Status Quo Bias and Risk Preference: an Experimental Investigation

Status quo bias and Risk preference: An Experimental investigation

Insaf Bekir1 University of Sousse, Sousse, Tunisia [email protected]

Abstract: Using a laboratory experiment, we investigate whether link can be established between status quo bias – that is the tendency to maintaining a previously chosen alternative - and risk preference. Our findings show that the attitude towards risk and individual criteria are strongly correlated with the status quo bias, which directly and indirectly affects the individual decision-making process. The results obtained in this paper provide some insights into consumer behavior, firms' strategy and policy effectiveness.

Keywords: Status Quo Bias, Risk preferences, individual behavior, experiment.

JEL codes: C91, D81, D91 .

1 Insaf Bekir, Rue lavalette Skanes 5000 Monastir. Tunisia. [email protected]. +21698676644.

Status quo bias and attitude towards Risk: An Experimental investigation.

Insaf Bekir Faten Doss

1. Introduction

The conventional economic approach, considers that economic agents (individuals, managers and government regulators) act to maximize their best interest, have a well defined preferences and are consistent rational actors. In this model of human behavior, the fundamental feature of the rational decision is that only preference properties of the alternative choices determine the individual’s decision. So, neither the order in which the alternatives are presented, nor any other features (eg. social norms, the context in, impulsiveness, limited willpower…) should affect the individual’s selection. However, in the real world choices can be influenced by cognitive anomalies2. Particularly, one may prefer maintaining its current or previous decision over changing it. This cognitive bias is called the status quo bias. Face to new options decision makers may stick with the status quo alternative, for example, by purchasing the same product brand; by following customary company policy, by staying in the same job. Status quo can be seen as a cognitive shortcoming in the sense that the bias exists even when there is no evidence showing the status quo choice is better than the alternatives (Thaler and Benartzi, 2004; Samuelson and Zeckhauser,1988). However, change is the basic law of nature “It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change” said Charles Darwin. The status quo behavior is also in opposition to the positive view towards change that the literature on entrepreneurship and business has frequently acknowledged. Innovation is often said to be a necessary process for the success of a business. This idea is in conformity with the Schumpeterian vision of creative destruction process, by which innovations incessantly replace old methods and processes with better ones (Schumpeter 1942). Innovation is usually advanced to be an important driver of business. Often, businesses have no shortage in generating innovative ideas, putting them into practice is the actual bottleneck. Status quo behaviors can, then, have a negative impact on the successful application of the relevant policies or choices3. Furthermore, by influencing the decision making, Status quo bias could affect the effectiveness of public policies “For every bias identified for individuals, there is an accompanying bias in the public sphere” (Sunstein, 2014). Indeed the status quo bias could result in a procrastination that may lead policymakers to delay taking important actions. Orphanides (2015) suggests that procrastination may explain the reluctance of Federal Reserve’s to normalize its policy despite considerable evidence it should do so. The presence of status quo bias can constitute an eventual obstacle to the effectiveness of public policies in some relevant contexts such as, public health (people's eating habits), environment (failures in markets for tradable permits); quality of savings for retirement,...

2 The decision-making processes are very complicated in a way that even the most advanced methods cannot formulate them in a unique and universal theory. Because of that, researchers will always be looking to get closer in understanding the human’s behavior and attitude which explain the great tendency of economists to collaborate with neuroscientists in order to better explore the human brain.

3 Recent efforts are observed to integrate the behavior economics approach into policymaking like the Behavioral Insights Team (BIT) created by the U.K and the White House Social and Behavioral Science team created by the U.S. government.

Another driver of business performance and entrepreneurship is attitude towards the risk. A large literature, both in economics and managerial studies, addresses the issue of how the individual risk characteristics shape the behavior of the entrepreneur. Theoretical proposition of a positive correlation between risk attitudes and entrepreneur (Kihlstrom and Laffont, 1979) has been supported by recent empirical research (Stewart and Roth, 2001; Cramer et al, 2002; Caliendo et al, 2009; Batista and Umblijs, 2014). Moreover, if Individuals and entities expose themselves intentionally to risk and increase that exposure over time, it is because they believe that they can exploit these risks to advantage and generate value. This explains why firms get into emerging markets that have important political and economic risk or into technologies where the ground rules change on a daily basis. Indeed, the most successful companies in every sector and in each generation – General Motors in the 1920s, IBM in the 1950s and 1960s, Microsoft and Intel in the 1980s and 1990s and Google in the last decade- achieved their success not by keep away from risk but by seeking it out. Both innovation and risk preference are important determinants of entrepreneurship and business performance. It is, therefore interesting to study if the attitude towards risk of an individual influences her attitude towards change. Our paper is intended to shed further light on this question by studying the effect of risk preferences on the emergence of status quo bias. The aim of this study is therefore to provide a rigorous examination of the link between status quo bias behavior and position towards the risk. This will be undertaken using a laboratory experiment. By doing so, we combine research from two different fields, namely literature on status quo bias and the literature on risk-attitudes. We wish to contribute to the research in the intersection between these two fields. The contribution of this study is as follows: First, it quantifies the relationship between Status Quo Bias behavior and risk attitudes. Second, it adds experimental evidence to a growing literature exploring the status quo behavior, on one hand and the risk preference, on the other hand. Up to our knowledge, none of the previous studies has attempted a similar undertaking. The paper unfolds as follows. The next section provides a literature review, in which more light is shed on the status quo bias and risk preference. Section 3 is devoted to the methodology where the experimental design is explained. Section 4 provides the main results and discusses them. Section 5 concludes and highlights implications.

