attention economy

December 9, 2018

Transaction cost economics and privacy: looking at Hoofnagle and Whittington’s “Free”

As I’ve been reading about transaction cost economics (TCE) and independently scrutinizing the business model of search engines, it stands to reason that I should look to the key paper holding down the connection between TCE and privacy, Hoofnagle and Whittinton’s “Free: Accounting for the Costs of the Internet’s Most Popular Price” (2014).

I want to preface the topic by saying I stand by what I wrote earlier: that at the heart of what’s going on with search engines, you have a trade of attention; it requires imagining the user has have attention-time as a scarce resource. The user has a query and has the option to find material relevant to the query in a variety of ways (like going to a library). Often (!) they will do so in a way that costs them as little attention as possible: they use a search engine, which gives an almost instant and often high-quality response; they are also shown advertisements which consume some small amount of their attention, but less than they would expend searching through other means. Advertisers pay the search engine for this exposure to the user’s attention, which funds the service that is “free”, in dollars (but not in attention) to the users.

Hoofnagle and Whittington make a very different argument about what’s going on with “free” web services, which includes free search engines. They argue that the claim that these web services are “free” is deceptive because the user may incur costs after the transaction on account of potential uses of their personal data. An example:

The freemium business model Anderson refers to is popular among industries online. Among them, online games provide examples of free services with hidden costs. By prefacing play with the disclosure of personal identification, the firms that own and operate games can contact and monitor each person in ways that are difficult for the consumer to realize or foresee. This is the case for many games, including Disney’s “Club Penguin,” an entertainment website for children. After providing personal information to the firm, consumers of Club Penguin receive limited exposure to basic game features and can see numerous opportunities to enrich their play with additional features. In order to enrich the free service, consumers must buy all sort of enhancements, such as an upgraded igloo or pets for one’s penguin. Disney, like others in the industry, places financial value on the number of consumers it identifies, the personal information they provide, and the extent to which Disney can track consumer activity in order to modify the game and thus increase the rate of conversion of consumers from free players to paying customers.

There are a number of claims here. Let’s enumerate them:

This is an example of a ‘free’ service with hidden costs to users.
The consumer doesn’t know what the game company will do with their personal information.
In fact, the game will use the personal information to personalize pitches for in-game purchases that ‘enrich’ the free service.
The goal of the company is to convert free players to paying customers.

Working backwards, claim (4) is totally true. The company wants to make money by getting their customers to pay, and they will use personal information to make paying attractive to the customers (3). But this does not mean that the customer is always unwitting. Maybe children don’t understand the business model when they begin playing Penguin Club, but especially today parents certainly do. App Stores, for example, now label apps when they have “in-app purchases”, which is a pretty strong signal. Perhaps this is a recent change due to some saber rattling by the FTC, which to be fair would be attributable as a triumph to the authors if this article had influence on getting that to happen. On the other hand, this is a very simple form of customer notice.

I am not totally confident that even if (2), (3), and (4) are true, that that entails (1), that there are “hidden costs” to free services. Elsewhere, Hoofnagle and Whittington raise more convincing examples of “costs” to release of PII, including being denied a job and resolving identity theft. But being convincingly sold an upgraded igloo for your digital penguin seems so trivial. Even if it’s personalized, how could it be a hidden cost? It’s a separate transaction, no? Do you or do you not buy the igloo?

Parsing this through requires, perhaps, a deeper look at TCE. According to TCE, agents are boundedly rational (they can’t know everything) and opportunistic (they will make an advantageous decision in the moment). Meanwhile, the world is complicated. These conditions imply that there’s a lot of uncertainty about future behavior, as agents will act strategically in ways that they can’t themselves predict. Nevertheless, agents engage in contracts with some kinds of obligations in them in the course of a transaction. TCE’s point is that these contracts are always incomplete, meaning that there are always uncertainties left unresolved in contracts that will need to be negotiated in certain contingent cases. All these costs of drafting, negotiating, and safeguarding the agreement are transaction costs.

Take an example of software contracting, which I happen to know about from personal experience. A software vendor gets a contract from a client to do some customization. The client and the vendor negotiated some sort of scope of work ex ante. But always(!), the client doesn’t actually know what they want, and if the vendor delivers on the specification literally the client doesn’t like it. Then begins the ex post negotiation as the client tries to get the vendor to tweak the system into something more usable.

