Internet Appendix to Telecracy: Testing for Channels of Persuasion

Guglielmo Barone ∗ Francesco D’Acunto † Gaia Narciso ‡

April 8, 2013

∗Bank of and RCEA. †Haas School of Business, UC Berkeley. e-mail: francesco [email protected]. ‡Department of Economics, Trinity College Dublin, CReAM and IIIS.

1 A Additional Evidence and Results

In this section, we provide additional evidence in support of our test design and assumptions, as well as additional results.

1. (Non-)Sorting into new information sources

In the paper, we claim that an omission bias is not likely to explain our evidence. This is only true if after the shock to exposure, individuals do not sort into information sources they were not accessing before the shock. Otherwise, individuals may access negative information regarding Berlusconi and his party which was omitted on TV. Figure 1 shows that viewers who moved from news programs to digital TV massively sorted into all- entertainment channels, and not into digital news channels. The top panel of Figure 1 shows the change in the viewing shares of the two major Italian news programs, TG1 and TG5, from September 2008 until November 2010. TG1 dropped from 30 percent of viewers to 24 percent of viewers. Over the same period, TG5 dropped from 26 percent of viewers to about 22 percent of them. The bottom panel of Figure 1 plots the viewing share of new digital channels over the same period, for the corresponding daily time slot when TG1 and TG5 are aired. The share of non-news new digital channels soared from about 1 percent in September 2008 to more than 10 percent in December 2010. At the same time, the share of all-news channels, or channels where a news program is aired, barely increased over the same period. Hence, our results cannot be driven by voters who accessed new sources of information on digital TV. Before 2010 regional elections viewers who went digital did not sort into two alternative information sources either: newspapers and the internet. In Figure 2, we compare the average daily number of purchased and freely-distributed daily newspapers per hundred inhabitants for Switch off (black histograms) and No Switch off (gray histograms) provinces in 2008, 2009 and 2010 1. The number of newspapers per 100 inhabitants has decreased in Switch off provinces from 2008 to 2010. Moreover, it has decreased more than in No Switch off provinces over the same period. Both facts are

1Source: Accertamenti Diffusione Stampa (ADS), available at http : //www.adsnotizie.it/

2 inconsistent with western Piedmont voters sorting into reading newspapers after lower exposure to information on TV. In Figure 4, we plot the Search Volume Index (SVI) 2 computed by Google Analytics for searching names of three major Italian newspapers’ websites: La Repubblica, Corriere della Sera, and La Stampa. We look at searches for their colloquial names, and verify that the vast majority of searches for each of these terms is connected to the newspapers’ websites. The top graph shows the evolution of SVI for the three newspapers in the province (No Switch off), while the bottom graph refers to the province (Switch off) 3. Elections were held on March 28th and March 29th 2010. The search volume in Turin province has barely increased during the electoral campaign (January-March 2010). Moreover, the search volume in Alessandria province, a No Switch off area, has increased more than in Turin over the same period.4 The same is true for unreported indices for searches of other terms related to elections, such as the Italian for elections and candidates, the surnames of Piedmont regional election candidates, the surname of national political leaders and party names. Online newspapers or other election-related terms do not exhaust possible sources of information on the web. As shown in Figure 5 and in the paper, youngsters are more likely to access the internet than the elderly, and we do not find any mediating role of youngsters on the electoral behavior of towns, which adds to the irrelevance of internet-based information as an explaination of our results. We then look at the incapacitation channel: households who do not switch to digital TV by the deadline cannot access any TV signals: they are incapacitated to bias exposure. If this channel is relevant, households may sort into alternative information sources as a consequence of their TVs being blank. In Figure 3, we reproduce a picture in ”Rapporto Digitale 2011 -Capitolo Terzo: I nuovi consumi digitali”, p. 78. The picture plots the weekly ratio of households accessing any TV signals over the analogous ratio for the

2SVI is computed by attributing a value of 100 for the week when an item was searched the most. For all other weeks in the search period, the index is the ratio of searches that week over the searches in the maximal week. 3There is a minimal number of searches needed to compute the index. None of the other Piedmont provinces has enough volume of searches to compute the SVI in the period we look at. 4All SVI series peak in the week immediately after elections. This is at odds with the idea that users look for online newspapers to get informed before voting in an election. Elections were held on a Sunday and a Monday morning, until 3pm. If voters wanted to know exit polls and results of elections, they had to connect to the internet, given that 3pm is a working hour.

3 same week in the previous year for the three regions which switched off to digital TV in Autumn 2009. Switch off weeks refer to the 2009 wave, when Campania, Lazio and Western Piedmont moved to digital TV. If no households were subject to incapacitation, we should observe a ratio constantly around 100, even in the switch off period. As it is apparent from Figure 3: access to TV signals dropped by about 10 percentage points compared to the previous year during the switch off period. But the incapacitation effect was short-lived to be of interest to our test: two weeks after the switch, the effect had halved. 10 weeks after the switch, access rates were back to normal. This was before the electoral campaign for 2010 regional elections started (mid-January 2010). Overall, evidence is inconsistent with Western Piedmont voters moving into alternative sources of information after going digital.

