The Role of Time Preferences in Compliance With COVID-19 Preventive Behaviors in Iran: A Quasi-hyperbolic Discounting Approach

Abstract

Objectives:

This study aimed to investigate the role of time preferences in compliance with coronavirus disease 2019 (COVID-19) preventive behaviors in an adult population of Iran.

Methods:

A web-based questionnaire was utilized to conduct a cross-sectional survey of 672 Iranian adults. The parameters of time preferences were estimated using a quasi-hyperbolic discounting model, and the relationship between COVID-19 preventive behaviors and time preferences was examined using a probit regression model.

Results:

A significant association was observed between the preventive behaviors of COVID-19 and the levels of patience and present-biased preferences among the study participants. Individuals who exhibited low levels of patience were found to be 12.8 percentage points less inclined to follow preventive behaviors compared to those with high levels of patience. The likelihood of having good preventive behaviors of COVID-19 was found to decrease by 14.3 percentage points among individuals with a present bias as opposed to those with a bias toward future.

Conclusions:

Patience and present-biased preferences are important determinants of adopting preventive behaviors against COVID-19. These behavioral characteristics should be considered in the design of control and prevention programs. Considering people’s discounting behavior and time (in)consistency in their preferences in the design of COVID-19 policy interventions can provide valuable insights for developing tailored public health policy interventions.

INTRODUCTION

The coronavirus disease 2019 (COVID-19) pandemic has created an unprecedented global public health crisis [1], significantly impacting public health systems, global economies, daily lives, and individual behaviors and decision-making processes across the world. Iran was one of the earliest countries to experience a severe COVID-19 outbreak outside of China. As of February 2022, Iran had reported over 6.6 million confirmed cases and approximately 140 000 deaths related to COVID-19 [2].

Preventive actions like social distancing, stay-at-home policy, hand hygiene, and avoiding face touching have been proven effective in mitigating viral transmission [3,4]. Therefore, encouraging the adoption and maintenance of preventive behaviors has become a key focal point of public health policies aimed at controlling the COVID-19 crisis [5].

Numerous studies have shown that socio-demographic, socioeconomic, and health-related factors, as well as the healthcare system, customs, cultural attitudes toward public health, government policies, perceived risk and social support impact compliance with COVID-19 preventive measures [6-8]. Nonetheless, to tackle COVID-19 and potential future pandemics effectively and design successful interventions, it is crucial to acquire a more detailed understanding of the factors that influence compliance with public health measures. One important factor that has gained substantial attention in behavioral health economics research is individuals’ time preferences, which determine how they weigh immediate versus delayed outcomes. Time preferences or time discounting refer to the trade-offs individuals make between choices where the costs and benefits of choices occur at different points in time. Behavioral economists classify this type of decision-making as intertemporal preferences, which can be examined through experiments that require individuals to choose between receiving a smaller, immediate reward or a larger, delayed reward (e.g., receiving US$200 today vs. US$250 tomorrow) [9,10].

Time preference is a critical issue in health economics, as it plays a significant role in explaining health-related behaviors. It has been identified as an important determinant of various health-related choices and decisions, such as smoking [11], alcohol consumption [12], and physical activity [13]. These behaviors involve weighing the costs and benefits that occur over different time periods. For instance, when someone chooses to overeat, they may experience the immediate gratification of enjoying tasty food, but it can also increase his or her risk of developing obesity and cardiovascular diseases in the future.

Time preferences are particularly relevant in the context of COVID-19, where individuals must balance the immediate inconvenience of preventive measures against the long-term benefits of reducing virus transmission. Decision to comply with COVID-19 preventive behaviors involves a trade-off between current costs such as forgoing enjoying freedom, going outside, social interactions at present and uncertain future benefits, which include reduction in the spread of COVID-19, potential shortening of the pandemic, and end of the pandemic [14,15]. More specifically, preventive behaviors like mask-wearing and social distancing represent current costs. These actions involve immediate inconveniences that may be seen as a reduction in current utility (well-being). Conversely, the benefits of these behaviors, such as reduced risk of infection and potential reduction in the spread, shortening, and end of the pandemic, accrue in the future.

