DO ADS INFLUENCE EDITORS? ADVERTISING AND BIAS IN THE FINANCIAL MEDIA Jonathan Reuter and Eric Zitzewitz erly Journal of Economics(forthcoming) Current Draft: August 2005 abstract The independence of editorial content from advertisers' influence is a cornerstone of journalistic ethics We test whether this independence is observed in practice. We find that mutual fund recommendations are correlated with past advertising in three personal finance publications but not in two national news- papers. Our tests control for numerous fund characteristics, total advertising expenditures, and past mentions. While positive mentions significantly increase fund inflows, they do not successfully predict returns. Future returns are similar for the funds we predict would have been mentioned in the absence f bias, suggesting that the cost of advertising bias to readers is small. The authors would like to thank Susan Athey, George Baker, Brad Barber, David Beim, Jeremy Bulow, John Chalmers Diane Del Guercio, Alexander Dyck, Ray Fisman, Kenneth Fuller, Ronald Gilson, Shane Greenstein, Ro Gutierrez, Larry Harris Mikkelson. Theodore David Musto. Megan Partch. Avri d.john rea Nancy Rose, Greg Spears, Joel Waldfogel, David Yoffie, Steve Zelda nonymous referees, Edward Glaeser(the editor seminar participants at the Columbia Graduate School of Business d Business school. the U.s. Securities and E. Commission, Stanford, University of California at Berkeley, University of Oregon, the second annual International Inc Organization Conference, the 2004 Wharton Conference on Household Financial Decision-Making and Portfolio Choice, the 2004 NBER Industrial Organization Summer Institute for helpful suggestions and comments. Caroline Baylon provie excellent research assistance. Any remaining errors are our
DO ADS INFLUENCE EDITORS? ADVERTISING AND BIAS IN THE FINANCIAL MEDIA† Jonathan Reuter and Eric Zitzewitz Quarterly Journal of Economics (forthcoming) Current Draft: August 2005 Abstract The independence of editorial content from advertisers’ influence is a cornerstone of journalistic ethics. We test whether this independence is observed in practice. We find that mutual fund recommendations are correlated with past advertising in three personal finance publications but not in two national newspapers. Our tests control for numerous fund characteristics, total advertising expenditures, and past mentions. While positive mentions significantly increase fund inflows, they do not successfully predict returns. Future returns are similar for the funds we predict would have been mentioned in the absence of bias, suggesting that the cost of advertising bias to readers is small. †The authors would like to thank Susan Athey, George Baker, Brad Barber, David Beim, Jeremy Bulow, John Chalmers, Diane Del Guercio, Alexander Dyck, Ray Fisman, Kenneth Fuller, Ronald Gilson, Shane Greenstein, Ro Gutierrez, Larry Harris, Chris Mayer, Cynthia Montgomery, Wayne Mikkelson, Theodore Miller, David Musto, Megan Partch, Avri Ravid, John Rea, Nancy Rose, Greg Spears, Joel Waldfogel, David Yoffie, Steve Zeldes, four anonymous referees, Edward Glaeser (the editor), and seminar participants at the Columbia Graduate School of Business, Harvard Business School, the U.S. Securities and Exchange Commission, Stanford, University of California at Berkeley, University of Oregon, the second annual International Industrial Organization Conference, the 2004 Wharton Conference on Household Financial Decision-Making and Portfolio Choice, and the 2004 NBER Industrial Organization Summer Institute for helpful suggestions and comments. Caroline Baylon provided excellent research assistance. Any remaining errors are our own
I. Introduction Recently, there has been considerable interest in political media bias(Groseclose and Milyo 2004 Baron [2005, and Mullainathan and Shleifer 2005). There is also growing interest in whether the media biases its content to benefit advertisers. For their part, media outlets tend to strongly deny that such a pro-adviser bias exists. For example, a 1996 article in Kiplinger's Personal Finance printed statements from editors at a number of personal finance publications(including the three in our study) claiming that advertisers have no influence over published content. 2 Despite the important role that the media play in generating and disseminating information to consumers and investors, we are aware of few systematic ttempts to test the accuracy of these claims In this paper, we test for advertising bias within the financial media. Specifically, we study mutual fund recommendations published between January 1997 and December 2002 in five of the top six recipients of mutual fund advertising dollars. Controlling for observable fund characteristics and total family advertising expenditures, we document a positive correlation between a family's lagged advertising expenditures and the probability that its funds are recommended in each of the personal finance publications in our sample ( Money Magazine, Kiplinger's Personal Finance, and SmartMoney ). While we consider several alternative explanations below, the robustness of the correlation leads us to conclude that the most plausible explanation is the causal one, namely, that personal finance publications bias their recommendations--either consciously or subconsciously-to favor advertisers. In contrast, we find no such correlation between advertising and content in either national newspaper (the New York Times and Wall Street Journal) For example, Baker [1994] and Hamilton oth argue that the media biases its conte Ellman and Germano [2005] model this bias as from advertisers committing to punish stories. A related bias is posited by Dyck and 2003, who document publications that en the way earnings nnouncements are reported in a press release and the way they are reported in the media and argue that the correlation consistent with reporters biasing articles in exchange for to private information. 2Goldberg, Steven, "Do the ads tempt the editors?(influence of mutual fund advertising on personal finance publication ditors), " Kiplinger's Personal Finance, May 1996. The article was written in response to an earlier article in Fortune accusing Forbes of"turning downbeat stories into upbeat stories in order to keep advertisers happy- even at the risk of misleading their own readers 3One exception is Reuter [2002], who asks whether advertising biases wine ratings. We briefly discuss his findings below nother exception is Miller (2004), who examines a sample of firms that the SEC found guilty of accounting fraud and finds that the media is no less likely to break stories about firms in the 15 indu acknowledges that the use of industry-level advertising data may reduce the power of this test. More generally, our work relates other studies of correlations between expert opinion and business interests. For example, Lin and McNichols [1998 and Michaely and Womack [1999] find that sell-side analysts buy and sell recommendations favor the companies with which their employers do investment banking br Zitzewitz [2005] finds that figure skating judges are nationalistically biased and sell" bias to colleagues by engaging in vote trading
