By Stephen Morris
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Sample text
By the same logic, our model predicts that if slant is an important component of demand, (i) newspapers with high Rn should choose high values of yn and (ii) newspapers with high Rn should consequently be read relatively 52 M. GENTZKOW AND J. M. SHAPIRO more in zip codes with high rz . The strength of these relationships will identify the coefficient on yn rz . Note that assuming that Rn is correlated with yn is not equivalent to assuming that yn = yn∗ or that yn = idealn . That is, for the purposes of our demand analysis, we do not assume that slant is chosen to maximize profits, only that it is correlated with consumer ideology in the newspaper’s home market.
Congressional Record and Congressperson Data Our approach to measuring slant requires data on the frequency with which individual members of Congress use particular phrases. gov and parsed using an automated script that identifies the speaker of each passage. To increase the efficiency of our text analysis algorithm, we apply a standard preprocessing procedure that removes extremely common words (such as “to,” “from,” and “the”) and strips words down to shared linguistic roots (so that, for example, “tax cut” and “tax cuts” are identified as the same phrase).
Com). For each data base, we use an automated script to calculate the number of articles containing each phrase in each newspaper during calendar year 2005. Whenever possible, we exclude opinion content. Also, because 40 M. GENTZKOW AND J. M. SHAPIRO some newspapers do not archive reprinted wire stories with ProQuest, we exclude articles from the Associated Press, focusing instead on content originating with the newspaper. Appendix A provides additional details on the mechanics of these searches.