One of the people I read fairly regularly (though not enough to actually pay for a subscription) is Ted Gioia, who writes at honest-broker.com on Substack. His most recent post on the macroculture (mainstream media like the New York Times, Disney, etc.) vs the “microculture” (basically, people posting stuff on YouTube and other places) is free to read, and worth reading, given that pretty much everyone on Cohost, including me, is participating in the “microculture” one way or another.

You can read the article for yourself, so I won’t attempt to summarize it, but I do want to comment briefly on some of his points.

First, he doesn’t understand why people in the “macroculture” aren’t paying attention to what’s going on in the “microculture,” unlike the attitude of mainstream media to counter-culture media in the past. This is easy to explain: in the era he’s thinking of (1950s and 1960s), the major difference between mainstream media and “alternative” media was one of scale: your local alternative weekly was essentially the same sort of thing as your main local daily newspaper, except everything was smaller: it had writers and editors, it ran news stories and reviews, it had ads, it was printed on paper, and it was distributed either to readers’ homes or (more commonly) to places they frequented.

It was relatively easy for a talented writer or editor to move from the “minor leagues” of alternative media to “the show” of a mainstream newspaper, because those hiring them were the same sort of people doing the same sorts of things, and could evaluate them accordingly. Ditto for small presses vs. large book publishers, and indie music labels vs. large corporate labels.

But today there’s a world of difference between what mainstream media do and what people on YouTube, Patreon, etc., are doing. The medium is different, what it takes to succeed is different, how you get paid is different, and so on. No wonder people in mainstream media are out of touch; nothing in their experience has prepared them for this environment.

Second, when discussing why “microculture” will win, he doesn’t touch on a key point: YouTube, Patreon, Tiktok, Spotify, OnlyFans, etc., are brutally competitive environments that take in an extremely large number of aspirants to fame and fortune and pit them against each other in a contest to win the attention of readers, viewers, and listeners. Those who reach the top ranks have benefited from a fair measure of luck, but having bested others in the competitive free-for-all they are also on average very very good at doing what they need to do to attract an audience. No wonder their offerings can command audience sizes and the associated outsized rewards that rival those of the “macroculture.”

(And the rewards are indeed outsized. A writer in the 1960s working at the New York Times might make ten times more money than a writer at a 1960s alternative weekly, maybe a hundred times more at most. However, as I wrote in my post on the distribution of Patreon earnings, top Patreon projects make about a thousand times more money than typical projects.)

Finally, when Gioia writes that “even the biggest potential enemies of microculture (those billionaires in Silicon Valley) need it for their own survival,” he mistakes where the true power lies. Yes, YouTube (for example) creates very little on its own and instead relies on the work of people making and uploading videos (“user-generated content” or “UGC,” to use the ugly marketing term). But there are millions of them, and only one YouTube. If a top vlogger decides to leave YouTube, there’s a horde of people standing in line to take their place. But if YouTube kicks off a vlogger (big or small) then there are few if any comparable channels of distribution they can turn to.

That’s because hosting video at scale is a natural monopoly: only large and well-funded corporations can build the centralized hosting services required to serve an audience of hundreds of millions or more, the recommendation engines needed for artists to come to the attention of others and potentially “go viral,” and the ad networks that are typically the means by which artists can be paid.

It’s also the case that the money that these platforms make is mostly not from the top tier of artists: again, as I saw in my Patreon analysis, their revenue from the top 100 artists is likely matched by revenue from the next 1,000, which in turn is matched by revenue from the next 10,000, which in turn is matched by revenue from the next 100,000, and so on. Since most of their revenue is from a large mass of typical artists, each of whom individually has little power, the platform can afford to squeeze artists, retain as high a percentage of revenue as they feel they can get away with, and screw over artists in various ways both large and small.

