$2.5 Billion: The Big Number that “Big Radio” could owe each year if it paid music royalties at Pandora’s rates

While the controversy over the rates paid by both streaming services and webcasters continues to bubble, one simple question lurked in my mind:

If it paid for music at the rates paid by Webcasters like Pandora, how much might Big Radio owe in royalties to record labels and performing/features artists?

And so, in the post that follows I describe the conclusion and the method for estimating this potential pool of royalty dollars.

The Short Story:

Were so-called “Big Radio,” otherwise known as Terrestrial Radio, to pay for their performance of sound recordings at rates similar to that paid by Pandora for the performance online of the same recordings, I estimate that the royalty pool would be pretty big: approximately $2.5 billion.

Were Big Radio to pay for the performance of sound recordings at the statutory webcasting rates established by the US Copyright Royalty Board (aka, the CRB Default Rates), I estimate the royalty pool would expand considerably in size: approximately $4.7 billion.

I will leave it up to the reader to determine whether the NAB’s proposed payment of $100 million for sound recording performance royalties would constitute a discount from the payments estimated above. I will also leave it up to the reader to decide whether webcasters like Pandora should be paying more or less for music than they now pay. However, I felt a little Oranges to Oranges sort of comparison might be worthwhile in the Fairness debate.

The Long Story:

Estimating the royalty pool for terrestrial radio, were this industry required to pay for music at the rates that webcasters pay, is not entirely straight forward. That said, the challenge is not so technically or mathematically convoluted that it requires special certifications. There would be, as far as I can tell, two ways in which to make such an estimate:

The hard way. The most difficult way to calculate the number of radio plays across so-called Big Radio—counting all radio listeners (the comparable we need since webcasters pay for each play (i.e., stream) to each listener)—would be to get the number of so-called “spins” for each station in the United States and multiply those spins by the listening audience of each station. Aside: A spin simply refers to the performance of a musical track on a radio station, referring back to the days of vinyl records that would indeed require spinning for the music to be heard.

Therefore, if we were omniscient when it comes to radio performance data we would need: (a) each spin throughout each day of the year, (b) for each station in the country, and (c) listening audience at each moment of the day for each station. Problematically, neither (a) nor (b) nor (c) are directly reported (or perhaps even known) by most stations.

The easy way. A much easier way to estimate the total number of “listens” to spins on US Radio requires accepting one simple claim: Radio stations are playing music 24 hours a day, 7 days a week, and 365 days a year. This claim is not that hard to accept if you just turn on a radio and start turning the dial. Music — with a capital M — is playing across most of these stations at any point in time. Anyone with a radio could be listening to Music at any moment, and for this analysis which station is tuned or which song happens to be playing doesn’t matter.

Once we accept that Music is playing across nearly all stations throughout the day, we can treat the radio tuner like one “Big Radio.” And so, we simply need to recognize that there are only so many tracks this big tuner station could play = (Z number of tracks per hour) x (24 hours in a day) x (365 days per year).

Throughout that day, people (aka, the listening audience) are/is, well, listening to radio. Therefore, in addition to the number of tracks output by this big radio we need some estimate of the total radio audience — the population of people listening the music on the radio — throughout the day. Fortunately, there are organizations like Arbitron here in the US that make just these sorts of estimates.

In other words, we are counting spins only when people listen to them. And using industry estimates to ascertain the average number of people listening throughout the day—the so-called “Average Simultaneous Listeners” (ASL) metric. If you think ASL is crazy, note that it is effectively a shortcut to the Aggregate Tuning Hour (ATH) metric previously applied to calculate webcasting royalties.

And so, we can simply multiply the average daily size of the music radio listening audience by the number of tracks (i.e., spins) our big station could play during a year.

24 Hours in a Day
365 Days in a Year
12 Songs Played every Hour


105,120 <;- The Total Number of Spins that our aggregated "Big Radio" could play in a Year

In the analyses below I develop estimates based upon the assertion of 9, 12, and 15 songs playing per hour. I make use of Arbitron's published estimates of the US Listening Audience (aged 12 and greater) at various times of the day to estimate the average size of the listening audience throughout the day, which I place at around 8.8% of the 12+ population. I also introduce some room for error in this audience estimate, by reducing the percentage of listeners by 33% and expanding it by 33% for those who like to see this sort of range.

Below is an estimate grid for the royalty pool were Big Radio to pay sound recording performance royalties as if it were a “pureplay” webcaster, at the rate of $0.0011 (2012) per performance per listener established by settlement between Sound Exchange and a cohort of Webcasters:

And so, at 12 songs per hour given an average radio listening audience throughout the day of 8.8% of the population age 12 and higher, so-called Big Radio would owe $2,469,294,195 in royalties for the performance of sound recordings. If that Big Radio were treated like a Pureplay Webcaster.

This next table is an estimate grid for the royalty pool were Big Radio to pay sound recording performance royalties as if it were a run-of-the-mill webcaster, at the rate of $0.0021 (2012) per performance per listener established by the US Copyright Royalty Board:


It Big Radio were treated like a Default Webcaster, at 12 songs per hour given an average radio listening audience throughout the day of 8.8% of the population age 12 and higher, that Big Radio would owe $4,714,107,000 in royalties for the performance of sound recordings.

That’s a Big Number for a Big Radio.


Anti-scale in music licensing: The Spotify example

More often than not, startups are in pursuit of venture dynamics that exhibit benevolent economies of scale: cost per unit, or per user fall as the number of users increases.

When licensing key inputs, rational startups pursue ceilings and not floors: license terms that place limits on the obligations rather than really high minimums for the obligation.

Music service startups face their own very special sort of “anti-scale.” Really high minimum obligations alongside ceilings that scale 1:1 with user growth or music use.

Eric Eldon of Techcrunch claims to have otherwise “secret” information on the license terms accepted by Spotify:

Its deal structure with the labels requires either a $200 million annual payment, like what it had to do last year, or around 75% of total revenue (whichever is higher)

That “whichever is higher” aspect of the licensing context says it all: Anti-scale.

First, by agreeing to such a significantly large upfront and guarantee, Spotify hopes to develop a barrier to entry– to offer a similar model any new service may have to match the scale of this payment.

Second, rather than license costs quieting with user growth, these costs get louder. In other word, once the risk of whether Spotify can acquire users dissipates a bit such that the guarantee can be paid, the total cost pool proceeds to expand linearly with that user base–a floor for the obligations rather than a ceiling.

In contrast, major radio stations will offer a large fixed pool of money for their royalty pool, enabling costs per user (listener) to decline once revenues exceed the size of this pool (which they do by many multiples).

Perhaps, someday, music service startups will not have to be completely irrational to operate. I guess it’s all for the fame, and they will just make money selling t-shirts and autobiographies.