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Social networks and the demand for news
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Information Economics and Policy journal homepage: www.elsevier.com/locate/iep
Social networks and the demand for newsR Lisa M. George a,∗, Christian Peukert b,c a
Department of Economics, Hunter College and the Graduate Center, CUNY, 695 Park Ave., New York, NY 10065, USA Católica-Lisbon School of Business and Economics, Universidade Católica Portuguesa, Palma de Cima, Lisbon, Portugal c ETH Zürich, Center for Law and Economics, Haldeneggsteig 4, 8092 Zürich, Switzerland b
a r t i c l e
i n f o
Article history: Received 24 October 2017 Revised 13 September 2019 Accepted 11 October 2019 Available online xxx JEL classification: D22 L13 L82 Keywords: News Social networks Preference externalities Media Internet
a b s t r a c t Economic research has documented a robust, positive relationship between media consumption among minority individuals and the size of the minority population in local markets. The theoretical mechanism behind these “preference externaltities” has been understood to be the supply incentive to cater to large groups when fixed costs or other scale economies limit the number of viable products in a local market. We demonstrate that the supply-side mechanism is incomplete: the relationship holds not just for local but also national news outlets. We extend the concept of preference externalities to the demand side, establishing a relationship between the racial composition of local communities and the tendency to seek and share information on online social networks. Using data from a sample of 35,997 internet households and a sample of 11,479 Twitter users, we show that a larger local black population is associated with both larger network size and higher utilization for individual black Twitter users relative to white Twitter users and vice versa. Our results suggest that digitization can exacerbate inequality in news consumption, but also that policies to widen broadband access might narrow the gap.
1. Introduction Economic research documents a robust relationship between the demographic composition of media markets and minority media consumption. Metropolitan areas with a larger AfricanAmerican population, for example, see higher per capita newspaper readership among black individuals than markets with a smaller black population, all else equal. The relationship has been documented across racial and ethnic minorities in the context of news, music and television. The relationship between the size of a minority population and media consumption has been understood to arise from supplier incentives to target large groups. These incentives arise when fixed production costs or other scale economics limit the number of differentiated products a market can support. Empirical evidence sup-
R This paper was written with financial support from the Research Foundation of the City University of New York, and FCT – Portuguese Foundation of Science and Technology for the project UID/GES/00407/2013. We thank conference and seminar participants at the University of Zurich, the Searle Center on Law, Regulation and Economic Growth, the Media Economics Workshop, and Microsoft Research for helpful comments. ∗ Corresponding author. E-mail address: [email protected] (L.M. George).
© 2019 Elsevier B.V. All rights reserved.
ports the supply-side mechanism: markets with a larger AfricanAmerican population offer more black-targeted radio and newspaper content, while markets with more Hispanics provide more Spanish language television news programs.1 Yet despite results linking group size to the supply of targeted media, other evidence suggests that supply-side explanations for what have come to be known as “preference externalities” remain incomplete. In news markets, for example, a gap in consumption between minority and majority readers has persisted into the digital era despite massive reductions in fixed costs and proliferation of targeted outlets. The well known Pew Research Center surveys show that the gap in newspaper readership between AfricanAmerican and white readers has remained stable at about 5–6% between 1999 and 2016.2 At the same time, the economics literature has amassed evidence that local social networks affect a wide range of individual decisions in education, trade and public
1 The chapter on preference externalities in the Handbook of Media Economics provides a full theoretical treatment (Anderson and Waldfogel, 2016). See George and Waldfogel (2003) for empirical evidence in daily newspaper markets. See Waldfogel (2003) for evidence in radio, and Oberholzer-Gee and Waldfogel (2009); Wang and Waterman (2011) for results in television. 2 Details and annual data available at http://www.journalism.org/ media-indicators/newspapers-daily-readership-by-ethnic-group.
https://doi.org/10.1016/j.infoecopol.2019.100833 0167-6245/© 2019 Elsevier B.V. All rights reserved.
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choice.3 Since communication lies at the heart of news markets, it seems reasonably that larger peer populations might increase the tendency to seek, and to share, information. Social networks might thus deepen, or offset, the relationship between group size and media consumption that arise from targeted entry and help explain the persistence of a “digital divide” in news consumption. This paper examines the relationship between community composition, online social networks and the demand for news. We have two goals. The first is to demonstrate that patterns of minority news consumption cannot be explained solely by supplier incentives to target large groups. We do this by showing that per capita consumption of national news online among black households increases with the local black population, contradicting theoretical predictions of the strict supply-driven model. The second goal is to provide evidence that social networks can explain consumption patterns observed in the data but unexplained by the standard model. We do this by linking local community composition with social network size and utilization. Taken together, our results indicate a broader scope for individuals with shared preferences to influence each other through media markets than previously recognized. Uncovering the mechanism behind consumption inequality is important for understanding the effects of digitization. Supply-side models of preference externalities are grounded in a spatial framework where fixed production costs limit entry, so they naturally apply in local settings with few products. But if social networks influence the demand for information, then minority groups can see lower consumption of any targeted media, not solely local outlets. In other words, the important determinant of consumption inequality would not be the presence of local product markets, but rather the presence of local social networks. Moreover, in digital markets, firms increasingly steer resources to content most readily shared, forwarded and “liked.” If larger groups have a greater tendency to share information, then even when fixed costs are very low suppliers will face a differential incentive to serve the majority at the expense of minority preferences. Simply put, if the tendency to share information depends on local social networks, groups with a higher tendency to share will find themselves better served and better informed. Distinguishing the role of supply-side and demand-side factors in consumption inequality is also important for policy. Diversity and localism are founding principles motivating policies of the Federal Communications Commission. Supply-side factors in consumption inequality imply under-provision of targeted media, ameliorated with the minority ownership rules or production supports. Demand-side mechanisms suggest social foundations for under-consumption, best tackled with policies to increase broadband access, digital literacy, and community cohesion. Policies aimed at strengthening local social networks are now advocated by economists for improving a broad range of social outcomes (Jackson, 2011).4 Our empirical approach for the two goals is as follows. We first establish that the relationship between local minority population and local news consumption documented in the literature holds in online news markets. Closely following the empirical strategy in the literature, we show using a sample of 35,997 internet households that a larger local black population is associated with more online news visits among black households relative to white households. A larger white population is similarly associated with fewer news visits among blacks relative to whites. We then extend the analysis to national news outlets. With the same sam3 See Jackson (2011) for a comprehensive overview of social network applications in economics. 4 Note that our paper also relates to the large literature in sociology on homophily in social networks, see McPherson et al. (2001).
ple of internet households, we show that a larger local black population is associated with higher online consumption of national news among blacks relative to whites. The standard supply-side mechanism cannot explain these results, predicting instead that national media targeted at the median consumer should not be positively correlated with local minority populations.5 After showing that the supply-side explanation of preference externalities is incomplete, we look for evidence of a demand-side mechanism. Following the literature on social networks, we hypothesize that larger communities with shared tastes lead to larger social networks and more information sharing. We study this in the context of social media, asking whether a larger minority population is associated with more intense social media use. We offer two sets of evidence. Using our internet sample, we first show that a larger minority population in a locality is associated with more frequent visits to the social media outlets Facebook and Twitter. The result suggests that local populations affect social network use overall, but does not allow us to measure social network size or distinguish information sharing from more passive consumption. To study this we assemble a unique sample of 11,479 Twitter users coded for race. We find that a larger local black population is associated with a larger number of sources followed and a higher number of messages posted. A larger local white population is similarly associated with a larger number of sources followed and higher network use. Cross effects, the effect of a larger white population on a black users and vice versa, tend to reduce network size and participation, especially for minority users. We conclude that a larger local population with shared preferences increases the tendency to seek, and to share, information on online social networks. Our identification strategy for the news analysis closely follows the industrial organization literature on preference externalities in using independent variation in the minority and majority populations at the MSA level to capture shared tastes for media products (Waldfogel, 2003; George and Waldfogel, 2003). Unlike some earlier studies of preference externalities, we measure behavior with household data and thus can control for observable household characteristics. In studying social media, we follow the literature on peer effects and identify results from variation in community composition at a more localized geography (zip code or census place) (Bertrand et al., 20 0 0; Aizer and Currie, 2004). Our approach effectively compares social network size and activity among minority individuals in places with a larger minority population to social network size and activity among minority individuals in places with a smaller minority population. In addition to our direct contribution to the literature on preference externalities in media markets, our results also relates to a now-substantial literature on social network communications. Perhaps most closely related to our study are List and Price (2009) and Zhang and Zhu (2011), which consider the relationship between group size and the incentives to contribute to public goods. Halberstam and Knight (2016) investigate political communications in social networks, linking group size to majority and minority political interests. Our results are also relevant to the growing literature on the relationship between online social media and news readership (e.g. Sismeiro and Mahmood, 2018), online social media and political participation (e.g. Petrova et al., 2017), and the effects of user-generated content in news media (e.g. Yildirim et al., 2013). Our focus on the link between the de-
5 The simplest formulation would predict no correlation. However a negative correlation could arise if national news and local news are substitutes, so that minorities in markets with a low minority population would be more likely to choose national media over poorly-targeted local media. George and Waldfogel (2003) shows substitutability of local and national media in the case of the New York Times, but the topic has not been comprehensively studied.
