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The commenter “m” did some calculations to work out the relative performance of different countries in PISA vs. TIMSS, and in Math vs. Science.

pca-timss-pisa

m writes:

Dimension 1 is overall performance across all 4 (PISA Math, PISA Science, TIMMS Math, TIMMS science). Everything goes up with this dimension. Highest performers: Singapore, Chinese Taipei, Japan. Weakest performers: Turkey, UAE, Malta.

Dimension 2 separates stronger performers on TIMMS vs PISA: strongest performers on TIMMS relative to PISA are in order: Turkey, Korea, Russia, Hungary, United Arab Emirates, while strongest performers on PISA relative to TIMMS are: New Zealand, Canada, Australia, Norway, Italy. The five most balanced countries in the tradeoff are roughly: Slovenia, England, Hong Kong, Japan, USA.

Overperformance in TIMSS relative to PISA can arguably be used as a proxy for schooling quality, since it’s more dependent on academic/curricular skills than on raw intelligence. I am not surprised by the good figures for Korea, Russia, and to a lesser extent, the rest of East Asia and the post-Communist world. However, the UAE and Turkey are surprising.

Dimension 3 separates out Science nations vs Math nations: Most heavily Science vs Math: Slovenia, England, USA, New Zealand, Turkey and most heavily Math vs Science: Hong Kong, Malta, Korea, Italy, Norway.

As much as including TIMMS might be a worse proxy of “IQ” than just PISA, I have included in the above graphic a measure of using the PC1 overall performance score to convert to IQ, based on the assumption that England is 100 and Japan 104.3 as in your PISA conversion. There’s a bit of swing, not too much, compared to PISA alone.

m then extended his analysis to encompass Reading, which is unsurprisingly “less correlated with the other measures”:

pca-reading

as well as to the PIAAC Survey of Adult Skills:

pca-piaac

Comment:

Singapore’s the biggest relative loser when the skills measure is rolled in as well, with the least advantage on PIAAC skills relative to TIMMS / PISA. Most other countries gain compared to the other PCA, as they are more advantaged relative to England and the East Asians on young people’s life skills than they are on young people’s education measures.

 
• Category: Race/Ethnicity • Tags: PISA, Psychometrics, Statistics 
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1. The CEC results

Here they are. The turnout was 32%.

  • Sergey Sobyanin – 51.37%
  • Alexei Navalny – 27.24%
  • Ivan Melnikov – 10.69%
  • Sergey Mitrokhin – 3.51%
  • Mikhail Degtyaryov – 2.86%
  • Nikolai Levichev – 2.79%
  • Invalid ballots – 1.53%

2. Pre-elections opinion polls:

Navalny’s support – among those who indicated a clear preference for one candidate or another – rose from the single digits in June to around 20% on the eve of the elections (Levada, VCIOM, FOM, Synovate Comcon). All the polls – even including the SuperJob poll that only queried active workers, aka excluded pro-Sobyanin pensioners – gave Sobyanin more than 50% in the first round.

His actual result massively exceeded expectations. By common consensus, this was because the “party of the couch” won; although close to 50% of Muscovites were saying they were going to vote, only 32% ended up doing so. These were mainly Sobyanin supporters who were, nonetheless, loth to shift their butts to vote for an uninspiring if competent technocrat who had ran a most lacklustre campaign.

3. Election observers

In the SMS-ЦИК program, accredited election observers would send text messages from their polling stations with numbers from the protocols at their precinct. They could then be compared with the official CEC numbers.

And Sobyanin’s result here was 49.52%.

Mikhail Degtyaryov 2,77%
Nikolay Levichev 2,78%
Ivan Melnikov 10,82%
Sergey Mitrokhin 3,71%
Alexei Navalny 28,54%
Sergey Sobyanin 49,52%
spoiled ballots 1,86%
from home 4,61%

Does this mean he really did cheat Navalny out of a second round? Well, not necessarily.

Here’s a key caveat. Far from all polling stations were covered by the SMS-ЦИК. Their figures thus have a significant margin of error. I would speculate that the bias is, in fact, more likely to be in favor of Navalny than of Sobyanin, because the observers who would get involved in this project in the first place would likely be more liberal-leaning in the first place, would on average appear more frequently in the more oppositionist areas of town, and would and come to observe their local station.

Still, it’s not a shut and closed case. Someone should really make an analysis of which areas where covered by this program – and whether the sample really does favor Navalny as I reasonably hypothesized above.

4. Exit polls

These are, admittedly, all over the place. The Center of Political Technologies gave Sobyanin 56% and Navalny 29%; FOM – Sobyanin 52.5%, Navalny – 29%; VCIOM – Sobyanin 53%, Navalny 29%. An exit poll carried out by Navalny’s supporters gave him 35.6% to Sobyanin’s 46%, while the Communist Party claimed their candidate Melnikov performed much better, with 19%, than he did according to the official 11 – though their poll still gave Sobyanin a first round victory with 51%.

In conclusion, four out of five exit polls gave a first round victory to Sobyanin. The only one that didn’t was carried out by explicit supporters of the opposition candidate.

5. Statistical evidence

The art of electoral fraud detection via statistical means has come a long way (and has – probably not coincidentally – been mostly spearheaded by Russian mathematicians). You can read the details here.

Suffice to say that for a relatively homogenous city like Moscow, it is expected that each candidates turnout to vote share graph should resemble a Gaussian curve. And here it is for 2013: The mean for Sobyanin is 51.65%, and for Navalny – 28.1%.

moscow-2013-elections-gauss

Or, expressed in the form of a “heat graph” for any one candidate in which the turnout at each station is graphed to the result there, it should form a single concentrated dot. A long tail leading up and to the right, as well as additional distinct dots – especially if they are concentrated at around the 100%/100% – constitutes strong evidence for systemic election fraud.

In regards Moscow, its elections were clean up to and including 2003 or so. But then it started growing ever thicker tails, and additional concentrations popped up, to reach absolutely bizarre and astounding levels in the 2009 City Council elections and the 2011 parliamentary elections. But then it seems obvious that some kind of order and directive was passed down to clean them up, and the graphs snapped back to what they were before 2004 during the 2012 Presidential elections.

moscow-elections-2011-2012

Now here is the heat graph for 2013. Which of the above does it most closely resemble?

moscow-2013-elections-heat

The verdict: As in 2012, but not in 2004-2009, the Moscow mayoral election of 2013 didn’t see any significant fraud and Sobyanin won legitimately in the first round.

(Republished from Da Russophile by permission of author or representative)
 
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Faced with the utter failure of their doom-laden projections for Russia’s population future to describe reality – it’s population is now not only growing in absolute terms, but even barring migration its number of births now virtually equals the number of deaths – the more guttural elements of the interwebs are now resorting to another strategy: “But it’s all due to Muslims anyway!”

A bizarre alliance of neocons, Western chauvinists, crazy Russian nationalists, Islamist fanatics, and plain Russophobes have been peddling the imminent prospect of a Muslim-majority Russian Army and a Russabia ruled from the Caucasus Emirates for almost a decade. But one does not have to be a proponent of mass Muslim immigration, or to deny that serious problems of radicalization exist in some Russian Muslim communities, to call out such projections for the fear-mongering BS they really are. Here is a graph that decisively refutes the “Russabia” thesis:

russia-will-become-majority-muslim-not

The percentage of births in Russia’s traditionally Muslim” republics in the North Caucasus (Agygea, Dagestan, Ingushetia, Kabardino-Balkaria, Karachay-Cherkessia, Chechnya) and the Volga (Bashkortostan, Tatarstan) is a mere 13%-14% of the total – and shows no signs of increasing at a sustained and rapid rate.

It should furthermore be noted that of the above only Dagestan, Chechnya, and Ingushetia have predominantly Muslim population – and their share of total Russian births, at just a little above 5%, are today virtually the same as they were in 2006. This is especially relevant because the vast bulk of Russia’s problems with Islamic fundamentalism and armed opposition to Russian state power are concentrated there.

Only about 50% (give or take) of the populations of the other five republics is Muslim, so if anything – despite the graph being partially balanced out by Muslim immigrants in Moscow and other non-Muslim regions – it substantially overstates the actual degree of Muslim demographic influence. Needless to say, the Orthodox Russians (and ethnic Tatars) who make up half of Tatarstan’s population aren’t going on jihad to restore the Qasim Khanate anytime soon.

It should be stressed that even the figures above will only start coming into effect two decades or so down the line. That is to say, only about 13% of 20-year olds in the 2030′s will have have been born in Muslim republics; the percentage of those belonging to Muslim-majority ethnicities will be even lower, at maybe 9% or 10%. How Muslims are supposed to constitute a majority in the Army with those kinds of figures must remain a mystery.

