[Frank Costanza]

More problematic is a concern that congressional citations and media citations do not follow the same data generating process. For instance, suppose that a factor besides ideology affects the probability that a legislator or reporter will cite a think tank, and suppose that this factor affects reporters and legislators differently. Indeed, John Lott and Kevin Hasset have invoked a form of this claim to argue that our results are biased toward making the media appear more conservative than they really are. They note:



“For example, Lott (2003, Chapter 2) shows that the New York Times’ stories on gun regulations consistently interview academics who favor gun control, but uses gun dealers or the National Rifle Association to provide the other side … In this case, this bias makes [Groseclose and Milyo’s measure of] the New York Times look more conservative than is likely accurate. (2004, 8)”



However, it is possible, and perhaps likely, that members of Congress practice the same tendency that Lott and Hassett have identified with reporters—that is, to cite academics when they make an anti-gun argument and to cite, say, the NRA when they make a pro-gun argument. If so, then our method will have no bias. On the other hand, if members of Congress do not practice the same tendency as journalists, then this can cause a bias to our method. But even here, it is not clear which direction the bias will occur. For instance, it is possible that members of Congress have a greater (lesser) tendency than journalists to cite such academics. If so, then this will cause our method to make media outlets appear more liberal (conservative) than they really are.



In fact, the criticism we have heard most frequently is a form of this concern, but it is usually stated in a way that suggests the bias is in the opposite direction. Here is a typical variant: “It is possible that (1) Journalists care more about the ‘quality’ of a think tank than do legislators (e.g. suppose they prefer to cite a think tank with a reputation for serious scholarship than another group that is known more for its activism); and (2) the liberal think tanks in the sample tend to be of higher quality than the conservative think tanks.” If statements (1) and (2) are true, then our method will indeed make media outlets appear more liberal than they really are. That is, the media will cite liberal think tanks more, not because they prefer to cite liberal think tanks, but because they prefer to cite high-quality think tanks. On the other hand, if one statement is true and the other is false, then our method will make media outlets appear more conservative than they really are. (E.g. suppose journalists care about quality more than legislators, but suppose that the conservative groups in our sample tend to be of higher quality than the liberal groups. Then the media will tend to cite the conservative groups disproportionately, but not because the media are conservative, rather because they have a taste for quality. This will cause our method to judge the media as more conservative than they really are.) Finally, if neither statement is true, then our method will make media outlets appear more liberal than they really are. Note that there are four possibilities by which statements (1) and (2) can be true or false. Two lead to a liberal bias and two lead to a conservative bias.



To test this concern, we collected two variables that indicate whether a think tank or policy group is more likely to produce quality scholarship. The first variable, staff called fellows, is coded as 1 if any staff members on the group’s website are given one of the following titles: fellow (including research fellow or senior fellow), researcher, economist, or analyst. The second variable, closed membership, is coded as a 0 if the web site of the group asks visitors to join the group and 1 otherwise. The idea behind this is that more activist groups are more likely to recruit laypersons for things such as protests and letter-writing campaigns to politicians. More scholarly groups are less likely to engage in these activities.



Both variables seem to capture the conventional wisdom about which think tanks are known for quality scholarship. For instance, of the top-25 most-cited groups in Table 1, the following had both closed membership and staff called fellows: Brookings, Center for Strategic and International Studies, Council on Foreign Relations, AEI, RAND, Carnegie Endowment for Intl. Peace, Cato, Institute for International Economics, Urban Institute, Family Research Council, and Center on Budget and Policy Priorities. Meanwhile, the following groups, which most would agree are more commonly known for activism than high-quality scholarship, had neither closed membership nor staff called fellows: ACLU, NAACP, Sierra Club, NRA, AARP, Common Cause, Christian Coalition, NOW, and Federation of American Scientists.[37]



These two variables provide some weak evidence that statement (1) is true—that journalists indeed prefer to cite high-quality groups more than legislators do. When we restrict the sample only to citations from the top-44 most cited think tanks (recall it is only these 44 that receive their own estimate of aj and bj), journalists cite these think tanks approximately 46% more frequently in our data set than legislators cite them. (This is due simply to the fact that the data set we collect for media outlets is approximately 46% larger than the data set we collect for Congress.) However, if we restrict the sample only to the top-44 think tanks that also have closed membership, then the media cite this set of groups 82% more frequently than legislators do. Thus, to the extent closed membership indicates quality, this result suggests that the 'mso-spacerun:yes'> (Recall that high ADA scores indicate that the group is liberal.) The correlation between staff called fellows and the average ADA score is also negative, specifically -.071.



This evidence suggests that, if anything, our estimates are biased in the direction of making the media look more conservative than they really are. However, because the correlations are so close to zero, we believe that any bias is small.