So the chief winners in the “war between the macroculture and the microculture” are not going to be writers, musicians, or graphic and video artists. They are going to be the large corporations that control distribution in the (ironically-named) “creator economy,” and the “Silicon Valley billionaires” who own them and run them.

kastelpls (@highimpactsex) - 2023-12-06 21:01

yeah, reading this was interesting because it’s not like i necessarily disagree with the claim that microculture (or as i prefer to call it, subcultures) is “winning”. indeed, it’s the companies that provide the infrastructure/ecosystem of microcultures that are winning. they’ve bet on the rise of fresh talents providing “user-generated content” and basically won as people became skeptical of mainstream media.

it’s true that traditional media like NYT is failing at least and that’s because they refuse to adapt to the times or hire actual people outside their circle. but i expect them to truck on for a little bit longer because their prestige/brand is still wroth it – i’m more surprised that newspapers and magazines have not followed the cynical path of reuters leasing their brand to anyone who pays.

i think what will end up happening (if it hasn’t now) is the creation of social classes within microcultures. there’s clearly a group of people who has made a living out of this while many others don’t. it’s hard for me to see how this isn’t some fractured community.

Frank Hecker (@hecker) - 2024-12-07 12:36

Thanks for stopping by to comment! You’ve hit the nail on the head — I have nothing to add to what you’ve said.

[This post and its associated comments were originally published on Cohost.]

Note to stats nerds: I too have read Clauset, et al., and am well aware that many things claimed to follow a power law actually do not. (For example, this appears to be true for Patreon earnings.) But “I fought the log-normal distribution and the log-normal distribution won” doesn’t have quite the same ring to it.

If you happen to listen to “Discover Weekly” on Spotify (as I do), or regularly check out musicians on Bandcamp (as I also do) then from time to time you may have thought to yourself, “Wow, this is really good! Why haven’t I ever heard of them?” Apparently there are more musicians with real talent than there are popular and successful musicians, and sometimes the most talented are not necessarily the most successful.

This experience is not confined to music, but applies to other areas as well. For example, I suspect that hidden in the lower half of Patreon projects by number of patrons there are writers and artists whose work is as worthy as that of those who occupy the top 100 places.

Why should this be? That’s the key question for today’s post. It’s a very political question, in that proposed answers are often used to justify existing distributions of fame and wealth, or alternately to deny those justifications. I’m still exploring this general topic, so you can consider this just one take on the subject, with others possibly to follow in future posts. (WARNING: This will be a bit long.)

The proposed answer I’m going to present in this post is adapted from Sornette, et al., “The fair reward problem: the illusion of success and how to solve it.” The general idea is that when we look at things like the number of views on YouTube, the number of listens on Spotify, or the distribution of Patreon earnings, we’re seeing the effects of 1) individual talents influenced by lots of little things that add up (and hence result in a normal distribution), which then 2) combine through multiplication to produce a log-normal distribution of overall skill at artistic (or other) endeavors, and are then 3) supplemented by luck to produce a power law distribution (at least at the high end, and possibly at the low end as well).

It all adds up

Let’s start with a specific talent, for example, having a good singing voice. I’m no anatomist or vocal coach, but I can think of lots of little things that might influence this: size, structure, and health of the vocal cords, size and structure of the mouth, throat, nasal passages, and sinuses; size, shape, and motility of the tongue, lung capacity and diaphragm strength; the ability to control one’s voice (e.g., to accurately hit certain pitches); and so on.¹

Some of these things might be determined at conception (genetic heritage), some in utero (by random acts of development), and some after birth (e.g., being able to get a music education and afford voice lessons). The relative importance of these can be and is debated, but my point here is simply that most of these influences act relatively independently of each other, and together they add up to determine the overall quality of a person’s singing voice.

If multiple independent things do add up together to determine vocal quality, the result when we look at voice quality across the population as a whole should be a so-called Gaussian or “normal”² distribution, with a hump in the middle representing people with average singing voices, a tail at the left representing people with below-average singing voices, and a similar tail at the right representing people with above-average singing voices.