Despite a substantial body of research focusing on demographic and attitudinal factors influencing compliance with COVID-19 preventive behaviors, we aim to use the lens of behavioral economics to explore the role of time preferences in shaping these behaviors, an area that has received relatively little attention. Understanding how individuals make decisions during such a crisis, particularly concerning their time preferences, is essential for designing effective public health interventions. This knowledge can enhance our understanding of the factors that influence compliance with health guidelines during public health emergencies such as pandemics and provide valuable insights for the development of tailored public health policy interventions. The objective of this study was to investigate the role of time preferences in adherence to COVID-19 preventive measures among a sample of Iranian adults.

Theoretical Framework

Traditional economic models assume that individuals make rational decisions to maximize their lifetime expected utilities based on the present and future costs and benefits associated with their choices. Individuals consistently devalue future outcomes in comparison to current ones. This model assumes that the degree to which future outcomes are discounted is consistent over time. This implies that individuals have stable, time-consistent preferences, typically represented by exponential discounting [16]. For instance, if an individual plan to quit smoking tomorrow rather than today, and assuming all other factors remain constant, that individual will consistently choose not to smoke when tomorrow arrives, as their preferences are time-consistent. In contrast, behavioral economics incorporates psychological insights into economics, highlighting time-inconsistent preferences in intertemporal decision-making, also known as present-biased preferences. Time-inconsistent preferences are exhibited by the hyperbolic discounting function, in which the discount rate for immediate future choices is higher than that for distant future choices. Present-bias preferences refer to individuals’ inclination to assign greater significance to short-term costs and benefits compared to those in the long term. These individuals overestimate immediate gratification over long-term health benefits, such as opting to relax instead of exercising. For present-biased individuals (i.e. individuals with a strong preference for immediate reward), the immediate costs of healthy choices outweigh future benefits [17]. Essentially, this implies that people are more influenced by immediate outcomes and exhibit a lesser future-oriented perspective [16,18,19], leading to suboptimal behaviors and a lower likelihood of adhering to preventive measures. Present bias serves as an explanatory factor for why individuals sometimes fail to act in their own best interests and encounter challenges in complying with preventive health behaviors, such as practicing social distancing, even when they genuinely desire to do so. This also explains why individuals exhibit preference reversal and fail to pursue the behavior change plans they have previously established, often delaying beneficial behaviors such as quitting smoking [20]. Future-biased individuals (i.e. individuals with a strong preference for larger delayed reward) prioritize long-term benefits over short-term inconveniences. They focus on future utility gains, such as reduced risk, even if it requires temporary hardships.

The quasi hyperbolic discounting model proposed by Laibson [21] provides insights into time-inconsistent preferences, which is crucial for understanding individuals’ compliance with preventive behaviors. The utility function associated with quasi-hyperbolic discounting is expressed as follows:

$$U_t\left(u_t,u_{t+1},u_{t+2},\dots \right)=u_t+\beta \delta u_{t+1}+\beta {\delta }^2{u}_{t+2}+\cdots +\beta {\delta }^T{u}_T=u_t+\beta {\sum }_{t=1}^T {\delta }^t{u}_t$$ (1)

Here, U_t denotes lifetime utility, u_t signifies the utility in period (t), δ signifies the conventional time-consistent preferences or propensity for long-term patience, and β represents time-inconsistent preferences, commonly referred to as present bias. The quasi-hyperbolic model postulates that individuals apply a discount rate of δ to two consecutive future periods, while also applying a discount rate of δβ between the present and the next period. When β equals 1, time preferences are entirely accounted for by δ. Consequently, the quasi-hyperbolic discount model aligns with the standard(exponential) model. However, when β is less than 1, it signifies that individuals exhibit present-biased preferences. This implies that they assign greater importance to current utility compared to future utility, which in turn suggests the presence of self-control challenges. Conversely, if β is greater than 1, individuals are future-biased [21].

