Happy to Take Some Risk: Investigating the Dependence of Risk Preference on Mood Using Biometric Data

Bachir Kassas, Marco A. Palma, and Maria Porter Food and Resource Economics Department University of Florida [email protected]

DRAFT Please do not circulate or cite without author permission Selected paper prepared for presentation at the 2019 Agricultural and Applied Economics Association Annual Meeting, Atlanta, Georgia, July 21-23, 2019

Copyright 2019 by Stephen Morgan and Bachir Kassas. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.

1. Introduction

The importance of risk preferences in driving individual decisions under a wide range of economic settings has intensified the focus on understanding the main factors underlying individual behavior under risk. One of the key components found to risk-taking behavior is the mood state under which individuals make their decisions. In fact, a large body of work has documented the impact of mood on risk preferences (Cahlíková & Cingl, 2017; Drichoutis & Nayga Jr, 2013; Fehr-Duda,

Epper, Bruhin, & Schubert, 2011; Kamstra, Kramer, & Levi, 2003; Kliger & Levy, 2003; Kuhnen

& Knutson, 2011). However, despite the multitude of research on this topic, the literature carries contradictory results, implying that the mechanism through which mood influences how individuals make decisions under risk is still a subject of debate. This study is aimed at examining differences in the reported effect of incidental mood on risk attitudes arriving from key elements in the experimental protocol, namely the mood measurement manipulation check and risk preference elicitation mechanism used. To this end, facial expression analysis technology and pupil dilation measures are utilized as an alternative to the conventional survey-based mood measurement task when investigating the effect of positive and negative moods on risky decisions.

The value of incorporating psychophysiological data in the analysis of the effect of mood on risk preferences, and individual behavior more generally, is underscored by highlighting a major drawback (mood dilution) from using surveys to elicit subjects’ moods. The results are compared across two popular risk preference instruments in the experimental economics literature, Holt-

Laury (HL) and Eckel-Grossman (EG), to assess the stability of risk preferences, and treatment effects, under different elicitation mechanisms (Eckel & Grossman, 2002; Holt & Laury, 2002).

The commonly adopted procedure for investigating the effect of incidental mood on risk preferences in the laboratory is to conduct a three-stage design, which is comprised of mood inducement, mood measurement, and risk preference elicitation (Bruyneel, Dewitte, Franses, &

Dekimpe, 2009; Conte, Levati, & Nardi, 2018; Drichoutis & Nayga Jr, 2013; Fessler, Pillsworth,

& Flamson, 2004; Kim & Kanfer, 2009; Treffers, Koellinger, & Picot, 2016). The mood measurement stage, which serves as a manipulation check to test the effectiveness of mood inducement, is usually conducted through self-reported surveys, predominantly the Positive and

Negative Affect Schedule (PANAS) survey. We argue for a mood dilution that results from imposing an intermediate stage (taking the survey) between mood inducement and risk preference elicitation. We show that subjects’ induced mood is significantly diluted by the time they complete the mood measurement survey and are ready to reveal their preferences in the risk task. We demonstrate the bias in the results caused by this mood dilution, which can be resolved by using psychophysiological data instead of surveys for mood measurement.

While testing the effect of positive and negative moods on risk preferences, we further divided each mood treatment (positive, negative, and neutral) into two groups. The first group, hereafter diluted group, followed the conventional three-stage design by sequentially completing mood inducement, followed by mood measurement using PANAS, and finally risk preference elicitation using HL and EG.1 Conversely, the second group, hereafter undiluted group, skipped the intermediate PANAS survey and went straight through from mood inducement to the risk preference elicitation stage. Facial expression analysis software and pupil dilation were used on both groups (diluted and undiluted) to measure subjects’ moods before and during mood inducement and right before they started the risk preference tasks.

1 The order of the HL and EG tasks was randomized across subjects to account for any ordering effects. Furthermore, the stakes were normalized across the two tasks to get a more comparable measure of risk preferences that is not driven by differences in stakes. Since the HL task is prone to inconsistencies (subjects having multiple switches in the multiple price list), while the EG does not allow for inconsistent behavior, inconsistent subjects were dropped from the analysis to allow for a more direct comparison of the treatment effects across the two mechanisms.

