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The Effect of Media on Voters: Field Experiment at the

Moscow Mayoral Elections.

Maxim Mironov, Alexandra Petrachkova*

[email protected], [email protected]

October 2014

Abstract

This paper studies the effect of negative campaign at the 2013 Moscow mayoral election. The

newspaper which criticized the incumbent mayor was distributed near the entrances of randomly

selected 20 metro stations during 4 weeks prior the election date. We find that the incumbent

mayor got 1.48% less votes at the voting stations located near the points of newspaper

distribution. Next, we document the evidence that weekend distribution has 2.4 bigger effect on

votes compared to the working day distribution. Finally, we find that the evening distribution is

about two times more effective than the morning distribution.

JEL classification: D72, L82, P26

Keywords: Elections, Negative campaign, Political economy, Transitional Economy,

Media, Voting behavior.

* This paper has benefited significantly from suggestions by Juan Pedro Gómez, Garen Markarian, Paolo

Porchia, Marco Trombetta, and seminar participants at the IE Business School.

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IntroductionDoes the media affect voting behavior? A large body of evidence suggests that the media

plays an important role in political outcomes. However, most of the existing evidence comes

from established democracies with stable political system and competitive media market. One of

a few exceptions is an important paper by Enikolopov, Petrova, and Zhuravskaya (2011) which

analyzes the impact of the only independent federal channel NTV on the results of the 1999

parliamentary elections in Russia. The authors provide evidence that exposure to alternative

point of view significantly decreases the vote for the government party, increases the combined

vote for major opposition parties, and decreases the turnout.

Our paper expands the evidence of how the media affects the voting behavior in emerging

democracies. We design a fully randomized field experiment to measure the effect of negative

campaign on voting behavior. One month prior to the 2013 Moscow mayoral election we

published the newspaper that criticized policies of the incumbent mayor. We handed out around

130 000 of the newspaper copies near the entrances of randomly selected 20 metro stations. At

each station we distributed the newspaper either in the morning or in the evening, either during

working days or on weekends, either in a color version or in a black and white. Then we

compared the election results at the voting stations where the newspaper was distributed with the

results at those stations where the newspaper was not distributed.

This paper makes three contributions to the literature. First, we show that negative

campaign has a significant impact on voting behavior. The newspaper decreased the percentage

of votes for the incumbent mayor by 1.48 percentage points. Second, the effect of the weekend

distribution is 2.4 times larger than the effect of the working day distribution. Finally, it is two

times more efficient to hand out the newspapers during the evenings compared to the mornings.

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The main goal of our paper is to analyze how negative campaign affects the voting

preferences. We published a newspaper which criticized the incumbent mayor, Sergei Sobyanin.

The newspaper articles discussed alleged corruption of the city government. To design our field

experiment, we took all metro stations in Moscow and excluded the stations located outside

Moscow and the stations within the metro circle line. We excluded central stations because a

significant portion of people who use these metro stations are not locals. Many office buildings,

shopping centers, and tourist attractions are located in the center. Thus, the effect of the

newspaper at these stations might be diluted. These selection criteria lead to the main sample of

116 metro stations. To create a treatment sample where our newspaper was distributed we also

excluded metro stations with adjacent bus or train stations. The majority of people who use these

stations are not local, thus the effect of the newspaper cannot be measured. Next, we randomly

selected two pairs of adjacent metro lines. We hired two managers to supervise distributors, so

the choice of adjacent lines was necessary to facilitate their job. The first pair of lines was Blue

West and Dark-blue West, the second pair of lines was Dark-blue East and Red North-East. For

each of 20 stations in our treatment sample we randomly assigned 3 variables: a) color or black

and white version of the newspaper, b) evening or morning distribution, and c) working days or

weekend distribution.

Next, we identify 15 closest voting stations to each metro station located no further than 2

kilometers from the metro station. We divide them into three groups: the closest 5 voting

stations, from 6th to 10th closest stations, and from 11th to 15th closest stations. Our final

sample includes 1485 voting stations and the treatment sample includes 233 voting stations. As a

baseline for our analysis we take the 2012 presidential election which was held 18 months prior

the Moscow mayoral elections. We find no statistically different results in voting behavior

between the entire and treatment sample. We use the results of the presidential elections as

control variables in our empirical analysis.

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Our first important result is that our newspaper decreased the votes for Sobyanin by 1.48

percentage point. This effect decreases with the distance from a metro station where the

newspaper was distributed. The effect at the 5 closest voting stations is -1.89%, the effect at the

6-10th closest voting stations is -1.24%, and the effect at the 11-15th closest voting stations is -

0.77% (statistically insignificant). This result is not surprising. People who live further from

metro stations are less likely to use the metro system for commuting. Thus, the probability that

they receive our newspaper decreases with the distance from metro. On average, our negative

campaign decreased the number of votes for Sobyanin by 10.17 votes at every voting station

participated in the experiment. The total effect is estimated as minus 2,369 votes for Sobyanin.

Who got these votes lost by the incumbent mayor? Three out of five competing candidates

benefited from our campaign. Mitrokhin (Yabloko, liberals) got additional 0.66% at the voting

stations where the newspaper was distributed, Navalny (People’s alliance, liberals) got +0.55%

and Melnikov (Communist Party) got +0.31%.

Finally, we analyze which ways of the newspaper distribution are more efficient. We find

no difference in the results between color and black and white version of the newspaper.

However, given the higher printing costs of a color version, it is more cost efficient to distribute

black and white newspaper. The weekend distribution is more efficient than the distribution on

working days. The effect of the weekend distribution is minus 2.44% of votes for Sobyanin, and

the effect of the working day distribution is -1.07%. We also find that it is more efficient to

distribute in the evenings compared to the mornings. The effect of the morning distribution is a

0.96% decrease in votes for Sobyanin while the effect of the evening distribution is a 1.88%

decrease.

Our research contributes to a growing literature which analyzes the effects of news media

on political behavior. The earlier media studies used data from surveys to measure the

association between a reported media exposure and political views. For example, White et al.

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(2005) find strong correlations between specific media slant and viewers’ political attitudes.

However, this research design may give biased results because individuals seek information in

accordance with their specific political views. Recent contributions to the literature employ

natural experiments (Enikolopov, Petrova, and Zhuravskaya (2011) and field experiments

(Gerber et al (2009)) to measure media effects on voting preferences.