A final anecdote gives some compelling evidence that our method is not biased. Note that none of the above arguments suggest a problem with the way our method ranks media outlets. Now, suppose that there is no problem with the rankings, yet our method is plagued with a significant bias that systematically causes media outlets to appear more liberal (conservative) than they really are. If so, then this means that the three outlets we find to be most centrist (Newshour with Jim Lehrer, Good Morning America, and Newsnight with Aaron Brown) are actually consast four broad empirical regularities emerge from our results. In this section we document the regularities and analyze their significance for some theories about the industrial organization of the news industry.



First, we find a systematic tendency for the U.S. media outlets to slant the news to the left. As mentioned earlier, this is inconsistent with basic spatial models of firm location such as Harold Hotelling’s (1929) and others. In such models the median firm locates at the ideal location of the median consumer, which our results clearly do not support.



Some scholars have extended the basic spatial model to provide a theory why the media could be systematically biased. For instance, James Hamilton (2004) notes that news producers may prefer to cater to some consumers more than others. In particular, Hamilton notes that young females tend to be one of the most marginal news consumers (i.e. they are the most willing to switch to activities besides reading or watching the news). Further, this group often makes the consumption decisions for the household. For these two reasons, advertisers are willing to pay more to outlets that reach this group. Since this young females tend to be more liberal on average, a news outlet may want to slant its coverage to the left. Thus, according to Hamilton’s theory, U.S. news outlets slant their news coverage leftward, not in spite of consumer demand, but because of it.[38]



A more compelling explanation for the liberal slant of news outlets, in our view, involves production factors, not demand factors. As Daniel Sutter (2001) has noted, journalists might systematically have a taste to slant their stories to the left. Indeed, this is consistent with the survey evidence that we noted earlier. As a consequence, “If the majority of journalists have left-of-center views, liberal news might cost less to supply than unbiased news (444).” David Baron (2004) constructs a rigorous mathematical model along these lines. In his model journalists are driven, not just by money, but also a desire to influence their readers or viewers. Baron shows that profit-maximizing firms may choose to allow reporters to slant their stories, and consequently in equilibrium the media will have a systematic bias.[39]



A second empirical regularity is that the media outlets that we examine are fairly centrist relative to members of Congress. For instance, as Figure 2 shows, all outlets but one have ADA scores between the average Democrat and average Republican in Congress. In contrast, it is reasonable to believe that at least half the voters consider themselves more extreme than the party averages.[40] If so, then a basic spatial model, where are firms are constrained to charge the same exogenous price, implies that approximately half the media outlets should choose a slant outside the party averages.[41] Clearly, our results do not support this prediction.



Moreover, when we add price competition to the basic spatial model, then, as Mullainathan and Schleifer (2003) show, even fewer media outlets should be centrist. Specifically, their two-firm model predicts that both media firms should choose slants that are outside the preferred slants of all consumers. The intuition is that in the first round, when firms choose locations, they want to differentiate their products significantly, so in the next round they will have less incentive to compete on price. Given this theoretical result, it is puzzling that media outlets in the U.S. are not more heterogeneous. We suspect that, once again, the reason may lie with production factors. For instance, one possibility may involve the sources for news stories—what one could consider as the raw materials of the news industry. If a news outlet is too extreme, many of the newsmakers may refuse to grant interviews to the reporters.



A third empirical regularity involves the question whether reporters will be faithful agents of the owners of the firms for which they work. That is, will the slant of their news stories reflect their own ideological preferences or the firm’s owners? The conventional wisdom, at least among left-wing commentators, is that the latter is true. For instance, Eric Alterman (2003) entitles a chapter of his book “You’re Only as Liberal as the Man Who Owns You.” A weaker assertion is that the particular news outlet will be a faithful agent of the firm that owns it. However, our results provide some weak evidence that this is not true. For instance, although Time magazine and CNN’s Newsnight are owned by the same firm (Time Warner), their ADA scores differ substantially, by 9.4 points.[42] Further, almost half of the other outlets have scores between the scores of Newsnight and Time Magazine.



A fourth regularity concerns the question whether one should expect a government-funded news outlet to be more liberal than a privately-funded outlet. “Radical democratic” media scholars Robert McChesney and Ben Scott claim that it will. For instance, they note “[Commercial journalism] has more often served the minority interests of dominant political, military, and business concerns than it has the majority interests of disadvantaged social classes (2004, 4).” And conservatives, who frequently complain that NPR is far left, seem also to agree. However, our results do not support such claims. If anything, the government-funded outlets in our sample (NPR’s Morning Edition and Newshour with Jim Lehrer) have a slightly lower average ADA score (61.0), than the private outlets in our sample (62.8).[43] Related, some claim that a free-market system of news will produce less diversity of news than a government-run system. However, again, our results do not support such a claim. The variance of the ADA scores of the privately run outlets is substantially higher (131.3) than the variance of the two government-funded outlets that we examine (55.1).



In interpreting some of the above regularities, especially perhaps the latter two, we advise caution. For instance, with regard to our comparisons of government-funded vs. privately-funded news outlets, we should emphasize that our sample of government-funded outlets is small (only two), and our total sample of news outlets might not be representative of all news outlets.