Our results point to a significant mood dilution among the diluted group, who went through the conventional three-stage experimental design. This was evident in the comparison of the mood measurement of those subjects during mood inducement and right before starting the risk task, which showed a substantial decay in the induced mood as a result of the intermediate PANAS survey. This mood dilution was further manifested in the treatment effects, where there was no significant change in the risk preferences of the diluted group across the mood treatments as opposed to a significant decrease in the risk aversion of both positive and negative mood treatments in the undiluted group. Based on the results obtained from the HL and EG tasks, the effect of mood on risk preferences did seem task dependent. Specifically, the treatment effects were more pronounced under the HL task, where the decrease in risk aversion was significant under both the positive and negative mood treatments. Conversely, the risk preferences obtained from the EG task showed no significant effect for the negative mood treatment and only a marginally significant effect for the positive mood treatment.

The contribution of this study is threefold. First, it highlights some of the useful applications of psychophysiological data in behavioral and experimental economics research.

Second, it underscores a major issue (mood dilution) in using conventional survey methods to elicit subjects’ moods, thus proposing a modification that can enhance the accuracy of experimental designs used to study the effect of incidental mood on individual preferences. Third, it demonstrates the dependency of the relationship between induced mood and risk preferences on the choice of risk preference elicitation mechanism, whereby highlighting the importance of the experimental task used and its influence on the observed treatment effects.

Perhaps a broader benefit from this research is derived from the significant role that mood plays in shaping our behavior. The literature addressing the effect of mood on individual behavior is ever-growing across several disciplines including economics, psychology, sociology, marketing, and nutrition. For instance, positive mood has been linked to enhanced cognitive ability, productivity, patience, and healthy eating behavior (Erez & Isen, 2002; Fedorikhin & Patrick,

2010; Ifcher & Zarghamee, 2011; Isen, 2008). On the other hand, negative mood has been implicated with criminality, drug abuse, obesity, overspending, immorality, and antisocial behavior (Blumenthal, 2005; Burke Jr, Burke, & Rae, 1994; Cryder, Lerner, Gross, & Dahl, 2008;

Ganem, 2010; Lerner, Small, & Loewenstein, 2004; Weiss, Griffin, & Mirin, 1992; Zeeck, Stelzer,

Linster, Joos, & Hartmann, 2011). This highlights the importance of developing experimental protocols that can accurately gauge the effect of mood on individual preferences.

The rest of the paper is organized as follows: Section 2 reviews the relevant literature, while section 3 describes the experimental design and data. Section 4 presents a simple theoretical model that was used to obtain point estimates of the coefficient of relative risk aversion under each treatment. Section 5 contains a discussion of the results and section 6 highlights the main findings and concludes.

2. Literature Review

The economic perspective of the role of mood in decision-making under uncertainty has been conceptualized through changes in the probability weighing of different outcomes. This framework is evident in early models of anticipated including aversion (Bell,

1982) and disappointment aversion (Gul, 1991; Loomes & Sugden, 1986). More importantly, it also seems to be the main theme in more recent work, which focused on studying the effect of mood experienced at the time of making the risky decision (Au, Chan, Wang, & Vertinsky, 2003;

Bassi, Colacito, & Fulghieri, 2013; Campos-Vazquez & Cuilty, 2014; Fehr-Duda et al., 2011;

Grable & Roszkowski, 2008; Kamstra et al., 2003; Kuhnen & Knutson, 2011). The general notion advanced by these studies is that positive (negative) mood drives people to become more optimistic

(pessimistic), which in turn leads them to fixate more on positive (negative) outcomes and make riskier (safer) decisions. For instance, Fehr-Duda et al. (2011) conducted an experiment and found that pre-existing good mood causes individuals to adopt a more optimistic approach when weighing the probability of outcomes in a risky situation. Moreover, Kuhnen and Knutson (2011) asserted that individuals tend to be overconfident and take more financial risk under a positive mood, while Bassi et al. (2013) argued that good weather positively influences individuals’ moods which in turn increases their risk-taking behavior.

Despite ample evidence of a lower risk aversion under positive mood and vice versa, the results of several experimental studies have either partially or completely contradicted this view

(Arkes, Herren, & Isen, 1988; Bruyneel et al., 2009; Conte et al., 2018; Drichoutis & Nayga Jr,

2013; Leith & Baumeister, 1996; Lerner & Keltner, 2001; Nygren, Isen, Taylor, & Dulin, 1996;

Stanton, Reeck, Huettel, & LaBar, 2014; Treffers et al., 2016; Yuen & Lee, 2003; Zhao, 2006).

