Transcript
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DETERMINING TELEVISION ADVERTISING RATES

By

Benjamin J. Bates Paper presented at the 33rd International Communication

Association conference, Dallas TX, May 1983. A revised version was published as:

Bates, Benjamin J. "Determining Television Advertising Rates." In R. N. Bostrom (Ed.), Communication Yearbook 7 (pp. 462-475). Beverly Hills: Sage, 1983.

Contact Info: Benjamin J. Bates School of Journalism & Electronic Media University of Tennessee Knoxville, TN 37996-0333 [email protected]

ABSTRACT Previous work has identified various factors as

contributing to the setting of television rates, without giving indication or empirical verification of the manner or size of such effects. This study proposes that such quantification of effects upon television spot advertising rates can be achieved through the development of a predictive model for such rates over time. Using multiple observations from the period 1973-1981, the development of such a model provided strong evidence that the identified factors, and the factor of inflation, did affect the setting of spot rates. In addition, the examination of rates over time permitted the application of analysis of covariance techniques which indicated that the effects of certain factors had significantly changed over that period.

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DETERMINING TELEVISION ADVERTISING RATES Commercial television stations are, for the most part,

supported through the sale of a part of their broadcast schedule to advertisers. This time is sold primarily in the form of short spots interspersed among the stations' entertainment programming. As the supply of these spots is fairly constant, the rates which stations charge for these spots in their schedule are quite dependent upon the demand among advertisers for those spots. Since the aim of advertising is to convey a message to an audience effectively, and television is essentially a medium to reach an audience, the demand for advertising on a station will be dependent upon the audience that advertising message reaches.

That is, since advertisers are actually purchasing access to an audience, it is expected that the price that advertisers would be willing to pay for that spot will depend upon that audience. Therefore the price that stations are able to get for their spots will depend on the audience that stations can attract for those spots. In previous studies over the last fifteen years, a number of audience factors have been identified as contributing to the determination of prices for broadcast time on television.

In 1967, W. T. Kelley undertook a survey of broadcast managers in the Philadelphia area, and found that market size and quality, station coverage, and competition were all major factors which were considered in the rate-setting process. Kelley did not, however, provide any evidence or proposals as to the manner in which these factors contributed to the determination of rates. In an 1976 article, S. M. Besen attempted to fill this gap in part by deriving an empirical model for the value of television time. Using pure, or block, time rates as the dependent variable, Besen's model included measures of market size, competition, network affiliation, and

the station's broadcast frequency. While providing empirical evidence of effects, the work was limited in that the model did not directly consider advertising spot rates and was limited to the examination of a single year. French & McBrayer (1979), in a qualitative article looking at the factors which determine station spot rates, found local market rates to be strongly influenced by three major factors: demand, competition, and ratings. And demand, they stated, was largely determined by the size of the market and local economic conditions.

This study will involve the empirical verification and measurement of those factors cited in the previous studies, namely the size of the market, the quality of the market, local economic conditions, the direct competition from other commercial stations in the market, differences in station coverage, the network affiliation (if any) of the station, and the broadcast band in which the station operates. It will refine the cited factors where appropriate, define reasonable measures, and then examine the possible effects of those measured factors upon the rates for television spot advertising.

It is not the goal of this project to provide a deterministic model of the rate setting process and proclaim its validity. The actual manner in which rates are set are too indistinct, relying more upon instinct and response to market forces rather than specific models or formulae. However, such processes are likely to involve non-concrete consideration of certain key factors. By modeling the apparent relationship between these factors and rates, it is possible to provide hard evidence in support of the role such factors play in the determination of advertising rates. The goal of this research is to provide such evidence.

METHOD As the factors that this study addresses are considered to be

determinants of spot rates, it was decided to base empirical

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analysis upon predictive modeling procedures. These would primarily involve the development of linear or essentially linear models using a combination of real and categorical (indicator) variables. As the various studies made no proposals or predictions as to the nature of effects, this modeling process would not be directed towards the determination of a single predictive model for advertising spot rates. Rather, through the model building process and the consideration of alternative models, those factors having a general significant impact can be identified, and general indicators of the particular effects determined. The appearance of effects of similar size and type across models would, in fact, reinforce the significance and validity of a factor in the setting of rates.