Related, in our attempts to explain these patterns, we in no way claim to have provided the last word on a satisfactory theory. Nor do we claim to have performed an exhaustive review of potential theories in the literature. Rather, the main goal of our research is simply to demonstrate that it is possible to create an objective measure of the slant of the news. Once this is done, as we hope we have demonstrated in this section, it is easy to raise a host of theoretical issues to which such a measure can be applied.











Appendix



We believe that the most appropriate model specification is the one that we used to generate estimates in Table 3. However, in this Appendix we show how the estimates change when we adopt alternative specifications.



Recall, that we excluded observations in which the journalist or legislator gave an ideological label to the think tank or policy group. The first column of Table A1 lists ADA estimates when instead we include these observations, while maintaining all the other assumptions that we used to create Table 3—e.g. that we use 44 actual think tanks and 6 mega think tanks, etc. As mentioned earlier, when we include labeled observations, the main effect is to make the media outlets appear more centrist. For example, note that this causes the New York Times’ score to become more conservative by about 3.8 points; while it makes the score of the Fox News’ Special Report become more liberal by 1.8 points.



In column 2 we report the results when we exclude citations of the ACLU (while we maintain all the other model specifications we used to construct Table 3, including the decision to omit labeled observations).



In columns 3 to 8 we report the results when, instead of using 44 actual think tanks and 6 mega think tanks, we use 48 (respectively, 47, 46, 45, 43, and 42) actual and 2 (respectively 3, 4, 5, 7, and 8) mega think tanks.



In columns 1 to 4 of Table A2 we report the results when, instead of using 44 actual think tanks and 6 mega think tanks, we use 54 (respectively 64, 74, and 84) actual think tanks and 6 mega think tanks. That is, we let the total number of think tanks that we use change to 60, 70, 80, and 90.



In column 5 of Table A2 we use sentences as the level of observation instead of citations. That is, for instance, suppose that a news outlet lists a four-sentence quotation from a member of a think tank. In the earlier analysis we would count this as one observation. However, the estimates in column 5 would treat this as four observations. One problem with this specification is that the data are very lumpy—that is, some quotes contain an inordinate number of sentences, which cause some anomalies. One is that some relatively obscure think tanks become some of the most-cited under this specification. For instance, the Alexis de Tocqueville Institute, which most readers would agree is not one of the most well-known and prominent think tanks, is the 13th most-cited think tank by members of Congress, when we use sentences as the level of observation. It is the 58th most-cited, when we use citations as the level of observation. Members of Congress cited it only 35 times, yet they cited an average of 39 sentences for each citation. This compares to approximately five sentences per citation for the other think tanks. Meanwhile, it is one of the 30 least-cited think tank by the media.[44] A related problem is that these data are serially correlated. That is, for instance, if a given observation for the New York Times is a citation to the Brookings Institution, then the probability is high that the next observation will also be a citation to the same think tank (since the average citation contains more than one sentence). However, the likelihood function that we use assumes that the observations are not serially correlated. Finally, related to these problems, the estimates from this specification sometimes are in stark disagreement with common wisdom. For instance, the estimates imply that the Washington Times is more liberal than Good Morning America. For these reasons, we base our conclusions on the estimates that use citations as the level of observation, rather than sentences.

[Frank Costanza]

http://www.sscnet.ucla.edu/polisci/f...s/image004.gif

[1] Our sample includes policy groups that are not usually called think tanks, such as the NAACP, NRA, and Sierra Club. To avoid using the more unwieldy phrase “think tanks and other policy groups” we often use a shorthand version, “think tanks.” When we use the latter phrase we mean to include the other groups, such as NAACP, etc.

[2] Eighty-nine percent of the Washington correspondents voted for Bill Clinton, and two percent voted for Ross Perot.

[3] “Finding Biases on the Bus,” John Tierney, New York Times, August, 1, 2004. The article noted that journalists outside Washington were not as liberal. Twenty-five percent of these journalists favored Bush over Kerry.

[4] “Ruling Class War,” New York Times, September 11, 2004.

[5] Cambridge and Berkeley’s preferences for Republican presidential candidates have remained fairly constant. In the House district that contains Cambridge, Bob Dole received 17 percent of the two-party vote in 1996, and George W. Bush received 19 percent in 2000. In the House district that contains Berkeley, Bob Dole received 14 percent of the two-party vote, and George W. Bush received 13 percent.

[6] Some scholars claim that news outlets cater not to the desires of consumers, but to the desires of advertisers. Consequently, since advertisers have preferences that are more pro-business or pro-free-market than the average consumer, these scholars predict that news outlets will slant their coverage to the right of consumers’ preferences. (E.g., see Parenti, 1986, or Herman and Chomsky, 1988.) While our work finds empirical problems with such predictions, Sutter (2002) notes several theoretical problems. Most important, although an advertiser has great incentive to pressure a news outlet to give favorable treatment to his own product or his own business, he has little incentive to pressure for favorable treatment of business in general. Although the total benefits of the latter type of pressure may be large, they are dispersed across a large number of businesses, and the advertiser himself would receive only a tiny fraction of the benefits.