I. Introduction Recently, there has been considerable interest in political media bias (Groseclose and Milyo [2004], Baron [2005], and Mullainathan and Shleifer [2005]). There is also growing interest in whether the media biases its content to benefit advertisers.1 For their part, media outlets tend to strongly deny that such a pro-adviser bias exists. For example, a 1996 article in Kiplinger’s Personal Finance printed statements from editors at a number of personal finance publications (including the three in our study) claiming that advertisers have no influence over published content.2 Despite the important role that the media plays in generating and disseminating information to consumers and investors, we are aware of few systematic attempts to test the accuracy of these claims.3 In this paper, we test for advertising bias within the financial media. Specifically, we study mutual fund recommendations published between January 1997 and December 2002 in five of the top six recipients of mutual fund advertising dollars. Controlling for observable fund characteristics and total family advertising expenditures, we document a positive correlation between a family’s lagged advertising expenditures and the probability that its funds are recommended in each of the personal finance publications in our sample (Money Magazine, Kiplinger’s Personal Finance, and SmartMoney). While we consider several alternative explanations below, the robustness of the correlation leads us to conclude that the most plausible explanation is the causal one, namely, that personal finance publications bias their recommendations—either consciously or subconsciously—to favor advertisers. In contrast, we find no such correlation between advertising and content in either national newspaper (the New York Times and Wall Street Journal). 1For example, Baker [1994] and Hamilton [2004] both argue that the media biases its content to benefit advertisers, and Ellman and Germano [2005] model this bias as arising from advertisers committing to punish publications that run negative stories. A related bias is posited by Dyck and Zingales [2003], who document a positive correlation between the way earnings announcements are reported in a press release and the way they are reported in the media and argue that the correlation is consistent with reporters biasing articles in exchange for access to private information. 2Goldberg, Steven, “Do the ads tempt the editors? (influence of mutual fund advertising on personal finance publication editors),” Kiplinger’s Personal Finance, May 1996. The article was written in response to an earlier article in Fortune accusing Forbes of “turning downbeat stories into upbeat stories in order to keep advertisers happy—even at the risk of misleading their own readers.” 3One exception is Reuter [2002], who asks whether advertising biases wine ratings. We briefly discuss his findings below. Another exception is Miller [2004], who examines a sample of firms that the SEC found guilty of accounting fraud and finds that the media is no less likely to break stories about firms in the 15 industries with the highest propensity to advertise, but acknowledges that the use of industry-level advertising data may reduce the power of this test. More generally, our work relates to other studies of correlations between expert opinion and business interests. For example, Lin and McNichols [1998] and Michaely and Womack [1999] find that sell-side analysts’ buy and sell recommendations favor the companies with which their employers do investment banking business. Zitzewitz [2005] finds that figure skating judges are nationalistically biased and “sell” bias to colleagues by engaging in vote trading. 1
Mutual funds recommendations are a good laboratory in which to test for advertising bias. Product recommendations are a form of content that advertisers might expect to benefit most from biasing. Muti funds are numerous and because they are financial assets their ex-ante and ex-post quality is relatively asy to observe. The availability of detailed data on funds'objective characteristics helps us control for differences in schools of thought about product quality in a way that would be difficult in other settings For example, suppose we found that gm advertised more and received better reviews than Toyota in Muscle Cars. Here one might conclude that Muscle Cars simply likes muscle cars, and GM advertises there to reach its readers. With mutual funds, differences in schools of thought about fund selection are largely over the relative importance of variables we observe in our data, such as past returns and expenses. What we interpret as possible evidence of bias is when, controlling for these and other factors, a publication is more likely to recommend funds from the mutual fund families that have advertised the most within its pages For mutual fund families to benefit from biased recommendations, at least some set of investors must rely upon them. Therefore, after testing for bias, we examine the impact that the mentions in our sample have on fund flows. controlling for past media mentions and a variety of fund characteristics, a single additional positive media mention for a fund is associated with inflows ranging from 7 to 15 percent of its assets over the following 12 months. While investors appear to respond to the media mentions, we find that the media mentions have little ability to predict future returns. Interestingly, this is not due to the pro-advertiser bias When we predict which funds would have been mentioned in the absence of bias, we find little difference in future returns. However, we do find that the personal finance publications would perform significantly better by simply recommending funds with the lowest expense ratios in their investment objectives It is a puzzle why readers respond to recommendations that do not predict future returns. Conditional on their behavior, however, publications selecting funds to mention from the large number available have a valuable set of favors to bestow. In deciding whether to reward an advertiser, publications trade off the benefits(encouraging future advertising) with the costs(harming one's reputation by mentioning a lower quality fund). When there is a wide selection of advertisers funds to recommend, the cost of advertising bias AReuter [2002 finds that advertisers in Wine Spectator receive slightly higher ratings than non-advertisers, controlling for ratings in Wine Advocate, which does not accept advertising. While Wine Spectator claims to use blind tastings to rate wines-a claim intended to increase reader confidence in the ratings-approximately half of the ratings difference is associated with the selective retasting of advertisers'wines. The rest of the rating difference may, in fact, be due to different schools of thought about how to rate wine
