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What Drives Media Slant? Evidence From U.S. Daily Newspapers
Abstract
We construct a new index of media slant that measures the similarity of a news outlet's language to that of a congressional Republican or Democrat. We estimate a model of newspaper demand that incorporates slant explicitly, estimate the slant that would be chosen if newspapers independently maximized their own profits, and compare these profit-maximizing points with firms' actual choices. We find that readers have an economically significant preference for like-minded news. Firms respond strongly to consumer preferences, which account for roughly 20 percent of the variation in measured slant in our sample. By contrast, the identity of a newspaper's owner explains far less of the variation in slant. Copyright 2010 The Econometric Society.
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Cites
Testing for Weak Instruments in Linear IV Regression
Stock, J., & Yogo, M. (2002) · doi:10.3386/t0284 · Cited by 1,842
Extracting Policy Positions from Political Texts Using Words as Data
LAVER, M., BENOIT, K., & GARRY, J. (2003) · American Political Science Review · doi:10.1017/s0003055403000698 · Cited by 1,377
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Exposure to ideologically diverse news and opinion on Facebook
Bakshy, E., Messing, S., & Adamic, L. A. (2015) · Science · doi:10.1126/science.aaa1160 · Cited by 3,245
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Flaxman, S., Goel, S., & Rao, J. M. (2016) · Public Opinion Quarterly · doi:10.1093/poq/nfw006 · Cited by 1,992
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Prior, M. (2013) · Annual Review of Political Science · doi:10.1146/annurev-polisci-100711-135242 · Cited by 1,125
Financial Literacy, Financial Education, and Economic Outcomes
Hastings, J. S., Madrian, B. C., & Skimmyhorn, W. L. (2013) · Annual Review of Economics · doi:10.1146/annurev-economics-082312-125807 · Cited by 1,041
Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook
Lee, D., Hosanagar, K., & Nair, H. S. (2018) · Management Science · doi:10.1287/mnsc.2017.2902 · Cited by 1,069
Ideological Segregation Online and Offline *
Gentzkow, M., & Shapiro, J. M. (2011) · The Quarterly Journal of Economics · doi:10.1093/qje/qjr044 · Cited by 980
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Martin, G. J., & Yurukoglu, A. (2017) · American Economic Review · doi:10.1257/aer.20160812 · Cited by 666
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Levy, R. (2021) · American Economic Review · doi:10.1257/aer.20191777 · Cited by 624
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Young, L., & Soroka, S. (2012) · Political Communication · doi:10.1080/10584609.2012.671234 · Cited by 609
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Enikolopov, R., Petrova, M., & Zhuravskaya, E. (2011) · American Economic Review · doi:10.1257/aer.101.7.3253 · Cited by 568
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
Self-citationGentzkow, M., Shapiro, J. M., & Taddy, M. (2019) · Econometrica · doi:10.3982/ecta16566 · Cited by 419
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Self-citationA Turn Toward Avoidance? Selective Exposure to Online Political Information, 2004-2008
Garrett, R. K., Carnahan, D., & Lynch, E. K. (2013) · Political Behavior · doi:10.1007/s11109-011-9185-6 · Cited by 311
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Feldman, L., Hart, P. S., & Milosevic, T. (2017) · Public Understanding of Science · doi:10.1177/0963662515595348 · Cited by 172
Michalopoulos, S., & Xue, M. M. (2021) · The Quarterly Journal of Economics · doi:10.1093/qje/qjab003 · Cited by 165
Gentzkow, M., Shapiro, J., & Taddy, M. (2016) · doi:10.3386/w22423 · Cited by 159
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Greenstein, S., & Zhu, F. (2012) · American Economic Review · doi:10.1257/aer.102.3.343 · Cited by 118
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Kulkarni, V., Ye, J., Skiena, S., & Wang, W. Y. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1388 · Cited by 71
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Porumbescu, G. (2017) · International Public Management Journal · doi:10.1080/10967494.2016.1269859 · Cited by 37
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Germano, F., & Sobbrio, F. (2020) · Journal of Public Economics · doi:10.1016/j.jpubeco.2020.104188 · Cited by 11
Nordbrandt, M. (2022) · Frontiers in Political Science · doi:10.3389/fpos.2022.921188 · Cited by 5
Metadata
- Venue
- Econometrica
- Publisher
- The Econometric Society
- Published
- 2010
- Volume
- 78
- Issue
- 1
- Pages
- 35-71
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W2891649320
- Cited by
- 1,483
- References
- 2
- FWCI
- 127.29
- Type
- journal article
- Language
- en
- Open access
- closed
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