[7] One of the most novel features of the Lott-Hasset paper is that to define unbiased, it constructs a baseline that can vary with exogenous factors. In contrast, some studies define unbiased simply as some sort of version of “presenting both sides of the story.” To see why the latter notion is inappropriate, suppose that a newspaper devoted just as many stories describing the economy under President Clinton as good as it did describing the economy as bad. By the latter notion this newspaper is unbiased. However, by Lott and Hasset’s notion the newspaper is unbiased only if the economy under Clinton was average. If instead it was better than average, Lott and Hasset (as many would recognize as appropriate, including us) would judge the newspaper to have a conservative bias. Like Lott and Hasset, our notion of bias also varies with exogenous factors. For instance, suppose after a series of events liberal (conservative) think tanks gain more respect and credibility (say, because they were better at predicting those events), which causes moderates in Congress to cite them more frequently. By our notion, for a news outlet to remain unbiased, it also must cite the liberal (conservative) think tanks more frequently. The only other paper of which we are aware that also constructs a baseline that controls for exogenous events is Tim Groeling and Samuel Kernell’s (1998) study of presidential approval. These researchers examine the extent to which media outlets report increases and decreases in the president’s approval, while controlling for the actual increases and decreases in approval (whether reported by the media or not). The focus of the paper, however, is on whether news outlets have a bias toward reporting good or bad news, not on any liberal or conservative bias.

[8] New York Times Executive Editor Howell Raines accepting the “George Beveridge Editor of the Year Award” at a National Press Foundation dinner, shown live on C-SPAN2 February 20, 2003.

[9] Paul Krugman, “Into the Wilderness,” New York Times, November 8, 2002.

[10] Al Franken (2003, 3) Lies and the Lying Liars Who Tell Them: A Fair and Balanced Look at the Right.

[11] Bill Moyers, quoted in “Bill Moyers Retiring from TV Journalism,” Frazier Moore, Associated Press Online, December 9, 2004.

[12] Groseclose, Levitt, and Snyder (1999) argue that the underlying scales of interest group scores, such as those compiled by the Americans for Democratic Action, can shift and stretch across years or across chambers. This happens because the roll call votes that are used to construct the scores are not constant across time, nor across chambers. They construct an index that allows one to convert ADA scores to a common scale so that they can be compared across time and chambers. They call such scores adjusted ADA scores.

[13] Importantly, this conversion affects congressional scores the same way that it affects media scores. Since our method can only make relative assessments of the ideology of media outlets (e.g. how they compare to members of Congress or the average American voter), this transformation is benign. Just as the average temperature in Boston is colder than the average temperature in Philadelphia, regardless if one uses a Celsius scale or Fahrenheit scale, all conclusions we draw in this paper are unaffected by the choice to use the 1999 House scale or the 1980 House scale.

[14] In the Appendix we report the results when we do include citations that include an ideological label. When we include this data, this does not cause a substantial leftward or rightward movement in media scores—the average media score decreased by approximately 0.6 points, i.e. it makes the media appear slightly more conservative. Perhaps the greater affect was to cause a media outlets to appear more centrist. For instance, the New York Times and CBS Evening News tended to give ideological labels to conservative think tanks more often than they did to liberal think tanks. As a consequence, when we include the labeled observations, their scores respectively decreased (i.e. became more conservative) by 3.8 and 1.6 points. Meanwhile, Fox News’ Special Report tended to do the opposite. When we included labeled observations, its score increased (i.e., became more liberal) by 1.8 points. We think that such an asymmetric treatment of think tanks (ie to give labels more often to one side) is itself a form of media bias. This is why we base our main conclusions on the non-labeled data.

[15] Groseclose, Levitt, and Snyder (1999) have not computed adjusted scores for years after 1999. One consequence of this is that members who first entered Congress in 2001 do not have adjusted scores. ocrat. Third, even if the new members were not representative, this fact alone would not cause a bias in our method. To see this, suppose that these omitted members were disproportionately extreme liberals. To estimate ADA scores for a media outlet, we need estimates of the citation behavior of a range of members with ideologies near the ideology of the media outlet. If we had omitted some extreme liberal members of Congress, this does not bias our estimate of the citation pattern of the typical liberal, it only makes it less precise, since we have less data for these members. If, on the other hand, new members behaved differently from old members who have the same adjusted ADA score, then this could cause a bias. For instance, suppose new members with a 70 real ADA score tend to cite conservative think tanks more often than do old members with a 70 score. Then this would mean that Congress’s citation patterns are really more conservative than we have recorded. This means the media’s citation patterns are really more liberal (relative to Congress) than they appear in our data set, which would mean that the media is really more liberal than our estimates indicate. However, we have no evidence to believe this (or the opposite) is the case. And even if it were, because the new members are such a small portion of the sample, any bias should be small.



