Pope Breaking News: the Effect of TV Coverage on Voters and Financial Markets

Pope Breaking News: The Eﬀect of TV Coverage on Voters and Financial Markets∗

This is a preliminary draft (Jan 2019) Please do not circulate

Abstract

To what extent does a politician’s TV coverage in campaign drive up his vote shares? Do investors of politically-connected ﬁrms take into account candidates’ TV exposure? This paper addresses those questions by exploiting a natural experiment: during the 2013 Italian electoral campaign Pope Benedict XVI suddenly resigned, and politics TV coverage dropped sharply. I show that Berlusconi’s lower exposure (-26 p.p.) caused a vote share loss of 2 p.p., which ultimately determined his defeat. On the stock market, the arrival of Pope news lead investors to penalize Mediaset (Berlusconi’s broadcaster), in anticipation of looser political ties.

Keywords: TV coverage, Media, voting, political ties JEL codes: L82, D72, G14, G41, Z12, Z13

∗I am grateful to Guido Legnante and to Paola Migliorino for sharing their data. I thank Alberto Abadie, Ruben Durante, Massimo Guidolin, Andrea Ichino, Eliana La Ferrara, David Martinez de Lafuente, Andrea Mattozzi and Paolo Pinotti for their helpful suggestions. I also thank participants to the MEWG seminars at EUI for their comments and feedback. †European University Institute (EUI) 1 Introduction

“The instrument (the telescreen, it was called) could be dimmed, but there was no way of shutting it oﬀ completely” (George Orwell, 1984).

When elections approach, candidates become ubiquitous in TV: they participate to debates, grant interviews, give speeches, and exploit any chance to increase their exposure. In the US, an equal-time rule specifies that television broadcast stations must provide an equivalent opportunity to any opposing political candidates who request it. Similar laws have been adopted in other countries (e.g. Italy, France), where broadcasters must accord each contender precisely equal speaking time and coverage in the final weeks of the campaign. Altogether, those regulatory efforts indicate a worldwide consensus on the relevance of TV exposure to electoral outcomes. A growing body of research suggests that years of TV fruition can affect citizens’ political decisions (DellaV- igna and Kaplan, 2007; Campante and Hojman, 2013; Durante, Pinotti and Tesei, 2017;). Moreover, there is evidence that politicians’ campaigns can affect voters in different ways, such as by priming the issues underlying vote choice (Iyengar and Simon, 2000; Druckman, 2004) or images of integrity and leadership (Funk, 1999; McGraw and Ling, 2003; Druckman and Holmes, 2004). But to what extent does candidates’ TV coverage drive up their votes in the short span of a campaign? Despite the general consensus, little conclusive evidence has been produced to this effect. A plausible reason is that the impact is difficult to identify: TV coverage and vote shares will be correlated even if exposure did not affect voters’ support, as both depend on a politician’s prominence in the political arena. In this paper I avoid this endogeneity problem by exploiting a natural experiment. On February 11 2013, during the last weeks before the Italian general elections, the Pope Benedict XVI announced his intention to renounce to the papacy. The event was unexpected, given its rarity (it only occurred 5 times in history, and last in 1415). After the Pope’s declaration, Italian media immediately shifted to a blanket coverage of related contents, causing a marked turn of attention from the upcoming elections. I identify the effect of exposure in TV on vote shares by instrumenting candidates’ visibility with the presence of Pope news. I measure voters’ support to candidates in two ways: first with a stock-market-based index, then using survey data. The results from the two analyses are concurrent: I find that the decline in visibility translated into a significant vote share loss for the right-wing leader Silvio Berlusconi, a candidate whose communication strategy heavily relied on TVs. His dip in television exposure (-26 percentage points) translated into a vote share decline of roughly 2 percentage points, an effect large enough to cause his electoral defeat (the left-wing won by 0.4 percentage points). I assess the robustness of the TV-visibility mechanism by exploring the channels through which Pope news could have affected Berlusconi’s vote share. A large part of his former voters appears to have shifted towards the new-comer in the political arena “Movimento 5 Stelle” (M5S). This group’s communication strategy hinged on the internet, but it was otherwise largely comparable to that of Berlusconi’s party: both strongly depended on media-savvy and charismatic leaders (Fella and Ruzza, 2013; Jones and Pasquino, 2015; Verbeek and Zaslove,

1 2016; Durante, Pinotti and Tesei, 2017). I find that the Pope’s announcement had no significant effect on interest in politics, voting participation or religiosity. Instead, it induced a shift in main source of political information for some of former Berlusconi voters: the 2008-supporters who in 2013 switched to M5S. In fact, the TV shock significantly increased their probability to resort to internet as principal source of political information. Therefore, I argue that the main mechanism behind Berlusconi’s vote loss after Pope news is the exposure to the different information environment of some former supporters, who changed their political affiliation in favour of the party with an internet-centred propaganda. Having proved the common thought about the relationship between TV coverage and votes, this paper poses an ancillary question: do investors of politically-connected firms take into account candidates’ TV exposure? I investigate the effect on the financial market by studying whether the stock price of firms connected to candidates is responsive to their TV coverage. I analyse the case of Mediaset (Berlusconi’s broadcaster), whose business has been found to benefit from Berlusconi’s office holding (Della Vigna, Durante, Knight and La Ferrara, 2017). I find that when Pope news arrived, Mediaset shares underperformed an otherwise similar synthetic control portfolio. Investors believed the coverage shock to decrease Berlusconi’s vote shares, and they devalued Mediaset in anticipation of its looser future political ties. I provide supportive evidence to exclude potential alternative mechanisms (such as lower Mediaset advertising revenues), and I pass the results to robustness checks. This paper relates to a wide and growing body of empirical studies on media economics, and in particular on the influence of TV on voting behaviours. In this strand, several studies have assessed the effect of long exposures to different types of TV contents, such as news (DellaVigna and Kaplan, 2007; Enikolopov et al., 2011) or entertainment programs (Durante, Pinotti and Tesei, 2017). This work contributes to this literature by providing a causal estimate of the impact of TV coverage on votes in the short span of the campaign. The findings are coherent with the idea suggested by Prat (2018), namely that relative media power can be well approximated by attention shares.1 He finds that each of the three biggest US media groups could individually determine the results of most presidential elections, with broadcasters being the most powerful players among the media. A second wide literature to which this paper relates studies connections between firms and politicians (see, e.g. Khwaja and Mian, 2005; Faccio, 2006; Fisman, Fisman, Galef, Khurana and Wang, 2012; Cingano and Pinotti, 2013; Bertrand, Bombardini and Trebbi, 2014; Tahoun, 2014; Akey, 2015). The present work exploits the event-study methodology, which owes its diffusion to researches such as Roberts (1990), Fisman (2001), and Johnson and Mitton (2003). This paper applies the synthetic control method (Abadie and Gardeazabal, 2003; Abadie, Diamond and Hainmueller, 2015) to stock-market data (e.g. Guidolin and La Ferrara, 2007; Acemoglu, Johnson, Kermani, Kwak and Mitton, 2016). The results on the negative reaction of Mediaset to the TV visibility shock are congruent with the findings by Della Vigna, Durante, Knight and La Ferrara (2017). The authors provide evidence of firms shifting their advertising spending towards Mediaset when Berlusconi was in office in the hope of obtaining political benefits; this, in turn, increased Mediaset profitability in coincidence with Berlusconi’s office holding.

1Prat deﬁnes the power of a media organization as its ability to induce voters to make electoral decisions they would not make if reporting were unbiased.

2 The remainder of the paper is organized as follows: After introducing the background (section 2), I perform an instrumental variable analysis to estimate the eﬀect of TV coverage on a stock-market based measure of Berlusconi’s electoral support (section 3). Then, I repeat the analysis using survey data, and I test the mechanisms (section 4). In section 5, I turn to study the impact of the TV shock on the Cumulative Abnormal Returns of Mediaset, comparing the latter to a synthetic control portfolio. I then test the channel for the Mediaset decline and I perform robustness checks (section 6). Section 7 concludes.

2 Background: Berlusconi and the 2013 elections

The electoral campaign for the 2013 Italian general elections started with the selection of party leaders in the late fall of 2012 and culminated with the elections on February 23, 2013. At the end of 2012, six predominant parties were animating the Italian political debate: the four bigger ones were “Partito democratico” (PD), “Popolo della libertà” (PDL), “Movimento Cinque Stelle” (M5S), and “Scelta civica” (SC ), respectively guided by Pierluigi Bersani, Silvio Berlusconi, Beppe Grillo and the former Prime Minister, Mario Monti2. At the onset of the campaign, Berlusconi was under criminal investigation (since 2011) for alleged sexual relationships with minors and abuse of office, and in January 2013 his the request to adjourn the trial was rejected. In addition, he had just closed (in December 2012) an extremely long and expensive divorce. With such premises, many had considered his candidacy doomed from the onset of the campaign. Against all odds, he was instead able to turn the negative-coverage of media at his advantage and he lost the elections by a small margin. Political analysts Bobba, Legnante, Roncarolo and Seddone (2013) describe Berlusconi’s 2013 communication strategy as extremely mediatic, a mix of political promises (like the guarantee to personally refund the tax on housing) and personal gossips. When he participated to the TV show of his historically adverse journalist, Michele Santoro, he drew an audience of 8.67 million people: the next day (January 11), journalists and experts of all political factions unanimously defined the episode as a “Berlusconi show”. Bobba et al. (2013) note that, while Berlusconi and Monti had comparable visibility within coverage on newscasts, only the former’s approval appeared impervious to the negativity of coverage3. In other words, the more Berlusconi appeared on TV, the more his consensus benefited from it. The newspaper “La Stampa” counted that only in the first 20 days of campaign (including non-working days) Berlusconi accepted 54 invitations to TV programs: more than two per day4. He appeared extensively not only on main broadcasters, but also on minor regional channels: just during that 20 days Berlusconi’s speech-time lasted 28 hours 56 minutes and 32 seconds 5. As a term of comparison, Monti had 20 hours and 13 minutes. In that time span, 395 million people watched the tycoon on the main news programs: this means that, on average, one month before the elections Italians had already seen him about once every three days6. Berlusconi was certainly planning to keep appearing on TV at such pace during the whole campaign. He had taken into account that, right before the vote, part of the audience would be “distracted” by

2Beppe Grillo was not the party’s oﬃce runner, but he was the main mediatic ﬁgure of the party. Other smaller factions were “Fare per fermare il declino” and “Rivoluzione Civile”, respectively guided by Oscar Giannino and Antonio Ingroia. 3The authors’ statistics were built selecting one newscast for each national broadcast company (Rai, Mediaset, La7 and Sky). 4“La Stampa”, January 15 2013. 5Auditel data. 6The broadcasts were: RAI, Mediaset and La7.

