Behavioral economics
Morteza Khorsandi; Mahnoush Abdollah Milani; Teimour Mohammadi; Pardis Hejazi
Abstract
The effect of income on subjective well-being, often used as a key measure of well-being, has been widely studied. However, various dimensions of this relationship remain unexplored. The current study aimed to examine the nonlinear effect of income on the subjective well-being of 58 countries over during ...
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The effect of income on subjective well-being, often used as a key measure of well-being, has been widely studied. However, various dimensions of this relationship remain unexplored. The current study aimed to examine the nonlinear effect of income on the subjective well-being of 58 countries over during 2005–2020. The analysis relied on two distinct scenarios. The Panel Smooth Threshold Regression (PSTR) model, derived from regime-switching models, was employed for the analysis. Additionally, the study investigated the effects of income, unemployment, inflation, life expectancy, and income inequality on subjective well-being. The findings revealed that in a nonlinear relationship, the effect of GDP on subjective well-being diminishes at a certain threshold value of income inequality. Consequently, while policymakers aim to increase national income and reduce income inequality to enhance well-being, it is crucial to recognize that further reductions in inequality beyond a certain threshold may reduce the effect of income on well-being. This suggests that after a certain threshold, governments should prioritize reallocating resources toward other essential needs rather than solely focusing on reducing income inequality.1.IntroductionWell-being is one of the primary indicators of development and a crucial element in social progress, making it a growing focus for policymakers. In a seminal 1974 article, Easterlin found that wealthy individuals are generally happier than their poorer countrymen. However, at a cross-national level, the average happiness in wealthier nations does not exceed that of poorer nations. Furthermore, despite significant economic growth in the United States between 1944 and 1970, no corresponding increase in average happiness was observed. These findings became known as the Easterlin Paradox. Easterlin contends that while economic growth may boost happiness in the short term, it has no lasting impact (over 10 years or more) on a nation’s happiness. Policymakers, seeking to address the question of what constitutes a fair level of income inequality, have thought of various policies. For some, the relationship between income inequality and economic growth is the primary focus of policymaking. Easterlin contends that while economic growth may boost happiness in the short term, it has no lasting impact (over 10 years or more) on a nation’s happiness. Policymakers, seeking to address the question of what constitutes a fair level of income inequality, have thought of various policies. For some, the relationship between income inequality and economic growth is the primary focus of policymaking. Research in the field of happiness economics has sought to explain the Easterlin Paradox and adjust macroeconomic policies accordingly. To date, the threshold factor (in the case of the effect of income on subjective well-being) has often been determined exogenously, visually, or based on the assumption of a linear relationship. The present study sought to answer the following question: Does income affect subjective well-being, taking into account the threshold factor of income and income inequality?2.Materials and MethodsThe present study used the Panel Smooth Threshold Regression (PSTR), which is a generalized version of the Panel Threshold Regression (PTR) model introduced by Gonzales et al. (2005). This nonlinear model extends regime-switching models, where regimes are determined by a threshold variable. The explanatory variables included inflation, unemployment, life expectancy, and gross domestic product (GDP) adjusted for purchasing power parity (PPP). The data for these variables was sourced from the World Bank, while the inequality dispersion ratio was obtained from the World Inequality Database. Numerous studies have investigated the effect of macroeconomic variables on subjective well-being indices. Such studies tend to examine inflation and unemployment together, with their potential interdependence typically overlooked. The dependent variable was subjective well-being, assessed using various components and scales. The data on subjective well-being was obtained from the World Happiness Report database. The report employs the life ladder scale, in which individuals rate their subjective well-being on a 1–10 scale.3.Results and DiscussionVarious factors influence the subjective well-being of countries, with income emerging as a key determinant that has been extensively studied. However, certain aspects of this relationship remain underexplored. Using income inequality as a threshold factor, the present study examined the nonlinear effect of income on subjective well-being across a sample of 58 countries. Two scenarios were analyzed to address the main research question. The first scenario examined the linear relationship between income and subjective well-being. The findings revealed that income has a positive and significant impact on subjective well-being, whereas income inequality exerts a significantly negative effect.The second scenario examined the nonlinear relationship using the PSTR model, which extends regime-switching models. The results indicated that while income continues to positively influence subjective well-being, the magnitude of this effect diminishes as income inequality increases.Drawing on the theory of relative deprivation, the study demonstrated that income inequality significantly affects subjective well-being. Moreover, in line with the tunnel effect theory, it was shown that changes in living conditions (e.g., increasing income inequality) can weaken the positive effect of income on subjective well-being.At an income inequality threshold of 2.16, the coefficient representing the effect of income on subjective well-being decreases from 0.1 to 0.09. Additionally, the findings from the first scenario confirmed that income inequality has a significantly negative effect on subjective well-being, with a coefficient of -0.058.4.ConclusionThe study of subjective well-being, alongside economic well-being, has garnered significant attention among economists. In economics, well-being is traditionally assessed through an individual’s capacity to purchase goods and services. However, subjective well-being encompasses a broader range of factors beyond income, focusing on overall quality of life. As a result, governments should consider subjective well-being as a critical aspect of policymaking, given its broader scope and its measurability through subjective and composite indicators. Equally important is addressing the social cost of inadequate subjective well-being. Mental illnesses are a leading cause of pain and suffering, significantly reducing productivity. Strengthening social connections can foster positive psychological effects, which, in turn, improve physical health. Thus, prioritizing subjective well-being could encourage governments to a shift in the reallocation of resources from solely physical health to mental health. In addition, enhancing subjective well-being can help reduce both psychological and physical costs in society. Rising income inequality has been shown to diminish the impact of income on subjective well-being. Consequently, if policymakers aim to promote well-being by fostering national income growth and reducing income inequality, it is essential to recognize that reducing inequality beyond a certain threshold may weaken the positive effect of income on subjective well-being. This suggests that after a certain threshold, governments should prioritize reallocating resources toward other essential needs rather than solely focusing on reducing income inequality.
