Supplementary Material B for “Whose News? Class-Biased Economic Reporting in the United States”
Alan M. Jacobs∗ J. Scott Matthews Professor, Associate Professor, Department of Political Science, Department of Political Science, University of British Columbia Memorial University [email protected] of Newfoundland 0000-0002-1008-2551 [email protected] 0000-0001-9738-3447
Timothy Hicks Eric Merkley Associate Professor, SSHRC Postdoctoral Fellow, School of Public Policy, Department of Political Science, University College London University of Toronto [email protected] [email protected] 0000-0001-6012-8418 0000-0001-7647-9650
February 19, 2021
∗Jacobs, Matthews, and Hicks rotate author orderings across their joint papers to reflect equal contributions among themselves. Contents
B1 Categorizing Articles as Relating to ‘The Economy’2 B1.1 Economic Relevance: Instructions to Coders...... 3 B1.2 Measuring Economic Perceptions ...... 9
B2 Model Specification Decisions 12
B3 Coding of Newspaper Ownership 14 B3.1 Newspaper Ownership Data Collection Procedure...... 14 B3.2 Newspaper Ownership Coding Rules ...... 14 B3.3 Sources on Newspaper Ownership...... 15
B4 Extra Regression Results Referenced in Main Paper 21
1 B1 Categorizing Articles as Relating to ‘The Economy’
One concern about our raw news content data might be that our search procedures on Lexis, Lexis-Nexis Academic, and Factiva may have yielded a substantial share of “false positives”: articles that contained one of our search terms but were not in fact about the U.S. economy. We employed a machine-learning approach to refining our sample and minimizing this problem. We describe this procedure here.
We began by creating a set of definitions and coding rules that would allow a team of research assistants to read an article and determine whether it was relevant to the U.S. economy. The full coding rules are reproduced in Section B1.1. The rules take an expansive view of economic news as reporting on anything relating to the incomes, production activities (including inputs to production), assets, or liabilities of individuals or collectivities (e.g., firms, governments, social groups) in the United States. Among the topics captured by the rule were, for instance, macroeconomic developments, macroeconomic policies, government taxation and spending, firm activities, consumer activity, labor markets and skills, and infrastructure.
We implemented this coding procedure at two levels of stringency. The most permissive coding rule deemed an article “relevant” if it contained a single mention of something that met this broad definition of the economy of the United States. This rule likely captured a substantial share of articles that were not primarily about the U.S. economy. A more restrictive coding rule classified an article as “Lede-Relevant” by applying the same mention-of-the-economy decision rule only to the headline and first 400 characters of text in each article.1 Given standard jour- nalistic practices of headline- and lede-writing, this second rule tended to select more precisely for newspaper articles that had a substantial focus on some aspect of the economy.
With these coding rules in hand, we tasked three research assistants with classifying a randomly-drawn subsample of 2,400 articles from the full sample for their relevance to the economy.2 We then split these 2,400 classifications into a training sample (80%) and a held-out testing sample (20%). The 1,920 classified articles in the former were used to train a Support Vector Machine (SVM) using the Scikit-learn Python package (Pedregosa et al. 2011). The SVM used the “linear” kernel, and we performed a grid search for the best-performing classifier across C ∈ [1 × 10−8, 1 × 106].3 The best classifier was selected on the basis of K = 5-fold
1We allowed that the reference to the United States itself might occur later in the article or be implicit, given that these were all U.S. newspapers. 2The RAs received training, and we performed multiple pilot rounds to hone the coding rules and check for consistency of coding across the RAs. Tests of inter-coder reliabilty based on the final round of training indicated a reliable coding instrument. Specifically, Krippendorff’s Alpha (Krippendorff, 2012) for a three-level ordinal classification of economic mentions (i.e., code yl v. y v. n; see sectionB1.1) was .802 (95% CI 0.726, 0.869). 3We searched across 15 values of C, equally spaced in log-space through that range.
2 cross-validation, with the “score” metric for the selection being “precision” in order to minimize the number of false positives in the final sample and so reduce the noise we are modeling.4 We trained two classifiers: one for the full-text sample (SVM F ull) and one for a sample including only the headline and first 400 characters of each article (SVM Lede).5
Once the classifiers were trained, we then assessed their performance on the held-out sam- ples. SVM F ull and SVM Lede exhibited average precision scores of 0.86 and 0.79,6 respectively, as well as average recall scores of 0.86 and 0.75, respectively.7 Satisfied with this performance, we then applied the trained classifiers to the full corpus, and used these classifications to further restrict the sample.
The resulting samples from the various steps in this process of corpus restriction are sum- marized in Table B1. Figure B1 shows the temporal variation in each of three tone measures that arise from more or less restrictive definitions of the underlying article sample.8 Specifically, it displays time series for all non-business section articles (NB); non-business section articles SVM-classified as mentioning the economy (NB/Econ); and non-business section articles SVM- classified as having headline-and-lede text about the economy (“NB/Econ/Lede”). As can be seen, the three series are highly correlated.Given that, we use the “lede-relevant” series in our analyses given our core empirical goal of measuring the tone of economic news that is readily accessible to the mass electorate: not only do headlines and ledes signal the core content of an article, but they are almost certainly more likely to be read than text that appears later in the article.
B1.1 Economic Relevance: Instructions to Coders
We reproduce here the instructions provided to coders classifying articles as being or not being about the economy.
N.b. All articles have been pre-selected to have the word “economy” or “economic” in them, as well as a mention of “United States” or any of the 50 U.S. states.
Define “the economy” as relating to anything on income, production, assets, or liabilities of individuals or collectivities (i.e. firms, governments, social groups). This would include:
4 TP P recision ≡ , where TP and FP are the numbers of true positives and false positives, respectively. TP +FP 5We included any words that began within the first 400 characters, even if they went beyond this point, in order to avoid word fragments. 6The grid search procedure yielded CF ull = 0.44 and CLede = 0.0 – with the obvious superscript notation. 7 TP Recall ≡ , where FN is the number of false negatives. TP +FN 8This figure is illustrative, only. Its construction does not account for entry to and exit from the sample of various newspapers at different points in time.