Mutual funds recommendations are a good laboratory in which to test for advertising bias. Product recommendations are a form of content that advertisers might expect to benefit most from biasing. Mutual funds are numerous and because they are financial assets their ex-ante and ex-post quality is relatively easy to observe. The availability of detailed data on funds’ objective characteristics helps us control for differences in schools of thought about product quality in a way that would be difficult in other settings. For example, suppose we found that GM advertised more and received better reviews than Toyota in Muscle Cars. Here one might conclude that Muscle Cars simply likes muscle cars, and GM advertises there to reach its readers.4 With mutual funds, differences in schools of thought about fund selection are largely over the relative importance of variables we observe in our data, such as past returns and expenses. What we interpret as possible evidence of bias is when, controlling for these and other factors, a publication is more likely to recommend funds from the mutual fund families that have advertised the most within its pages. For mutual fund families to benefit from biased recommendations, at least some set of investors must rely upon them. Therefore, after testing for bias, we examine the impact that the mentions in our sample have on fund flows. controlling for past media mentions and a variety of fund characteristics, a single additional positive media mention for a fund is associated with inflows ranging from 7 to 15 percent of its assets over the following 12 months. While investors appear to respond to the media mentions, we find that the media mentions have little ability to predict future returns. Interestingly, this is not due to the pro-advertiser bias. When we predict which funds would have been mentioned in the absence of bias, we find little difference in future returns. However, we do find that the personal finance publications would perform significantly better by simply recommending funds with the lowest expense ratios in their investment objectives. It is a puzzle why readers respond to recommendations that do not predict future returns. Conditional on their behavior, however, publications selecting funds to mention from the large number available have a valuable set of favors to bestow. In deciding whether to reward an advertiser, publications trade off the benefits (encouraging future advertising) with the costs (harming one’s reputation by mentioning a lowerquality fund). When there is a wide selection of advertisers’ funds to recommend, the cost of advertising bias 4Reuter [2002] finds that advertisers in Wine Spectator receive slightly higher ratings than non-advertisers, controlling for ratings in Wine Advocate, which does not accept advertising. While Wine Spectator claims to use blind tastings to rate wines—a claim intended to increase reader confidence in the ratings—approximately half of the ratings difference is associated with the selective retasting of advertisers’ wines. The rest of the rating difference may, in fact, be due to different schools of thought about how to rate wines. 2
may not be that large, especially if one does not internalize the reputational spillover to ones peers. This is ournalism profession ed an ethical sanction against advertising bias, because the returns to favoring advertisers might otherwise be high II. Data Our tests for advertising bias require data on advertising expenditures, media mentions, and mutual fund characteristics. Data on monthly mutual fund advertising expenditures by publication and fund family were purchased from Competitive Media Research(CMR). According to these data, the mutual fund in- dustry's annual advertising ditures averaged approximately $307 million during our 1996-2002 sample period, with $80 million(26%) going to national newspapers and $119 million(39%) going to consumer The publications we study include five of the six top recipients of mutual fund advertising between 1998 and 2002: the Wall Street Journal($48.5 million per year), Money($22.1 million), New York Times(S14.0 million), Kiplinger's Personal Finance($12.2 million), and SmartMoney($8.7 million).(We attempted to gather media mentions for Mutual Funds( S14.0 million)but were unable to access its content electronically. In total, the publications in our sample account for approximately 45 percent of the mutual fund industrys advertising expenditures. Naturally, mutual fund advertising is a more important source of revenue for the personal finance publications than the national newspapers. Whereas mutual fund advertising accounts for 3.8 percent of advertising revenues at the Wall Street Journal and 1.1 percent at the New York Times, it accounts for 15 percent at Money, 16 percent at SmartMoney, and 28 percent at Kiplinger's. We also gather edia mentions from Consumer Reports, which does not accept advertisin The media mention data vary across publications and are summarized in Table I. Since these data had to be hand collected, for several publications we restrict attention to particular articles or columns. For the New York Times, we track funds mentioned in a column from the Sunday Business section titled"Investing With. The column spotlights a particular fund, interviewing fund managers and providing details such For print publications, CMR tracks the size of each advertisment and estimates a dollar cost for the advertisement based on the publication's quoted advertising rates and any likely discount. Comparing CMR's estimates of total print advertising revenue for the New York Times and Wall Street Journal to figures reported in the parent companies'10K filings, the CMr estimates often differ from the actual revenues by less than 10 percent
may not be that large, especially if one does not internalize the reputational spillover to ones peers. This is precisely why the journalism profession developed an ethical sanction against advertising bias, because the returns to favoring advertisers might otherwise be high. II. Data Our tests for advertising bias require data on advertising expenditures, media mentions, and mutual fund characteristics. Data on monthly mutual fund advertising expenditures by publication and fund family were purchased from Competitive Media Research (CMR).5 According to these data, the mutual fund industry’s annual advertising expenditures averaged approximately $307 million during our 1996-2002 sample period, with $80 million (26%) going to national newspapers and $119 million (39%) going to consumer magazines. The publications we study include five of the six top recipients of mutual fund advertising between 1998 and 2002: the Wall Street Journal ($48.5 million per year), Money ($22.1 million), New York Times ($14.0 million), Kiplinger’s Personal Finance ($12.2 million), and SmartMoney ($8.7 million). (We attempted to gather media mentions for Mutual Funds ($14.0 million) but were unable to access its content electronically.) In total, the publications in our sample account for approximately 45 percent of the mutual fund industry’s advertising expenditures. Naturally, mutual fund advertising is a more important source of revenue for the personal finance