Software contracting often resolves this by getting off the fixed cost contracting model and onto a cost-and-materials contact that allows billing by hours of developer time. Alternatively, the vendor can internalize the costs into the contract by inflating the cost “estimates” to cover for contingencies. In general, this all amounts to having more contract and a stronger relationship between the client and vendor, a “bilateral dependency” which TCE sees as a natural evolution of the incomplete contract under several common conditions, like “asset specificity”, which means that the asset is specialized to a particular transaction (or the two agents involved in it). Another term for this is lock-in, or the presence of high switching costs, though this way of thinking about it reintroduces the idea of a classical market for essentially comparable goods and services that TCE is designed to mitigate against. This explains how technical dependencies of an organization become baked in more or less constitutionally as part of the organization, leading to the robustness of installed base of a computing platform over time.

This ebb and flow of contract negotiation with software vendors was a bit unsettling to me when I first encountered it on the job, but I think it’s safe to say that most people working in the industry accept this as How Things Work. Perhaps it’s the continued influence of orthodox economics that makes this all seem inefficient somehow, and TCE is the right way to conceptualize things that makes better sense of reality.

But back to the Penguins…

Hoofnagle and Whittington make the case that sharing PII with a service that then personalizes its offerings to you creates a kind of bilateral dependence between service and user. They also argue that loss of privacy, due to the many possible uses of this personal information (some nefarious), is a hidden cost that can be thought of as an ex post transaction cost that is a hazard because it has not been factored into the price ex ante. The fact that this data is valuable to the platform/service for paying their production costs, which is not part of the “free” transaction, is an indication that this data is a lot more valuable than consumers think it is.

I am still on the fence about this.

I can’t get over the feeling that successfully selling a user a personalized, upgraded digital igloo is such an absurd example of a “hidden cost” that it belies the whole argument that these services have hidden costs.

Splitting hairs perhaps, it seems reasonable to say that Penguin Club has a free version, which is negotiated as one transaction. Then, conditional on the first transaction, it offers personalized igloos for real dollars. This purchase, if engaged in, would be another, different transaction, not an ex post renegotiation of the original contract with the Disney. This small difference changes the cost of the igloo from a hidden transaction cost into a normal, transparent cost. So it’s no big deal!

Does the use of PII create a bilateral dependence between Disney and the users of Penguin Club? Yes, in a sense. Any application of attention to an information service, learning how to use it and getting it to be part of your life, is in a sense a bilateral dependence with a switching cost. But there are so many other free games to play on the internet that these costs seem hardly hidden. They could just be understood as part of the game. Meanwhile, we are basically unconcerned with Disney’s “dependence” on the consumer data, because Disney can get new users easily (unless the user is a “whale”, who actual pays the company). And “dependence” Disney has on particular users is a hidden cost for Disney, not for the user, and who cares about Disney.

The cases of identity theft or job loss are strange cases that seem to have more to do with freaky data reuse than what’s going on with a particular transaction. Purpose binding notices and restrictions, which are being normed on through generalized GDPR compliance, seem adequate to deal with these cases.

So, I have two conclusions:

(1) Maybe TCE is the right lens for making an economic argument for why purpose binding restrictions are a good idea. They make transactions with platforms less incomplete, avoiding the moral hazard of ex post use of data in ways that incurs asymmetrically unknown effects on users.

(2) This TCE analysis of platforms doesn’t address the explanatorily powerful point that attention is part of the trade. In addition to being concretely what the user is “giving up” to the platform and directly explaining monetization in some circumstances, the fact that attention is “sticky” and creates some amount of asset-specific learning is a feature of the information economy more generally. Maybe it needs a closer look.

References

Hoofnagle, Chris Jay, and Jan Whittington. “Free: accounting for the costs of the internet’s most popular price.” UCLA L. Rev. 61 (2013): 606.

December 6, 2018

Data isn’t labor because using search engines is really easy

A theme I’ve heard raised in a couple places recently, including Ibarra et al. “Should We Treat Data As Labor?” and the AI Now 2018 Report, is that there is something wrong with how “data”, particularly data “produced” by people on the web, is conceptualized as part of the economy. Creating data, the argument goes, requires labor. And as the product of labor, it should be protected according to the values and practices of labor movements in the past. In particular, the current uses of data in, say, targeted advertising, social media, and search, are exploitative; the idea that consumers ‘pay’ for these services with their data is misleading and ultimately unfair to the consumer. Somehow the value created by the data should be reapportioned back to the user.

This is a sexy and popular argument among a certain subset of intellectuals who care about these things. I believe the core emotional appeal of this proposal is this: It is well known that a few well-known search engine and social media companies, namely Google and Facebook, are rich. If the value added by user data were in part returned to the users, the users, who are compared to Google and Facebook not rich, would get something they otherwise would not get. I.e., the benefits for recognizing the labor involved in creating data is redistribution of surplus to The Rest of Us.