3 Article N Newspaper First year Total Non-Business Lede-Relevant Per qtr Atlanta Journal-Constitution 1991 70,035 53,187 11,954 126 Chicago Sun-Times 1992 54,746 38,966 8,103 92 Chicago Tribune 1985 154,493 118,145 28,030 242 Columbus Dispatch 1992 32,629 25,884 9,797 111 Dallas Morning News 1992 77,026 56,371 10,852 126 Denver Post 1993 39,678 28,350 7,484 90 Detroit Free Press 1994 39,134 25,489 7,115 91 Kansas City Star 1994 57,208 49,990 20,364 261 Los Angeles Times 1985 191,357 141,408 23,788 205 Minneapolis Star-Tribune 1991 35,833 26,675 7,857 84 New York Times 1981 243,926 174,022 25,792 192 Orange County Register 1987 34,795 26,306 7,700 71 Orlando Sentinel 1990 84,578 70,514 22,545 235 Philadelphia Inquirer 1994 45,332 33,720 9,441 115 Pittsburgh Post-Gazette 1993 82,396 63,778 11,526 139 Plain Dealer 1992 50,599 39,212 10,387 115 Providence Journal 1994 44,293 36,720 11,792 144 Salt Lake Tribune 1994 28,754 21,371 7,220 87 San Diego Union-Tribune 1983 63,762 51,364 12,294 145 San Francisco Chronicle 1989 46,362 35,971 8,102 84 San Jose Mercury News 1994 51,305 39,492 11,887 143 St Louis Post-Dispatch 1989 75,532 58,984 14,177 138 Sun-Sentinel 1993 69,203 51,641 11,744 147 Tampa Bay Times 1987 69,769 59,035 13,798 124 Tampa Tribune 1990 33,474 28,410 7,655 97 The Boston Globe 1988 98,898 78,985 18,520 185 The Houston Chronicle 1991 72,068 52,282 11,439 127 The Miami Herald 1983 116,032 88,374 19,092 174 The Oregonian 1987 75,625 62,430 21,071 199 USA Today 1989 49,540 32,206 5,191 51 Wall Street Journal 1991 70,281 70,281 39,597 528 Washington Post 1981 205,422 145,306 27,393 228 Total 1981 2,464,085 1,884,869 463,707 153
Table B1: Summary of newspaper articles included in the sample. “Lede-Relevant” is pre- filtered for non-business-section articles. “Per qtr” provides the article-N for the “Lede- Relevant” column.
4 4
3
2
1
0
-1 Average News Economic Tone -2
-3 1980q1 1984q1 1988q1 1992q1 1996q1 2000q1 2004q1 2008q1 2012q1
NB NB/Econ NB/Econ/Lede
Figure B1: Time series plot of the mean of three standardized, circulation-weighted quarterly measures of the tone of newspaper reporting about the economy. Vertical shading indicates recessionary period as defined by the Federal Reserve of St. Louis’s FRED database series.
• a firm • an industry • unemployment • prices or inflation (retail, commodity, energy, etc) • GDP • (aggregated) income measures • trade • interest rates • capital flows (into/out of/within the U.S.) • labor supply (into/out of/within U.S) • hiring/firing • The working class, workers as a group • Investors as a group • other economically defined groups: e.g., homeowners, business owners, classes, income groups, an occupation as a group • stock market performance • corporate investment • corporate profits • household or personal incomes
5 • Consumer behavior (in some aggregate form) • Consumer or business confidence • poverty or inequality in incomes or wealth • government revenues and spending • monetary policy • trade policy (except sanctions targeted at foreign countries for foreign policy reasons) • energy sector/energy production or other natural-resource inputs into economy • infrastructure • Skills (if some reference to work/labor/labor markets/occupations) • advertising • government deficits and debt
The mention of these things could refer to them in any way. It could be a mention of their level, their nature/kind, a change in them, a shortage/excess of or demand for them, their price, how they are distributed (across regions, firms, groups, sectors, etc.), and so on.
To count as a mention of the economy, the mention must refer to some economic phenomenon at some level of aggregation higher than that of specific, named individuals. The level of aggregation need not be national. It could, for instance, be at the level of a firm, a local area, a region, a sector of the economy, a factor of production (labor, capital), a set of actors (bond investors, low-wage workers), etc. The economic content just cannot be strictly about specific people named in the article - although an article may also discuss the economic circumstances of named individuals and still be coded as mentioning “the economy” if the higher level of aggregation is also present.
Only count references to economy that refer to recent past, present or future — not to decades earlier unless there’s a connection made to recent past, present, or future. For in- stance, a reference in the 1980s to the Great Depression would not count, unless there is some implication, lesson, connection, etc being drawn to something in the recent past, present, or future.
In general, policies with possible economic effects (e.g., health care policy) do not count as a mention of the economy on their own, without some explicit mention of an economic development that they are affecting. Note that we are NOT looking for causes or effects of economic phenomena, which is what policies are. We are looking for mentions of economic phenomena/activity/developments themselves — like the phenomena in the above list.
Of course, if an article about economic policy also mentions some economic phenomenon that could be affected, then the article counts as a “yes.” Also, interest rates should generally be considered a “yes” (even though they are in part a policy choice by the central bank) since they are a price.
6 Moreover, also consider government revenues/taxes, government spending, deficits and debt to be “yesses” because they are standardly considered components of the economy. In other words, even though these could be seen as policies, they are ALSO economic developments in themselves.
The fact that the U.S. isn’t explicitly mentioned in a lede does not stop us from coding a “yl.” Given that these are U.S. papers, the U.S. context will often be taken for granted. In fact, since these are U.S. papers, you should infer the economic stuff mentioned in the lede is referring to the U.S. unless you see information in the article (could be anywhere) indicating that it is NOT. The point is that the U.S. reader of U.S. paper will certainly be assuming it’s the U.S. unless they’re told otherwise.
Count U.S.-based or owned firms as firms in the U.S. even if the reference is to their activities outside the U.S.
Do NOT count mentions of campaign contributions, campaign spending, or campaign fundraising as economic mentions. However, references to taxpayers having to pay for campaign activities DOES count.