Behavioral economics
Habib Morovat; Ali Asghar Salem; Shayan Mohammad Sharifi
Abstract
Several factors influence the growth and development of the stock market. One of these factors is the behavior and performance (investment return) of individual investors. Individual investors are motivated to invest in the stock market for various reasons, such as long-term capital growth, dividends, ...
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Several factors influence the growth and development of the stock market. One of these factors is the behavior and performance (investment return) of individual investors. Individual investors are motivated to invest in the stock market for various reasons, such as long-term capital growth, dividends, and hedging against the decline in purchasing power due to inflation. However, their performance in the market, in addition to general economic and stock market conditions, depends on individual characteristics. In this respect, the present study aimed to examine the significance of demographic, behavioral and investment-related factors on the performance of individual investors in the Tehran Stock Exchange. The demographic factors included age, gender, risk tolerance, patience level, etc. Behavioral biases included factors such as overconfidence and loss aversion, while the investment-related factors encompassed experience, skills, and the frequency of portfolio restructuring. Using a systematic sampling, the study collected the data from 240 questionnaires completed by individual investors in the Tehran Stock Exchange. The ordinal logit model was employed to analyze the data. The results showed that age, gender, and risk tolerance did not significantly affect the performance of individual investors. However, the patience level had a positive and significant effect on performance, with more patient investors achieving higher returns. Overconfidence and loss aversion were found to have a significantly negative effect on performance. Finally, investment experience and skills had a significantly positive effect on the investor performance.IntroductionTraditional finance assumed that investors make rational decisions in the stock market, particularly regarding risk-return trade-offs and utility maximization. However, psychologists have found that human behavior is not as rational as economists assume. Stock market anomalies and empirical research show that investors often act in ways that deviate from rational expectations. These anomalies can be explained by the emerging field of behavioral finance. Behavioral finance explores how psychological factors influence the actions of individuals or groups as investors, analysts, and portfolio managers. It also seeks to understand how emotions and cognitive biases affect the behavior of individual investors (Kengatharan & Kengatharan, 2014).Behavioral biases are defined as systematic errors in judgment. Recent studies have identified over 100 such biases in individual investor behavior. Some researchers refer to these biases as heuristic rules (rules of thumb), while others describe them as beliefs, judgments, or preferences. Psychological factors include heuristic rules or cognitive shortcuts related to information processing, memory errors, emotional and/or motivational influences, and social factors such as family upbringing or cultural norms (Pompian, 2021).The Tehran Stock Exchange (TSE) has been one of the most profitable markets (in terms of nominal yield) in the world in recent years. Compared to other parallel investment markets, it has achieved the highest nominal returns over the past 10 and 5 years. The present study aimed to assess the effect of individual factors on the performance of individual investors in the TSE. These factors were grouped into three general categories: economic–demographic characteristics (e.g., age, gender, education, income level, risk tolerance, and patience level), behavioral biases (e.g., loss aversion and overconfidence), and investment-related characteristics (e.g., investment skills, experience in the stock market, and the frequency of portfolio restructuring. The data was collected in 2021 and 2022. A systematic sample of 240 individual investors was surveyed both in-person and online. Multivariate regression and ordinal logit models were used for data analysis.Materials and MethodsUsing a systematic sampling, the study selected a sample of 240 investors active in the TSE. The data was collected through in-person and online surveys. Multivariate regression was applied to examine the causal relationship between the factors influencing the individual investors’ returns. Since the dependent variable was ordinal, the ordinal logit model was used to conduct the analysis.Results and DiscussionIn order to see if there is a significant relationship between explanatory and dependent variables, we used multiple regression. Since the dependent variable is an ordinal variable, the ordinal logit regression model was used to fit the model. The model fitting results are presented in the table below.Table 1. Model Estimation Results Using the Ordered Logit Model MethodProbabilityOdd ratioCoef*Variables0.3240.7533-0.2833Gender0.4040.9899-0.0101Age0.6331.07690.0760Education0.1591.18040.1659Income0.3790.9627-0.0380Risk tolerance0.0002.8739***1.0557Patience0.0130.7539**-0.2824Overconfidence0.0530.9867*-0.0136Loss aversion0.0031.4317***0.3589Experience0.0281.3268**0.2828Investment skill0.0571.2929*0.2561Stock share0.3290.9408-0.0610Portfolio restructuring0.2300.8534-0.1585Initial investment0.1903Pseudo R2***134.82LR Chi2(8)Source: research calculations based on STATA software *, **, *** Coefficients are significant at 10%, 5%, and 1% levels, respectively.All