[16] In fact, for all members of Congress who switched parties, we treated them as if they were two members, one for when they were a Democrat and one for when they were a Republican.

[17] The party averages reflect the midpoint of the House and Senate averages. Thus, they give equal weight to each chamber, not to each legislator, since there are more House members than senators.

[18] Table 3, in the “Estimation Results” section, lists the period of observation for each media outlet.

[19] We assert that this statement is more likely to be made by a conservative because it suggests that government spending is filled with wasteful projects. This, conservatives often argue, is a reason that government should lower taxes.

[20] We were directed to this passage by Sutter’s (2001) article, which also seems to adopt the same definition of bias that we adopt.

[21] Like us, Mullainathan and Shleifer (2003) define bias as an instance where a journalist fails to report a relevant fact, rather than chooses to report a false fact. However, unlike us, Mullainathan and Shleifer define bias as a question of accuracy, not a taste or preference. More specific, their model assumes that with any potential news story, there are a finite number of facts that apply to the story. By their definition, a journalist is unbiased only if he or she reports all these facts. (However, given that there may be an unwieldy number of facts that the journalist could mention, it also seems consistent with the spirit of their definition that if the journalist merely selects facts randomly from this set or if he or she chooses a representative sample, then this would also qualify as unbiased.) As an example, suppose that, out of the entire universe of facts about free trade, most of the facts imply that free trade is good. However, suppose that liberals and moderates in Congress are convinced that it is bad, and hence in their speeches they state more facts about its problems. Under Mullainathan and Shleifer’s definition, to be unbiased a journalist must state more facts about the advantages of free trade—whereas, under our definition a journalist must state more facts about the disadvantages of free trade. Again, we emphasize that our differences on this point are ones of semantics. Each notion of bias is meaningful and relevant. And if a reader insists that “bias” should refer to one notion instead of the other, we suggest that he or she substitute a different word for the other notion, such as “slant.” Further, we suggest that Mullainathan and Shleifer’s notion is an ideal that a journalist perhaps should pursue before our notion. Nevertheless, we suggest a weakness of Mullainathan and Shleifer’s notion: It is very inconvenient for empirical work, and perhaps completely infeasible. Namely, it would be nearly impossible—and at best a very subjective exercise—for a researcher to try to determine all the facts that are relevant for a given news story. Likewise, it would be very difficult, and maybe impossible, for a journalist to determine this set of facts. To see this, consider just a portion of the facts that may be relevant to a news story, the citations from experts. There are hundreds, and maybe thousands, of think tanks, not to mention hundreds of academic departments. At what point does the journalist decide that a think tank or academic department is so obscure that it does not need to be contacted for a citation? Further, most think tanks and academic departments house dozens of members. This means that an unbiased journalist would have to speak to a huge number of potential experts. Moreover, even if the journalist could contact all of these experts, a further problem is how long to talk to them. At what point does the journalist stop gathering information from one particular expert before he or she is considered unbiased? Even if a journalist only needs to contact a representative sample of these experts, a problem still exists over defining the relevant universe of experts. Again, when is an expert so obscure that he or she should not be included in the universe? A similar problem involves the journalist’s choice of stories to pursue. A news outlet can choose from a huge—and possibly infinite—number of news stories. Although Mullainathan and Shleifer’s model focuses only on the bias for a given story, a relevant source of bias is the journalist’s choice of stories to cover. It would be very difficult for a researcher to construct a universe of stories from which journalists choose to cover. For instance, within this universe, what proportion should involve the problems of dual-career parents? What proportion should involve corporate fraud?

[22] Originally we used Stata to try to compute estimates. With this statistical package we estimate that it would have taken eight weeks for our computer to converge and produce estimates.

[23] However, Hamilton also notes that CBS covered roll calls by the American Conservative Union more frequently than the other two networks. Nevertheless, one can compute differences in frequencies between roll calls from the ADA and ACU. These differences show CBS to be more liberal than ABC and NBC. That is, although all three networks covered ADA roll calls more frequently than they covered ACU roll calls, CBS did this to a greater extent than the other two networks did.

[24] Other anecdotes that Sperry documents are: (i) a reporter, Kent MacDougall, who, after leaving the Journal, bragged that he used the “bourgeois press” to help “popularize radical ideas with lengthy sympathetic profiles of Marxist economists”; (ii) another Journal reporter who, after calling the Houston-based MMAR Group shady and reckless, caused the Journal to lose a libel suit after jurors learned that she misquoted several of her sources; (iii) a third Journal reporter, Susan Faludi (the famous feminist) characterized Safeway as practicing “robber baron” style management practices.

[25] See http://people-press.org/reports/disp...3?ReportID=215 for a description of the survey and its data. See also Kurtz (2004) for a summary of the study.