2. The literature review

Through a set of field and lab experiments, Samuelson and Zeckhauser (1988) showed, in a seminal paper, that individuals tend to maintain a previously chosen alternative more frequently than would be predicted by the standard model of choice. They coined the term Status Quo Bias. This finding has lead to the development of decision making models attempting to capture status quo effects. Samuelson and Zeckhauser (1988) distinguish three categories of explanations for the status quo bias: rational decision-making, cognitive misperceptions and psychological commitment. In the first category, status quo can be the rational choice as it takes place in similar conditions. It also might be due to transition costs when the loss of switching to alternative choice is higher than the gains of maintaining the first one. Presence of uncertainty in the decision making setting can also provide an

3 explanation of status quo choice. In this case, individuals will switch to the unknown choice alternatives only when the current choice no longer satisfies them. The second category explaining the status quo bias is cognitive misperceptions. The reason can be loss aversion. As individuals weigh losses heavier than gains, they are reluctant to change and therefore biased towards their status quo option (Kahneman, Knetsh, Thaler, 1991). Psychological commitment can also cause individuals to maintain their status quo option. It can be due to the sunk costs invested in the status quo option that make people staying in that alternative even if it is no longer a realistic undertaking. Feeling of regret for bad choices can also be advanced for explaining the status quo choice. Kahenman and Tversky (1982) found that individuals are more frustrated by bad outcomes resulting from new actions taken, than by similar bad outcomes derived from inaction. The opposite was found to be true too. Individuals are more overjoyed when a good outcome resulted from action than from inaction (Landman 1987). Another explanation is self-perception theory. People use their past experiences as a guide for present and future decision-making (Festinger and Carlsmith, 1959; Langer, 1983).The status quo bias has been researched in experiments as well as in field studies in different contexts. The status quo bias effects have been observed in the markets for electric services (Hartman et al., 1991), retirement savings (Madrian and Shea, 2001), car insurance (Johnson et al, 1993) organ donations (Johnson and Goldstein, 2003), medicare (Ericson and Keith, 2014), entrepreneurship (Burmeister and Schade, 2007) and in the financial markets (Kempf and Ruenzi, 2006). However, Dean (2008) and Ren (2014) report that for small choice sets the bias is small in magnitude and sometimes absent. List (2003, 2004) shows that professional traders are less prone to the bias. Some scholars discuss the status quo bias in combination with other topics like loss aversion (Kahneman et al 1991, Samuelson and Zeckhauser 1988, Thaler, 1980) and ambiguity aversion (Roca, Hogarth, and Maule, 2006; Bewley, 1986, Maltz and Romagnoli, 2016). In the same vein, our paper is intended to shed further light on this question by studying the effect of risk preferences on the emergence of status quo bias. More specifically, our experiment explores the extent to which individual risk preference could affect the presence and magnitude of the status quo bias.

Risk is omnipresent in decision-making. The extent to which people are willing to take on risk constitutes their risk preferences. Attitudes towards risk are one of the primitives of economics. It is a fundamental element in standard theories of risk and uncertainty such as subjective expected utility (Savage, 1954) and prospect theory (Kahneman and Tversky, 1979). Economists have developed a variety of methodologies to elicit and assess individual risk attitudes depending on the question one wants to answer, as well as the characteristics of the sample population. The traditional and indirect method is to analyze market behavior, (Blume and Friend, 1975, Bucciol and Miniaci, 2011, Drèze, 1981, Szpiro, 1986, Cohen and Einav, 2007),Chetty, 2006, de Linde Leonard, 2012).The more current and direct way is to study the choices individuals make among risky alternatives in an experiment which revealed the preferences. This methodology uses the multiple price list instrument (MPL), popularized by Holt and Laury (2002) method. In the MPL, the participants are requested to choose, into a list, among two lotteries, a "safe" lottery A and a risky lottery B. The common probability for

4 both lotteries is gradually increased, as one move down the list (from 0.1 to 1). Risk aversion is then measured by the number of times the subject chooses the safe lottery. In sum, various experimental works have been directed for studying either the Status Quo Bias or risk preference. Strikingly, works dedicated to the combination of both of them together are inexistent. Up to our knowledge, no direct link between the status quo bias and risk preference has been thoroughly investigated in the literature so far. This gap leads to some uncertainty regarding the nature and magnitude of the relationship between the two phenomena. The existence of such associations, and their significance as well as their magnitude, is an empirical issue to which attention is now turned.