publications than the national newspapers. Whereas mutual fund advertising accounts for 3.8 percent of advertising revenues at the Wall Street Journal and 1.1 percent at the New York Times, it accounts for 15 percent at Money, 16 percent at SmartMoney, and 28 percent at Kiplinger’s. We also gather media mentions from Consumer Reports, which does not accept advertising. The media mention data vary across publications and are summarized in Table I. Since these data had to be hand collected, for several publications we restrict attention to particular articles or columns. For the New York Times, we track funds mentioned in a column from the Sunday Business section titled “Investing With.” The column spotlights a particular fund, interviewing fund managers and providing details such 5For print publications, CMR tracks the size of each advertisment and estimates a dollar cost for the advertisement based on the publication’s quoted advertising rates and any likely discount. Comparing CMR’s estimates of total print advertising revenue for the New York Times and Wall Street Journal to figures reported in the parent companies’ 10K filings, the CMR estimates often differ from the actual revenues by less than 10 percent. 3
as past returns, expense ratios, and the fund familys contact information. For Money, we focus on the composition of the Money 100 list, a list of recommended mutual funds published once a year between 1998 and 2002. Similarly, for Consumer Reports we focus on articles listing recommended equity funds, bond funds, or both. For each of these publications, we classify the mutual fund mentions as positive Since Kiplinger's Personal Finance and SmartMoney do not publish lists of recommended funds anal gous to the Money 100 list, for these two publications we analyze every article containing the word"fund. For articles that focus on mutual funds(rather than mention them in passing), we classify the article as making recommendations across investment objectives, within a particular investment objective, or within a particular mutual fund family. We also classify each mutual fund mention as positive or negative. As uggested by the representative article titles reported in Table I, this rarely involved close judgment calls When we could not determine whether a mention was positive or negative, we dropped the mention from our sample: we did this for 8 of the 783 mentions in Kiplinger's and 16 of the 2, 417 mentions in SmartMoney Finally, for the Wall Street Journal, we focus on a daily column titled"Fund Track, "that mentions funds either because they are the subject of news(such as manager turnover) or because their managers are being quoted on an issue. In the (pre-scandal) time period we study, being quoted on an issue in the Wall Street Journal is presumably positive exposure for the managers fund. However, since "Fund Track" rarely rec- ommends a course of action for fund investors, mentions in the Wall Street Journal are potentially different from mentions in the other publications we study. At a minimum, mentions in the Wall Street Journal can be viewed as proxies for the news coverage of specific funds Data on U.S. mutual fund returns and characteristics come from CRSP. The unit of observation is fund i in month t and the sample period is January 1996 through December 2002. Table II reports summary statistics for the full sample of mutual funds and for funds receiving mentions from a given publication in month t. Univariate comparisons indicate that funds receiving positive media mentions tend to be larger, ad bond funds, but excludes money market funds because they are rarely mentioned in the publications we study. For mutual inflows, and other characteristics, and include one observatie per fund per month in our sample. Also, since we merge the media mention data with the CRSP Survivor-Bias Free US Mutual Fund Database using ticker, our sample is limited to funds for which w e able to locate a ticker for at least one share class Because magazines are typically available on the newsstand in the month prior to the month stated on their cover, we code mentions in the month t+ I issue of Money, Kiplinger's, Smart Money, and Consumer Reports as occurring in month t. For mple, we code mentions in the June 1998 issue of Money as occurring in May 1998. In contrast, mentions in a June 1998 issue of the Wall Street Journal or New York Times are coded as occurring in June 1998
as past returns, expense ratios, and the fund family’s contact information. For Money, we focus on the composition of the Money 100 list, a list of recommended mutual funds published once a year between 1998 and 2002. Similarly, for Consumer Reports we focus on articles listing recommended equity funds, bond funds, or both. For each of these publications, we classify the mutual fund mentions as positive. Since Kiplinger’s Personal Finance and SmartMoney do not publish lists of recommended funds analogous to the Money 100 list, for these two publications we analyze every article containing the word “fund.” For articles that focus on mutual funds (rather than mention them in passing), we classify the article as making recommendations across investment objectives, within a particular investment objective, or within a particular mutual fund family. We also classify each mutual fund mention as positive or negative. As suggested by the representative article titles reported in Table I, this rarely involved close judgment calls. When we could not determine whether a mention was positive or negative, we dropped the mention from our sample; we did this for 8 of the 783 mentions in Kiplinger’s and 16 of the 2,417 mentions in SmartMoney. Finally, for the Wall Street Journal, we focus on a daily column titled “Fund Track,” that mentions funds either because they are the subject of news (such as manager turnover) or because their managers are being quoted on an issue. In the (pre-scandal) time period we study, being quoted on an issue in the Wall Street Journal is presumably positive exposure for the manager’s fund. However, since “Fund Track” rarely recommends a course of action for fund investors, mentions in the Wall Street Journal are potentially different from mentions in the other publications we study. At a minimum, mentions in the Wall Street Journal can be viewed as proxies for the news coverage of specific funds. Data on U.S. mutual fund returns and characteristics come from CRSP. The unit of observation is fund i in month t and the sample period is January 1996 through December 2002.6 Table II reports summary statistics for the full sample of mutual funds and for funds receiving mentions from a given publication in month t. 7 Univariate comparisons indicate that funds receiving positive media mentions tend to be larger, 6Our sample includes all domestic equity funds, international equity funds, hybrid funds (which invest in debt and equity), and bond funds, but excludes money market funds because they are rarely mentioned in the publications we study. For mutual funds with multiple share classes, we calculate fund-level returns, inflows, and other characteristics, and include one observation per fund per month in our sample. Also, since we merge the media mention data with the CRSP Survivor-Bias Free US Mutual Fund Database using ticker, our sample is limited to funds for which we were able to locate a ticker for at least one share class. 7Because magazines are typically available on the newsstand in the month prior to the month stated on their cover, we code mentions in the month t + 1 issue of Money, Kiplinger’s, SmartMoney, and Consumer Reports as occurring in month t. For example, we code mentions in the June 1998 issue of Money as occurring in May 1998. In contrast, mentions in a June 1998 issue of the Wall Street Journal or New York Times are coded as occurring in June 1998. 4