This paper is also related to the literature that measures the impact of negative campaigning

on election results for a target, her competitors, and the turnout. Lau and Pomper (2004) analyze

negative campaigns for the Senate elections in the US from 1992 to 2002 and find that although

they have no affect on the overall stability of political system, they are not an effective strategy

to gain votes. As for the turnout, Ansolabehere and Iyengar (1995) found that negative

campaigns demobilize voters, although later studies show that voters are actually more resilient

to negativity than they were previously thought to be (Brooks, 2006).

This paper also contributes to the literature that studies the right timing for political

communication. Some researchers study the timing of voting decision from voter’s perspective

(Fournier et al., 2004) in order to understand responsiveness to a campaign. Others measure

effectiveness of message delivery depending on its proximity to the election day (Nickerson,

(2007), Panagopoulos (2010)).

The paper proceeds as follows. Section 1 describes analytical framework. Section 2 gives

background information on Moscow mayoral election and important events preceded it. Section

3 presents the data and experiment design. Section 4 discusses empirical strategy. Section 5

discusses the results. We conclude in Section 6.

1. AnalyticalFrameworkThe role of mass media in election campaigns constitutes a core research area in political

science. The persuasiveness of mass media communication has been debated since the dawn of

modern social science. Until the 1980s academics called into question the ability of mass media

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to have important effects on voter attitudes and behavior. Klapper (1960) summarizes: “(a) mass

communication by itself does not act as a necessary and sufficient cause of audience effects and

(b) mass communication typically reinforces existing conditions, rather than changing them”. In

contrast, the majority of recent studies make a different conclusion, finding that media can have

substantial effects. Using the variety of research designs scholars have measured the influence of

different types of media (newspapers, TV, radio). DellaVigna and Kaplan (2007) analyze the

impact of Fox News in 20 percent of US towns between 1996 and 2000. The entry of the pro-

Republican channel convinced from 3 to 28 percent of its viewers to vote for this party

candidates in the presidential and senate elections. Gerber, Karlan, and Bergan (2009) show that

randomly assigned subscriptions to the conservative Washington Times or the more liberal

Washington Post increased votes for the Democratic candidate among subscribers to both

newspapers suggesting that exposure to media is sometimes more important than the media

slant.

Several recent papers employ experiments to measure not only the influence of media

implied by the slant of its coverage but also more direct ways of communication like

advertising. Gerber et al (2011) describe a large-scale experiment in paid political advertising

that has been conducted in 2006 when around $2 million of TV and radio ads on behalf of one of

the candidates were assigned randomly (in terms of volume and date). Results indicate that such

ads have strong but short-lived effects on voter preferences. Gerber and Green (2008) review

dozens of studies of the alternative methods of voter mobilization. They conclude that personal

canvassing can increase the turnout by more than 8 percent, while a call from a volunteer can

raise it by 2.5 percent and several mails boost the turnout by one percentage point.

A growing literature studies the effects of negative campaigning on turnout, votes for an

attacker and a target, and political system itself. Findings are controversial. Lau, Sigelman, and

Rovner (2007) analyze 111 papers about the effects of negative campaigns. They conclude that

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such campaigns are not so effective to gain votes as some practitioners may think, although they

tend to be more memorable. At the same time they do not suppress the turnout.

The majority of studies on mass media influence on voting behavior are based on

experiments and surveys conducted in developed democracies while the evidence of the effects

of media on voting outside the developed world is rare. However, one could expect larger

impact of media on political outcomes in a country with weak democratic institutions where

media market and political landscape are not so competitive, and ideological platforms of

political parties are not so well-understood. Enikolopov, Petrova, and Zhuravskaya (2011)

confirm this hypothesis: a mere availability of the only independent from the government TV

channel NTV to three-quarters of Russia’s population before the 1999 parliamentary elections

decreased aggregate vote for the government party by 8.9 percentage points, increased the

combined vote for major opposition parties by 6.3 percentage points, and decreased the turnout

by 3.8 percentage points.

The aim of our paper is to study the effects of mass media on voting choices in an emerging

democracy such as Russia in 2013. We created an anti-government newspaper which criticized

the policies of the incumbent mayor of Moscow. This newspaper was distributed one month

prior the election date. Specifically, we analyze the following questions.

Question 1: Does a newspaper with the negative slant affect the turnout, votes for a

target (incumbent mayor), votes for other candidates?

Although the timing of communication is one of the key parameters of any campaign,

studies that examine the impact of message timing are surprisingly rare and produce

controversial results. Nickerson (2007) finds that messages delivered closer to the election day

are more effective while Panagopoulos (2010) argues that appeals delivered early during a

campaign cycle can also be effective. Krupnikov (2011) finds that negativity demobilizes when

a voter already selected a candidate and negative information is about this preferred candidate.

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Question 2: At what time of the day and days of the week communication is more

effective?

On the one hand, in the morning we may consume information more efficiently than in the

evening when we are tired after a working day. On the other hand, it is not convenient to read a

newspaper on a way to work in metro while in the evening you can read it at home and share it

with your relatives. During working days we are keener to receive “serious” information,

although we have less time for it. On weekend we are more relaxed but probably do not want to

hear anything about politics. Again, during weekends it is easier to read a newspaper in metro

even in the morning.

Question 3: What is more effective to distribute a color or black and white newspaper?

On the one hand, color images are more visually appealing. On the other hand, color newspapers

historically are associated with tabloids whereas quality broadsheets were printed in black and

white. The most reputable Russian daily Vedomosti (joint venture of the Wall Street Journal and

Financial times) switched to a color edition only 5 years ago.

2. BackgroundInformationThe Moscow mayoral election of 2013 was the first election in 9 years. The mayor of

Moscow was elected between 1991 and 2004. In 2004 the election system was abolished. The

Moscow mayor as well as governors of other Russian regions were nominated by the president

of Russia and then approved by a legislative body of the region. Following the 2011–12 Russian

protests triggered by falsification of the 2011 parliamentary election1, President Dmitry

Medvedev offered to reintroduce the direct elections of the governors and the mayor of Moscow.

The corresponding legislation was approved by the Russian Parliament.

1 See Enikolopov et al. (2012) for the detailed justification of the electoral fraud during the 2011 parliamentary

election.

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On June 5, 2013 the incumbent mayor of Moscow Sergei Sobyanin announced his

resignation from the post and after short time confirmed his intention to stand for election held

on September 8.2 Five other candidates were allowed to participate in the elections3 - Ivan

Melnikov (Communist Party of the Russian Federation), Nikolai Levichev (a Just Russia),

Mikhail Degtyaryov (LDPR), Sergei Mitrokhin (Yabloko), and Alexei Navalny4 (People’s

Alliance).