Finally, the Muslim demographic expansion is self-limiting. A lot of the people who push Russabia (and Eurabia) are apparently under the impression that their typical family has 6 children, which in turn will have 6 children, and so on until they squeeze out everyone else. This is completely and utterly wrong. In Russia, at least, the only Muslim region with a TFR higher than the replacement level rate is Chechnya; as of 2009, it was at 3.38 children per woman, compared to 1.97 in Ingushetia, 1.96 in Dagestan, and far less in all the others – in fact, both Kabardino-Balkaria’s and Tatarstan’s TFR of 1.51 was *less* than the Russian average of 1.54. As such, far from reflecting any innate demographic strength, the current high rates of natural increase seen in Russia’s Muslim republics – or even more specifically, in Dagestan, Ingushetia, and Chechnya – are due in large part to the youthfulness of those regions’ populations. Young populations have, by definition, few old people (hence low mortality) and many young people (hence high natality). Considering that *all* of Russia’s Muslim regions with the partial exception of Chechnya – which, however, accounts for a mere 1% of its population and 2% of its newborn – are rapidly undergoing demographic transition, this is necessarily a temporary state of affairs.

(Republished from Da Russophile by permission of author or representative)
 
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One of the most reliable indicators of influence is access to cars. They are the standard symbol of affluence and middle-class status the world over. They are also far more understandable at the everyday level than things like the PPP GDP per capita, or the number of burgers your national McWage will buy.

Following on my last post, which focused on production, let’s now examine another indicator: The number of cars bought in any given year per 1,000 people.

auto-sales-russia-cee

As we can see from the graph above, Russians (22/1,000 as of 2012) are now buying more new cars per person than any other Central-East European country. Now, this is NOT to say that they are richer than the Czechs (18/1,000), or even the Poles (9/1,000) and Estonians (18/1,000). The latter countries’ markets are already substantially saturated and close to Western levels of auto ownership, while Russia still has some catching up to do; furthermore, they don’t have tariffs on imported second-hand cars, whereas Russia’s are quite substantial. It is also probably true that on average Czechs buy higher quality and more expensive cars than Russians. Nonetheless, the difference between Russia and countries like post-crisis Latvia (7/1,000) and Hungary (7/1,000) are now so wide that it’s hard to argue that the latter are still substantially more prosperous.

auto-sales-russia-and-other-countries

The difference is of a similar magnitude to today’s Greece (6/1,000), in the wake of its economic depression – and has also gained on other countries that were part of developed Europe but hard-hit by the crisis like Spain (17/1,000), Portugal (11/1,000), Ireland (20/1,000), and Italy (26/1,000). In a very real sense, the fact that ordinary Russians can now more readily afford relatively big-ticket items like automobiles than citizens of some countries long considered to be past of the developed world is quite a momentous affair. In fact, not only are they being overtaken by Russia, but by Brazilians (20/1,000) and the Chinese (14/1,000) too, even if the last BRICS member India (3/1,000) continues to be mediocre. That said, there is still a very considerable gap between Russia and the truly front-tier countries like Germany (41/1,000) and the US (47/1,000).

(Republished from Da Russophile by permission of author or representative)
 
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One common trope about the Russian economy is that it has virtually no manufacturing to speak of and lives off “oil rents” that can collapse any day.

Whiles there is a small nugget of truth to this assertion, but by and large it is simply false. It is true that a great chunk of Russian exports do accrue to hydrocarbons and metals, because that is its comparative advantage in trade. That said, there are plenty of Russian products on the domestic market. The automobile industry is a good and representative example of this because they it’s a stalwart of many national economies and there exist reliable and easily accessible statistics on it.

Car Production Car Sales Autos self-sufficiency
Czech Rep. 1,178,938 193,795 608%
Mexico 3,001,974 987,747 304%
South Korea 4,557,738 1,530,585 298%
Poland 647,803 328,532 197%
Japan 9,942,711 5,369,721 185%
Germany 5,649,269 3,394,002 166%
Turkey 1,072,339 817,620 131%
China 19,271,808 19,306,435 100%
Argentina 764,495 832,026 92%
Brazil 3,342,617 3,802,071 88%
South Africa 539,424 623,921 86%
France 1,967,765 2,331,731 84%
Russia 2,231,737 3,141,551 71%
USA 10,328,884 14,785,936 70%
UK 1,576,945 2,333,763 68%
Sweden 162,814 326,441 50%
Italy 671,768 1,534,889 44%
Ukraine 76,281 263,604 29%
Australia 209,730 1,112,132 19%

As such, I decided to compile a representative list of countries, with data on production and sales for 2012 drawn from OICA, in order of the ratio of their auto production to new auto sales – that is, their degree of self-sufficiency in cars.As we can see above, while Russia is perhaps rather lower than average, its domestic auto manufacturing industry nonetheless manages to satiate 71% of demand for new cars.

This is quite comparable to France, the US, and the UK, and is vastly higher than a similarly resource-dependent rich country, Australia. Quite a lot of other resource-heavy countries like Saudi Arabia, Venezuela, and Norway don’t produce cars at all. Mexico is a huge exception, but the reason for that is that it borders the US and the US has outsourced quite a lot of its auto industry south of the border to take advantage of lower labor costs – a situation analogous to the Germans’ outsourcing of car production to Spain in the 1980′s, and Central-East European countries like the Czech Republic, Slovakia, Hungary, and Poland in the 2000′s.

(Republished from Da Russophile by permission of author or representative)
 
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In the wake of Russia’s Internet penetration breaking the 50% mark (now – 55%) and overtaking Germany in total number of users last year, we now have news that Russian overtook German as its second most popular language. It is used on 5.9% of all the world’s websites. It is projected that Russia will maintain this position for a few years. Also .ru has become the world’s most popular country-level domain.

internet-most-popular-languages

This is quite a remarkable achievement considering Russia’s limited number of Internet users relative to the much more populous Spanish and Chinese speaking worlds (even if Internet penetration in the latter regions is a bit lower). I wonder why that could be the case? One theory is that Latin Americans simply don’t read much, while creating websites in China may be trickier than in the West because of greater controls over the Internet. (Also hanzi are much more space-economical than alphabet-based writing systems, so what might take a few pages in English may only require one page in Chinese; that is another possible explanation). That would also explain why the world’s less than 100 million native German speakers are also far ahead of those far more numerous nationalities. Alternatively, maybe there’s simply more spam blogs or pages hosting copied content in Russian.

Here is a trends graph. As of March 27 (the date of this article), Russian has clearly at 5.9% edged past German which is now at 5.7%.

(Republished from Da Russophile by permission of author or representative)
 
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In one of the recent posts on corruption, commentator AP wrote:

Kids from Moscow are having trouble getting into universities now because entrance, based on exam results, skews the chances of acceptance in favor of those students from corrupt regions where they can buy better results. Moscow is less corrupt than, say, Dagestan so Dagestani students perform much better on entrance exams.

Is this true? Seeing as how the Russian state doesn’t release Unified State Exam (USE) results by region, probably due to PC considerations, at first this assertion might appear to be unanswerable. However, there is a way to get round the problem.

(1) We know the PISA-derived IQ’s of some 43 Russian regions (which account for about 75% of its school-age population).

(2) The Russian government DOES release the the numbers of maximum scores in the USE tests by region. In this post we will consider the data for 2012. Furthermore, we know that at least at the federal level, these results tend to form bell curves.

(3) One of the primary “proofs” of electoral fraud in the Russian elections was the presence of spikes at convenient increments of 5%. In the case of USE fraud, we only have access to data for 100% scores and measuring the fatness of that tail should give us a clue as to its relative magnitude. (While it is possible and even likely that school administrators and regions would take care not to create too many maximum marks on the notoriously hard USE tests, far from everybody will follow said precautions. After all, if many regions didn’t even bother to smoothen the spikes to conceal fraud in the elections, is it realistic to posit that they’d take greater care around trifles like exams?).

(4) We know the number of 16 year old’s per Russian region from the 2010 Census, who would have participated in the 2012 exam season.

(5) We know the normal distribution.

The blue bars below show the number of top-scoring exams per region as a multiple of Russian 18 year olds there with an expected IQ of 130 or more, based on the region’s average PISA scores and a standard deviation of 15. The red bars show the same thing, with the major exception that an average IQ of 96 – that is, the national average – is assumed for ALL Russian regions.

unified-state-exam-fraud

As we can see above, the most suspicious results are mostly from ethnic Russian oblasts such as Stavropol, Kaluga, Rostov, Perm, and Vladimir, with the two big exceptions being Mari El and Chuvashia. To the contrary, Dagestan – the biggest Caucasian Muslim republic – has very few top scores relative to the number of very bright people we can expect to find there relative to most other Russian regions.

Finally, the reason that the red bar is a lot higher than the blue bar in Moscow, and to a lesser extent Saint-Petersburg, probably doesn’t have anything to do with foul play, but with the fact that their average IQ’s are about 106.6 and 102.6, respectively (i.e. considerably higher than the national average of 96). So while they generate a relatively disproportionate number of top USE scores, that is presumably because they attract the bulk of Russia’s most intellectual families (the so-called “cognitive clustering” effect).

