Is privacy dead?

May 9 JDN 2459342

It is the year 2021, and while we don’t yet have flying cars or human-level artificial intelligence, our society is in many ways quite similar to what cyberpunk fiction predicted it would be. We are constantly connected to the Internet, even linking devices in our homes to the Web when that is largely pointless or actively dangerous. Oligopolies of fewer and fewer multinational corporations that are more and more powerful have taken over most of our markets, from mass media to computer operating systems, from finance to retail.

One of the many dire predictions of cyberpunk fiction is that constant Internet connectivity will effectively destroy privacy. There is reason to think that this is in fact happening: We have televisions that listen to our conversations, webcams that can be hacked, sometimes invisibly, and the operating system that runs the majority of personal and business computers is built around constantly tracking its users.

The concentration of oligopoly power and the decline of privacy are not unconnected. It’s the oligopoly power of corporations like Microsoft and Google and Facebook that allows them to present us with absurdly long and virtually unreadable license agreements as an ultimatum: “Sign away your rights, or else you can’t use our product. And remember, we’re the only ones who make this product and it’s increasingly necessary for your basic functioning in society!” This is of course exactly as cyberpunk fiction warned us it would be.

Giving up our private information to a handful of powerful corporations would be bad enough if that information were securely held only by them. But it isn’t. There have been dozens of major data breaches of major corporations, and there will surely be many more. In an average year, several billion data records are exposed through data breaches. Each person produces many data records, so it’s difficult to say exactly how many people have had their data stolen; but it isn’t implausible to say that if you are highly active on the Internet, at least some of your data has been stolen in one breach or another. Corporations have strong incentives to collect and use your data—data brokerage is a hundred-billion-dollar industry—but very weak incentives to protect it from prying eyes. The FTC does impose fines for negligence in the event of a major data breach, but as usual the scale of the fines simply doesn’t match the scale of the corporations responsible. $575 million sounds like a lot of money, but for a corporation with $28 billion in assets it’s a slap on the wrist. It would be equivalent to fining me about $500 (about what I’d get for driving without a passenger in the carpool lane). Yeah, I’d feel that; it would be unpleasant and inconvenient. But it’s certainly not going to change my life. And typically these fines only impact shareholders, and don’t even pass through to the people who made the decisions: The man who was CEO of Equifax when it suffered its catastrophic data breach retired with a $90 million pension.

While most people seem either blissfully unaware or fatalistically resigned to its inevitability, a few people have praised the trend of reduced privacy, usually by claiming that it will result in increased transparency. Yet, ironically, a world with less privacy can actually mean a world with less transparency as well: When you don’t know what information you reveal will be stolen and misused, you will constantly endeavor to protect all your information, even things that you would normally not hesitate to reveal. When even your face and name can be used to track you, you’ll be more hesitant to reveal them. Cyberpunk fiction predicted this too: Most characters in cyberpunk stories are known by their hacker handles, not their real given names.

There is some good news, however. People are finally beginning to notice that they have been pressured into giving away their privacy rights, and demanding to get them back. The United Nations has recently passed resolutions defending digital privacy, governments have taken action against the worst privacy violations with increasing frequency, courts are ruling in favor of stricter protections, think tanks are demanding stricter regulations, and even corporate policies are beginning to change. While the major corporations all want to take your data, there are now many smaller businesses and nonprofit organizations that will sell you tools to help protect it.

This does not mean we can be complacent: The war is far from won. But it does mean that there is some hope left; we don’t simply have to surrender and accept a world where anyone with enough money can know whatever they want about anyone else. We don’t need to accept what the CEO of Sun Microsystems infamously said: “You have zero privacy anyway. Get over it.”

I think the best answer to the decline of privacy is to address the underlying incentives that make it so lucrative. Why is data brokering such a profitable industry? Because ad targeting is such a profitable industry. So profitable, indeed, that huge corporations like Facebook and Google make almost all of their money that way, and the useful services they provide to users are offered for free simply as an enticement to get them to look at more targeted advertising.

Selling advertising is hardly new—we’ve been doing it for literally millennia, as Roman gladiators were often paid to hawk products. It has been the primary source of revenue for most forms of media, from newspapers to radio stations to TV networks, since those media have existed. What has changed is that ad targeting is now a lucrative business: In the 1850s, that newspaper being sold by barking boys on the street likely had ads in it, but they were the same ads for every single reader. Now when you log in to CNN.com or nytimes.com, the ads on that page are specific only to you, based on any information that these media giants have been able to glean from your past Internet activity. If you do try to protect your online privacy with various tools, a quick-and-dirty way to check if it’s working is to see if websites give you ads for things you know you’d never buy.