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mographic composition of local communities and online behavior also contributes to a now substantial literature on geography and the internet, initiated by Sinai and Waldfogel (2004) and continued with, for example, Agrawal et al. (2015). Our work also contributes to research in marketing that studies the extent to which social activity acts as a complement or substitute to other activities, for example as in Seiler et al. (2017) and Zhang et al. (2017). We emphasize at this stage that our work relies on broad classifications of race rooted in the census definitions of AfricanAmerican/black and non-Hispanic white populations. Like other studies, our aim is not to study race per se. We study consumption differences across race because of well-documented evidence that media preferences differ by race in measurable ways (e.g., George and Waldfogel, 2003; Waldfogel, 2003) and because of extensive evidence of that race plays a substantial role in peer groups and local social networks (e.g., Bertrand et al., 20 0 0; Aizer and Currie, 2004). While individuals with different racial background may prefer different news topics due to a complex mix of socioeconomic, ethnic and cultural factors, race captures this mix of factors that together constitute a distinct set of preferences of interest in research, policy and practice. Population by race thus offers a clear and practical measure of shared tastes in a market that links our results to policy and the literature. For economy of language we refer throughout the paper to black and white populations and individuals, recognizing that these are simplified, shorthand references to complex, culturally-based classifications. The paper proceeds as follows. Sections 2 and 3 describe our working data and empirical approach. Section 4 reports results on the demand for online news, demonstrating that observed patterns of preference externalities cannot be explained solely by supply incentives. Section 5 shows that community composition affects both the size and utilization of users’ social networks. Section 6 concludes the paper. 2. Data The first goal of our empirical work is to show that preference externalities operate at both the local and national level in online news markets, a pattern that cannot be explained solely by supplyside mechanisms. The second goal is to offer evidence that social networks play a role in explaining observed consumption patterns.
3
MSA population from the 2010 decennial census. The sample includes a small number of household demographics: race, age of oldest household member, income categories, and household size. 2.2. Social network activity data In studying the relationship between local group populations and social network activity we construct two samples. The first is average monthly online visits to Facebook and Twitter using the Comscore sample described above. The second is total “tweets” from a sample of sample of 9,065 white and 2,414 black users in 1,846 census places of the social network Twitter. Our measure of social network size is the number of individuals followed (“friends”) by each user in our Twitter sample. The working data are constructed as follows. The starting point for our sample is the dataset used in Petrovic´ et al. (2012). This data reports identification codes for approximately 51,879,0 0 0 tweets obtained from the Twitter Streaming API from June 30 to August 15, 2011. We extracted detailed user information for a 15% random sample using the Twitter REST API. We follow the literature to determine user geography (Mislove et al., 2012; Takhteyev et al., 2012) at the local level, making use of self-reported location in the form of “Place, State” in the United States to match with 2010 decennial census places. The resulting intermediate sample includes 322,215 users located in 10,709 places. We classify race based on profile photos. To construct the sample, we obtained pictures associated with 20,0 0 0 randomly selected user accounts. We use the Amazon Mechanical Turk (AMT) service to remove institutional profiles and code race. AMT workers were asked to classify pictures according to the categories “African American/Black”, “Asian”, “Caucasian/White”, “Non-Human” (pictures of pets, corporate logos, etc.), “Other” (pictures showing more than one person, or not showing enough detail for classification), and “Error” (no picture showing because of technical issues). Instructions explained that the racial categories reflect a social definition of race in the US and are not an attempt to define race biologically, anthropologically, or genetically. The average time taken to classify a picture was 26.4 seconds. The majority of pictures were classified as “Caucasian/White”, a total of 9,078 users or 46%. A total of 2,416 or 14% were classified as “African American/Black.”6 We restrict our sample to these accounts, producing a final sample of 11,479 unique users in 1,846 places.
2.1. News consumption data For our analysis of online news consumption, we study visits to news outlets using the Comscore Web Behavior Database. Our working sample records average monthly visits to local and nonlocal news outlets by 24,487 white and 11,510 black households across 321 Metropolitan Statistical Ares (MSA’s) in 2011. Comscore data record the complete browsing history of a nationally-representative sample of about 50,0 0 0 households in 2011. To form our working sample, we first designate each browsing instance in the database as a visit and aggregate to the household-domain-month. We identify news domains from the set defined by Burrelle’s Media Directory, Bulldog Reporter’s MediaPro Directory, the Newspaper Association of America website and Google News following the procedure in George and Hogendorn (2019). News visits are classified as local to a user if the home MSA for a local media outlet matches the home market of the individual. After classification, we further aggregate local and non-local visits to the household level. For our news analysis we define markets as MSA’s to match the literature on preference externalities, which treats MSA’s as the relevant market for major news outlets. With this definition, we exclude users residing outside of MSA’s. We also exclude users who never visited a social network or news site in 2011. We use
2.3. Sample statistics Table 1 summarizes the Comscore sample, covering both news and social media visits. The top portion of the table summarizes data for white households, the middle portion for black households, and the lower portion for the full sample. News visits average 13.1 per month overall, 1.4 of which are visits to a site local to the user. White news consumption is somewhat higher than consumption among blacks, at 15.0 versus 9.0 news visits overall and 1.8 versus 0.6 local visits. Users make an average of 21.5 visits per month to the online social media sites Facebook and Twitter, with visits somewhat higher among white households than black households (22.0 vs. 20.2 visits per month.) Black individuals comprise about one third of the sample. Table 2 summarizes the working Twitter data. As above, the top panel summarizes network activity among white users, the middle panel summarizes network activity among black users, and 6 The remaining categories were 20% “Not Human,” 15% “Other,” 4% Asian and 1% “Error”. We manually checked a random sample of classifications to check for false positives and to confirm the idea that there is no reason to expect a bias towards any of the racial categories regarding false negative classification errors in the “NonHuman” and “Other” categories.
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L.M. George and C. Peukert / Information Economics and Policy xxx (xxxx) xxx Table 1 Comscore sample statistics. Mean
S.D.
5%
95%
White (N=24,487) Social Media Visits News Visits Local News Visits Non-Local News Visits High Income HH HH Size HH Age Black Household ZipPop Black (1,000) ZipPop White (1,000) MSAPop Black (M) MSAPop White (M)
22.0 15.0 1.8 13.2 0.2 3.1 7.0 0.0 3.4 20.9 0.4 1.3
33.9 29.8 8.4 26.2 0.4 1.5 2.8 0.0 6.9 12.2 0.5 1.2
0.2 0.3 0.0 0.3 0.0 1.0 2.0 0.0 0.0 3.4 0.0 0.1
92.3 59.8 7.1 52.5 1.0 6.0 11.0 0.0 15.1 42.4 1.6 3.7
Black (N= 11,510) Social Media Visits News Visits Local News Visits Non-Local News Visits High Income HH HH Size HH Age Black Household ZipPop Black (1,000) ZipPop White (1,000) MSAPop Black (M) MSAPop White (M)
20.2 9.0 0.6 8.4 0.1 3.4 6.1 1.0 11.1 17.6 0.6 1.6
30.7 17.0 3.1 15.8 0.3 1.6 2.8 0.0 14.3 11.9 0.6 1.3
0.3 0.3 0.0 0.3 0.0 1.0 1.0 1.0 0.1 1.6 0.0 0.1
83.1 33.1 2.2 31.0 1.0 6.0 11.0 1.0 40.7 38.9 2.2 3.7
Total Social Media Visits News Visits Local News Visits Non-Local News Visits High Income HH HH Size HH Age Black Household ZipPop Black (1,000) ZipPop White (1,000) MSAPop Black (M) MSAPop White (M) Observations
21.5 13.1 1.4 11.7 0.2 3.2 6.7 0.3 5.8 19.9 0.4 1.4 35,997
32.9 26.6 7.2 23.5 0.4 1.5 2.8 0.5 10.5 12.2 0.6 1.2
0.2 0.3 0.0 0.3 0.0 1.0 2.0 0.0 0.0 2.6 0.0 0.1
89.9 51.3 5.3 45.8 1.0 6.0 11.0 1.0 26.6 41.5 1.6 3.7
measure.) Users send an average of 7,231 Tweets (median 3,749). The mean network size for users is 699 (median 326). Black users in the sample generate more information on the social network than white users, averaging, 9,980 versus 6,499 Tweets (median 5,199 vs. 3,438). Higher utilization among blacks is consistent with survey data on social network use, for example Pew Research Center (2014). The average network size is larger for whites (732 vs. 577), though the median is lower (321 vs. 344). We turn now to our empirical approach. 3. Empirical approach Our empirical framework follows the literature on preferences externalities. We take a similar approach in examining both news consumption and social media activity, with refinements to more closely align with theory on peer effects, detailed below. We hypothesize that a larger black population in a market will be associated with higher per capita local news consumption among blacks. We similarly predict that a larger local white population will be associated with higher per capita local news consumption among whites. Cross effects are ambiguous, since larger group populations might contribute to higher consumption overall while shifting targeting toward the larger group. In a supplyside framework, these relationships should hold for local media, where fixed costs limit supply, but not for national media. In a demand-side framework, a larger pool of individuals with shared tastes would impact both local and national media consumption as well as social network activity. We express our hypotheses more formally as: W W Vi,k = α0 + α1 Bk + α2Wk + i,k
(1)
B B Vi,k = β0 + β1 Bk + β2Wk + i,k
(2)
W measures news consumption or other outcome variable where Vi,k
the lower panel shows activity for the combined sample. There are some highly active outliers in our sample, so we report both means and medians. (We estimate our empirical models either without outliers or in logs, which reduces the skewness in the
B is similarly for a white individual i residing in market k and Vi,k defined for a black individual. The variable Wk measures the number of whites in the market and Bk the number of blacks. Supply models of preference externalities predict that a larger black population should exert a greater influence on consumption among blacks than among whites, or β 1 > α 1 , and correspondingly that a larger white population would exert more influence on consumption among whites than among blacks, β 2 < α 2 . To test the relationship, we pool the data and estimate 1 and 2 as a single equa-
Table 2 Twitter sample statistics. Total Mean
Median
White (N=9,065) Total Tweets Friends (Sources Followed) PlacePop White (1,000) PlacePop Black (1,000)
6,499.0 732.3 273.3 119.5
3,438.0 321.0 97.2 30.2
Black (N=2,414) Total Tweets Friends (Sources Followed) PlacePop White (1,000) PlacePop Black (1,000)
9,980.8 576.7 318.4 204.6
Total Total Tweets Friends (Sources Followed) PlacePop White (1,000) PlacePop Black (1,000) Observations
7231.2 699.6 282.8 137.4 11,479
S.D.
