Does advertising revenue increase or diminish content differentiation in media markets? This paper shows that an increase in the technically feasible number of ad breaks per video leads to an increase in content differentiation between several thousand YouTube channels. I exploit two institutional features of YouTube's monetization policy to identify the causal effect of advertising on the YouTubers' content choice. The analysis of around one million YouTube videos shows that advertising leads to a twenty percentage point reduction in the YouTubers' probability to duplicate popular content, i.e., content in high demand by the audience. I also provide evidence of the economic mechanism behind the result: popular content is covered by many competing YouTubers; hence, viewers who perceive advertising as a nuisance could easily switch to a competitor if a YouTuber increased her number of ad breaks per video. This is less likely, however, when the YouTuber differentiates her content from her competitors.

No abstract

We present a new measure for the political position of news outlets based on politicians' selective sharing of news items. Politicians predominantly share news items that are in line with their political position, hence, one can infer the political position of news outlets from the politicians' revealed preferences over news items. We apply our measure to twelve major German media outlets by analyzing tweets of German Members of Parliament (MPs) on Twitter. For each news outlet under consideration, we compute the correlation between the political position of the seven parties in the 19th German Bundestag and their MPs' relative number of Twitter referrals to that outlet. We find that three outlets are positioned on the left, and two of them are positioned on the right. Several robustness checks support our results. We also apply our procedure to nine major media outlets from the USA and find that two outlets are positioned on the right, five are positioned on the left of the political spectrum.

The importance of user-generated content is growing as media consumption is moving online; yet, investigations of media bias on user-generated content platforms are rare. We develop a novel procedure to detect coverage bias -i.e., bias in the amount of coverage certain topics or issues receive -on user-generated content platforms. We proceed in two steps. First, we focus on a sample of homogeneous observations and control for observable differences. Second, we compare the coverage of our observations between different language versions of the same platform in a difference-in-differences framework, which allows us to disentangle coverage bias from unobserved heterogeneity between observations. We apply our procedure to Wikipedia and examine whether it has a coverage bias in its biographies of German (and French) Members of Parliament (MPs). Our analysis reveals a small to medium size coverage bias against MPs from the center-left parties in Germany and in France. A plausible explanation are partisan contributions to the Wikipedia biographies, as we show by analyzing patterns of authorship and Wikipedia's talk pages for the German case. Practical implications of our results include raising users' awareness of coverage bias when searching for and processing information obtained on user-generated content platforms.

No abstract