Exponential Growth Bias Research Articles

Pandemics and the great evolutionary mismatch.

Dezecache et al. argue that affiliation and contact-seeking are key responses to danger. These natural social tendencies are likely to hinder the observance of physical distancing during the current pandemic. We need internet access at this time, not only to promote freedom of expression, but also to promote public health.

Strategically managing the artificially intelligent firm

Purpose Artificial Intelligence is a general-purpose technology that is bound to affect every industry. To develop successful corporate and business strategies with AI, leaders have to capture the technological opportunity, understand the economics of AI and lead organizational change. Design/methodology/approach What to do with AI? How to set up for AI? Where is the value? Initial answers to these three fundamental questions are derived based on an interdisciplinary and integrated literature review. Findings AI leaders pursue competitive advantage by taking up the technological offer to predict-prescribe-automate, they enact machine learning by entering into a portfolio of experiments within a modular strategic framework, they organize the business to form of man-machine teams following the concept of human-agent collectives and they avoid falling victim to exponential growth bias. Practical implications In a business environment where the cognification of everything will lead to exponential growth of both, volume of data and intelligence of machines, it will be paramount for strategic leaders to have a robust AI strategy in place and to develop the competencies required for managing the Artificially Intelligent Firm. Originality/value This overview enables strategic leaders to prepare themselves and their organizations for AI. It spearheads making required connections between technology, economics and management of AI.

Exponential Growth Bias in the Prediction of Covid-19 Spread and Economic Expectation

Exponential growth bias (EGB) is the pervasive tendency of people to perceive a growth process as linear when, in fact, it is exponential. In this paper, we document that people exhibit EGB when asked to predict the number of COVID-19 positive cases in the future. The bias is positively correlated with optimistic expectations about the future macroeconomic conditions and personal economic circumstances, and investment in a risky asset. We design four interventions to correct EGB and evaluate them through a randomized experiment. In the first treatment (Step), participants make predictions in several short steps; in the second and third treatments (Feedback-N and Feedback-G) participants are given feedback about their prediction errors either in the form of numbers or graphs; and in the fourth treatment (Forecast), participants are offered a forecast range of the future number of cases, based on a statistical model. Our results show that a) Step helps mitigate EGB relative to Baseline, b) Feedback-N, Feedback-G, and Forecast significantly reduce bias relative to both Baseline and Step, c) the interventions decrease risky investment and help moderate future economic expectations through the reduction in EGB. The results suggest that nudges, such as behaviorally informed communication strategies, which correct EGB can also help rationalize economic expectations.

Who is a passive saver under opt-in and auto-enrollment?

Defaults have been shown to have a powerful effect on retirement saving behavior yet there is limited research on who is most affected by defaults and whether this varies based on features of the choice environment. Using administrative data on employer-sponsored retirement accounts linked to survey data, we estimate the relationship between retirement saving choices and individual characteristics – long-term discounting, present bias, financial literacy, and exponential-growth bias – under two distinct choice environments: an opt-in regime and an auto-enrollment regime. Consistent with our conceptual model, we find that the determinants of following the default and contribution behavior are regime-specific. Under the opt-in regime, financial literacy plays an important role in predicting total contributions, active saving choices, and maxing out contributions in the tax-preferred account. In contrast, under the auto-enrollment regime, present bias is the most significant behavioral predictor of contribution behavior. A causal interpretation of the estimates suggests that auto-enrollment increases saving primarily among those with low financial literacy.

How Financial Literacy Impacts Retirement Savings: The Role of Present Bias and Exponential Growth Bias

Lack of saving for retirement is a major issue, especially in soon-to-be ageing societies. In this paper we examine the impact of financial literacy on retirement savings, as well as its effect on present bias and exponential growth bias, which have been identified as detrimental to savings in previous studies. We find that better financial literacy is related with a more accurate perception of exponential growth, which stimulates retirement savings. In addition, financially literate individuals are less affected by present bias when they save for their retirement.

