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Time preferences and consumer behaviour

An individual’s level of patience is an important determinant in the trade-off between current and future consumption. This column explores the relationship between individuals’ patience for monetary payoffs and their health behaviours, energy use, and financial outcomes. The authors decompose patience into short-run impulsiveness versus long-run impatience, and also explore the role of alternative measures of patience, such as self-reported willpower.

In economics, patience is measured as the ‘rate of time preference’, which is a function reflecting the amount of present consumption one would be willing to forego in order to increase future consumption by a certain amount. Individuals’ rates of time preferences are often elicited by surveys using questions involving choices about different monetary amounts at different points in time. Previous research identifies connections between such elicited time preferences and a variety of outcomes including body mass index, smoking, drinking, and preventive healthcare utilisation (e.g. Chabris et al. 2008, Bradford 2010, Sutter et al. 2013, Courtemanche et al. forthcoming).  

Some economists and psychologists have noted that individuals' time preferences seem to change over time, with individuals appearing more patient for decisions that are farther in the future (Thaler 1981, Ainslie 1991, Laibson 1997). Such preferences are labelled ‘time-inconsistent’ or ‘present-biased’ because they may lead an individual to regret having consumed resources too soon.

One interpretation of present-biased preferences is that time preferences consist of two distinct components:

  • Short-run impulsiveness; and
  • Long-run impatience. 

Some have argued that impulsiveness leads consumers to make decisions that are not in their true best interests, and thus that a person’s wellbeing could be improved through policy interventions that increase the present costs of impulsive decisions with future consequences (or reduce the present costs of more patient decisions with future benefits). For instance, higher food costs can actually be beneficial to those susceptible to self-control problems with snacking (Cutler et al. 2003). Researchers have documented several aspects of consumer behaviour (such as choices about exercising, completing homework, eating, and car purchases) that seem to indicate present bias (Ariely and Wertenbroch 2002, Dellavigna and Malmendier 2006, Ruhm 2012, Allcott and Wozny forthcoming).

In a recent study (Bradford et al. 2014), we make three contributions to this literature.

  • First, we examine how survey-elicited time preferences are related to individuals’ decisions about a large and diverse set of real-world outcomes, including those across the domains of health, energy, and finance. 

To our knowledge, we are the first to estimate the link between elicited time preferences and outcomes such as self-assessed physical and mental health, health-related limitations, snacking, binge drinking, sunscreen and seatbelt use, variables related to home and automobile energy use, among others. 

  • Second, our study allows us to measure time-inconsistent discounting and to disentangle whether the diverse group of observed relationships are driven by time-consistent preferences, present bias, or both. 
  • Third, we assess how the predictive power of time preferences elicited over monetary outcomes compares to that of other time preferences measured across different domains, and whether present bias is evident only in estimates based on elicited monetary measures.

Patience for money, health, and energy use

To accomplish these objectives, we conducted a survey of 1,325 respondents chosen to be representative of the US adult population. We asked respondents a series of questions about their preference for either a smaller, sooner payment or a larger, later payment. For example, we asked people if they would prefer to receive $24 today or $30 in one month. A random subset of individuals received their chosen option in the form of an Amazon gift card. By varying the amount of money and the delay between outcomes, we were able to measure a person’s patience for monetary outcomes and also distinguished between short-run impulsiveness and long-run patience.

We next measured a variety of health behaviours, energy use behaviours, and financial outcomes, and examined their association with our estimates of individuals’ patience. Relatively patient individuals reported better overall health, better mental health, and fewer health-related activity limitations than did impatient individuals. Relatively more patient individuals were also more likely to have health insurance, consumed fewer snacks per day, smoked fewer cigarettes, and reported fewer episodes of binge-drinking during the previous month.

For energy use behaviours, more patient individuals were more likely to drive a car with high fuel economy (at least 25 miles per gallon), to live in a well-insulated residence, and to have installed energy-efficient lighting. Interestingly, car fuel economy was also associated with individuals’ short-run impulsiveness, whereas energy-efficient lighting decisions were associated with long-run patience, suggesting that car purchases involve a substantial ‘impulse’ component, whereas light bulb purchases do not. We also found that more patient individuals carried lower credit card balances compared to more impatient individuals.