Of course one problem is that we don’t have PISA data for all Russian regions. Maybe the Chechens do all the cheating then?

Probably not. Chechnya only had a total of five top scored USE results (for comparison, Moscow had 654 top results). In the graph below I produced results for all Russian regions, but with an unavoidable concession: In the case of those regions with no results from PISA, I had to make do with assuming a regional IQ of 96 (as per the Russian national average).

unified-state-exam-fraud-2

In so doing, yet another major region of likely fraud crops up: Bryansk. This oblast, along with Vladimir, produces as many top USE results as a percentage of its 18 year old population as does the intellectual capital, Moscow. Kalmykia, Kirov, and Lipetsk also join the list of Russian regions with suspiciously good USE results (probably not entirely coincidentally, Lipetsk and Kalmykia – along with Ingushetia – were the three regions whose USE results raised suspicions to the extent that they were rechecked).

He also makes the comment:

The schools with the top math students in the country stopped winning Olympiads, while private schools with politically connected kids started to win them…

No obvious way to statistically analyze this, but what we can say with some confidence is that there is no major ethnic angle to this:

share-of-olympiad-scholars-russia

As we can see above, the Central and North-West regions of Russia, which contain the cognitive hotbeds of the two capitals, massively surpass the number of people from the North Caucasus in the share of “Olympians” (basically students who did really well and get benefits) in the annual university cohort.

This is pretty much what we can expect on the basis of the average IQ differentials between these regions.

(Republished from Da Russophile by permission of author or representative)
 
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One of the keystones of the “Dying Bear” meme is the factoid that abortions outnumber births in Russia. As Mark Steyn put it, “When it comes to the future, most Russian women are voting with their fetus.” The only problem is that there is no causal relation between abortions and demographic health whatsoever – and for that matter it is no longer even true factually.

russia-abortion-statistics

There were 814,149 abortions in 2012, which is less than 50% of the 1,896,263 births during the same period. As we can see from the graph above, abortion as a method of fertility control was specific to the Soviet era and has been in rapid decline since the mid-1990′s. In fact Russia’s abortion rate is now basically equivalent to America’s during the early 1980′s, a decade after Roe vs. Wade. The real story about abortions in Russia is that they have been plummeting in all its regions in the past two decades as it steadily becomes a “normal country” in this as on an array of other indicators; its overall numbers of abortions per live births (43%) are now rapidly converging on the 10%-30% range typical of other developed nations.

But this chart also brings us to another point. A recent Weekly Standard article by Daniel Halper, which makes errors beyond demography (no, Putin did NOT invite Boyz II Men to sing fertility chants), reviews a new book by Jonathan V. Last about how the long supposedly doesn’t have enough babies. Not only does he claim that there are 13 abortions per live births in Russia – a statistic that was last true a decade before the book was published! – but that it “suggests a society that no longer has the will to live.” In that case, what would have made of the RSFSR circa 1965, when abortions reached an all time peak of 27 per 10 live births? Well, in 1965 the birth rate (15.7/1000) was double the death rate (7.6/1000), and the total fertility rate was at an entirely sound and replacement level rate of 2.14 children per woman!

That is the problem with moralistic rhetoric of the “voting with their fetuses” variety. Not only in Russia’s case is it now increasingly wrong at a basic factual level, along with the “voting with their feet” brouhaha over the non-existence emigration crisis, but it doesn’t even describe how the world works in general. Abortion rates were world historically high in the post-Stalin USSR, but at the same time it had eminently sustainable fertility stats. On the other hand, modern Poland – the lovechild of Anglo mainstream conservatives like Mark Steyn and Jonathan Last – has a blanket ban on abortions, but its fertility rate of 1.3 children per woman is now considerably lower than “dying Russia’s” 1.7 or so children per woman.

In reality, abortion tends to be low in low-fertility and high-fertility advanced societies alike, because people get access to pills and realize that wearing a condom is preferable to getting an STD, being saddled with child support, and/or undergoing the physical and psychological stress of an abortion.

(Republished from Da Russophile by permission of author or representative)
 
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I have recently been cleaning up my old posts.

When I moved from Sublime Oblivion to here, the pictures remained hosted at the old site (there were too many of them to auto-import). So I’ve been going through ancient posts, manually reattaching pictures (so that they are now hosted at wordpress.com) and making the categories and tags system more comprehensive.

This allowed me the opportunity to reread (or rather, skim) many of my older posts. I summarize the experience here.

In short, the original Da Russophile at blogger was… too Russophile. Unreasonably so.

The Sublime Oblivion of 2009-2010 in its Russia coverage was characterized by a “bizarre fusion” of eco-leftism, Stratforian realism, and Spenglerian mysticism. As in 2008 there were many good articles, but overall it was patchy and frequently ideologized… and falling far short of the punchy, trope-breaking spirit that characterizes it today, and which it should have always aspired to.

In 2011 I moderated, the Russian coverage at S/O reached its peak, and I got into journalism. The pharma hack of early 2012 that crippled S/O was, in retrospect, a blessing in disguise: It allowed me to finally partition the Russia stuff and the everything else stuff into different domains.

As of today, I objectively believe my blog has never been better – and there are ambitious plans for a new translation website and ongoing work on the book Dark Lord of the Kremlin.

Since I started in January 9, 2008, Da Russophile (first in blogger; then as part of Sublime Oblivion; and finally, as now, as its own WordPress.com site) has been visited a total of nearly one million times. Thank you all for reading.

(Republished from Da Russophile by permission of author or representative)
 
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One of the most common tropes against Russia is that critical (independent, democratic, etc) journalists there are dying like flies, presumably because of the “culture of impunity” created by Putin or even on his express orders. It is rarely mentioned that the statistical chances of a Russian journalist dying by homicide is an order of magnitude lower than in several countries widely recognized to be “democratic” such as Brazil, Mexico, Columbia, and the Philippines, or that – unlike Turkey or Israel (!) – Russia does not imprison any journalists on account of their professional work. To this end, I compiled a “Journalism Security Index” to get a more objective picture than the politicized rankings produced by outfits like Freedom House that put Russia on par with Zimbabwe.

As usual in these situations, a few graphs are worth thousands of words.

The graph above shows the numbers of journalists killed in Russia for every year since 1992 as compared with other “democratic” countries like Brazil, Mexico, India, and Colombia. As one can see, the situation has improved greatly in the past three years, with only one journalist (in Dagestan) getting killed in 2011; meanwhile, the situation in Mexico has deteriorated to levels unseen in Russia since the early 1990′s. Does this mean that Felipe Calderón is the next Stalin? Or is it that he is just faced with a drugs war that is rapidly spiraling out of control?

However, even this likely overstates the risks to Russian journalists, because there are simply a great many of them. According to the latest UN data, there were 102,300 newspaper journalists in Russia, far more than in Brazil (6,914) or India (16,079), and while data for the other two does not exist, I will assume that there are as many journalists per capita in Colombia (so 1,670) and three times as many in Mexico (13,027) as in Brazil. You can adjust the latter two figures within the bounds of plausibility but as you will see, this would not make a cardinal difference. So let’s start calculating annual homicides per 100,000 newspaper journalists (latest figure) – a rough but valid proxy for the general level of journalistic peril in any given country.

Wow! You can’t see anything past Colombia! Let’s remove it.

So once you make some necessary adjustments for respective journalist populations, it emerges that Russian journalists have been relatively safe compared to other democratic countries throughout virtually its entire post-Soviet history. They are now safer by orders of magnitude. (The dip in Brazil’s and Mexico’s rates in 2012 are artificial as only half the year has passed).

Finally, homicides per 100,000 journalists are compared with the population as a whole. As one can see from the above graph, Russian journalists were always safer than the average Russian citizen, and are now safer by an order of magnitude. Only one Russian journalist was killed in 2010 and 2011 for a rate of about 0.5/100,000 per year, relative to an overall homicide rate of slightly less than 10/100,000. The average journalist is far less likely to have criminal or binge drinking proclivities than the average citizen (factors that account for the overwhelming bulk of homicides in Russia) so it is right and proper that their homicide rate should also be well below the national average.

The same cannot be said of the other countries we are comparing Russian journalists to. In 2010, the homicide rate in Mexico was 18/100,000 (vs. 77/100,000 for journalists), in Brazil it was 25/100,000 (vs. 14/100,000 for journalists in 2010, but soaring to 87/100,000 in 2011), and in India it was 3.4/100,000 (vs. 12/100,000 for journalists).

It need hardly be mentioned at this point that for most of the “democratic” Yeltsin period, life was riskier for Russian journalists than under “authoritarian” Putin and his “stooge” Medvedev. There were 41 journalists killed in Russia from 1992-1999, compared to 30 from 2000-2008, and 6 from 2009-today (of which 5 occurred in 2009). Does this then mean that Yeltsin, not Putin, was the real Stalin? Of course not. The journalist killings in the 1990′s were a product of the chaos and lawlessness of that time, much like the narco-related killings decimating the ranks of Colombian, Brazilian, and Mexican journalists today. As one can see from the graph above, killings of Russian journalists have always been substantially correlated with the overall homicide rate; the latter began to sustainably decline from the mid-2000′s, and from 2009, journalist killings appear to have followed suit.