• Rule 1:
– If an article mentions something that fits this definition of “the economy” — and that mention is related to the USA — within the headline or the lede (we will define the number of characters counting as the lede): ∗ Code: “yl”
• Rule 2:
– If Rule 1 does not lead to “yl”. . . – If an article mentions something that fits this definition of “the economy” — and that mention is related to the USA — anywhere within the text: ∗ Code: “y”
• Rule 3:
– Otherwise: ∗ Code: “n”
“Mention” can be just that, such that the bulk of the article may actually be concerned with other matters.
7 By “mention the economy”, we also mean to include those articles that refer to any actor(s) expressing their own views on those matters, not just objective facts.
Some “articles” may actually be “letters” to the newspaper, but these should be coded according to the same rules, above.
Some “articles” may actually be just be one-line pointers to the real article. These should be coded according to the rules above, to the extent that it is clear that the real article would meet the respective criteria.
8 B1.2 Measuring Economic Perceptions
The measures of economic perceptions that we use are calculated using data from University of Michigan’s Surveys of Consumers (SoC) (see above, Section A2. We use variables that are constructed directly by the SoC. These constructed measures are endorsed as appropriate aggregations of the individual-level responses by the organization that administers the surveys. However, the choice of exactly how to construct these summary measures is not a neutral one in that it entails an implicit choice about how the resulting measures should respond (or not) to responses that fall in the middle/neutral and DK categories.
For example, the RetroBust measure from the SoC is the same for the case of (%Bettert =
25, %W orset = 20) and for the case of (%Bettert = 45, %W orset = 40), but these are sub- stantively different sets of responses, and one might reasonably think that the difference should be reflected by a summary measure. One way to reflect the difference would be to scale the measure by the total number of directional responses, thus taking into account changes in the proportion of respondents providing a directional (better or worse, favorable or unfavorable, good or poor) response as compared to a neutral response (e.g., the midpoint response option or “don’t know” (DK)). The following alternative measures, denoted with the S (for scaled) superscript, have this feature:
S • RetroBust = (%Bettert − %W orset)/(%Bettert + %W orset).
S • NewsBust = (%F avorablet − %Unfavorablet)/(%F avorablet + %Unfavorablet).
S • GovtHandlingt = (%Goodt − %P oort)/(%Goodt + %P oort).
S S Note that RetroBust and GovtEcont come with an implicit assumption that the DK and mid-point responses are not distinguished from each other – having an effect on the measure only as a combined residual category since the responses for a given time period (t) sum to 100. A second alternative approach, then, is to scale the direction of the measures by the sum of all substantive response categories – i.e. excluding only the DK response category:
SDK • RetroBust = (%Bettert − %W orset)/(%Bettert + %W orset + %Samet).
SDK • NewsBust = (%F avorablet − %Unfavorablet)/(%F avorablet + %Unfavorablet +
%NoNewst).
SDK • GovtHandlingt = (%Goodt − %P oort)/(%Goodt + %P oort + %F air).
(The underlying data generating process for the NewsBus data is such that responses can sum to more than 100 because respondents are able to report remembering both favorable and unfavorable news.)
9 The effects of applying these two approaches to scaling are minimal for our data. As can be seen from Tables B2–B4, the correlations across the respective measures are extremely high. This reflects the temporal stability in the proportions of mid-point and DK responses to the underlying questions. Unsurprisingly, then, re-estimating the models that capture the association between these measures and our economic news tone measure yields unchanged inferences – as shown in Tables B5–B7.
(1)
Std S SDK RetroBust RetroBust RetroBust Std RetroBust 1 S RetroBust 0.998 1 SDK RetroBust 1.000 0.999 1
Table B2: Correlation matrix for the various measures of perceptions of business conditions.
(1)
Std S SDK NewsBust NewsBust NewsBust Std NewsBust 1 S NewsBust 0.971 1 SDK NewsBust 0.995 0.988 1
Table B3: Correlation matrix for the various measures of business news perceptions.
(1)
Std S SDK GovtHandlingt GovtHandlingt GovtHandlingt Std GovtHandlingt 1 S GovtHandlingt 0.995 1 SDK GovtHandlingt 1.000 0.996 1
Table B4: Correlation matrix for the various measures of perceptions of government economic handling.
10 (1) (2) (3) b se b se b se RetroBust−1 0.75 0.04 S RetroBust−1 0.75 0.04 SDK RetroBust−1 0.75 0.04 T onet 0.30 0.06 0.29 0.06 0.30 0.06 Newspaper FE Yes Yes Yes R2 0.88 0.88 0.88 N 135 135 135 Portmanteau Q 41.8 42.7 42.2 Portmanteau Q: p 0.39 0.36 0.38
Table B5: Comparison of association between our economic news tone measure and various ways of measuring perceptions of business conditions.
(1) (2) (3) b se b se b se NewsBust−1 0.61 0.06 S NewsBust−1 0.59 0.06 SDK NewsBust−1 0.59 0.06 T onet 0.43 0.08 0.41 0.08 0.44 0.08 Newspaper FE Yes Yes Yes R2 0.78 0.77 0.78 N 135 135 135 Portmanteau Q 46.5 51.1 47.9 Portmanteau Q: p 0.22 0.11 0.18
Table B6: Comparison of association between our economic news tone measure and various ways of measuring business news perceptions.
(1) (2) (3) b se b se b se GovtHandlingt−1 0.88 0.04 S GovtHandlingt−1 0.89 0.04 SDK GovtHandlingt−1 0.88 0.04 T onet 0.12 0.05 0.11 0.05 0.12 0.05 Newspaper FE Yes Yes Yes R2 0.89 0.90 0.89 N 135 135 135 Portmanteau Q 45.7 42.6 44.6 Portmanteau Q: p 0.25 0.36 0.28
Table B7: Comparison of association between our economic news tone measure and various ways of measuring perceptions of government economic handling.
11 B2 Model Specification Decisions
As noted, the goal in Section is not to estimate a model of the data-generating process (DGP) underlying economic news tone, but to produce descriptive inferences about the extent to which various income growth measures are associated with T onei,t, conditional on one another. The issue that arises is that we want statistical inferences about these partial correlations to be appropriate – i.e., the estimated standard errors need to be correctly constructed – though this is difficult to ensure without a model of the DGP. We also need to take into account the particular panel structure of the data. That our income variables are measured at the national- , rather than newspaper-, level implies that contemporaneous correlation of errors between newspapers may be a problem. Indeed, even with newspaper fixed effects (FE), residuals from models estimated by Ordinary Least Squares (OLS) were found to be heavily correlated across panels. We also find non-trivial levels of within-newspaper autocorrelation of residuals, which may reflect (deliberately) unmodelled features of the DGP on the explanatory variable side or omitted lagged dependent variables.
