Investigating risk seeking behaviour under stress

Investigating risk seeking behaviour under stress

Magnus Bratli Ingjer & Øyvind Sanner Sjaastad Supervisor: Aysil Emirmahmutoglu

Master’s thesis, Economic and Business Administration, Behavioral Economics

NORWEGIAN SCHOOL OF ECONOMICS

This thesis was written as a part of the Master of Science in Economics and Business Admin- istration at NHH. Please note that neither the institution nor the examiners are responsible - through the approval of this thesis - for the theories and methods used, or results and conclusions drawn in this work.

Acknowledgements

This thesis represents the completion of our Master of Science in Business Analysis and Performance Management at the Norwegian School of Economics. The process of writing this thesis have demanded a lot of time and energy. However, the trade-off has been increased knowledge about experimental methods and behavioral economics, subjects which has interested us long before writing this thesis.

The making of this thesis would not have been possible without the excellent feedback from our supervisor, Aysil Emirmahmutoglu. We are both humble and thankful for all the time she has invested. She provided enthusiasm and support beyond our expectations, which helped mold our thesis to a product we can be proud of. We would also like to thank our family and friends for their continuous support, and for making our five year journey at NHH fulfilling and enjoyable.

Finally, writing this thesis would not have been possible without the participation of our fellow NHH students in our study. Thank you.

Bergen, December 19, 2021

Abstract

People often need to make important social and economic decisions while under the influence of acute stress. Thus, it is crucial to understand how stress affects our decision-making capabilities.

While recent research suggests that stress may influence decision-making, it remains inconclusive as to whether any behavioural change can be identified at all. The current study seeks primarily to examine how stress affects risky financial decisions by individuals and evaluates any sex- associated differences.

To evaluate how stress affects individual risk attitudes in financial decision-making, we adopted an online experimental survey approach. The 442 individual study participants were randomly assigned by lots to either a stressed condition or a control condition group. The Stroop task was utilised as the stressor, while the control group performed a similar task without the stressful elements.

We elicit risk attitudes through two different multiple price list (MPL) designs, namely the Holt and Laury (2002) and Eckel and Grossman (2008) tasks. In the former, each participant is given ten paired lottery choice decisions, wherein either lottery A (safe) or lottery B (risky) must be selected. The Eckel and Grossman task presents the participants with nine distinct gambles, each with a 50/50 probability of either outcome. The first gamble represents a certain payoff, and each subsequent gamble becomes progressively riskier while offering a higher potential reward.

Results from both risk elicitation methods reveal that there is no significant difference identified in terms of behaviour between stressed and non-stressed individuals across the overall population.

While we were unable to identify any significant behavioural differences between stressed and non-stressed men, we found that women exposed to stress were more risk-averse as compared to the control group for the Holt and Laury lottery (HL) task. While this behaviour is in agreement with a substantial proportion of the literature, we did not find that stress alters female risk-taking behaviour in the EG task.

1 Introduction 1

2 Theory and Literature review 3

2.1 Measuring the Risk Attitudes of Decision-Makers . . . 3

2.1.1 Holt and Laury . . . 3

2.1.2 Eckel and Grossman . . . 5

2.1.3 Self-assessment . . . 5

2.1.4 Risk-Taking Behavior in Lottery Games . . . 5

2.2 Stress . . . 6

2.2.1 Stressor - The Stroop Task . . . 7

2.2.2 How Stress Affects Decision-making . . . 8

2.2.3 Differences in Sex . . . 9

3 Methodology 10 3.1 Design of the Experiment . . . 10

3.1.1 Part 1 - Demographic Questionnaire and Self-risk Assessment . . . 10

3.1.2 Part 2 - Treatment vs Control group . . . 11

3.1.3 Part 3 - Measuring of Risk . . . 13

3.2 Conducting the Experiment . . . 15

3.2.1 Online Experiments . . . 15

3.2.2 Power Calculations . . . 16

3.2.3 Recruitment . . . 17

3.2.4 Ethical Concerns . . . 18

4 Hypotheses and Empirical Strategy 19 4.1 Hypotheses . . . 19

4.2 Empirical Strategy . . . 19

5 Results and Analysis 22 5.1 Sample . . . 22

5.2 How Stress Influences Decision-Making . . . 24

5.2.1 Risk Aversion as Revealed by the Holt and Laury Task . . . 24

5.2.1.1 Risk Aversion as Revealed by Safe Choices . . . 24

5.2.1.2 Risk Aversion as Revealed by CRRA Intervals . . . 26

5.2.2 Risk Aversion as revealed by Eckel and Grossman . . . 27

5.3 Analysis . . . 29

5.4 Correlation Between Elicitation Methods . . . 31

5.5 OLS Regression Models . . . 32 iii

5.5.1 HL Regression Models . . . 32

5.5.2 EG Regression Models . . . 34

5.5.3 Test of OLS Assumptions . . . 36

6 Limitations, Conclusions and Further Research 39 6.1 Limitations . . . 39

6.2 Conclusions . . . 40

6.3 Further Research . . . 42

References 44 A Appendix 51 A.1 Instructions for the participants during the Survey . . . 51

A.2 Email sent to students . . . 56

A.3 HL risk values transformed to EG risk values . . . 57

A.4 Socioeconomic characteristics . . . 58

A.5 Consistent and inconsistent participants in the treatment- and control group . . 58

A.6 Cross comparison of the participants in each risk classification . . . 58

A.7 Durbin Watson- and Breusch-Pagan tests for regression models . . . 59

1 Original Holt and Laury (2002) design . . . 4

2 Holt & Laury task modified according to Reynaud and Couture (2012) . . . 13

3 Modified version of Eckel and Grossman (2002) task . . . 14

4 Socioeconomic characteristics of subjects by group. . . 23

5 Risk aversion classifications based on lottery choices in control group . . . 26

6 Risk aversion classifications based on lottery choices in treatment group . . . . 27

7 EG - risk aversion classified based on safe lottery choices in control group . . . 28

8 EG - risk aversion classified based on safe lottery choices in treatment group . . 28

9 Proportion of Subjects by risk class using lottery tasks . . . 29

10 Spearman rank correlation coefficients are used to identify the correlation be- tween the HL, EG and SA risk elicitation methods . . . 32

11 Holt and Laury multiple OLS regression . . . 33

12 Eckel and Grossman multiple OLS regression . . . 35

List of Figures

2 Domen.et.al (2011) Self-Assessment Scheme . . . 11

3 Stroop task - incongruent word . . . 12

4 Stroop task - congruent word . . . 12

5 Possible outcomes from a statistical test (Van Voorhis and Morgan, 2007) . . . 16

6 OLS regression sensitivity to large outliers . . . 21

7 Probability of safe choices in each decision . . . 25

8 Probability of safe choices in each decision, adjusted for inconsistencies and monotonicity . . . 26

9 Holt and Laury OLS: Histogram and normal probability plot for the regression standardized residuals . . . 36

10 Eckel and Grossman OLS: Histogram and normal probability plot for the regres- sion standardized residuals . . . 37

11 Holt and Laury OLS: Scatterplot of the regression standardized residuals and predicted values . . . 37

12 Eckel and Grossman OLS: Scatterplot of the regression standardized residuals and predicted values . . . 38

v

1 Introduction

Stress is a key factor that highly dictates an individual’s decision-making abilities. Viewed through an evolutionary lens, an individual’s ability to make decisions under stress, for instance, if faced with a lion attack, strongly determines their chance of survival (McEwen, 2007).

