Tag: richard posner

Economics of expertise and information services

We have no considered two models of how information affects welfare outcomes.

In the first model, inspired by an argument from Richard Posner, the are many producers (employees, in the specific example, but it could just as well be cars, etc.) and a single consumer. When the consumer knows nothing about the quality of the producers, the consumer gets an average quality producer and the producers split the expected utility of the consumer’s purchase equally. When the consumer is informed, she benefits and so does the highest quality producer, at the detriment of the other producers.

In the second example, inspired by Shapiro and Varian’s discussion of price differentiation in the sale of information goods, there was a single producer and many consumers. When the producer knows nothing about the “quality” of the consumers–their willingness to pay–the producer charges all consumers a profit-maximizing price. This price leaves many customers out of reach of the product, and many others getting a consumer surplus because the product is cheap relative to their demand. When the producer is more informed, they make more profit by selling as personalized prices. This lets the previously unreached customers in on the product at a compellingly low price. It also allows the producer to charge higher prices to willing customers; they capture what was once consumer surplus for themselves.

In both these cases, we have assumed that there is only one kind of good in play. It can vary numerically in quality, which is measured in the same units as cost and utility.

In order to bridge from theory of information goods to theory of information services, we need to take into account a key feature of information services. Consumers buy information when they don’t know what it is they want, exactly. Producers of information services tailor what they provide to the specific needs of the consumers. This is true for information services like search engines but also other forms of expertise like physician’s services, financial advising, and education. It’s notable that these last three domains are subject to data protection laws in the United States (HIPAA, GLBA, and FERPA) respectively, and on-line information services are an area where privacy and data protection are a public concern. By studying the economics of information services and expertise, we may discover what these domains have in common.

Let’s consider just a single consumer and a single producer. The consumer has a utility function \vec{x} \sim X (that is, sampled from random variable X, specifying the values it gets for the consumption of each of m = \vert J \vert products. We’ll denote with x_j the utility awarded to the consumer for the consumption of product j \in J.

The catch is that the consumer does not know X. What they do know is y \sim Y, which is correlated with X is some way that is unknown to them. The consumer tells the producer y, and the producer’s job is to recommend to them j \in J that will most benefit them. We’ll assume that the producer is interested in maximizing consumer welfare in good faith because, for example, they are trying to promote their professional reputation and this is roughly in proportion to customer satisfaction. (Let’s assume they pass on costs of providing the product to the consumer).

As in the other cases, let’s consider first the case where the acting party has no useful information about the particular customer. In this case, the producer has to choose their recommendation \hat j based on their knowledge of the underlying probability distribution X, i.e.:

\hat j = arg \max_{j \in J} E[X_j]

where X_j is the probability distribution over x_j implied by X.

In the other extreme case, the producer has perfect information of the consumer’s utility function. They can pick the truly optimal product:

\hat j = arg \max_{j \in J} x_j

How much better off the consumer is in the second case, as opposed to the first, depends on the specifics of the distribution X. Suppose X_j are all independent and identically distributed. Then an ignorant producer would be indifferent to the choice of \hat j, leaving the expected outcome for the consumer E[X_j], whereas the higher the number of products m the more \max_{j \in J} x_j will approach the maximum value of X_j.

In the intermediate cases where the producer knows y which carries partial information about \vec{x}, they can choose:

\hat j = arg \max_{j \in J} E[X_j \vert y] =

arg \max_{j \in J} \sum x_j P(x_j = X_j \vert y) =

arg \max_{j \in J} \sum x_j P(y \vert x_j = X_j) P(x_j = X_j)

The precise values of the terms here depend on the distributions X and Y. What we can know in general is that the more informative is y is about x_j, the more the likelihood term P(y \vert x_j = X_j) dominates the prior P(x_j = X_j) and the condition of the consumer improves.

Note that in this model, it is the likelihood function P(y \vert x_j = X_j) that is the special information that the producer has. Knowledge of how evidence (a search query, a description of symptoms, etc.) are caused by underlying desire or need is the expertise the consumers are seeking out. This begins to tie the economics of information to theories of statistical information.

Formalizing Posner’s economics of privacy argument

I’d like to take a more formal look at Posner’s economics of privacy argument, in light of other principles in economics of information, such as those in Shapiro and Varian’s Information Rules.

By “formal”, what I mean is that I want to look at the mathematical form of the argument. This is intended to strip out some of the semantics of the problem, which in the case of economics of privacy can lead to a lot of distracting anxieties, often for legitimate ethical reasons. However, there are logical realities that one must face despite the ethical conundrums they cause. Indeed, if there weren’t logical constraints on what is possible, then ethics would be unnecessary. So, let’s approach the blackboard, shall we?

