The Du et al. (2002)
study of cultural differences exemplifies a growing body of research concerned with group differences in discounting. One of the first studies to take this approach was the Green, Fry, and Myerson (1994)
study of age differences. In a follow-up study, Green, Myerson, Lichtman, Rosen, and Fry (1996)
examined the effects of income on temporal discounting in older adults (mean age = 71 years), comparing a lower income group (median income less than $10,000 per year and living in subsidized housing) with a higher income group (median income approximately $50,000 per year). The two groups were approximately equal in years of education. Green et al. (1996)
observed that for both groups, their data were well described by a hyperbola (Equation 2
), but the lower income older adults discounted delayed rewards much more steeply than the higher income older groups. A younger group (mean age = 33 years) of upper income adults was also tested, and no age difference was observed between the two upper income groups. The relative stability of discounting in adults between the ages of 30 and 70 parallels previous findings of the stability of personality traits over this age range (Costa & McCrea, 1989
) and suggests that in adults, income comes to play a larger role than age in discounting.
The effects of income on discounting are intriguing. The steeper discounting of delayed consequences may be consistent with certain stereotypes regarding impulsive decision making by the poor, yet Green et al.’s (1996)
results run counter to what would be expected on the basis of the effect of amount of reward on rate of discounting. It might be assumed that the same nominal amount would be worth more to a low-income individual. Given that larger delayed amounts are discounted less steeply than smaller amounts (e.g., Green et al., 1997
), it would seem to follow that low-income individuals would discount a given amount less steeply than high-income individuals. Yet Green et al.’s (1996)
results suggest that the opposite is the case, even when the level of education is equivalent in both groups. Further research that examines the basic mechanisms underlying income and magnitude effects is clearly needed to explicate the relationship between the effect of amount and the effect of income.
In two studies that examined differences between groups defined on the basis of personality traits, Ostaszewski (1996
compared extraverts with introverts and high with low impulsive groups (Eysenck, Eysenck, & Barrett, 1985
) as well as high and low sensation seekers (Zuckerman, 1971
). In both studies, the extravert and high impulsive groups showed steeper discounting of hypothetical delayed rewards than did the introvert and low impulsive groups. There were no differences, however, in rates of temporal discounting between the high and low sensation seekers. Interestingly, Ostaszewski (1997)
found that the opposite pattern was observed with probability discounting. That is, with hypothetical probabilistic rewards, the rate of discounting did not differ between extraverts and introverts or between high and low impulsive groups, whereas high sensation seekers showed less steep discounting (i.e., they were less risk averse) than low sensation seekers. These results, like those discussed in the previous section, pose problems for single-process theories of discounting.
Logue and Anderson (2001)
recently reported a novel application of the discounting measure that is relevant here. They tested individuals with various levels of experience in college and university administration on a discounting task in which they chose between a smaller, immediate amount and a larger, delayed amount of money for the budget of their administrative unit. For example, they were presented with a series of hypothetical choices such as, “The administrator to whom you report says that s/he will give your unit $6,680 right now” versus “The administrator to whom you report says that s/he will give your unit $20,000 in 3 years.” The procedure was generally similar to that described previously (see ). A number of delays were used ranging from 1 week to 12 years, and at each delay, the amount available immediately was adjusted to determine the amount judged equivalent in value to the delayed amount.
Those with administrative experience were more likely to report considering long-term consequences when describing their decision making in hypothetical scenarios. Yet, these same experienced administrators discounted the value of future budgetary amounts significantly more steeply than those without experience. One implication of these findings is that experience teaches educational administrators (among others) not to trust promises of future funds. Another, more general implication is that the discounting behavior characteristic of a particular group may reflect what one learns as a member of that group about real-world contingencies.
Although group-specific experiences (such as those shared by university administrators) may be a cause of group differences in discounting, another possibility is that individual differences in discounting may play a role in determining group membership. For example, individual differences in discounting may determine, in part, which people are more likely to become substance abusers. Indeed, research measuring performance on discounting tasks has found that cigarette smokers tend to discount delayed rewards more steeply than nonsmokers (Baker et al,, 2003
; Bickel et al., 1999
; Mitchell, 1999
; Reynolds, Richards, Horn, & Karraker, 2004
). Interestingly, Mitchell (1999)
observed no differences between smokers and nonsmokers in the discounting of probabilistic rewards, a finding that could pose yet another difficulty for single-process discounting theories, although Reynolds et al. (2004)
did find that smokers discounted probabilistic rewards more steeply than nonsmokers.