PREDICTING RETIREMENT SAVINGS USING SURVEY MEASURES OF EXPONENTIAL‐GROWTH BIAS AND PRESENT BIAS

In a nationally representative sample, we predict retirement savings using survey‐based elicitations of exponential‐growth bias (EGB) and present bias (PB). We find that EGB, the tendency to neglect compounding, and PB, the tendency to value the present over the future, are highly significant and economically meaningful predictors of retirement savings. These relationships hold controlling for cognitive ability, financial literacy, and a rich set of demographic controls. We address measurement error as a potential confound and explore mechanisms through which these biases may operate. Back of the envelope calculations suggest that eliminating EGB and PB would increase retirement savings by approximately 12%. (JEL D91, D14)

Exponential‐growth Bias in Experimental Consumption Decisions

Exponential‐growth bias (EGB) is the tendency to neglect the power of compounding interest, which has been found to be widespread in the population. A person with EGB will misperceive the intertemporal budget constraint, overestimating lifetime wealth and underestimating the differences in the cost of consumption across periods. We test five comparative static predictions implied by EGB: (1) compound interest will increase consumption when the elasticity of intertemporal substitution is greater than 1; (2) higher interest rates lead to more compounding and hence increase consumption; (3) budget‐neutral delays in income will increase consumption; (4) the person will exhibit a form of dynamic inconsistency that depends solely on the current account balance and is independent of time preferences; and (5) framing the frequency of interest in shorter units increases consumption. We test these predictions using an induced‐value consumption–savings experiment in the laboratory, and find evidence in support of all predictions against the rational benchmark. We consider rules of thumb as alternative hypotheses and find that they cannot explain the results, although they contribute to some findings.

Exponential growth bias matters: Evidence and implications for financial decision making of college students in the U.S.A.

This paper tests the exponential growth bias of undergraduate students at a top-level university in the United States and explores the potential drivers that influence the size of this bias. Here, we find that bias matters, even for college students, in making savings and debt decisions. We observe that the individuals who have already taken on debt are more biased in their compound savings estimates, while those who have experience with savings products are less biased. Interestingly, we detect no significant differences in the results between freshmen and upperclassmen, despite the findings that significantly more upperclassmen claim to have previously learned about compounding interest and are more aware of compound growth. We believe that these findings entail some strong policy implications and we urge policy makers in the U.S. to consider both a more extensive compound savings formula training curriculum and a focus on bias “awareness“.

Predicting Retirement Savings Using Survey Measures of Exponential-Growth Bias and Present Bias

In a nationally-representative sample, we predict retirement savings using survey- based elicitations of exponential-growth bias (EGB) and present bias (PB). We find that EGB, the tendency to neglect compounding, and PB, the tendency to value the present over the future, are highly significant and economically meaningful predictors of retirement savings. These relationships hold controlling for cognitive ability, financial literacy, and a rich set of demographic controls. We address measurement error as a potential confound and explore mechanisms through which these biases may operate. Back of the envelope calculations suggest that eliminating EGB and PB would increase retirement savings by approximately 12 percent.

Individual preferences and the exponential growth bias

The exponential growth bias (EGB) refers to individuals’ underestimation of the effects of exponential growth and has been shown to affect important financial decisions such as retirement savings. We develop and test a novel experimental design to evaluate the magnitude of the EGB based on multiple price lists. Our design simultaneously elicits the EGB as well as subjects’ utility curvatures and time preferences. Allowing for non-linear utility, we structurally estimate the magnitude of the EGB as about half as large as when linear utility is assumed. These results shed light on the relationship between individual preferences and the EGB.

Exponential-growth bias and overconfidence

There is increasing evidence that people underestimate the magnitude of compounding interest. However, if people were aware of their inability to make such calculations they should demand services to ameliorate the consequences of such deficiencies. In a laboratory experiment, we find that people exhibit substantial exponential-growth bias but, more importantly, that they are overconfident in their ability to answer questions that involve exponential growth. They also exhibit overconfidence in their ability to use a spreadsheet to answer these questions. This evidence explains why a market solution to exponential-growth bias has not been forthcoming. Biased individuals have suboptimally low demand for tools and services that could improve their financial decisions.