2. Experimental design

In May 2016, we run a paper and pencil experiment among 195 students at the university of Economics and management of Sousse, Tunisia. The participation to this experiment lasts nearly 20 minutes and ends with real payoffs. The choice of subjects was built on the educational level (third year of License, Master, or Ph.D. students) since the experiment requires a certain level of comprehension. Our experience consists of two parts. The First part is a within experience, composed of two sessions. The first session, denoted risk-experience, is used to detect the attitude towards risk by using a first lottery choices list4. The second session serves to establish the status quo bias through a second choices list. We denoted it status quo-experience. The second part is a status quo survey test. We denoted it status quo survey experience. Figure 1 provides a model experience design.

Risk Experience Within Experience

Status Quo Experience

Whole Experience

Status Quo Survey Experience

Figure 1. Experience design

4 The construction of the list was inspired by Holt et laury (2002). We have just accommodated the amounts of money to fit the participant revenues.

Each participant received an envelope containing four sheets of paper: the instructions of experiment (see Appendix 1), the first lottery choices list sheet, the second lottery choices list sheet, the status quo survey sheet, a questionnaire sheet.

The within experience

Risk-experience

In the Risk-experience, subjects began by indicating their choice between option A and option B, for each of the ten paired lottery choices in the first lottery choices list sheet (see Appendix 2), with the understanding that one of these choices would be selected at random ex-post and played to determine the earnings for the option selected (Holt and Laury, 2002). 1-r Given that the utility function considered is u(x)  x for x  0, we have risk preference for r  0, risk neutrality for r  0, and risk aversion for r  0 (Holt and Laury, 2002). Following this, the payoff numbers for the lotteries were chosen so that Risk aversion is measured by the number of “safe” decisions A chosen by the participant: the risk-loving person change from the safe lottery A to risky lottery B at the second choice (exponential utility); the risk-neutral person switches from safe lottery A to risky lottery B at the fifth choice (linear utility) and the risk-averse person cross over to risky lottery B at the seventh choice (square root utility). Therefore the total number of “safe” A choices will be used as an indicator of risk aversion.

Status quo-experience

By the end of the within experiment first session (Risk-experience), subjects were invited to participate to the status quo-experience. They were asked to pull out the second lottery choices list sheet involving nine quadruplet lottery choices and were asked to choose between four lottery options: the previous lotteries A, B and two new lotteries C and D for each of the lottery choices (see Appendix 3). This menu of quadruplet lottery choices is structured so that the upkeep of previous lottery choices can be used to infer the degree of status quo bias in the participant’s behavior. Options C and D are created such as to be equivalent to initial lotteries A and B in terms of expected utility for each risk preference. We choose the payoff numbers for the lottery C so that the risk neutral choice pattern (four safe choices A followed by six risky choices B) will be equivalent to the pattern of choosing lottery C for the four first decisions and lottery D for the later six decisions . Lottery C provides the same level of linear utility than safe lottery A (risky lottery B) for the four first choices (for the last six choices) so that a neutral risk subject will be indifferent between the lotteries A and C for the four first choices and indifferent between B and D for the last six choices. Similarly, The risk averse choice pattern (six safe choices A followed by four risky choices B) will be equivalent to the pattern of choosing lottery C for the six first decisions and lottery D for the later four decision. Lottery C provides the same level of squared utility than safe lottery A (risky lottery B) for the six first choices (for the last four choices) so that an averse-risk subject will be indifferent between the lotteries A and C for the six first choices and indifferent between B

6 and D for the last four choices. At the end of the within experiment, a random draw was done in order to identify the winning decision that everyone will be paid based on it. The numbers related to nineteen decisions made by the subjects, in both the Risk-experience and the Status quo-experience, were written in small papers (e.g. session 1-decision 2; session 2-decision 5 …). They were put into a transparent covered bole. One of the participants, selected by chance, shacked the bole very well and picked only one small paper. Subjects were informed that they will get the payoff assigned to their choice made in the written decision that will appear. Since each proposed option was a lottery, payoffs associated to choices are their expected payoff. Before starting the experiment, all the lotteries’ expected payoffs were calculated for both sessions: Risk-experience and the Status quo-experience. Those calculations were not shown to subjects. They were just told that they will be paid simply an average of the amounts of money proposed in each option with taking into consideration the probabilities associated to each one of them.

Status quo survey experience

After announcing the result of the draw, participants passed to the second part of the experiment. They were asked to pull out the third paper from the envelope, which was a simple questionnaire (see Appendix 4)5. Participants were facing six different situations. They were asked to imagine themselves really in those situations, facing three choices and had to select one among them. Among the three choices, one alternative occupies the position of status quo and the two others are new alternatives. After finishing the questionnaire, subjects were asked to bring out the last paper from the envelope. It is a complementary and anonymous questionnaire (Appendix 5). Subjects had to complete it with their personal information: age, gender, revenue. At the end of the experiment, each participant was invited to keep the small paper in the bottom of the envelope containing his/her personal number, return the four papers in the envelope and wait for the experimenter to come and get it. They were asked to wait a moment for their earned money since the experimenter has to bring out the four papers from the envelope, look for the choice made by the subject for the selected decisions in the draw, determine the amount of money earned and put it in the envelope. Once this work is done, participants were called according to their personal numbers and got their payoffs.

