-10-

III. The Conceptual Model: What Probably Determines Radio Revenues and

Station Prices?

A statistical correlation between two variables can never prove that
changes in one of them have “caused” the variations in the other. Proper
inferences about causation require some kind of logical relationship --
formally called a “model” or a “theory.” This section provides such a
conceptual framework. 8/

The factors that influence a radio station's revenues differ greatly in
importance from place to place and station to station. Some are clearly
measurable, while others are not only unmeasurable but sometimes even
unknowable. Nonetheless, certain average tendencies are likely to hold across
the nation and across sufficiently large samples of stations. This study
poses a number of measurable factors that arguably might influence revenues,
explains the reasons for selecting them, and then examines the available data
to see which effects are statistically discernible.

The point of the study is to discover generally applicable relationships
rather than to deal with the isolated case. For example, one may know from

personal experience that the closing of a daily newspaper in some metropolitan

(continued)

follow the results below without any particular knowledge of these issues. A
standard technical reference is J. Johnston, Econometric Methods, 2nd ed.
(McGraw-Hill, 1972). The units of observation used in the regressions below
are, strictly speaking, firms rather than radio stations. Some firms in the
data sets operate only AM stations, some operate only FM stations, and some
are joint AM-FM operations. The difference is technical rather than
substantive, and it does not bear upon the common-sense interpretation of the
results. The statistical package SHAZAM (by Kenneth J. White, Rice
University) was used for all calculations.

8/  Econometricians should take the framework presented here as & “reduced
form" rather than a “structural” model.


~31-

APPENDIX A

A Comparison of Revenue Determinants for

1976 through 1980

The main data files for this study were originally set up with station
revenues, income (CSI), and numbers of stations (NSTA) for 1978. After much
of the data gathering and regression analysis were completed, however, data on
station revenues became available for 1979 and 1980. Although time constraints
prevented re-analysis of all variables, a limited set of regressions were run
on 1979 and 1980 revenues, together with 1976, 1977, and 1978 revenues, for
the large market sample. The results of these regressions are shown in Table
A-l. The most notable features of these coefficients are their relative
stability from year-to-year and their general conformity with the 1978 results
presented in the main text. Another notable feature is the consistent, al~-
though slight, year-to-year increase in the coefficient on FM power (FMPWR).
This increase probably reflects FM's growing share of the total radio audience
(see text, footnote 24).

The regressions in Table A-l have a slightly larger sample of stations
for 1978 (N = 825) than the large market regressions in the text (N = 812).
The reason is that new data became available between the work on the original
regressions and the work for Table A-l. Since the results were so close,
however, it was thought not worthwhile to recalculate the original regres-
sions. The regressions in Table A-1 also have slightly different sample sizes
from year to year, due to annual variations and other problems in the FCC

files. Although it would have been possible to calculate the coefficients


~27-

Table Six

Comparison of Revenue Determinants and Trading Price
Determinants for Stations that Changed Hands’
During January 1980 through June 1981
(all variables, except dummies » in natural logs; parentheses show t-scores)

Dependent Trading Trading 1978 1978
Variable Priced Priceb Revenuec Revenueb
Independent
Variable
AMDPWR 0.152 0.148 0.125 0.131
(3.54) (3.31) (3.34) (3.21)
AMNPWR 0.064 0.061 0.065 0.070
(4.66) (4.3) (5.48) (5.40)
AMFREQ -0.126 -0.119 -0.055 ~0.060
(2.85) (2.58) (1.40) (1.42)
FMPWR 0.080 0.081 0.064 0.067
(10.41) (10.20) (9.56) (9.15)
cSI78 0.526 0.560 0.511 0.502
(10.28) (10.16) (11.01) (9.91)
NSTA ~0.363 -0.408 -0.386 ~0.382
(4.65) (4.85) (5.45) (4.95)
CORDUM 0.643 0.629 0.752 0.703
(dummy) (6.03) (5.56) (7.90) (6.78)
FOURDM ~0.007 -0.003 -0.075 ~0.064
(dummy ) (-0.07) (0.03) (0.85) (0.67)
Intercept 5.432 5.075 4.634 4.739
(9.04) (7.81) (8.48) (7.94)
F 65.62 58.31 64.61 55.27
R2 ADJ 0.47 0.47 0.50 0.46
N 578 517 514 517

NOTES: a. For all stations in the data set.
b. For stations with 1978 revenues greater than zero.
c. For stations with 1978 revenues greater than $10,000.


-36-

disparity in residual variance between the upper and lower regressions in a
pair is due simply to sampling variation. Therefore, based upon the “before
adjustment” ratios where the observations are ranked by CSI, AMFREQ, or NSTA,
one can be 95 per cent confident that the estimated regression suffers from
heteroskedasticity.

In an attempt to eliminate heteroskedasticity, the data were adjusted by
dividing all variables (which are already in natural lograrithms) by the
logarithm of consumer spendable income (CSI78). 4/ The results of the G-Q
tests after this adjustment are also shown in Table B-l. There are substan—
tial reductions in two of three ratios that showed heteroskedasticity before
adjustment, but one can nevertheless reject with 95 per cent confidence the
hypothesis that the adjusted regression is homoskedastic. 5/ Thus one must be
alert to the possibility that the true significance levels (t-scores) for
regressions estimated in the text above are lower than the levels shown.

Time limitations prevented use of more ambitious methods of correcting
the data for heteroskedasticity. It seems unlikely, however, that even “per-
fect" adjustment would render insignificant more than a few of the coeffi-
cients that are shown as significant in the text tables. The reasons are
several. First, a Q-G test performed on the small-market data ranked by CSI
produced a ratio of residual variances (1.24) that was well below the critical
F-test value (1.47 for 69 degrees of freedom in both numerator and denomina~

tor). Second, the results in the text display marked consistency across three

4/ GQ call this “the cheapest method of adjusting for heteroskedasticity”
and note that it “appears to work moderately well in a variety of cases." Op.
cit., p. 119.

5/ Similar adjustments were performed with NSTA, the square root of CSI, and
the square root of (CSI x NSTA). Reductions of the ratios were less after
these adjustments than after the adjustment by CSI.


of their revenues from "new" advertising (as opposed to advertising drawn away
from old stations). The insignificant relationship between revenue and night-
time protection indicates that the FCC's policy of not protecting signals of
certain low power fulltime local service ("Class IV") stations probably has,
on the average, no significant adverse economic impact on those stations. 5/
This finding suggests further that if and when the FCC should decide to
authorize new fulltime local service stations (for example, on the new 1605-
1705 kHz band established by the 1979 World Administrative Radio Conference),
the stations will probably have a much greater collective economic value if
the FCC spaces them geographically with concern only for daytime coverage

areas than if it gives them nighttime signal protection against one another.

