Danish DREAM model.
Green = Danish origin.
Dark blue = immigrants from ‘developed countries’ (direct translation: ‘more developed countries’).
Turquoise = descendants of immigrants from -ll-.
Red = immigrants from ‘lesser-developed countries’.
Grey = descendants of -ll-.

They calculate in the report (p.123) that when looking at the financial net contributions to the government over the lifespan of an individual the estimated net present value (…NPV) of a male immigrant from a lesser-developed country is -0,28 mio. kroner ($50k), whereas the NPV of a female immigrant from a lesser-developed country is -4,4 mio. kroner ($800k). The NPV of a new-born male descendant of an immigrant from a lesser developed country is -0,17 mio. kroner ($30.000), and the NPV of a new-born female descendant of an immigrant from a lesser-developed country is -3,13 mio. kroner ($570k). The NPVs of immigrants from more-developed countries are 3,04 mio. kroner/$553k (males) and -0,65 mio. kroner/-$118k (females). The estimates are from 2004 and they are sensitive to changes in policy, but not that sensitive.

*Off topic, but I thought I should mention it anyway: The Florida Birth Defects Registry in 1999 estimated the lifetime costs for a child with Down Syndrome to be nearly $500,000. A Danish estimate would be much higher, but note that this cost estimate is significantly lower than the cost estimate of an average female immigrant from a lesser-developed country. In the 90es it was despite this not uncommon in Denmark to see political arguments to the effect that we needed to import immigrants from the Third World in order to save the Danish welfare state from economic ruin in the long run.

*Anyway, they remark in the Welfare Commission report that:

‘The negative contributions pr. person for immigrants and descendants from lesser-developed countries have a significant effect on the total future public-sector budget-balance problem, because both these groups are growing fast. In 2003 these two groups made up 4,7 % of the population, whereas they in 2040 are expected to make up 11,8% of the population, if the present (low) level of immigration is unchanged.’

(“De negative bidrag pr. person for indvandrere og efterkommere fra mindre udviklede lande har en betydelig effekt på det samlede fremtidige finansieringsproblem for den offentlige sektor, fordi begge disse grupper vokser med betydelig hast. I 2003 udgjorde de to grupper tilsammen 4,7 pct. af befolkningen, mens de i 2040 forventes at udgøre 11,8 pct. af befolkningen, hvis den nuværende (lave) indvandring fastholdes.” – p.125)

*As mentioned before, the overlap between the number of people who are in fact full-time recipients of a given public transfer payment and the number of people who have received a certain type of transfer payment only during a short time period over the course of the year depends on the nature of the transfer. A way to measure the average duration people receive a certain type of transfer is to divide the number of calculated full-time recipients with the number of people who have at some point during the year received the transfer. Immigrants from non-Western countries who receive temporary transfers on average receive those transfers for a longer period of time than do people of Danish origin or immigrants from Western countries and this is particularly the case when it comes to kontanthjælp: Non-Western immigrants who receive kontanthjælp on average receive it for 52% of the year, whereas the corresponding number for people of Danish origin is 40% – which is again significantly higher than the number for Western immigrants, which is 31-32% (judging from the graph on page 104; no numbers are given in the text).

January 31, 2012 Posted by | data, demographics, economics, immigration | Leave a comment

Basic Pharmacology Principles

A very good introductionary lecture on pharmacology:

I decided to post some wikipedia links to a few of the concepts he covers in the lecture below (however I’m pretty sure the lecture is the more efficient way to learn this stuff, at least the basics):

Receptor antagonist.
EC50 (half maximal effective concentration).
Dose-response relationship.