It was also decided to model this process over a period of time. Consideration of the factors and rates over time would allow for the reliable examination of the general significance of the factors without restricting the validity of the procedure and the results to a single period. In addition, the presence of multiple observations from a single station could allow for the direct consideration of a factor's effect as well as reflect the possible presence of additional explanatory factors. Further, the examining of rates over time would allow for consideration of possible changes in the size or significance of the various factors' effects over time, and the possible identification of trends.

The measures used for this analysis were constructed to take advantage of data which are generally available. For the basic dependent factor of rate, or the price of television spots, it was decided to use the highest rate quoted for a thirty second (:30) television spot, as reported in the annual editions of either the TV Factbook or the Broadcasting Yearbook. As these rates were to be collected and analyzed over a period of time, only those rates which could be reliably determined to be valid at a

particular point in time were used, and were noted in conjunction with that date. The associated date was then used to match the rate information with other dated measures.

A second dependent variable was constructed by adjusting the rate data for the effects of inflation. The adjustment was made through the use of the Consumer Price Index (as advertising spots were considered to be finished goods) in an attempt to render the rates collected over time comparable. As will be pointed out in the analysis section, this attempt was only partially successful.

The measurement of some of the cited factors as independent variables was quite straightforward. Reports of the network affiliation and broadcast frequency of stations were obtained from the TV Factbook and Broadcasting Yearbook. A simple nominal measure was constructed to indicate whether a particular station's frequency, or channel, assignment was in the UHF or the VHF bands. A nominal measure was also constructed to indicate the primary network affiliation of the station, if any.

A consideration of the factors cited by the three studies reveals the need for the refinement or addition of another factor, as the size of the audience for a message or station is not directly considered. The factor of audience size would seem to be the single most important factor in differentiating audiences, and thus the demand for broadcast spots, yet it is not directly cited in those three previous studies. It would appear, however, that indirect consideration was given to audience size in the inclusion of the market size factor, though reliance on that measure alone presumes that all stations in the market cover that market both fully and equally well. Both of these assumptions are generally suspect, leaving an audience size measure of questionable reliability and validity. Some of the studies attempted to rectify this condition with the added

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consideration of the factor of station coverage differences, removing major problems, but still leaving the consideration of audience size to indirect means.

Thus, it would seem appropriate to include the factor of audience size among the potential factors affecting television spot rates. This factor's inclusion in the analysis is not only theoretically indicated, but it should also provide for a more reliable analysis and relieve any confounding of those factors which vary with station coverage.

It was decided to measure audience size through the use of the Average Daily Circulation (ADC) for the station for a given season, as measured by Arbitron and reported in the TV Factbook. While not giving a specific measure of absolute audience at any given time, the ADC is a reasonably valid and reliable indicator of the number of households who can, and do, watch that station regularly over a period of time. Thus, it can be considered a good measure of the potential (or likely) audience for any station.

The measurement of several other factors depended to a certain extent upon the specification of television broadcast markets. For the purposes of this study, it was decided to base market definitions upon those used by Arbitron, as reported in the Broadcasting Yearbook, only treating Arbitron's "supplemental" markets as separate markets. From this basis, the factor of market size was measured relatively by the rank of the market as reported by Arbitron for a season in the middle of the data collection period, resulting in an ordinal measure. Unranked markets were arbitrarily assigned ranks falling below the lowest of the ranked markets.

The factor of direct competition from other stations was measured by the number of commercial television stations licensed and operating in the market at the given date. Noncommercial stations were not included as they did not

provide competition for broadcast spots, although they did provide some competition for audience. The station's competition in the market, however, will also affect audience size, as it reflects the number of stations which must share the potential audience. That is, the actual audience for a station will be a share of potential audience, and that share is determined in large part by the competition a station faces.

This leaves only the rather ill-defined factors of market quality and local economic conditions which had been cited in earlier studies. One aspect of both factors which should also be of particular interest to potential advertisers would be the wealth of the market; the ability of those in the audience to actually purchase the advertised goods or services. One widely available measure of this ability to buy is the Effective Buying Income (EBI) measure, as developed and reported by Sales & Marketing Management, Inc., in their annual Survey of Buying Power. For this study, a scaled, relative measure based upon the median market EBI was constructed. This market quality measure segmented the median market EBI as to whether it was, in comparison with the national median, over 20% above, between 5-20% above, within 5%, between 5-20% below, or over 20% below.

