Newspaper language controls |  |  | X

  * 1.

Notes: 2SLS estimates. Cross-section with newspaper-level observations
weighted their total circulation. The dependent variable is newspaper language
similarity with FNC (the average probability that a snippet from a newspaper
is predicted to be from FNC). The right-hand side variable of interest is
instrumented FNC viewership relative to averaged CNN and MSNBC viewership. All
columns include state fixed effects and demographic controls as listed in
Appendix Table A.2. Column 2 also includes channel controls (population shares
with access to each of the three TV channels). Column 3 controls for generic
newspaper language features (vocabulary size, avg. word length, avg. sentence
length, avg. article length). Standard errors are clustered at the state level
(in parenthesis): * p $<$ 0.1, ** p $<$ 0.05, *** p $<$ 0.01.

###  Robustness: Historical circulation weights

Table C.8: Cable News Effects on Newspaper Content (2SLS): 1995 Circulation
Weights

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3)
---|---|---|---
FNC Viewership (rel. to CNN/MSNBC) | 0.556*** | 0.538*** | 0.561***
| (0.165) | (0.162) | (0.180)
K-P First-Stage F-Stat | 21.357 | 21.311 | 19.157
N observations | 1928 | 1928 | 1928
State FE | X | X | X
Demographic controls | X | X | X
Channel controls |  | X | X
Newspaper language controls |  |  | X

  * 1.

Notes: 2SLS estimates. Cross-section with newspaper-county-level observations
weighted by newspaper circulation in 1995 (the pre-FNC era) in each county.
The dependent variable is newspaper language similarity with FNC (the average
probability that a snippet from a newspaper is predicted to be from FNC). The
right-hand side variable of interest is instrumented FNC viewership relative
to averaged CNN and MSNBC viewership. All columns include state fixed effects
and demographic controls as listed in Appendix Table A.2. Column 2 also
includes channel controls (population shares with access to each of the three
TV channels). Column 3 controls for generic newspaper language features
(vocabulary size, avg. word length, avg. sentence length, avg. article
length). Standard errors are multiway-clustered at the county and at the
newspaper level (in parenthesis): * p $<$ 0.1, ** p $<$ 0.05, *** p $<$ 0.01.

###  Robustness: Relative circulation weights

Table C.9: Cable News Effects on Newspaper Content (2SLS): Relative Circ.
Weights $\times$ Sampled Households

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3)
---|---|---|---
FNC Viewership (rel. to CNN/MSNBC) | 0.521*** | 0.509*** | 0.922**
| (0.173) | (0.172) | (0.442)
K-P First-Stage F-Stat | 20.790 | 20.973 | 20.801
N observations | 3781 | 3781 | 3781
State FE | X | X | X
Demographic controls | X | X | X
Channel controls |  | X | X
Newspaper language controls |  |  | X

  * 1.

Notes: 2SLS estimates. Cross-section with newspaper-county-level observations
weighted by their circulation share in each county, multiplied by the number
of surveyed individuals for each county by Nielsen. The dependent variable is
newspaper language similarity with FNC (the average probability that a snippet
from a newspaper is predicted to be from FNC). The right-hand side variable of
interest is instrumented FNC viewership relative to averaged CNN and MSNBC
viewership. All columns include state fixed effects and demographic controls
as listed in Appendix Table A.2. Column 2 also includes channel controls
(population shares with access to each of the three TV channels). Column 3
controls for generic newspaper language features (vocabulary size, avg. word
length, avg. sentence length, avg. article length). Standard errors are
multiway-clustered at the county and at the newspaper level (in parenthesis):
* p $<$ 0.1, ** p $<$ 0.05, *** p $<$ 0.01.

Table C.10: Cable News Effects on Newspaper Content (2SLS): Relative Circ.
Weights $\times$ County Pop

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3)
---|---|---|---
FNC Viewership (rel. to CNN/MSNBC) | 0.436*** | 0.439*** | 0.342*
| (0.151) | (0.150) | (0.204)
K-P First-Stage F-Stat | 20.773 | 21.150 | 20.439
N observations | 3781 | 3781 | 3781
State FE | X | X | X
Demographic controls | X | X | X
Channel controls |  | X | X
Newspaper language controls |  |  | X

  * 1.

Notes: 2SLS estimates. Cross-section with newspaper-county-level observations
weighted by their circulation share in each county, multiplied by the county
population. The dependent variable is newspaper language similarity with FNC
(the average probability that a snippet from a newspaper is predicted to be
from FNC). The right-hand side variable of interest is instrumented FNC
viewership relative to averaged CNN and MSNBC viewership. All columns include
state fixed effects and demographic controls as listed in Appendix Table A.2.
Column 2 also includes channel controls (population shares with access to each
of the three TV channels). Column 3 controls for generic newspaper language
features (vocabulary size, avg. word length, avg. sentence length, avg.
article length). Standard errors are multiway-clustered at the county and at
the newspaper level (in parenthesis): * p $<$ 0.1, ** p $<$ 0.05, *** p $<$
0.01.

