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Open Access 16.10.2023

No news is bad news: local news intensity and firms’ information environments

verfasst von: Kristian D. Allee, Ryan Cating, Caleb Rawson

Erschienen in: Review of Accounting Studies

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Abstract

We examine the effects of local newspapers on firms’ information environments. With newspaper employment dropping precipitously in the last few decades, we posit that these changes will harm local firms’ information environments. Consistent with local news improving information environments, we find that volatility, spreads, and illiquidity increase as local newspaper intensity declines and that this is associated with firms’ importance in their local economy. We further find that for firms that are more important in their community, or have busy analysts, less local newspaper intensity is associated with significantly lower analyst accuracy and higher forecast dispersion. This is consistent with local newspapers improving information environments, even for sophisticated and likely remote information intermediaries. We also investigate how stakeholders respond to declines in local news and find that managers increase the amount of forward-looking disclosures while analysts increase coverage. These results provide insights into the methods by which stakeholders attempt to improve firms’ information environments when local news coverage fades.
Hinweise

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
“There is no substitute for a local newspaper that is doing its job.”
-Warren Buffett1

1 Introduction

Local newspapers are lauded for their monitoring of local events and organizations, providing current information and interpretation of local political, commercial, and social issues. Despite the documented political and social importance of local news (Martin and McCrain 2019), employment by newspapers is declining (Abernathy 2020; Walker 2021), having dropped 75% from 2000 to 2021, with most of these losses coming at the local news level.2 This decline in local newspapers likely has significant political, social, and economic implications for not just the local community, but also for the broader community that depends on local newspaper journalists as a source of information (Radcliffe and Wallace 2021). In this study, we ask whether the intensity of local newspapers relative to the local economy (i.e., local news intensity) influences the information available about local publicly traded firms in the capital markets. Specifically, we use information about local newspaper employment to examine the association between the intensity of local news coverage and local firms’ information environments.
There are several reasons to expect an association between local newspapers and local firms’ information environments. First, research finds that national media coverage of a firm affects its trading volume, liquidity, and returns by reducing investors’ disclosure processing costs, uncovering private information, and directing investor attention (e.g., Bushee et al. 2010; Peress 2014; Blankespoor et al. 2018; Lawrence et al. 2018). If local media performs a similar yet incremental role to national media outlets, we would expect similar changes in investors’ information. Indeed, research finds that most information originates with local newspapers and is subsequently picked up by the national news media, suggesting that the absence of local newspapers would result in informational gaps in firm coverage (Waldman 2011; Cagé et al. 2020). This pattern of news sourcing and distribution could result in a decline in the news coverage of local firms, as less local news may lead to fewer stories about local firms being picked up by national media and less local dissemination of stories generated by the national media. Thus, total news coverage about the local firm would decline. Second, monitoring corporate actions is a fundamental responsibility of news media (e.g., Miller 2006; Call et al. 2022), and local newspapers are often a primary source of “accountability” journalism in a community (Waldman 2011). Local reporters are more likely to have detailed information about and access to local firms, due to their geographic proximity and the demand from local stakeholders for this information (Heese et al. 2022).
However, local monitoring may not significantly affect firms’ information environments for several reasons. First, local news might be perceived as less credible, due to incentives to bias firm coverage to cater to local firms that purchase advertising from local news outlets and to subscribers employed by local firms (Gurun and Butler 2012).3 Second, national news outlets could provide sufficient information for capital markets, rendering local news irrelevant for investors. Finally, the percentage of people who read newspapers has also declined substantially, while the percentage of Americans who obtain their news primarily from television has risen (Pew 2006; Shearer 2021). With the decline in newspaper circulation potentially being offset by local and network television news coverage, there may not be an overall impact on firms’ information environments, due to lower local newspaper intensity.
Using data from the Bureau of Labor Statistics (BLS), we measure the level of local news intensity in each city (MSA) as the percentage of local jobs in the newspaper publishing industry. Our measurement approach provides several advantages over alternative methods of capturing local newspaper coverage. First, scaling by all local employment reduces the risk that changes in the local economy (and thus local firms’ own profitability or performance) drive any observed association with information environments. Second, BLS data is available quarterly, allowing us to observe timely changes in local news presence over our entire sample period. This methodology contrasts with recent accounting and finance research that uses infrequent events, such as newspaper closures, to capture changes in local newspaper coverage (e.g., Gao et al. 2020; Kim et al. 2021).4 The BLS data that we employ has been extensively used in the social science literatures (e.g., Belasen and Polachek 2008; Duqi et al. 2021; Prager and Schmitt 2021) to capture regional trends and differences in employment yet remains underutilized in accounting and finance research.
Using the level and change in local news intensity, we document a relatively small average local news intensity and a striking decline over time. Specifically, local newspaper jobs account for 0.14 percent of total local jobs and over 90 percent of our sample has newspaper employment growing slower (or shrinking faster) than the local economy as a whole. In addition, many of these losses appear gradually, with rounds of layoffs in specific papers or departments slowly reducing the newspaper staff until a paper’s closure is simply the inevitable closing of the door in an already empty newsroom (or merging with a regional newspaper). To understand the association between local news presence and firms’ information environments, we focus on the volatility of stock returns, number of volatile days, bid-ask spreads, and liquidity. Consistent with local news improving the firm’s information environments, we find that volatility, spreads, and illiquidity increase as local newspaper intensity decreases. This relation holds after controlling for national news coverage, local unemployment, changes in total local employment, declines in local newspaper establishments, firm characteristics, and industry and time fixed effects.
To capture variation in treatment intensity, we next partition the sample based on the relative importance of the firm in its local economy and its litigation risk. If local newspapers face limited resources, they are likely to focus their reporting on the firms with the most importance to their economic area or firms most likely to have newsworthy events. Thus, a change in local news intensity would most likely affect the information environments of firms currently receiving local news attention. Classifying important firms as those with a market value greater than 10 percent of the total market value of firms in their MSA, we find that the increase in volatility (i.e., the volatility of returns and number of volatile days) as local news intensity declines is significantly more pronounced for firms that are more important in their local economy. We also find that the increase in spreads and illiquidity associated with declines in local news intensity is driven by firms with greater relative importance in their local area. Additionally, we find that that decreases in local news intensity is associated with decreases in the volatility of firms with high litigation risk while being simultaneously associated with increases in spreads and illiquidity. This is consistent with local news both uncovering significant stories for these firms and investors perceiving local newspapers as a firm monitor, lowering information asymmetry. Taken together, these results are consistent with local newspapers focusing their efforts more on firms where their efforts are more likely to be meaningful.
We next examine whether local news intensity is associated with information intermediaries, focusing specifically on analysts. If analysts benefit from the higher quality information environment created by local news (i.e., act as beneficiaries of local news coverage), then analyst forecast accuracy (dispersion) should be higher (lower) for firms with more local news. However, analyst forecast characteristics could remain unchanged if analysts increase effort in response to the information vacuum arising from lower local news. We find that firms that are relatively important in areas with lower local news intensity are associated with significantly greater analyst forecast dispersion and less accurate forecasts, consistent with local news intensity improving firm information environments, even for sophisticated information intermediaries. We also examine how the effect of local newspaper intensity on analyst forecasting outcomes varies with the busyness of analysts and find that, while firms with busy analysts have lower accuracy and increased dispersion, higher local newspaper intensity partially alleviates these detriments and is associated with more accurate forecasts with less dispersion. Conversely, firms with analysts who are less busy are unaffected by the weakened information environment.
Finally, we examine the ramifications of changes in local newspaper intensity and find an immediate negative impact on firms’ information environments. However, this impact gradually disappears over the following 24 months. To investigate why there is only a short-term and not long-term impact, we explore the behavior of information intermediaries and the extent to which investors access other information. Consistent with information intermediaries attempting to fill the information void left behind by newspapers, we find that analysts are more likely to increase coverage of firms with decreases in local newspaper intensity and that managers are more likely to increase their forecasts and other forward-looking disclosures after a decrease in newspaper intensity.
Our paper makes several contributions. First, we contribute to the literature by providing evidence for practitioners’ concerns about the widespread decline in local news. While prior studies have documented increased fraud and related costs to the loss of local news (e.g., Gao et al. 2020), the evidence on capital market implications is sparse. Given the importance of the business press for firms’ information environments and stakeholders’ invested wealth, information loss in local news coverage could be mitigated by national or global media and intermediary efforts. Our evidence suggests there is an effect on capital market information, highlighting the importance of local media for financial information as well.
Second, we contribute to the literature examining media by expanding the nascent understanding of local media presence, thus answering the call of Miller and Skinner (2015) for research examining broader types of media outlets. Prior research suggests that local news may in fact worsen the information environment, due to newspapers having incentives to bias coverage or emphasize positive news when covering local firms (Gurun and Butler 2012). Our results provide evidence of a harmful effect on firms’ information environments when local news decreases, counterbalancing results in prior research. Specifically, using unique proxies for local news intensity, we document that information environments worsen when local news coverage shrinks.
Third, we contribute to the literature on information intermediaries by examining the role that local news plays in both analysts’ and managers’ information production. Consistent with analysts at least partially filling the void left behind by lower local news, we find that analyst coverage is higher when local news is less significant. Further, after a decline in local news, we find that analysts increase coverage, managers increase the number of forecasts, and managers provide more forward-looking information in their prepared remarks in earnings conference calls. Taken together, our evidence suggests a gradual shift to a new information equilibrium as the role of local newspapers diminishes.
Finally, Blankespoor et al. (2020) discuss the “news costs” associated with a firm’s information environment. Specifically, they suggest that the costs of incorporating news into stock prices include awareness costs, acquisition costs, and integration costs. We document that local news is instrumental in producing value relevant information on local firms, especially when the firm is more important in its local economy. Thus, journalists can reduce the awareness and acquisition costs of investors by making relevant information easier to access and acquire and could also help with the integration costs by placing firm events into context. That is, local news provides mosaic-like information that helps these interested parties put the pieces together with other factors affecting the company. Further, awareness and acquisition costs likely differ for a local journalist when compared to a national journalist, as the information that local journalists have access to and are interested in acquiring is unique relative to national journalists.
Prior literature finds evidence of national media coverage improving firms’ information environments. Using large national news outlets, studies document that more media coverage is associated with lower information asymmetry, greater liquidity, faster incorporation of information into price, and lower insider trading profits (e.g., Soltes 2009; Bushee et al. 2010; Drake et al. 2014; Rogers et al. 2017; Twedt 2016; Blankespoor et al. 2018; Guest 2021). Consistent with the media playing a monitoring role, the press acts as a “watchdog” for accounting fraud by disseminating information from other information intermediaries (such as managers, analysts, auditors, and lawyers) and by undertaking original investigation and analysis (e.g., Miller 2006; Dyck et al. 2010; Cowle et al. 2023). Researchers document the media’s skepticism in their decision to cover a firm or event and their style of coverage, with media being more likely to cover firms with deteriorating performance or to take contrarian perspectives (e.g., Niessner and So 2018; Jacobs 2020).
However, there is also evidence that media coverage can be biased for several reasons. First, news outlets must satisfy their readers, often choosing to cover firms that are of interest to their readers or slanting the type or tone of news toward the preferences of their readers (e.g., Miller 2006; Gentzkow and Shapiro 2010; Ahern and Sosyura 2015; Rees and Twedt 2022). Second, news outlets must also satisfy firms that provide them with advertising revenue, leading to more positive coverage of firms spending more on advertising (Gurun and Butler 2012). Third, news outlets are susceptible to influence from firms’ investor relations departments, perhaps to establish or preserve relationships with management or because low-cost firm-provided information is attractive to resource-constrained news outlets (e.g., Solomon 2012; Blankespoor and de Haan 2020; Ru et al. 2020).
Nearly all prior evidence of media coverage influencing firms’ information environments is based on examining large national news outlets. However, local news has unique characteristics that may influence firms’ information environments. Compared to national news outlets, local reporters could have better access to information about local firms, as their geographic proximity makes visiting firm sites or observing operations less cumbersome. Formal interactions between reporters and firm management could be more likely due to investments in long-term relationships, and informal interactions could be facilitated by social interactions, due to living in the same area. In addition, local firms are likely to have outsized effects on the local economy and local politics. For example, they have a local workforce, contract with local businesses, sponsor local youth sports teams, influence local government decisions, and have more potential to help or harm the local environment. Because of these potential employment, economic, political, and social effects, readers are more likely to demand information about these firms from local news outlets, which, in turn, provides incentives for these news outlets to allocate more of their resources to monitoring information about these firms than national news outlets would.
However, there are several reasons local news might not be able to improve firms’ information environments. First, while local news is likely to focus more resources on local firms, local news outlets still have far fewer resources than national news outlets. Having fewer resources suggests less ability to hire reporters with business-specific training and experience.5 In addition, the resource constraints that local news outlets face can create incentives to bias local firm coverage to maintain advertising revenue (Gurun and Butler 2012) or the goodwill of local readers who benefit from the presence of these firms, which could reduce the informativeness of local news coverage. Finally, if the information is sufficiently important to influence stock price movements, then national news outlets—especially those focusing on business news—likely have incentives to obtain and disseminate the information to their readers, reducing the need for local news outlets.
Despite these unique features of local news outlets, few studies have focused on them, leading to calls for more research into a broader set of outlets beyond the national media (Miller and Skinner 2015). The limited research examining local newspapers has found evidence of decreased stock trading when weather prevents newspaper delivery in a pre-internet period (Engelberg and Parsons 2011) or during newspaper strikes (Peress 2014). It has also found that abnormally positive coverage is associated with greater future market-wide returns (Gurun and Butler 2012). Additionally, recent papers find evidence suggesting that a loss of local newspapers reduces the monitoring of local governments (Gao et al. 2020) and is associated with increased local facility violations issued by regulatory agencies (Heese et al. 2022) and increased dividend payouts (Kim et al. 2021).
Our study builds on this literature by (1) focusing on overall local news intensity, rather than individual articles or newspaper closures, using a measure that allows us to capture more timely and gradual changes in local news than the extreme outcome of closure; (2) examining how local news intensity is associated with the flow of firms’ information and their broader information environments; and (3) examining more recent years, when investors have had easy access to online news and TV from many locations, potentially reducing the influence of local news intensity for capital markets.