According to polls two months before the elections, Sobyanin was expected to get 56% of

the total vote, while the opposition leader Navalny could count only for 6%.5 Sobyanin did not

run any visible campaign6 relying on his high rating assured by his close ties with the Kremlin

and on his monopolistic access to television.7 At the same time Navalny ran unprecedented,

American-style campaign which was funded exclusively by private donations from 16,700

people from all over Russia. For the entire campaign period, the candidate greeted voters 89

times in large gatherings outside of metro stations and set up 2,756 mobile “cubes” inscribed

with his campaign platform throughout the city. Around 20,000 volunteers fanned across

2 Sobyanin was nominated as Mayor in 2010 and resigned from the post two years before the end of his term.

The reasons behind this decision are unclear. Maria Zheleznova et al. report headlined "Sobyanin leaves for legitimacy" in Vedomosti 05.06.2013 says Moscow Mayor Sergei Sobyanin has decided to take part in the early mayoral election in an attempt to look more legitimate in the eyes of Muscovites, as United Russia risks losing its positions in the regional election in the city in 2014. In fact, although Sobyanin is one of the leaders of United Russia, in the mayoral election he preferred to run as an independent candidate.

3 In order to be registered candidates were required to pass a “municipal filter” introduced by the law of 2012. Every candidate was required to gain support from at least 6% (110) of municipal deputies from no less than 75% Moscow municipalities. Given that United Russia controlled most of municipalities, it was especially difficult to pass the filter for opposition candidates. Controversially, Sobyanin helped them get enough signatures from municipal candidates, in particular for opposition activist Alexei Navalny. According to Sobyanin, it would be wrong to deny Muscovites the possibility to show their attitude to Navalny's point of view. Source: http://www.bbc.co.uk/russian/russia/2013/07/130709_navalny_signatures_mayor_elections

4 Navalny gained his prominence in Russia and outside Russia via his blog, hosted on the website LiveJournal, where he publishes his investigations about corruption in Vladimir Putin administration. In July 2013 after the registration as a candidate for the Moscow mayoral elections he was convicted of embezzlement and was sentenced to five years in prison. He was released from prison a day after sentencing after 15 000 protested in the center of Moscow. This prison fine was suspended in October 2013. However, several criminal investigations are opened against him.

5 See the results reported by a major Russian polling firm “Foundation Obschestvennoe Mnenie” on September 2, 2013 http://fom.ru/Politika/11062

6 Navalny’s Campaign to be Moscow Mayor by Robert W.Orttung, Institute for European, Russian, and Eurasian Studies at the George Washington University Elliott School of International Affairs

7 For example, the federal channel NTV gave Sobyanin more than 20 minutes on August 29 2013 (11 days before the elections) http://www.ntv.ru/novosti/651379/

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Moscow to hand out 23 million copies of election materials. As a result, at least 43% of

Muscovites have seen his election materials.8

The results of the elections were the following: Sergei Sobyanin – 51.37%, Alexei Navalny

– 27.24%, Ivan Melnikov – 10.69%, Sergei Mitrokhin – 3.51%, Mikhail Degtyaryov – 2.86%,

Nikolai Levichev – 2.79%, invalid ballots – 1.53%. The turnout was 32.07%.9

3. DataandExperimentDesignThe data for this field experiment were collected during Moscow mayoral pre-election

campaign in 2013. The total costs of the experiment were 19,000 dollars which were financed by

me, my husband Maxim Mironov, and 6 our friends. We created a newspaper which criticized

policies of the incumbent mayor, Sergei Sobyanin. A copy of the newspaper can be found in

Appendix 1. The goal of the experiment was to distribute the newspaper near entrances of the

Moscow metro stations and analyze the effects of this newspaper on the voting behavior. We

excluded the following types of stations from participation in the experiment:

a. Stations within the circle line. These metro stations are situated in the city center

where a lot of business centers, government offices, company headquarters, and tourist

attractions are located. People who use these stations are less likely to live in the

adjacent areas.

b. Stations close a train or local bus station. A lot of people commute from Moscow

oblast (the region surrounding the city of Moscow) using local trains and buses. Then

they switch to metro network. Thus, the major part of traffic at these metro stations

constitutes of people not living in the neighborhood. The effect of the newspaper

distribution near these stations would be diluted.

8 Candidate’s information. http://report.navalny.ru/media/navalny_report.pdf 9http://www.moscow_city.vybory.izbirkom.ru/region/region/moscow_city?action=show&root=1&tvd=277200

01368293&vrn=27720001368289&region=77&global=&sub_region=0&prver=0&pronetvd=null&vibid=27720001368293&type=234

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c. Stations located outside the city of Moscow. In the last years several stations were

built outside administrative borders of Moscow. People who live in these areas do not

vote at Moscow elections.

These selection criteria yield to a sample of 87 metro stations out of 152 stations existed as

July 2013.10 See Appendix 2 for the map of Moscow metro. Then were hired 4 managers.

Manager 1 was responsible for distribution of the newspapers at West part of Blue and Dark-

blue lines, Manager 2 was responsible for distribution at South part of Green and Light-green

lines, Manager 3 was responsible for North part of Grey and Green lines, and Manager 4 was

responsible for North-East part of Red line and East part of Dark-blue line. The directions were

chosen randomly, so each manager covers two adjacent lines. The managers were supposed to

hire and monitor distributors which should have handed out newspapers at the entrances of

metro stations. The managers were promised a bonus which depends on the percentage decrease

of the votes for Sobyanin, the incumbent candidate. The goal of the bonus was to provide

incentives for managers to move between the metro stations and monitor their distributors.

Unfortunately, as was revealed by our investigation, Managers 2 and 3 did not complete

their tasks. They faked money receipts from distributors, provided faulty reports, and no

evidence were found that any newspaper was distributed.11 We should admit that about 50% of

our budget was wasted due to this fact and we excluded these managers from the experiment.

Managers 1 and 4 distributed the newspaper at 20 metro stations. These are all stations located at

the corresponding metro lines (Blue West, Dark-blue West, Dark-blue East, and Red North-

East) which satisfy the criteria above. The people who live on the West side of Moscow are a bit

10 Official number of station was 192. If two or more lines intersect then each station on the intersection is

counted as a separate station. We counted station according to the geographical location. If several stations are located at the same place, we treated them as a single station.

11 We discussed these issues with several professionals working on this market. They told us that such kind of cheating is common, especially during pre-election campaign when demand for distributors is high.