In fact, I consider it a very welcome recent development that video streaming is finally a way to watch TV shows by actually paying for them instead of having someone else pay for the right to shove ads in my face. I can’t remember the last time I heard a TV ad jingle, and I’m very happy about that fact. Having to spend 15 minutes of each hour of watching TV to watch commercials may not seem so bad—in fact, many people may feel that they’d rather do that than pay the money to avoid it. But think about it this way: If it weren’t worth at least that much to the corporations buying those ads, they wouldn’t do it. And if a corporation expects to get $X from you that you wouldn’t have otherwise paid, that means they’re getting you to spend that much that you otherwise wouldn’t have—meaning that they’re getting you to buy something you didn’t need. Perhaps it’s better after all to spend that $X on getting entertainment that doesn’t try to get you to buy things you don’t need.

Indeed, I think there is an opportunity to restructure the whole Internet this way. What we need is a software company—maybe a nonprofit organization, maybe a for-profit business—that is set up to let us make micropayments for online content in lieu of having our data collected or being force-fed advertising.

How big would these payments need to be? Well, Facebook has about 2.8 billion users and takes in revenue of about $80 billion per year, so the average user would have to pay about $29 a year for the use of Facebook, Instagram, and WhatsApp. That’s about $2.50 per month, or $0.08 per day.

The New York Times is already losing its ad-supported business model; less than $400 million of its $1.8 billion revenue last year was from ads, the rest being primarily from subscriptions. But smaller media outlets have a much harder time gaining subscribers; often people just want to read a single article and aren’t willing to pay for a whole month or year of the periodical. If we could somehow charge for individual articles, how much would we have to charge? Well, a typical webpage has an ad clickthrough rate of 1%, while a typical cost-per-click rate is about $0.60, so ads on the average webpage makes its owners a whopping $0.006. That’s not even a single cent. So if this new micropayment system allowed you to pay one cent to read an article without the annoyance of ads or the pressure to buy something you don’t need, would you pay it? I would. In fact, I’d pay five cents. They could quintuple their revenue!

The main problem is that we currently don’t have an efficient way to make payments that small. Processing a credit card transaction typically costs at least $0.05, so a five-cent transaction would yield literally zero revenue for the website. I’d have to pay ten cents to give the website five, and I admit I might not always want to do that—I’d also definitely be uncomfortable with half the money going to credit card companies.

So what’s needed is software to bundle the payments at each end: In a single credit card transaction, you add say $20 of tokens to an account. Each token might be worth $0.01, or even less if we want. These tokens can then be spent at participating websites to pay for access. The websites can then collect all the tokens they’ve received over say a month, bundle them together, and sell them back to the company that originally sold them to you, for slightly less than what you paid for them. These bundled transactions could actually be quite large in many cases—thousands or millions of dollars—and thus processing fees would be a very small fraction. For smaller sites there could be a minimum amount of tokens they must collect—perhaps also $20 or so—before they can sell them back. Note that if you’ve bought $20 in tokens and you are paying $0.05 per view, you can read 400 articles before you run out of tokens and have to buy more. And they don’t all have to be from the same source, as they would with a traditional subscription; you can read articles from any outlet that participates in the token system.

There are a number of technical issues to be resolved here: How to keep the tokens secure, how to guarantee that once a user purchases access to an article they will continue to have access to it, ideally even if they clear their cache, delete all cookies, or login from another computer. I can’t literally set up this website today, and even if I could, I don’t know how I’d attract a critical mass of both users and participating websites (it’s a major network externality problem). But it seems well within the purview of what the tech industry has done in the past—indeed, it’s quite comparable to the impressive (and unsettling) infrastructure that has been laid down to support ad-targeting and data brokerage.

How would such a system help protect privacy? If micropayments for content became the dominant model of funding online content, most people wouldn’t spend much time looking at online ads, and ad targeting would be much less profitable. Data brokerage, in turn, would become less lucrative, because there would be fewer ways to use that data to make profits. With the incentives to take our data thus reduced, it would be easier to enforce regulations protecting our privacy. Those fines might actually be enough to make it no longer worth the while to take sensitive data, and corporations might stop pressuring people to give it up.