[26] This comes from the estimates for the “Republican” coefficient that they list in their Table 7. These estimates indicate the extent to which a newspaper is more likely to use a negative headline for economic news when the president is Republican. .

[27] Sometimes even liberals consider NPR left-wing. As Bob Woodward notes in The Agenda (1994, p. 114). “[Paul] Begala was steaming. To him, [OMB Director, Alice] Rivlin symbolized all that was wrong with Clinton’s new team of Washington hands, and represented the Volvo-driving, National Public Radio-listening, wine-drinking liberalism that he felt had crippled the Democratic Party for decades.”

[28] To test that NPR is to the right of Joe Lieberman we assume that we have measured the ideological position of Lieberman without error. Using the values in Table 2 and 3, the t-test for this hypothesis is t = (74.2 – 66.3)/1.0 = 7.9. This is significant at greater than 99.9% levels of confidence. To test that NPR is to the right of the New York Times, we use a likelihood ratio test. The value of the log likelihood function when NPR and the NY Times are constrained to have the same score is -78,616.64. The unconstrained value of the log likelihood function is -78,609.35. The relevant value of the likelihood ratio test is 2(78,616.64-78,609.35). This is distributed according to the Chi-Square distribution with one degree of freedom. At confidence levels greater than 99.9% we can reject the hypothesis that the two outlets have the same score.

[29] Of the reports written by Matt Drudge, he cited the Brookings Institution twice (actually once, but he listed the article for two days in row) the ACLU once, Taxpayers for Common Sense once, and Amnesty International once. On June 22, 2004, the Drudge Report listed a link to an earlier version of our paper. Although that version mentioned many think tanks, only one case would count as a citation. This is the paraphrased quote from RAND members, stating that the media tends to cite its military studies less than its domestic studies. (The above quote from PERC was not in the earlier version, although it would also count as a citation.) At any rate, we instructed our research assistants not to search our own paper for citations.

[30] Nevertheless, we still report how our results change if instead we use median statistics. See footnotes 34 and 35.

[31] The year 1999 was somewhat, but not very, atypical. During the rest of the 1990s on average 17.6 senators received scores between 33 and 67, approximately half as many as would be expected if scores were distributed uniformly. See http://www.adaction.org/votingrecords.htm for ADA scores of senators and House members.

[Frank Costanza]

[33] To see this, imagine a state with three districts, each with the same distribution of voters. (Thus, the median voter in each district has an ideology identical to the median voter of the state.) Now suppose that a Republican state legislature redraws districts so that Democratic voters are transferred from districts 1 and 2 to district 3. Suppose Republican voters are transferred in the opposite direction. Necessarily, the increase in Democratic voters in district 3 is twice the average increase in Republican voters in districts 1 and 2. Next, suppose that the expected ideological score of a representative is a linear function of the fraction of Democratic voters in his or her district. Then it will necessarily be the case that the expected average ideological score of the representatives in this hypothetical state will be identical to the expected average before redistricting. However, the same will not be true of the median score. It will be expected to decrease (i.e. to become more conservative).

[34] A clever alternative measure, suggested to us by David Mayhew, is to use a regression-based framework to estimate the expected ADA score of a legislator whose district is perfectly representative of the entire U.S. In the 2000 presidential election Gore won 50.27% of the two-party vote (including D.C.). Suppose we could construct a hypothetical congressional district with an identical Gore-vote percentage. It is reasonable to believe that the expected adjusted ADA score of the legislator from such a district is a good measure of the ideology of the centrist U.S. voter, and this appropriately adjusts for any biases due to gerrymandered districts, exclusion of D.C. voters, and the small-state biases in the Senate. To estimate this, we regressed (i) the 1999 adjusted ADA scores of members of Congress on (ii) Gore’s percentage of the two-party vote in the legislator’s district. In this regression we included observations from the Senate as well as the House. (Remember that adjusted scores are constructed so that they are comparable across chambers.) The results of the regression were: ADA Score = -46.48 + 1.91 Gore Vote. This implies 49.53 as the expected ADA score of a district in which the Gore vote was 50.27%. We repeated this analysis using, instead, adjusted ADA scores from 1998, 1997, 1996 and 1995. In the latter three years we used the Clinton share of the 2-party vote, and we used Clinton’s national share, 54.74% as the share of the representative district. These years give the following respective estimates of the ADA score of the centrist U.S. voter: 48.83, 48.99, 47.24, and 47.41. The average of these five measures is 48.40. Since this number is 1.7 points les than the mean-based measure of the centrist voter (50.1), if one believes that it is the more appropriate measure, then our main conclusions (based on the mean-based measure) are biased rightward—that is, the more appropriate conclusion would assert that the media are an additional 1.7 points to the left of the centrist voter.

Yet another measure is based on median scores of the House and Senate. The average Senate median over the five years was 58.19, while the average House median was 40.61. (Again, both these figures include phantom D.C. legislators, and the Senate score is weighted by state population.) The midpoint is 49.4, which is 0.7 points more conservative than our mean-based measure. If one believes that this is the more appropriate measure of centrist, then, once again, this implies that our media estimates are biased in the direction of making them more conservative than they really are.