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wealthier than an average Muscovite and the people who live on the East side are a bit poorer

than an average Muscovite. We randomize on the following variables:

a. Color or black and white. A half of the stations were randomly assigned to color

newspapers and another half were assigned to black and white newspapers.

b. Working or weekend. Since it was more difficult to find distributors who were willing

to work on weekends, 30% of the stations were randomly assigned to weekend

distribution (Saturday and Sunday) and 70% were assigned to working day distribution

(Monday through Friday). Ex-ante probabilities of distribution were 1/3 for weekend

and 2/3 for working days.

c. Morning or evening. Morning is from 8.00 to 10.00 during working days and 10.00 to

12.00 during weekends. Evening is 18.00 to 20.00 during working days and 19.00 to

21.00 during weekends. Since it is not convenient to distribute simultaneously on Red

line North-East and Dark-blue line East (there is no a transfer station between these

lines, so the manager could not monitor effectively two lines simultaneously), all

working day stations on Red line were assigned to evenings, and all working day station

on Dark blue line were assigned to mornings (this choice was random). All other

stations were assigned randomly with probability 50%. As a result 9 stations were

assigned to morning distribution and 11 stations were assigned to evening distribution.

Distributers were instructed to hand out newspapers to people who enter a station in the

morning and who exit the station in the evening. People, who exit the station in the

morning and who enter the station in the evening are less likely to live in the

neighborhood.

Two distributors hand out newspapers at each metro station during 2 hours. They typically

distributed from 700 to 900 newspapers during this time frame. The distribution was made

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during 4 weeks preceding the election date (September 8, 2013). About 130,000 copies were

handed out. Table 1 presents the list of metro stations where the newspaper was distributed.

[Insert Table 1 here]

We selected 116 metro stations located outside of the metro circle line as our main sample

of metro stations.12 We excluded the city center stations and stations outside the city of Moscow.

Central stations cannot serve as a good comparable to analyze the treatment-control sample

differences. The people who live in the city center are richer than inhabitants of other districts

and have different voting preferences. Thus, inclusion of central stations would bias our control

sample. Then, we chose up to 15 closest voting stations located not further than 2 kilometers

from a given metro station.13 If one lives further than 2 kilometers from the nearest metro station

(25 minutes walking distance) it is less likely that he or she uses this metro station for

commuting. Our final sample includes 1485 voting stations out of 3590 voting stations located

in Moscow. Such significant attrition is caused by a few reasons. First, we exclude all voters

who live in the city center. Second, we exclude the districts located outside of the Moscow Ring

Road which are still considered administrative districts of Moscow (e.g. Zelenograd, “New

Moscow”, Solncevo). Finally, we eliminate the districts without close access to metro lines (e.g.

Zapadnoye Degunino, Golovinskiy district). The sample of 1485 voting stations includes 44.5%

of the total number of Moscow voters.

In 2013 numbering of the voting stations was reassigned. Thus, the direct match between

voting stations in 2012 and 2013 to compare the results of the presidential and mayoral elections

is not possible. We choose the voting stations located exactly at the same address and match

them with new voting stations. It is common that several voting stations are located at the same

address. Different houses from the neighborhood are assigned to different voting stations. In this

12 We applied criteria a) and c) from the above selection. We excluded transport hubs from our treatment

sample (criterion b)) because the newspaper distribution would be inefficient near these stations. However, they can be included in the control sample of voting stations.

13 Citizens are assigned to voting stations based on their home address.

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case we calculated averages among the voting stations located at the same address at the

presidential election and assign them to new voting stations at the Moscow mayoral election

located at the same address.

Then, we select 15 closest voting stations to the 20 metro stations of our treatment sample,

where we distributed the newspaper. The treatment sample includes 233 voting stations. Table 2

presents results of the presidential election for the entire and the treatment sample of voting

stations. We split voting stations on 3 groups: 5 closest to a metro station, the 6th to 10th closest

and the 11th to 15th closest to a metro station. As we can see from the table, 5 closest voting

stations are located on average at 484 meters from a metro station, the 6th to 10th closest a

located at 789 meters from a metro station, and the 11th to15th closest are located 987 meters

from a metro station. On average, the voting stations from the entire sample are located at 729

meters from a metro station. The voting stations from the treatment sample are located on

average at 727 meters from a metro station. The number of votes for different candidates do not

vary significantly across samples. Putin from United Russia gets 45.26% and 44.82% at the

entire sample and at the treatment sample of voting stations accordingly. The number of votes

for other candidates exhibit a similar pattern: Zyuganov from Communist Party – 19.22% and

19.19%, Zhirinovsky from LDPR – 5.95% and 5.88%, Mironov from a Just Russia –5.16% and

5.18%, Prokhorov from Civic Platform – 21.91% and 22.38%. All the differences between

relative numbers are statistically insignificant.


Table 3 presents the results of the Moscow mayoral election. We can see that the difference

between votes for Sobyanin from United Russia, is quite substantial for the entire sample and

the treatment sample. At the 5 closest voting stations the percentages of votes are 48.25% and

46.40% (the difference is 1.86%, t-stats is 3.40), at the 6th to 10th closest stations Sobyanin

results are 48.60% and 46.54% (the difference is 2.06%, t-stats is 3.19), at the 11th to 15th

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closest station the percentages of votes are 49.32% and 48.26% (the difference is 1.06%, t-stats

is 1.27). If we consider all voting stations, the difference would be 1.8% (t-stats 4.79).

Preliminary analysis reveals that the differences for Degtyarev, LDPR (2.73% and 2.62%) and

Levichev (3.01% and 3.00%) are not substantial and statistically insignificant. Three candidates

show a statistically significant increase in number of votes between the entire sample and

treatment sample: Melnikov, Communist Party (11.06% and 11.38%, t-stats is 2.18), Mitrokhin,

Yabloko (3.84% and 4.46%, t-stats is 7.58) and Navalny, People’s Alliance (29.25% and

30.22%, t-stats is 2.87).


Basic analysis of summary statistics allows us to conclude that our campaign is likely to

cause damage to Sobyanin. He got 1.8% less votes at the treatment sample stations compared to

the entire sample stations. It is not surprising because the newspaper criticized him and his

policies. The main winners from the campaign were Mitrokhin (+0.83%) and Navalny

(+0.97%).