diagnostic control tests for goodness of fit were conducted, including tests for heteroscedasticity (Breusch-Pagan test), collinearity (VIF test), and parallel regressions across different categories (Brant’s test). The null hypotheses of no heteroscedasticity, no collinearity, and the parallel regression assumption were not rejected. In non-linear models such as the logit model, the parameter sign and p-value provide insights into the direction and significance of the effect of coefficients. However, interpreting results directly on the log-odds scale is often impractical. Similarly, expressing results in terms of odds ratios presents challenges, as odds ratios are frequently misunderstood. One common misconception is treating odds ratios as probabilities, which they are not. For these reasons, it is more meaningful to interpret logit models using predicted probability scale, as this approach aligns better with research questions focused on understanding how covariates affect the probability of an event occurring. Table 2 illustrates how the probability of being placed in each category of the dependent variable (investment performance) changes with variations in explanatory variables (patience, overconfidence, investment experience, and financial literacy)-assuming all other variables are held constant at their mean values. For instance, the probability that the most patient individual (patience = 5) falls into the category with the poorest investment performance (I have lost a lot) is 0.006. In contrast, the probability that the same individual belongs to the category with the best investment performance (I have made a lot of profit) is 0.285.Table 2. Final Effects of Changing Explanatory VariablesInvestment performanceI have lost a lot.I have lost.There is no change in my capital.I have made a profit.I have made a lot of profit. Explanatory variables0.275***0.338***0.278***0.102***0.006**1Patience0.117***0.24***0.385***0.24**0.016***20.044***0.118***0.34***0.451***0.046***30.016***0.048***0.198***0.616***0.122***40.006***0.018**0.87***0.604***0.285***50.057***0.145***0.367***0.293***0.035***1Overconfidence0.074***0.177***0.384***0.336***0.027***20.095***0.211***0.390***0.281***0.020***30.122***0.246***0.383***0.231***0.015***40.155***0.279***0.365***0.187***0.012**50.195**0.308***0.337***0.150***0.009*60.155***0.278***0.365***0.188***0.012**1Investment experience0.114***0.236***0.387***0.245***0.017***20.083***0.192***0.388***0.310***0.024***30.06***0.151***0.371***0.382***0.034***40.004***0.115***0.337***0.455***0.047***50.11***0.231***0.388***0.252***0.017***1Investment skills (financial literacy)0.084***0.194***0.389***0.308***0.023***20.064***0.158***0.375***0.368***0.032***30.048***0.127***0.35***0.430***0.042**40.036***0.1***0.315***0.490***0.056***5Source: Research calculations based on STATA software. *, **, *** Coefficients are significant at 10%, 5%, and 1% levels, respectively.These findings demonstrate that greater patience is associated with higher investment performance. However, the relationship between patience and the likelihood of belonging to specific investment performance categories is non-linear. For highly patient individuals, the probability of being in higher performance categories increases up to the third category and then declines. In contrast, for the variable of overconfidence, the probability of the most overconfident individual (overconfidence = 6) achieving the highest investment performance category (I have made a lot of profit) is only 0.009, which is statistically significant at the 10% level. Meanwhile, the probability of this same individual falling into the lowest performance category (I have lost a lot) is 0.195. Non-linear relationships are evident between all explanatory variables and the predicted probabilities of being in various investment performance categories. This non-linearity underscores the value of analyzing final effects over relying solely on the interpretation of coefficients and odds ratios in logit models.ConclusionThe current study aimed to examine the individual factors influencing the performance of individual investors in the TSE, incorporating new explanatory variables and a new model-fitting technique. The analysis using the ordinal logit model revealed that variables such as age, gender, and risk tolerance do not significantly impact the performance of individual investors. However, patience was found to have a positive and significant effect on investment performance, with more patient individuals achieving higher returns. Conversely, overconfidence and loss aversion exhibited negative and significant effects on performance. Additionally, investment experience and skills were shown to positively and significantly influence investors’ performance. Moreover, the findings indicated that TSE investors are affected by behavioral biases, which have a significantly negative impact on their performance, as well as on the overall efficiency and stability of the market. For example, loss aversion leads investors to hold onto losing investments for prolonged periods, reducing portfolio returns. Similarly, overconfidence causes investors to overestimate their ability to evaluate investment opportunities, often disregarding negative signals that could suggest avoiding or selling certain stocks. The study also demonstrated that investment skills (e.g., financial literacy) had a significantly positive impact on investment performance. In contrast, general literacy and unrelated educational backgrounds showed no significant effect on performance. Overall, individuals with greater investment experience and higher levels of patience tend to achieve better outcomes and earn higher returns in the TSE.