3 Data and descriptives

3.1 Data and sample selection

Our sample consists of all quarterly observations of public companies between 2006 and 2019. We begin our sample in 2006 because we require prior year employment information for cities where firms are headquartered, and the Office of Management and Budget (OMB) implemented major revisions to its standards for defining the boundaries of metropolitan and micropolitan statistical areas (MSAs) during 2003 and 2004 (OMB 2000). This revision resulted in notable changes in data availability and consistency from other government agencies, which rely on the OMB to report MSA-level data.
Data on firm characteristics come from Compustat, CRSP, IBES, and Ravenpack. Information on newspaper employment comes from the Bureau of Labor Statistics Quarterly Census of Employment and Wages (BLS-QCEW). The BLS-QCEW collects information on employment in different industries from state unemployment insurance programs covering over 95 percent of all jobs available in the United States and publishes information quarterly on a county and MSA-level basis (Bureau of Labor Statistics and Department of Labor 2022). BLS data has been widely used in the finance and economics literatures as a primary source of employment data (e.g., Cascino et al. 2021; Duqi et al. 2021; Gao et al. 2019; Prager and Schmitt 2021; Rouen 2020). Since the BLS publishes data confidentially, data is withheld for a given county when the publication of the data would potentially reveal the identity of employers (mostly due to only one or two employers of a type being present). Thus, we exclude observations with missing BLS data (~11 percent of sample observations).

3.2 Intensity of local newspapers

Our proxy for the extent of local newspapers is the intensity of employment by newspaper publishers in the MSA of firms’ headquarters.6 Specifically, we compute Newspaper_Intensity as the number of full- or part-time employees working at newspapers divided by the number of employees working for all employers.7 We compute Newspaper_Intensity on an MSA level, where we collapse all counties in an MSA with data from the BLS to identify aggregate MSA level employment.
To highlight how Newspaper_Intensity is associated with changes in newspaper employment, Fig. 1 graphs the percentage of employees in Pittsburgh and the United States as a whole who are employed by a newspaper publisher during our sample period. Consistent with a broad decline in newspaper employment, the solid line shows that the percentage of employees working for newspapers in the United States declined over 70 percent from 0.29 percent of all employees in 2005 (one in 345 employees) to 0.08 percent of all employees in 2019 (one in 1,250 employees).8 Pittsburgh largely experienced a similar trend over the same 15 years. However, the dashed line shows that there were years where Pittsburgh experienced greater decreases, compared to national trends, as well as years of relatively stable newspaper employment. These relative differences drive cross-sectional variation in Newspaper_Intensity. Importantly, the events outlined in the Fig. 1 also highlight that most declines in newspaper employment are driven by layoffs, reductions in coverage, or frequency of publications and not due to closures, as prior literature has argued (e.g., Gao et al. 2020; Kim et al. 2021).

