Steffen Huck, Nora Szech, and Lukas M. Wenner, “More Effort with Less Pay: On Information Avoidance, Belief Design and Performance.” CESifo Working Paper No. 5542, October, 2015 (pdf).
• If information can influence your expectations and motivations, you might want to avoid acquiring information. For instance, knowing that there are high stakes riding on your performance might lead you to choke under the pressure. This paper reports on an experiment that tests whether people might choose to avoid information about their precise (piece-rate) wage.
• The experiment is of the “real effort” variety, that is, subjects are asked to engage in a tedious, painstaking task: typing 60-character nonsensical alphanumeric strings, mixed uppercase and lowercase, backwards into a computer. For each correctly typed string, a piece rate is paid, and the subjects have one hour in which to type (or not, as they wish). In case they want to not work, they have a magazine at hand to help pass the hour.
• The piece rate is random; with probability .5, it is quite low – .1 euro per string – and with probability .5 it is significant: 1 euro per string. The different conditions of the experiment turn on: whether the workers know their wage (Full Info); whether they choose to be or not to be informed of their wage until after the work hour is over (Info Choice); and whether they have no option but to remain uninformed (No Info). A fourth condition is where workers know that their wage is .55 euro per string, which is the weighted average of the high and low piece rates, and hence might serve as the expected wage for workers who do not know their wage.
• Not surprisingly, if you know your wage, you work harder if the wage is high than if it is low (and you work at an in-between rate if you know you earn the medium wage). If you get to decide whether or not to be told your wage, well, 30 out of 95 subjects choose not to be told. These uninformed folks work like machines, even outpacing subjects who know they have a high piece rate.
• Perhaps those subjects who choose not to learn their wage rate happen to be really productive workers, that is, maybe the option of not knowing the wage within the Info Choice group selects for the best workers. This possibility is tested by the No Info treatment, where subjects don’t know their piece rate in advance, but not by choice: they are not given the option of learning their wage. But these subjects also perform at a very high level, so selection effects don’t seem to drive the result: not possessing wage information tends to bolster productivity.
• When asked about their decisions, those subjects who choose to learn their piece rate in advance tend to explicitly note their interest in tailoring their effort to the rate. Those who choose not to learn their piece rate tend to mention the effect that a low rate would have on their motivation, and/or a fear that a high rate might lead to anxiety.
• The authors adapt a model of Brunnermeier and Parker (2005; pdf here) to indicate that a high piece rate can both increase the revenue from effort, and also increase the costs of supplying effort: a high rate might induce anxiety, for instance. Further, agents receive utility not just from what happens today but also some utility (today) from anticipation of what is likely to happen next period. The anticipation utility provides a channel for utility-maximizing agents to hold biased beliefs, overoptimistic views of what the future will bring. With a high enough anticipatory utility, agents will prefer to avoid knowledge of their wage: they are information avoiders.
Saturday, May 6, 2017
Bobadilla-Suarez, Sunstein, and Sharot (2016) on Under-Delegation
Sebastian Bobadilla-Suarez, Cass R. Sunstein, and Tali Sharot, “Are Choosers Losers? The Propensity to Under-Delegate in the Face of Potential Gains and Losses.” February 15, 2016 (pdf, perhaps somewhat updated, available here).
• People seem to value control over decisions, and are willing to sacrifice (in terms of expected returns) to make a decision themselves rather than to delegate. Perhaps for decisions involving losses, however, people might prefer to delegate, to insulate themselves a bit from painful choices.
• This paper reports on two laboratory experiments that are conducted to look at delegation versus control. Sample sizes are rather small: 26 subjects for the first experiment, and 25 for the second. In some trials, participants can delegate the choice to an advisor, after being informed of the reliability and cost of the advisor. (In reality, the advisor is a computer player programmed with the appropriate reliabilities.)
• Both experiments start with a learning phase. Participants are shown two geometrical shapes, and are asked to pick the better one. They are not told what criteria go into “better,” but if they pick correctly, they receive a higher payoff (or avoid a bigger loss) than if they choose incorrectly. Unbeknownst to the players, there is no underlying rule for “better,” the computer just designates, randomly, one of the two shapes as better. But humans are good at finding patterns in random events!