Why then does Russia have one of the lousiest reputations for journalist killings in the world, whereas a purely statistical analysis implies that it is in fact now extremely safe relative to several other “democratic” countries like Brazil, Mexico, the Philippines, India, and Colombia, and does not imprison any journalists unlike Turkey or Israel?

Ultimately, I think it has much to do with the unhinged hostility of the Western media to Russia. Case in point, let’s look at The Guardian’s coverage.

When a journalist is killed in Mexico or Brazil, it is reported placidly and matter of factly, the newspaper restricting itself to: Names and identities (four journos from Veracruz; Mario Randolfo Marques Lopes); possible culprits (“the work of the cartels”; “accusing local officials of corruption”); some basic context, e.g. quantity of other journalist killings in the recent past. And apart from a final sentence or two noting that “corruption means it is often difficult to define where the authorities stop and organised crime begins”, that is pretty much the harshest judgment they make.

Now turn to the Guardian’s coverage of the sole Russian journalist killed in the past three years – Khadzhimurad Kamalov, in Dagestan, 2011. The difference begins with the titles. What used to be “Four Mexican journalists murdered in last week” or Brazilian journalist and girlfriend kidnapped and murdered” now becomes “Truth is being murdered in Putin’s bloody Russia.” And it continues in the same vein, with rhetoric being substituted for facts: “Crimes against freedom bathed in slothful impunity”; “Inside Moscow, rulers who pay lip service to human rights parade only an indifference that makes them complicit in these crimes” (is Calderón or Dilma Rousseff complicit in journalist killings in their countries?); “How many more, Mr Putin? How long are we supposed to mourn fellow journalists who died trying to tell us, and their fellow Russians, what a slack, slimy, savage state you run?”

No further comment is necessary.

(Republished from Da Russophile by permission of author or representative)
 
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Since yesterday, the following image from an article by liberal journalist Evgenya Albats has been making the rounds on the Internet. It shows that whereas Putin’s official tally was 65%, independent observers put it close to or below the 50% marker that would necessitate a second round, such as Golos’ 51% and Citizen Observer’s 45%. Predictably, these figures were seized upon by the liberals to condemn the legitimacy of the elections. As Putin ended up getting 63.6%, while the average of all observers was 50.2%, one could conclude that the level of fraud was 13% or more.

However, as pointed out by Kireev, this is a gross misuse of statistics for political ends, because of the severe sampling problems: Golos observers were concentrated in Moscow, St.-Petersburg, and a few other large cities where Putin is less popular, while Citizen Observer is almost entirely confined to the capital. The website http://sms.golos.org/ collates the results from all the big Russian observer projects, and from the regional data, we can see that about half the election protocols compiled to create these figures were from Moscow; almost another quarter were from Moscow oblast and St.-Petersburg.

Nonetheless, while looking through the regional data, I realized that if it were to be adjusted for its pro-Moscow (anti-Putin) sampling bias, we could get a fairly a good estimate for the level of fraud in this election; or at least, an upper limit for it. And so that’s what I proceeded to do.

After assembling the data, I came up with the following table. The first column are the different provinces. The second column is Putin’s vote according to the observer protocols for that region. The third column is Putin’s vote according to the Central Election Commission. The third column is the difference between the two. It may represent fraud, but it may also be (1) sampling bias – more on this later, (2) natural margins of error, which are especially high in regions where there were few observers. The fourth column is the total number of ballots (both real and spoiled) cast in this region.

ВВП (набл.) ВВП (ЦИК) Х бул.
Республика Адыгея (Адыгея) 59.72 64.07 4.35 220481
Республика Башкортостан 63.21 76.38 13.17 2300258
Республика Карелия 50.56 55.38 4.82 309439
Республика Коми 70.28 65.02 -5.26 525780
Республика Марий Эл 57.11 59.98 2.87 381148
Республика Татарстан (Татарстан) 72.21 82.70 10.49 2378904
Удмуртская Республика 62.50 65.75 3.25 784405
Чувашская Республика – Чувашия 59.01 62.32 3.31 702957
Алтайский край 50.90 57.35 6.45 1175430
Краснодарский край 59.96 63.72 3.76 2692090
Красноярский край 55.33 59.53 4.20 1303846
Приморский край 40.97 57.31 16.34 989669
Ставропольский край 66.80 64.47 -2.33 1195740
Хабаровский край 52.21 56.15 3.94 653997
Амурская область 59.35 62.84 3.49 399704
Архангельская область 54.83 57.97 3.14 575014
Астраханская область 60.46 68.76 8.30 432603
Белгородская область 49.22 59.30 10.08 899973
Брянская область 50.01 64.02 14.01 699848
Владимирская область 50.03 53.49 3.46 638010
Волгоградская область 58.91 63.41 4.50 1278416
Вологодская область 58.88 59.44 0.56 608595
Воронежская область 55.05 61.34 6.29 1304344
Ивановская область 60.33 61.85 1.52 519239
Иркутская область 50.49 55.45 4.96 1072723
Калининградская область 47.82 52.55 4.73 457483
Калужская область 54.18 59.02 4.84 506933
Кемеровская область 66.51 77.19 10.68 1642580
Курганская область 53.68 63.39 9.71 482391
Курская область 56.91 60.45 3.54 606717
Ленинградская область 55.86 61.90 6.04 810757
Липецкая область 54.70 61.00 6.30 626535
Московская область 52.71 56.85 4.14 3545368
Мурманская область 58.58 60.05 1.47 407311
Нижегородская область 53.58 63.90 10.32 1857953
Новгородская область 51.49 57.91 6.42 309970
Новосибирская область 52.15 56.34 4.19 1352726
Омская область 42.80 55.55 12.75 974829
Оренбургская область 50.91 56.89 5.98 1014937
Орловская область 45.22 52.84 7.62 450151
Пензенская область 53.43 64.27 10.84 765541
Пермский край 59.62 62.94 3.32 1170209
Ростовская область 58.15 62.66 4.51 2113180
Рязанская область 54.16 59.74 5.58 620967
Самарская область 52.98 58.56 5.58 1557667
Саратовская область 60.52 70.64 10.12 1323161
Сахалинская область 53.76 56.30 2.54 228350
Свердловская область 60.06 64.50 4.44 2073983
Тверская область 54.67 58.02 3.35 667496
Томская область 50.93 57.07 6.14 458311
Тульская область 58.16 67.77 9.61 867569
Тюменская область 73.19 73.10 -0.09 836179
Ульяновская область 53.99 58.18 4.19 666159
Челябинская область 60.76 65.02 4.26 1729399
Забайкальский край 60.94 65.69 4.75 498407
Ярославская область 48.58 54.53 5.95 670972
Город Москва 45.11 46.95 1.84 4247438
Город Санкт-Петербург 50.33 58.77 8.44 2388567
Ханты-Мансийский автономный округ 65.12 66.41 1.29 707504
Чукотский автономный округ 44.84 72.64 27.80 29337
Территория за пределами РФ 65.17 73.19 8.02 441931
Всего в регионах с наблюдателями 56.11 61.97 5.86 63151581
РОССИЯ 63.60 71701665

Sources: CEC regions data, Kommersant elections map, Golos’ SMS-ЦИК observer data aggregation project.

As you can see, the figures are more or less as what we can expect from analysis already published on this blog. In Moscow, fraud is minimal, the difference between observer protocols and the official result being less than 2%. We can be fairly certain about this: The protocols analyzed have data on over a million, i.e. some 1,021,810 votes, out of a total of 4,247,438 cast; at almost 25%, this is excellent coverage. Furthermore, the real fraud figure may be smaller than the 1.84% given above because the observers made sure to cover all the stations with the most suspicious 2011 results.

Coverage in St.-Petersburg is far smaller at 5%, but the fraud figure of 8.44% can still be treated as very reliable. It is backed up by other statistical evidence.

To get a figure for the regions in SMS-ЦИК dataset, which accounted for 88% of Russia’s total votes, I took the regional observer protocols’ figures for Putin and weighing them by the total number of ballots in that region. My final fraud figure using this method came out to 5.86%.

Five Caveats

This is not a conclusive fraud figure, of course, there still being at least five factors that would further influence it. Two of them are negative, one is probably neutral, and two are positive.

(1) This is a negative factor, but one that is very hard to quantify. The pro-Putin votes are weighted according to turnout, however, it is also the case that regions with greater turnout tend to have more fraud – this is because one of the most common methods of fraud is inflating turnout that almost invariably benefits Putin. But it is important stress that this relationship does not necessarily imply fraud, for it is also the case that there are subgroups of the Russian population – primarily, rural dwellers – among whom turnout is naturally higher. So we can expect turnout to be higher in some of the more rural provinces without fraud being responsible. Separating out the two is extremely tricky and is closely tied to a related problem – to what extent is fraud, or subgroups with specific voting patterns, responsible for Putin’s and United Russia’s long tails?