###  Robustness: Absolute and relative FNC viewership

Table C.11: Cable News Effects on Newspaper Content (2SLS): Different
Instruments

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3) | (4) | (5) | (6)
---|---|---|---|---|---|---
FNC Viewership (rel. to CNN) | 0.157 | 0.274 |  |  |  |
| (0.143) | (0.365) |  |  |  |
FNC Viewership (rel. to MSNBC) |  |  | 0.409*** | 1.073** |  |
|  |  | (0.126) | (0.470) |  |
FNC Viewership (absolute) |  |  |  |  | 0.075 | 1.085*
|  |  |  |  | (0.197) | (0.649)
K-P First-Stage F-stat | 39.173 | 23.314 | 32.120 | 23.834 | 15.011 | 7.715
N observations | 3781 | 3781 | 3781 | 3781 | 3781 | 3781
State FE | X | X | X | X | X | X
Demographic controls | X | X | X | X | X | X
Channel controls | X | X | X | X | X | X
Newspaper language controls | X | X | X | X | X | X
Circulation weights | X |  | X |  | X |
Circulation share weights |  | X |  | X |  | X

  * 1.

Notes: 2SLS estimates. Cross-section with newspaper-county-level observations.
In uneven columns, we weight observations by newspaper circulation in each
county (following column 3 of Table 4). In even columns, we weight
observations by their circulation share in each county, multiplied by the
number of surveyed individuals for each county by Nielsen. The dependent
variable is newspaper language similarity with FNC (the average probability
that a snippet from a newspaper is predicted to be from FNC). The right-hand
side variable of interest is (i) instrumented FNC viewership relative to CNN
viewership: Viewership (FNC - CNN)) in columns 1 and 2, (ii) instrumented FNC
viewership relative to MSNBC viewership: Viewership (FNC - MSNBC)) in columns
3 and 4, and (iii) absolute instrumented FNC viewership: Viewership FNC). All
columns include state fixed effects, demographic controls as listed in
Appendix Table A.2, channel controls (population shares with access to each of
the three TV channels), and controls for generic newspaper language features
(vocabulary size, avg. word length, avg. sentence length, avg. article
length). Standard errors are multiway-clustered at the county and at the
newspaper level (in parenthesis): * p $<$ 0.1, ** p $<$ 0.05, *** p $<$ 0.01.

###  Robustness: Dropping observations and clustering

Table C.12: Cable News Effects on Newspaper Content (2SLS): State Clustering

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3)
---|---|---|---
FNC Viewership (rel. to CNN/MSNBC) | 0.314** | 0.311** | 0.318**
| (0.120) | (0.117) | (0.135)
K-P First-Stage F-Stat | 35.682 | 36.496 | 37.544
N observations | 3781 | 3781 | 3781
State FE | X | X | X
Demographic controls | X | X | X
Channel controls |  | X | X
Newspaper language controls |  |  | X

  * 1.

Notes: 2SLS estimates. Cross-section with newspaper-county-level observations
weighted by newspaper circulation in each county. The dependent variable is
newspaper language similarity with FNC (the average probability that a snippet
from a newspaper is predicted to be from FNC). The right-hand side variable of
interest is instrumented FNC viewership relative to averaged CNN and MSNBC
viewership. All columns include state fixed effects and demographic controls
as listed in Appendix Table A.2. Column 2 also includes channel controls
(population shares with access to each of the three TV channels). Column 3
controls for generic newspaper language features (vocabulary size, avg. word
length, avg. sentence length, avg. article length). Standard errors are
clustered at the state level (in parenthesis): * p $<$ 0.1, ** p $<$ 0.05, ***
p $<$ 0.01.

Figure C.2: Cable News Effects on Newspaper Content (2SLS): Dropping
Individual Newspapers

The histogram on the left (bin width 0.005) shows the $Viewership_{js}$
coefficients according to our main specification (Table 4, column 3), but
leaving out each individual newspaper once. The histogram on the right (bin
width 0.025) shows the distribution of the t-values from the same regressions.

###  Mechanisms: Language features and topics

Table C.13: 2SLS: Cable News Effects on Text Readability Metrics (2SLS)

Dep. variable | Vocab. size | Len. words | Len. sent | Len. art
---|---|---|---|---
FNC Viewership (rel. to CNN/MSNBC) | -0.227 | 0.885 | 0.154 | 0.863
| (0.543) | (0.740) | (0.393) | (0.554)
K-P First-Stage F-Stat | 36.380 | 36.380 | 36.380 | 36.380
N observations | 3781 | 3781 | 3781 | 3781
State FE | X | X | X | X
Demographic controls | X | X | X | X
Channel controls | X | X | X | X
Corpus size control | X | X | X | X

  * 1.