ome from larger fund families, and have higher returns and inflows over the prior 12 months than their peers. They are less likely to charge investors a sales commission(load), but their expense ratios are roughly comparable. Relative to the actual distribution of mutual funds across investment objectives, mentions in the publications we study focus disproportionately on general domestic equity funds Funds receiving positive mentions belong to families that spend a greater percentage of family assets on both print and non-print advertising and, since these families are larger, spend much more than average in absolute terms. Interestingly, the sample of funds recommended by Consumer Reports also come from families that spend an above-average amount on advertising. This suggests that advertising may be corre- lated with characteristics that are unobservable to the econometrician but that the financial media uses to rank funds. Consequently, our tests for advertising bias control for fund families'general level of advertis- ing. Examining the share of print advertising by publication reveals that funds receiving mentions from a oublication tend to come from families with higher than average levels of advertising in that publication II. Does Advertising Influence the Media? A. Motivation and Empirical Framework To motivate our tests for advertising bias, consider the mutual funds that appear on Money magazine's annual Money 100 list during our sample period. In an average year, 83.8 percent of families that spent more than $I million on advertising in Money over the prior 12 months are mentioned on the Money 100 list at least once. In contrast, only 7.2 percent of families that did not advertise in Money over the prior 12 months are mentioned. This difference partially reflects the fact that heavy advertisers tend to manage more mutual funds than non-advertisers. However, an individual fund from a heavy advertiser is more than twice as likely to be included on the Money 100 list as an individual fund from a non-advertiser ( 3.0 percent versus 1.3 percent). This difference is consistent with pro-advertiser bias, but obviously does not control for any of the mutual fund or mutual fund family characteristics that might lead publications to rank one mutual fund over another. In particular, one might worry that"high quality"'mutual funds are both more likely to advertise and more likely to receive positive media mentions [Milgrom and Roberts, 1986. To address this concern, we turn to multivariate tests for advertising bias
come from larger fund families, and have higher returns and inflows over the prior 12 months than their peers. They are less likely to charge investors a sales commission (load), but their expense ratios are roughly comparable. Relative to the actual distribution of mutual funds across investment objectives, mentions in the publications we study focus disproportionately on general domestic equity funds. Funds receiving positive mentions belong to families that spend a greater percentage of family assets on both print and non-print advertising and, since these families are larger, spend much more than average in absolute terms. Interestingly, the sample of funds recommended by Consumer Reports also come from families that spend an above-average amount on advertising. This suggests that advertising may be correlated with characteristics that are unobservable to the econometrician but that the financial media uses to rank funds. Consequently, our tests for advertising bias control for fund families’ general level of advertising. Examining the share of print advertising by publication reveals that funds receiving mentions from a publication tend to come from families with higher than average levels of advertising in that publication. III. Does Advertising Influence the Media? A. Motivation and Empirical Framework To motivate our tests for advertising bias, consider the mutual funds that appear on Money magazine’s annual Money 100 list during our sample period. In an average year, 83.8 percent of families that spent more than $1 million on advertising in Money over the prior 12 months are mentioned on the Money 100 list at least once. In contrast, only 7.2 percent of families that did not advertise in Money over the prior 12 months are mentioned. This difference partially reflects the fact that heavy advertisers tend to manage more mutual funds than non-advertisers. However, an individual fund from a heavy advertiser is more than twice as likely to be included on the Money 100 list as an individual fund from a non-advertiser (3.0 percent versus 1.3 percent). This difference is consistent with pro-advertiser bias, but obviously does not control for any of the mutual fund or mutual fund family characteristics that might lead publications to rank one mutual fund over another. In particular, one might worry that “high quality” mutual funds are both more likely to advertise and more likely to receive positive media mentions [Milgrom and Roberts, 1986]. To address this concern, we turn to multivariate tests for advertising bias. 5
Our general approach is to ask whether lagged publication-level advertising expenditures are correlated with the probability of receiving a media mention, controlling for all of the mutual fund and mutual fund family characteristics that publications might reasonably use to rank funds. Consider predicting positive mentions in a particular publication using the following specificatio Mention, t=a +?