In modern society, although individuals do not frequently face life-threatening situations, the influence which stress plays on decision-making remains interesting due to the constant pressures of daily life. Exposure to stress could be related to competitive forces, workload, time pressures, or the threat of job loss. Therefore, it is a rather common experience to feel stress when faced with critical political, social, or economic decisions. Acknowledging how stress can affect our economic preferences and decision-making capacities could help to improve our understanding of the heterogeneity of preferences, notably regarding financial decisions.

Traditional economic models are based on rigid assumptions. For example, the existence of stable preferences is an important assumption when defining rationality within this field. In this schema, individuals are described as systematically evaluating choices based on the available information and believed to act rationally by thoroughly evaluating the benefits and costs of each alternative decision before selecting the utility-maximising alternative. However, the burgeoning literature surrounding this field of research highlights the non-stationary preferences that exist under many circumstances (Camerer et al., 2004), notwithstanding the importance of external variables and the emotional state of an individual during the decision-making process itself (Tversky and Kahneman, 1974). The ability to comprehend the extent of such instabilities is important in understanding the role of stress in terms of fluctuating choices and preferences.

While the psychological literature which examines how stress affects the decision-making process is growing, the literature on how this can be applied to financial questions remains incomplete and inconclusive. In particular, there is only a limited extant understanding as to how stress influences economic preferences which are fundamental to economic models and individual decision-making. While some research suggests that stress increases an individual’s risk preference (Preston et al., 2007; Van den Bos et al., 2009), others have found that stress does not affect the decision-making process (Delaney et al., 2014).

The present study aims to examine how stress affects risky financial decision-making. In this context, a risky decision is one where the outcome probabilities are known. Individual risk behaviour is measured by two risk elicitation methods, known as the Holt and Laury (2002) task and the Eckel and Grossman (2002) task, respectively. These are represented in an online survey in which half of the participants were exposed to stress through a task known as the Stroop task.

This context allowed for the following main hypotheses to be tested:

• Stressful stimuli influence an individual’s risk attitude in terms of risky financial decision- making.

In addition, we sough to investigate whether there are any differences in how stress influences the financial decisions of males vs. females. Whereas the literature suggests that women become more risk-averse under influence of stress, stress seemingly impact male risk preferences to a much lesser extent (Preston et al., 2007; Cano-López et al., 2016). Also, many studies suggest that stress tend to amplify existing gender differences in terms of risk-seeking (Mather et al., 2009). As a result, we hypothesise the following:

• Stressful stimuli influence women‘s risk attitude in terms of risky financial decision- making.

• Stressful stimuli do not influence men‘s risk attitude in terms of risky financial decision- making.

This paper has the following structure. Chapter 2 examines the relevant theory and existing literature related to decision-making and stress. Chapter 3 describes the research methodology, including a description of the experimental design and how the experiment was conducted.

Chapter 4 specifies the hypothesis and the empirical strategy that was utilised. Chapter 5 presents the results and an analysis of the data. Finally, chapter 6 presents the conclusions of the study and outlines the study’s limitations and recommendations for further research.

2 Theory and Literature review

This chapter includes relevant theory in addition to a literature review. The first subchapter presents the risk elicitation methods pioneered by Holt and Laury (2002) and Eckel and Gross- man (2002). Subsequently, we will present expected utility theory as a framework for explaining risk-taking in such experiments. Finally, we include existing literature related to how risky decision-making is influenced by stress.

2.1 Measuring the Risk Attitudes of Decision-Makers

Decision-making is generally described by researchers as“the process of selecting one specific option amongst a list of potential alternatives”(Von Winterfeldt and Edwards, 2007; Miller and Byrnes, 2001). The decision-making process comprises specific phases that include identifying the problem, searching for information, identifying alternatives, selecting the alternative with the highest utility, and implementing the selection (Schramm-Nielsen, 2011). This process can be altered from time to time under the influence of intuition, family, peer pressure, memory, prejudice, emotions, motivation, stress, psychoactive substances, personality traits, and problem- solving (Klaczynski et al., 2001).

A risky decision indicates that the decision-maker knows the probability of each conceivable outcome. The Multiple Price list (MPL) format is a straightforward means of eliciting individual risk attitudes (Andersen et al., 2006). In this approach, the individual can choose between one or more different gambles, whereupon their choices implicitly reveal their risk preferences. The MPL format has several advantages in that it is easy for subjects to understand the task, simple to implement, and the task motivates subjects to answer truthfully.

2.1.1 Holt and Laury

The experimental HL lottery task is one of the most widely-used risk elicitation procedures (Anderson and Mellor, 2008; Csermely and Rabas, 2016). In this task, the subjects choose between ten paired gambles. Following the original format, the task is constructed so that the participant can choose between a “safe” option (A) and a “risky” option (B) for ten different decisions. Each option contains two lotteries, wherein the payoffs remain constant throughout all of the decisions. However, for each subsequent decision, the probability of each outcome changes. For the purpose of illustration, for the first column in lottery A the probability starts at 10 % and increases by the same amount until it reaches 100 % in the last row. In lottery A’s second column, the pattern is reversed. Here, the probability of getting payoff x starts at 90 % and decreases until it reaches 0 % in row ten. The same pattern can be seen for option B. Table 1 shows the original design by Holt and Laury (2002).

Option A Option B

10 % of 2 $ or 90 % of 1.6 $ 10 % of 3.85 $ or 90 % of 0.10 $ 20 % of 2 $ or 80 % of 1.6 $ 20 % of 3.85 $ or 80 % of 0.10 $ 30 % of 2 $ or 70 % of 1.6 $ 30 % of 3.85 $ or 70 % of 0.10 $ 40 % of 2 $ or 60 % of 1.6 $ 40 % of 3.85 $ or 60 % of 0.10 $ 50 % of 2 $ or 50 % of 1.6 $ 50 % of 3.85 $ or 50 % of 0.10 $ 60 % of 2 $ or 40 % of 1.6 $ 60 % of 3.85 $ or 40 % of 0.10 $ 70 % of 2 $ or 30 % of 1.6 $ 70 % of 3.85 $ or 30 % of 0.10 $ 80 % of 2 $ or 20 % of 1.6 $ 80 % of 3.85 $ or 20 % of 0.10 $ 90 % of 2 $ or 10 % of 1.6 $ 90 % of 3.85 $ or 10 % of 0.10 $ 100 % of 2 $ or 0 % of 1.6 $ 100 % of 3.85 $ or 0 % of 0.10 $

Table 1: Original Holt and Laury (2002) design

Experimental lotteries such as HL make it possible to measure individual risk attitudes through the constant relative risk aversion (CRRA) coefficient. The HL task is considered the standard method of measuring the CRRA coefficient because the task contains multiple decision situations, wherein each situation represents a choice between two lotteries, one of which is a safe option while the other is risky (Anderson and Mellor, 2008). As a result, the lottery values remain fixed for all the decisions, whereas the probabilities of attaining the higher and lower amounts are varied systematically. The point where an individual switches from option A to Option B can be used to measure their risk aversion. While an individual that switches early can be considered risk-seeking, an individual that switches late is risk-averse. By using this approach, it is possible to create a risk-utility function that is based on the transformation of an individual‘s selected decisions (Abdellaoui et al., 2011).