One interpretation of the finding of differences in temporal discounting between smokers and nonsmokers is that the tendency to discount delayed consequences steeply may be a contributing cause of smoking. Consistent with this interpretation, Kollins (2003)
found that the age at which college students report having first smoked a cigarette is negatively correlated with the rate at which they discount delayed hypothetical monetary rewards. In addition, Reynolds et al. (2004)
reported that how steeply an individual discounted delayed rewards was a significantly better predictor of whether he or she was a smoker than how steeply the individual discounted probabilistic rewards. It is possible, of course, that smoking may somehow lead to an increase in discounting rates, rather than smoking being the result of a tendency to steeply discount delayed outcomes. Prospective studies are necessary to distinguish between these possibilities.
It may be noted that although Bickel et al. (1999)
found steeper discounting by current smokers than by those who had never smoked cigarettes, they also reported that the discounting rates of ex-smokers did not differ significantly from those who had never smoked. As Bickel et al. (1999)
pointed out, the similarity of discounting by ex-smokers and never-smokers could mean that just as those who discount more steeply may be more likely to become smokers, of those who smoke, those who discount least steeply may be the most likely to quit. Petry’s (2001 a)
finding that currently abstinent alcoholics show rates of discounting intermediate between those of currently drinking alcoholics and nonalcoholic controls is also consistent with the idea that discounting rates may predict success at giving up cigarettes and alcohol, but as yet this hypothesis has not been specifically tested. Because it is also possible that use may affect discounting rates for both substances, prospective studies are needed to determine the direction of causality with respect to quitting just as they are needed with respect to understanding how substance abusers come to have different temporal discounting rates in the first place.
Researchers also have examined discounting in users of substances other than alcohol and nicotine. For example, Madden et al. (1997)
studied opioid-dependent patients and found greater discounting of hypothetical monetary rewards by the substance abusers than by non-drug-using controls. Kirby, Petry, and Bickel (1999)
replicated this finding using delayed monetary rewards that participants had a chance of actually receiving. In addition, Vuchinich and Simpson (1998)
studied college students and found that both heavy social drinkers (Experiment 1) and problem drinkers (Experiment 2) showed greater temporal discounting of hypothetical monetary rewards than light social drinkers.
Petry and Casarella (1999)
suggested that some of the factors that determine group membership may have additive effects on discounting. They reported that substance abusers who were also problem gamblers discounted delayed monetary rewards more steeply than those who had only substance abuse problems, who in turn, showed steeper temporal discounting than those with neither substance abuse nor gambling problems. Similarly, individuals with a primary diagnosis of pathological gambling with a history of substance abuse discounted delayed rewards more steeply than pathological gamblers without a history of substance abuse, who in turn, showed steeper discounting than controls (Petry, 2001b
). Holt, Green, and Myerson (2003)
, however, reported no statistically significant differences in temporal discounting between college students categorized as gamblers and nongamblers on the basis of their scores on the South Oaks Gambling Screen (SOGS; Lesieur & Bloom, 1987
), although group differences in probability discounting were observed.
One possible explanation for the discrepancy between the results of the Holt et al. (2003)
and Petry (2001b)
studies is that the relation between temporal discounting rate and the tendency to gamble depends on the degree to which gambling is pathological. Consistent with this view, the gamblers in the Holt et al. study, although they typically gambled at least once a week, had many fewer gambling-related problems (as indicated by much lower SOGS scores) than the pathological gamblers studied by Petry (2001b)
. Indeed, Alessi and Petry (2003)
have recently shown that within a group of pathological gamblers, SOGS scores were significantly related to discounting rate. In this context, it also should be noted that as gambling becomes more pathological, comorbidity issues arise. That is, pathological gambling is typically associated with financial problems as well as with other psychiatric disorders (e.g., Ibáñez et al., 2001
). This comorbidity raises questions as to the extent to which observed differences in temporal discounting rate are attributable to pathological gambling per se or to other associated problems.
Studies of substance abusers are consistent in reporting that such individuals discount delayed monetary rewards more steeply than do controls. Another interesting question is whether substances of abuse themselves are discounted more steeply than are other kinds of rewards. Indeed, Madden et al. (1997)
found that people addicted to heroin, who were more impulsive than controls with respect to monetary rewards, tended to discount hypothetical delayed amounts of heroin even more steeply than they discounted money (see the left graph in ). Moreover, those addicts who discounted money more steeply tended to discount heroin rewards more steeply. In an analogous study, Bickel et al. (1999)
found that current cigarette smokers, who discounted delayed monetary rewards more steeply than nonsmokers, tended to discount hypothetical amounts of cigarettes even more steeply than money (see the right graph in ).