(Own-Publication Advertising t-1)+BZi, t-1+8k, t +Ei. here Mentionit equals one if fund i receives a positive mention in the publication in month t and zero otherwise, Own-Publication Advertising i t-1 measures lagged advertising expenditures in the publication by fund i's family, Zi t-1 contains numerous control variables, Sk. t is an investment objective-by-month fixed effect, and Eit is a fund-by-month disturbance term. To test whether advertising and content are related we estimate equation(1)and test whether i is statistically different from zero. The identifying assumption equired to give this test a causal interpretation is that advertising within a publication be uncorrelated with any unobserved fund characteristics that would cause its readers to want the publication to mention the advertisers fund. For products whose quality is partially or totally subjective, the fact that advertising is endogenous would lead us to seriously question this assumption. However, in the context of mutual funds, where er post product quality is objective and easil tified, we believe the assumption may be casona From a financial perspective, mutual fund investors should seek to maximize risk-adjusted returns on an after-expense basis. Therefore, within each investment objective, publications should seek to identify those funds with the highest expected future returns and the lowest expenses. Since Carhart [1997 finds low fund expenses to be a good predictor of future returns, we control for fund is lagged expense ratio. As other potential predictors of future returns, we include fund is log return over the prior twelve months, its lagged log return squared, and its Morningstar rating at the end of the prior calendar year. In addition to predictors of future returns, publications should also focus on the form of distribution S Mutual funds with multiple share classes can earn a different Morningstar rating for each share class. Therefore, to control for Morningstar rating we begin with five dummy variables that indicate whether one or more of fund i's share classes earned a Morningstar rating of one, two, three, four, or five stars. We then scale each dummy variable by the fraction of dollars under nanagement receiving each rating
Our general approach is to ask whether lagged publication-level advertising expenditures are correlated with the probability of receiving a media mention, controlling for all of the mutual fund and mutual fund family characteristics that publications might reasonably use to rank funds. Consider predicting positive mentions in a particular publication using the following specification: Mentioni,t = α + γ(Own-Publication Advertisingi,t−1 ) + βZi,t−1 + δk,t + εi,t, (1) where Mentioni,t equals one if fund i receives a positive mention in the publication in month t and zero otherwise, Own-Publication Advertisingi,t−1 measures lagged advertising expenditures in the publication by fund i’s family, Zi,t−1 contains numerous control variables, δk,t is an investment objective-by-month fixed effect, and εi,t is a fund-by-month disturbance term. To test whether advertising and content are related, we estimate equation (1) and test whether ˆγ is statistically different from zero. The identifying assumption required to give this test a causal interpretation is that advertising within a publication be uncorrelated with any unobserved fund characteristics that would cause its readers to want the publication to mention the advertiser’s fund. For products whose quality is partially or totally subjective, the fact that advertising is endogenous would lead us to seriously question this assumption. However, in the context of mutual funds, where ex post product quality is objective and easily quantified, we believe the assumption may be reasonable. From a financial perspective, mutual fund investors should seek to maximize risk-adjusted returns on an after-expense basis. Therefore, within each investment objective, publications should seek to identify those funds with the highest expected future returns and the lowest expenses. Since Carhart [1997] finds low fund expenses to be a good predictor of future returns, we control for fund i’s lagged expense ratio. As other potential predictors of future returns, we include fund i’s log return over the prior twelve months, its lagged log return squared, and its Morningstar rating at the end of the prior calendar year.8 In addition to predictors of future returns, publications should also focus on the form of distribution 8Mutual funds with multiple share classes can earn a different Morningstar rating for each share class. Therefore, to control for Morningstar rating we begin with five dummy variables that indicate whether one or more of fund i’s share classes earned a Morningstar rating of one, two, three, four, or five stars. We then scale each dummy variable by the fraction of dollars under management receiving each rating. 6
that most appeals to their readers. For example, to the extent that personal finance publications appeal to ivestors who prefer to purchase direct-marketed funds rather than employ a broker and pay a load, these publications should be more likely to recommend no-load funds. Since families of no-load funds should then be more likely to advertise in the personal finance publications, Z includes a dummy variable indicating whether fund i charges a load; it also includes the level of fund is 12b-1(marketing and distribution) fee As additional measures of potential investor interest in fund i, we include log dollars under management within both fund i and the fund family to which it belongs, log net inflows into fund i over the prior twelve months, and the number of mentions in each of the other publications in our sample over the prior twelve months. Since mutual fund families that advertise may differ systematically from those that do not--either because advertisers have systematically higher expected future returns or because investors are more likely to value reviews of funds from families they learned about through advertising--z also includes total print and non-print advertising expenditures by fund is family over the prior 12 months B. Testing for Advertising Bias In Table III, we estimate equation(1)separately for each type of media mention. For example, the dependent ariable in the column titled"SmartMoney Positive"equals one if we coded fund i as receiving a positive mention in SmartMoney in month t and zero otherwise. Estimation is via logit and includes a separate fixed effect for each investment objective-by-month combination. The number of observations in this column reflects the number of mutual funds each month with the same investment objectives as those receiving a positive mention in SmartMoney. The explanatory variable of interest is advertising expenditures by fund i's family within SmartMoney over the prior 12 months, which we refer to as " own-publication advertising expenditures. Standard errors are reported below the coefficients and cluster on mutual fund family Moulto 1990 Looking across the columns in Table Ill, the coefficents on own-publication advertising are positive and re not mentioned in the n, our tests for advertising bias effectively condition on the investment objectives that publications choose to focus on each issue and ask, within these investment objectives, whether advertising expenditures influence which funds are mentioned Since we observe advertising expenditures at the mutual fund family level and many families offer funds that span the set of Ivestment objectives, we have insufficient statistical to test whether the choice of investment objectives favors advertisers 7