5%
95%
9,386.5 2,695.4 403.6 218.1
193.0 43.0 6.5 0.2
22,746.0 2,000.0 1,212.8 661.8
5,199.5 344.0 114.3 93.6
14,214.1 1,118.5 440.6 243.8
212.0, 53.0 7.8 1.8
35,453.0 1,773.0 1,212.8 661.8
3749.0 326.0 103.7 36.7
10,680.1 2,450.4 412.0 226.4
199.0 44.0 6.8 0.2
25,283.0 1,995.0 1,212.8 661.8
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tion :
Vi,k = α0 + (β0 − α0 )b + α1 Bk + α2Wk + (β1 − α1 )bBk + (β2 − α2 )bWk + i,k
(3)
where b is a dummy variable for black individuals and the key hypothesis tests are embedded in the coefficients on the interaction terms (β1 − α1 ) > 0 and (β2 − α2 ) < 0. Identification in the basic model comes from variation in the sizes of the black and white population at the market (MSA) level, Bk and Wk . Importantly, group populations are in levels not shares, since absolute size is the relevant metric in markets with scale economies. To account for potential bias from unobserved factors correlated with market population and outcome measures, we include MSA fixed effects. This is the primary identification strategy in the literature. Since we work with household data, we also include household controls for household size, age and education. With market fixed effects, we cannot separately identify the α 1 and α 2 coefficients. However, the interaction coefficients showing the relative effects (β1 − α1 ) and (β2 − α2 ) necessary for our hypothesis tests are identified. We can from these terms infer whether a larger black community increases news consumption among black individuals more than for white individuals, and correspondingly whether a larger white community increases consumption among white individuals more than for black individuals. Thus the sign of the interaction terms (β1 − α1 ) and (β2 − α2 ) summarize the hypothesis tests of primary interest in this specification and we focus on these estimates in our interpretation of results. Our complete specification is:
Vi,k =α0 + (β0 − α0 )b + (β1 − α1 )bBk + (β2 − α2 )bWk + ηi Xi + γk + i,k
(4)
To account for skewness in count data, we report two versions of our specification. The first estimates Eq. (4) in levels but without outliers, with cutoffs at the 95th percentile of news consumption. The second estimates consumption in logs using the full sample. The second specification has the additional advantage of admitting a proportional demand response to larger population. (In other words, the effect of an additional white person in a community might have a smaller effect on behavior once the white population grows large.) Our social media specifications follow a similar identification approach. For our examination of visits to online social media outW and V B represent the number of vislets using Comscore data, Vi,k i,k its (or log visits) to social media outlets online. We report aggregate social media visits as our primary specification, with a breakdown of Facebook and Twitter in Appendix A. For our investigation of the link between local community composition and social networks using the Twitter sample, we similarly hypothesize that individuals in a larger community with shared preferences will supply more information to the online social network, i.e., post more messages, than individuals with a smaller local offline community. We also predict that individuals in a larger offline community with shared preferences will form larger networks, i.e. connect with more users, than individuals with a smaller local social network. We again report both count specifications without outliers and log specifications with the full sample. Our Twitter data is at the individual rather than household level, so we do not require household demographics, which we do not observe.7 We do include a control for month of entry into the Twitter sample. 7
We do not observe individual characteristics of Twitter users other than location and race. But reiterating an earlier point, race in our framework, as in other studies of preference externalities, represents a bundle of individual characteristics that collectively captures shared environment and shared tastes. Our aim is not to estimate the effect of the local black (or white) population on black (or white) net-
5
We do adjust our social media Twitter specifications in one important way to reflect the literature on local (offline) social network formation. This literature links the size of a network to the probability of meeting individuals with similar preferences in a community, which is typically a smaller geographic unit than a media market/MSA. We thus reformulate our Twitter specification to measure the black and white populations by city or town (census place) rather than metropolitan area. For the Comscore specification we use zipcodes. We use the corresponding local geography as fixed effects, with MSA results in Appendix A for comparison. In terms of Eq. 3, we hypothesize that a black person in a community with many blacks would be expected to post more messages and follow more users than a black person in a community with fewer blacks (β 1 > 0). A white person in a community with a larger white population should similarly post more tweets and follow more users than a white person in a community with fewer whites (α 2 > 0). As with our news analysis, the effect of individuals with opposite preferences is theoretically ambiguous. Additional individuals of opposite race might still increase the number of potential peers, though perhaps less than the effect of additional individuals of the same race. The potential for larger social networks thus predicts greater network activity. However, additional individuals of opposite race can increase the transaction costs of finding like-minded peers, potentially decreasing the size of the local social network and decreasing network activity. The net effect of a larger white population on social network activity among blacks is thus again an empirical question.8 We turn now to estimation of our specifications for news consumption, social media visits, social media activity and social network size. 4. Results: group populations and demand for online news Table 3 reports the effect of group populations on visits to local and non-local news outlets. The first two columns show count regressions excluding outliers, while the third and fourth column report log specifications. The pattern of coefficients in the first and third column follow the prediction of the model, with the effect of “own” group population exerting a larger influence than a larger population with different preferences. This is shown with the positive interaction term in row two and negative interaction term in row three. Increasing the black population in an MSA by 1 million increases local news visits among blacks by about 0.11. Increasing the white population in an MSA by 1 million decreases local news visits among blacks by about 0.04. These estimates are remarkably similar to 0.16 and −0.04 in George and Waldfogel (2003). However counter to the prediction of supply-side model, coefficient estimates in columns (2) and (4) also show a greater effect of larger local group populations. This result that local group populations influence national media consumption cannot be explained by the standard supply-side of preference externalities, which would predict no effect of local populations on consumption of nationallytargeted media. 5. Results: group populations and social network activity With evidence above that supplier incentives to target large groups cannot explain observed consumption patterns, we examine work activity holding individual characteristics constant, but rather to estimate the effect of more individuals with shared outlook and preferences captured by race on individual actions. Thus individual heterogeneity is subsumed in our model in group characteristics. 8 See Currarini et al. (2009) and Currarini et al. (2010) for a more thorough and nuanced theoretical treatment of how utility for common tastes and transaction costs in matching lead to equilibrium segregation in social networks.
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L.M. George and C. Peukert / Information Economics and Policy xxx (xxxx) xxx Table 3 Group populations and the demand for online news.
Black Household Black Household × MSAPop Black (M) Black Household × MSAPop White (M) Constant Mean Y N
Visits (1) Local
Visits (2) Non-Local
Log Visits (3) Local
Log Visits (4) Non-Local
−0.1352∗ ∗ (0.0138) 0.1053∗ ∗ (0.0355) −0.0402∗ ∗ (0.0139) 0.2558∗ ∗ (0.0184) 0.37 36,733
−3.0144∗ ∗ (0.3502) 2.4132∗ ∗ (0.5788) −0.8195∗ (0.3612) 7.1622∗ ∗ (0.4698) 9.33 36,733
−0.3952∗ ∗ (0.0328) 0.2627∗ ∗ (0.0651) −0.1287∗ ∗ (0.0262) −0.9623∗ ∗ (0.0554) −0.74 24,066
–.3409∗ ∗ (0.0320) 0.3170∗ ∗ (0.0615) −0.0989∗ ∗ (0.0318) 0.8097∗ ∗ (.0493) 1.34 38,959
Notes: Dependent variable in column (1) is average monthly visits to local news outlets by sample households. Dependent variable in column (2) is average monthly visits to non-local news outlets by sample households. Dependent variables in columns (3) and (4) are local and non-local visits to news outlets expressed in logs. Group populations are measured at the market (MSA) level. All specifications include MSA fixed effects and categorical controls for household income, size and age, not shown. Standard errors clustered by market: + p < 0.10, ∗ p < 0.05, ∗ ∗ p < 0.01.
Table 4 Group populations and online social networks.