2. Exposure to information of elderly and youngsters

In our analysis, we argue that the elderly are more exposed to TV than others. Also, we argue that youngsters access the internet more than any other age group. These claims are supported by evidence in Figure 5, based on data from Istat. We plot the percentage of Italians who watch TV daily (left y-axis), and who access the Internet more than once a week (right y-axis) against age brackets. Exposure to TV is captured by the solid line. 95% of retirement-age individuals (i.e. those individuals aged 60 or higher) watch TV daily. This share drops to 92% for individuals aged 20 to 44. The dashed line plots exposure to the internet by age group. Exposure to the internet steadily decreases with age, moving from about 80% of people between 20 and 24 to less that 10% of people aged 65 or higher. In the paper, we show that when Piedmont towns are sorted by the ratio of youngsters, we detect no mediating effects of youngsters on Berlusconi candidate vote share change. One might be concerned that the ratio of youngters does not vary enough across towns. Thus, our test might be not powerful enough. The mean ratio of youngsters is 5.3% below the bottom tercile, 9.3% above the top tercile. Moving from the lowest to the highest tercile doubles the ratio of youngsters. Means are low though, and one might still be concerned by limited variation. We address this concern in the bottom graph of

4 Figure 5, which plots the vote share obtained by a new party candidate from Movimento 5 Stelle in 2010 Regional Elections, against the ratio of youngsters in Piedmont towns. The new party conducted a massive internet campaign, mainly addressing young voters, and consisting of blogs, internet meetups, and so on. The bottom-left graph plots the candidate vote share against the ratio of youngsters for all Piedmont towns. There is a clear positive relationship between vote share and ratio of youngsters. Since this effect might be driven by towns in the Val di Susa area, where the new candidate obtained up to 30% of votes, we trim the vote share of the Movimento 5 stelle candidate to 6% in the bottom-right graph. The positive relationship between vote share and ratio of youngsters is still apparent.

3. Additional Specification Tests and Robustness

In Figure 7, we provide additional graphical evidence of Berlusconi candidate performance in 2010 in Western and Eastern Piedmont compared to his performance in 2005. We plot the Berlusconi candidate vote share in 2010 against that in 2005, without exploiting the spatial heterogeneity across towns. Gray points are towns in the control area (east), while black points are towns which switched to digital TV before 2010 elections (west). The solid grey line plots a linear predictor for control towns, while the dashed black line does the same for treated towns. The dashed line is always below the solid one: in Western towns, the Berlusconi candidate was more likely to do worse with respect to 2005 than in Eastern towns. Although harder to capture, the dashed line diverges more from the solid one in towns where the vote share in 2005 was lower. If 2005 vote share is a proxy for the historical support for Berlusconi, this is consistent with results on historical support in the paper. We provide additional specification tests and robustness to alternative explanations in Table 1. In panel A and B, we use a quadratic and a quartic RD polynomial in the distance from the border, respectively. Magnitude of coefficients and statistical significance are similar to those reported in the paper. We do not report results for higher-order polynomials, since an overfitting problem at the boundary may arise when allowing for too many terms to be estimated. Coefficients and significance do not change

5 using higher degree polynomials though. In panel C, we allow for heterogenous effects of the running variable adding interactions between border segment fixed effects and all terms of the cubic distance polynomial. Results are similar to those in the baseline specifications of the paper. Moving on to alternative explanations, in panel D we ask if a novel political party in the 2010 elections, Movimento 5 Stelle, could explain our result. Movimento 5 Stelle might have been more effective in the west, and it might have convinced more voters who favored Berlusconi in 20055. We include the ratio of votes for the Movimento 5 Stelle candidate in 2010 6, as well as its interaction with the treatment condition. The unreported coefficient on the Movimento 5 Stelle candidate vote share is indeed significant and negative, but the interaction is not, and our main result is unaltered. In panel E we check if our effect is driven by towns in north-eastern or south-western Piedmont, hence restricting the analysis not only in longitude, but also in latitude. We exclude towns above 45 ◦300 and below 44 ◦300 in latitude. Results do not change.

4. Placebo Interaction Effects

In the paper we show that the effect of the shock to bias exposure is stronger in towns with more elderly and least educated voters, while it does not differ in towns with more (less) youngsters, or higher (lower) social capital. We interpret this as suggesting that cognitive biases are relevant to explain our findings. To corroborate this interpretation, Table 2 estimates additional interaction effects of our treatment indicator with several variables. None of these interactions is significantly difference from zero, i.e. treated towns above the top tercile, or below the bottom tercile sorting by variables in the captions do not vote differently from other treated towns. Sorting vairables include: income per capita, unemployment rate, TV subscriptions per household (intensity of TV usage), number of farms per capita (rural areas), number of chemist shops per capita (level of services), percentage of kids below age 6 who attend kindergarten (working mothers), and average tax rate. Hence, being older or less educated makes the effect of the drop stronger, but

5It is unclear if this explanation contrasts with an effect of digital TV: voters might have heard less about Berlusconi campaign on TV, hence having more attention to allot to the new party campaign. 6Since this party was not on the ballot in 2005, this ratio can be vacously interpreted as the change in vote share for the party candidate between 2010 and 2005.

6 many other characteristics do not mediate or mitigate the effect we find.

5. Omission Bias and Agenda-Setting

Larcinese et al.(2011) show that liberal-leaning US newspapers omit negative economic information when a Democratic administration is in power. Information omission is particularly relevant to our analysis. As Besley and Prat(2006) and Anderson and MacLaren(2012) show, a media bias consisting of information omission may have a long run effect on rational consumers, who do not know if and when information is omitted. Evidence in the previous section speaks to this point: treated voters do not select into new information sources after moving to non-information digital TV channels. It is unlikely that they had a chance to learn information which was previously omitted. To analyze the issue more directly, Figure 6 plots the change in Berlusconi candidate vote share between 2010 and 2005 against the change in unemployment between 2010 and 2001 for all Piedmont towns. Black points are towns in the control area (east), while gray points are towns in the treated area (west). Suppose that biased channels did not provide information regarding worsening employment conditions before 2010 regional elections. Voters in areas where unemployment did not increase may have not been aware of rising unemployment: concerns about the economy would not affect their beliefs about the Berlusconi candidate, nor their voting behavior. But those in areas where unemployment had risen dramatically from 2001 to 2010 did not need to hear from the news about unemployment: it was salient to them. If unreported rising unemployment explained our result, one should observe that the drop in the Berlusconi candidate vote share was higher in high unemployment growth towns, and higher in the west than in the east. The picture, on the contrary, shows that towns where Berlusconi candidate vote share decreased the most had no rise or a moderate rise in unemployment. In towns were unemployment increased more, Berlusconi candidate vote share decreased less. Sorting towns by change in unemployment shows that the higher drop in Berlusconi candidate vote share in the West was homogenous, and not concentrated in areas with soaring unemployment.