Behavioral economics
Taha Shishegari; Farhad Ghaffari
Abstract
Conventional economics posits that the presence of arbitrage in financial markets forces market participants to act rationally in order to maximize profits. This assumption underpins the efficient market hypothesis (EMH). However, in recent years, behavioral economics has challenged the assumption of ...
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Conventional economics posits that the presence of arbitrage in financial markets forces market participants to act rationally in order to maximize profits. This assumption underpins the efficient market hypothesis (EMH). However, in recent years, behavioral economics has challenged the assumption of market efficiency and rational behavior by demonstrating the significant impact of seemingly irrelevant factors (e.g., weather conditions, air temperature, and pollution) on financial markets. The present research aimed to compare the explanatory power of these two perspectives by analyzing daily data from the Tehran Stock Exchange index over two periods: February 20th, 2022 to February 19th, 2023, and February 20th, 2023 to February 19th, 2024. The study relied on the daily data on the growth rate of the dollar as an explanatory variable for the total stock market index growth from a conventional economics perspective. From a behavioral economics viewpoint, the analysis incorporated variables such as air temperature, weather conditions, and the pollution index. Given the nature of financial markets, the study used the EGARCH method for analysis. The results indicated that during the period from February 20th, 2022 to February 19th, 2023, when the dollar rate exhibited a significant upward trend, the explanatory power of behavioral variables decreased, with some even losing their significance in explaining the total stock market index. However, during the period from February 20th, 2023 to February 19th, 2024-when the exchange rate remained relatively stable-behavioral variables had a significant impact on the total stock market index. Introduction In the conventional economics perspective, which has long dominated the analysis of financial markets, actors are assumed to behave rationally. This means that they adjust their beliefs accurately (according to Bayes’ rule), aligning their subjective probabilities with reality and making decisions based on expected utility. However, in recent decades, the deviation of conventional economics theories from empirical data—along with the emergence of large, persistent, and severe price bubbles in financial markets—has led a group of economists to question the explanatory power of conventional theories and the assumption of rational behavior in financial markets. The present study aimed to address the duality between conventional and behavioral economics within the context of Iran’s developing economy. Focusing the country’s unique economic conditions, the study sought to determine which perspective—conventional or behavioral economics—provides a better explanation for stock market behavior. Two distinct time periods were analyzed: 1) from February 20th, 2022 to February 19th, 2023, during which the exchange rate (U.S. dollar) nearly doubled as a representative variable of the macroeconomic situation; and 2) from February 20th, 2023 to February 19th, 2024, when the exchange rate remained relatively stable, increasing by about 40%. The impact of behavioral variables on stock market returns was examined in two scenarios: one characterized by significant changes in macroeconomic variables and the other by more moderate changes. Conventional economics suggests that humans act rationally when the data is clear and the analysis is straightforward. However, as complexity increases and data becomes less clear, individuals tend to deviate from rational behavior due to limited rationality (Thaler, 2009). This hypothesis was tested by focusing on the two time periods of the study. In the first period, when macroeconomic variables exhibited a clear and specific trend, conventional economic theories were expected to provide a more accurate explanation of stock market behavior, with the influence of behavioral variables likely to decrease. Conversely, in the second period, when macroeconomic variables lacked a clear direction, it was anticipated that behavioral economics—along with variables rooted in psychological influences and the internal states of actors—would offer a better explanation for stock market performance. In this study, environmental variables such as air temperature, atmospheric conditions, and air pollution were considered representative of behavioral variables. The analysis investigated the impact of behavioral variables on the Tehran Stock Exchange index. Materials and Methods Financial sector data often exhibit heteroskedasticity, which makes the use of linear structures for estimation and modeling problematic. Additionally, fluctuations in financial data tend to cluster, indicating that the variance is self-explanatory. These characteristics make ARCH and GARCH models particularly suitable for modeling in this context. When using ARCH and GARCH models, it is essential for the estimated coefficients to be non-negative, which can present challenges in the estimation process. To address this issue, EGARCH models, which is the logarithmic form of the GARCH model, can be employed. This approach eliminates the need to impose the non-negativity condition on the variance coefficients. The current study estimated the daily growth rate of the total index of Tehran Stock Exchange over two separate time periods: from February 20th, 2022 to February 19th, 2023, and from February 20th, 2023 to February 19th, 2024. The analysis applied the AR(1) model and incorporated both behavioral and conventional variables into the variance component of the model to explain fluctuations in the total index efficiency. Results and Discussion During the first period (February 20th, 2022 to February 19th, 2023), the exchange rate experienced a clear and significant increase of 100%. Market players, adhering closely to conventional economic theories, operated under the assumption of rational and optimizing behavior. As a result, the exchange rate variable became more effective in explaining market fluctuations, while some behavioral variables, such as climate and air pollution, lost their explanatory power in the variance equation. In the second period (February 20th, 2023 to February 19th, 2024), the conventional variable (currency growth rate) became less significant and transparent. Market players increasingly relied on behavioral variables, which