Black Household Black Household × ZipPop Black (1,000) Black Household × ZipPop White (1,000) Constant Mean Y N
Social Media Visits (1)
Log Social Media Visits (2)
−0.9120 (0.6518) 1.1920∗ ∗ (0.2495) −0.6644∗ ∗ (0.2270) 11.9248∗ ∗ (0.7377) 14.69 31,157
−0.1159+ (0.0644) 0.1755∗ ∗ (0.0266) −0.0226 (0.0229) 1.3064∗ ∗ (0.0667) 1.78 33,471
Notes: Dependent variable in column (1) is average monthly visits to online social media (Facebook and Twitter) by sample households. Dependent variable in column (2) is log visits. Group populations are measured at the local community (zip) level. All specifications include zip fixed effects and categorical controls for household income, size and age, not shown, see Appendix A for full specification. Standard errors clustered by zip: + p < 0.10, ∗ p < 0.05, ∗ ∗ p < 0.01.
whether social networks might play a role in explaining preference externalities in the demand for news and information. As a first step, we consider the relationship between group population and visits to online social networks, estimating Eq. (4) with visits to Facebook and Twitter using our Comscore sample. Table 4 reports results. Coefficients follow the same pattern as in Section 4, with evidence that a larger number of individuals with shared tastes leads to more visits to social media sites. Cross effects are weaker than in Table 3, but are negative on both specifications. Results are similar with market-level populations and fixed effects, shown in Appendix A. The results above using our Comscore sample link population and social media activity. In doing so, they suggest that group populations play a role in the demand for information. However, our Comscore sample does not allow us to study the size of social networks or the supply of information to the network. For this we turn to our Twitter sample. We first examine the relationship between local group populations and total “Tweets”, reporting results in Table 5. We then turn to the relationship between local group populations and the size of online social networks in Table 6. The first column in each table again reports results in levels without outliers (95th percentile of the dependent variable) and the second column reports log results with the full sample. Results in Table 5 show the same pattern as results with online social media activity: the number of messages posted to the social network is higher for blacks in communities with more blacks
Table 5 Local group population and Twitter activity.
Black Black × PlacePop Black (1,000) Black × PlacePop White (1,000) Entry Month Constant Mean Y N
Tweets (1)
Log Tweets (2)
283.4734∗ (137.8003) 1.6043∗ ∗ (0.3339) −0.5764∗ ∗ (0.1625) 407.5308∗ ∗ (5.3194) −247764.9526∗ ∗ (3299.9186) 5,236.37 9,872
0.0232 (0.0394) 0.0004∗ (0.0002) −0.0002+ (0.0001) 0.1027∗ ∗ (0.0009) −55.8020∗ ∗ (0.5744) 8.07 10,540
Notes: Dependent variable in column (1) is total tweets. Dependent variable in column (2) is log tweets. Group populations are measured at the local community (place) level. Standard errors clustered by community: ++ p < 0.10, ∗ p < 0.05, ∗ ∗ p < 0.01.
than for blacks with a smaller community black population, and is lower for blacks in communities with more whites. A larger local community with shared preferences increases users’ supply of information to the local network.9 9 We find similar results when we focus our analysis only on re-tweeted messages. We do not find that a larger local community with shared preferences in-
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Black Black × PlacePop Black (1,000) Black × PlacePop White (1,000) Entry Month Constant Mean Y N
7
among whites. There is also evidence of negative cross effects, with coefficients in row 3 both less than zero.
Friends (1)
Log Friends (2)
−8.9676 (12.7942) 0.0739∗ ∗ (0.0279) −0.0804∗ ∗ (0.0144) 10.7359∗ ∗ (0.5217) –6,234.6313∗ ∗ (324.0403) 431.51 9856
−0.0780∗ (0.0389) 0.0004∗ ∗ (0.0001) −0.0003∗ ∗ (0.0001) 0.0409∗ ∗ (0.0013) –19.5900∗ ∗ (0.7985) 5.79 10,532
6. Conclusions
Notes: Dependent variable in column (1) is total tweets. Dependent variable in column (2) is log tweets. Group populations are measured at the local community (place) level. Standard errors clustered by community: + p < 0.10, ∗ p < 0.05, ∗ ∗ p < 0.01.
Table 5 relates group population to information supply. We also look for evidence of whether larger local populations contribute to larger online social networks, not only more intense usage.10 Table 6 reports the relationship between group populations and the number of sources followed on Twitter, referred to as “friends”. Again, we present levels in column (1) and logs in column (2). The coefficient pattern is again repeated in this table: “own” group populations are positively related to social network size: more blacks in a locality leads blacks to form larger social networks and more whites in a locality are associated with larger social networks
We establish a relationship between the racial composition of local communities and the tendency to seek and share information on online social networks. Using race to represent group preferences, we find that a larger local black population increases online social network activity among black users relative to white users and vice versa. We further show using data from internet households that the effect of community composition is not limited to information exchange on social media, but also affects demand for news. We demonstrate that the effect operates for both local and national outlets and thus cannot be explained solely by supplier incentives to target mass tastes. Our results suggest new ways of understanding the impacts of digitization in media markets. From a supply-side perspective, reductions in fixed costs brought about by digitization should reduce the gap between majority and minority news consumption. The tendency for groups with larger social networks to more actively seek and share information offsets this effect, potentially increasing inequality in news consumption. If media firms increasingly direct resources toward content that gathers tweets, shares and “likes”, groups with a higher tendency to share may find themselves better served, and better informed. Our results highlight the continuing importance of geography in digital markets, suggesting that policies to widen broadband access and strengthen community cohesion can play a role in narrowing the readership gap. Future research could expand on our framework and study partisan media in addition to local and non-local news. Appendix A
creases the number of “followers”, which we interpret as a measure of influence. This is an interesting avenue for future research. Results available upon request. 10 We cannot observe the type of accounts a user is following, for example to distinguish between users that follow relatively many news outlets. This remains an interesting avenue for future research.
Table 7 Group populations and online social networks (supplemental specifications).
Black Household Black Household × ZipPop Black (1,000) Black Household × ZipPop White (1,000) High Income HH HH Size Constant Mean Y N
Visits (1) Local FE
Log Visits (2) Local FE
Visits (3) MSA FE
Log Visits (4) MSA FE
−0.9120 (0.6518) 1.1920∗ ∗ (0.2495) −0.6644∗ ∗ (0.2270) −0.7984∗ (0.3603) 1.5873∗ ∗ (0.0802) 11.9248∗ ∗ (0.7377) 14.69 31,157
−0.1159+ (0.0644) 0.1755∗ ∗ (0.0266) −0.0226 (0.0229) −0.1055∗ ∗ (0.0369) 0.2175∗ ∗ (0.0075) 1.3064∗ ∗ (0.0667) 1.78 33,471
−1.7454∗ ∗ (0.4794) 1.2972∗ ∗ (0.1984) −0.3328∗ (0.1665) −0.8813∗ ∗ (0.2765) 1.6185∗ ∗ (0.0802) 12.7620∗ ∗ (0.6873) 14.76 33,721
−0.1127∗ (0.0527) 0.1651∗ ∗ (0.0251) −0.0143 (0.0171) −0.1490∗ ∗ (0.0318) 0.2212∗ ∗ (0.0075) 1.3918∗ ∗ (0.0618) 1.78 35,997
Notes: Dependent variable in column (1) is average monthly online visits to online social media (Facebook and Twitter) by sample households. Dependent variable in column (2) is log visits. Group populations are measured at the MSA level. All specifications include MSA fixed effects and categorical controls for household income, size and age (not shown). Standard errors clustered by zip: + p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.
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L.M. George and C. Peukert / Information Economics and Policy xxx (xxxx) xxx Table 8 Group Populations and Online Social Networks (Facebook, Twitter).
Black Household Black Household × ZipPop Black (1,000) Black Household × ZipPop White (1,000) High Income HH HH Size Constant Mean Y N
Facebook (1) Visits
Twitter (2) Visits
Facebook (3) Log Visits
Twitter (4) Log Visit
–1.3902∗ (0.6180) 1.0491∗ ∗ (0.2342) −0.6040∗ ∗ (0.2156) −0.8476∗ (0.3454) 1.5200∗ ∗ (0.0775) 11.1785∗ ∗ (0.6871) 14.08 31,157
0.4782∗ ∗ (0.0988) 0.1429∗ ∗ (0.0399) −0.0605+ (0.0347) 0.0492 (0.0507) 0.0673∗ ∗ (0.0107) 0.7463∗ ∗ (0.1357) 0.62 31,157
−0.1461∗ (0.0642) 0.1710∗ ∗ (0.0264) −0.0205 (0.0228) −0.1050∗ ∗ (0.0369) 0.2126∗ ∗ (0.0075) 1.2653∗ ∗ (0.0662) 1.73 33,382
0.4510∗ ∗ (0.0919) 0.0667∗ (0.0330) −0.0224 (0.0317) 0.0914+ (0.0474) 0.0912∗ ∗ (0.0104) –1.0025∗ ∗ (0.0960) −0.92 15,093
Notes: Dependent variables in columns (1) and (2) are average monthly online visits to Facebook and Twitter by sample households. Dependent variables in columns (3) and (4) are log visits. Group populations are measured at the local community (zip) level. All specifications include zip fixed effects and categorical controls for household income, size and age, not shown. Standard errors clustered by zip: + p < 0.10, ∗ p < 0.05, ∗ ∗ p < 0.01.
Supplementary material Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.infoecopol.2019. 100833. References Agrawal, A., Catalini, C., Goldfarb, A., 2015. Crowdfunding: geography, social networks, and the timing of investment decisions. J. Econ. Manag. Strat. 24 (2), 253–274. Aizer, A., Currie, J., 2004. Networks or neighborhoods? correlations in the use of publicly-funded materity care in california. J. Public Econ. 88, 2573–2585. Anderson, S.P., Waldfogel, J., 2016. Preference Externalities in Media Markets. In: Anderson, S.P., Strömberg, D., Waldfogel, J. (Eds.), Handbook of Media and Economics, 1A. North-Holland, pp. 3–40. Bertrand, M., Luttmer, E.F.P., Mullainathan, S., 20 0 0. Network effects and welfare cultures. Q. J. Econ. 115 (4), 1019–1055. Currarini, S., Jackson, M.O., Pin, P., 2009. An economic model of friendship: homophily, minorities, and segregation. Econometrica 77 (4), 1003–1045. Currarini, S., Jackson, M.O., Pin, P., 2010. Identifying the roles of race-based choice and chance in high school friendship network formation. Proc. Natl. Acad. Sci. 107 (11), 4857–4861. George, L., Hogendorn, C., 2019. Local news online: aggregators, geo-Targeting and the market for local news. J. Ind. Econ.. forthcoming George, L., Waldfogel, J., 2003. Who affects whom in daily newspager markets? J. Polit. Econ. 111 (4), 765–784. Halberstam, Y., Knight, B.G., 2016. Homophily, group size, and the diffusion of political information in social networks: evidence from twitter. J. Public Econ. 143, 73–88. Jackson, M.O., 2011. An Overview of Social Networks and Economic Applications, 1. North-Holland, pp. 511–585.
