VII Lecture

Shaping effect

Wrap up of the previous lecture

• Coherent arbitrariness of preferences is consistent with downward sloping demand curves.

• Anchoring effect determines a highly arbitrary foundational choice that restricts the range of future choices.

• Metrical problem: do past choices influence utility or do they bias the subjective estimation of utility?

Introduction• If the recursive causal relation between preferences and choices

affects utility rather than biasing its subjective estimation then we would observe the emergence of market specific patterns of behavior even without anchoring effect.

• Need of a direct test of shaping effect intended as an affiliation of individuals’ evaluations to previous market prices even if such affiliation is excluded by design.

• Evidence of shaping effect is crucial to distinguish between discovered preferences hypothesis and preferences construction hypothesis.

Experiment on shaping effect in median price selling auctions

• Test of the predictive implications of DPH and PCH in repeated median price selling auctions (Tufano 2009).

• Median price selling auction: participants submit a form with a range of questions on bids expressing their willingness to accept money for an auctioned good. The bids are the lowest prices at which they are willing trade.

• The median bid is the market price and the subjects with bids lower than the market-price trade and get paid accordingly.

Budget constraint• Subjects receive a participation fee.

• In median price auction, participants express WTA valuations through a form with prices ranging from a minimum (slightly positive) to a maximum amount equal to the participation fee. The progression is geometric-like.

• The budget constraint is the participation fee (endowment) and it is equal for everyone.

• Subjects respective earnings are calculated at the end of the auction session.

Example of the form

Predictive implications of DPH and PCH for median price selling auctions

• Assumptions: 1) no correlation of individual values, 2) no possibility of buying and selling back auctioned good.

• DPH-based prediction. The variance of the mean bids within a single auction varies in accordance to the error pattern (if symmetric or asymmetric), but the relation of the mean bids between several auctions remains unchanged: equality of mean bids across markets.

• PCH-based prediction. The mean bids variance reduces within a single auction: bids converge towards the market price. The mean bids variance might increase between different auctions (Tufano 2009).

Experiment implementation.Preliminary steps

• Subjects take part to six market periods of a median price selling auction in which an unpleasant mixture of vinegar and Lucozade is auctioned.

• Subjects get a participation fee of 3.00 $.

• Each subject tastes 30ml of the liquid before the auction starts. This allows for reflective learning (Plott 1996).

• Subjects are randomly assigned to two markets.

Experiment implementation. Controlling for anchoring effect

• After testing 30 ml the liquid each subject in the two groups is asked the following question: would you be willing to drink again for a payment of x?

• The value of x is the specific anchor manipulation and it can be Low (£ 0.05), Medium (£ 0.25) and High (£ 1.25).

• We have two treatments. In T1, the participant majority in a market (i.e., (n + 1)/2) was exposed to the low anchor whereas the minority (i.e., (n - 1)/2) to the medium anchor; in T2, the majority was exposed to the high anchor whereas the minority to the medium anchor.

• The price feedbacks in the two treatments had to be different if market bids exhibit anchoring effects by being biased toward the given anchor.

Experiment implementation.The auction starts

• Subjects are asked to submit a form with a range of questions on bids expressing their WTA money to drink 60 ml of the liquid.

• The prices range from a minimum of 0.01$ to a maximum of 3.00$ (budget constraint). The progression is geometric like with a maximum step of 0.20$.

• The experimenter orders the lowest WTA valuations and selects the median price for each market.

• The subjects whose bids are lower than the market price drink the liquid and get paid accordingly.

Implications• Median price auctions are a strongly competitive markets that

need to clear.

• If subjects bid their true values, then market clearing price obtains.

• This does not mean that all the subjects have to truthfully bid: above median bidders have an incentive to lower their asks, while below median bidders do not.

• Issue addressed: is the median market price the same across auctions?

Methodological justification of the interactive structure and auctioned good

INTERACTIVE STRUCTURE

• It is simple, incentive compatible and guarantees interactive learning (Binmore 1999).

• Revealing true preferences is a weakly dominant strategy.