7 6. Double sorting of interaction effects

In the paper, we investigate which characteristics are typical of people who do not filter out the bias towards Berlusconi over time. In this section, we provide graphical evidence when we double-sort towns based on two characteristics, and check how each of them contributes to the main effect. The set of graphs on the left in Figure 8 plot the change in Berlusconi candidate vote share between 2010 and 2005 against the distance from the border when restricting the analysis to four groups of towns: those with ratio of elderly above the top tercile and ratio of youngsters below the lowest tercile (top left), those with ratio of elderly above the top tercile and ratio of youngsters above the top tercile (top right), those with ratio of elderly below the lowest tercile and ratio of youngsters below the lowest tercile (bottom left), and those with ratio of elderly above the lowest tercile and ratio of youngsters above the top tercile (bottom right). In line with our interpretation of interaction effects, the difference in the change in vote share between east and west Piedmont is apparent in towns above the top tercile for ratio of elderly, for any level of ratio of youngsters. On the contrary, there is no difference if we look at towns with low ratio of elderly, for any ratio of youngsters. The set of graphs on the right of Figure 8 repeat the same exercise for the ratio of elderly and the ratio of college-educate graduates in a town. The difference is apparent when looking at towns with high ratio of elderly, whatever the ratio of educated voters. It disappears across towns with low elderly and high educated voters (bottom right), in line with our interpretation. Finally, the effect of being a treated towns is detected for low ratio of elderly and low ratio of educated people, although it is less apparent than in the other subgroups.

7. Effect of switch to digital TV on Berlusconi candidate’s competitors

Effect on competitors. In Table 3, we estimate Equation 1 in the paper using the change in vote share of Berlusconi candidate main opponent (columns (1) and (2)), of a

8 novel party candidate 7 (columns (3) and (4)), and of the extreme right candidate (columns (5) and (6)) as dependent variables. The main opponent has benefitted the most from the drop in Berlusconi candidate vote share. The effect is less robust statistically than in Table 2 of the paper, and magnitudes differ. The center-left candidate gained 3.3 and 3.8 percentage points, while Berlusconi candidate lost 4.5 and 5.4 percentage points. Results and comparisons are similar in panel B. None of the other candidates seem to gain from the drop of Berlusconi candidate vote share. Thus, results are hardly consistent with 2005 Berlusconi supporters having showed up at 2010 Elections and consistently voted for one of the other candidates on the ballot.

8. External Validity of the Main Effect

In the paper, we present results for regressing the change in Berlusconi candidate vote share between Piedmont () and Liguria (Imperia and Savona) to verify if the main effect we document is valid across geographic areas. Figure 9 plots the change in the Berlusconi candidate vote share between 2010 and 2005 against distance from the border between towns in the Cuneo province (treated) and in Liguria (control). Treated towns are assigned a positive distance. The change averages around zero in Liguria towns, and around -4 percentage points in Piedmont. We check if results survive across elections. We analyze the link between digital TV usage and Berlusconi candidates performances in 2011 and 2006 province elections for the nine provinces where elections were held in May 2011. Some provinces switched in Autumn 2010. We compare them to those still allowing for both analogic and digital TV in May 2011. Rules for province and regional elections are very similar, which makes it easy to interpret results in light of our discussion so far. In Figure 10, we plot the vote share of Berlusconi candidates in May 2011 against that in May 2006 8. Black bubbles are provinces which were allowing for analogic TV in May 2011 9. Gray bubbles are

7The novel party which ran in 2010 is Movimento 5 Stelle. 8The border province of Gorizia is excluded because Berlusconi party entered different coalitions in 2006 and 2011 9Lucca (Toscana), Campobasso (Molise) and Reggio Calabria (Calabria).

9 provinces which had switched to digital TV in Autumn 2010 10. The square is Macerata, where elections were held and won by Berlusconi candidate in 2009, then voided due to irregularities. They were repeated again in 2011 11. The dashed line plots the predicted relationship if vote shares were the same in the two elections. Provinces still allowing for analogic TV lie above the line. All but one of the provinces which had switched off to digital TV before 2011 elections lie below the line, suggesting that Berlusconi candidates in these regions, and there only, performed worse in 2011 than in 2006.

9. Debiasing Mechanism in other Italian Regions

Despite idiosyncratic regional effects, we should observe patterns consistent with the proposed debiasing mechanism in all regions, since switching to digital TV has been an option for all Italian households since 2008. In Table 4, we enlist regions with elections in 201012 in decreasing order by the ratio of digital TV users (third column). The second column shows the mean historical support for Berlusconi candidate. The last three columns report correlation coefficients (p-values in brackets) for the change in Berlusconi candidate vote share between 2010 and 2005 and the ratio of elderly, or dummies equal to one for towns in the top and bottom third of the ratio of elderly distribution. We look at two dimensions: ratio of digital TV users (the more individuals watch digital TV, the less individuals are exposed to biased information), and historical support for Berlusconi (the lower the historical support, the stronger the effect of a drop in exposure to bias). In Figure 11 we plot the ratio of digital TV users against the historical support for Berlusconi. Each bubble represents one of the 11 regions with elections in March 2010. Bubble size is proportional to the correlation between change in Berlusconi candidate vote share, and the dummy capturing towns with most elderly. The correlation is negative for

10Vercelli (Piedmont), Mantova (Lombardia), (Lombardia), Treviso (Veneto), Ravenna (Emilia- Romagna). 11Macerata was still allowing for both transmission mechanisms around both elections 12Calabria and Campania are excluded. Calabria authority has not published election results online, nor on paper. When contacted, local officials said that data are accessible at the Region headquarters, but there is no electronic or paper version. In Campania, a center-left governor (Mr. Bassolino) won 2005 elections with more than 65% of votes. In 2008, he was accused on several criminal grounds, including fraud, corruption and false testimony. His own party asked him to resign, but he refused. Berlusconi candidate won easily in 2010.