II. Statistical Methodology

The study uses a standard statistical technique called multiple
regression analysis. A full explanation is beyond the scope of this paper.
The basic idea, however, may perhaps be grasped from Figures One through
Five. Assume a sample of daytime-only AM stations, with data on (a) the
stations' revenues and power levels, (b) the number of competing stations in
each of their markets, and (c) the incomes of consumers in those markets.
Then draw a graph with station power on the horizontal axis and revenue on the
vertical axis. For such a graph, one should expect the data points for the
sample to scatter in a pattern something like Figure One. That is, stations

with higher power levels usually can be heard by more people and therefore

5/ Another way to state this result is that “watt for watt,” Class IV AM
stations appears to be no less valuable economically on the average than other
nighttime AM operations (i.e., those stations that have nighttime protection).


-14-

income (CSI), (e) number of commercial radio stations, (f) total number of
radio stations and (g) the number of TV stations in the market area. 9/
Automobile registrations were hypothesized to show a positive relationship
with radio revenues due to the large amount of radio listening that alledgedly
occura during “drivetime." Newspaper circulation is hypothesized to have a
negative relationship with radio revenues because newspapers are competitively
important’ as local advertising alternatives to radio. Consumer income (taken
in this study as total rather than per capita) is hypothesized to show a
positive relationship with radio revenues, since a larger “pool” of consumer
income should on the average translate into more lucrative business
conditions, while the numbers of commercial radio stations, total radio
stations, and TV stations are -~ for competitive reasons ~~ hypothesized to

affect revenues negatively. 10/

Iv. Revenue Determinants

Ae Large Markets

The large market data set covers 1125 radio stations (in 812 separate

observations). Of these, 350 are AM-only, 149 are FM-only, and 313 are joint

—

g/ Consumer Spendable Income is similar to Consumer Disposable Income as
reported by the Department of Commerce. CSI, which is estimated by the
Standard Rate and Data Service, has the advantage of being available quickly
on a yearly basis for the nation's 400 largest radio markets, together with
comparable data on retail sales, automobile registrations, updated population,
and the like.

10/ Total population for each market area has not been used in this study as
an independent variable because of the presumption that “people without
4ncome" would not be a revenue source for stations. Total consumer income
presumably captures poth a “pure” population effect for a given market area

and the average citizen's purchasing power, since total income is simply per
capita income multiplied by population. Per capita auto registrations and per
capita daily newspaper circulation were used, on the contrary, to avoid double
counting of the pure population impact and at the same time to provide
measures of the intensity of both automobile driving and newspaper readership.


-32-

with an identical (and smaller) sample for all five years, this exercise was
judged not worth the effort that would have been involved. Finally, the NSTA
numbers used in each year are all for 1978, due to the lack of time available
during the project to build up 4 complete new set of station numbers for 1976-
78 and 1979-80. Thie circumstance appears to be reflected in the relatively

lower t-scores on NSTA for 1976-77 and 1979-80, as opposed to the t-score for
1978.


-ili-

This study estimates the effects on radio stations' revenues and trading
prices of the following variables: daytime AM power, nighttime AM power, AM
frequency, FM power, consumer income, number of competing stations in a market
area, and whether a station is located in the central Part or on the fringes
of its market area. These variables show approximately the same percentage
effects on trading prices as on revenues. On the other hand, the following
variables appear to have no statistically significant associations with
revenues: newspaper circulation, automobile registration, and number of TV
stations in a market area; whether an AM station has nighttime signal
protection and/or uses a directional antenna; and whether a station has
network affiliation. The data suggest somewhat more than one-half of a new
station's revenues are likely on average to come from advertising dollars not
previously spent on radio, while somewhat less than one-half are likely to
come from advertising that previously went to old stations. The nighttime
protection variable's statistical non-significance suggests that “class IV"
local AM stations (which lack nighttime protection) do not on average appear
to suffer a relative earnings’ handicap vis-a-vis comparably powered stations
that have nighttime protection. The study recommends, therefore, that the FCC

consider a “class IV" approach for any allocation of new channels to the AM
band.

The study uses multiple regression analysis. All specifications are double-
logarithmic. Three data sets are examined: about 1100 stations in “large”
markets (defined according to the Census Bureau's "Standard Metropolitan
Statistical Areas” -- SMSA's), about 300 stations in "small" markets (non-SMSA
counties), and about 700 stations (in both large and small markets) that
changed hands in 1980 and 1981. Revenue data are examined for 1976 through

1980, with emphasis on 1978. Similar statistical results are observed across
all three data sets.


-25-

To investigate further the relationships between revenues and trading
prices, 1978 revenue data from FCC files for stations traded from January 1980
through June 1981 were regressed against the same set of determinants (except
TIME). Table Five presente these results, and Table Six compares the revenue
determinants and trading price determinants to one another directly. The
reults again are basically similar, with two exceptions: (1) the coefficient
in the revenue regressions on AMFREQ falls below the 95 percent significance
level, and (2) the coefficient on FMPWR is much higher in the trading price
regressions. There is no obvious explanation for the insignificance of the
AMFREQ coefficient. The higher FMPWR coefficient is due, no doubt, to buyers’
expectations that FM revenues will grow more rapidly in future years than will
AM revenues. This phenomenon has been noted in the trade press. 24/ In
addition to the regression analysis, a direct comparison of trading prices to
revenues was also performed. The results are presented in Table Seven. Here
aleo one can see a marked premium for FM stations, again based no doubt upon

investors’ expectations that FM will continue to outpace AM.

VI. Implications of the Results

These findings should be interpreted with caution for several reasons.
First, apparently no comparable study is publicly available. A single
unreplicated statistical study is not necessarily a suitable basis for strong
policy conclusions. Moreover, the present study is not based on strictly

random samples. And its data are necessarily limited to past years -- years

24/ See, e.g., Broadcasting, January 12, 1981, p. 48, and January 21, 1980,
p. 54, which report on the relative popularity among broadcast investors of FM
Properties. FM's share of the radio audience has risen above 50 per cent
nationally in recent years and is now 65 per cent or more in many larger urban
markets.


-ll-

area was followed by a significant increase in local radio stations’ adver—
tising revenues. One might thereupon reasonably hypothesize that radio
revenues in most other markets are systematically related to local newspaper
circulation and moreover that such a relationship will show up meaningfully in
the available statistics. But until the data actually are consulted, one
should maintain the notion as an untested hypothesis. It will always be
possible that such a relationship does not actually hold on the average across
the nation. Or even if it does hold, the available statistics may be so
error-ridden as to conceal the true relationship.

Certain potentially important factors have been ruled out as objects of
this study altogether (a) because they are not covered by available data, (b)
because they cannot currently be measured, (c) because there is no clear
consensus or well-understood “theory” about how they are likely to affect
revenues, or (d) for some combination of these three reasons. Some
potentially important revenue determinants that might be so categorized, and
that have not been included in this study, include management quality,
audience demographics, and "formate" (i.e., type of programming, particularly
type of music played).