U(x) =x^{r 1}/(1 r) (1)

In equation (1) x represents the lottery prize. The parameter that is to be estimated is the CRRA coefficient, which is denoted by r. R > 0 corresponds to risk-aversion, r < 0 to risk-seeking and r

= 0 is normally equivalent to risk-neutrality.

However, the HL task includes the possibility of a biased CRRA coefficient. Such biased coefficients can occur when participants try to weigh the probabilities (Abdellaoui et al., 2011).

This, in turn, may lead to more risk-averse behaviour for higher probabilities and more risk- seeking behaviour for lower probabilities (Tversky and Kahneman, 1992). The complexity of the HL tasks requires higher cognitive mathematical abilities which could, in turn, influence the actions of some participants to a significant degree and, as a consequence, disturb the inference of the task output (Eckel and Grossman, 2002).

2.1.2 Eckel and Grossman

The EG task was proposed as a simpler method to quantify attitudes to risk. This elicitation method originally asks the participants to choose one of five gambles (Eckel and Grossman, 2002). There is a 50 % chance of winning either payoff, A or B. For each subsequent gamble, the standard deviation between the payoffs increases in addition to the absolute possible payoff.

Thus, selecting gambles further down the line indicates a higher degree of risk-seeking behaviour.

While the HL task calculates the CRRA coefficient based on when the participant shifts from lottery A to B, in the EG task the CRRA coefficient is determined by the single choice made by the participant.

2.1.3 Self-assessment

Alongside MPL list methods, one can also use self-assessment (SA) questionnaires to access risk attitudes. These questionnaires have the advantage of being less complex, less time- consuming, and less costly than experimental tasks (Lönnqvist et al., 2015). The self-reported risk measurement scheme is based on the work of Dohmen et al. (2011) and has also been reported by Reynaud and Couture (2012) to be a reliable predictor of risk preferences in the HL task. The SA is based on an 11-point scale. A score of 0 (i.e., “not willing to take risks at all”) on this scale would imply that the person is very risk-averse, whereas a score of 10 is indicative of a highly risk-seeking individual. A risk-neutral individual on this scale would be 5 (i.e. “risk is not relevant to my decision”).

2.1.4 Risk-Taking Behavior in Lottery Games

The expected utility theory (EUT) can be applied to better understand individual risk behaviour in the aforementioned lottery tasks. The well cited theory states that a decision-maker chooses between risky prospects through a comparison of their utility values(Von Neumann and Mor- genstern, 2007). These values are again derived from calculating the weighted sums of utility, multiplied by their respective probabilities (Mongin, 2009). A utility equation can be derived from the EUT, as illustrated in equation 2 below (Von Neumann and Morgenstern, 2007):

U(C1,C2,p1,p2) =p1⇤v(c1) +p2⇤v(c2) (2) In this utility(U) equation, v(c1) andv(c2)represent the weighted sum of some function of consumption, whereasp1andp2are the respective probabilities for each state.

In this theory, each individual has a utility function that is based on whether they are risk- seeking, risk-neutral or risk-loving (Weber et al., 2002). Risk-averse individuals display a concave utility function, as illustrated in the left graph in figure 1. Whereas these individuals according to the EUT will select the safe option A at least the four first times in the original Holt and Laury (2002) task. A risk-neutral decision-maker can be characterised by a straight utility

function. Accordingly, they would switch from the safe option A to the risky option B precisely on the fifth decision because this is when the expected value of option B becomes the highest. A risk-seeking individual exhibits a convex utility function and would switch from option A to B no later than the fourth decision in the HL task.

Figure 1: Utility curves for three levels of risk affinity (Policnomics, 2012)

Furthermore, research on risk preferences in the Holt and Laury (2002) task has revealed that the majority of respondent’s (2/3) show risk averse behaviour, which increases when the incentives rise. Harrison et.al (2005) also backs up these findings in a similar lottery game where the stakes are varied. For the Eckel and Grossman (2002) task, the pattern is also that the majority of respondents are risk averse. These risk averse tendencies are also replicated in similar studies (Eckel and Grossman, 2008; Binswanger, 1980; Dave et al., 2010).

2.2 Stress

Stress is amongst the most commonly experienced human emotions and can be defined as a mental or emotional tension/strain that results from demanding or adverse circumstances (Jackson, 2013). Within the literature, there is currently a consensus that the presence of stress induces physiological, psychological, and behavioural reactions (Kudielka et al., 2009).

Stressors, defined as events triggering the release of stress hormones, can either be chronic (long-term) or else acute (short-term). In addition, they can be physiological (e.g., hunger, illness, pain) or psychological (e.g., uncertainty, anxiety, fear). Multiple stressors can be experienced simultaneously by a given individual with additive impacts (Liu and Weiss, 2002). The human physiological response to a stressor can change both an individual’s cognition and behaviour (Sapolsky, 2000; Juster et al., 2010), while the complexity of a decision might serve as a source of stress within even relatively simple decisions (e.g., meal planning) and such stressors could be exacerbated if other external stressors are present. Either way, stress increases the decision cost,

for example, due to diverted attention and/or a more costly or interrupted process for gathering information. To compensate for the increased cognitive costs, individuals often make use of heuristics to reduce the cost of making a decision.

Over the years, the accumulated literature shows that stress influences an individual‘s decision- making processes (Keinan, 1987; Starcke et al., 2008; Galvan and Rahdar, 2013), and each stage of the decision-making process can be impacted (Moschis, 2007). A few studies indicate that stress positively affects decision-making, yet most demonstrate a negative effect (Staal, 2004). While an individual may act or think more rationally when stress is not present, many studies show a tendency towards more irrational decisions under the influence of stress. On this topic, prior work highlights how stress makes the decision-making process more hurried and unsystematic, with less time spent evaluating alternatives (Mather and Lighthall, 2012; Galvan and Rahdar, 2013). Thus, the presence of high stress levels can lead to premature closure, which means that the individual decides what to do before all the alternatives are fully evaluated.

Decisions that must be made in a risky environment can generally be solved strategically, which means that it is possible to evaluate each option with respect to potential outcomes (Brand et al., 2006). According to Leder et al. (2013), acute stress impairs an individual’s ability to think strategically. Their study shows that participants in a stressed condition execute lower levels of strategic reasoning relative to the control group.

2.2.1 Stressor - The Stroop Task

In a laboratory setting, several stress induction protocols are available. According to Karthikeyan et al. (2011), examples of available laboratory stressors include the Trier Social Stress Test (TSST); the Stroop colour and word test; the mental arithmetic task, and the cold pressor test.

While some of these tests are physical, notably the latter in which the individual lowers their hand into ice-cold water to induce stress (Wirch et al., 2006), others are more complex, such as the TSST, which is known as the stress induction gold standard. Cognitively demanding tasks such as the Stroop (1935) task have also been shown to induce stress. When Dickerson and Kemeny (2004) measured cortisol levels in subjects after the completion of the Stroop task, they found them to have been significantly increased, indicating that the task has elicited a stress response. This finding aligns with Reunad and Blondin (1997) who reported that the Stroop task is an efficient laboratory stressor. The literature also highlights the effectiveness of conducting the Stroop task in a computerised form (Pehlivano˘glu et al., 2005). Today, the Stroop task is frequently used in experiments within physiological, psychological, and biochemical research (Tulen et al., 1989).