A second measure of market quality was included, in the form of the forecast growth rate for the market. These forecasts, in the form of the projected growth in households in a market, were also obtained from the annual Survey of Buying Power volumes, and the rates scaled and segmented in the same manner as the EBI measure. It should be noted that the precise market definitions used in the Survey of Buying Power measures did not always precisely match those of Arbitron, although in all cases the major population center(s) were in accord.

All measures were obtained on an annual basis where

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needed to match the dependent variables obtained for commercial stations within the continental United States over the period 1974-1981. It should be noted, however, that valid rates were obtained from these sources for earlier periods, and those rates reported for 1973 were also included, along with the corresponding measures of the independent variables. An additional variable was then constructed to denote and label the stations for which data were collected and included for analysis. The resulting data set was then restricted to those data from stations for which at least four observations of rates were reported over the collection period. This yielded a set of 856 observations from a total of 164 stations for preliminary analysis. Later, the data set was expanded to include observations from other stations in the same markets as those in the initial set of observations. This second, expanded data set included a total of 1068 observations from 232 stations.

ANALYSIS The analysis for the significance of the cited factors began

as an exploratory data analysis leading to the development of predictive models for spot rates. First, the data sets were examined to identify the significant predictive factors for television spot rates. Note was taken of the different types of measures used to derive the various independent variables, and those initial measures were treated or transformed where appropriate to fit the kinds of effects or relationships revealed in these exploratory procedures.

The analysis began with an examination of the cross-correlation matrix among the ten variables. Several of the measures evidenced a correlation with the dependent variables of spot rates (RATE) and the spot rates adjusted for inflation (ADJRATE). Those correlations greater than 0.1 are listed by factor in Table 1. A highly significant correlation was evidenced between the audience size measure (ADC) and

the dependent variables, tending to confirm the importance and significance of this factor in the setting of television spot rates. High correlations were also found for the initial variables measuring the factors of market size (MKT), station competition (COMP), the date of the observed rate (YEAR), broadcast band (UHF), and one of the market quality measures (EBI). It should also be noted that there was a significant amount of cross-correlation among the independent variables as well.

[Table 1 about here] The high correlation of audience size with rates

suggested the initial appropriateness of regression procedures in the modelling process. As there was no initial reason to assume the inherent linearity of the relationship between audience size and spot rates, it was decided to examine a variety of essentially linear regression models. An examination of the plot of RATE vs. ADC indicated the likelihood of an exponential relationship. To include the consideration of these types of relationship in the modeling process, variables for the natural logarithms of RATE (LNRATE), ADJRATE (LNADJ), and ADC (LNADC), and the computation of regressions involved the original as well as transformed variables.

These regressions, which are summarized in Table 2, indicated that the regression procedures "explained" about two-thirds of the variation in spot rates between stations and over time. These regressions also suggested the relative validity of the adjustment for inflation and the appropriateness of the logarithmic transformations, in that an examination of the R2 statistic indicated that the models employing those adjustments provided a better fit to the data than the models without the adjusted variables. The validity of these "essentially linear"

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models was further supported by the analyses of the residuals resulting from these regressions, as those residuals evidenced less systematic effects.

[Table 2 about here] While the use of the audience size measure in essentially

linear models "explained" a great deal of the variation in rates, it also left some to be explained by other factors. The residuals of these regressions were examined, and all showed a high correlation with the YEAR variable, even those coming from models using the adjusted rates as dependent variables. This correlation with time, in fact, was in all cases higher than the correlation with any other single factor.

This finding was somewhat of a surprise, as it was surmised that correcting for inflation would remove any such effects in models using the adjusted rates. The manner in which the YEAR variable appeared to contribute to the determination of price indicated, however, that while adjusting for the effects of inflation was appropriate, the Consumer Price Index was not a good measure of inflation in television spot advertising rates.

Thus, the factor of time, as measured by the YEAR variable, was entered into all of the developing models, resulting in a series of multiple regressions which are summarized in Table 3. All the resulting linear models proved to be significant predictors of rate, both when considered in toto and by variable. The addition of the YEAR variable increased in all cases the predictive power of the model, resulting in one case of a model which explained over 80 percent of the variation in rates. At this time, it was decided to remove the linear, additive, models from further analysis. Not only was the inappropriateness of these models suggested in the analyses of residuals, but the presumption of constant additive effects for the factors which the model implied also

seemed invalid, particularly in comparison with the multiplicative effects presumed in the other models.