3. Results

Based on the results of the Risk-experience, we identify the subject behavior towards risk. The classification of risk attitudes was based on the lower number of times subjects had chosen the option A before switching to B. Subjects who choose A for 0;1;2 or 3 times before switching to B are considered as risk loving and are assigned the code 1, subjects who choose A for 4 or 5 times before switching to B are considered as risk neutral persons and are assigned the code 2 and subjects who choose for 6 times or over before switching to B are considered as risk-

5 The questionnaire is inspired from Samuelson and Zeckhauser (1988). Situations are modified and updated to fit the current state.

7 averse persons and are assigned the code 3. From the Status quo-experience, we identify the status quo behavior by measuring the number of same choices kept by participants between the two sessions of the within experiment. In the Status quo survey experience, subjects had to choose between three different options (a, b and c) for 6 different situations. In order to facilitate the calculation, we have chosen to make option c is the status quo while options a and b are the alternatives. In order to know the status quo tendency, we have done a summation of the number of times subjects had chosen the option c. To be considered as a status quo person, subjects should have selected the option c at least three times. In sum status quo tendency has been assessed in two ways: (i) we analyzed results of each part of experience separately; (ii) we evaluate results of the whole experience by summing the total number of status quo choices from both Status quo-experience and Status quo survey experience. Among the 190 subjects who faced the whole experience choices, 58 had made a status quo bias choice (29.74%). “Twelve choices” was the highest number of status quo choices made by subject but it exists only for 0.51%; “zero choice” was the lowest level of status quo choices made with a frequency of 2.05%; While the most frequent situation is “four choices”, it was found for 39 times which represent 20% in total.

[Insert Table 1 around here]

Table 1 displays the distribution of subjects according to their characteristics and their status quo choices. The table shows that 71.70% of risk-averse participants have made status quo choices, while 89.55% of risk loving participants and 82.67% of the risk-neutral participants do not show any status quo bias. Risk-averse people seem to be the most concerned by the status quo tendency in opposition of risk seeking and neutral people. People who are afraid of losing tend to stick with their actual situation without taking the risk of changing it. Besides, only the first category of revenue was characterized by its tendency to status quo (68.09% of subjects having revenue under 50DT/month). Yet, the rest of categories showed a remarkable tendency to the inexistence of status quo bias: 78.26% of the second category (revenue between 50 and 100TD); 88.10% of the third category (revenue between 100 and 150TD) and 83.78% of the fourth category (revenue over 150TD). Regarding the gender, only 14.58% of men made status quo choices while 34.69% of women do present status quo bias. Finally, we noticed that 93.81% of participants aged 22 years old or less were no status quo whereas 62.20% of participants aged more than 22 years old were status quo.

In order to go a step further with our analysis, let us now examine the relationship between individual risk preference and status quo behavior while controlling for individuals’ income, age and gender. Given that our endogenous variable, i.e., the status quo behavior, can be expressed as both a count variable (the number of status quo choices) or a qualitative variable taking a dichotomous form (Y=1 if individual has status quo behavior and Y = 0 if not), we use a negative binomial regression for the first case and a logistic regression for the second case. Moreover, the effect of risk attitude is tested using a variable taking a dichotomous form (1; 2 or 3 if the individual is respectively risk-loving, risk-neutral or risk-averse). The individual monthly income has 4 values: 1 if it is less than 50 DT, 2 if it is between 50 and

100 DT, 3 if it is between 100 and 150DT and 4 if it is more than 150 DT. The variable gender is binary, equal to 1 if the individual is a male and 0 if the individual is female. The variable age is also binary, equal to 1 if individuals are less than 22 years old and 2 if they are older. Although we conducted the two parts of the study jointly, we present the results sequentially distinguishing results from different states. The presentation of the finding is structured in the following way: First, the within experience results are provided and analyzed via both negative binomial regression and logistic regression. Second, we compare these results to those provided by the Status quo survey experience. Finally, we use aggregated data for status quo behavior collected from the whole experience, to highlight the interaction between status quo behavior and risk attitudes.

3.1 Within experience results

Table 2 reports the results of both negative binomial regression and logistic regression of the within experience with the goodness-of-fit measures.

[Insert Table 2 around here]

In both regressions, the obtained small p-value from the LR test (0.0000) indicates that at least one of the regressions’ coefficients is different from zero and so our model is statistically significant as a whole. The dispersion parameter Alpha is found significantly greater than zero (0.1474501) which indicates that our parameters are over-dispersed and the negative binomial regression model is appropriate. Moreover, given the fact that negative binomial regressions model the log of the expected count as a function of explanatory variables, the coefficients are interpreted in terms of difference in the logs of expected counts of the endogenous variable. For the risk behavior variable, it has a positive relationship with the dependent variable with both regressions since its coefficient is strictly positive in both regressions (higher than zero). With the negative binomial regression, this independent variable has an incidence rate ratio (IRR) equal to 1.17 which is strictly positive (superior to one). This enables us to say that risk-averse persons tend to exhibit status quo bias 17% more than the other risk- preference persons. With the logistic regression, the risk behavior variable has an Odds-Ratio equal to 2.2 which is strictly positive (superior to one). Risk-averse persons are 2.2 times more likely to be concerned by status quo bias than other risk-preference persons. For the revenue variable, it has a negative relationship with the dependent variable with both regressions. With the negative binomial regression, this independent variable has an IRR equal to 0.87 which means that higher revenues tend to be 13% less affected by status quo than the others categories of revenue. But in the logistic regression, the revenue variable has an Odds-Ratio equal to 0.54 which is inferior to one. The age variable has a strictly positive coefficient in each of the regressions. With the negative binomial regression, the age variable has an IRR equal to 2.19. So, elderly persons tend to be 119% more affected by the status quo bias than less aged. With the logistic regression, the Odds-Ratio of the age variable is equal to 9.98 (> 1). So, elderly persons are 9.98 times more likely than younger people to be affected by the status quo bias.

