The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
The Market for News
Open accessAbstract
We investigate the market for news under two assumptions: that readers hold beliefs which they like to see confirmed, and that newspapers can slant stories toward these beliefs. We show that, on the topics where readers share common beliefs, one should not expect accuracy even from competitive media: competition results in lower prices, but common slanting toward reader biases. On topics where reader beliefs diverge (such as politically divisive issues), however, newspapers segment the market and slant toward extreme positions. Yet in the aggregate, a reader with access to all news sources could get an unbiased perspective. Generally speaking, reader heterogeneity is more important for accuracy in media than competition per se.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
REMEMBERING: A STUDY IN EXPERIMENTAL AND SOCIAL PSYCHOLOGY
Bartlett, F. C., & Burt, C. (1933) · British Journal of Educational Psychology · doi:10.1111/j.2044-8279.1933.tb02913.x · Cited by 7,475
Lord, C. G., Ross, L., & Lepper, M. R. (1979) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.37.11.2098 · Cited by 4,583
Cited by
Social Media and Fake News in the 2016 Election
Allcott, H., & Gentzkow, M. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.211 · Cited by 6,767
Iyengar, S., Sood, G., & Lelkes, Y. (2012) · Public Opinion Quarterly · doi:10.1093/poq/nfs038 · Cited by 2,996
Filter Bubbles, Echo Chambers, and Online News Consumption
Flaxman, S., Goel, S., & Rao, J. M. (2016) · Public Opinion Quarterly · doi:10.1093/poq/nfw006 · Cited by 1,992
Red Media, Blue Media: Evidence of Ideological Selectivity in Media Use
Iyengar, S., & Hahn, K. S. (2009) · Journal of Communication · doi:10.1111/j.1460-2466.2008.01402.x · Cited by 1,940
The Fox News Effect: Media Bias and Voting
DellaVigna, S., & Kaplan, E. (2007) · The Quarterly Journal of Economics · doi:10.1162/qjec.122.3.1187 · Cited by 1,602
A New Era of Minimal Effects? The Changing Foundations of Political Communication
Bennett, W. L., & Iyengar, S. (2008) · Journal of Communication · doi:10.1111/j.1460-2466.2008.00410.x · Cited by 1,558
Ideological Segregation Online and Offline *
Gentzkow, M., & Shapiro, J. M. (2011) · The Quarterly Journal of Economics · doi:10.1093/qje/qjr044 · Cited by 980
Myopic Voters and Natural Disaster Policy
HEALY, A., & MALHOTRA, N. (2009) · American Political Science Review · doi:10.1017/s0003055409990104 · Cited by 931
Bias in Cable News: Persuasion and Polarization
Martin, G. J., & Yurukoglu, A. (2017) · American Economic Review · doi:10.1257/aer.20160812 · Cited by 666
Why Do Partisan Media Polarize Viewers?
Levendusky, M. S. (2013) · American Journal of Political Science · doi:10.1111/ajps.12008 · Cited by 587
The Effect of Newspaper Entry and Exit on Electoral Politics
Gentzkow, M., Shapiro, J. M., & Sinkinson, M. (2011) · American Economic Review · doi:10.1257/aer.101.7.2980 · Cited by 497
Corporate social responsibility and media coverage
Cahan, S. F., Chen, C., Chen, L., & Nguyen, N. H. (2015) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2015.07.004 · Cited by 365
Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis
Budak, C., Goel, S., & Rao, J. M. (2016) · Public Opinion Quarterly · doi:10.1093/poq/nfw007 · Cited by 336
Local News and National Politics
MARTIN, G. J., & McCRAIN, J. (2019) · American Political Science Review · doi:10.1017/s0003055418000965 · Cited by 331
The Impact of Media Censorship: 1984 or Brave New World?
Chen, Y., & Yang, D. Y. (2019) · American Economic Review · doi:10.1257/aer.20171765 · Cited by 250
Partisan Media Exposure and Attitudes Toward the Opposition
Levendusky, M. (2013) · Political Communication · doi:10.1080/10584609.2012.737435 · Cited by 236
Strategic Extremism: Why Republicans and Democrats Divide on Religious Values
Glaeser, E. L., Ponzetto, G. A. M., & Shapiro, J. M. (2005) · The Quarterly Journal of Economics · doi:10.1162/003355305775097533 · Cited by 234
News Media Channels: Complements or Substitutes? Evidence from Mobile Phone Usage
Xu, J., Forman, C., Kim, J. B., & Van Ittersum, K. (2014) · Journal of Marketing · doi:10.1509/jm.13.0198 · Cited by 180
Feldman, L., Hart, P. S., & Milosevic, T. (2017) · Public Understanding of Science · doi:10.1177/0963662515595348 · Cited by 172
The Consequences of Broader Media Choice: Evidence from the Expansion of Fox News
Hopkins, D. J., & Ladd, J. M. (2014) · Quarterly Journal of Political Science · doi:10.1561/100.00012099 · Cited by 162
Ardèvol-Abreu, A., & Gil de Zúñiga, H. (2017) · Journalism & Mass Communication Quarterly · doi:10.1177/1077699016654684 · Cited by 153
Measuring the news and its impact on democracy
Watts, D. J., Rothschild, D. M., & Mobius, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912443118 · Cited by 121
Greenstein, S., & Zhu, F. (2012) · American Economic Review · doi:10.1257/aer.102.3.343 · Cited by 118
Opinion Dynamics with Confirmation Bias
Allahverdyan, A. E., & Galstyan, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099557 · Cited by 90
Lu, Y., & Lee, J. K. (2019) · Journalism & Mass Communication Quarterly · doi:10.1177/1077699018811295 · Cited by 86
(2012) · Cited by 39
Ranking for engagement: How social media algorithms fuel misinformation and polarization
Germano, F., Gómez, V., & Sobbrio, F. (2026) · Journal of Public Economics · doi:10.1016/j.jpubeco.2026.105589 · Cited by 14
Opinion dynamics via search engines (and other algorithmic gatekeepers)
Germano, F., & Sobbrio, F. (2020) · Journal of Public Economics · doi:10.1016/j.jpubeco.2020.104188 · Cited by 11
Few voices, strong echo: Measuring follower homogeneity of politicians’ Twitter accounts
Rusche, F. (2024) · New Media & Society · doi:10.1177/14614448221099860 · Cited by 6
Metadata
- Authors
- Mullainathan, S., & Shleifer, A.
- Venue
- American Economic Review
- Publisher
- American Economic Association
- Published
- 2005-08-01
- Volume
- 95
- Issue
- 4
- Pages
- 1031-1053
- ISSN
- 0002-8282 (ISSN-L)
- OpenAlex
- W3123401135
- Cited by
- 1,180
- References
- 2
- FWCI
- 122.26
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.