Notes: 2SLS estimates. Cross-section with newspaper-county-level observations
weighted by newspaper circulation in each county. The dependent variable is
vocabulary size in column 1, average word length in column 2, average sentence
length in column 3, and average total article length in column 4. The right-
hand side variable of interest is instrumented FNC viewership relative to
averaged CNN and MSNBC viewership. All columns include state fixed effects,
demographic controls as listed in Appendix Table A.2, channel controls
(population shares with access to each of the three TV channels), and a
control for the size of the newspaper-specific corpus. Standard errors,
multiway-clustered at the county and at the newspaper level, in parenthesis. *
p $<$ 0.1, ** p $<$ 0.05, *** p $<$ 0.01.

In Table C.13, re-run our main specification, but instead of bigram-based
similarity with FNC, we regress vocabulary size (normalized by the total size
of the corpus, column 1), average word length (column 2), average sentence
length (column 3), and average article length (column 4) on instrumented FNC
viewership relative to MSNBC and CNN. As before, we include demographic and
channel controls. We also account for the size of the newspaper-specific
corpus.353535The number of articles scraped is given by the availability on
NewsLibrary. It does not seem to follow a pattern: correlation between corpus
size and circulation by newspaper is rather small, around 0.3. The correlation
between similarity with FNC and corpus size is, if anything, negative (around
-0.21). None of the coefficients are significant or close to significant.
These results are consistent with the interpretation that our main results are
driven by FNC-specific bigrams that diffuse into local newspaper
language.363636The insignificance of the coefficients in Table C.13 should not
come as a surprise given that the main results in Table 4 barely change when
we move from column 2 to column 3 (where generic newspaper language controls
are introduced).

Table C.14: Cable News Effects on Newspaper Content (2SLS): Conditioning on
Topics

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3)
---|---|---|---
FNC Viewership (rel. to CNN/MSNBC) | 0.223** | 0.223** | 0.258**
| (0.101) | (0.101) | (0.107)
K-P First-Stage F-Stat | 37.499 | 37.290 | 35.341
N observations | 3781 | 3781 | 3781
State FE | X | X | X
Demographic controls | X | X | X
Channel controls |  | X | X
Newspaper language controls |  |  | X

  * 1.

Notes: 2SLS estimates. Cross-section with newspaper-county-level observations
weighted by newspaper circulation in each county. The dependent variable is
newspaper language similarity with FNC (the average probability that a snippet
from a newspaper is predicted to be from FNC). The right-hand side variable of
interest is instrumented FNC viewership relative to averaged CNN and MSNBC
viewership. All columns include state fixed effects, demographic controls as
listed in Appendix Table A.2, and average topic share controls. Column 2 also
includes channel controls (population shares with access to each of the three
TV channels). Column 3 controls for generic newspaper language features
(vocabulary size, avg. word length, avg. sentence length, avg. article
length). Standard errors are multiway-clustered at the county and at the
newspaper level (in parenthesis): * p $<$ 0.1, ** p $<$ 0.05, *** p $<$ 0.01.

###  Mechanisms: Descriptive Evidence on Demand Side

A relevant question is whether the effects of contagious slant are driven by
supply or demand – that is, a direct influence on news producers, or else
theirs response to changes in reader preferences. Newspaper readers,
influenced by their cable news consumption, might demand more slanted news
content. On the supply side, owners, editors, or journalists exposed to
certain channels may borrow the associated slanted content and push it to
consumers.

The previous literature has produced mixed evidence, with some work showing
that slant across U.S. newspapers reflects the political preferences of
readers rather than producers [Gentzkow and Shapiro,, 2010], while other work
has shown that media owners do influence news content [Gilens and Hertzman,,
2000, Martin and McCrain,, 2019, Mastrorocco and Ornaghi,, 2020, Szeidl and
Szucs,, 2021]. Given the declining revenues in the local news industry during
the 2005-2008 time period [Evans,, 2009, Rolnik et al.,, 2019, Djourelova et
al.,, 2021], it could be that newspapers have less leeway to deviate from
consumer preferences in favor of producer preferences. On the other hand,
lower revenues might also reduce newsmaking resources. Then, borrowing content
from national platforms may provide a cheap production alternative to original
reporting.

There is good reason to be skeptical of a supply-side mechanism in our case,
given that variation comes from shifts in viewership due to channel
positioning. Journalists, editors, and others involved in news production are
sophisticated news consumers. These individuals will view their preferred
cable news shows regardless of local channel position. Thus our instrument
will not affect them. Hence, any supply-side mechanism is suspect and would
require strong evidence.