Why should this be? Think of having a really excellent singing voice as being the equivalent of flipping a coin and having it come up heads (say) 95 or more times out of 100 times, and having a really bad singing voice as having it come up up tails 95 or more times out of 100 times. There are relatively few ways that you can have a coin flip come up heads (or tails) 95% of the time, but many (many) more ways that you can have a coin flip come up roughly half heads and half tails. So, in practice most people would end up in the middle, not at the tails—thus the central hump.

Go forth and multiply

Suppose a person has an excellent, or at least well above average, singing voice. Does that mean they’ll have success as, say, a singer-songwriter? No, because a singer-songwriter by definition also needs to be able to write songs; more specifically, they need to write both melodies and lyrics. So there are now three things that they should ideally be well above average in, and those things are relatively independent: there are people who can sing well but not write catchy melodies, people who can write catchy melodies but not write good lyrics, and so on.³

To pull some numbers out of the air, let’s say that 20% of the population can sing pretty well, but only 5% can write catchy melodies and only 10% can write reasonably good lyrics. Then out of 100 million people we’d expect 20 million (20% of 100 million) to be able to sing well, 1 million to be able to sing well and write catchy melodies (5% of 20 million), and 100,000 (10% of 1 million) to be able to do all three. So in this example only 1 in 1,000 (100,000 out of 100 million) people have what it takes to be a singer-songwriter.

But it doesn’t end there. A potential singer-songwriter who wants to be successful also has to have the drive and conscientiousness to write and record lots of songs, the energy to tour, the ability to connect with audiences, the savvy to navigate the music business, and so on. Each of these requirements further reduces the potential talent pool, so that it may be that in a population of 100 million people there’s only about one in a million people who have what it takes to be a successful singer-songwriter.⁴

You can apply a similar analysis to any creative endeavor: writing novels, drawing comics, filming movies, making interesting podcasts or entertaining game run-throughs, and so on. In all of these cases many relatively independent factors will multiply together to determine overall skill-based success. The result will be a log-normal distribution: instead of most people clustering around an average level of success, with smaller tails to the left and right (as in a normal distribution), almost all people will have little or no success, and will thus form a very large cluster to the left. There will then be a very long right tail, with only a very few people in the extreme right of the tail having great success.

So, to sum up thus far: individual talents are hypothesized to be due to the additive effects of many independent factors, and thus to be normally distributed. However, having an overall skill set conducive to success in a given field of endeavor is hypothesized to be depend on the multiplicative effects of many different and relatively independent talents, and thus to follow a log-normal distribution.⁵

Luck and pluck

However, having an individual talent or even an overall set of talents is not necessarily sufficient to achieve success; often a fair amount of luck contributes to success, artistic and otherwise. We are used to attributing people’s success to talent and hard work—and of course people who are themselves successful are often the most extreme proponents of this. (After all, who wants to think that their own success is partly—let alone mostly—a matter of chance?)

This habit is so pervasive that we often attribute success wholly to merit even when it’s explicitly made clear that that’s not the case. For example, when people think of the 19th century novels by Horatio Alger, Jr. (when they think of them at all), they think of them as portraying the rewards that come from hard work. But if you actually read some of them (as I did), it’s clear that they are really stories of “pluck” and “luck”, with the latter typically given pride of place.

For example, in Alger’s first two novels, “Ragged Dick”, a homeless shoe-shine boy, is hard-working, honest, and open to new opportunities. But he becomes the successful businessman “Richard Hunter” only through a series of circumstances that combine mundane good fortune with incredibly implausible coincidences.

So, how might the effects of luck be modeled? Sornette, et al., treat it as an random additive component on top of what they call “overall skill.” They assume that overall skill is log-normally distributed (as discussed above) and acts to increase one’s already-achieved success by some percentage during each time period, with the exact percentage depending on the overall skill. Luck then acts to randomly enhance or counteract this effect of overall skill in each time period.

