Here’s the link. I don’t usually cover this sort of stuff, but I have quoted extensively from the report below because this is some nice data, and nice data sometimes disappear from the internet if you don’t copy it in time.
The sample sizes here are large (“The total number of respondents was 10,195 (c. 1,000 per country).”) and a brief skim of the wiki article about Chatham House hardly gives the impression that this is an extreme right-wing think tank with a hidden agenda (for example Hilary Clinton received the Chatham House Prize just a few years ago). Data was gathered online, which of course might lead to slightly different results than offline data procurement strategies, but if anything this to me seems to imply that the opposition seen in the data might more likely be a lower bound estimate than an upper bound estimate; older people, rural people and people with lower education levels are all more opposed than their counterparts, according to the data, and these people are less likely to be online, so they should probably all else equal be expected if anything to be under-sampled in a data set relying exclusively on data provided online. Note incidentally that if you wanted to you could probably sort of infer some implicit effect sizes; e.g. by comparing the differences relating to age and education, it seems that age is the far more important variable, at least if your interest is in the people who agree with the statement provided by Chatham House (of course when you only have data like this you should be very careful about making inferences about the importance of specific variables, but I can’t help noting here that part of the education variable/effect may just be a hidden age effect; I’m reasonably certain education levels have increased over time in all countries surveyed).
“Drawing on a unique, new Chatham House survey of more than 10,000 people from 10 European states, we can throw new light on what people think about migration from mainly Muslim countries. […] respondents were given the following statement: ‘All further migration from mainly Muslim countries should be stopped’. They were then asked to what extent did they agree or disagree with this statement. Overall, across all 10 of the European countries an average of 55% agreed that all further migration from mainly Muslim countries should be stopped, 25% neither agreed nor disagreed and 20% disagreed.
Majorities in all but two of the ten states agreed, ranging from 71% in Poland, 65% in Austria, 53% in Germany and 51% in Italy to 47% in the United Kingdom and 41% in Spain. In no country did the percentage that disagreed surpass 32%.”
“Public opposition to further migration from Muslim states is especially intense in Austria, Poland, Hungary, France and Belgium, despite these countries having very different sized resident Muslim populations. In each of these countries, at least 38% of the sample ‘strongly agreed’ with the statement. […] across Europe, opposition to Muslim immigration is especially intense among retired, older age cohorts while those aged below 30 are notably less opposed. There is also a clear education divide. Of those with secondary level qualifications, 59% opposed further Muslim immigration. By contrast, less than half of all degree holders supported further migration curbs.”
“Of those living in rural, less populated areas, 58% are opposed to further Muslim immigration. […] among those based in cities and metropolitan areas just over half agree with the statement and around a quarter are less supportive of a ban. […] nearly two-thirds of those who feel they don’t have control over their own lives [supported] the statement. Similarly, 65% of those Europeans who are dissatisfied with their life oppose further migration from Muslim countries. […] These results chime with other surveys exploring attitudes to Islam in Europe. In a Pew survey of 10 European countries in 2016, majorities of the public had an unfavorable view of Muslims living in their country in five countries: Hungary (72%), Italy (69%), Poland (66%), Greece (65%), and Spain (50%), although those numbers were lower in the UK (28%), Germany (29%) and France (29%). There was also a widespread perception in many countries that the arrival of refugees would increase the likelihood of terrorism, with a median of 59% across ten European countries holding this view.”
i. Fire works a little differently than people imagine. A great ask-science comment. See also AugustusFink-nottle’s comment in the same thread.
iii. I was very conflicted about whether to link to this because I haven’t actually spent any time looking at it myself so I don’t know if it’s any good, but according to somebody (?) who linked to it on SSC the people behind this stuff have academic backgrounds in evolutionary biology, which is something at least (whether you think this is a good thing or not will probably depend greatly on your opinion of evolutionary biologists, but I’ve definitely learned a lot more about human mating patterns, partner interaction patterns, etc. from evolutionary biologists than I have from personal experience, so I’m probably in the ‘they-sometimes-have-interesting-ideas-about-these-topics-and-those-ideas-may-not-be-terrible’-camp). I figure these guys are much more application-oriented than were some of the previous sources I’ve read on related topics, such as e.g. Kappeler et al. I add the link mostly so that if I in five years time have a stroke that obliterates most of my decision-making skills, causing me to decide that entering the dating market might be a good idea, I’ll have some idea where it might make sense to start.
“Are stereotypes accurate or inaccurate? We summarize evidence that stereotype accuracy is one of the largest and most replicable findings in social psychology. We address controversies in this literature, including the long-standing and continuing but unjustified emphasis on stereotype inaccuracy, how to define and assess stereotype accuracy, and whether stereotypic (vs. individuating) information can be used rationally in person perception. We conclude with suggestions for building theory and for future directions of stereotype (in)accuracy research.”
A few quotes from the paper:
“Demographic stereotypes are accurate. Research has consistently shown moderate to high levels of correspondence accuracy for demographic (e.g., race/ethnicity, gender) stereotypes […]. Nearly all accuracy correlations for consensual stereotypes about race/ethnicity and gender exceed .50 (compared to only 5% of social psychological findings; Richard, Bond, & Stokes-Zoota, 2003).[…] Rather than being based in cultural myths, the shared component of stereotypes is often highly accurate. This pattern cannot be easily explained by motivational or social-constructionist theories of stereotypes and probably reflects a “wisdom of crowds” effect […] personal stereotypes are also quite accurate, with correspondence accuracy for roughly half exceeding r =.50.”
“We found 34 published studies of racial-, ethnic-, and gender-stereotype accuracy. Although not every study examined discrepancy scores, when they did, a plurality or majority of all consensual stereotype judgments were accurate. […] In these 34 studies, when stereotypes were inaccurate, there was more evidence of underestimating than overestimating actual demographic group differences […] Research assessing the accuracy of miscellaneous other stereotypes (e.g., about occupations, college majors, sororities, etc.) has generally found accuracy levels comparable to those for demographic stereotypes”
“A common claim […] is that even though many stereotypes accurately capture group means, they are still not accurate because group means cannot describe every individual group member. […] If people were rational, they would use stereotypes to judge individual targets when they lack information about targets’ unique personal characteristics (i.e., individuating information), when the stereotype itself is highly diagnostic (i.e., highly informative regarding the judgment), and when available individuating information is ambiguous or incompletely useful. People’s judgments robustly conform to rational predictions. In the rare situations in which a stereotype is highly diagnostic, people rely on it (e.g., Crawford, Jussim, Madon, Cain, & Stevens, 2011). When highly diagnostic individuating information is available, people overwhelmingly rely on it (Kunda & Thagard, 1996; effect size averaging r = .70). Stereotype biases average no higher than r = .10 ( Jussim, 2012) but reach r = .25 in the absence of individuating information (Kunda & Thagard, 1996). The more diagnostic individuating information people have, the less they stereotype (Crawford et al., 2011; Krueger & Rothbart, 1988). Thus, people do not indiscriminately apply their stereotypes to all individual members of stereotyped groups.” (Funder incidentally talked about this stuff as well in his book Personality Judgment).
One thing worth mentioning in the context of stereotypes is that if you look at stuff like crime data – which sadly not many people do – and you stratify based on stuff like country of origin, then the sub-group differences you observe tend to be very large. Some of the differences you observe between subgroups are not in the order of something like 10%, which is probably the sort of difference which could easily be ignored without major consequences; some subgroup differences can easily be in the order of one or two orders of magnitude. The differences are in some contexts so large as to basically make it downright idiotic to assume there are no differences – it doesn’t make sense, it’s frankly a stupid thing to do. To give an example, in Germany the probability that a random person, about whom you know nothing, has been a suspect in a thievery case is 22% if that random person happens to be of Algerian extraction, whereas it’s only 0,27% if you’re dealing with an immigrant from China. Roughly one in 13 of those Algerians have also been involved in a case of ‘body (bodily?) harm’, which is the case for less than one in 400 of the Chinese immigrants.
v. Assessing Immigrant Integration in Sweden after the May 2013 Riots. Some data from the article:
“Today, about one-fifth of Sweden’s population has an immigrant background, defined as those who were either born abroad or born in Sweden to two immigrant parents. The foreign born comprised 15.4 percent of the Swedish population in 2012, up from 11.3 percent in 2000 and 9.2 percent in 1990 […] Of the estimated 331,975 asylum applicants registered in EU countries in 2012, 43,865 (or 13 percent) were in Sweden. […] More than half of these applications were from Syrians, Somalis, Afghanis, Serbians, and Eritreans. […] One town of about 80,000 people, Södertälje, since the mid-2000s has taken in more Iraqi refugees than the United States and Canada combined.”
“Coupled with […] macroeconomic changes, the largely humanitarian nature of immigrant arrivals since the 1970s has posed challenges of labor market integration for Sweden, as refugees often arrive with low levels of education and transferable skills […] high unemployment rates have disproportionately affected immigrant communities in Sweden. In 2009-10, Sweden had the highest gap between native and immigrant employment rates among OECD countries. Approximately 63 percent of immigrants were employed compared to 76 percent of the native-born population. This 13 percentage-point gap is significantly greater than the OECD average […] Explanations for the gap include less work experience and domestic formal qualifications such as language skills among immigrants […] Among recent immigrants, defined as those who have been in the country for less than five years, the employment rate differed from that of the native born by more than 27 percentage points. In 2011, the Swedish newspaper Dagens Nyheter reported that 35 percent of the unemployed registered at the Swedish Public Employment Service were foreign born, up from 22 percent in 2005.”
“As immigrant populations have grown, Sweden has experienced a persistent level of segregation — among the highest in Western Europe. In 2008, 60 percent of native Swedes lived in areas where the majority of the population was also Swedish, and 20 percent lived in areas that were virtually 100 percent Swedish. In contrast, 20 percent of Sweden’s foreign born lived in areas where more than 40 percent of the population was also foreign born.”
vi. Book recommendations. Or rather, author recommendations. A while back I asked ‘the people of SSC’ if they knew of any fiction authors I hadn’t read yet which were both funny and easy to read. I got a lot of good suggestions, and the roughly 20 Dick Francis novels I’ve read during the fall I’ve read as a consequence of that thread.
“On the basis of an original survey among native Christians and Muslims of Turkish and Moroccan origin in Germany, France, the Netherlands, Belgium, Austria and Sweden, this paper investigates four research questions comparing native Christians to Muslim immigrants: (1) the extent of religious fundamentalism; (2) its socio-economic determinants; (3) whether it can be distinguished from other indicators of religiosity; and (4) its relationship to hostility towards out-groups (homosexuals, Jews, the West, and Muslims). The results indicate that religious fundamentalist attitudes are much more widespread among Sunnite Muslims than among native Christians, even after controlling for the different demographic and socio-economic compositions of these groups. […] Fundamentalist believers […] show very high levels of out-group hostility, especially among Muslims.”
ix. Portal: Dinosaurs. It would have been so incredibly awesome to have had access to this kind of stuff back when I was a child. The portal includes links to articles with names like ‘Bone Wars‘ – what’s not to like? Again, awesome!
x. “you can’t determine if something is truly random from observations alone. You can only determine if something is not truly random.” (link) An important insight well expressed.
xi. Chessprogramming. If you’re interested in having a look at how chess programs work, this is a neat resource. The wiki contains lots of links with information on specific sub-topics of interest. Also chess-related: The World Championship match between Carlsen and Karjakin has started. To the extent that I’ll be following the live coverage, I’ll be following Svidler et al.’s coverage on chess24. Robin van Kampen and Eric Hansen – both 2600+ elo GMs – did quite well yesterday, in my opinion.
xii. Justified by More Than Logos Alone (Razib Khan).
“Very few are Roman Catholic because they have read Aquinas’ Five Ways. Rather, they are Roman Catholic, in order of necessity, because God aligns with their deep intuitions, basic cognitive needs in terms of cosmological coherency, and because the church serves as an avenue for socialization and repetitive ritual which binds individuals to the greater whole. People do not believe in Catholicism as often as they are born Catholics, and the Catholic religion is rather well fitted to a range of predispositions to the typical human.”
I have had a look at two sources, the Office of Refugee Resettlement’s annual reports to Congress for the financial years 2013 and 2014. I have posted some data from the reports below. In the cases where the page numbers are not included directly in the screen-caps, all page numbers given below are the page numbers of the pdf version of the documents.
I had some trouble with how to deal with the images included in the post; I hope it looks okay now, at least it does on my laptop – but if it doesn’t, I’m not sure I care enough to try to figure out how to resolve the problem. Anyway, to the data!
The one above is the only figure/chart from the 2014 report, but I figured it was worth including here. It’s from page 98 of the report. It’s of some note that, despite the recent drop, 42.8% of the 2014 US arrivals worked/had worked during the year they arrived; in comparison, only 494 of Sweden’s roughly 163.000 asylum seekers who arrived during the year 2015 landed a job that year (link).
All further images/charts below are from the 2013 report.
It’s noteworthy here how different the US employment gap is to e.g. the employment gap in Denmark. In Denmark the employment rate of refugees with fugitive status who have stayed in the country for 5 years is 34%, and the employment rate of refugees with fugitive status who have stayed in the country for 15 years is 37%, compared to a native employment rate of ~74% (link). But just like in Denmark, in the US it matters a great deal where the refugees are coming from:
“Since their arrival in the U.S., 59 percent of refugees in the five-year population worked at one point. This rate was highest for refugees from Latin America (85 percent) and lowest for refugees from the Middle East (48 percent), while refugees from South/Southeast Asia (61 percent) and Africa (59 percent) were positioned in between. […] The highest disparity between male and female labor force participation rates was found for respondents from the Middle East (64.1 percent for males vs. 34.5 percent for females, a gap of 30 points). A sizeable gender gap was also found among refugees from South/Southeast Asia (24 percentage points) and Africa (18 percentage points), but there was hardly any gap among Latin American refugees (3 percentage points). Among all refugee groups, 71 percent of males were working or looking for work at the time of the 2013 survey, compared with 49 percent of females.” (p.94)
Two tables (both are from page 103 of the 2013 report):
When judged by variables such as home ownership and the proportion of people who survive on public assistance, people who have stayed longer do better (Table II-16). But if you consider table II-17, a much larger proportion of the refugees surveyed in 2013 than in 2008 are partially dependent on public assistance, and it seems that a substantially smaller proportion of the refugees living in the US in the year 2013 was totally self-reliant than was the case 5 years earlier. Fortunately the 2013 report has a bit more data on this stuff (p. 107):
The table has more information on page 108, with more details about specific public assistance programs.Table II-22 includes data on how public assistance utilization has developed over time (it’s clear that utilization rates increased substantially during the half-decade observed):
Some related comments from the report:
“Use of non-cash assistance was generally higher than cash assistance. This is probably because Medicaid, the Supplemental Nutrition Assistance Program (SNAP), and housing assistance programs, though available to cash assistance households, also are available more broadly to households without children. SNAP utilization was lowest among Latin Americans (37 percent) but much higher for the other groups, reaching 89 to 91 percent among the refugees from Africa and the Middle East. […] Housing assistance varied by refugee group — as low as 4 percent for Latin American refugees and as high as 32 percent for refugees from South/Southeast Asia in the 2013 survey. In the same period, other refugee groups averaged use of housing assistance between 19 and 31 percent.” (pp. 107-108)
The report includes some specific data on Iraqi refugees – here’s one table from that section:
The employment rate of the Iraqis increased from 29.8% in the 2009 survey to 41.3% in 2013. However the US female employment rate is still actually not much different from the female employment rates you observe when you look at European data on these topics – just 29%, up from 18.8% in 2009. As a comparison, in the year 2010 the employment rate of Iraqi females living in Denmark was 28% (n=10163) (data from p.55 of the Statistics Denmark publication Indvandrere i Danmark 2011), almost exactly the same as the employment rate of female Iraqis in the US.
Of note in the context of the US data is perhaps also the fact that despite the employment rate going up for females in the time period observed, the labour market participation rate of this group actually decreased between 2009 and 2013, as it went from 42.2% to 38.1%. So more than 3 out of 5 Iraqi female refugees living in the US are outside the labour market, and almost one in four of those that are not are unemployed. A few observations from the report:
“The survey found that the overall EPR [employment rate, US] for the 2007 to 2009 Iraqi refugee group in the 2013 survey9 was 41 percent (55 percent for males and 29 percent for females), a steady increase in the overall rate from 39 percent in the 2012 survey, 36 percent in the 2011 survey, 31 percent in the 2010 survey, and 30 percent in the 2009 survey. As a point of further reference, the EPR for the general U.S. population was 58.5 percent in 2013, about 17 percentage points higher than that of the 2007 to 2009 Iraqi refugee group (41.3 percent). The U.S. male population EPR was nine percentage points higher than the rate for Iraqi males who arrived in the U.S. in 2007 to 2009 (64 percent versus 55 percent), while the rate for the Iraqi females who arrived in the U.S. in 2007 to 2009 was 24 points higher for all U.S. women (53 percent versus 29 percent). The difference between the male and female EPRs among the same group of Iraqi refugees (26 percentage points) also was much larger than the gap between male and female EPRs in the general U.S. population (11 points) […] The overall unemployment rate for the 2007 to 2009 Iraqi refugee group was 22.9 percent in the 2013 survey, about four times higher than that of the general U.S. population (6.5 percent) in 2013” (pp. 114-115).
i. A very long but entertaining chess stream by Peter Svidler was recently uploaded on the Chess24 youtube account – go watch it here, if you like that kind of stuff. The fact that it’s five hours long is a reason to rejoice, not a reason to think that it’s ‘too long to be watchable’ – watch it in segments…
People interested in chess might also be interested to know that Magnus Carlsen has made an account on the ICC on which he has played, which was a result of his recent participation in the ICC Open 2016 (link). A requirement for participation in the tournament was that people had to know whom they were playing against (so there would be no ultra-strong GMs playing using anonymous accounts in the finals – they could use accounts with strange names, but people had to know whom they were playing), so now we know that Magnus Carlsen has played under the nick ‘stoptryharding’ on the ICC. Carlsen did not win the tournament as he lost to Grischuk in the semi-finals. Some very strong players were incidentally kicked out in the qualifiers, including Nepomniachtchi, the current #5 in the world on the FIDE live blitz ratings.
ii. A lecture:
iii. Below I have added some new words I’ve encountered, most of them in books I’ve read (I have not spent much time on vocabulary.com recently). I’m sure if I were to look all of them up on vocabulary.com some (many?) of them would not be ‘new’ to me, but that’s not going to stop me from including them here (I included the word ‘inculcate’ below for a reason…). Do take note of the spelling of some of these words – some of them are tricky ones included in Bryson’s Dictionary of Troublesome Words: A Writer’s Guide to Getting It Right, which people often get wrong for one reason or another:
Conurbation, epizootic, equable, circumvallation, contravallation, exiguous, forbear, louche, vituperative, thitherto, congeries, inculcate, obtrude, palter, idiolect, hortatory, enthalpy (see also wiki, or Khan Academy), trove, composograph, indite, mugginess, apodosis, protasis, invidious, inveigle, inflorescence, kith, anatopism, laudation, luxuriant, maleficence, misogamy (I did not know this was a word, and I’ll definitely try to remember it/that it is…), obsolescent, delible, overweening, parlay (this word probably does not mean what you think it means…), perspicacity, perspicuity, temblor, precipitous, quinquennial, razzmatazz, turpitude, vicissitude, vitriform.
iv. Some quotes from this excellent book review, by Razib Khan:
“relatively old-fashioned anti-religious sentiments […] are socially acceptable among American Left-liberals so long as their targets are white Christians (“punching up”) but more “problematic” and perhaps even “Islamophobic” when the invective is hurled at Muslim “people of color” (all Muslims here being tacitly racialized as nonwhite). […] Muslims, as marginalized people, are now considered part of a broader coalition on the progressive Left. […] most Left-liberals who might fall back on the term Islamophobia, don’t actually take Islam, or religion generally, seriously. This explains the rapid and strident recourse toward a racial analogy for Islamic identity, as that is a framework that modern Left-liberals and progressives have internalized and mastered. The problem with this is that Islam is not a racial or ethnic identity, it is a set of beliefs and practices. Being a Muslim is not about being who you are in a passive sense, but it is a proactive expression of a set of ideas about the world and your behavior within the world. This category error renders much of Left-liberal and progressive analysis of Islam superficial, and likely wrong.”
“To get a genuine understanding of a topic as broad and boundless as Islam one needs to both set aside emotional considerations, as Ben Affleck can not, and dig deeply into the richer and more complex empirical texture, which Sam Harris has not.”
“One of the most obnoxious memes in my opinion during the Obama era has been the popularization of the maxim that “The arc of the moral universe is long, but it bends towards justice.” It is smug and self-assured in its presentation. […] too often it becomes an excuse for lazy thinking and shallow prognostication. […] Modern Western liberals have a particular idea of what a religion is, and so naturally know that Islam is in many ways just like United Methodism, except with a hijab and iconoclasm. But a Western liberalism that does not take cultural and religious difference seriously is not serious, and yet all too often it is what we have on offer. […] On both the American Left and Right there is a tendency to not even attempt to understand Islam. Rather, stylized models are preferred which lead to conclusions which are already arrived at.”
“It’s fine to be embarrassed by reality. But you still need to face up to reality. Where Hamid, Harris, and I all start is the fact that the vast majority of the world’s Muslims do not hold views on social issues that are aligned with the Muslim friends of Hollywood actors. […] Before the Green Revolution I told people to expect there to be a Islamic revival, as 86 percent of Egyptians polled agree with the killing of apostates. This is not a comfortable fact for me, as I am technically an apostate.* But it is a fact. Progressives who exhibit a hopefulness about human nature, and confuse majoritarian democracy with liberalism and individual rights, often don’t want to confront these facts. […] Their polar opposites are convinced anti-Muslims who don’t need any survey data, because they know that Muslims have particular views a priori by virtue of them being Muslims. […] There is a glass half-full/half-empty aspect to the Turkish data. 95 percent of Turks do not believe apostates should be killed. This is not surprising, I know many Turkish atheists personally. But, 5 percent is not a reassuring fraction as someone who is personally an apostate. The ideal, and frankly only acceptable, proportion is basically 0 percent.”
“Harris would give a simple explanation for why Islam sanctions the death penalty for apostates. To be reductive and hyperbolic, his perspective seems to be that Islam is a totalitarian cult, and its views are quite explicit in the Quran and the Hadith. Harris is correct here, and the views of the majority of Muslims in Egypt (and many other Muslim nations) has support in Islamic law. The consensus historical tradition is that apostates are subject to the death penalty. […] the very idea of accepting atheists is taboo in most Arab countries”.
“Christianity which Christians hold to be fundamental and constitutive of their religion would have seemed exotic and alien even to St. Paul. Similarly, there is a much smaller body of work which makes the same case for Islam.
A précis of this line of thinking is that non-Muslim sources do not make it clear that there was in fact a coherent new religion which burst forth out of south-central Arabia in the 7th century. Rather, many aspects of Islam’s 7th century were myths which developed over time, initially during the Umayyad period, but which eventually crystallized and matured into orthodoxy under the Abbasids, over a century after the death of Muhammad. This model holds that the Arab conquests were actually Arab conquests, not Muslim ones, and that a predominantly nominally Syrian Christian group of Arab tribes eventually developed a new religion to justify their status within the empire which they built, and to maintain their roles within it. The mawali (convert) revolution under the Abbasids in the latter half of the 8th century transformed a fundamentally Arab ethnic sect, into a universal religion. […] The debate about the historical Jesus only emerged when the public space was secularized enough so that such discussions would not elicit violent hostility from the populace or sanction form the authorities. [T]he fact is that the debate about the historical Muhammad is positively dangerous and thankless. That is not necessarily because there is that much more known about Muhammad than Jesus, it is because post-Christian society allows for an interrogation of Christian beliefs which Islamic society does not allow for in relation to Islam’s founding narratives.”
“When it comes to understanding religion you need to start with psychology. In particular, cognitive psychology. This feeds into the field of evolutionary anthropology in relation to the study of religion. Probably the best introduction to this field is Scott Atran’s dense In Gods We Trust: The Evolutionary Landscape of Religion. Another representative work is Theological Incorrectness: Why Religious People Believe What They Shouldn’t. This area of scholarship purports to explain why religion is ubiquitous, and, why as a phenomenon it tends to exhibit a particular distribution of characteristics.
What cognitive psychology suggests is that there is a strong disjunction between the verbal scripts that people give in terms of what they say they believe, and the internal Gestalt mental models which seem to actually be operative in terms of informing how they truly conceptualize the world. […] Muslims may aver that their god is omniscient and omnipresent, but their narrative stories in response to life circumstances seem to imply that their believe god may not see or know all things at all moments.
The deep problem here is understood [by] religious professionals: they’ve made their religion too complex for common people to understand without their intermediation. In fact, I would argue that theologians themselves don’t really understand what they’re talking about. To some extent this is a feature, not a bug. If the God of Abraham is transformed into an almost incomprehensible being, then religious professionals will have perpetual work as interpreters. […] even today most Muslims can not read the Quran. Most Muslims do not speak Arabic. […] The point isn’t to understand, the point is that they are the Word of God, in the abstract. […] The power of the Quran is that the Word of God is presumably potent. Comprehension is secondary to the command.”
“the majority of the book […] is focused on political and social facts in the Islamic world today. […] That is the best thing about Islamic Exceptionalism, it will put more facts in front of people who are fact-starved, and theory rich. That’s good.”
“the term ‘fundamentalist’ in the context of islam isn’t very informative.” (from the comments).
Below I have added some (very) superficially related links of my own, most of them ‘data-related’ (in general I’d say that I usually find ‘raw data’ more interesting than ‘big ideas’):
*My short review of Theological Correctness, one of the books Razib mentions.