Indeed, the research addressing the effect of incidental mood on risk-taking behavior has produced inconsistent results. On the one hand, some researchers have either supported or opposed the notion above, that is, reported evidence of higher (or lower) risk aversion with induced positive mood and vice versa. Conversely, others found support for only one valence (positive or negative mood) or even no effect of induced mood on risk preferences.

The main driving factors for this divergence in experimental findings might lie in particular elements of the experimental design. Table 1 provides a summary of some of the main studies investigating the effect of induced mood on risk preferences in the lab and emphasizes the significant differences with respect to the experimental tasks employed in each of the three stages.

We contribute to this literature by focusing on the potential discrepancies in results arriving from Table 1. Review of Main Results Surrounding the Effect of Incidental Mood on Risk Preferences Study Mood Inducement Mood Measurement Risk Preference Eliciation Main Findings Arkes et al. (1988) handing candy none willingness-to-pay for lottery tickets positive mood both increased and decreased risk aversion (2 experiments) Bruyneel et al. (2009) imagining scenarios surveys buying lottery tickets no effect of positive nor negative mood on risk aversion Cahlikova & Cingl (2017) social surveys, heart rate, saliva multiple price list increases risk averion for men Campos-Vasquez & Cuilty (2014) negative factual info surveys multiple price list increases risk aversion Chou et al. (2007) videos surveys choice dilemma questionnaire positive mood decreases risk aversion and vice versa Colasante et al. (2017) music & pictures surveys multiple price list both positive and negative moods increase risk aversion Conte et al. (2018) videos surveys multiple price list both positive and negative moods decrease risk aversion Drichoutis & Nayga (2013) experiencing success/failure surveys multiple price list both positive and negative moods increase risk aversion Fehr-Duda et al. (2011) none surveys certainty equivalence for lotteries positive mood decreases risk aversion only in women Fessler et al. (2004) memory recollection surveys choice between a safe and risky gamble negative mood decreases (increases) risk aversion in men (women) Guiso et al. (2018) videos none multiple price list negative mood increases risk aversion Johnson & Tversky (1983) negative anecdotes surveys level of concern with negative events negative affect increases Kim & Kanfer (2009) videos surveys choice dilemma questionnaire negative mood decreases risk aversion Lee & Andrade (2015) videos surveys framed gambling negative mood both increased and decreased risk aversion depending on context Lerner & Keltner (2001) memory recollection surveys (only in pilot) asian desease task negative mood both increases and decreases risk aversion Mittal & Ross (1998) anecdotes none valuation of lotteries positive mood increases risk aversion and vice versa Nygren et al. (1996) handing candy none gambling positive mood increased (decreased) risk aversion in high (low) loss frame Raghunathan & Pham (1999) anecdotes surveys (only in pilot) choice between a safe and risky gamble negative mood both increases and decreases risk aversion Stanton et al. (2014) videos surveys lottery with gain and loss frame positive mood decreases risk aversion, no effect for negaive mood Treffers et al. (2016) videos surveys multiple price list sadness increases risk aversion, no effect on , , or Vastfjall et al. (2008) reminder of natural disasters surveys likelihood of positive and negative events negative mood increases pessimism Wright & Bower (1992) memory recollection none likelihood of positive and negative events positive mood increases and vice versa Yip & Cote (2013) social anxiety surveys (after risk task) choice between a safe and risky gamble anxiety increases risk aversion Yuen & Lee (2003) videos surveys choice dilemma questionnare negative mood increases risk aversion, no effect for positive Zhao (2006) experiencing success/failure surveys multiple price list positive mood increases risk aversion and vice versa differences in the mood measurement task and risk preference elicitation mechanism used. First, we evaluate the reliability of using survey-based mood measurement manipulation checks.

Specifically, we utilize facial expression analysis technology and pupil dilation measures to point to a potential bias stemming from the common practice of using surveys to measure subjects’ moods. Furthermore, we address the dependency of experimental results on the specific risk preference elicitation mechanism employed by investigating the treatment effects under two of the most commonly used risk tasks in the experimental economics literature, namely the Holt-Laury

(HL) and Eckel-Grossman (EG) tasks (Eckel & Grossman, 2002; Holt & Laury, 2002).