The contemporaneous correlation of errors rules out dynamic panel estimators like Blundell- Bond and the static estimator of Newey-West, as they assume zero correlation across panels. Consequently, our approach is to present results from two estimation strategies. The first, which is our baseline specification for the models we estimate in Section , uses a heavily dynamic specification with four lags (reflecting our quarterly unit of analysis) of the dependent variable, as well as allowing for any remaining autocorrelation in the errors with newspaper-specific AR1 errors, coupled with panel-corrected standard errors (Beck and Katz 1995) to account for contemporaneous correlation of errors.9 The second, more minimalist strategy uses a static specification with Driscoll and Kraay (1998) standard errors, which adjust for correlation across panels and autocorrelation up to a specified lag length, which we set as four. While this standard-error correction is justified asymptotically in time periods (T ), Monte Carlo results also demonstrate very good performance even for panels of N = 20 and T = 25 (Driscoll and Kraay 1998, 554). Our analyses typically have N = 32 and T ≈ 130.
To be more precise, our base dynamic specification is the following regression:
4 3 T X T one,k X Q q X g g ∆T onei,t = βi + βi · T imet + β · T onei,t−k + βq · Qtrt + β · δInct + t , (1) k=1 q=1 g∈G
T where: βi denotes newspaper fixed effects (with newspapers indexed by i); βi denotes newspaper- q specific time trends; Qtrt denotes dummies for (three of) the four annual quarters; and G is a set of income quantiles (e.g. {P 0 − 10,P 40 − 60}) that varies by estimated model as we ex-
9 The length of our time series, with mean(Ti) ≈ 85, should obviate any concern about Nickell (1981) bias arising from unit FE and the lagged dependent variable(s).
12 plore the relationship between T onei,t and growth in different parts of the income distribution. Meanwhile, our base static specification is as follows:
3 T X Q q X g g T onei,t = βi + βi · T imet + βq · Qtrt + β · δInct + t , (2) q=1 g∈G
where the same notation applies.
13 B3 Coding of Newspaper Ownership
B3.1 Newspaper Ownership Data Collection Procedure
Data collection proceeded in three steps.
1. Identifying owning company. The “History” and “Ownership” sections of each news- paper’s website were examined to identify the company that currently owns that news- paper as well as any changes in ownership over time.
2. Classifying owning company. With the owning company or companies identified, the company was classified as public, private, or non-profit for each quarter based on cross- validated information from the company’s Wikipedia entry and from Mergent Online. On Mergent Online, information was drawn from the sections on history, highlights, and ownership for each company.
3. Validation. For those newspapers and time periods in which the two datasets overlapped, classifications were validated against the newspaper ownership data from Dunaway and Lawrence (2015), shared by Johanna Dunaway. Only one discrepancy was found. Whereas Dunaway and Lawrence classify Tribune Publishing Co. as private in 2004, 2006, and 2008, we code the company as public until the end of 2007, when Sam Zell bought the company and took it private. (We then code the company as public as of the end of 2012 again, but this is outside the scope of Dunaway and Lawrence’s dataset.)
B3.2 Newspaper Ownership Coding Rules
Ownership types are coded as follows:
• Public: If the majority of shares of the owning company are available for trade among the general public in the stock market, the ownership type is coded as “Public.” Subsidiaries of public companies, where the public company owns at least 50% of the voting stock, were coded as “Public.”
• Private: If the company is closely held by private individuals and families or the ma- jority of its voting shares are not available for trade among the general public in the stock market, the ownership type is classified as “Private.” If the owning company is a private subsidiary of a private company, which owns at least 50% of its voting stock, the newspaper is coded as “Private.”
14 • Non-profit: This category includes newspapers that are owned by non-profit organiza- tions.
Each paper is given an ownership classification for each quarter that it appears in our dataset. In the case of a change in owning company or ownership type mid-quarter, we code that quarter as whichever ownership type prevailed for the majority of the quarter.
B3.3 Sources on Newspaper Ownership
Atlanta Journal-Constitution
https://en.wikipedia.org/wiki/The_Atlanta_Journal-Constitution
https://en.wikipedia.org/wiki/Cox_Enterprises
http://www.mergentonline.com/basicsearch.php
Chicago Sun-Times
https://en.wikipedia.org/wiki/Chicago_Sun-Times
https://en.wikipedia.org/wiki/Sun-Times_Media_Group
http://www.mergentonline.com/basicsearch.php
https://en.wikipedia.org/wiki/Wrapports
https://en.wikipedia.org/wiki/James_C._Tyree
Chicago Tribune
https://www.chicagotribune.com/ct-history-of-tribune-ownership-saga-20160204-htmlstory.html
https://en.wikipedia.org/wiki/Tribune_Media#Public_corporation_second_time
https://en.wikipedia.org/wiki/Tribune_Publishing
http://www.mergentonline.com/basicsearch.php
Columbus Dispatch
https://en.wikipedia.org/wiki/The_Columbus_Dispatch
https://en.wikipedia.org/wiki/Dispatch_Broadcast_Group
http://www.mergentonline.com/basicsearch.php
Gale Directory of Publications and Broadcast Media: 1994 Volume
Dallas Morning News
https://en.wikipedia.org/wiki/The_Dallas_Morning_News
http://www.mergentonline.com/basicsearch.php
15 https://en.wikipedia.org/wiki/Belo https://en.wikipedia.org/wiki/A._H._Belo
Denver Post https://en.wikipedia.org/wiki/The_Denver_Post https://en.wikipedia.org/wiki/Digital_First_Media http://www.mergentonline.com/basicsearch.php
Detroit Free Press https://www.gannett.com/who-we-are/history/ http://www.mergentonline.com/basicsearch.php https://en.wikipedia.org/wiki/Gannett https://en.wikipedia.org/wiki/Knight_Ridder https://en.wikipedia.org/wiki/Detroit_Free_Press
Gale Directory of Publications and Broadcast Media: 1994 Volume