offered a better explanation for fluctuations in the total stock market index. The estimated coefficient for the conventional variable (foreign exchange growth rate) lost its significance during this period. The results showed that air temperature had a negative and significant impact on fluctuations in the growth index during both periods, consistent with the findings of previous studies. Conclusion This study analyzed two distinct economic periods: one marked by significant growth in foreign exchange rates, and the other characterized by relative stability in the foreign exchange market. The objective was to examine the behavior of financial actors and compare the explanations provided by conventional and behavioral perspectives on financial markets using the available data. According to the results from the two estimated models, the exchange rate growth (as the representative variable of the conventional view) had a significant and positive impact on stock index fluctuations during the first period, when exchange rates exhibited a clearly upward movement. However, this variable lost its significance in the second period, when exchange rates remained relatively stable. During the second time, the explanatory power shifted to behavioral variables such as weather conditions, pollution, and air temperature.
Behavioral economics
Mohammad Amin Zandi
Abstract
The precise measurement of individual time preferences in assessing the economic plans that individuals are involved in, in the estimation of social time preferences, in the assessment of environmental and health plans is very crucial. The purpose of this research is to estimate and also describe the ...
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The precise measurement of individual time preferences in assessing the economic plans that individuals are involved in, in the estimation of social time preferences, in the assessment of environmental and health plans is very crucial. The purpose of this research is to estimate and also describe the method of estimating individual intertemporal preferences. The sample is 70 students of Allameh Tabataba'i (A.S) and Payam Noor Universities. For this purpose, the experimental method, which allows controlling the confounding variables, is used. In order to estimate the discount function among various functions, the hyperbolic function had a better fit on the data. In this type of function, the discount does not take place at a fixed rate, but with the extension of the selection period, the discount decreases. The fitting of data using the hyperbolic function showed that this kind of discounting is consistent with past research. The average individual discount rate obtained was 0.0615 with a standard deviation of 0.796.1.IntroductionDecisions with varying consequences across different time periods are referred to as intertemporal choices. The scope of these decision types is extensive in human life, encompassing economic considerations like saving for retirement, investing in stocks, choosing between mortgage and renting, buying insurance, planning for student loan repayments, initiating a business, budgeting, planning for financial issues, buying energy-efficient equipment, purchasing a car, planning for estate, and deciding on the retirement withdrawal strategy. Moreover, decisions extend to non-economic realms, including investing in education, practicing delayed gratification in daily life, making choices regarding health and wellness, selecting a career path, deciding on healthcare options, engaging in environmental protection, and establishing education budgets for children. In essence, a myriad of intertemporal decisions shape the course of an individual’s life.In Samuelson’s framework for intertemporal choices, the total utility is determined as the weighted sum of utility across each time period. (1) The weight in each period is determined by the discount function. (2) represents the total utility from the perspective of the current period (i.e., t). T is the final period of life. signifies the instantaneous utility in the period t+k. is the discount function. k denotes the time delay from the present moment, and ρ is the instantaneous discount rate reflecting time preferences. The discount function, as incorporated in this model, takes the form of an exponential function. When computing the growth rate of the discount function, we have: (3) The growth rate of the discount function is independent of the delay in receiving goods (or rewards) postponed from the present time (i.e., k). This implies that altering the delay period for receiving delayed goods does not lead to a change in a person’s intertemporal preferences. For instance, if an individual favors receiving one apple today over receiving two apples tomorrow, this preference should extend to preferring one apple in one year over receiving two apples in one year and one day. This is the example introduced by Strotz (1955) to illustrate temporal consistency.Experimental research based on the discounted utility model has highlighted its limitations. First, extensive studies indicate that the discount rate tends to decrease as the delay in receiving the reward increases (Chapman, 1996; Heller & Pender, 1996; Redelmeier, 1993; Thaler, 1981). In other words, the growth rate of the discount function should also be contingent on the delay in receiving the goods (or reward). The second observed shortcoming in these investigations is termed inverse utility. This occurs when an individual prefers $1000 today to $1100 tomorrow but favors $1100 one year and one day later over $1000 one year later. Consequently, the behaviors noted in these studies lack time consistency. Additional research has identified instances of reverse preferences in individuals (Elster, 1979; Laibson, 1997; O'Donoghue & Rabin, 1999). The exponential discount function employed in the discounted utility model falls short in explaining such phenomena, as it conducts discounting at a fixed rate, irrespective of whether the delay in receiving the bonus increases or decreases.To address this issue, Mazur (1987) made modifications to the discount function originally proposed by Bam and Rachlin (1969) by incorporating k into the denominator. The adjustment resulted in a discount function that overcame the shortcomings of the exponential function. This hyperbolic function found extensive application in subsequent research and demonstrated a better alignment with the data acquired from experiments. The hyperbolic function is expressed as follows:(4) Here, k represents the discount rate, and D signifies the delay in receiving the reward from the present