Factors likely to influence revenues divide into two basic categories,
the individual station characteristics and market characteristics. Among
measurable revenue-determining station characteristics, perhaps the most
obvious is transmitting power. Power influences revenue due to its impact
both on the station's geographic coverage area and on the quality (i.e., the
signal-to-noise ratio) of its signal. Therefore the relationship between
power and revenue should be positive. Three types of power are treated -~
daytime power for AM stations, nighttime power for AM stations, and FM

power. AM day and night power are two separate components of a station's


~17-

equal. 15/ Unfortunately, the coefficients of the dummy variables do not have
such a straightforward “percentage” interpretation. 16/

As shown in Table Two, the following variables appear to be statistically
significant’ determinants of radio broadcasting revenues: (1) AM daytime
Power, (2) AM nighttime power, (3) AM frequency, (4) FM power, (5) consumer
spendable income in the market area, (6) the number of competing stations in
the market area, and (7) whether the station is in a “core” county (as opposed
to a "ring" county) of a multi-county market. The significance levels are
quite high compared to much cross-section economic research and generally are
confirmed across all three sets. 17/

The following variables do not appear as statistically significant
determinants of revenues: (1) number of TV stations in the market area, (2)
per capita automobile registrations in the market area, (3) per capita daily
newspaper circulation in the market area, (4) the number of daily newspapers
in the market area (coefficient not reported in Table Two), (5) whether or not
the station uses a directional AM antenna, (6) whether or not the station is

AM class IV, and (7) whether or not the station has network affiliation.

15/ The t-score of 9.07 indicates that the true value of the coefficient,

assuming only sampling errors, stands a 95 per cent chance of lying between
0.344 and 0.564.

16/ They do show, however, a mathematical formula for predicting the effect
of a given condition. For example, CORDUM will predict the advantage of a
station's being located in a central county for any given set of AM and FM
powers. But this advantage will decrease steadily for higher power levels
(and/or higher levels of other variables like CSI). And when a dummy variable
is not significantly different from zero, it shows that the presence of a
given condition has no statistically significant impact on revenues.

17/ But see Appendix B for a discussion of possible bias in the calculation
of significance levels.


-13-

coverage areas by lowering signal strengths in certain geographic direc-
tions. (Offsetting this confinement, however, may be the more effective use
of the directional station's power in other directions.) Class IV AM status
is hypothesized also to reduce revenues because Class IV stations do not have
“nighttime protection." Class IV stations operate with 1000 watts during the
day and 250 watts at night, with non-directional antennas. They are each
assigned one among six AM frequencies reserved for Class IV stations only.
Unlike other AM stations operating after sunset, they lack nighttime "protec~
tion." They are spaced across the country so that they do not interfere with
one another during the day. They therefore enjoy effective daytime coverage
areas reaching perhaps 15 to 20 miles from their transmitters. At night,
however, “skywave" conditions cause their signals to travel much farther and
thereby to interfere with one another. This interference is so great that
their effective nighttime coverage areas extend out no more than about five
miles from their transmitters. On other AM channels, stations are placed
farther apart and/or use nighttime directional antennas, so that their
“protected” signals have greater effective nighttime coverage areas (per watt)
than do Class IV stations. Therefore, Class IV stations are hypothesized to
have lower revenues, all other things being equal, than do other AM stations.
Network affiliation and “core” county locations (in multicounty markets) are
hypothesized to raise stations' revenues, network affiliation because it
presumably offers a station's listeners a commercially superior service to
independent stations and core location because it centers a station's signal
in the most heavily populated parts of multi-county markets.

The market-specific factors considered in this study (all for 1978) are
(a) per capita automobile registrations, (b) per capita daily newspaper

circulation, (c) number of daily newspapers, (d) total consumer spendable


-26-

Table Five

1978 Revenue Determinants for Stations that
Changed Hands from January 1980 through June 1981
(all variables, except dummies, in natural logs; parentheses show t-scores)

Equation No. 1 2 3
Independent
Variable
AMDPWR 0.125 0.123 0.122
(3.34) (3.25) (3.23)
AMNPWR 0.065 0.066 0.066
(5.48) (5.54) (5.52)
AMFREQ -0.055 -0.054 -0.053
(1.40) (1.38) (1.35)
FMPWR 0.064 0.064 0.063
(9.56) (9.46) (9.45)
CSI78 0.511 0.457 0.451
(11.01) (11.26) (11.27)
NSTA -0.386
(5.45)
CSTA -0.330
(4.93)
WSTA -0.323
(4.87)
CORDUM 0.752 0.820 0.821
(dummy) (7.90) (8.47) (8.47)
FOURDUM -0.075 0.077 -0.079
(dummy) (0.85) (0.88) (0.90)
Intercept 4.634 5.164 5.221
(8.48) (10.33) (10.54)
F 64.61 63.32 63.15
R2 aps 0.50 0.49 0.49

N 514 514 514


revenue

total consumer
— income in the
market area

Figure Five

Equation Three gives a mathematical expression for this same positive

relationship:

R= a, + b,I. (3)

The discussion so far has dealt only with relationships between two vari-
ables at one time. Each example has had the same “dependent” variable ——
revenue -- and only one “independent” variable -- power, competition, Or
income. Such two-variable relationships can be presented either as
mathematical equations or as simple two-dimensional graphs. But ‘what if one
wants to represent simultaneously the relationships of revenues to power,
competition, and income? In such a case, it is no longer possible to portray
the mathematical relationship as a graph or scatter diagram. On the other
hand, it is still possible to portray the relationship as an equation.
Equation Four, for example, expresses such relationships as one might expect
between a station's revenues (R=-the dependent variable) and its power (P),
number of competing stations (N), and consumer income (I):

R=A+b,P -bN+ byI. (4)




-23-

frequency, (4) FM power, (5) consumer spendable income in the market area, (6)
total radio stations and commercial radio stations in the Market area, (7)
location in the “core” county of a multi-county market, and (8) Class IV AM
status. (The last two variables are zero-one dummies.) The results of the
trading price regressions are shown in Table Four. An additional linear
variable, TIME, was used to test the hypothesis that station prices increased
according to a linear trend over the 18-month period. 21/

The trading price regressions show results similar to the revenue
regressions, in terms of both the absolute values and the significance levels
of the coefficients. Notable again is the insignificance of the Class IV
dummy. TIME is also insignificant, although it has the expected positive
sign. Perhaps the most notable difference between the trading price results
and the previous regressions is that in the former, the alternative measures
of competition (CSTA and WSTA) do not offer better fits than does the simple
measure of total stations (NSTA). 22/ The overall similarity of results,
however, is consistent with a hypothesis that the market for radio broadcast
properties functions well insofar as it puts the “correct” economic values on
the determinants of trading prices. And the fact that similar results came
from separate data sets speaks well for the robustness of the methodology used

here. 23/

21/ TIME was the month in which Broadcasting reported the transaction, with
January 1980 taken as month number one and June 1981 as number 18.

22/ Based on their t-scores. There is no readily apparent explanation for
this result.

23/ Actually, the two data sets have a small overlap. About 17 per cent of
the observations in the trading price file were also in the large markets
file, and about three per cent were in the small markets file. The small
markets and large markets files are mutually exclusive to one another.


-20-

Three alternative measures of competition (NSTA, CSTA, and WSTA) were
tested, and all were found statistically significant. These last results from
the large market data set suggest that the number of commercial stations in
the market is a better measure of competitive impact than is simply the total
number of stations in the market, and that the weighted index number is
perhaps slightly better still. These results are not surprising. On the
other hand, they are not repeated in the small markets and trading price data

sets (see below).