I don’t have a problem personally with that redistributive impulse. However, I don’t think the “data is labor” argument actually makes much sense.

Why not? Well, let’s take the example of a search engine. Here is the transaction between a user and a search engine:

Alice types a query, “avocado toast recipes”, into the search engine. This submits data to the company computers.
The company computers use that data to generate a list of results that it deems relevant to that query.
Alice sees the results, and maybe clicks on one or two of them, if they are good, in the process of navigating to the thing she was looking for in the first place.
The search engine records that click as well, in order to better calibrate how to respond to others making that query.

We might forget that the search engine is providing Alice a service and isn’t just a ubiquitous part of the infrastructure we should take for granted. The search engine has provided Alice with relevant search results. What this does is (dramatically) reduce Alice’s search costs; had she tried to find the relevant URL by asking her friends, organically surfing the web, or using the library, who knows what she would have found or how long it would take her. But we would assume that Alice is using the search engine because it gets her more relevant results, faster.

It is not clear how Alice could get this thing she wants without going through the motions of typing and clicking and submitting data. These actions all seem like a bare minimum of what is necessary to conduct this kind of transaction. Similarly, when I got to a grocery store and buy vegetables, I have to get out my credit card and swipe it at the machine. This creates data–the data about my credit card transaction. But I would never advocate for recognizing my hidden labor at the credit card machine is necessary to avoid the exploitation of the credit card companies, who then use that information to go about their business. That would be insane.

Indeed, it is a principle of user interface design that the most compelling user interfaces are those that require the least effort from their users. Using search engines is really, really easy because they are designed that way. The fact that oodles of data are collected from a person without that person exerting much effort may be problematic in a lot of ways. But it’s not problematic because it’s laborious for the user; it is designed and compelling precisely because it is labor-saving. The smart home device industry has taken this even further, building voice-activated products for people who would rather not use their hands to input data. That is, if anything, less labor for the user, but more data and more processing on the automated part of the transaction. That the data is work for the company, and less work for the user, indicates that data is not the same thing as user labor.

There is a version of this argument that brings up feminism. Women’s labor, feminists point out, has long been insufficiently recognized and not properly remunerated. For example, domestic labor traditionally performed by women has been taken for granted, and emotional labor (the work of controlling ones emotions on the job), which has often been feminized, has not been taken seriously enough. This is a problem, and the social cause of recognizing women’s labor and rewarding it is, ceteris paribus, a great thing. But, and I know I’m on dicey ground here, so bear with me, this does not mean that everything that women do that they are not paid to do is unrecognized labor in the sense that is relevant for feminist critiques. Case in point, both men and women use credit cards to buy things, and make telephone calls, and drive vehicles through toll booths, and use search engines, and do any number of things that generate “data”, and in most of these cases it is not remunerated directly; but this lack of remuneration isn’t gendered. I would say, perhaps controversially, that the feminist critique does not actually apply to the general case of user generated data much at all! (Though is may apply in specific cases that I haven’t thought of.)

So in conclusion, data isn’t labor, and labor isn’t data. They are different things. We may want a better, more just, political outcome with respect to the distribution of surplus from the technology economy. But trying to get there through an analogy between data and labor is a kind of incoherent way to go about it. We should come up with a better, different way.

So what’s a better alternative? If the revenue streams of search engines are any indication, then it would seem that users “pay” for search engines through being exposed to advertising. So the “resource” that users are giving up in order to use the search engine is attention, or mental time; hence the term, attention economy.

Framing the user cost of search engines in terms of attention does not easily lend itself to an argument for economic reform. Why? Because search engines are already saving people a lot of that attention by making it so easy to look stuff up. Really the transaction looks like:

Alice pays some attention to Gob (the search engine).
Gob gives Alice some good search results back in return, and then…
Gob passes on some of Alice’s attention through to Bob, the advertiser, in return for money.

So Alice gives up attention but gets back search results and the advertisement. Gob gets money. Bob gets attention. The “data” that matters is not the data transmitted from Alice’s computer up to Gob. Rather, the valuable data is the data that Alice receives through her eyes: of this data, the search results are positively valued, the advertisement is negatively valued, but the value of the bundled good is net positive.