3 the popular music festival Sanremo (February 12-16). Indeed, he tried to have the festival shifted to after the elections, plainly conceding that “the festival steals share and [...] it makes communication more diﬃcult”7. Certainly, Berlusconi could not foresee that in the morning of February 11 Pope Joseph Ratzinger would have announced his decision to renounce to the papacy. The news was ﬁrst spread on media outlets at 11:46 (Italian time) by the Italian agency Ansa, and then re-bounced at worldwide level. In Italy, the Pope’s decision had immediately a wide resonance. Quoting the journalist and news-director for “La7” broadcast, Enrico Mentana, “politics, chronicle, economics and sport are assigned a back-seat” as all the news programs (TG1, TG5 and TGLa7) focused on the Pope-news8.

3 TV and Berlusconi’s electoral support

3.1 Data on TV coverage

I use TV-coverage data from the TV content analysis for the 2013 campaign by the Italian National Election Study (Itanes). The dataset contains detailed information about all the main Italian TV programs, chosen on the basis of typology, broadcaster and audience share9, and it encompasses all the instalments aired between December 1, 2012 and March 3, 2013. The data include the first 12 actors that took part in each TV unit, the way they are presented and their characteristics. A TV unit is either one of the headlines in TV news programs, one of the first 3 news reports, or one of the main themes in talk shows and “pop” programs; an individual is considered present in the unit if he was either mentioned twice, quoted or interviewed. The TV units were then aggregated to create a measure of daily visibility for each of the main actors. Note that this measure encompasses not only news programs or politically themed shows, but also those hinging upon entertainment. The rationale for this is that non-politically themed programs accounted for a relevant part of politicians’ TV appearances during campaign (Legnante, Mancini, Mazzoleni and Roncarolo, 2013; Bianchi, Chianale and Pulvirenti, 2014). Table 1 contains the summary of the TV coverage of different actors, during the three windows of interest: 1) before Pope news, 2) between Pope news and the end of the Sanremo festival, and 3) after the end of Sanremo up until pre-election day. For each actor, I test the difference in mean-coverage between the periods, and I find that Pope news significantly disrupted coverage of Berlusconi, Monti and Bersani. Notice that Beppe Grillo’s TV coverage decreases only marginally after Pope news (from 7 to 4 percent): this is because the communication strategy of Movimento Cinque Stelle was mainly focused on internet and on direct contact with people. After the end of Sanremo, as elections approached, TV coverage of Berlusconi and Bersani increased again significantly. Grillo’s TV visibility grew because of indirect referral by other party leaders in final TV con- frontations. 7Corriere della sera, Feb 8 2013. 8Businesspeople, “Telegiornali delle 20 a confronto” Feb 11 2013. 9The list of the 13 programs is in the appendix.

4 Table 1: Summary statistics for TV coverage

(1) (2) (3) (4) (5) Coverage of: Berlusconi Monti Bersani Grillo Pope (1) Dec.1 - Feb.11: 0.345 0.389 0.234 0.070 0.005 (0.016) (0.016) (0.014) (0.008) (0.002) (2) Feb. 11-16: 0.151 0.173 0.114 0.040 0.138 (0.015) (0.016) (0.012) (0.011) (0.049) (3) Feb. 16-22: 0.332 0.242 0.348 0.316 0.051 (0.053) (0.049) (0.043) (0.042) (0.021)

Diﬀerence (2)-(1): -0.195∗∗∗ -0.216∗∗∗ -0.119∗∗∗ -0.029∗∗ 0.132∗∗∗ (0.021) (0.022) (0.018) (0.013) (0.046) Diﬀerence (3)-(2): 0.181∗∗∗ 0.069 0.233∗∗∗ 0.276∗∗∗ -0.086 (0.086) (0.052) (0.045) (0.044) (0.052)

Note: Standard errors in parenthesis.The table lists the results of two-tailed tests for diﬀerence

in means, for TV appearance of the main four candidates and of the Pope. Diﬀerences are

taken across time-windows (before, during the “Pope shock”, and after it), with standard errors

robust for heteroskedasticity. Data: Itanes monitoring of TV campaign 2013.

Figure 1 shows the pattern of TV appearances in a window of 20 days around the event-date: when Pope news arrived, the attention of media was suddenly diverted from politics (the Pope’s visibility obviously displays an inverted pattern). I perform a battery of Wald-tests for the existence of structural breaks in the series of TV coverage (appendix table A.1). When I impute the date of Pope news – February 11 – as known break-date, all the tests reject the null hypothesis of no-break. Similarly, when I perform the test for unknown break-date, Feb. 11 is “picked” by the series of Berlusconi, Monti and the Pope.

5 Figure 1: TV coverage of actors around the event date. Data: Itanes monitoring of TV campaign 2013

3.2 Data on electoral support

While studies about voting intentions usually rely on opinion polls, this possibility is precluded for my analysis. The reason is that the last available 2013 opinion poll dates February 8, while Pope news arrived only three days later10. In event studies, the literature suggests to rely on prediction markets, as they provide a good (and continuous) measure of electoral support (Wolfers and Zitzewitz, 2006; Snowberg, Wolfers and Zitzewitz, 2011). In 2013, however, none of the existing prediction markets traded futures on the Italian elections11. I use an alternative measure of voters’ support along the campaign, which was produced by the company Borsari (a publisher specialized in ﬁnancial analysis). During the campaign of 2013, Borsari published daily reports on its website12 (each one received on average 120’000 visits), displaying a measure of the advantage of the left-wing party (PD) over Berlusconi’s right-wing party (PDL), for the “Camera dei Deputati” chamber elections. This series uses stock market aggregate indexes, without building on prices of single companies or non-stock-market inputs (e.g. polls)13. Given the latter, the measure could be published uninterruptedly every

10The Italian law (Article 8.1, Law 28, 2000) states that each electoral opinion poll that is disseminated in mass media must have its data published on the website www.sondaggipoliticoelettorali.it. The same law impedes pollsters to publish their polls during the 15 days prior to the elections. This restriction discouraged the data collection (or at least their later posting): none of the polls in the website continue beyond February 8. 11With one exception: the group Intrade, which covered Italian 2013 elections but went into liquidation in 2015 (data are no longer available). 12www.borsarireport.it 13The series assembles the following indexes: BTP-BUND 10 years spread, Dax30, FTSE-Mib, US 30yrs T-bond, and the exchange

6 day until the one prior to elections, without fear of breaking the pre-ballot silence. The rationale behind the exploitation of stock-market indexes as “electoral thermometer” relies on the idea that financial markets mirror investors’ beliefs and information about elections; this is well known in the economics and finance literature. Forsythe, Frank, Krishnamurthy and Ross (1995) test and confirm that markets can efficiently aggregate information. As politicians’ platforms are capitalized in equity prices of politically-sensitive stocks and indexes, those are informative of electoral trends (Knight, 2006). Leblang and Mukherjee (2005) find that rational expectations of higher inflation under left-wing administrations lowers the volume and the mean price of stocks traded, not only during the incumbency of left-wing governments but also before elections if traders expect the left-wing party to win. Gemmill (1992) examines the behaviour of the stock and options markets in London during the 1987 election, and finds a close relationship between opinion polls and the FTSE 100 Index of share prices. Mattozzi (2008) uses the stock market to measure the electoral support of office- runners throughout the campaign. Let me define the “Fictitious Vote Share” (FVS) for party X in pre-election time t as the percentage of votes that party X would obtain if the ballot took place at that time t. I construct a FVS digitizing Borsari reports: the series varies with 15 minutes frequency, and at every intraday tick i of the campaign it tells what would be the difference in percent vote share between left- and right-wing parties if the ballot took place at that same time i. More simply, if the series attains a value of 10, it indicates that PD would have an advantage of 10 percentage points of vote share over PDL if the elections were in that moment. As a first mean to validate the ability of FVS to capture voting intentions, I plot it against the series of opinion polls. Figure 2 shows that, both in values and in trends, opinion polls and the FVS are similar14. As above explained, only the FVS was produced until one day before elections, and its prediction of the true election outcome was significantly better than that of rival measures. The left-wing PD won by 0.4 percentage points against the PDL. While the FVS predicted a 1 percentage point margin, the polls anticipated instead an average of 6.2 percentage points margin. I leave further corroborations of the FVS to section 3.4. Following the structure of previous section, I perform the test for structural breaks also on the FVS series. Both when I impute a known break-date and an unknown one, the test rejects at 99% confidence level the null hypothesis of no-structural break in correspondence of the exact moment (month, day and hour) of Pope’s announcement. Figure 3 shows the changes in trend of the FVS series: after the Pope news, and up until the closure of the Sanremo festival (the evening of Feb 16), the FVS exhibits an upward trend, while it decreases in other periods of the campaign.

rate Euro-dollar 14Pollsters listed on the governmental website sondaggipoliticoelettorali.it. The graph excludes pollsters whose sampling method, sample size or reference population was not kept constant over time, or series with less than 200 surveyed individuals per poll.

7 20 15 10 5 Distance: left-right in % in left-right Distance: 0 1/2/2013 1/21/2013 1/31/2013 2/12/2013 2/21/2013

Fictitious Vote Share (FVS) tecné Ipsos IspoRicerche Demopolis Election result

Figure 2: The FVS and a selection of opinion polls. Data: FVS from digitization of Borsari reports.

Figure 3: The FVS trends. Data: FVS from digitization of Borsari reports.

8 The change in FVS trend suggests that the TV diversion from politics affected more negatively the PDL than its rival PD (a more in-depth discussion is provided in section 3.4). Part of this difference is attributable to the relative magnitude of the shock for each of the two parties: before the Pope-news, Berlusconi was enjoying consistently more TV coverage than Bersani (PD’s leader). Therefore, the visibility of both politicians fell, but Berlusconi “lost” relatively more of it. This is coherent with the findings by Bobba et al. (2013), who considered TVs to be a more effective “persuasion” mean for Berlusconi than for other party leaders. Thus, when choosing the media platforms for their communication strategy, politicians appear to tie their choice to the characteristics of their electorate: e.g. the intensity of TV use is closely correlated to the relevance of TV in a politician’s target voting group. This mechanism relates to the literature on media slant: Gentzkow and Shapiro (2010), for instance, show that newspaper readers have a significant preference for likeminded news, and that firms in turn strongly respond to consumer preferences.