3.3 Descriptive statistics

Table 1 provides descriptive statistics for the variables used in our primary analysis. Variable definitions are provided in Appendix A. Firms in our sample are headquartered in MSAs with an average 0.14 percent of employees working for newspapers. We observe significant cross-sectional variation with an interquartile range of 0.08 percent to 0.19 percent. Notably, the interquartile range in 2006 was 0.17 to 0.31 percent, while the interquartile range in 2019 was 0.05 to 0.10 percent (untabulated). Other descriptive statistics reported in Table 1 are largely consistent with prior literature. For example, we find that our volatility and illiquidity measures are right skewed (Bonaime and Ryngaert 2013; Brookman and Thistle 2013; Tang and Zhang 2020), with the mean (median) number of days per firm-quarter with an absolute return greater than 5 percent being 5.1 (2).
Table 1
Descriptive statistics
 
N
Mean
SD
P25
Median
P75
Newspaper_Intensity
109,535
0.139
0.073
0.080
0.129
0.186
Relative_Chg_News
109,535
-0.092
0.079
-0.126
-0.087
-0.051
Fut_Ret_Volatility
109,535
2.843
1.908
1.555
2.286
3.522
#Volatile_Days
109,535
5.148
6.999
0.000
2.000
7.000
Spreads
109,535
0.389
0.807
0.045
0.105
0.306
Illiquidity
109,535
0.246
1.745
0.000
0.002
0.018
Size
109,535
6.990
1.937
5.594
6.970
8.281
Leverage
109,535
0.251
0.230
0.046
0.210
0.389
Book-to-Market
109,535
0.547
0.526
0.235
0.451
0.758
ROA
109,535
-0.007
0.059
-0.004
0.006
0.018
Turnover
109,535
2.221
1.908
1.023
1.711
2.753
Earnings_Volatility
109,535
0.874
4.477
0.080
0.164
0.361
Ret_Volatility
109,535
0.124
0.070
0.073
0.105
0.155
Institutional_Own
109,535
0.652
0.297
0.440
0.739
0.896
Coverage
109,535
1.852
0.859
1.386
1.946
2.485
#PRs
109,535
4.449
1.353
3.664
4.543
5.263
Chg_All_Employment
109,535
1.127
2.088
0.578
1.504
2.266
Unemployment_Rate
109,535
5.776
2.267
4.050
5.134
7.494
Newspaper_Closure
109,535
0.584
0.493
0.000
1.000
1.000
This table presents descriptive statistics for our full sample. Variables definitions are in Appendix A, and all continuous variables are winsorized at the 1st and 99th percentiles

4 Research design and results

4.1 Local newspapers and firms’ information environments

We expect that local newspaper intensity will be associated with firms’ information environments, as local journalists investigate and report on the activities of local firms. To examine this, we use four proxies for firms’ information environments.
If local newspapers improve firms’ overall information environments, we should observe lower stock volatility as more information about the firm is getting reported on. Thus, our first two proxies capture different aspects of volatility. First, we examine the standard deviation of daily returns over the following quarter to capture overarching stock volatility (Future_Ret_Volatility). Second, because local newspapers may be associated with more consistent news coverage of the firm and thus fewer extreme information shocks, we consider the number of trading days in the following quarter with an absolute return of at least 5 percent (#Volatile_Days). We predict that the flow of information will be more consistent for firms in areas with stronger local news intensity, manifesting as more consistent information search (i.e., smaller dispersion in investor search patterns) and more consistent trading interest (i.e., smaller stock volatility) over a given period. More frequent but smaller updates to firm price would generate less stock volatility than deserts of information followed by more extreme news events.9 Thus, we use stock volatility to capture the frequency with which information reaches investors (Kothari et al. 2009).
Third, we predict that, with more local news intensity, there will be less information asymmetry reflected in the capital markets about firm value. Following prior research (e.g., Soltes 2009; Bushee et al. 2010), we examine average daily bid-ask spreads over the following quarter (Spreads) to capture the level of information asymmetry. If information is flowing more frequently and faster, then we should observe less on average information asymmetry (i.e., lower spreads).
Finally, following Amihud (2002); Fong et al. (2017), we examine stock illiquidity, which is computed as the daily average of the absolute returns divided by the dollar volume traded over the following quarter (Illiquidity). We examine illiquidity as greater information available about a firm should be associated with lower illiquidity (i.e., high liquidity), as there is less uncertainty about firm activities.
For each measure of firms’ information environment, we use the following OLS regression where Information_Environment is equal to one of the above four proxies:10
$$Information\_Environment={\beta }_{1} Newspaper\_Intensity+{\beta }_{2} Controls+\varepsilon$$
(1)
The vector of control variables includes firm characteristics associated with a firm’s information environment, including size, leverage, book-to-market, ROA, turnover, earnings and returns volatility, institutional ownership, analyst coverage, and the number of press releases that a firm provides. To control for changes in the local economy, we also control for the percentage change in total employment for the MSA where the firm is headquartered, the local unemployment rate where the firm is headquartered, and an indicator variable that captures whether there was a decrease in the number of newspaper establishments.11 Further, we include year, quarter, and industry fixed effects in all models and cluster standard errors by MSA-year, as that is the level at which treatment is assigned (Abadie et al. 2023).12
Table 2 presents the results from Eq. (1). Consistent with local newspapers improving firms’ information environments, Columns 1 and 2 indicate that less local news intensity is associated with significant increases in return volatility and the number of volatile days over the subsequent quarter. Furthermore, Columns 3 and 4 indicate that less local newspaper employment is also associated with higher bid-ask spreads and illiquidity. Taken together, these results are consistent with local newspapers providing a valuable role in firms’ information environments by reducing volatility and information asymmetry.
Table 2
Newspaper intensity
Dependent Variable:
Fut_Ret_Volatility
#Volatile_Days
Spreads
Illiquidity
(1)
(2)
(3)
(4)
Newspaper_Intensity
-1.061***
-3.922***
-0.277***
-0.853***
(-7.252)
(-6.134)
(-3.627)
(-4.073)
Size
-0.305***
-0.891***
-0.182***
-0.133***
(-36.097)
(-26.500)
(-39.156)
(-13.066)
Leverage
0.626***
2.350***
0.180***
0.341***
(11.756)
(12.985)
(10.231)
(8.475)
Book-to-Market
0.309***
1.297***
0.135***
0.353***
(10.112)
(11.174)
(10.333)
(9.309)
ROA
-5.188***
-18.989***
-1.042***
-0.550**
(-29.081)
(-33.613)
(-8.937)
(-2.186)
Turnover
0.061***
0.189***
-0.059***
-0.074***
(13.468)
(9.605)
(-19.072)
(-11.612)
Earnings_Volatility
0.012***
0.028***
0.007***
0.001
(5.444)
(3.948)
(4.641)
(0.374)
Ret_Volatility
6.775***
25.608***
-0.090
-1.044***
(39.087)
(34.464)
(-1.155)
(-4.722)
Institutional_Own
-0.253***
-1.221***
-0.454***
-0.410***
(-9.859)
(-12.680)
(-24.191)
(-9.413)
Coverage
0.069***
0.223***
-0.092***
-0.151***
(5.531)
(5.115)
(-12.400)
(-8.259)
#PRs
0.139***
0.438***
0.088***
0.064***
(10.124)
(9.710)
(19.778)
(5.240)
Chg_All_Employment
-0.058***
-0.308***
-0.021***
-0.036***
(-4.819)
(-5.630)
(-6.032)
(-3.998)
Unemployment_Rate
-0.037***
-0.138***
-0.004
-0.002
(-4.647)
(-4.149)
(-1.050)
(-0.222)
Newspaper_Closure
0.045*
0.150
0.001
0.014
(1.742)
(1.555)
(0.097)
(0.869)
Constant
3.650***
7.137***
1.863***
1.175***
(32.911)
(14.688)
(19.323)
(9.725)
Qtr & Year FE
Yes
Yes
Yes
Yes
Industry FE
Yes
Yes
Yes
Yes
R-Squared
53.3%
53.2%
44.3%
11.0%
Observations
109,535
109,535
109,535
109,535
This table reports the results from Eq. (1) examining the implications of local newspaper intensity. Newspaper_Intensity is equal to the percentage of all employees in the firm’s MSA who are working for newspapers. Variable definitions are in Appendix A. Significance levels of 10%, 5%, and 1% are represented by *, **, and ***, respectively. t-statistics are reported in parentheses, and standard errors are clustered by MSA-year

