Media Persuasion and Corporate Social Responsibility

Abstract

Can media affect corporate executives’ ideology beliefs and change corporate social re-

sponsibility (CSR) policies? In this paper, we investigate how CSR scores are affected

by the expansion of a conservative media conglomerate, Sinclair Broadcast Group, in

local TV markets. The entry of Sinclair to local TV markets is not related to the re-

gion’s demographic or political characteristics. In a difference-in-differences setting, we

find that firms reduce CSR activities after exposure to Sinclair TV, consistent with the

media persuasion hypothesis. The effect is present in all three subcategories of CSR:

environmental, social, and governance aspects. The effect is stronger for firms that are

with low institutional ownership, in the sin industries, or have previously high CSR

scores. We also find a larger effect during times with high political polarization. We

find no relation between Sinclair exposure and firm performance, measured by ROA

or Tobin’s Q, in a difference-in-differences setting.

Keywords: corporate social responsibility, media persuasion, political economics

1 Introduction

Does media affect corporate behavior? Previous papers have found that media coverage

plays a role in firms’ decision-making due to their concerns of reputation risk.1 In this paper,

we explore a different channel through which media may change corporate behavior, that is,

the media persuasion channel. The channel refers to the possibility that media could change

firm policy through influencing the opinions and beliefs of corporate executives and directors

on certain matters. Specifically, we study the media’s persuasive power by a conservative

media conglomerate, Sinclair Broadcast Group, on one particular corporate policy that is

strongly tied to ideology beliefs: corporate social responsibility (henceforth, CSR).

Multiple papers in political economics have shown voter behavior is subject to media

influence and persuasion (see DellaVigna and Kaplan (2007), Chiang and Knight (2011),

Gentzkow (2006), and Miho (2018) among others). When the same question is posed to

corporate behavior, ex ante there is no obvious answer. Decisions of large corporations are

mostly made by executives and directors. These professionals are highly educated, and on

average more sophisticated cognitively than voters consisting of the general public. Suppose

that executives and directors rationally choose CSR activities maximizing an objective func-

tion given all their information set, they should be able to filter out any biases in the media

environment and stick to the optimal policy. Hence, the media influence on them could be

minimal. We refer to this the rational expectation hypothesis, which predicts no effect of

media on CSR. On the other hand, the literature on managerial style and corporate policies

suggest that executive beliefs and traits are influenced by external environments and events.2

1Dyck, Volchkova, and Zingales (2008) document the governance role of media coverage in Russia. Liuand McConnell (2013) show that managers are sensitive to the level and tone of media coverage of the dealwhen deciding whether to abandon an acquisition attempt. Baloria and Heese (2018) find that firms subjectto the threat of slanted coverage suppress the release of negative information.

2Bernile, Bhagwat, and Rau (2017) find that natural disasters in CEOs’ early childhood affect the risk-taking activities of firms. Malmendier, Tate, and Yan (2011) show that CEOs who grew up during the GreatDepression are averse to debt and lean excessively on internal finance. Dittmar and Duchin (2015) find

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In this view, it is very likely that executives can be influenced by the media environment

firms operate in, just like the voters. Even when CSR activities are rationally determined,

both the objective function and the information set amid decision-making can be changed by

media. We call this the media persuasion hypothesis. We test these two opposing hypothe-

ses empirically by exploiting the natural experiment induced by the expansion of Sinclair

Broadcast Group (henceforth, Sinclair) in local TV markets. We choose to study local TV

markets for media persuasion because local news is found to be the most trustworthy source

according to Pew Research Center.3

Sinclair was founded in 1986 and experienced fast growth in the following decade. It

eventually went public in 1995, and now is the largest owner/operator of local TV stations

in US. According to the company’s website, as of 2019, it owns or operates 193 TV stations,

reaches more than 90 TV markets and an audience of 39% of the U.S. population.4 We obtain

data from the company’s 10-K filings on the geographic distribution of its TV stations across

the country from 1996 to 2016. Figure 1 shows the maps of distribution in the years of 1996,

2001, 2006, 2011, and 2016, providing snapshots of influenced counties every five years in the

sample period. The snapshots show that counties with access to Sinclair are not concentrated

in a small number of geometric regions, but almost evenly spread across the country over

time. Sinclair has been known to be conservative and right-leaning since it was founded.

Several media outlets have published articles expressing the concern for Sinclair’s expansion

due to its conservative slant. In academic research, Martin and McCrain (2019) study

broadcast transcripts and find that Sinclair stations focus more on national news, which

tends to be more politically charged and has a significant rightward shift in the ideological

that past professional experiences of CEOs affect corporate policies. Benmelech and Frydman (2015) arguethat past military experiences shape CEOs’ belief system, and firms with military CEOs engage in moreconservative corporate policies and ethical behavior.

3CNN published a news article online in 2018 “Why Sinclair matters: Local news is Americans’ No. 1 newssource”. See details at https://www.cnn.com/2018/04/02/politics/sinclair-trust-in-local-news/index.html.

4DellaVigna and Kaplan (2007) study whether the expansion of FOX News Channel in cable TV marketschanges voter behavior, and report that Fox News reaches an audience of 17.3% of viewers in 2000.

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slant of coverage. They interpret this as a supply-side change rather than being driven by

viewer demand, as viewers appear to prefer the more local-heavy mix of coverage to the more

national-heavy one, and viewership decreased following Sinclair operation.

The gradual expansion of Sinclair across the country since 1990s creates staggered changes

in exposure to Sinclair TV in different regions, and serve as the natural experiment for us to

investigate the relation between a local firm’s exposure to Sinclair TV and its CSR scores.

CSR has been shown to be strongly tied to the ideological beliefs of corporate executives.

Hong and Kostovetsky (2012) show that Democratic investment managers tend to hold port-

folios with better CSR policies compared to their Republican peers. Similarly, Di Giuli and

Kostovetsky (2014) find that firms score higher on CSR when they have Democratic rather

than Republican CEOs and directors. CSR is closely related to political ideology, because

the Democratic Party emphasizes more on issues related to CSR, such as environment pro-

tection, anti-discrimination laws, employee protection, and social welfare. In contrast, the

core value system of the Republican party is based on free market, small governments, pri-

vate property rights, and is less friendly with labor unions. Hence the media persuasion

hypothesis predicts CSR decreasing upon Sinclair’s influence, while the rational expectation

hypothesis predicts no change in CSR.

One might be concerned that the expansion of Sinclair is correlated with local firms’ CSR

policy due to the underlying change in regional political and economic factors. For example,

Sinclair could choose to enter a county, because they observed or successfully anticipated the

shift in its residents’ political ideology to the conservative side. Hence it is the underlying

shift in population political sentiment driving both Sinclair entry and the decrease in local

firms’ CSR spending, rather than CSR policies changing as a response to Sinclair exposure.

The evidence in Martin and McCrain (2019) suggests that it is unlikely to be the case, as

viewership decreases upon the entry of Sinclair. Also from reading Sinclair’s 10-K reports,

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our impression is that the expansion is mostly driven by profitability concerns with a focus

on the mid-sized markets, as acquisition costs in these markets tend to be lower than larger

markets.

To further address this concern, we investigate the relation between Sinclair presence and

a host of economic, political, and demographic characteristics of a county. In a multi-period

Logit model, we regress the dummy variable of Sinclair exposure on lagged economic and

ideology-related variables including unemployment rate, the most recent presidential voting

result, the percentage of population with college or higher education, and the percentage of

female, we do not find any significant correlations. Local firms’ CSR policy is unlikely to be

correlated to other omitted county level characteristics in this regression. More importantly,

the decision to enter a TV market made by Sinclair is a choice among the cross section

of regions in the same year, while in the analysis of regressing CSR on Sinclair exposure,

we always include firm fixed effects so that the coefficient estimates of the latter regression

are generated by time-series variation within the same firm. Hence we take the entrance of

Sinclair to a county as a plausibly exogenous event when studying the CSR policy of a firm

located in that county.

Furthermore, in the baseline, we estimate the effect of Sinclair exposure on CSR scores in

a difference-in-differences setting, using firms whose headquarters are located in TV markets

with at least one Sinclair TV stations as treated firms, and firms in TV markets without

Sinclair TV stations yet as control firms. A nice feature with the data for identification is

that the change of Sinclair presence is determined by the local TV market, which does not

coincide with its state. A local TV market is defined by the “Designated Market Area”

(henceforth, DMA), also referred to as a media market. There are around 210 DMAs in

US. There are normally multiple DMAs in one state, and one DMA can also span multiple

states. For example, the DMA of “Philadelphia” covers counties in three states: Delaware,

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New Jersey, and Delaware. This allows us to control for time-varying state effects that are

related to unobserved political and economic factors within a state. Firm fixed effects and

state-year interaction fixed effects are included in all specifications throughout the paper.

Our finding supports the media persuasion hypothesis. The baseline results show that

the post-Sinclair CSR score on average is lower than the pre-Sinclair CSR by 0.33, which

is 15% of the sample standard deviation of CSR (2.244). We also calculate CSR scores in

three sub-categories: social, environmental, and governance aspects. All three subcategories

are affected, with the environmental score being reduced the most, by 16.7% of the sample

standard deviation, the social score being reduced by 12.7% of the sample standard deviation,

and the governance score being reduced by 7% of the sample standard deviation.