*An analysis of Danish data conducted by the Rockwool Foundation found that for family-reunificated spouses/relatives etc. to fugitives, 22 % were employed after having lived in Denmark for five years (the family-reunificated individuals, that is, not the fugitives themselves). Only one in three of the family-reunificated individuals had managed to find a job after having stayed here for fifteen years. The employment rate of family-reunificated to immigrants is 49 % for people who have been in the country for 5 years, and the number is below 60 % after 15 years. In Denmark, the employment rate of immigrants from non-Western countries was 47,7 % in November 2013, compared to 73,8 % for people of (…’supposedly’, see also my comments and observations here) Danish origin, according to numbers from Statistics Denmark (link). When you look at the economic performance of the people with fugitive status themselves, 34 % are employed after 5 years, but that number is almost unchanged a decade later – only 37 % are employed after they’ve stayed in Denmark for 15 years.
Things of course sometimes look even worse at the local level than these numbers reflect, because those averages are, well, averages; for example of the 244 fugitives and family-reunificated who had arrived in the Danish Elsinore Municipality within the last three years, exactly 5 of them were in full-time employment.
*Rotherham child sexual exploitation scandal (“The report estimated that 1,400 children had been sexually abused in the town between 1997 and 2013, predominantly by gangs of British-Pakistani Muslim men […] Because most of the perpetrators were of Pakistani heritage, several council staff described themselves as being nervous about identifying the ethnic origins of perpetrators for fear of being thought racist […] It was reported in June 2015 that about 300 suspects had been identified.”)
*A memorial service for the terrorist and murderer Omar El-Hussein who went on a shooting rampage in Copenhagen last year (link) gathered 1500 people, and 600-700 people also participated at the funeral (Danish link).
*Pew asked muslims in various large countries whether they thought ‘Suicide Bombing of Civilian Targets to Defend Islam [can] be Justified?’ More than a third of French muslims think that it can, either ‘often/sometimes’ (16 %) or ‘rarely’ (19 %). Roughly a fourth of British muslims think so as well (15 % often/sometimes, 9 % rarely). Of course in countries like Jordan, Nigeria, and Egypt the proportion of people who do not reply ‘never’ is above 50 %. In such contexts people often like to focus on what the majorities think, but I found it interesting to note that in only 2 of 11 countries (Germany – 7 %, & the US – 8 %) queried was it less than 10 % of muslims who thought suicide bombings were not either ‘often’ or ‘sometimes’ justified. Those numbers are some years old. Newer numbers (from non-Western countries only, unfortunately) tell us that e.g. fewer than two out of five Egyptians (38%) and fewer than three out of five (58%) Turks would answer ‘never’ when asked this question just a couple of years ago, in 2014.
*A few non-data related observations here towards the end. I do think Razib is right that cognitive psychology is a good starting point if you want to ‘understand religion’, but a more general point I would make is that there are many different analytical approaches to these sorts of topics which one might employ, and I think it’s important that one does not privilege any single analytical framework over the others (just to be clear, I’m not saying that Razib’s doing this); different approaches may yield different insights, perhaps at different analytical levels, and combining different approaches is likely to be very useful in order to get ‘the bigger picture’, or at least to not overlook important details. ‘History’, broadly defined, may provide one part of the explanatory model, cognitive psychology another part, mathematical anthropology (e.g. stuff like this) probably also has a role to play, etc., etc.. Survey data, economic figures, scientific literatures on a wide variety of topics like trust, norms, migration analysis, and conflict studies, e.g. those dealing with civil wars, may all help elucidate important questions of interest, if not by adding relevant data then by providing additional methodological approaches/scaffoldings which might be fruitfully employed to make sense of the data that is available.
vi. The Level and Nature of Autistic Intelligence. Autistics may be smarter than people have been led to believe:
“Autistics are presumed to be characterized by cognitive impairment, and their cognitive strengths (e.g., in Block Design performance) are frequently interpreted as low-level by-products of high-level deficits, not as direct manifestations of intelligence. Recent attempts to identify the neuroanatomical and neurofunctional signature of autism have been positioned on this universal, but untested, assumption. We therefore assessed a broad sample of 38 autistic children on the preeminent test of fluid intelligence, Raven’s Progressive Matrices. Their scores were, on average, 30 percentile points, and in some cases more than 70 percentile points, higher than their scores on the Wechsler scales of intelligence. Typically developing control children showed no such discrepancy, and a similar contrast was observed when a sample of autistic adults was compared with a sample of nonautistic adults. We conclude that intelligence has been underestimated in autistics.”
I recall that back when I was diagnosed I was subjected to a battery of different cognitive tests of various kinds, and a few of those tests I recall thinking were very difficult, compared to how difficult they somehow ‘ought to be’ – it was like ‘this should be an easy task for someone who has the mental hardware to solve this type of problem, but I don’t seem to have that piece of hardware; I have no idea how to manipulate these objects in my head so that I might answer that question’. This was an at least somewhat unfamiliar feeling to me in a testing context, and I definitely did not have this experience when doing the Mensa admissions test later on, which was based on Raven’s matrices. Despite the fact that all IQ tests are supposed to measure pretty much the same thing I do not find it hard to believe that there are some details here which may complicate matters a bit in specific contexts, e.g. for people whose brains may not be structured quite the same way ‘ordinary brains’ are (to put it very bluntly). But of course this is just one study and a few personal impressions – more research is needed, etc. (Even though the effect size is huge.)
Slightly related to the above is also this link – I must admit that I find the title question quite interesting. I find it very difficult to picture characters featuring in books I’m reading in my mind, and so usually when I read books I don’t form any sort of coherent mental image of what the character looks like. It doesn’t matter to me, I don’t care. I have no idea if this is how other people read (fiction) books, or if they actually imagine what the characters look like more or less continuously while those characters are described doing the things they might be doing; to me it would be just incredibly taxing to keep even a simplified mental model of the physical attributes of a character in my mind for even a minute. I can recall specific traits like left-handedness and similar without much difficulty if I think the trait might have relevance to the plot, which has helped me while reading e.g. Agatha Christie novels before, but actively imagining what people look like in my mind I just find very difficult. I find it weird to think that some people might do something like that almost automatically, without thinking about it.
vii. Computer Science Resources. I recently shared the link with a friend, but of course she was already aware of the existence of this resource. Some people reading along here may not be, so I’ll include the link here. It has a lot of stuff.
[Warning: Long post].
I’ve blogged data related to the data covered in this post before here on the blog, but when I did that I only provided coverage in Danish. Part of my motivation for providing some coverage in English here (which is a slightly awkward and time consuming thing to do as all the source material is in Danish) is that this is the sort of data you probably won’t ever get to know about if you don’t understand Danish, and it seems like some of it might be worth knowing about also for people who do not live in Denmark. Another reason for posting stuff in English is of course that I dislike writing a blog post which I know beforehand that some of my regular readers will not understand. I should perhaps note that some of the data is at least peripherally related to my academic work at the moment.
The report which I’m covering in this post (here’s a link to it) deals primarily with various metrics collected in order to evaluate whether treatment goals which have been set centrally are being met by the Danish regions, one of the primary political responsibilities of which is to deal with health care service delivery. To take an example from the report, a goal has been set that at least 95 % of patients with known diabetes in the Danish regions should have their Hba1c (an important variable in the treatment context) measured at least once per year. The report of course doesn’t just contain a list of goals etc. – it also presents a lot of data which has been collected throughout the country in order to figure out to which extent the various goals have been met at the local levels. Hba1c is just an example; there are also goals set in relation to the variables hypertension, regular eye screenings, regular kidney function tests, regular foot examinations, and regular tests for hyperlipidemia, among others.
Testing is just one aspect of what’s being measured; other goals relate to treatment delivery. There’s for example a goal that the proportion of (known) type 2 diabetics with an Hba1c above 7.0% who are not receiving anti-diabetic treatment should be at most 5% within regions. A thought that occurred to me while reading the report was that it seemed to me that some interesting incentive problems might pop up here if these numbers were more important than I assume they are in the current decision-making context, because adding this specific variable without also adding a goal for ‘finding diabetics who do not know they are sick’ – and no such goal is included in the report, as far as I’ve been able to ascertain – might lead to problems; in theory a region that would do well in terms of identifying undiagnosed type 2 patients, of which there are many, might get punished for this if their higher patient population in treatment as a result of better identification might lead to binding capacity constraints at various treatment levels; capacity constraints which would not affect regions which are worse at identifying (non-)patients at risk because of the existence of a tradeoff between resources devoted to search/identification and resources devoted to treatment. Without a goal for identifying undiagnosed type 2 diabetics, it seems to me that to the extent that there’s a tradeoff between devoting resources to identifying new cases and devoting resources to the treatment of known cases, the current structure of evaluation, to the extent that it informs decision-making at the regional level, favours treatment over identification – which might or might not be problematic from a cost-benefit point of view. I find it somewhat puzzling that no goals relate to case-finding/diagnostics because a lot of the goals only really make sense if the people who are sick actually get diagnosed so that they can receive treatment in the first place; that, say, 95% of diabetics with a diagnosis receives treatment option X is much less impressive if, say, a third of all people with the disease do not have a diagnosis. Considering the relatively low amount of variation in some of the metrics included you’d expect a variable of this sort to be included here, at least I did.
The report has an appendix with some interesting information about the sex ratios, age distributions, how long people have had diabetes, whether they smoke, what their BMIs and blood pressures are like, how well they’re regulated (in terms of Hba1c), what they’re treated with (insulin, antihypertensive drugs, etc.), their cholesterol levels and triglyceride levels, etc. I’ll talk about these numbers towards the end of the post – if you want to get straight to this coverage and don’t care about the ‘main coverage’, you can just scroll down until you reach the ‘…’ point below.
The report has 182 pages with a lot of data, so I’m not going to talk about all of it. It is based on very large data sets which include more than 37.000 Danish diabetes patients from specialized diabetes units (diabetesambulatorier) (these are usually located in hospitals and provide ambulatory care only) as well as 34.000 diabetics treated by their local GPs – the aim is to eventually include all Danish diabetics in the database, and more are added each year, but even as it is a very big proportion of all patients are ‘accounted for’ in the data. Other sources also provide additional details, for example there’s a database on children and young diabetics collected separately. Most of the diabetics which are not included here are patients treated by their local GPs, and there’s still a substantial amount of uncertainty related to this group; approximately 90% of all patients connected to the diabetes units are assumed at this point to be included in the database, but the report also notes that approximately 80 % of diabetics are assumed to be treated in general practice. Coverage of this patient population is currently improving rapidly and it seems that most diabetics in Denmark will likely be included in the database within the next few years. They speculate in the report that the inclusion of more patients treated in general practice may be part of the explanation why goal achievement seems to have decreased slightly over time; this seems to me like a likely explanation considering the data they present as the diabetes units in general are better at achieving the goals set than are the GPs. The data is up to date – as some of you might have inferred from the presumably partly unintelligible words in the parenthesis in the title, the report deals with data from the time period 2013-2014. I decided early on not to copy tables into this post directly as it’s highly annoying to have to translate terms in such tables; instead I’ve tried to give you the highlights. I may or may not have succeeded in doing that, but you should be aware, especially if you understand Danish, that the report has a lot of details, e.g. in terms of intraregional variation etc., which are excluded from this coverage. Although I far from cover all the data, I do cover most of the main topics dealt with in the publication in at least a little bit of detail.
The report concludes in the introduction that for most treatment indicators no clinically significant differences in the quality of the treatment provided to diabetics are apparent when you compare the different Danish regions – so if you’re looking at the big picture, if you’re a Danish diabetic it doesn’t matter all that much if you live in Jutland or in Copenhagen. However some significant intra-regional differences do exist. In the following I’ll talk in a bit more detail about some of data included in the report.
When looking at the Hba1c goal (95% should be tested at least once per year), they evaluate the groups treated in the diabetes units and the groups treated in general practice separately; so you have one metric for patients treated in diabetes units living in the north of Jutland (North Denmark Region) and you have another group of patients treated in general practice living in the north of Jutland – this breakdown of the data makes it possible to not only compare people across regions but also to investigate whether there are important differences between the care provided by diabetes units and the care provided by general practitioners. When dealing with patients receiving ambulatory care from the diabetes units all regions meet the goal, but in Copenhagen (Capital Region of Denmark, (-CRD)) only 94% of patients treated in general practice had their Hba1c measured within the last year – this was the only region which did not meet the goal for the patient population treated in general practice. I would have thought beforehand that all diabetes units would have 100% coverage here, but that’s actually only the case in the region in which I live (Central Denmark Region) – on the other hand in most other regions, aside from Copenhagen again, the number is 99%, which seems reasonable as I’m assuming a substantial proportion of the remainder is explained by patient noncompliance, which is difficult to avoid completely. I speculate that patient compliance differences between patient populations treated at diabetes units and patient populations treated by their GP might also be part of the explanation for the lower goal achievement of the general practice population; as far as I’m aware diabetes units can deny care in the case of non-compliance whereas GPs cannot, so you’d sort of expect the most ‘difficult’ patients to end up in general practice; this is speculation to some extent and I’m not sure it’s a big effect, but it’s worth keeping in mind when analyzing this data that not all differences you observe necessarily relate to service delivery inputs (whether or not a doctor reminds a patient it’s time to get his eyes checked, for example); the two main groups analyzed are likely to also be different due to patient population compositions. Differences in patient population composition may of course also drive some of the intraregional variation observed. They mention in their discussion of the results for the Hba1c variable that they’re planning on changing the standard here to one which relate to the distributional results of the Hba1c, not just whether the test was done, which seems like a good idea. As it is, the great majority of Danish diabetics have their Hba1c measured at least annually, which is good news because of the importance of this variable in the treatment context.
In the context of hypertension, there’s a goal that at least 95% of diabetics should have their blood pressure measured at least once per year. In the context of patients treated in the diabetes units, all regions achieve the goal and the national average for this patient population is 97% (once again the region in which I live is the only one that achieved 100 % coverage), but in the context of patients treated in general practice only one region (North Denmark Region) managed to get to 95% and the national average is 90%. In most regions, one in ten diabetics treated in general practice do not have their blood pressure measured once per year, and again Copenhagen (CRD) is doing worst with a coverage of only 87%. As mentioned in the general comments above some of the intraregional variation is actually quite substantial, and this may be a good example because not all hospitals are doing great on this variable. Sygehus Sønderjylland, Aabenraa (in southern Jutland), one of the diabetes units, had a coverage of only 67%, and the percentage of patients treated at Hillerød Hospital in Copenhagen (CRD), another diabetes unit, was likewise quite low, with 83% of patients having had their blood pressure measured within the last year. These hospitals are however the exceptions to the rule. Evaluating whether it has been tested if patients do or do not have hypertension is different from evaluating whether hypertension is actually treated after it has been discovered, and here the numbers are less impressive; for the type 1 patients treated in the diabetes units, roughly one third (31%) of patients with a blood pressure higher than 140/90 are not receiving treatment for hypertension (the goal was at most 20%). The picture was much better for type 2 patients (11% at the national level) and patients treated in general practice (13%). They note that the picture has not improved over the last years for the type 1 patients and that this is not in their opinion a satisfactory state of affairs. A note of caution is that the variable only includes patients who have had a blood pressure measured within the last year which was higher than 140/90 and that you can’t use this variable as an indication of how many patients with high blood pressure are not being treated; some patients who are in treatment for high blood pressure have blood pressures lower than 140/90 (achieving this would in many cases be the point of treatment…). Such an estimate will however be added to later versions of the report. In terms of the public health consequences of undertreatment, the two patient populations are of course far from equally important. As noted later in the coverage, the proportion of type 2 patients on antihypertensive agents is much higher than the proportion of type 1 diabetics receiving treatment like this, and despite this difference the blood pressure distributions of the two patient populations are reasonably similar (more on this below).
Screening for albuminuria: The goal here is that at least 95 % of adult diabetics are screened within a two-year period (There are slightly different goals for children and young adults, but I won’t go into those). In the context of patients treated in the diabetes units, the northern Jutland Region and Copenhagen/RH failed to achieve the goal with a coverage slightly below 95% – the other regions achieved the goal, although not much more than that; the national average for this patient population is 96%. In the context of patients treated in general practice none of the regions achieve the goal and the national average for this patient population is 88%. Region Zealand was doing worst with 84%, whereas the region in which I live, Region Midtjylland, was doing best with a 92% coverage. Of the diabetes units, Rigshospitalet, “one of the largest hospitals in Denmark and the most highly specialised hospital in Copenhagen”, seems to also be the worst performing hospital in Denmark in this respect, with only 84 % of patients being screened – which to me seems exceptionally bad considering that for example not a single hospital in the region in which I live is below 95%. Nationally roughly 20% of patients with micro- or macroalbuminuria are not on ACE-inhibitors/Angiotensin II receptor antagonists.
Eye examination: The main process goal here is at least one eye examination every second year for at least 90% of the patients, and a requirement that the treating physician knows the result of the eye examination. This latter requirement is important in the context of the interpretation of the results (see below). For patients treated in diabetes units, four out of five regions achieved the goal, but there were also what to me seemed like large differences across regions. In Southern Denmark, the goal was not met and only 88 % had had an eye examination within the last two years, whereas the number was 98% in Region Zealand. Region Zealand was a clear outlier here and the national average for this patient population was 91%. For patients treated in general practice no regions achieved the goal, and this variable provides a completely different picture from the previous variables in terms of the differences between patients treated in diabetes units and patients treated in general practice: In most regions, the coverage here for patients in general practice is in the single digits and the national average for this patient population is just 5 %. They note in the report that this number has decreased over the years through which this variable has been analyzed, and they don’t know why (but they’re investigating it). It seems to be a big problem that doctors are not told about the results of these examinations, which presumably makes coordination of care difficult.
The report also has numbers on how many patients have had their eyes checked within the last 4 years, rather than within the last two, and this variable makes it clear that more infrequent screening is not explaining anything in terms of the differences between the patient populations; for patients treated in general practice the numbers are still here in the single digits. They mention that data security requirements imposed on health care providers are likely the reason why the numbers are low in general practice as it seems common that the GP is not informed of the results of screenings taking place, so that the only people who gets to know about the results are the ophthalmologists doing them. A new variable recently included in the report is whether newly-diagnosed type 2 diabetics are screened for eye-damage within 12 months of receiving their diagnosis – here they have received the numbers directly from the ophthalmologists so uncertainty about information sharing doesn’t enter the picture (well, it does, but the variable doesn’t care; it just measures whether an eye screen has been performed or not) – and although the standard set is 95% (at most one in twenty should not have their eyes checked within a year of diagnosis) at the national level only half of patients actually do get an eye screen within the first year (95% CI: 46-53%) – uncertainty about the date of diagnosis makes it slightly difficult to interpret some of the specific results, but the chosen standard is not achieved anywhere and this once again underlines how diabetic eye care is one of the areas where things are not going as well as the people setting the goals would like them to. The rationale for screening people within the first year of diagnosis is of course that many type 2 patients have complications at diagnosis – “30–50 per cent of patients with newly diagnosed T2DM will already have tissue complications at diagnosis due to the prolonged period of antecedent moderate and asymptomatic hyperglycaemia.” (link).
The report does include estimates of the number of diabetics who receive eye screenings regardless of whether the treating physician knows the results or not; at the national level, according to this estimate 65% of patients have their eyes screened at least once every second year, leaving more than a third of patients in a situation where they are not screened as often as is desirable. They mention that they have had difficulties with the transfer of data and many of the specific estimates are uncertain, including two of the regional estimates, but the general level – 65% or something like that – is based on close to 10.000 patients and is assumed to be representative. Approximately 1% of Danish diabetics are blind, according to the report.
Foot examinations: Just like most of the other variables: At least 95 % of patients, at least once every second year. For diabetics treated in diabetes units, the national average is here 96% and the goal was not achieved in Copenhagen (CRD) (94%) and northern Jutland (91%). There are again remarkable differences within regions; at Helsingør Hospital only 77% were screened (95% CI: 73-82%) (a drop from 94% the year before), and at Hillerød Hospital the number was even lower, 73% (95% CI: 70-75), again a drop from the previous year where the coverage was 87%. Both these numbers are worse than the regional averages for all patients treated in general practice, even though none of the regions meet the goal. Actually I thought the year-to-year changes in the context of these two hospitals were almost as interesting as the intraregional differences because I have a hard time explaining those; how do you even set up a screening programme such that a coverage drop of more than 10 % from one year to the next is possible? To those who don’t know, diabetic feet are very expensive and do not seem to get the research attention one might from a cost-benefit perspective assume they would (link, point iii). Going back to the patients in general practice on average 81 % of these patients have a foot examination at least once every second year. The regions here vary from 79% to 84%. The worst covered patients are patients treated in general practice in the Vordingborg sygehus catchment area in the Zealand Region, where only roughly two out of three (69%, 95% CI: 62-75%) patients have regularly foot examinations.
Aside from all the specific indicators they’ve collected and reported on, the authors have also constructed a combined indicator, an ‘all-or-none’ indicator, in which they measure the proportion of patients who have not failed to get their Hba1c measured, their feet checked, their blood pressure measured, kidney function tests, etc. … They do not include in this metric the eye screening variable because of the problems associated with this variable, but this is the only process variable not included, and the variable is sort of an indicator of how many of the patients are actually getting all of the care that they’re supposed to get. As patients treated in general practice are generally less well covered than patients treated in the diabetes units at the hospitals I was interested to know how much these differences ‘added up to’ in the end. For the diabetes units, 11 % of patients failed on at least one metric (i.e. did not have their feet checked/Hba1c measured/blood pressure measured/etc.), whereas this was the case for a third of patients in general practice (67%). Summed up like that it seems to me that if you’re a Danish diabetes patient and you want to avoid having some variable neglected in your care, it matters whether you’re treated by your local GP or by the local diabetes unit and that you’re probably going to be better off receiving care from the diabetes unit.
Some descriptive statistics from the appendix (p. 95 ->):
Sex ratio: In the case of this variable, they have multiple reports on the same variable based on data derived from different databases. In the first database, including 16.442 people, 56% are male and 44% are female. In the next database (n=20635), including only type 2 diabetics, the sex ratio is more skewed; 60% are males and 40% are females. In a database including only patients in general practice (n=34359), like in the first database 56% of the diabetics are males and 44% are females. For the patient population of children and young adults included (n=2624), the sex ratio is almost equal (51% males and 49% females). The last database, Diabase, based on evaluation of eye screening and including only adults (n=32842), have 55% males and 45% females. It seems to me based on these results that the sex ratio is slightly skewed in most patient populations, with slightly more males than females having diabetes – and it seems not improbable that this is to due to a higher male prevalence of type 2 diabetes (the children/young adult database and type 2 database seem to both point in this direction – the children/young adult group mainly consists of type 1 patients as 98% of this sample is type 1. The fact that the prevalence of autoimmune disorders is in general higher in females than in males also seems to support this interpretation; to the extent that the sex ratio is skewed in favour of males you’d expect lifestyle factors to be behind this.
Next, age distribution. In the first database (n=16.442), the average and the median age is 50, the standard deviation is 16, the youngest individual is 16 and the oldest is 95. It is worth remembering in this part of the reporting that the oldest individual in the sample is not a good estimate of ‘how long a diabetic can expect to live’ – for all we know the 95 year old in the database got diagnosed at the age of 80. You need diabetes duration before you can begin to speculate about that variable. Anyway, in the next database, of type 2 patients (n=20635), the average age is 64 (median=65), the standard deviation is 12 and the oldest individual is 98. In the context of both of the databases mentioned so far some regions do better than others in terms of the oldest individual, but it also seems to me that this may just be a function of the sample size and ‘random stuff’ (95+ year olds are rare events); Northern Jutland doesn’t have a lot of patients so the oldest patient in that group is not as old as the oldest patient from Copenhagen – this is probably but what you’d expect. In the general practice database (n=34359), the average age is 68 (median=69) and the standard deviation is 11; the oldest individual there is 102. In the Diabase database (n=32842), the average age is 62 (median=64), the standard deviation is 15 and the oldest individual is 98. It’s clear from these databases that most diabetics in Denmark are type 2 diabetics (this is no surprise) and that a substantial proportion of them are at or close to retirement age.
The appendix has a bit of data on diabetes type, but I think the main thing to take away from the tables that break this variable down is that type 1 is overrepresented in the databases compared to the true prevalence – in the Diabase database for example almost half of patients are type 1 (46%), despite the fact that type 1 diabetics are estimated to make up only 10% of the total in Denmark (see e.g. this (Danish source)). I’m sure this is to a significant extent due to lack of coverage of type 2 diabetics treated in general practice.
Diabetes duration: In the first data-set including 16.442 individuals the patients have a median diabetes duration of 21,2 years. The 10% cutoff is 5,4 years, the 25% cutoff is 11,3 years, the 75% cutoff is 33,5 years, and the 90% cutoff is 44,2 years. High diabetes durations are more likely to be observed in type 1 patients as they’re in general diagnosed earlier; in the next database involving only type 2 patients (n=20635), the median duration is 12.9 years and the corresponding cutoffs are 3,8 years (10%); 7,4 years (25%); 18,6 years (75%); and 24,7 years (90%). In the database involving patients treated in general practice, the median duration is 6,8 years and the cutoffs reported for the various percentiles are 2,5 years (10%), 4,0 (25%), 11,2 (75%) and 15,6 (90%). One note not directly related to the data but which I thought might be worth adding here is that of one were to try to use these data for the purposes of estimating the risk of complications as a function of diabetes duration, it would be important to have in mind that there’s probably often a substantial amount of uncertainty associated with the diabetes duration variable because many type 2 diabetics are diagnosed after a substantial amount of time with sub-optimal glycemic control; i.e. although diabetes duration is lower in type 2 populations than in type 1 populations, I’d assume that the type 2 estimates of duration are still biased downwards compared to type 1 estimates causing some potential issues in terms of how to interpret associations found here.