(2) The neutral factor (more or less) are the margins of error that come from only having a very limited numbers of observers in the more remote regions. For instance, it seems pretty unlikely there was 5% fraud AGAINST Putin in the Komi republic. ;) I am assuming that since there margins can either be positive or negative, they will largely cancel themselves out by the time we calculate the aggregate total.

(3) This is a negative factor. Some regions, accounting for 12% of the total votes, are missing from the SMS-ЦИК dataset: Altai Republic, Buryatia, Daghestan, Ingushetia, Kabardino-Balkaria, Kalmykia, Karachay-Cherkess, Mordovia, Sakha Republic, North Ossetia, Tyva, Khakassia, Chechnya, Kamchatka krai, Kirov oblast, Kostroma oblast, Magadan oblast, Smolensk oblast, Tambov oblast, Jewish autonomous oblast, Nenets autonomous oblast, Yamalo-Nenets autonomous oblast, and Baikonur (Kazakhstan).

The FOM exit poll data showed that even though the North Caucasus was the region most wracked by fraud, it also showed, at 68.4%, the highest genuine support for Putin. The election in Stavrapol krai appear to have been fair – the official figure there was actually higher than the observers’ – so let’s leave its result as is. Assuming that turnout in the ethnic minority republics of the North Caucasus was only 50% or so, as seems more likely based on anecdotal evidence rather than the 90%-like official turnout, then the real, average Putin vote across those areas would then be about 71% – still above the Russian national average, but only moderately so – as opposed to the official 89%. This would raise Putin’s real average score by a bit, but by less than he would lose from the large amount of fraud embodied in them.

Similar things can be said, albeit to a smaller extent, for the other ethnic republics (a few of which, like Buryatia, seem to be quiet fair; others, like Mordovia, which are as fraudulent as anything observed in the North Caucasus). The average Putin vote officially in all the non-North Caucasus, non-observed regions is 68%; of the ethnic Russian majority ones, only about 62%. These regions are already almost or entirely consistent with the national average, so they will have only the most insignificant impacts.

Including all the other regions will up the official score to 63.6% (by definition), but will also increase both the level of fraud and Putin’s real score. So perhaps Putin will go up to 57.0% (thanks to the genuine North Caucasus votes), but fraud will also increase to maybe 6.6%.

(4) Now this is already looking very bad, as bad as the 2011 elections, but fortunately there are two major mitigating factors. First, just as nationwide observers are biased towards Moscow, then logically at the regional level they would likewise be biased towards major urban areas. If a crew of observers volunteer in some Russian backwater province, after hearing Navalny’s call over the Internet, chances are they would hail from the big local urban center. And there are significant voting differences between town and city in Russia, with the rural voters consistently both turning out in greater numbers and giving the Kremlin candidate around 10% or even 15% more votes (e.g., in FOM’s last pre-elections poll, only 43% of Muscovites and 47% of people living in cities of more than one million said they’d vote for Putin, compared to 51% of small towners and 58% of rural folks).

Now some 25% of Russia’s population is rural, and another significant part lives in small towns; the observer presence there is all but minimal, in any one region. As such, the observer protocol figures would systemically understate Putin’s vote. To what extent? Crude back of the envelope calculation, but I think it’s valid: 25% of a subgroup that gives Putin 10% more would give him 2.5% more, and they are very much underrepresented in the poll; add another 0.5% for the small town people. Putin’s real score rises to 60.0%, while his fraud score is compressed to 3.6%; total remains, by definition, at 63.6%.

(5) It is also known that observers concentrated most on polling stations that had a legacy of suspicious results from the 2011 elections. Since it is likely that those stations are still more likely – relative to others – to be bad apples this time round, the focus on them means that the level of fraud may be further artificially skewed.

Interpretation

There are many ways one can interpret these results.

One can cite the 5.86% figure as the most precise one, but one that doesn’t take into account a number of complicating factors. Alternatively, one could argue for a significantly lower figure, like 3.6% – or even lower once you adjust for the last factor. Alternatively, one can argue that the positive factors cancel out the first factor, which is unknown in magnitude but surely significant, and so return to a fraud estimate of 4%-6%. This range would back the two most comprehensive exit polls, FOM which gives Putin 59.3% (possible fraud: 4.3%) and VCIOM, which gives him 58.3% (possible fraud: 5.3%).

Either way, one thing is absolutely clear: A proper analysis of the observer protocols statistics can in no way support the theory that Putin got less than 50%.

(Republished from Sublime Oblivion by permission of author or representative)
 
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In the aftermath of the 2011 Duma elections, the Russian blogosphere was abuzz with allegations of electoral fraud. Many of these were anecdotal or purely rhetorical in nature; some were more concrete, but variegated or ambiguous. A prime example of these were opinion polls and exit polls, which variably supported and contradicted the Kremlin’s claims that fraud was minimal. But there was also a third set of evidence. Whatever problems Russia may have, a lack of highly skilled mathematicians, statisticians and programmers certainly isn’t one of them. In the hours and days after the results were announced, these wonks drew on the Central Electoral Commission’s own figures to argue the statistical impossibility of the election results. The highest of these fraud estimates were adopted as fact by the opposition. Overnight, every politologist in the country – or at least, every liberal politologist – became a leading expert on Gaussian distributions and number theory.

While I don’t want to decry Churov, the head of the Central Electoral Commission, for making subjects many people gave up back in 8th grade fun and interesting again, I would like to insert a word of caution: lots of math and numbers do not necessarily prove anything, and in fact – generally speaking – the more math and numbers you have the less reliable your conclusions (not making this up: the research backs me up on this). Complicated calculations can be rendered null and void by simple but mistaken assumptions; the sheer weight of figures and fancy graphs cannot be allowed to crowd out common sense and strong diverging evidence. Since the most (in)famous of these models asserts that United Russia stole 15% or more of the votes, it is high time to compile a list of alternate models and fraud estimates that challenge that extremely unlikely conclusion – unlikely, because if it were true, it would essentially discredit the entirety of Russian opinion polling for the last decade.

In this post, I will compile a list of models built by Russian analysts of the scale of electoral fraud in the 2011 Duma elections. I will summarize them, including their estimates of aggregate fraud in favor of United Russia, and list their possible weak points. The exercise will show that, first, the proper methodology is very, very far from settled and as such all these estimates are subject to (Knightian) uncertainty; but second, many of them converge to around 5%-7%, which is about the same figure as indicated by the most comprehensive exit poll. This is obviously very bad but still a far cry from the most pessimistic and damning estimates of 15%+ fraud, which would if they were true unequivocally delegitimize the Russian elections.

The Magical Beard (16% fraud)

The long-time elections watcher and phycist Sergey Shpilkin (podmoskovnik) has probably written the most popular article on the use of statistical analysis to detect electoral fraud. The first piece of evidence of fraud is that as turnout increases, so does United Russia’s share of the vote; the effect is not observed for the other parties, whose share remains constant or even declines. Below is the graph for Moscow.

And below, courtesy of Maxim Pshenichnikov (oude_rus), is the same graph as a “heat map” for all Russia.

But that’s not all. A second problem is that turnout in Russia does not follow a normal, or Gaussian distribution. The laws of probability dictate that if you throw a coin 100 times, it is fairly unlikely that the “heads” will turn up exactly 50% of the time; however, as you repeat this experiment a dozen, a hundred, and then a thousand times, the average should converge to 50%. A graph of all these experiments should be in the form of a bell curve, with a peak at the midway point and falling away rapidly on either side. Theoretically, this should also hold for turnout, and this is in fact what we see in for elections in countries such as Mexico, Bulgaria, Sweden, Canada, Poland, and Ukraine. As we can see, there are suspicious peaks at 100% turnout in some of the less developed democracies like Ukraine, Bulgaria, and even Poland; and Ukraine’s Gaussian distribution breaks down beyond about 90% turnout altogether. Nonetheless, the overwhelming indications are that all these countries conduct almost fully free and fair elections.

But these laws do not seem to apply to Russia, including for the most recent Duma elections. Not only does the normal distribution break down on the right hand side of the graph, from about the 60% turnout point, but there begin to appear consistent peaks at “convenient” intervals of 5%, as if the polling stations with 70%, 75%, 80%, 90%, and 100% turnout were working to targets! Though the most recent election seems marginally better than the 2007 Duma election and the 2008 Presidential election, the overall indication is one of rampant shenanigans and fraud.

Graphing the number of polling stations, as done by Pshenichnikov, at which every party got a certain percentage of the votes, exposes United Russia as the black sheep of the political family. Regular spikes at 5% intervals begin from 50% onwards, at which point the Gaussian distribution breaks down and is stretched away into oblivion – producing what is now jocularly referred to as “Churov’s beard.”