[35] If instead we use medians, the figure is 54.9

[36] Another concern is that, although Republicans and Democrats are given debate time nearly proportional to their number of seats, one group might cite think tanks more frequently than the other. The above reasoning also explains why this will not cause a bias to our method.

[37] Despite its name, the Federation of American Scientists is more of a lobbying group than a scholarly think tank. Indeed, like most other well-known lobbying groups, its address is on K Street in Washington, D.C.

[38] Sutter (2001) similarly notes that demand factors may be the source of liberal bias in the newspaper industry. Specifically, he notes that liberals may have a higher demand for newspapers than conservatives, and he cites some suggestive evidence by Goff and Tollison (1990), which shows that as the voters in a state become more liberal, newspaper circulation in the state increases.

[39] Perhaps an even more interesting, in Baron’s model news consumers, in equilibrium, can be persuaded in the direction of the bias of the news outlet, despite the fact that they understand the equilibrium of the game and the potential incentives of journalists to slant the news.

[40] A simple model supports this assertion. Suppose that in every congressional district, voters have ideal positions that are uniformly distributed between -1 and 1, where -1 represents the most liberal voter and 1 represents the most conservative voter. Assume that a voter is a Democrat if and only if his or her ideal position is less than 0. Four candidates, two Republican and two Democrat, simultaneously choose positions in this space. Next they compete in two primary elections, where the Republican voters choose between the two Republican candidates, and likewise for the Democratic primary. Each voter votes for the candidate that is nearest his or her ideological position, and if two candidates are equidistant, then the voter flips a coin. (This assumption implies that voters are myopic in the primary election. If, instead, the voters were fully rational, then it can easily be shown that the candidates will choose even more centrist positions, which means that even more voters will consider themselves more extreme than the party averages.) Assume that candidates maximize the votes that they receive in the general election (i.e. the votes they receive in the primary election are only a means to winning votes in the general election). Then this setup implies that in equilibrium both Democratic candidates will locate at -.5, and both Republican candidates will locate at .5. Each winner of the primary has a 50% chance at winning the general election. Once this is repeated across many districts, then the expected number of voters who consider themselves more extreme than the party averages will be 50%.

[41] For instance, suppose that consumers are distributed uniformly between -1 and 1. Suppose that there are 20 news outlets, and suppose that consumers choose the outlet that is closest to them. It is easy to show that the unique, pure-strategy equilibrium is for two firms to locate at -.9, two firms locate at -.7, … , and two firms locate at .9.

[42] This difference, however, is not statistically significant at the 95% confidence level. A likelihood ratio test, constraining Time and NewsNight to have the same score gives a log-likelihood function that is 1.1 units greater than the unconstrained function. This value, multiplied by two, follows a Chi-Squared distribution with one degree of freedom. The result, 2.2, is almost significant at the 90% confidence level, but not quite. (The latter has a criterion of 2.71). We obtained similar results when we tested, the joint hypothesis that (i) Newsnight and Time have identical scores and that (ii) all three network morning news shows have scores identical to their respective evening news shows. A likelihood ratio test gives a value of 8.04, which follows a Chi-Squared distribution with four degrees of freedom. The value is significant at the 90% confidence level (criterion = 7.78), but not at the 95% confidence level (criterion = 9.49). Our hunch is that with more data we could show conclusively that at least sometimes different news outlets at the same firm produce significantly different slants. We suspect that, consistent with Baron’s (20xx) model, editors and producers, like reporters, are given considerable slack, and that they are willing to sacrifice salary in order to be given such slack.

[43] This result is broadly consistent with Djakov, McLiesh, Nenova, and Shleifer’s (2003) notion of the public choice theory of media ownership. This theory asserts that a government-owned media will slant news in such a way to aid incumbent politicians. If so, some reasonable theories (e.g. Black, 1958) suggest that the slant should conform to the median view of the incumbent politicians. We indeed find that the slant of the government-funded outlets in the U.S. on average is fairly close to the median politicians’ view. In fact, it is closer to the median view than the average of the privately-funded outlets that we examine. See Lott (1999) for an examination of a similar public-choice theory applied to the media and the education system in a country.

[44] Geoffrey Nunberg (2004), in a critique of an earlier version of our paper, deserves credit for first noting the problems with the sentence-level data involving Alexis de Tocqueville Institute. Our earlier version gave approximately equal focus to (i) estimates using citations as the level of observation and (ii) estimates using sentences as the level of observation. Partly due to his critique, the current version no longer focuses on sentences as observations. We did not have the same agreement with the rest of his criticisms, however. See Groseclose and Milyo (2004) for a response to his essay.

[Mulletsoldier]

Originally Posted by Newman

Yes, the MAJOR news outlets are slightly left, with the majority of smaller news outlets being the more extreme (MSNBC).