Although the majority of studies, as evident in Table 1, have used some form of survey as a manipulation check to measure subject’s moods post-inducement, some researchers tried to avoid including this intermediate step between mood inducement and preference elicitation. This was accomplished by either relying on previous experiments that validated a specific mood inducement procedure (Arkes et al., 1988; Guiso, Sapienza, & Zingales, 2018; Mittal & Ross Jr, 1998; Nygren et al., 1996; Wright & Bower, 1992), conducting a pilot study to test the effect of a specific mood inducement method on subjects’ moods (Lerner & Keltner, 2001; Raghunathan & Pham, 1999), or conducting the mood measurement survey after the subjects have completed the risk preference elicitation task (Yip & Côté, 2013). While these solutions hold the potential to remedy some of the bias from mood dilution, they too carry their own disadvantages. For one, relying on a pilot study or previous work entails using a different subject pool to judge the success of a certain mood inducement method. This requires the researcher to work based on an unchecked belief that the mood inducement procedure will have the same effect on the subject pool used in their current study. On the other hand, including the mood measurement survey after the risk task might confound the change in mood driven by the mood inducement stage with the change resulting from making decisions under uncertainty (i.e., participating in the risk task might in and of itself have a significant effect on the subject’s mood). Finally, using surveys in general – as an intermediate stage or through pilots and previous studies – implies relying on self-reported measures, the accuracy of which is never guaranteed since they are prone to error from misreporting and/or misunderstanding. Our study contributes to this literature by proposing an alternative method for measuring subjects’ moods that overcomes the drawbacks stated above. Using facial expression analysis technology and pupil dilation measures allows the researcher to conduct the mood inducement and mood measurement manipulation check simultaneously, thus removing the need for self-reported surveys and all the potential problems they carry.

It is worth noting that facial expression analysis software has been previously used in the literature (Kahyaoğlu & Ican, 2017; Nguyen & Noussair, 2014). For instance, Nguyen and

Noussair (2014) employed this technology to monitor subjects’ facial expressions as they made choices between a safe and a risky lottery alternative. Kahyaoğlu and Ican (2017) also utilized facial expression analysis to investigate the effect of experienced mood on participants’ choices in the popular television show Deal or No Deal. However, these studies did not employ mood manipulations as a treatment variable in a controlled environment. Rather, they only served to associate different facial expressions with corresponding choices in risky situations. Our study adds to this work by utilizing biometric data to isolate the effect of incidental positive and negative moods on risk preferences, thus highlighting a useful application of this technology in refining existing experimental protocols.

3. Experimental Design

The experiment was conducted using 187 undergraduate students from Texas A&M University, where each participant was paid a $5 show up fee and was also given the chance to earn more money based on their decisions in the risk preference tasks. Upon arrival to their respective session, participants provided consent and were seated in front of a computer screen, which displayed the experimental instructions. A webcam was located at the top of the screen to record the subjects’ expressions during the experiment. As discussed later, this information was fed to the facial expression analysis software to measure subjects’ moods. An eye-tracking device, which was placed at the base of the computer screen, was also used to measure pupil size and discern the degree of pupil dilation exhibited during the different stages of the experiment. The experimental protocol consisted of mood inducement, mood measurement, and risk preference elicitation as described below.

3.1 Mood Inducement

The participants were randomly assigned to one of three mood treatments (positive, neutral, and negative). Based on their treatment, subjects were induced with their respective moods using three- minute videos. The positive mood treatment was presented with short clips from Mr. Bean (a classic British comedy show starring the famous comedian Rowan Atkinson). On the other hand, the negative mood subjects were shown a video of animal mistreatment and the neutral mood group watched an automobile driving down a road.2 Although several mood inducement methods have been reported in the literature, video clips seem to be the predominant choice and they were used in this study since they provide an easy and direct way of manipulating subjects’ moods.

Moreover, they require the subjects to look continuously at the computer screen, which makes them well suited to use with facial expression analysis technology and pupil dilation measures.