The Stroop colour and word task was first introduced by John Ridley Stroop in 1935. The Stroop task traditionally shows the subjects various words printed in different colours and then asks them to name the ink colour. Two versions initially existed. First, the congruent version

consisted of ink colours that matched the words (e.g., the word ‘red’ was printed in red ink).

The second incongruent version consisted of words printed in inconsistent colours. For instance, a subject is shown the word ‘red’ printed in green ink. In this second version, the participants are required to perform a less automated task (such as naming the print colour) while inhibiting the interference arising from a more automated task (i.e., reading the word) (MacLeod and Dunbar, 1988). The difficulty that arises when trying to inhibit the automatic process (in naming the printed word instead of the ink colour) is named the Stroop effect and is what causes the stress(Stroop, 1935).

2.2.2 How Stress Affects Decision-making

The extant research literature on how stress influences an individual�s risky financial decision- making process is generally inconsistent. Within the gain domain, many research studies suggest that acute stress increases an individual’s gambling tendency (i.e. favours more risk-seeking behaviours with a more convex utility function; (Preston et al., 2007; Starcke et al., 2008; Mather et al., 2009; Van den Bos et al., 2009; Pabst et al., 2013). Delaney et al. (2014), found that stress does not influence an individual’s financial decision-making process through a laboratory experiment that used the HL task to measure risk aversion. This contention is supported by Kandasamy (2014) who conducted a study in which the participants played a computerised decision task that offered two choices based on real monetary payoffs. Each choice contained a lottery with different payoffs and probabilities, thereby allowing participants to value the payoffs according to their individual utility. This study also found that acute stress, as measured by cortisol elevation, did not affect individual financial risk-taking.

An experimental study by Porcelli and Delgado (2009) identifies a higher presence of reflection bias (meaning risk avoidance relating to gains and conversely risk-seeking for losses) for participants in a stressed condition. This finding is also supported by Galvan and Rahdar (2013).

According to Mather and Lighthall (2012), stress has a tendency to make individuals disregard negative information and pay more attention to positive information. They found that, when people are considering alternative decisions, they are more likely to focus on the upsides of the alternatives they are evaluating rather than the downsides.

Many studies have suggested that the effect of acute stress on decision-making is both rapid and dependent on its timing (Jones et al., 2011; Pabst et al., 2013; Kocher et al., 2013). Ben Zur and Breznitz (1981) found that time pressure has a tendency to make people select choices of lower risk, in addition to increasing the time spent considering the potential downsides for decisions.

Interestingly, Kocher and Sutter (2006) showed that time pressure decreases individuals’ depth of reasoning. Time constraints inherently change our capacity for information processing in such a way that less information is evaluated and incorporated into the decision-making process (Rieskamp and Hoffrage, 2008; Ibanez et al., 2009). Related research into risk-taking under

pressure of time constraints revealed that risk-taking was differentially affected in terms of gains, losses, and mixed options (Young et al., 2012; Kocher et al., 2013).

Many psychological studies on decision-making under stress make use of situations or hypothet- ical situations that otherwise offer only small incentives. This seems unfortunate, as multiple studies have proven that incentives affect study outcomes, as illustrated in a review by Camerer and Hogarth (1999). However, both Wik et al. (2004) and Battalio et al. (1990) were unable to identify any differences arising between the lottery decisions of the participants in a study that used both real and hypothetical payoffs.

When people evaluate and select a decision amongst many alternatives, the process itself can generate psychological stress to some degree. The presence of variable alternatives might make it more challenging to select a preferred option when entering risky situations. According to Baradell and Klein (1993), the presence of neutral settings makes a decision-maker utilise his or her accumulated experience to make inferences about what data is meaningful and to generate options.

2.2.3 Differences in Sex

In a meta-analysis conducted by Byrnes et.al (1999) they found well documented differences in risk taking for sexes. These findings supports the general perception that women generally pick less risky alternatives, view risks as being more significant, and participate in behaviours that involve less risk (Ball et al., 2010). The literature on whether any differences exist between males and females in terms of how stress affects their respective financial decision-making is somewhat more equivocal. Preston et al. (2007) found that women in a stressed condition who were anticipating giving a speech scored better, as compared to the control group, on the Iowa Gambling Task (IGT). Meanwhile, those men who experienced stress scored worse relative to the control group. In summary, the study reports that women under stress become more risk-averse than men. This study has since been replicated by Lighthall et al. (2009), who suggest that acute stress further amplifies existing gender differences in terms of risk-seeking. Thus, men tend to become more risk-seeking, while women show more risk-averse tendencies. The study points to evolutionary principles as a possible explanation for these differences. However, other researchers do not identify any differences in decision-making between males and females experiencing a stressed condition (Starcke et al., 2008; Pabst et al., 2013). Cano-Lopez et al. (2016) were unable to identify any effects of acute stress on financial decisions in a study conducted solely on males.

3 Methodology

This chapter is divided into two subchapters. The first subchapter delves into the nature of experimental design through a chronological description of the various parts. The second subchapter details how each experiment was executed. This includes an elaboration on the nature of online experiments, power calculations and a description of the recruitment process in addition to ethical considerations.

3.1 Design of the Experiment

Our study utilised a ‘between-subject’ design, one which offers multiple benefits as compared to the within-subject design alternative. According to Charness et al. (2012), the ‘within-subject’

design can be adversely affected by carry-over and demand effects. The demand effect occurs when participants interpret the experimenters’ intentions and change their behaviour accordingly, whether consciously or unconsciously (Rosenthal, 1976). Such effects are likely to be stronger in the within-subject design and such spill over effects may arise when an effect is “carried over”

to another experiment (Charness et al., 2012).

The experiment can be decomposed into three constituent parts, namely (1) demographic questions and a self-assessment of risk tendencies; (2) a stressor that is randomly applied to half the participants; and (3) two different risk elicitation methods. The following sections will give a detailed description of each component with a justification of their inclusion within the design.

The entire survey, as shown to participants, is included in Appendix A.1.

3.1.1 Part 1 - Demographic Questionnaire and Self-risk Assessment

The first part of the experiment involved asking the participants questions relating to their background, including their sex, age, employment, and current educational status. The inclusion of these background variables allowed us to conduct a more extensive analysis. We found it especially interesting to include the variable of sex because of the reported differences in terms of risk-taking between the sexes. Other studies have found education to play a role in risk-taking and, therefore, it is of interest to know when the participant has completed or started their economic education. By adding these variables, we could control for different effects that potentially affected the outcome of the experiment. Doing so allowed us to filter (via dependent variable) the effect that the stress protocol had on the individual’s risk preferences.

At the end of Part 1 of the survey, the participants were asked to self-evaluate their own attitudes to risk via a SA questionnaire. The questionnaire is based on Dohmen et al. (2011) and asks the participants to rank their risk attitudes, as illustrated in figure 2 below. This allowed us to check whether there is consistency arising between the participants’ reported risk attitudes and their actions.

Figure 2: Domen.et.al (2011) Self-Assessment Scheme

3.1.2 Part 2 - Treatment vs Control group

The second part of the survey randomly inflicted the stress protocol on half of the sample. This group is hereafter referred to as the treatment group. The group not subjected to the stress protocol is hereafter referred to as the control group and were asked to perform a similar task, structured in such a way so as not to induce stress. Because our study seeks to evaluate whether any behavioural differences arise between how the treatment and control groups react to the stressor, clear instructions were needed to avoid any confusion.