3.3 Analysis: the eﬀect of Pope news on FVS

In order to relate TV coverage to Berlusconi’s electoral support along the campaign, I adopt an instrumental variable approach to identify the average eﬀect on Berlusconi’s vote share for the days in which, because of the Pope news, TVs attention was diverted from the election campaign15 I estimate the following equation by 2SLS:

IV FVSi = β0 + β1 Pd + β2Ti + i (1)

The outcome variable FVS i is the diﬀerence between left- and right-wing party in time i (i= intraday, with 15 minutes frequency). TVd is the percentage of TV units, in a day d, in which Berlusconi was present; I instrument

TVd with a dummy variable, Pd, taking value 1 when TVs covered the renounce by the Pope and 0 otherwise;

Ti is a time trend over intraday units. Table 2 presents the results with two different versions of my instrument: in the table, “Pope news” is a dummy equal to 1 in the days when Pope news were covered by TV. “Pope + Sanremo news” is a dummy equal to 1 when TV covered either the Pope news or the Sanremo festival, which contributed to lengthen the period when Berlusconi disappeared from TV. Column 1 presents the first-stage for the Pope-news instrument: the Pope-shock on average decreased the TV presence of Berlusconi by 26 percentage points; the effect is statistically significant and the null hypothesis of weak-instrument is rejected at 99% confidence level (F-test at the bottom of the table). Column 2 displays the second stage: An extra 1% Berlusconi-coverage reduces the advantage of left-wing over right-wing party by 0.03 percentage points. The reduced form (column 3) contains the overall effect that Pope news had on Berlusconi’s electoral support: instead of continuing to shrink, the relative distance increased by 0.75 percentage points. The other columns of table 2 show that the effect is even greater when one considers the instrument “Pope+ Sanremo news”. Column 4 shows, once again, the first stage: Pope news and Sanremo news jointly reduced Berlusconi’s coverage by roughly 28 percentage points. The effect of Berlusconi’s presence on TV is now much larger (column 5); this is coherent with what suggested by figures 3 and 1: the Sanremo festival meant pulling the end of the no-TV window towards the election date,

15Durante and Zhuravskaya (2018), for instance, use a similar approach to analyse TV coverage of episodes in the conﬂict between Israel and Palestine.

9 namely where the TV-campaign seems to have been more efficient in closing the FVS gap (see the steeper trend in figure 3 after Feb 16). In fact, when considering jointly Pope-news and Sanremo, the total effect on FVS is an increment by almost 2 percentage points.

Table 2: IV estimation: the eﬀect of Pope news on TV coverage and FVS

(1) (2) (3) (4) (5) (6) Dep. var.: TV Berl FVS FVS TV Berl FVS FVS b/se b/se b/se b/se b/se b/se Pope news -25.89∗∗∗ 0.75∗∗∗ (1.17) (0.28)

TV Berlusconi -0.03∗∗∗ -0.06∗∗∗ (0.01) (0.01)

Pope+Sanremo -27.83∗∗∗ 1.75∗∗∗ (1.00) (0.26)

Time trend Yes Yes Yes Yes Yes Yes Observations 802 802 802 802 802 802 F-test (weak-ins) 487.42 781.88 mean of dep 37.48 8.28 8.28 37.48 8.28 8.28

Note: IV regressions. The dummies “Pope news” and “Pope news+Sanremo” are instruments for “TV Berlusconi”, namely the percentage of TV units in a day in which Berlusconi is present. Heteroskedasticity-robust standard errors are in parentheses. Data: Itanes TV monitoring of 2013 campaign.

Is such a small loss in vote share economically significant? Given that the final election resulted in a PD-PDL difference of 0.4 percentage points, the “Pope news” effect, although small, was strong enough to overturn the electoral outcome and determine the right-wing defeat. Absent the Pope’s announcement, Berlusconi would have won the 2013 elections.

3.4 The eﬀect of Pope news in Itanes 2013 RCS

In this subsection I use additional data from the Rolling Cross Section (RCS) dataset by the Italian National Election Study (Itanes) to test the results obtained in previous section, and then explore the mechanisms underlying Berlusconi’s loss in vote share after Pope news. The dataset consists of a series of electoral surveys conducted on a representative sample of the Italian population, before and after national parliamentary elections. I focus on the 2013 wave: it contains 8723 surveyed individuals before the elections and 3008 in the follow- up, and it includes questions on voting choice for 2008 and 2013 elections, general political preferences, media consumption and religious participation. The pre-electoral wave of the Itanes 2013 RCS covers all days between January 5 and February 23, the one before the elections. Itanes collected the data every day up to the one before the election and published roughly 1 year after the election16. Using the answers to the Itanes RCS, I

16The surveys were realized with the CAWI method (Computer Assisted Web Interviewing). On average, 203 individuals were surveyed each day. The post-electoral follow-up survey took place between March 27 and April 8, 2013, and participants were randomly selected with stratiﬁcation by gender, age, residence, political interest and orientation. The redemption rate for the follow-up sample was 91%.

10 Comparison: FVS and Itanes survey data 24 10 8 23 6 22 4 21 Fictitious VoteFictitious PD-PDL Share: Popularity 0-100: Bersani-Berlusconi 0-100: Popularity 2 Jan 28 Feb 4 Pope news SR ends Feb 22

Polynomial of popularity rates: Bersani-Berlusconi FVS

Figure 4: The figure compares two measures of the distance PD-PDL: Dots: “FVS” by the Borsari group; Line: polynomial fitting the answers to the Itanes RCS survey, measuring the distance in popularity between Bersani and Berlusconi. For each survey respondent, the distance in popularity was constructed using the question “From 0 to 100, how much do you like ...?” and subtracting Berlusconi’s score from Bersani’s. (Note: the measures use different scales, see the right and left y-axes in the graph). Data: FVS from digitization of Borsari reports; survey measure from Itanes RCS 2013. construct measures of voters’ opinion on candidates, as well as indexes for their “interest in politics”, “intention to abstain from voting”, “timing of voting decision”, “main source of information on elections” and “religiosity”.

3.4.1 Comparison of FVS to survey data

I first test whether survey and stock market data suggest similar paths of voters’ opinions. Using the RCS, I thus construct a new index to be compared to the FVS (which, as said, captures the distance between PD and PDL). More specifically, I exploit the question “On a scale from 0 to 100, how much do you like candidate...?”, and I subtract Berlusconi’s score from Bersani’s. I then set one measure against the other: figure 4 shows that the two series exhibit close patterns. This further validates the FVS and it suggests that the variation in popularity of left- and right-wing coalitions is closely related to changes in leaders’ appeal. While the FVS offered a high-frequency measure of the difference PD-PDL, it did not allow to disentangle the effects of Pope news for each of the two parties separately. I therefore exploit the survey data to isolate the trends of popularity for each leader. Figure 5 shows the appraisal patterns of the four main candidates in 2013 elections (Berlusconi, Bersani, Grillo and Monti). The figure plots the local polynomials fitting the survey answers. Only Berlusconi (top left panel) suffered a significant decrease in popularity in the window between Pope news and the end of Sanremo Festival. A formal test of those results is contained in table 3: column 1 shows that, while Berlusconi gained popularity overall during the period of the campaign, he lost 2.14 percentage points during the no-TV window. The appraisal of other leaders was not significantly affected (columns 2, 3 and 4). The distance between Bersani (PD) and Berlusconi (PDL) shrank during the entire campaign period,

11 From 0 to 100, how much do you like candidate 'X'?

Berlusconi Bersani 66 38 64 37 62 36 60 35 34 58 Jan 5 Jan 16 Jan 28 Pope news SR ends Jan 5 Jan 16 Jan 28 Pope news SR ends

Grillo Monti 58 58 56 56 54 54 52 52 50 50 Jan 5 Jan 16 Jan 28 Pope news SR ends Jan 5 Jan 16 Jan 28 Pope news SR ends

local polynomial of the score

Figure 5: Local polynomials ﬁtting the answers of Itanes RCS respondents to the question: “From 0 to 100, how much do you like candidate X ?”.

Table 3: Voters’ opinion on candidates (Data: Itanes RCS 2013)

(1) (2) (3) (4) (5) Berlusconi Bersani Monti Grillo Bersani-Berlusconi Dependent variable: How much do you like...? Time trend 0.092∗∗ -0.116∗∗∗ -0.142∗∗∗ 0.174∗∗∗ -0.207∗∗∗ (0.039) (0.035) (0.024) (0.028) (0.066)

noTV -2.142∗∗∗ 0.639 1.068 1.304 2.653∗ (0.792) (0.973) (0.869) (1.289) (1.560) Observations 7679 7623 7673 7428 7606 Mean of dependent 36.40 58.83 52.13 53.42 22.48 Ordinary Least Squares regressions. Dependent variables measure politicians’ agreeableness on a 0-100 scale, using the question: “From 0 to 100, how much do you like candidate X?”. “No-TV” is a dummy equal to 1 if the respondent was interviewed in the days between Pope’s announcement and the end of Sanremo Festival. Day-level clustered standard errors in parentheses. Data: Itanes RCS 2013.

12 but increased by sightly more than 2.5 percentage points during the no-TV window (column 5). These results conﬁrm the ﬁndings in section 3.3.

3.4.2 Testing the channels

Up to this point, the analysis on both survey and stock-market data confirmed that Pope news caused a loss in Berlusconi’s popularity. I now turn to investigating the channels at work behind this effect. I will argue that the loss suffered by PDL was channelled by media visibility, not so much by other mechanisms (such as shifts in voters’ religiosity or in voting participation). Using the Itanes RCS data I compare voters’ choices in the 2008 and 2013 elections. Figure 6 shows for every 2008 party the percentage of voters migrating to each 2013 vote-option. Out of respondents who voted Berlusconi’s coalition in 2008, 54% voted for him again in 2013. Only a small fraction switched to the left-wing PD (5%); in a similar way, only 8% chose Monti’s party (SC) or abstained from vote. Notably, however, 25% of those who voted Berlusconi in 2008 switched to the newcomer Movimento Cinque Stelle. In light of the final electoral result, where M5S was the most voted single party in the 2013 elections, it is notable that 40% of respondents who voted M5S were previously supporting the PDL coalition (appendix figure A.1). For this reason, voters who shifted towards Grillo’s party are particularly relevant to the analysis of the mechanisms underlying Berlusconi’s vote loss. To understand the differences in preferences between voters who remained PDL supporters and those who became M5S voters, I analyse respondents’ opinions about the last Berlusconi government (2008-2011). Figure A.2 plots the densities of the answers to the question “How positively do you evaluate the performance of Berlusconi’s 2008-11 government?”, where respondents who voted PDL in 2008 are grouped by their 2013 vote choice. Among those former Berlusconi voters, the ones that had the most positive opinion chose to vote for him again in 2013, not surprisingly. The second-most positive opinion belongs to voters who abstained from vote in 2013. The third most positive judgement was given by the group of voters who switched to M5S in 2013. A similar pattern is revealed when former Berlusconi supporters were asked about their judgement on each of the candidates of the 2013 elections (figure A.3 in appendix): While Berlusconi had the highest density of positive opinions, Grillo was the second most valued candidate. Given the above, it is sensible to claim that, after Berlusconi, Grillo was the second most preferred candidate option for former PDL voters. In addition, voters who actually switched from PDL in 2008 to M5S in 2013 appear to have taken their decision later in time (closer to the elections), compared to both those who passed from left-wing PD in 2008 to M5S in 2013 and to those who kept voting Berlusconi (appendix table A.2).