Next, smoking. In the first database (n=16.442), 22% of diabetics smoke daily and another 22% are ex-smokers who have not smoked within the last 6 months. According to the resource to which you’re directed when you’re looking for data on that kind of stuff on Statistics Denmark, the percentage of daily smokers was 17% in 2013 in the general population (based on n=158.870 – this is a direct link to the data), which seems to indicate that the trend (this is a graph of the percentage of Danes smoking daily as a function of time, going back to the 70es) I commented upon (Danish link) a few years back has not reversed or slowed down much. If we go back to the appendix and look at the next source, dealing with type 2 diabetics, 19% of them are smoking daily and 35% of them are ex-smokers (again, 6 months). In the general practice database (n=34.359) 17% of patients smoke daily and 37% are ex-smokers.
BMI. Here’s one variable where type 1 and type 2 look very different. The first source deals with type 1 diabetics (n=15.967) and here the median BMI is 25.0, which is comparable to the population median (if anything it’s probably lower than the population median) – see e.g. page 63 here. Relevant percentile cutoffs are 20,8 (10%), 22,7 (25%), 28,1 (75%), and 31,3 (90%). Numbers are quite similar across regions. For the type 2 data, the first source (n=20.035) has a median BMI of 30,7 (almost equal to the 1 in 10 cutoff for type 1 diabetics), with relevant cutoffs of 24,4 (10%), 27,2 (25%), 34,9 (75%), and 39,4 (90%). According to this source, one in four type 2 diabetics in Denmark are ‘severely obese‘ and more diabetics are obese than are not. It’s worth remembering that using these numbers to implicitly estimate the risk of type 2 diabetes associated with overweight is problematic as especially some of the people in the lower end of the distribution are quite likely to have experienced weight loss post-diagnosis. For type 2 patients treated in general practice (n=15.736), the median BMI is 29,3 and cutoffs are 23,7 (10%), 26,1 (25%), 33,1 (75%), and 37,4 (90%).
Distribution of Hba1c. The descriptive statistics included also have data on the distribution of Hba1c values among some of the patients who have had this variable measured. I won’t go into the details here except to note that the differences between type 1 and type 2 patients in terms of the Hba1c values achieved are smaller than I’d perhaps expected; the median Hba1c among type 1s was estimated at 62, based on 16.442 individuals, whereas the corresponding number for type 2s was 59, based on 20.635 individuals. Curiously, a second data source finds a median Hba1c of only 48 for type 2 patients treated in general practice; the difference between this one and the type 1 median is definitely high enough to matter in terms of the risk of complications (it’s more questionable how big the effect of a jump from 59 to 62 is, especially considering measurement error and the fact that the type 1 distribution seems denser than the type 2 distribution so that there aren’t that many more exceptionally high values in the type 1 dataset), but I wonder if this actually quite impressive level of metabolic control in general practice may not be due to biased reporting, with GPs doing well in terms of diabetes management being also more likely to report to the databases; it’s worth remembering that most patients treated in general practice are still not accounted for in these data-sets.
Oral antidiabetics and insulin. In one sample of 20.635 type 2 patients, 69% took oral antidiabetics, and in another sample of 34.359 type 2 patients treated in general practice the number was 75%. 3% of type 1 diabetics in a sample of 16.442 individuals also took oral antidiabetics, which surprised me. In the first-mentioned sample of type 2 patients 69% (but not the same amount of individuals – this was not a reporting error) also took insulin, so there seems to be a substantial number of patients on both treatments. In the general practice sample included the number of patients on insulin was much lower, as only 14% of type 2 patients were on insulin – again concerns about reporting bias may play a role here, but even taking this number at face value and extrapolating out of sample you reach the conclusion that the majority of patients on insulin are probably type 2 diabetics, as only roughly one patient in 10 is type 1.
Antihypertensive treatment and treatment for hyperlipidemia: Although there as mentioned above seems to be less focus on hypertension in type 1 patients than on hypertension in type 2 patients, it’s still the case that roughly half (48%) of all patients in the type 1 sample (n=16.442) was on antihypertensive treatment. In the first type 2 sample (n=20635), 82% of patients were receiving treatment against hypertension, and this number was similar in the general practice sample (81%). The proportions of patients in treatment for hyperlipidemia are roughly similar (46% of type 1, and 79% and 73% in the two type 2 samples, respectively).
Blood pressure. The median level of systolic blood pressure among type 1 diabetics (n=16442) was 130, with the 75% cutoff intersecting the hypertension level (140) and 10% of patients having a systolic blood pressure above 151. These numbers are almost identical to the sample of type 2 patients treated in general practice, however as earlier mentioned this blood pressure level is achieved with a lower proportion of patients in treatment for hypertension. In the second sample of type 2 patients (n=20635), the numbers were slightly higher (median: 133, 75% cutoff: 144, 90% cutoff: 158). The median diastolic blood pressure was 77 in the type 1 sample, with 75 and 90% cutoffs of 82 and 89; the data in the type 2 samples are almost identical.
Here’s my first post about the book. In this post I’ll continue my coverage where I left off in my first post. A few of the chapters covered below I did not think very highly of, but other parts of the coverage are about as good as you could expect (given problems such as e.g. limited data etc.). Some of the stuff I found quite interesting. As people will note in the coverage below the book does address the religious dimension to some extent, though in my opinion far from to the extent that the variable deserves. An annoying aspect of the chapter on religion was to me that although the author of the chapter includes data which to me cannot but lead to some very obvious conclusions, the author seems to be very careful avoiding drawing those conclusions explicitly. It’s understandable, but still annoying. For related reasons I also got annoyed at him for presumably deliberately completely disregarding which seems in the context of his own coverage to be an actually very important component of Huntington’s thesis, that conflict at the micro level seems to very often be between muslims and ‘the rest’. Here’s a relevant quote from Clash…, p. 255:
“ethnic conflicts and fault line wars have not been evenly distributed among the world’s civilizations. Major fault line fighting has occurred between Serbs and Croats in the former Yugoslavia and between Buddhists and Hindus in Sri Lanka, while less violent conflicts took place between non-Muslim groups in a few other places. The overwhelming majority of fault line conflicts, however, have taken place along the boundary looping across Eurasia and Africa that separates Muslims from non-Muslims. While at the macro or global level of world politics the primary clash of civilizations is between the West and the rest, at the micro or local level it is between Islam and the others.”
This point, that conflict at the local level – which seems to be the type of conflict level you’re particularly interested in if you’re researching civil wars, as also argued in previous chapters in the coverage – according to Huntington seems to be very islam-centric, is completely overlooked (ignored?) in the handbook chapter, and if you haven’t read Huntington and your only exposure to him is through the chapter in question you’ll probably conclude that Huntington was wrong, because that seems to be the conclusion the author draws, arguing that other models are more convincing (I should add here that these other models do seem useful, at least in terms of providing (superficial) explanations; the point is just that I feel the author is misrepresenting Huntington and I dislike this). Although there are parts of the coverage in that chapter where I feel that it’s obvious the author and I do not agree, I should note that the fact that he talks about the data and the empirical research makes up for a lot of other stuff.
Anyway, on to the coverage – it’s perhaps worth noting, in light of the introductory remarks above, that the post has stuff on a lot of things besides religion, e.g. the role of natural resources, regime types, migration, and demographics.
“Elites seeking to end conflict must: (1) lead followers to endorse and support peaceful solutions; (2) contain spoilers and extremists and prevent them from derailing the process of peacemaking; and (3) forge coalitions with more moderate members of the rival ethnic group(s) […]. An important part of the two-level nature of the ethnic conflict is that each of the elites supporting the peace process be able to present themselves, and the resulting terms of the peace, as a “win” for their ethnic community. […] A strategy that a state may pursue to resolve ethnic conflict is to co-opt elites from the ethnic communities demanding change […]. By satisfying elites, it reduces the ability of the aggrieved ethnic community to mobilize. Such a process of co-option can also be used to strengthen ethnic moderates in order to undermine ethnic extremists. […] the co-opted elites need to be careful to be seen as still supporting ethnic demands or they may lose all credibility in their respective ethnic community. If this occurs, the likely outcome is that more extreme ethnic elites will be able to capture the ethnic community, possibly leading to greater violence.
It is important to note that “spoilers,” be they an individual or a small sub-group within an ethnic community, can potentially derail any peace process, even if the leaders and masses support peace (Stedman, 2001).”
“Three separate categories of international factors typically play into identity and ethnic conflict. The first is the presence of an ethnic community across state boundaries. Thus, a single community exists in more than one state and its demands become international. […] This division of an ethnic community can occur when a line is drawn geographically through a community […], when a line is drawn and a group moves into the new state […], or when a diaspora moves a large population from one state to another […] or when sub-groups of an ethnic community immigrate to the developed world […] When ethnic communities cross state boundaries, the potential for one state to support an ethnic community in the other state exists. […] There is also the potential for ethnic communities to send support to a conflict […] or to lobby their government to intervene […]. Ethnic groups may also form extra-state militias and cross international borders. Sometimes these rebel groups can be directly or indirectly sponsored by state governments, leading to a very complex situation […] A second set of possible international factors is non-ethnic international intervention. A powerful state may decide to intervene in an ethnic conflict for a variety of reasons, ranging from humanitarian support, to peacekeeping, to outright invasion […] The third and last factor is the commitment of non-governmental organizations (NGOs) or third-party mediators to a conflict. […] The record of international interventions in ethnic civil wars is quite mixed. There are many difficulties associated with international action [and] international groups cannot actually change the underlying root of the ethnic conflict (Lake and Rothchild, 1998; Kaufman, 1996).”
“A relatively simple way to think of conflict onset is to think that for a rebellion to occur two conditions need to be satisfactorily fulfilled: There must be a motivation and there must be an opportunity to rebel.3 First, the rebels need a motive. This can be negative – a grievance against the existing state of affairs – or positive – a desire to capture resource rents. Second, potential rebels need to be able to achieve their goal: The realization of their desires may be blocked by the lack of financial means. […] Work by Collier and Hoeffler (1998, 2004) was crucial in highlighting the economic motivation behind civil conflicts. […] Few conflicts, if any, can be characterized purely as “resource conflicts.” […] It is likely that few groups are solely motivated by resource looting, at least in the lower rank level. What is important is that valuable natural resources create opportunities for conflicts. To feed, clothe, and arm its members, a rebel group needs money. Unless the rebel leaders are able to raise sufficient funds, a conflict is unlikely to start no matter how severe the grievances […] As a consequence, feasibility of conflict – that is, valuable natural resources providing opportunity to engage in violent conflict – has emerged as a key to understanding the relation between valuable resources and conflict.”
“It is likely that some natural resources are more associated with conflict than others. Early studies on armed civil conflict used resource measures that aggregated different types of resources together. […] With regard to financing conflict start-up and warfare the most salient aspect is probably the ease with which a resource can be looted. Lootable resources can be extracted with simple methods by individuals or small groups, are easy to transport, and can be smuggled across borders with limited risks. Examples of this type of resources are alluvial gemstones and gold. By contrast, deep-shaft minerals, oil, and natural gas are less lootable and thus less likely sources of financing. […] Using comprehensive datasets on all armed civil conflicts in the world, natural resource production, and other relevant aspects such as political regime, economic performance, and ethnic composition, researchers have established that at least some high-value natural resources are related to higher risk of conflict onset. Especially salient in this respect seem to be oil and secondary diamonds […] The results regarding timber […] and cultivation of narcotics […] are inconclusive. […] [An] important conclusion is that natural resources should be considered individually and not lumped together. Diamonds provide an illustrative example: the geological form of the diamond deposit is related to its effect on conflict. Secondary diamonds – the more lootable form of two deposit types – makes conflict more likely, longer, and more severe. Primary diamonds on the other hand are generally not related to conflict.”
“Analysis on conflict duration and severity confirm that location is a salient factor: resources matter for duration and severity only when located in the region where the conflict is taking place […] That the location of natural resources matters has a clear and important implication for empirical conflict research: relying on country-level aggregates can lead to wrong conclusions about the role of natural resources in armed civil conflict. As a consequence of this, there has been effort to collect location-specific data on oil, gas, drug cultivation, and gemstones”.
“a number of prominent studies of ethnic conflict have suggested that when ethnic groups grow at different rates, this may lead to fears of an altered political balance, which in turn might cause political instability and violent conflict […]. There is ample anecdotal evidence for such a relationship [but unfortunately little quantitative research…]. The civil war in Lebanon, for example, has largely been attributed to a shift in the delicate ethnic balance in that state […]. Further, in the early 1990s, radical Serb leaders were agitating for the secession of “Serbian” areas in Bosnia-Herzegovina by instigating popular fears that Serbs would soon be outnumbered by a growing Muslim population heading for the establishment of a Shari’a state”.
“[One] part of the demography-conflict literature has explored the role of population movements. Most of this literature […] treats migration and refugee flows as a consequence of conflict rather than a potential cause. Some scholars, however, have noted that migration, and refugee migration in particular, can spur the spread of conflict both between and within states […]. Existing work suggests that environmentally induced migration can lead to conflict in receiving areas due to competition for scarce resources and economic opportunities, ethnic tensions when migrants are from different ethnic groups, and exacerbation of socioeconomic “fault lines” […] Salehyan and Gleditsch (2006) point to spill-over effects, in the sense that mass refugee migration might spur tensions in neighboring or receiving states by imposing an economic burden and causing political stability [sic]. […] Based on a statistical analysis of refugees from neighboring countries and civil war onset during the period 1951–2001, they find that countries that experience an influx of refugees from neighboring states are significantly more likely to experience wars themselves. […] While the youth bulge hypothesis [large groups of young males => higher risk of violence/war/etc.] in general is supported by empirical evidence, indicating that countries and areas with large youth cohorts are generally at a greater risk of low-intensity conflict, the causal pathways relating youth bulges to increased conflict propensity remain largely unexplored quantitatively. When it comes to the demographic factors which have so far received less attention in terms of systematic testing – skewed sex ratios, differential ethnic growth, migration, and urbanization – the evidence is somewhat mixed […] a clear challenge with regard to the study of demography and conflict pertains to data availability and reliability. […] Countries that are undergoing armed conflict are precisely those for which we need data, but also those in which census-taking is hampered by violence.”
“Most research on the duration of civil war find that civil wars in democracies tend to be longer than other civil wars […] Research on conflict severity finds some evidence that democracies tend to see fewer battledeaths and are less likely to target civilians, suggesting that democratic institutions may induce some important forms of restraints in armed conflict […] Many researchers have found that democratization often precedes an increase in the risk of the onset of armed conflict. Hegre et al. (2001), for example, find that the risk of civil war onset is almost twice as high a year after a regime change as before, controlling for the initial level of democracy […] Many argue that democratic reforms come about when actors are unable to rule unilaterally and are forced to make concessions to an opposition […] The actual reforms to the political system we observe as democratization often do not suffice to reestablish an equilibrium between actors and the institutions that regulate their interactions; and in its absence, a violent power struggle can follow. Initial democratic reforms are often only partial, and may fail to satisfy the full demands of civil society and not suffice to reduce the relevant actors’ motivation to resort to violence […] However, there is clear evidence that the sequence matters and that the effect [the increased risk of civil war after democratization, US] is limited to the first election. […] civil wars […] tend to be settled more easily in states with prior experience of democracy […] By our count, […] 75 percent of all annual observations of countries with minor or major armed conflicts occur in non-democracies […] Democracies have an incidence of major armed conflict of only 1 percent, whereas nondemocracies have a frequency of 5.6 percent.”
“Since the Iranian revolution in the late 1970s, religious conflicts and the rise of international terror organizations have made it difficult to ignore the facts that religious factors can contribute to conflict and that religious actors can cause or participate in domestic conflicts. Despite this, comprehensive studies of religion and domestic conflict remain relatively rare. While the reasons for this rarity are complex there are two that stand out. First, for much of the twentieth century the dominant theory in the field was secularization theory, which predicted that religion would become irrelevant and perhaps extinct in modern times. While not everyone agreed with this extreme viewpoint, there was a consensus that religious influences on politics and conflict were a waning concern. […] This theory was dominant in sociology for much of the twentieth century and effectively dominated political science, under the title of modernization theory, for the same period. […] Today supporters of secularization theory are clearly in the minority. However, one of their legacies has been that research on religion and conflict is a relatively new field. […] Second, as recently as 2006, Brian Grim and Roger Finke lamented that “religion receives little attention in international quantitative studies. Including religion in cross-national studies requires data, and high-quality data are in short supply” […] availability of the necessary data to engage in quantitative research on religion and civil wars is a relatively recent development.”
“[Some] studies [have] found that conflicts involving actors making religious demands – such as demanding a religious state or a significant increase in religious legislation – were less likely to be resolved with negotiated settlements; a negotiated settlement is possible if the settlement focused on the non-religious aspects of the conflict […] One study of terrorism found that terror groups which espouse religious ideologies tend to be more violent (Henne, 2012). […] The clear majority of quantitative studies of religious conflict focus solely on inter-religious conflicts. Most of them find religious identity to influence the extent of conflict […] but there are some studies which dissent from this finding”.
“Terror is most often selected by groups that (1) have failed to achieve their goals through peaceful means, (2) are willing to use violence to achieve their goals, and (3) do not have the means for higher levels of violence.”
“the PITF dataset provides an accounting of the number of domestic conflicts that occurred in any given year between 1960 and 2009. […] Between 1960 and 2009 the modified dataset includes 817 years of ethnic war, 266 years of genocides/politicides, and 477 years of revolutionary wars. […] Cases were identified as religious or not religious based on the following categorization:
1 Not Religious.
2 Religious Identity Conflict: The two groups involved in the conflict belong to different religions or different denominations of the same religion.
3 Religious Wars: The two sides of the conflict belong to the same religion but the description of the conflict provided by the PITF project identifies religion as being an issue in the conflict. This typically includes challenges by religious fundamentalists to more secular states. […]
The results show that both numerically and as a proportion of all conflict, religious state failures (which include both religious identity conflicts and religious wars) began increasing in the mid-1970s. […] As a proportion of all conflict, religious state failures continued to increase and became a majority of all state failures in 2002. From 2002 onward, religious state failures were between 55 percent and 62 percent of all state failures in any given year.”
“Between 2002 and 2009, eight of 12 new state failures were religious. All but one of the new religious state failures were ongoing as of 2009. These include:
• 2002: A rebellion in the Muslim north of the Ivory Coast (ended in 2007)
• 2003: The beginning of the Sunni–Shia violent conflict in Iraq (ongoing)
• 2003: The resumption of the ethnic war in the Sudan [97% muslims, US] (ongoing)
• 2004: Muslim militants challenged Pakistan’s government in South and North Waziristan. This has been followed by many similar attacks (ongoing)
• 2004: Outbreak of violence by Muslims in southern Thailand (ongoing)
• 2004: In Yemen [99% muslims, US], followers of dissident cleric Husain Badr al-Din al-Huthi create a stronghold in Saada. Al-Huthi was killed in September 2004, but serious fighting begins again in early 2005 (ongoing)
• 2007: Ethiopia’s invasion of southern Somalia causes a backlash in the Muslim (ethnic- Somali) Ogaden region (ongoing)
• 2008: Islamist militants in the eastern Trans-Caucasus region of Russia bordering on Georgia (Chechnya, Dagestan, and Ingushetia) reignited their violent conflict against Russia (ongoing)” [my bold]
“There are few additional studies which engage in this type of longitudinal analysis. Perhaps the most comprehensive of such studies is presented in Toft et al.’s (2011) book God’s Century based on data collected by Toft. They found that religious conflicts – defined as conflicts with a religious content – rose from 19 percent of all civil wars in the 1940s to about half of civil wars during the first decade of the twenty-first century. Of these religious conflicts, 82 percent involved Muslims. This analysis includes only 135 civil wars during this period. The lower number is due to a more restrictive definition of civil war which includes at least 1,000 battle deaths. This demonstrates that the findings presented above also hold when looking at the most violent of civil wars.” [my bold]
“This comprehensive new Handbook explores the significance and nature of armed intrastate conflict and civil war in the modern world.
Civil wars and intrastate conflict represent the principal form of organised violence since the end of World War II, and certainly in the contemporary era. These conflicts have a huge impact and drive major political change within the societies in which they occur, as well as on an international scale. The global importance of recent intrastate and regional conflicts in Afghanistan, Pakistan, Iraq, Somalia, Nepal, Côte d’Ivoire, Syria and Libya – amongst others – has served to refocus academic and policy interest upon civil war. […] This volume will be of much interest to students of civil wars and intrastate conflict, ethnic conflict, political violence, peace and conflict studies, security studies and IR in general.”
I’m currently reading this handbook. One observation I’ll make here before moving on to the main coverage is that although I’ve read more than 100 pages and although every single one of the conflicts argued in the introduction above to be motivating study into these topics aside from one (the exception being Nepal) involve muslims, the word ‘islam’ has been mentioned exactly once in the coverage so far (an updated list would arguably include yet another muslim country, Yemen, as well). I noted while doing the text search that they seem to take up the topic of religion and religious motivation later on, so I sort of want to withhold judgment for now, but if they don’t deal more seriously with this topic later on than they have so far, I’ll have great difficulties giving this book a high rating, despite the coverage being in general actually quite interesting, detailed and well written so far – chapter 7, on so-called ‘critical perspectives’ is in my opinion a load of crap [a few illustrative quotes/words/concepts from that chapter: “Frankfurt School-inspired Critical Theory”, “approaches such as critical constructivism, post-structuralism, feminism, post-colonialism”, “an openly ethical–normative commitment to human rights, progressive politics”, “labelling”, “dialectical”, “power–knowledge structures”, “conflict discourses”, “Foucault”, “an abiding commitment to being aware of, and trying to overcome, the Eurocentric, Orientalist and patriarchal forms of knowledge often prevalent within civil war studies”, “questioning both morally and intellectually the dominant paradigm”… I read the chapter very fast, to the point of almost only skimming it, and I have not quoted from that chapter in my coverage below, for reasons which should be obvious – I was reminded of Poe’s Corollary while reading the chapter as I briefly started wondering along the way if the chapter was an elaborate joke which had somehow made it into the publication, and I also briefly was reminded of the Sokal affair, mostly because of the unbelievable amount of meaningless buzzwords], but that’s just one chapter and most of the others so far have been quite okay. A few of the points in the problematic chapter are actually arguably worth having in mind, but there’s so much bullshit included as well that you’re having a really hard time taking any of it seriously.
Some observations from the first 100 pages:
“There are wide differences of opinion across the broad field of scholars who work on civil war regarding the basis of legitimate and scientific knowledge in this area, on whether cross-national studies can generate reliable findings, and on whether objective, value-free analysis of armed conflict is possible. All too often – and perhaps increasingly so, with the rise in interest in econometric approaches – scholars interested in civil war from different methodological traditions are isolated from each other. […] even within the more narrowly defined empirical approaches to civil war studies there are major disagreements regarding the most fundamental questions relating to contemporary civil wars, such as the trends in numbers of armed conflicts, whether civil wars are changing in nature, whether and how international actors can have a role in preventing, containing and ending civil wars, and the significance of [various] factors”.
“In simplest terms civil war is a violent conflict between a government and an organized rebel group, although some scholars also include armed conflicts primarily between non-state actors within their study. The definition of a civil war, and the analytical means of differentiating a civil war from other forms of large-scale violence, has been controversial […] The Uppsala Conflict Data Program (UCDP) uses 25 battle-related deaths per year as the threshold to be classified as armed conflict, and – in common with other datasets such as the Correlates of War (COW) – a threshold of 1,000 battle-related deaths for a civil war. While this is now widely endorsed, debate remains regarding the rigor of this definition […] differences between two of the main quantitative conflict datasets – the UCDP and the COW – in terms of the measurement of armed conflict result in significant differences in interpreting patterns of conflict. This has led to conflicting findings not only about absolute numbers of civil wars, but also regarding trends in the numbers of such conflicts. […] According to the UCDP/PRIO data, from 1946 to 2011 a total of 102 countries experienced civil wars. Africa witnessed the most with 40 countries experiencing civil wars between 1946 and 2011. During this period 20 countries in the Americas experienced civil war, 18 in Asia, 13 in Europe, and 11 in the Middle East […]. There were 367 episodes (episodes in this case being separated by at least one year without at least 25 battle-related deaths) of civil wars from 1946 to 2009 […]. The number of active civil wars generally increased from the end of the Cold War to around 1992 […]. Since then the number has been in decline, although whether this is likely to be sustained is debatable. In terms of onset of first episode by region from 1946 to 2011, Africa leads the way with 75, followed by Asia with 67, the Western Hemisphere with 33, the Middle East with 29, and Europe with 25 […]. As Walter (2011) has observed, armed conflicts are increasingly concentrated in poor countries. […] UCDP reports 137 armed conflicts for the period 1989–2011. For the overlapping period 1946–2007, COW reports 179 wars, while UCDP records 244 armed conflicts. As most of these conflicts have been fought over disagreements relating to conditions within a state, it means that civil war has been the most common experience of war throughout this period.”