3.2 Status quo survey experience Results

[Insert Table 3 around here]

Table 3 reports the results of both negative binomial regression and logistic regression of the second part of experience: the Status quo survey experience. According to the negative binomial regression, the dispersion parameter Alpha is small but strictly positive (equal to 4.24e-12) which indicates that our parameters are over-dispersed and consequently the negative binomial regression model fits to our data more than the Poisson regression model. Also the small value of the LR chi (2) test’s p-value (equal to 0.0008 < 1%) shows that there is at least one of the regression’s coefficient is different from zero which indicates that our model is statistically significant as a whole. Through the negative binomial regression, only the risk behavior variable is statistically significant and having a positive relationship with the status quo behavior (the dependent variable). Its IRR is equal to 1.17 (superior to one), so risk-averse people tend to be 17% more touched with the status quo bias than people with other risk preferences. As for, the logistic regression, results show that our model is statistically significant as a whole (the LR chi (2) test’s p-value is equal to 0.0000 <1%) and there are two statistically significant independent variables which are the risk behavior and the revenue. For the risk behavior variable, the Odds-Ratio is equal to 2.02 (strictly positive): risk-averse persons tend to be 2 times more likely to be affected by the status quo bias than other risk preferences’ persons. Revenue variable has a negative relationship with the dependent variable Its Odds-Ratio is equal to 0.72 (< 1).

3.3 The whole experience results

[Insert Table 4 around here]

Table 4 reports the results of both negative binomial regression and logistic regression of the experience on its whole. From a first look, we can see that the dispersion parameter Alpha is small but still strictly positive (equal to 9.12e-08) which indicates that our parameters are over-dispersed and so the negative binomial regression model is the appropriate and not the Poisson regression model. Furthermore, both regressions provide almost the same results since they have the same p- value of the LR chi (2) test (equal to 0.0000 <1%), so our model for both of them is statistically significant as a whole. Also, through both regressions there are three independent variables that explain the status quo bias (the dependent variable) which are the risk behavior, the revenue, and the age. The risk behavior variable has a positive relationship with the status quo bias in both regressions since its coefficient in both of them is strictly positive (higher than zero). With the negative binomial regression, the risk behavior variable has a strictly positive IRR (equal to 1.18 superior to 1). So, risk-averse people tend to be 18% more concerned by the status quo bias than people with other risk preferences, while with the logistic regression, the risk behavior variable has a strictly positive Odds-Ratio (equal to 3.92

> 1): Risk-averse persons are 3.92 times more likely to be affected by the status quo bias than other risk preferences persons. For the revenue variable, a negative relationship exists between this variable and the status quo bias with both regressions since its coefficient in both regressions is strictly negative (less than zero). The revenue variable has an IRR equal to 0.9 (inferior to one) which is viewed through the negative binomial regression. So higher revenues tend to be 10% less touched by the status quo bias than the others revenues’ categories. In the logistic regression, the revenue variable has an Odds-Ratio equal to 0.51 (inferior to one). For the age variable, it has a strictly positive coefficient in both regressions (both are higher than zero). With the negative binomial regression, the IRR of the age variable is equal to 1.57 (superior to one). So, elderly persons tend to be 57% more concerned by the status quo bias than younger ones. And with the logistic regression, the Odds-Ratio of the age variable is equal to 19.51 (superior to one). Elderly people are 19.51 times more likely to be touched by the status quo bias than younger people. Taking together the results of tables 2, 3 and 4 that summarize outcomes issued from both regressions for three experiences, it has been found that the risk aversion attitude, the low level of revenue, and a certain age can affect directly and even greatly the status quo behavior

In sum, we can interpret the results as follows: (i) Risk-averse people are the one to have status quo tendency in opposition of risk seeking and even neutral people. People who are afraid of losing tend to stick with their actual situation without taking the risk of changing it; (ii) People with small incomes show a status quo propensity in contrast with those who have much larger incomes which could be explained by the fact that people when having small wealth tend to keep their previous decisions since they are afraid of losing all of their assets by taking the risk of changing their decisions; (iii) Males tend more than females to be non- status quo. This finding may be explained by the fact that, females, by nature, tend to look more than males for stability; (iv) Teenagers (22 years old and less) tend to be non-status quo which could be explained by the fact that due to their young age, they have more the sense of adventure. So they take more risks and tend to change their previous decisions by curiosity.