• In the delegation phase of experiment one, participants are given the option, prior to being shown the shapes, to delegate their decision to an advisor (actually, a chooser) whose probability of choosing correctly is revealed in advance, along with the cost of using the advisor. After the delegate-or-not decision, the two shapes are revealed, a choice is made (although, if made by an advisor, not revealed), and onto the next round (60 in total). Subjects do not learn the outcome of individual trials, but are compensated at the end on a random subset of ten of the trials.
• Given that the subjects themselves have a 50% chance of choosing correctly, it is easy to determine when the expected monetary payoff to the subject is higher by delegating the decision to the advisor (thanks to those known probabilities and costs; incidentally, the price of using the advisor is only imposed if the advisor is used, and if the advisor chooses correctly).
• The advisors are configured (costs and accuracy) in such a way that the subjects would do best (in expected monetary terms) by delegating on half of the trials. Instead, subjects choose to delegate only about 30% of the time, whether they are choosing for gains or to avoid losses. The departures from “optimal” play are almost all in the direction of failing to delegate the decision when expected returns would have been higher with delegation.
• Subjects’ perceptions of their own abilities to choose are canvassed, and the aversion to delegation is not the result of excessive optimism with respect to the accuracy of their own choices.
• The second experiment is nearly identical to the first one, except that the decision to choose or to delegate is now made each round after the shapes themselves have been revealed. Still, there is substantial, even slightly worse, under-delegation.
• The fact that under-delegation also is present in the “loss” trials indicates that people, at least in this setting, don’t seem to want to shield themselves from painful decisions through delegation.
• People seem to value control over decisions, and are willing to sacrifice (in terms of expected returns) to make a decision themselves rather than to delegate. Perhaps for decisions involving losses, however, people might prefer to delegate, to insulate themselves a bit from painful choices.
• This paper reports on two laboratory experiments that are conducted to look at delegation versus control. Sample sizes are rather small: 26 subjects for the first experiment, and 25 for the second. In some trials, participants can delegate the choice to an advisor, after being informed of the reliability and cost of the advisor. (In reality, the advisor is a computer player programmed with the appropriate reliabilities.)
• Both experiments start with a learning phase. Participants are shown two geometrical shapes, and are asked to pick the better one. They are not told what criteria go into “better,” but if they pick correctly, they receive a higher payoff (or avoid a bigger loss) than if they choose incorrectly. Unbeknownst to the players, there is no underlying rule for “better,” the computer just designates, randomly, one of the two shapes as better. But humans are good at finding patterns in random events!
• In the delegation phase of experiment one, participants are given the option, prior to being shown the shapes, to delegate their decision to an advisor (actually, a chooser) whose probability of choosing correctly is revealed in advance, along with the cost of using the advisor. After the delegate-or-not decision, the two shapes are revealed, a choice is made (although, if made by an advisor, not revealed), and onto the next round (60 in total). Subjects do not learn the outcome of individual trials, but are compensated at the end on a random subset of ten of the trials.
• Given that the subjects themselves have a 50% chance of choosing correctly, it is easy to determine when the expected monetary payoff to the subject is higher by delegating the decision to the advisor (thanks to those known probabilities and costs; incidentally, the price of using the advisor is only imposed if the advisor is used, and if the advisor chooses correctly).
• The advisors are configured (costs and accuracy) in such a way that the subjects would do best (in expected monetary terms) by delegating on half of the trials. Instead, subjects choose to delegate only about 30% of the time, whether they are choosing for gains or to avoid losses. The departures from “optimal” play are almost all in the direction of failing to delegate the decision when expected returns would have been higher with delegation.
• Subjects’ perceptions of their own abilities to choose are canvassed, and the aversion to delegation is not the result of excessive optimism with respect to the accuracy of their own choices.
• The second experiment is nearly identical to the first one, except that the decision to choose or to delegate is now made each round after the shapes themselves have been revealed. Still, there is substantial, even slightly worse, under-delegation.
• The fact that under-delegation also is present in the “loss” trials indicates that people, at least in this setting, don’t seem to want to shield themselves from painful decisions through delegation.
Thursday, May 4, 2017
Gilovich and Kumar (2015) on Experiences versus Material Goods
Thomas Gilovich and Amit Kumar, “We’ll Always Have Paris: The Hedonic Payoff from Experiential and Material Investments.” Advances in Experimental Social Psychology 51: 147-187, 2015. [The version outlined below has different page numbers (and perhaps other discrepancies) from the version available here.]