Be Small Markets

A second sample consists of 100 counties not located in the top 400
markets. Leaving aside non-commercial stations and stations whose financial
data in FCC files could not be used due to gaps or inconsistencies, the
remaining stations in these counties furnished a total of 306 stations for the
sample (in 208 observations), of which 93 are AM-only, 17 are FM-only, and 98
are joint AM-FM (two-station) operations. The regression procedures were
generally the same as for the large-market sample, except that fewer variables
were tested for significance. 18/

As shown in Table Three, the following variables were statistically
significant as determinants of small-market stations' revenues: (a) AM
daytime power, (b) AM nighttime power, (c) AM frequency, (d) FM power, (e)
consumer spendable income, and (f) number of stations in the market. The
dummy variables for class IV status and directional antennas were not

significant.

18/ Due to their insignificance in the large-market sample, the following
variables were thought likely to be insignificant here also and therefore were
not tested for the small-market sample: automobile registrations, newspaper
circulation, number of TV stations, and network affiliation. Core~county
location and “weighted” number of competing stations are not relevant, since
all the small markets are single counties.


Il.

III.

Iv.

Vv.

VI.

Appendix A.

Appendix B.

2.
3.
4
5.

Table of Contents

ADBETACtecececscccccecvccccscevsscscscccsvescccesesesvevecvcedsd

Acknowledgements..

eevil

Introduction and Summary.cccccecsscvcccrcccccscccsccsscscssceceel

Statistical Methodology... 4

eee eee rere sy

The Conceptual Model: What Probably Determines
Radio Revenues and Station Prices. ccccesscecccececccesseeeclO

Revenue Determinants.... onl

A. Large MarketS.cccecccccccccccrccccccccccccesecccsescsceseel4
B. Small Markets.ressesescsvcccvccccssseveccceveseseesseces sel
Determinants of Trading PriceS..cceccccscccccccccvecccccsscesell
Implications of the Results.cssccecscescccesccescccscveccvvesele
A Comparison of Revenue Determinants

for 1976 through 1980. ..ccccccccccccccccccccceveccccccsssveedl

Heteroskedasticity and Biased Significance Tests...sseeeeeeee 34

Tables

Definitions of Variables....ssessseeeeee
Determinants of Revenues, Large Markets
Determinants of Revenues, Small Markets
Determinants of Trading Prices..ccecccccccccccesccccsveceeseee ld
1978 Revenue Determinants for Stations that Changed
Hands from January 1980 through 1981..
Comparison of Revenue Determinants and Trading
Determinants for Stations that Changed Hands
from January 1980 through 198l.cccccsccccccesecs
Trading Prices vs. 1978 Revenues.......
Revenue Determinants for Large Market
Stations, 1976-80. .cecccccccuvescsescvccccscvecccvves
Tests for Heteroskedasticity.

rereerrery.)





In this equation, A is still called an "intercept" term even though it is
impossible to portray it pictorially as intercepting anything; and likewise
by, bo, and bz may still be called "slope coefficients” or simply “coeffi-
cients” (the more common terminology). The effect of power on revenue is
shown by bj, the effect of competition by b2, and the effect of income by b3
(in each case holding the other two independent variables constant).
Regression analysis may again be called upon to estimate the intercept
and the coefficients in such a multi-variable equation, just as in our earlier
case of the two-variable equation. Such estimates can be bounded by standard
errors, which indicate ranges within which the true values of the coeffi-
cients are likely to lie. These range estimates serve to “quantify the uncer—
tainty” that virtually always underlies a particular regression equation.
Mathematically, all variables in this study (except dummy variables) have
been transformed into their natural logarithms, and then the relationships
among them have been estimated in linear form. This technique is not without
technical drawbacks, but it has several distinct advantages: (1) it is widely
used and understood, particularly among econometricians; (2) it is computa-
tionally straightforward and economical; (3) it allows relationships to take
curvilinear form and thereby exhibit diminishing marginal returns; and (4) it
allows relationships to be stated as percentage changes of both dependent and
independent variables, independently of the original measurement units. 6/

Ordinary least squares estimation is used exclusively. i

6/ To economists, these double~log revenue functions are analogous to “Cobb-
Douglas" production functions. There is a large technical literature in the
general area,

a, Ordinary least squares (OLS) is a technical term for the commonest
computational method of fitting a regression equation. The analysis of its
strengths and weaknesses is, in fact, at the core of modern econometric
theory. The non-technical reader of this paper should, however, be able to


vil

Acknowledgements

Work on this study began in late 1979. Helpful suggestions along the way
have been made by Steve Brenner, Nina Cornell, Jerry Duvall, Lex Felker, Phil
Gieseler, Ken Gordon, Dan Kelley, Jon Levy, Peter Pitsch, Jack Robinson, Fred
Ruppel and Doug Webbink. They are not responsible, of course, for any errors
that remain. Thanks also to Jackie Singleton, Marie Moyd, Terry Matsumoto,
and Vivette Hart for typing; and to Pat Allen, Jim Gilmartin, Dave Santucci,
Ken Stern, and especially Joe Bluemel and Fred Ruppel, for research

assistance,


-15-

AM-FM (i.e., two-station) operations. These stations are spread among 100
metropolitan areas selected at random from the largest 400 radio markets in
the country. All of these markets are defined by county boundaries. i/

The data are from three main sources. Revenues are from stations’ FCC
Form 324 filings. Data on stations' power levels, frequencies, and other
characteristics are from Broadcasting Yearbook, 1979, Data on each matket's
consumer spendable income, automobile registrations, and daily newspaper
circulation are from the Standard Rate and Data Service, as of December 31,
1978.

Multiple regression analysis was used to estimate the separate effects
upon radio stations’ 1978 revenues of the fifteen variables defined in Table
One. 12/ All regressions were fitted as linear in logarithms, except for the
dummy variables. 13/ Basic results are shown in Table Two. The table shows

estimated coefficients and intercept terms for four slightly different forms

il/ The study's original intent was to include all commercial stations on the
air in the 100 markets as of December 31, 1978. This total would have given
about 895 observations. Inconsistencies and gaps in data sources limited the
useful sample, however, to 812. The 400 metropolitan areas are the country's
major radio markets as defined by the Standard Rate and Data Service (SRDS),
as of December 31, 1978. The SRDS list consists principally of the Census
Bureau's 318 SMSA's for 1978 plus 82 additional markets selected by SRDS. The
SRDS supplemental markets are all single counties with large urban
populations, All SMSA's, except those in New England, are defined by county
boundaries. Most of the larger SMSA's consist of more than one county. The
present study substitutes the Census Bureau's New England County Metropolitan
areas for New England SMSA's, in order to keep all markets defined
consistently in terms of county boundaries.

12/ The results reported in the text use not only 1978 revenues as the
dependent variable, but also 1978 values for all the independent variables.
The selection was made because 1978 revenues were the most recent available
data in FCC files when the study began. In order to compare the 1978 results
with both previous and subsequent years, however, limited regressions for 1976
through 1980 are reported in Appendix A. Nothing in those results presents
any significant contradiction to the results presented here in the main text.

