The Effect of Media on Voters: Field Experiment at the Moscow Mayoral

Home , Bagrationovskaya, Mayor of Moscow, Sergey Sobyanin, Slavyansky Bulvar

The Effect of Media on Voters: Field Experiment at the

Moscow Mayoral Elections.

Maxim Mironov, Alexandra Petrachkova*

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.

Introduction Does 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.

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.

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.

(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. Analytical Framework The 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

5 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

6 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.

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. Background Information The 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.

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/

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. Data and Experiment Design The 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®ion=77&global=&sub_region=0&prver=0&pronetvd=null&vibid=27720001 368293&type=234

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.

11 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

12 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.

13 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.

[Insert Table 2 here]

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

14 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).

[Insert Table 3 here]

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

15 between Mironov and Levichev is 0.206, correlation between Prokhorov and Mitrokhin is 0.116, correlation between Prokhorov and Navalny is 0.588.

[Insert Table 4 here]

4. Empirical Strategy The 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:

Sobyaniniiiiii 1234 Newspaper  PutinP  TurnoutP  DistMetro , (1)

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

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

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

election, TurnoutPi is a turnout rate at the 2012 presidential election, DistMetroi 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:

Candidateiiiiiii 12345 Newspaper  PutinP  TurnoutP  Dist  CandidateP , (2)

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

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

16 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:

Turnoutiiiiii 1234 Newspaper  PutinP  TurnoutP  DistMetro , (3)

where Turnouti 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:

Sobyaniniiiiii 12345 Newspaper  PutinP  TurnoutP  DistMetro  Color  (4)

Sobyaniniiiiii 12345 Newspaper  PutinP  TurnoutP  DistMetro  Working  (5)

Sobyaniniiiiii 12345 Newspaper  PutinP  TurnoutP  DistMetro  Evening  (6)

Sobyanin  Newspaper  PutinP  TurnoutP  DistMetro  iiiii1234 (7) 56Coloriii Working   7 Evening  

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

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

(0 for weekends), and Eveningi 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.

5. Results We 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

18 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

[Insert Table 5 here]

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.

19 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.

6. Conclusions This 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.

References Ansolabehere, Stephen and Shanto Iyengar, 1995, “Going Negative: How Political

Advertisements Shrink and Polarize the Electorate”, New York: Free Press

Brooks, Deborah J., 2006, “The Resilient Voter: Moving Toward Closure in the Debate

Over Negative Campaigning and Turnout”, Journal of Politics, 68 (3) 684–696

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

Voting”, Quarterly Journal of Economics, 122(3): 1187-1234

Enikolopov, Ruben, Maria Petrova, and Ekaterina Zhuravskaya, 2011, “Media and Political

Persuasion: Evidence from Russia”, American Economic Review, 101(12): 3253–3285

Enikolopov, Ruben, Vasily Korovkin, Maria Petrova, Konstantin Sonin and Alexei

Zakharov, 2012, “Field Experiment Estimate of Electoral Fraud in Russian Parliamentary

Elections”, Proceedings of the National Academy of Sciences of the United States of America,

110 (2): 448-452

Fournier, Patrick, Richard Nadeaua, André Blaisa, Elisabeth Gidengilb, and Neil Nevittec,

2004, “Time-of-Voting Decision and susceptibility to campaign effects”, Electoral Studies, 23

(4): 661–681

Gallagher, Mary E. and Daniela Stockmann, 2011, “Remote Control: How the Media

Sustain Authoritarian Rule in China”, Comparative Political Studies, 44(4): 436-467

Gerber, Alan S., Donald P. Green, 2008, “Get Out the Vote. How to Increase Voter

Turnout”, Brookings Institution Press

Gerber, Alan S., James G. Gimpel, Donald P. Green, Daron R. Shaw, 2011, “How Large and Long-Lasting are the Persuasive Effects of Televised Campaign Ads: Results from a

Randomized Field Experiment”, American Political Science Review, 105 (1) 135-150

Gerber, Alan S., Dean Karlan, and Daniel Bergan, 2009, “Does the Media Matter? A Field