• Experiences provide long-lasting memories, at least relative to material goods; experiences are gifts that keep on giving. Perhaps subjective well-being (SWB) and income would be more closely related if people shifted their spending from material goods to experiential goods.
• Vacations, concerts, dining out: these are examples of experiential goods. Electronic gadgetry and clothing fall into the material goods category. People consistently report higher happiness from experiential goods relative to material goods.
• Experiences are better for SWB for a variety of reasons. First, they are less subject to the adaptation which, over times, erodes the pleasure boost given by a material acquisition. Experiences are more pleasurable (and interesting) to talk about, and can become embedded in our identity, our story.
• Adaptation is helpful in allowing people to come to terms with negative events, so if bad experiences are not subject to adaptation, we could suffer long-term from unpleasant moments. But experiences tend to be malleable, so that bad experiences – the vacation where it rained the whole time – tend to be looked at more favorably, through rose-tinted glasses, over time. Indeed, the negative features of experiences can become, in retrospect, their chief virtues.
• Even in anticipation of consumption, experiences bring more pleasure than material goods. Waiting in line is less unpleasant when it is to see a play than to buy a gadget. And after the fact, people tend to think of money spent on experiences as having been better spent than that expended on material objects.
• The social element that is attached to many experiences is one of the sources of their SWB advantages. And talking about experiences after the fact is one of their social elements. Talking about material possessions is a bit of a snooze fest.
• The endowment effect is connected to an unwillingness to part with something that we “own”; people tend to show larger endowment effects, greater attachments, to potential experiences than to material goods.
• Material possessions like cars are relatively easy to compare along quality dimensions – your neighbor’s Ferrari is probably better than your Ford. But who had a better vacation, or a better time at the concert? These are harder to compare, and hence, experiences to some extent protect us against unfavorable social comparisons. And if we enjoy our concert, we are not much concerned about opportunity costs, those potentially better concerts we sacrificed to go to “ours”.
• If you buy a bad car, you regret it, you have buyer’s remorse. But the more frequent regret in the experiential realm concerns foregone opportunities, not buyer’s remorse. And it is easy to find the good in a bad experience, but not so easy to find the upside of a malfunctioning computer. “Surprises” tend to be positive when they arise during experiences, but negative when they are connected to material goods!
• Some material goods are purchased with the intention of using them to enhance experiences, such as watching movies with your friends and family in a home video center. But perhaps the actual consumption of these goods is less social and less experiential and even less common than anticipated. If people have the experiences in mind when they make the purchases, they may be systematically fooled, as it were, into overspending on what turn out to be merely material goods.
• Perhaps connoisseurs are able to convert material goods (collectibles, say) into experiential goods, by harnessing the same social, identity, and even narrative advantages that other types of experiences present.
• Can we design cities, and our lives, to maximize experience?
• Experiences provide long-lasting memories, at least relative to material goods; experiences are gifts that keep on giving. Perhaps subjective well-being (SWB) and income would be more closely related if people shifted their spending from material goods to experiential goods.
• Vacations, concerts, dining out: these are examples of experiential goods. Electronic gadgetry and clothing fall into the material goods category. People consistently report higher happiness from experiential goods relative to material goods.
• Experiences are better for SWB for a variety of reasons. First, they are less subject to the adaptation which, over times, erodes the pleasure boost given by a material acquisition. Experiences are more pleasurable (and interesting) to talk about, and can become embedded in our identity, our story.
• Adaptation is helpful in allowing people to come to terms with negative events, so if bad experiences are not subject to adaptation, we could suffer long-term from unpleasant moments. But experiences tend to be malleable, so that bad experiences – the vacation where it rained the whole time – tend to be looked at more favorably, through rose-tinted glasses, over time. Indeed, the negative features of experiences can become, in retrospect, their chief virtues.
• Even in anticipation of consumption, experiences bring more pleasure than material goods. Waiting in line is less unpleasant when it is to see a play than to buy a gadget. And after the fact, people tend to think of money spent on experiences as having been better spent than that expended on material objects.
• The social element that is attached to many experiences is one of the sources of their SWB advantages. And talking about experiences after the fact is one of their social elements. Talking about material possessions is a bit of a snooze fest.