January 30, 2012 Posted by | biology, Lectures, medicine, Pharmacology, wikipedia | Leave a comment


i. “We live in a society exquisitely dependent on science and technology, in which hardly anyone knows anything about science and technology.” (Carl Sagan)

ii. “What we perceive today as elegant, natural selection created as simply as gravity creates a river. The water will flow downhill, every other parameter is free.” (John Hawks)

iii. “Time is a great teacher. Unfortunately, it kills all its pupils.” (Hector Berlioz)

iv. “A great many people think they are thinking when they are merely rearranging their prejudices.” (William James)

v. “The greater the man, the less is he opinionative, he depends upon events and circumstances.” (Napoléon Bonaparte)

vi. “The time you enjoy wasting is not wasted time.” (Bertrand Russell)

vii. “If everybody thought before they spoke, the silence would be deafening.” (George Barzan)

viii. “Most people are theists not because they were “reasoned into” believing in God, but because they applied Occam’s razor at too early an age. Their simplest explanation for the reason that their parents, not to mention everyone else in the world, believed in God, was that God actually existed. The same could be said for, say, Australia.” (Mencius Moldbug, quote found here)

ix. “The bore is usually considered a harmless creature, or of that class of irrational bipeds who hurt only themselves.” (Maria Edgeworth, Thoughts on Bores)

x. “If there is any person to whom you feel a dislike, that is the person of whom you ought never to speak.” (Richard Cecil)

xi. “For when any one explains himself guardedly, nothing is more uncivil than to put a new question.” (Jean Paul Richter)

xii. “Old men delight in giving good advice as a consolation for the fact that they can no longer provide bad examples.” (Rochefoucauld)

xiii. “You are in a pitiable condition when you have to conceal what you wish to tell.” (Publilius Syrus)

xiv. “We desire nothing so much as what we ought not to have.” (-ll-)

xv. “But nothing is so hard for those who abound in riches, as to conceive how others can be in want.” (Jonathan Swift)

xvi. “Good manners is the art of making those people easy with whom we converse. Whoever makes the fewest persons uneasy is the best bred in the company.” (-ll-)

xvii. “When people talk listen completely. Don’t be thinking what you’re going to say. Most people never listen.” (Ernest Hemingway)

xviii. “Not everything that is more difficult is more meritorious.” (Thomas Aquinas)

xix. “Everyday words are inherently imprecise. They work well enough in everyday life that you don’t notice. Words seem to work, just as Newtonian physics seems to. But you can always make them break if you push them far enough.” (Paul Graham, via. LW)

January 30, 2012 Posted by | quotes | Leave a comment

Divorce data

From page 18 of this book, Divorce: causes and consequences. The book also mentions a 2002 CDC report which found that after three years, 12 % of all marriages had ended in divorce or separation; after 5 years, 20 % of all first-marriages had ended, after 10 years; 33 %. After 15 years the number is 43 %. Maybe there’s such a thing as a ‘7-year-itch’, but the divorce likelihood is statistically much higher in the years before that and divorce risk in general is highly front-loaded. If 20 % of all marriages have ended in divorce after 5 years and the likelihood that a marriage will end in divorce after 50 years is 50%, that means that 40 % of all divorces that do happen (…at least within a 50 year time frame) take place during the first 5 years of marriage. In the US, first marriages that end in divorce last about eight years [on average].

US divorce patterns might not be similar to those found other places, so it makes sense to add some data from the UK (same link): “According to [a 2004] survey, husbands engaged in extramarital affairs in 75% of cases; wives in 25%. In cases of family strain, wives’ families were the primary source of strain in 78%, compared to 22% of husbands’ families. Emotional and physical abuse were more evenly split, with wives affected in 60% and husbands in 40% of cases. In 70% of workaholism-related divorces it was husbands who were the cause, and in 30%, wives. The 2004 survey found that 93% of divorce cases were petitioned by wives, very few of which were contested. 53% of divorces were of marriages that had lasted 10 to 15 years, with 40% ending after 5 to 10 years. The first 5 years are relatively divorce-free, and if a marriage survives more than 20 years it is unlikely to end in divorce.”

As should be clear from the above passages, divorce patterns are not the same across countries. In the US, people are more likely to divorce early on, whereas the Brits tend to wait longer before they split up. CDC probably has more US data here if Plamus or Gwern (or others?) are interested in taking a closer look, but I didn’t find what I was looking for and I didn’t want to spend a lot of time searching for that data.

