This paper was prepared for John B. Taylor and Michael Woodford, Editors

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In some cases, at least, anchoring may be rational behavior for respondents.  They may

rationally assume that the deviser of the questionnaire uses some information (in this case,

about typical people’s incomes) when devising the questionnaire.  Not fully remembering

their own income, they may rely on the information in the brackets to help them answer

better.  If the brackets do contain information, then it is rational for subjects to allow

themselves to be influenced by the brackets.

While anchoring undoubtedly has an information-response component in many

circumstances, it has also been shown that anchoring behavior persists even when

information is absent.  In one experiment Tversky and Kahneman (1974), subjects were

given simple questions whose answers were in percentages, e.g., the percentage of African

nations in the United Nations.  A wheel of fortune with numbers from 1 to 100 was spun

before the subjects.  Obviously, the number at which the wheel of fortune stopped had no

relevance to the question just asked.  Subjects were asked whether their answer was higher

or lower than the wheel of fortune number, and then to give their own answer.

Respondents’ answers were strongly influenced by the “wheel of fortune.”  For example,

the median estimates of the percentage of African countries in the United Nations were 25

and 45 for groups that received 10 and 65, respectively, as starting points (p. 184).

Values in speculative markets, like the stock market, are inherently ambiguous.  Who

would know what the value of the Dow Jones Industrial Average should be?  Is it really

“worth” 6,000 today?  Or 5,000 or 7,000? or 2,000 or 10,000?  There is no agreed-upon

economic theory that would answer these questions.  In the absence of any better

information, past prices (or asking prices or prices of similar objects or other simple

comparisons) are likely to be important determinants of prices today.

That anchoring affects valuations, even by experts, was demonstrated by Northcraft and

Neale (1987) in the context of real estate valuation.  All subjects were taken to a house for

sale, asked to inspect the house for up to 20 minutes, and were given a ten-page packet of

information about the house and about other houses in the area, giving square footage and

characteristics of the properties, and prices of the other properties.  The same packet was

given to all subjects except that the asking price of the property under consideration and its

implied price per square foot were changed between subjects.  Subjects were asked for their

own opinions of its appraisal value, appropriate listing price, purchase price, and the lowest

offer the subject would accept for the house if the subject were the seller.  The real estate

agents who were given an asking price of $119,900 had a mean predicted appraisal value

of $114,204, listing price of $117,745, purchase price of $111,454 and a lowest acceptable

offer of $111,136, while the real estate agents who were given an asking price of $149,900

had a mean appraisal value of $128,754, listing price of $130,981, predicted purchase price

of $127,318, and a lowest offer of $123,818.  The changed asking prices thus swayed their

valuations by 11% to 14% of the value of the house.  Similar results were found with

amateur subjects.  While this experiment does not rule out that the effect of the asking price

was due to a rational response to the assumed information in the asking price, the effects of

asking price are remarkably large, given that so much other information on the house was

also given.  Moreover, when subjects were asked afterwards to list the items of information

that weighed most heavily in their valuations, only 8% of the expert subjects and only 9%

of the amateur subjects listed asking price of the property under consideration among the

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6

The notion that speculative prices approximately describe "random walks" was first proposed

by Bachelier (1900, 1964).  It became widely associated with the efficient markets hypothesis, the

hypothesis that market prices efficiently incorporate all available information, with the work of Fama

(1970).  For further information on the literature on the random walk and efficient markets theory see

also Cootner (1964), Malkiel (1981), and Fama (1991).

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For a discussion of the anomaly, see Backus, Foresi and Telmer (1995) and Froot and Thaler

(1990).

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top three items.  Note that the valuation problem presented to these subjects is far less

difficult or ambiguous than the problem of determining the “correct” value for the stock

market, since here they are implicitly being asked to assume that the comparable properties

are correctly valued.  (See also McFadden, 1974 and Silberman and Klock, 1989.)

One might object that the notion that anchoring on past prices helps determine present

price in the stock market might be inconsistent with the low serial correlation of stock price

changes, that is with the roughly random-walk behavior of daily or monthly stock prices that

has been widely noted.

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  This conclusion is not warranted however.  Models of “smart

money” (i.e., people who are unusually alert to profit opportunities in financial markets)

seeking to exploit serial correlation in price, models which also include ordinary investors,

are consistent with the implications that serial correlation is low and yet the anchoring

remains important for the level of stock prices (see Shiller, 1984, 1990).

By extension from these experimental results, it is to be presumed that very many

economic phenomena are influenced by anchoring.  Gruen and Gizycki (1993) used it to

explain the widely observed anomaly

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 that forward discounts to not properly explain

subsequent exchange rate movements.  The anchoring phenomenon would appear relevant

to the “sticky prices” that are so talked about by macroeconomists.  So long as past prices

are taken as suggestions of new prices, the new prices will tend to be close to the past prices.

The more ambiguous the value of a commodity, the more important a suggestion is likely

to be, and the more important anchoring is likely to be for price determination.

The anchoring phenomenon may help to explain certain international puzzles observed

in financial markets.  U.S. investors who thought in the late 1980s that Japanese stock price–

earnings ratios were outrageously high then may have been influenced by the readily-

available anchor of (much lower) U.S. price–earnings ratios.  By the mid 1990s, many U.S.

investors feel that the Tokyo market is no longer overpriced (see Shiller, Kon-Ya and

Tsutsui, 1996), even though price–earnings ratios remain much higher than in the U.S.

perhaps because the anchor of the widely-publicized high Tokyo price–earnings ratios of the

late 1980s appears to be another anchor.

Anchoring may also be behind certain forms of money illusion.  The term money

illusion, introduced by Fisher (1928), refers to a human tendency to make inadequate

allowance, in economic decisions, for the rate of inflation, and to confuse real and nominal

quantities.  Shafir, Diamond and Tversky (1997) have shown experimentally that people

tend to give different answers to the same hypothetical decision problem depending on

whether the problem was presented in a way that stressed nominal quantities or in a way that

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