[Table 3 about here] It seemed that a fairly firm basis had been established for

the determination and development of predictive models for spot rates in television. At this point, the remaining factor measures (such as affiliation, competition, and market size and quality) were molded, where necessary, into a set of indicator variables which would demonstrate their significance to the model, and thus their role in the determination of rates. The precise derivation of the indicator variables for the nominal and ordinal measures was accomplished through an iterative procedure of model-building and residual analysis.

This procedure involved the construction of models which included the factor in question, then fitting the same model without that factor. Examination of the resulting residuals was then used to indicate the manner in which the factor under development contributed to the original predictive model. Separate indicator variables were then constructed to mirror that effect. In addition, it should be noted that as factors were converted to indicator variables, the appropriateness of those constructed variables were checked by repeating the above process.

After the iterative procedures were used to develop the appropriate variables, a series of stepwise regression procedures was used to identify the significant indicator variables within the various model permutations. With the focus on the transformed, multiplicative, models, the effects of the individual indicator variables upon the prediction of rates were assumed to be multiplicative, resulting in what could be considered to be either a bonus or a discount associated with a certain state of the basic factor. As the procedure involved the consideration of multiple models, both a forward and

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backwards selection procedure was instituted in the stepwise regressions, resulting in the consideration of eight separate linear models, with multiple R statistics running between .83510 and .92080. A brief summary of these models, fitted to the data set of 1068 observations, is given in Table 4.

[Table 4 about here] The effects of station affiliation were apparent in all eight

models. In all cases, independent stations were found to have significantly lower station rates than other stations. Rates for independent stations were about half of those for network affiliated stations with similar audiences. In addition, spot rates for NBC affiliates were found to be uniformly about 10% lower than the rates for affiliates of the other two major networks, when all other factors were taken into account. No significant difference was found between ABC and CBS affiliation in any of the models. An indicator variable was also used to distinguish the affiliates of the Spanish International Network (SIN), and this variable was found to make a significant contribution to the prediction of spot rates in all but two of the models. Also, SIN affiliate rates were significantly different from those for the other network audience (ceteris paribus) for half of the models, and only marginally significant with opposite effects for the other half. This variability was possibly due to the small number of observations from SIN affiliates, and the limitation of the SIN affiliates to the largest markets.

Significant effects for the factor of market size were found in all but two of the predictive models. The market size measure was segmented into three groups through the use of indicator variables: the top twenty five markets, the next twenty five markets (ranks 26-50), and all other markets. The size, direction, and significance of these indicator variables differed greatly for the two treatments of the audience size

measure in the models. This was not totally unexpected, as the use of transformations placed differing emphases on certain ranges of audience size, and the ADC and market size factors were highly correlated. Still, there was evidence of a separate effect accruing to the market size factor, although for one treatment that effect was of marginal significance.

The factor of station competition was also found to have significant effects towards the prediction of spot rates, though the size of these effects also varied somewhat with the particular treatment of the audience size measure. In particular, a bonus of around 20 percent for all stations in markets having at least four stations was indicated, although no statistically significant difference was noted for markets of four, five, six, or more stations. That is, although rates were generally higher in those markets with at least four competing commercial television stations, no additional increase was then noted for the addition of other stations to the market. This result was fairly uniform across models. Differences in station rates were also noted between stations with one, two, and three competing stations, although the size of these effects evidenced greater variation. This, again, was not totally unexpected, as the number of stations in a market is highly correlated with market and audience size.

The two measures of market quality also proved to make significant contributions to the predictive models, which indicated the presence of some effect upon station spot rates, although the precise nature of that effect varied from model to model. In all cases, however, the basic effect indicated was that stations in markets with higher EBI or higher growth rates were able to get higher prices for their spots, with effects in the range of ten percent.

Finally, examination of these linear models indicated that stations in the VHF band were able to charge higher rates for

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their spots than stations situated in the UHF band, but this bonus was uniformly less than ten percent. Thus, the consideration of the multiple models confirmed the significance of the cited factors in the determination of television spot rates, although the effects did not appear to be uniform across models for all factors.

For a final estimation of the specific size of the effects, it was necessary to pick out a specific predictive model. On the basis of predictive power and other indicators of fit such as residual analysis, it appeared that the best predictive model was that using LNRATE as the dependent variable and LNADC as the primary independent variable. The specific estimations of coefficients, and thus effects, are given in Table 5, along with the appropriate statistical measures.