4. Conclusion

In this paper, we have investigated the relationship between the risk preferences and status quo bias. We found that risk preferences attitudes along with revenues’ level, gender, and the decision-maker age can separately or even all of them together provide strong explanation of the status quo behavior. Our experiment showed that risk-averse people, low revenues, females, elderly people are more likely to develop status quo tendency than any other ones. Our findings provide potential explanation to status quo bias and offer some insights into consumer behavior, firms’ strategy, and policy effectiveness. This cognitive anomaly affecting the decision-making may result in additional costs both at the individual level and at the societal level. The status quo bias can lead individuals to repeat or keep the same decision unthinkingly for no other reason than it is the default option and this can occurs even when the alternative may generate a better payoff for the individual. Thereby, people with a status quo tendency can keep their previously decided retirement plans even in the case where it is no more the optimal choice to make; replicate unhealthy eating

11 behaviors, refuse to go along with any new strategy proposed by their teammate and oppose to any government policy. Managers and policy makers can also adopt the same behavior and take on the strategy summed up by the English wartime slogan “keep calm and carry on”. By behaving this way, all these underestimate the risk and the cost of status quo bias resulting in sub-optimal choices. The recognition of such behavior anomalies can have an impact on the prediction of individual behavior and therefore, on the effectiveness of public policies. It can also provide a basis for improving the design of the policy in some contexts. Indeed, managerial and policy makers can either implement incentive policies to mitigate behavioral anomalies or exploit these anomalies for their own benefit. In the specific context of public policy the government can use a nudge which is a policy aimed at modifying behavior without modifying the set of decision options. This policy does not forbid, penalize or reward any particular option; instead, it conducts people to a particular choice by changing the default option or benchmark (world Bank, 2015). On the contrary, decision-makers could not only not correct this biased way of thinking (the status quo bias), but also deepen it because with it, they could more easily achieve their objectives. It has been shown, in this paper, that status quo bias and risk aversion behaviors are closely interrelated, so actions that checks to encourage (respectively counteracts) status quo behavior should involve efforts to fight (respectively promote) risk-aversion behavior. Controlling status quo bias pays greater dividends if it is coupled with the control of risk aversion. For instance, implementing nudges for modifying behavior anomalies related to status quo bias might be weaker if risk aversion is not taken into account. To drive economic agents to opt for the innovative alternatives, decision maker should offer warranties and shed light on the opportunities provided by the new options referring to the successful experiences of well known companies which achieved their success not by avoiding risk but by seeking it out.

Although we have offered new empirical evidence regarding the relationship between status quo bias and risk preference, we have not investigated whether these results are robust to higher amounts, more choices and repetition. It would be interesting, as well to investigate observational data for exploring the presence of status quo and identifying its determinants.

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Table 1. Status quo distribution for the whole experience

Presence of Status Quo Absence of Status Quo Risk-loving 10.45% 89.55% Neutrailty 17.33% 82.67% Risk- Aversion 71.70% 28.3% R ≤ 50 68.09% 31.91% 50 150 16.22% 83.78% Male 14.58% 85.42% Female 34.69% 65.31% Age ≤ 22 6.19% 93.81% Age > 22 62.20% 37.80%

Table 2. Regression estimates of the within experience

Negative binomial regression Logistic regression

Status quo bias Estimates IRR Estimates Odds Ratio

Risk behavior .1565775** 1.169501 .7817002*** 2.185184 (.0769517) (.2628415) Revenue -.1336488** .8748973 -.6152731*** .5404933 (.0592081) (.2164526) Gender -.0534331 .9479693 -.4425522 .6423948 (.1442874) (.5226268) Age .7832796*** 2.188638 2.300835*** 9.982518 (.1235971) (.4330848)

Pseudo R2 0.0831 0.3373 Log likelihood -356.27275 -79.223856 LR chi2(4) 64.58 80.63 p-value 0.0000 0.0000 Alpha .1474501 - p-value .0681485 - Standard errors are indicated between brackets. (**) and (***) are statistically significant values at thresholds of respectively 5% and 1%.

Table 3. Regression estimates of the Status quo survey experience

Negative binomial regression Logistic regression

Status quo bias Estimates IRR Estimates Odds Ratio

Risk behavior .1596984** 1.173157 .7028564*** 2.019513 (.0663813) (.2159164) Revenue -.0731892 .929425 -.3222443** .7245212 (.0506259) (.1637277) Gender .0888934 1.092964 .2615593 1.298954 (.123016) (.3882389) Age .1230265 1.130914 .5503995 1.733946 (1051613) (.3343348)

Pseudo R2 0.0290 0.1194 Log likelihood -318.76464 -117.36745 LR chi2(4) 19.02 31.84 p-value 0.0008 0.0000 Alpha 4.24e-12 - Standard errors are indicated between brackets. (**) and (***) are statistically significant values at thresholds of respectively 5% and 1%.