2 The scenery did change a bit in the neutral mood inducement clip to avoid the video being boring and accidentally resulting in a negative valence. 3.2 Mood measurement

Two different mood measurement methods were utilized in this experiment. The first is the positive and negative affect schedule (PANAS), which is a survey consisting of a series of questions that measure the extent to which subjects are various positive and negative moods. Different variants of this survey have been adapted in previous studies. The version used in this experiment includes 20 questions that span 10 positive and 10 negative moods. The positive moods are happy, amused, enthusiastic, interested, determined, excited, inspired, strong, proud, and attentive. On the other hand, the negative moods are sad, angry, afraid, upset, distressed, nervous, ashamed, guilty, irritable, and hostile. Subjects answer on a scale of 1 (very slightly or not at all) to 5 (extremely) how much they are feeling each of the above moods. The subject’s answers are then used to infer the degree to which the individual is experiencing a positive, neutral, or negative mood.

The second mood measurement method uses a facial expression analysis software called

AFFDEX (Broch-Due, Kjærstad, Kessing, & Miskowiak, 2018; McDuff et al., 2016; Stöckli,

Schulte-Mecklenbeck, Borer, & Samson, 2018). This software was adapted and configured to capture the slightest changes in facial muscle movement and measure the extent to which the subject is experiencing each of seven main emotional states (, sadness, anger, , , fear, ). It is completely noninvasive and operates through any webcam. It works by keeping a massive database of facial expressions associates with different positive and negative emotions. Once the subjects face is recognized by the software, it automatically compares the displayed facial expressions with this database to provide an accurate estimate of his/her current mood state. It then generates an index for each emotional state that reflects the degree to which the subject’s expression aligns with that mood. This technology operates at 30Hz, meaning that it generates 30 observations for each per second. Considering the fact that pupils dilate when the individual is emotionally stimulated, pupil dilation measures were collected from the eye-tracking device to supplement the conclusions derived from the facial expression analysis software.

Although the PANAS survey is highly used in the literature (Conte et al., 2018; Ifcher &

Zarghamee, 2011; Kim & Kanfer, 2009; Lerner & Keltner, 2001; Treffers et al., 2016; Watson,

Clark, & Tellegen, 1988), it constitutes an additional step (taking the survey) that occurs between mood inducement and risk preference elicitation. This could result in mood dilution where the induced mood from the short videos is attenuated by the time the subject completes the survey and is ready to start the subsequent risk preference elicitation stage. Conversely, the AFFDEX software can be used to measure the subjects’ mood during the mood inducement stage since the webcam is used to record facial expressions while the subjects are watching the mood inducement videos.

This eliminates the need for an intermediate step (taking the survey) which helps avoid this issue.

In order to test for the potential mood dilution resulting from the PANAS survey, each mood treatment was split into two groups labeled diluted and undiluted. Subjects in the diluted group followed the conventional three-stage experimental design, which consisted of mood inducement using the short videos, followed by mood measurement using the PANAS survey, and finally risk preference elicitation. On the other hand, subjects in the undiluted group skipped the

PANAS survey and went straight from mood inducement to risk preference elicitation. Facial expression analysis and pupil dilation measures were used to record moods of both diluted and undiluted subjects before and during the mood inducement video as well as right before they started the risk preferences tasks.

3.3 Risk Preference Elicitation

Subjects’ risk preferences were elicited using two popular risk preference elicitation mechanisms in the experimental economics literature, the Holt-Laury (HL) and Eckel-Grossman (EG) tasks

(Eckel & Grossman, 2002; Holt & Laury, 2002). The results from the two tasks were compared to assess any differences in the treatment effects arriving from the choice of experimental risk preference elicitation method. The order of the tasks was randomized across subjects to control for any ordering effects.

The HL task consists of a multiple price list (MPL) where subjects are presented with several choice sets as shown in table 1. The list consists of 10 rows, each corresponding to a choice set containing a safe lottery (A) and a risky lottery (B). The potential outcomes from both lotteries remain fixed across the choice sets, however, the probability of the outcomes change as the subject move down the list. Specifically, the probability of the higher outcome progressively increases for both lotteries. Lottery A starts with a higher expected return than lottery B in the first few choice sets (rows). However, as the probability on the higher outcome increases in both lotteries, the expected return of lottery B eventually surpasses that of lottery A in row 5. For each choice set, subjects are required to indicate which lottery they prefer, after which their risk preferences are calculated based on the point where they switch from the safe lottery (A) to the risky one (B).

Clearly, the further down the table the switch happens, the more risk-averse the individual is.

Despite its great suitability for accurately measuring risk preferences, one drawback in the HL task is susceptibility to inconsistent behavior, where subjects might make multiple switches down the list or choose the safe lottery in the last choice set (i.e., indicate their preference for a lower compared to a higher certain payment).