Table 4 shows correlations of votes between the presidential and the Moscow mayoral

election. Panel A presents the entire sample and Panel B describes the treatment sample. We can

see that correlations for candidates from the same party are quite high. Correlation between

votes for Putin and votes for Sobyanin is 0.655. Correlation between Melnikov and Zyuganov is

0.344. Correlation between Zhirinovsky and Degtyarev is 0.462. One exception is a Just Russia:

correlation between Mironov and Levichev is 0.063. Civic Platform was not present at the

mayoral election. However, two parties which share similar values were present: Yabloko and

People’s Alliance. Correlation between Prokhorov and Mitrokhin is 0.32 and correlation

between Prokhorov and Navalny is 0.695. The treatment sample exhibits a similar pattern of

correlations. Correlation between Putin and Sobyanin is 0.696, correlation between Melnikov

and Zyuganov is 0.368, correlation between Zhirinovsky and Degtyarev is 0.487, correlation

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between Mironov and Levichev is 0.206, correlation between Prokhorov and Mitrokhin is 0.116,

correlation between Prokhorov and Navalny is 0.588.


4. EmpiricalStrategyThe main purpose of our newspaper was to decrease the number of votes for Sobyanin.

Thus, we start our empirical analysis with estimation of the following regression:

1 2 3 4i i i i i iSobyanin Newspaper PutinP TurnoutP DistMetro , (1)

where i stands for index of a voting station, iSobyanin is percentage of votes for Sobyanin,

iNewspaper is a dummy that is equal to one if the newspaper was distributed at a metro station

close to the voting station, iPutinP is percentage of votes for Putin at the 2012 presidential

election, iTurnoutP is a turnout rate at the 2012 presidential election, iDistMetro is a distance

from a voting station to the metro station, and i is the error term. We estimate this regression

for different subsamples of voting stations depending on the distance to a metro station.

Specifically, we analyze the effect of the newspaper at the 5 closest voting stations, the 6th to

10th closest voting stations, and the 11th to 15th closest voting stations. The idea behind this

segmentation is that people who live further from a metro station are less likely to use metro for

commuting. Thus, the effect of the newspaper distribution should probably decrease with a

distance from a metro station.

Another interesting question is which candidates benefit from our campaign. To analyze

this issue we estimate the following regression:

1 2 3 4 5i i i i i i iCandidate Newspaper PutinP TurnoutP Dist CandidateP , (2)

where iCandidate is percentage of votes for a given candidate (Melnikov, Degtyarev, Levichev,

Mitrokhin, or Navalny) at the mayoral election, iCandidateP is percentage of votes for a

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candidate from the same (or similar) political party at the presidential election, all other

variables are the same as in (1).

Several papers (Ansolabehere, Iyengar (1995), Brooks (2006)) analyze the effect of

negative campaign on voter turnout. We also analyze how our newspaper affects turnout by

estimating the following regression:

1 2 3 4i i i i i iTurnout Newspaper PutinP TurnoutP DistMetro , (3)

where iTurnout is the voter turnout at the mayoral election. All other variables are defined in

equation (1).

Finally, we analyze how different newspaper characteristics affect the efficiency of the

campaign. We estimate the following regressions:

1 2 3 4 5i i i i i iSobyanin Newspaper PutinP TurnoutP DistMetro Color (4)

1 2 3 4 5i i i i i iSobyanin Newspaper PutinP TurnoutP DistMetro Working (5)

1 2 3 4 5i i i i i iSobyanin Newspaper PutinP TurnoutP DistMetro Evening (6)

1 2 3 4

5 6 7

i i i i i

i i i

Sobyanin Newspaper PutinP TurnoutP DistMetro

Color Working Evening

(7)

where iColor is a dummy that is equal to one if a color newspaper was distributed (0 if black

and white), iWorking is a dummy equal to one if the newspaper was distributed on working days

(0 for weekends), and iEvening is a dummy equal to one if the newspaper was distributed in the

evenings (0 for the morning distribution). All other variables are defined in equation 1. It is

important to note that all variable of interest were assigned randomly without any coordination

with the running candidates. Thus the proposed empirical specification does not suffer from

endogeneity issues and all estimated coefficients for variables of interest should be interpreted as

causal effects.

18

5. ResultsWe estimate equation (1) for different subsamples of voting stations. Table 5 presents the

results. We can see that the number of votes for Sobyanin is highly related to the number of

votes that Putin got during the presidential election. The related coefficient varies from 0.79 to

0.98 with t-stats from 15.3 to 34.5. On average, one percent additional vote for Putin translates

to 0.86% additional votes for Sobyanin (see column (4)). The coefficient for turnout at the

presidential election is negative and statistically significant. A possible explanation of this

negative coefficient may be falsifications observed during the election cycle of 2011-2012 (see

Enikolopov et al. (2012) for the details). The increase of votes for Putin and United Russia was

achieved by bringing pro-government voters from other regions to Moscow voting stations.

Thus, higher turnout during the presidential election ceteris paribus means higher share of

falsification in favor of Putin. The table results indicate that an additional percent of the turnout

in 2012 corresponds to 0.15% decrease in votes for Sobyanin (see column (4)). We also find a

negative and significant sign on Distance from metro. Extra kilometer from Metro corresponds

to 0.77% decrease in the number of votes for Sobyanin. A possible explanation might be that

people who live further from metro stations spend more time on commuting. Moscow is

notorious for its traffic jams and low quality of land public transport. Therefore they are in

general less happy with the city authorities. We find that the distribution of the newspaper has a

significant impact on votes for Sobyanin. For the entire sample of voting stations, the

distribution of the newspaper decreases the number of votes for Sobyanin by 1.48% percentage

point (t-stats 4.24). We can see that the effect is decreasing with distance from a metro station

where the newspaper was distributed. The effect of the newspaper on votes for Sobyanin is -

1.89% (t-stats 3.77) for the 5 closest voting stations, -1.24% (t-stats 2.37) for the 6-10 closest

voting stations, and -0.77% (statistically insignificant) for the the 11-15 closest voting stations.

A possible reason for this decrease with distance is that people who live further from the point of

19

distribution are less likely to get the newspaper. An average number of people who participated

in the mayoral election were 689 people per voting station. Thus, our newspaper campaign lead

to a decrease of Sobyanin votes by 10.17 at every voting station participated in the experiment.

Thus, the total effect of the newspaper campaign can be estimated as minus 2369 votes for

Sobyanin.14 Taking into account that the effective budget of our campaign was $9500, it gives

an estimation of $4.01 per vote.15


Next, we estimate equations (2) and (3) to analyze the effects of the newspaper campaign on

other candidates’ votes and the voter turnout. Table 6 describes the results. We can see from the

table that the campaign has a significant positive effect on votes for Melnikov, Mitrokhin,and

Navalny, and no effect on Degtyarev, Levichev, and the voter turnout. The biggest winner is

Mitrokhin, he gets additional 0.66% (t-stats 7.18) where the newspaper was distributed,

followed by Navalany – +0.55% (t-stats 1.93) and Melnikov – +0.31% (t-stats 1.88).

