 Starting point: individuals have private information, not accessible to firms, or easily incorporated into prices.
– About their preferences, income, health, ability to repay a loan, value of a project or asset, etc.
 Many theoretical and policy implications follow.
– Should not expect market outcomes to be efficient, especially for complex products: insurance, credit, financial assets, labor.
– Justification for government regulation and in some cases wholesale operation of certain markets.

 Pioneering contributions on asymmetric information were theoretical – though often with sound empirical motivation.
 However, relatively few statistical tests of the theory, or attempts to quantify degree of asymmetric information.
 Last decade has seen explosion of empirical research on asymmetric information.
 Facilitated by increasing availability of micro datasets on insurance, loans, financial markets, auctions, etc.

 Next three (more technical) lectures will discuss different aspects of this research and some of what we’ve learned.
Later Lectures
 Lecture 2: High Risk Consumer Credit Markets
 Lecture 3: Measuring Inefficiencies from Adverse Selection
 Lecture 4: Can Markets for Health Insurance Work?

 Today’s lecture: explore one particular idea, that will reappear in the next lectures, maybe hasn’t gotten the attention it deserves.
 Technology, i.e. data collection and processing, and the rise of predictive models, has changed the distribution of information.
 Private information of individuals arguably plays a diminished role in credit markets, insurance, financial markets.
 Instead, firms have increasing power to aggregate & analyze data.
 Causes of this development are fairly clear. But what are its implications? Do consumers capture the benefits? Can they be exploited with new information? Is new regulation called for?

 Discuss three examples
Credit Cards
Consumer Internet
Financial Markets
 Try to emphasize common themes
‒ Competition to utilize consumer or market data.
‒ Efficiency benefits (less adverse selection, better matching)
‒ Potential adverse effects
‒ Market structure of information industries

 Most cited economics paper on credit cards: “The Failure of
Competition in the Credit Card Market” (Ausubel, 1990 AER)
 Describes puzzling state of the US credit card market circa late
1980s (at the time, about $400 billion in annual charges).
 Industry had very low concentration:
 Top ten banks combined had only 30% of accounts;
 Second ten has less than 10%.
 Interest rates were high, generally uniform across customers, and “virtually constant” throughout the 1980s.
 Bank profits from credit cards also appeared to be very high.

From Ausubel (1990, AER)

 Ausubel’s asymmetric information theory of the market.
 Different types of customers, indistinguishable to banks
 A bank that offers low interest rates might disproportionately attract consumers who intend to borrow.
 Intention to borrow on a credit card (!) is a bad signal about someone’s ability to obtain credit => high default risk.
 So banks have an incentive to price high, and not undercut.
 Matches evidence on the industry circa 1990.

 Investments in data and processing power to classify and segment customers.
 Widespread adoption of personalized pricing and targeted “introductory” rates.
 Most famous example, and one of most successful companies in
1990s -- was Capital One.
“Using scientific testing on a massive scale, we gather huge amounts of information to help us tailor products and services to the individual consumer.”

 From Grodzicki (2013)

 Improved information and personalized offers and pricing mitigates adverse selection, but targeting may have some problematic features as well.
 Some consumers may be vulnerable to targeted offers
For example, short-term “teaser rate” offers, if response is to borrow for longer periods (Ausubel and Shui, 2005).
Borrowers with low credit scores seem particularly susceptible to penalty fees and other hidden costs. (Agarwal et al., 2013)

 From Agarwal et al. (2013)

 From Grodzicki (2013)

 Similar pattern in many credit and insurance markets
Aggregation of data and use of predictive modeling
Risk-based and targeted pricing to mitigate adverse selection.
 Examples from next three lectures
‒ Risk based pricing of consumer loans to counter adverse selection
(Einav, Jenkins, Levin, 2012, 2013 – Lecture 2)
‒ Risk based pricing of insurance can eliminate relationship between risk and purchased insurance (Einav, Finkelstein, Levin, 2010 - Lecture 3).
‒ Risk adjustment in managed health insurance markets as a way to reduce incentives to cream-skim or “lemon-drop” (part of Lecture 4).

 Traditional information problem in retail and advertising
Consumers may not know what exactly what products and services they want, or what their prices are.
Firms don’t know exactly who are their likely customers
 Consumer internet addresses both problems
 Difference from credit/insurance examples
Firms that have relevant information are often intermediaries rather than the producers of the goods or services.

 Clear efficiency benefits
Better matching of consumers to products and services,
More efficient, less time-consuming search process.
 Who captures the benefits?
Consumers? Producers? Intermediaries?
 Concerns of “excessive” targeting and market power?

 Platforms create value by making matches that wouldn’t have happened.
 What about matches that would have happened anyway?
 One concern is that platforms end up with undue influence.
 Google has almost 50% of US internet ad market.
eBay traffic from Google during large ad experiment (Blake, Nosko, Tadelis, 2013)

 Asymmetric information in financial markets
Kyle (1985) and Glosten-Milgrom (1985) theory of financial markets: market-makers quote spreads to protect themselves against “insiders”, i.e. informed traders.
 Trading in public equities has become extremely liquid
Information release is tightly regulated, plus security design, e.g. hard to see how an investor would have much private information about the value of an index-linked fund.
Obviously some exceptions … but nevertheless, suggests that investors should pay very low trading costs.

 Data mining and high-frequency trading
Traders that do not have any private information about fundamentals still maybe able to predict short run price moves.
Maybe not “private information” in the traditional sense, but an informational advantage conveyed by data processing.
 Basis of high-frequency trading in financial markets.
Identify correlations in asset prices and look for (very) short run violations of these relationships.
Similar data mining and statistics to the first two examples!
 Design of equities markets particularly conducive to this.

 From Budish, Cramton and Shim (2013)

 Advantaged traders are not “insiders” with access to propriety information.
 Rather they are “speed traders” who are fastest to detect arbitrage opportunities and exploit them.
 Intense competition to gather/utilize information: investments run into the hundreds of millions of dollars.

 Budish et al. theory of market microstructure
Spreads determined not by asymmetric information but by interaction of market makers and HFTs.
Advances in technology (speed, detection of price anomalies) leads to arms race; does not necessarily reduce trading costs!
 Why? Competition is on speed. Benefits flow the firms with the fastest technology not the ones offering the best price.
 In this case, changes in technology and the structure of information have gotten ahead of regulation & market design.

 Data aggregation and predictive modeling as a “general purpose technology” gradually diffusing into many industries: e.g. credit, insurance, retail, advertising, finance.
 Examples from today reflect similar underlying technology, but not necessarily with the same welfare implications.
 We are just seeing the tip of the iceberg in terms of how much individual information could become available to firms.
 May be more limits in Europe due to privacy regulation, but perhaps more likely that consumers will just adapt.

 Traditionally, we have viewed individual private information as central cause of market inefficiencies. Given changes in technology, perhaps need some degree of refocusing.
 Behavioral / consumer protection
Possibility that some people may be vulnerable as degree of targeting or personalization increases.
 Market power
Inherent scale economies in data aggregation and processing.
But probably too soon to say what broader implications will be for market structure information-intensive industries.

 Profound effects of technology on the structure of information in many markets.
 Pretty obvious once you think about it, but with many interesting implications.
 Lectures 2-4 on will pick up this theme in discussing markets with asymmetric information, with more detail, statistics, etc.