Tag Archives: wonky

List Of Estimates On Fraud In Russia’s 2012 Presidential Elections

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This post is a follow-up to a similar one for the 2011 Duma elections. It contains an extensive list of blogger, pundit and “expert” opinions on the extent of fraud in the 2011 Duma elections. Interspersed among these opinions and analyses are results from federal opinion polls, election monitors, and other evidence.

In general, it seems we can identify three “theses” or “clubs.” The 0% Club holds the idea that falsifications were non-existent or minimal; it is advanced by Kremlin officials and supported by many opinion polls. Its polar opposite is the 15% Club, which is – unlike in the Duma elections – now only claimed by opposition forces and some liberal and  Western media outlets. The 5% Club tends to arguee that Putin got a solid majority with some 56%-60% of the vote; almost all evidence converges to this figure. Most of the systemic opposition and arguably most Russians belong to this club.

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Why Golos’ Own Figures Support Only 3%-6% Fraud

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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.

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Measuring Churov’s Beard: The Mathematics Of Russian Election Fraud

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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.

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Demography III – Faces of the Future

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I developed a model on Russia’s future demographic development in Matlab. First, I will describe (non-mathematically) the essentials of how it works; then I will present a range of different possible scenarios. Our data is sourced from Rosstat and the Human Mortality Database.

Demography is a social science, and as such it is impossible to make any precise predictions. As such the strategy we will use is to present four different scenarios, which include Stagnation, Low Improvement, Medium Improvement and High Improvement. (The Transformation scenario I was thinking of doing would have involved some rather complicated math and as such I leave it to a later date). They will be described below. First, an examination of basic concepts.

Introduction

The biggest single factor by far in this model are future fertility trends. It basically determines whether the population will go up or down (improvements in mortality statistics only postpone, not alter, underlying trends). The fertility rate itself is the amount of children in any given year a woman could be expected to have, calculated by adding up age-specific birth rates. The amount required for long-term population stability is 2.1 children per woman (because in most countries slightly more boys are born than girls).

Mortality trends are more useful for ascertaining things such as future dependency ratios, which are important from an economics perspective (assuming the retirement age remains constant). It can also be argued that it is an ethical responsibility of society to maximize the (healthy and fulfilling) longevity of its citizens’ lives. The life expectancy is how long a person can expect to live based on the age-specific mortality indicators of the year in question.

Net immigration, in Russia as in many other countries, typically consists of bringing in masses of young workers which help boost the percentage of working-age people within a population. Its merits are debateable. While they certainly put in more than they take out, they can also cause social unrest and lower overall productivity (if they’re uneducated cheap labor). As such, in my opinion the Japanese method of substitituting capital for labor on the factory floor (it has more than a third of the world’s stock of industrial robots) is generally smarter than importing a diverse mob of car-burners (although perhaps I have an insufficient appreciation of the spiritual benefits of multiculturalism). Digressions aside, it is clear that after a relative migratory drought in the early to mid 2000′s that followed the huge influx of ethnic Russians from the Near Abroad, economic progress and impending labor shortages are drawing a new tide of migrants, and this time many more of them are non-Slavic Central Asians and Caucasians (a total of 287,000 in 2007, probably with many more not covered by the statistics).

With an understanding of the basics, we can now reveal our first scenario.

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