Interestingly, even though Mitrokhin and Navalny targeted similar electorate with liberal values,

there is no effect of votes for Prokhorov (pro-liberal candidate at the presidential election) on

Mitrokhin’s votes. However, there is strong and significant effect of Prokhorov’s votes on

Navalny’s votes – the coefficient is 0.6265 with t-stats equal to 4.03. All other candidates have

positive statistically significant link with their party peers in the presidential elections.

Finally, we estimate equation (4) to (7) to examine the efficiency of different types of

newspaper distribution. Table 7 presents the results. As the table data indicate, the coefficient for

Color is insignificant in both univariate and multivariate specifications (see columns (1) and

(4)). Thus, our data do not reveal whether color or black and white newspaper is more efficient

14 This number represents a lower bound of the total effect. Some of the newspapers were distributed to people

who use a metro station but do not live in this neighborhood. Thus the effect on their voting behavior cannot be estimated.

15 As was discussed in section Data, about 50% of our total budget of $19,000 was not used to the newspaper distribution due to the managerial fraud.

20

in political advertisement. Since the printing cost of a color newspaper is 70% higher than a

black and white one, we can conclude that in terms of cost per vote, a black and white

newspaper is more efficient. We can see that it is more efficient to distribute newspaper on

weekend rather than on a working day. The effect of the weekend distribution is minus 2.44% of

votes for Sobyanin, and the effect of the working day distribution is negative 1.07%. The

difference between working day and weekend distribution is 1.37 percentage points, significant

at 5% level (see column (2)). Multivariate specification in column (4) delivers the similar result:

the working-weekend difference is 1.39%, significant at 5% level. The possible explanation of

this empirical finding is that on weekends people have more possibility for comprehension of

new information. It is very difficult to read a newspaper in the rush hour when metro cars are

fully packed. Also, more political news is produced during working days. This fact increases a

total volume of consumed information during working days compared to weekends. Thus, the

information from the newspaper might be ousted by information from other sources. The

difference between evening and morning distribution is marginally significant at 14% level.

However, economic significance is quite substantial. The morning distribution corresponds to a

0.96% decrease in votes of Sobyanin whereas the evening distribution is associated with a

1.88% decrease (see column (3)). The effect of the evening distribution is almost 2 times larger.

The multivariate specification in column (4) shows the similar effect: coefficient for Evening is -

0.96% significant at 14%. The possible explanation for this empirical finding is that evening

distribution leads to higher readership per a copy of the distributed newspaper. People who get

the newspaper near the metro station at the evening are more likely to bring it to home and share

it to the family members. Whereas those who get the newspaper in the morning are more likely

to read only by themselves and not to carry it during the entire day.

21

6. ConclusionsThis paper presents the results of the unique field experiment. We published anti-

government newspaper and distributed it during a month prior the 2013 Moscow mayoral

elections. We find that this newsapse had a significant impact on voting behavior. The

incumbent mayor lost 1.48 percentage points of votes at the voting stations where the newspaper

was distributed. We estimate that the total impact of the newspaper as minus 2,369 votes for the

incumbent mayor with the average costs of $4 per vote. We randomly assigned different types of

the newspaper (color or black and white) and ways of distribution (evening or morning,

weekends or working days). We find that it is more efficient to distribute newspapers on

weekends compared to the working days and in the evenings compared to the mornings. Our

paper expands the important work by Enikolopov, Petrova, and Zhuravskaya (2011) which

analyzes the impact of the independent media on the voting behavior at the emerging

democracies.

We suggest several directions for future research. First, it is interesting to analyze when it is

more efficient to campaign: one week before the election, one month before, or several months

before. Second, it is important to compare different ways of distribution: handing in at the

streets, near metro stations, or mail box delivery. Finally, it is interesting to experiment with

different degree of criticism: mild, moderate, or aggressive wording.

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22

DellaVigna, Stefano, and Ethan Kaplan, 2007, “The Fox News Effect: Media Bias and

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Experiment Measuring the Effect of Newspapers on Voting Behavior and Political Opinions”,

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(4) 797–813

23

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Elections, 1999-2000, British Journal of Political Science 35(2): 191-208

24

Table 1. Metro Stations Where the Newspaper Was Distributed

The table presents a list of 20 metros stations where the newspaper was distributed. The station

selection procedure is described in section Data. Color/b-w is equal to “color” if a color newspaper was

distributed and “b-w” if a black and white newspaper was distributed. Working/weekend is equal to

“working” if the newspaper was distributed on working days (Monday through Friday) and “weekend” if

the newspaper was distributed on weekends (Saturday or Sunday). Evening/morning is equal to

“evening” if the newspaper was distributed from 18.00 to 20.00 on working days and from 19.00 to 21.00

on weekends. Evening/morning is equal to “morning” if the newspaper was distributed from 8.00 to

10.00 on working days and from 10.00 to 12.00 on weekends. Manager indicates the manager who was

responsible for distribution at the given station.

Metro line Station Color/b‐w Working/weekend Evening/morning Manager

(1) (2) (3) (4) (5) (6)

Red, North Krasnoselskaya color Working evening Manager 4

Red, North Sokolniki b‐w Weekend morning Manager 4

Red, North Preobrazhenskaya

Ploschad b‐w Working evening Manager 4

Red, North Cherkizovskaya b‐w Working evening Manager 4

Red, North Ulitsa Podbelskogo color working evening Manager 4

Dark blue, East Baumanskaya color weekend morning Manager 4

Dark blue, East Semenovskaya b‐w working morning Manager 4

Dark blue, East Partizanskaya b‐w weekend morning Manager 4

Dark blue, East Izmailovskaya color working morning Manager 4

Dark blue, East Pervomaiskaya color working morning Manager 4

Dark blue, West Park Pobedy b‐w weekend evening Manager 1

Dark blue, West Slavyansky Bulvar b‐w working evening Manager 1

Dark blue, West Molodezhnaya color weekend evening Manager 1

Dark blue, West Krylatskoe color working morning Manager 1

Dark blue, West Strogino color working evening Manager 1

Blue, West Studencheskaya color working morning Manager 1

Blue, West Kutuzovskaya b‐w working morning Manager 1

Blue, West Bagrationovskaya b‐w working evening Manager 1

Blue, West Filevsky Park b‐w weekend evening Manager 1

Blue, West Pionerskaya color working evening Manager 1

25

Table 2. Presidential Election Results, March 2012

The table presents the results of the presidential election that were held on March 4, 2012. The entire

sample includes voting stations located near 116 metro stations located outside of the central ring line.