4.2 Local journalist attention

Next, we examine whether local newspapers play a more significant role in the information environments of firms that are more likely to receive attention from local journalists. Specifically, given that local newspaper journalists are constrained with both their time and the amount of publication space, we expect that, when there are multiple public firms in the area, journalists will focus on firms that are more important or worthwhile of their scarce resources. However, while research documents that media serves as a governance mechanism to monitor firms and their behavior (Kölbel et al. 2017), there is also evidence that media are biased and produce information that can portray firms in a positive light, especially at the local level, in order to generate revenue from subscribers and advertisers (Bednar 2012; Gurun and Butler 2012). Large firms or those that are more exposed to receiving potentially negative news coverage may be more likely to influence local news coverage through advertising or other forms of soft influence.
To examine the impact of journalist attention, we use two different proxies. First, firms that are relatively more important in their area may be more likely to receive more newspaper attention and coverage, as their decisions and operations have a bigger impact on the local economy (e.g., greater local employment or charitable activity). To examine this, we define High_Rel_Importance as an indicator equal to one if the firm’s market value is more than 10 percent of the total market value in the MSA. This process identifies approximately 10 percent of all firms as having high relative importance.
Second, two key aspects of local journalism are investigative journalism and covering ongoing legal proceedings. For example, in 2003, the Rocky Mountain News uncovered fraudulent behavior at local public company Qwest Communications that was related to Qwest boosting its reported earning numbers (Accola et al. 2003). This coverage in turn led to multiple lawsuits and SEC enforcement.13 To examine whether local newspapers focus more on firms that are more likely to have issues that could be uncovered via investigative journalism, we define High_Litigation_Risk as an indicator equal to one if the firm has above median litigation risk, as computed by Kim and Skinner (2012).
We use a model similar to that in Table 2 but interact Newspaper_Intensity with either High_Rel_Importance or High_Litigation_Risk. We use the same vector of control variables from Eq. (1), as the dependent variables are the same. The interaction between Newspaper_Intensity and High_Rel_Importance or High_Litigation_Risk captures whether local newspaper journalists spend incrementally more time focusing on firms that are more significant in their MSA or face higher litigation risk.
Table 3 presents the results examining relative importance. The evidence continues to suggest that less local news increases return volatility, the number of volatile days, spreads, and illiquidity. Consistent with newspapers having an incrementally more significant impact on firms’ information environments when these firms are more significant to their city, we find that, across all four models, the interaction between Newspaper_Intensity and High_Rel_Importance is significantly negative. Further, the increases in average spreads and illiquidity associated with less local newsroom employment are notably exacerbated when the firm has high relative importance in its city. At first glance, this may seem illogical since local firms of higher importance are likely to be the last firms that local newsrooms drop coverage of. However, this line of reasoning assumes that both high and low importance firms were initially receiving coverage. Our results suggest that local newsrooms were, on average, only covering larger firms, with limited or no coverage of smaller ones.
Table 3
Newspaper intensity and high relative importance
Dependent Variable:
Fut_Ret_Volatility
#Volatile_Days
Spreads
Illiquidity
(1)
(2)
(3)
(4)
Newspaper_Intensity
-0.997***
-3.425***
-0.162**
-0.679***
(-6.127)
(-4.888)
(-1.971)
(-3.009)
High_Rel_Importance
0.136***
0.699***
0.246***
0.303***
(3.894)
(5.177)
(11.936)
(7.045)
High_Rel_Importance * Newspaper_Intensity
-0.556**
-3.491***
-1.002***
-1.357***
(-2.494)
(-4.150)
(-9.396)
(-5.362)
Size
-0.308***
-0.901***
-0.187***
-0.138***
(-35.862)
(-26.144)
(-37.786)
(-12.804)
Leverage
0.626***
2.354***
0.181***
0.343***
(11.755)
(13.002)
(10.317)
(8.520)
Book-to-Market
0.309***
1.294***
0.134***
0.352***
(10.110)
(11.180)
(10.314)
(9.305)
ROA
-5.189***
-18.998***
-1.044***
-0.553**
(-29.069)
(-33.623)
(-8.974)
(-2.201)
Turnover
0.062***
0.191***
-0.058***
-0.073***
(13.563)
(9.671)
(-18.883)
(-11.591)
Earnings_Volatility
0.011***
0.028***
0.007***
0.001
(5.415)
(3.904)
(4.571)
(0.329)
Ret_Volatility
6.772***
25.588***
-0.096
-1.052***
(39.043)
(34.413)
(-1.228)
(-4.751)
Institutional_Own
-0.249***
-1.203***
-0.446***
-0.402***
(-9.678)
(-12.529)
(-24.085)
(-9.310)
Coverage
0.070***
0.226***
-0.090***
-0.149***
(5.598)
(5.194)
(-12.229)
(-8.210)
#PRs
0.137***
0.432***
0.085***
0.061***
(9.990)
(9.567)
(19.119)
(4.972)
Chg_All_Employment
-0.057***
-0.304***
-0.020***
-0.034***
(-4.727)
(-5.535)
(-5.701)
(-3.824)
Unemployment_Rate
0.048*
0.155
0.005
0.018
(1.809)
(1.580)
(0.780)
(1.101)
Newspaper_Closure
3.625***
6.985***
1.819***
1.115***
(32.951)
(14.539)
(19.834)
(9.116)
Constant
-0.037***
-0.135***
-0.003
-0.001
(-4.579)
(-4.035)
(-0.852)
(-0.106)
Qtr & Year FE
Yes
Yes
Yes
Yes
Industry FE
Yes
Yes
Yes
Yes
R-Squared
53.3%
53.2%
44.5%
11.1%
Observations
109,535
109,535
109,535
109,535
This table reports the results from Eq. (1) examining the implications of local newspaper intensity and firms’ relative importance in their MSA. Newspaper_Intensity is equal to the percentage of all employees in the firm’s MSA who are working for newspapers. Variable definitions are in Appendix A. Significance levels of 10%, 5%, and 1% are represented by *, **, and ***, respectively. t-statistics are reported in parentheses, and standard errors are clustered by MSA-year
Table 4 reports results examining firms with ex ante high litigation risk. Consistent with firms with high litigation risk having more uncertainty, we find that High_Litigation_Risk is positively associated with volatility, spreads, and information uncertainty. Local news may serve more impactful investigative and monitoring roles for firms with high litigation risk, due to their higher risk and potentially riskier operations. Consistent with local newspapers uncovering and broadcasting useful information about these firms, we find that Newspaper_Intensity is associated with more volatile days for firms with greater litigation risk. Additionally, consistent with investors viewing local news as a monitor of firm behavior, we find that Newspaper_Intensity is associated with lower spreads and lower illiquidity for firms with greater litigation risk, as local newspapers may produce more information about these firms.
Table 4
Newspaper intensity and high litigation risk
Dependent Variable:
Fut_Ret_Volatility
#Volatile_Days
Spreads
Illiquidity
(1)
(2)
(3)
(4)
Newspaper_Intensity
-1.138***
-4.729***
-0.083
-0.133
(-6.886)
(-6.577)
(-0.891)
(-0.461)
High_Lit_Risk
0.193***
0.487***
0.150***
0.297***
(5.801)
(4.245)
(8.399)
(6.407)
High_Lit_Risk * Newspaper_Intensity
0.256
1.968***
-0.350***
-1.418***
(1.267)
(2.768)
(-3.093)
(-4.395)
Size
-0.332***
-0.978***
-0.194***
-0.146***
(-37.679)
(-27.839)
(-40.009)
(-13.975)
Leverage
0.544***
2.080***
0.143***
0.303***
(10.254)
(11.495)
(8.355)
(7.523)
Book-to-Market
0.265***
1.149***
0.116***
0.337***
(8.397)
(9.585)
(8.937)
(8.957)
ROA
-5.056***
-18.542***
-0.988***
-0.505**
(-28.648)
(-33.080)
(-8.549)
(-2.032)
Turnover
0.054***
0.166***
-0.062***
-0.077***
(11.981)
(8.441)
(-19.726)
(-11.865)
Earnings_Volatility
0.011***
0.027***
0.007***
0.001
(5.321)
(3.822)
(4.650)
(0.428)
Ret_Volatility
6.308***
24.061***
-0.299***
-1.252***
(35.472)
(31.668)
(-3.763)
(-5.490)
Institutional_Own
-0.243***
-1.192***
-0.448***
-0.402***
(-9.510)
(-12.387)
(-24.030)
(-9.343)
Coverage
0.064***
0.207***
-0.094***
-0.155***
(5.234)
(4.838)
(-12.608)
(-8.324)
#PRs
0.130***
0.404***
0.086***
0.066***
(9.786)
(9.244)
(18.895)
(5.152)
Chg_All_Employment
-0.056***
-0.300***
-0.020***
-0.034***
(-4.679)
(-5.552)
(-5.648)
(-3.783)
Unemployment_Rate
-0.036***
-0.135***
-0.004
-0.002
(-4.599)
(-4.114)
(-0.961)
(-0.210)
Newspaper_Closure
0.044*
0.146
-0.000
0.013
(1.703)
(1.524)
(-0.012)
(0.806)
Constant
3.872***
7.960***
1.926***
1.154***
(34.659)
(16.351)
(20.041)
(9.413)
Qtr & Year FE
Yes
Yes
Yes
Yes
Industry FE
Yes
Yes
Yes
Yes
R-Squared
53.5%
53.4%
44.6%
11.1%
Observations
109,535
109,535
109,535
109,535
This table reports the results from Eq. (1) examining the implications of local newspaper intensity and firms’ litigation risk. Newspaper_Intensity is equal to the percentage of all employees in the firm’s MSA who are working for newspapers. Variable definitions are in Appendix A. Significance levels of 10%, 5%, and 1% are represented by *, **, and ***, respectively. t-statistics are reported in parentheses, and standard errors are clustered by MSA-year
Taken together, the results in this section are consistent with local newspapers focusing their efforts more on firms that are more likely to be meaningful to their local communities and on firms that would benefit more from additional monitoring and are more likely to have underlying news or events to be uncovered.