While we measured time preferences over monetary outcomes paid as online gift cards, time preferences may be different depending on what type of consumption is being delayed. For instance, an individual who is patient for money or gift cards may not necessarily be patient with regard to health. As such, we considered several alternative measures of patience. We asked individuals to report (on a scale from 1 to 10) their patience, general willpower, and willpower over junk food. We also measured patience for health outcomes directly, in the context of a hypothetical drug for migraine relief where the time delay of effectiveness varied, using an identical methodology as to individuals’ preferences for monetary outcomes. We then examined if these alternative measures of patience were better predictors of health outcomes than was an individual’s patience for monetary outcomes. Interestingly, patience for money was a more consistent predictor of consumers’ health decisions than were the measures of health-related patience.

Because any one of the alternative measures of patience may be imperfect, we also combined all of them into a single patience index function, using a statistical technique developed by Lubotsky and Wittenberg (2006). The results were promising; our combined indicator of patience was a significant predictor (and in the expected direction) for 18 out of the 26 outcomes we considered, compared to 11 out of 26 using solely patience for monetary outcomes. These findings suggest that while any one measure of time preference may be noisy, combining several different measures can yield a measure of a person’s attitude over intertemporal outcomes that is more consistently predictive of important economic decisions. 


Our overall conclusion is that time preferences are related to a variety of consumer behaviours, with some of the strongest and most interesting associations obtained for outcomes that we judge to be most important to people’s long-term wellbeing, such as overall self-assessed health, binge drinking, and purchases of high mileage cars. Generally, both the time-consistent and time-inconsistent components of preferences are important, although their relative importance varies across the outcomes we examine.

Our study also suggests many areas for future research. For instance, would the results persist with larger samples or with preference elicitation strategies that provided respondents with larger rewards? Which of the outcomes examined in this investigation are most important and what other outcomes would be critical to analyse? Do the phenomena observed in this analysis show systematic patterns among subgroups stratified by characteristics such as age, gender, and socioeconomic status? Finally, which policy interventions would actually lead to improvements in social welfare and how could these policies be most effectively implemented?


Ainslie, G (1991), “Derivation of ‘Rational Economic Behavior from Hyperbolic Discount Curves.” American Economic Review 81, no. 2 (May): 334-340.

Allcott, H, and N Wozny (forthcoming), “Gasoline Prices, Fuel Economy, and the Energy Paradox”, Review of Economics and Statistics.

Ariely, D, and K Wertenbroch (2002), “Procrastination, Deadlines, and Performance: Self-Control by Precommitment”,  Psychological Science 13, no. 3: 219-224.

Bradford, W D (2010), “The Association Between Individual Time Preferences and Health Maintenance Habits”, Medical Decision Making 30: 99-112.

Bradford, D, C Courtemanche, G Heutel, P McAlvanah and C Ruhm (2014), “Time Preferences and Consumer Behavior”, National Bureau of Economic Research Working Paper No. 20320.

Chabris, C, D Laibson, C Morris, J Schuldt, and D Taubinsky (2008), “Individual laboratory-measured discount rates predict field behavior”, Journal of Risk and Uncertainty 37 (2008): 237-269.

Courtemanche, C, G Heutel, and P McAlvanah (forthcoming), “Impatience, Incentives, and Obesity.” Economic Journal

Cutler, D, E Glaeser, and J Shapiro (2003), “Why Have Americans Become More Obese?” Journal of Economic Perspectives 17, no. 3: 93-118.

Dellavigna, S, and U Malmendier (2006), "Paying Not to Go to the Gym." American Economic Review 96, no. 3: 694-719.

Laibson, D (1997), “Golden Eggs and Hyperbolic Discounting.” Quarterly Journal of Economics 112, no. 2: 443-477.

Lubotsky, D and M Wittenberg (2006), “Interpretation of Regressions with Multiple Proxies”, Review of Economics and Statistics 88, no. 3: 549-562.

Ruhm, C J (2012), “Understanding Overeating and Obesity”, Journal of Health Economics 31, no. 6: 781-796.

Sutter, M, M Kocher, D Rutzler, and S Trautmann (2013), “Impatience and uncertainty: Experimental decisions predict adolescents' field behaviour”, American Economic Review 103, no. 1: 510-531.

Thaler, R (1981), “Some Empirical Evidence on Dynamic Inconsistency”, Economics Letters 8, no. 3: 201-207.

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