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Information Economics and Policy xxx (xxxx) xxx
Contents lists available at ScienceDirect
Information Economics and Policy journal homepage: www.elsevier.com/locate/iep
Social networks and the demand for newsR Lisa M. George a,∗, Christian Peukert b,c a
Department of Economics, Hunter College and the Graduate Center, CUNY, 695 Park Ave., New York, NY 10065, USA Católica-Lisbon School of Business and Economics, Universidade Católica Portuguesa, Palma de Cima, Lisbon, Portugal c ETH Zürich, Center for Law and Economics, Haldeneggsteig 4, 8092 Zürich, Switzerland b
a r t i c l e
i n f o
Article history: Received 24 October 2017 Revised 13 September 2019 Accepted 11 October 2019 Available online xxx JEL classification: D22 L13 L82 Keywords: News Social networks Preference externalities Media Internet
a b s t r a c t Economic research has documented a robust, positive relationship between media consumption among minority individuals and the size of the minority population in local markets. The theoretical mechanism behind these “preference externaltities” has been understood to be the supply incentive to cater to large groups when fixed costs or other scale economies limit the number of viable products in a local market. We demonstrate that the supply-side mechanism is incomplete: the relationship holds not just for local but also national news outlets. We extend the concept of preference externalities to the demand side, establishing a relationship between the racial composition of local communities and the tendency to seek and share information on online social networks. Using data from a sample of 35,997 internet households and a sample of 11,479 Twitter users, we show that a larger local black population is associated with both larger network size and higher utilization for individual black Twitter users relative to white Twitter users and vice versa. Our results suggest that digitization can exacerbate inequality in news consumption, but also that policies to widen broadband access might narrow the gap.
1. Introduction Economic research documents a robust relationship between the demographic composition of media markets and minority media consumption. Metropolitan areas with a larger AfricanAmerican population, for example, see higher per capita newspaper readership among black individuals than markets with a smaller black population, all else equal. The relationship has been documented across racial and ethnic minorities in the context of news, music and television. The relationship between the size of a minority population and media consumption has been understood to arise from supplier incentives to target large groups. These incentives arise when fixed production costs or other scale economics limit the number of differentiated products a market can support. Empirical evidence sup-
R This paper was written with financial support from the Research Foundation of the City University of New York, and FCT – Portuguese Foundation of Science and Technology for the project UID/GES/00407/2013. We thank conference and seminar participants at the University of Zurich, the Searle Center on Law, Regulation and Economic Growth, the Media Economics Workshop, and Microsoft Research for helpful comments. ∗ Corresponding author. E-mail address: [email protected] (L.M. George).
© 2019 Elsevier B.V. All rights reserved.
ports the supply-side mechanism: markets with a larger AfricanAmerican population offer more black-targeted radio and newspaper content, while markets with more Hispanics provide more Spanish language television news programs.1 Yet despite results linking group size to the supply of targeted media, other evidence suggests that supply-side explanations for what have come to be known as “preference externalities” remain incomplete. In news markets, for example, a gap in consumption between minority and majority readers has persisted into the digital era despite massive reductions in fixed costs and proliferation of targeted outlets. The well known Pew Research Center surveys show that the gap in newspaper readership between AfricanAmerican and white readers has remained stable at about 5–6% between 1999 and 2016.2 At the same time, the economics literature has amassed evidence that local social networks affect a wide range of individual decisions in education, trade and public
1 The chapter on preference externalities in the Handbook of Media Economics provides a full theoretical treatment (Anderson and Waldfogel, 2016). See George and Waldfogel (2003) for empirical evidence in daily newspaper markets. See Waldfogel (2003) for evidence in radio, and Oberholzer-Gee and Waldfogel (2009); Wang and Waterman (2011) for results in television. 2 Details and annual data available at http://www.journalism.org/ media-indicators/newspapers-daily-readership-by-ethnic-group.
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choice.3 Since communication lies at the heart of news markets, it seems reasonably that larger peer populations might increase the tendency to seek, and to share, information. Social networks might thus deepen, or offset, the relationship between group size and media consumption that arise from targeted entry and help explain the persistence of a “digital divide” in news consumption. This paper examines the relationship between community composition, online social networks and the demand for news. We have two goals. The first is to demonstrate that patterns of minority news consumption cannot be explained solely by supplier incentives to target large groups. We do this by showing that per capita consumption of national news online among black households increases with the local black population, contradicting theoretical predictions of the strict supply-driven model. The second goal is to provide evidence that social networks can explain consumption patterns observed in the data but unexplained by the standard model. We do this by linking local community composition with social network size and utilization. Taken together, our results indicate a broader scope for individuals with shared preferences to influence each other through media markets than previously recognized. Uncovering the mechanism behind consumption inequality is important for understanding the effects of digitization. Supply-side models of preference externalities are grounded in a spatial framework where fixed production costs limit entry, so they naturally apply in local settings with few products. But if social networks influence the demand for information, then minority groups can see lower consumption of any targeted media, not solely local outlets. In other words, the important determinant of consumption inequality would not be the presence of local product markets, but rather the presence of local social networks. Moreover, in digital markets, firms increasingly steer resources to content most readily shared, forwarded and “liked.” If larger groups have a greater tendency to share information, then even when fixed costs are very low suppliers will face a differential incentive to serve the majority at the expense of minority preferences. Simply put, if the tendency to share information depends on local social networks, groups with a higher tendency to share will find themselves better served and better informed. Distinguishing the role of supply-side and demand-side factors in consumption inequality is also important for policy. Diversity and localism are founding principles motivating policies of the Federal Communications Commission. Supply-side factors in consumption inequality imply under-provision of targeted media, ameliorated with the minority ownership rules or production supports. Demand-side mechanisms suggest social foundations for under-consumption, best tackled with policies to increase broadband access, digital literacy, and community cohesion. Policies aimed at strengthening local social networks are now advocated by economists for improving a broad range of social outcomes (Jackson, 2011).4 Our empirical approach for the two goals is as follows. We first establish that the relationship between local minority population and local news consumption documented in the literature holds in online news markets. Closely following the empirical strategy in the literature, we show using a sample of 35,997 internet households that a larger local black population is associated with more online news visits among black households relative to white households. A larger white population is similarly associated with fewer news visits among blacks relative to whites. We then extend the analysis to national news outlets. With the same sam3 See Jackson (2011) for a comprehensive overview of social network applications in economics. 4 Note that our paper also relates to the large literature in sociology on homophily in social networks, see McPherson et al. (2001).
ple of internet households, we show that a larger local black population is associated with higher online consumption of national news among blacks relative to whites. The standard supply-side mechanism cannot explain these results, predicting instead that national media targeted at the median consumer should not be positively correlated with local minority populations.5 After showing that the supply-side explanation of preference externalities is incomplete, we look for evidence of a demand-side mechanism. Following the literature on social networks, we hypothesize that larger communities with shared tastes lead to larger social networks and more information sharing. We study this in the context of social media, asking whether a larger minority population is associated with more intense social media use. We offer two sets of evidence. Using our internet sample, we first show that a larger minority population in a locality is associated with more frequent visits to the social media outlets Facebook and Twitter. The result suggests that local populations affect social network use overall, but does not allow us to measure social network size or distinguish information sharing from more passive consumption. To study this we assemble a unique sample of 11,479 Twitter users coded for race. We find that a larger local black population is associated with a larger number of sources followed and a higher number of messages posted. A larger local white population is similarly associated with a larger number of sources followed and higher network use. Cross effects, the effect of a larger white population on a black users and vice versa, tend to reduce network size and participation, especially for minority users. We conclude that a larger local population with shared preferences increases the tendency to seek, and to share, information on online social networks. Our identification strategy for the news analysis closely follows the industrial organization literature on preference externalities in using independent variation in the minority and majority populations at the MSA level to capture shared tastes for media products (Waldfogel, 2003; George and Waldfogel, 2003). Unlike some earlier studies of preference externalities, we measure behavior with household data and thus can control for observable household characteristics. In studying social media, we follow the literature on peer effects and identify results from variation in community composition at a more localized geography (zip code or census place) (Bertrand et al., 20 0 0; Aizer and Currie, 2004). Our approach effectively compares social network size and activity among minority individuals in places with a larger minority population to social network size and activity among minority individuals in places with a smaller minority population. In addition to our direct contribution to the literature on preference externalities in media markets, our results also relates to a now-substantial literature on social network communications. Perhaps most closely related to our study are List and Price (2009) and Zhang and Zhu (2011), which consider the relationship between group size and the incentives to contribute to public goods. Halberstam and Knight (2016) investigate political communications in social networks, linking group size to majority and minority political interests. Our results are also relevant to the growing literature on the relationship between online social media and news readership (e.g. Sismeiro and Mahmood, 2018), online social media and political participation (e.g. Petrova et al., 2017), and the effects of user-generated content in news media (e.g. Yildirim et al., 2013). Our focus on the link between the de-
5 The simplest formulation would predict no correlation. However a negative correlation could arise if national news and local news are substitutes, so that minorities in markets with a low minority population would be more likely to choose national media over poorly-targeted local media. George and Waldfogel (2003) shows substitutability of local and national media in the case of the New York Times, but the topic has not been comprehensively studied.