Kansas City Star https://en.wikipedia.org/wiki/The_Kansas_City_Star https://en.wikipedia.org/wiki/Knight_Ridder https://en.wikipedia.org/wiki/McClatchy https://en.wikipedia.org/wiki/Capital_Cities/ABC_Inc. http://www.mergentonline.com/basicsearch.php
Los Angeles Times https://en.wikipedia.org/wiki/Los_Angeles_Times https://en.wikipedia.org/wiki/Tribune_Media#Public_corporation_second_time https://en.wikipedia.org/wiki/Tribune_Publishing http://www.mergentonline.com/basicsearch.php https://en.wikipedia.org/wiki/Times_Mirror_Company
The New York Times https://en.wikipedia.org/wiki/The_New_York_Times_Company http://www.mergentonline.com/basicsearch.php
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Minneapolis Star-Tribune https://en.wikipedia.org/wiki/Star_Tribune https://en.wikipedia.org/wiki/Cowles_Media_Company
16 https://en.wikipedia.org/wiki/McClatchy https://www.nytimes.com/1997/11/14/business/the-media-business-mcclatchy-in-1.4-billion-cowles-deal.html http://www.mergentonline.com/basicsearch.php
Orange County Register https://en.wikipedia.org/wiki/Orange_County_Register https://en.wikipedia.org/wiki/Freedom_Communications http://www.mergentonline.com/basicsearch.php
Orlando Sentinel https://en.wikipedia.org/wiki/Orlando_Sentinel https://en.wikipedia.org/wiki/Tribune_Publishing http://www.mergentonline.com/basicsearch.php
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Philadelphia Inquirer https://en.wikipedia.org/wiki/The_Philadelphia_Inquirer https://en.wikipedia.org/wiki/Philadelphia_Media_Holdings https://en.wikipedia.org/wiki/Philadelphia_Media_Network http://www.mergentonline.com/basicsearch.php https://www.philafound.org/ https://en.wikipedia.org/wiki/The_Philadelphia_Foundation
Pittsburgh Post-Gazette https://www.post-gazette.com/about/history https://en.wikipedia.org/wiki/Block_Communications https://en.wikipedia.org/wiki/Pittsburgh_Post-Gazette http://www.mergentonline.com/basicsearch.php
Plain Dealer https://en.wikipedia.org/wiki/The_Plain_Dealer https://en.wikipedia.org/wiki/Advance_Publications http://www.mergentonline.com/basicsearch.php
Providence Journal https://en.wikipedia.org/wiki/The_Providence_Journal http://www.fundinguniverse.com/company-histories/the-providence-journal-company-history/ https://en.wikipedia.org/wiki/Belo
17 https://en.wikipedia.org/wiki/A._H._Belo http://www.mergentonline.com/basicsearch.php https://en.wikipedia.org/wiki/GateHouse_Media
Salt Lake Tribune https://en.wikipedia.org/wiki/The_Salt_Lake_Tribune#cite_note-9 https://en.wikipedia.org/wiki/Digital_First_Media https://en.wikipedia.org/wiki/Tele-Communications_Inc. http://www.mergentonline.com/basicsearch.php
San Diego Union-Tribune https://en.wikipedia.org/wiki/The_San_Diego_Union-Tribune https://en.wikipedia.org/wiki/Platinum_Equity https://en.wikipedia.org/wiki/Copley_Press http://www.mergentonline.com/basicsearch.php
San Francisco Chronicle https://en.wikipedia.org/wiki/San_Francisco_Chronicle https://en.wikipedia.org/wiki/Chronicle_Publishing_Company https://en.wikipedia.org/wiki/Hearst_Communications http://www.mergentonline.com/basicsearch.php
San Jose Mercury News https://en.wikipedia.org/wiki/The_Mercury_News https://en.wikipedia.org/wiki/Knight_Ridder https://en.wikipedia.org/wiki/Digital_First_Media https://en.wikipedia.org/wiki/McClatchy http://www.mergentonline.com/basicsearch.php
St Louis Post-Dispatch https://en.wikipedia.org/wiki/St._Louis_Post-Dispatch https://en.wikipedia.org/wiki/Pulitzer,_Inc. https://en.wikipedia.org/wiki/Lee_Enterprises
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Sun-Sentinel https://en.wikipedia.org/wiki/Sun-Sentinel https://en.wikipedia.org/wiki/Tribune_Media#Public_corporation_second_time
18 https://en.wikipedia.org/wiki/Tribune_Publishing http://www.mergentonline.com/basicsearch.php
Gale Directory of Publications and Broadcast Media: 1994 Volume
Tampa Bay Times https://en.wikipedia.org/wiki/Tampa_Bay_Times https://en.wikipedia.org/wiki/Poynter_Institute https://en.wikipedia.org/wiki/Times_Publishing_Company http://www.mergentonline.com/basicsearch.php
Tampa Tribune https://en.wikipedia.org/wiki/The_Tampa_Tribune https://en.wikipedia.org/wiki/Media_General https://www.bloomberg.com/research/stocks/private/snapshot.asp?privcapId=13634261 http://www.mergentonline.com/basicsearch.php
The Boston Globe https://en.wikipedia.org/wiki/The_Boston_Globe https://en.wikipedia.org/wiki/The_New_York_Times_Company http://www.mergentonline.com/basicsearch.php
The Houston Chronicle https://en.wikipedia.org/wiki/Houston_Chronicle https://en.wikipedia.org/wiki/Hearst_Communications http://www.mergentonline.com/basicsearch.php
The Miami Herald https://www.britannica.com/topic/The-Miami-Herald https://en.wikipedia.org/wiki/Miami_Herald https://en.wikipedia.org/wiki/Knight_Ridder https://en.wikipedia.org/wiki/McClatchy http://www.mergentonline.com/basicsearch.php
The Oregonian https://en.wikipedia.org/wiki/The_Oregonian https://en.wikipedia.org/wiki/Advance_Publications http://www.mergentonline.com/basicsearch.php
USA Today
19 https://en.wikipedia.org/wiki/USA_Today https://en.wikipedia.org/wiki/Gannett http://www.mergentonline.com/basicsearch.php
Gale Directory of Publications and Broadcast Media: 1994 Volume
Wall Street Journal https://en.wikipedia.org/wiki/The_Wall_Street_Journal http://www.mergentonline.com/basicsearch.php https://en.wikipedia.org/wiki/Dow_Jones_%26_Company https://en.wikipedia.org/wiki/News_Corp_(2013%E2%80%93present) https://en.wikipedia.org/wiki/Bancroft_family http://www.mergentonline.com/basicsearch.php
Washington Post https://en.wikipedia.org/wiki/The_Washington_Post https://en.wikipedia.org/wiki/Graham_Holdings http://www.mergentonline.com/basicsearch.php
20 B4 Extra Regression Results Referenced in Main Paper
(1) b se p Recessiont -0.43 0.07 0.00 Newspaper FE Yes Newspaper Trends Yes Seasonal FE Yes 4 lags of DV Yes R2 0.34 N 2847 N newspapers 32 Mean Ti 89.0 Min Ti 60 Max Ti 130 Min Y eari,t 1982 Max Y eari,t 2014 Corr. psar1 AR1-p 0.84
Table B8: Descriptive inferences regarding the association between the tone of economic news reporting across newspapers (T onei,t) and an indicator of economic recession at the quarterly level. Model estimated by OLS with Beck and Katz (1995) panel corrected standard errors.