For robustness, we conduct the following tests. (i) The analysis on temporal dynamic

effects shows that there is no difference in pre-treatment trends between treated and control

firms. The effect of Sinclair exposure on CSR is permanent and becomes more pronounced

as exposure time goes by. (ii) We run placebo tests by simulating pseudo Sinclair entry years

to sample TV markets, and compare the coefficient estimate on Sinclair exposure from the

real data with the distribution of the coefficient estimates from pseudo samples, and finding

the significance of the original result. (iii) The baseline results are also robust to adding

time-varying industry-year fixed effects, to alternative exposure measures, and to sub-period

analysis splitting the sample to two 10-year periods.

We further investigate which types of firms reduce CSR activities more upon Sinclair ex-

posure. Chen, Dong, and Lin (2019) document that institutional investors engage in socially

responsible investing to attract investors and have a real impact on CSR. We conjecture

that firms with low institutional ownership reduce their CSR activities more upon Sinclair

exposure. Cahan, Chen, Chen, and Nguyen (2015) show that firms in sin industries (whose

business is related to alcohol, tobacco, and gambling) especially relies on using CSR to es-

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tablish a positive image of their firms. That is, they do “extra” CSR compared to other

firms. Sinclair exposure could make executives of these firms feel less “guilty” about their

business and reduce their CSR more. Similarly, we conjecture that the effect of Sinclair on

CSR reduction should be more pronounced for firms with previously high CSRs. We add

an interaction term of Sinclair exposure and the dummy variable indicating these types of

firms to conduct cross-sectional analysis. The results are consistent with our conjecture. We

find the effect is stronger for firms with lower institutional ownership, in “sin” industries,

with previously high CSR scores. We also hypothesize that the media persuasion power is

stronger during times of high political polarization, and find supporting evidence. Lastly,

we ask a related question about whether Sinclair exposure has impact on performance of

treated firms, and we find no impact on performance, proxied by ROA or Tobin’s Q.

Our paper contributes to literature in several ways. First, it adds to the burgeoning CSR

literature, complementing earlier findings that CSR is closely tied to the ideology beliefs

of executives and board directors. This is also connected to the broader topic on manager

style/experience and firm policies. Managers’ personal preferences matter for corporate

policies. In this particular scenario, what managers watch on their TV changes CSR. Second,

we present evidence showing that not only CSR is related to executive ideology beliefs, but

such beliefs can also be molded by the local media environment. It complements existing

studies investigating the relation between media and firm behavior. Previous papers focus

on the disciplining role of media via managers’ reputation costs, while we focus on the

media persuasion channel. Lastly, the paper is the first in the literature using the expansion

of Sinclair across the country as an exogenous shift to the conservative side in local media

environment. Several papers have used the expansion of FOX cable news for similar purposes,

and do not consider the local TV markets.5

The rest of the paper is organized as follows. Section 2 describes the history of the

5See DellaVigna and Kaplan (2007) and Baloria and Heese (2018) among other.

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expansion of Sinclair in the local TV market, and investigates the relation between county

characteristics and Sinclair entry. Section 3 presents empirical results of Sinclair entry on

local firm CSR in the region. Section 4 investigates the effect of Sinclair on firm performance.

Section 5 concludes.

2 The expansion of Sinclair in local TV markets

2.1 Local TV markets and the history of Sinclair

The important feature of local TV station is that it is a form of public goods which tends

to serve public interests for the local community, and charges no fee from its viewers. Also it

is more difficult to distinguish liberal versus conservative sources for local news than on cable

or radio talk shows. As a result, it reaches an ideologically diverse audience. Democrats and

Republicans are about equally as likely to watch local news, according to Pew Research

Center. The local TV news has outpaced national news outlets both in terms of trust scores

and viewership rates. More people receive their news from local television stations than

from any other source. According to a 2017 Pew Research Center study, 37% of Americans

get their news from local television news. That’s higher than cable news (28%) or network

television news (26%). Local TV news is also ranked as the most trusted source of news.6

A local TV market is defined by a “Designated Market Area” (DMA), also referred to

as a media market. DMAs are determined by the Nielsen Company and impact the cost of

advertising in a specific area.7 There are around 210 DMAs covering the whole country and

are usually defined based on metropolitan areas, with suburbs often being combined within.

6Around 80% of Americans trust local news outlets, higher than the 60% for nationalnews and 14% for social networks, according to a the Pew Research Center. See the de-tails at https://www.journalism.org/2017/05/10/americans-attitudes-about-the-news-media-deeply-divided-along-partisan-lines/.

7The Nielson Company is a market research and measurement company. The more viewers in a particularDMA, the more an advertisement will cost.

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Viewers in the same DMA receive the same or similar media coverage. Typically there are

multiple DMAs in one state. As there are around 3000 counties in U.S., one DMA always

includes multiple counties, and in some cases, can also span multiple states. The local TV

market is regulated by the Federal Communications Commission (FCC), which was created

by Congress in 1934 in the Communications Act for the purpose of “regulating interstate

and foreign commerce in communication by wire and radio so as to make available, so far as

possible, to all the people of the United States”. Local TV stations need to obtain licenses

issued by the FCC to operate in a particular DMA. On its website, the FCC states that

“whenever we review an application —whether to build a new station, modify or renew the

license of an existing station or sell a station —we must determine if granting the application

would serve the public interest. We expect station licensees to be aware of the important

problems and issues facing their local communities.” Hence, in order to obtain an license to

operate a station, the local TV station must meet the needs and interests of the community

it serves. Meanwhile FCC emphasizes that it is not responsible for selecting the material

stations air, as the First Amendment and the Communications Act expressly prohibit it from

censoring broadcast matter.

Sinclair originated in 1971 when Julian Sinclair Smith bought an UHF station WBFF-TV

in Baltimore, Maryland. His four sons founded the Sinclair Broadcast Group, Inc. in 1986,

after acquiring several existing UHF stations in Baltimore, Pittsburgh and Columbus. The

company’s station portfolio expanded to 59 stations in 1995 and it went public in the same

year. The rapid growth is partly fueled through outright purchase of stations, but also driven

by a creative usage of local marketing agreements, or LMAs. LMAs are a type of contract in

which one company agrees to operate a radio or television station owned by another party.

In essence, it is a lease or time-buy for the operating license. This allows Sinclair to bypass

many regulation rules once imposed by FCC regarding the ownership of operating licenses

for the purpose of facilitating competition and fostering diversity in media.

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Currently, Sinclair is the largest owner/operator of local TV stations in US. It is also the

biggest producer of local news. Each week, the company reportedly produces 2,400 hours of

local news.8 It airs original programming from its 193 TV channels in more than 90 DMAs

across the country.9 Extending from coast to coast, Sinclair now covers more than 39% of all

American households. While it owns/operates the largest number of TV stations in America,

most of its target users are not aware of its existence.10 Sinclair has achieved this evident

anonymity thanks to its unique expansion method over the last few decades: acquiring and

operating local news stations without re-branding them as parts of Sinclair network. For

example, Sinclair runs an ABC affiliate station in Dayton, Ohio, and a Fox affiliated station

in Oklahoma City. Therefore, local TV stations with a wide range of political and social

ideologies (from FOX to CBS and NBS) are now owned or operated by Sinclair. In May

2017, Sinclair announced its intention to buy Tribune Media and acquire its 42 stations. The

deal, had it been successful, would have resulted in Sinclair having stations available in 72%

of all households in US. The deal received criticism from multiple special-interest groups,

as well as politicians from both the Democratic and Republican parties, who felt that the

deal would give Sinclair an effective oligopoly on local TV broadcasting. In 2018, Tribune

announced the termination of the merger agreement.11

Owned by eminent Republican donors, Sinclair’s programs are widely seen as having a

conservative bias.12 Martin and McCrain (2019) study broadcast transcripts and find that

8See the report by CBS news at: https://www.cbsnews.com/news/sinclair-broadcast-group-what-you-need-to-know/.

9See the full list here: http://sbgi.net/tv-channels/.10Sinclair is often referred to as the under-the-radar company, by US prime time news outlets. For ex-

ample, see https://www.theguardian.com/media/2017/aug/17/sinclair-news-media-fox-trump-white-house-circa-breitbart-news. Similarly, Sinclair’s CEO is not as famed as Rupert Murdoch, the founder of the NewsCorp.

11The deal also caused backlash for the FCC Chairman Ajit Pai, who had reversed several decades-old policies pertaining to broadcast ownership as well as some related policies implemented during theadministration of President Barack Obama that interfered with Sinclair’s attempts to expand through furtherstation acquisitions.