METHODS

Study Design and Sampling Strategy

An online cross-sectional survey was conducted among Iranian adults aged 18 and older in response to the difficulties associated with conducting in-person surveys during the COVID-19 pandemic. This study was conducted during the first wave of the COVID-19 pandemic in the spring of 2020, before the availability of vaccines against the virus in Iran. The single population proportion formula was employed to calculate the sample size, incorporating a 95% confidence level, a 5% sampling error, and an assumed proportion of individuals adhering to COVID-19 preventive measures of 50% (at the onset of the pandemic, we lacked an established estimate of compliance with preventive behaviors, so we used a 50% prevalence for sample size calculations). The initial sample size was set at 385, but it was ultimately increased to 672. This sample size was approved during the initial ethics review. This decision was made to enhance statistical power, improve generalizability, and ensure data robustness and minimize sampling error. Convenience sampling was employed to recruit participants for the online survey. This method involved selecting individuals who were readily accessible and willing to participate, enabling us to collect data efficiently during a time-sensitive period. Participants received a web-based questionnaire through popular social media platforms in Iran, specifically WhatsApp and Telegram. This strategy facilitated the inclusion of individuals from diverse socioeconomic backgrounds within the general population, as these platforms are widely used in Iran. Before data collection, potential participants were informed of the voluntary nature of their participation in the study. Completion of the questionnaire served as an indication of informed consent to participate.

We obtained data through a pre-structured questionnaire for collecting data on demographic and socioeconomic characteristics and preventive behaviors of COVID-19; and a choice0based task for measuring individual time preferences.

Outcome Variable

For the outcome variable, we defined a binary indicator to represent whether an individual complied with COVID-19 preventative behaviors. It was determined by evaluating an individual’s preventive behaviors through a set of five questions, which included inquiries about their adherence to social distancing guidelines, use of gloves and masks, handwashing, and disinfection practices for cell phones and laptops. The answers were evaluated with four options: never/seldom, occasionally, often, and always. We converted the scores of a dependent variable derived from a Likert scale into a binary format by categorizing the responses into two distinct groups based on a threshold method, where the mean of item scores served as the threshold [22]. Individuals whose scores were higher than the mean score of the sample were considered those with good preventive behaviors for COVID-19.

Primary Dependent Variables

Time preferences

The time preferences measure was constructed based on questions used in previous related studies [9,23,24]. Expert opinion was used to confirm the face validity of the time preferences questionnaire. A pilot study was conducted to pre-test the questionnaire to ensure participants’ understanding of the questions regarding inter-temporal trade-offs, which resulted in slight modifications to the instrument. To estimate individuals’ time preferences, we posed binary monetary choices between near and distant future scenarios. Responses to these choices were utilized in the computation of time consistent and time-inconsistent preference parameters from Laibson [21]’s quasi-hyperbolic discounting model. This study employed two time frames to estimate the parameters of the model: In the case of the near future, participants were required to select between receiving a smaller hypothetical sum of money immediately or a larger sum of money one week later. On the other hand, for the distant future, the binary choices involved choosing between receiving a smaller hypothetical sum of money six months later or a larger sum of money six months and one week later. The fixed immediate amount was 1.5 million Iranian rials (IRR; equivalent to US$ 35.71 using an exchange rate of US$1=IRR 42 000), while delayed sums ranged from 1.6 million IRR to 2.75 million IRR. For example, we asked participants the following question: Which would you prefer: 1.5 million IRR today or 1.75 million IRR a week later? In the quasi-hyperbolic model, individuals discount between any two consecutive future time periods at rate δ. Therefore, parameter δ was calculated using the following formula:

$$\delta =IRR 1.500.000/(minimum amount of money six months and one week later over IRR 1.500.000 in six months later)$$ (2)

Due to the multiple-choice format of the time preference questionnaire, the discount factor (δ parameter) is restricted to a discrete set of values. Specifically, the instrument offers four possible options for δ: 0.93, 0.85, 0.66, and 0.54. Due to the relatively small number of participants with a δ value of 0.66 (only 9.38% of participants exhibited a δ parameter value of 0.66), the categories δ=0.66 and 0.54 were combined for analysis. Consequently, the resulting δ parameter was classified into three distinct categories: low (δ=0.54 and 0.66), medium (δ =0.85), and high patience (δ =0.93).

Similarly, according to the quasi-hyperbolic discounting model, individuals discount between now and the next period at a rate of δβ. We calculated parameter β, based on responses to near future trade-offs and the previously computed δ parameter. The formula used is as follows:

$$\beta =IRR 1.500.000/\left(\right(minimum amount of money willing to accept in one week over receiving IRR 1.500.000 now)*\delta )$$ (3)

The estimated β parameter was classified according to Laibson [21]’s model, which categorizes individuals into three groups based on their time preferences: those with present bias (β<1), those with no present bias (β=1), and those with future bias (β>1). The methods used to determine the δ and β thresholds in this study were consistent with those employed in previous research [21,25]. The measure of time preferences was developed based on prior related studies, and its validity was confirmed qualitatively through expert opinion. The task was pretested to ensure that participants fully understood the questions regarding intertemporal monetary choices.