“There were 3 million deaths from civil wars with no international intervention between 1946 and 2008. There were 1.5 million deaths in wars where intervention occurred. […] In terms of region, there were approximately 350,000 civil war-related deaths in both Europe and the Middle East from the years 1946 to 2008. There were 467,000 deaths in the Western Hemisphere, 1.2 million in Africa, and 3.1 million in Asia for the same period […] In terms of historical patterns of civil wars and intrastate armed conflict more broadly, the most conspicuous trend in recent decades is an apparent decline in absolute numbers, magnitude, and impact of armed conflicts, including civil wars. While there is wide – but not total – agreement regarding this, the explanations for this downward trend are contested. […] the decline seems mainly due not to a dramatic decline of civil war onsets, but rather because armed conflicts are becoming shorter in duration and they are less likely to recur. While this is undoubtedly welcome – and so is the tendency of civil wars to be generally smaller in magnitude – it should not obscure the fact that civil wars are still breaking out at a rate that has been fairly static in recent decades.”
“there is growing consensus on a number of findings. For example, intrastate armed conflict is more likely to occur in poor, developing countries with weak state structures. In situations of weak states the presence of lootable natural resources and oil increase the likelihood of experiencing armed conflict. Dependency upon the export of primary commodities is also a vulnerability factor, especially in conjunction with drastic fluctuations in international market prices which can result in economic shocks and social dislocation. State weakness is relevant to this – and to most of the theories regarding armed conflict proneness – because such states are less able to cushion the impact of economic shocks. […] Authoritarian regimes as well as entrenched democracies are less likely to experience civil war than societies in-between […] Situations of partial or weak democracy (anocracy) and political transition, particularly a movement towards democracy in volatile or divided societies, are also strongly correlated to conflict onset. The location of a society – especially if it has other vulnerability factors – in a region which has contiguous neighbors which are experiencing or have experienced armed conflict is also an armed conflict risk.”
“Military intervention aimed at supporting a protagonist or influencing the outcome of a conflict tends to increase the intensity of civil wars and increase their duration […] It is commonly argued that wars ending with military victory are less likely to recur […]. In these terminations one side no longer exists as a fighting force. Negotiated settlements, on the other hand, are often unstable […] The World Development Report 2011 notes that 90 percent of the countries with armed conflicts taking place in the first decade of the 2000s also had a major armed conflict in the preceding 30 years […] of the 137 armed conflicts that were fought after 1989 100 had ended by 2011, while 37 were still ongoing”
“Cross-national, aggregated, analysis has played a leading role in strengthening the academic and policy impact of conflict research through the production of rigorous research findings. However, the […] aggregation of complex variables has resulted in parsimonious findings which arguably neglect the complexity of armed conflict; simultaneously, differences in the codification and definition of key concepts result in contradictory findings. The growing popularity of micro-studies is therefore an important development in the field of civil war studies, and one that responds to the demand for more nuanced analysis of the dynamics of conflict at the local level.”
“Jason Quinn, University of Notre Dame, has calculated that the number of scholarly articles on the onset of civil wars published in the first decade of the twenty-first century is larger than the previous five decades combined”.
“One of the most challenging aspects of quantitative analysis is transforming social concepts into numerical values. This difficulty means that many of the variables used to capture theoretical constructs represent crude indicators of the real concept […] econometric studies of civil war must account for the endogenising effect of civil war on other variables. Civil war commonly lowers institutional capacity and reduces economic growth, two of the primary conditions that are consistently shown to motivate civil violence. Scholars have grown more capable of modelling this process […], but still too frequently fail to capture the endogenising effect of civil conflict on other variables […] the problems associated with the rare nature of civil conflict can [also] cause serious problems in a number of econometric models […] Case-based analysis commonly suffers from two fundamental problems: non-generalisability and selection bias. […] Combining research methods can help to enhance the validity of both quantitative and qualitative research. […] the combination of methods can help quantitative researchers address measurement issues, assess outliers, discuss variables omitted from the large-N analysis, and examine cases incorrectly predicted by econometric models […] The benefits of mixed methods research designs have been clearly illustrated in a number of prominent studies of civil war […] Yet unfortunately the bifurcation of conflict studies into qualitative and quantitative branches makes this practice less common than is desirable.”
“Ethnography has elicited a lively critique from within and without anthropology. […] Ethnographers stand accused of argument by ostension (pointing at particular instances as indicative of a general trend). The instances may not even be true. This is one of the reasons that the economist Paul Collier rejected ethnographic data as a source of insight into the causes of civil wars (Collier 2000b). According to Collier, the ethnographer builds on anecdotal evidence offered by people with good reasons to fabricate their accounts. […] The story fits the fact. But so might other stories. […] [It might be categorized as] a discipline that still combines a mix of painstaking ethnographic documentation with brilliant flights of fancy, and largely leaves numbers on one side.”
“While macro-historical accounts convincingly argue for the centrality of the state to the incidence and intensity of civil war, there is a radical spatial unevenness to violence in civil wars that defies explanation at the national level. Villages only a few miles apart can have sharply contrasting experiences of conflict and in most civil wars large swathes of territory remain largely unaffected by violence. This unevenness presents a challenge to explanations of conflict that treat states or societies as the primary unit of analysis. […] A range of databases of disaggregated data on incidences of violence have recently been established and a lively publication programme has begun to explore sub-national patterns of distribution and diffusion of violence […] All of these developments testify to a growing recognition across the social sciences that spatial variation, territorial boundaries and bounding processes are properly located at the heart of any understanding of the causes of civil war. It suggests too that sub-national boundaries in their various forms – whether regional or local boundaries, lines of control established by rebels or no-go areas for state security forces – need to be analysed alongside national borders and in a geopolitical context. […] In both violent and non-violent contention local ‘safe territories’ of one kind or another are crucial to the exercise of power by challengers […] the generation of violence by insurgents is critically affected by logistics (e.g. roads), but also shelter (e.g. forests) […] Schutte and Weidmann (2011) offer a […] dynamic perspective on the diffusion of insurgent violence. Two types of diffusion are discussed; relocation diffusion occurs when the conflict zone is shifted to new locations, whereas escalation diffusion corresponds to an expansion of the conflict zone. They argue that the former should be a feature of conventional civil wars with clear frontlines, whereas the latter should be observed in irregular wars, an expectation that is borne out by the data.”
“Research on the motivation of armed militants in social movement scholarship emphasises the importance of affective ties, of friendship and kin networks and of emotion […] Sageman’s (2004, 2008) meticulous work on Salafist-inspired militants emphasises that mobilisation is a collective rather than individual process and highlights the importance of inter-personal ties, networks of friendship, family and neighbours. That said, it is clear that there is a variety of pathways to armed action on the part of individuals rather than one single dominant motivation”.
“While it is often difficult to conduct real experiments in the study of civil war, the micro study of violence has seen a strong adoption of quasi-experimental designs and in general, a more careful thinking about causal identification”.
“Condra and Shapiro (2012) present one of the first studies to examine the effects of civilian targeting in a micro-level study. […] they show that insurgent violence increases as a result of civilian casualties caused by counterinsurgent forces. Similarly, casualties inflicted by the insurgents have a dampening effect on insurgent effectiveness. […] The conventional wisdom in the civil war literature has it that indiscriminate violence by counterinsurgent forces plays into the hands of the insurgents. After being targeted collectively, the aggrieved population will support the insurgency even more, which should result in increased insurgent effectiveness. Lyall (2009) conducts a test of this relationship by examining the random shelling of villages from Russian bases in Chechnya. He matches shelled villages with those that have similar histories of violence, and examines the difference in insurgent violence between treatment and control villages after an artillery strike. The results clearly disprove conventional wisdom and show that shelling reduces subsequent insurgent violence. […] Other research in this area has looked at alternative counterinsurgency techniques, such as aerial bombings. In an analysis that uses micro-level data on airstrikes and insurgent violence, Kocher et al. (2011) show that, counter to Lyall’s (2009) findings, indiscriminate violence in the form of airstrikes against villages in the Vietnam war was counterproductive […] Data availability […] partly dictates what micro-level questions we can answer about civil war. […] not many conflicts have datasets on bombing sorties, such as the one used by Kocher et al. (2011) for the Vietnam war.”
Yesterday I gave some of the reasons I had for disliking the book; in this post I’ll provide some of the reasons why I kept reading. The book had a lot of interesting data. I know I’ve covered some of these topics and numbers before (e.g. here), but I don’t mind repeating myself every now and then; some things are worth saying more than once, and as for the those that are not I must admit I don’t really care enough about not repeating myself here to spend time perusing the archives in order to make sure I don’t repeat myself here. Anyway, here are some number from the coverage:
“Twenty-two high-burden countries account for over 80 % of the world’s TB cases […] data referring to 2011 revealed 8.7 million new cases of TB [worldwide] (13 % coinfected with HIV) and 1.4 million people deaths due to such disease […] Around 80 % of TB cases among people living with HIV were located in Africa. In 2011, in the WHO European Region, 6 % of TB patients were coinfected with HIV […] In 2011, the global prevalence of HIV accounted for 34 million people; 69 % of them lived in Sub-Saharan Africa. Around five million people are living with HIV in South, South-East and East Asia combined. Other high-prevalence regions include the Caribbean, Eastern Europe and Central Asia . Worldwide, HIV incidence is in downturn. In 2011, 2.5 million people acquired HIV infection; this number was 20 % lower than in 2001. […] Sub-Saharan Africa still accounts for 70 % of all AIDS-related deaths […] Worldwide, an estimated 499 million new cases of curable STIs (as gonorrhoea, chlamydia and syphilis) occurred in 2008; these findings suggested no improvement compared to the 448 million cases occurring in 2005. However, wide variations in the incidence of STIs are reported among different regions; the burden of STIs mainly occurs in low-income countries”.
“It is estimated that in 2010 alone, malaria caused 216 million clinical episodes and 655,000 deaths. An estimated 91 % of deaths in 2010 were in the African Region […]. A total of 3.3 billion people (half the world’s population) live in areas at risk of malaria transmission in 106 countries and territories”.
“Diarrhoeal diseases amount to an estimated 4.1 % of the total disability-adjusted life years (DALY) global burden of disease, and are responsible for 1.8 million deaths every year. An estimated 88 % of that burden is attributable to unsafe supply of water, sanitation and hygiene […] It is estimated that diarrhoeal diseases account for one in nine child deaths worldwide, making diarrhoea the second leading cause of death among children under the age of 5 after pneumonia”
“NCDs [Non-Communicable Diseases] are the leading global cause of death worldwide, being responsible for more
deaths than all other causes combined. […] more than 60 % of all deaths worldwide currently stem from NCDs .
In 2008, the leading causes of all NCD deaths (36 million) were:
• CVD [cardiovascular disease] (17 million, or 48 % of NCD deaths) [nearly 30 % of all deaths];
• Cancer (7.6 million, or 21 % of NCD deaths) [about 13 % of all deaths]
• Respiratory diseases (4.2 million, or 12 % of NCD deaths) [7 % of all deaths]
• Diabetes (1.3 million, 4 % of NCD deaths) .” [Elsewhere in the publication they report that: “In 2010, diabetes was responsible for 3.4 million deaths globally and 3.6 % of DALYs” – obviously there’s a lot of uncertainty here. How to avoid ‘double-counting’ is one of the major issues, because we have a pretty good idea what they die of: “CVD is by far the most frequent cause of death in both men and women with diabetes, accounting for about 60 % of all mortality”].
“Behavioural risk factors such as physical inactivity, tobacco use and unhealthy diet explain nearly 80 % of the CVD burden”
“nearly 80 % of NCD deaths occur in low- and middle-income countries , up sharply from just under 40 % in 1990 […] Low- and lower-middle-income countries have the highest proportion of deaths from NCDs under 60 years. Premature deaths under 60 years for high-income countries were 13 and 25 % for upper-middle-income countries. […] In low-income countries, the proportion of premature NCD deaths under 60 years is 41 %, three times the proportion in high-income countries . […] Overall, NCDs account for more than 50 % of DALYs [disability-adjusted life years] in most counties. This percentage rises to over 80 % in Australia, Japan and the richest countries of Western Europe and North America […] In Europe, CVD causes over four million deaths per year (52 % of deaths in women and 42 % of deaths in men), and they are the main cause of death in women in all European countries.”
“Overall, age-adjusted CVD death rates are higher in most low- and middle-income countries than in developed countries […]. CHD [coronary heart disease] and stroke together are the first and third leading causes of death in developed and developing countries, respectively. […] excluding deaths from cancer, these two conditions were responsible for more deaths in 2008 than all remaining causes among the ten leading causes of death combined (including chronic diseases of the lungs, accidents, diabetes, influenza, and pneumonia)”.
“The global prevalence of diabetes was estimated to be 10 % in adults aged 25 + years […] more than half of all nontraumatic lower limb amputations are due to diabetes [and] diabetes is one of the leading causes of visual impairment and blindness in developed countries .”
“Almost six million people die from tobacco each year […] Smoking is estimated to cause nearly 10 % of CVD […] Approximately 2.3 million die each year from the harmful use of alcohol. […] Alcohol abuse is responsible for 3.8 % of all deaths (half of which are due to CVD, cancer, and liver cirrhosis) and 4.5 % of the global burden of disease […] Heavy alcohol consumption (i.e. ≥ 4 drinks/day) is significantly associated with an about fivefold increased risk of oral and pharyngeal cancer and oesophageal squamous cell carcinoma (SqCC), 2.5-fold for laryngeal cancer, 50 % for colorectal and breast cancers and 30 % for pancreatic cancer . These estimates are based on a large number of epidemiological studies, and are generally consistent across strata of several covariates. […] The global burden of cancer attributable to alcohol drinking has been estimated at 3.6 and 3.5 % of cancer deaths , although this figure is higher in high-income countries (e.g. the figure of 6 % has been proposed for UK  and 9 % in Central and Eastern Europe).”
“At least two million cancer cases per year (18 % of the global cancer burden) are attributable to chronic infections by human papillomavirus, hepatitis B virus, hepatitis C virus and Helicobacter pylori. These infections are largely preventable or treatable […] The estimate of the attributable fraction is higher in low- and middle-income countries than in high-income countries (22.9 % of total cancer vs. 7.4 %).”
“Information on the magnitude of CVD in high-income countries is available from three large longitudinal studies that collect multidisciplinary data from a representative sample of European and American individuals aged 50 and older […] according to the Health Retirement Survey (HRS) in the USA, almost one in three adults have one or more types of CVD [11, 12]. By contrast, the data of Survey of Health, Ageing and Retirement in Europe (SHARE), obtained from 11 European countries, and English Longitudinal Study of Aging (ELSA) show that disease rates (specifically heart disease, diabetes, and stroke) across these populations are lower (almost one in five)”
“In 1990, the major fraction of morbidity worldwide was due to communicable, maternal, neonatal, and nutritional disorders (47 %), while 43 % of disability adjusted life years (DALYs) lost were attributable to NCDs. Within two decades, these estimates had undergone a drastic change, shifting to 35 % and 54 %, respectively”
“Estimates of the direct health care and nonhealth care costs attributable to CVD in many countries, especially in low- and middle-income countries, are unclear and fragmentary. In high-income countries (e.g., USA and Europe), CVD is the most costly disease both in terms of economic costs and human costs. Over half (54 %) of the total cost is due to direct health care costs, while one fourth (24 %) is attributable to productivity losses and 22 % to the informal care of people with CVD. Overall, CVD is estimated to cost the EU economy, in terms of health care, almost €196 billion per year, i.e., 9 % of the total health care expenditure across the EU”
“In the WHO European Region, the Eastern Mediterranean Region, and the Region of the Americas, over 50 % of women are overweight. The highest prevalence of overweight among infants and young children is in upper-to-middle-income populations, while the fastest rise in overweight is in the lower-to-middle-income group . Globally, in 2008, 9.8 % of men and 13.8 % of women were obese compared to 4.8 % of men and 7.9 % of women in 1980 .”
“In low-income countries, around 25 % of adults have raised total cholesterol, while in high-income countries, over 50 % of adults have raised total cholesterol […]. Overall, one third of CHD disease is attributable to high cholesterol levels” (These numbers seem very high to me, but I’m reporting them anyway).
“interventions based on tobacco taxation have a proportionally greater effect on smokers of lower SES and younger smokers, who might otherwise be difficult to influence. Several studies suggest that the application of a 10 % rise in price could lead to as much as a 2.5–10 % decline in smoking [20, 45, 50, 56].”
“The decision to allocate resources for implementing a particular health intervention depends not only on the strength of the evidence (effectiveness of intervention) but also on the cost of achieving the expected health gain. Cost-effectiveness analysis is the primary tool for evaluating health interventions on the basis of the magnitude of their incremental net benefits in comparison with others, which allows the economic attractiveness of one program over another to be determined [More about this kind of stuff here]. If an intervention is both more effective and less costly than the existing one, there are compelling reasons to implement it. However, the majority of health interventions do not meet these criteria, being either more effective but more costly, or less costly but less effective, than the existing interventions [see also this]. Therefore, in most cases, there is no “best” or absolute level of cost-effectiveness, and this level varies mainly on the basis of health care system expenditure and needs .”
“The number of new cases of cancer worldwide in 2008 has been estimated at about 12,700,000 . Of these, 6,600,000 occurred in men and 6,000,000 in women. About 5,600,000 cases occurred in high-resource countries […] and 7,100,000 in low- and middle-income countries. Among men, lung, stomach, colorectal, prostate and liver cancers are the most common […], while breast, colorectal, cervical, lung and stomach are the most common neoplasms among women […]. The number of deaths from cancer was estimated at about 7,600,000 in 2008 […] No global estimates of survival from cancer are available: Data from selected cancer registries suggest wide disparities between high- and low-income countries for neoplasms with effective but expensive treatment, such as leukaemia, while the gap is narrow for neoplasms without an effective therapy, such as lung cancer […]. The overall 5-year survival of cases diagnosed during 1995– 1999 in 23 European countries was 49.6 % […] Tobacco smoking is the main single cause of human cancer worldwide […] In high-income countries, tobacco smoking causes approximately 30 % of all human cancers .”
“Systematic reviews have concluded that nutritional factors may be responsible for about one fourth of human cancers in high-income countries, although, because of the limitations of the current understanding of the precise role of diet in human cancer, the proportion of cancers known to be avoidable in practicable ways is much smaller . The only justified dietary recommendation for cancer prevention is to reduce the total caloric intake, which would contribute to a decrease in overweight and obesity, an established risk factor for human cancer. […] The magnitude of the excess risk [associated with obesity] is not very high (for most cancers, the relative risk (RR) ranges between 1.5 and 2 for body weight higher than 35 % above the ideal weight). Estimates of the proportion of cancers attributable to overweight and obesity in Europe range from 2 %  to 5 % . However, this figure is likely to be larger in North America, where the prevalence of overweight and obesity is higher.”
“Estimates of the global burden of cancer attributable to occupation in high-income countries result in the order of 1–5 % [9, 42]. In the past, almost 50 % of these were due to asbestos alone […] The available evidence suggests, in most populations, a small role of air, water and soil pollutants. Global estimates are in the order of 1 % or less of total cancers [9, 42]. This is in striking contrast with public perception, which often identifies pollution as a major cause of human cancer.”
“Avoidance of sun exposure, in particular during the middle of the day, is the primary preventive measure to reduce the incidence of skin cancer. There is no adequate evidence of a protective effect of sunscreens, possibly because use of sunscreens is associated with increased exposure to the sun. The possible benefit in reducing skin cancer risk by reduction of sun exposure, however, should be balanced against possible favourable effects of UV radiation in promoting vitamin D metabolism.”
I read the first nine chapters of this very long book a while back, and I decided to have another go at it. I have now read chapters 10-18, the first seven of which deal with ‘Profiles of Vulnerable Populations’ (including chapters about: Gender and Sexually Transmitted Diseases (10), Adolescents and STDs Including HIV Infection (11), Female Sex Workers and Their Clients in the Epidemiology and Control of Sexually Transmitted Diseases (12), Homosexual and Bisexual Behavior in Men in Relation to STDs and HIV Infection (13), Lesbian Sexual Behavior in Relation to STDs and HIV Infection (14) (some surprising stuff in that chapter, but I won’t cover that here), HIV and Other Sexually Transmitted Infections in Injection Drug Users and Crack Cocaine Smokers (15), and STDs, HIV/AIDS, and Migrant Populations (16)), and the last two of which deal with ‘Host Immunity and Molecular Pathogenesis and STD’ (Chapters about: ‘Genitourinary Immune Defense’ (17) and ‘Normal Genital Flora’ (19 as well as ‘Pathogenesis of Sexually Transmitted Viral and Bacterial Infections’ (19) – I have only read the first two chapters in that section so far, and so I won’t cover the last chapter here. I also won’t cover the content of the first of these chapters, but for different reasons). The book has 108 chapters and more than 2000 pages, so although I’ve started reading the book again I’m sure I won’t finish the book this time either. My interest in the things covered in this book is purely academical in the first place.
Some observations and comments below…
“A major problem when assessing the risk of men and women of contracting an STI [sexually transmitted infection], is the differential reporting of sexual behavior between men and women. It is believed that women tend to underreport sexual activity, whereas men tend to over-report. This has been highlighted by studies assessing changes in reported age at first sexual intercourse between successive birth cohorts15 and by studies that compared the numbers of sex partners reported by men and by women.10,13,16, 17, 18 […] There is widespread agreement that women are more frequently and severely affected by STIs than men. […] In the studies in the general population that have assessed the prevalence of gonorrhea, chlamydial infection, and active syphilis, the prevalence was generally higher in women than in men […], with differences in prevalence being more marked in the younger age groups. […] HIV infection is also strikingly more prevalent in women than in men in most populations where the predominant mode of transmission is heterosexual intercourse and where the HIV epidemic is mature […] It is generally accepted that the male-to-female transmission of STI pathogens is more efficient than female-to-male transmission. […] The high vulnerability to STIs of young women compared to young men is [however] the result of an interplay between psychological, sociocultural, and biological factors.33”
“Complications of curable STIs, i.e., STIs caused by bacteria or protozoa, can be avoided if infected persons promptly seek care and are managed appropriately. However, a prerequisite to seeking care is that infected persons are aware that they are infected and that they seek treatment. A high proportion of men and of women infected with N. gonorrhoeae, C. trachomatis, or T. vaginalis, however, never experience symptoms. Women are asymptomatic more often than men. It has been estimated that 55% of episodes of gonorrhea in men and 86% of episodes in women remain asymptomatic; 89% of men with chlamydial infection remain asymptomatic and 94% of women.66 For chlamydial infection, it has been well documented that serious complications, including infertility due to tubal occlusion, can occur in the absence of a history of symptoms of pelvic inflammatory disease.65”
“Most population-based STD rates underestimate risk for sexually active adolescents because the rate is inappropriately expressed as cases of disease divided by the number of individuals in this age group. Yet only those who have had intercourse are truly at risk for STDs. For rates to reflect risk among those who are sexually experienced, appropriate denominators should include only the number of individuals in the demographic group who have had sexual intercourse. […] In general, when rates are corrected for those who are sexually active, the youngest adolescents have the highest STD rates of any age group.5”
“Although risk of HPV acquisition increases with number of partners,67,74,75 prevalence of infection is substantial even with limited sexual exposure. Numerous clinic-based studies,76,77 supported by population-based data, indicate that HPV prevalence typically exceeds 10% among young women with only one or two partners.71”
“while 100 years ago young men in the United States spent approximately 7 years between [sexual] maturation and marriage, more recently the interval was 13 years, and increasing; for young women, the interval between menarche and marriage has increased from 8 years to 14. […] In 1970, only 5% of women in United States had had premarital intercourse by age 15, whereas in 1988, 26% had engaged in intercourse by this age. However, in 1988, 37% of never married 15-17-year-olds had engaged in intercourse but in 2002, only 30% had. Comparable data from males demonstrated even greater declines — 50% of never married 15-17-year-olds reported having had intercourse in 1988, compared with only 31% in 200299”
“Infection with herpes simplex type 2 (HSV-2) is extremely common among FSWs [female sex workers], and because HSV-2 infection increases the likelihood of both HIV acquisition in HIV-uninfected individuals, and HIV transmission in HIV-infected individuals, HSV-2 infection plays a key role in HIV transmission dynamics.100 Studies of FSWs in Kenya,67 South Africa,101 Tanzania,36 and Mexico72 have found HSV-2 prevalences ranging from 70% to over 80%. In a prospective study of HIV seronegative FSWs in Nairobi, Kenya, 72.7% were HSV-2 seropositive at baseline.67 Over the course of over two years of observation […] HSV-2 seropositive FSWs were over six times more likely to acquire HIV infection than women who were HSV-2 seronegative.”