10 black bubbles, positive for gray ones. If our mechanism is true, we should observe that: i) fixing a value of historical support, the higher the percentage of digital TV users, the stronger the debiasing effect, especially among the elderly, the larger the bubble; ii) fixing a ratio of digital TV users, the lower the historical support, the stronger the debiasing effect, the larger the bubble. These patterns are consistent with the picture.13 In Figure 12, we plot the change in turnout against the ratio of elderly for all regions where elections where held in 2010. Black points are towns where Berlusconi candidate in 2010 obtained fewer votes, in absolute terms, than the candidate in 2005. Gray points are towns where the candidate in 2010 obtained more votes than in 2005. For each graph, the title consists of the name of the region, the percentage of digital TV users as of March 2010, and the historical support for Berlusconi candidates in previous Regional elections (average of shares in 1995, 2000 and 2005). According to our mechanism, we should observe that turnout drops more the more the elderly, and Berlusconi candidate is more likely to lose votes in towns where turnout drops more and with more elderly. This should be especially true for regions with more digital TV users, and where historical support for Berlusconi candidate was lower. Figure 12 is consistent with this patterns of the debiasing mechanism.

10. Evidence from 2011 Mayoral elections

In 2011, mayoral elections were held in several Italian towns. For completeness, we show evidence from those elections too, despite many caveats which make it hard to interpret the results. First, electoral rules for mayoral elections differ based on town size. One the one hand, towns with more than 15,000 inhabitants have similar rules to province elections: if no candidate obtains the absolute majority of votes, the two most voted candidates take part in a second round, when the winner is elected. But given the low amount of resources needed to set up a list at the town level, this creates incentives for many unaffiliated, ad hoc lists to form just before elections, and to run in the first round hoping to obtain negotiating power with candidates who access the second round to

13Unfortunately, no Region exists with low historical support and high ratio of digital TV users. Not even the two regions we do not report, Calabria and Campania, satisfy these characteristics.

11 include specific policy issues in their agenda. Hence, vote shares are hardly interpretable across elections. On the other hand, towns below 15,000 have a first-past-the-post system: the candidate who obtains most votes, irrespective of the percentage, is elected mayor. This creates incentives for local candidates to set up highly inclusive lists, which often avoid any references to conflictive national parties. In most small towns, no symbols for Berlusconi or other national parties appear on the ballot 14 . This creates a selection issue, which is likely to be relevant to our setting: large towns, where we do observe candidates affiliated with Berlusconi, are those where it is salient and easier to buy a decoder and connect to digital TV before the switch off deadline, given the presence of electronic stores and megastores. Hence, digital TV users were likely to be high even for large towns which had not switched before 2011 elections. Second, coalitions at the town level are often determined by local politics. It is common to observe different coalitions across elections, which makes it hard to interpret the changes in candidates vote shares. In addition, mayoral elections allow for a disjoint vote: a voter can pick a party by checking its symbol, but also a candidate to mayor from a different coalition. This procedure is

14To give an idea of how pervasive this phenomenon is, the following list reports towns in Piedmont which had Mayoral elections in 2011 and where Berlusconi party symbol, or any reference to its name, ap- peared on the ballot: , , , , , , , Torino, Domodossola. Below is the list of towns where the symbol, or any reference to it, did not appear on ballots: Casorzo, Castelnuovo Belbo, Cortanze, Nizza Monferrato, Olmo Gentile, Piov`a Massaia, Quaranti, Roatto, San Paolo Solbrito, Scurzolengo, , , , , , , , , Gavi, Isola Sant’Antonio, , , , , Odalengo, , , Ponti, , Terzo, Barbaresco, , Capruana, , Casteldelfino, , , , , , , , Lequio , Martiniana , Melle, , , PezzoloValle Uzzone, , , , Roccaforte Mondov`ı, , , , , , , , , , , , , , , , , , , Suno, Albiano d’, , , , , , , , Ceres, , , , Cuorgne’, , Fiano, , , , Lusiglie’, , , , , , , , , , Porte, Ronco , Samone, , , , , , Arborio, , , Borgo d’Ale, , , , , , , , , , , , , Castelletto Cervo, Coggiola, Dorzano, Trivero, Veglio, Belgirate, Cesara, Macugnaga, Premosello-Chiovenda, Quarna Sotto, Trasquera. All ballots and information about Mayoral elections are available at http : //elezionistorico.interno.it/

12 common in mayoral elections:15 results are often driven by candidates’ own personality and records. Finally, 20% of towns which had elections in 2011 did so before the regular 5-year electoral cycle was over, due to governing coalition implosions or other motives for mayor resignations, which are likely to have a large influence in following election results despite no change in the Berlusconi party ideology at the national level. Keeping all these caveats in mind, the top graph of Figure 13 plots the Berlusconi candidate vote share in the first round of 2011 elections for towns where a Berlusconi candidate is identifiable, against his share in the first round of the previous elections. Black points are towns whose provinces had not officially switched as of May 2011, while gray towns had switched before elections. The dashed line emphasizes the 2010 vote share predicted by previous elections vote share. This graph does not seem to offer any clear evidence. We try to make evidence more comparable across towns, and select the sample: we exclude all towns were previous elections were not held in 2006; all towns where Berlusconi party was in different coalitions in the 2006 and 2011 elections; all towns where local, unaffiliated lists were admitted to the second round instead of major coalitions. Panel B of Figure 13 plots the vote share of Berlusconi candidate in 2011 against the one in 2006. Again, the evidence is inconclusive. The main concern is that we do not observe the ratio of digital TV users at the town level, which is relevant given the selection issues mentioned above.