4.3 Newspaper intensity and analyst forecasts

While our previous results speak to the implications of newspaper intensity for stockholders, reductions in employment at local newspapers may also directly impact other information intermediaries, such as analysts. Prior research finds that more frequent news coverage in the largest newspapers in the country is associated with stronger market reactions to analyst revisions (Bradshaw et al. 2021) and that news coverage of analyst revisions in turn increases the initial market reaction to the revision (Ahn et al. 2019), suggesting that the media and analysts can function as complements. Therefore, to the extent that analysts rely on local news coverage for information influencing their forecasts, analysts’ forecasts may suffer (i.e., be more dispersed and less accurate) when there is not sufficient local news coverage.
To examine this further, we examine how local newspaper coverage is associated with analyst coverage and forecast characteristics. Local newspaper coverage could help analysts by providing additional information, facilitating analysts’ coverage of local firms, and increasing the accuracy and precision of their estimates, consistent with the notion of intermediaries serving as complements (Lang and Lundholm 1996). Alternatively, robust local newspaper coverage could make the incremental value provided by covering a firm less, due to the analyst providing less additional non-public information about the firm leading to lower coverage. Additionally, if local newspapers are not providing useful information to analysts while making forecasts, we would expect to see no association between newspaper intensity and the accuracy or dispersion of analyst forecasts. To examine this, we rerun Eq. (1) but with analyst coverage, accuracy, and dispersion as the dependent variables.
Column 1 of Table 5 indicates that, for our full sample, there is evidence of a substitutive relation between analysts and local newspapers. Specifically, when newspaper intensity is lower, analyst coverage is higher. Column 2 documents a complimentary association between local news and analysts, such that lower newspaper intensity is associated with less accurate analyst forecasts. Column 3 indicates that Newspaper_Intensity by itself is not associated with analyst forecast dispersion. Columns 4 through 6 report the results examining the role of firms’ importance to their city. The base effect of Newspaper_Intensity when looking at coverage (accuracy) is unchanged (insignificant). However, we find evidence consistent with newspaper intensity helping analysts, leading to more accurate and less dispersed forecasts specifically when the firm is more important in its MSA.14 Furthermore, Column 4 indicates that the association between coverage and newspaper intensity is weaker (though still present) when the firm is more important in its MSA.
Table 5
Newspaper intensity and analysts
Dependent Variable:
Coverage
Accuracy
Dispersion
Coverage
Accuracy
Dispersion
(1)
(2)
(3)
(4)
(5)
(6)
Newspaper_Intensity
-0.479***
4.959**
-1.214
-0.491***
1.545
-0.260
(-7.915)
(2.337)
(-1.297)
(-7.699)
(0.637)
(-0.257)
High_Rel_Importance
   
-0.078***
-2.829***
0.881**
   
(-4.281)
(-3.179)
(2.336)
High_Rel_Importance * Newspaper_Intensity
   
0.225**
18.810***
-5.250*
   
(2.210)
(2.931)
(-1.810)
Size
0.273***
1.691***
-0.992***
0.275***
1.701***
-0.999***
(94.697)
(7.888)
(-10.700)
(92.254)
(7.769)
(-10.672)
Leverage
0.119***
-3.693***
2.011***
0.119***
-3.724***
2.019***
(7.764)
(-4.572)
(4.688)
(7.774)
(-4.607)
(4.703)
Book-to-Market
0.043***
-0.297
0.818**
0.043***
-0.288
0.815**
(6.226)
(-0.486)
(2.265)
(6.284)
(-0.471)
(2.257)
ROA
-0.941***
43.037***
-13.952***
-0.941***
43.113***
-13.973***
(-15.706)
(6.721)
(-5.248)
(-15.692)
(6.734)
(-5.254)
Turnover
0.077***
0.226*
-0.092*
0.077***
0.218
-0.089
(29.541)
(1.682)
(-1.692)
(29.319)
(1.608)
(-1.635)
Earnings_Volatility
-0.006***
-6.189***
2.473***
-0.006***
-6.187***
2.473***
(-7.158)
(-19.376)
(11.625)
(-7.112)
(-19.375)
(11.622)
Ret_Volatility
-0.529***
8.437
-5.586***
-0.527***
8.546*
-5.611***
(-7.316)
(1.633)
(-2.604)
(-7.296)
(1.654)
(-2.615)
Institutional_Own
0.305***
2.687***
-1.426***
0.302***
2.647***
-1.410***
(24.105)
(5.243)
(-5.849)
(24.011)
(5.176)
(-5.778)
Coverage
 
-0.408
1.118***
 
-0.404
1.116***
 
(-1.258)
(5.810)
 
(-1.255)
(5.838)
#PRs
0.081***
-1.321***
0.561***
0.082***
-1.306***
0.555***
(15.675)
(-7.164)
(6.030)
(15.615)
(-7.101)
(5.942)
Chg_All_Employment
0.000
0.323***
-0.083*
-0.001
0.312***
-0.078*
(0.023)
(2.771)
(-1.797)
(-0.213)
(2.661)
(-1.698)
Unemployment_Rate
-0.008***
-0.205
0.132**
-0.008***
-0.227
0.138**
(-2.927)
(-1.169)
(2.355)
(-2.960)
(-1.265)
(2.415)
Newspaper_Closure
0.002
-0.084
0.006
-0.001
-0.041
0.002
(0.330)
(-0.394)
(0.071)
(-0.210)
(-0.192)
(0.019)
Constant
-1.187***
-10.241***
3.327***
-1.175***
-9.475***
3.085***
(-9.490)
(-4.862)
(3.787)
(-9.328)
(-4.429)
(3.401)
Qtr & Year FE
Yes
Yes
Yes
Yes
Yes
Yes
Industry FE
Yes
Yes
Yes
Yes
Yes
Yes
R-Squared
65.9%
52.4%
51.0%
66.0%
52.4%
51.0%
Observations
109,535
102,536
92,684
109,535
102,536
92,684
This table reports the results from regressing Newspaper_Intensity on analyst coverage and forecast characteristics. Newspaper_Intensity is equal to the percentage of all employees in the firm’s MSA who are working for newspapers. Variable definitions are in Appendix A. Significance levels of 10%, 5%, and 1% are represented by *, **, and ***, respectively. t-statistics are reported in parentheses, and standard errors are clustered by MSA-year
Prior literature documents that, due to resource constraints, busy analysts (i.e., analysts who follow more firms than their peers) tend to have lower quality outcomes (e.g., Clement 1999; Clement and Tse 2003; Driskill et al. 2020). To the extent that local news helps analysts be more efficient with their constraints, we would expect to see the impact of local news to be the greatest for busy analysts. To investigate this, we first create a firm-quarter level measure of analyst busyness by computing the average of the number of firms an analyst follows across all analysts following the firm. We then define Busy_Analysts equal to one if the firm quarter is in the top tercile of average analyst busyness during the year. We then rerun Eq. (1) looking at analyst accuracy and dispersion and interact Busy_Analysts with Newspaper_Intensity. Table 6 presents the results of these analyses. We find no association between newspaper intensity and forecast characteristics for firms with non-busy analysts. In line with prior literature, we find a negative association between Busy_Analysts and the quality of their outputs (i.e., less accurate and more dispersed forecasts), indicating that busy analysts perform, on average, worse than non-busy analysts (Demmer et al. 2019; Driskill et al. 2020). Consistent with local newspapers assisting busy analysts, we find that newspaper intensity is only associated with greater accuracy and lower dispersion when the firm has busy analysts. This indicates that, for firms with non-busy analysts, the incremental value of local newspapers for analysts may be less, as they have greater capacity to engage in their own information gathering and processing behavior.
Table 6
Newspaper intensity and busy analysts
Dependent Variable:
Accuracy
Dispersion
(1)
(2)
Newspaper_Intensity
-1.192
0.038
(-0.369)
(0.032)
Busy_Analysts
-1.267***
0.312**
(-2.704)
(2.039)
Busy_Analysts * Newspaper_Intensity
12.877***
-2.498*
(2.874)
(-1.852)
Size
1.681***
-0.991***
(7.847)
(-10.683)
Leverage
-3.775***
2.020***
(-4.686)
(4.727)
Book-to-Market
-0.300
0.816**
(-0.490)
(2.257)
ROA
43.361***
-13.995***
(6.763)
(-5.258)
Turnover
0.226*
-0.092*
(1.675)
(-1.688)
Earnings_Volatility
-6.191***
2.473***
(-19.372)
(11.625)
Ret_Volatility
8.572*
-5.628***
(1.658)
(-2.622)
Institutional_Own
2.700***
-1.431***
(5.278)
(-5.865)
Coverage
-0.470
1.123***
(-1.448)
(5.850)
#PRs
-1.308***
0.561***
(-7.117)
(6.004)
Chg_All_Employment
0.324***
-0.083*
(2.782)
(-1.792)
Unemployment_Rate
-0.204
0.131**
(-1.164)
(2.348)
Newspaper_Closure
-0.089
0.007
(-0.417)
(0.081)
Constant
-9.458***
3.172***
(-4.580)
(3.685)
Qtr & Year FE
Yes
Yes
Industry FE
Yes
Yes
R-Squared
52.4%
51.0%
Observations
102,536
92,684
This table reports the results from Eq. (1) examining the implications of local newspaper intensity and firms with busy analysts. Newspaper_Intensity is equal to the percentage of all employees in the firm’s MSA who are working for newspapers. Variable definitions are in Appendix A. Significance levels of 10%, 5%, and 1% are represented by *, **, and ***, respectively. t-statistics are reported in parentheses, and standard errors are clustered by MSA-year