First, explicit instructions on how to execute the task were given to all participants as shown in full in Appendix A.1. The stress protocol we used for this study is the Stroop task. This task is designed as a game in which participants were asked to identify the print colour of the letters of a word that is shown on the computer screen. After the participants read the initial instructions, they were each given four practice trials. The purpose is to increase their understanding of the upcoming task and to address any misunderstandings before they begin. Subsequently, the participants were randomly assigned to either the control (no stress stimuli) or treatment group (stress stimuli). By doing so, we were more likely to be able to create comparable groups, thereby reducing any potential selection bias.

In the Stroop task, the treatment group had to respond to incongruent words. To illustrate, a word classified as incongruent could be the word RED printed in the colour blue, as illustrated in figure 3. In this sample, participants would need to answer the question by pressing the keyboard button "B" corresponding to the print colour blue. Pressing any other keyboard button

would make an error message quickly appear on their screen before leading into the next task.

Our experiments included the colours red, blue, yellow, and green, represented by the keyboard buttons "R", "B", "Y" and "G", respectively. Inconsistencies arise within the literature in terms of how many words the Stroop task should include, but we have chosen to include 25 words.

To increase the stress load on participants within the treatment group, they were informed that they have a 90 second time limit (corresponding to 3.6 seconds per word) to complete all 25 words. To increase their stress level further, they were also told to try to maximise their number of correct answers.

Figure 3: Stroop task - incongruent word

The control group was given a Stroop task that was modified so as not to expose the subjects to any stress stimuli. The task only included congruent words, wherein the print colour of the letters of the word were identical to the word itself, as illustrated in figure 4. In contrast to tasks involving incongruent words, a task involving congruent words is considered less mentally challenging and does not inflict stress on the participants. This allows us to isolate possible causal effect of stress on risk-taking. It was necessary to include a task for the control group to ensure that both groups spent approximately the same amount of time on the experiment.

Any differences in temporal pressure would reduce our ability to isolate the stress factor. In contrast to the treatment group, the control group was explicitly told not to worry about any time restrictions and to take as much time as needed. The intention was to make them more relaxed and decrease the probability of the task causing any stress.

Figure 4: Stroop task - congruent word

3.1.3 Part 3 - Measuring of Risk

The purpose of the third part of the survey was to elicit individual risk attitudes through lottery experiments. With risk attitude being the dependent variable in our study, it was important to use an experimental design that allowed empirical measurement of this variable. We selected two distinct risk elicitation methods, specifically (1) a modified version of the Holt-Laury task (2002) and, (2) a modified version of the Eckel and Grossmann (2002) task. The order in which the tasks are presented to the participants were randomised.

The HL task was designed in such a way that the participants were asked to choose either option A (safe) or option B (risky) across 10 decision situations. The HL task we have included is based on the original framework with a few modifications. All payoffs have been multiplied by a factor of 500 and converted to NOK, an approach similar to that proposed by Reynaud and Couture (2012). This conversion method results in payoffs ranging from 50 to 1,925 NOK in the HL task. Importantly, this method does not alter the implied CRRA range. A complete overview of the decision situations, together with the payoff outcomes of each probability, can be seen in table 2.

Prob. 1 Prob. 2 Option A Option B Implied Range of CRRA*

Payoff difference*

(A-B) Payoff 1 Payoff 2 Payoff 1 Payoff 2

10 % 90 % 1000 800 1925 50 r 0.95 1057.7

20 % 80 % 1000 800 1925 50 r 0.95 415

30 % 70 % 1000 800 1925 50 -0.95 < r 0.49 247.5

40 % 60 % 1000 800 1925 50 -0.49 < r 0.15 80

50 % 50 % 1000 800 1925 50 -0.15 < r0.15 -87.5

60 % 40 % 1000 800 1925 50 0.15 < r0.41 -255

70 % 30 % 1000 800 1925 50 0.41 < r0.68 -422.5

80 % 20 % 1000 800 1925 50 0.68 < r0.97 -590

90 % 10 % 1000 800 1925 50 0.97 < r1.37 -757.5

100 % 0 % 1000 800 1925 50 r > 1.37 -925

*Not shown to participants

Table 2: Holt & Laury task modified according to Reynaud and Couture (2012)

Because of the possibility of biased CRRA coefficients, we also included an adaptation of a different risk elicitation method, namely the EG task. The EG task allowed for less cognitively demanding choices compared to the HL task. In the HL task the participants had to be familiar with calculating sums by using different probabilities contrary to the EG task where the proba- bilities are fixed. Hence the HL task could yield biased CRRA values due to the participants possible lack of mathematical abilities. As reported by Eckel et.al (2010) the EG task produces results that are more precise and less noisy than the HL task for participants that exhibit low

mathematical abilities. However, the EG task simplicity comes at a cost of providing less finer categorization than the HL task.

In the EG task, participants were presented with nine different gambles from which they were asked to select the one that they preferred. The chance of obtaining a high/low payoff is 50/50 for every gamble. Each subsequent gamble involved a potentially higher absolute payoff, but is also accompanied by a higher variance in terms of outcomes. Thus, the gambles gradually became progressively riskier. In this study, we changed both the payoffs and the number of gambles originally introduced by Eckel and Grossman (2002) to make it possible to compare the results with the adapted HL task. To achieve this, the payoffs in the EG task are primarily selected so as to obtain identical CRRA intervals relative to the adapted HL task (Reynaud and Couture, 2012). Thus, In both tasks, a CRRA coefficient of less than -0.15 implies a risk-seeking decision-maker; a CRRA coefficient within the interval of [-0.15, 0.15] one who is risk-neutral, while a CRRA coefficient of greater than 0.15 implies a risk-averse decision-maker (Reynaud and Couture, 2012; Sauter et al., 2015). All payoffs are expressed in terms of NOK, and a summary of the adapted EG task can be seen in table 3.

Lottery Prob 1 Prob 2 Payoff 1 Payoff 2 Implied Range of CRRA*

Expected value*

Lottery 1 50 % 50 % 850 850 r > 1.37 850

Lottery 2 50 % 50 % 680 1083.5 0.97 < r1.37 881.75

Lottery 3 50 % 50 % 510 1360 0.68 < r0.97 935

Lottery 4 50 % 50 % 340 1662.5 0.41 < r0.68 1001.25 Lottery 5 50 % 50 % 255 1827.5 0.15 < r0.41 1041.25 Lottery 6 50 % 50 % 170 1944.5 -0.15 < r0.15 1057 Lottery 7 50 % 50 % 127.5 1974 -0.49 < r 0.15 1050.75 Lottery 8 50 % 50 % 85 1984.5 -0.95 < r 0.49 1034.75

Lottery 9 50 % 50 % 21 1987 r 0.95 1004

*Not shown to participants

Table 3: Modified version of Eckel and Grossman (2002) task

The participants had the voluntary option to enter a random drawing of monetary rewards that was based on their decisions in both tasks. In total, two participants were drawn as winners, one for each task. In the HL task, the winner selected their preferred option for ten different decision-alternatives. We drew a number between 1-10, which represented the winning HL decision number, and a corresponding lottery, that was the basis of the winners cash price. To illustrate, if the number seven was drawn and the winner has selected option A, there is a 70%

change of 100 NOK being the reward, and a 30% chance of 800 NOK. In terms of the EG task, the winner has selected their preferred lottery that formed the basis for their potential reward.