No, privacy isn’t dead. But it’s dying. If we want to save it, we have a lot of work to do.

The fable of the billionaires

May 31 JDN 2458999

There are great many distortions in real-world markets that cause them to deviate from the ideal of perfectly competitive free markets, and economists rightfully spend much of their time locating, analyzing, and mitigating such distortions.

But I think there is a general perception among economists, and perhaps among others as well, that if we could somehow make markets perfectly competitive and efficient, we’d be done; the world, or at least the market, would be just and fair and all would be good. And this perception is gravely mistaken. To make that clear to you, I offer a little fable.

Once upon a time, widgets were made by hand. One person, working for one eight-hour day, could make 100 widgets. Most people were employed making widgets full-time. The wage for making widgets was $1 per widget.

Then, an inventor came up with a way to automate the production of widgets. For $100 per day, the same cost to hire a worker to make 100 widgets, the machine could instead make 101 widgets.

Because it was 1% more efficient, businesses began adopting the new machine, and now made slightly more widgets than before. But some workers who had previously made widgets were laid off, while others saw their wages fall to only $0.99 per widget.


If there were more widgets, but fewer people were getting paid less to make them, where did the extra wealth go? To the inventor, of course, who now owns 10% of all widget production and has billions of dollars.

Later, another inventor came up with an even better machine, which could make 102 widgets in a day. And that inventor became a billionare too, while more became unemployed and wages fell to $0.98 per widget.

And then there was another inventor, and another, and another; and today the machines can make 200 widgets in a day and wages are only $0.50 per widget. We now have twice as many widgets as we used to have, and hundreds of billionaires; yet only half as many people now work making widgets as once did, and those who remain make only half of what they once did.

Was this market inefficient or uncompetitive? Not at all! In fact it was quite efficient: It delivered the most widgets for the least cost every step of the way. And the first round of billionaires didn’t get enough power to keep the next round from innovating even better and also becoming billionaires. No one stole or cheated to get where they are; the billionaires really made it to the top by being brilliant innovators who made the world more efficient.

Indeed, by the standard measures of economic surplus, the world has gotten better with each new machine. GDP has gone up, wealth has gone up. Yet millions of people are out of work, and millions more are making pitifully low wages. Overall the nation seems to be worse off, even though all the numbers keep saying things are getting better.

There are some relatively simple solutions to this problem: We could tax those billionaires, and use the money to provide public goods to everyone else; and then the added wealth from doubling our quantity of widgets would benefit everyone and not just the inventors who made it happen. Would that reduce the incentives to innovate? A little, perhaps; but it’s hard to believe that most people who would be willing to invent something for $1 billion wouldn’t be willing to do so for $500 million or even for $50 million. At some point that extra money really isn’t benefiting you all that much. And what’s the point of incentivizing innovation if it makes life worse for most of our population?

In the real world there are lots of other problems, of course. Corruption, regulatory capture, rent-seeking, collusion, and so on all make our markets less efficient than they could have been. But even if markets were efficient, it’s not clear that they would be fair or just, or that they would be making most people’s lives better.

Indeed, I’m not convinced that most billionaires really got where they are by being particularly innovative. I can appreciate the innovations made by Cisco and Microsoft, but what brilliant innovation underlies Facebook or Amazon? The Internet itself is a great innovation (largely created by DARPA and universities), but is using it to talk to people or sell things really such a great leap? Tesla and SpaceX are innovative, but they have largely been money pits for Elon Musk, who inherited a good chunk of his wealth and made most of the rest by owning shares in PayPal. Yet even if we suppose that all the billionaires got where they are by inventing things that made the economy more efficient, it’s still not clear that they deserve to keep that staggering wealth.

I think the fundamental problem is that we have mentally equated ‘value of marginal product’ with ‘what you rightfully earn’. But the former is dependent upon the rest of the market: Who you are competing with, what your customers want. You can work very hard and be very talented, but if you’re making something that people aren’t willing to pay for, you won’t make any money. And the fact that people won’t pay for something doesn’t mean it isn’t valuable: If you produce public goods, they could benefit many people a great deal but still not draw in profits. Conversely, the fact that something is profitable doesn’t necessarily make it valuable: It could just be a very effective method of rent-seeking.

I’m not saying we should do away with markets; they’re very useful, and they do have a lot of benefits. But we should acknowledge their limitations. We should be aware not only that real-world markets are not perfectly efficient, but also that even a perfectly efficient market wouldn’t make for the best possible world.