13/ A dummy variable takes a value of zero when a given condition is absent,
and a value of one when the condition is present. For example, stations with
AM directional “antennas receive a value of one for the variable DADUM. For
other stations, DADUM is zero.







the economic effects of regulation. To the author's knowledge, this study is
the only publicly available multivariate statistical analysis of radio
stations' revenue determinants, and therefore it should help fill a gap in our
basic knowledge about the economics of telecommunications regulation. 2/

The determinants of a radio station's revenues are a mixture of
measurable and non-measurable items. The study deals directly with just the
measurable determinants. It uses a common statistical technique, called
multiple regression analysis, that isolates the separate influences of two or
more statistical determinants -~"independent variables"-- on a “dependent
variable.” This study examines two different dependent variables: (a) the
revenues and (b) the trading prices of radio stations. The type of regression
analysis used below allows one to make such statements as, "On the average,
and other things being equal, an increase of x per cent in an AM station's
daytime power is associated with an increase of J per cent in that station's
xvevenue.” Finally, even though regression analysis deals explicitly with only
the measurable factors, it does not overlook the nonmeasurable. Indeed, one
strength of this method is that it offers a rough quantification of the
uncertainty underlying its estimates. For example, it allows one to predict
how often the revenues of a station with certain characteristics are likely to

fall inside or outside a given “confidence interval.” 3/

2/ There have been a number of similar previous studies, however, on the
statistical determinants of television stations' revenues. For a survey, see
Federal Communications Commission, Network Inquiry Special Staff, The
Determinants of Television Station Profitability (June 1980).

3/ Alternatively, the “correlation coefficient" estimates the amount of
variation accounted for, and not accounted for, by a particular regression
equation. Such interpretations of the correlation coefficient often are not
useful for econometric studies, however. For an explanation see, e.g.,
Potluri Rao and Roger L. Miller, Applied Econometrics (1971), pp. 13-20.


Statistical Determinants of Radio

Stations’ Revenues and Trading Prices

I. Introduction and Summary

This study is a statistical analysis of certain factors that influence
(a) the revenues of radio broadcasting stations and (b) the prices at which
those stations change hands. There are at least three reasons for such a
study. The first is to examine the economic implications of the FCC's radio
broadcasting allocation/assignment policies. For example, if one takes
revenues as an approximate measure of a station's economic worth to society,
would a collection of small stations on a given frequency be more or less
valuable in total than a few large stations on the same frequency? 1/ Answers
to such questions may contribute to the economic evaluation of FCC policies.
The second reason is to provide information potentially useful to the private
sector in evaluating the economic potential of broadcast properties. Although
abstract numbers like those presented here can never be complete substitutes
for detailed (and normally confidential) information about specific pro-
perties, such data may usefully complement the private sector's other infor-

mation sources. The third reason is simply to provide general information on

1/ Dealing here exclusively with economic criteria does not necessarily imply
that society should not use additional criteria for evaluating the social
worth of radio stations. Moreover, the “social economic worth" of a station
will not always coincide with its private economic worth (which might be more
or less than the social worth in any particular case). As a theoretical
measure of social economic worth, “value added” is more appropriate than the
gross revenue measure used here. Value-added data would probably not change
any major conclusions of this study, however, due to their close correlation
to revenues for radio stations (since radio stations purchase few “raw
materials” inputs). In any event value-added data were not used here because
of the belief that non-uniform accounting practices among radio stations would
unnecessarily introduce errors into the statistical results.


-34-

Appendix B

Heteroskedasticity and Biased Significance Tests

Significance tests for parameters estimated by ordinary least squares
(OLS) regression generally assume that the disturbance term has a constant
variance across the entire range of observations. When this assumption of
“homoskedasticity” is violated, a regression is said to display "heteroskedas-
ticity.” A consequence of heteroskedasticity may be bias in the significance
tests for the estimated parameters, such that an insignificant relationship
appears to be significant. 1/ Therefore, if heteroskedasticity is present in
the regressions above, then the estimated coefficients actually may be less
significant than the OLS t-scores indicate.

A popular and relatively simple test for heteroskedasticity is the method
of Goldfield and Quandt (G-Q). 2/ That method is used here. Briefly, it
involves separating the observations into three groups, rank-ordered by an
independent variable thought to be 4 likely source of heteroskedasticity, and
then running separate regressions on the top and bottom groups. If the postu~
lated regression model displays heteroskedasticity then the ratio of residual
variances for the two separate regressions should diverge significantly from

unity (judged by an “F" test with appropriate degrees of freedom).

—_——

1/ Many econometrics textbooks discuss the problem of heteroskedasticity.

The most accessible and compact discussion is probably in J. Johnston,
Econometric Methods, 2nd ed.(1972), pp. 214-221.

2/ S.M. Goldfeld and R.E. Quandt, "Some Tests for Homoscedasticity,” Journal
of the American Statistical Association, 60 (1965), pp» 539-547, summarized in
Johnston, op. cit., P- 219.



-22-

Vv. Determinants of Trading Prices

Trading price data and basic station data were taken from Broadcasting
magazine, January 1980 through June 1981, on all radio station sales. The
file has data on 745 stations, 167 of which were joint AM-FM (two-station)
operations, 282 were AM-only, and 129 were FM-only. The stations sold were
categorized as being in either SMSA markets or non-SMSA single~county
markets. The total numbers of radio stations in the respective SMSA's and
counties were taken from the 1979 Broadcasting Yearbook. Data on 1978
consumer spendable income in the same SMSA's and counties were taken from the
Standard Rate and Data Service. 19/

As with the data set on revenue determinants, the dependent variable
(trading price) and all independent variables for the trading price set (again
except dummies) were transformed into their natural logarithms. Data on
newspaper circulation, automobile registrations, television stations, and
radio network affiliations were not included in these regressions, due to
their non-significance in the revenue-determinants' regressions. 20/ The
following independent variables were tested, then, as possible determinants of

trading prices: (1) AM daytime power, (2) AM nighttime power, (3) AM

—

19/ Therefore all the basic data for this section of the study are from
relatively accessible public sources, as opposed to confidential FCC files.
Although the files on station sales are not closed to the public (as are FCC
files on stations’ financial data), they are difficult to use. There are
variable lags in the time between FCC approval of an actual station sale and
Broadcasting magazine's report of the sale, but no particular gain in
precision was seen in using dates reported in FCC records, especially since
the 18~month period for study was itself chosen arbitrarily. Sales reported
at less than $10,000 were omitted from the sample, due to the belief that they
did not reflect arms-length market values.