Here, if the winner chose lottery 6, their monetary reward was determined by the probabilities and payoffs of this specific pick. More specifically, this entailed a lottery with a 50% probability of either 170 NOK or 1944.5 NOK being the reward. We believe that the inclusion of real monetary incentives, where one winner would be selected for each task, is a good mechanism to incentive participants to answer truthfully.

Finally, we would like to mention that the CRRA coefficients derived from both experiments have received some critical analysis. As mentioned by Holt and Laury (2002), the results from experiments with scaled-up real payments, such as those used in our experiment, are inconsistent with CRRA. However, our intention in this experiment is not to perfectly measure risk preferences, but rather to study whether one group is relatively more or less risk-averse than another when exposed to stressors. The use of both measurement methods can also be defended because they are both relatively simple ways of measuring risk preferences, which is, after all, the primary goal of this study.

3.2 Conducting the Experiment

This subchapter details the execution of the experiment. We will first elaborate on the concept of online experiments before we present the platform used to execute the study. Second, we will present the power calculations used to ascertain the necessary sample size. Subsequently, our approach to collecting data through the recruitment process will be described prior to a brief discussion of ethical concerns.

3.2.1 Online Experiments

We chose to conduct the study as an online experiment in contrast to other studies in which stress is elicited in laboratory experiments. Several factors speak in favour of online experiments over traditional laboratory experiments. First, when the online experiment is designed, the automated distribution process allows it to be executed rapidly at a lower cost (Barnhoorn et al., 2015). As compared to traditional laboratory experiments, Rademacher and Lippke (2007) highlight the advantageous nature of online experiments in terms of obtaining a large sample size.

In contrast to a laboratory setting, online experiments potentially create a more neutral decision- making environment for participants, as it offers the opportunity for them to complete the experiment within the comfort of their homes. This type of environment could reduce par- ticipants’ initial stress levels (Duersch et al., 2009; Vinogradov and Shadrina, 2013) which, considering that stress is our primary focus, is advantageous to this study. Further, when com- paring the results of online experiments with those conducted within a laboratory setting, many studies found the results to be consistent (Behrend et al., 2011).

In terms of stress stimuli, the most reliable stress-inducement methods undeniably require

physical presence within a laboratory setting. This allows laboratory experiments to utilise the most popular and widely tested mechanisms to induce stress, including the cold pressor test and the TSST. However, uncertainty regarding the circumstances surrounding the COVID-19 pandemic and the potential implications for organising a physical setting in which to collect responses, made it difficult to justify this solution. Therefore, conducting the study as an online experiment was deemed the most practical solution available.

3.2.2 Power Calculations

To avoid spending unnecessary time and money on experiments that are unlikely to produce reliable results, we performed power calculations. This is a useful tool for determining the ideal sample size for a particular study. In this context, power refers to the probability of rejecting a false null hypothesis, as demonstrated in figure 5. In other words, the power function assumes that the null hypothesis is incorrect (Van Voorhis and Morgan, 2007). As Cohen (1992) mentions, statistical power depends primarily on three factors, namely the significance criteriona ; the sample size (N), and the population effect size (ES). He also highlights that power depends upon different distributions. The general concept of power calculations is that, to detect small effects, large sample sizes are needed. Nevertheless, most power analyses are based on many simplifying assumptions and will, therefore, not always yield definitively correct answers.

Figure 5: Possible outcomes from a statistical test (Van Voorhis and Morgan, 2007)

To calculate power, we used the statistical software package GPower. In line with the recommen- dations of Cohen (1992) regarding research within the field of behavioural science, we operated with a significance levela (of 0.05) and a power (1 b) of 0.80. We also wanted to employ a t-test to see if any differences arose between the means of the two groups. According to Cohen (1992), the effect sizes for a t-test are, in general, 0.2 for small effects, 0.5 for medium effects, and 0.8 for large effects. When calculating the total sample size for conducting a two-tailed t-test, we ended up with 52 for detecting large effects, 128 for medium effects, and 788 for small effects. In terms of multiple regression analyses, Cohen (1992) recommends that we use an

effect size of 0.02 to detect small effects, 0.15 for medium effects, and 0.35 for large effects.

By using such effect sizes, we can calculate that the total sample sizes for a given multiple regression, when using two predictors, should be 485, 68, and 31, respectively. When increasing the number of predictors to 3, the equivalent numbers are 550, 77, and 36, respectively.

Ultimately, 442 participants were recruited to take part in our study. Based on our sample size, we therefore believe we recruited sufficient participants to detect both large and medium effects in both tests. Given both temporal and budgetary constraints, we deemed this to be a satisfactory result.

3.2.3 Recruitment

After the experiment had been designed, applied, and approved by our supervisor, it was ready to be distributed amongst potential respondents. The recruitment process aimed primarily at gathering a cross-section of the student body at the Norwegian School of Economics (NHH).

We gained access to the full mailing lists for both master’s and bachelor students by contacting the school administration. In order to gain access to the necessary information, we gave consent to conditions relating to data handling and, more specifically, agreed to abide by EU GDPR regulations. The survey was distributed to potential subjects via individually tailored emails containing an anonymous personal link to the Qualtrics survey. The email, which can be found in Appendix A.2., contained little information about the nature of the survey, other than that it related to the topic of decision-making. Additionally, to gain the trust of potential subjects, the confidentiality of the data was highlighted. This served as an approach that sought to increase the participants’ honesty and obtain a higher response rate (Saunders et al., 2012).

To eliminate multiple responses from single individuals, each individual link was restricted so that it could only be clicked once per device. Thus, a single individual could potentially enter the survey from both a computer and a mobile device. However, it was not possible to fully complete the survey if the participant accessed it via a mobile device. A computer was necessary, which was strongly indicated in the disseminated email. The JavaScript programming language we used to create the Stroop task part of the survey made it impossible for the participants to complete using a mobile device. As a result, they would become stuck at this stage and forced to exit the survey.

Ultimately, while we were able to obtain 442 completed questionnaires, a total of 767 responses were collected in Qualtrics. This number includes those individuals who started the survey but did not complete it. Although some individuals may indeed have naturally quit the survey before completion, we suspect that the low completion rate of 57.6% was heavily influenced by the issue of people entering the survey using a mobile device.

3.2.4 Ethical Concerns

The online execution of our experiment naturally involves differential ethical benefits and concerns when compared to in-person research. From an ethical perspective, the two main benefits of conducting online experiments are that it is easier to ensure complete anonymity, while participants feel reduced social pressure when wanting to quit the study (Barchard and Williams, 2008). However, even though the experiment was conducted online, one of our primary ethical concerns was the issue of anonymity. To be able to draw winners from both lotteries, we made it possible for the participants to voluntarily enter their email addresses at the end of the study, as shown in Appendix A.1. This survey design allowed us to identify participants who had a desire to enter the prize drawing, and thereby remove their anonymity.

To minimise this ethical concern, we followed the guidelines from Barchard and Williams (Barchard and Williams, 2008). Through the consent form, participants were informed that the data would not be anonymous, as illustrated at the beginning of Appendix A.1.. In addition, other features of the consent form, which are also in keeping with Barchard and Williams (2008)’

recommendations, include an option for withdrawing from the study. By including this option, participants can withdraw and not feel pressured to complete the study. Another measure was to restrict physical and electronic access to the collated data, ensuring that it could only be observed by the researchers.