5 Additional analyses

5.1 Declines in local newspapers

An important motivation for examining the implications of local news presence is the widely discussed decline in local news, a growing problem that has been exacerbated by conditions during the COVID pandemic (LeDuc 2020). While our main analysis examines the association between current levels of newspaper employment and firms’ immediate information environment, changes in newspaper intensity may also influence information environments. Specifically, given the broad declines in newspaper employment over the past two decades, information environments as a whole may have either worsened or remain unchanged, as other information intermediaries and investors have been forced to change how and where they source firm information. Thus, in further analyses, we assess whether the documented associations exist specifically for declines in local news presence. To perform this analysis, we examine the percentage change in newspaper employment over the prior 12 months minus the percentage change in all employment in the MSA over the prior 12 months (Relative_Chg_News).15
In Table 7, we run models similar to Eq. (1) but examine the association between Relative_Chg_News and firms’ information environments. Similar to the main results, we find that decreases in newspaper intensity are associated with increased return volatility, the number of volatile days, bid-ask spreads, and illiquidity in the following quarter.16 These findings further confirm the main results and suggest that changes in newspaper employment have an immediate impact on local firms’ information environments.
Table 7
Relative change in newspaper intensity
Dependent Variable:
Fut_Ret_Volatility
#Volatile_Days
Spreads
Illiquidity
(1)
(2)
(3)
(4)
Relative_Chg_News
-0.248*
-1.199**
-0.094**
-0.154*
(-1.765)
(-2.258)
(-2.199)
(-1.715)
Size
-0.306***
-0.893***
-0.182***
-0.133***
(-36.299)
(-26.630)
(-39.189)
(-13.073)
Leverage
0.610***
2.294***
0.176***
0.329***
(11.488)
(12.744)
(10.228)
(8.379)
Book-to-Market
0.305***
1.282***
0.134***
0.350***
(9.945)
(11.013)
(10.324)
(9.296)
ROA
-5.206***
-19.056***
-1.047***
-0.564**
(-29.060)
(-33.565)
(-8.959)
(-2.234)
Turnover
0.061***
0.189***
-0.059***
-0.074***
(13.406)
(9.578)
(-19.076)
(-11.638)
Earnings_Volatility
0.011***
0.027***
0.007***
0.001
(5.305)
(3.802)
(4.585)
(0.300)
Ret_Volatility
6.830***
25.807***
-0.076
-1.000***
(39.078)
(34.403)
(-0.976)
(-4.550)
Institutional_Own
-0.252***
-1.217***
-0.454***
-0.409***
(-9.715)
(-12.549)
(-24.173)
(-9.400)
Coverage
0.074***
0.240***
-0.091***
-0.147***
(5.879)
(5.501)
(-12.295)
(-8.065)
#PRs
0.137***
0.433***
0.088***
0.063***
(10.043)
(9.633)
(19.667)
(5.150)
Chg_All_Employment
-0.046***
-0.265***
-0.018***
-0.026***
(-3.946)
(-5.084)
(-5.544)
(-3.033)
Unemployment_Rate
-0.021***
-0.080***
0.000
0.011
(-2.868)
(-2.723)
(0.047)
(1.186)
Newspaper_Closure
0.040
0.130
-0.001
0.011
(1.529)
(1.324)
(-0.143)
(0.676)
Constant
3.285***
5.773***
1.766***
0.885***
(31.309)
(12.871)
(18.912)
(8.624)
Qtr & Year FE
Yes
Yes
Yes
Yes
Industry FE
Yes
Yes
Yes
Yes
R-Squared
53.2%
53.1%
44.3%
10.9%
Observations
109,535
109,535
109,535
109,535
This table reports the results from examining the implications of changes in local newspaper intensity and firms’ information environments. Relative_Chg_News is equal to the percentage change in newspaper employment in the prior 12 months minus the percentage change in all employment in the prior 12 months in the firm’s MSA. Variable definitions are in Appendix A. Significance levels of 10%, 5%, and 1% are represented by *, **, and ***, respectively. t-statistics are reported in parentheses, and standard errors are clustered by MSA-year
While Table 7 examines the immediate ramifications of changes in newspaper employment for firms’ information environment, the long-term impacts are unclear, as other information intermediaries may step in and fill the information gap left by lower local newspaper employment. To examine this, we continue to use a similar model but instead look at the association between changes in newspaper employment and information environments over longer windows. Specifically, in Table 8 Panel A (B) [C], we compute our information environment proxies over the following six (12) [24] months instead of three months. Consistent with local newspapers improving firms’ information environments, we continue to find, in Panel A, that declines in local newspaper employment are associated with higher spreads and greater illiquidity over six months. However, consistent with other information intermediaries stepping in to fill the information void left behind by local newspapers, we find weaker (or no) association between declines in newspaper employment and firms’ information environments in the following 12 and 24 months.
Table 8
Relative change in newspaper intensity – long term effects
Panel A: Dependent variable measurement window = 6 months
Dependent Variable:
Fut_Ret_Volatility
#Volatile_Days
Spreads
Illiquidity
(1)
(2)
(3)
(4)
Relative_Chg_News
-0.125
-1.053
-0.093**
-0.232*
(-0.922)
(-1.144)
(-2.294)
(-1.811)
Controls
Yes
Yes
Yes
Yes
Qtr & Year FE
Yes
Yes
Yes
Yes
Industry FE
Yes
Yes
Yes
Yes
R-Squared
59.6%
60.6%
44.4%
9.5%
Observations
109,535
109,535
109,535
109,535
Panel B: Dependent variable measurement window = 12 months
Dependent Variable:
Fut_Ret_Volatility
#Volatile_Days
Spreads
Illiquidity
(1)
(2)
(3)
(4)
Relative_Chg_News
0.016
1.289
-0.076*
-0.257
(0.144)
(0.981)
(-1.955)
(-1.587)
Controls
Yes
Yes
Yes
Yes
Qtr & Year FE
Yes
Yes
Yes
Yes
Industry FE
Yes
Yes
Yes
Yes
R-Squared
63.3%
65.9%
44.9%
8.7%
Observations
109,535
109,535
109,535
109,535
Panel C: Dependent variable measurement window = 24 months
Dependent Variable:
Fut_Ret_Volatility
#Volatile_Days
Spreads
Illiquidity
(1)
(2)
(3)
(4)
Relative_Chg_News
0.000
0.866
-0.074*
-0.185
(0.080)
(0.345)
(-1.953)
(-0.817)
Controls
Yes
Yes
Yes
Yes
Qtr & Year FE
Yes
Yes
Yes
Yes
Industry FE
Yes
Yes
Yes
Yes
R-Squared
62.3%
64.8%
44.4%
8.1%
Observations
109,535
109,535
109,535
109,535
Panel A (B) [C] reports the results from examining the implications of changes in local newspaper intensity and firms’ information environments over the following six (12) [24] months. Relative_Chg_News is equal to the percentage change in newspaper employment in the prior 12 months minus the percentage change in all employment in the prior 12 months in the firm’s MSA. Variable definitions are in Appendix A. Significance levels of 10%, 5%, and 1% are represented by *, **, and ***, respectively. t-statistics are reported in parentheses, and standard errors are clustered by MSA-year