Here, we provide some descriptive evidence on the demand side by comparing the
effects of cable-channel exposure on news content to the parallel effects on
Republican vote share documented in previous work [Martin and Yurukoglu,,
2017, Ash et al.,, 2021]. If the contagious-slant effects are driven by
responses to a cable news-induced demand shift among readers, we would expect
newspapers to react most in counties where the FNC effect on Republican voting
is largest. To test for this possibility, we use a machine learning approach
to model heterogeneous treatment effects across counties in the response to
the channel-position instrument. This generalized model – a causal forest from
Athey et al., [2019] – allows the reduced form effect of channel position to
vary flexibly with local covariates. After collapsing the data to the county
level, we train two models – one with newspaper slant as the outcome, and a
second with 2008 Republican vote share as the outcome.

Using the trained causal forests, we predict county-specific treatment effects
for both outcomes. Appendix Figure C.3 shows that the predicted effect sizes
for each outcome are highly correlated across counties (with a Pearson’s
correlation of 0.25). Further, Appendix Table C.15 shows that similar
covariates predict a strong response for both news similarity and Republican
vote share. Overall, these findings suggest that the types of counties who are
responsive to cable news exposure in their voting are also responsive in the
associated news content similarity. We interpret this result as descriptive
evidence for the relative importance of demand-side effects.

Figure C.3: Predicted treatment effects of instrument: similarity to FNC
versus Republican votes in 2008

Binned scatterplots (16 bins) of the predicted treatment effect of FNC
exposure (relative to CNN) on Republican vote shares in 2008 (standardized)
against the predicted treatment effects of same instrument on predicted
similarity to FNC (standardized). Cross-section with county-level
observations. No controls are included. In grey (next to the axes), we show
the distributions of the underlying variables.

Table C.15: The five covariates most associated with a response to the the
instrument (county-level)

Strong response of $\text{Slant}_{k}$ | Strong response of voting
---|---
% Black population | % High school graduates
% High school graduates | % Republican votes 1996
% Republican votes 1996 | % Age group 80s
% Asian population | Gini
% Age group 80s | % Age group 70s

_Notes:_ Covariates most associated with a strong response of newspaper
similarity $\text{Slant}_{k}$ (left column) and Republican vote shares (right
column) to the instrument. The covariates were identified using heterogeneous
treatment effect estimation via instrumental variables as proposed by Athey et
al., [2019]. The top-listed covariate represents the most associated one, the
second covariate the second-most associated one, etc. All effects estimated at
the county level. The newspaper-county level similarity values (as used in the
main results) are averaged at the county-level weighting each newspaper-county
observation by the newspaper circulation.

###  Mechanisms: Slant contagion and polarization

Table C.16: Cable News Effects on Newspaper Content (2SLS): By Historical
Republican Vote Shares

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3)
---|---|---|---
FNC Viewership (rel. to CNN/MSNBC) | -0.280 | 0.061 | 0.327**
| (0.305) | (0.092) | (0.127)
K-P First-Stage F-Stat | 9.235 | 18.261 | 16.627
N observations | 1459 | 1249 | 1068
Rep. vote share 1996 in 1st tercile | X |  |
Rep. vote share 1996 in 2nd tercile |  | X |
Rep. vote share 1996 in 3rd tercile |  |  | X
State FE | X | X | X
Demographic controls | X | X | X
Channel controls | X | X | X
Newspaper language controls | X | X | X

  * 1.

Notes: 2SLS estimates. Cross-section with newspaper-county-level observations
weighted by newspaper circulation in each county. The dependent variable is
newspaper language similarity with FNC (the average probability that a snippet
from a newspaper is predicted to be from FNC). The right-hand side variable of
interest is instrumented FNC viewership relative to averaged CNN and MSNBC
viewership. Column 1 only includes newspaper-county-level observations from
counties where the Republican votes share in 1996 (pre-FNC era) lies in the
lowest tercile. In column 2, we include observations from counties in the
second tercile, and in column 3 from those in the highest tercile. All columns
include state fixed effects, demographic controls as listed in Appendix Table
A.2, channel controls (population shares with access to each of the three TV
channels), and generic newspaper language features (vocabulary size, avg. word
length, avg. sentence length, avg. article length). Standard errors are
multiway-clustered at the county and at the newspaper level (in parenthesis):
* p $<$ 0.1, ** p $<$ 0.05, *** p $<$ 0.01.

Table C.17: Polarizing Effect of Cable News: Separate Reduced-Form Effects of
FNC and CNN/MSNBC

Dep. variable: $\text{Slant}_{ijs}$=$\Pr(FNC|\textrm{Text}_{ijs})$ | (1) | (2) | (3)
---|---|---|---
FNC Position (absolute) | 0.067*** | 0.002 | -0.031***
| (0.022) | (0.019) | (0.008)
CNN/MSNBC Position (average) | 0.040 | 0.020 | 0.031**
| (0.031) | (0.032) | (0.014)
N observations | 872 | 1858 | 1040
Endorsed Democrat | X |  |
No (Known) Endorsement |  | X |
Endorsed Republican |  |  | X
State FE | X | X | X
Demographic controls | X | X | X
Channel controls | X | X | X
Newspaper language controls | X | X | X

  * 1.