And in Moscow, United Russia’s curve looks even more ridiculous. The twin peaks that Yabloko has are either because their vote was stolen at some places and not at others, or they did not have a proper Gaussian to begin with. (Note how practically all the Moscow polling stations with machines cluster at around 30% for United Russia, strongly indicating that the second, bigger peak at around 50% is falsified; see these two clusters in more graphic form here).

Then there’s the matter of abnormal turnout patterns. Cui bono? Quite clearly, United Russia. Returning to Shpilnikov’s work, as you can see below, the higher the turnout, the greater the relative discrepancy between votes for United Russia and the opposition parties.

The author then proceeds to “normalize” United Russia’s results, making the blanket assumption that the correlation between high turnout and higher votes is entirely due to fraud and that it is valid to extend the correlation between votes for United Russia relative to the other parties observed for stations will turnout lower than 50% to every other polling station. Its adjusted results vastly differ from its official results, with the numbers of falsified votes soaring once turnout at any individual polling station exceeds 50% and rapidly converging to near total falsification once turnout rises to 70% and above.

At this point, it is possible to “integrate” the adjusted results curve, to calculate United Russia’s real result. The conclusions are devastating. According to Shpilkin’s final calculations, cited by GOLOS, out of 32 million votes for United Russia, only half of them – some 16.2 million – are “normal”, whereas the other 15.8 million are “anomolous.” This means that in reality it only got 33.7% of the vote, as opposed to the official 49.3%, implying a 15.6% degree of fraud.

Party Official vote Duma seats Real result Real Duma seats
United Russia 49.3% 238 33.7% 166
Communists 19.2% 92 25.1% 124
Fair Russia 13.2% 64 17.3% 85
Liberal Democrats 11.7% 56 15.3% 75
Yabloko 3.4% 4.5%
Patriots of Russia 1.0% 1.3%
Right Cause 0.6% 0.8%
spoiled ballots 1.6% 2.1%

This would clearly make the Duma elections illegitimate, as the will of the Russian electorate – a truly multi-party parliament – is not reflected. If the elections were fair, United Russia would lose its majority and have to rely on coalitions with other parties to pursue its legislative agenda. It would appear that the non-systemic opposition has a clear mandate to demand a rerun.

Not so fast. This claim of 15% fraud is contrary to the entirety of Russian opinion polling, which generally predicted United Russia would get 50%, and to the results of the most comprehensive exit poll, which gave it 43%. Furthermore, as other bloggers rushed to point out, Shpilkin makes many highly questionable assumptions that challenge the credibility of his estimates, for instance, he doesn’t back up his claim that the correlation of higher turnout with more votes for United Russia (and is in fact contradicted by electoral patterns in advanced democracies like Germany and the UK).

PS. You can read an alternate explanation of this method in English by Anton, a Russian blogger living in Finland.

What About Limey?

The mathematician Sergey Kuznetsov wrote a long piece at eruditor.ru attempting to rebut Shpilkin’s conclusions. He starts off by pointing out that the Gaussian distribution achieved by conducting multiple coin tossing experiments is artificial because conditions remain identical. The same cannot be said if some of the experimenters continue tossing coins, while others of their kind begin to favor using dice with “heads” on five of their faces. Likewise, in a country with many socio-economically and culturally idiosyncratic regions such as Russia, Gaussian distributions are not inevitable.

As for the peaks at 5% intervals, they are products of elementary number theory. There must be a jump at 50% because the fraction 1/2, among other fractions n/m, appears more frequently than any other. The same can be said for other “nice” fractions: 2/3, 3/4, 4/5, and so on. Not only fraudsters like these “beautiful” fractions; its an intrinsic property of number theory itself. This is demonstrated below by Ruslan Enikeev (singpost), who built a frequency distribution of the natural outcome of multiple elections with 600 participants; as you can see below, there are very prominent spikes at all the “nice” fractions.

And guess what? If we are to build Pshenichnikov’s graph in “The Magical Beard” but at much finer resolutions, like Kuznetsov did, we get the following. Note how the other parties also get their spikes at “nice” fractions!

So you say that a correlation between higher turnout and more votes for United Russia means mass electoral fraud? If that’s the case, Britain must be a banana republic. Below is the relation between turnout and votes for the Conservatives and New Labour in the 2010 general elections (and this pattern is common to every British region).

Nor are British voters big fans of the Gaussian distribution either.

PS. At this point, I should also note that I observed lots of small peaks for the 2007 Ukraine elections (i.e. after its Orange Revolution) in this blog post.

That said, it should be noted that Kuznetsov acknowledges that the fat tail, and some of the 5% intervals that cannot be explained by number theory – e.g., 65%, 70%, 85%, 90%, 95% – means that a lot of fraud probably did happen.

PS. This has been pretty much confirmed by bloggers such as gegmopo4 (“Happy Pictures“) and Dmitry Kobak (“Party of Scoundrels and Thieves and 10 Sigma“).

The Reichstag Is Burning Since 2002!

The programmer Sergey Slyusarev (jemmybutton) also gave his two kopeiki on election fraud. He pointed out that as in the UK, the turnout for the 2002 Bundestag elections did not follow a perfect Gaussian either; in particular, a lower turnout in East Germany contributed to a second, smaller peak to the left of the main one. He also notes that higher turnouts correlated with more votes for the conservative alliance and fewer votes for the social democrat / green alliance.

Just as Kuznetsov above, he also discussed how pure number theory can explain most of the peaks along 5% intervals. However, even after making adjustments for it, there remained peaks at 75%, 85%, and the fat tail in general that he could not explain as being natural.

I would add that that is understandably so, if we consider this graph of North Ossetia’s results from Pshenichnikov. The biggest irony is that they didn’t even HAVE TO do it to ensure a big United Russia win. The “natural” Gaussian for UR (from the few free and fair stations) seems to be only a few percentage points short of the artificial peak. There’s idiots and then there’s bureaucrats.

He goes into further really wonky elections stuff later on in his post. There are no firm insights or conclusions arising from it, so I’ll refrain from summarizing it.

Trust Me On Arabs In Israel

The blogger, and aspiring Sinologist Vitaly Shishakov (svshift) doesn’t have original models, but does have a lot of useful links. He gives further examples of countries where higher turnouts result in more votes for certain parties and of where turnout does not follow Gaussian distributions. One example is Israel, where Arab turnout in local elections is consistently, stunningly higher than in Jewish ones. As both are still in significant part traditionalist societies, one wonders if the same applies to the Caucasus states (a possibility I raised in my Al Jazeera article). Read him here and here.

Revealing The Real Israel

The blogger levrrr does not believe that there is significant electoral fraud in Israel; and he agrees with Dmitry Kobak that this is patently not the case in Russia. Nonetheless, the curious patterns observed in the 2009 elections in that socio-culturally diverse society are a good reminder that just because it looks strange doesn’t necessarily mean surreptitious activities are afoot.

Unlike in many other countries, the distribution of voting stations by the percentage of votes each party obtained in them is most definitely not standard. Yisrael Beiteinu is log-normal; Likud is a Gaussian with two peaks (like Yabloko in Moscow); Kadima is kind of Gaussian but with a huge plateau; and the two fundamentalist parties (Shas and United Torah Judaism) have a weirdly long and fat tail. So no wonder Avigdor Lieberman is virtually the only foreign statesman to approve of Russia’s elections!

Comparing it to Pshenichnikov’s graph of Russia, there are striking comparative resemblances: Yabloko resembles Shas; the LDPR and Fair Russia resemble Yisrael Beiteinu; the KPRF resembles Likud; and apart from the spiked tail, United Russia looks like Kadima.

Like United Russia, the higher the turnout, the more votes Kadima gets, as in the graph below. The effect is neutral for Likud (as for the Russian opposition parties), and it is negative for Yisrael Beiteinu.

Nonetheless, Israel’s turnout is an indisputable Gaussian; there is no separate peak for the Arabs. (I would note that they have ultra-high turnouts only for local elections, not national ones). Less than 0.1% of polling stations saw a turnout of more than 95%, whereas this figure is more than 5% for the recent Russian elections. I assume that’s almost all fraud, as there are only so many barracks in Russia where everyone goes to vote en masse.

Dangerous Curves (5%-6% fraud)

The economist Sergey Zhuravlev (zhu_s) argues that the correlation between higher turnout and higher votes for United Russia is meaningless because of the “silent majority” effect. Voters for the opposition can be expected to turn out in full force, whereas people without any specific grievances against the “party of power” – who expect it to win with or without their participation – can turn out at varying rates in different regions, depending on their satisfaction with its performance and its success at mobilizing its supporters. As for United Russia’s unusually long tail, that can be explained by the very fact of its getting many votes. A party like Yabloko whose support base hovers in the lower single digits can be expected to have a very narrow peak at the beginning; a party like United Russia, which enjoys a great deal of supports with large geographic variation, will naturally have a far wider spread.