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mographic composition of local communities and online behavior also contributes to a now substantial literature on geography and the internet, initiated by Sinai and Waldfogel (2004) and continued with, for example, Agrawal et al. (2015). Our work also contributes to research in marketing that studies the extent to which social activity acts as a complement or substitute to other activities, for example as in Seiler et al. (2017) and Zhang et al. (2017). We emphasize at this stage that our work relies on broad classifications of race rooted in the census definitions of AfricanAmerican/black and non-Hispanic white populations. Like other studies, our aim is not to study race per se. We study consumption differences across race because of well-documented evidence that media preferences differ by race in measurable ways (e.g., George and Waldfogel, 2003; Waldfogel, 2003) and because of extensive evidence of that race plays a substantial role in peer groups and local social networks (e.g., Bertrand et al., 20 0 0; Aizer and Currie, 2004). While individuals with different racial background may prefer different news topics due to a complex mix of socioeconomic, ethnic and cultural factors, race captures this mix of factors that together constitute a distinct set of preferences of interest in research, policy and practice. Population by race thus offers a clear and practical measure of shared tastes in a market that links our results to policy and the literature. For economy of language we refer throughout the paper to black and white populations and individuals, recognizing that these are simplified, shorthand references to complex, culturally-based classifications. The paper proceeds as follows. Sections 2 and 3 describe our working data and empirical approach. Section 4 reports results on the demand for online news, demonstrating that observed patterns of preference externalities cannot be explained solely by supply incentives. Section 5 shows that community composition affects both the size and utilization of users’ social networks. Section 6 concludes the paper. 2. Data The first goal of our empirical work is to show that preference externalities operate at both the local and national level in online news markets, a pattern that cannot be explained solely by supplyside mechanisms. The second goal is to offer evidence that social networks play a role in explaining observed consumption patterns.
3
MSA population from the 2010 decennial census. The sample includes a small number of household demographics: race, age of oldest household member, income categories, and household size. 2.2. Social network activity data In studying the relationship between local group populations and social network activity we construct two samples. The first is average monthly online visits to Facebook and Twitter using the Comscore sample described above. The second is total “tweets” from a sample of sample of 9,065 white and 2,414 black users in 1,846 census places of the social network Twitter. Our measure of social network size is the number of individuals followed (“friends”) by each user in our Twitter sample. The working data are constructed as follows. The starting point for our sample is the dataset used in Petrovic´ et al. (2012). This data reports identification codes for approximately 51,879,0 0 0 tweets obtained from the Twitter Streaming API from June 30 to August 15, 2011. We extracted detailed user information for a 15% random sample using the Twitter REST API. We follow the literature to determine user geography (Mislove et al., 2012; Takhteyev et al., 2012) at the local level, making use of self-reported location in the form of “Place, State” in the United States to match with 2010 decennial census places. The resulting intermediate sample includes 322,215 users located in 10,709 places. We classify race based on profile photos. To construct the sample, we obtained pictures associated with 20,0 0 0 randomly selected user accounts. We use the Amazon Mechanical Turk (AMT) service to remove institutional profiles and code race. AMT workers were asked to classify pictures according to the categories “African American/Black”, “Asian”, “Caucasian/White”, “Non-Human” (pictures of pets, corporate logos, etc.), “Other” (pictures showing more than one person, or not showing enough detail for classification), and “Error” (no picture showing because of technical issues). Instructions explained that the racial categories reflect a social definition of race in the US and are not an attempt to define race biologically, anthropologically, or genetically. The average time taken to classify a picture was 26.4 seconds. The majority of pictures were classified as “Caucasian/White”, a total of 9,078 users or 46%. A total of 2,416 or 14% were classified as “African American/Black.”6 We restrict our sample to these accounts, producing a final sample of 11,479 unique users in 1,846 places.
2.1. News consumption data For our analysis of online news consumption, we study visits to news outlets using the Comscore Web Behavior Database. Our working sample records average monthly visits to local and nonlocal news outlets by 24,487 white and 11,510 black households across 321 Metropolitan Statistical Ares (MSA’s) in 2011. Comscore data record the complete browsing history of a nationally-representative sample of about 50,0 0 0 households in 2011. To form our working sample, we first designate each browsing instance in the database as a visit and aggregate to the household-domain-month. We identify news domains from the set defined by Burrelle’s Media Directory, Bulldog Reporter’s MediaPro Directory, the Newspaper Association of America website and Google News following the procedure in George and Hogendorn (2019). News visits are classified as local to a user if the home MSA for a local media outlet matches the home market of the individual. After classification, we further aggregate local and non-local visits to the household level. For our news analysis we define markets as MSA’s to match the literature on preference externalities, which treats MSA’s as the relevant market for major news outlets. With this definition, we exclude users residing outside of MSA’s. We also exclude users who never visited a social network or news site in 2011. We use
2.3. Sample statistics Table 1 summarizes the Comscore sample, covering both news and social media visits. The top portion of the table summarizes data for white households, the middle portion for black households, and the lower portion for the full sample. News visits average 13.1 per month overall, 1.4 of which are visits to a site local to the user. White news consumption is somewhat higher than consumption among blacks, at 15.0 versus 9.0 news visits overall and 1.8 versus 0.6 local visits. Users make an average of 21.5 visits per month to the online social media sites Facebook and Twitter, with visits somewhat higher among white households than black households (22.0 vs. 20.2 visits per month.) Black individuals comprise about one third of the sample. Table 2 summarizes the working Twitter data. As above, the top panel summarizes network activity among white users, the middle panel summarizes network activity among black users, and 6 The remaining categories were 20% “Not Human,” 15% “Other,” 4% Asian and 1% “Error”. We manually checked a random sample of classifications to check for false positives and to confirm the idea that there is no reason to expect a bias towards any of the racial categories regarding false negative classification errors in the “NonHuman” and “Other” categories.
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L.M. George and C. Peukert / Information Economics and Policy xxx (xxxx) xxx Table 1 Comscore sample statistics. Mean
S.D.
5%
95%
White (N=24,487) Social Media Visits News Visits Local News Visits Non-Local News Visits High Income HH HH Size HH Age Black Household ZipPop Black (1,000) ZipPop White (1,000) MSAPop Black (M) MSAPop White (M)
22.0 15.0 1.8 13.2 0.2 3.1 7.0 0.0 3.4 20.9 0.4 1.3
33.9 29.8 8.4 26.2 0.4 1.5 2.8 0.0 6.9 12.2 0.5 1.2
0.2 0.3 0.0 0.3 0.0 1.0 2.0 0.0 0.0 3.4 0.0 0.1
92.3 59.8 7.1 52.5 1.0 6.0 11.0 0.0 15.1 42.4 1.6 3.7
Black (N= 11,510) Social Media Visits News Visits Local News Visits Non-Local News Visits High Income HH HH Size HH Age Black Household ZipPop Black (1,000) ZipPop White (1,000) MSAPop Black (M) MSAPop White (M)
20.2 9.0 0.6 8.4 0.1 3.4 6.1 1.0 11.1 17.6 0.6 1.6
30.7 17.0 3.1 15.8 0.3 1.6 2.8 0.0 14.3 11.9 0.6 1.3
0.3 0.3 0.0 0.3 0.0 1.0 1.0 1.0 0.1 1.6 0.0 0.1
83.1 33.1 2.2 31.0 1.0 6.0 11.0 1.0 40.7 38.9 2.2 3.7
Total Social Media Visits News Visits Local News Visits Non-Local News Visits High Income HH HH Size HH Age Black Household ZipPop Black (1,000) ZipPop White (1,000) MSAPop Black (M) MSAPop White (M) Observations
21.5 13.1 1.4 11.7 0.2 3.2 6.7 0.3 5.8 19.9 0.4 1.4 35,997
32.9 26.6 7.2 23.5 0.4 1.5 2.8 0.5 10.5 12.2 0.6 1.2
0.2 0.3 0.0 0.3 0.0 1.0 2.0 0.0 0.0 2.6 0.0 0.1
89.9 51.3 5.3 45.8 1.0 6.0 11.0 1.0 26.6 41.5 1.6 3.7
measure.) Users send an average of 7,231 Tweets (median 3,749). The mean network size for users is 699 (median 326). Black users in the sample generate more information on the social network than white users, averaging, 9,980 versus 6,499 Tweets (median 5,199 vs. 3,438). Higher utilization among blacks is consistent with survey data on social network use, for example Pew Research Center (2014). The average network size is larger for whites (732 vs. 577), though the median is lower (321 vs. 344). We turn now to our empirical approach. 3. Empirical approach Our empirical framework follows the literature on preferences externalities. We take a similar approach in examining both news consumption and social media activity, with refinements to more closely align with theory on peer effects, detailed below. We hypothesize that a larger black population in a market will be associated with higher per capita local news consumption among blacks. We similarly predict that a larger local white population will be associated with higher per capita local news consumption among whites. Cross effects are ambiguous, since larger group populations might contribute to higher consumption overall while shifting targeting toward the larger group. In a supplyside framework, these relationships should hold for local media, where fixed costs limit supply, but not for national media. In a demand-side framework, a larger pool of individuals with shared tastes would impact both local and national media consumption as well as social network activity. We express our hypotheses more formally as: W W Vi,k = α0 + α1 Bk + α2Wk + i,k
(1)
B B Vi,k = β0 + β1 Bk + β2Wk + i,k
(2)
W measures news consumption or other outcome variable where Vi,k
the lower panel shows activity for the combined sample. There are some highly active outliers in our sample, so we report both means and medians. (We estimate our empirical models either without outliers or in logs, which reduces the skewness in the
B is similarly for a white individual i residing in market k and Vi,k defined for a black individual. The variable Wk measures the number of whites in the market and Bk the number of blacks. Supply models of preference externalities predict that a larger black population should exert a greater influence on consumption among blacks than among whites, or β 1 > α 1 , and correspondingly that a larger white population would exert more influence on consumption among whites than among blacks, β 2 < α 2 . To test the relationship, we pool the data and estimate 1 and 2 as a single equa-
Table 2 Twitter sample statistics. Total Mean
Median
White (N=9,065) Total Tweets Friends (Sources Followed) PlacePop White (1,000) PlacePop Black (1,000)
6,499.0 732.3 273.3 119.5
3,438.0 321.0 97.2 30.2
Black (N=2,414) Total Tweets Friends (Sources Followed) PlacePop White (1,000) PlacePop Black (1,000)
9,980.8 576.7 318.4 204.6
Total Total Tweets Friends (Sources Followed) PlacePop White (1,000) PlacePop Black (1,000) Observations
7231.2 699.6 282.8 137.4 11,479
S.D.