“Surveys in the UK133 and New Zealand134 found that approximately 7% of men reported ever paying for sex. A more recent telephone survey in Australia found that almost 16% of men reported having ever paid for sex, with 1.9% reporting that they had paid for sex in the past 12 months.135 Two national surveys in Britain found that the proportion of men who reported paying women for sex in the previous 5 years increased from 2.0% in 1990 to 4.2% in 2000.14 A recent review article summarizing the findings of various surveys in different global regions found that the median proportion of men who reported “exchanging gifts or money for sex” in the past 12 months was approximately 9-10%, whereas the proportion of men reporting who engaged in “paid sex” or sex with a sex worker was 2-3%.136”
“There are currently around 175-200 million people documented as living outside their countries of birth.3 This number includes both voluntary migrants, people who have chosen to leave their country of origin, and forced migrants, including refugees, trafficked people, and internally displaced people.4 […] Each year about 700 million people travel internationally with an estimated 50 million originating in developed countries traveling to developing ones.98 […] Throughout history, infectious diseases of humans have followed population movements. The great drivers of population mobility including migration, economic changes, social change, war, and travel have been associated with disease acquisition and spread at individual and population levels. There have been particularly strong associations of these key modes of population mobility and mixing for sexually transmitted diseases (STDs), including HIV/AIDS. […] Epidemiologists elucidated early in the HIV/AIDS epidemic that there was substantial geographic variability in incidence, as well as different risk factors for disease spread. As researchers better understood the characteristics of HIV transmission, its long incubation time, relatively low infectivity, and chronic disease course, it became clear that mobility of infected persons was a key determinant for further spread to new populations.6 […] mobile populations are more likely to exhibit high-risk behaviors”
“Studies conducted over the past decade have relied on molecular techniques to identify previously noncultivable organisms in the vagina of women with “normal” and “abnormal” flora. […] These studies have confirmed that the microflora of some women is predominated by species belonging to the genus Lactobacillus, while women having BV [bacterial vaginosis] have a broad range of aerobic and anaerobic microorganisms. It has become increasingly clear that even with these more advanced tools to characterize the microbial ecology of the vagina the full range of microorganisms present has yet to be fully described. […] the frequency and concentration of many facultative organisms depends upon whether the woman has BV or Lactobacillus-predominant microflora.36 However, even if “normal” vaginal microflora is restricted to those women having a Lactobacillus-dominant flora as defined by Gram stain, 46% of women are colonized by G. vaginalis, 78% are colonized by Ureaplasma urealyticum, and 31% are colonized by Candida albicans.36 […] Nearly all women are vaginally colonized by obligately anaerobic gram-negative rods and cocci,36 and several species of anaerobic bacteria, which are not yet named, are also present. While some species of anaerobes are present at higher frequencies or concentrations among women with BV, it is clear that the microbial flora is complex and cannot be defined simply by the presence or absence of lactobacilli, Gardnerella, mycoplasmas, and anaerobes. This observation has been confirmed with molecular characterization of the microflora.26, 27, 28, 29, 30, 31, 32, 33, 34, 35”
Vaginal pH, which is in some sense an indicator of vaginal health, varies over the lifespan (I did not know this..): In premenarchal girls vaginal pH is around 7, whereas it drops to 4.0-4.5 in healthy women of reproductive age. It increases again in post-menopausal women, but postmenopausal women receiving hormone replacement therapy have lower average vaginal pH and higher numbers of lactobacilli in their vaginal floras than do postmenopausal women not receiving hormone replacement therapy, one of several findings indicating that vaginal pH is under hormonal control (estrogen is important). Lactobacilli play an important role because those things produce lactic acid which lowers pH, and women with a reduced number of lactobacilli in their vaginal floras have higher vaginal pH. Stuff like sexual intercourse, menses, and breastfeeding all affect vaginal pH and -microflora, as does antibiotic usage, and such things may play a role in disease susceptibility. Aside from lowering pH some species of Lactobacilli also play other helpful roles which are likely to be important in terms of disease susceptibility, such as producing hydrogen peroxide in their microenvironments, which is the kind of stuff a lot of (other) bacteria really don’t like to be around: “Several clinical studies conducted in populations of pregnant and nonpregnant women in the United States and Japan have shown that the prevalence of BV is low (4%) among women colonized with H2O2-producing strains of lactobacilli. By comparison, approximately one third of women who are vaginally colonized by Lactobacillus that do not produce H2O2 have BV.45, 46, 47“.
My interest in the things covered in this book is as mentioned purely academical, but I’m well aware that some of the stuff may not be as ‘irrelevant’ to other people reading along here as it is to me. One particularly relevant observation I came across which I thought I should include here is this:
“The lack of reliable plenotypic methods for identification of lactobacilli have led to a broad misunderstanding of the species of lactobacilli present in the vagina, and the common misperception that dairy and food derived lactobacilli are similar to those found in the vagina. […] Acidophilus in various forms have been used to treat yeast vaginitis.144 Some investigators have gone so far as to suggest that ingestion of yogurt containing acidophilus prevents recurrent Candida vaginitis.145 Nevertheless, clinical studies of women with acute recurrent vulvovaginitis have demonstrated that women who have recurrent yeast vaginitis have the same frequency and concentration of Lactobacillus as women without recurrent infections.146 […] many women who seek medical care for chronic vaginal symptoms report using Lactobacillus-containing products orally or vaginally to restore the vaginal microflora in the mistaken belief that this will prevent recurrent vaginitis.147 Well-controlled trials have failed to document any decrease in vaginal candidiasis whether orally or vaginally applied preparations of lactobacilli are used by women.148 Microbial interactions in the vagina probably are much more complex than have been appreciated in the past.”
As illustrated above, there seems to be some things ‘we’ know which ‘people’ (including some doctors..) don’t know. But there are also some really quite relevant things ‘we’ don’t know a lot about yet. One example would be whether/how hygiene products mediate the impact of menses on vaginal flora: “It is unknown whether the use of tampons, which might absorb red blood cells during menses, may minimize the impact of menses on colonization by lactobacilli. However, some observational data suggests that women who routinely use tampons for catamenial protection are more likely to maintain colonization by lactobacilli compared to women who use pads for catamenial protection”. Just to remind you, colonization by lactobacilli is desirable. On a related and more general note: “Many young women use vaginal products including lubricants, contraceptives, antifungals, and douches. Each of these products can alter the vaginal ecosystem by changing vaginal pH, altering the vaginal fluid by direct dilution, or by altering the capacity of organisms to bind to the vaginal epithelium.” There are a lot of variables at play here and my reading of the results indicate that it’s not always obvious what is actually the best advice. For example an in this context large (n=235) prospective study about the effect of N-9, a compound widely used in contraceptives, on vaginal flora “demonstrated that N-9 did have a dose-dependent impact on the prevalence of anaerobic gram-negative rods, and was associated with a twofold increase in BV (OR 2.3, 95% CI 1.1-4.7).” Using spermicides like those may on the one hand perhaps decrease the likelihood of getting pregnant and perhaps lower the risk of contracting a sexually transmitted disease during intercourse, but on the other hand usage of such preparations may also affect the vaginal flora in a way which may make users more vulnerable to sexually transmitted diseases by promoting E. coli colonization of the vaginal flora. On a more general note, “The impact of contraceptives on the vaginal ecosystem, including their impact on susceptibility to infection, has not been adequately investigated to date.” The book does cover various studies on different types of contraceptives, but most of the studies are small and probably underpowered, so I decided not to go into this stuff in more detail. An important point to take away here is however that there’s no doubt that the vaginal flora is important for disease susceptibility: “longitudinal studies [have] showed a consistent link between increased incidence of HIV, HSV-2 and HPV and altered vaginal microflora […] there is a strong interaction between the health of the vaginal ecosystem and susceptibility to viral STIs.” Unfortunately, “use of probiotic products for treatment of BV has met with limited success.”
I should note that although multiple variables and interactions are involved in ‘this part of the equation’, it is of course only part of the bigger picture. One way in which it’s only part of the bigger picture is that the vaginal flora plays other roles besides the one which relates to susceptibility to sexually transmitted disease – one example: “Studies have established that some organisms considered to be part of the normal vaginal microflora are associated with an increased risk of preterm and/or low birth weight delivery when they are present at high-density concentrations in the vaginal fluid”. (And once again the lactobacilli in particular may play a role: “high-density vaginal colonization by Lactobacillus species has been linked with a decreased risk of most adverse outcomes of pregnancy”). Another major way in which this stuff is only part of the equation is that human females have a lot of other ways to defend themselves as well besides relying on bacterial colonists. If you don’t like immunology there are some chapters in here which you’d be well-advised to skip.
Back when I read Kenwood and Lougheed, the first economic history text I’ve read devoted to such topics, the realization of how much the world and the conditions of the humans inhabiting it had changed during the last 200 years really hit me. Reading this book was a different experience because I knew some stuff already, but it added quite a bit to the narrative and I’m glad I did read it. If you haven’t read an economic history book which tells the story of how we got from the low-growth state to the high-income situation in which we find ourselves today, I think you should seriously consider doing so. It’s a bit like reading a book like Scarre et al., it has the potential to seriously alter the way you view the world – and not just the past, but the present as well. Particularly interesting is the way information in books like these tend to ‘replace’ ‘information’/mental models you used to have; when people know nothing about a topic they’ll often still have ‘an idea’ about what they think about it, and most of the time that idea is wrong – people usually make assumptions based on what they know about, and when things about which they make assumptions are radically different from anything they know, they will make wrong assumptions and get a lot of things seriously wrong. To take an example, in recent times human capital has been argued to play a very important role in determining economic growth differentials, and so an economist who’s not read economic history might think human capital played a very important role in the Industrial Revolution as well. Some economic historians thought along similar lines, but it turns out that what they found did not really support such ideas:
“Although human capital has been seen as crucial to economic growth in recent times, it has rarely featured as a major factor in accounts of the Industrial Revolution. One problem is that the machinery of the Industrial Revolution is usually characterized as de-skilling, substituting relatively unskilled labor for skilled artisans, and leading to a decline in apprenticeship […] A second problem is that the widespread use of child labor raised the opportunity cost of schooling (Mitch, 1993, p. 276).”
I mentioned in the previous post how literacy rates didn’t change much during this period, which is also a serious problem with human-capital driven Industrial Revolution growth models. Here’s some stuff on how industrialization affected the health of the population:
“A large body of evidence indicates that average heights of males born in different parts of western and northern Europe began to decline, beginning with those born after 1760 for a period lasting until 1800. After a recovery, average heights resumed their decline for males born after 1830, the decline lasting this time until about 1860. The total reduction in average heights of English soldiers, for example, reached 2 cm during this period. Similar declines were found elsewhere […] in the case of England, it is clear that the decline in the average height of males born after 1830 occurred at a time when real wages were rising […] in the period 1820–70, the greatest improvement in life expectancy at birth occurred not in Great Britain but in other western and northwest European countries, such as France, Germany, the Netherlands, and especially Sweden […] Even in industrializing northern England [infant mortality] only began to register progress after the middle of the nineteenth century – before the 1850s, infant mortality still went up […] It is clear that economic growth accelerated during the 1700–1870 period – in northwestern Europe earlier and more strongly than in the rest of the continent; that real wages tended to lag behind (and again, were higher in the northwest than elsewhere); and that real improvements in other indicators of the standard of living – height, infant mortality, literacy – were often (and in particular for the British case) even more delayed. The fruits of the Industrial Revolution were spread very unevenly over the continent”
A marginally related observation which I could not help myself from adding here is this one: “three out of ten babies died before age 1 in Germany in the 1860s”. The world used to be a very different place.
Most people probably have some idea that physical things such as roads, railways, canals, steam engines, etc. made a big difference, but how they made that difference may not be completely clear. As a person who can without problems go down to the local grocery store and buy bananas for a small fraction of the hourly average wage rate, it may be difficult to understand how much things have changed. The idea that spoilage during transport was a problem to such an extent that many goods were simply not available to people at all may be foreign to many people, and I doubt many people living today have given it a lot of thought how they would deal with the problems associated with transporting stuff upstream on rivers before canals took off. Here’s a relevant quote:
“The difficulties of going upstream always presented problems in the narrow confines of rivers. Using poles and oars for propulsion meant large crews and undermined the advantages of moving goods by water. Canals solved the problem with vessels pulled by draught animals walking along towpaths alongside the waterways.”
Roads were very important as well:
“Roads and bridges, long neglected, got new attention from governments and private investors in the first half of the eighteenth century. […] Over long hauls – distances of about 300 km – improved roads could lead to at least a doubling of productivity in land transport by the 1760s and a tripling by the 1830s. There were significant gains from a shift to using wagons in place of pack animals, something made possible by better roads. […] Pavement was created or improved, increasing speed, especially in poor weather. In the Austrian Netherlands, for example, new brick or stone roads replaced mud tracks, the Habsburg monarchs increasing the road network from 200 km in 1700 to nearly 2,850 km by 1793”
As were railroads:
“As early as 1801 an English engineer took a steam carriage from his home in Cornwall to London. […] In 1825 in northern England a railroad more than 38 km long went into operation. By 1829 engines capable of speeds of almost 60 kilometers an hour could serve as effective people carriers, in addition to their typical original function as vehicles for moving coal. In England in 1830 about 100km of railways were open to traffic; by 1846 the distance was over 1,500 km. The following year construction soared, and by 1860 there were more than 15,000 km of tracks.”
How did growth numbers look like in the past? The numbers used to be very low:
“Economic historians agree that increases in per capita GDP remained limited across Europe during the eighteenth century and even during the early decades of the nineteenth century. In the period before 1820, the highest rates of economic growth were experienced in Great Britain. Recent estimates suggest that per capita GDP increased at an annual rate of 0.3 percent per annum in England or by a total of 45 percent during the period 1700–1820 […] In other countries and regions of Europe, increases in per capita GDP were much more limited – at or below 0.1 percent per annum or less than 20 percent for 1700–1820 as a whole. As a result, at some time in the second half of the eighteenth century per capita incomes in England (but not the United Kingdom) began to exceed those in the Netherlands, the country with the highest per capita incomes until that date. The gap between the Netherlands and Great Britain on the one hand, and the rest of the continent on the other, was already significant around 1820. Italian, Spanish, Polish, Turkish, or southeastern European levels of income per capita were less than half of those occurring around the North Sea […] From the 1830s and especially the 1840s onwards, the pace of economic growth accelerated significantly. Whereas in the eighteenth century England, with a growth rate of 0.3 percent per annum, had been the most dynamic, from the 1830s onwards all European countries realized growth rates that were unheard of during the preceding century. Between 1830 and 1870 the growth of GDP per capita in the United Kingdom accelerated to more than 1.5 percent per year; the Belgian economy was even more successful, with 1.7 percent per year, but countries on the periphery, such as Poland, Turkey, and Russia, also registered annual rates of growth of 0.5 percent or more […] Parts of the continent then tended to catch up, with rates of growth exceeding 1 percent per annum after 1870. Catch-up or convergence applied especially to France, Germany, Austria, and the Scandinavian countries. […] in 1870 all Europeans enjoyed an average income that was 50 to 200 percent higher than in the eighteenth century”
To have growth you need food:
“In 1700, all economies were based very largely on agricultural production. The agricultural sector employed most of the workforce, consumed most of the capital inputs and provided most of the outputs in the economy […] at the onset of the Industrial Revolution in England , around 1770, food accounted for approximately 60 percent of the household budget, compared with just 10 percent in 2001 (Feinstein, 1998). But it is important to realise that agriculture additionally provided most of the raw materials for industrial production: fibres for cloth, animal skins for leather, and wood for building houses and ships and making the charcoal used in metal smelting. There was scarcely an economic activity that was not ultimately dependent on agricultural production – even down to the quill pens and ink used by clerks in the service industries. […] substantial food imports were unavailable to any country in the eighteenth century because no country was producing a sufficient agricultural surplus to be able to supply the food demanded by another. Therefore any transfer of labor resources from agriculture to industry required high output per worker in domestic agriculture, because each agricultural worker had to produce enough to feed both himself and some fraction of an industrial worker. This is crucial, because the transfer of labor resources out of agriculture and into industry has come to be seen as the defining feature of early industrialization. Alternative paradigms of industrial revolution – such as significant increases in the rate of productivity growth, or a marked superiority of industrial productivity over that of agriculture – have not been supported by the empirical evidence.”
“Much, though not all, of the increase in [agricultural] output between 1700 and 1870 is attributable to an increase in the intensity of rotations and the switch to new crops […] Many of the fertilization techniques (such as liming and marling) that came into fashion in the eighteenth century in England and the Netherlands had been known for many years (even in Roman times), and farmers had merely chosen to reintroduce them because relative prices had shifted in such a way as to make it profitable once again. The same may also be true of some aspects of crop rotation, such as the increasing use of clover in England. […] O’Brien and Keyder […] have suggested that English farmers had perhaps two-thirds more animal power than their French counterparts in 1800, helping to explain the differences in labor productivity. The role of horsepower was crucial to increasing output both on and off the farm […] [Also] by 1871 an estimated 25 percent of wheat in England and Wales was harvested by mechanical reapers, considerably more than in Germany (3.6 percent in 1882) or France (6.9 percent in 1882)”
“It is no coincidence that those places where agricultural productivity improved first were also the first to industrialize. For industrialization to occur, it had to be possible to produce more food with fewer people. England was able to do this because markets tended to be more efficient, and incentives for farmers to increase output were strong […] When new techniques, crop rotations, or the reorganization of land ownership were rejected, it was not necessarily because economic agents were averse to change, but because the traditional systems were considered more profitable by those with vested interests. Agricultural productivity in southern and eastern Europe may have been low, but the large landowners were often exceedingly rich, and were successful in maintaining policies which favored the current production systems.”
I think I talked about urbanization in the previous post as well, but I had to include these numbers because it’s yet another way to think about the changes that took place during the Industrial Revolution:
“On the whole, European urban patterns [in the mid-eighteenth century] were not very different from those of the late Middle Ages (i.e. between the tenth and the fourteenth centuries). The only difference was the rise of urbanization north of Flanders, especially in the Netherlands and England. […] In Europe, in the early modern age, fewer than 10 percent of the population lived in urban centers with more than 10,000 inhabitants. At the end of the twentieth century, this had increased to about 70 percent. In 1800 the population of the world was 900 million, of which about 50 million (5.5 percent) lived in urban centers of more than 10,000 inhabitants: the number of such centers was between 1,500 and 1,700, and the number of cities with more than 5,000 inhabitants was more than 4,000. At this time Europe was one of the most urbanized areas in the world […], with about one third of the world’s cities being located in Europe […] In the nineteenth century urban populations rose in Europe by 27 million […] (by 22.5 million in 1800–70) and the number of cities with over 5,000 inhabitants grew from 1,600 in 1800 to 3,419 in 1870. On the whole, in today’s developed regions, urbanization rates tripled in the nineteenth century, from 10 to 30 percent […] With regard to [European] centers with over 5,000 inhabitants, their number was 86 percent higher in 1800 than in 1700, and this figure increased fourfold by 1870. […] Between 1700 and 1800 centers with more than 10,000 inhabitants doubled. […] On the world scale, urbanization was about 5 percent in 1800, 15–20 percent in 1900, and 40 percent in 2000”
There’s a lot more interesting stuff in the book, but I had to draw a line somewhere. As I pointed out in the beginning, if you haven’t read a book dealing with this topic you might want to consider doing it at some point.
I’m currently reading this book.
This is not the first economic history text I read on ‘this’ topic; a while back I read the Kenwood and Lougheed text. However as that book ‘only’ covers the time period from 1820-2000 and does not limit the coverage to Europe I’ve felt that I’ve had some gaps in my knowledge base, and reading this book was one way for me to try to fill the gaps. The book also partly bridges the gap between Whittock (coverage ends around 1550) and K&L. K&L is a good text, and although this book is also okay so far I’m far from certain I’ll read the second volume as it seems unnecessary – part of the justification for reading this book was precisely that the time period covered does not perfectly overlap with K&L. Interestingly, without really having had any intention to do so I have actually over the last few years covered a very large chunk of British history (Britain was the biggest player in the game during the Industrial Revolution, so naturally the book spends quite a few pages on her in this book); I’ve also in the past dealt with the Roman invasion of Britain, Harding had relevant stuff about Bronze Age developments, Heather had stuff about both the period under Roman rule and about later Viking Age developments, and of course then there’s Whittock. Include WW1 and WW2 book reading and offbeat books like Bryson’s At Home as well as stuff like Wikipedia’s great (featured) portal about the British Empire, which I’ve also been browsing from time to time, and it starts to add up – thinking about it, I’m probably at the point where I’ve read more (/much more?) British history than I have Danish history…
Anyway, back to the book. It has a lot of data, and I love that. Unfortunately it also spends some pages talking about macro models which have been used to try to make sense of that data (or was that actually what they were meant to do? Sometimes you wonder…), and I don’t like that very much. Most models assume things about the world which are blatantly false (which makes it easy for me to dismiss them and hard for me to take them seriously), a fact which the authors fortunately mention during the coverage (“the “Industrial Revolution in most growth models shares few similarities with the economic events unfolding in England in the 18th century””) – and I consider many of these and similar models to be, well, to a great extent a load of crap. An especially infuriating combination is the one where economic theorists have combined the macro modelling approach and historicism and have tried to identify ‘historical laws’. Mokyr and Voth argue in the first chapter that:
“A closer collaboration between those who want to discern general laws and those who have studied the historical facts and data closely may have a high payoff.”
To which I say: The facts/data guys should stay the hell away from those ‘other people’ (this was where I ended up – I called them different things in earlier drafts of this post). The views of people who’re working on trying to identify general Historical Laws should be disregarded altogether – they’re wasting their time and the time of the people who read their stuff. The people who do should read Popper instead.
The data which is included in the book is nice, and the book has quite a few tables and figures which I had to omit from the coverage. I’d say most people should be able to read the book and get a lot out of it, but people who’re considering reading it should keep in mind that it’s an economic history textbook and not ‘just’ a history text – “The approach is quantitative and makes explicit use of economic analysis, but in a manner that is accessible to undergraduates” – so if you’ve never heard about, say, the Heckscher–Ohlin model for example, there’ll be some stuff which you’ll not understand without looking up some stuff along the way. But I think most people should be able to take a lot away from the book even so. I may be biased/wrong.
Below some observations from the first three chapters, I’ve tried to emphasize key points for the readers who don’t want to read it all:
“the transition to modern economic growth was a long-drawn-out process. Even in the lead country, the United Kingdom, the annual growth rate of per capita income remained less than 0.5 percent until well into the nineteenth century. Only after 1820 were rates of growth above 1 percent per annum seen, and then only in a handful of countries.” [a ‘growth argument’ was incidentally, if I remember correctly, part of the reason why K&L decided to limit their coverage to 1820 and later.]
“The population–idea nexus [the idea that larger populations -> more ideas -> higher growth] is key in many unified growth models. How does this square with the historical record? As Crafts (1995) has pointed out, the implications for the cross-section of growth in Europe and around the world are simply not borne out by the facts – bigger countries did not grow faster. Modern data reinforce this conclusion: country size is either negatively related to GDP per capita, or has no effect at all. The negative finding seems plausible, as one of the most reliable correlates of economic growth, the rule of law (Hansson and Olsson, 2006), declines with country size. […] the European experience after 1700 [also] does not suggest that the absolute size of economies is a good predictor of the timing of industrialization.”
“Most “constraints on the executive” took the form of rent-seeking groups ensuring that their share of the pie remained constant. Unsurprisingly, large parts of Europe’s early modern history read like one long tale of gridlock, with interest groups from local lords and merchant lobbies to the Church and the guilds squabbling over the distribution of output. […] None of the groups that offered resistance to the centralizing agendas of rulers in France, Spain, Russia, Sweden, and elsewhere were interested in growth. Where they won, they did not push through sensible, longterm policies. They often replaced arbitrary taxation by the ruler with arbitrary exactions by local monopolies. […] Economically successful but compact units were frequently destroyed by superior military forces or by the costs of having to maintain an army disproportionate to their tax base. The only two areas that escaped this fate enjoyed unusual geographical advantages for repelling foreign invasions – Britain and the northern Netherlands. Even these economies were burdened by high taxation […] A fundamental trade-off [existed]: a powerful central government was more effective in protecting an economy from foreign marauders, but at the same time the least amenable to internal checks and balances.”
“In many models of long-run growth, the transition to self-sustaining growth is almost synonymous with rising returns to education, and a rapid acceleration in skill formation. […] Developments during the Industrial Revolution in Britain appear largely at variance with these predictions. Most evidence is still based on the ability to sign one’s name, arguably a low standard of literacy (Schofield, 1973). British literacy rates during the Industrial Revolution were relatively low and largely stagnant […] School enrollment rates did not increase much before the 1870s […] A recent literature survey, focusing on the ability to sign one’s name in and around 1800, rates this proportion at about 60 percent for British males and 40 percent for females, more or less at a par with Belgium, slightly better than France, but worse than the Netherlands and Germany […] The main conclusion appears to be that, while human-capital-based approaches hold some attractions for the period after 1850, few growth models have much to say about the first escape from low growth.”
“The average population growth rate in Europe in 1700–50 was 3.1 percent, ranging between 0.3 percent in the Netherlands and 8.9 percent in Russia […] Figure 2.1 […] shows two measures of fertility for England, 1540–2000. The first is the gross reproduction rate (GRR), the average number of daughters born per woman who lived through the full reproductive span, by decade. Such a woman would have given birth to nearly five children (daughters plus sons), all the way from the 1540s to the 1890s. Since in England 10–15 percent of each female cohort remained celibate, for married women the average number of births was nearly six. The demographic transition to modern fertility rates began only in the 1870s in England, as in most of Europe, but then progressed rapidly. […] population growth [after 1750] occurred everywhere in Europe. Annual rates of growth were between 0.4 percent and 1.3 percent, except for France and Ireland. Europe’s population more than doubled in 1800–1900, compared with increases of 32 percent in 1500–1600, 13 percent in 1600–1700, and 56 percent in 1700–1800 […] population growth was, at best, weakly associated with economic development […] [From] 1800–1900, France grew by 65 percent, from 29 million to 41 million. In the same period England and Wales grew from under 9 million to over 30 million, and Germany grew from about 25 million to 56 million.”
“Mortality, especially for infants, remained extremely high in eastern Europe. Blum and Troitskaja (1996) estimate that life expectancy at birth in the Moscow region at mid-century [~1850] was about twenty-four years, compared with life expectancies of around forty years in western Europe. Birth rates in eastern Europe were also much higher than in the west.”
“The population of Europe in 1815 was 223 million. By 1913, 40 million people had emigrated to the New World. […] By 1900, more than a million people a year were emigrating to the United States, the primary destination for most Europeans. […] More than half of some nationalities returned to Europe from the United States […] Internally there was substantial migration of population from country to city as incomes rose. From 1815 to 1913 the rural population [in Europe] grew from 197 to 319 million. But the urban population expanded from 26 million in 1815 to about 162 million in 1913 (Bairoch, 1997).” [26 million out of 223 million is roughly 10 percent of Europe’s population living in urban areas at that time; 10 percent is a very small number – it corresponds to the proportion of the English population living in towns around the year 1000 AD… (link).]
