Behavioral economics
Morteza Khorsandi; Mahnoush abdollahmilani; Teymur Mohamadi; pardis hejazi
Abstract
The effect of income on subjective-wellbeing (as one of the criteria for measuring mental well-being) has been considered in many studies but various dimensions of this effect have not yet been studied. The study aims to investigate the nonlinear effect of income on the subjective-wellbeing of 58 selected ...
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The effect of income on subjective-wellbeing (as one of the criteria for measuring mental well-being) has been considered in many studies but various dimensions of this effect have not yet been studied. The study aims to investigate the nonlinear effect of income on the subjective-wellbeing of 58 selected countries during 2005 to 2020, which has been studied in two scenarios. For this purpose, a PSTR model developed from regime change models has been used. In the present study, the effects of income, unemployment, inflation, life expectancy, and income inequality on subjective-wellbeing have also been investigated. According to the obtained results, in a nonlinear relationship, the effect of GDP on subjective well-being at a certain threshold value of income inequality is decreasing. Therefore, if increasing national income and reducing income inequality as a factor affecting welfare is considered by politicians, it is also important to note that reducing inequality from a certain threshold onwards reduces the impact of income on welfare. This means that from a certain threshold on income inequality, the focus of governments on reducing income inequality should be reduced so that resources are spent on essentials.
Behavioral economics
Habib Morovat; Ali Asghar Salem; shayan mohammadsharifi
Abstract
Several factors are effective in the growth and development of the stock market. One of these factors is the behavior and performance of individual investors in these markets. Individual investors are interested in investing in the stock market for various reasons, such as long-term capital growth, dividends, ...
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Several factors are effective in the growth and development of the stock market. One of these factors is the behavior and performance of individual investors in these markets. Individual investors are interested in investing in the stock market for various reasons, such as long-term capital growth, dividends, and hedging against the decline in purchasing power caused by inflation. But their performance in this market, in addition to the general economic and stock market conditions, depends on the individual characteristics of the investor. Therefore, in this study, an attempt has been made to determine the significance of demographic characteristics such as age and gender, risk-taking and degree of patience, behavioral biases such as overconfidence and loss aversion, and investment characteristics such as experience and investing skill, and frequency of portfolio restructuring on the individual investor performance in the Tehran stock market. For this purpose, using systematic sampling, the required information was collected from 240 questionnaires from the population of individual investors in the Tehran stock market. Data analysis using the ordinal logit model showed that the variables of age, gender, and degree of risk taking do not have a significant effect on the performance of real investors. The degree of patience of people has a positive and significant effect on the performance of investors, and more patient people get more returns. Overconfidence and loss aversion have a negative and significant effect on investors' performance, and finally, investment experience and skill have a positive and significant effect on investors' performance.
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.