Figure 13 The subjective value of a delayed reward (expressed as a proportion) plotted as a function of time until its receipt. Data in the left graph are from "Impulsive and Self-Control Choices in Opioid-Dependent Patients and Non-Drug-Using Control Participants: (more ...)
In studies using hypothetical rewards, Odum, Madden, Badger, and Bickel (2000)
also reported that people addicted to heroin discounted delayed heroin more steeply than delayed money, and Coffey, Gudleski, Saladin, and Brady (2003)
reported that individuals dependent on crack/cocaine discounted delayed cocaine rewards more steeply than they discounted hypothetical monetary rewards. Giordano et al. (2002)
replicated the finding that hypothetical heroin rewards were discounted more steeply than money by opioid-dependent individuals and also found that mild opiate deprivation increased the degree to which they discounted both types of reward.
It is possible, of course, that the reported differences in rates of discounting monetary and drug rewards could represent magnitude effects. That is, a given amount of a drug might be discounted more steeply than a given amount of money if the drug reward was worth less than the monetary reward. It is important to note that the Madden et al. (1997)
, Odum et al. (2000)
, Giordano et al. (2002)
, and Coffey et al. (2003)
studies all tried to control for possible effects of amount when comparing the monetary and drug rewards. For example, Madden et al. (1997)
compared hypothetical choices between amounts of money available immediately and $1,000 available after a delay with choices between amounts of heroin available immediately and $1,000 worth of heroin (based on the current street price) available after a delay, whereas Odum et al. (2000)
also adjusted the amount of heroin based on price, but did so on an individual basis.
Similarly, Petry (2001a)
reported that alcoholics discounted hypothetical monetary rewards (e.g., $100) less steeply than hypothetical alcohol rewards of equivalent monetary value (e.g., 15 bottles of an alcoholic beverage). This finding is similar to that observed with participants addicted to heroin (Madden et al., 1997
) and cigarette smoking participants (Bickel et al., 1999
), and it provides further evidence that abused substances are discounted more steeply than money, at least by the substance abusers. Thus, not only may substance abusers differ from nonabusers in terms of discounting, but in addition, those who tend to show less self-control may, as a result, engage in behavior involving rewards that, by their nature, tend to undermine self-control, thereby exacerbating the problem.
The question then arises whether non-substance abusers also discount abused substances more steeply than money. If so, this would strongly suggest that there is something about the substances themselves that causes them to be steeply discounted. Although it may not be meaningful to ask how steeply individuals without addiction discount heroin, it is possible to determine how steeply individuals without alcoholism discount alcohol. Petry’s (2001a)
study addressed this interesting issue. She found that although nonalcoholics discounted both hypothetical money and alcohol less steeply than alcoholics, alcoholics and nonalcoholics both discounted alcohol more steeply than money. These results are consistent with the idea that substances that potentially may be abused are inherently subject to steeper discounting.
There is, however, another possible interpretation. Odum and Rainaud (2003)
have suggested that the difference in rates of discounting money and drugs of abuse (e.g., alcohol) may be due, at least in part, to the fact that drugs of abuse are primary/consumable reinforcers whereas money is a conditioned/nonconsumable reinforcer. Indeed, using participants who reported no problems with food, alcohol, or money, Odum and Rainaud found that there was no difference in the rates at which hypothetical amounts of food and alcohol were discounted, although both types of reward were discounted more steeply than money. Thus, the key distinction may be between immediately consumable and nonconsumable rewards rather than between rewards that have the potential for abuse and those that do not (although the current obesity epidemic suggests that food may have the potential to be an abused substance; World Health Organization, 2000
Finally, we would like to point out that all of the discounting functions shown in are hyperbola-like (i.e., based on our fits of Equation 3
to data provided by the authors of the original studies) and that a hyperbola-like function fit the data significantly better than either an exponential decay function or a hyperbola (Equations 1
, respectively). In the case of the discounting of delayed heroin rewards by participants with heroin addiction, for example, the proportion of variance accounted for (R2
) by the fit of Equation 3
was .866, compared with .336 and .771 for the fits of Equations 1
, respectively. The finding that Equation 3
provided the best fit to the discounting of delayed drug rewards extends the generality of the hyperbola-like discounting function not only to different groups but also to kinds of rewards quite different from those considered previously.