that most appeals to their readers. For example, to the extent that personal finance publications appeal to investors who prefer to purchase direct-marketed funds rather than employ a broker and pay a load, these publications should be more likely to recommend no-load funds. Since families of no-load funds should then be more likely to advertise in the personal finance publications, Z includes a dummy variable indicating whether fund i charges a load; it also includes the level of fund i’s 12b-1 (marketing and distribution) fee. As additional measures of potential investor interest in fund i, we include log dollars under management within both fund i and the fund family to which it belongs, log net inflows into fund i over the prior twelve months, and the number of mentions in each of the other publications in our sample over the prior twelve months. Since mutual fund families that advertise may differ systematically from those that do not—either because advertisers have systematically higher expected future returns or because investors are more likely to value reviews of funds from families they learned about through advertising—Z also includes total print and non-print advertising expenditures by fund i’s family over the prior 12 months. B. Testing for Advertising Bias In Table III, we estimate equation (1) separately for each type of media mention. For example, the dependent variable in the column titled “SmartMoney Positive” equals one if we coded fund i as receiving a positive mention in SmartMoney in month t and zero otherwise. Estimation is via logit and includes a separate fixed effect for each investment objective-by-month combination. The number of observations in this column reflects the number of mutual funds each month with the same investment objectives as those receiving a positive mention in SmartMoney. 9 The explanatory variable of interest is advertising expenditures by fund i’s family within SmartMoney over the prior 12 months, which we refer to as “own-publication advertising” expenditures. Standard errors are reported below the coefficients and cluster on mutual fund family [Moulton 1990]. Looking across the columns in Table III, the coefficents on own-publication advertising are positive and 9Because funds with investment objectives that are not mentioned in the publication in month t are excluded from the estimation, our tests for advertising bias effectively condition on the investment objectives that publications choose to focus on each issue and ask, within these investment objectives, whether advertising expenditures influence which funds are mentioned. Since we observe advertising expenditures at the mutual fund family level and many families offer funds that span the set of investment objectives, we have insufficient statistical power to test whether the choice of investment objectives favors advertisers. 7
tatistically significant at the l-percent level for positive mentions in all three personal finance publications. 0 The coefficients are also economically significant. For Money, the marginal effect of Sl million in family advertising expenditure is to increase the probability a of positive mention for each of its funds by 0.2% compared with a predicted probability(at sample means) of 0.5%. For Kiplinger's, those probabilities are 0. 1% and 0.08%, respectively, and for SmartMoney they are 0. 2% and 0. 2%. Put differently, variation own-publication advertising has more explanatory power for positive mentions in each of the personal finance publications than variation in fund expenses, and about the same explanatory power as past returns As another way of gauging the economic significance of our findings, we use the coefficients reporte in Table Ill to predict the set of funds we would expect each publications to mention, first including the influence of own-publication advertising and then excluding it. For example, if SmartMoney mentioned 10 aggressive growth funds favorably in month t, we treat the 10 aggressive growth funds with the highest predicted values based on our estimates of equation (1)as predicted mentions that include the influence of own-publication advertising. We then repeat this exercise, setting the coefficient on own-pu advertising equal to zero For the Money 100 list, the overlap in the two sets of predicted mentions is 91.5% suggesting that 8-9 funds were replaced on the list by advertisers'funds that had otherwise just missed the cutoff. For positive mentions in Kiplinger's and SmartMoney, the overlap is 77.0% and 77.9%, respectively In contrast to the results for the personal finance publications, the coefficient on own-publication adver- tising is a precisely estimated zero for the Wall Street Journal and negative, but statistically indistinguishable from zero, for the New York Times. Since the three personal finance publications receive between a much larger share of their advertising revenues from mutual funds than the newspapers, our findings are consistent with advertising expenditures influencing fund rankings in those publications relatively more dependent on mutual fund advertising. Of course, for Wall Street Journal, the lack of a statistically significant correlation between advertising and mentions could also reflect that mentions in the"Fund Track" column are a mixture of positive and negative, and driven primarily by news with respect to negative mentions, advertising bias predicts that y will be negative, making publica- tions less likely to include advertisers'funds in negative mentions. Here, evidence of bias is weaker. For oThe correlations between advertising and content reported in Tables III and IV are robust to the inclusion of additional fund characteristics, such as fund age, manager turnover, and the standard deviation of fund returns over the prior 36 months
statistically significant at the 1-percent level for positive mentions in all three personal finance publications.10 The coefficients are also economically significant. For Money, the marginal effect of $1 million in family advertising expenditure is to increase the probability a of positive mention for each of its funds by 0.2% compared with a predicted probability (at sample means) of 0.5%. For Kiplinger’s, those probabilities are 0.1% and 0.08%, respectively, and for SmartMoney they are 0.2% and 0.2%. Put differently, variation in own-publication advertising has more explanatory power for positive mentions in each of the personal finance publications than variation in fund expenses, and about the same explanatory power as past returns. As another way of gauging the economic significance of our findings, we use the coefficients reported in Table III to predict the set of funds we would expect each publications to mention, first including the influence of own-publication advertising and then excluding it. For example, if SmartMoney mentioned 10 aggressive growth funds favorably in month t, we treat the 10 aggressive growth funds with the highest predicted values based on our estimates of equation (1) as predicted mentions that include the influence of own-publication advertising. We then repeat this exercise, setting the coefficient on own-publication advertising equal to zero. For the Money 100 list, the overlap in the two sets of predicted mentions is 91.5%, suggesting that 8-9 funds were replaced on the list by advertisers’ funds that had otherwise just missed the cutoff. For