Notes: Reduced form estimates. Cross-section with newspaper-county-level
observations weighted by newspaper circulation in each county. The dependent
variable is newspaper language similarity with FNC (the average probability
that a snippet from a newspaper is predicted to be from FNC). The two right-
hand side variables of interest are (i) the absolute position of FNC
viewership (Position FNC) and the (ii) average of the absolute positions of
CNN and MSNBC (Position 0.5(CNN+MSNBC)). In column 1 we only include
newspapers that endorsed the Democratic Presidential candidate in 1996 (pre-
FNC era). In column 2, we focus on newspapers that did not endorse either
candidate (or for which endorsement data is not available). Column 3 considers
only newspapers that endorsed the Republican candidate. All columns include
state fixed effects, demographic controls as listed in Appendix Table A.2,
channel controls (population shares with access to each of the three TV
channels), and generic newspaper language features (vocabulary size, avg. word
length, avg. sentence length, avg. article length). Standard errors are
multiway-clustered at the county and at the newspaper level (in parenthesis):
* p $<$ 0.1, ** p $<$ 0.05, *** p $<$ 0.01.


Lee84, R. Lefèvre9, A. Leflat39, S. Legotin80, O. Leroy10, T. Lesiak33, B.
Leverington16, H. Li71, L. Li62, P. Li16, X. Li66, Y. Li6, Y. Li6, Z. Li67, X.
Liang67, T. Lin60, R. Lindner47, V. Lisovskyi14, R. Litvinov26, G. Liu71, H.
Liu5, S. Liu6, X. Liu3, A. Loi26, J. Lomba Castro45, I. Longstaff58, J.H.
Lopes2, G. Loustau49, G.H. Lovell54, Y. Lu6, D. Lucchesi27,m, S. Luchuk40, M.
Lucio Martinez31, V. Lukashenko31, Y. Luo3, A. Lupato61, E. Luppi20,g, O.
Lupton55, A. Lusiani28,r, X. Lyu5, L. Ma6, S. Maccolini19,e, F. Machefert11,
F. Maciuc36, V. Macko48, P. Mackowiak14, S. Maddrell-Mander53, O. Madejczyk34,
L.R. Madhan Mohan53, O. Maev37, A. Maevskiy81, D. Maisuzenko37, M.W.
Majewski34, J.J. Malczewski33, S. Malde62, B. Malecki47, A. Malinin79, T.
Maltsev42,v, H. Malygina16, G. Manca26,f, G. Mancinelli10, R. Manera
Escalero44, D. Manuzzi19,e, D. Marangotto25,o, J. Maratas9,u, J.F. Marchand8,
U. Marconi19, S. Mariani21,47,h, C. Marin Benito11, M. Marinangeli48, P.
Marino48, J. Marks16, P.J. Marshall59, G. Martellotti30, L. Martinazzoli47,j,
M. Martinelli24,j, D. Martinez Santos45, F. Martinez Vidal46, A. Massafferri1,
M. Materok13, R. Matev47, A. Mathad49, Z. Mathe47, V. Matiunin38, C.
Matteuzzi24, K.R. Mattioli84, A. Mauri31, E. Maurice11,b, J. Mauricio44, M.
Mazurek35, M. McCann60, L. Mcconnell17, T.H. Mcgrath61, A. McNab61, R.
McNulty17, J.V. Mead59, B. Meadows64, C. Meaux10, G. Meier14, N. Meinert75, D.
Melnychuk35, S. Meloni24,j, M. Merk31,78, A. Merli25, L. Meyer Garcia2, M.
Mikhasenko47, D.A. Milanes73, E. Millard55, M. Milovanovic47, M.-N. Minard8,
L. Minzoni20,g, S.E. Mitchell57, B. Mitreska61, D.S. Mitzel47, A. Mödden14,
R.A. Mohammed62, R.D. Moise60, T. Mombächer14, I.A. Monroy73, S. Monteil9, M.
Morandin27, G. Morello22, M.J. Morello28,r, J. Moron34, A.B. Morris74, A.G.
Morris55, R. Mountain67, H. Mu3, F. Muheim57, M. Mukherjee7, M. Mulder47, D.
Müller47, K. Müller49, C.H. Murphy62, D. Murray61, P. Muzzetto26,47, P.
Naik53, T. Nakada48, R. Nandakumar56, T. Nanut48, I. Nasteva2, M. Needham57,
I. Neri20,g, N. Neri25,o, S. Neubert74, N. Neufeld47, R. Newcombe60, T.D.
Nguyen48, C. Nguyen-Mau48, E.M. Niel11, S. Nieswand13, N. Nikitin39, N.S.
Nolte47, C. Nunez84, A. Oblakowska-Mucha34, V. Obraztsov43, D.P. O’Hanlon53,
R. Oldeman26,f, M.E. Olivares67, C.J.G. Onderwater77, A. Ossowska33, J.M.
Otalora Goicochea2, T. Ovsiannikova38, P. Owen49, A. Oyanguren46,47, B.
Pagare55, P.R. Pais47, T. Pajero28,47,r, A. Palano18, M. Palutan22, Y. Pan61,
G. Panshin82, A. Papanestis56, M. Pappagallo18,d, L.L. Pappalardo20,g, C.
Pappenheimer64, W. Parker65, C. Parkes61, C.J. Parkinson45, B. Passalacqua20,
G. Passaleva21, A. Pastore18, M. Patel60, C. Patrignani19,e, C.J. Pawley78, A.
Pearce47, A. Pellegrino31, M. Pepe Altarelli47, S. Perazzini19, D. Pereima38,
P. Perret9, K. Petridis53, A. Petrolini23,i, A. Petrov79, S. Petrucci57, M.
Petruzzo25, T.T.H. Pham67, A. Philippov41, L. Pica28, M. Piccini76, B.
Pietrzyk8, G. Pietrzyk48, M. Pili62, D. Pinci30, J. Pinzino47, F. Pisani47, A.
Piucci16, Resmi P.K10, V. Placinta36, J. Plews52, M. Plo Casasus45, F.
Polci12, M. Poli Lener22, M. Poliakova67, A. Poluektov10, N. Polukhina80,c, I.
Polyakov67, E. Polycarpo2, G.J. Pomery53, S. Ponce47, D. Popov5,47, S.
Popov41, S. Poslavskii43, K. Prasanth33, L. Promberger47, C. Prouve45, V.
Pugatch51, A. Puig Navarro49, H. Pullen62, G. Punzi28,n, W. Qian5, J. Qin5, R.