In our interpretation of Posner’s argument, there are a number of applicants for a job, i \in I, where the number of candidates is n = \left\vert{I}\right\vert. Let’s say each is capable of performing at a certain level based on their background and aptitude, x_i. Their aptitude is sampled from an underlying probability distribution x_i \sim X.

There is an employer who must select an applicant for the job. Let’s assume that their capacity to pay for the job is fixed, for simplicity, and that all applicants are willing to accept the wage. The employer must pick an applicant i and gets utility x_i for their choice. Given no information on which to base her choice, she chooses a candidate randomly, which is equivalent to sampling once from X. Her expected value, given no other information on which to make the choice, is E[X]. The expected welfare of each applicant is their utility from getting the job (let’s say it’s 1 for simplicity) times their probability of being picked, which comes to \frac{1}{n}.

Now suppose the other extreme: the employer has perfect knowledge of the abilities of the applicants. Since she is able to pick the best candidate, her utility is \max x_i. Let \hat i = arg\max_{i \in I} x_i. Then the utility for applicant \hat i is 1, and it is 0 for the other applicants.

Some things are worth noting about this outcome. There is more inequality. All expected utility from the less qualified applicants has moved to the most qualified applicant. There is also an expected surplus of (\max x_i) - E[X] that accrues to the totally informed employer. One wonders if a “safety net” were to be provided those who have lost out in this change; if it could be, it would presumably be funded from this surplus. If the surplus were entirely taxed and redistributed among the applicants who did not get the job, it would provide each rejected applicant with \frac{(\max x_i) - E[X]}{n-1} utility. Adding a little complexity to the model we could be more precise by computing the wage paid to the worker and identify whether redistribution could potentially recover the losses of the weaker applicants.

What about intermediary conditions? These get more analytically complex. Suppose that each applicant i produces an application y_i which is reflective of their abilities. When the employer makes her decision, her expectation of the performance of each applicant is

P(x_i \vert y_i) \propto P(y_i \vert x_i)P(x_i)

because naturally the employer is a Bayesian reasoner. She makes her decision by maximizing her expected gain, based on this evidence:

arg\max E[P(x_i \vert y_i)] =

arg\max \sum_{x_i} x_i p(x_i \vert y_i) =

arg\max \sum_{x_i} x_i p(y_i \vert x_i) p(x_i)

The particulars of the distributions X and Y and especially P(Y \vert X) matter a great deal to the outcome. But from the expanded form of the equation we can see that the more revealing y_i is about x_i< the more the likelihood term p(y_i \vert x_i) will overcome the prior expectations. It would be nice to be able to capture the impact of this additional information in a general way. One would think that providing limited information about applicants to the employer would result in an intermediate outcome. Under reasonable assumptions, more qualified applicants would be more likely to be hired and the employer would accrue more value from the work.

What this goes to show is how ones evaluation of Posner's argument about the economics of privacy really has little to do with the way one feels about privacy and much more to do with how one feels about the equality and economic surplus. I've heard that a similar result has been discovered by Solon Barocas, though I'm not sure where in his large body of work to find it.

Notes on Posner’s “The Economics of Privacy” (1981)

Lately my academic research focus has been privacy engineering, the designing of information processing systems that preserve privacy of their users. I have been looking the problem particularly through the lens of Contextual Integrity, a theory of privacy developed by Helen Nissenbaum (2004, 2009). According to this theory, privacy is defined as appropriate information flow, where “appropriateness” is determined relative to social spheres (such as health, education, finance, etc.) that have evolved norms based on their purpose in society.

To my knowledge most existing scholarship on Contextual Integrity is comprised by applications of a heuristic process associated with Contextual Integrity that evaluates the privacy impact of new technology. In this process, one starts by identifying a social sphere (or context, but I will use the term social sphere as I think it’s less ambiguous) and its normative structure. For example, if one is evaluating the role of a new kind of education technology, one would identify the roles of the education sphere (teachers, students, guardians of students, administrators, etc.), the norms of information flow that hold in the sphere, and the disruptions to these norms the technology is likely to cause.

I’m coming at this from a slightly different direction. I have a background in enterprise software development, data science, and social theory. My concern is with the ways that technology is now part of the way social spheres are constituted. For technology to not just address existing norms but deal adequately with how it self-referentially changes how new norms develop, we need to focus on the parts of Contextual Integrity that have heretofore been in the background: the rich social and metaethical theory of how social spheres and their normative implications form.

Because the ultimate goal is the engineering of information systems, I am leaning towards mathematical modeling methods that trade well between social scientific inquiry and technical design. Mechanism design, in particular, is a powerful framework from mathematical economics that looks at how different kinds of structures change the outcomes for actors participating in “games” that involve strategy action and information flow. While mathematical economic modeling has been heavily critiqued over the years, for example on the basis that people do not act with the unbounded rationality such models can imply, these models can be a first step and valuable in a technical context especially as they establish the limits of a system’s manipulability by non-human actors such as AI. This latter standard makes this sort of model more relevant than it has ever been.