• The endowment effect is connected to an unwillingness to part with something that we “own”; people tend to show larger endowment effects, greater attachments, to potential experiences than to material goods.
• Material possessions like cars are relatively easy to compare along quality dimensions – your neighbor’s Ferrari is probably better than your Ford. But who had a better vacation, or a better time at the concert? These are harder to compare, and hence, experiences to some extent protect us against unfavorable social comparisons. And if we enjoy our concert, we are not much concerned about opportunity costs, those potentially better concerts we sacrificed to go to “ours”.
• If you buy a bad car, you regret it, you have buyer’s remorse. But the more frequent regret in the experiential realm concerns foregone opportunities, not buyer’s remorse. And it is easy to find the good in a bad experience, but not so easy to find the upside of a malfunctioning computer. “Surprises” tend to be positive when they arise during experiences, but negative when they are connected to material goods!
• Some material goods are purchased with the intention of using them to enhance experiences, such as watching movies with your friends and family in a home video center. But perhaps the actual consumption of these goods is less social and less experiential and even less common than anticipated. If people have the experiences in mind when they make the purchases, they may be systematically fooled, as it were, into overspending on what turn out to be merely material goods.
• Perhaps connoisseurs are able to convert material goods (collectibles, say) into experiential goods, by harnessing the same social, identity, and even narrative advantages that other types of experiences present.
• Can we design cities, and our lives, to maximize experience?
Tuesday, May 2, 2017
Clark (2014) on Adaptation and Easterlin
Andrew E. Clark, “Adaptation and the Easterlin Paradox.” Paris School of
Economics, October, 2014 [pdf].
• One explanation of the Easterlin Paradox is that people care about relative, not absolute, incomes. A second explanation, addressed here, is that people adapt their reference point to reflect a higher income and so receive no extra subjective well-being (SWB) when the adaptation is complete.
• The existing empirical evidence shows a good deal of adaptation to permanently higher incomes, but often, not full adaptation. Increasing income inequality, where richer people have recent higher-than-normal gains, can lead to a positive association in cross-sectional data between income and SWB.
• But perhaps other contributors to well-being – employment life, health, family life, for example – are not as subject to adaptation as is income. Unemployment seems to be fairly resistant to adaptation – many people are as worse off (in terms of happiness) a few years into unemployment as they are when they first become unemployed. The same lack of adaptation applies to falls into poverty. This is the case only on average, of course, and perhaps we could invest in resilience so that adaptation to negative events would be more common.
• On average, the happiness of marriage is subject to adaptation, so that the initial happiness boost (in many countries) is eliminated over time. Divorce is associated with a notable fall in subjective well-being that precedes the divorce for a few years, but by and large, this decline is eliminated a few years post-divorce through adaptation. Having children has no impact, on average, in long-term SWB.
• People somewhat adapt to bad health events, but that adaptation tends to be far from complete. (Cosmetic surgery brings lasting SWB benefits (page 11)!)
• One explanation of the Easterlin Paradox is that people care about relative, not absolute, incomes. A second explanation, addressed here, is that people adapt their reference point to reflect a higher income and so receive no extra subjective well-being (SWB) when the adaptation is complete.
• The existing empirical evidence shows a good deal of adaptation to permanently higher incomes, but often, not full adaptation. Increasing income inequality, where richer people have recent higher-than-normal gains, can lead to a positive association in cross-sectional data between income and SWB.
• But perhaps other contributors to well-being – employment life, health, family life, for example – are not as subject to adaptation as is income. Unemployment seems to be fairly resistant to adaptation – many people are as worse off (in terms of happiness) a few years into unemployment as they are when they first become unemployed. The same lack of adaptation applies to falls into poverty. This is the case only on average, of course, and perhaps we could invest in resilience so that adaptation to negative events would be more common.
• On average, the happiness of marriage is subject to adaptation, so that the initial happiness boost (in many countries) is eliminated over time. Divorce is associated with a notable fall in subjective well-being that precedes the divorce for a few years, but by and large, this decline is eliminated a few years post-divorce through adaptation. Having children has no impact, on average, in long-term SWB.
• People somewhat adapt to bad health events, but that adaptation tends to be far from complete. (Cosmetic surgery brings lasting SWB benefits (page 11)!)
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