Quagliani12, B. Quintana8, N.V. Raab17, R.I. Rabadan Trejo10, B. Rachwal34,
J.H. Rademacker53, M. Rama28, M. Ramos Pernas55, M.S. Rangel2, F.
Ratnikov41,81, G. Raven32, M. Reboud8, F. Redi48, F. Reiss12, C. Remon
Alepuz46, Z. Ren3, V. Renaudin62, R. Ribatti28, S. Ricciardi56, K. Rinnert59,
P. Robbe11, A. Robert12, G. Robertson57, A.B. Rodrigues48, E. Rodrigues59,
J.A. Rodriguez Lopez73, A. Rollings62, P. Roloff47, V. Romanovskiy43, M.
Romero Lamas45, A. Romero Vidal45, J.D. Roth84, M. Rotondo22, M.S. Rudolph67,
T. Ruf47, J. Ruiz Vidal46, A. Ryzhikov81, J. Ryzka34, J.J. Saborido Silva45,
N. Sagidova37, N. Sahoo55, B. Saitta26,f, D. Sanchez Gonzalo44, C. Sanchez
Gras31, R. Santacesaria30, C. Santamarina Rios45, M. Santimaria22, E.
Santovetti29,k, D. Saranin80, G. Sarpis61, M. Sarpis74, A. Sarti30, C.
Satriano30,q, A. Satta29, M. Saur5, D. Savrina38,39, H. Sazak9, L.G.
Scantlebury Smead62, S. Schael13, M. Schellenberg14, M. Schiller58, H.
Schindler47, M. Schmelling15, T. Schmelzer14, B. Schmidt47, O. Schneider48, A.
Schopper47, M. Schubiger31, S. Schulte48, M.H. Schune11, R. Schwemmer47, B.
Sciascia22, A. Sciubba30, S. Sellam45, A. Semennikov38, M. Senghi Soares32, A.
Sergi52,47, N. Serra49, L. Sestini27, A. Seuthe14, P. Seyfert47, D.M.
Shangase84, M. Shapkin43, I. Shchemerov80, L. Shchutska48, T. Shears59, L.
Shekhtman42,v, Z. Shen4, V. Shevchenko79, E.B. Shields24,j, E. Shmanin80, J.D.
Shupperd67, B.G. Siddi20, R. Silva Coutinho49, G. Simi27, S. Simone18,d, I.
Skiba20,g, N. Skidmore74, T. Skwarnicki67, M.W. Slater52, J.C. Smallwood62,
J.G. Smeaton54, A. Smetkina38, E. Smith13, M. Smith60, A. Snoch31, M.
Soares19, L. Soares Lavra9, M.D. Sokoloff64, F.J.P. Soler58, A. Solovev37, I.
Solovyev37, F.L. Souza De Almeida2, B. Souza De Paula2, B. Spaan14, E. Spadaro
Norella25,o, P. Spradlin58, F. Stagni47, M. Stahl64, S. Stahl47, P. Stefko48,
O. Steinkamp49,80, S. Stemmle16, O. Stenyakin43, H. Stevens14, S. Stone67,
M.E. Stramaglia48, M. Straticiuc36, D. Strekalina80, S. Strokov82, F.
Suljik62, J. Sun26, L. Sun72, Y. Sun65, P. Svihra61, P.N. Swallow52, K.
Swientek34, A. Szabelski35, T. Szumlak34, M. Szymanski47, S. Taneja61, T.
Tekampe14, F. Teubert47, E. Thomas47, K.A. Thomson59, M.J. Tilley60, V.
Tisserand9, S. T’Jampens8, M. Tobin6, S. Tolk47, L. Tomassetti20,g, D. Torres
Machado1, D.Y. Tou12, M. Traill58, M.T. Tran48, E. Trifonova80, C. Trippl48,
G. Tuci28,n, A. Tully48, N. Tuning31, A. Ukleja35, D.J. Unverzagt16, E.
Ursov80, A. Usachov31, A. Ustyuzhanin41,81, U. Uwer16, A. Vagner82, V.
Vagnoni19, A. Valassi47, G. Valenti19, N. Valls Canudas44, M. van Beuzekom31,
M. Van Dijk48, H. Van Hecke66, E. van Herwijnen80, C.B. Van Hulse17, M. van
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Cardona83, E. Vilella Figueras59, P. Vincent12, G. Vitali28, A. Vollhardt49,
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A. Xu4, J. Xu5, L. Xu3, M. Xu7, Q. Xu5, Z. Xu5, Z. Xu4, D. Yang3, Y. Yang5, Z.
Yang3, Z. Yang65, Y. Yao67, L.E. Yeomans59, H. Yin7, J. Yu70, X. Yuan67, O.
Yushchenko43, E. Zaffaroni48, K.A. Zarebski52, M. Zavertyaev15,c, M. Zdybal33,
O. Zenaiev47, M. Zeng3, D. Zhang7, L. Zhang3, S. Zhang4, Y. Zhang4, Y.
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1Centro Brasileiro de Pesquisas Físicas (CBPF), Rio de Janeiro, Brazil
2Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
3Center for High Energy Physics, Tsinghua University, Beijing, China
4School of Physics State Key Laboratory of Nuclear Physics and Technology,
Peking University, Beijing, China
5University of Chinese Academy of Sciences, Beijing, China
6Institute Of High Energy Physics (IHEP), Beijing, China
7Institute of Particle Physics, Central China Normal University, Wuhan, Hubei,
China
8Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IN2P3-LAPP, Annecy,
France
9Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France
10Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France
11Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
12LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3,
Paris, France
13I. Physikalisches Institut, RWTH Aachen University, Aachen, Germany
14Fakultät Physik, Technische Universität Dortmund, Dortmund, Germany
15Max-Planck-Institut für Kernphysik (MPIK), Heidelberg, Germany
16Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg,
Germany
17School of Physics, University College Dublin, Dublin, Ireland
18INFN Sezione di Bari, Bari, Italy
19INFN Sezione di Bologna, Bologna, Italy
20INFN Sezione di Ferrara, Ferrara, Italy
21INFN Sezione di Firenze, Firenze, Italy
22INFN Laboratori Nazionali di Frascati, Frascati, Italy
23INFN Sezione di Genova, Genova, Italy