Risk preferences

Studies have indicated that people’s investment choices may be influenced by their tendency towards taking risks. Since the results of investments are unpredictable, people’s approach towards uncertainty or risk can have an impact on their investment decisions. To measure risk preferences, we employed a general risk question, where participants were queried to assess their willingness to take risks on a scale ranging from 1 to 10, with 1 denoting a reluctance to take risks and 10 indicating a full willingness to take risks [26]. While risk preferences are commonly estimated through lotteries and probability choices, these methods can be complex and difficult to understand. Therefore, a general risk query was utilized as a reliable, valid and straightforward approach to evaluate risk preferences [26]. We categorized willingness to take risks as follows: low for scores of 1, 2, 3, and 4; medium for scores of 5, 6, and 7; and high for scores of 8, 9, and 10.

Other Variables

Other explanatory variables were sex (male, female), age group (<20, 21-30, 31-40, 41-50, and >50), marital status (married and single/divorced/widower), level of education (high school or less/bachelor degree, postgraduate degree), economic status (low, middle and high), pre-existence of chronic diseases (yes/no). Health status was assessed using a self-rated scale ranging from 1 to 10, which classified health into three categories: poor for scores of 1, 2, 3, and 4; fair for scores of 5, 6, and 7; and good for scores of 8, 9, and 10.

Statistical Analysis

We used the probit regression model to analyze the association between time preferences, namely long–run patience (δ) (i.e. time-consistent preferences) and present-biasedness (β) (i.e. time-inconsistent preferences), and preventive behaviors for COVID-19. This model is based on a latent variable model. If y* represents the latent or unobservable variable:

$$\begin{gathered} y*={\beta }_0+{\sum }_{j=1}^k{\beta }_j{x}_j+\epsilon , Where, \beta j are the coefficients of the independent variables xj, xj are the independent variables, \\ \epsilon \sim Normal (0, 1) and y=1 if y*>0, y=0 if y*<0 \end{gathered}$$ (4)

Then the probability of having good preventive behaviors of COVID-19, y, is obtained by

$$P(y=1\mid x)=P\left(y^{\ast }>0\mid x\right)=\Phi \left({\beta }_0+\sum _{j=1}^k \beta x\right)$$ (5)

Ф shows the cumulative standard normal distribution function. The probit model was employed utilizing maximum likelihood estimation. To facilitate interpretation, the estimated coefficients were transformed into average marginal effects, which illustrate how a one-unit change in a dependent variable impacts the likelihood of the outcome variable [27]. The preventive behaviors for COVID-19 were modeled as a function of time-inconsistent and time-consistent preferences, risk preferences, and covariates including socio-demographics, socioeconomic status, and health-related variables.

$$preventive behaviors of COVID19=\alpha 1+\alpha 2\delta +\alpha 2\beta +\alpha 3R+\alpha 4C+\epsilon$$ (6)

All statistical analysis was carried out in Stata version 17 (StataCorp., College Station, TX, USA).

Ethics Statement

By responding to the online questionnaire, participants explicitly demonstrated their voluntary agreement to take part in the survey after being fully informed about the nature and purpose of the study. The Ethics Committee of Kermanshah University of Medical Sciences approved the study (IR.KUMS. REC.1399.032).

RESULTS

A total of 672 people between the ages of 18 and 73 completed the questionnaire (mean±standard deviation, 35.10± 9.49 years). The proportion of participants with good preventive behaviors was 49.1%. The results indicated that, among the participants in this study, 24.7% exhibited a low willingness to take risks, 52.7% demonstrated a medium willingness, and 22.6% displayed a high willingness to take risks. In the sample, 38.4% of respondents exhibited present-biased preferences (β<1), 48.2% of respondents were classified as exponential discounters (β =1), indicating time-consistent preferences. Additionally, 13.4% were identified as future-biased (β>1). The majority of respondents (62.3%) exhibited high levels of patience, while 17.9% and 19.8% displayed medium and low levels of patience, respectively (Table 1).

Table 1.