time. The discount rate in the hyperbolic discount function is given by: (5) In this rate, there is an inherent consideration for the delay in receiving goods (or rewards) from the present time. Consequently, the discount rate will undergo changes corresponding to alterations in this interval. This adjustment serves to rectify the deficiencies noted in this functional form.The findings of the meta-analysis on discount rates, encompassing both experimental and empirical methods, reveal that the variance of discount rates obtained from experimental approaches is lower than that observed in empirical methods. This discrepancy can be attributed to several factors. First, the limited availability of field data for determining time preferences contributes to the higher variance in empirical results. In addition, there is no available field data in which individuals make comparative choices. Third, the complexity arises from the numerous intervening variables influencing real-world data, making it challenging to isolate and analyze specific factors. The estimates obtained from experimental methods demonstrate greater predictability of intertemporal behaviors in the real world.Despite the significant importance of individual time preferences and the consistent data yielded by the experimental method, this approach has been underutilized for measuring individual time preferences in Iran. In this respect, the present research aimed to estimate and describe a methodology for calculating individual intertemporal preferences through the experimental method.2.Materials and MethodsThere are four experimental methods for measuring time preferences. The first method is the choice task, where subjects are prompted to select between a smaller reward in the present or near future and a larger reward in the distant future. Some studies implement this experiment using actual rewards, while others use hypothetical or non-financial rewards, such as a hypothetical job offer. The second method is known as matching tasks, in which subjects are asked to answer a question and fill in the blank. A common structure for this method is exemplified by questions like: 20,000 dollars now or … dollars one year later. Experiments use both real and hypothetical currencies. The third method is termed rating task. Here, subjects are exposed to the rewards provided at specific time intervals. They are tasked with rating the (un)attractiveness of these proposals. The fourth method is called pricing task, where subjects are requested to specify their willingness to pay for a hypothetical reward at a certain time (Feredrick et al., 2002).The present study used the method of choice task, and the task design was based on validated designs (Calluso et al., 2015a, 2015b, 2017, 2020). Each subject was exposed to a series of intertemporal choices, including receiving a fixed amount of money (14500 Tomans) immediately or a variable amount (22000, 36500, 44000, 59000, 66000, 80000, 88000 Tomans) across six time intervals (i.e., 7, 15, 30, 60, 90, and 180 days later). Consequently, the subjects were presented with 42 intertemporal choices, and each question was repeated 10 times. The subjects thus answered a total of 420 questions in a randomly distributed order. To determine the monetary values in intertemporal choices, the study converted the previously-researched valid monetary values into Iranian currency based on the purchasing power parity (PPP) index, utilizing the Central Bank data. The PPP index can be defined as the number of currency units a country needs to purchase the same quantity of goods and services in the domestic market that can be bought with US dollars.3.Results and DiscussionThe hyperbolic function, prevalent in most recent studies and previously discussed, was employed to estimate the discount rate. In this function, as the delay increases, the discount rate concurrently decreases. To obtain this rate for each tested individual, the research relied on conventional process from past research studies (Calluso et al., 2015a, 2015b, 2017, 2020; Iodice et al., 2017; Kable & Glimcher, 2007; Li et al., 2013). Concerning each delay period (7, 15, 30, 60, 90, and 180 days), a ratio of responses was obtained, where subjects expressed a preference for the future over the present, taking into account the delayed reward amounts. Subsequently, the Points of Subjective Equivalence (PSE) was calculated, representing the amount at which subjects chose an equal number of future and present options. To achieve this, the study estimated a logistic function that regressed the preference ratio of future-to-present responses on the reward amounts. Using this function, the research determined the amount equivalent to fifty percent of the frequency of the ratio of future-to-present preferences (i.e., PSE). Then the following formula was used to calculate the subjective value for each delay period: (6) The immediate reward was set at 14,500 Tomans. The subjective value was then normalized to the immediate reward. Subsequently, the discount rate for each subject was determined by fitting a hyperbolic function (Grossbard & Mazur, 1986; Laibson, 1997) to the relationship between the subjective value and the delay time in receiving the delayed reward.(7) Below is the scatter diagram depicting delays by day and the PSE for the aforementioned three subjects. Figure 1.The scatter diagram of delays by day and the PSE Source: Research resultsThe graphs illustrate that individuals with lower discount rates exhibit a lower PSE in delays, whereas those with higher discount rates demonstrate correspondingly higher PSE.Table 1 presents the results of estimating the individual discount rates for the three subjects.Table 1. Discount rate for the three subjectsR SquareSignificanceDiscount rateSubject0.8071significant0.0182patient0.7965significant0.0484average0.8028significant0.1173hastySource: Research results4.ConclusionThe estimation of the individual discount rate derived from this research confirmed the hyperbolic nature of the individual discount function, yielding a rate of 0.0615. In the evaluation of economic plans, the calculation involves determining the benefits and costs associated with the plan. A comparison of the benefits and costs is used to determine whether the plan is economical or not. Yet this proves challenging due to the presence of time preferences and the time value of money, the occurrence of benefits and costs at different points in times, and the varying weight of these factors in economic plans over time. Therefore, it seems less feasible to judge whether the plan is economical or not simply by adding benefits and costs.