12According to federal filings, Sinclair’s current Chairman David Smith has contributed $206,650 to Re-publicans and $132,350 to Democrats in congressional and presidential campaigns since 1995. He also gave

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Sinclair stations focus more on national news, which tends to be more politically charged

and has a significant rightward shift in the ideological slant of coverage. Sinclair regularly

produces a centralized news segment or commentary and sends them to stations across the

country for broadcast. For example, one of such news commentaries is the so-called “must

runs”, where local TV program hosts from different stations across the country are expected

to read and broadcast from the same transcript. It has received wide criticism because of its

conservative biases. Sinclair’s political orientation has attracted much attention from other

media in recent years. For example, New York Times describes Sinclair as a “conservative

giant” and alleges that Sinclair uses its TV stations “to advance a mostly right-leaning

agenda”.13

2.2 Sinclair expansion and county characteristics

One might question the validity of using the expansion of Sinclair as a natural experiment

to study its impact on local firms’ CSR policy, due to the concern that unobserved changes

in demographics or political ideology in the local region drives both the entry of Sinclair

and firms’ CSR policy. In this section, we address this concern by investigating directly

the relation between the presence of Sinclair and a host of characteristics of the county. As

explained earlier in Section 2.1, Sinclair exposure is the same across a DMA, while one DMA

always span multiple counties. There are around 210 DMAs and 3000 counties across the

country in our sample. On average, one DMA includes 14 counties. Using county level data

gives us finer information regarding a region’s population characteristics, compared to using

DMA level data. In the regression analysis, we also use DMA level data constructed by

$36,000 to two political action committees (PACs) that have consistently contributed more to Republicansthan to Democrats.

13Other examples include the Washington Post, which calls Sinclair “with a long history of favoringconservative causes and candidates on its newscasts”. It published articles such as “Under New Ownership,WJLA-TV Takes a Slight Turn to the Right” in 2014, and “Heres What Happened the Last Time SinclairBought a Big-City Station” in 2017, “Trump said Sinclair is far superior to CNN. What we know about theconservative media giant” in 2018.

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aggregating or averaging country level data.

We compile data on the expansion of Sinclair in local TV markets from 1996 to 2016

from a variety of sources, including Sinclair’s 10-K filings, the company’s website, the FCC

database, and the Capital IQ Key Development database. We complement our data by also

manually adding to it multiple features of each Sinclair-affiliated TV station that may have

been absent from the aforementioned sources. Our final data includes a rich menu of Sinclair

stations’ characteristics over time, including the date of entry, other affiliated networks, the

type of contract Sinclair has with other parties involved in each station, the political standing

of the affiliated network, the number of Sinclair-associated TV stations in a particular DMA.

At any given point of time, there could be zero to four Sinclair TV stations in one DMA.

County level demographic data from 1995 to 2016 is mostly from the U.S. Census Bureau. We

collect data on total population, the percentage of the population that is above 65 years old,

the percentage that have college or higher education, the percentage of female, the percentage

of the Hispanic, the percentage of the African American. The unemployment data is from

the Bureau of Labor Statistics.14 To measure the political ideology data, we download the

county level presidential election voting data from Harvard Dataverse.15 The variable is the

percentage of votes for a Republican presidential candidate, and by construction it is updated

every four years. We are especially interested in county level variables that are related to

its economic status and ideology situation including unemployment rate, the percentage of

Republican votes, the percentage of population with college or higher education, and the

percentage of female. These could be potential drivers of Sinclair entry if it is caused by

ideology shifts of the residents.

As a preliminary investigation, we compare sample means of the above characteristics

between country-year observations with one or more local Sinclair stations and country-year

14We thank Antonela Andonia Miho for generously sharing the data used in her paper Miho (2018).15The dataset can be accessed at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.79

10/DVN/VOQCHQ.

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observations without. Table 1 presents the results. The table shows that in total there are

13,210 observations with Sinclair exposure, and 28,720 ones without. For the four variables

related to economic status and political ideology of the regions (unemployment rate and

percentages of population with college and higher education, female, Republican votes), the

mean values in the two samples are almost identical across the two samples: 6.6% vs. 6.5%

for unemployment rate, 58.9% vs. 59.9% for the percentage of Republican votes, 18% vs.

18% for the percentage of population with college or higher education, 50.2% vs. 50.1%

for the percentage of female. It suggests that the entry of Sinclair and its decision to stay

in a region is unlikely driven by lcoal economic or political factors. For other demographic

variables, the exposed sample has slightly larger population (the log of population is 10.38 vs.

10.20), a slightly higher percentage of senior population (16.6% vs. 16.2%), lower percentage

of Hispanic (6.4% vs. 9.1%), and lower percentage of African American (8.1% vs. 8.7%).

We continue to investigate the dependence of Sinclair entry in a multi-variate setting.

We run a multi-period Logit regression as follows,

Sinclairc,t = α + β1Unemploymentc,t−1 + β2Republican Votesc,t−1+

β3College or higher education c,t−1 + β4Femalec,t−1 + γOther controlsc,t−1 + εc,t.

(1)

Sinclairc,t is a dummy variable that takes a value of one if the county c has at least one

local TV station associated with Sinclair in year t, and zero otherwise. All explanatory

variables are lagged by one year. The key variables are unemployment rate, the percentage

of Republican votes, the percentage of population with college or higher education, and the

percentage of female. Other controls include the log of total population, the percentages of

the senior, Hispanic, and African American.

Panel A of Table 2 shows the results. For comparison, Column (2) includes state fixed

effects and year fixed effects while Column (1) does not. We cluster standard errors at the

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DMA level, as the dependent variable co-move perfectly among the counties that belong to

the same DMA. Columns (3) and (4) repeat the analysis using OLS. In Panel B, we conduct

the analysis using DMA level data. This is because Sinclair exposure is the same across

counties in the same DMA. We construct DMA level data by either aggregating (for pop-

ulation) or averaging (percentages) across the county level data. In all eight specifications,

we find no relationship between Sinclair exposure and unemployment rate, the percentage

of Republican votes, the percentage of population with college or higher education, and the

percentage of female, confirming the univariate comparison results in Table 1.

In several specifications, we find that there is a marginal effect by the population size.

This is consistent with our prior that Sinclair mostly target mid-sized markets. In fewer

cases, there is also an effect from the percentage of the senior. Since the size of population

and the percentage of the senior in a particular county is unlikely to change much over time,

and we include firm fixed effects when we study the relation between Sinclair exposure and

CSR, we conclude that reverse causality is not likely a concern for the purpose of our study.

3 The effect of Sinclair on CSR

In this section, we test the two hypothesis: the rational expectation hypothesis that

predicts no change in CSR after the exposure to Sinclair, and the media persuasion hypothesis

that predicts a decrease in CSR. We select all COMPUSTAT firms with available CSR data

covered by the KLD dataset from 1996 to 2016. We then merge the CSR and COMPUSTAT

data with the geographical distribution of Sinclair TV stations by the county of a firm’s

headquarter. Since we test whether executives and directors of a firm can be influenced by

local TV stations, and most likely they work and live around the headquarter, using the

location information of headquarters should be the appropriate choice.

The analysis in the section is summarized as follows. First, we describe how we construct

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variables of firms characteristics and CSR scores. Then we develop a baseline regression

testing the relation between CSR and Sinclair exposure. We conduct various robustness

checks of the baseline. We also test the cross-sectional variation of this relation based on

certain firm characteristics and its headquarter’s county information. Lastly, we investigate

whether firm performance is affected by Sinclair exposure through the channel of CSR.

3.1 Firm characteristics and CSR construction

We download data from COMPUSTAT on firm financials from 1996 to 2015 to construct

control variables. Previous literature has shown that CSR score is related to firm size, lever-

age, profitability, Tobin’s Q, cash flow, sales growth, advertising costs, and R&D expenses.16

Accordingly, we construct the variables of Size, Leverage, ROA, Tobin′s Q, Cash F low,

Sales Growth, Advertising Cost and R&D. The details of the variable construction are

described in the Appendix.

CSR scores are downloaded from the Kinder, Lydenberg, and Domini (KLD) database

(now MSCI ESG KLD STATS database), which is widely used in the literature that in-

vestigates the determinants and consequences of CSR policies (e.g. Cahan et al. (2015),

among others). KLD evaluates CSR performance in three main categories: environmental,

social, and governance aspects. We denote scores in these three by ENV , SOC, and GOV .

Each category further includes many small groups of points. For example, the environmen-

tal category includes indicators of waste management, carbon emissions, natural resource

use, supply chain management, water stress, and other related concerns; The social category

includes indicators of community, human right, employee relations, diversity, and product.

The governance category includes indicators such as governance system, controversial invest-

ments, corruption and political instability etc. For each dimension of CSR, the dataset first

16Recent papers such as Cahan et al. (2015) and Cao, Liang, and Zhan (2019) summarized the controlvariables used in previous studies.

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examines the presence (or absence) of a list of “strengths” and “concerns” for each firm.

A one point increase in the CSR score indicates that a firm has made a positive change in

one CSR indicator. Such a change can happen through a one level shift from a concern to

neutral, or from neutral to a strength. Since the CSR scores are calculated as an aggregation

of positive points and negative points, CSR score has a mean of -0.18 and median of 0 in

our sample.