Table 4. Regression estimates of the whole experience

Negative binomial regression Logistic regression

Status quo bias Estimates IRR Estimates Odds Ratio

Risk behavior .1623779*** 1.176305 1.367194*** 3.924324 (.0466039) (.3178151) Revenue -.1074641*** .8981088 -.6634793*** .5150562 (.0360335) (.249571) Gender .0041011 1.004109 .2777203 1.320117 (.0877206) (.6308588) Age .4518564*** 1.571226 2.971126*** 19.51388 (.0746578) (.510699)

Pseudo R2 0.1072 0.4911 Log likelihood -399.24529 -60.401103 LR chi2(4) 95.89 116.58 p-value 0.0000 0.0000 Alpha 9.12e-08 - p-value .0000279 - Standard errors are indicated between brackets. (**) and (***) are statistically significant values at thresholds of respectively 5% and 1%.

APPENDICES

Appendix 1: Instructions used in the experiment

Session N°1

The course of the experiment:

During this session, you will get a paper including a table composed of ten decisions. For each one of them, you are invited to choose one of the two proposed options: option A or option B. You have to know that there is neither a right nor wrong response. Your choice should be only guided by you own personal preferences. The options: each option represents a lottery assigned with a probability.

Your payment: Your gains will be conducted based on the chosen option in one of the decisions which will be selected through a random draw.

Session N°2

The course of the experience:

During this session, you will take a questionnaire in your hand. You are invited to answer the present questions by ticking the option that you prefer. For a certain purpose, you have to pick one and only one option in each question. All of the rest of the participants with you are having the same questions as you. Also, you have to know that there is neither a right nor wrong answers. Only your behavior will matter. Your answers will be treated anonymously.

Appendix 2 : Session N°1

Will you please complete the following table, showing in each time whether you prefer option A or option B.

Your Option A Option B Choice (A or B) 4 DT with a probability of 10% 7.7 DT with a prob. of 10% Decision 1 3.2 DT with a prob. of 90% 0.2 DT with a prob. of 90%

4 DT with a prob. of 20% 7.7 DT with a prob. of 20% Decision 2 3.2 DT with a prob. of 80% 0.2 DT with a prob. of 80%

4 DT with a prob. of 30% 7.7 DT with a prob. of 30% Decision 3 3.2 DT with a prob. of 70% 0.2 DT with a prob. of 70%

4 DT with a prob. of 40% 7.7 DT with a prob. of 40% Decision 4 3.2 DT with a prob. of 60% 0.2 DT with a prob. of 60%

4 DT with a prob. of 50% 7.7 DT with a prob. of 50% Decision 5 3.2 DT with a prob. of 50% 0.2 DT with a prob. of 50%

4 DT with a prob. of 60% 7.7 DT with a prob. of 60% Decision 6 3.2 DT with a prob. of 40% 0.2 DT with a prob. of 40%

4 DT with a prob. of 70% 7.7 DT with a prob. of 70% Decision 7 3.2 DT with a prob. of 30% 0.2 DT with a prob. of 30%

4 DT with a prob. of 80% 7.7 DT with a prob. of 80% Decision 8 3.2 DT with a prob. of 20% 0.2 DT with a prob. of 20%

4 DT with a prob. of 90% 7.7 DT with a prob. of 90% Decision 9 3.2 DT with a prob. of 10% 0.2 DT with a prob. of 10%

Decision 10 4 DT with a prob. of 100% 7.7 DT with a prob. of 100%

Appendix 3: Session N°2

Please answer the following questions:

Question1: Tick only the option that you prefer: Option A: 4 dinars with a probability of 10%, 3.2 dinars with a probability of 90% □ Option B: 7.7 dinars with a probability of 10%, 0.2 dinars with a probability of 90% □ Option C: 5 dinars with a probability of 10%, 3.09 dinars with a probability of 90% □ Option D: 3 dinars with a probability of 10%, 3.29 dinars with a probability of 90% □ Question2: Tick only the option that you prefer: Option A: 4 dinars with a probability of 20%, 3.2 dinars with a probability of 80% □