24INFN Sezione di Milano-Bicocca, Milano, Italy
25INFN Sezione di Milano, Milano, Italy
26INFN Sezione di Cagliari, Monserrato, Italy
27Universita degli Studi di Padova, Universita e INFN, Padova, Padova, Italy
28INFN Sezione di Pisa, Pisa, Italy
29INFN Sezione di Roma Tor Vergata, Roma, Italy
30INFN Sezione di Roma La Sapienza, Roma, Italy
31Nikhef National Institute for Subatomic Physics, Amsterdam, Netherlands
32Nikhef National Institute for Subatomic Physics and VU University Amsterdam,
Amsterdam, Netherlands
33Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of
Sciences, Kraków, Poland
34AGH - University of Science and Technology, Faculty of Physics and Applied
Computer Science, Kraków, Poland
35National Center for Nuclear Research (NCBJ), Warsaw, Poland
36Horia Hulubei National Institute of Physics and Nuclear Engineering,
Bucharest-Magurele, Romania
37Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI),
Gatchina, Russia
38Institute of Theoretical and Experimental Physics NRC Kurchatov Institute
(ITEP NRC KI), Moscow, Russia
39Institute of Nuclear Physics, Moscow State University (SINP MSU), Moscow,
Russia
40Institute for Nuclear Research of the Russian Academy of Sciences (INR RAS),
Moscow, Russia
41Yandex School of Data Analysis, Moscow, Russia
42Budker Institute of Nuclear Physics (SB RAS), Novosibirsk, Russia
43Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI),
Protvino, Russia, Protvino, Russia
44ICCUB, Universitat de Barcelona, Barcelona, Spain
45Instituto Galego de Física de Altas Enerxías (IGFAE), Universidade de
Santiago de Compostela, Santiago de Compostela, Spain
46Instituto de Fisica Corpuscular, Centro Mixto Universidad de Valencia -
CSIC, Valencia, Spain
47European Organization for Nuclear Research (CERN), Geneva, Switzerland
48Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL),
Lausanne, Switzerland
49Physik-Institut, Universität Zürich, Zürich, Switzerland
50NSC Kharkiv Institute of Physics and Technology (NSC KIPT), Kharkiv, Ukraine
51Institute for Nuclear Research of the National Academy of Sciences (KINR),
Kyiv, Ukraine
52University of Birmingham, Birmingham, United Kingdom
53H.H. Wills Physics Laboratory, University of Bristol, Bristol, United
Kingdom
54Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom
55Department of Physics, University of Warwick, Coventry, United Kingdom
56STFC Rutherford Appleton Laboratory, Didcot, United Kingdom
57School of Physics and Astronomy, University of Edinburgh, Edinburgh, United
Kingdom
58School of Physics and Astronomy, University of Glasgow, Glasgow, United
Kingdom
59Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom
60Imperial College London, London, United Kingdom
61Department of Physics and Astronomy, University of Manchester, Manchester,
United Kingdom
62Department of Physics, University of Oxford, Oxford, United Kingdom
63Massachusetts Institute of Technology, Cambridge, MA, United States
64University of Cincinnati, Cincinnati, OH, United States
65University of Maryland, College Park, MD, United States
66Los Alamos National Laboratory (LANL), Los Alamos, United States
67Syracuse University, Syracuse, NY, United States
68School of Physics and Astronomy, Monash University, Melbourne, Australia,
associated to 55
69Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de
Janeiro, Brazil, associated to 2
70Physics and Micro Electronic College, Hunan University, Changsha City,
China, associated to 7
71Guangdong Provencial Key Laboratory of Nuclear Science, Institute of Quantum
Matter, South China Normal University, Guangzhou, China, associated to 3
72School of Physics and Technology, Wuhan University, Wuhan, China, associated
to 3
73Departamento de Fisica , Universidad Nacional de Colombia, Bogota, Colombia,
associated to 12
74Universität Bonn - Helmholtz-Institut für Strahlen und Kernphysik, Bonn,
Germany, associated to 16
75Institut für Physik, Universität Rostock, Rostock, Germany, associated to 16
76INFN Sezione di Perugia, Perugia, Italy, associated to 20
77Van Swinderen Institute, University of Groningen, Groningen, Netherlands,
associated to 31
78Universiteit Maastricht, Maastricht, Netherlands, associated to 31
79National Research Centre Kurchatov Institute, Moscow, Russia, associated to
38
80National University of Science and Technology “MISIS”, Moscow, Russia,
associated to 38
81National Research University Higher School of Economics, Moscow, Russia,
associated to 41
82National Research Tomsk Polytechnic University, Tomsk, Russia, associated to
38
83DS4DS, La Salle, Universitat Ramon Llull, Barcelona, Spain, associated to 44
84University of Michigan, Ann Arbor, United States, associated to 67