The final sample consists of 24,217 firm-year observations of 2,434 firms in the period of

1996-2016. The summary statistics of firm characteristics and CSR variables are presented

in Table 3. Firm characteristics are lagged by one year. Panel A shows the distribution of

each variable in the whole sample. The mean firm assets is $13.2 billion with a median of

$1 billion. The average leverage is 21.1%. The firms are profitable with an average ROA

(operating income divided by total assets) of 13%, and cash flow (operating cash flow divided

by total assets) of 19.6%. Average Tobin’s Q is 1.96 and average sales growth is 12.4%. There

are large variations of the CSR scores. The aggregate CSR score has a mean of -0.18, with

a standard deviation of 2.244. Average ENV is 0.043 with a standard deviation of 0.682.

Average SOC is -0.01 with a standard deviation of 1.824. Average GOV is -0.213 with a

standard deviation of 0.677.

Panel B compares firm characteristics and CSR variables between firm-year observations

with Sinclair exposure (treated) and firm-year observations without Sinclair exposure (con-

trol). The status of exposure is lagged by one year. There are several differences in firm

characteristics between the treated and the control firm-year observations. For example,

in the treated sample, asset size is smaller (average total assets of $9.2 billion vs. $14.1

billion), leverage is higher (0.224 vs. 0.208), ROA is higher (0.146 vs. 0.126). This shows

the necessity to control for these firm characteristics and use firm fixed effects in empirical

testing.

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3.2 The baseline

The baseline regression tests the two hypotheses directly by investigating whether there

is a negative relation between CSR and a firm’s exposure to local Sinclair TV stations. We

regress CSR on Sinclair exposure and control variables in a difference-in-differences setting.

The regression equation is specified as follows,

CSRit = αi + αst + βSinclair TVit−1 + γ′Xit−1 + εit (2)

where i, s, t index firms, states, and years, respectively. Sinclair TVit−1 is a dummy variable

if the headquarter of firm i is located in a DMA with at least one Sinclair TV stations in year

t−1. Xit is a vector of control variables, including size, leverage, ROA, Tobin’s Q, cash flow,

sales growth, advertising costs, and R&D expenses. αi and αst represent firm fixed effects

and the state-year interaction fixed effects. We cluster the standard errors at the DMA-year

level, as the Sinclair exposure is the same for firms located in the same DMA in the same

year. The rational expectation hypothesis predicts that β = 0, and the media persuasion

hypothesis predicts that β < 0.

The results of regression are presented in Table 4. In Columns (1) and (3), firm and

year fixed effects are included. In Columns (2) and (4), firm fixed effects and the state-year

interaction fixed effects are included. In all specifications, the coefficient on Sinclair TV is

statistically significant and negative. The evidence supports the media persuasion hypothesis

and not the rational expectation hypothesis. The economic magnitude is also large. Take

Column (4) as an example, it shows that β = −0.331. This means that post-Sinclair CSR

score is lower than the pre-Sinclair CSR score by 0.331 for treated firms, this is 14.8% of the

sample standard deviation (2.244) of CSR.

Next, we use the CSR scores in three subcategories: environmental (ENV ), governance

16

(GOV ), and social (SOC) as the dependent variable, and run the same baseline regression in

Equation 2. We are interested in how Sinclair exposure might affect these component scores

differently. The results are shown in Table 5. The exposure to Sinclair decreases CSR scores

in all three categories. In terms of economic magnitude, Column (4) indicates that the post-

Sinclair environmental score is lower than the pre-Sinclair score by 0.114, which is 16.7% of

the variable’s sample standard deviation (0.114/0.682=16.7%); Column (5) shows that the

post-Sinclair social score is lower than the pre-Sinclair score by 0.086, which is 12.7% of the

variable’s sample standard deviation (0.086/0.677=12.7%); and Column (6) shows that the

post-Sinclair governance score is lower than the pre-Sinclair score by 0.131, which is 7% of

the variable’s sample standard deviation (0.131/1.824=7%). These results indicate that the

effect of Sinclair exposure is the largest on firms’ environmental scores, and the lowest on its

governance scores. This is consistent with the belief system of some conservative groups.17

3.3 Robustness of the baseline

In this section, we further address the econometric concerns of endogeneity and conduct

various robustness exercises.

3.3.1 Reverse causality

Section 2 shows that the existence of Sinclair TV stations is not driven by county level

variables such as the unemployment rate, the percentage of Republican votes, the percentage

of population with college or higher education, and the percentage of female. We conclude

that ideology shifts in a particular region does not cause Sinclair exposure. In this section,

17Public attitudes around climate, energy, and environmental issues are strongly correlated with party ide-ology, where other kinds of science issues are not. For example, see “of Democrats with high levels of scienceknowledge, just about nine out of ten people trust environmental scientists. Of Republicans with high levelsof science knowledge? Less than half trust environmental scientists.” in https://grist.org/article/climate-change-is-the-one-area-of-science-republicans-tend-to-doubt/.

17

we follow Bertrand and Mullainathan (2003) to conduct a dynamic test between the relation

of CSR and Sinclair exposure. This test investigates both pre-exposure and post-exposure

time trends of the exposure effects, hence can help alleviate potential concerns related to

reverse causality. Specifically, we run the following regressions,

CSRit = αi + αst + β1Sinclair TVi,≥t+5 + β2Sinclair TVi,t+4 + β3Sinclair TVi,t+3

+ β4Sinclair TVi,t+2 + β5Sinclair TVi,t+1 + β6Sinclair TVi,t−1 + β7Sinclair TVi,t−2

+ β8Sinclair TVi,t−3 + β9Sinclair TVi,t−4 + β10Sinclair TVi,≤t−5 + γ′Xit−1 + εit,

(3)

where αi and αst represent firm fixed effects and state-year fixed effects. It is the same

regression equation as the baseline Equation 2 except that the original Sinclair TV dummy

is replaced by ten event year dummy variables. Sinclair TVi,≥t+5 is a dummy variable that

takes the value of one if the firm i is exposed to Sinclair TV five or more years later, and

zero otherwise. Similarly, Sinclair TVi,t+4 indicates exposure to Sinclair TV for a firm four

years later, and Sinclair TVi,t−4 indicates exposure to Sinclair TV for a firm four years prior

to Sinclair entry. For firms whose headquarters are located in DMAs without any Sinclair

TV stations in all years, these ten dummy variables take a value of zero. We also run the

same regression for subcategories of environmental, social, and governance scores.

The results are presented in Table 6. The dependent variables are CSR, environmental,

social, and governance scores from Column (1) to (4). The coefficients of the first five dummy

variables (Sinclair TVi,≥t+5, ..., Sinclair TVi,t+1) stand for effects of reverse causality. Since

CSR is related to the corporate executives’ ideology belief, any correlation between CSR and

future Sinclair exposure suggests that local ideology shifts drive Sinclair entry. Across the

four columns, all five coefficients are not statistically different from zero.

The coefficients of the last five dummy variables (Sinclair TVi,t−1, ..., Sinclair TVi,≤t+5)

stand for the effect of past Sinclair exposure on CSR over time. We find that media persuasion

18

takes time, and significant lower reduction in CSR does not occur until the third year after

Sinclair exposure. But the effect is persistent and grows larger over time. Three years after

Sinclair exposure, CSR drops by 0.411, which is 18.3% of the sample standard deviation

(0.411/2.244=18.3%); four years after exposure, CSR drops by 0.536, 23.9% of the standard

deviation (0.536/2.244=23.9%); five or more years after exposure, CSR drops by 1.022, 45.5%

of the standard deviation (1.022/2.244=45.5%).

When we investigate environmental, social, and governance scores separately, we find

that the environmental scores are affected immediately, just one year after Sinclair entry

to a local area, shown in Column (2). Column (3) shows that social scores do not change

until after the forth year. The effect also grows with time, as the coefficient for the 5th year

dummy is much larger. Column (4) shows that there is no effect on governance, when we

replace the Sinclair TV dummy in the baseline by event year dummy variables. Overall, the

results are consistent with earlier findings presented in Table 5. The environmental score is

affected the most by the exposure to Sinclair TV, and the governance score is affected by

the least amount.

Next, we use a figure to present the regression result of Equation 3. Figure 2 plots the

coefficient estimates of the ten event year dummy variables indicating the relative timing

of Sinclair exposure and CSR in the y-axis. The x-axis shows the time relative to Sinclair

exposure, corresponding to the relative year between CSR and Sinclair exposure. The dashed

lines are the 90% confidence intervals of the coefficient estimates. The four panels show

the coefficients when the dependent variable is the CSR score and its three subcategories:

environmental, social, and governance score, respectively. This figure shows clearly that the

parallel trends assumption is likely to be satisfied between the treated firms and the control

firms before the exposure period. And CSR only decreases after exposure to Sinclair, not

vice versa.

19

3.3.2 The placebo test

There may exist shocks or variables, omitted from the baseline specification, that coincide

with Sinclair entry to the target DMAs. The staggered nature of Sinclair entry across time

and space has the ability to mitigate this concern to some degree. In that, there is a low

probability that series of unobserved shocks take place with the same timing as that of

Sinclair entry and in the same affected counties. Regardless, we attempt to re-examine the

validity of the main results by conducting a placebo analysis.