However good or bad luck does not affect everyone equally, but rather depends on a person’s appetite for risk: those who take more risks may benefit more than others from a given event of good luck, or may suffer more than others from an event of bad luck.

We see this, for example, in the Horatio Alger novels discussed above: the luckiest event of Dick’s young life occurs when he rashly leaps from a ferry to save a young boy who’s fallen overboard. The boy turns out to be the son of a business owner, who rewards Dick with a job in his establishment; the boy’s mother subsequently gives Dick a thousand dollars as a token of her own gratitude.⁶ But of course, in real life Dick’s selfless act might have brought no reward at all, or even resulted in his own death.

Modeling the effects of luck

As with overall skill, Sornette, et al., model the degree of risk taking using a log-normal distribution, presumably because, as with skill, it arises from several factors multiplied together: personality, social and economic situation, and so on. Thus some people would have an order of magnitude or more appetite for risk than others, and would be disproportionately rewarded (or punished) for their good (or bad) luck. Based on their simulation exercises they come to two conclusions:

First, they claim that the effects of luck may combine with the effects of overall skill to convert the distribution of overall success from a log-normal distribution to a power law distribution (as shown in the right hand graph above): at the top end would be people who benefited from extraordinary good luck and a taste for risk, beyond what their overall skills might justify, and likewise at the bottom end could be people with relatively high overall skills who have suffered bad luck of various kinds.

Second, they point out that in the short term it may be difficult to impossible to separate the effects of talent vs. luck. Only after a few years or even decades will it likely become apparent who has real staying power based on true talent and whose success was simply a matter of being in the right place at the right time, and little more than that.

Sornette, et al., give the example of success in investing in this context, but we also see this in artistic fields such as music. For example, think of all the acts who had #1 hits in their day but have had no lasting impact whatsoever. Think also of those acts who never achieved success due to bad luck of various kinds—internal conflicts, label troubles, financial problems, ill health or the death of a band member, or simply being out of step with contemporary trends—but who were rediscovered years later and acknowledged as exceptional artists. (Of course, in the meantime they lost out on the cumulative rewards of the success that eluded them.)

Conclusion

Sornette, et al., present one view of the interaction between talent, luck, and the society that rewards them both. As I mentioned above, there are other possible models as well, some putting more stress on the importance of luck, some putting more stress on talent, and others highlighting different factors, like the initial circumstances from which artists emerge.

But the overall picture is fairly clear: almost all would-be artists will be unsuccessful in both relative and absolute terms, and only a few artists will be truly successful. The question then becomes, what, if anything, should society do in terms of changing this picture? In particular, what stance should we take in terms of supporting current artists, or encouraging more people to become artists?

This is an especially pertinent question given the fear (or hope, as some might say) that we will be overwhelmed with a flood of AI-generated art (or “art,” in quotes, as some might say). Should we just accept that this is the way things are, and that it’s pointless to try to change it? I don’t personally believe that, but my thoughts on the matter are not yet fully-formed enough to summarize here. I’ll try to do that in a future post.

Royal Assassin (@RoyalAssassin) - 2023-02-26 16:42

Imagine being lucky enough to be born with a predisposition for having a good work ethic. Alas.

Frank Hecker (@hecker) - 2023-02-26 17:58

Yep, there are a lot of predispositions I wish I was born with :-(

Mightfo (@Mightfo) - 2023-02-26 16:46

Great post. Im also reminded of the quote by Gould “I am, somehow, less interested in the weight and convolutions of Einstein’s brain than in the near certainty that people of equal talent have lived and died in cotton fields and sweatshops.“

Even if you exclude economic factors, the vector of “did they pursue something they had talent for?” is a big factor imo. What are the chances of discovering different talents? Of pursuing those talents over other careers or other ways to spend time? Different cultures reward different talents and highlight different talents. Preexisting industries can be key to cultivate talents, like how voice acting in Japan is a lot more developed than most elsewhere. Audience size and language are also a major intersection- i think Finnish and Romanian are particularly beautiful languages, but they dont have the same audience to provide reverberating support for music and so on as English, Chinese, Spanish, etc.