[Table 5 about here] As a further indication of the type of effects evidenced by

these factors, a series of analyses of covariance models was also derived from the enlarged data set. These models allow for the examination of the fitted models on a year by year basis, and allow the consideration of the uniformity of effects over time. As the relatively small number of observations from certain years made estimation impossible due to problems of singularity, the analysis was restricted to the period from 1975 to 1979, inclusive, using the expanded data set of 1068 observations. With the restriction, the data set for analysis was reduced to 812 observations. Further, the consideration of the analysis of covariance findings will be restricted to the LNRATE-LNADC model which was selected as the "best" predictive model for television spot rates.

The basic analysis of covariance table yielded several test statistics which indicated the validity of this approach. While these statistics indicated the general significance of the predictive model and of the covariate YEAR, they also

indicated that that portion of the model remaining was possibly not uniform over the period. That is, the statistics indicated the likelihood that some or all of the modeled factors' effects may have changed over time. The analysis of covariance procedure used provided for, along with the general model estimation, the fitting of separate models for each of the covariate values. This provided estimates of coefficients for each year.

Several interesting developments emerged from a consideration of these estimated coefficients. First, it appeared that there were statistically significant differences among the fitted coefficients for most of the factors, in the sense that either some coefficients could not be statistically distinguished from zero (indicating the possibility of no effect), or that one or more of the estimates greatly varied from other years' coefficients for that variable. While some of this may have resulted in part from the small sample size of some component factors, enough regularity in the pattern of estimates remained in some cases to indicate some interesting relationships in the model.

First, it was noted that the effect of the broadcast band factor effectively disappeared after 1976. Uniform, significant coefficients were estimated for the corresponding indicator variable for 1975 and 1976, but in all following years the fitted coefficients were an order of magnitude lower, and could not be reliably distinguished from zero. Thus there was no evidence that a station's frequency had an effect upon the determination of its rates after 1976.

In a similar vein, there seemed to be a change in the size of the effects attributable to station competition over time. Specifically, the coefficients for the indicator variable reflecting those markets large enough to field an independent station (i.e. four or more stations) evidenced two distinct levels. There appeared to be a sizeable effect for that factor

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until 1978, when the effect was about half that estimated for the previous three years.

The analysis of covariance also indicated an interesting elaboration of the effect of station affiliation with NBC which had been noted in the earlier models. The results of this procedure indicated that there seemed to be no significant difference in rates among network affiliated stations until 1977, when the "discounting" effects of NBC affiliation began. In fact, the coefficient for the NBC indicator variable showed steady increase from that point, indicating a worsening of the relative position of NBC affiliates. This, it should be noted, coincided with the dramatic fall of the effectiveness of NBC's prime time programming in attracting audience and the resultant fall of NBC to a distant third in the ratings race.

Another regularity which was noted was the overall consistency of the the market quality measure EBI. While the individual coefficients showed a fair amount of variability, the overall effect noted from high to low values remained fairly consistent. The consistency of the other market quality measure, household growth, was not as evident.

The coefficients for the other indicator variables, and the real audience measure LNADC, remained fairly consistent. As no other general pattern in the estimated coefficients emerged, they were judged to have had consistent effects over the period 1975-1979.

CONCLUSIONS The goal of this research was to provide quantitative

verification of the role of certain specified factors in the determination of television spot advertising rates. The development of a predictive model accounting for about 85 percent of the variation in spot ad rates over a period of eight years for a group of 232 stations across the U. S. serves as the

basis for that verification. While not specifically implying causation in the direction of factors to rates, the unlikelihood of rates affecting factors such as broadcast frequency, audience size, and station affiliation infer an asymmetrical relationship in the other direction. That is, the presence of such an effective, useful, predictive model supports the claim that the factors represented by the variables in the model do have some sort of effect upon the setting of television advertising rates.

In the general models, some effect was indicated for all of the factors mentioned in the earlier studies by Kelley(1967), Besen(1976), and French & McBrayer(1979). Specifically, the empirical study of spot rates indicated the following general and specific effects.

The factor with the greatest effect upon the determination of spot rates in television was the audience size. This effect was strong, and was fairly consistent, explaining on its own roughly two thirds of the variation in rates between stations over time. The second most important factor found in the study, time, was not mentioned specifically in the earlier research. It is a factor, however, that would only show up in a consideration over time. The interesting result of this analysis was not only the affirmation of the importance of considering inflation over time, but the models' indication that the rate of inflation in television spot advertising rates was roughly twice that indicated by the Consumer Price Index over the same period. Inflation in spot rates, the model estimated, ran about 19 percent a year over the period for which data were collected.