This is my roundabout way of beginning to investigate the fascinating field of privacy economics. I am a new entrant. So I found what looks like one of the earliest highly cited articles on the subject written by the prolific and venerable Richard Posner, “The Economics of Privacy”, from 1981.

Richard Posner, from Wikipedia

Wikipedia reminds me that Posner is politically conservative, though apparently he has changed his mind recently in support of gay marriage and, since the 2008 financial crisis, the laissez faire rational choice economic model that underlies his legal theory. As I have mainly learned about privacy scholarship from more left-wing sources, it was interesting reading an article that comes from a different perspective.

Posner’s opening position is that the most economically interesting aspect of privacy is the concealment of personal information, and that this is interesting mainly because privacy is bad for market efficiency. He raises examples of employers and employees searching for each other and potential spouses searching for each other. In these cases, “efficient sorting” is facilitated by perfect information on all sides. Privacy is foremost a way of hiding disqualifying information–such as criminal records–from potential business associates and spouses, leading to a market inefficiency. I do not know why Posner does not cite Akerlof (1970) on the “market for ‘lemons'” in this article, but it seems to me that this is the economic theory most reflective of this economic argument. The essential question raised by this line of argument is whether there’s any compelling reason why the market for employees should be any different from the market for used cars.

Posner raises and dismisses each objective he can find. One objection is that employers might heavily weight factors they should not, such as mental illness, gender, or homosexuality. He claims that there’s evidence to show that people are generally rational about these things and there’s no reason to think the market can’t make these decisions efficiently despite fear of bias. I assume this point has been hotly contested from the left since the article was written.

Posner then looks at the objection that privacy provides a kind of social insurance to those with “adverse personal characteristics” who would otherwise not be hired. He doesn’t like this argument because he sees it as allocating the costs of that person’s adverse qualities to a small group that has to work with that person, rather than spreading the cost very widely across society.

Whatever one thinks about whose interests Posner seems to side with and why, it is refreshing to read an article that at the very least establishes the trade offs around privacy somewhat clearly. Yes, discrimination of many kinds is economically inefficient. We can expect the best performing companies to have progressive hiring policies because that would allow them to find the best talent. That’s especially true if there are large social biases otherwise unfairly skewing hiring.

On the other hand, the whole idea of “efficient sorting” assumes a policy-making interest that I’m pretty sure logically cannot serve the interests of everyone so sorted. It implies a somewhat brutally Darwinist stratification of personnel. It’s quite possible that this is not healthy for an economy in the long term. On the other hand, in this article Posner seems open to other redistributive measures that would compensate for opportunities lost due to revelation of personal information.

There’s an empirical part of the paper in which Posner shows that percentage of black and Hispanic populations in a state are significantly correlated with existence of state level privacy statutes relating to credit, arrest, and employment history. He tries to spin this as an explanation for privacy statutes as the result of strongly organized black and Hispanic political organizations successfully continuing to lobby in their interest on top of existing anti-discrimination laws. I would say that the article does not provide enough evidence to strongly support this causal theory. It would be a stronger argument if the regression had taken into account the racial differences in credit, arrest, and employment state by state, rather than just assuming that this connection is so strong it supports this particular interpretation of the data. However, it is interesting that this variable ways more strongly correlated with the existence of privacy statutes than several other variables of interest. It was probably my own ignorance that made me not consider how strongly privacy statutes are part of a social justice agenda, broadly speaking. Considering that disparities in credit, arrest, and employment history could well be the result of other unjust biases, privacy winds up mitigating the anti-signal that these injustices have in the employment market. In other words, it’s not hard to get from Posner’s arguments to a pro-privacy position based of all things on market efficiency.

It would be nice to model that more explicitly, if it hasn’t been done yet already.

Posner is quite bullish on privacy tort, thinking that it is generally not so offensive from an economic perspective largely because it’s about preventing misinformation.

Overall, the paper is a valuable starting point for further study in economics of privacy. Posner’s economic lens swiftly and clearly puts the trade-offs around privacy statutes in the light. It’s impressively lucid work that surely bears directly on arguments about privacy and information processing systems today.


Akerlof, G. A. (1970). The market for” lemons”: Quality uncertainty and the market mechanism. The quarterly journal of economics, 488-500.

Nissenbaum, H. (2004). Privacy as contextual integrity. Wash. L. Rev., 79, 119.

Nissenbaum, H. (2009). Privacy in context: Technology, policy, and the integrity of social life. Stanford University Press.

Posner, R. A. (1981). The economics of privacy. The American economic review, 71(2), 405-409. (jstor)