If people marry young, the likelihood of divorce is much higher than if they do not. Here’s another graph from the book (p.36):

This link between age and divorce risk is not controversial, the wikipedia link has more:

“Success in marriage has been associated with higher education and higher age. 81% of college graduates, over 26 years of age, who wed in the 1980s, were still married 20 years later. 65% of college graduates under 26 who married in the 1980s, were still married 20 years later. 49% of high school graduates under 26 years old who married in the 1980s, were still married 20 years later.[28]” […] In 2009, 2.9% of adults 35–39 without a college degree were divorced, compared with 1.6% with a college education.[30]”

The book notes (p.40) that: “Interracial marriages are more likely to disrupt than marriages in which both spouses are the same race and ethnicity.[36] Interracial marriages have a 10 percent higher chance of failure in the first 10 years than same-race marriages (41 percent versus 31 percent).”

Here’s another interesting tidbit from the wikipedia article:

“According to a study published in the American Law and Economics Review, women currently file slightly more than two-thirds of divorce cases in the United States.[67] There is some variation among states, and the numbers have also varied over time, with about 60% of filings by women in most of the 19th century, and over 70% by women in some states just after no-fault divorce was introduced, according to the paper. Evidence is given that among college-educated couples, the percentages of divorces initiated by women is approximately 90%.” [my emphasis – again, from the wikipedia link above. This 90% estimate is comparable to the British estimate above and maybe I should have emphasized that one as well. Anyway, it seems that females are much more likely to file for divorce than are males, and it seems that they are pretty much almost always the ones to file for divorce when both partners have a high education level. There’s needless to say more than one model of marriage dynamics that fits those facts, but in order to get a good model, you probably need to include in such a model the fact that males are on average much more likely to cheat on their partner than are females.]

The figure below (the book, p.32) on household data is somewhat unrelated to the above, but worth posting:

Part iii of this previous post of mine has some related Danish household data. Danish readers might also want to reread this post if they want to know more about some related Danish numbers. Right now I’m considering having a closer look at Statistics Denmark’s data on marriage/divorce-patterns – I know they have data on this stuff – so I might write another post on this subject at a later point in time.

January 29, 2012 Posted by | books, data, demographics, marriage, wikipedia | Leave a comment

Blog overhaul

It’s been a gradual process that started out last year, but I think I’m pretty much ‘there’ by now – at least I’ve come a long way. So what has happened?

Well, I’ve removed a lot of posts from the site. I’ve posted 1450 posts by now (this post is number 1450), and I’ve pulled 372 from the site altogether. I didn’t do all of that today or yesterday, this was a gradual process. Even though I’ve taken down a lot of stuff, there are still 1071 posts in the archives available for everyone to read. Most of the stuff I deleted was quite bad and a lot of the posts were posts I wrote during the first year (the blogging learning curve isn’t all that steep). That being said, I should be clear about the fact that ‘low quality’ was but one of three choice parameters under consideration. The other two parameters of interest were ‘political content’ and ‘personal content’. The last couple of days I dealt with the last one in a systematic way, as I also noted on the twitter.

Political stuff doesn’t much interest me anymore, and I used to have strong opinions about that stuff. If I hadn’t blogged in the past and I were about to start up a blog at this point in time, I’m quite certain I’d see no major need to, say, upload tape recordings of political discussions I had with other people 4-5 years ago to the archives of the blog for everybody to listen to at their leisure. The old low-quality political posts have only been in my archives for the last couple of years because I never came around to removing them; now I have. The selection mechanism hasn’t been all that fine-grained, so I’m sure there’s plenty of bad stuff still around and the fact that I’ve not pulled a political post should not be interpreted as ‘current me’ supporting the views expressed in the post – maybe I just never got around to removing it, maybe I overlooked it because I hadn’t categorized it properly, maybe it contained some data that alleviated the problem that the views expressed in the post were stupid, or perhaps I thought it would be weird if there was a gap of several months in the archives even though I’ve posted relatively regularly for most of the period I’ve been blogging, or…

Incidentally, I should probably take the time to note that ‘low quality’ and ‘political content’ were very much correlated post traits – far most of the posts I’ve taken down were political posts. Politics is the Mind-Killer and just because you think of yourself as an independent and reasonable person doesn’t mean that you don’t commit a lot of the same mistakes that all those other unreasonable people make all the time, in part because just like everyone else, you have a strong need to validate and justify the political views you subscribe to. See also this.