20/ An exception was made in the case of the dummy variable for AM Class IV
stations, which was included in the trading price regressions because of its
policy relevance -~ even though it also was insignificant in the revenue
regressions.


hae



-21-

Table Three
Determinants of Revenues, Small Markets

Dependent Variable: 1978 Revenue
(all variables, except dummies, in natural logs; parentheses show t-scores)

Equation No. 1 2 Means

Independent

Variables logs originals

AMDPWR 0.256 0.260 6.455 1736.8
(5.47) (5.55)

AMNPWR 0.040 0.034 2.368 591.4
(2.15) (1.83)

AMFREQ -0.180 -0.178 6.498 1120.4
(3.73) (3.70)

FMPWR 0.055 0.055 4.887 11424.0
(6.54) (6.59)

csr 0.359 0.367 12.062 250,970
(5.01) (5.13)

NSTA -0.369 1.027 3.45
(3.92)

CSTA -0.400 0.968 3.18

(4.07)

FOURDM -0.058 -0.052 0.2356

(dummy) (0.47) (0.42)

DADUM 0.152 0.172 0.1731

(dummy) (1.23) (1.39)

Intercept 7.332 7.213
(8.93) (8.74)

1978 Revenues 12,159 237 ,580

F 19.41 19.66

R2 ADJ 0.42 0.42

N 208 208




-12-

revenue-producing assets not only because the FCC licenses many AM stations
with different night and day power levels, but also because night signals and
day signals have radically different geographic coverage characteristics on
the frequencies used for AM broadcasting. (By contrast, signals on the
frequencies used for FM broadcasting propagate essentially the same during the
day as at night, and consequently the FCC licenses FM stations for the same
night- and daytime power levels.) Another important technical characteristic
that may affect an AM station's revenues is its frequency. During the day-
time, AM stations on the lower frequencies have much larger coverage areas
than stations on the higher AM frequencies (given equal power levels). In
fact, a favorable frequency can overcome substantial daytime power differ~
ences. For example, a 5000 watt AM station in the vicinity of 600 kHz will
typically have a larger daytime coverage area than a 50,000 watt station on
1500 kHz. The relationship of AM frequency to station revenue is therefore
hypothesized to be negative, indicating lower revenues to stations at higher
frequencies, all other things being equal.

Additional station characteristics included in this study are (a) use of
directional AM antennas, (b) whether a station in a multi-county market is ina
“core” or “ring” county, (c) whether a station has network affiliation, and (a)
whether a station is AM Class IV. Core-county location and network affiliation
are hypothesized to have positive influences on a station's revenue, while
directional antenna use and Class IV status are hypothesized as having negative
influences. Rationales for these hypotheses follow. It is worth mentioning
again, however, that no matter how reasonable they seem, these hypotheses may not
survive examination of the data (as seen below in Section IIt).

Use of directional AM antennas is hypothesized to reduce potential

revenues because the stations concerned must to some extent confine their


-29-

crease in the number of stations in the large market data set is associated
with a decrease in old stations’ revenues, on the average, of about 0.55 per
cent, and that an increase in the number of stations by ten per cent (a much
more likely situation) is associated with an average decline in per-station
revenues of about 5.1 per cent. 25/ In other words, these figures indicate
that, on the average, most of a new station's revenues can be expected to come
from “new" advertising; less than half of its revenue is likely to be drawn
away from other stations. (Of course, any such statement must be qualified by
the probability that individual stations in individual markets will diverge
from the average. But about as many will probably be above the average as
below it.)

The insignificance of the Class IV dummy variable in all of the regres—
sions, across all three data sets, suggests that Class IV AM stations are just
as viable and just as valuable economically as other nighttime AM broadcast-
ers, on a watt-for-watt basis. As noted above (p. 13), Class IV AM stations
interfere severely with one another at night. This interference severely
limits each station's nighttime geographic coverage. One might expect, there-
fore, that Class IV stations should suffer economically as compared to
stations with nighttime signal protection. Yet, the data fail to reveal any

such handicap. 26/

25/ With the coefficient of 0.55, an increase in stations by 20 per cent is
associated with a decrease in per-station revenues of about 9.4 per cent; and
an increase in station revenues of 30 percent is associated with a decrease in
per-station revenues of about 13 per cent. The average number of commercial
stations per market in the large market data set is 20.7, so that one new
station in the representative market would represent an increase of about five
per cent in the number of stations, associated on the average with a decline
of about 2.6 per cent in per~station revenues.

26/ A possible explanation of this finding is that most of a Class IV
station's potential audience typically lives within about five miles of the
transmitter so that listeners living beyond five miles are usually not
numerous enough to attract large advertising revenues for Class IV stations.


-30-

This lack of a handicap suggests that if the FCC has the opportunity to
establish new AM channels, it should consider assigning stations there without
nighttime protection. An opportunity to apply such an approach may arise in
the late 1980's, when ten new AM channels may be assigned in the band 1605 to
1705 kilohertz. (This band was established by the 1979 World Administrative
Radio Conference. Implementation of assignments in the Western hemisphere
might not be undertaken until almost 1990, however.) The current distribution
of Class IV stations indicates that these new channels could probably accom-
modate something like 1500 fulltime local service stations without nighttime
protection. If nighttime protection should be given, on the other hand, the
new channels’ could probably accommodate only 300 to 400 stations. 27/ Thus it
appears that the total economic value of the new channels might be four or
five times greater for an allocation of unprotected nighttime operations than
for an allocation of protected assignments.

The results above also have an implication for future statistical analy-
sis of radio revenue determinants. The FCC decided in March 1982 to abolish
mandatory annual financial reporting for broadcasters. 28/ These reports
furnished the revenue data used in this study. The trading price regressions
presented above suggest, however, that essentially the same results may be
expected when analyzing certain policy questions whether one uses trading
prices or revenues as the dependent variable. Thus, both non-government
analysts and the FCC should be able in the future to use publicly available
trading price data for making many policy-oriented statistical inferences
about the determinants of radio broadcasting revenues.

—————

27/ | Current Class IV channels accommodate about 150 stations each; thus the
estimate of 1500 stations for ten new channels. Calculations during the
Commission's 9 kHz studies showed that ten new channels on the existing AM
band would probably accommodate only 300 to 400 local service stations with
nighttime protection.

28/ Docket No. 80-190, 47 F.R. 13345 (1982). The data formerly were filed on
FCC form number 324.


AMDPWR
AMFREQ
AMNPWR

CARSPC

CIRCPC

CORDUM

Csi78

CSTA

DADUM

FMPWR

FOURDM

NETDUM

NSTA

WSTA

-18-

Table One

Definitions of Variables

Daytime AM power in watts, transformed to natural logarithms.
AM frequency in kilohertz, transformed to natural logarithms.
Nighttime AM power in watts, transformed to natural logarithms.

Per capita automobile registrations in the market
area,transformed to natural logarithms.

Per capita daily newspaper circulation in the market
area,transformed to natural logarithms.

Equals one if station is located in the “core” county of a multi-.
county SMSA or in a one-county market, and equals zero if
astation is in a “ring” county of a multi-county SMSA.

Consumer spendable income in the market area for 1978 as reported
by the Standard Rate and Data Service in thousands of dollars,
transformed to natural logarithms.

Number of commercial radio stations in the market area,
transformed to natural logarithms.

Equals one if the station uses a directional AM antenna (day or
night) and equals zero if the station does not use an AM
directional antenna.

FM power in watts, transformed to natural logarithms.

Equals one if the station is a Class IV AM; otherwise equals

zero.