aUniversidade Federal do Triângulo Mineiro (UFTM), Uberaba-MG, Brazil
bLaboratoire Leprince-Ringuet, Palaiseau, France
cP.N. Lebedev Physical Institute, Russian Academy of Science (LPI RAS),
Moscow, Russia
dUniversità di Bari, Bari, Italy
eUniversità di Bologna, Bologna, Italy
fUniversità di Cagliari, Cagliari, Italy
gUniversità di Ferrara, Ferrara, Italy
hUniversità di Firenze, Firenze, Italy
iUniversità di Genova, Genova, Italy
jUniversità di Milano Bicocca, Milano, Italy
kUniversità di Roma Tor Vergata, Roma, Italy
lAGH - University of Science and Technology, Faculty of Computer Science,
Electronics and Telecommunications, Kraków, Poland
mUniversità di Padova, Padova, Italy
nUniversità di Pisa, Pisa, Italy
oUniversità degli Studi di Milano, Milano, Italy
pUniversità di Urbino, Urbino, Italy
qUniversità della Basilicata, Potenza, Italy
rScuola Normale Superiore, Pisa, Italy
sUniversità di Modena e Reggio Emilia, Modena, Italy
tUniversità di Siena, Siena, Italy
uMSU - Iligan Institute of Technology (MSU-IIT), Iligan, Philippines
vNovosibirsk State University, Novosibirsk, Russia