Why “marginal productivity” is no excuse for inequality

May 28, JDN 2457902

In most neoclassical models, workers are paid according to their marginal productivity—the additional (market) value of goods that a firm is able to produce by hiring that worker. This is often used as an excuse for inequality: If someone can produce more, why shouldn’t they be paid more?

The most extreme example of this is people like Maura Pennington writing for Forbes about how poor people just need to get off their butts and “do something”; but there is a whole literature in mainstream economics, particularly “optimal tax theory”, arguing based on marginal productivity that we should tax the very richest people the least and never tax capital income. The Chamley-Judd Theorem famously “shows” (by making heroic assumptions) that taxing capital just makes everyone worse off because it reduces everyone’s productivity.

The biggest reason this is wrong is that there are many, many reasons why someone would have a higher income without being any more productive. They could inherit wealth from their ancestors and get a return on that wealth; they could have a monopoly or some other form of market power; they could use bribery and corruption to tilt government policy in their favor. Indeed, most of the top 0.01% do literally all of these things.

But even if you assume that pay is related to productivity in competitive markets, the argument is not nearly as strong as it may at first appear. Here I have a simple little model to illustrate this.

Suppose there are 10 firms and 10 workers. Suppose that firm 1 has 1 unit of effective capital (capital adjusted for productivity), firm 2 has 2 units, and so on up to firm 10 which has 10 units. And suppose that worker 1 has 1 unit of so-called “human capital”, representing their overall level of skills and education, worker 2 has 2 units, and so on up to worker 10 with 10 units. Suppose each firm only needs one worker, so this is a matching problem.

Furthermore, suppose that productivity is equal to capital times human capital: That is, if firm 2 hired worker 7, they would make 2*7 = $14 of output.

What will happen in this market if it converges to equilibrium?

Well, first of all, the most productive firm is going to hire the most productive worker—so firm 10 will hire worker 10 and produce $100 of output. What wage will they pay? Well, they need a wage that is high enough to keep worker 10 from trying to go elsewhere. They should therefore pay a wage of $90—the next-highest firm productivity times the worker’s productivity. That’s the highest wage any other firm could credibly offer; so if they pay this wage, worker 10 will not have any reason to leave.

Now the problem has been reduced to matching 9 firms to 9 workers. Firm 9 will hire worker 9, making $81 of output, and paying $72 in wages.

And so on, until worker 1 at firm 1 produces $1 and receives… $0. Because there is no way for worker 1 to threaten to leave, in this model they actually get nothing. If I assume there’s some sort of social welfare system providing say $0.50, then at least worker 1 can get that $0.50 by threatening to leave and go on welfare. (This, by the way, is probably the real reason firms hate social welfare spending; it gives their workers more bargaining power and raises wages.) Or maybe they have to pay that $0.50 just to keep the worker from starving to death.

What does inequality look like in this society?
Well, the most-productive firm only has 10 times as much capital as the least-productive firm, and the most-educated worker only has 10 times as much skill as the least-educated worker, so we might think that incomes would vary only by a factor of 10.

But in fact they vary by a factor of over 100.

The richest worker makes $90, while the poorest worker makes $0.50. That’s a ratio of 180. (Still lower than the ratio of the average CEO to their average employee in the US, by the way.) The worker is 10 times as productive, but they receive 180 times as much income.

The firm profits vary along a more reasonable scale in this case; firm 1 makes a profit of $0.50 while firm 10 makes a profit of $10. Indeed, except for firm 1, firm n always makes a profit of $n. So that’s very nearly a linear scaling in productivity.

Where did this result come from? Why is it so different from the usual assumptions? All I did was change one thing: I allowed for increasing returns to scale.

If you make the usual assumption of constant returns to scale, this result can’t happen. Multiplying all the inputs by 10 should just multiply the output by 10, by assumption—since that is the definition of constant returns to scale.

But if you look at the structure of real-world incomes, it’s pretty obvious that we don’t have constant returns to scale.

If we had constant returns to scale, we should expect that wages for the same person should only vary slightly if that person were to work in different places. In particular, to have a 2-fold increase in wage for the same worker you’d need more than a 2-fold increase in capital.