13 2013 PDL PD M5S SC no vote 2008 PDL 0.54 0.05 0.25 0.08 0.08 1 (N=439) (N=40) (N=204) (N=65) (N=62)

PD 0.03 0.62 0.24 0.08 0.03 1 (N=20) (N=457) (N=172) (N=60) (N=23)

other 0.14 0.35 0.23 0.19 0.09 1 (N=35) (N=88) (N=57) (N=48) (N=22)

no vote 0.08 0.11 0.39 0.05 0.37 1 (N=16) (N=22) (N=83) (N=11) (N=79)

Figure 6: Vote shares in 2013, by 2008 vote choice

In sum, those “switchers” had preferences compatible with both a vote for PDL and for M5S, were undecided for most of the campaign, and ended up voting M5S. Despite the relatively large size of this group, the bottom left panel in figure 5 (showing Grillo’s popularity over the campaign) does not exhibit a sudden upward shift in correspondence to Berlusconi’s support decline. This can be explained with the group’s pattern of slow and late transition from PDL to M5S (appendix table A.2). Given this scheme of preferences, voters who “abandoned” Berusconi’s coalition were most likely to do so in favour of either M5S or abstention. In what follows, I investigate the extent to which Pope news contributed to each of the the two possible outcomes. First, I ask whether the diversion of TVs decreased individuals’ interest in politics (for simplicity, I will use the short “no-TV” to indicate the relevant time window, namely Feb 11-Feb 16). I perform ordered-logit regressions where the dependent variable is a measure of political interest derived from the RCS questionnaire. I perform separate estimations for respondents in different 2013 vote groups (appendix table A.3). The coefficients of the “no-TV” dummy show that interest was not significantly affected for any of the voting groups; supporters of PDL exhibit the largest negative but non significant effect on interest. Vice versa, former Berlusconi voters who then switched to M5S exhibit the largest -non significant- increase in interest. I repeat the regressions using the whole sample of voters including respondents who did not vote for Berlusconi in 2008. Once again, there are no significant changes in interest in politics with the advent of Pope news, and the patterns are similar to those mentioned above (appendix table A.4).

I then look via OLS regressions at whether the lack of politics coverage brought about a change in voting participation of former Berlusconi supporters. The dependent variable is a measure of individuals’ likelihood to abstain from voting derived from the RCS questionnaire. The results do not highlight any signiﬁcant abstention pattern in any of the voting groups (appendix table A.5). Similarly, there are no signiﬁcant changes when the analysis is repeated on all respondents (appendix table A.6).

14 Overall, both in the general sample of RCS respondents and in the subgroup of former Berlusconi voters, there is no indication that the arrival of Pope news induced a significantly lower interest in politics or decreased voting participation. Given this, when politics disappeared from TV it is plausible to believe that voters searched for information elsewhere, in particular individuals who were still undecided on who to vote. I test this using once again the RCS survey; in particular, the follow-up wave asks respondents’ main source of political information for the 2013 elections. Figure 7 summarizes the data: the plot shows the share of respondents who reported each media type, by 2013 vote group. Independently of political affiliation TV and Internet were by far the two most common sources17. Ideally, a test for shifts in information source requires data on daily consumption of each media type, for all the campaign days. Given the unavailability of such data, I create a time-varying measure of main political information mean by imputing the day of the person’s first questionnaire to her post-electoral answer (e.g. if an individual surveyed on January 30 in the follow-up survey mentioned TV as main source, the dummy for “TV” would take value 1 on January 30). I argue that the answer on “main information mean” given after the elections can be a measure, however noisy, of the respondent’s main source around the day when she was first surveyed. In the psychology literature, respondents of surveys have been found to resort to inferences that use partial information from memory to construct an answer (Bradburn and Shevell, 1987). The authors illustrate as follows: some situations of life inhibit or facilitate recall by individuals. Because of this, when probed for facts about events in their past (e.g. During the last 2 weeks, on days when you drank liquor, about how many drinks did you have?) respondents answers are “altered”: they respond relying on what they recall, which is biased by the presence of the salient events. Mapping these statements in the context of present paper: if the first-wave survey was a sufficiently unusual event for the respondents, when the second wave probed for facts about the whole campaign period (e.g. “What was your main political information source during the campaign?”), people answered giving “more weight” to memories from the day of their first questionnaire. In the appendix (table A.7), I provide additional evidence that such a mechanism is at work in the present case.

17Note that“Internet” does not encompass newspaper online-reading, which instead was categorized under “newspapers”. Inter- net usage corresponds to visits to politicians’ websites, use social media, participation to online political forums/debates/events, subscription to email list, actively being in contact with party-members via email/social media.

15 eysmlrbt ntr fcecet n fsgicnelevel. significance of and coefficients of term in both similar very hi dpino nenta anifrainsuc.Ipraty osgicn ieec xssi em of terms in exists difference significant no in Importantly, others from source. different information significantly main not as are results internet window the of “no-TV” shows adoption the C votedtheir during radio Panel who surveyed or respondents first 0). magazines the individuals from TV, namely group, different voters, this friends, statistically of on one group others only third the than the again likely for being less “friends” relied internet for used They have estimate to (the source. others than information likely political more points main percentage as 22.8 were window regression “no-TV” the the shows during B surveyed voting Panel “Newspapers” from significant). switched and statistically who “Friends” voters being indicated information. estimate for likely first political results less the of (only they source means main days, information their other main as in as internet surveyed indicate first later respondents to to others Compared than likely more not were period elections the voted before who weeks individuals sub-groups: relevant two the as well as respondents of PDL sample full the analyse I mation. 18 pedxtbeA8rpiae h eut ntefl e frsodns nldn hs h eie ale n h eut are results The on. earlier decided who those including respondents, of set full the on results the replicates A.8 table Appendix sn h el-rae aibe,Its hte oenw nue wthi oreo oiia infor- political of source in switch a induced news Pope whether test I variables, newly-created the Using al ipasterslso L ersin ntesbe frsodnswocoewot oei h three the in vote to who chose who respondents of subset the on regressions OLS of results the displays 4 Table nbt 08ad21 lcin,ad2008- and elections, 2013 and 2008 both in

Percentages within vote group 15 30 45 60 0 TV 18 PD ntegnrlsml PnlA,rsodnsfis uvyddrn h “no-TV” the during surveyed first respondents A), (Panel sample general the In . iue7 anifrainma y21 oechoice vote 2013 by mean information Main 7: Figure Friends PDL PDL Newspapers PDL n20 ovoting to 2008 in oeswosice to switched who voters 16 M5S Magazines PDL M5S SC n20 n i o wthi 03 In 2013. in switch not did and 2008 in n21.I hsgop niiul first individuals group, this In 2013. in Radio M5S No voteNo n2013. in Internet references to TV. Given the randomly selected sample of Itanes RCS surveys, the findings in table 4 cannot originate from a sample selection bias. Therefore, they indicate a shift in political information source with the advent of Pope news. Former Berlusconi voters with higher reliance on internet after Pope news are those who switched vote towards a party with an internet-based propaganda. In contrast, voters who did not rely more on internet in that window did not change party affiliation. The different electoral outcomes occurred despite the members of the two groups were equally uncertain on who to vote at the time of the survey, and had similar opinions on candidates. In synthesis, the above results indicate that the TV shock did not significantly impact the interest nor participation of former Berlusconi voters. Instead, for some of them it induced a change in the likelihood to resort to Internet for electoral information; those individuals are those who in 2013 switched to M5S, the party with an internet-based propaganda. What role did religiosity play in these dynamics? Given the that the shock consisted of unexpected “Church news”, one could argue the higher relevance of Christian themes triggered the above mentioned patterns. There- fore, I check whether the arrival of Pope news brought about changes in religious participation, to test the prevalence of a media channel versus a religious one. I exploit the RCS first-wave survey question “Excluding ceremonies ( marriages, funerals, etc.) how often do you attend religious celebrations?”. 19 I create dummies for different levels of religious participation, which I then use as dependent variables of OLS regressions (appendix table A.9). Independently of the religiosity level, there are no significant changes in religious participation around the “no-TV” window. The same is true if religiosity is regressed on a dummy for the presence of Pope news, thus excluding the diversion due to the Sanremo festival (see appendix table ??). I then investigate religiosity changes distinguishing former PDL supporters by their 2013 political affiliation. I perform ordered-logit regressions and I find that the diversion from politics did not significantly affect individuals’ religiosity in either of the voting groups (appendix table A.10). The analysis on the whole sample of respondents shows similarly insignificant changes (appendix table A.11). Therefore, the absence of significant changes in religious attitudes indicates that Berlusconi’s vote loss did not have a religious motive. Pope news had no impact on individuals’ interest in politics nor on voting participation. Instead, the Pope news induced the exposure of some PDL supporters to a different information environment (Internet); this, in turn, has contributed to their change political affiliation towards the party with internet-based communication (M5S).

4 Mediaset performance

The first part of the paper showed that Pope-news caused a drop in Berlusconi’s electoral support, confirming the common thought that campaign TV visibility has a positive effect on vote. In the remainder of present work I test whether such a belief was strong enough to be taken into account by stock market investors. More specifically, I analyse whether the stock price of Berlusconi’s broadcaster (Mediaset) was responsive to his exposure in TV; I conduct an event study around the date of Pope news whereby I test the performance of Mediaset stocks compared to that of a synthetic control.

19Appendix ﬁgure A.4 displays a summary of religiosity distributions by voting choice in 2013.