Table 2. Holt-Laury Risk Task Description Option A Option B Choice $8 if the die roll is 1 $15.4 if the die roll is 1 ______$6.4 if the die roll is 2-10 $0.4 if the die roll is 2-10

$8 if the die roll is 1-2 $15.4 if the die roll is 1-2 ______$6.4 if the die roll is 3-10 $0.4 if the die roll is 3-10

$8 if the die roll is 1-3 $15.4 if the die roll is 1-3 ______$6.4 if the die roll is 4-10 $0.4 if the die roll is 4-10

$8 if the die roll is 1-4 $15.4 if the die roll is 1-4 ______$6.4 if the die roll is 5-10 $0.4 if the die roll is 5-10

$8 if the die roll is 1-5 $15.4 if the die roll is 1-5 ______$6.4 if the die roll is 6-10 $0.4 if the die roll is 6-10

$8 if the die roll is 1-6 $15.4 if the die roll is 1-6 ______$6.4 if the die roll is 7-10 $0.4 if the die roll is 7-10

$8 if the die roll is 1-7 $15.4 if the die roll is 1-7 ______$6.4 if the die roll is 8-10 $0.4 if the die roll is 8-10

$8 if the die roll is 1-8 $15.4 if the die roll is 1-8 ______$6.4 if the die roll is 9-10 $0.4 if the die roll is 9-10

$8 if the die roll is 1-9 $15.4 if the die roll is 1-9 ______$6.4 if the die roll is 10 $0.4 if the die roll is 10

$8 if the die roll is 1-10 $15.4 if the die roll is 1-10 ______

The EG task is much simpler and avoids problems with inconsistent behavior. However, it provides coarser estimates of risk preferences compared with HL. In the EG task, subjects are presented with only one choice set containing 6 lottery alternatives as shown in table 2. Thus, they only make one choice instead of 10. The main appeal of this mechanism is that all lotteries offer

50/50 odds of a high or low payoff, which makes them easier to understand and process than lotteries that vary the probability of outcomes. The first lottery offers a sure payment of $5.6 (the high and low payoffs are the same), while lotteries 2-5 are structured to offer progressively increasing expected returns, but with an increasing variance (risk) as well. The last lottery offers the same expected payoff as lottery 5 but with a higher risk and is included to detect risk-seeking behavior. Risk preferences are calculated by comparing the subject’s chosen lottery with the adjacent alternatives (the lotteries before and after the one chosen).

Table 3. Eckel-Grossman Risk Task Description Gamble (50/50 Lottery) Low Payoff High Payoff Gamble 1 $5.6 $5.6

Gamble 2 $4.8 $7.2

Gamble 3 $4.0 $8.8

Gamble 4 $3.2 $10.4

Gamble 5 $2.4 $12.0

Gamble 6 $0.4 $14.0

4. Experimental Design

1.1 Estimating the Relative Risk Aversion Coefficient

Subjects’ choices in the HL and EG tasks were used to obtain point estimates of the relative risk aversion coefficient. The model used here was adapted from (Dave, Eckel, Johnson, & Rojas,

2010), where we assume a constant relative risk aversion (CRRA) utility function of the form:

#()* (1) !(#|%) = 1 + %

such that # indicates the monetary payoff and % denotes the coefficient of relative risk aversion.

The expected utility associated with each lottery - is then calculated as follows:

(2) .!/ = 0[23 × !(#3|%)] , ∀8 = 1,2 3

where 23 is the probability associated with payoff #3 and 8 indicate the outcome (2 potential outcomes per lottery alternative).

If we denote by .!: and .!; the utility of the lotteries in a binary choice set, we can then construct a simple probabilistic choice rule as follows:

.!: (3) <%(=>?@AB D) = .!: + .!;

This forms the basis for the logistic conditional logarithmic likelihood function A(%|E/) which can be maximized with respect to % based on the subjects’ decisions (E/) in the HL and EG risk tasks.

The parameter % can then be specified as a function of treatments and individual characteristics

% = F(GH) to test for treatment effects and individual heterogeneity. The resulting modified likelihood function can be written as A(%, H|E/, G/).

1.2 Coding the Choice Data

Subjects’ choices between the lotteries in the HL and EG tasks were coded as a binary variable E/ for use in the model described above. Coding this variable for the HL task was straightforward.