He outlines an alternative method that involves plotting the growth of each party’s share of the vote against the numbers of polling stations giving them a certain level of support. In a society where there are no regional differences in voting preferences and no falsifications, the graphs for each party can be expected to converge to a vertical center. In real life, regional differences flatten out this “ideal” vertical form, especially at the top and bottom. This is because both many stations with little support for a particular party, and the few stations with high support for a particular party, contribute only a small share of the votes to that party; most of its votes accrue to the many stations where support for that party is not far from the national average. This method eliminates the “flattening effect” observed in Shpilkin’s work where the mere fact of high popularity makes United Russia’s spread look unnaturally wide. As we can see below, all parties have substantial spreads in regional support; they are just on different scales.

From the graph above, United Russia is seen to enjoy an “S-effect”, in which stations where they got more than 70% – concentrated in the ethnic minority republics – contributed one fifth of its total vote; the kinks observed in that region are especially suspicious and indicative of mass fraud. This “S-effect” took away votes from the Communists and LDPR, creating an analogous “J-effect” at the bottom of their graphs. Yabloko too has an “S-effect”, if much lower in overall scale relative to United Russia, due to its relatively good performance in the two capitals; elsewhere, it is now just a forgotten relic of the 1990′s.

Whereas there is much evidence of fraud in Moscow, Zhuravlev has some of the strongest evidence against it as shown in the graph below. United Russia has a very natural curve, with no kinks observed at the at the top-right; instead, it has a “J-curve” at the bottom, presumably in the hipster Moscow districts with high support levels for Yabloko (a thesis corroborated by Yabloko’s prominent S-curve).

To resolve the possible falsifications arising from the S-effects and J-effects (with the caveat that they are not always indicative of fraud – e.g., Moscow with its Yabloko-friendly hipster districts), Zhuravlev suggests taking the median: i.e., the party voting shares such that half the polling stations have lower numbers and the other half have higher numbers. This effectively cuts out the S-effects and J-effects. The result is that United Russia loses 6% points relative to its official results, leaving it marginally below a Duma majority with 220 seats.

Of course, this approach too has its problems. It seems to me that kinks are only going to be observed where results are “drawn to plan” (as in some of the ethnic minority republics); where fraud is decentralized, the degree of fraud will itself be a wide spread, and as such not reflected in kinks or S-curves. His conclusion that fraud in Moscow was minimal contrasts with a whole heap of contrary evidence.

Zhuravlev expands on his thoughts on falsifications and the economics of political choice in a follow-up blog post.

Churov’s Defense (minimal fraud)

The Election Results: An Analysis of Electoral Preferences by Vladimir Churov. This isn’t the first time the head of the Central Elections Commission, a physicist with some Petersburg connections to Putin, has had to dodge incoming bullets from the election nerds and LJ malcontents. In response to criticisms of the last round of elections, in 2008 he co-authored an article in an attempt to rebut the critics.

His basic approach is to explain the idiosyncrasies of Russia election patterns in terms of voter behavior. At the beginning, he brings forth the standard criticism against the view that voter behavior must necessarily conform to normal distributions, i.e. it’s not a uniform series of experiments but the choices of a heterogeneous population we are talking about. The authors then proceed to build a model of electoral preferences for Russia’s different population groups in a quest to see how well it conforms with reality. Unlike everyone else on this list, he is analyzing the Presidential election of 2008, but that’s fine because according to Shpilkin it was one of the most falsified.

As shown in the graph above, rural polling stations and urban polling stations reveal starkly different voting patterns. I can see that the latter is described by an (almost internationally standard) log-normal curve; rural voters are the ones who create the fat tail. The other polling stations are various special ones, e.g. in closed institutions or the military, but only account for 1% of the total voters so their overall effect is small. The difference between turnout in the cities and the country is explained “deeper and stronger mutual relations” existing in the latter, whereas urban dwellers are a more amorphous mass. And I would remind the reader at this point that United Russia is more popular in the countryside.

At some level this does make sense – anybody who has lived in a Russian village (or even a small town) can confirm that people there know each other far better than in a big city or a metropolis like Moscow. I can easily imagine a social activity like voting will logically draw a higher participation. He makes a further interesting argument regarding the relation between turnout and the size of the voter list at polling stations (see “Size Matters, Baby” below for a nice graph by Pshenichnikov illustrating this). Basically, turnout at urban polling stations with smaller voter lists begins to converge to converge with results from rural polling stations with bigger voter lists; but unlike in towns, the vast bulk of votes in rural areas accrue to polling stations with small voter lists, where turnout is very high.

And though there are fewer rural voters than urban voters, the number of polling stations is about evenly split between the two – because the average rural polling station has a smaller voter list than the average urban polling station. Adding the results from city stations and rural stations together produces the fat tail on the turnout graphs.

In summary, the overall turnout distribution by polling station is merely the sum of how different Russian population groups vote: urban voters, rural voters, institutional voters (e.g. soldiers).

Worried about the “cragginess” of the graph? Just the result of ordinary fluctuations. It increases when you analyze it at higher resolutions and fades away to nothing at the lowest resolutions.

Plotting the voter turnout distribution not against the number of polling stations but against the number of voters voting in places at any particular turnout will naturally diminish the fatness of the tail (because as pointed out above the polling stations with small voter lists will have the highest turnouts).

As before, the same general turnout pattern is observed in terms of rural and urban voting patterns when plotted against voter numbers.

Churov further argues that the proportional votes for each candidate are NOT huge affected by the turnout. What tendency Medvedev has to win more votes relatively at higher turnouts is down to the increasing influence of the rural vote. A close up of the voting figures for the 75%-100% is presented.

As far as I can see, Churov makes an important point (and in large part convincing) point about the different voting patterns that describe rural and urban voters, and especially the effect of the size of the polling station’s voter list on the turnout. However, he patently fails to address the main concerns of his critics for one simple reason.

He only analyzed the results from 25 regions of European Russia. Which ones? They are not even identified (apart from Kaliningrad, Murmansk and Arghangelsk oblasts, and the Nenets autonomous region, which are mentioned in passing as included). If there is a link telling us what the other 21 are, I cannot find it. And the biggest problem is that, of course, fraud is highly variant by Russian regions. For instance, see Aleksandr Kireev‘s (kireev) map of his estimates of election fraud. Note that three of the four regions actually cited by Churov are green, i.e. indicating that they had little or no fraud in the 2011 elections. As Russian political culture hasn’t changed much in the past three years, they presumably looked similar in 2008.

I strongly suspect that for his analysis Churov merely handpicked the most electorally honest regions he could find and worked from there. Why else include only the 25 regions, with 21% of Russia’s voters and 23% of its voting stations, when he obviously has access to the Central Election Commission’s entire database just like any other blogger? These suspicions are further reinforced by the lack of spikes at regular 5% intervals that everyone else who compiled turnout distributions at the federal level found. He makes some good arguments but the overall conclusions that there is no or minimal fraud is not credible.

Separate The Wheat From The Chaff (5%-7%; 6.6% fraud)

The computer programmer hist_kai takes a relatively simple approach. He plots the number of people voting for United Russia under every 0.1% point interval to get the graph below.

Then he removed all voices for United Russia at 5% intervals, in a 0.5% swathe left and right. This gives a level of fraud of 0.7%.

Then he removes all polling stations where United Russia got more than 75%. This gives a total fraud level of 7.3%.

This is highly unscientific, of course. Some polling stations where United Russia got less than 75% would have been dirty, and some where it got more than 75% would have been clean. Still, it’s a useful way to demonstrate that even removing all the places where it got huge amounts of the vote would have only modestly impacted United Russia’s total tally and would have still clearly left it as the biggest winner.

A group from Samarcand Analytics (Alex Mellnik, John Mellnik and Nikolay Zhelev) issued a study using the a similar method to hist_kai, though they cut off the top quintile of turnout as opposed to all stations registering more than 75% support for United Russia. They justified this on the basis that it was only the quintile with the highest turnout that voted for United Russia in a spectacularly non-Gaussian distribution.

Because of the aforementioned observations that higher turnout correlates with more votes for United Russia, its score after this adjustment is reduced to 42.7%. This implies a possible fraud of 6.6%. The adjusted results for all parties are as follows:

Party

Percent of the vote

Percent without high-turnout polling stations

United Russia

49.3

42.7

Communist Party

19.2

21.2

A Just Russia

13.2

15.2

LDPR

11.7

13.3

Yabloko

3.4

4.0

Patriots of Russia

1.0

1.1

Right Cause

0.6

0.7

Despite the methodological problems with this relatively crude method, it’s worth noting that the adjusted results by party are highly congruent with the results of the FOM exit poll, the most comprehensive one.

Rise of the Machines (6%-7%; 17% fraud)

There are very significant and suspicious discrepancies between polling stations with machine voting and polling stations were counted by hand. The former, on average, are a lot lower.