The treatment sample includes voting stations located near 20 stations where the newspaper was

distributed. See section Data for the detailed description of the sample construction procedures Column

(1) includes 5 closest voting stations to a metro station, column (2) includes 6th to 10th closest voting

stations to a metro station, column (3) includes 11th to 15th closest voting stations to a metro station, and

column (4) includes 15 closest voting stations to a metro station. The numbers in the tables indicate the

average percentage of votes received by different candidates. Turnout is the average voter turnout at the

presidential election. Distance to a metro indicates the average distance from voting stations to a metro

station.

Candidate 5 closest

voting stations 6‐10 closest

voting stations 11‐15 closest voting stations

15 closest voting stations

(1) (2) (3) (4)

Putin, United Russia

Entire sample 0.4500 0.4502 0.4590 0.4526

Treatment sample 0.4471 0.4416 0.4604 0.4482

Zyuganov, Communist party

Entire sample 0.1928 0.1922 0.1915 0.1922


Zhirinovskiy, LDPR

Entire sample 0.0593 0.0589 0.0606 0.0595


Mironov, a Just Russia

Entire sample 0.0516 0.0519 0.0511 0.0516


Prokhorov, Civic platform

Entire sample 0.2211 0.2214 0.2136 0.2191


Turnout

Entire sample 0.5922 0.5817 0.5881 0.5875


Distance to a metro station, km

Entire sample 0.484 0.789 0.987 0.729


Number of voting stations

Entire sample 562 508 415 1485

Treatment sample 100 80 53 233

26

Table 3. Moscow Mayoral Election Results, September 2013

The table presents the results of the Moscow mayoral election that were held on September 8, 2013.

The entire sample includes voting stations located near 116 metro stations located outside of the central

ring line. The treatment sample includes voting stations located near 20 stations where the newspaper

was distributed. See section Data for the detailed description of the sample construction procedures.

Column (1) includes 5 closest voting stations to a metro station, column (2) includes 6th to 10th closest

voting stations to a metro station, column (3) includes 11th to 15th closest voting stations to a metro

station, and column (4) includes 15 closest voting stations to a metro station. The numbers in the tables

indicate the average percentage of votes received by different candidates. Turnout is the average voter

turnout at the mayoral election. Distance to a metro indicates the average distance from voting stations to

a metro station.

Candidate 5 closest

voting stations 6‐10 closest

voting stations 11‐15 closest voting stations


(1) (2) (3) (4)

Sobyanin, United Russia

Entire sample 0.4825 0.4860 0.4932 0.4867


Melnikov, Communist party

Entire sample 0.1108 0.1111 0.1097 0.1106


Degtyarev, LDPR

Entire sample 0.0274 0.0273 0.0272 0.0273


Levichev, a Just Russia

Entire sample 0.0313 0.0297 0.0290 0.0301


Mitrokhin, Yabloko

Entire sample 0.0390 0.0388 0.0369 0.0384


Navalny, People’s Alliance

Entire sample 0.2949 0.2927 0.2891 0.2925


Turnout

Entire sample 0.3295 0.3259 0.3235 0.3266


Distance to a metro station, km

Entire sample 0.4843 0.7890 0.9872 0.7291


Number of voting stations

Entire sample 562 508 415 1485

Treatment sample 100 80 53 233

27

Table 4. Correlations of Votes between Presidential and Moscow Mayoral Elections

The table presents the correlation of votes between the presidential and the Moscow mayoral

elections. Panel A presents the results for the entire sample which includes voting stations located near

116 metro stations located outside of the central ring line. The entire sample includes 1485 voting

stations. Panel B shows correlations for the treatment sample which includes voting stations located near

20 stations where the newspaper was distributed. The treatment sample includes 233 voting stations.See

section Data for the detailed description of the sample construction procedures.

Panel A. Entire sample

Putin Zyuganov Zhirinovskiy Mironov Prokhorov

Sobyanin 0.655 ‐0.165 0.422 ‐0.167 ‐0.649

Melnikov ‐0.299 0.344 ‐0.275 0.144 0.201

Degtyarev 0.337 ‐0.113 0.462 ‐0.097 ‐0.395

Levichev 0.156 ‐0.046 0.202 0.063 ‐0.197

Mitrokhin ‐0.340 0.106 ‐0.241 0.175 0.320

Navalny ‐0.635 0.045 ‐0.437 0.087 0.695

Panel B. Treatment sample

Putin Zyuganov Zhirinovskiy Mironov Prokhorov

Sobyanin 0.696 ‐0.350 0.051 ‐0.236 ‐0.541

Melnikov ‐0.251 0.368 ‐0.141 0.034 0.143

Degtyarev 0.222 ‐0.183 0.487 ‐0.049 ‐0.302

Levichev ‐0.031 0.030 0.158 0.206 ‐0.069

Mitrokhin ‐0.184 0.067 0.030 0.085 0.116

Navalny ‐0.649 0.221 ‐0.125 0.181 0.588

28

Table 5. Votes for Sobyanin and Newspaper Distribution

The table presents the OLS regressions which analyze the relation between votes for Sobyanin and the

newspaper distribution. Votes for Sobyanin is percentage of votes for Sobyanin at the Moscow mayoral

election. Newspaper is a dummy equal to one if the newspaper was distributed at a metro station close to

the voting station, Putin, 2012 is percentage of votes for Putin at the 2012 presidential election, Turnout,

2012 is a turnout rate at the 2012 presidential election, Distance from metro is a distance from a voting

station to the metro station. Column (1) includes 5 closest voting stations to a metro station, column (2)

includes 6th to 10th closest voting stations to a metro station, column (3) includes 11th to 15th closest

voting stations to a metro station, and column (4) includes 15 closest voting stations to a metro station.

The numbers in parentheses are standard errors. *, **, and *** indicate statistical significance at the

10%, 5%, and 1% levels.

Dependent variable: Votes for Sobyanin


6‐10 closest voting stations

11‐15 closest voting stations


(1) (2) (3) (4)

Newspaper ‐0.0189 ‐0.0124 ‐0.0077 ‐0.0148

(0.005)*** (0.0052)** (0.0076) (0.0033)***

Putin, 2012 0.8146 0.9793 0.7861 0.8552

(0.0394)*** (0.0403)*** (0.0514)*** (0.0248)***

Turnout, 2012 ‐0.0927 ‐0.0885 ‐0.2241 ‐0.1450

(0.0331)*** (0.04)** (0.0427)*** (0.0217)***

Distance from metro ‐0.0062 ‐0.0077 ‐0.0175 ‐0.0077

(0.007) (0.0053) (0.0067)*** (0.003)**

R‐sq 0.4483 0.5595 0.3801 0.4581

Number of obs 562 507 415 1484

29

Table 6. Votes for Other Candidates and Newspaper Distribution

The table presents the OLS regressions which analyze the relation between votes for different candidates

and the newspaper distribution. Melnikov, Degtyarev, Levichev, Mitrokhin, and Navalny indicate

percentage of votes received by a relevant candidate at the Moscow mayoral election. Putin, Zyuganov,

Zhirinovskiy, Mironov, and Prokhorov indicate the percentage of votes received by a relevant candidate

at the presidential election. Turnout, 2013 is the voter turnout at the mayoral election. Turnout, 2012 is

the voter turnout at the presidential election. All other variables are defined in Table 5. The numbers in

parentheses are standard errors. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%

levels.