Following Fracassi, Petry, and Tate (2016), we start by obtaining the empirical distribu-

tion of Sinclair entry years to different DMAs. Using this distribution, we re-assign these

years randomly to the sample DMAs and re-estimate our baseline specification. Through

1,000 rounds of random assignments, we obtain 1,000 samples with pseudo Sinclair entry

years to different DMAs. Therefore, if our main results are driven by Sinclair entry (i.e.,

exposure to Sinclair media) and not a data fluke, then the random reassignment of the entry

dates should generate no patterns between pseudo Sinclair exposure and CSR. On the flip

side, if the baseline results are driven by chance (not by the exposure to Sinclair media),

then the placebo samples should still yield results not significantly different from our main

result, as the main driver of the outcome variable (CSR) still resides in the testing frame-

work. After the 1,000 rounds of estimation, we plot the histogram of the distribution for

the t-statistics of the main coefficient (β) in Figure 3. The vertical axis shows the frequency

of the t-statistics and the red, dashed line on the left shows the t-statistic of the baseline

regression model (-5.933). This t-statistic lies in a range that is clearly the lower 1% of

the placebo distribution. We obtain similar results (untabulated) when we run additional

placebo tests on the three subcategory scores on CSR.

20

3.3.3 Other robustness checks

We conduct several additional robustness tests to the baseline specification as follows and

present the results in Table 7. All original control variables are included, and for the sake of

brevity, their regression estimates are not presented.

(i) Include industry-year fixed effects in the baseline specification, to control for time-

varying industry effects in CSR activity. Industries are defined by 2-digit SIC codes. The

result remains and is presented in Panel A.

(ii) Use alternative Sinclair exposure measures to replace the Sinclair TV dummy vari-

able in the baseline. We construct three measures. TV # is the number of local Sinclair TV

stations in year t − 1 in the DMA where the firm’s headquarter is located. There could be

multiple local TV stations associated with Sinclair. In the sample, possible values of TV #

are 0, 1, 2, 3, and 4. Average Exposure is the average number of local Sinclair TV stations

in the DMA per year from the beginning of the sample period (1996) to t−1. Y ears Present

is the number of years since Sinclair first enters the DMA to t− 1. We conjecture that the

effect should be stronger if there are more Sinclair TV stations in a DMA or if there is a

longer history of such influence. We find supporting evidence in Columns (2), (3), and (4)

of Panel B. If we also include the original Sinclair TV dummy variable in the regression,

then only Average Exposure has marginally explanatory power.

(iii) Investigate if the effect of Sinclair TV on CSR is only concentrated in earlier or later

periods of the sample. We split the sample period to two 10-year periods of 1996-2006, and

2007-2016. Then we run the baseline separately for these two time periods. The results are

presented in Panel C. The effect is strong in both periods, with β1 = −0.32 in the first half

of the sample period, and β1 = −0.245 in the second half.

(iv) We further control for county level demographic variables and the results are in Panel

21

D. Columns (1) and (2) do not include firm level controls, while Columns (3) and (4) do.

Columns (1) and (3) include firm fixed effects and year fixed effects, and Columns (2) and (4)

include firm fixed effects and state-year fixed effects. Across all specifications, the dummy

variable Sinclair TV remains statistically significant and the magnitude is similar to that in

the baseline.

3.4 Cross-sectional analysis

In this section, we exploit cross-sectional variation in the effect of Sinclair TV on CSR

scores. Essentially, we estimate difference-in-differences-in-differences regression models. We

hypothesize that, within treated firms, there are some types of firms that are affected more

than others. These tests serve several purposes. First, they provide direct evidence on

differential treatment effects for different types of firms. Second, it is possible that unobserved

trends in CSR or other unobserved factors affect firms headquartered in DMAs with or

without Sinclair TV stations differently. By identifying firms within treated DMAs that

are more likely to be affected by Sinclair exposure, the triple-difference estimator can help

alleviate these concerns. Lastly, it provides indirect evidence about the channels regarding

how Sinclair exposure reduces firm CSR scores. We conduct cross-sectional tests on several

dimensions.

First, Chen et al. (2019) document that an increasing number of institutional investors

have committed to integrating environmental, social, and governance concerns into their

capital allocation process to meet clients demand for sustainable investments. Institutional

shareholders can influence CSR directly through CSR-related proposals. We conjecture

that while executives’ ideology beliefs are influenced by the local media environment, such

influence is constrained by institutional investors. We construct a dummy variable Low IOR

for firms with low institutional ownership in year t − 1, and add an interaction term of

22

Low IOR × Sinclair TV in the baseline regression. The coefficient of the interaction term

is expected be negative. That is, firms with low institutional ownership reduce their CSR

activities more than firms with high institutional ownership. The result is consistent with

the conjecture, presented in Column (1) of Table 8. It shows that the average effect of

Sinclair TV on CSR for low institutional ownership firms is almost twice as large as that

for high institutional ownership firms.

Second, Cahan et al. (2015) find that firms in sin industries (firms producing alcohol,

tobacco, and gambling) use CSR to establish a positive image of their firms. Ex ante,

it is not clear whether these firms would reduce their CSR activities more or less upon

Sinclair exposure. On one hand, executives in these firms could start feel less “guilty” for

the negative social impact of their business, after exposure of Sinclair TV, and reduce CSR

more than firms in other industries. On the other hand, if their revenue is strongly tied with

the public image they establish, they could be constrained in reducing CSR. Empirically,

we find evidence documenting a larger effect of the former. Column (2) of Table 8 shows a

strongly negative coefficient on the interaction term of Sinclair TV and the dummy variable

indicating a firm belonging to the sin industry.

Third, we investigate whether the effect of Sinclair is different for firms with historically

high CSR scores vs. low CSR scores. We hypothesize that the persuasion effect should be

larger for firms with previously high CSR scores. For executives at firms with low CSR

scores, their ideology beliefs could be already aligned with what is dissipated by Sinclair TV

stations, leaving little room to for Sinclair to change CSR. Column (3) of Table 8 confirms

the conjecture.

Lastly, the media persuasion power should be the most pronounced in times when the

country is highly divided in their political beliefs. We obtain the data on political polarization

from PEW research center. According to PEW, the magnitude of political polarization is

23

manifest in two measures: disagreement among Democrats and Republicans on the state of

the economy and disagreement among Democrats and Republicans on the sitting presidents’

approval. High Polarization is a dummy variable indicating years above the 90% percentile

of these two polarization variables. We interact the Sinclair TV dummy variable with

periods of high polarization in the baseline specification. Table 9 show the results. Column

(1) uses the disagreement on economic health as the polarization variable, while Column

(2) uses the disagreement on presidential approval as the polarization variable. For both

measures, we find that the effect of Sinclair exposure is larger when there is high political

polarization.

4 The effect of Sinclair on firm performance

Previous analysis has demonstrated a large effect of Sinclair exposure on firm CSR. A

related question is whether such effects on CSR would also affect firm performance. There are

opposing views and evidence in the literature, regarding whether CSR benefits shareholders.

One view argues that socially recognized good firms can also be beneficial to shareholders,

hence creating value.18 Several other papers hold the view that CSR is just another manifest

of agency issues. Corporate resources are used for purposes of executives’ reputation gain,

sacrificing shareholder interests.19 If the exposure to Sinclair TV changes firm CSR activities,

by studying whether firm performance is affected, we can provide some indirect evidence to

18Ferrell, Liang, and Renneboog (2016) show that there is a positive relation between CSR and firm value,and that CSR attenuates the negative relation between managerial entrenchment and value. Deng, Kang,and Low (2013) document that high CSR firms experience higher acquirer announcement returns and longterm performance when they make acquisitions. Edmans (2011) documents a positive relation betweenemployee satisfaction and long-run stock returns.

19Di Giuli and Kostovetsky (2014) find that increases in firm CSR ratings are associated with negativefuture stock returns and declines in firm ROA, and conclude that any benefits to stakeholders from socialresponsibility come at the direct expense of firm value. Kruger (2015) studies stock price reactions to eventsrelated to CSR, and finds that investors react negatively to both negative and positive events on CSR, justmore so for negative events. Masulis and Reza (2014) find that as corporate giving increases, shareholdersreduce their valuation of firm cash holdings.

24

this question.

We regress firm performance, measured by ROA and Tobin’s Q, on the lagged Sinclair TV

dummy variable, controlling for lagged firm characteristics such as size, leverage, cash flow,

sales growth, advertising costs, and R&D expenses. Since it is intended to be a difference-in-

differences setting, firm fixed effects and the state-year interaction fixed effects are included.

The results are presented in Table 10. We find no significant effects on firm performance.

We interpret the results of two possibilities. The first reason could be that the CSR chan-

nel is simply not large enough to impact firm performance. As the previous literature has

shown mixed evidence between CSR and firm value, it is very likely that for some firms

lower CSR rating is associated with better firm performance, while for others the relation

can be the opposite. Hence on average, we do not observe a significant impact of Sinclair on

firm performance through the channel of CSR. The second reason could be that by affecting

executives’ ideology beliefs, Sinclair can also change other firm policies in subtle ways. For

example, executives might choose different investment projects due to their ideology shift,

or their risk-taking appetite can be affected. All these other channels could also impact firm

value. We conclude that, despite its large impact on firm CSR, Sinclair exposure does not

change firm performance.