Behavioral economics
Habib Morovat; Syrous Omidvar; Roya Eskandary
Abstract
Risk and uncertainty are key factors in making economic decisions. Since individual attitudes towards risk can greatly influence choices, it is crucial to understand the determinants of such preferences in order to predict and comprehend individuals’ behavior. The present study aimed to investigate ...
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Risk and uncertainty are key factors in making economic decisions. Since individual attitudes towards risk can greatly influence choices, it is crucial to understand the determinants of such preferences in order to predict and comprehend individuals’ behavior. The present study aimed to investigate the impact of several factors on individuals’ attitudes towards risk, specifically the degree of risk aversion, by examining individuals’ optimism and patience (time preference). The study used a questionnaire to collect data from a sample of 304 individuals in Iran selected through random sampling. The research method was a multivariate regression model. The findings indicated that both optimism and income have a significant negative effect on risk aversion, while age has a significant positive effect. Furthermore, the study found that patience does not have a significant impact on risk aversion.IntroductionRisk and uncertainty are critical factors that heavily influence most economic decisions, including investment, education, employment, and the decision to buy a house or insurance. Such decisions involve an element of risk, so they are highly influenced by individuals’ attitudes towards risk. In developing countries, such as Iran, most individuals typically experience unstable incomes, limited access to insurance, and possess few assets to cushion the impact of severe economic shocks. As a result, individuals in these circumstances are more exposed to risk, and these factors can significantly influence their attitudes towards risk. Understanding the determinants of these preferences is crucial to comprehending and predicting people’s behavior, as different attitudes towards risk lead to different choices. The present study was to examine how certain factors, such as optimism and patience (time preference rate), influence individuals’ attitudes towards risk. In addition, socio-economic variables were included as control variables to account for their potential impact.Materials and MethodsTo gather data on individuals’ degrees of risk aversion, optimism, and patience, this study used a questionnaire based on internationally recognized surveys. The model was then estimated by the general multivariate regression through the ordinary least squares (OLS) method.Results and DiscussionThe descriptive information related to demographic variables is presented in Table 1. Table 1: Frequency distribution of demographic sVariableVariable levelFrequencyRelative FrequencyGenderMale15549Female16051Total315100Marital statusSingle21970Married9630Total315100AgeLess than 20 years165Between 20-30 years17255Between 30-40 years10032Above 40 years278Total315100Level of educationHigh school41Diploma–BA9931BA–MA13944MA–PhD7323Total315100Employment statusUnemployed5116Retired10Housewife279School student72University student13142Employed9831Total315100The economic situationIncome below 1 million Tomans11236Income between 1–3 million Tomans10637Income between 3–6 million Tomans6320Income above 6 million Tomans3411Total315100Source: research findingsModel EstimationThe OLS method was used to estimate the model.Table 2. Model estimation resultsRA The dependent variableprobabilityt-statCoefficientsVariables0.00-3.71-0.027 * * *Optimism0.0023.070.0003 * * *Wealth0.000-63.60.29 * * *Income0.418-0.81-0.017Patience0.024-26.2-0.19 * *Gender0.0222.310.23 * *Single0.0005.630.044 * * *Age0.2151.240.06Education0.00075.52.16 * * *_Cons29.13F(8,295)304Number of obs0.000Prob > F0.44R-squared0.63Root MSE0.42Adj R-squared * The coefficient is significant at 10 % level, * * The coefficient is significant at 5 % level and *** The coefficient is significant at 1 % level.Source: research calculationsThe Brush-Pagan and VIF test show that there is no heteroskedasticity and collinearity at estimated residuals.As shown in the table, as the individual’s level of optimism increases, their degree of risk aversion decreases, which is consistent with previous research conducted by Felten and Gibson (2014) and Duhman et al. (2018). In addition, the study found that wealth has a direct and significant impact on risk aversion in Iran, which aligns with the findings of Agassi et al. (2015) and Qanbili (2016). However, this result contradicts the research conducted by Ronald and Grable (2010), and therefore, the effect of wealth on risk aversion warrants further discussion and reflection.Previous research suggests that there is a likelihood that the effect of wealth on risk aversion in Iran may be opposite to that observed in other countries. This could potentially be attributed to errors in measuring wealth In Iran, where information regarding individuals’ assets and wealth is often unclear. In this respect, the present study relied on indicators such as car and house ownership and their estimated values, which