There is also an ethical concern in online experiments concerning the age of the participants (Barchard and Williams, 2008). Contrary to a physical, in-person meeting with the participants, whereupon age can be verified, online experiments in Qualtrics offer no such opportunity. To solve the ethical concern regarding age, our recruitment process targeted students at NHH. Here, the students are all over 18 and can be considered legally old enough to consent to participation.

4 Hypotheses and Empirical Strategy

This chapter will first present the different hypotheses the current study evaluates. Following this, we will elaborate on overarching empirical strategies that were employed to evaluate our hypotheses.

4.1 Hypotheses

The purpose of this study was to investigate how stress affects individuals who make risky financial decisions. The study was conducted by using a between-subjects design – hence we had one treatment group and one control group. The treatment group was exposed to the stressful stimuli in the form of the Stroop stress task, whereas the control group was presented with no such stimuli. Our experiment measured the output of reported risk-taking for both the EG and HL tasks.

Our main hypothesis focuses on the difference arising in observed risk attitudes between the control and treatment groups when exposed to the relevant stimuli. Since the literature yields diverging results, it is difficult to anticipate how the stressful stimuli will affect the subjects.

Nevertheless, our hypothesis is that stress does influence an individual’s decision-making, as delineated below.

• Stressful stimuli do influence an individual’s risk attitude in terms of risky financial decision-making.

In addition, examining whether stress elicits any differences in terms of stress responses between genders is also interesting. For instance, Preston et al. (2007) report that women, in general, become more risk-averse than men when exposed to stress. The hypothesis related to how decision-making is influenced by stress for each gender can be seen below:

• Stressful stimuli influence women‘s risk attitude in terms of risky financial decision- making.

• Stressful stimuli do not influence men‘s risk attitude in terms of risky financial decision- making.

4.2 Empirical Strategy

Our empirical strategy was based on a variety of tests and analyses that try to identify statistical differences in risky behaviour arising between the control and treatment groups. This included the use of two-tailed independent t-tests as well as the Mann-Whitney test.

Additionally, an OLS regression model was used to further examine whether any other variables influenced our results. An OLS estimator minimises the residual sum of the squares. The

principle of least squared is thus used to identify the parameters in the linear function, given specific explanatory variables. Thus, the OLS method allows unknown parameters to be estimated via a linear regression model. Additionally, the measurement of closeness is based on the sum of the square errors that are made in prediction Y, for any given value of X. A multiple regression model that includes four explanatory variables, and is included in our study, is shown below:

Y_i=b₀+b₁Dt reat ment+b₂D_{f emale}+b₃Deducat ionH igher+b₄Dem ployment+e_i (3) The explanatory variables includes: (1)Dt reat ment which takes the value of 1 if the participant belongs to the treatment group, and 0 for the control group (2)D_{f emale}which takes the value of 1 if the participant is female, and 0 if male (3)Educat ionH igher which takes the value of 1 if the participant is a masters or doctorate student, and 0 if bachelor or lower, and (4)Dem ployment

which takes the value of 1 if the participant have a part-time or full time job, or if they are not working.

The same regression model is used to analyse both risk elicitation methods. The models only differentiate based on the dependent variable Yi, which measures the observed risk attitude in the HL and EG tasks, respectively. For both elicitation methods, the dependent variable used in the regression is defined as the EG value for each subject, thus taking a value between 1 and 9. Since we are unable to obtain this value directly from the HL tasks, we convert the CRRA values we receive in the HL scale to the EG scale. For instance, an individual with an observed CRRA interval between 0.68-0.97 in the HL task will have a converted EG value of 3 in the regression, which corresponds to relatively risk-averse behaviour. Normally the dependent variable would be calculated as the mean CRRA coefficient (r) for each subject (Holt and Laury, 2002). However, this adjustment should not be problematic, as the values we use are based on the same CRRA intervals and because we are using the same risk measure in both regressions to facilitate inferences from the analysis. A summary of the corresponding HL and EG values is shown in Appendix A.3. Additionally,b0represents the constant andeserves as the residual which captures all the variation that cannot otherwise be explained by the variables.

Using such OLS regression models requires multiple assumptions to be fulfilled in order to draw meaningful inferences regarding the population for a given sample (Stock and Watson, 2006):

1. The conditional distribution of ui given Xi has a mean of zero. While the error term in the regression model contains "other factors", the assumption is a mathematical statement which says that these "other factors" must be unrelated to Xi in the sense that, regardless of the chosen value for X, the error term u must have a mean value of 0 and not be subject to systematic patterns. A mathematical expression of this assumption may be written as E (ui | Xi) = 0.

2. Independently and identically distributed data. This is based on the assumption that Xi

,Yi,i=1,. . . , n must be independently and identically distributed across observations in the sample. Hence, all the observations used in the OLS regression must be obtained by random sampling based on a single large population.

3. Large outliers are unlikely. Observing extreme values of Xi and Yi as compared to the normal data range should be unlikely. The presence of large outliers that receive heavy weighting when estimating the unknown regressor coefficients could result in i values that are meaningless. An OLS regression is sensitive to such large outliers, as shown in Figure 6ri (Stock and Watson, 2006).

Figure 6: OLS regression sensitivity to large outliers

4. No perfect multicollinearity. Multicollinearity takes place if two or more independent variables within a regression model correlate. We have perfect multicollinearity when there is an exact linear relationship between independent variables. Such correlation is problematic, because independent variables need to be independent. Given a high enough correlation between independent variables, it becomes difficult to fit the model or to interpret the results.

5 Results and Analysis

This chapter will attempt to answer the research question by analysing the data collected from the survey. First, we will describe the sample. Second, descriptive statistics for both risk elicitation methods will be presented, which serve to give an indication of the direction of the findings. In the HL task, we report the participants’ average safe choices and implied CRRA intervals to measure their risk attitudes, while the EG task only measures risk attitude through the CRRA interval. Thus, this section only includes a descriptive analysis. Third, we include statistical analysis of our results. Fourth, we perform a correlation analysis to check participants’

consistency across different elicitation methods. Finally, we conduct a multiple OLS regression analysis to evaluate whether any other variables influenced our results.

5.1 Sample

The recruitment process aimed primarily at gathering a cross-sectional cohort from the student body at the Norwegian School of Economics. In total, 442 individuals participated in the experiment by finishing the Qualtrics survey. However, two of these individuals did not consent to the confidentiality agreement, which automatically ended their survey. This reduced the sample size to 440 participants.

When considering the entire sample of 440 participants, 283 (64.3%) were male and 157 (35.7%) were female. The average age was 25.5 years within an age range of 18 to 34. While students accounted for 84% of the total sample, 23% and 9%, respectively, worked in either a part-time or full-time capacity. As such, some of the participants attended studies in addition to working.

In terms of educational level, 170 participants (39%) were undergraduates (attending a bachelor programme), while 259 (59%) were graduates (attending a master’s programme). Additionally, a total of 409 participants (93%) had either finished or were attending a programme within the field of business administration or economics. A summary of the descriptive statistics can be seen in Appendix A.4.