15For instance, in the 2011 Naples elections, an independent, renowned candidate obtained 28%. Vote shares of parties supporting him summed up to 17%. His personal score allowed him to take part in the second round, where he beated the Berlusconi candidate, who had obtained about 40% of votes in the first round.

13 References

Simon Anderson and John MacLaren. Media Mergers and Media Bias with Rational Consumers. Journal of the European Economic Association, 10(4):831–859, 2012.

Timothy Besley and Andrea Prat. Handcuffs for the Grabbing Hand? Media Capture and Government Accountability. American Economic Review, 96(3):720–736, 2006.

Colin Cameron and Douglas Miller. Robust Inference with Clustered Data. Handbook of Empirical Economics and Finance, pages 1–28, 2011.

Timothy Conley. GMM Estimation with Cross-Sectional Dependence. Journal of Econometrics, 105(1):59–83, 1999.

Sthephen Donald and Kevin Lang. Inference with Differences-in-differences and Other Panel Data. Review of Economics and Statistics, 89:221–233, 2007.

Valentino Larcinese, Jim Snyder, and Riccardo Puglisi. Partisan Bias in Economic News: Evidence on the Agenda Setting Behavior of US Newspapers. Journal of Public Economics, 95(9-10):1178–1189, 2011.

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Table 1: Additional Specification Tests and Robustness

(1) (2) (3) (4) (5) (6) (7) (8) Full Sample < 75 km < 50 km < 25 km

A. Quadratic polynomial, distance from the border Switch off -0.047 -0.045 -0.050 -0.047 -0.055 -0.053 -0.060 -0.056 Clust. Province 0.009*** 0.009*** 0.008*** 0.008*** 0.011*** 0.011*** 0.010*** 0.011*** R2 0.397 0.394 0.397 0.396 0.424 0.418 0.439 0.437

B. Quartic polynomial, distance from the border Switch off -0.061 -0.057 -0.054 -0.051 -0.047 -0.044 -0.055 -0.050 Clust. Province 0.012*** 0.012*** 0.012*** 0.011*** 0.009*** 0.008*** 0.009*** 0.008*** R2 0.404 0.399 0.399 0.398 0.428 0.424 0.443 0.440

C. With interactions border segment*distance polynomial (cubic) Switch off -0.052 -0.049 -0.049 -0.046 -0.045 -0.043 -0.052 -0.049 Clust. Province 0.011*** 0.011*** 0.011*** 0.011*** 0.010*** 0.009** 0.010*** 0.010*** R2 0.422 0.418 0.414 0.415 0.445 0.443 0.471 0.473 D. Movimento 5 Stelle effect Switch off -0.076 -0.073 -0.071 -0.070 -0.061 -0.061 -0.057 -0.051 Clust. Province 0.009*** 0.009*** 0.008*** 0.008*** 0.014*** 0.013*** 0.011*** 0.009*** R2 0.413 0.411 0.409 0.410 0.436 0.434 0.441 0.439 E. Excluding unmatched towns (West and East) Switch off -0.050 -0.046 -0.053 -0.050 -0.051 -0.048 -0.044 -0.040 Clust. Province 0.011*** 0.011*** 0.011*** 0.012*** 0.013*** 0.014** 0.012** 0.012** R2 0.378 0.375 0.384 0.381 0.398 0.390 0.444 0.435 Observations 841 841 831 831 709 709 456 456

Electoral controls yes yes yes yes yes yes yes yes Socio-dem. controls yes yes yes yes yes yes yes yes Border segment f.e. yes yes yes yes yes yes yes yes Weighted LS no yes no yes no yes no yes Observations 1,206 1,206 1,161 1,161 928 928 552 552

This table reports results for estimating the spatial RDD model described in Equation 1 in the paper, when the RDD polynomial is cubic in the distance from the border. Each observation is a town in Piedmont. In all columns, the dependent variable is the change in Berlusconi candidate vote share between 2010 and 2005 Regional Elections. Switch off is a dummy variable which equals one for treated towns, zero otherwise. Columns report results for the Full Sample, and for limiting the analysis to towns within 75Km, 50Km and 25Km from the border in both directions. In odd columns, observations are unweighted. In even columns, observations are weighted by the average of the log of voters in 2010 and 2005. Significance is as follows: *10%, **5%, ***1%.