were self-reported by the participants and might be subject to bias.The study findings indicated that income has a significant and negative impact on risk aversion in Iran, which is aligned with previous research conducted by Wright (2012; 2014) and Shah et al. (2020). Moreover, it was found that gender has a significant effect on risk aversion, with females being more risk-averse than males. This finding is consistent with Banir and Newbert (2016), Hosseinnejad and Haddadi (2016), and Mohammadi-Majed (2018).The findings also revealed that age has a significantly positive impact on risk aversion in Iran, which is in line with the results of Dankers and Van Suest (1999) and Menadia et al. (2016). Finally, the results showed that time preference rate and education do not have a significant impact on risk aversion in Iran.ConclusionThis research examined the impact of several factors on individuals’ risk aversion in Iran. The investigation of the research hypotheses demonstrated that variables such as optimism and income have a significantly inverse relationship with risk aversion, with higher levels leading to decreased risk aversion. Wealth and age have a significantly positive impact on risk aversion, with higher levels leading to increased risk aversion. Furthermore, the variables of time preference rate and education were found to have no significant effect on risk aversion in Iran. The study also found that married individuals are more risk-averse than single ones, and females are more risk-averse than males.The results indicated that young people, males, and the individuals with higher incomes and lower wealth tend to accept risk more readily. The findings can provide fresh insight for investment consulting and insurance companies in Iran.
Behavioral economics
Mohaddeseh Pouralimardan; Heshmatolah Asgari
Abstract
The main goal of this article is an applied investigation of one of the types of biases caused by overconfidence, under the heading of bias in expected relative wage (or individual overplacement) and its relationship with time preferences (in the form of a proxy of people's patience) based on the Friehe ...
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The main goal of this article is an applied investigation of one of the types of biases caused by overconfidence, under the heading of bias in expected relative wage (or individual overplacement) and its relationship with time preferences (in the form of a proxy of people's patience) based on the Friehe & Pannenberg (2020) method. The data gathering tool of this investigation has been a two-stage questionnaire. 204 staff and faculty members of Ilam university completed the questions related to the questionnaire in two stages. Based on the ordinary least squares and semi-parametric model, the relationship between bias in wage and time preferences was examined in four stages. The results of research models in four stages showed that there is a negative and significant correlation between bias in expected relative wage (or bias in the distribution of the relative wage of people of the same age-peers) and time preferences. This means that people who are more patient, will have less bias (overplacement) on average. Examining the impact of current relative wage on bias showed that there is a positive and significant correlation between bias and current relative wage; This means that the current relative wage of individuals is not effective in reducing bias, and the higher the individual's current relative wage, the individual's bias will be greater. Also, the results showed that there is a positive and significant correlation between bias and extraversion, a negative and significant correlation between bias and neuroticism and a negative and significant correlation between bias and agreeableness.
Behavioral economics
Maryam Shahlaee; Mehdi Pedram; Narges Hajimoladarvish
Abstract
Acquiring information about expectations is difficult as individuals' beliefs are unobservable. Thus, how expectation forms and how to model expectation is an open question in economic modelling that has been addressed recently by experimental economics. In this article, in order to identify expectations, ...
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Acquiring information about expectations is difficult as individuals' beliefs are unobservable. Thus, how expectation forms and how to model expectation is an open question in economic modelling that has been addressed recently by experimental economics. In this article, in order to identify expectations, we examine the behaviour of subjects when encountering new exchange rates in an experiment. Furthermore, in this experiment, differences of expectation formation among participants and their relation to cognitive abilities are analysed. To motivate people, incentive payments are used. In our setting, while the rational expectation hypothesis is not supported, the adaptive expectation is not rejected. Agents form their expectations in the same way regardless of their cognitive ability. In this context, individuals overreact to the new quantity of exchange rate which is assumed as a noisy perception. This finding is considered as evidence of emotional behaviours in the exchange market.