5 Conclusions

CSR has been shown to be strongly tied to the ideology beliefs of executives and direc-

tors. In this paper, we use an exogenous shift in local media environment, the expansion

of Sinclair, to study the impact of conservative local media slant on firm CSR. We test two

opposing hypotheses, the rational expectation hypothesis vs. the media persuasion hypoth-

esis. The former predicts no change in CSR upon Sinclair exposure, as corporate executives

and directors should be able to filter out media biases in the local TV markets and stick to

25

the optimal level of CSR. The media persuasion hypothesis predicts a negative impact on

CSR, as the conservative value system emphasizes more on smaller governments, a market

economy, and less on social welfare and environment protection. We find support for the

latter. The result is not driven by reverse causality or omitted variables. The impact is larger

for firms with low institutional ownership, in sin industries, and with previously high CSR

scores. The media persuasion power is also stronger during times with high political polar-

ization. Despite the large impact of Sinclair on firm CSR, we do not find any effect on firm

performance. This can be due to the different relation between CSR and firm performance

for different firms, or other channels of firm policy change induced by Sinclair exposure.

26

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29

Figure 1: The Distribution of Sinclair TV stations by county

This figure illustrates the distribution of Sinclair TV stations in each county in U.S. over time from 1996-2016, by providing five snapshotsevery five years. The figure is color-coded by the number of Sinclair TV stations in each county.

(a) 1996 (b) 2001 (c) 2006

(d) 2011 (e) 2016

30

Figure 2: Dynamic effects around Sinclair entry

This figure shows the dynamic effects of Sinclair exposure on local firms’ CSR. On the y-axis, the graph plots the coefficient estimates of theten event year dummy variables indicating the relative timing of Sinclair exposure and CSR, specified in Equation 3. The x-axis shows the timerelative to Sinclair exposure, corresponding to the relative year between CSR and Sinclair. The dashed lines are the 90% confidence intervals ofthe coefficient estimates. Confidence intervals are calculated from standard errors clustered by DMA-year. The four panels show the coefficientswhen the dependent variable is the CSR score and its three subcategories: environmental, social, and governance score, respectively.

(a) CSR (b) ENV

(c) SOC (d) GOV

Figure 3: The placebo test

This figure reports placebo test results by randomizing the treatment effect among sample firms. Similarto Fracassi et al. (2016) and Cornaggia, Cornaggia, and Israelsen (2017), we use the original distributionof the Sinclair entry years to randomly reassign the placebo entry years to different DMAs and re-estimateits effect using the baseline specification in Equation 2 1,000 times. We then plot the distribution of thet-statistics of β, the coefficient estimate of the Sinclair TV dummy variaable, in the following histogram.The vertical dashed red line indicates the t-statistic of the true baseline regression. The dependent variableis the CSR score based on KLD database. All specifications include firm fixed effects and the state-yearinteraction fixed effects. Standard errors are clustered at the DMA-year level.

31

32

Table 1: County characteristics by Sinclair exposure

The table shows county characteristics of two groups of observations: the Sinclair-exposed sample includes county-year observations with atleast one local Sinclair TV station, while the un-exposed sample includes the rest of the county-year observations. Total population, thepercentage of the population that is above 65 years old, the percentage that have college education or higher, the percentage of female, thepercentage of the Hispanic, the percentage of the African American is from the U.S. Census Bureau. The unemployment data is from Bureauof Labor Statistics. The county level presidential election voting data is from the Harvard Dataverse database.

Sinclair-exposed Sample Un-exposed Sample

Mean Median StdEv Mean Median StdEv

Economic and ideology-related variables

Unemployment Rate 0.066 0.061 0.026 0.065 0.059 0.029

% Republican Votes 0.589 0.590 0.116 0.599 0.617 0.138

% College or Higher Education 0.180 0.160 0.084 0.180 0.158 0.084

% Female 0.502 0.505 0.021 0.501 0.505 0.021

Other demographic characteristics

Log of Total Population 10.383 10.267 1.278 10.198 10.087 1.539

% Population above 65 Years Old 0.166 0.162 0.042 0.162 0.157 0.045

% Hispanic 0.064 0.027 0.116 0.091 0.034 0.138

% African American 0.081 0.025 0.126 0.087 0.019 0.145

Observations 13,210 28,720

Table 2: Sinclair exposure and county characteristics

This table reports coefficients and t-statistics in the parenthesis of the regression specified in Equation 1.The dependent variable is a dummy variable that takes a value of one if a county has at least one localTV stations associated with Sinclair in year t, and zero otherwise. The explanatory variables are laggedby one year, and include all county-level characteristics in Table 1. Panel A uses county level data. PanelB uses DMA level data. DMA level data is constructed by aggregating (for population) or averaging (forpercentages) county level data. Column (1) and (2) estimates the equation using a multi-period Logit, whileColumn (3) and (4) estimates the same equation with OLS. CStandard errors are clustered at the DMAlevel in all specifications. *, **, and *** indicate significance at the 0.1, 0.05, and 0.01 levels, respectively.

Panel A: County-year observations

(1) (2) (3) (4)Logit Logit OLS OLS

Unemployment Rate -0.194 -1.606 -0.040 -0.319(-0.058) (-0.437) (-0.056) (-0.582)

% Republican Votes -0.672 0.058 -0.152 0.009(-0.611) (0.053) (-0.671) (0.058)

% College or Higher Education -1.222 -0.084 -0.261 -0.020(-1.126) (-0.089) (-1.159) (-0.134)

% Female -3.423 -0.269 -0.656 -0.043(-1.236) (-0.128) (-1.187) (-0.137)

Log of Total Population 0.190∗∗ -0.063 0.038∗∗ -0.009(2.291) (-0.789) (2.401) (-0.776)

% Population above 65 Years Old 3.798∗ -1.783 0.786∗ -0.241(1.803) (-0.718) (1.786) (-0.695)

% Hispanic -2.119 0.585 -0.365 0.079(-1.285) (0.439) (-1.568) (0.406)

% African American -0.645 -0.143 -0.143 -0.020(-0.598) (-0.100) (-0.633) (-0.087)

Observations 41930 40474 41930 41930Adjusted R2 0.022 0.253State FE NO YES NO YESYear FE NO YES NO YES

33

Panel B: DMA-year observations

(1) (2) (3) (4)Logit Logit OLS OLS

Unemployment Rate 2.080 0.479 0.328 0.084(0.635) (0.057) (0.630) (0.100)

% Republican Votes -2.371 -0.631 -0.406 0.004(-1.487) (-0.219) (-1.626) (0.014)

% College or Higher Education 1.414 -6.590 0.243 -0.555(0.517) (-1.146) (0.531) (-0.790)

% Female -0.045 16.016 0.145 1.381(-0.003) (0.764) (0.073) (0.578)

Log of Total Population 0.004∗∗∗ 0.009∗∗∗ 0.001∗∗∗ 0.001∗∗∗

(3.645) (4.885) (3.546) (5.106)

% Population above 65 Years Old 8.519∗ -8.372 1.420∗ -1.216(1.800) (-1.018) (1.739) (-1.036)

% Hispanic -1.280 0.187 -0.140 0.029(-0.993) (0.080) (-0.949) (0.109)

% African American 0.227 -0.422 0.020 -0.057(0.180) (-0.128) (0.087) (-0.141)

Observations 2842 2564 2842 2842Adjusted R2 0.069 0.243State FE NO YES NO YESYear FE NO YES NO YES

34

Table 3: Summary Statistics

This table shows the summary statistics of firm characteristics constructed using data from COMPUSTAT,and CSR scores and its components using data from Kinder, Lydenberg, and Domini (KLD). The details ofvariable construction for firm characteristics are described in the Appendix. ENV , SOC, and GOV are CSRscores of sub-categories in environmental, social, and governance aspects. Panel A shows the distributionstatistics of the variables in the whole sample. Panel B compares the variables between treated firm-yearobservations and control firm-year observations. A firm i in year t−1 is in the treated sample if it is exposedto Sinclair TV in year t− 1, and in the control sample otherwise. Firm characteristics being compared aremeasured in year t− 1, and the CSR scores are measured in year t.

Panel A: Distribution statistics

Mean SD P25 Median P75

Firm characteristics:Assets 13.194 77.258 0.521 1.602 5.219

Leverage 0.211 0.194 0.041 0.181 0.322

ROA 0.130 0.139 0.061 0.126 0.195

Tobin’s Q 1.961 1.373 1.098 1.469 2.249

Cash Flow 0.196 0.258 0.033 0.096 0.259

Sales Growth 0.124 0.285 -0.004 0.078 0.189

Advertising Costs 0.012 0.033 0.000 0.000 0.006

R&D 0.037 0.082 0.000 0.000 0.034

CSR and sub-categories:CSR -0.180 2.244 -1.000 0.000 1.000

ENV 0.043 0.682 0.000 0.000 0.000

SOC -0.010 1.824 -1.000 0.000 1.000

GOV -0.213 0.677 -1.000 0.000 0.000

Observations 24,217

Panel B: Difference in Characteristics by Treatment Status

Treated Firm-years Control Firm-years

Assets 9.186 14.145

Leverage 0.224 0.208

ROA 0.146 0.126

Tobin’s Q 1.885 1.979

Cash Flow 0.137 0.210

Sales Growth 0.104 0.128

Advertising Costs 0.013 0.012

R&D 0.020 0.041

Observations 4,643 19,574

35

Table 4: CSR and Sinclair TV exposure

This table reports coefficients and t-statistics in the parenthesis of the baseline specified in Equation 2 in adifference-in-differences setting. The dependent variable is the CSR score of firm i in year t. All explanatoryvariables are measured in year t− 1. The main variable of interests is the dummy variable Sinclair TV thatis equal to one if the headquarter of a firm is located in a DMA with at least one Sinclair TV stations, andzero otherwise. The control variables are described in the Appendix. Columns (1) and (2) report univariateregression results, while Columns (3) and (4) include control variables of firm characteristics. Standarderrors are clustered at the DMA-year level. *, **, and *** indicate significance at the 0.1, 0.05, and 0.01levels, respectively.