As for the last parameter, the personal stuff, there’s no arguing that I’ve written a lot of stuff here over time that I’d not want some random guy on the street to know about me. Maybe not a lot of posts, but if you include parameters like ‘post length’ and ‘size of comment section’ (comment sections which not rarely remained active for perhaps a week after the post was written) in the analysis, it actually turned out to be quite a bit of material. Much of the stuff I’ve taken down was the kind of stuff you’d not want somebody you don’t know very well but might want to get to know better in the future, like a potential future close friend or girlfriend, to have access to all at once right from the get-go – to have that person read stuff like that could easily end up colouring that person’s perception of me, perhaps irrevocably, causing him or her to get the wrong idea and think that I’m someone I’m actually not. “You have to dole out your crazy in little pieces, you can’t do it all at once.”

I’ve had this problem with the blog for some time now; there’d be this person or that (in Real Life) which I’d like to tell about it, but I’ve always felt that given what was currently there to be found in the archives, I really would not feel comfortable telling them about it. Now I’ve changed the equation by removing some of the most personal stuff here. As I also tweeted(?) earlier, if you’ve left a comment that you really liked or you’d like to review a discussion we had here that is no longer available, give me a heads-up and I’ll mail you (/or something like that). Again – I’ve deleted nothing, all of it is still ‘in here’. The obvious alternative to this solution model was a two-tier posting system, where some posts would be password protected and others (most) would be available for all to read. I didn’t like that model, but maybe I’ll change my mind about that later on.

Given that people like to comment on the personal posts, the fact that I’ve taken down quite a few of those also means that the number of comments has probably dropped significantly, and that the blog looks less active than it used to do. A week ago the blog had approximately 2000 comments in the archives – now that number has been reduced significantly. That’s a shame, but I hope you guys will still comment here in the future despite the fact that some of the stuff you’ve written in the past has now been taken down. Anyway – comment sections are discussion fora, not history books.

One last change I’ve made is to drastically reduce the number of categories. There are currently 342 categories in the sidebar to your right which is arguably still way too much, but when I started this process there were more than 700. I hope this will make the blog a little easier to navigate.

January 29, 2012 Posted by | meta, personal | Leave a comment

Wikipedia articles of interest

1. Dutch East India Company.

“The Dutch East India Company (Dutch: Vereenigde Oost-Indische Compagnie, VOC, “United East India Company”) was a chartered company established in 1602, when the States-General of the Netherlands granted it a 21-year monopoly to carry out colonial activities in Asia. It was the second multinational corporation in the world (the British East India Company was founded two years earlier) and the first company to issue stock.[2] It was also arguably the first megacorporation, possessing quasi-governmental powers, including the ability to wage war, imprison and execute convicts,[3] negotiate treaties, coin money, and establish colonies.[4]

Statistically, the VOC eclipsed all of its rivals in the Asia trade. Between 1602 and 1796 the VOC sent almost a million Europeans to work in the Asia trade on 4,785 ships, and netted for their efforts more than 2.5 million tons of Asian trade goods. By contrast, the rest of Europe combined sent only 882,412 people from 1500 to 1795, and the fleet of the English (later British) East India Company, the VOC’s nearest competitor, was a distant second to its total traffic with 2,690 ships and a mere one-fifth the tonnage of goods carried by the VOC. The VOC enjoyed huge profits from its spice monopoly through most of the 17th century.[5] […]

By 1669, the VOC was the richest private company the world had ever seen, with over 150 merchant ships, 40 warships, 50,000 employees, a private army of 10,000 soldiers, and a dividend payment of 40% on the original investment.[23] […]

However, from there on [1730] the fortunes of the VOC started to decline. Five major problems, not all of equal weight, can be adduced to explain its decline in the next fifty years to 1780.[38]