Ill try to think more later about an idea of how things should be in this regard and maybe share those thoughts.

Frank Hecker (@hecker) - 2023-02-26 18:03

Your comments on place and time are very much on point. One of the papers I didn’t highlight was about how succeeding in the contemporary art world (i.e,, the sorts of art featured in, say, ArtForum) is highly influenced by the prestige level of the institutions an artist is associated with very early in their careers: art schools, galleries, etc. A great artist who comes from the middle of nowhere is going to find it difficult to impossible to achieve success.

I’d love to hear your thoughts on “how things should be”. (As I said, my own are still half-baked.)

This is another brief follow-up to my “Life in Patreonia” post, based on this analysis. It can be summed up as follows: there is no significant correlation between the number of patrons of a Patreon project and the earnings per patron, although the earnings per patron does decline very slightly as projects become more popular.

The numbers: across almost 130,000 projects reporting nonzero earnings from monthly charges in December 2022, average earnings per patron was $6.83 and median earnings per patron was $4.50. Fitting a simple linear model predicts that each additional patron is worth $3.98. There’s a fair amount of variability, though: one project earned less than a penny per patron, while another earned over a thousand dollars from just one patron. (As you might have guessed, it was an NFSW project.)

I also looked into the (in)famous Kevin Kelly claim that creators aiming to make a living in the Internet age just need to find “1,000 true fans” willing to pony up $100 or more per year. How’s that working out for Patreon projects?

Only 31 Patreon projects (about 0.02% of all projects with nonzero monthly earnings) met the specific criteria of having 1,000 or more patrons and per patron earnings of $100 or more per year (assuming December 2022 earnings were representative). If we use the looser criterion of earning $100,000 or more per year (regardless of the number of patrons), 238 projects met that, about 0.18% of all projects with nonzero monthly earnings in December 2022. I think finding 1,000 “true fans” is a lot harder than Kelly thought.

Mightfo (@Mightfo) - 2023-02-07 19:54

i literally just watched an overall good video that pushed the 1000 true fans thing as a partial solution to the issues of the attention economy, this puts it into a lot of perspective…

Frank Hecker (@hecker) - 2023-02-07 20:56

Thanks for stopping by! Yeah, Kevin Kelly in particular has been dining out on the “1000 true fans” thing for years; it’s still the most popular post on his site. (But to give Kelly his due, his “scenius” post is excellent and I think essentially correct, even though he didn’t invent the term himself.)

[This post was originally published on Cohost.]

(My apologies, I couldn’t think of a clever headline for this.) In a comment on my “Life in Patreonia” post, @tekgo asked whether the number of patrons of Patreon projects was distributed in a similar way to the earnings for Patreon projects.

The short answer is “yes, it is.” The long answer is here. The in-between answer is that in the sample of about 218,000 projects, the number of patrons per project appears to have a log-normal distribution (like earnings), with a median number of 6 patrons per project. The chances of having more than 10 patrons is about 40%, the chances of having more than 100 is less than 10%, and the chances of having more than 1,000 is less than 1%.

[This post and its associated comments were originally published on Cohost.]

If you’re like me, you probably contribute to a project on Patreon. You may have even started a project on Patreon yourself, or are considering doing so.

Patreon boasts about its success: “8 million+ monthly active patrons … 250,000+ creators on Patreon … $3.5 billion paid out to creators.” Other sites run articles like “How Much Money Can You Make on Patreon?” and “25 Patreon Statistics You Need to Know.” There’s even a Patreon project devoted to collecting and publishing such statistics on an ongoing basis.