17 Table 4: Main source of political information for the 2013 election

Panel A - full sample (1) (2) (3) (4) (5) (6) Internet TV Friends Newspapers Magazines Radio

“No-TV” 0.026 0.009 -0.028∗ -0.017 0.007 0.007 (0.031) (0.039) (0.014) (0.030) (0.013) (0.014) Time trend -0.000 -0.000 0.001 0.000 -0.000 0.000 (0.001) (0.001) (0.000) (0.001) (0.000) (0.000) Observations 1611 1611 1611 1611 1611 1611 Mean of dependent 0.31 0.45 0.04 0.10 0.02 0.03

Panel B - Voters 2008 = PDL, 2013 = M5S (1) (2) (3) (4) (5) (6) Internet TV Friends Newspapers Magazines Radio “No-TV” 0.228∗∗∗ -0.094 -0.074∗∗ 0.011 -0.033 -0.022 (0.062) (0.106) (0.034) (0.075) (0.022) (0.017) Time trend -0.003 0.002 0.001 0.000 0.001 -0.001 (0.002) (0.002) (0.002) (0.002) (0.001) (0.001) Observations 133 133 133 133 133 133 Mean of dependent 0.37 0.43 0.05 0.09 0.02 0.03

Panel C - Voters 2008 = PDL, 2013 = PDL (1) (2) (3) (4) (5) (6) Internet TV Friends Newspapers Magazines Radio

“No-TV” 0.062 0.080 0.042 -0.074 -0.030∗ -0.028 (0.107) (0.121) (0.032) (0.092) (0.018) (0.020) Time trend -0.001 -0.001 -0.001 0.001 0.000 0.001 (0.002) (0.003) (0.001) (0.002) (0.001) (0.001) Observations 199 199 199 199 199 199 Mean of dependent 0.31 0.48 0.02 0.13 0.02 0.02

Note: Ordinary Least Squares regressions. Dependent variables are dummies for whether a media type was the main source of electoral political information.“No-TV” dummy equals 1 if the respondent of the follow-up survey was ﬁrst interviewed during the no-TV window. Panel A refers to the full sample of voters. Panel B refers to voters of Berlusconi’s coalition in 2008 who switched to M5S in 2013. Panel C refers to voters who voted Berlusconi in 2008 and again voted PDL in 2013. Day-level clustered standard errors in brackets (day of ﬁrst interview). The regressions exclude voters who chose who to vote more than 3 weeks before elections. Data: Itanes RCS 2013.

18 4.1 Data

I fix the Pope’s announcement at February 11, 12:00 am, given that the news first appeared on the media at 11:46 am. The financial data on Mediaset stocks come from Bloomberg, whereas company news are taken from the Eikon platform by Thompson Reuters. The timing of the events was retrieved from the website of Ansa, the main Italian agency for first-hand news. Data were collected at intraday ticks and averaged at hour level.

4.2 Abnormal Returns, CARs and the Synthetic Control method

To exclude the eﬀect of large market movements during the event window, I calculate the abnormal returns for the relevant days. Following the methodology suggested by Campbell, Lo and MacKinlay (1997), I use an augmented market model: M rt = α + βrt + γDt + et (2)

M where rt stands for the per-period return of the stock, rt is the returns of the market index FTSE Mib (where

Mediaset is listed), Dt is a set of dummies for company-events (announcement of dividends and deals, or changes in outstanding shares) that are orthogonal to the electoral campaign; finally, et is the unexplained residual, the “abnormal return”. Given the way the model is built, abnormal returns of the firm do not reflect concomitant information released by the firm at time t, (e.g. news concerning profits). I define an “estimation window” as the period of time that is used for the estimation of the model in equation 2: it goes from January 2 (h9) to February 11 (h11), for a total of 256 trading hours, namely covering all the trading periods from the beginning of the electoral campaign up to the event. My post-event window spans for 14 trading hours, until the market closure on February 13.20

From the abnormal returns estimated in 2, I produce the series of cumulative abnormal returns {CARt} as:

t X CARt = ej (3)

j=t0 where t0 marks the time of the renounce to the papacy. This measure allows me to assess the persistence of the shock over time. In order to control for the effect of unobservable factors that might impact the common trend of Mediaset and the other firms, I construct a synthetic control (SC) for Mediaset using the methodology suggested by Abadie and Gardeazabal (2003). A synthetic control is a (Jx1) vector of weights W, with J equal to the size of the “donor-pool” (i.e. the group of companies in the control group); its elements are numbers in [0,1] and sum to 1: choosing a specific vector is equivalent to choose a synthetic control.

Let X1 be the (Kx1) vector containing the values of some pre-intervention characteristics that are considered to be predictors of Mediaset’s abnormal returns; then let X0 be the [KxJ] matrix of donors-values for the same variables. The vector of weights W is chosen so to minimize

20This is the window covering the stock-market relevant visibility shock, namely when coverage of politics was unexpectedly low. The day Feb 14-16 are discarded because the Sanremo festival was scheduled a log time before: under the assumption that markets are eﬃcient, information is taken into account when it is released.

19 K X 2 vk(X1k − X0kW ) (4) k=1 where vk is a weight that reflects the relative importance of characteristic k in measuring the distance X1 −X0W . My main specification is similar to the one in Guidolin and La Ferrara (2007), with relevant characteristics k from the pre-event window being (a) the mean of abnormal returns (b) the variance of abnormal returns (c) the OLS beta of the market model that regresses a firm’s returns on the market index return, and (d) the variance of abnormal returns from the beginning of February to Pope news. In this main specification, my pre-event window for the synthetic matching covers 100 periods before Pope news: this excludes large market movements occurred in January and improves the fit of SC to Mediaset (weights and technical details in appendix section A.2.3). However, alternative specifications produce similar results, with longer or shorter pre-event windows of 46 and 246 trading hours (see appendix sections A.2.4 and following)21). As indicated in the literature, I test my results with synthetic controls produced using all pre-treatment abnormal returns lags as predictors, like in Ferman and Pinto (2017) (appendix section A.2.5) and no other covariates, following Kaul et al (2015) (appendix section A.2.6). In all of the above cases, the results that I obtain are similar to the ones in the main specification. Ferman, Pinto and Possebom (2018) discuss the risk of specification-search by the researchers, with results being manipulated through “cherry picking” of donors. Indeed, the selection of units to include in the donor pool is crucial for the reliability of the SC analysis. I borrow my firm-selection method from the literature, and in particular from Guidolin and La Ferrara (2007). I take all the 12 companies that are listed in the “All-share MEDIA Italy” index, to which Mediaset belongs. I then discard the companies that do not comply with the following criteria set out in Abadie et al.(2015): (1) No (known) connection with Berlusconi (2) No activity in sectors other than that of Mediaset (3) Being continuously traded during the period under analysis. My final donor pool counts 9 firms (see the appendix for a list). I then assess whether the Pope news had any cumulative impact on the portfolios under analysis. Figure 8 contains my main result: it plots the cumulative abnormal return of Mediaset and of the control portfolio for periods included in the event window. In the figure, Mediaset CAR remains relatively close to the control up until the end of the day of Pope news (Feb 11), while in the morning of the day after the two start to diverge more markedly. This is compatible with the interpretation that investors only realize the “threat” to TV coverage after the evening of February 11, when politics went completely out of coverage. Figure 9 presents the gap between Mediaset CAR and that of its synthetic control: the TV shock was perceived as negative news by Mediaset investors. Appendix figure A.2.1 plots the difference in the series of abnormal returns during the evaluation window, showing that the synthetic control follows the Mediaset series, and that the gap widens after the Pope news.

21246 is the whole campaign period. 46 periods, instead, is the total time between Pope news and a shock that FVS exhibits on January 31; a discussion of such shock is provided in the appendix section A.1.2

20 .01 .005 0 -.005 Cumulative Cumulative Returns Abnormal -.01 -.015 Feb 11,h12 Feb 11,h16 Feb 12,h11 Feb 12,h15

Mediaset Synthetic control

Figure 8: CAR of Mediaset and of the synthetic control .01 0 -.01 -.02 Gap in Cumulative Cumulative Gap in Returns Abnormal -.03 Feb 11,h12 Feb 11,h16 Feb 12,h11 Feb 12,h15

Figure 9: CAR diﬀerence between mediaset its synthetic control

21 5 Testing the channel and robustness checks

Abadie et al.(2010) argue that large sample inferential techniques are not adequate to comparative case-studies in cases of small donor pools (such as the present one). They therefore suggest to run a number of robustness checks.

5.1 Leave-one-out distribution

A first necessary verification entails checking whether the findings are exclusively driven by one of the units in the donor pool (Abadie et al, 2015). To test this, I iteratively leave each one of the possible controls out of the donor pool, and repeat the synthetic control estimation. Figure 10 plots all the new gaps as well as the original one: since all of them result in a negative cumulative abnormal return similar to the original series, this shows that the effect found is not driven by a particular company in the donor pool. .01 0 CAR -.01 -.02 -.03 Feb 11,h12 Feb 11,h16 Feb 12,h11 Feb 12,h15

Ced left out Gedi left out RCS left out Cairo left out Class Ed. left out Poligraﬁci left out Sole 24 ore left out Mediaset

Figure 10: Leave-one-out distribution

5.2 In-space placebo

A “placebo in space” is a falsification exercise that consists of considering a unit in the donor pool as treated unit, and in building a synthetic control with all the others (including the original treated one, i.e. Mediaset); this is then repeated for all members of the donor pool. Applying this idea allows to compare the estimated effect of the Pope news on Mediaset to the distribution of placebo effects obtained for other companies. The

22 effect of Mediaset would be significant if it appeared unusually large compared to the distribution of placeboes. As done by Abadie et al. (2010), I first measure the quality of the SC with the pre-event “mean squared prediction error” (MSPE), namely the average of the squared discrepancies between per Mediaset abnormal returns the ones of its synthetic counterpart during the pre-event period; then, when I derive the distribution of placebos, I exclude two companies whose pre-event RMSPE is larger than twice the one of Mediaset. .04 .02 0 Gap in CAR Gap in -.02 -.04 Feb 11,h12 Feb 11,h16 Feb 12,h11 Feb 12,h15

Ced Gedi RCS Cairo Class editori Poligraﬁci Sole 24 ore Mediaset

Figure 11: Placebo in space

Figure 11 plots the placebo gaps in CAR: the bold line represents the gap between Mediaset and its synthetic control; the other lines show the corresponding gaps for the “fictitious treatments”. While the effect found for Mediaset is overall large compared to the placebos, the robustness is only “guaranteed” after February 12, h11. This suggests that the effect became apparent a few hours later.

5.3 Testing the electoral channel

Could Pope news be inherently bad for Mediaset, i.e. independently from the shock to Berlusconi’s vote share? I provide two types of evidence to assess the robustness of the “electoral channel”: the ﬁrst is anecdotal and it concerns the way Mediaset contracts are drafted; the other consists of a “placebo in-time”, checking Mediaset reaction when other Pope news arrived.