There was a steady erosion of intra-Asiatic trade by changes in the Asiatic political and economic environment that the VOC could do little about. These factors gradually squeezed the company out of Persia, Suratte, the Malabar Coast, and Bengal. The company had to confine its operations to the belt it physically controlled, from Ceylon through the Indonesian archipelago. The volume of this intra-Asiatic trade, and its profitability, therefore had to shrink.
The way the company was organized in Asia (centralized on its hub in Batavia) that initially had offered advantages in gathering market information, began to cause disadvantages in the 18th century, because of the inefficiency of first shipping everything to this central point. This disadvantage was most keenly felt in the tea trade, where competitors like the EIC and the Ostend Company shipped directly from China to Europe.
The “venality” of the VOC’s personnel (in the sense of corruption and non-performance of duties), though a problem for all East-India Companies at the time, seems to have plagued the VOC on a larger scale than its competitors. To be sure, the company was not a “good employer”. Salaries were low, and “private-account trading” was officially not allowed. Not surprisingly, it proliferated in the 18th century to the detriment of the company’s performance.[39] From about the 1790s onward, the phrase perished by corruption (also abbreviated VOC in Dutch) came to summarize the company’s future.
A problem that the VOC shared with other companies was the high mortality and morbidity rates among its employees. This decimated the company’s ranks and enervated many of the survivors.
A self-inflicted wound was the VOC’s dividend policy. The dividends distributed by the company had exceeded the surplus it garnered in Europe in every decade but one (1710–1720) from 1690 to 1760. However, in the period up to 1730 the directors shipped resources to Asia to build up the trading capital there. Consolidated bookkeeping therefore probably would have shown that total profits exceeded dividends. In addition, between 1700 and 1740 the company retired 5.4 million guilders of long-term debt. The company therefore was still on a secure financial footing in these years. This changed after 1730. While profits plummeted the bewindhebbers only slightly decreased dividends from the earlier level. Distributed dividends were therefore in excess of earnings in every decade but one (1760–1770). To accomplish this, the Asian capital stock had to be drawn down by 4 million guilders between 1730 and 1780, and the liquid capital available in Europe was reduced by 20 million guilders in the same period. The directors were therefore constrained to replenish the company’s liquidity by resorting to short-term financing from anticipatory loans, backed by expected revenues from home-bound fleets.”

2. Plastid.

3. Collision cascade.

4. Ablation.

5. Second Persian invasion of Greece. A nice image from the article:

6. Tidal force.

7. La Tène culture.

8. Effective population size (genetics)

“the number of breeding individuals in an idealized population that would show the same amount of dispersion of allele frequencies under random genetic drift or the same amount of inbreeding as the population under consideration.”

January 28, 2012 Posted by | anthropology, biology, genetics, history, Physics, wikipedia | Leave a comment

Mistakes were made (but not by me)

Here’s the link, you should go order the book. I’ve been thinking about what it had to say on and off for the last few days, and I’m reasonably certain this is now a book which is on my mental ‘you-must-read-this’ list (another book on that list is Rochefoucould). Some chapters are better than others, but I’d say chapter 7 alone should probably increase your likelihood of getting to live in a happy marriage/relationship by more than enough to justify the costs in expected value terms, if you internalize the stuff that’s in there (/and you can find someone in the first place!).

Some quotes and comments below:

“Cognitive dissonance is a state of tension that occurs whenever a person holds two cognitions (ideas, attitudes, beliefs, opinions) that are psychologically inconsistent, such as “Smoking is a dumb thing to do because it could kill me” and “I smoke two packs a day.” Dissonance produces mental discomfort, ranging from minor pangs to deep anguish; people don’t rest easy until they find a way to reduce it.”

“You can see one immediate benefit of understanding how dissonance works: Don’t listen to Nick. [Nick’s a hypothetical guy who’s just bought a flashy car he couldn’t really afford] The more costly a decision, in terms of time, money, effort, or inconvenience, and the more irrevocable its consequences, the greater the dissonance and the greater is the need to reduce it by overemphasizing the good things about the choice made. Therefore, when you are about to make a big purchase or an important decision […] don’t ask someone who has just done it. That person will be highly motivated to convince you that it is the right thing to do.”

“one of the most entrenched convictions in our culture [is] that expressing anger or behaving aggressively gets rid of anger. […] decades of experimental research have found exactly the opposite: that when people vent their feelings aggressively they often feel worse, pump up their blood pressure, and make themselves even angrier.”