“This positive correlation of fertility and income [they talk a little about that stuff in the text but I won’t cover it here – see Bobbi Low’s coverage here if you’re interested, the Swedish pattern is also observed elsewhere] became negative in Europe in the period of the demographic transition after 1870, and there seems to be no association between income and fertility in high-income–low-fertility societies today. The numbers of children present in the households of married women aged 30–42 in both 1980 and 2000 were largely uncorrelated with income in Canada, Finland, Germany, Sweden, the United Kingdom, and the United States […] This suggests that the income–fertility relationship within societies changed dramatically over time.”
“Between 1665 and 1800 total revenue in England rose from 3.4 percent of GDP to at least 12.9 percent. In France, meanwhile, taxes slipped from 9.4 percent in the early eighteenth century to only 6.8 percent in 1788 […] In 1870 central government typically raised only between 20 and 40 percent of their revenue through taxes on wealth or income. The remainder came from customs and, especially after the liberalization of trade in the 1850s and 1860s, excise duties […] In most countries the tax burden was often no higher in 1870 than it had been a century earlier. Most central governments’ taxes still amounted to less than 10 percent of GDP.”
“by 1870 institutions were more different across Europe than they had been in 1700. Suffrage where it existed in 1700 was generally quite restricted. By 1870 there were democracies with universal male suffrage, while other polities had no representation whatsoever. In 1700 public finance was an arcane art and taxation an opaque process nearly everywhere. By 1870 the western half of Europe had adopted many modern principles of taxation, while in the east reforms were very slow.”
I’ve read some articles etc. about this stuff before, but I’ve never read ‘the textbook’. I have now. Well, I’ve read a textbook anyway. I am not super impressed by the book, and I decided to give it two stars on goodreads. Maybe it deserves three, it’s in that neighbourhood.
So what’s the book about? Here’s what they write in the introduction:
“In this book the fundamental approach is to describe the classification of diabetes, risk factors for diabetic retinopathy and lesions of diabetic retinopathy, and explain the significance of these lesions in terms of progression of the disease, recommended treatment and consequences for vision. Methods of screening for diabetic retinopathy and other retinal conditions that are more frequent in diabetes or have similar appearances to diabetic retinopathy are also discussed.”
They deal with main concepts and they provide a lot of examples and case histories along the way. As is always the case in books like these many of the case histories are really quite depressing – I was considering skipping them altogether at one point after a particularly ‘bad one’, but I decided to read those parts anyway; they make up a substantial part of the book.
As you might have inferred from the remarks above, diabetic retinopathy is diabetes-related eye disease. How many diabetics are impacted by this? A rather large number, it turns out (well, I already knew that and I’ve talked about it before, but…):
“Diabetic retinopathy is a leading cause of adult blindness in the US, reported by Fong et al. in 2004 to result in blindness for over 10,000 people with diabetes per year. Moss reported the 10-year incidence of blindness in the Wisconsin Epidemiological study of Diabetic Retinopathy to be 1.8%, 4.0% and 4.8% in the younger-onset, older-onset taking insulin, and older-onset not taking insulin groups, respectively. Respective 10-year rates of visual impairment were 9.4%, 37.2% and 23.9%. […] In the Wisconsin study, proliferative retinopathy occurred in 67% of people with type 1 diabetes for 35 or more years. One would therefore expect that two-thirds of people with type 1 diabetes would need laser treatment for proliferative diabetic retinopathy during their lifetime. […] In patients with type 2 diabetes, the rate of proliferative diabetic retinopathy is not as high but it is estimated that 1 in 3 patients with type 2 diabetes will develop sight-threatening diabetic retinopathy requiring laser during their lifetime. […] Despite major advances in treatment and early detection of diabetic eye disease, the ageing demographic and increased incidence of diabetes is resulting in greater numbers of diabetic visually impaired people in the population.” [my emphasis. Numbers differ across countries and there are a lot more numbers in the book, but these estimates provide some context; this is a complication that affects a huge number of diabetics.]
In the book they talk a lot about how you can use tiny (with sizes measured in microns!) and very short-lasting laser pulses to treat the damaged blood vessels in the eyes, and that stuff’s quite interesting. Equally interesting is the fact that people seem to be treating without really knowing exactly why the treatment works:
“The effectiveness of focal laser treatment may be due, in part, to the closure of leaky microaneurysms, but the specific mechanisms by which focal photocoagulation reduces macular oedema is not known. Studies have shown histopathological changes and biochemical changes,[19,20] which have been suggested as mechanisms for improvement in macular oedema although some investigators have suggested alternative mechanisms for clearance of the oedema such as the application of Starling’s law and improved oxygenation. […] the mechanism by which laser treatment improves the prognosis of sight-threatening diabetic retinopathy is ill-understood.”
A lot has happened when it comes to treatment over the last decades, as patients in the pre-laser era would often simply lose their vision because no good treatment options existed. A lot of people still do lose their vision to diabetes as mentioned above, but with the advent of laser treatments the prognosis has improved a lot. There are some adverse effects associated with these treatments, e.g. in the form of laser scars or scotomas and (paradoxical?) development of macular oedema afterwards (“McDonald showed that 43% of the treated eyes in his study developed increased macular oedema 6–10 weeks following laser treatment.”), and it doesn’t always work (“if there is ischaemia that involves the central fovea, laser treatment in isolation is unlikely to improve the vision.” “It is not uncommon to successfully treat one area of leakage and subsequently find leakage appearing in a completely different area around the fovea of the same eye.”). But it’s still a big step in the right direction. Laser therapy is however surgical management of tissue damage, and some people are of course hoping to develop pharmacological treatment options as well. In that context I should note that in a way it was fun to read a medical textbook written by people who know less about some aspects of the stuff I’m reading about than do people I’ve met personally (people like Toke Bek). Latanoprost is being evaluated in a clinical trial right now as a drug which might be used to slow the progression of diabetic retinopathy in diabetics, but they don’t talk about that at all in the ‘Future advances in the management of diabetic retinopathy’-chapter (however on the other hand you can’t really blame them for not including this stuff, as that idea postdates the book..).
It should be noted – and they do this repeatedly throughout the book – that the damage to the small blood vessels in the eyes and the subsequent retinal ischaemia/bleeding etc. leading to vision loss in diabetics is strongly linked to factors such as glycemic control and (systemic) blood pressure. This means that improvements in glycemic control and blood pressure management will, if they can be achieved, also translate into better outcomes along these dimensions over time. A factor pulling in the other direction (‘more blind people’) is the high number of current and future undiagnosed type two diabetics who’ll incur extensive tissue damage without knowing it before getting their diagnoses:
“In the UKPDS study it was observed that up to 50% of [type 2] patients had some detectable form of tissue damage at diagnosis, the majority of this being background diabetic retinopathy. […] Retinopathy is the commonest finding, with about 30% of all subjects newly diagnosed having detectable retinal lesions.”
This patient population poses some problems also because these people will by definition not be included in national screening programs. A related point they do not touch upon in the book is of course that non-compliant patients, the ones most likely to benefit from participation, would also be expected to be less likely than other patient groups to participate in screening programs; so even in places where you have national screening programs and so on you’ll likely still have some ‘theoretically preventable’/’excess’ diabetes-related blindness in the future. Perhaps I talk about screening programs as if I think they’re a good idea, but if that’s the case it’s because some forms of them are almost certainly pretty much a no-brainer – see e.g. this post. The book also spends a chapter on that stuff, unsurprisingly coming to the conclusion that screening is probably a good idea (there’s also consensus about which method of screening is best: “There is widespread agreement that digital photography is the best method of screening for sight-threatening DR.”). It’s worth noting in the context of the complication rates that it’s easier to spot eye damage than other types of tissue damage, and that this may provide part of the explanation for why this complication is so often found at diagnosis compared to other types of complications – here’s a relevant passage from the book:
“Retinopathy is often the easiest complication to detect because the smallest of lesions (microaneurysms) can be visualized long before any change to the subjective function of the eye would be apparent. Retinopathy tracks closely with nephropathy, and so careful screening of renal function needs to be carried out in those who have retinopathy and vice versa.”
The book has a lot more stuff, but I know that most readers probably aren’t too interested in this topic so I figured a rather limited coverage of the book would be preferable to most readers. One of multiple reasons why I did not give it a higher rating is that they repeat themselves quite a few times, covering the same stuff in multiple chapters. Unless you’re a diabetic there’s also no good reason why you should read the book as it is quite technical. Most diabetics will probably find it hard to read.
Here’s the first post I wrote about the book, here’s goodreads. I didn’t know I’d finish it this soon or I’d probably only have written one post about the book. Anyway – things didn’t change much along the way and I ended up giving it 2 stars on goodreads. Some of the stuff was really weak and I think I’m closer to one star than three. There’s quite a bit of speculation, and there are quite a few anecdotes. Many of the findings which are covered are not dealt with in nearly enough detail for them to be really all that trustworthy. The authors often use cautious language, but they don’t talk that much about why such cautious language is necessary, certainly not in any great detail; some readers will know, others won’t.
It should be noted that the book was easy to read, and so I never really seriously contemplated not finishing it. The theoretical frameworks presented in the book I think tend to constitute useful ways to frame specific problems or useful ways to think about behavioural patterns, but I didn’t feel like they actually added much new knowledge; lots of theoretical stuff covered in the book was already known to me, and one main motivation for reading the book was to get some data as well in order to figure out the extent to which given behavioural patterns matched expectations. I don’t really think they delivered in that respect. I should note that you can’t really fault the authors for the fact that not a lot of good science exists in some of these specific areas (you can blame them to the extent that they’re the ones conducting bad research of course, but let’s not go there…), but you can fault them for not being more open about how uncertain many of the observations and conclusions drawn in the book really are. As mentioned above they do use cautious language with a lot of qualifications and so on (a brief word search told me that they used the word ‘perhaps’ 10 times during the first 100 pages of the book; ‘may be’ is incidentally also used 10 times during the first 100 pages, and ‘maybe’ and ‘it seems’ are used 3 times each as well.), but unless you’re familiar with how scientific research works and know a little bit of statistics it’ll not be perfectly obvious to you why such cautious language is even necessary; they spend very little time discussing the sometimes blatantly obvious and huge limitations of the studies they cover. I consider this to be a problem in that I believe a lot of people who don’t know a lot about how science works will read this book – it’s easy to read and filled with anecdotes, and while reading the book I certainly didn’t get the impression that the intended readership consists of mechanical engineering graduate students from MIT. You can try to go from ‘how 55 mostly-white female college students from one specific study conducted in American town X near Florida (or wherever…) think about X’ to ‘how women think about X’, but the conclusions you draw from that one study probably need some additional support to be taken all that seriously. Don’t take this to mean that e.g. cross-cultural differences, to take an example, are not covered in the book; they are. The problem is that studies covered are most of the time not put into any sort of ‘proper context’. They’ll often jump from, say, ’55 women in study X’ to ‘women’ without any comments, and I’m sure some people will miss the fact that those women are actually the same. The ‘from ’55 women’ to ‘women” transition is not even the worst type; it’s far better than the transitions that involve an unknown number of women, because the authors can’t even be bothered to tell us how many people participated in the study behind the finding they happen to talk about now. As might be inferred, the authors will often only spend a few lines – perhaps a whole paragraph if things are going well – on a study, mostly just summarizing the conclusions from the paper, and then quickly move on to another couple of studies dealing with another matter. It gets really funny when a reported conclusion from a study sparks that familiar thought, ‘There’s just no way in hell a study that small had the power to actually show what they just claimed that it showed with any degree of confidence!‘ It often feels to me as if they’re rushing while covering the studies, and I can’t shake off the impression that part of the reason is that at least some of those studies really wouldn’t stand up to any close scrutiny.
Adding a few remarks about how this stuff (whatever ‘this stuff’ may be) matches up with, say, what we know about how romance novels are written, may be useful to some people, and you may gain a better understanding of the theoretical principles by reading a few remarks about how the sexual experiences of male bass players in bands can help enlighten us here; but these are not the methods usually applied in order to elucidate matters in the books I read. In more than a few sections there’ll be no studies at all, just some theoretical remarks interspersed within the mountains of illustrative anecdotes. “As anyone who has experienced junior high school knows…” Don’t get me wrong, there’s a reason I kept reading – there’s some interesting stuff in there. But this is a very different kind of book from the ones I usually read, and I mostly don’t consider it to be different in a good way.
All in all: Too much talk, too little substance.
Some quotes and observations from the second half of the book:
“As a highly social species, we are constantly threatened by potential mate poachers who try to lure our partners, be it for brief sexual encounters or for a more permanent relationship. We also face the risk that our partners might be tempted to leave the relationship in hopes of “trading up” to a more desirable partner. Among both dating and married couples, the Buss Lab’s research has revealed findings similar to those in our study: Women often use sex in many different ways to protect their relationships. They give in to their partners’ sexual requests in an attempt to keep them happy, they act “sexy” to take their partners’ mind off potential competitors, and they perform sexual favors or succumb to sexual pressures to entice their partners into staying. […] Women are motivated to have sex to mate guard because the costs of not doing so can be catastrophic. […] Having sex, even though it does not always work as planned, is partly designed to prevent infidelity and keep a couple from breaking up.”
“One study found that 79 percent of women who had affairs became emotionally involved with, or fell in love with, their affair partners. Although this finding may seem obvious, it is in stark contrast to the experiences of men, of whom only about a third become emotionally enmeshed. According to one study, most men’s motivations for sex outside their primary relationship are more a matter of desire for sexual variety.”
“Studies consistently show that men report higher levels of sex drive than women. This holds true for college students, middle-aged people, and even eighty- and ninety-year-olds. Men are also much more likely than women to say they want more sex than they are currently getting, whether measured among married persons or couples in the early stages of dating. In a study of 1,410 American men and 1,749 American women, 32 percent of women between the ages of eighteen and twenty-nine reported a lack of sexual interest in the previous year, compared to 14 percent of men in the same age group.”
“I love my husband, but when you’ve been married for awhile, let’s face it—sex just isn’t that exciting anymore. It’s all so predictable. Even when we try to be “spontaneous” it’s almost comical because I can predict his every move. I have sex because I feel I “owe” it to him as his wife, and also because I love him and want to keep him happy. The truth is, though, most of the time I just lie there and make lists in my head. I grunt once in awhile so he knows I’m awake, and then I tell him how great it was when it’s over. It seems to be working. We’re happily married.
—heterosexual woman, age 48″
[One of many quotes of this kind from the book – often interview snippets like these are used to illustrate a point/problem. Quite a few of these quotes are incidentally thoroughly depressing to read. ‘We’re happily married’… But then again, what do I know?]
“Research indicates that women agree to unwanted sex more often than men do—but not by as great a margin as one might predict. One study of married couples found that 84 percent of wives and 64 percent of husbands “usually” or “always” complied with having sex when their spouse wanted to and they did not. […] What determines whether a woman will feel happy or remorseful after engaging in consensual unwanted sex? Probably the best predictor is whether the behavior occurred because of what psychologists refer to as approach versus avoidance motives. Approach-motivated behaviors refer to acts done in an effort to achieve a positive or pleasurable experience. In the sexual arena, this would mean, for example, that a woman agrees to have unwanted sex because she wants to make her partner happy and to feel that she is a good mate. That motivation would likely result in her feeling good about her decision. Avoidance-motivated behaviors, on the other hand, refer to behaviors undertaken to avoid negative or painful outcomes. This could mean agreeing to have sex out of fear of losing one’s partner or making the partner angry or disappointed. Consenting to sex to avoid negative outcomes more often than not leads to feelings of shame and remorse. […]
There is often a fine line between unwanted sex and rape. This is especially true when rape occurs in a long-term relationship, where the couple has engaged in consensual sexual intercourse in the past. Women who are sexually abused in marital relationships frequently define it as rape only if physical force or harm is involved. And research shows that when a woman is sexually abused in a committed relationship she is more likely to make excuses for her partner such as “He’s only like that when he’s drunk” or “I should know better than to provoke him.” They also tend to minimize the situation by claiming things like “It’s only happened a couple of times.” […] According to the National Violence Against Women Survey of eight thousand women, approximately 15 percent of the women had been raped and 3 percent had experienced attempted rape. Sixty-two percent of the assaults were by a past or current partner, and the likelihood of physical injury was higher with intimate partners than with strangers.”
“The value of women’s virginity shifted dramatically with the introduction of the birth control pill in 1961. […] In the landmark 1953 Kinsey report surveying nearly six thousand American women, 40 percent reported being nonvirgins before marriage. In a 1994 survey of more than 1,600 American women, approximately 80 percent of the women who were born between 1953 and 1974 reported having had premarital sex. […] The average age for a woman to lose her virginity also radically changed during this time period. In 1950, the average age for a woman to first engage in sexual intercourse—or at least admit to it—was twenty. In 2000, the average age was sixteen.” […] A study conducted in the Meston Sexual Psychophysiology Lab of more than four hundred Canadian university women showed that 72 percent of women of European ancestry had engaged in premarital sex compared with a much lower 43 percent of Southeast Asian women, most of whom were ethnic Chinese.”
“According to one study, approximately 25 percent of women in their thirties have had sex with five to ten different partners since age eighteen, and just over 10 percent have had sex with more than twenty-one different partners. By contrast, only about 15 percent of women in their late teens and early twenties have had between five and ten sexual partners, and approximately one-third have had sexual intercourse with only one person.” [here’s a post – unfortunately in Danish – with some Danish numbers.]
“Men possess [a] psychological tic, the sexual overperception bias, which is the tendency to overinfer women’s sexual interest based on ambiguous information. […] when a woman smiles at a man, men often infer sexual interest, when in many cases the woman is simply being friendly or polite. Other ambiguous cues—a touch on the arm, standing close, or even holding eye contact for a split second longer than usual—trigger men’s sexual overperception bias. As a consequence, women can exploit men’s overperception bias for economic gain, in what has been called a “bait and switch” tactic, a strategy that involves persuading men to expend resources as part of courtship, but then failing to follow through on an implied “promise” of sex.
Research has also found that most men find most women at least somewhat sexually attractive, whereas most women do not find most men sexually attractive at all.”
“Not all friends-with-benefits relationships result in unmitigated, mutually beneficial sexual bliss. Women who have these relationships also report some disadvantages. These include developing romantic feelings for the friend (65 percent), harming the friendship (35 percent), causing negative emotions (24 percent), and risking negative sexual side effects such as sexually transmitted diseases (10 percent). Interestingly, the vast majority of women, 73 percent, never explicitly discuss the ground rules or expectations for these relationships.”
“[I have sex] to get my way or to persuade my husband into something I really want and he might be opposed to.
—heterosexual woman, age 31
I will often use sex as leverage in my relationship to get what I want.
—heterosexual woman, age 27 […]
In modern Western cultures, […] direct exchanges tend to be far less common, or at least less explicit. Nonetheless, sexual economics sometimes continues to influence why women have sex within marriage. The exchange of sex may not be for economic resources per se, but rather for reciprocal favors. […] Sexual economics play out across cultures in many forms. On the mating market, women accrue significant power as a result of men’s sexual psychology—their desire for sexual variety, their sex drive, their sexual overperception bias, their persistent sexual fantasies, and a brain wired to respond to visual stimulation. As the valuable resource over which men compete, women can, and some often do, exercise that power to exchange their sexual resources for benefits, including food, gifts, special favors, grades, career advancement, or entrée into the movie business. In some of these exchanges, there is no sharp line demarcating honest courtship, seduction, and prostitution.”
“many women, when asked what motivated them to have sex, […] respond by saying they were deceived by a man, verbally coerced, plied with drugs or alcohol, or physically forced. These are not ways in which women want to have sex. But they are nonetheless some of the reasons women end up having sex. […] A recent study of online dating ads explored the extent to which men and women provide deceptive information about themselves. The researchers compared men’s and women’s advertised height, weight, age, and other characteristics with actual measured height and weight and independently verified age.
Fifty-five percent of the men, compared with 41 percent of women, lied about their height. Women were somewhat more likely than men to shade the truth about their weight. Overall, an astonishing 81 percent of the sample engaged in some form of deception, be it about physical characteristics, income, habits such as smoking or drinking, or political beliefs.”
“According to a survey of over 1,400 women aged eighteen to fifty-nine years, American women have sex about 6.3 times per month. The average is somewhat higher among twenty-to thirty-year-olds (7.5 times per month) and somewhat lower among fifty-to sixty-year-olds (four times per month).”
Go ahead – judge me…
I guess covering a book like this here is a great way to stop potential readers from being able to read my blog at work ever again… Oh well. I’m not sure the post is actually NSFW; it’ll probably depend upon where you work. Anyway, the book is written by Cindy Meston and David Buss, the latter of which I have quoted before here on this blog in various contexts. I decided to have a go at the book after I’d decided that the Duncombe et al book was crap. There’s some overlap, but fortunately not too much (or I’d also have thrown away this book).
I consider the book to be light reading and that’s part of why I’m reading it now; Mas-Colell is not light reading. I’m not too impressed and I’m only at a 2-star evaluation at this point, having read roughly half of the book. Much of the research presented is of questionable validity due to reliance on self-reported data [here’s a relevant link] and small n studies, and the book is less data-driven than I’d expected. Often they’ll neglect to even tell you about the n’s and only talk about the percentages, so you have no clue if those 36 percent they’re talking about are actually just 9 college educated women out of 25. You could look up the studies yourself, true, but if you need to do that in order to figure out if the authors’ inferences can be trusted or not how much value does the book really add? There are some interesting notes and observations, but it subtracts a lot that you can’t always tell if they can really be trusted or not.
Some stuff from the first half of the book below:
“Back in the 1930s, a study examined five thousand marriages performed in a single year, 1931, to determine where the bride and groom lived before their wedding. One-third lived within five blocks of each other and more than one-half lived within a twenty-block radius.” [Things have changed since then, but probably less than you’d think.]
“DNA fingerprinting studies reveal that roughly 12 percent of women get pregnant by men other than their long-term mates” [Yeah, well… I know I’ve touched upon this one before, but people seem to keep writing books in which they make claims about these things which are probably not true, and as long as they do that I’ll keep repeating that those estimates are most likely wrong.]
“Research reveals that women find certain body movements to be more attractive than others. […] Nonreciprocal same-sex touching—when a man touches another man’s back, for example—is a well-documented signal of dominance. Women see “touchers” as having more status, a key component of a man’s mate value. Space maximization movements, as when a man stretches his arms or extends his legs, are another dominance signal. Those who display open body positioning—for example, by not having their arms folded across the chest—are judged to be more potent and persuasive.
Evolutionary psychologist Karl Grammer and his colleagues conducted a study in three singles bars in Pennsylvania. They coded men’s nonverbal behaviors and then examined which ones were linked with making “successful contact” with a woman in the bar—defined as achieving at least one minute of continuous conversation with her. They found five classes of men’s movements linked with successul contact: more frequent short, direct glances at women; more space maximization movements; more location changes; more nonreciprocated touches; and a smaller number of closed-body movements.”
“Why a sense of humor is so important in sexual attraction has been the subject of much scientific debate. One critical distinction is between humor production (making others laugh) and humor appreciation (laughing at others’ jokes). There’s a sex difference—men define a woman with a good sense of humor as someone who laughs at their jokes! Men especially like women who are receptive to their humor in sexual relationships. Women, in contrast, are attracted to men who produce humor, and that’s true for all types of relationships, from one-night stands to lifelong matings.”
“A person’s mood at the time of an initial encounter is an important factor in determining attraction—positive feelings lead to positive evaluations of others and negative feelings lead to negative evaluations. In fact, anyone or anything simply present when positive or negative feelings are aroused also tends to be liked or disliked as a consequence.”
“when it comes to actually choosing a long-term sexual partner, it is more the rule than the exception that “similars” attract. Several studies have shown substantial similarity between husbands and wives in their attitudes about faith, war, and politics, as well as similarities in their physical health, family background, age, ethnicity, religion, and level of education. Dating and married couples are similar in physical attractiveness, and young married couples even tend to be matched in weight. The “matching hypothesis”—as named by social psychologists—is so strong that observers react negatively when they perceive couples who are mismatched on levels of attractiveness. There is one notable exception—a beautiful woman and a less-attractive man. In this scenario, consistent with evolutionary logic, people judging the mismatched pairs ascribe wealth, intelligence, or success to the man.”
“All of the nerve endings in the vagina lie in the outer portion of the vagina, near the opening. This means that women are sensitive to light touch or stimulation of their vaginas only when it is applied to this outer region. Further inside the vagina there are sensory receptors that respond to more intense pressure. Vaginas probably evolved this way because having highly sensitive nerve endings threaded throughout the vagina would have made the extended penetration of sex painful.
Because of the way the vagina is designed, some women find stimulation of the vaginal opening the most pleasurable aspect of penetration. And because the nerve endings become less sensitive after repeated stimulation, some women say that penetration feels most enjoyable at first entry. Taking short breaks during sexual activity to focus on other erogenous zones allows the nerve endings in the vagina time to regain their sensitivity. Breaks allow women to reexperience the initial entry pleasure.” [Wondering why stuff like this was not covered during sex ed…]
“By 2001, there were no fewer than twenty-six distinct definitions of women’s orgasm in the research literature.”
“In a survey of over 1,600 American women ages eighteen to fifty-nine, only 29 percent of the women overall said they were able to have an orgasm with a partner. Sixty-one percent—more than twice as many—said they were able to have an orgasm when they masturbated.” [I found these numbers depressing.]