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Table 2: Placebo Interaction Effects

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) Income p.c. Unemployment TV subscriptions Farms Chemist Shops % Kids in Average Tax p.h.h. p.c. p.c. kindergarten Rate Switch off -0.053 -0.043 -0.054 -0.045 -0.052 -0.043 -0.059 -0.046 -0.064 -0.054 -0.061 -0.053 -0.058 -0.046 Clust. Province 0.012*** 0.012*** 0.011*** 0.011*** 0.011*** 0.014** 0.012*** 0.011*** 0.011*** 0.015*** 0.012*** 0.012*** 0.011*** 0.013**

Switch off*Top 3 -0.002 -0.004 0.005 0.004 -0.003 0.001 0.005 -0.003 0.011 0.015 0.003 0.008 0.007 0.001 Clust. Province 0.006 0.006 0.005 0.007 0.004 0.004 0.004 0.004 0.008 0.008 0.005 0.006 0.006 0.005

Switch off*Bottom 3 -0.007 -0.008 -0.006 -0.003 -0.012 -0.014 -0.001 0.001 0.007 0.002 0.004 0.003 -0.002 -0.005 Clust. Province 0.006 0.006 0.006 0.008 0.007 0.009 0.009 0.011 0.012 0.014 0.003 0.004 0.006 0.011

T3, B3, ratio yes yes yes yes yes yes yes yes yes yes yes yes yes yes

16 Electoral controls yes yes yes yes yes yes yes yes yes yes yes yes yes yes Socio-dem. controls yes yes yes yes yes yes yes yes yes yes yes yes yes yes Border segment f.e. yes yes yes yes yes yes yes yes yes yes yes yes yes yes < 50 km no yes no yes no yes no yes no yes no yes no yes Observations 1,206 928 1,206 928 1,206 928 1,206 928 1,206 928 1,206 928 1,206 928 R2 0.400 0.421 0.401 0.420 0.402 0.429 0.400 0.420 0.400 0.421 0.401 0.422 0.402 0.422

This Table reports results for estimating variations of the following spatial RDD model:

0 ∆Berlusconi10−05ipb = α + γswitchoffp + γ1switchoffp × T opthird + γ2switchoffp × Bottomthird + Xpre10ipδ + f(distancei) + Φb + ipb Each observation is a town in Piedmont. Switch off is a dummy variable which equals one for treated towns, zero otherwise. Columns (1) and (2) focus on interactions of treatment effect with the average income per capita in a town. Switchoff*Top 3 is one if a town is in the treatment group and above the top tercile of towns sorted by income per capita, zero otherwise. Switchoff*Low is one if a town is in the treatment group and below the lowest tercile of towns sorted by income per capita, zero otherwise. Columns (3) and (4) repeat the same exercise for the unemployment ratio. Columns (5) and (6) repeat it for the number of public TV subscription, columns (7) and (8) for the number of farms per capita, columns (9) and (10) for the number of chemist shops per capita, columns (11) and (12) for the percentage of kids below 6 years old who attend a kindergarten in town, and columns (13) and (14) for the averate tax rate. In even columns the analysis is limited to towns within 50Km from the border. In all columns, observations are weighted by the average of the log of voters in 2010 and 2005. Cluster prov. standard errors are clustered at the province level (8 clusters), and corrected as in Cameron and Miller(2011) for downward bias. Significance is as follows: *10%, **5%, ***1%. Table 3: Effect of Switch Off to digital TV on competitors vote shares

(1) (2) (3) (4) (5) (6) Main Opponent New Political Offer Extreme right

A. Distance from the border

Switch off 0.033 0.038 -0.005 -0.002 -0.001 0.001 Clust. province 0.019 0.013** 0.012 0.009 0.002 0.001 Wild Bootstrap 0.028 0.016** 0.023 0.004 0.003 0.001 Spatial HAC 0.009*** 0.008*** 0.006 0.005 0.002 0.002

R2 0.386 0.413 0.329 0.274 0.305 0.355

B. Cubic polynomial, distance from the border

Switch off 0.044 0.026 -0.006 0.003 0.001 0.002 Clust. province 0.015** 0.012* 0.013 0.005 0.003 0.002 Wild Bootstrap 0.023* 0.017 0.009 0.006 0.003 0.002 Spatial HAC 0.009*** 0.009*** 0.008 0.004 0.003 0.002

R2 0.402 0.419 0.370 0.285 0.308 0.361

17 Electoral controls yes yes yes yes yes yes Socio-dem. controls yes yes yes yes yes yes Border segment f.e. yes yes yes yes yes yes < 50 km no yes no yes no yes Observations 1,206 928 1,206 928 1,260 928

This table reports results for estimating variations of the following spatial RDD model:

0 ∆Dep var = α + γswitchoffp + Xpre10ipδ + f(distancei) + Φb + ipb Each observation is a town in Piedmont. In columns (1) and (2), the dependent variable is the change in the center-left candidate vote share (Ms. Bresso) between 2010 and 2005. In columns (3) and (4), it is the vote share of the candidate of a novel party (Movimento 5 Stelle) candidate in 2010. In columns (5) and (6), it is the change in the extreme-right candidate vote share between 2010 and 2005. In Panel A the RDD polynomial in the distance of a town from the border is linear. In Panel B, it is cubic. Switch off is a dummy variable which equals one for treated towns, zero otherwise. Odd columns report results for the Full Sample. In even columns the analysis is limited to towns within 50Km from the border in both directions. In all columns, observations are weighted by the average of the log of voters in 2010 and 2005. For each specification, three sets of standard errors are reported. In Clust. province, s.e. are clustered at the province level (8 clusters), and corrected for downward bias as in Cameron and Miller(2011). Wild bootstrap s.e. follow the procedure suggested by Donald and Lang(2007). Columns (1) to (6) are based on 900 repetitions of bootstraps, while columns (7) and (8) on 100 repetitions. Spatial HAC s.e. allow for spatial dependence of unknown form following Conley(1999). Significance is as follows: *10%, **5%, ***1%. Table 4: Digital TV usage and Voting Behavior across Italian Regions

Regions N. of Historical Digital Δ Berlusconi 10-05 towns support TV users Ratio of Top Bottom Elderly Third Third

Western Piedmont 565 49% 100% -0.332 -0.300 0.169 [0.000] [0.000] [0.001]

Lazio 318 46% 75% -0.098 -0.128 0.110 [0.081] [0.022] [0.051]

Eastern Piedmont 641 50% 40% -0.131 -0.072 0.013 [0.001] [0.069] [0.736]