positive mentions in Kiplinger’s and SmartMoney, the overlap is 77.0% and 77.9%, respectively. In contrast to the results for the personal finance publications, the coefficient on own-publication advertising is a precisely estimated zero for the Wall Street Journal and negative, but statistically indistinguishable from zero, for the New York Times. Since the three personal finance publications receive between a much larger share of their advertising revenues from mutual funds than the newspapers, our findings are consistent with advertising expenditures influencing fund rankings in those publications relatively more dependent on mutual fund advertising. Of course, for Wall Street Journal, the lack of a statistically significant correlation between advertising and mentions could also reflect that mentions in the “Fund Track” column are a mixture of positive and negative, and driven primarily by news. With respect to negative mentions, advertising bias predicts that γ will be negative, making publications less likely to include advertisers’ funds in negative mentions. Here, evidence of bias is weaker. For 10The correlations between advertising and content reported in Tables III and IV are robust to the inclusion of additional fund characteristics, such as fund age, manager turnover, and the standard deviation of fund returns over the prior 36 months. 8
negative mentions in Kiplinger's, the coefficient on own-publication advertising is negative but statistically indistinguishable from zero: for negative mentions in SmartMoney, the coefficient is also negative and statis- tically insignificant, but quite close to zero. Nevertheless, for both publications, we can reject the hypothesis that the marginal effects of own-publication advertising are equal for positive and negative mentions(at the 5-percent level). This fact casts doubt on one alternative explanation for our findings. Namely, if past advertising in a publication directly increases reader demand for information on advertiser's funds, we would expect advertising to predict more positive mentions and more negative mentions. However in Table Ill,we find evidence that advertising expenditures increase positive mentions more than negative mentions Before exploring the robustness of our main results, several of the coefficients on the control variables deserve mention. First, counter to our expectations, few of the coefficients on the total print and non-print advertising expenditure variables are statistically significant. The fact that the coefficients on total print advertising expenditures are positive for both types of negative mentions, suggests that Kiplinger's and SmartMoney may be responding to subscriber demand for negative reviews on funds they 've seen advertised in general (rather than specifically in Kiplinger's or SmartMoney). Second, the probability of receiving both positive and negative mentions is increasing in the size of fund i and decreasing in the size of its family Third, the probability of receiving both positive and negative mentions is increasing in the level of the fund is expense ratio for every publication except Consumer Reports. Fourth, funds experiencing inflows good returns, and(though not reported)favorable Morningstar ratings over the prior 12 months are more likely to receive positive mentions, while outflows and low returns and ratings are associated with negative mentions. Fifth, with the exception of the New York Times, the probability of receiving a positive mention lower for load funds than for no-load funds 1 I As discussed above, load fund families are less likely to advertise in publications catering to do-it-yourself investors,and ss likely to mention their funds. Including a load dummy variables controls for this effect, but as an additional robustness check, we restrict our sample pad funds and re-estimate equation(1)for mentions in the thre personal finance publications. For positive mentions, the estimated coefficients on own-publication advertising are uniformly larger than those reported in Table Ill, and statistically significant at the l-percent level. For negative mentions, both coefficients remain negative but statistically indistinguishable from zero
negative mentions in Kiplinger’s, the coefficient on own-publication advertising is negative but statistically indistinguishable from zero; for negative mentions in SmartMoney, the coefficient is also negative and statistically insignificant, but quite close to zero. Nevertheless, for both publications, we can reject the hypothesis that the marginal effects of own-publication advertising are equal for positive and negative mentions (at the 5-percent level). This fact casts doubt on one alternative explanation for our findings. Namely, if past advertising in a publication directly increases reader demand for information on advertiser’s funds, we would expect advertising to predict more positive mentions and more negative mentions. However in Table III, we find evidence that advertising expenditures increase positive mentions more than negative mentions. Before exploring the robustness of our main results, several of the coefficients on the control variables deserve mention. First, counter to our expectations, few of the coefficients on the total print and non-print advertising expenditure variables are statistically significant. The fact that the coefficients on total print advertising expenditures are positive for both types of negative mentions, suggests that Kiplinger’s and SmartMoney may be responding to subscriber demand for negative reviews on funds they’ve seen advertised in general (rather than specifically in Kiplinger’s or SmartMoney). Second, the probability of receiving both positive and negative mentions is increasing in the size of fund i and decreasing in the size of its family. Third, the probability of receiving both positive and negative mentions is increasing in the level of the fund i’s expense ratio for every publication except Consumer Reports. Fourth, funds experiencing inflows, good returns, and (though not reported) favorable Morningstar ratings over the prior 12 months are more likely to receive positive mentions, while outflows and low returns and ratings are associated with negative mentions. Fifth, with the exception of the New York Times, the probability of receiving a positive mention is lower for load funds than for no-load funds.11 11As discussed above, load fund families are less likely to advertise in publications catering to do-it-yourself investors, and these publications are less likely to mention their funds. Including a load dummy variables controls for this effect, but as an additional robustness check, we restrict our sample to no-load funds and re-estimate equation (1) for mentions in the three personal finance publications. For positive mentions, the estimated coefficients on own-publication advertising are uniformly larger than those reported in Table III, and statistically significant at the 1-percent level. For negative mentions, both coefficients remain negative but statistically indistinguishable from zero. 9