For each choice set, the variable E/ took the value 0 if the subject chose the safe lottery A and 1 otherwise. Since each subject made 10 choices in HL, each had 10 observations of E/ in this task.

In order to code the decisions in the EG task in a comparable manner, we follow the procedure used in Dave et al. (2010). Although subjects made only one choice between 6 alternatives in this task, we constructed 5 hypothetical binary choice sets as follows: Choice set 1: Gamble 5 vs. Gamble 6 Choice set 2: Gamble 4 vs. Gamble 5 Choice set 3: Gamble 3 vs. Gamble 4 Choice set 4: Gamble 2 vs. Gamble 3 Choice set 5: Gamble 1 vs. Gamble 2

It is important to note that this produces a list similar to the one in the HL task, where each choice set contains two alternatives with the safe lottery on the left-hand side. The validity of this coding method has already been asserted by Dave et al. (2010). To understand how subjects’ decisions are translated into the coded variable, suppose a subject chooses gamble 2 in the EG task. Obviously, this means the subject prefers gamble 2 to all other gambles in the choice set, meaning they will select gamble 2 in choice sets 4 and 5. It also implies the subject prefers gamble

3 to 4, gamble 4 to 5, and gamble 5 to 6. Hence, they will choose gambles 3, 4, and 5 in choice sets 3, 2, and 1 respectively. With 0 denoting the choice of the safe alternative and 1 the risky alternative, the vector E/ is coded as [0, 0, 0, 0, 1] in this case. Similar logic can be applied to determine the binary choices associated with the other possible decisions in the EG task.

5. Results and Discussion

5.1 Analysis of Mood Inducement

The AFFDEX measures for anger, sadness, and joy are summarized for the undiluted and diluted subjects in tables 4 and 5 respectively.3 The results are separately reported for each treatment before and during the mood inducement video and right before the subjects started the risk task.

Looking at the undiluted subjects in table 4, we see that the positive mood inducement video resulted in a substantial increase in joy. Moreover, this positive effect on mood was still present at the time subjects participated in the risk preference tasks, assuring that any changes in risk

3 Although the AFFDEX produces measures for seven emotional states (anger, sadness, fear, surprise, contempt, disgust, and joy), we only focus on anger, sadness, and joy since they are more closely related to the substance of the mood inducement videos. preferences between the neutral and positive mood treatments are driven by this effect. On the other hand, while subjects in the negative mood treatment did not display higher measures of anger and sadness during the mood inducement video, their negative affect seams to increase at the time they participated in the risk tasks, suggesting a lag in the effect of the negative video on subjects’ moods.

Table 4. Summary of AFFDEX measures for Undiluted Subjects Anger Sadness Joy (Std. Error) (Std. Error) (Std. Error) Neutral (n=63) Before Video 0.031 0.307 0.939 (0.016) (0.217) (0.799) During Video 0.050 0.086 0.004 (0.030) (0.047) (0.002) Right Before Risk Tasks 0.002 0.229 0.002 (0.000) (0.202) (0.000) Positive (n=63) Before Video 0.537 0.140 4.487 (0.519) (0.096) (2.217) During Video 1.153 0.848 20.763 (0.760) (0.668) (4.939) Right Before Risk Tasks 0.309 0.107 19.968 (0.219) (0.083) (6.840) Negative (n=61) Before Video 0.251 0.154 3.576 (0.156) (0.130) (2.339) During Video 0.102 0.127 1.056 (0.053) (0.059) (0.910) Right Before Risk Tasks 2.869 1.983 0.002 (2.634) (1.361) (0.000)

The benefit achieved from utilizing biometric tools (i.e., facial expression analysis and pupil dilation measures) becomes clear when we consider the AFFDEX measures for the diluted subjects, who completed the PANAS survey between mood inducement and risk preference elicitation. As shown in table 5, while the positive mood inducement video did substantially increase subjects’ joy, this effect drastically decays by the time the subjects completed the PANAS survey and were ready to start the risk tasks. Moreover, the delayed effect of the negative mood inducement video also disappeared, where the measures on anger and sadness are extremely low for the negative mood treatment right before the subjects started the risk tasks. This result provides clear indication supporting the mood dilution phenomenon inherent in the survey-based mood manipulation checks.