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

Sinclair TV -0.402∗∗∗ -0.341∗∗∗ -0.396∗∗∗ -0.331∗∗∗

(-7.085) (-6.093) (-6.969) (-5.933)

Size -0.088∗∗ -0.093∗∗∗

(-2.520) (-2.600)

Leverage 0.135 0.121(1.098) (0.989)

ROA 0.351∗∗∗ 0.402∗∗∗

(2.666) (3.020)

Tobin’s Q -0.022 -0.020(-1.349) (-1.328)

Cash Flow 0.275∗∗∗ 0.299∗∗∗

(3.795) (3.916)

Sales Growth 0.039 0.038(0.871) (0.817)

Advertising Costs -1.626∗ -1.841∗

(-1.729) (-1.921)

R&D -0.474∗ -0.435(-1.675) (-1.535)

Observations 24210 24191 24210 24191Adjusted R2 0.500 0.502 0.501 0.503Firm FE YES YES YES YESYear FE YES NO YES NOState-Year FE NO YES NO YES

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Table 5: Subcategories of CSR: environmental, social, and governance scores

This table reports coefficients and t-statistics in the parenthesis of the baseline specified in Equation 2 in adifference-in-differences setting. The dependent variables are subcategories of CSR: environmental (ENV ),governance (GOV ), and social scores (SOC) of firm i in year t. All explanatory variables are measuredin year t − 1. The main variable of interests is the dummy variable Sinclair TV that is equal to one ifthe headquarter of a firm is located in a DMA with at least one Sinclair TV stations, and zero otherwise.The control variables are described in the Appendix. Columns (1), (2), and (3) report univariate regressionresults, while Columns (3), (4), and (5) include control variables of firm characteristics. Firm fixed effectsand the state-year interaction fixed effects are included in all specifications. Standard errors are clusteredat the DMA-year level. *, **, and *** indicate significance at the 0.1, 0.05, and 0.01 levels, respectively.

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

ENV SOC GOV ENV SOC GOV

Sinclair TV -0.122∗∗∗ -0.077∗ -0.142∗∗∗ -0.114∗∗∗ -0.086∗∗ -0.131∗∗∗

(-6.527) (-1.768) (-7.251) (-6.150) (-1.973) (-6.826)Size -0.098∗∗∗ 0.117∗∗∗ -0.112∗∗∗

(-8.226) (4.132) (-10.045)Leverage 0.091∗∗∗ 0.004 0.026

(2.683) (0.038) (0.667)ROA 0.069∗ 0.384∗∗∗ -0.051

(1.714) (3.495) (-1.059)Tobin’s Q -0.019∗∗∗ 0.017 -0.018∗∗∗

(-3.798) (1.415) (-3.883)Cash Holding 0.122∗∗∗ 0.044 0.133∗∗∗

(5.871) (0.736) (4.970)Sales Growth 0.029∗∗ -0.047 0.056∗∗∗

(2.449) (-1.270) (3.477)Advertising Costs -0.834∗∗∗ -1.065 0.058

(-3.135) (-1.393) (0.169)R&D -0.163∗∗ -0.303 0.031

(-1.972) (-1.437) (0.281)Observations 24191 24191 24191 24191 24191 24191Adjusted R2 0.431 0.551 0.404 0.434 0.552 0.408Firm FE YES YES YES YES YES YESState-Year FE YES YES YES YES YES YES

37

Table 6: Dynamic effects around Sinclair entry

This table reports coefficients and t-statistics in the parenthesis of the regression equation 3 showing dynamiceffects of Sinclair TV entry to a DMA to local firms’ CSR. Sinclair TV≥t+5 is a dummy variable that takesthe value of one if the firm is exposed to Sinclair TV five or more years later, and zero otherwise. Similarly,Sinclair TVt+4 indicates exposure to Sinclair TV for a firm four years later, and Sinclair TVt−4 indicatesexposure to Sinclair TV for a firm four years ago. For firms whose headquarters are located in DMAs withoutany Sinclair TV stations in all years, these ten dummy variables take a value of zero. In Columns (2), (3),and (4) we change the dependent variable to subcategories of environmental, social, and governance scores,respectively. Firm fixed effects and the state-year interaction fixed effects are included in all specifications.Standard errors are clustered at the DMA-year level. *, **, and *** indicate significance at the 0.1, 0.05, and0.01 levels, respectively.

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

CSR ENV SOC GOV

Sinclair TV≥t+5 -0.035 -0.097 -0.016 0.077(-0.194) (-1.514) (-0.141) (1.277)

Sinclair TVt+4 -0.122 -0.096 0.038 -0.064(-0.592) (-1.285) (0.294) (-0.877)

Sinclair TVt+3 -0.106 -0.099 -0.008 0.002(-0.520) (-1.378) (-0.065) (0.028)

Sinclair TVt+2 0.020 -0.034 -0.001 0.056(0.102) (-0.512) (-0.010) (0.799)

Sinclair TVt+1 -0.079 -0.038 -0.013 -0.029(-0.352) (-0.457) (-0.092) (-0.448)

Sinclair TVt−1 -0.180 -0.138∗ -0.011 -0.030(-0.894) (-1.922) (-0.086) (-0.450)

Sinclair TVt−2 -0.112 -0.060 -0.003 -0.048(-0.477) (-0.759) (-0.017) (-0.728)

Sinclair TVt−3 -0.411∗ -0.226∗∗ -0.189 0.003(-1.702) (-2.184) (-1.228) (0.044)

Sinclair TVt−4 -0.536∗∗ -0.134 -0.315∗ -0.086(-2.422) (-1.512) (-1.948) (-1.115)

Sinclair TV≤t−5 -1.022∗∗∗ -0.197∗∗ -0.766∗∗∗ -0.058(-4.262) (-2.223) (-4.741) (-0.729)

Observations 24191 24191 24191 24191Adjusted R2 0.503 0.433 0.552 0.407Controls YES YES YES YESFirm FE YES YES YES YESState-Year FE YES YES YES YES

38

39

Table 7: Other robustness tests

This table shows the results of various robustness tests to the baseline specified in Equation 2. The controls include all firm characteristicsin the baseline. Standard errors are clustered at the DMA-year level. Panel A shows the regression results of including the industry-yearinteraction fixed effects. Industries are defined by SIC 2-digit codes. Panel B shows the regression results when we use alternative measures forSinclair exposure. TV # is the number of local Sinclair TV stations in year t−1 in the DMA the firm resides in. It takes the value of 0, 1, 2, 3,and 4. Average Exposure measures the average number of local Sinclair TV stations in the DMA per year since 1996 to t− 1. Y ears Presentis the number of years since Sinclair first enters the DMA. Panel C conducts sub-period analysis by splitting the sample period of 1996-2016to two 10-year period, 1996-2006 and 2007-2016. *, **, and *** indicate significance at the 0.1, 0.05, and 0.01 levels, respectively.