Lombardia 1,420 53% 25% -0.058 0.023 0.007 [0.030] [0.386] [0.788]

Liguria 235 42% 22% -0.148 -0.121 0.030 [0.024] [0.006] [0.643]

Umbria 92 35% 17% -0.243 -0.177 0.060 [0.020] [0.092] [0.569]

Veneto 581 50% 16% 0.014 0.038 -0.001 [0.735] [0.365] [0.973]

Toscana 287 34% 16% -0.006 -0.011 0.043 [0.915] [0.841] [0.466]

Emilia-Romagna 347 34% 13% -0.278 -0.171 0.088 [0.000] [0.002] [0.106]

Marche 239 37% 11% -0.231 -0.131 0.178 [0.000] [0.044] [0.006] Basilicata 131 31% 7% 0.060 -0.041 -0.082 [0.494] [0.642] [0.353]

This table reports summary statistics and correlation coefficients for regions where elections were held in 2010. For each region, the left panel reports the number of towns, the Historical support for Berlusconi candidates in previous elections (average of vote shares in 1995, 2000 and 2005) and the percentage of TV viewers on digital TV. The right panel reports correlation coefficients between the change in Berlusconi candidate vote share between 2010 and 2005 at the town level, and the ratio of elderly, a dummy which equals one for towns above the top tercile of towns sorted by ratio of elderly, and a dummy which equals one for towns below the bottom tercile of towns sorted by ratio of elderly. P-values for the significance of correlation coefficients are reported in brackets. Regions are enlisted in decrasing order based on the percentage of digital TV users as of March 2010.

18 12

News digital 10 Other Digital 8

6

4

2 Viewing shares News (vs) all- (vs) News shares Viewing channels digital entertainment 0

Figure 1: From biased information to all-entertainment

31 TG1 TG5

29

27

25

23

21 Viewing share major Italian news programs news programs Italian major share Viewing

19

12

News digital 10 Other Digital 8

6

4

2 Viewing shares News (vs) all- (vs) News shares Viewing channels digital entertainment 0

In this graph, the top panel plots the viewing share of the two major Italian news programs, which air daily from 8 to 8:30pm, TG1 and TG5, from January 2008 to December 2011. The bottom panel plots the viewing share of new digital TV channels over the same period, where the share of all-entertainment channels and those involving news programs are split up.

31 TG1 TG5 19 29

27

25

23

21 Viewing share major Italian news programs news programs Italian major share Viewing

19

Figure 2: (Non-)Sorting into Newspapers after switch off to Digital TV

9.5

9.0 Switch off No Switch off 8.5

distributed 8.0

7.5 inhabitants

100 7.0

newspapers

for 6.5 of

daily 6.0

Number 5.5

5.0 2008 2009 2010

This picture plots average daily number of purchased and freely-distributed daily newspapers per hundred inhabitants in Western (black histograms) and Eastern (gray histograms) towns in the period when digital TV was made available, and before 2010 Regional Elections.

Figure 3: Incapacitation to access TV signals after the switch

110

105

100 TV vs. previous TV 95

90

85

year (Lazio, Campania, year (Lazio, Campania, Piedmont) 80 Households watching

This picture is a one-to-one reproduction of ”Rapporto Digitale 2011 - Capitolo Terzo: I nuovi consumi digitali”, p. 78. For each week around the first wave of switch off deadlines (Autumn 2010), the picture shows the percent of households accessing any TV signals compared to the same week in the previous year for Piedmont, Lazio and Campania.

20 Share of viewers of Italian major talk shows (%) 30 25 20 BALLARO' 15 PORTA A PORTA 10 MATRIX 5 OTTO E MEZZO 0 SANTORO

Figure 4: (Non-)Sorting into internet information sources after switch off to Digital TV

Google SVI indices during 2010 electoral campaign in the East (top) and the West (bottom)

80 repubblica 2010 Elections

70 corriere

stampa 60

50

40

30

20

100 2010 Elections repubblica

90 corriere

stampa 80

70

60

50

40

This picture plots the Google SVI index for searches of three major Italian newspapers with online websites during the 2010 regional elections campaign. The top graph refers to internet users in Alessandria province (East, control), while the bottom graph to the Turin province (West, treated). 21 Figure 5: Exposure to Information sources of Elderly and Youngsters

90 100 Internet (left) TV (right) 80 98 70

60 96

50 94 40

30 92

20 90 10

0 88 18‐12345678919 20‐24 25‐34 35‐44 45‐54 55‐59 60‐64 65‐74 75+

Share of new party candidate vs. ratio of youngsters

In the top graph, we plot the percentage of individuals who watch television daily (measured on the left y-axis), and who access the Internet more than once a week (measured on the right y-axis) against age groups in Italy. The solid line refers to exposure to TV, while the dashed line to the exposure to the internet. In the bottom graph we plot the vote share of a new political party candidate who ran an aggressive internet campaing against the ratio of youngsters in Piedmont towns. In the left graph, all Piedmont towns are plotted. In the right graph, we only look at towns where the new party candidate vote share was below 6%.

22 Figure 6: Agenda Setting: Unemployment .4 .2 0 -.2 -.4 -.6 Change in Berlusconi Candidate vote share 10-05

-.1 0 .1 .2 Change in unemployment 10-01 (%)

This graph plots the change in Berlusconi candidate vote share between 2010 and 2005 against the change in unemployment between 2010 and 2001 across Piedmont towns. Black points are towns in the east (control), while gray points are towns in the West (treated area).

23 Figure 7: Vote Shares of Berlusconi candidate in 2010 and 2005 for treated and control towns .8 .6 .4 .2 Vote Share Berlusconi Candidate 2010 0

0 .2 .4 .6 .8 Vote Share Berlusconi Candidate 2005 This picture plots the vote share of Berlusconi candidate in 2010 against that in 2005 across Piedmont towns. Gray points are control towns (East), while black points are treated towns (West). The dashed line is a linear predictor for the average of treated observations, while the solid line is a linear predictor for the average of control observations.