If there is something unjust about this economic situation, it has to be due to the way consumer’s attention is being managed by Gob. Interestingly, those who have studied the value of ‘free’ services in attentional terms have chalked up a substantial consumer surplus due to saved attention (Brynjolfsson and Oh, 2012) This appears to be the perspective of management scientists, who tend to be pro-business, and is not a point repeated often by legal scholars, who tend to be more litigious in outlook. For example, legal scholarship has detailed the view of how attention could be abused through digital market manipulation (Calo, 2013).

Ironically for data-as-labor theorists, the search-engine-as-liberator-of-attention argument could be read as the view that what people get from using search engines is more time, or more ability to do other things with their time. In other words, we would use a search engine instead of some other, more laborious discovery mechanism precisely because it would cost us net negative labor. That absolutely throws a wrench in any argument that the users of search engines should be rewarded on dignity of labor grounds. Instead, what’s happened is that search engines are ubiquitous because consumers have undergone a phase transition in their willingness to work to discover things, and now very happily use search engines which, on the whole, seem like a pretty good deal! (The cost of being-advertised-to is small compared to the benefits of the search results.)

If we start seeing search engines as a compelling labor-saving device rather than a exploiter of laborious clickwork, then some of the disregard consumers have for privacy on search engines becomes more understandable. People are willing to give up their data, even if they would rather not, because search engines are saving them so much time. The privacy harms that come as consequence, then, can be seen as externalities to what is essentially a healthy transaction, rather than a perverse matter of a business model that is evil to the bone.

This is, I wager, on the whole a common sense view, one that I’d momentarily forgotten because of my intellectual milieu but now am ashamed to have overlooked. It is, on the whole, far more optimistic than other attempt to characterize the zeitgeist of new technology economy.

Somehow, this rubric for understanding the digital economy appears to have fallen out of fashion. Davenport and Beck (2001) wrote a business book declaring attention to be “the new currency of business”, which if the prior analysis is correct makes more sense than data being the new currency (or oil) of business. The term appears to have originated in an article by Goldhaber (1997). Ironically, the term appears to have had no uptake in the economics literature, despite it being the key to everything! The concept was understood, however, by Herbert Simon, in 1971 (see also Terranova, 2012):

In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.

(A digitized version of this essay, which amazingly appears to be set by a typewriter and then hand-edited (by Simon himself?) can be found here.)

This is where I bottom out–the discover that the line of thought I’ve been on all day starts with Herbert Simon, that the sciences of the artificial are not new, they are just forgotten (because of the glut of other information), and exhaustingly hyped. The attention economy discovered by Simon explains why each year we are surrounded with new theories about how to organize ourselves with technology, when perhaps the wisest perspectives on these topics are ones that will not hype themselves because their authors cannot tweet from the grave.

References

Arrieta-Ibarra, Imanol, et al. “Should We Treat Data as Labor? Moving beyond” Free”.” AEA Papers and Proceedings. Vol. 108. 2018.

Brynjolfsson, Erik, and JooHee Oh. “The attention economy: measuring the value of free digital services on the Internet.” (2012).

Calo, Ryan. “Digital market manipulation.” Geo. Wash. L. Rev. 82 (2013): 995.

Davenport, Thomas H., and John C. Beck. The attention economy: Understanding the new currency of business. Harvard Business Press, 2001.

Goldhaber, Michael H. “The attention economy and the net.” First Monday 2.4 (1997).

Simon, Herbert A. “Designing organizations for an information-rich world.” (1971): 37-72.

Terranova, Tiziana. “Attention, economy and the brain.” Culture Machine 13 (2012).

1 Comment

May 10, 2008

The phrase “attention economy” denotes the idea that attention is one of the most important scarce resources at work in the economy, I think this is a profound insight that has not yet been sufficiently developed.

I need to investigate further into what has been written on the subject already, but my impression is that so far the idea has the most currency in the world of slick web business, and almost none in the world of rigorous theoretical economics.

Here are some reasons, off the top of my head, why I think that’s too bad:

Since attention, and the limits of our ability to attend, largely determine what information we are able to pull from the environment and how much we are able to process it, a theory of attention in economics is necessary for an accurate theory of bounded rationality.
Economics has been pretty poor at accounting for the role of advertising in the economy. Since the advertising industry is largely concerned with capturing the attention of audiences, a theory of the role of attention in the economy would provide a lot of explanatory force.
My understanding is that robust results in hedonic psychology show that happiness is primarily a matter of attention and only secondarily a mater of circumstances. To the extent that economic theories attempt to be normative and utilitarian (as many do), these psychological results demand that economics notice what people are attending to.

I think that a rigorous theory of the attention economy could turn a lot of economics on its head. I hope to approach this subject again in future posts.

Tag: attention economy