“research shows that men are actually more likely than women to “fall in love at first sight,” which may be the result of an evolutionary adaptation. Men generally are more quickly swayed by physical appearance when choosing a partner than are women, who tend to rely on a wider range of signals, including scent and personality, for the initial spark of attraction. […] The qualities women seek, particularly in a long-term mate, take a longer period of time to evaluate. “Love at first sight” is just more straightforward for men.
Beyond that first rush of emotion, men also appear to stay in love longer: A study that assessed 231 college dating couples from 1972 through 1974 refuted the stereotype that women are the lovers and men are the leavers. The study found that women were more likely than men to break up a relationship [this part should be old news to ‘regular readers’ – see e.g. this post (“The 2004 survey found that 93% of divorce cases were petitioned by wives”)], and they were also more likely to see the breakup coming well in advance. […] There is also some evidence to suggest that breaking up a relationship is more traumatic for men than for women. Obviously it depends on the circumstances of both the relationship and the breakup, but in general, after a breakup, men tend to report more loneliness and depression.”
“Whereas 53 percent of men in one study said that they would have sex without kissing, only 15 percent of women said they would consider sex with someone without first kissing them. […] “Bad” kissing is definitely a sexual turnoff for most women. One study found that 66 percent of women (as compared with 58 percent of men) admitted that sexual attraction evaporated after a bad kiss.”
“Within the United States, Americans purchase some 2,136,960 tubes of lipstick and 2,959,200 jars of skin care products every day. Roughly three hundred thousand American women undergo breast augmentation surgical procedures each year.” [I was curious about the latter number because that sounded high to me, but it seems to check out; see this link] […] “women spend nearly ten times as much on appearance-enhancement products as men do.”
“Studies conducted in Germany [used] digital photography to capture what women wore to singles bars and interview[ed] them afterward. Using a computer program that calculated the percentage of skin revealed by women’s clothing choices, they discovered that women in the most fertile phase of their ovulation cycles wore more revealing clothing and showed more skin than women in the nonfertile phase. Ovulating women dress for sexual success. Another group of researchers, led by UCLA evolutionary psychologist Martie G. Haselton, found that women in the fertile phase of their cycles wore nicer and more fashionable clothes and showed more upper and lower body skin than the same women in the low-fertility phase of their cycles.”
“Why would women intentionally evoke jealousy, given that it is a dangerous emotion, known to be linked to physical violence and even murder? One clue comes from the circumstances in which women use the tactic. Although many couples are equally committed to each other, a substantial minority—39 percent according to one study—exhibit an involvement imbalance in which one partner is more committed to the relationship than the other. Within this group, when the man is the more committed partner, only 26 percent of women report intentionally evoking jealousy. In sharp contrast, when the woman is more committed to the relationship, 50 percent of the women resort to jealousy evocation.
Women’s strategic provocation of a partner’s jealousy serves three functions. First, it increases her partner’s perception of her desirability. The sexual interest of others is a gauge of a partner’s overall mate value. Second, a partner’s response to a jealousy-triggering situation provides a litmus test of the level of his or her commitment. For example, if a man is indifferent when his partner sits seductively in another man’s lap, it may signal a lack of allegiance, and the level of his jealousy can be a signal of the depth of his emotional dedication to the relationship. Perhaps most important is the third function—to increase a partner’s commitment. This is especially true among men, who are much more likely to commit to a woman whom they perceive to be highly desired by other men. A jealous man becomes more smitten, comes to believe that he is lucky to be with his partner, and so doubles his dedication.”
I finished the book. I ended up at two stars on goodreads – it didn’t improve towards the end. If I had to sum it up in just a few words, I’d say something like this: ‘You’ll learn a lot of stuff about the region from reading this book, but the book isn’t actually all that great.’ The first few chapters I’ve yet to talk about here covered economic factors, and the last ones were brief chapters about specific subregions, both regional entities of Russia (e.g. The Volga region, the Urals, Siberia, …) as well as other regional entitites of the FSU (e.g. the Central Asian republics, the Eastern European countries of Ukraine, Moldova and Belarus, a chapter about the Baltics, etc..). I’ve already talked a lot about the book here, so I’ll limit my coverage of the last part of the book to some observations from the remaining chapters which I’ve posted below.
“about one-third of all Russians now claim that they never read” […]
“about 5% of Russia’s gross domestic product (GDP) is produced by agriculture and another 5% by forestry […] In Russian society 100 years ago, 80% of the people were peasants. […] Today 15% of workers in Russia are employed in forestry or agriculture; this remains a much higher rate than in the West, where it is under 3% […] Because the collective farming was notoriously inefficient, people were tacitly encouraged by the authorities to take care of themselves and to grow their own food. Small plots of land (averaging 0.06 ha) were grudgingly given out by the Soviet authorities to the urban residents, so that some food could be grown around cities. […] Villagers had slightly larger plots of land (usually 0.10–0.20 ha) immediately next to their houses to grow their own food. […] These tiny plots yielded an astonishing 30% of the total agricultural produce in the country in 1980, and yield even more today. […] Fewer than 20% of all vegetables are produced on large farms. […]
“Because Soviet agriculture was so inefficient […], the Soviet Union had to import about one-fifth of its total calories by the early 1980s, making it the largest single importer of food on earth […] About one-quarter of all economic expenditures in the Soviet Union were on food. […] In 2005 over $16 billion was spent by Russia to import food — almost 17% of all imports for the year. The cost went up to $35 billion by 2008 […] Although for some African nations food constitutes one-third of all imports, for a typical European country food accounts for under 10% of imports (under 5% in the United States)”
“Russia is a country of heavy smokers; 65% of its men smoke, as compared to 35% in France or 22% in the United States. Fewer Russian women smoke (about 10%), but their number is increasing (World Health Organization, 2007).”
“The service sector was greatly underdeveloped in the Soviet Union, because the government always gave the highest priority to heavy industry. Although mass transit was well developed, other services lagged far behind Western norms. After World War II only 10% of all workers were in the service sector, and by 1990 only 25%, as compared to over 70% in the United States at that time. […] recent years have seen a massive increase in the relative importance of services”
“about 80% of all those commuting to work in Russian cities do so by bus […] In Russia only 14% of travel happens by plane, as compared to 40% by automobile and 33% by train. The proportion of air travel is higher than in the United States because a lot fewer people travel by private car in Russia (under 10% of all passenger-kilometers, as opposed to almost 85% in the United States).”
“Russia had over 44,000 km of petroleum pipelines and over 150,000 km of gas pipelines in 2008. […] Although less glamorous than trains or planes, pipelines move more freight, about 55% of the total […] Of these, 59% move natural gas and 41% move petroleum.”
“About 27% of the Russian population had online access in 2008 (38 million users) […] Internet access is about as common in Russia now as it is in Turkey or Brazil, but not nearly as common as in developed Asia or Europe.”
“Not only were goods not necessarily available at the Soviet shops, but entire categories of stores simply did not exist. For example, there were no shopping malls with brand-name stores, because there were no brands; all clothing was made by the state, with minimal differences among the available models. There were no craft stores, no car dealerships, and no home improvement stores.” (reminded me of this)
“In Northern Eurasia or the former Soviet Union (FSU), there are 15 countries in four groups: the Baltic states; Russia, Belarus, Ukraine, and Moldova; the three states in the trans-Caucasus; and the five states of Central Asia. Russia is presently divided into seven regions, distinguished on the basis of political units.” Here’s a brief overview from the book, click to view full size:
[Again an illustration of why I don’t always trust the author’s numbers: The population figures here are completely off, as a lot of people seem to have been left out. If you add all the population figures they only add up to 84 million, even though the country has more than 140 million inhabitants. There is no explanation in the text for why these numbers don’t add up. My motivation for including the table above both derived from my desire to once again illustrate this aspect and from the fact that it was easier to add the table than it would have been to list the Federal districts myself.] […]
“The Russian Caucasus is included in the South federal district, which occupies 600,000 km2 and contains 23 million people in 13 subjects of federation […] the South district of Russia is the second most densely populated territory after the Central district, with an average density of 40/km2. It is also the least urbanized region, with only 58% of its population living in cities. It leads the country in fertility […] it is also the poorest region among the seven federal districts, with only half of Russia’s average gross regional product (GRP) per capita. […] The poorest three republics in Russia are war-torn Chechnya (GRP unknown) and its neighbors Ingushetiya (about 15% of the national average) and Dagestan (about one-third of the national average). These are also the areas with the highest unemployment (24%), highest poverty rate, and highest fertility […] Chechnya is years away from being a prosperous and stable society, and this is one area in Russia where travel is not advisable.”
“The Ural Mountains are a treasure trove of resources: coal, iron ore, manganese, titanium, chromium, gold, copper, nickel, vanadium, marble, and many other minerals. This is the richest area in all of Russia with respect to nonferrous metals and gemstones. Over 1,000 minerals are found in the Urals […] Now that Tyumen Oblast and the two autonomous okrugs are included in the Urals district, the region has also become by far the richest area in Russia with respect to petroleum and natural gas, accounting for over 70% of all Russia’s oil and more than 80% of its natural gas reserves. […] The oil and natural gas fields of what was then the West Siberia economic region were discovered in the 1960s and developed in the 1970s. In 1965 this area produced only 1 million metric tonnes (mmt) of petroleum, but by 1985 it was […] 400 mmt […] The production of oil in this area dropped dramatically in the 1990s because of the economic downturn, to about 200 mmt per year in 1995, but has since risen to about 320 mmt. This number is unlikely to increase farther, because the oil fields are rapidly being depleted.”
“Siberia is pivotal to Russia’s economic might. It is part of Asiatic Russia and is usually defined as [I thought this choice of words was problematic. See the wiki] the land east of the Urals and west of the Lena River, sometimes including the entire watershed of the Lena. Thus the territory west of Siberia is European Russia, and the land east of it is the Far East, also called the Russian Pacific. […] Siberia thus defined (5.1 million km2) is just a little smaller than the largest (Far East) federal district, and is bigger than the European Union (EU) in size. Although it accounts for about one third of Russia’s territory, it has only 20 million residents, giving it an average population density of only 3.9 people/km2. […] It has few people, plenty of natural resources, and a very cold continental climate. Like the rest of Russia, Siberia is losing population fast […] The overall decline is about –0.6% per year, among the fastest in Russia.” […]
“The [Russian Far East] has merely 6.7 million residents [spread out over 6.2 million km2], giving it a population density of 1.1/km2— the lowest average density in Russia, and only one-third of Canada’s density. To put it another way, this huge region is settled by only about half as many people as live in Moscow. […] With respect to economic development, the southern part of the region along the Trans-Siberian Railroad is more or less contiguously settled. In the north, there are three isolated clusters of development (around Yakutsk, Magadan, and Petropavlovsk), with virtually untouched wilderness in between. […] The Far East has lost about 1 million people since 1991.”
“The history of Ukraine’s statehood is a long and convoluted one, but essentially centers on internal struggles between pro-Russian and pro-Polish groups and on its emerging nationalism since the mid-18th century, with perpetually shifting affinities and borders. Areas of western Ukraine have seen hundreds of border adjustments in the past five centuries […] Ukraine in this sense is a classic example of a political transition zone in perpetual search of an identity. Post-Soviet Ukraine remains in the same position today” […]
“Close to a million Moldovans have left the country for employment in the construction, retail, food, and textile industries of Russia, Ukraine, Turkey, Italy, and France. [The current population of the country amounts to ~4 million people…]
“Uzbekistan’s leading export is not oil [like Kazakhstan], but cotton; its major industry is not machine building [-ll-], but textiles. It does have limited natural gas supplies, but very little petroleum. In short, it has relatively little to offer to the world […] Uzbekistan has some of the worst corruption in the world as measured by Transparency International, and it also has one of the most brutal and least transparent judicial systems. In particular, opposition journalists are persecuted and sometimes disappear without a trace. […] Kyrgyzstan is another struggling economy in the region. Although it was the first Central Asian state to launch market reforms and political democratization in the early 1990s, it soon fell out of pace with Kazakhstan and Russia because of internal political tensions. […] a bloody revolt […] deepening economic crisis […] pervasive corruption […] Tajikistan is the least developed, poorest, and most mountainous country in the FSU. […] a bitter civil war […] Islamist movements […] an increasingly vocal Muslim population […] unresolved border disputes […] frequent border closures […] Turkmenistan is the most closed society of Central Asia. Its development was severely hampered by 15 years of […] autocratic rule […] Its economy […] is one of the least privatized in the FSU, with about 70% of all assets still state owned. […] Central Asia remains one of the remotest areas of the world, far away from the economic powerhouses of Asia, Europe, or North America, and is entirely landlocked.” [Sounds like a great place to visit!]
I’ve now read roughly two-thirds of the book so I figured I might as well post another post about the book, even though I’m not actually particularly impressed with the stuff I’ve read since the last post. My current goodreads rating is now much closer to two stars than three. Topics which I’ve read about since the last post include: The Geopolitical Position of Russia in the World (chapter 9); Demographics and Population Distribution (chapter 10); Cities and Villages (chapter 11); Social issues – Health, Wealth, Poverty, and Crime (chapter 12); Cultures and Languages (chapter 13); Religion, Diet, and Dress (chapter 14); Education, Arts, Sciences, and Sports (chapter 15); Tourism (chapter 16); Oil, Gas, and Other Energy Sources (chapter 17, the first in Part IV, about economics); and Heavy Industry and the Military Complex (chapter 18).
The author applies a data-centered approach most of the time, and I love that! …which makes it harder for me to be critical of the stuff than it otherwise might have been. However critical I must be, and some chapters are much better than others. In one specific chapter he includes numbers which anyone with two brain-cells can tell are complete bullshit, without adding many critical remarks – according to the crime per capita estimates provided in that chapter, Russia’s crime/capita numbers are less than one-fourth of those of the UK. Yeah.. On a related note, an implicit assumption often rearing its ugly head in the text is that the economic data provided towards the end of the Soviet Era accurately reflected economic conditions. Stuff like that – numbers and the problem of how to interpret them and when in particular to be cautious – cause a few problems along the way. Even (semi-?)valid numbers and estimates are not always put into the proper context, so for example 2002 numbers and 2009 numbers (or numbers from the early 90es and numbers from the 2000s) are given in consecutive paragraphs without attention to the problem that these numbers may not be comparable. I’m not sure the author knows what a standard deviation is, so I am not sure this is the kind of person you want writing a book with a lot of data. He’s far from always uncritical, this must be said, but there’s still a trust issue here for me to deal with in that I often don’t think he’s nearly as skeptical and precise as he ought to be; he draws conclusions not fully supported by the data he uses to support the conclusions in question more than a few times. It should be mentioned that at least in part the trust problem arises due to the scope of the book; as can be inferred from the topics listed above nobody can claim to be an expert on all of this stuff, so you need to take some things on faith. But the problem is surely aggravated by some of the more ‘soft’, not-too-data centered chapters, where he’s just in my view way too uncritical of Soviet material (/propaganda) and seem to try to make Soviet life out to be better than you’d conclude that it had been if you were to just judge by the numbers he provides himself and not ignoring obvious less-than-flattering interpretations. Here’s an example of the kind of stuff I find problematic:
“By and large, the [health] care was decent. A Soviet worker who came down with flu, for example, just needed to dial the local clinic’s phone in the morning and stay in bed; the physician on call would come and visit the worker at home, usually later that same day. Physicians were accustomed to spending about half of their workday making house calls.”
My first thought: F..¤#$£ inefficient as hell, and probably hellishly expensive! Here’s a related observation:
“The Soviet Union also had one of the longest average hospital stays in the world, because home care was viewed as inherently inferior, while hospital beds were free. A typical hospitalization would last for 2–3 weeks, and frequently over a month.”
Given this kind of information, it really should be no surprise that:
“By the end of the Soviet period, the U.S.S.R. had the highest ratio of doctors to patients in the world”.
But here’s the thing – the word ‘inefficient’ isn’t mentioned once in that chapter. The lots of doctors/capita is interpreted as a great thing, not a serious problem indicating severe inefficiencies in health care delivery. The same chapter started out with some pure gold which really set the pace for the rest of that chapter:
“The Soviet Union had what was arguably one of the best health care systems in the world. Surprised? If you have seen Michael Moore’s film Sicko, you may not be: Moore depicts Cuba as an example of a socialist state with a free, universal health care system that has produced impressive results. This is something many Americans and even some Europeans have a hard time imagining.”
(Naturally) I was very close to stopping reading altogether there – ‘arguably’ indeed. He’s talking about a country where the life expectancy was below 70 years (in 1990), far from the top 10 percent of the world (but ‘within the best third’, which is the only observation regarding the relative performance he includes..). Instead of stopping reading there I decided instead to adjust my expectations downwards and to just start paying a lot more attention to the raw data (and where it was coming from) and a lot less attention to the author’s observations and interpretations of said data. I think this was a good decision. I don’t think the author always understands what he’s talking about although I’m sure he does sometimes. What I’m also sure of is that his standards of evidence are different from mine.
Another illustrative quote and some related observations from chapter 12 below:
“The health care system went through a major restructuring on short notice [in the 90s], with support from the state abruptly declining to a fraction of its former amount due to rising inflation rates and to unwillingness or inability to pay more.”
In light of the data above it probably wouldn’t be outrageous to assume that said ‘unwillingness’ was presumably at least a little related to the fact that the system which was set up was inefficient and provided far from impressive health outcomes. Of course there were other reasons as well, relating to political economy stuff and so on. But he never comes close to even saying this. Even weirder, he talks about “fewer doctors” being one explanation for the worsened health outcomes during the post-Soviet period on the very same page that he provides data making it very clear that the number of doctors was not the problem. Judging from the data he provides himself on that page, the raw number of physicians in Russia was pretty much identical in 1990 and 2000 (though it was a little lower in 1995), and it was notably higher than both years in 2005 (and so the number of physicians/1000 people was if anything higher in 2000 than in 1990 judging from that data, as the nation underwent a significant population decline during the period – something he documents himself in the book and talks about in some detail).
Obvious conclusions from the data are not always drawn, and questionable conclusions from the same data sometimes seem to be. But there’s a lot of data and there’s a lot of good stuff as well, and so I felt I should add some data from the chapters mentioned above below. The book is a mixed bag at this point. I’m learning a lot, but I feel like I have to be a lot more cautious about trusting the information provided than I usually need to be when I’m reading a book. I have never felt any need to worry about the author lying to me about how kidneys work while reading McPhee et al, or about the author using very questionable data to draw conclusions without pointing out that there’s some uncertainty here. Blinnikov isn’t uncritical, but compared to some of the publications I have made a habit of reading at this point reading this book occasionally feels a bit like reading an elephant’s account of his brother’s trip to the porcelain shop – this stuff seems too close to politics for comfort, and the author isn’t as careful and unbiased in his coverage as I’d have liked. Anway, quotes below (my bold):
“Since 1992 […] Russia has been steadily losing people to the tune of 500,000 or so per year, and this has become a firmly established phenomenon. […] the average Russian man is expected to live only 61 years, and the average Russian woman 74. The reasons for this discrepancy are complex, but the factor most commonly cited is the high rate of alcoholism among Russian men […] only about 100,000 legal migrants come to Russia each year, while about 500,000 people are lost per year due to the fertility–mortality imbalance. […] About 16% of the Russian population has completed a college education (vs. 28% in the United States) […] Only three-quarters of all households in Russia have running water, while only 71% have flush toilets. […] 82% of urban dwellers have central heat provided by a power plant, while 50% of rural dwellers depend on wood-burning brick ovens or on coal boilers.” […]
“sanitary norms set in 1922 dictated the size of the minimal livable space at 9 m2 […] per person. This remained unchanged over the entire Soviet period and without respect to local needs […] As illustrated in Bater (1996), the actual space available toward the end of the U.S.S.R. ranged from 13 m2 in Estonia to 7 m2 in Turkmenistan, with 10 m2 being the national average. […] On average, one person has 19 m2 in which to live [today]. […]
The level of urbanization rose through the 20th century: In 1900 almost 80% of the Russian Empire consisted of peasants; in 1950 the U.S.S.R. had an urbanization level of 52%; in 1970 it was 62%; and since 1990 Russia’s level has been 74%. […] Even by 2005, only 7% of the total agricultural output in Russia was produced on private farms. The kolkhozy were restructured into joint-stock cooperative ventures, but their management practices remained essentially unchanged. Although the workers collectively own each enterprise now, the head manager typically has the controlling vote, and the enterprise continues to be inefficient. In 2005, the output of the Russian agricultural sector was 40% less than in 1990; the sown acreage had decreased at least 30%; and the number of cattle had decreased by 46%. Russia today imports a little less than half of the food it needs to feed its own population—one of the highest rates of foreign-food dependency in the world” [at least he commented upon the inefficiency here, otherwise I would have. I’ll add here that it’s likely that the 1990 numbers can’t be trusted, so although this is not the impression you get from reading the book the extent to which this is a ‘true decline’ is probably still to some extent an open question.]
“there were 31,800 murders and attempted murders in Russia in 2000, versus only 22,200 in 2007. The majority of contract killings were perpetrated by the mob against prominent businessmen and journalists in the mid-1990s (Volkov, 1999); such attacks are now rare. Most domestic homicides happen between spouses and involve alcohol.” […]
“Russia had over 1 million prisoners in 1995, and about 872,000 10 years later. Seven percent of the inmates in 2005 were women, and about 17% were repeat offenders.” […]
“The Transparency International organization’s global Corruption Perception Index for 2007 ranked Russia very much near the bottom, in 143rd place out of 179 countries—right above Togo” […]
“in Russia about 80% of people have been baptized in the Orthodox faith, but only 44% profess belief in a God, and merely 12% attend church on a monthly basis.” […] 22% are agnostics who are not sure whether there is a God, and about 22% call themselves atheists. By comparison, in the United States about 75% of people consider themselves Christians, and about 40% attend a religious ceremony at least once a month.” […] About 25% [of Russians] embrace a vague syncretic worldview that recognizes the existence of spirits, karma, and reincarnation, and affirms divination, talismans, tarot, and yoga as legitimate practices, while simultaneously professing adherence to the Russian Orthodox Church (which vehemently condemns all of these things).” […]
“many universities are located in Moscow and St. Petersburg: In 2000, 171 (19%) were found in Moscow and 77 (8%) in St. Petersburg, with a total of 914 colleges and universities, public and private, in the entire country.” […]
In the late 1970s, over 150 full-length movies were made in the U.S.S.R. per year. Russian film production practically ceased in 1992–1996 due to lack of funding, with merely 20–30 produced per year; it began again in the mid-1990s with Hollywood-wannabe gangster flicks sponsored by shady businessmen. […] By comparison, Hollywood produced over 400 movies in 1996. […] About 120 new movies come out every year in Russia now […] The number of modern multiplex cinemas in Russia went up from 8 in 1995 to 185 in 2001″ […]
“In real terms (after adjustment for inflation), the salary of a PhD-level senior researcher decreased by a factor of 10 between 1989 and 1999, whereas many other professions supported by state budgets did not see a comparable decline. Thus, if in the late Soviet period a Moscow city bus driver had a salary slightly lower than that of a physics professor, by the end of the Yeltsin period the bus driver was making five to seven times more than the professor. The result, predictably, was a drastic reduction in the number of scientists. […]
By the end of the Soviet period, about 30 million people per year took advantage of resorts and sanatoria in the Russian Federation alone, not counting the other republics. Most were domestic tourists. The number of organized tourists in Russia abruptly plunged to a mere 8 million per year following the economic collapse of 1991, however. […] In 2008 36.5 million Russians crossed the nation’s borders; 11 million of these crossings were for tourist trips, and 2 million business trips. […] Russia sends five times as many tourists abroad as it receives.” […]
“The U.S.S.R. was the largest producer of oil and natural gas in the world by the early 1980s, surpassing the United States and Saudi Arabia with production from the giant fields in western Siberia […] [Russia] remains the world leader in natural gas production and is currently second in petroleum production […] The share of [the energy] sector went up from only 12% of the total gross domestic product (GDP) in 1991 to 31% in 2002. […] The distribution of energy production in Russia is very uneven. The oil and gas fields in western Siberia produce 69% of all the petroleum and 91% of all the natural gas […] In 2007, 4 companies in the top 20 in Russia were engaged in metal production, heavy machinery production, or other heavy manufacturing” […]
“Perhaps the heaviest legacy […] of the Soviet economy was its military–industrial complex, called in Russian the […] VPK. Its presence was pervasive: Entire cities were built around steel mills, aluminum smelters, tank manufacturers, chemical factories, or nuclear weapons facilities. Over 50% of the country’s industrial output in the 1980s was generated by this sector. […] According
to some estimates, in the late Soviet period about one-quarter of all industrial workers in the country (5 million people) were employed by the VPK, including almost 1 million researchers at over 2,000 institutes and factories, and the sector accounted for almost 20% of the country’s gross domestic product (GDP). Hundreds of research labs, institutes, and factories were scattered over a few dozen small and medium-sized cities that did not appear on any maps […] They were largely declassified, renamed, and finally put on maps by 2000. Most remain closed to casual visitors, however, and even Russia’s residents (let alone foreigners) require special permits to enter.”
I’m currently reading this book, and I like it so far. The book has stuff on physical geography (relief and hydrography, climate, biomes, and environmental stuff), the history and politics of the area/region, cultural and social geography (demographics and population distribution/structure, cultural stuff including religion and language etc.), some stuff about economic factors of interest, as well as some chapters providing more details about the specific regions towards the end of the book. The book mostly deals with Russia, but there’s stuff about other post-Soviet states as well.