23 5.3.1 Mediaset advertising contracts

An alternative explanation for the negative reaction of Mediaset stocks could hinge upon losses from advertising: if advertisers broke their contracts with the broadcaster, fearing an audience shift towards other outlets, this would explain the negative CAR. Given the way Mediaset contracts were drafted, its stocks cannot however have suffered from a shock to advertising after Pope news. More specifically, the document “Palinsesto Publitalia 2013” (setting the conditions for advertising on Mediaset outlets), specifies the following:

• The right of withdrawal from advertising contracts could be exercised only with a minimum prior notice of 60 days. Therefore, if the Pope news brought about a shift (perceived or actual) in audience towards other broadcasters, clients could not respond to this by immediately cancelling their advertising with Mediaset. In sum, the stocks of the firm could not have suffered a damage for “forgone profits from existing clients”.

• In the “Show schedule” section (Programmazione, in Italian), the document details the conditions under which the airing of the advertising shall occur. While the client can choose a “window” ( in terms of hours and week days) in which his advertising shall be aired, Mediaset reserves the right to change the schedule without prior notice, declaring the scheduling as merely indicative and not binding: the company is only required to air the advertising within the next 7 days from the original time. Given these clauses, Mediaset cannot have suﬀered a damage from breach of contracts, when Pope-news called for a complete restructuring of the show schedule.

• In the “Material delivery” section, Mediaset specifies that the clients willing to advertise on its outlets shall send all the materials at least 8 days before the airing date. As a consequence, any new client has its advertising aired at least 8 days after signing the contract. If Pope-news lowered Mediaset’s “appeal”, this would have translated into a loss of clients only if advertisers believed the shift in audience-share to persist for at least 8 days, which is implausible. For this reason, it is implausible that Mediaset stocks suffered from “forgone profits from future clients”

In sum, the contractual conditions used by Mediaset in its advertising contracts exclude the possibility that Pope-news aﬀected the company through the advertising channel: Mediaset cannot have lost potential clients nor present ones, nor it had to pay reimbursement for breach of contract conditions.

5.3.2 Placebo in time: the appointment of a new Pope

In this subsection I test the validity of the “electoral channel”, namely that Mediaset stock goes down exclusively because Berlusconi loses voters’ support. In particular, I perform a “placebo-in time” around the date of other Pope-news: the appointment of the new Pope (J. M. Bergoglio), occurred roughly two weeks after the elections. If Pope-news were inherently bad for Mediaset stocks independently from Berlusconi’s vote share (in other words: if Mediaset tended to perform poorly whenever the Pope or related themes were in the news), then a similar gap should be observed right after the papal election (i.e. at market opening on March 14, 2013). Figure 12 shows the comparison between Mediaset and the synthetic control at that date22. The two series do 22This “placebo-in-time-synthetic-control” uses the same weight scheme (i.e. the same SC) as the one of the main section; the diﬀerence is only in the time frame over which the series are plotted.

24 not systematically diverge as in ﬁgure 9, supporting the idea that Mediaset stocks did not react per se to Pope news, but to the distraction of the audience from elections. .01 0 -.01 -.02 Gap in Cumulative Cumulative Gap in Returns Abnormal -.03 March 14,h9 March 14,h15 March 15,h12 March 14,h17

Figure 12: Placebo: CAR gap when the new Pope is elected

6 Conclusion

There is general consensus that a candidate’s TV exposure during the campaign is crucial for success in the electoral competition. However, little conclusive evidence has been produced to this effect. This paper contributes to the literature by providing a causal estimate of the impact of TV visibility on vote share, in the short span of a campaign. The identification strategy relies on a natural experiment: during the 2013 Italian general elections the Pope Benedict XVI announced his intention to renounce to the papacy. After his declaration, Italian TVs shifted to a blanket coverage of related contents, causing a marked turn of attention from the upcoming elections. I identify the effect of exposure in TV on vote shares by instrumenting candidates’ visibility with the presence of Pope news. I find that Berlusconi’s dip in television coverage (-26 percentage points) translated into a vote share loss of about 2 percentage points, and this overturned the result of elections: the left-wing won by 0.4 percentage points. The mechanism underlying is a change in political information outlet for some voters: the Pope’s announcement lead a subgroup of former Berlusconi supporters to shift their main source of political information towards Internet. The exposure to a different information environment induced a change in their political affiliation in favour of M5S, the party with an internet-centred propaganda. When Pope news diverted the TVs attention from politics, the stock of Mediaset (Berlusconi’s broadcaster) underperformed an otherwise similar synthetic control portfolio. Investors believed TV visibility to increase

25 Berlusconi’s vote shares; since Mediaset benefited from Berlusconi’s previous offices, after the TV shock the stock market devalued Mediaset in anticipation of looser future political ties for the firm. The take away of this paper consists of three points: First, TV coverage can impact voting behaviour even in a very short time span. Second, the effect of candidates’ TV exposure can be strong enough to overturn the result of the elections. Third, markets react to common thoughts, independently of the availability of evidence supporting the beliefs.

References

[1] Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the eﬀect of California’s tobacco control program. Journal of the American statistical Association, 105(490), 493-505.

[2] Abadie, A., Diamond, A., & Hainmueller, J. (2015), Comparative politics and the synthetic control method. American Journal of Political Science, 59(2), 495-510.

[3] Abadie, A., & Gardeazabal, J. (2003). The economic costs of conﬂict: A case study of the Basque Country. American economic review, 93(1), 113-132.

[4] Acemoglu, D., Johnson, S., Kermani, A., Kwak, J., & Mitton, T. (2016). The value of connections in turbulent times: Evidence from the United States. Journal of Financial Economics, 121(2), 368-391.

[5] Akey, P. (2015). Valuing changes in political networks: Evidence from campaign contributions to close congressional elections. The Review of Financial Studies, 28(11), 3188-3223.

[6] Bertrand, M., Bombardini, M., & Trebbi, F. (2014). Is it whom you know or what you know? An empirical assessment of the lobbying process. American Economic Review, 104(12), 3885-3920.

[7] Bobba, G., Legnante, G., Roncarolo, F., & Seddone, A. (2013). Candidates in a negative light. The 2013 Italian election campaign in the media. Rivista italiana di scienza politica, 43(3), 353-380.

[8] Botosaru, I., & Ferman, B. (2017). On the Role of Covariates in the Synthetic Control Method.

[9] Bradburn, N. M., Rips, L. J., & Shevell, S. K. (1987). Answering autobiographical questions: The impact of memory and inference on surveys. Science, 236(4798), 157-161.

[10] Campbell, J. , Lo, A.W. , & MacKinlay, C. A. (1997). The econometrics of ﬁnancial markets. Princeton: Princeton University Press.

[11] Campante, F. R., & Hojman, D. A. (2013). Media and polarization: Evidence from the introduction of broadcast TV in the United States. Journal of Public Economics, 100, 79-92.

[12] Cingano, F., & Pinotti, P. (2013). Politicians at work: The private returns and social costs of political connections. Journal of the European Economic Association, 11(2), 433-465.

[13] DellaVigna, S., & Kaplan, E. (2007). The Fox News eﬀect: Media bias and voting. The Quarterly Journal of Economics, 122(3), 1187-1234.

[14] Druckman, J. N. (2004). Priming the vote: Campaign eﬀects in a US Senate election. Political Psychology, 25(4), 577-594.

[15] Durante, R., Pinotti, P., & Tesei, A. (2017). The political legacy of entertainment TV.

[16] Durante, R., & Zhuravskaya, E. (2018). Attack When the World Is Not Watching? US News and the Israeli-Palestinian Conﬂict. Journal of Political Economy, 126(3), 1085-1133.

[17] Faccio, M. (2006). Politically connected ﬁrms. American economic review, 96(1), 369-386.

26 [18] Ferman, B., & Pinto, C. (2017). Placebo tests for synthetic controls.

[19] Ferman, B., Pinto, C., & Possebom, V. (2018). Cherry picking with synthetic controls.

[20] Fisman, D., Fisman, R. J., Galef, J., Khurana, R., & Wang, Y. (2012). Estimating the value of connections to Vice-President Cheney. The BE Journal of Economic Analysis & Policy, 12(3).

[21] Fisman, R., (2001), Estimating the Value of Political Connections, The American Economic Review, 91(4): 1095-1102.

[22] Forsythe, R., Frank, M., Krishnamurthy, V., & Ross, T. (1995). Using Market Prices to Predict Election Results: The 1993 UBC Election Stock Market. The Canadian Journal of Economics / Revue Canadienne D’Economique, 28(4a), 770-793. doi:10.2307/135930

[23] Gemmill, G. (1992). Political risk and market eﬃciency: tests based in British stock and options markets in the 1987 election. Journal of Banking & Finance, 16(1), 211-231.

[24] Gentzkow, M. (2006). Television and voter turnout. The Quarterly Journal of Economics, 121(3), 931-972.

[25] Guidolin, M., & La Ferrara, E. (2007). Diamonds Are Forever, Wars Are Not: Is Conﬂict Bad for Private Firms?. The American Economic Review, 97(5), 1978-1993.

[26] Johnson, S., & Mitton, T. (2003). Cronyism and capital controls: evidence from Malaysia. Journal of ﬁnancial economics, 67(2), 351-382.

[27] Kaul, A., Klossner, S., Pfeifer, G., & Schieler, M. (2015). Synthetic Control Methods: Never Use All Pre-Intervention Outcomes Together With Covariates.

[28] Khwaja, A. I., & Mian, A. (2005). Do lenders favor politically connected ﬁrms? Rent provision in an emerging ﬁnancial market. The Quarterly Journal of Economics, 120(4), 1371-1411.

[29] Knight, B. (2006). Are policy platforms capitalized into equity prices? Evidence from the Bush/Gore 2000 presidential election. Journal of Public Economics, 90(4-5), 751-773.

[30] Leblang, D., & Mukherjee, B. (2005). Government partisanship, elections, and the stock market: examining American and British stock returns, 19302000. American Journal of Political Science, 49(4), 780-802.

[31] Legnante, G., Mancini, P., Mazzoleni, P., & Roncarolo, F. (2013). Campagna elettorale sui media.

[32] Mattozzi, A. (2008). Can we insure against political uncertainty? Evidence from the US stock market. Public Choice, 137(1-2), 43-55.

[33] Prat, A. (2018). Media power. Journal of Political Economy, 126(4), 1747-1783.

[34] Roberts, B. E. (1990). A dead senator tells no lies: Seniority and the distribution of federal beneﬁts. American Journal of Political science, 31-58.

[35] Snowberg, E., Wolfers, J., and Zitzewitz, E. (2011). How prediction markets can save event studies (No. w16949). National Bureau of Economic Research.

[36] Tahoun, A. (2014). The role of stock ownership by US members of Congress on the market for political favors. Journal of Financial Economics, 111(1), 86-110.