“Dissonance is bothersome under any circumstance, but it is most painful to people when an important element of their self-concept is threatened – typically when they do something that is inconsistent with their view of themselves. […] Because most people have a reasonably positive self-concept, believing themselves to be competent, moral, and smart, their efforts at reducing dissonance will be designed to preserve their positive self-images.[21]”

Much of the book is about how this happens in more detail; how people justify themselves and their actions, how it works. Most people do bad things now and then, and most people who do bad things think that they are deep down good people, which means that the bad behaviour causes dissonance. So people try to reduce it by justifying themselves and their bad behaviours in all kinds of ways; like denying they did what they did, downplaying the consequences, tell themselves that the other guy deserved it, ect.
A very important part of human behaviour is the path-dependence aspects to it, and the book spends quite a bit of time on this one. They introduce a kind of pyramid-model of decision-making; you start out at the top when you take some decision or another, there are a lot of places you can end up, and gradually you’ll move down the pyramid while you justify your choice and perhaps make new choices which have been impacted by the way you started out your descent. When people have made up their mind about some subject or another, it’s very hard to change it; and the longer time passes by, the more a person will invest in the idea and justify his or her stance (and perhaps …the more additional ‘ex ante questionable actions’ will have been undertaken along the way). The intensity and the commitment increase as we keep defending ourselves and strengthening our position. As the book put it: “How do you get an honest man to lose his ethical compass? You get him to take one step at a time, and self-justification will do the rest.” Of course stuff like confirmation bias ‘helps out’ along the way too. Interestingly, decisions taken relatively recently can even change the past – our memories are an important part of ourselves, and they are much more malleable than people perhaps tend to think. So people also tend to ‘rewrite the past’ to better deal with the present. Humans are not above selectively remembering and focusing on some things from the past which aid the narrative we’ve constructed about ourselves nor are we above forgetting other things. We will go through a lot of trouble to defend our narrative, our persona. From the book:

“When two people produce entirely different memories of the same event, observers usually assume that one of them is lying. […] But most of us, most of the time, are neither telling the whole truth nor intentionally deceiving. We aren’t lying; we are self-justifying. All of us, as we tell our stories, add details and omit inconvenient facts; we give the tale a small, self-enhancing spin; that spin goes over so well that the next time we add a slightly more dramatic embellishment; we justify that little white lie as making the story better and clearer – until what we remember may not have happened that way, or even may not have happened at all. […] History is written by the victors, and when we write our own histories, we do so just as the conquerors of nations do: to justify our actions and make us look and feel good about ourselves and what we did or what we failed to do. If mistakes were made, memory helps us remember that they were made by someone else. If we were there, we were just innocent bystanders. […] We remember the central events of our life stories. But when we do misremember, our mistakes aren’t random. The everyday, dissonance-reducing distortions of memory help us make sense of the world and our place in it, protecting our decisions and beliefs. The distortion is even more powerful when it is motivated by the need to keep our self-concept consistent; by the wish to be right; by the need to preserve self-esteem; by the need to excuse failures or bad decisions; or by the need to find an explanation, preferably one safely in the past […like parent-blaming, one they take up later on – US], of current problems. […] memory researchers love to quote Nietzsche: “‘I have done that,’ says my memory. ‘I cannot have done that,’ says my pride, and remains inexorable. Eventually – memory yields.”

I could write a lot more, but I’ll cut it short. As I said in the beginning, this is one of those books you should read.

January 28, 2012 Posted by | books, Psychology | Leave a comment

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.”

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).

January 26, 2012 Posted by | cancer, health care, medicine | Leave a comment

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).

January 25, 2012 Posted by | data, demographics, denmark, economics, education, immigration | Leave a comment

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.

January 24, 2012 Posted by | marriage, studies | Leave a comment

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.

January 24, 2012 Posted by | books, data, demographics, Geography, personal, Psychology | 1 Comment


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).

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.

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).

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).

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)

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]”

January 19, 2012 Posted by | Africa, biology, data, Geography, Paleontology, Psychology, quotes, wikipedia | 1 Comment

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.

January 14, 2012 Posted by | personal | Leave a comment

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!

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.

January 11, 2012 Posted by | economics, Psychology, random stuff | 3 Comments

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)

January 9, 2012 Posted by | biology, Geography, health, history, Physics, wikipedia | Leave a comment

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:

January 3, 2012 Posted by | books, genetics, health care, Khan Academy, mathematics, Psychology, wikipedia | Leave a comment