24 Figure 8: Double sorting and interaction effects

Elderly and Youngsters Elderly and Educated 25

This two graphs sort towns based on the ratio of elderly and the ratio of youngsters (left) and the ratio of elderly and the ratio of college-educated people (right). We plot the change in Berlusconi candidate vote share between 2010 and 2005 against the distance of a town from the treatment border, where treated towns are assigned a positive distance. Figure 9: External Validation: Change in Berlusconi candidate performance around treatment border (Piedmont vs. Liguria)

This graph depicts the change in Berlusconi candidate vote share between 2010 and 2005 regional elections for towns in two neighboring regions, Piedmont and Liguria, against their distance from the border. Piedmont towns (Cuneo province) switched to digital TV before 2010 regional elections. They are assigned a positive distance from the border. Liguria towns (Imperia and Savona provinces) switched off to digital TV after 2010 elections. Voters chose for two different sets of candidate in the two regions, and elections were held on the same day. Observations are trimmed at the 1-99 percentile levels of the change in Berlusconi candidate vote share.

26 Figure 10: External Validation: Change in Berlusconi candidate performance across Province Elections (2011 vs. 2006) .6 .5 .4 .3 .2 Vote Share Berlusconi Candidate May 2011 .2 .3 .4 .5 .6 Vote Share Berlusconi Candidate May 2006

No Switch off before May 2011 Elections in May 2009 void, repeated in May 2011 Switch off in Autumn 2010

This graph depicts the vote share of Berlusconi candidate in 2011 province elections against the vote share in 2006 elections for 9 Italian provinces where elections were held in May 2011. Black bubbles are provinces which had not switched to digital TV as of May 2011. Gray bubbles are provinces which had switched in Autumn 2010. The square is Macerata (Marche), where elections won by the Berlusconi party candidate were held in June 2009. Elections were then declared void due to irregularities. After one year of vacancy of power, elections were held again in May 2011.

27 Figure 11: Debiasing Mechanism across Regions: Digital TV, Historical Support, and Ratio of Elderly 100 80 60 40 Digital TV users (March 2010) 20 0

30 35 40 45 50 55 Historical Support for Berlusconi

This graph plots the percentage of digital TV users in March 2010 against the historical support for Berlusconi for regions where elections where held in 2010. Historical support is the average of Berlusconi candidates vote shares in 1995, 2000 and 2005. Bubble sizes are proportional to the correlation between the change in Berlusconi candidate vote share between 2010 and 2005, and a dummy for towns above to the top tercile of the ratio of elderly. Bubbles are black if the correlation is negative, gray if it is positive.

28 Figure 12: Turnout and elderly across Italian Regions .2 .2 .2 .2 0 0 0 0 -.2 -.2 -.2 -.2 Change in Turnout 10-05 Change in Turnout 10-05 Change in Turnout 10-05 Change in Turnout 10-05 -.4 -.4 -.4 -.4 .3 .4 .5 .6 .7 .8 .3 .4 .5 .6 .7 .8 .3 .4 .5 .6 .7 .8 .3 .4 .5 .6 .7 .8 Ratio of elderly Ratio of elderly Ratio of elderly oldratio .2 .2 .2 .2 0 0 0 0 29 -.2 -.2 -.2 -.2 Change in Turnout 10-05 Change in Turnout 10-05 Change in Turnout 10-05 -.4 -.4 -.4 -.4 .3 .4 .5 .6 .7 .8 .3 .4 .5 .6 .7 .8 .3 .4 .5 .6 .7 .8 .3 .4 .5 .6 .7 .8 Ratio of elderly Ratio of elderly Ratio of elderly Ratio of elderly .2 .2 .2 0 0 0 -.2 -.2 -.2 Change in Turnout 10-05 Change in Turnout 10-05 Change in Turnout 10-05 -.4 -.4 -.4 .3 .4 .5 .6 .7 .8 .3 .4 .5 .6 .7 .8 .3 .4 .5 .6 .7 .8 Ratio of elderly Ratio of elderly Ratio of elderly

In each graph, we plot the change in turnout between 2010 and 2005 against the ratio of elderly over total population for regions where elections where held in 2010. Black points are towns where Berlusconi candidate in 2010 obtained fewer votes, in absolute terms, than the candidate in 2005. Gray points are towns where the candidate obtained more votes than in 2005. For each graph, the title consists of the name of the region, the percentage of digital TV users as of March 2010, and the historical support for Berlusconi candidates in previous regional elections (average of shares in 1995, 2000 and 2005). Figure 13: Change in Berlusconi candidate performance across Mayoral Elections (2011 vs. 2006)

A. All towns with elections in 2011 .8 .6 .4 .2 Berlusconi Candidate Vote Share 2011 0

0 .2 .4 .6 .8 Berlusconi Candidate Vote Share previous elections

B. Towns with same Berlusconi coalition in 2006 and 2011 .8 .6 .4 .2 Berlusconi Candidate Vote Share 2011 0

.1 .2 .3 .4 .5 .6 Berlusconi Candidate Vote Share 2006

These graphs depict the vote share of Berlusconi candidate in May 2011 mayoral elections against the vote share in previous elections for towns where identifiable signs of Berlusconi party and the main opposition party were on the ballot. Black points are towns which had not switched to digital TV as of May 2011. Gray points are towns which had switched before May 2011. The top graph uses all towns. The bottom graph excludes towns where the electoral cycle ending in 2011 had not started in 2006, and where Berlusconi coalition was not the same across elections.

30