5%
95%
9,386.5 2,695.4 403.6 218.1
193.0 43.0 6.5 0.2
22,746.0 2,000.0 1,212.8 661.8
5,199.5 344.0 114.3 93.6
14,214.1 1,118.5 440.6 243.8
212.0, 53.0 7.8 1.8
35,453.0 1,773.0 1,212.8 661.8
3749.0 326.0 103.7 36.7
10,680.1 2,450.4 412.0 226.4
199.0 44.0 6.8 0.2
25,283.0 1,995.0 1,212.8 661.8
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tion :
Vi,k = α0 + (β0 − α0 )b + α1 Bk + α2Wk + (β1 − α1 )bBk + (β2 − α2 )bWk + i,k
(3)
where b is a dummy variable for black individuals and the key hypothesis tests are embedded in the coefficients on the interaction terms (β1 − α1 ) > 0 and (β2 − α2 ) < 0. Identification in the basic model comes from variation in the sizes of the black and white population at the market (MSA) level, Bk and Wk . Importantly, group populations are in levels not shares, since absolute size is the relevant metric in markets with scale economies. To account for potential bias from unobserved factors correlated with market population and outcome measures, we include MSA fixed effects. This is the primary identification strategy in the literature. Since we work with household data, we also include household controls for household size, age and education. With market fixed effects, we cannot separately identify the α 1 and α 2 coefficients. However, the interaction coefficients showing the relative effects (β1 − α1 ) and (β2 − α2 ) necessary for our hypothesis tests are identified. We can from these terms infer whether a larger black community increases news consumption among black individuals more than for white individuals, and correspondingly whether a larger white community increases consumption among white individuals more than for black individuals. Thus the sign of the interaction terms (β1 − α1 ) and (β2 − α2 ) summarize the hypothesis tests of primary interest in this specification and we focus on these estimates in our interpretation of results. Our complete specification is:
Vi,k =α0 + (β0 − α0 )b + (β1 − α1 )bBk + (β2 − α2 )bWk + ηi Xi + γk + i,k
(4)
To account for skewness in count data, we report two versions of our specification. The first estimates Eq. (4) in levels but without outliers, with cutoffs at the 95th percentile of news consumption. The second estimates consumption in logs using the full sample. The second specification has the additional advantage of admitting a proportional demand response to larger population. (In other words, the effect of an additional white person in a community might have a smaller effect on behavior once the white population grows large.) Our social media specifications follow a similar identification approach. For our examination of visits to online social media outW and V B represent the number of vislets using Comscore data, Vi,k i,k its (or log visits) to social media outlets online. We report aggregate social media visits as our primary specification, with a breakdown of Facebook and Twitter in Appendix A. For our investigation of the link between local community composition and social networks using the Twitter sample, we similarly hypothesize that individuals in a larger community with shared preferences will supply more information to the online social network, i.e., post more messages, than individuals with a smaller local offline community. We also predict that individuals in a larger offline community with shared preferences will form larger networks, i.e. connect with more users, than individuals with a smaller local social network. We again report both count specifications without outliers and log specifications with the full sample. Our Twitter data is at the individual rather than household level, so we do not require household demographics, which we do not observe.7 We do include a control for month of entry into the Twitter sample. 7
We do not observe individual characteristics of Twitter users other than location and race. But reiterating an earlier point, race in our framework, as in other studies of preference externalities, represents a bundle of individual characteristics that collectively captures shared environment and shared tastes. Our aim is not to estimate the effect of the local black (or white) population on black (or white) net-
5
We do adjust our social media Twitter specifications in one important way to reflect the literature on local (offline) social network formation. This literature links the size of a network to the probability of meeting individuals with similar preferences in a community, which is typically a smaller geographic unit than a media market/MSA. We thus reformulate our Twitter specification to measure the black and white populations by city or town (census place) rather than metropolitan area. For the Comscore specification we use zipcodes. We use the corresponding local geography as fixed effects, with MSA results in Appendix A for comparison. In terms of Eq. 3, we hypothesize that a black person in a community with many blacks would be expected to post more messages and follow more users than a black person in a community with fewer blacks (β 1 > 0). A white person in a community with a larger white population should similarly post more tweets and follow more users than a white person in a community with fewer whites (α 2 > 0). As with our news analysis, the effect of individuals with opposite preferences is theoretically ambiguous. Additional individuals of opposite race might still increase the number of potential peers, though perhaps less than the effect of additional individuals of the same race. The potential for larger social networks thus predicts greater network activity. However, additional individuals of opposite race can increase the transaction costs of finding like-minded peers, potentially decreasing the size of the local social network and decreasing network activity. The net effect of a larger white population on social network activity among blacks is thus again an empirical question.8 We turn now to estimation of our specifications for news consumption, social media visits, social media activity and social network size. 4. Results: group populations and demand for online news Table 3 reports the effect of group populations on visits to local and non-local news outlets. The first two columns show count regressions excluding outliers, while the third and fourth column report log specifications. The pattern of coefficients in the first and third column follow the prediction of the model, with the effect of “own” group population exerting a larger influence than a larger population with different preferences. This is shown with the positive interaction term in row two and negative interaction term in row three. Increasing the black population in an MSA by 1 million increases local news visits among blacks by about 0.11. Increasing the white population in an MSA by 1 million decreases local news visits among blacks by about 0.04. These estimates are remarkably similar to 0.16 and −0.04 in George and Waldfogel (2003). However counter to the prediction of supply-side model, coefficient estimates in columns (2) and (4) also show a greater effect of larger local group populations. This result that local group populations influence national media consumption cannot be explained by the standard supply-side of preference externalities, which would predict no effect of local populations on consumption of nationallytargeted media. 5. Results: group populations and social network activity With evidence above that supplier incentives to target large groups cannot explain observed consumption patterns, we examine work activity holding individual characteristics constant, but rather to estimate the effect of more individuals with shared outlook and preferences captured by race on individual actions. Thus individual heterogeneity is subsumed in our model in group characteristics. 8 See Currarini et al. (2009) and Currarini et al. (2010) for a more thorough and nuanced theoretical treatment of how utility for common tastes and transaction costs in matching lead to equilibrium segregation in social networks.
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L.M. George and C. Peukert / Information Economics and Policy xxx (xxxx) xxx Table 3 Group populations and the demand for online news.
Black Household Black Household × MSAPop Black (M) Black Household × MSAPop White (M) Constant Mean Y N
Visits (1) Local
Visits (2) Non-Local
Log Visits (3) Local
Log Visits (4) Non-Local
−0.1352∗ ∗ (0.0138) 0.1053∗ ∗ (0.0355) −0.0402∗ ∗ (0.0139) 0.2558∗ ∗ (0.0184) 0.37 36,733
−3.0144∗ ∗ (0.3502) 2.4132∗ ∗ (0.5788) −0.8195∗ (0.3612) 7.1622∗ ∗ (0.4698) 9.33 36,733
−0.3952∗ ∗ (0.0328) 0.2627∗ ∗ (0.0651) −0.1287∗ ∗ (0.0262) −0.9623∗ ∗ (0.0554) −0.74 24,066
–.3409∗ ∗ (0.0320) 0.3170∗ ∗ (0.0615) −0.0989∗ ∗ (0.0318) 0.8097∗ ∗ (.0493) 1.34 38,959
Notes: Dependent variable in column (1) is average monthly visits to local news outlets by sample households. Dependent variable in column (2) is average monthly visits to non-local news outlets by sample households. Dependent variables in columns (3) and (4) are local and non-local visits to news outlets expressed in logs. Group populations are measured at the market (MSA) level. All specifications include MSA fixed effects and categorical controls for household income, size and age, not shown. Standard errors clustered by market: + p < 0.10, ∗ p < 0.05, ∗ ∗ p < 0.01.
Table 4 Group populations and online social networks.