Option B: 7.7 dinars with a probability of 20%, 0.2 dinars with a probability of 80% □ Option C: 5 dinars with a probability of 20%, 2.95 dinars with a probability of 80% □ Option D: 3 dinars with a probability of 20%, 3.43 dinars with a probability of 80% □ Question3: Tick only the option that you prefer: Option A: 4 dinars with a probability of 30%, 3.2 dinars with a probability of 70% □ Option B: 7.7 dinars with a probability of 30%, 0.2 dinars with a probability of 70% □ Option C: 5 dinars with a probability of 30%, 2.77 dinars with a probability of 70% □ Option D: 3 dinars with a probability of 30%, 3.6 dinars with a probability of 70% □ Question4: Tick only the option that you prefer: Option A: 4 dinars with a probability of 40%, 3.2 dinars with a probability of 60% □ Option B: 7.7 dinars with a probability of 40%, 0.2 dinars with a probability of 60% □ Option C: 5 dinars with a probability of 40%, 2.54 dinars with a probability of 60% □ Option D: 3 dinars with a probability of 40%, 3.84 dinars with a probability of 60% □ Question5: Tick only the option that you prefer: Option A: 4 dinars with a probability of 50%, 3.2 dinars with a probability of 50% □ Option B: 7.7 dinars with a probability of 50%, 0.2 dinars with a probability of 50% □ Option C: 6 dinars with a probability of 50%, 1.9 dinars with a probability of 50% □ Option D: 3 dinars with a probability of 50%, 4.2 dinars with a probability of 50% □ Question6: Tick only the option that you prefer: Option A: 4 dinars with a probability of 60%, 3.2 dinars with a probability of 40% □ Option B: 7.7 dinars with a probability of 60%, 0.2 dinars with a probability of 40% □ Option C: 6 dinars with a probability of 60%, 2.75 dinars with a probability of 40% □ Option D: 3 dinars with a probability of 60%, 4.75 dinars with a probability of 40% □ Question7: Tick only the option that you prefer: Option A: 4 dinars with a probability of 70%, 3.2 dinars with a probability of 30% □ Option B: 7.7 dinars with a probability of 70%, 0.2 dinars with a probability of 30% □ Option C: 6 dinars with a probability of 70%, 4.16 dinars with a probability of 30% □ Option D: 3 dinars with a probability of 70%, 8.19 dinars with a probability of 30% □ Question8: Tick only the option that you prefer: Option A: 4 dinars with a probability of 80%, 3.2 dinars with a probability of 20% □ Option B: 7.7 dinars with a probability of 80%, 0.2 dinars with a probability of 20% □ Option C: 6 dinars with a probability of 80%, 7 dinars with a probability of 20% □ Option D: 3 dinars with a probability of 80%, 20.97 dinars with a probability of 20% □ Question9: Tick only the option that you prefer: Option A: 4 dinars with a probability of 90%, 3.2 dinars with a probability of 10% □ Option B: 7.7 dinars with a probability of 90%, 0.2 dinars with a probability of 10% □ Option C: 6 dinars with a probability of 90%, 15.5 dinars with a probability of 10% □ Option D: 3 dinars with a probability of 90%, 94.67 dinars with a probability of 10% □

Appendix 4: Questionnaire

All of the following situations are fictional situations. So please focus on each one of them, imagine yourself as if you are really in that situation and choose only one of the proposed alternatives:

1. You are on a trip and you have lost your Sony camera (16Mpx LCD model 2010) and you want to buy a new one so you will be able to keep all the best moments of your trip. You have these choices available to you: a- Buy a Canon camera full options (20Mpx Full HD) model 2016 for 600 DT. b- Buy a PVC camera with the same characteristics of your old one for 199 DT.

c- Buy the same old camera for the same price (350 DT).

2. Your Samsung Smartphone (S3 MP3-player) had fallen into the water. You want to buy a new one. You have the following choices: a- Buy an iPhone : an optimal duration of 6h for 800 DT b- Buy an Alcatel Smartphone : an optimal duration of 20h for 300 DT c- Buy the same old one: an optimal duration of 10h for 500 DT

3. You are an employee at a public company with a fixed salary of 800DT/month; a- Quit your job and create your own business with 6/10 chances of gain (having 1500 DT/month), 1/10 chances to get the same old revenue (800 DT/month) and 3/10 chances of loss (having 500 DT/month). b- Quit your job and partner a businessman with 4/10 chance of gain (2000 D/month), 4/10 chances of having the same old revenue (800 DT/month) and 2/10 chances of loss (300 DT/month). c- Keep your current job.

4. You are the new headmaster of human resources and development of a company. The administration asked you to hand them a new budget allocation of that department. You knew that the ex-chief had the following allocation: 60% for the new recruits, 30% for training their employees and 10% for the materials’ development. a- Focus on materials’ development; 30% - 10% - 60%. b- Divide the budget between the recruits (50%) and materials’ development (50%) c- Keep the same old allocation: 60% - 30% - 10%.

5. You are an employee with a fixed salary of 800DT/month and you can get a bonus of 200DT/month (depending on your performance) and you inherited a 500m² filed. In order to be able to buy your dream house, you can: a- Quit your job, work in your field (agriculture) and in one year, you will have 6/10 chances of buying the house along with a monthly gain of 1500DT, 2/10 chances of maintaining your current situation and 2/10 chances of losing everything. b- Keep your current job; rent the field for 500DT/month and getting a bank loan in order to buy the house (the bank will cut 400DT/month for your salary). c- Keep your current job; sell the field for 100MD and buy the house you want along with a 10MD as a remaining amount.

6. You are the new financial chief in an airport. You want to put a plan of budget allocation. The ex-chief has the following allocation : 70% for aircraft’s maintenance and 30% of runways’ maintenance a- Reverse the old allocation: 30% for the aircraft’s and 70% for the runways. b- Divide the budget equally between the both of them (50% - 50%). c- Keep the same old allocation: 70% - 30%.

Appendix 5: complementary and anonymous questionnaire

Please fill in the following by either completing or checking the appropriate box:

1. Age : ____ years old 2. Your net monthly income :

a) Less than 50DT/month □

b) Between 50 and 100 DT/month □

c) Between 100 and 150 DT/month □

d) More than 150 DT/month □ 3. Gender : M. □ F. □

THANKS FOR YOUR

PARTICIPATION