The remaining contributions to the predictive models were made by indicator variables. It should be noted that the possible confounding of market size with a number of the indicator variables, such as those for the factors of competition and market quality, acts to restrict the kind of conclusions

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which can be drawn. In addition, the differing strengths of the contributions to the predictive model made by the indicator variables for separate aspects of factors make any specific statements about relative strengths of those factors' impacts difficult.

Having mentioned the appropriate caveats, the following conclusions and interpretations are offered. The factor of station affiliation proved to be fairly significant in the determination of station rates, particularly in the distinction between network and non-network affiliates. This may have resulted in part from the relative accuracy of the audience size measure, ADC, as an indicator of prime time audience for stations. Traditionally, and for most markets, independent stations are not equal competitors with network stations during that time, as the strength of independent stations in attracting an audience lies in periods outside normal prime-time, whereas prime time spots usually yield the highest rates. Thus, the audience size measure upon which rates are predominantly based is apt to be skewed for independent stations as a result of their apparent inability to compete on an equal basis with network affiliates during prime time.

This may also explain the finding of the growing difference in rates for NBC affiliates. The models indicated that, over the sample period, NBC affiliates generally earned less than ABC or CBS affiliates. In fact, the analysis of covariance indicated that this was a growing difference over the period from 1975 to 1979, roughly matching that period when NBC was not competing on an equal basis during prime time impact upon station rates, at least when measured in terms of the number of commercial television stations licensed to a market. In general, the models indicated that the more competition, the higher the rates in a market, at least to the point where there were at least four commercial stations in the market. Despite initial

appearances, this is not an unexpected finding. The audience size measure is more a measure of potential audience than actual audience, so the amount of competition will then indicate how close potential and actual audience is to be for a station. That is, the ADC measure for a station with no direct competition (i.e., located in a single station market) is apt to match the actual viewing audience for that station, while the actual viewing audience for a station with competition is likely to be only a fraction of the ADC (potential audience) measure. Thus, the pattern of discounts and bonuses for the set of indicator variables is not unexpected. The effects demonstrated through these independent variables may reflect the differences between a station's potential audience and their actual share of prime time audience.

It should be noted that for the final predictive model, the discount for the single station market was actually less than that for the two station market, indicating that there was likely to be some monopoly benefit accruing to stations without competition. However, the nature of the variables and measures used did not allow for a precise separation of effects. It should further be noted that the number of stations in a market is confounded with market size, further clouding the precise nature of this effect.

Lesser effects were noted for the two market quality measures. Stations in markets with higher than average Effective Buying Income measures tended to have higher rates than stations in markets with lower than average measures. The bonus accruing to those stations ranged between seven and ten percent in the various predictive models. As for the growth measure of market quality, that also showed some effect in the predicted direction, with stations in markets with significantly higher growth rates (20 percent above the national rate) having about a ten percent higher rates than stations in other markets.

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It should be noted that these differences showed up only in those markets with extreme differences in growth rates from the national average, indicating that this measure is likely considered only subjectively in the determination of rates.

There did appear to be a UHF/VHF difference in the rates charged by stations for spot advertising. This effect was small, however, amounting to only about a seven percent difference, and did not contribute a great deal to the explanatory power of the models. In fact, the separate analysis of covariance indicated that this effect seems to have disappeared after 1976. The disappearance of effect can possibly be traced to the rise of cable systems in fringe areas, where the signal strength differences (resulting in differing picture quality) are greatest, and the resultant diminishing of qualitative differences between UHF and VHF stations which might result in differences in audience and thus the rates stations can charge for their advertising spots.