Occasionally you’ll find someone injecting a note of caution, as in a relatively in-depth analysis from five years ago. But the one set of statistics I could never find was about exactly how Patreon earnings were distributed across the whole set of projects, including what typical Patreon projects could expect to earn, and whether there was a straightforward way to characterize that distribution of earnings. So I decided to try doing that myself.

If you’re interested in the gory details, see “Distribution of Earnings Among Patreon Projects Charging by the Month.” The document is CC0-licensed, as is the R code used to create it. However the actual dataset I used (from Graphtreon) is not publicly available; if you want to replicate my work you’ll need to pay for the data yourself. (For what it’s worth, I don’t have a problem with Graphtreon charging for this; it took work to collect this data, and it has commercial value.)

One way to think about Patreon is to think of it as its own country (or state, or province), one with a population of over a hundred thousand. More specifically, Patreon had about 220,000 projects that reported their number of patrons as of December 2022, but only about 130,000 of them reported nonzero earnings from monthly charges. (About 80,000 projects didn’t report their earnings publicly at all, a few thousand charge by the podcast or video, not by the month, and a few hundred reported zero earnings.) Those are the projects I looked at in my analysis.

If Patreon were a country (“Patreonia”) then it would be by far the most unequal country on earth. As you can see in the left graph above, project earnings drop off extremely fast once you go past the top-ranked projects. In fact, the drop-off is so extreme that it’s better visualized using a so-called “log-log” plot, like the right graph above. While the top projects on Patreon earn hundreds of thousands of dollars a month, the median project (half earn more, half earn less) earns $25 a month, or less than a dollar a day.

This level of inequality is greater than in any country in the world; if you’re familiar with Gini coefficients, the coefficient for “Patreonia” is 0.84, while the country with the highest level of inequality is apparently South Africa, with a coefficient of 0.63. (A Gini value of 0 means income is equally shared, while a value of 1 indicates “perfect inequality” — one person gets all the income, everyone else gets nothing.)

I find it helpful to consider “Patreonia” as consisting of four separate groups of projects, each ten times larger than the last; together these four subsets account for almost all of the projects that I had valid data for. You can think of them as communities of different sizes and economic circumstances.

Patreon Heights

The first group, the “0.1%” of “Patreonia,” consists of the top 100 projects with nonzero earnings from monthly charges. The median project in “Patreon Heights” had almost 3,900 patrons and a monthly income in December 2022 of almost $25,000, or about $300,000 a year. This corresponds to an especially affluent neighborhood in an especially affluent county in the US, like Loudoun County, Virginia, which has a median household income of around $150,000, the highest of any US county.

Patreon Grove

The second group, the “1%” of “Patreonia,” consists of the next 1,000 projects with nonzero earnings from monthly charges. The median project in “Patreon Grove” had almost 800 patrons and a monthly income in December 2022 of about $4,200, or about $50,400 a year. This is well under the current median household income in the US, which is about $70,000. A US county with a comparable median household income and population is Crockett County, Texas, a rural county in the western part of the state.

Patreonville

The third group consists of the next 10,000 projects with nonzero earnings from monthly charges. The median project in “Patreonville” had just over 100 patrons and a monthly income in December 2022 of about $650, or about $7,800 a year. This is well below the US Federal poverty line of $12,880 for a single-person household, and is lower than the median household income for any county in the US, even lower than that for Adjuntas, Puerto Rico, the poorest jurisdiction for which the US Census Bureau has data. (The median household income for Adjuntas is around $12,000 a year.)

The rest of Patreonia

The fourth and final group consists of the next 100,000 projects with nonzero earnings from monthly charges. The median project in the rest of “Patreonia” had 5 patrons and a monthly income in December 2022 of about $28, or about $340 a year. This is comparable to incomes in the poorest countries on Earth, places like Somalia, Uzbekistan, or the Democratic Republic of Congo.

Patreon and the “creator economy”

At this point you might say to me, “Frank, these are really stupid comparisons. You can’t compare someone running a side gig on Patreon to a person eking out a meager living in the world’s poorest countries.” And you’re right, but: Patreon, Substack, OnlyFans, and similar services are pitched to “creators” as a way to earn at least a partial living by “monetizing” their “content.”