[37] Wolfers, J., & Zitzewitz, E. (2006). Prediction markets in theory and practice (No. w12083). National Bureau of Economic Research.

27 Appendix

A.1: Appendix for TV coverage analysis

A.1.1 List of TV programs monitored by Itanes

The dataset by Itanes on TV monitoring encompasses the following programs: The ﬁrst 3 news of the News Programs (Tg1, Tg5, TgLa7, SkyTG24 ) Ballar`o Servizio Pubblico Porta a Porta Quinta Colonna Striscia la Notizia Le invasioni barbariche Che tempo che fa La telefonata di Belpietro Festival di Sanremo.

Table A.1: Wald tests for structural breaks in TV coverage

(1) (2) (3) (4) (5) Coverage of: Berlusconi Monti Bersani Grillo Pope date/p-value date/p-value date/p-value date/p-value date/p-value

Known date: Feb 11 Feb 11 Feb 11 Feb 11 Feb 11 0.0192 0.0003 0.0680 0.0000 0.0000

Unknown date Feb 11 Feb 11 Jan 2 Feb 17 Feb 11 (auto-detected): 0.0624 0.0006 0.1489 0.0000 0.0016

The table displays the tests for the presence of structural breaks in the TV appearances of the 4 main political candidates and of the Pope. Data: Itanes monitoring of TV campaign 2013

28 2013 PDL PD M5S SC no vote 2008 PDL 0.86 0.07 0.40 0.35 0.33 (N=439) (N=40) (N=204) (N=65) (N=62) PD 0.04 0.75 0.33 0.33 0.12 (N=20) (N=457) (N=172) (N=60) (N=23)

other 0.07 0.14 0.11 0.26 0.12 (N=35) (N=88) (N=57) (N=48) (N=22)

no vote 0.03 0.04 0.16 0.06 0.43 (N=16) (N=22) (N=83) (N=11) (N=79) 1 1 1 1 1

Figure A.1: Vote shares in 2008, by 2013 vote choice. Data: Itanes RCS 2013.

How good (0-100) was Berlusconi's '08-'11 government? .02 .015 .01 Kernel density Kernel .005 0 0 20 40 60 80 100 (Sample: voters who voted Berlusconi in 2008) Vote choice in 2013: M5S PD PDL SC No vote

Figure A.2: “From 0 to 100, how good was Berlusconi’s government from 2008 to 2011?” The image shows the densities of answers for voters of Berlusconi in 2008, stacked by 2013 vote choice. Data: Itanes RCS 2013

29 From 0 to 100, how much do you like 2013 candidate ...? .02 .015 .01 Kernel density Kernel .005 0 0 20 40 60 80 100 (Sample: voters who voted Berlusconi in 2008) Opinion on 2013 candidates: Berlusconi Bersani Grillo Monti

Figure A.3: “From 0 to 100, how much do you like candidate X ?” The image shows the densities of voters’ answers for former Berlusconi voters (those who voted PDL coalition in 2008). Data: Itanes RCS 2013

Table A.2: Timing of vote decision, by vote combination 2008-2013

“In 2013, when did you decide which candidate to vote?” (1) (2) (3) (4) A long time Some weeks One week Inside the before elections before elections before elections ballot cabine Vote combinations: 2008 = PDL, 2013 = PDL 55.93% 20.62% 18.36% 5.08% (N=198) (N=73) (N=65) (N=18) 2008 = PDL, 2013 = M5S 31.54% 22.82% 30.20% 15.44% (N=47) (N=34) (N=45) (N=23) 2008 = PD, 2013 = M5S 48.26% 19.19% 24.42% 8.14% (N=83) (N=33) (N=42) (N=14)

Summary table: timing of vote decisions. Timing is measured by the question: “When did you decided on your vote?”. For each vote combination (listed on the left), table entries give the share of voters who decided at a given point in time (timings are listed under column headers 1-4). Data : Itanes RCS 2013.

30 Table A.3: Ordered logit: interest in politics of former PDL voters, by 2013 vote

(1) (2) (3) (4) (5) PD PDL M5S SC Abstained Interest in politics (from low to high)

Time trend 0.026 -0.001 -0.005 0.029 -0.033 (0.027) (0.007) (0.009) (0.019) (0.020) noTV -0.035 -0.372 0.192 -1.059 0.101 (1.080) (0.328) (0.425) (0.865) (0.698) Observations 38 435 200 63 62 Mean of dep var 1.76 2.05 1.90 1.84 1.84

Ordered logit regressions on respondents who voted PDL in 2008. The sependent variable is: “Interest in politics”, as derived from the RCS survey question: “How much are you interested in politics?” (answers range: 0-3). “No-TV” is a dummy equal to 1 if the respondent was interviewed in the days between Pope’s announcement and the end of Sanremo Festival. The columns contain regressions on diﬀerent subsamples of voters, following their 2013 vote (column headers indicate the voted party). Day-level clustered standard errors in parentheses. Data: Itanes RCS 2013.

Table A.4: Interest in politics, by 2013 vote choice

(1) (2) (3) (4) (5) PD PDL M5S SC Abstained Dependent: Interest in politics

Time trend 0.005 0.000 0.005 0.000 0.010 (0.006) (0.006) (0.005) (0.010) (0.013) “No-TV” 0.288 -0.537 0.242 0.108 -0.927∗∗∗ (0.211) (0.350) (0.213) (0.471) (0.342) Observations 654 534 609 201 211 Mean of dependent 2.09 2.00 1.87 1.78 1.58

Ordered logit regressions. The dependent variable is “Interest in politics” from the question: “How much are you interested in politics?” (answers range: 0-3). “No-TV” is a dummy equal to 1 if the respondent was interviewed in the days between Pope’s announcement and the end of Sanremo Festival. The columns contain regressions on diﬀerent sub-samples of voters, following their 2013 vote column headers indicate the voted party). Day-level clustered standard errors in parentheses. Data: Itanes RCS 2013.

31 Table A.5: Probability of abstention for former PDL voters, by 2013 vote choice

(1) (2) (3) (4) (5) PD PDL M5S SC Abstained Dependent: Probability to abstain

Time trend -0.008 -0.009∗ -0.006 -0.011 -0.024 (0.023) (0.006) (0.007) (0.016) (0.023) noTV 1.088 0.318 0.195 -0.095 0.232 (1.514) (0.295) (0.231) (1.206) (0.564) Observations 37 431 195 63 62 Mean of dependent 4.46 3.64 4.76 3.98 7.58

Ordered logit regressions on respondents who voted for PDL in 2008. The dependent variable is “Probability of abstention” as measured by the RCS question: “From 0 to 10, how likely are you to not go to vote in the upcomgin elections?”.“No-TV” is a dummy equal to 1 if the respondent was interviewed in the days between Pope’s announcement and the end of Sanremo Festival. The columns contain regressions on diﬀerent subsamples of voters, following their 2013 vote (column headers indicate the voted party). Day-level clustered standard errors in parentheses. Data: Itanes RCS 2013.

Table A.6: Probability of abstention, by 2013 vote choice

(1) (2) (3) (4) (5) PD PDL M5S SC Abstained Dependent: Probability to abstain

Time trend -0.010∗ -0.011∗ -0.007 -0.010 0.003 (0.006) (0.006) (0.006) (0.009) (0.010) “No-TV” 0.077 0.347 -0.328∗ -0.657∗∗ 0.042 (0.229) (0.339) (0.197) (0.324) (0.399) Observations 650 530 597 201 207 Mean of dependent 2.78 3.83 4.48 3.32 8.09

Ordered logit regressions. The dependent variable is “Probability of abstention” measured by question: “From 0 to 10, how likely are you to not go to vote?”. “No-TV” is a dummy equal to 1 if the respondent was interviewed in the days between Pope’s announcement and the end of Sanremo Festival. The columns contain regressions on diﬀerent subsamples of voters, following their 2013 vote (column headers indicate the voted party). Day-level clustered standard errors in parentheses. Data: Itanes RCS 2013.

32 Table A.7: Multinomial Logit for the inﬂuence of Church in 2013 election

(Baseline category: no inﬂuence at all)

“How much did the Church inﬂuence the election?” Very much Quite A little b/se b/se b/se

noTV 1.914∗∗∗ 0.703∗∗ 0.306 (0.678) (0.280) (0.189)

Time trend -0.159∗∗∗ -0.082∗∗∗ -0.052∗∗∗ (0.009) (0.004) (0.002) Observations 2585

Multinomial logit model: baseline category is ‘no influence at all’; SE in parenthesis. The question was asked in the second wave of Itanes RCS survey. Respondents were all surveyed after the elections. “No-TV” is a dummy taking value 1 if the respondent to the second wave was first surveyed during the days between Pope news and the end of the Sanremo festival. When the first interview was during the “noTV” window, respondents are significantly more likely to later report the Church impact as high or very high, as compared to null. This validates the idea that individuals’ retrieval of campaign memories is biased towards the period when they had the first survey. Data: Itanes RCS 2013

Religiosity by 2013 vote choice 4.5 4 3.5 3 2.5 PDL Abstained PD M5S SC

Figure A.4: Coefficient plot: Each column displays the coefficient and the 95% confidence intervals of an OLS regression of “Religiosity” on one party dummy and no constant term. The measure of religiosity is derived from the survey question: Excluding ceremonies (weddings, funerals etc), how frequently do you attend religious celebrations?. Larger values correspond to higher religious participation. Dummies for parties take value 1 if the survey respondent voted that party in 2013 election. Data: Itanes RCS 2013.