5.2 Responses to weakened firm information environments

To better understand the ramifications of decreased local newspaper presence, and the response of other information intermediaries, we next examine the role and extent of other information sources. When firms have significant changes in their levels of local news intensity, other information intermediaries may respond to remediate the volatility and information asymmetry effects we document in our primary analyses. To more directly examine this, we next examine how changes in local news intensity are associated with four alternative information sources.
First, we investigate analyst coverage of firms following declines in local newspaper intensity, as Healy and Palepu (2001) posit that financial intermediaries such as analysts will engage in private information production to uncover managers’ superior information. Managers’ superior information is likely increased following a decline in local news coverage which would typically hold them accountable or disseminate information to the capital markets. Accordingly, we expect that financial analysts will increase their coverage of local firms following a decline in local newspaper intensity.
Second, managers may take steps themselves to allay investor concerns following a decrease in independent monitoring by local newspapers by improving their firms’ information environments. To investigate this, we examine the number of management forecasts that mangers provide in the following quarter and the amount of forward-looking statements in the manager-prepared narrative section of the conference call.17 These measures capture two alternative disclosure mechanisms that managers use to provide additional information about the current and future activities of the firm.
Finally, we examine how changes in local news intensity are associated with changes in how institutional investors acquire information about a firm by looking at the amount of attention a firm receives on the Bloomberg terminal. Specifically, we follow Ben-Rephael et al. (2017) and use data from Bloomberg that captures how many users deliberately search for and read news articles about a firm. These data are standardized into hourly counts and compared to averages in attention over the prior month before Bloomberg creates an hourly attention score by firm. Since we are interested in how stakeholders change their information acquisition behavior, we compute the number of days in the following quarter with abnormal Bloomberg attention. If stakeholders fill the information gap left behind when local newspaper intensity decreases by turning to articles in the Bloomberg terminals (which are more national and industry focused sources), we should observe an increase in Bloomberg attention when newspaper intensity decreases.
For all four dependent variables, we use the following model:
$$Information\; Intermediaries={\beta }_{1}\; Relative\_Chg\_Newspapers+{\beta }_{2-k}\; Controls+\varepsilon$$
(2)
where Information Intermediaries is equal to either the natural log of analysts following the firm, the natural log of the number of management forecasts provided over the following three months, the percentage of forward-looking disclosures in the manager-prepared narrative section of a conference call, or the natural log of the number of days with Bloomberg attention scores in the following three months. We continue to control for firm characteristics and changes in the local economy, as these are likely to be associated with the extent of information available via alternative information intermediaries.
Table 9 presents the results of our analyses of how other stakeholders react to the weakened information environment brought about by a decrease in newspaper intensity. Consistent with analysts helping to fill the void left when other sources of information decline, Column 1 indicates that there is an increase in the number of analysts covering firms when local newspaper employment decreases. Columns 2 and 3 indicate that managers also respond to the change in local news by increasing the number of forecasts they provide and by providing more forward-looking information in the manager-prepared narrative portion of conference calls. Finally, the insignificant coefficient in Column 4 is not consistent with stakeholders changing their information acquisition behavior by increasing their news acquisition about the firm from the Bloomberg terminal when local news becomes less available. Taken together, these results help explain the decreased association between changes in newspaper intensity and firms’ information environments in the long term. Specifically, the results in Tables 7, 8, and 9 are consistent with firms’ information environments immediately deteriorating when local news decreases, upon which other information intermediaries then step in to help fill the information vacuum, thus potentially limiting the long-term impacts of decreased local news.
Table 9
Relative change in newspaper intensity and alternative information
Dependent Variable:
Coverage
# Mgmt Forecasts
% MPN Forward Looking
Bloomberg Attention
(1)
(2)
(3)
(4)
Relative_Chg_News
-0.081**
-0.089*
-0.010**
-0.058
(-2.019)
(-1.956)
(-2.247)
(-0.843)
Size
0.273***
0.037***
-0.004***
0.353***
(94.521)
(9.493)
(-8.239)
(60.575)
Leverage
0.112***
0.232***
0.004**
0.501***
(7.238)
(11.735)
(2.171)
(17.022)
Book-to-Market
0.041***
-0.026***
0.001
0.019
(5.972)
(-3.642)
(0.838)
(1.201)
ROA
-0.956***
1.249***
-0.102***
-1.065***
(-15.956)
(20.445)
(-14.501)
(-8.937)
Turnover
0.078***
0.011***
0.002***
0.101***
(29.575)
(4.844)
(7.429)
(27.036)
Earnings_Volatility
-0.006***
-0.002***
-0.000*
0.005***
(-7.343)
(-2.755)
(-1.677)
(2.930)
Ret_Volatility
-0.506***
-0.784***
0.051***
1.938***
(-7.005)
(-8.435)
(6.412)
(14.393)
Institutional_Own
0.306***
0.383***
0.019***
0.067**
(24.417)
(25.684)
(10.775)
(2.396)
Coverage
 
0.151***
0.013***
0.317***
 
(21.603)
(15.661)
(26.105)
#PRs
0.081***
0.027***
-0.000
0.167***
(15.422)
(6.042)
(-0.725)
(13.922)
Chg_All_Employment
0.006**
-0.008**
0.001
-0.004
(1.994)
(-2.368)
(1.553)
(-0.838)
Unemployment_Rate
-0.001
-0.021***
-0.003***
0.012*
(-0.309)
(-6.095)
(-7.103)
(1.955)
Newspaper_Closure
0.001
-0.018**
-0.001
0.016
(0.110)
(-2.211)
(-0.807)
(1.351)
Constant
-1.342***
-0.220***
0.128***
-2.942***
(-10.806)
(-3.621)
(9.539)
(-23.806)
Qtr & Year FE
Yes
Yes
Yes
Yes
Industry FE
Yes
Yes
Yes
Yes
R-Squared
65.9%
37.5%
10.9%
62.8%
Observations
109,535
109,535
109,535
109,535
This table reports the results from examining the implications of changes in local newspaper intensity for alternative information sources. Relative_Chg_News is equal to the percentage change in newspaper employment in the prior 12 months minus the percentage change in all employment in the prior 12 months in the firm’s MSA. Variable definitions are in Appendix A. Significance levels of 10%, 5%, and 1% are represented by *, **, and ***, respectively. t-statistics are reported in parentheses, and standard errors are clustered by MSA-year

5.3 Direct or indirect effect on analysts

The results in Table 9 suggest a substitutive relation between declines in newspaper intensity and both analyst coverage and management forecasts. However, prior research has argued that management forecasts and analyst coverage can be substitutes themselves at times and that management forecasts can influence analyst forecast characteristics (e.g., Altschuler et al. 2015; Kim and Song 2015). To examine whether management forecasts influence the relation between newspaper coverage and analyst forecasts, we conduct a path analysis (untabulated) to directly examine how relative changes in newspaper intensity affect analyst coverage both directly and indirectly through management forecasts. We center all variables first by year and then by industry and thus exclude fixed effects. In this setting, the reliability of path analysis is somewhat restricted, given the required assumptions (e.g., that changes in newspaper employment first impact management forecasting and then impact analysts in future periods). However, we find that, as newspaper employment drops relative to all employment in an MSA, both management forecasts and analyst coverage increase. Additionally, we find evidence of both a direct and indirect impact through more management forecasts of changes in newspaper employment on analyst coverage.

5.4 Manufacturing intensity

An alternative explanation for our main results is that firms’ information environments suffer when the local economy systematically changes, and that change in turn influences the employment distribution of a city. If this is the case, our results may not be due to newspapers influencing firms’ information environments specifically but instead are due to Newspaper_Intensity capturing changes (i.e., deterioration) in the local economy. To investigate this further, we rerun our main analysis looking at the employment intensity of manufacturing instead of newspaper publishers.18 We choose manufacturing specifically for this test for two reasons. First, similar to newspapers, manufacturing has experienced a systematic decrease in employment over the past several decades (Harris 2020). During our sample period, we observe manufacturing employment decreasing from 8.7% of all jobs to 6.8%. Second, also similar to newspaper employment, manufacturing employment intensity has significant cross-sectional variation between cities, allowing for sufficient power to test for any impact on information environments.
Our results from this analysis (untabulated) indicate that there is no consistent association between levels of (or relative changes in) manufacturing intensity and firms’ information environments. Additionally, we find no association between relative changes in manufacturing intensity and analyst coverage, management forecasts, or forward-looking disclosures. This is consistent with our main results being driven by newspaper employment specifically and not overarching trends or changes in the local economy.

5.5 Firm fixed effects regressions

As noted above, the inferences from the results in Table 2 remain similar if we instead employ year, quarter, and firm fixed effects. Further, Table 7 documents that the results in Table 2 remain consistent in a changes framework using the Relative_Chg_News variable and are therefore already examining firm specific changes. However, Tables 2, 3, 4, 5, and 6 are performed using the levels of Newspaper_Intensity and using industry fixed effects for two primary reasons.
First, our investigation of how newspaper intensity affects local firms’ information environments is best facilitated by controlling for time and industry fixed effects as opposed to time and firm fixed effects. We are interested in investigating how plausibly exogenous variation in newspaper intensity affects firms’ information environments, holding unobservable industry-specific factors constant. Specifically, the variation of interest in our study is the within-time (as both national levels or norms of newspaper intensity and the role of alternative information sources, such as blogs or online-only news outlets, varies) and within-industry (as the role of newspapers varies across industries based on reader demands) variation in newspaper intensity. Given that firms rarely change the location of their headquarters, the only within-firm variation in newspaper intensity arises from changes in newspaper intensity in their city which is subtle from quarter to quarter. This creates some limitations for tests examining changes in newspaper intensity or using a firm fixed effect research design. Thus, we instead continue to use a research design controlling for time and industry fixed effects, as we believe that this best isolates the variation of interest in our study.
Second, as evidence of the discussion above, while the general tenor of our results holds with firm fixed effects, the tests with the most limited within-firm variation in the variables of interest, especially as they relate to the dependent variables, are the ones where the results using firm fixed effects are weakest. With the exception of Table 5, the results are largely similar if we instead use a firm fixed effects design. Specifically, in Table 5, when the dependent variable is coverage, accuracy, or dispersion (variables that also have very little within-firm quarter-to-quarter variation), a test using firm fixed effects and controlling for time effects is essentially a test of whether coverage, accuracy, or dispersion, are affected by the slight change in Newspaper_Intensity from the prior quarter. This lack of variation in our dependent variables results in us not finding consistent statistical results for these tests.

