A Deeper Insight Into How We Studied Media Slant

Table of Links

Abstract and 1 Introduction 2. Data

3. Measuring Media Slant and 3.1. Text pre-processing and featurization

3.2. Classifying transcripts by TV source

3.3. Text similarity between newspapers and TV stations and 3.4. Topic model

4. Econometric Framework

4.1. Instrumental variables specification

4.2. Instrument first stage and validity

5. Results

5.1. Main results

5.2. Robustness checks

6. Mechanisms and Heterogeneity

6.1. Local vs. national or international news content

6.2. Cable news media slant polarizes local newspapers

7. Conclusion and References

Online Appendices

A. Data Appendix

A.1. Newspaper articles

A.2. Alternative county matching of newspapers and A.3. Filtering of the article snippets

A.4. Included prime-time TV shows and A.5. Summary statistics

B. Methods Appendix, B.1. Text pre-processing and B.2. Bigrams most predictive for FNC or CNN/MSNBC

B.3. Human validation of NLP model

B.4. Distribution of Fox News similarity in newspapers and B.5. Example articles by Fox News similarity

B.6. Topics from the newspaper-based LDA model

C. Results Appendix

C.1. First stage results and C.2. Instrument exogeneity

C.3. Placebo: Content similarity in 1995/96

C.4. OLS results

C.5. Reduced form results

C.6. Sub-samples: Newspaper headquarters and other counties and C.7. Robustness: Alternative county matching

C.8. Robustness: Historical circulation weights and C.9. Robustness: Relative circulation weights

C.10. Robustness: Absolute and relative FNC viewership and C.11. Robustness: Dropping observations and clustering

C.12. Mechanisms: Language features and topics

C.13. Mechanisms: Descriptive Evidence on Demand Side

C.14. Mechanisms: Slant contagion and polarization

4.2. Instrument first stage and validity

Figure C.3 visualizes the first-stage relation between the FNC channel position (relative to the averaged position of CNN and MSNBC) and FNC viewership (also relative to CNN/MSNBC). Table C.1 shows coefficients and standard errors in tabular format. The relationship is significantly negative and similar without controls (panel a) and with the addition of controls (panel b). A one-standard-deviation decrease in the relative channel position (11 positions in the lineup) increases relative viewership by about 10% of a standard deviation (0.041 rating points). A one-tenth of a rating point equals roughly 45 minutes per month of (additional) viewership per household.

Hence, our first-stage coefficient means that decreasing the channel position of FNC by 11 (while holding the positions of CNN and MSNBC constant) would increase the viewership of FNC and decrease the viewership of CNN/MSNBC such that the FNC-to-CNN/MSNBC viewership difference goes up by 22 minutes per month. The tables below report Kleinbergen-Paap cluster-robust first-stage F-statistics (consistently >30, indicating a well-powered first stage).

Beyond relevance, we assume monotonicity, that the exclusion restriction holds, and exogeneity. Monotonicity appears plausible in our context (i.e., that the channel position influences TV viewership in the same direction for all counties, and thus, that higher positions would not systematically increase viewership). For the exclusion restriction, we assume that the channel position affects local news reporting only through its effect on cable news viewership. Third, exogeneity demands that Positionjs is uncorrelated with ijs. That is, the channel position is not endogenously selected with county-specific preferences for conservative or liberal news reporting. The main identification problem is that channel positions could be allocated strategically in response to local factors correlated with conservative news messaging.

Martin and Yurukoglu (2017) provide a detailed discussion and several checks supporting the exogeneity assumption. Their qualitative research highlights that channel positions have an important arbitrary, historical component with significant inertia and path dependence. Quantitatively, they document an absent correlation of the instrument with Republican vote shares before the introduction of FNC. Similarly, Ash and Galletta (2023) show that the instrument is unrelated to demographic characteristics that predict policy preferences or news channel viewership. We apply the same checks to our newspaper-county-level data, finding no association between channel positions and county characteristics otherwise important for our endogenous regressor or outcome (Appendix C.2). Additionally, a placebo check based on local news from 1995 and 1996 (the pre-FNC/MSNBC era) results in insignificant estimates (Appendix C.3).