Fox news is centrist...why would I not admit to that?



I'm not admitting to anything...I am repeating what the authors said:

"Our results show a strong liberal bias."

And once again, not quantifying your statements, because you don't like real numbers.

The strong bias is defined as relative number of outlets, and not necessarily their ADA score. And you would hate to admit that this study found, just like most other media studies, a more or less centrist viewpoint expressed in mainstream media.

Well it it easy to amuse you...

It is, especially with simple spelling errors.

Now where did it state a conservative bias for local media outlets? Radio is a no brainer but where did it state the majority of regional and local newspapers because I can't find it.

Public coverage, sorry. Misinterpretation on my part.

If you didn't find something wrong with this study, I would think something is wrong with you.

If you constantly dole it out in support of your opinion, without ever recognizing its Swiss-Cheese constitution, I think something is equally as wrong with you.

Dan Rather?

Forged documents...

Nah..they would never do that.

Hm, well one isolated case versus systematic and deliberate bias admitted from a top-level producer. Hmm, which is worse?

Actually they are called progressives....the L word isn't used that much here.

The L Word is a good show.

How many American news programs do you watch?

How many news anchors are following Obama on his trip and how many followed McCain on that same trip?

But Obama is "news".....thats it.

I'm not entirely sure what this had to do with my statement? Several studies (other than this piece of crap you love to cite, lol) have shown that opinions in the news media are extremely funneled, and just like your study here, mostly centrist in nature.

[Frank Costanza]

Originally Posted by Mulletsoldier

And once again, not quantifying your statements, because you don't like real numbers.

LMAO....Oh how I love the attempt.

Come on, you can do better than that...

The strong bias is defined as relative number of outlets, and not necessarily their ADA score. And you would hate to admit that this study found, just like most other media studies, a more or less centrist viewpoint expressed in mainstream media.

Not according to them!

"What is the typical ADA score of members of Congress who exhibit the same frequency (2:1) in their speeches? This is the score that we would assign to the New York Times. Our results show a strong liberal bias. All of the news outlets except Fox News’ Special Report and the Washington Times received a score to the left of the average member of Congress."

There MUST be a way to spin this in your favor..

Be creative!

It is, especially with simple spelling errors.

youer wlemcome.

Public coverage, sorry. Misinterpretation on my part.

I'm sure it was

If you constantly dole it out in support of your opinion, without ever recognizing its Swiss-Cheese constitution, I think something is equally as wrong with you.

You think that regardless of the study....

But come on, you can be more witty than that...





[/quote]Hm, well one isolated case versus systematic and deliberate bias admitted from a top-level producer. Hmm, which is worse?
[/quote]

Yes, the documented proof of those top level producers is overwhelming.

Deliberate bias? Sorry, its centrist.

I'm not entirely sure what this had to do with my statement? Several studies (other than this piece of crap you love to cite, lol) have shown that opinions in the news media are extremely funneled, and just like your study here, mostly centrist in nature.

I was just curious to how many US news shows you watch or US newspapers you read. Thats all.

Mullet, every study that disagrees with your point of view is a piece of crap. We all know this. You are extremely consistent.

[Frank Costanza]

And can you post those studies please? I don't think you did last time we had this go around.

[atjnutrition2]

I'm sure I'll get jumped on a little for this...because I'm coming in a little late. There are arguments on both sides of the fence on whether the media has either a liberal or conservative slant. I won't state which side I'm on as I don't believe it's exactly relevant to what I have to say.

My issue with media outlets is their use of quantifiers and headlines. For example...one headline I saw on a Time magazine cover read "Is John McCain healthy enough to be president?" The article itself may have been centrist in nature with the only purpose to highlight the past health of a candidate. The headline however, paints a different picture. It creates a doubt or thought in the reader's mind which can't help but be unfavorable to Sen. McCain no matter the slant or non-slant of the article. The reader goes into the arcticle with a bias that Mr. McCain is unhealthy and possibly not fit to be elected.

As far as quantifiers go...this is what frustrates me the most. I'll use the U.S. presence in Iraq because of the numerous examples. Quantifiers such as "horrific, terrible"....and the like create a bias for the reader. For example compare the two sentences:

Today in Iraq an horrific explosion violently killed 30 civilians in a crowded market. U.S. military units in the area were ineffective in stopping this tragedy.

Today in Iraq 30 civilian were killed by a suicide bomber while shopping in a local market, despite the presence of an American military unit in the vicinity.

Both examples convey the same information, but to some, the first two sentences can paint a much different picture. To those who already oppose the U.S. presence in Iraq, the first sentences and the qualifiers would further reinforce their position. To those who support the U.S. presence in Iraq, the first sentences comes across as inflammatory and anti-U.S.

I understand that the job of a reporter is to paint a picture. However, regardless of your viewpoints, the irresponsible use of qualifiers and headlines can further the view that the media is either liberal or conservative.

That's my two cents worth on this one.