Finally, there is the consideration of the market size, which was presumed to have been largely dismissed by the consideration of the audience size. The development of the predictive models did indicate directly (and possibly indirectly) the presence of a market size factor above and beyond audience size. The precise nature of the market size effect indicated by the model, however, was dependent upon the nature of the relationship assumed between audience size and rates in the particular model which was fitted (that relationship being determined by the transformations used). Such differences in effects, while reaffirming the link between audience and market size, also indicates a differential effect at various levels of market/audience size. Should this area be studied further, this is one aspect which should be further investigated, as this procedure was hampered in that regard by a possibly too

restrictive measurement of market size. In summation, it appears reasonable to reach the following

conclusion from this analysis. First, that the factors of audience and market size, station competition, station affiliation, broadcast band, and market quality/local economic conditions did have some kind of influence in the determination of television spot advertising rates. A listing of these factors, ranked by their relative importance of their contribution, is given in Table 6. Second, that there has been inflation in spot rates, and the rate of that inflation has been about twice that of the CPI. And last, but not necessarily least, that the effects of factors upon rates need not be, and have not been, constant; that changing conditions may diminish or even invalidate the significance of specific factors, or bring forth new factors to consider in the determination of rates.

[Table 6 about here] BIBLIOGRAPHY

Besen, S. M. "The Value of Television Time."

Southern Economic Journal, 42:435-441 (1976) Broadcasting. Yearbook. (annual) French, W. A., and McBrayer, J. T. "How Television Stations

Price Their Service." Journal of Advertising, 8:15-18 (1979)

Kelley, W. J. "How Television Stations Price Their Service." Journal of Broadcasting, 11:313-323 (1967)

Sales and Marketing Management, Inc. Survey of Buying Power. (annual)

Television Digest, Inc. Television Factbook. (annual)

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Table 1. Correlations

Correlation with Variable RATE ADJRATE

ADC .7832 .8502 MKT -.4950 -.5530 COMP .4558 .4909 YEAR .2984 .1936 EBI -.2184 -.2421 UHF .1387 .1574

Note: all the above correlations were significant at the p=.01 level

Table 2. Initial Regressions

Variables Rate Audience

R-square

RATE ADC .61352 ADJRATE ADC .72288 LNRATE ADC .63199 LNADJ ADC .69935 LNRATE LNADC .66762 LNADJ LNADC .73723

Note: the F-test statistic for all the regressions indicated their significance at a level of p<.001

Table 3. Multiple Regressions

Variables Dependent Independent

R-square

RATE ADJRATE LNRATE LNADJ LNRATE LNADJ

ADC, YEAR ADC, YEAR ADC, YEAR ADC, YEAR LNADC, YEAR LNADC, YEAR

.67428

.74164

.78342

.74717

.80957

.77947

Note: the F-test statistics for all regressions indicated their significance at a level of p<.001

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Table 4. Predictive Models

Variablesa Forward Selection Backwards Selection

Rate Audience Mult R R2 Mult R R2

LNRATE LNADC .92049 .84730 .92078 .84783 LNRATE ADC .90951 .82720 .91041 .82884LNADJ LNADC .90871 .82576 .90910 .82646LNADJ ADC .89588 .80200 .89689 .80441

Note: the F-test statistics for all models indicated their significance at a level of p<.001 a The stepwise procedure also selected from the full set of indicator variables and the variable YEAR in all cases Table 5. Estimates of Effectsa

Factor Variable Partial Coefficient T-Stat

Constant -.49926 -1.420 Audience LNADC .70111 .72056 31.906+++Inflation YEAR .68332 .17264 30.370+++Bdcst Band UHF' .07210 .07094 2.346+Network INDb .37406 .79780 13.089+++Affiliation SINb .10242 .45169 3.341+++ NBCb .12431 .10662 4.0654++Competition ONESTNb .11455 .16134 3.742+" TWOSTN .19065 .24874 6.302+++ FOUR+- -.14904 -.19519 -4.891+++Mkt Size TOP25b -.29171 -.63604 -9.900+++ NEXT4 -.12483 -.18446 -4.023+++Mkt Quality HIEBI' -.07108 -.072008 -2.312+ LOEBI .05862 .05337 1.905 HIHHGROb -.06938 -.054387 -2.2574

+ p<.05 " p<.01 +++ p<.001 aWith LNRATE as dependent variable, using the backwards

selection procedure bDue to the requirements of the particular statistical package used, these indicator variables were given a value of 1 if true, and 2 if false (i.e. not having the indicated value)

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Table 6.Factors Affecting the Determination of Rates (ranked in order of relative importance)

Factor Variables

1 Audience Size ADC, LNADC 2 Time (Inflation) YEAR 3 Network Affiliation IND, SIN, NBC 4 Market Size TOP25, NEXT25 5 Competition ONESTN, TWOSTN, FOUR+ 6 Market Quality HIEBI, LOEBI, HIHHGRO

7 Broadcast Band UHF


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