Equals one if the station is a network affiliate; otherwise
equals zero.

Number of radio stations in the market area, transformed to
natural logarithms.

Number of television stations in the market area, transformed to
natural logarithms.

Weighted index number of commercial radio stations in the
marketarea, tranformed to natural logarithms. For the index,
stations in “ring” counties of multi-county markets are given a
weight of 0.5, while all other stations receive weights of 1.0.




-37-

separate data sets. And third, sampling experiments by G-Q over a variety of
cases indicate that OLS for heteroskedastic regressions typically biases a
coefficient's variance estimator by factors of 2.0 or less. 6/ Since division
by 2.0 would still leave most of the significant t-scores in the text at 1.96
or more, the basic thrust of the findings above is probably not affected by

the model's obvious departure from homoskedasticity.

/ Op. cit., pp. 102-118.


-35-

There were no strong a priori reasons in this study to suspect that rank
ordering by some particular independent variable was most likely to reveal
heteroskedasticity. 3/ Therefore, seven different G-Q tests were performed
with the large market data file, with the observations rank-ordered succes~
sively by 1978 revenue (the dependent variable) and the following six indepen-—
dent variables: (a) coneumer spendable income, (b) AM day power, (c) AM night
power, (d) AM frequency, (e) FM power, and (f) number of stations. The re-
sults of these tests are shown in Table B-1. A ratio of unity for the residu-
al variances from each “upper” regression to its corresponding "lower" regres~

sion would indicate the total absence of heteroskedasticity. In these

Table B-1

Tests for Heteroskedasticity

Observations Ratio of High to Low Critical Value
Ranked by @ Residual Variance for F-test
Before Adj. After Adj.b (95%)

REV78 1.04 1,22 1.20
CSI78 1.59 1.14 1.20
AMDPWR 1.11 1.09 1.22
AMNPWR 1,08 1.10 1.21
AMFREQ 1.34 1.29 1.20
FMPWR 1.10 1.10 1.20
NSTA 2.02 1.50 1.20

Notes: (a) Variables defined in text, Table 1.
(b) All variables divided by CSI.

tests, therefore, a ratio greater than the critical F-value for any single

pair means one can reject with 95 per cent confidence the hypothesis that the

3/ It is commonly suggested in the econometric literature, however, that
heteroskedasticity 1s most likely where there is great variation in the size
distribution of a variable. E.g., S.M. Goldfeld and R.E. Quandt, Nonlinear
Methods in Econometrics (1972), p. 78.


revenue

Power

Figure One

will on average have higher advertising revenues. The relationship is a
scatter of points rather than a smooth line because such statistical date can
never be “controlled” (4.e., corrected) for all the factors that create
variations in individual stations’ revenue generating potentials. Such
factors may be quantifiable influences like a station's frequency, the incomes
of its listeners, or the number of competing stations in its market area; or
they may be non-measurable phenomena like the quality of a station's
Management or the peculiar “tastes” of listeners in its geographical area.

It is important to note that the factors we do not (or perhaps cannot)
measure may not always be critical for the purpose at hand. Statisticians
have found that such extra influences often can be set aside (at least
temporarily), so that one may draw the sort of “average” line shown in Figure
Two. A precise mathematical expression for such a line can be derived by the
techniques of “regression” analysis, The correspondence between a regression

line and its mathematical expression are shown in Figure Three and Equation


The process of developing mathematical estimates for a and b from a real-world
data set is often called “fitting” an equation.

The preceding exposition has dealt only with the relationship between our
hypothetical stations‘ power levels and revenues, But the example also
assumed data were available on both the number of competing stations and total
consumer income in each station's market area, The latter two types of data
may also be graphed separately in relation to revenues, as shown in Figures
Four and Five. But note that Figure Four shows an important difference:

Here, one should expect the relationship to be “negative.” That is, the

revenue

no. of
— ——a competing
stations

Figure Four

greater the number of competing stations, the lower a station's expected
revenues. A mathematical expression that reflects this relationship is
Equation Two. Here, a is the vertical intercept, N the number of stations,
and b, the slope term. Note that by has a negative sign, indicating that the
regression line slopes downwards:

R= a -b.N. (2)

Figure Five shows the expected positive relationship between station revenues

and consumer incomes.


Statistical Determinants of Radio
Stations' Revenues and Trading Prices*

James A. Brown, Jr.

Working Paper No. 9

Office of Plans and Policy
Federal Communications Commission
Washington, D.C. 20554

August 1982

*The opinions and conclusions in this
the author.

paper are solely the responsibility of
They do not necessarily reflect the policies or views of the

Federal Communications Commission or any other organization or individual.


-28-

Table Seven
Trading Prices vs. 1978 Revenues for Radio Stations
Traded, January 1980 through June 1981a

Ratios of Trading Price
to Revenues

Weighted Unweighted Average Average Number of
Averageb Average Price Revenue Sales
All Properties 2.57 2.73 901,850 350,960 514
AM only 1.92 2.22 596,990 310 ,860 251
FM only 3.74 3.94 1,250,100 334,640 104
AM-FM 2.72 2.72 1,155,300 424,930 159

Notes: a. Stations with either trading prices or 1978 revenues less than
$10,000 are omitted from these calculations.
b. Weighted by revenue; equivalent to average price divided by
average revenue.

that will be much more distant before any major policy based on its results
could be implemented. So the discussion below should be taken only as a
series of hypotheses not contradicted by the available data. On the other
hand, the results have plausibility for at least three reasons. First, they
are generally consistent across all three data sets, the third of which has
absolutely no claims of statistical randomness. Second, all the statistically
significant coefficients have the predicted signs and thereby are consistent
with theory and our expectations. Third, the coefficients have significance
levels quite high by ordinary standards of economic research.

The coefficients on competition cluster in the neighborhood of -0.55 for
the large markets (based on WSTA in Table Two), -0.40 for the small markets
(using CSTA in Table Three), and -0.35 for the trading price data set (Tables

Four and Five). These numbers indicate for example, that a one per cent in-


~16-

of the basic model. That is, there are four slightly different equations.
Changing the "specification" in this way causes the estimated coefficients and
intercepts to vary somewhat from equation to equation. The first equation
tests the maximum number of variables. The second, third, and fourth equa-
tions test fewer variables, and they also try alternative measures of market
competitiveness: The second equation takes the total number of stations in
the market area (NSTA) as the measure of competitiveness, whereas the third
equation takes only the number of commercial stations (CSTA). The fourth
equation takes a weighted index of commercial stations (WSTA) as the measure
of competition, where stations in “ring”. counties of SMSA markets count only
as one-half a station each and all others count as full stations. Mean values
of the dependent variables are shown in the two right-hand columns.

Table Two may be read as follows. The values in parentheses are “t-
scores" for the coefficients immediately above them. A t-score of 1.96 or
more is taken to mean that the corresponding coefficient is statistically
significant at the 95 per cent level. 14/ Except for the dummies, the
coefficients themselves may be read as saying that a one per cent increase in
the particular variable is associated with an indicated percentage change in
stations' 1978 revenues. For example, the coefficient on CSI78 in the first
equation indicates that, on the average, an increase of one per cent in 1978
consumer spendable income was associated in this sample with an estimated

increase of about 0.45 per cent in stations’ revenues, all other things being

14/ In other words, the odds are 95-to-5 or better that the true coefficient
is not zero and has the same sign as the estimated coefficient.