21 (1) (2) b se p b se p P 0−20 δInct 1.64 1.29 0.20 2.78 2.04 0.18 P 20−40 δInct -3.71 4.03 0.36 -2.52 5.67 0.66 P 40−60 δInct 17.96 9.83 0.07 35.45 13.83 0.01 P 60−80 δInct -22.63 9.13 0.01 -43.95 12.73 0.00 P 80−100 δInct 7.89 1.77 0.00 11.78 2.33 0.00 Newspaper FE Yes Yes Newspaper Trends Yes Yes Seasonal FE Yes Yes 4 lags of DV Yes No R2 0.35 0.59 N 2842 3008 N newspapers 32 32 Mean Ti 88.8 94.0 Min Ti 60 Max Ti 128 Min Y eari,t 1982 1982 Max Y eari,t 2014 2014 Corr. psar1 DK/AR4 AR1-p 0.44
Table B9: Descriptive inferences regarding the association between the tone of economic news reporting across newspapers (T onei,t) and income growth at various points of the income dis- tribution. Model 1 estimated by OLS with Beck and Katz (1995) panel corrected standard errors. Model 2 estimated by OLS with Driscoll and Kraay (1998) standard errors.
(1) (2) (3) (4) b se p b se p b se p b se p P 0−20 δInct 1.43 1.43 0.32 2.53 2.08 0.23 1.20 1.48 0.42 2.09 2.21 0.35 P 20−40 δInct 1.21 3.25 0.71 7.84 4.29 0.07 0.02 4.24 1.00 5.56 6.38 0.39 P 40−60 δInct -2.26 6.09 0.71 -4.00 9.31 0.67 P 60−80 δInct -7.94 5.45 0.15 -16.17 7.40 0.03 P 80−100 δInct 6.55 1.87 0.00 9.59 2.76 0.00 5.37 1.75 0.00 7.22 2.59 0.01 Newspaper FE Yes Yes Yes Yes Newspaper Trends Yes Yes Yes Yes Seasonal FE Yes Yes Yes Yes 4 lags of DV Yes No Yes No R2 0.34 0.58 0.34 0.58 N 2842 3008 2842 3008 N newspapers 32 32 32 32 Mean Ti 88.8 94.0 88.8 94.0 Min Ti 60 60 Max Ti 128 128 Min Y eari,t 1982 1982 1982 1982 Max Y eari,t 2014 2014 2014 2014 Corr. psar1 DK/AR4 psar1 DK/AR4 AR1-p 0.79 0.98
Table B10: Descriptive inferences regarding the association between the tone of economic news reporting across newspapers (T onei,t) and income growth at various points of the income distribution. Models 1 and 3 estimated by OLS with Beck and Katz (1995) panel corrected standard errors. Models 2 and 4 estimated by OLS with Driscoll and Kraay (1998) standard errors.
22 (1) (2) (3) b se p b se p b se p P 0−20 δInct -0.55 1.08 0.61 -0.58 1.09 0.59 -0.56 1.08 0.60 P 20−40 δInct -0.85 3.27 0.80 -0.20 2.58 0.94 -0.73 3.09 0.81 P 40−60 δInct 2.45 7.95 0.76 1.77 4.28 0.68 P 60−80 δInct -0.79 7.89 0.92 1.31 4.28 0.76 P 80−100 δInct 4.01 1.51 0.01 3.81 1.47 0.01 3.92 1.33 0.00 Newspaper FE Yes Yes Yes Newspaper Trends Yes Yes Yes Seasonal FE Yes Yes Yes 4 lags of DV Yes Yes Yes R2 0.33 0.33 0.33 N 2843 2843 2843 N newspapers 32 32 32 Mean Ti 88.8 88.8 88.8 Min Ti 60 60 60 Max Ti 128 128 128 Min Y eari,t 1982 1982 1982 Max Y eari,t 2014 2014 2014 Corr. psar1 psar1 psar1 AR1-p 0.84 0.76 0.82
Table B11: Descriptive inferences regarding the association between the tone of economic news reporting across newspapers (T onei,t) and annual income growth (replicated four times, once for each quarter) at various points of the income distribution. Models estimated by OLS with Beck and Katz (1995) panel corrected standard errors.
(1) (2) (3) b se p b se p b se p P 0−20 δInct 4.22 3.03 0.16 4.18 3.05 0.17 3.87 3.01 0.20 P 20−40 δInct -8.28 9.40 0.38 -5.34 6.30 0.40 -5.36 8.26 0.52 P 40−60 δInct 10.48 23.85 0.66 -4.79 13.10 0.71 P 60−80 δInct -16.35 20.40 0.42 -8.41 11.27 0.46 P 80−100 δInct 12.78 3.91 0.00 12.19 3.84 0.00 11.05 3.42 0.00 Newspaper FE Yes Yes Yes Newspaper Trends Yes Yes Yes 1 lag of DV Yes Yes Yes R2 0.43 0.43 0.43 N 736 736 736 N newspapers 32 32 32 Mean Ti 23.0 23.0 23.0 Min Ti 16 16 16 Max Ti 33 33 33 Min Y eari,t 1982 1982 1982 Max Y eari,t 2014 2014 2014 Corr. psar1 psar1 psar1 AR1-p 0.06 0.04 0.01
Table B12: Descriptive inferences regarding the association between the tone of economic news reporting across newspapers (T onei,t) and income growth at various points of the income distribution. Newspaper-year unit of observation. Models estimated by OLS with Beck and Katz (1995) panel corrected standard errors.