When Formulas Fail: On the Variability of the Exponential Growth Bias

This paper evaluates the possible benefits and drawbacks of the formal formula learning of compound growth as it pertains to eliminating, or at least reducing, the exponential growth bias in various household savings and debt decisions. In our main experimental study, we determine if the ability to calculate the simple compound savings formula only assists in its direct area of application with an available calculator, or if this knowledge extends into similar exponentially-based savings and debt decisions when either a calculator is prohibited or when the formula is unknown. In the process of tackling this research question, we develop a measure for the exponential growth bias that naturally extends over different tasks and parameter settings. Our findings suggest that learning the compound savings formula does much more than eliminate the exponential growth bias for individuals in the savings domain with an available calculator. In fact, we find evidence that these individuals post less biased savings and debt estimates in the absence of a calculator, suggesting that the knowledge of this formula may aid in developing a more general, intuitive grasp of exponential effects. On the other hand, we find that too much dependence on these formulas can have adverse effects, as a number of participants who knew the compound savings formula mistakenly applied a variation of it in the debt domain leading to insensible answers well above the initial loan balance.

How to Decrease the Amortization Bias: Experience vs. Rules

We conduct an experimental study that tests the effectiveness of de-biasing a certain form of exponential growth bias found in household finance debt decisions, called the amortization bias. We provide 251 bachelor students at a German university with a short tutorial based on one of three learning methods: experiential learning, learning a simple “I Owe More” debt rule-of-thumb, as well as learning an extended, but more accurate version of the “I Owe More” debt rule. Immediately after completing these tutorials, we retest for the amortization bias and find a significant bias improvement in all three treatments. More importantly, after confronting the same participants with similar debt scenarios approximately three weeks later, we find that those who had previously received a debt tutorial maintain a significantly larger bias improvement over the control group. However, during this short period, most of the individuals who learned the simple and complex rules-of-thumb could no longer apply the rule and reverted back to their biased answers, while the experiential learning group best retained their improvement in bias. We find evidence in this experiment that experience-based learning may be better suited to produce long-lasting improvements for attenuating the amortization bias.

EXPONENTIAL-GROWTH BIAS AND LIFECYCLE CONSUMPTION

Exponential-growth bias (EGB) is the tendency for individuals to partially neglect compounding of exponential growth. We develop a model wherein biased agents misperceive the intertemporal budget constraint, and derive conditions for overconsumption and dynamic inconsistency. We construct an incentivized measure of EGB in a US-representative population and find substantial bias, with approximately one third of subjects estimated as the fully biased type. The magnitude of the bias is negatively associated with asset accumulation, and does not respond to a simple graphical intervention.

Misunderestimation: Exponential-Growth Bias and Time-Varying Returns

Exponential-growth bias is the tendency to neglect the compounding of interest. The economics literature has used the fact that a biased agent in many circumstances will underestimate the value of assets that grow according to compound interest. We show that the opposite can also be true. It is always possible to make an agent who underestimates exponential growth to overestimate the value of an asset that grows exponentially. This paradoxical phenomenon arises when interest rates vary over time. This gives rise to the averaging effect of exponential-growth bias, which causes agents to perceive the mean return to exceed the true mean. Consequently, biased agents will strictly prefer assets with time-varying returns over equivalent constant-return assets. With sufficient variation in returns, any biased agent will overestimate the true value of an asset for any time horizon.

Exponential Growth Bias Matters: Evidence and Implications for Financial Decision Making of College Students in the U.S.A.