Reading it feels a little like reading a very detailed wikipedia article (~450 pages long) and I must admit that I’ve probably lost a little more respect for humanities students along the way while reading this; again it’s not that the book is bad, far from it, but I feel pretty sure you don’t add much value to an education by including courses such as ones dealing with material like this. The ability of a university student to read and understand a book like this will tell you very little about their abilities as nine out of ten high schoolers technically ought be able to do that without problems. Also, reading the book will take a normal person at most a couple of days, so if an employer has a position that really requires one to know stuff like what’s in the book I don’t see how it could ever be a big deal if the applicant doesn’t – the situation is a bit different if the individual doesn’t know multi-variable calculus and that is a requirement. A depressing point is that even though this is an easy read, a course dealing with the stuff in this book is probably potentially a lot more useful than are many other courses those students might have taken instead (art history, Hebrew studies, theatre research, Indology (“In this course, students will be introduced to the basic Indian systems of Yoga, both in its ancient texts and practices and in its modern practice and will pay particular attention to the development of Yoga in Denmark in the 20th century.”),…) (all examples in the previous parenthesis taken directly from the University of Copenhagen course catalogue).
This is not the first book about Russia/USSR I read, but most of the stuff I’ve read so far has only dealt with the history of the country/region; this book adds a lot of stuff because it deals with a lot of other things as well. I think he actually handles the history part quite well, but of course it’s not a very detailed account.
Below I’ve added some observations from the first third of the book or so:
“Russia has over 120,000 rivers over 10 km long, which collectively create 2.3 million km of waterways. Fifty-four percent of their flow enters the Arctic Ocean, with only 15% entering the Pacific. Another 8% of water flows to the Atlantic Ocean via the Black and Baltic Seas, and 23% to the Aral-Caspian interior basin with no outlet to the ocean. […] The [Volga] basin occupies only 8% of the country, but is home to 40% of its population. […] The Volga loses 7% of its annual flow to human consumption. Its flow has been reduced by about 20% in the last 100 years. The Siberian rivers primarily flow north to the Arctic Ocean, with the exception of the Amur, which flows east into the Pacific.”
“Climatologists generally consider the following factors important in producing a particular climate type: Latitude, […] Elevation above sea level […] Proximity to the ocean […] Presence of ocean currents […] Prevalent wind direction […] Position relative to a mountain range […] Cloud cover and dust […] Human infrastructure.” [there are further details in the book about how these factors impact the climate of the FSU, in broad terms, but I won’t go into the details here…]
“Only a fraction of the Russian population (8%) lives near a seacoast […] Compare this to the United States, where two-thirds of all people live within 200 km of a coast” […] [I’ve previously blogged this map, and it’s pretty handy if you want to know more about the details of where people live – more than three out of four Russians live in the European part of the country, and so Siberia is relatively empty. If you want to know more about the population density of the US, I’ve blogged that stuff before as well here.]
“The biomes of Northern Eurasia are similar to those of Europe or North America: tundra in the north; taiga and deciduous forests in the middle; steppe and desert in the south. The extreme south has deserts or subtropical Mediterranean-like shrub vegetation. […] For millions of years, Northern Eurasia and North America were connected to each other […] This resulted in an array of animals and plants that are shared by these two regions. […] The flora and fauna of India (which is on the same continent as Russia), on the other hand, are completely dissimilar to Northern Eurasia’s; they are more like Africa’s. […] Many animal genera or even species are identical in North America and Northern Eurasia […] If an exact match is missing, there is usually a pretty good substitute/vicariant species” […]
“The overall diversity of the plants and animals in Russia is not great, because of its northern location. For example, there are 11,000 species of vascular plants, 30 of amphibians, 75 of reptiles, 730 of birds, and 320 of mammals in the Russian Federation. By comparison, the United States (a more southern country half the size of Russia) has 19,000 species of vascular plants, 260 of amphibians, 360 of reptiles, 650 of birds, and 360 of mammals.”
“In Northern Eurasia, the taiga is a huge biome (covering over half of all Russia) […] The boreal forests of Eurasia make up about 21% of the world’s total tree cover on 5.3 million km2 […] Soils of the taiga are poor in nutrients and acidic […] Steppe forms in areas with moisture deficit that precludes tree growth. Although steppes are on average warmer than most of the forested biomes to the north, it is really the lack of water that determines the tree boundary. […] The classic Eurasian steppe is treeless […] There are few places where virgin steppe can still be seen. As in North America, over 99% of this biome in Eurasia was plowed under in the 19th and 20th centuries.” […]
“With its spacious, rainless interior, Eurasia is home to the northernmost deserts in the world. […] The main deserts in North America are found at latitudes between 25º and 35ºN, whereas in Eurasia they occur between 38º and 44ºN. […] Altogether, the Central Asian deserts occupy 3.5 million km2 — an area as large as Saudi Arabia and Iran combined.” […]
“The exact sequence and elevation of the vegetation belts [of a mountain range] are determined by the direction of the slope (north-facing slopes are always colder and have a lower treeline) and by local climatic and biological factors. The treeline, for example, occurs at 300 m in the polar Urals and the Khibins in the Kola Peninsula in the Arctic, but at 2,000 m in the Carpathian mountains, 2,500 m in the Caucasus, and above 3,000 m in much of Central Asia” […]
“The U.S.S.R. was one of the largest polluters of air on the planet, and Russia still is today […] Between 2000 and 2005, an average big city in Russia saw a 30% increase in air pollutants. […] Although there has been some increase in production since 2000, Russia generally pollutes less today than it did 20 years ago. However, a major new contributor to air pollution is car exhaust. Moscow, for example, had only 500,000 automobiles in the late 1980s. Today there are about 4 million cars and trucks in the city […] In 2007, Russia as a whole had 195 passenger cars per 1,000 people […] In the late Soviet period, Russia had only 50 cars per 1,000 people.” […]
“Every spring, Moscow faucets run with brownish-tinged water smelling faintly of manure; it enters the Moscow water supply system from agricultural fields upstream.” […]
“At the end of the Soviet period, the U.S.S.R. boasted over 40 [nuclear] reactors at 15 sites (today Russia has 31 reactors at 10 operating plants), not counting a few dozen small research reactors at scientific institutes. By comparison, the United States has slightly over 100 commercial reactors, Japan has 63, and France has 59. […] Nuclear pollution is unevenly concentrated in the FSU, and much of the information about former accidents is still classified. […] the highest levels of such pollution are found in and around Chernobyl (northern Ukraine, southeastern Belarus, and southwestern Russia); in the Novaya Zemlya islands and Semey, Kazakhstan; and at the production facilities in Sarov, Kyshtym, and a few cities near Krasnoyarsk. Furthermore, there are several submarine staging areas where offshore dumping of nuclear waste took place in the Far East and off the Kola Peninsula. Beyond these areas, there are a smattering of sites polluted by radiation—for example, in European Russia in Ivanovo and Perm Oblasts close to Moscow, as well as in the Komi Republic […] Unlike in the United States, information on the actual location of [toxic waste] sites in Russia or other post-Soviet states is not readily available. […] These sites number in the hundreds, if not in the thousands” […]
“The eventual rise of Moscow to the preeminent position among Russian cities had to do with some pure luck and the political talents of the early princes there, but it also owed a good deal to geography: Originally an insignificant wooden fort (established in 1147), it was located at a perfect midpoint between the sources of the Dnieper and the Volga. It was situated on a tributary (the Moscow) of a tributary (the Oka) of the Volga—not on the main water artery, but close enough to Smolensk (100 km to the west in the Dnieper basin) that the Dnieper headwaters could be easily reached. In the age before highways, all transportation of goods took place by rivers. […] The main exploratory push and the expansion of the Russian frontier across Siberia came in the mid-17th century with the new Romanov dynasty […] in less than one century (from 1580 to 1650), the Russian state was extended from Tyumen in western Siberia all the way to Okhotsk on the Pacific Coast! Of course, this vast area was not fully settled by any means, but about two dozen forts were built at strategic locations. […] Every major Siberian city that was established during this period is situated on a big river. The movement was somewhat analogous to the opening of the American West, except that it was driven less by farmers and more by fur traders […] The early settlers were a highly mobile force, not interested in farming or other sedentary pursuits. […] In comparison, the movement to the west, north, and south was much slower, because more developed states and tribes there made rapid expansion impossible.”
“By the start of World War I in 1914, the Russian Empire included most of Ukraine, Belarus, and Moldova (Bessarabia); Finland, Armenia, Azerbaijan, and Georgia; the Central Asian states (Russian Turkestan); Lithuania, Latvia, and Estonia; significant portions of Poland; and some Turkish cities in the Balkans. Only about 45% of its population consisted of ethnic Russians. The total population was 125 million in 1897, the time of the first Russian census. Alaska was sold in 1867 to the United States […] After a bitter civil war […] in 1917–1922 […] U.S.S.R. […] reconstituted itself within the former borders of the Russian Empire, with the
exceptions of Finland, Poland, the Baltic states, much of western Ukraine and Belarus, and Moldova. This may be explained by not only political and cultural but also geographic factors. […] northern Eurasia forms a large, easily-defensible area bounded by some of the highest mountains in the world on the south, by the frozen Arctic Ocean on the north, and by the Pacific Ocean on the east. It is much more open and vulnerable in the west, and this is precisely where all the major wars were fought. Once these boundaries were reclaimed by the Soviets in the 1920s, there was relatively little change for 70 years.” […]
“It is important to understand that the Russian Federation today is not merely a smaller U.S.S.R. It is qualitatively different from either the Russian Empire or the U.S.S.R. The latter two had fewer than 50% ethnic Russians and had external borders with nations of very different cultures (e.g., Hungary, Turkey, Iran, Afghanistan), whereas Russia is over 80% ethnically Russian and mainly borders other Russian-speaking territories in Ukraine, Belarus, or Kazakhstan […] Although Russia remains the biggest state in the world by area, it is half of its original size and is now only 9th in terms of population” […]
“The average Soviet citizen had less than 20% of the square footage available to the average American, and perhaps about 40% of the level available to the average European. In addition, over half of the country’s population had no access to indoor plumbing. […] In the late 1980s, over 60% of the Soviet Union’s industrial output was in the form of heavy machinery (tractors, turbines, engines, etc.), thought to be necessary for the production of better goods and weapons. Less than 30% was accounted for by consumer goods.” […]
“The important geographic outcome of 1991 was that a single, unitary state, the U.S.S.R., with its capital in Moscow, was replaced on the world maps by 15 newly independent states (NIS), each with its own capital, president, parliament, and so on. Twelve of these would soon form the Commonwealth of Independent States (CIS), a military and economic alliance; three others, the Baltics, would be admitted to the North Atlantic Treaty Organization (NATO) and the European Union (EU) in 2004. From 1991 on, the political and economic changes in each NIS were decoupled to a large extent from those in others, and proceeded along individualized trajectories. There were very rapid reforms in the Baltic states, almost no reforms in Uzbekistan and Belarus, and intermediate levels of reforms in others.”
“The aim of this article is to integrate empirical research on divorce risks in Europe and to explain the variation of empirical findings between European countries by the different levels of modernization and differences in the strength of marriage norms. We focus on the effects of premarital cohabitation, the presence of children, and the experience with parental divorce on marital stability. More than 260 studies on divorce risks could be identified, and 120 were used for further meta-analytical examinations. We show that there is considerable heterogeneity of divorce risks within as well as between countries. Explaining the variation of effect sizes between European countries, it could be shown that in countries where more rigid marriage norms prevail cohabitation has a stronger effect on marital stability than in countries where marriage norms are weaker. Furthermore, the lower the divorce barriers are, the weaker is the association between the parental divorce and the divorce risk of the offspring.”
Some data and results from the paper (click tables and figures to see them in a higher resolution):
The table shows the estimated effect sizes of premarital cohabitation on the divorce risk in various European countries; a positive effect size indicates a higher likelihood of divorce among couples who lived together before they got married, whereas a negative effect size indicates a smaller divorce risk for couples who did not cohabitate before they got married. They note in the paper that, “The European overall effect indicates a positive relationship between cohabitation and the risk of divorce, that is, cohabiting couples have a 33 per cent higher risk to divorce than couples who do not share a common household before marriage.” However the effecs are highly heterogenous across countries, and more specifically they find that: “In countries in which traditional marriage norms are strongly institutionalized, cohabitation has a stronger effect than in countries in which marriage norms are weaker.” The institutional framework is important. The Q-statistic is a heterogeneity-measure – read the paper if you want the details..
What about children? Here’s a brief summary:
Effect sizes are almost universally negative (children = smaller risk of divorce) and a lot of them are highly significant (more than half of them are significant at the 1% confidence level). As they note, “The presence of children strongly decreases the risk of divorce”. Note that the effect sizes vary but tend to be large; in the Netherlands, the country with the largest effect size, married couples with children are 70% less likely to divorce than are couples without children. The average estimated effect size is 50% so this is a huge effect. However I would be cautious about making a lot of inferences based on this finding without at the very least having a closer look at the studies on which these results are based; for example it’s unclear if they have taken into account that there may be unobserved heterogeneity problems playing a role when comparing married couples with- and without children here; lots of marriages break up early on (using Danish data I have previously estimated that once the marriage has lasted 9 years, half of the total divorce risk the Danish couple confronted ex ante will basically have been accounted for; i.e. the total risk that you’ll divorce your partner during the first 9 years is as big as is the risk that you’ll do it at any point after the 9th year of marriage – see the last figure in this post), and it does not seem unlikely e.g. that sampled marriages involving children may, ceteris paribus, have lasted a longer time on average than have sampled marriages without children (most European couples get married before they have children so the likelihood that a couple will have children is positively correlated with the marriage duration), meaning that these marriages were less likely to get broken up, regardless of the children. If they conditioned on marriage duration when calculating these effects this particular problem is dealt with, but I don’t know if they did that (and I’m not going to go through all those studies in order to find out..) and there may be a lot of other ways in which marriages with and without children differ; differences that may also relate to divorce probability (education, income, labour market status, …). Note that the fact that the studies included in the meta-study are longitudinal studies does not on its own solve the potential ‘duration problem’ (/selection problem); you can easily follow two couples for the same amount of time and still have radically different (ex ante) divorce likelihoods – and comparing unadjusted (group?) hazard rates and making conclusions based on those seems problematic if you have selection issues like these. Researchers aren’t stupid, so the studies here may all have taken care of this particular potential problem. But I’m sure there are problems they haven’t handled. Caution is warranted – part of the estimated ‘children effect’ is likely not to go through the children at all.
How about the parents? How does the fact that your parents got divorced impact your own likelihood of divorce?
“Nearly all the reported effect sizes indicate positive associations between the stability of the parental marriage and the stability of children’s marriage”. There are huge cross-country differences – in Italy an individual whose parents got divorced is almost three times as likely to get divorced him/herself as is an individual whose parents did not divorce, whereas the risk increase in Poland amounts to only (a statistically insignificant) 14%.
Lastly, I’ll note that:
“No empirical support was found for any of our hypotheses which link the level of modernization to the risk of divorce. A least with respect to the divorce risk, we considered the level of socioeconomic development not to be an important macro-variable. Also, we could not find any significant relationships between the strength of divorce barriers and the effect of children on marital stability.”
I would not have expected these results if you’d asked me beforehand. Then again e.g. the differences in socioeconomic development among the countries included here are not that big, so it may just be a power issue.
“Thirty-five percent of U.S. adults say that at one time or another they have gone online specifically to try to figure out what medical condition they or someone else might have.
These findings come from a national survey by the Pew Research Center’s Internet & American Life Project. Throughout this report, we call those who searched for answers on the internet “online diagnosers”.
When asked if the information found online led them to think they needed the attention of a medical professional, 46% of online diagnosers say that was the case. Thirty-eight percent of online diagnosers say it was something they could take care of at home and 11% say it was both or in-between.
When we asked respondents about the accuracy of their initial diagnosis, they reported:
41% of online diagnosers say a medical professional confirmed their diagnosis. An additional 2% say a medical professional partially confirmed it.
35% say they did not visit a clinician to get a professional opinion.
18% say they consulted a medical professional and the clinician either did not agree or offered a different opinion about the condition.
1% say their conversation with a clinician was inconclusive.
Women are more likely than men to go online to figure out a possible diagnosis. Other groups that have a high likelihood of doing so include younger people, white adults, those who live in households earning $75,000 or more, and those with a college degree or advanced degrees.”
The quotes above are from a Pew report, Health Online 2013, published earlier this year. Below I’ve added some more data from the report, as well as a few comments. You can click the tables to view them in a higher resolution.
“Looking more broadly at the online landscape, 72% of internet users say they looked online for health information of one kind or another within the past year. […] 77% of online health seekers say they began at a search engine such as Google, Bing, or Yahoo. Another 13% say they began at a site that specializes in health information, like WebMD. Just 2% say they started their research at a more general site like Wikipedia […] 39% of online health seekers say they looked for information related to their own situation. Another 39% say they looked for information related to someone else’s health or medical situation. […] As of September 2012, 81% of U.S. adults use the internet and, of those, 72% say they have looked online for health information in the past year. [Incidentally, according to this Pew report, the number of online Americans is actually 85%, but it’s in that neighbourhood… Note that 72% of 81% is just 58% (they say 59% in the report later, probably due to rounding) – so almost half of all Americans don’t look for health information online. That’s a lot of people.] […]
Females are more likely to be online diagnosers, as are young people, whites, rich people, and college-educated individuals (when we compare the females with males, the young people with the old, the white people with the non-white, etc. See also the remarks in the update..). Note that education is basically a step-function here; the more education you get, all else equal the more likely you are to try to diagnose yourself online. Note also that some of these differences are really huge; roughly 10 percent of people without a HS diploma answered that they’d looked online to diagnose a condition during the last year, whereas half of all college-educated individuals answered in the affirmative.
A potentially important thing to have in mind when comparing the numbers for insured and uninsured individuals is that internet usage and health insurance status probably covary; I believe it’s likely that uninsured people are also less likely to use the internet. Low-income individuals with short educations are much less likely to be online, independent of age (see the link above).
“Twenty-six percent of internet users who look online for health information say they have been asked to pay for access to something they wanted to see online. […] Of those who have been asked to pay, just 2% say they did so. [I was very surprised that that number was strictly larger than zero…] Fully 83% of those who hit a pay wall say they tried to find the same information somewhere else. Thirteen percent of those who hit a pay wall say they just gave up. […] Respondents living in lower-income households were significantly more likely than their wealthier counterparts to say they gave up at that point. Wealthier respondents were the likeliest group to say they tried to find the same information elsewhere.”
Do remember when looking at the numbers above that health status and education are related variables; lower educated people are more likely to be in poorer health than are higher educated people on average, in part because of lifestyle choices (I’ve written about these differences before – see e.g. this post (and note that there’s a lot of stuff in those links – and that I have a lot more links for you if you don’t find them satisfactory, as I’ve done academic work in this field and am quite familiar with the literature on the links between education and health.)). Yet even when conditioning on online status (low-educated individuals are less likely to be online), individuals with low educations are still, all other things being equal, much less likely than are the college educated to look online for many types of health information.
Update: To illustrate how much trouble you might get into if you don’t have in mind the differences in internet adoption rates across social strata, I decided to add a few more numbers. The numbers are from the Offline Adults report, to which I also link above:
People without a high school diploma are roughly 10 times as likely not to use the internet as are people with a college degree; 41% of people without a HS diploma don’t use the internet – 4% of college-educated don’t. For individuals with an income below $30k, one in four don’t use the internet, whereas roughly 5% of those with an income north of $50k don’t. It’s very safe to say that not all subgroups included in some of the specific types of response data above are equally representative of the groups from which they are derived. Note also that potential drivers of the relevant intragroup differences here may be very important if one were to try to find ways to ‘bridge the information gap’; for example if some of the low-educated individuals who don’t use the internet can’t read, finding ways to provide them with internet access may not make much difference.
I should point out here that based just on the observations above it’s impossible to say anything about the details of what drives these results. It’s not clear e.g. how big a role the age variable plays when it comes to the contribution from income and education; old people on a pension have much lower incomes (but higher net savings) than most people who’re still active in the labour market (link), and older people are also significantly less likely to have college degrees and more likely to not have a high school diploma. The significance tests they report which are meant to indicate whether or not e.g. the results for people with an income of $30-50k are different from the results for people with incomes below $30k don’t take stuff like that into account, they’re just of a ‘let’s ignore everything else and compare the numbers’-kind and so can’t really be trusted. Maybe income doesn’t matter once you’ve taken age and education into account. I’m not saying this is the case, but given the data you can’t say if that’s true or not. Disentangling the ‘pure partial effects’ would be nice, but that’s likely to be a lot harder than it looks; multicollinearity is likely a problem, and some of the correlated regressors display non-linear relationships (e.g. income-age – see the link above). Be careful about which conclusions you draw.
i. I’ve played some good chess over the last few weeks. I’m currently participating in an unrated chess tournament – the format is two games per evening (one with the white pieces and one with the black), with 45 minutes per person per game. The time control means that although the games aren’t rated, they’re at least long enough to be what I’d consider ‘semi-serious’.
Here’s a recent game I played, from that tournament – I was white. It wasn’t without flaws on my part but it was ‘good enough’ as he was basically lost out of the opening. I wasn’t actually sure if 7.Qd4 could be played (this should tell you all you need to know about how much I know about the Pirc…) but I was told after the game that it was playable – my opponent had seen it in a book before, but he’d forgotten how the theory went and so he made a blunder. It was the second game that evening, played shortly after I’d held my opponent, a ca. 2000 FIDE rated player, to a draw in the first game. I mention the first game also because I think it’s quite likely that the outcome of that game played a role in the mistake he made in the second game. The average rating of my opponents so far has been 1908 (I’ve also drawn a 2173 FIDE guy along the way, though the chess in that case was not that great), and I’m at +1 after six games. I’ve beaten FMs before in bullet and blitz, but as mentioned these games are a tad more serious than, say, random 3 minute games online, and this is one of the first times I’ve encountered opponents as strong as this in a ‘semi-serious’ setting. And I’m doing quite well. It probably can’t go on, but I’m enjoying it while it lasts.
ii. An interesting medical lecture about vaccines:
“This paper assesses gender disparities in federal criminal cases. It finds large gender gaps favoring women throughout the sentence length distribution (averaging over 60%), conditional on arrest offense, criminal history, and other pre-charge observables. Female arrestees are also significantly likelier to avoid charges and convictions entirely, and twice as likely to avoid incarceration if convicted. Prior studies have reported much smaller sentence gaps because they have ignored the role of charging, plea-bargaining, and sentencing fact-finding in producing sentences. Most studies control for endogenous severity measures that result from these earlier discretionary processes and use samples that have been winnowed by them. I avoid these problems by using a linked dataset tracing cases from arrest through sentencing. Using decomposition methods, I show that most sentence disparity arises from decisions at the earlier stages, and use the rich data to investigate causal theories for these gender gaps.”
Here’s what she’s trying to figure out: “In short, I ask: do otherwise-similar men and women who are arrested for the same crimes end up with the same punishments, and if not, at what points do their fates diverge?”
Some stuff from the paper:
“The estimated gender disparities are strikingly large, conditional on observables. Most notably, treatment as male is associated with a 63% average increase in sentence length, with substantial unexplained gaps throughout the sentence distribution. These gaps are much larger than those estimated by previous research. This is because, as the sequential decomposition demonstrates, the gender gap in sentences is mostly driven by decisions earlier in the justice process—most importantly sentencing fact-finding, a prosecutor-driven process that other literature has ignored.
But why do these disparities exist? Despite the rich set of covariates, unobservable gender differences are still possible, so I cannot definitively answer the causal question. However, several plausible theories have testable implications, and I take advantage of the unusually rich dataset to explore them. I find substantial support for some theories (particularly accommodation of childcare responsibilities and perceived role differences in group crimes), but that these appear only to partially explain the observed disparities.” […]
“Columns 11-12 of Table 5 show that the gender gap is substantially larger among black than non-black defendants (74% versus 51%). The race-gender interaction adds to our understanding of racial disparity: racial disparities among men significantly favor whites,29 but among women, the race gap in this sample is insignificant (and reversed in sign). The interaction also offers another theory for the gender gap: it might partly reflect a “black male effect”—a special harshness toward black men, who are by far the most incarcerated group in the U.S. […] This theory only goes so far, however — the gender gap even among non-blacks is over 50%, far larger than the race gap among men.”
“Nutritional factors affect blood glucose levels, however there is currently no universal approach to the optimal dietary strategy for diabetes. Different carbohydrate foods have different effects on blood glucose and can be ranked by the overall effect on the blood glucose levels using the so-called glycaemic index. By contributing a gradual supply of glucose to the bloodstream and hence stimulating lower insulin release, low glycaemic index foods, such as lentils, beans and oats, may contribute to improved glycaemic control, compared to high glycaemic index foods, such as white bread. The so-called glycaemic load represents the overall glycaemic effect of the diet and is calculated by multiplying the glycaemic index by the grammes of carbohydrates.
We identified eleven relevant randomised controlled trials, lasting 1 to 12 months, involving 402 participants. Metabolic control (measured by glycated haemoglobin A1c (HbA1c), a long-term measure of blood glucose levels) decreased by 0.5% HbA1c with low glycaemic index diet, which is both statistically and clinically significant. Hypoglycaemic episodes significantly decreased with low glycaemic index diet compared to high glycaemic index diet. No study reported on mortality, morbidity or costs.”
v. I started reading Dinosaurs Past and Present a few days ago. It’s actually a quite short and neat book, but I haven’t gotten very far as other things have gotten in the way. I just noticed that a recently published PlosOne study deals with some of the same topics covered in the book – I haven’t read it yet but if you’re curious you can read the article on Forearm Posture and Mobility in Quadrupedal Dinosaurs here.