Descriptive results

Variables	n (%)
Having good preventive behaviors
Yes	330 (49.1)
No	342 (50.9)
Time preferences
Patience
High patience (patient)	419 (62.3)
Medium patience	120 (17.9)
Low patience (impatient)	133 (19.8)
Present-biased preferences
Future biased	90 (13.4)
Exponential discounter (unbiased)	324 (48.2)
Present biased	258 (38.4)
Willingness to take risk (risk preferences)
Low	166 (24.7)
Medium	354 (52.7)
High	152 (22.6)
Age (y)
<20	24 (3.6)
21-30	207 (30.8)
31-40	277 (41.2)
41-50	122 (18.2)
>50	42 (6.3)
Sex
Male	372 (55.4)
Female	300 (44.6)
Marital status
Single	380 (56.5)
Married	292 (43.4)
Education
High school or less	304 (45.2)
Bachelor degree	200 (29.8)
Postgraduate degree	168 (25.0)
Socioeconomic status
Low	164 (24.4)
Middle	324 (48.2)
High	184 (27.4)
Health status
Poor	58 (8.6)
Fair	230 (34.2)
Good	384 (57.1)
Pre-existence of chronic diseases
Yes	90 (13.4)
No	580 (86.6)

There was a significant relationship between patience, present bias, and the likelihood of having good preventive behaviors of COVID-19. The likelihood of having good preventive behaviors against COVID-19 was 12.8 percentage points lower for individuals with low patience (i.e. impatient) compared to those with high patience. Moreover, the probability of having good preventive behaviors against COVID-19 increased by 14.3 percentage points for individuals with present bias compared to those with future bias. The results also indicated that the likelihood of exhibiting good preventive behaviors against COVID-19 was 14.1 percentage points lower for individuals with a high level of willingness to take risks compared to those with a low level of willingness to take risks. A statistically significant inverse relationship was found between being male and the likelihood of practicing good preventive behaviors for COVID-19. Additionally, belonging to higher socioeconomic groups and possessing good health status were found to be statistically correlated with the adoption of effective preventive behaviors compared to their counterparts (Table 2).

Table 2.

The results of probit regression

Variables	Marginal effect	SE	p-value
Time preferences
Patience
Low	-0.128	0.054	0.017
Medium	0.116	0.056	0.038
High	Reference
Present-biased preferences
Future bias	Reference
Present bias	-0.143	0.065	0.028
Unbiased (exponential discounter)	-0.069	0.066	0.300
Willingness to take risk (risk preferences)
Low	Reference
Medium	-0.054	0.047	0.252
High	-0.141	0.054	0.009
Age (y)
<20	Reference
21-30	0.083	0.106	0.435
31-40	0.080	0.108	0.458
41-50	0.131	0.111	0.239
>50	0.228	0.117	0.051
Sex
Male	-0.179	0.041	0.000
Female	Reference
Marital status
Single	Reference
Married	-0.016	0.045	0.722
Education
High school or less	Reference
Bachelor degree	-0.041	0.046	0.374
Postgraduate degree	-0.089	0.049	0.071
Socioeconomic status
Low	Reference
Middle	0.093	0.047	0.046
High	0.110	0.053	0.037
Health status
Poor	Reference
Fair	0.155	0.070	0.028
Good	0.214	0.070	0.002
Pre-existence of chronic diseases
Yes	-0.088	0.058	0.131
No	Reference
No. of obs=670, log likelihood=-422.008, LR chi2(19)=84.51, Prob>chi2=<0.001, Pseudo R2=0.0910

SE, standard error; obs, observations; LR, likelihood ratio; Prob, probability value (p-value).

DISCUSSION

This study explored how time preferences influence COVID-19 preventive behaviors among Iranian adults. It used the quasi-hyperbolic discounting model to measure time preferences, specifically time-consistent preferences and present bias. The study found that individual time preferences can predict adherence to COVID-19 preventive measures, providing new insights into this relationship. To our knowledge, it is the first published study to examine this relationship using the quasi-hyperbolic discounting model.

Our study found that individuals with lower patience and present-biased preferences are less likely to adhere to COVID-19 preventive behaviors. This aligns with previous research on time discounting in health-related decisions, such as adherence to medical advice. Our findings contribute to the literature on behavioral economics and public health during pandemics.

These findings confirm previous studies demonstrating a link between higher time discounting and lower engagement in COVID-19 preventive behaviors [15,28,29]. For instance, Lloyd et al. [15] found a negative correlation between delay discounting rates and adherence to social distancing measures. This association, however, was not observed in cases of active violation of lockdown guidelines, such as group gatherings. Interestingly, those who devalued rewards more rapidly displayed a reduced perceived ability to effectively practice social distancing.