According to graphs compiled by Sergey Shpilkin, the turnout looks a lot more Gaussian in polling stations equipped with machines; those without feature very fat tails, rising to a much sharper spike at 100%. Compare the turnout graph below for polling stations with machines with the average turnout graph in the section “The Magical Beard.”

Across the same territorial electoral commissions, United Russia got an average of only 36.6% at polling stations equipped with voting machines; this is compared to its 54.2% result elsewhere. This would seem to indicate huge fraud, as machines are harder to tamper with. But this is only assuming that there is no consistent difference between polling stations with and without voting machines.

But this may not be merited as urban, more accessible areas can generally be expected to have a higher likelihood of hosting voting machines, and they are also precisely the places where United Russia has done less well in these elections. On the other hand, if both machines and hand ballots are falsified – e.g. as seems to be the case in Karachay-Cherkessia – this indicator would be a false negative.

In a joint project, Maxim Pshenichnikov and Dmitry Kobak (kobak) compiled a list of disparities between machine and hand ballot results in each of Russia’s cities. They return substantially smaller estimates of overall fraud, albeit there are huge differences between regions. The average calculated by Pshenichnikov is 6.3%. This figure he termed “коибатость”, i.e. which we may translate as “machination.” As you can see in the graph below, the city with the highest measure of fraud – as measured by the machine / hand ballot discrepancy, which has its methodological problems – is Astrakhan, with more than 30% fraud in favor of United Russia. In third or fourth position follows Moscow, with slightly less than 20% fraud in favor of United Russia.

The average calculated by Kobak is 6%-7%. His method is slightly different from – and more rigorous than – Pshenichnikov’s, because whereas the latter calculated “global” machination he confined himself to “local” machination, i.e. he only used the statistics from those polling stations which had at least one voting machine for his comparison with the results from voting machines. Apart a histogram similar to the one above also produces this stunning map of machine and hand ballot voting in Russia’s urban regions: The “green meteors” are results from hand voting, the “red meteors” (which aren’t usually near as trail-blazing) are the results from machine voting.

Kobak is unsure as to why the big discrepancy with Shpilkin’s figures. He emphasizes that Shpilkin’s 37% figure for United Russia cannot be taken at face value because machines tend to be present in larger cities where United Russia does less well; but does consider the 17% figure (the federal average) an important estimate, despite its being much different from his own 6%-7% estimate (the average by region).

One theory he suggests is that in even in those regions where United Russia has a high results, there are few machines and many individuals sites are without them; there, the difference between hand voting and machine voting results is modest at 7%. But when counting up these results on the federal level, these high-United Russia support regions only contribute a little to the aggregate total at well below their true weight (because few of them have machines and can be counted); while contributing a lot to the hand voting totals. Hence the possible source of the huge (and “misleading”) 17% discrepancy.

Meteors of Mendacity (11% fraud)

Dmitry Kobak (kobak) is another big skeptic of the official results. Like Shpilkin, he considers the turnout / voting correlation in favor of United Russia damning, and has some nice graphs to illustrate it. For an election to be fair, the meteors have to be flying to the left and their trails have to be horizontal – a condition that United Russia fails to fulfill. See above for extensive criticism of this assertion.

He calculates the real result by cutting away all the data from polling stations with “suspiciously high turnout”, which he puts at anything bigger than 60% or 50%. Due to United Russia getting far fewer votes in places where turnout is low, that has the effect of reducing its result from 49.3% to 36% and 34%, respectively.

Needless to say his graphs look nice, but they hide a very crude method. Cutting off at 60% essentially dismisses half the entire electorate. He addresses this concern by taking the minimum of United Russia’s voting curve in relation to the turnout, then sums the results up to get a real score of 38%. This implies 11% fraud.

This seems more realistic than the 15%+ obtained by Shpilkin, which clashes so badly with the results of exit polls and opinion polls, if still towards their absolutely lowest margins of error. And needless to say the fairness of taking United Russia’s minimum – and assigning anything above it to fraud – is highly questionable. Using the regional turnout and voting data for the 2010 UK general election provided by _ab_, would the same method not “prove” massive fraud in favor of the Tories?

He also reproduces Shpilkin’s normalization method, producing a real result of 34% for United Russia and hence fraud of 15%. However, even he rejects the method as too harsh and simplistic, ignoring local specifics.

His analysis of the applicability of Benford’s Law to the Russian elections saw no interesting results.

Size Matters, Baby

Maxim Pshenichnikov points out that the larger the amount of voters at any polling station the lower a result United Russia tends to get there. Is it because fraud is harder when there are more people? Or is because smaller stations would probably tend to be in rural and more remote areas, which are usually more pro-United Russia? He doesn’t comment. You decide.

Questioning Russian Behavior

That the correlation between higher turnout and more votes for United Russia is indicative of fraud has two main arguments against it, as we saw above: First, the logic of the “silent majority”, and second, comparisons with other countries like the UK, Germany, and Israel. The blogger vmenshov attempts to prove that this “silent majority” thesis does not apply to Russia, and that the effect really is down to vote stealing on United Russia’s behalf.

So Is It Time To Get The Barber?

Back in 2007, Churov promised to shave off his beard if the elections were unfair. Should we send him the barber then?

It’s a hard question. That there is statistical evidence indicating some degree of fraud is beyond dispute. What’s at stake is the scale. Much like United Russia’s results in Moscow, there are two big clusters: I will simplify them to the 5% Thesis and the 15% Thesis. (There is also a 0% Thesis, as argued by Churov and Kremlin spokespersons; not as if they have much of a choice on the matter. But I think most of us can agree that just the results from Chechnya alone discredit this group).

The 5% Thesis is maintained by Sergey Zhuravlev and the aggregate regional discrepancies between districts with and without machine voting; it is also the figure suggested by practically every opinion poll and exit poll.

The 15% Thesis, most prominently advanced by Sergey Shpilkin and Dmitry Kobak, has become the banner figure of the opposition. If they are right the current composition of the Duma does not reflect the will of the Russian electorate and as such the elections have to be honestly rerun for the system to win back its legitimacy.

The problem with it is that it relies on three fundamental assumptions about Russian elections which. Kirill Kalinin, writing for Slon.ru, identifies these three assumptions thus:

  1. The lack of a “normal” Gaussian turnout and voting distribution.
  2. Suspicious spikes at regular intervals in the turnout and voting distribution.
  3. A positive correlation between turnout and votes for United Russia.

The problem is that all of these assumptions have been argued to be invalid in the Russian context. That said, there are powerful counter-arguments too. By the numbers:

  1. A heterogeneous population and examples of similar phenomenon from advanced democracies throw doubt on this argument, BUT none have tails quite as fat or spikes quite as sharp as does United Russia.
  2. The spikes may, in part, be a product of number theory. But as turnout rises above 60%, they become too sharp to be attributed to number theory alone; and besides, number theory can only explain spikes at common fractions, not at places like 85% or 95%.
  3. The thesis of the “silent majority” and myriad examples from other countries severely weaken this assumption.

It’s good that this election has inspired bloggers, activists and scientists to delve into the interesting and undeveloped world of electoral fraud analysis. They may well be truly groundbreaking original research on the subject lurking somewhere on Runet.

Nonetheless, there remain huge uncertainties; one must guard against the deceptive simplicity and aesthetic richness of most of these arguments. A further peril is that, understandably, this discussion is extremely politicized. As a rule, proponents of the 15% Thesis are liberals to whom United Russia really is a party of scoundrels and thieves and Putin is a cancer on the nation. Likewise, all proponents of the 0% Thesis and some of the proponents of the 5% Thesis are more politically conservative and sympathetic to the Kremlin’s viewpoint that things are basically alright.

My own view on the matter is that the 15% Thesis is extremely unlikely to be true because if it were valid, it would essentially invalidate the entirety of Russian opinion polling – and the work of hundreds of experienced professionals – for at least the last decade; prior to the 2011 Duma elections, only a single poll gave United Russia less than 49%. And we are expected to believe their actual result was 35% or even less? A claim this extraordinary needs truly extraordinary evidence to be credible, but the evidence that has actually been presented is full of questionable assumptions. Which is, in fact, quite ordinary in the world of social science.

Which is not a bad thing. Let the debate go on. Churov can keep his beard, but a web camera or three to let people know he ain’t hiding anything in it wouldn’t go amiss.

(Republished from Sublime Oblivion by permission of author or representative)
 
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Anatoly Karlin
About Anatoly Karlin

I am a blogger, thinker, and businessman in the SF Bay Area. I’m originally from Russia, spent many years in Britain, and studied at U.C. Berkeley.

One of my tenets is that ideologies tend to suck. As such, I hesitate about attaching labels to myself. That said, if it’s really necessary, I suppose “liberal-conservative neoreactionary” would be close enough.

Though I consider myself part of the Orthodox Church, my philosophy and spiritual views are more influenced by digital physics, Gnosticism, and Russian cosmism than anything specifically Judeo-Christian.