33 Table A.8: Main source of political information for the 2013 election (All RCS respondents)

Panel A - full sample (1) (2) (3) (4) (5) (6) Internet TV Friends Newspapers Magazines Radio

“No-TV” 0.048∗ -0.021 -0.019∗ 0.001 0.000 -0.003 (0.027) (0.024) (0.010) (0.019) (0.007) (0.009) Time trend -0.000 -0.000 0.000 0.000 -0.000 0.000 (0.001) (0.001) (0.000) (0.000) (0.000) (0.000) Observations 2682 2682 2682 2682 2682 2682 Mean of dependent 0.35 0.43 0.04 0.11 0.02 0.03

Panel B - Voters 2008 = PDL, 2013 = M5S (1) (2) (3) (4) (5) (6) Internet TV Friends Newspapers Magazines Radio “No-TV” 0.193∗∗∗ -0.075 -0.053∗∗ 0.006 -0.021 -0.026∗ (0.048) (0.088) (0.024) (0.061) (0.015) (0.015) Time trend -0.002 0.001 0.001 0.000 0.001 -0.000 (0.002) (0.002) (0.001) (0.001) (0.001) (0.001) Observations 200 200 200 200 200 200 Mean of dependent 0.44 0.39 0.04 0.07 0.01 0.03

Panel C - Voters 2008 = PDL, 2013 = PDL (1) (2) (3) (4) (5) (6) Internet TV Friends Newspapers Magazines Radio

“No-TV” -0.007 0.055 0.072∗∗ -0.049 -0.017∗ -0.034∗∗ (0.060) (0.080) (0.036) (0.059) (0.009) (0.016) Time trend -0.001 -0.000 -0.001 0.001 0.000 0.001 (0.002) (0.002) (0.000) (0.001) (0.000) (0.001) Observations 438 438 438 438 438 438 Mean of dependent 0.27 0.51 0.03 0.13 0.01 0.02

Ordinary Least Squares regressions. Dependent variables are dummies for whether a media type was the main source of electoral political information.“No-TV” dummy equals 1 if the respondent of the follow-up survey was ﬁrst interviewed during the no-TV window. Panel A refers to the full sample of voters. Panel B refers to voters of Berlusconi’s coalition in 2008 who switched to M5S in 2013. Panel C refers to voters who voted Berlusconi in 2008 and again voted PDL in 2013. Day-level clustered standard errors in brackets (day of ﬁrst interview). The sample is the full set of respondents to Itanes Rolling Cros Section (RCS) 2013.

34 Table A.9: Religious participation, by religiosity level

“How frequently do you attend religious celebrations?” (1) (2) (3) (4) (5) (6) (7) (8) Everyday 2-3/week 1/week 2-3/month 1/month 2-3/year 1/year Never : Time trend 0.001∗∗∗ 0.001∗∗ 0.000 0.001 -0.000 -0.000 -0.001 -0.001 (0.000) (0.000) (0.001) (0.000) (0.000) (0.001) (0.000) (0.001) “No-TV” -0.005 -0.015 -0.018 -0.020 -0.001 -0.014 0.032 0.043 (0.010) (0.016) (0.030) (0.029) (0.018) (0.023) (0.028) (0.034) Observations 1242 1242 1242 1242 1242 1242 1242 1242 Mean of dependent 0.01 0.03 0.22 0.07 0.07 0.23 0.08 0.29

Ordinary Least Squares regressions. The dependent variables are dummies marking the responses to the question: “Excluding ceremonies (weddings etc.) how frequently do you attend religious celebrations?”. “No TV” is a dummy equal to 1 if the respondent was interviewed in the days between Pope’s announcement and the end of Sanremo Festival. The columns contain regressions for individuals at each religiosity level (column headers indicate the category). Day-level clustered standard errors in parentheses. Data: Itanes RCS 2013.

Table A.10: Religious participation for former PDL voters, by 2013 vote choice

(1) (2) (3) (4) (5) PD PDL M5S SC Abstained Dependent: Religious participation

Time trend 0.144∗∗ -0.013 0.019 -0.008 0.002 (0.067) (0.009) (0.014) (0.023) (0.032) noTV -2.915 -0.013 0.226 0.564 -0.351 (2.020) (0.411) (0.681) (1.513) (1.070) Observations 19 215 83 30 21 Mean of dependent 4.11 3.53 3.58 4.20 4.00

Ordered logit regressions on respondents who voted PDL in 2008. The dependent variable is “Religious Participation” (based on question: “Excluding ceremonies (weddings etc.) how frequently do you attend religious celebrations?” (frequency ordered low to high). “No- TV” is a dummy equal to 1 if the respondent was interviewed in the days between Pope’s announcement and the end of the Sanremo Festival. The columns contain regressions on diﬀerent subsamples of voters, following their vote in 2013 (column headers indicate the voted party). Day-level clustered standard errors in parentheses. Data: Itanes RCS 2013.

35 Table A.11: Religious participation, by 2013 vote choice

(1) (2) (3) (4) (5) PD PDL M5S SC Abstained Dependent: Religious participation

Time trend 0.023∗∗∗ -0.002 0.018∗∗ -0.027∗ -0.009 (0.007) (0.008) (0.008) (0.014) (0.013) noTV -0.211 -0.380 -0.444 -0.093 0.879 (0.357) (0.377) (0.319) (0.819) (0.603) Observations 306 274 280 91 89 Mean of dependent 3.32 3.61 3.00 3.86 3.34

Ordered logit regressions. The dependent variable is “Religious Participation” (based on question: “Excluding ceremonies (weddings etc.) how frequently do you attend religious celebrations?” (frequency ordered low to high). “No-TV” is a dummy equal to 1 if the respondent was interviewed in the days between Pope’s announcement and the end of the Sanremo Festival. The columns contain regressions on diﬀerent subsamples of voters, following their vote in 2013 (column headers indicate the voted party). Day-level clustered standard errors in parentheses. Data: Itanes RCS 2013.

A.1.2 Might football news aﬀect vote share?

Figure 3 in the main text displayed a second remarkable jump in the FVS serie (other than the one in correspondence of Pope news); such discontinuity, occurred on Jan 31 at 9 a.m., is highlighted by the red vertical line in ﬁgure A.5.

Figure A.5: A second gap in FVS 14 10 6 perc. points difference perc. points (L-R) 2 Jan10,2013 Jan 21,2013 Jan 31,2013 Pope Sanremo ends

Fitted values Fitted values Fitted values Fictitious Vote Share (FVS)

On Jan 31 two of the main Italian newspapers23 reported: “Berlusconi gains vote share as his football club,

23“Corriere della sera”, “Il Fatto Quotidiano”.

36 “A.C. Milan”, bought the player Mario Balotelli from the Manchester City football club.” While the newspapers’ conclusion seems to be far-fetched, the jump in FVS appears to support their reading. Admittedly, January 31, h9 is the day/hour when the player first entered the A.C. Milan pitch, thereby confirming the rumours on his acquisition. The “A.C. Milan” is based in the city of Milan, where in 2013 the right-wing party PDL had the closest “têteátête”with the leftist PD, playing there the Senate majority bonus (i.e. extra seats in the Senate assigned to the party with more votes). In fact, the newspaper “Corriere della sera” reported that the football manoeuvre could have shifted as many as 100’000 votes exactly in the Lombardia region (the one of Milan). Previously, during the campaign, the subscribers of A.C. Milan had received letters encouraging to vote for Berlusconi in the upcoming elections, arguing that “A.C. Milan is the football club with the highest number of titles in the world [...] this indicates [Berlusconi’s] ambitious and successful management practice, one that is desirable for the government as well”. A final point by the newspapers hinges upon the fact that Italian law24 prohibits propaganda from one day before the elections until the end of the ballot: given this, Berlusconi could have aimed at protracting his “message” by exploiting the derby of A.C. Milan, scheduled in Milan during the electoral-silence window. One point could cast doubt on the above interpretation of the jump in the FVS: the news of the possible acquisition of Mario Balotelli by A.C. Milan came out for the very first time on January 29. In that moment, the FVS did not display any jump, and it stayed rather flat; on the other hand, the football market is characterised by a consistent amount of fake news, which could have meant that investors did not believe the claims. 24Law April 4, 1956 number 212

37 A.2: Appendix for Mediaset analysis

A.2.3 Main speciﬁcation for Mediaset SC:

Table A.2.12: Scheme for SC in main speciﬁcation

RMSPE: .0048612 Company weights: ced .259 mch 0 gedi .308 mon 0 rcsm 0 pol 0 cai .434 s24 0 cle 0

Predictor balance:

Mediaset SC

Variance AR .0000252 .0000277 Variance AR (Feb) .000026 .0000297 AR .0002499 -.0005409 beta 1.1109 .338

Matrix of weights:

Variance Variance AR beta AR AR (Feb)

Variance AR .49676 Variance AR (Feb) 0 .49823 AR 0 0 .00365 beta 0 0 0 .00135

38 .02 .01 0 -.01 Gap in Gap in CAR AR and -.02 -.03 Jan 25, h12 Jan 30, h14 Feb 7, h14 Feb 11, h12

Figure A.2.1: The gap between Mediaset and SC

39 A.2.4 Robustness checks on Mediaset: pre-event window of 46 periods:

Table A.2.13: Scheme for SC robustness check: 46 pre-event periods

RMSPE: .0044372 Company weights: ced 0 mch 0 gedi .344 mon 0 rcsm 0 pol 0 cai .574 s24 0 cle .082

Predictor balance:

Mediaset SC

Variance AR .0000252 .0000299 Variance AR (Feb) .000026 .00003 AR .00018 -.00037 beta 1.1109 .43575

Matrix of weights:

Variance Variance AR beta AR AR (Feb)

Variance AR .55639 Variance AR (Feb) 0 .43294 AR 0 0 .00810 beta 0 0 0 .00255

40 .01 .005 0 -.005 Cumulative Cumulative Returns Abnormal -.01 -.015 Feb 11,h12 Feb 11,h16 Feb 12,h11 Feb 12,h15

Mediaset Synthetic control

Figure A.2.2: The gap between Mediaset and SC in the robustness check with 46 pre-event periods

41 A.2.5 Robustness checks on Mediaset: pre-event window of 246 periods:

Table A.2.14: Scheme for SC robustness check: 246 pre-event periods

RMSPE: .0062355 Company weights: ced 0 mch 0 gedi .447 mon 0 rcsm 0 pol 0 cai .437 s24 0 cle .117

Predictor balance:

Mediaset SC

Variance AR .0000252 .0000348 Variance AR (Feb) .000026 .0000363 AR 1.02e-12 -4.55e-12 beta 1.1109 .4742718

Matrix of weights:

Variance Variance AR beta AR AR (Feb)

Variance AR .61267322 Variance AR (Feb) 0 .36652897 AR 0 0 .00681955 beta 0 0 0 .01397826

42 .01 .005 0 -.005 Cumulative Cumulative Returns Abnormal -.01 -.015 Feb 11,h12 Feb 11,h16 Feb 12,h11 Feb 12,h15

Mediaset Synthetic control

Figure A.2.3: The gap between Mediaset and SC in the robustness check with 246 pre-event periods

A.2.6 Robustness checks on Mediaset: only pre-event AR, no other covariates

Table A.2.15: Scheme for SC robustness check: only AR and no other covariates

RMSPE: .0261512 Company weights: ced .004 mch .975 gedi .002 mon .003 rcsm .006 pol .003 cai .002 s24 0 cle .004

Predictor balance:

Mediaset SC

AR .0002499 .0002509

43 .03 .02 .01 0 Cumulative Cumulative Returns Abnormal -.01

Feb 11,h12 Feb 11,h16 Feb 12,h11 Feb 12,h15

Mediaset Synthetic control

Figure A.2.4: The gap between Mediaset and SC in the robustness check with no covariates other than AR