In the same vein, Byrne et al. [28] discovered a correlation between higher delay discounting and risky decision-making with inadequate mask-wearing practices and lack of adherence to social distancing guidelines among American adults. Müller and Rau [29] also revealed that an individual’s level of patience is positively correlated with their tendency to stay at home and avoid crowded places. In a study by Rafaï et al. [30], patience has been discovered as a predictor of compliance. These support our findings that time preferences significantly influence behavior during health crises.

Our research contributes to the existing literature by demonstrating that time preferences also predict adherence to a range of recommended behaviors during a global pandemic, an area that has been less explored in prior literature. In contrast to the theoretically expected outcome where higher discounting rates would be linked to lower adherence to preventive behaviors, opposite findings have been observed in some studies [14,31,32]. For instance, Wismans et al. [32] found that individuals who placed less value on delayed rewards tended to report higher levels of adherence to social distancing and hygiene protocols. However, the noted positive correlation between the discount rate and adherence was modest. Another investigation revealed that those with a higher discount rate, indicating a stronger present bias, were more inclined to comply with COVID-19 public health protocols [14]. To conclude, despite conflicting findings, the concept of discounting processes could serve as a useful framework for examining compliance with pandemic interventions.

Understanding time preferences is essential for developing tailored interventions that align with different population segments. Individuals with present bias respond better to immediate incentives, while those with future-oriented preferences are influenced by long-term health outcomes. Interventions offering immediate rewards, such as financial incentives or minimizing current costs, can motivate adherence to COVID-19 preventive behaviors. These initiatives can include deferring loan repayments, providing low-cost internet access, and offering benefit packages to vulnerable populations (such as free services and food vouchers) can help individuals prioritize long-term benefits over short-term costs. This approach has proven effective in promoting compliance with antiretroviral therapy, smoking cessation, and weight reduction. Financial incentives can effectively motivate compliance by emphasizing immediate benefits, such as reduced financial burdens or improved health outcomes. These strategies aim to increase adherence to pandemic policies and enhance their success [33].

Our research findings suggest that individuals who exhibit a greater inclination towards taking risks are less likely to engage in COVID-19 preventive behaviors. This aligns with previous studies that have also observed a positive correlation between risk aversion and adherence to preventive measures for COVID-19 [30,34,35]. For instance, Chan et al. [35] conducted a study that revealed that areas where people were more willing to take risks had a higher compliance rate with mobility restrictions. Additionally, another study indicated that individuals with higher levels of risk aversion were more likely to avoid meeting more than five other people and even refrained from meeting their own family members [34]. This supports our findings that time preferences play a critical role in shaping behavior during health crises.

This study has several limitations that warrant consideration. Firstly, we employed convenience sampling, which facilitated rapid data collection but may introduce sampling bias, affecting the generalizability of our results to the broader Iranian population. Secondly, our reliance on online surveys via WhatsApp and Telegram may limit participation among older individuals unfamiliar with technology, potentially skewing results. However, this approach allowed us to capture a diverse range of participants from different regions and socioeconomic backgrounds. This diversity can enhance the study’s external validity despite potential biases. Additionally, our data relies on self-reported responses, which may be influenced by social desirability bias.

Lastly, time preferences seem related to individual traits, which may shift under certain external contexts (e.g., a pandemic wave peak). However relevant literature has shown heterogeneous results. Some studies have showed that the first wave of COVID-19 decreased patience [36,37], some studies have reported no change in patience or time discounting [38,39] and some studies have that the first wave of COVID-19 increased patience [40].

In conclusion, our findings reveal a significant positive correlation between time-consistent preferences (reflecting patience) and engagement in preventive behaviors. Conversely, time-inconsistent preferences (indicating present bias) were associated with lower compliance. Considering this link, interventions emphasizing immediate or short-term benefits for oneself and others could enhance motivation. Additionally, immediate financial incentives may function as effective nudges, encouraging more rational decision-making regarding COVID-19 and other pandemic preventive measures. These findings indicate that accounting for heterogeneous time preferences can significantly enhance the effectiveness of public health interventions during crises such as the COVID-19 pandemic or potential future pandemics.