Not a month goes by without another blog post, news story, or website heralding the “creator economy.” It’s attracting the attention of venture capitalists, who’ve funded a host of startups, all eager to help you realize your dreams as an artist, writer, musician, filmmaker, game designer, or “influencer,” in return for just a few percent off the top.

But the reality? Not so rosy. What I’ve tried to do is to put some more numbers behind that assertion.

P.S. to math-savvy web developers: If you’d like to put together a different kind of “how much money can you make on Patreon?” calculator, it’s pretty easy to calculate the odds of a project earning more than a given amount of money on Patreon. Patreon earnings for the month I analyzed were best fit by a log-normal distribution with μ of 3.33 and σ of 1.84. So to estimate the probability of earning more than x dollars, you can plug x into the formula for the log-normal cumulative distribution function (you’ll need the erf() function for this), and then subtract the result from 1. For example, the probability of earning more than $100 a month is 0.24, or about 1 in 4, while the probability of earning more than $1,000 a month is 0.026, or less than 3 percent.

@Janet (@Janet) - 2023-01-21 11:05

messed up.

but you dont even have to go online. same goes for GEMA in germany. thats the state org handling musical rights, only a few percent of artists (mostly song writers and producers) earn anything at all. if you are a tiny band, there is no way you will ever get money from GEMA, yet any music made will automatically be handled by GEMA, except in case you find another org, but GEMA had a monopoly so you were sool. Some years ago another such org was founded just so you wouldnt have to deal with GEMA anymore… ah but im not really in the know, i only read about it when the new org C3S was founded

Frank Hecker (@hecker) - 2023-01-21 12:38

Thanks for stopping by to comment! I believe the US has the same system for music, a duopoly between ASCAP and BMI.

june (@junelinked) - 2023-01-22 05:54

a little different; ASCAP/BMI are performance rights only (and do have some small, private, for-profit competitors - notably SESAC) whereas GEMA is an integrated CMO (mechanical + performance), and actually has more restrictive assignment provisions at least in part since ASCAP/BMI are under consent decrees. the market dynamics end up similar though, small writers are lucky to make enough to hit the payout threshold

Frank Hecker (@hecker) - 2023-01-22 11:52

Thank you for correcting me!

jeroknite (@jeroknite) - 2023-01-21 13:46

… Of course the poorest place in the US is in Puerto Rico :c

Frank Hecker (@hecker) - 2023-01-21 14:15

Thanks for the comment! I was curious about this, and checked the median household income statistics on Wikipedia. The 40 poorest jurisdictions in the US and its territories are in Puerto Rico, as are 58 of the 60 poorest.

Maynard (@Quelklef) - 2023-01-21 14:20

is there large variance in the size of patreon projects (≈ number of people involved)? If we account for this by, say, dividing earnings by number of people, do the graphs seriously change?

(My first reaction to the graph was that ”this makes sense; the large projects get the most funding but also have to pay out to more people”. But actually I have no clue what the distribution of project sizes on patreon looks like, nor if size correlates with earnings)

Maynard (@Quelklef) - 2023-01-21 14:32

Say we very generously assume that all Patreon Heights projects are run by 100 people, Patreon Grove projects by 10, Patreonville projects by 1, and that the rest of Patreonia consist of abandoned projects that people forgot to unsubscribe from.

Then in Patreon Heights we have median income per person per month of $250; in Patreon Grove we get $420; and in Patreonville we get $650

Frank Hecker (@hecker) - 2023-01-21 15:53

You can find a list of the highest-earning Patreon projects at https://graphtreon.com/top-patreon-earners. You can check them out yourself (I haven’t), I suspect that the highest-earning projects aren’t run by anywhere near 100 people.