Black Household Black Household × ZipPop Black (1,000) Black Household × ZipPop White (1,000) Constant Mean Y N
Social Media Visits (1)
Log Social Media Visits (2)
−0.9120 (0.6518) 1.1920∗ ∗ (0.2495) −0.6644∗ ∗ (0.2270) 11.9248∗ ∗ (0.7377) 14.69 31,157
−0.1159+ (0.0644) 0.1755∗ ∗ (0.0266) −0.0226 (0.0229) 1.3064∗ ∗ (0.0667) 1.78 33,471
Notes: Dependent variable in column (1) is average monthly visits to online social media (Facebook and Twitter) by sample households. Dependent variable in column (2) is log visits. Group populations are measured at the local community (zip) level. All specifications include zip fixed effects and categorical controls for household income, size and age, not shown, see Appendix A for full specification. Standard errors clustered by zip: + p < 0.10, ∗ p < 0.05, ∗ ∗ p < 0.01.
whether social networks might play a role in explaining preference externalities in the demand for news and information. As a first step, we consider the relationship between group population and visits to online social networks, estimating Eq. (4) with visits to Facebook and Twitter using our Comscore sample. Table 4 reports results. Coefficients follow the same pattern as in Section 4, with evidence that a larger number of individuals with shared tastes leads to more visits to social media sites. Cross effects are weaker than in Table 3, but are negative on both specifications. Results are similar with market-level populations and fixed effects, shown in Appendix A. The results above using our Comscore sample link population and social media activity. In doing so, they suggest that group populations play a role in the demand for information. However, our Comscore sample does not allow us to study the size of social networks or the supply of information to the network. For this we turn to our Twitter sample. We first examine the relationship between local group populations and total “Tweets”, reporting results in Table 5. We then turn to the relationship between local group populations and the size of online social networks in Table 6. The first column in each table again reports results in levels without outliers (95th percentile of the dependent variable) and the second column reports log results with the full sample. Results in Table 5 show the same pattern as results with online social media activity: the number of messages posted to the social network is higher for blacks in communities with more blacks
Table 5 Local group population and Twitter activity.
Black Black × PlacePop Black (1,000) Black × PlacePop White (1,000) Entry Month Constant Mean Y N
Tweets (1)
Log Tweets (2)
283.4734∗ (137.8003) 1.6043∗ ∗ (0.3339) −0.5764∗ ∗ (0.1625) 407.5308∗ ∗ (5.3194) −247764.9526∗ ∗ (3299.9186) 5,236.37 9,872
0.0232 (0.0394) 0.0004∗ (0.0002) −0.0002+ (0.0001) 0.1027∗ ∗ (0.0009) −55.8020∗ ∗ (0.5744) 8.07 10,540
Notes: Dependent variable in column (1) is total tweets. Dependent variable in column (2) is log tweets. Group populations are measured at the local community (place) level. Standard errors clustered by community: ++ p < 0.10, ∗ p < 0.05, ∗ ∗ p < 0.01.
than for blacks with a smaller community black population, and is lower for blacks in communities with more whites. A larger local community with shared preferences increases users’ supply of information to the local network.9 9 We find similar results when we focus our analysis only on re-tweeted messages. We do not find that a larger local community with shared preferences in-
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L.M. George and C. Peukert / Information Economics and Policy xxx (xxxx) xxx Table 6 Local group population and social network size.
Black Black × PlacePop Black (1,000) Black × PlacePop White (1,000) Entry Month Constant Mean Y N
7
among whites. There is also evidence of negative cross effects, with coefficients in row 3 both less than zero.
Friends (1)
Log Friends (2)
−8.9676 (12.7942) 0.0739∗ ∗ (0.0279) −0.0804∗ ∗ (0.0144) 10.7359∗ ∗ (0.5217) –6,234.6313∗ ∗ (324.0403) 431.51 9856
−0.0780∗ (0.0389) 0.0004∗ ∗ (0.0001) −0.0003∗ ∗ (0.0001) 0.0409∗ ∗ (0.0013) –19.5900∗ ∗ (0.7985) 5.79 10,532
6. Conclusions
Notes: Dependent variable in column (1) is total tweets. Dependent variable in column (2) is log tweets. Group populations are measured at the local community (place) level. Standard errors clustered by community: + p < 0.10, ∗ p < 0.05, ∗ ∗ p < 0.01.
Table 5 relates group population to information supply. We also look for evidence of whether larger local populations contribute to larger online social networks, not only more intense usage.10 Table 6 reports the relationship between group populations and the number of sources followed on Twitter, referred to as “friends”. Again, we present levels in column (1) and logs in column (2). The coefficient pattern is again repeated in this table: “own” group populations are positively related to social network size: more blacks in a locality leads blacks to form larger social networks and more whites in a locality are associated with larger social networks
We establish a relationship between the racial composition of local communities and the tendency to seek and share information on online social networks. Using race to represent group preferences, we find that a larger local black population increases online social network activity among black users relative to white users and vice versa. We further show using data from internet households that the effect of community composition is not limited to information exchange on social media, but also affects demand for news. We demonstrate that the effect operates for both local and national outlets and thus cannot be explained solely by supplier incentives to target mass tastes. Our results suggest new ways of understanding the impacts of digitization in media markets. From a supply-side perspective, reductions in fixed costs brought about by digitization should reduce the gap between majority and minority news consumption. The tendency for groups with larger social networks to more actively seek and share information offsets this effect, potentially increasing inequality in news consumption. If media firms increasingly direct resources toward content that gathers tweets, shares and “likes”, groups with a higher tendency to share may find themselves better served, and better informed. Our results highlight the continuing importance of geography in digital markets, suggesting that policies to widen broadband access and strengthen community cohesion can play a role in narrowing the readership gap. Future research could expand on our framework and study partisan media in addition to local and non-local news. Appendix A
creases the number of “followers”, which we interpret as a measure of influence. This is an interesting avenue for future research. Results available upon request. 10 We cannot observe the type of accounts a user is following, for example to distinguish between users that follow relatively many news outlets. This remains an interesting avenue for future research.
Table 7 Group populations and online social networks (supplemental specifications).
Black Household Black Household × ZipPop Black (1,000) Black Household × ZipPop White (1,000) High Income HH HH Size Constant Mean Y N
Visits (1) Local FE
Log Visits (2) Local FE
Visits (3) MSA FE
Log Visits (4) MSA FE
−0.9120 (0.6518) 1.1920∗ ∗ (0.2495) −0.6644∗ ∗ (0.2270) −0.7984∗ (0.3603) 1.5873∗ ∗ (0.0802) 11.9248∗ ∗ (0.7377) 14.69 31,157
−0.1159+ (0.0644) 0.1755∗ ∗ (0.0266) −0.0226 (0.0229) −0.1055∗ ∗ (0.0369) 0.2175∗ ∗ (0.0075) 1.3064∗ ∗ (0.0667) 1.78 33,471
−1.7454∗ ∗ (0.4794) 1.2972∗ ∗ (0.1984) −0.3328∗ (0.1665) −0.8813∗ ∗ (0.2765) 1.6185∗ ∗ (0.0802) 12.7620∗ ∗ (0.6873) 14.76 33,721
−0.1127∗ (0.0527) 0.1651∗ ∗ (0.0251) −0.0143 (0.0171) −0.1490∗ ∗ (0.0318) 0.2212∗ ∗ (0.0075) 1.3918∗ ∗ (0.0618) 1.78 35,997
Notes: Dependent variable in column (1) is average monthly online visits to online social media (Facebook and Twitter) by sample households. Dependent variable in column (2) is log visits. Group populations are measured at the MSA level. All specifications include MSA fixed effects and categorical controls for household income, size and age (not shown). Standard errors clustered by zip: + p < 0.10, ∗ p < 0.05, ∗∗ p < 0.01.
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L.M. George and C. Peukert / Information Economics and Policy xxx (xxxx) xxx Table 8 Group Populations and Online Social Networks (Facebook, Twitter).
Black Household Black Household × ZipPop Black (1,000) Black Household × ZipPop White (1,000) High Income HH HH Size Constant Mean Y N
Facebook (1) Visits
Twitter (2) Visits
Facebook (3) Log Visits
Twitter (4) Log Visit
–1.3902∗ (0.6180) 1.0491∗ ∗ (0.2342) −0.6040∗ ∗ (0.2156) −0.8476∗ (0.3454) 1.5200∗ ∗ (0.0775) 11.1785∗ ∗ (0.6871) 14.08 31,157
0.4782∗ ∗ (0.0988) 0.1429∗ ∗ (0.0399) −0.0605+ (0.0347) 0.0492 (0.0507) 0.0673∗ ∗ (0.0107) 0.7463∗ ∗ (0.1357) 0.62 31,157
−0.1461∗ (0.0642) 0.1710∗ ∗ (0.0264) −0.0205 (0.0228) −0.1050∗ ∗ (0.0369) 0.2126∗ ∗ (0.0075) 1.2653∗ ∗ (0.0662) 1.73 33,382
0.4510∗ ∗ (0.0919) 0.0667∗ (0.0330) −0.0224 (0.0317) 0.0914+ (0.0474) 0.0912∗ ∗ (0.0104) –1.0025∗ ∗ (0.0960) −0.92 15,093
Notes: Dependent variables in columns (1) and (2) are average monthly online visits to Facebook and Twitter by sample households. Dependent variables in columns (3) and (4) are log visits. Group populations are measured at the local community (zip) level. All specifications include zip fixed effects and categorical controls for household income, size and age, not shown. Standard errors clustered by zip: + p < 0.10, ∗ p < 0.05, ∗ ∗ p < 0.01.
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Please cite this article as: L.M. George and C. Peukert, Social networks and the demand for news, Information Economics and Policy, https://doi.org/10.1016/j.infoecopol.2019.100833