• The price is a reliable basis to infer the median subject’s preferences.

AUCTIONED GOOD

• It avoids field price censoring, affiliated beliefs of field prices, affiliated beliefs about the commodity quality (Harrison et al. 2004).

• It avoids correlation of values across subjects.

• It allows for independent choices.

Method of analysis• Statistical test based on the analysis of bids’ variances within

and between markets.

• Two-way ANOVA model.

• Null hypothesis: mean bids variance across markets is convergent (corresponding to DPH predictive implications).

• Alternative hypothesis: the mean bid variance across markets is divergent (corresponding to PCH predictive implications).

Control for anchoring effect• Use of data in period 1 to test anchoring effect in the short run such to

exclude anchoring erosion due market forces.

• If anchoring effect matters, then subjects’ bids should vary with the anchor.

• Hypothesis: given the different treatments (T1 and T2) and the corresponding different price feedbacks, we should observe a market price biased toward the low (high) anchor in T1 (T2). Therefore, price feedbacks had to be lower in T1 than in T2.

• The means of bids in Period 1 across treatments and anchors are not different from each other.

Illustration

Data analysis in period 1• Both DPH and PCH predict equality of market means in

period 1 as result of correct randomization.

• Correct randomization: the assignment to a given market of individuals with certain preferences or tastes can be equally probable. Therefore, on average, there should be the same distribution of tastes and, consequently, of bids across markets.

• No statistically relevant difference among means of bids in Period 1 (F(31, 168) = 1.114; p = 0.324).

Data analysis in period 6• DPH predicts no statistically significant differences of

the mean bids variance between markets, while PCH predicts a statistically significant differences of the mean bids variance across market.

• It is observed a statistically significant difference across market mean bids in period 6. (F(31, 168) = 7.093; p = 0.000).

• Empirical support for PCH.

Cross periods results• Variation in opposite direction of the sum of squares between and

within markets.

• Between markets variation: increase from 26.260 in Period 1 to 78.338 in Period 6.

• Within market variation: decrease from 127.751 in Period 1 to 59.851 Period 6.

• The total variation explained by the variation between markets passes from an R 2 = 0.171 in Period 1 to an R 2 = 0.567 in Period 6.

Illustration

Note• In Fig. 1, the market means of bids with data from Periods 1 to 6 are plotted; that is, each point

of the set scattered in Fig. 1 represents the mean of bids for a given market in Period 1, in Period 2, and so on through Period 6. Of course, if two means are identical, only one marker is displayed; alternatively, if two means differ only a little, two markers with some overlap are shown. Hence, Fig. 1 clearly reports an increase of the variance of market means across periods. This is a qualitative indication of market specific patterns and, consequently, of shaping.

Implication for anomalies erosion in repeated markets

• The erosion of anomalies provides empirical evidence that is convergent with theoretical predictions.

• However this evidence is not univocal and we have showed a divergent empirical evidence in repeated markets.

• The erosion of anomalies might be a support for some theoretical predictions (i.e. reduction of the mean bids variance within a market) but not necessarily a support of discovered preferences hypothesis (i.e. reduction of the mean bids variance between markets).

• It follows that PCH might account for this conflicting evidence.

Implications for metrical problem

• The experiment provides a distinction between anchoring (bias) and shaping effect.

• The repetition of market interactions shapes our preferences (hedonic utility).

• This possibility can be explained by PCH.

Is this the end of the story?• We can claim that there are substantial shaping effects

in determining the emergence of prices.

• This means that our preferences can be constructed in market interactions.

• This does not imply that we are completely devoid of any inborn preferences structure, which is explanatory relevant for economic behavioral regularities.

Conclusions• Problem of distinguishing between shaping utility and biasing its

subjective estimation.

• The experimental test of shaping effect provides evidence of a reduction of the mean bids variance within markets and an increase of the mean bids variance between markets.

• This evidence can be explained as a shaping effect phenomenon.

• Shaping utility is a concrete possibility of repeated markets interactions.

• Empirical evidence of shaping effect are supportive of PCH.