Maynard (@Quelklef) - 2023-01-21 22:07

Hmm, yeah. Looks like at least the top few are mostly podcasts featuring a handful of people. Presumably they employ editors and publicizers, etc, but I would be shocked if any number was approaching 100

Maynard (@Quelklef) - 2023-01-21 22:19

A four-host podcast making almost $200k a MONTH is absolutely bonkers; holy shit

Frank Hecker (@hecker) - 2023-01-21 15:49

Thanks for commenting! I cover some of this in the detailed document I linked to, in the section ”How are earnings and the number of patrons related?”; I’m not sure if you’ve had the chance to look at that or not. The short answer is that mean earnings per patron was $6.83 (with a standard deviation of $12.01), and the median earnings per patron was $4.50. Only 15% of projects have earnings per patron over $10, and very few have earnings per patron over $20.

Maynard (@Quelklef) - 2023-01-21 22:18

Ah, I had missed that link; sorry! (That is a very nice analysis)

I think I phrased my question poorly. I’m not interested in earnings versus number of patreons, but versus number of creators. Looking at Graphtreon, seems like this data isn’t available? I guess if it’s not something Patreon asks for people to self-report on, then it’s not really possible to generate in bulk.

Frank Hecker (@hecker) - 2023-01-22 00:30

To my knowledge there’s no source of data on the number of people per Patreon project. That’s why I was careful to refer to ”projects” not ”creators”.

Mightfo (@Mightfo) - 2023-01-21 21:44

Im not sure how related this is, but this reminds me of an experiment a music site did where they released two beta versions: One where you could see the number of views a song got, and one where you couldnt. The one where you could see the views, completely random songs would get a snowball effect of views and people would be really into them, but those songs were not significant at all on the other.

I feel like that sort of ”attention economy snowballing” is a key part of disproportionate success dynamics like this.

Frank Hecker (@hecker) - 2023-01-22 00:32

A very good comment, thank you for foreshadowing some of what I hope to be able to write about in future.

㋬Zen1th (@zenith391) - 2023-01-23 16:16

That’s very true, and it’s also why I like cohost. You can’t see like counts or follow numbers which brings us closer to the version where ’we can’t see the number of views’. I might be following complete nobodies or the most popular account here, I can’t tell the difference.

Pat (@tekgo) - 2023-01-23 16:29

As you noted in the caveats a number of projects with high patron counts don’t report earnings. Looking at the Graphtreon top projects list only 14 of 50 report their earnings. I’m curious if you graph all the projects by number of patrons(that report that data) does it have a similar distribution to the earnings rank graphs?

Frank Hecker (@hecker) - 2023-01-23 16:59

Thanks for commenting! Yes, the distribution of number of patrons per project has a similar sharp drop-off once you get past the top 100, 1000, etc. I actually did an analysis of this as well, but the analysis document was getting really long and I decided to focus primarily on earnings. I may do a separate document discussing the number of patrons vs. rank in number of patrons. I’m going to guess that it follows a log-normal distribution too.

exodrifter (@exodrifter) - 2023-01-24 02:35

I have a lot of complicated feelings about the ”creator economy” and this is part of it. Many of the people I know that participate only do it on the side for fun and use the extra income to afford a few things relevant to the creative pursuit they are doing for fun. And although we are small, we also like to support each other, but every time money changes hands, the platform takes a cut…

Meanwhile, I would like to work full time on my creative pursuits, but it’s hard to imagine how that would work given the slim odds. I don’t really think I’m trying to say anything in particular here, it’s just difficult for me to think about.

Frank Hecker (@hecker) - 2023-01-24 19:28

Thanks for commenting! To repeat what I wrote earlier, I am not trying to discourage people who want to supplement their income via Patreon or similar services. My main target was/is VCs and startups that are pushing services like this as the answer for people who want to support themselves full-time (or nearly so) as artists.

Censa (@censa) - 2023-01-29 23:23

huh this was really interesting. Thank you for sharing your findings!