To assess whether the sample can be considered representative of the students at the Norwegian School of Economics, we compared it to statistics provided by the Norwegian School of Economics in their annual report for 2020. According to the report, the share of female students is approximately 40%, which also is the school’s explicit goal in terms of male/female ratio. In total, 3,315 students attended the school in 2020, with the proportion of bachelor and master’s students being 73.9% and 26.1%, respectively. Even though our sample included slightly fewer female participants as compared to the NHH average, we argue that it is relatively aligned in terms of the distribution of undergraduate and graduate students, as our sample size is heavily skewed towards graduates (59%) than the school as a whole (26.1%). Nevertheless, the sample can still be considered representative of the school.

Of the total sample, 218 subjects were randomised to the treatment group and 222 to the control group. In terms of sex, the treatment group was 61.5% male and 38.5% female, while the control group was 67.1% male and 32.9% female. The mean ages in the treatment and control groups were 23.8 and 23.2 years, respectively. This can be considered reasonable, considering that 84%

are students in both groups. The educational level in the treatment group were mainly bachelor (33.5%) and master’s students (63.3%), whereas only 1.4% were doctoral students. Additionally, 1.83% of the 218 subjects in the treatment group could be classified as ‘other’ in terms of their educational level. For the control group, the equivalent numbers would be 43.7%, 54.5%, 0%, and 1.8%, respectively. When considering the occupational level of the treatment group, 9.6%

responded that they had a part-time job, 22% answered that they had a full-time job, whereas 1.4% were unemployed. The control group had approximately the same distribution, with 8.6%

in a part-time job, 23.9% in a full-time job, and 1.9% unemployed. Table 4 with all the described characteristics is shown below:

Treatment (N=218) Control (N=222)

Male 61.5% 67.1%

Age 23.8 years 23.2 years

Student 84.0% 84.0%

Educational level

- Bachelors degree 33.5% 43.7%

- Masters degree 63.3% 54.5%

- Doctorate degree 1.4% 0%

- Other 1.8% 41.8%

Business/ Economics 93.1% 92.8%

Work

- Part-time job 9.6% 8.6%

- Full-time job 22.0% 23.9%

- Unemployed 1.4% 1.4%

Table 4: Socioeconomic characteristics of subjects by group.

It is important to check whether the randomization process resulted in random samples. This is done by comparing the distribution of the treatment sample to the control sample through a nonparametric Mann-Whitney U Test for two unpaired groups. The null hypothesis indicates identical distributions for both groups. With respect to sex, a Mann-Whitney test gives a p-value

= 0.217, which means that we have no evidence of any significant differences between the groups in terms of gender distribution. While this high p-value does not give sufficient evidence to reject the null hypothesis of distinct populations, it is not the equivalent of stating that the samples are identical. This only suggests that we have no compelling evidence that there is a significant difference in gender distribution between the two groups. In terms of employment status, we

similarly do not have sufficient evidence to reject a null hypothesis of identical populations. The same is true for the distribution of students, with a p-value of 0.93.

When addressing educational level, the Mann-Whitney test gives a p-value of 0.024, which allows for the rejection of the null hypothesis and supports the conclusion that the populations are distinctly different. Considering the higher skew towards master’s students in the treatment group, this result is not unexpected. Consequently, in terms of age, it is not surprising that a Mann-Whitney test gives a p-value = 0.004, as master’s students tend to be older than bachelor’s students.

5.2 How Stress Influences Decision-Making

5.2.1 Risk Aversion as Revealed by the Holt and Laury Task

While the HL data can be analysed using a variety of statistical models, there are primarily two models which are typically used to make inferences regarding individual risk preferences (Harrison and Rutström, 2008). One method, based on the original model introduced by Holt and Laury (2002), uses the average number of safe choices that each participant has for all ten lottery decisions. The second model implicitly reveals the CRRA interval for each participant, based on when they switch from safe option A to risky option B (Chakravarty and Roy, 2009).

The next sections present the findings from each of these methods in turn. Subsequently, a statistical analysis of these results will be performed.

5.2.1.1 Risk Aversion as Revealed by Safe Choices

Figure 7 shows the average proportion of participants who picked safe option A, by group, for all ten lottery pairs. The decision number is shown on the horizontal axis, while the probability of selecting option A for a given decision is shown on the vertical axis. The solid blue and green lines represent the average decision made within the treatment and control groups, respectively.

The predicted choices of a risk-neutral individual are represented by the dashed (orange) line.

This type of risk-neutral individual is expected to select option A, provided that the expected value is higher than the expected value of option B. As such, option A should be chosen with a probability of 1 for the first four decisions and a probability of 0 thereafter. A subject who switches to the risky option B later on reveals risk-averse tendencies, whereas a subject who switches earlier indicates risk-seeking behaviour.

Figure 7: Probability of safe choices in each decision

Because the expected value of option A is only higher for decisions 1-4, as shown in figure Y, it is useful to distinguish these choices from decisions 5-9 in which only risk-inclined individuals would select option B. We notice from figure 7 that most of the participants picked the safe option A while the expected payoff for the riskier option B was low, before gradually switching to option B as the expected payoff increased. However, both groups seemingly switched later and at a lower rate than a risk-neutrality assumption would predict. Both the treatment- and control groups revealed strong risk-averse tendencies.

If a subject is rational with constant preferences, then only one switch from safe option A to the riskier option B should take place. A subject can be considered inconsistent if they make multiple transitions between options. In the treatment group, 20 subjects (9.2%) exhibited such transitory behaviour in vacillating from option B to A. This number is similar to the control group in which 18 subjects (8.1%) exhibited such behaviour. A summary of these inconsistencies may be found in Appendix A.5. Several reasons could explain this type of behaviour. The subjects could be genuinely indifferent towards which lottery they select (Andersen et al., 2006) or else simply disrespect monotonicity (Casari et al., 2011). Alternatively, a mistake could have occurred by the relevant subject. Figure 8 shows an adjusted graph, removing those inconsistent participants. While implementing this adjustment allows us to observe behaviour that is more closely aligned to the risk-neutrality assumption at both tails, there are no other significant observable behaviour changes.

Figure 8: Probability of safe choices in each decision, adjusted for inconsistencies and mono- tonicity

5.2.1.2 Risk Aversion as Revealed by CRRA Intervals

To examine the effect of the experimental conditions while controlling for sample characteristics we can use CRRA intervals. While the CRRA coefficient’s implied bounds can be calculated for every row in the payoff matrix as previously shown in Table 2, the bounds are otherwise identical to those used by Holt and Laury (2002). Accordingly, table 5 shows the number of safe choices with corresponding CRRA intervals for the control group after Chakravarty et al.

(2009).

Number of Safe Choices

Number of subjects

Range of CRRA Risk preference classification

Proportion of choices

Cumulative proportion of

choice

0-1 7 r 0.95 RL3 3.2 % 3.2%

2 4 -0.95 < r 0.49 RL2 1.8 % 5%

3 26 -0.49 < r 0.15 RL1 11.7 % 16.7 %

4 48 -0.15 < r0.15 RN 21.6 % 38.3 %

5 32 0.15 < r0.41 RA1 14.4 % 52.7 %

6 47 0.41 < r0.68 RA2 21.2 % 73.9 %

7 33 0.68 < r0.97 RA3 14.9 % 88.7 %

8 9 0.97 < r1.37 RA4 4.1 % 92.8 %

9-10 16 r > 1.37 RA5 7.2 % 100 %

Table 5: Risk aversion classifications based on lottery choices in control group

The number of safe choices for each subject implicitly reveals their CRRA range, corresponding to a specific risk preference classification. For the purposes of illustration, a subject who picks