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 monthsstatistically 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 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 γ will be negative, making publica￾tions 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