6 Conclusion

Newspapers and their journalists play a vital role in gathering and disseminating information to the public. However, in the past two decades, local newspapers have suffered from systematic layoffs and closures that have reduced the number of newspaper employees by over 75 percent. We examine the effect of this trend on firms’ information environments by developing a continuous measure of local newspaper intensity. Consistent with reduced monitoring by local newspapers having detrimental effects on firms’ information environments, we find that lower newspaper intensity is associated with higher return volatility, more frequent volatile return days, higher bid-ask spreads, and more illiquidity of a firm’s stock. We also document that each of these effects is exacerbated when a firm is more important to the local economy. Furthermore, firms and their shareholders are not the only stakeholders who suffer from reduced local newspaper monitoring. Financial analysts covering the firm are also adversely affected as evidenced by their increased forecast dispersion and lowered accuracy for economically important firms.
Finally, we find that the negative effects on a firm’s information environment are short-lived, as other stakeholders step in to repair the weakened information environment. Specifically, we find that analysts, management, and investors increase their information production and data gathering activities following a decrease in newspaper employment, which at least partially offsets the negative impact on firms’ information environments.
The results of this study inform financial market participants, such as financial analysts, current and potential shareholders, and managers, about the importance of local newspapers and the implications of the continued decline in the newspaper industry. Additionally, this paper is focused on the ramifications of and initial response to decreases in newspaper intensity. Future research could consider whether stakeholders are opportunistically taking advantage of the decline in the newspaper industry and how different types of investors are affected by levels and changes to newspaper intensity. Finally, we outline one way to utilize BLS Quarterly Census of Employment and Wages data to capture one aspect of the local economy surrounding a firm’s headquarters. Future research can use similar methodologies to investigate the ramifications of other types of employment and how changes in the regional economy may impact firms’ operations or stock market performance.

Acknowledgements

We thank Beth Blankespoor, Mike Crawley, Andrew Grice, Patrick Hopkins, Blair Marquardt, participants at the 2023 University of North Texas Accounting Research Conference, and multiple anonymous current and former business journalists for helpful comments. We also thank Penelope Abernathy, Alex Dixon, Zachary Metzger, and the UNC Hussman School of Journalism and Media for discussions on data availability and issues related to the expanding U.S. news desert. Allee and Rawson acknowledge the support of the Open Access Publishing Fund administered through the University of Arkansas Libraries. Allee acknowledges the support of the Doyle Z. Williams Chair in Professional Accounting at the Sam M. Walton College of Sam M Walton College of Business and the University of Melbourne.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creativecommons.​org/​licenses/​by/​4.​0/​.

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Anhänge

Appendix A. Variable definitions

Newspaper_Intensity
= Percentage of all employees in an MSA working for a newspaper publisher (NAICS code 51111).
Relative_Chg_News
= Percentage change in newspaper employment in prior 12 months – percentage change in all employment in prior 12 months.
High_Rel_Importance
= Indicator equal to one if the firm’s market value is more than 10 percent of the total market value in the MSA.
High_Lit_Risk
= Indicator equal to one if the firm has above median litigation risk. Litigation risk is calculated per model 2 in Table 7 of Kim and Skinner (2012).
Busy_Analysts
= Indicator equal to one if the average number of firms that the firm’s analyst follows is in the top tercile by year.
Accuracy
= Negative 100 times the absolute value of mean analyst surprise divided by lagged stock price.
Dispersion
= 100 times the standard deviation of analyst forecasts divided by stock price.
Fut_Ret_Volatility
= Standard deviation of daily returns over the following three months.
#Volatile_Days
= Number of trading dates with absolute returns greater than 5 percent over the following three months.
Spreads
= Average of 100 * daily bid-ask spread over the following three months. Bid-ask spreads are computed as the ask minus bid divided by the midpoint.
Illiquidity
= Average of the Amihud (2002) illiquidity measure over the following three months.
Size
= Natural log of market value.
Leverage
= Total liabilities divided by total assets.
Book-to-Market
= Book value of equity divided by market value.
ROA
= Income before extraordinary items divided by total assets.
Turnover
= Share volume in the prior 12 months divided by shares outstanding.
Earnings_Volatility
= Standard deviation of earnings in the prior 16 quarters.
Ret_Volatility
= Standard deviation of monthly returns in the prior 48 months.
Institutional_Own
= Percentage of stock owned by institutions.
Coverage
= Natural log of 1 plus the number of analysts making annual EPS estimates.
#PRs
= Natural log of 1 plus the number of press releases provided in the prior three months.
Chg_All_Employment
= Percentage change in the number of all employees in an MSA over the prior 12 months.
Unemployment_Rate
= Average unemployment rate in an MSA over the prior 12 months.
Newspaper_Closure
= Indicator variable equal to one if the number or newspaper establishments in an MSA has declined over the previous 12 months.
Fußnoten
2
Specifically, according to the Bureau of Labor Statistics Quarterly Census of Employment and Wages, there were 415,405 employees at newspapers in January 2000 and 105,138 employees in December of 2021.
 
3
For example, late night TV host John Oliver has discussed the growing trend of sponsored content in local TV news and how it can result in lower overall trust of local media (Oliver 2021).
 
4
While newspaper closures reduce newspaper employment, we find that there is a consistent decrease in newspaper employment well before the actual closure of a newspaper. That is, because closures are often preceded by multiple rounds of layoffs and consolidations, a newspaper closure often does not result in a shocking or significant change in employment. For example, in untabulated analysis, we find that the decrease in newspaper intensity for cities with a newspaper closure is ~37 percent from three years pre closure to three years post closure with only ~7 percent of this occurring in the closure year. This compares to an ~43 (~8) percent decrease in newspaper intensity during the same seven years (pseudo-close year) for a control group of cities matched on size and year. Furthermore, discussions with local news reporters revealed that business reporters are likely some of the first employees to be let go as layoffs arise and would not likely still be around at the time of the newspaper closure.
 
5
This is likely compounded by few journalists wanting to cover businesses. As one retired business reporter we interviewed described it: “Nobody dreams of being a business journalist. They just end up there.”
 
6
Throughout the paper, we use the terms “MSA” and “city” interchangeably. We match the address of each firms’ headquarters to a county and MSA based on zip codes.
 
7
Specifically, “Newspaper Publishing Establishments” (NAICS code 51111 during our sample period).
 
8
In 2005 there were 370,380 newspaper employees and approximately 130 million jobs in the United States. By the end of 2019, there were only 126,692 newspaper employees, despite overall national employment increasing to over 150 million jobs.
 
9
For example, in an interview with a local business reporter, he noted that, after cuts in business reporters in an area, the stock price of a local pharmaceutical company increased dramatically upon the announcement that one of its drugs had received FDA approval. He opined that, if the prior local (business) news reporting had been maintained, the approval would not have surprised the market as much because more regular coverage of the company would have encouraged more gradual incorporation of the information before the final announcement.
 
10
As the number of volatile trading days is a count variable, we also run this utilizing a Poisson regression (untabulated) and find consistent results.
 
11
Our data on establishments comes from the BLS, which defines them as an “economic unit that produces goods or services, usually at a single physical location, and that is engaged in one or predominantly one type of economic activity” (https://​www.​bls.​gov/​bls/​glossary.​htm).
 
12
If we instead employ year, quarter, and firm fixed effects (untabulated) or instead cluster standard errors by MSA, our inferences remain similar.
 
14
In untabulated analyses, we also examine whether the association between newspaper intensity and market outcomes varies with the level of analyst coverage, as the relation between analysts and local newspapers may differ based on the firms’ information environment. We replace High_Rel_Importance with indicators equal to one when the firm is in the bottom decile of spreads or illiquidity and find that the relation between local newspaper intensity and coverage and dispersion (accuracy) is lower (higher) when the firm has low spreads or low illiquidity.
 
15
We subtract the change in overall employment to capture changes in the local economy as a whole. For example, if the number of jobs in a city increased by 3 percent in the prior year but the number of newspaper jobs increased by only 1 percent, we want to capture the relatively slower growth in employment as a decrease in the intensity of newspaper employment.
 
16
In untabulated analysis, we interact the relative change in newspaper employment with High_Rel_Importance and find that relative declines in newspaper intensity continue to be associated with increased volatility and the number of volatile days for all firms. Additionally, similar to Table 3, the association with spreads and illiquidity is isolated exclusively among firms that are more important to their city.
 
17
Specifically, we classify each sentence of the manager-prepared narrative as being forward looking or non-forward looking following Bozanic et al. (2018) and compute the percentage of all sentences that are forward looking.
 
18
Specifically, we examine employment in NAICS industries 31–33, which capture all forms of manufacturing.
 
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Metadaten
Titel
No news is bad news: local news intensity and firms’ information environments
verfasst von
Kristian D. Allee
Ryan Cating
Caleb Rawson
Publikationsdatum
16.10.2023
Verlag
Springer US
Erschienen in
Review of Accounting Studies
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI
https://doi.org/10.1007/s11142-023-09811-7