Dependent variable: Melnikov Degtyarev Levichev Mitrokhin Navalny Turnout, 2013

(1) (2) (3) (4) (5) (6)

Newspaper 0.0031 ‐0.0007 0.0003 0.0066 0.0055 0.0026

(0.0016)* (0.0007) (0.0007) (0.0009)*** (0.0029)* (0.0035)

Putin, 2012 ‐0.0945 0.0317 0.0425 ‐0.0838 ‐0.1853 ‐0.1210

(0.0133)*** (0.006)*** (0.0057)*** (0.0146)*** (0.0451)*** (0.0262)***

Turnout, 2012 0.0270 ‐0.0080 ‐0.0192 0.0184 0.1035 0.5728

(0.0106)** (0.0045)* (0.0048)*** (0.0061)*** (0.019)*** (0.0229)***

Dist. from metro 0.0039 ‐0.0005 ‐0.0037 0.0008 0.0061 0.0043

(0.0015)*** (0.0006) (0.0007)*** (0.0008) (0.0026)** (0.0032)

Zyuganov, 2012 0.3339

(0.032)***

Zhirinovskiy, 2012 0.3048

(0.0221)***

Mironov, 2012 0.1442

(0.0366)***

Prokhorov, 2012 0.0153 0.6265

(0.0144) (0.0446)***

R‐sq 0.1577 0.2299 0.0670 0.1557 0.4997 0.3027

Number of obs 1484 1484 1484 1484 1484 1484

30

Table 7. Votes for Sobyanin and Different Types of Newspaper

The table presents the OLS regressions which analyze the relation between votes for Sobyanin and

different types of the newspaper. Color is a dummy that is equal to one if a color newspaper was

distributed (0 if black and white), Working is a dummy equal to one if the newspaper was distributed on

working days (0 for weekends), and Evening is a dummy equal to one if the newspaper was distributed

in the evenings (0 for the morning distribution). All other variables are defined in Table 5. The numbers

in parentheses are standard errors. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%

levels.

Dependent variable: Votes for Sobyanin

(1) (2) (3) (4)

Newspaper ‐0.0179 ‐0.0244 ‐0.0096 ‐0.0201

(0.0048)*** (0.0058)*** (0.0048)** (0.0075)***

Putin, 2012 0.8564 0.8541 0.8612 0.8607

(0.0248)*** (0.0247)*** (0.0251)*** (0.0251)***

Turnout, 2012 ‐0.1447 ‐0.1437 ‐0.1494 ‐0.1481

(0.0217)*** (0.0217)*** (0.0219)*** (0.0219)***

Dist. from metro ‐0.0075 ‐0.0075 ‐0.0076 ‐0.0074

(0.0031)** (0.003)** (0.003)** (0.003)**

Color (vs. b‐w) 0.0057 0.0016

(0.0062) (0.0064)

Working (vs. weekend) 0.0137 0.0139

(0.0067)** (0.0068)**

Evening (vs. morning) ‐0.0092 ‐0.0096

(0.0062) (0.0064)

R‐sq 0.4584 0.4596 0.4589 0.4605

Number of obs 1484 1484 1484 1484

31

Appendix1.Newspaper“TheTruthaboutMoscow”

Page 1 contains a short bio of Sergey Sobyanin, emphasizing that he several times changed

his political views. During Soviet times he was a member of the Communist Party of the Soviet

Union. After collapse of the Soviet Union, he quit the Communist Party and became pro-

democratic activist. He was appointed as a mayor of Kogalym, a city in the oil province of

Khanty-Mansiysk. The rumors were that this appointment was related to his friendship with

local oil oligarchs. After Putin was elected the president in 2000, Sobyanin again changed his

political views and became a member of the United Russia, pro-Putin political party. He was a

strong Putin advocate and actively promoted the law to increase the presidential term from 4 to 7

years. Eventually the presidential terma was increased to 6 years. His political career flourished

in the last 10 years. In 2005 he was appointed as a head of the presidential administration and in

2010 he was appointed as the mayor of Moscow.

Page 2 contains two articles about alleged corruption in refurbishing of the city side-walks

and urban forestry. The city budget spent 130 million dollars on paving slabs. However in 102

places the slabs were not laid (according to the documents, the paving slabs in these places were

laid). In many places the paving slabs were destroyed after a few months (if laid properly they

should serve for 25-30 years). Several media sources stated that the wife of Sergey Sobyanin,

Irina Sobyanina, was the owner of the company that produced paving slabs. This company was

the main contractor to repair sidewalks in Khanty-Mansiysk, where Sobyanin was a mayor in the

beginning of 2000s. The forestry of Tverskaya also raised a lot of question. According to expert

estimates, a set of a small tree, a bench, and a litter box which were installed in the city center

should cost around 10,000 dollars. However, the city government paid 100,000 dollars for each

set. The company which served as a contractor for this project is related to Vladimir Recin, the

former vice-mayor of Moscow.

32

Page 3 contains the article about illegal migrants. The illegal migration causes a lot of

tensions in Moscow. In fact, the city government and affiliated entities are the main source of

demand for illegal labor. Officially, the budget pays about $800-$1000 per month for low-

qualified labor. This money is enough to attract students, retired people, and residents of the

adjacent areas. However, corrupt officials pay around $250-$300 per month to illegal

immigrants and pocket the difference. Thus local low-qualified labor cannot get the job and

corrupt officials earn $550-$700 from each illegal migrant. The total corruption in this market is

estimated as 17 billion dollars per year.

Page 4 contains several jokes about Vladimir Putin, Sergey Sobyanin, and Elena Baturina

(the wife of the former mayor of Moscow).

37

Appendix2.MoscowMetroMap

- metro stations where local transport hubs are located (train stations, bus stations, etc.)

- metro stations located outsides of the city of Moscow

- metro stations where the newspaper was distributed