This is a bit counter-intuitive, so let me explain a bit further. If a 2-fold increase in capital results in a 2-fold increase in wage for a given worker, that’s increasing returns to scale—indeed, it’s precisely the production function I assumed above.
If you had constant returns to scale, a 2-fold increase in wage would require something like an 8-fold increase in capital. This is because you should get a 2-fold increase in total production by doubling everything—capital, labor, human capital, whatever else. So doubling capital by itself should produce a much weaker effect. For technical reasons I’d rather not get into at the moment, usually it’s assumed that production is approximately proportional to capital to the one-third power—so to double production you need to multiply capital by 2^3 = 8.

I wasn’t able to quickly find really good data on wages for the same workers across different countries, but this should at least give a rough idea. In Mumbai, the minimum monthly wage for a full-time worker is about $80. In Shanghai, it is about $250. If you multiply out the US federal minimum wage of $7.25 per hour by 40 hours by 4 weeks, that comes to $1160 per month.

Of course, these are not the same workers. Even an “unskilled” worker in the US has a lot more education and training than a minimum-wage worker in India or China. But it’s not that much more. Maybe if we normalize India to 1, China is 3 and the US is 10.

Likewise, these are not the same jobs. Even a minimum wage job in the US is much more capital-intensive and uses much higher technology than most jobs in India or China. But it’s not that much more. Again let’s say India is 1, China is 3 and the US is 10.

If we had constant returns to scale, what should the wages be? Well, for India at productivity 1, the wage is $80. So for China at productivity 3, the wage should be $240—it’s actually $250, close enough for this rough approximation. But the US wage should be $800—and it is in fact $1160, 45% larger than we would expect by constant returns to scale.

Let’s try comparing within a particular industry, where the differences in skill and technology should be far smaller. The median salary for a software engineer in India is about 430,000 INR, which comes to about $6,700. If that sounds rather low for a software engineer, you’re probably more accustomed to the figure for US software engineers, which is $74,000. That is a factor of 11 to 1. For the same job. Maybe US software engineers are better than Indian software engineers—but are they that much better? Yes, you can adjust for purchasing power and shrink the gap: Prices in the US are about 4 times as high as those in India, so the real gap might be 3 to 1. But these huge price differences themselves need to be explained somehow, and even 3 to 1 for the same job in the same industry is still probably too large to explain by differences in either capital or education, unless you allow for increasing returns to scale.

In most industries, we probably don’t have quite as much increasing returns to scale as I assumed in my simple model. Workers in the US don’t make 100 times as much as workers in India, despite plausibly having both 10 times as much physical capital and 10 times as much human capital.

But in some industries, this model might not even be enough! The most successful authors and filmmakers, for example, make literally thousands of times as much money as the average author or filmmaker in their own country. J.K. Rowling has almost $1 billion from writing the Harry Potter series; this is despite having literally the same amount of physical capital and probably not much more human capital than the average author in the UK who makes only about 11,000 GBP—which is about $14,000. Harry Potter and the Philosopher’s Stone is now almost exactly 20 years old, which means that Rowling made an average of $50 million per year, some 3500 times as much as the average British author. Is she better than the average British author? Sure. Is she three thousand times better? I don’t think so. And we can’t even make the argument that she has more capital and technology to work with, because she doesn’t! They’re typing on the same laptops and using the same printing presses. Either the return on human capital for British authors is astronomical, or something other than marginal productivity is at work here—and either way, we don’t have anything close to constant returns to scale.

What can we take away from this? Well, if we don’t have constant returns to scale, then even if wage rates are proportional to marginal productivity, they aren’t proportional to the component of marginal productivity that you yourself bring. The same software developer makes more at Microsoft than at some Indian software company, the same doctor makes more at a US hospital than a hospital in China, the same college professor makes more at Harvard than at a community college, and J.K. Rowling makes three thousand times as much as the average British author—therefore we can’t speak of marginal productivity as inhering in you as an individual. It is an emergent property of a production process that includes you as a part. So even if you’re entirely being paid according to “your” productivity, it’s not really your productivity—it’s the productivity of the production process you’re involved in. A myriad of other factors had to snap into place to make your productivity what it is, most of which you had no control over. So in what sense, then, can we say you earned your higher pay?

Moreover, this problem becomes most acute precisely when incomes diverge the most. The differential in wages between two welders at the same auto plant may well be largely due to their relative skill at welding. But there’s absolutely no way that the top athletes, authors, filmmakers, CEOs, or hedge fund managers could possibly make the incomes they do by being individually that much more productive.