Panel A: Industry-year fixed effects

(1) (2)

Sinclair TV -0.333∗∗∗ -0.287∗∗∗

(-6.031) (-5.303)

Observations 24210 24191Adjusted R2 0.521 0.522Controls YES YESFirm FE YES YESState-Year FE NO YESIndustry-Year FE YES YES

Panel B: Alternative Sinclair exposure measures

(1) (2) (3) (4) (5) (6) (7)

Sinclair TV -0.331∗∗∗ -0.325∗∗∗ -0.323∗∗∗ -0.337∗∗∗

(-5.936) (-5.802) (-5.780) (-4.868)Sinclair TV: TV # -0.066 -0.043

(-1.543) (-0.994)Sinclair TV: Ave. Exposure -0.111∗∗ -0.090∗

(-2.122) (-1.694)Sinclair TV: Years Present -0.022∗∗∗ 0.001

(-2.598) (0.140)

Observations 24191 24191 24191 24191 24191 24191 24191Adjusted R2 0.503 0.503 0.503 0.503 0.503 0.503 0.503Controls YES YES YES YES YES YES YESFirm FE YES YES YES YES YES YES YESState-Year FE YES YES YES YES YES YES YES

Panel C: Sub-period analysis

(1) (2)1996-2006 2007-2016

Sinclair TV -0.320∗∗∗ -0.245∗∗

(-4.099) (-2.429)

Observations 8907 14979Adjusted R2 0.709 0.512Controls YES YESFirm FE YES YESState-Year FE YES YES

Panel D: Controlling for County-Level Characteristics

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

Sinclair TV -0.382∗∗∗ -0.371∗∗∗ -0.373∗∗∗ -0.358∗∗∗

(-6.216) (-5.629) (-6.074) (-5.429)

Unemployment rate 1.778 1.539 1.762 1.464(0.905) (0.568) (0.885) (0.539)

% Republican Voters 2.339 1.739 2.280 1.675(1.133) (0.679) (1.137) (0.656)

% College or Higher Education -2.895 -2.853∗ -2.934 -2.882∗

(-1.505) (-1.665) (-1.516) (-1.667)

% Female 19.056∗∗ 21.162∗∗ 19.642∗∗ 21.663∗∗

(2.226) (2.324) (2.296) (2.370)

Total Population 0.000 0.000∗ 0.000 0.000∗

(0.801) (1.757) (0.957) (1.871)

% Population above 65 years old -7.990∗∗ -4.776 -8.346∗∗ -5.092(-1.969) (-1.113) (-2.041) (-1.181)

% Hispanic -0.055 1.329 -0.021 1.365(-0.026) (0.549) (-0.010) (0.570)

% African American 1.603 -0.602 1.018 -1.134(0.637) (-0.250) (0.403) (-0.468)

Observations 19,336 19,330 19,336 19,330Adjusted R2 0.517 0.518 0.517 0.519Firm-Level Controls NO NO YES YESFirm FE YES YES YES YESYear FE YES NO YES NOState-Year FE NO YES NO YES

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Table 8: Cross-Sectional tests: firm characteristics

This table shows the results of various cross-sectional tests to the baseline specified in Equation 2, byadding interaction terms of the Sinclair TV dummy variable with other firm characteristics. Column(1) tests whether firms with low institutional ownership reduce their CSR activities more after Sinclairexposure, compared to firms with high institutional ownership. Low IOR is a dummy variable indicatinglow institutional ownership. Column (2) tests whether firms in sin industries reduce their CSR more uponSinclair exposure. The sin industries include alcohol, tobacco, and gambling firms. Sin Industry is adummy variable indicating if the firm belongs to these industries. Column (3) test whether firms withhistorically high CSR scores reduce their future CSR more upon Sinclair exposure. The controls include allfirm characteristics in the baseline. All specifications include firm fixed effects and the state-year interactionfixed effects. Standard errors are clustered at the DMA-year level. *, **, and *** indicate significance at the0.1, 0.05, and 0.01 levels, respectively.

(1) (2) (3)

Sinclair TV -0.361*** -0.330*** -0.329***(-5.182) (-5.914) (-5.877)

Low IOR × Sinclair TV -0.286***(-2.662)

Low IOR 0.296***(5.676)

Sin Industry × Sinclair TV -1.623***(-2.849)

High CSR × Sinclair TV -1.025***(-4.116)

Observations 19,254 24,191 24,191Adjusted R2 0.517 0.503 0.504Controls YES YES YESFirm FE YES YES YESState-Year FE YES YES YES

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Table 9: The effect of Sinclair exposure when political polarization is high

This table shows the larger effects of Sinclair exposure on CSR during politically polarizing times. The timeseries data for political polarization is from PEW research. According to PEW, the magnitude of politicalpolarization is manifest in two measures: disagreement among Democrats and Republicans on the stateof the economy and disagreement among Democrats and Republicans on the sitting presidents’ approval.High Polarization is a dummy variable indicating years above the 90% percentile of the polarization vari-ables. Column (1) uses the disagreement on economic health as the polarization variable, while Column (2)uses the disagreement on presidential approval as the polarization variable. The controls include all firmcharacteristics in the baseline. All specifications include firm fixed effects and the state-year interaction fixedeffects. Standard errors are clustered at the DMA-year level. *, **, and *** indicate significance at the 0.1,0.05, and 0.01 levels, respectively.

(1) (2)Disagreement on Disagreement onEconomic Health Presidential Approval

High Polarization × Sinclair TV -0.216∗∗

(-2.201)

High Polarization -0.093∗

(-1.778)

High Polarization × Sinclair TV -0.283∗∗∗

(-2.804)

High Polarization -0.096∗

(-1.807)

Sinclair TV -0.222∗∗∗ -0.318∗∗∗

(-3.141) (-4.673)

Observations 22150 24210Adjusted R2 0.435 0.443Controls YES YESFirm FE YES YESState-Year FE YES YES

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Table 10: Firm performance and the Sinclair exposure

This table reports coefficients and t-statistics in the parenthesis of a regression studying the effects of Sinclairexposure on firm performance in a difference-in-differences setting. Firm performance is measured by ROAColumn (1), and by Tobin’s Q in Column (2). All explantory variables are lagged by one year. Bothspecifications include firm fixed effects and state-year fixed effects. Standard errors are clustered at theDMA-year level. *, **, and *** indicate significance at the 0.1, 0.05, and 0.01 levels, respectively.

(1) (2)

ROA Tobin’s Q

Sinclair TV -0.001 -0.016(-0.335) (-0.515)

Size -0.003 -0.582∗∗∗

(-0.961) (-14.526)

Leverage -0.070∗∗∗ -0.612∗∗∗

(-5.753) (-5.153)

Cash Flow 0.069∗∗∗ 0.898∗∗∗

(4.555) (7.769)

Sales Growth 0.114∗∗∗ 0.376∗∗∗

(15.029) (6.071)

Advertising Costs 0.539∗∗∗ 1.796∗

(4.375) (1.813)

R&D -0.522∗∗∗ 4.564∗∗∗

(-8.219) (8.346)

Observations 24,191 24,191Adjusted R2 0.641 0.651Firm FE YES YESState-Year FE YES YES

43

Appendix

Variable Description

• CSR : (Corporate Social Responsibility Index) is from Kinder, Lydenberg, and Domini (KLD)database. This database measures firms CSR performance in three main categories: environmen-tal (ENV), social (SOC), and governance (GOV) domains.

• ENV : Is the sub-category of CSR index that rates firms based on their environment-related perfor-mance. We compute this variable as the sum of KLD’s Positive Environment Performance Indicatoridentifiers (e.g. waste management, energy efficiency, etc.) less the sum of Negative EnvironmentPerformance Indicator (e.g. toxic emissions, water stress, and alike).

• GOV : Is the sub-category of CSR index that rates firms based on their corporate-governance-relatedperformance.

• SOC : Is the sub-category of CSR index that rates firms based on their environment-related perfor-mance.

• Sinclair TV : A dummy variable that equals one for treated DMAs, i.e. those that experience SinclairMedia entry, after the treatment.

• Sinclair TV: Ave. Exposure : The average over time of the number of Sinclair TV stations in acounty.

• Sinclair TV: TV # : The annual number of Sinclair TV stations in a county (as part of the DMAwherein Sinclair TV is active).

• Sinclair TV: Years Present : The number of years that Sinclair TV is present in a county (as partof the DMA wherein Sinclair TV is active).

• Advertising Costs : Advertising expenses from the Compustat, scaled by total assets.

• Assets : Firm’s asset value as reported by the Compustat.

• Cash Flow : Operating cash flow, defined as the of earnings before extraordinary items plus depre-ciation, divided by the lag of firm’s total assets.

• High political polarization :

• IOR : Firm’s institutional ownership, is from Thomson-Reuters Ownership Data available on WRDS.We use information on 13F forms to obtain data on institutional ownership.

• Leverage : The sum of a firm’s long term debt and debt in current liabilities, divided by total assets(DLTT + DLC / AT).

• Low institutional ownership : Is a dummy variable that equals one for firm-year observations wherethe level of firm’s institutional ownership is below the sample median, and equals zero otherwise.

• Prev. High CSR: (previous high CSR) is a dummy variable that equals one for firms with high CSRprior to Sinclair entry to their respective counties. The variable is constructed as follows: first, overthe life of the firm, we compute the average CSR rating for the firm using the KLD data. For treatedfirms, we compute this average only for the time period before Sinclair entry. For control firms, wecompute this mean over the entire study period. Then, we assign a dummy (Prev. High CSR) of 1to sample firms with CSR rating in the top 30 percentile.

• R&D : Firm’s R&D expenses from Compustat database, scaled by total assets.

• ROA : (Return on Assets), is the ratio of firm’s net income to total assets.

44

• Sales growth : is the percentage growth rate between the firm’s sales in year t and year t− 1.

• Sin industries : Is defined as in Hong and Kacperczyk (2009) and Cahan et al. (2015), as a dummyvariable that equals one for (1) firms with SIC codes 21002199 which are beer and liquor producers,(2) firms with SIC codes 20802085 which are tobacco firms, and (3) firms with NAICS codes 7132,71312, 713210, 71329, 713290, 72112, and 721120 which are gambling firms, and equals zero otherwise.

• Size : Natural logarithm of the firms’ total assets (AT).

• Tobin’s Q : Common shares outstanding times closing price, plus total assets less common equity,divided by total book assets.

45