Perception Rents in the Market for Advice
It is commonly thought that a picture is worth a thousand words. If that is so, one might ask how much data is a piece of advice worth. In other words, if advice is important than we should be able to measure it in two ways: How much data would a rational decision maker be willing to give up in order to receive a piece of advice from a person who has just engaged in the decision problem he or she is about to engage in and how much would that person be willing to pay for such advice from a person with a given set of characteristics. A related, and perhaps more interesting, question deals with the impact of what psychologists call the â€œrepresentativeness-conservativeâ€ bias in Bayesian updating and its implications for the process of advice given and following. For example, when updating oneâ€™s beliefs, a rational Bayesian decision maker is expected to place a certain amount of weight on his previous prior (or the base rate) and a certain amount on new information (the sample) as it arrives. How much weight depends on the strength of his or her prior. If a decision maker places more than the Bayes-optimal weight on the prior (or base rate) he or she is called â€œconservativeâ€ while if excessive weight is placed on the sample he or she is considered to be subject to the â€œrepresentativeâ€ bias thinking that the sample they received is in some sense representative of the population from which it was drawn. Such people fail to take base rates or priors sufficiently into account. These concerns have wide ranging implications for our research on advice giving and following. For example, if we could measure the degree to which a decision maker is subject to one of the biases discussed above, could we correlate that characteristic to the decision makerâ€™s willingness to follow advice. More precisely, if conservatives are reluctant to update their priors on the basis of new information, are they therefore less inclined to pay for advice and also follow it once it is given? Put differently, since decision makers who are subject to the representativeness bias place too much weight on sample information and discount their prior, would they be willing to pay more for data than their conservative cohorts? Also who is more persuadable, conservatives or representatives? In another vein we are interested in this proposed research in investigating the market for influence and advice. More precisely, all countries in the modern world have markets for advice. Some of these markets are formal and are occupied by consulting firms vying for consulting contracts while others are informal where the currency is influence. Presidents listen to some people whose advice they seek while giving the cold shoulder to others who are out of favor. C.E.O.'s insist on name brand consulting firms (like Arthur Anderson until they proved themselves unworthy) but shun smaller firms whose quality may be higher and price lower but whose reputations have not been established. In all such markets there is a perception that certain people or types of people are worth listening to. These perceptions amount to broad stereotypes that may bestow huge rents on some of the agents in the market. Such stereotypes, if they persist, can lead to what we will call "perception rents", i.e. amounts paid for the advice of agents in excess of the expected informational content contained in their opinion. If such rents are substantial, they present us with a potentially large inefficiency. For example, say that Bob has a Ph.D. in Economics while Ed has only a Masters degree and for that reason Bob is perceived to be a better advisor. Because of this Bob may obtain a "perception rent " on the market. In our research we study an experimental market for advice in an attempt to measure perception rents. The results of our pilot experiments generate several interesting findings but leave too much unresolved to be definitive at this point. First our preliminary results find that in general our client subjects are willing to pay more for advice from any type advisor than the expected value of information contained in that advice. In other words, people impute more value to advice than that advice could be expected to have. Second, males and science majors appear to be collecting large perception rents as compared to females and either economics, business or humanities majors. Finally, while the advice offered by science majors is in fact better than that offered by others, the perception rents that science majors collect far outweighs their marginal addition to the profits of clients. These results are aggregate market results but we fear they mask explanations that can only be uncovered by running more subjects and investigating hypotheses on the disaggregated level. This is what we propose to do here. We propose try to disaggregate these results in an effort to discover how they are derived. Here we look at two things. First we look to see if there is a consensus in the market about expert quality. For example, while the mean price of advice for a science major may be highest, do all types of clients agree that they make the best advisors, i.e. are all types willing to pay more for their advice than that of others or is their high price driven by their own egotistical assessment of their value. In other words, how is their price composed? Second, our pilot experiments were not designed to uncover within subjects comparisons. More precisely, in the work proposed here we intend to bring subjects into the lab and give them a decision task that will sort them according to their degree of conservatism and representativeness. We will then have them engage in our experiment and look to see if this categorization has explanatory power for their behavior inwhat we call the I-S game to be described below. For example, are scientists more likely to be Bayesians and hence process data correctly? If so, does that explain their increased value in the market? Who follows advice more Bayesians or those subject to the representative bias? Finally we use our design to actually measure the informational content of advice defined by the number of sample observations a piece of advice is worth to a decision maker
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