23 (1) (2) (3) (4) b se p b se p b se p b se p P 0−20 δInct 3.17 2.99 0.29 3.13 3.07 0.31 3.25 3.28 0.32 2.99 3.45 0.39 P 40−60 δInct -8.74 9.26 0.35 -6.91 9.23 0.45 -4.42 9.50 0.64 -0.62 9.48 0.95 P 90−100 δInct 8.36 2.81 0.00 P 95−100 δInct 6.59 2.34 0.00 P 99−100 δInct 4.37 1.75 0.01 P 99.9−100 δInct 2.84 1.28 0.03 Newspaper FE Yes Yes Yes Yes Newspaper Trends Yes Yes Yes Yes 1 lag of DV Yes Yes Yes Yes R2 0.42 0.42 0.41 0.40 N 736 736 736 736 N newspapers 32 32 32 32 Mean Ti 23.0 23.0 23.0 23.0 Min Ti 16 16 16 16 Max Ti 33 33 33 33 Min Y eari,t 1982 1982 1982 1982 Max Y eari,t 2014 2014 2014 2014 Corr. psar1 psar1 psar1 psar1 AR1-p 0.22 0.29 0.74 0.54
Table B13: Descriptive inferences regarding the association between the tone of economic news reporting across newspapers (T onei,t) and income growth at various points of the income distribution. Newspaper-year unit of observation. Models estimated by OLS with Beck and Katz (1995) panel corrected standard errors.
(1) (2) (3) (4) b se p b se p b se p b se p P 0−20 δInct 1.19 1.31 0.36 1.15 1.33 0.39 1.16 1.35 0.39 1.10 1.37 0.42 P 40−60 δInct -1.31 4.18 0.75 -0.28 4.08 0.95 1.09 4.02 0.79 2.65 3.89 0.50 P 90−100 δInct 4.15 1.33 0.00 P 95−100 δInct 3.19 1.07 0.00 P 99−100 δInct 2.01 0.75 0.01 P 99.9−100 δInct 1.27 0.52 0.01 Newspaper FE Yes Yes Yes Yes Newspaper Trends Yes Yes Yes Yes Seasonal FE Yes Yes Yes Yes 4 lags of DV Yes Yes Yes Yes R2 0.34 0.34 0.34 0.33 N 2842 2842 2842 2842 N newspapers 32 32 32 32 Mean Ti 88.8 88.8 88.8 88.8 Min Ti 60 60 60 60 Max Ti 128 128 128 128 Min Y eari,t 1982 1982 1982 1982 Max Y eari,t 2014 2014 2014 2014 Corr. psar1 psar1 psar1 psar1 AR1-p 0.92 0.86 0.75 0.65
Table B14: Descriptive inferences regarding the association between the tone of economic news reporting across newspapers (T onei,t) and income growth at various points of the income distribution. All models estimated by OLS with Beck and Katz (1995) panel corrected standard errors.
24 (1) (2) (3) (4) b se p b se p b se p b se p P 0−20 δInct 1.16 1.31 0.38 1.11 1.32 0.40 1.10 1.33 0.41 1.08 1.33 0.42 P 40−60 δInct -3.23 5.92 0.59 -3.30 5.95 0.58 -3.99 5.97 0.50 -4.35 5.98 0.47 P 80−90 δInct 3.20 7.00 0.65 4.73 6.79 0.49 7.36 6.41 0.25 9.25 6.08 0.13 P 90−100 δInct 3.69 1.61 0.02 P 95−100 δInct 2.67 1.25 0.03 P 99−100 δInct 1.52 0.81 0.06 P 99.9−100 δInct 0.94 0.52 0.07 Newspaper FE Yes Yes Yes Yes Newspaper Trends Yes Yes Yes Yes Seasonal FE Yes Yes Yes Yes 4 lags of DV Yes Yes Yes Yes R2 0.34 0.34 0.34 0.33 N 2842 2842 2842 2842 N newspapers 32 32 32 32 Mean Ti 88.8 88.8 88.8 88.8 Min Ti 60 60 60 60 Max Ti 128 128 128 128 Min Y eari,t 1982 1982 1982 1982 Max Y eari,t 2014 2014 2014 2014 Corr. psar1 psar1 psar1 psar1 AR1-p 0.96 0.95 0.91 0.89
Table B15: Descriptive inferences regarding the association between the tone of economic news reporting across newspapers (T onei,t) and income growth at various points of the income distribution. All models estimated by OLS with Beck and Katz (1995) panel corrected standard errors.
(1) (2) (3) (4) b se p b se p b se p b se p P 0−20 δInct 1.39 1.31 0.29 0.98 1.32 0.46 0.43 1.32 0.74 0.36 1.24 0.77 P 20−90 δInct -2.59 4.82 0.59 P 20−95 δInct 0.63 4.68 0.89 P 20−99 δInct 4.78 4.24 0.26 P 90−100 δInct 4.48 1.49 0.00 P 95−100 δInct 3.04 1.26 0.02 P 99−100 δInct 1.28 0.96 0.18 P 20−99.9 δInct 5.76 3.30 0.08 P 99.9−100 δInct 0.54 0.65 0.40 Newspaper FE Yes Yes Yes Yes Newspaper Trends Yes Yes Yes Yes Seasonal FE Yes Yes Yes Yes 4 lags of DV Yes Yes Yes Yes R2 0.34 0.34 0.34 0.34 N 2842 2842 2842 2842 N newspapers 32 32 32 32 Mean Ti 88.8 88.8 88.8 88.8 Min Ti 60 60 60 60 Max Ti 128 128 128 128 Min Y eari,t 1982 1982 1982 1982 Max Y eari,t 2014 2014 2014 2014 Corr. psar1 psar1 psar1 psar1 AR1-p 0.88 0.86 0.89 0.90
Table B16: Descriptive inferences regarding the association between the tone of economic news reporting across newspapers (T onei,t) and income growth at various points of the income distribution. All models estimated by OLS with Beck and Katz (1995) panel corrected standard errors.