This paper tests the exponential growth bias of undergraduate students at a top-level university in the United States and explores the potential drivers of this bias. We find that bias matters, even for college students, in making savings and debt decisions. In this sample, we observe that the individuals who have already taken on debt are more biased, while those who have experience with savings products are less biased. Moreover, those classified as possessing an awareness of compound growth as well as an ability to consistently calculate the compound savings equation are significantly less biased in different savings treatments than those who are unable to make the calculation, further demonstrating that learning the formula may also aid in making better intuitive estimates. Interestingly, we detect no significant differences of university learning in the results between freshmen and upperclassmen, with the exceptions that significantly more upperclassmen claim to have previously learned about compounding interest and are more aware of compound growth. We believe that these findings entail some strong policy implications and we urge policy makers to consider both a more extensive compound savings formula training curriculum to the current Common Core State Standards Initiative and a more experientially-based learning curriculum with a focus on bias “awareness” for these important savings and debt decisions.

Financial Education, Financial Competence, and Consumer Welfare

We introduce the concept of financial competence, a measure of the extent to which individuals’ financial choices align with those they would make if they properly understood their opportunity sets. Unlike existing measures of the quality of financial decision making, the concept is firmly rooted in the principles of choice-based behavioral welfare analysis; it also avoids the types of paternalistic judgments that are common in policy discussions. We document the importance of assessing financial competence by demonstrating, through an example, that an educational intervention can appear highly successful according to conventional outcome measures while failing to improve the quality of financial decision making. Specifically, we study a simple intervention concerning compound interest that significantly improves performance on a test of conceptual knowledge (which subjects report operationalizing in their decisions), and appears to counteract exponential growth bias. However, financial competence (welfare) does not improve. We trace the mechanisms that account for these seemingly divergent findings.

Exponential-Growth Bias and Overconfidence

There is increasing evidence that people underestimate the magnitude of compounding interest. However, if people were aware of their inability to make such calculations they should demand services to ameliorate the consequences of such deficiencies. In a laboratory experiment we find that people exhibit substantial exponential-growth bias, but, more importantly, that they are overconfident in their ability to answer questions that involve exponential growth. They also exhibit overconfidence in their ability to use a spreadsheet to answer these questions. This evidence explains why a market solution to exponential-growth bias has not been forth- coming. Biased individuals have suboptimally low demand for tools and services that could improve their financial decisions.

In Equations We Trust? Formula Knowledge Effects on the Exponential Growth Bias in Household Finance Decisions

Exponential growth effects play a major role in many household finance decisions. A systematic bias in dealing with exponential growth can lead to poor savings and debt decisions. In this paper, we extend previous research on the exponential growth bias in the savings and debt domains and provide a first experimental link between these two important fields of consumer financial decision making. We develop a measure for the exponential growth bias that naturally extends over different domains and parameter settings, and we explore the ramifications of being acquainted with the basic formula of exponential savings growth. Specifically, we analyze whether such formula knowledge helps only in calculating simple compound interest scenarios with a pocket calculator or if it provides benefits that go beyond this narrow field of application. We observe that—even without a pocket calculator—individuals who know the compound interest formula provide less biased estimates for problems from the savings domain and also for slightly more complicated debt amortization problems that also build on exponential growth effects. We conclude that being acquainted with the compound interest formula provides some intuitive grasp of exponential effects that can be helpful in a broader range of household finance decisions. At the same time, we observe that too much dependence on a calculator can have adverse effects: when equipped with a pocket calculator, a number of participants, both aware and unaware of the compound savings formula, provided persistently insensible answers greater than the initial loan balance in the debt domain.

Exponential-Growth Bias and Lifecycle Consumption

Exponential-growth bias (EGB) is the tendency for individuals to partially neglect compounding of exponential growth. We develop a model wherein biased agents misperceive the intertemporal budget constraint, and derive conditions for overconsumption and dynamic inconsistency. We construct an incentivized measure of EGB in a US-representative population and find substantial bias, with approximately one-third of subjects estimated as the fully-biased type. The magnitude of the bias is negatively associated with asset accumulation, and does not respond to a simple graphical intervention.