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

American Economic Journal: Applied Economics, 1:2, 35-52

Klapper, Joseph, 1960, “The Effects of Mass Communication”, New York: Free Press

Krupnikov, Yanna, 2011, “When Does Negativity Demobilize? Tracing the Conditional

Effect of Negative Campaigning on Voter Turnout”, American Journal of Political Science, 55

(4) 797–813

Lau, Richard R. and Gerald M. Pomper, 2004, “Negative Campaigning: an Analysis of US

Senate Elections”, Political Science

Lau, Richard R., Lee Sigelman, and Ivy B. Rovner, 2007, “The Effects of Negative Political

Campaigns: a Meta-Analytic Reassesment”, Journal of Politics, 69 (4): 1176–1209

Nickerson, David, 2007, “Quality is job one: Professional and Volunteer Voter Mobilization

Calls”, American Journal of Political Science, 51 (2): 269-282

Panagopoulos, Costas, 2011, “Timing is Everything? Primacy and Recency Effects in Voter

Mobilization Campaigns”, Political Behavior, 33: 79-93

White, Stephen, Sarah Oats, and Ian McAllister, 2005, “Media Effects and Russian

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

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 Preobrazhenskaya Red, North 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

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.

5 closest 6‐10 closest 11‐15 closest 15 closest Candidate voting stations voting stations voting stations 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 Treatment sample 0.1905 0.1937 0.1919 0.1919 Zhirinovskiy, LDPR Entire sample 0.0593 0.0589 0.0606 0.0595 Treatment sample 0.0579 0.0584 0.0611 0.0588 Mironov, a Just Russia Entire sample 0.0516 0.0519 0.0511 0.0516 Treatment sample 0.0523 0.0526 0.0499 0.0518 Prokhorov, Civic platform Entire sample 0.2211 0.2214 0.2136 0.2191 Treatment sample 0.2257 0.2299 0.2108 0.2238 Turnout Entire sample 0.5922 0.5817 0.5881 0.5875 Treatment sample 0.5998 0.5950 0.6071 0.5998 Distance to a metro station, km Entire sample 0.484 0.789 0.987 0.729 Treatment sample 0.479 0.840 1.024 0.727 Number of voting stations Entire sample 562 508 415 1485 Treatment sample 100 80 53 233

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.

5 closest 6‐10 closest 11‐15 closest 15 closest Candidate voting stations voting stations voting stations voting stations (1) (2) (3) (4) Sobyanin, United Russia Entire sample 0.4825 0.4860 0.4932 0.4867 Treatment sample 0.4640 0.4654 0.4826 0.4687 Melnikov, Communist party Entire sample 0.1108 0.1111 0.1097 0.1106 Treatment sample 0.1152 0.1156 0.1086 0.1138 Degtyarev, LDPR Entire sample 0.0274 0.0273 0.0272 0.0273 Treatment sample 0.0251 0.0271 0.0270 0.0262 Levichev, a Just Russia Entire sample 0.0313 0.0297 0.0290 0.0301 Treatment sample 0.0309 0.0289 0.0301 0.0300 Mitrokhin, Yabloko Entire sample 0.0390 0.0388 0.0369 0.0384 Treatment sample 0.0450 0.0455 0.0425 0.0446 Navalny, People’s Alliance Entire sample 0.2949 0.2927 0.2891 0.2925 Treatment sample 0.3058 0.3027 0.2947 0.3022 Turnout Entire sample 0.3295 0.3259 0.3235 0.3266 Treatment sample 0.3409 0.3290 0.3387 0.3363 Distance to a metro station, km Entire sample 0.4843 0.7890 0.9872 0.7291 Treatment sample 0.4792 0.8405 1.0236 0.7271 Number of voting stations Entire sample 562 508 415 1485 Treatment sample 100 80 53 233

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

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 5 closest voting 6‐10 closest 11‐15 closest 15 closest for Sobyanin stations voting stations voting stations 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

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

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

Appendix 1. Newspaper “The Truth about Moscow”

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.

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).

Appendix 2. Moscow Metro Map

- 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