25 (1) (2) (3) (4) b se p b se p b se p b se p P 0−90 δIncs,t 1.09 0.55 0.05 P 90−100 δIncs,t 1.96 0.41 0.00 P 95−100 δIncs,t 1.40 0.34 0.00 P 99−100 δIncs,t 0.60 0.25 0.02 P 99.9−100 δIncs,t 0.12 0.16 0.46 P 0−95 δIncs,t 1.65 0.65 0.01 P 0−99 δIncs,t 2.84 0.78 0.00 P 0−99.9 δIncs,t 3.74 0.81 0.00 Newspaper FE Yes Yes Yes Yes Newspaper Trends Yes Yes Yes Yes Seasonal FE Yes Yes Yes Yes 4 lags of DV Yes Yes Yes Yes R2 0.35 0.35 0.35 0.35 N 2775 2775 2775 2775 N newspapers 32 32 32 32 Mean Ti 86.7 86.7 86.7 86.7 Min Ti 56 56 56 56 Max Ti 127 127 127 127 Min Y eari,t 1982 1982 1982 1982 Max Y eari,t 2013 2013 2013 2013 Corr. psar1 psar1 psar1 psar1 AR1-p 0.99 1.00 0.91 0.83
Table B17: Estimates of the association between the tone of economic news reporting across newspapers (T onei,t) and state-level measures of top-income growth. All models estimated by OLS with Beck and Katz (1995) panel corrected standard errors.
(1) (2) (3) (4) b se p b se p b se p b se p P 99−100 δInct 2.01 0.75 0.01 1.41 0.76 0.06 1.23 0.76 0.11 1.07 0.76 0.16 P 0−20 δInct 1.16 1.35 0.39 0.94 1.24 0.45 0.69 1.23 0.57 0.69 1.19 0.57 P 40−60 δInct 1.09 4.02 0.79 0.60 3.68 0.87 -0.39 3.73 0.92 -0.30 3.61 0.93 δGDPt 0.10 0.06 0.09 0.06 0.06 0.34 ∆Unempt -0.27 0.13 0.04 -0.20 0.15 0.18 Newspaper FE Yes Yes Yes Yes Newspaper Trends Yes Yes Yes Yes Seasonal FE Yes Yes Yes Yes 4 lags of DV Yes Yes Yes Yes R2 0.34 0.34 0.34 0.34 N 2842 2842 2842 2842 N newspapers 32 32 32 32 Mean Ti 88.8 88.8 88.8 88.8 Min Ti 60 60 60 60 Max Ti 128 128 128 128 Min Y eari,t 1982 1982 1982 1982 Max Y eari,t 2014 2014 2014 2014 Corr. psar1 psar1 psar1 psar1 AR1-p 0.75 0.79 0.57 0.79
Table B18: Estimates of the association between the tone of economic news reporting across newspapers (T onei,t), national-level top-1% income growth, GDP growth, and the change in the national-level unemployment rate. All models estimated by OLS with Beck and Katz (1995) panel corrected standard errors.
26 (1) (2) (3) (4) b se p b se p b se p b se p P 99−100 ∆IncSharet 14.44 5.05 0.00 9.60 4.01 0.02 7.83 4.34 0.07 5.46 4.38 0.21 δS&P 500t 0.02 0.00 0.00 0.02 0.00 0.00 0.02 0.00 0.00 δGDPt 0.04 0.04 0.32 -0.01 0.05 0.80 ∆Unempt -0.23 0.11 0.03 Newspaper FE Yes Yes Yes Yes Newspaper Trends Yes Yes Yes Yes Seasonal FE Yes Yes Yes Yes 4 lags of DV Yes Yes Yes Yes R2 0.32 0.35 0.35 0.36 N 2842 2842 2842 2842 N newspapers 32 32 32 32 Mean Ti 88.8 88.8 88.8 88.8 Min Ti 60 60 60 60 Max Ti 128 128 128 128 Min Y eari,t 1982 1982 1982 1982 Max Y eari,t 2014 2014 2014 2014 Corr. psar1 psar1 psar1 psar1 AR1-p 0.96 0.04 0.04 0.27
Table B19: Estimates of the association between the tone of economic news reporting across newspapers (T onei,t), income growth at various points of the income distribution, and growth in various financial market indices. All models estimated by OLS with Beck and Katz (1995) panel corrected standard errors.
(1) b se p P 0−90 δInct 3.58 3.07 0.24 P 99−100 δInct 1.73 0.75 0.02 Newspaper FE Yes Newspaper Trends Yes Seasonal FE Yes 4 lags of DV Yes R2 0.33 N 2842 N newspapers 32 Mean Ti 88.8 Min Ti 60 Max Ti 128 Min Y eari,t 1982 Max Y eari,t 2014 Corr. psar1 AR1-p 0.93
Table B20: Estimates of the association between the tone of economic news reporting across newspapers (T onei,t) and income growth at various points of the income distribution. Income measured at national level, but matching the specifications that we use at the state level. All models estimated by OLS with Beck and Katz (1995) panel corrected standard errors.
27 References
Beck, Nathaniel, and Jonathan N. Katz. 1995. “What to do (and not to do) with Time-Series Cross-Section Data”. American Political Science Review 89 (3): 634–647. Driscoll, John C., and Aart C. Kraay. 1998. “Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data”. Review of Economics and Statistics 80 (4): 549–560. Dunaway, Johanna, and Regina G. Lawrence. 2015. “What Predicts the Game Frame? Media Ownership, Electoral Context, and Campaign News”. Political Communication 32 (1): 43– 60. Nickell, Stephen. 1981. “Biases in Dynamic Models with Fixed Effects”. Econometrica 49 (6): 1417–1426. Pedregosa, F., et al. 2011. “Scikit-learn: Machine Learning in Python”. Journal of Machine Learning Research 12:2825–2830.
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