=-24-

Table Four

Determinants of Radio Stations’ Trading Prices

(all variables, except dummies and TIME,

in natural logs;parentheses show t-scores)

Equation No. 1 2 3 Means

Independent

Variable logs originals

AMDPWR 0.152 0.150 0.148 5.570 1915.2
(3.54) (3.49) (3.46)

AMNPWR 0.063 0.065 0.064 2.439 710.64
(4.59) (4.72) (4.62)

AMFREQ 0.125 0.126 70.124 5.534 988.18
(2.84) (2.85) (2.80)

FYPWR 0.080 0.079 0.079 4.722 16081
(10.53) (10.45) (10.43)

CSI78 0.525 0.483 0.471 13.615
(10.27) (10.71) (10.53)

NSTA -0.361 2.049 14.384
(4.63)

CSTA 0.323 1.928 11.306

(4.37)
WSTA -0.304 1,901 10.901
(4.04)

CORDUM 0.640 0.714 0.710 0.872
(6.00) (6.61) (6.56)

FOURDUM -0.003 -0.010 -0.008 0.178
(0.027) (0.091) (0.08)

TIME 0.007 0.007 9.599
(1.21) (1.19)

Intercept 5.379 5.838 5.898
(8.93) (10.56) (10.68)

PRICE 13.089 851,410

F 58.54 65.03 57.60

R2 ADJ 0.48 0.47 0.47

N 578 578 578


-33-

Table A-1

Revenue Determinants for Large Market Stations, 1976-80
(All variables, except dummy, in natural logs; parentheses show t~scores.)

Year 1976 1977 1978 1979 1980
Independent
Variable
AMDPWR 0.165 0.172 0.162 0.162 0.157
(6.92) (7.06) (6.64) (6.44) (6.15)
AMNPWR 0.101 0.095 0.098 0.091 0.090
(11.93) (11.15) (11.37) (10.30) (10.05)
AMFRQ -0.106 0.122 0.119 ~-0.120 0.126
(4.11) (4.66) (4.54) (4.47) (4.61)
FMPWR 0.070 0.075 0.084 0.089 0.095
(13.47) (14.09) (15.64) (16.25) (17.16)
csré 0.335 0.365 0.452 0.397 0.404
(7.93) (8.58) (9.77) (8.98) (8.94)
nsTab ~0.160 0.214 0.366 ~ -0.247 0.249
(2.23) (2.97) (4.67) (3.31) (3.28)
CORDUM 0.496 0.476 0.456 0.553 0.524
(dummy ) (7.71) (7.39) (7.04) (8.32) (7.70)
Intercept 6.689 6.548 5.787 6.216 6.259
(14.37) (13.83) (11,33) (12.42) (12.14)
R2 ADJ 0.56 0.55 0.56 0.56 0.57
F 146.17 143.78 153.88 148.97 148.78
N 811 817 825 825 795

Notes: a. Consumer Spendable Income for each market in the year shown in the
column heading.
b. Number of Stations in the market in 1978.


Determinants of Revenues, Large Markets

Dependent Variable:

-19-

Table Two

1978 Revenue

(all variables, except dummies, in natural logs; parentheses show t-scores)

Equation No. 1 2 3 4 Means

Independent

Variable logs originals

AMDPWR 0.178 0.175 0.181 0.179 6.246 4419.3
(6.09) (6.01) (6.28) (6.28)

AMNPWR 0.091 0.094 0.093 0.093 3.256 2337.3
(8.19) (9.61) (9.59) (9.72)

AMFREQ 0.149 0.145 0.149 0.147 5.760 980.24
(4.65) (4.57) (4.75) (4.74)

FMPWR 0.076 0.075 0.075 0.074 5.576 23485
(13.20) (13.37) (13.40) (13.41)

CSI78 0.454 0.462 0.511 0.520 15.014 8,514,200
(9.07) (9.66) (11.59) (12.82)

NSTA 0.310 -0.371 2.926 26.034
(3.33) (4.58)

CSTA 0.539 2.838 20.720

(6.26)
WSTA -0.558 2.649 17.904
(7.20)

NIV -0.073 1.036 3.362
(0.93)

CIRCPC 0.035 ~1.262 0.329
(0.68)

CARSPC 0.123 0.643 0.542
(1.34)

CORDUM (dummy) 0.446 0.439 0.435 0.506 0.796
(6.64) (6.56) (6.59) (7.75)

FOURDUM (dummy) 0.065 0.048 0.047 0.038 0.151
(0.69) (0.56) (0.56) (0.46)

DADUM (dummy) 0.035 0.367
(0.50)

NETDUM (dummy) 0.042 0.390
(0.78)

Intercept 5.831 5.798 5.515 5.26
(10.06) (10.99) (11.64) (11.33)

1978 Revenue 12.993 829,450

F 74.49 120.54 125.44 128.83

R2 aps 0.54 0.54 0.55 0.56

N 812 812 812 812


The study uses three data sets. The first two are random samples of (a)
100 large markets, drawn from among the 400 largest radio markets in the
country, and (b) 100 small-market counties not among the top 400 markets,
Data on all commercial radio stations in each of the 100 large and 100 small
markets in 1978 are included (except for geps and inconsistencies in FCC
files). The third data set covers all commercial radio stations that changed
hands during the 18-month period January 1980 through June 1981. The three
data sets have 1125, 306, and 745 stations respectively.

The study's findings generally are consistent across the three data
sets. The statistical variables significantly associated in most equations
with revenues or station prices include: (a) station power levels, (b) AM
frequency, (c) consumer income in the market area, and (d) number of radio
stations (i.e., competition) in the market. On the other hand, the following
measures, which also were hypothesized to be statistical determinants, showed
no significant association with revenues: (a) use of directional AM antennas,
(b) network affiliation, (c) newspaper circulation in the market, (d) auto-
mobile registrations in the market, (e) number of TV stations in the market,
and (f£) whether an AM station has nighttime signal protection. 4/ The
consistency of these results across three separate samples gives them greater
credibility than they would have if based only on one or two samples.

Perhaps the most interesting policy implications of the study derive from
the estimated relationships (1) between a station's revenues and the number of
competing radio stations in its market and (2) between revenue and nighttime
signal protection. The competition variable suggests that new stations in an

average market are likely to attract something like fifty to seventy per cent

4A/ Nighttime signal protection (and the lack of it for Class IV AM stations)
is explained below on p. 13.


revenue

power

Figure Two

One, where R equals revenue; P equals power; b, is the "slope". coefficient,

which measures the steepness of the line; and a is the vertical “intercept”

41

Figure Three

term. (The slope b, equals the distance “c” in Figure Three divided by the

It shows how much the dependent variable changes when a given
y

distance “d".

independent variable increases or decreases a certain amount.

. R= a, +b,P @