Table 5. Summary of AFFDEX measures for Diluted Subjects Anger Sadness Joy (Std. Error) (Std. Error) (Std. Error) Neutral (n=63) Before Video 0.023 0.357 2.354 (0.009) (0.330) (1.149) During Video 0.106 0.476 0.140 (0.088) (0.431) (0.077) Right Before Risk Tasks 0.011 0.032 0.017 (0.006) (0.007) (0.014) Positive (n=63) Before Video 0.031 0.029 4.049 (0.017) (0.009) (2.655) During Video 0.129 0.092 14.668 (0.056) (0.033) (4.023) Right Before Risk Tasks 0.128 0.067 0.003 (0.120) (0.043) (0.001) Negative (n=61) Before Video 0.149 0.088 2.933 (0.116) (0.045) (2.168) During Video 0.192 0.462 0.227 (0.083) (0.204) (0.108) Right Before Risk Tasks 0.113 0.027 0.002 (0.108) (0.004) (0.000)

The results from the PANAS survey are presented for the diluted subjects in table 6. The undiluted subjects skipped this step and went straight through from mood inducement to risk preference elicitation. To generate comparable metrics for comparison between the PANAS and

AFFDEX, the measures on anger, sadness, happiness, and were taken individually from the PANAS survey and reported in table 6.4 Based on subjects’ self-reported measures in the

PANAS survey, we again see that the videos were effective in inducing subjects with the mood desired for their treatment. The measures on anger and sadness were the highest for the negative mood group. On the other hand, while happiness was similar between the positive and neutral mood treatments, amusement was highest for the positive mood group. This provides further evidence of the success of the mood inducement stage.

Table 6. Summary of PANAS by Treatment for Diluted Subjects Anger Sadness Happiness Amusement (Std. Error) (Std. Error) (Std. Error) (Std. Error) Neutral 1.000 1.219 3.313 2.406 (0.000) (0.087) (0.145) (0.167) Positive 1.121 1.515 3.394 3.515 (0.058) (0.164) (0.184) (0.190) Negative 3.097 3.161 1.581 1.226 (0.268) (0.267) (0.166) (0.111)

As mentioned earlier, an eye-tracking device was utilized to obtain pupil dilation measures that can be used to supplement the results from the AFFDEX software. The analysis and conclusions drawn here are based on the fact that pupils normally dilate when an individual is emotionally aroused and vice versa. This information can help further analyze the mood dilution phenomenon inherent in the PANAS and other survey-based mood measurement methods. Table

7 reports the left and right pupil size averaged for subjects in the positive and negative mood treatment. The results are broken down for the diluted and undiluted subjects. First, we observe that pupil size increases significantly during the video for both positive and negative mood groups, which further supports the success of the mood inducement stage. More importantly, we notice

4 While anger and sadness are common measures in AFFDEX and PANAS, the happiness and amusement were taken from the PANAS as the closest metrics that relate to the joy measure from AFFDEX. that while there is essentially no change in the pupil size of undiluted subjects right before the risk task, this was not the case for diluted subjects, where pupil size substantially shrinks by the time they were ready to reveal their risk preferences. This evidence strengthens the conclusion that sitting through an intermediate stage (mood measurement survey) between mood inducement and risk preference elicitation results in a decay in the subjects induced mood state.

Table 7. Summary of Pupil Dilation by Treatment Positive Negative Diluted Undiluted Diluted Undiluted (Std. Error) (Std. Error) (Std. Error) (Std. Error) Before Video Left Pupil 2.556 2.598 2.496 2.335 (0.140) (0.115) (0.162) (0.150) Right Pupil 2.538 2.594 2.394 2.335 (0.132) (0.119) (0.164) (0.134)

During Video Left Pupil 3.937 3.837 3.641 3.703 (0.101) (0.112) (0.162) (0.086) Right Pupil 3.862 3.761 3.464 3.705 (0.104) (0.116) (0.172) (0.105)

Right Before Risk Task Left Pupil 3.000 4.158 3.005 3.869 (0.100) (0.163) (0.119) (0.126) Right Pupil 2.892 4.138 2.779 3.840 (0.140) (0.146) (0.180) (0.124)

5.2 Analysis of Risk Preferences

The average number of safe choices made in the HL task is reported for each mood treatment in table 8.5 The results are also broken down for the diluted and undiluted subjects. As we can see, the average number of safe choices made by the positive and negative mood treatments is not significantly different from the control (neutral mood) for the diluted group, suggesting no

5 The data is only reported for the consistent subjects, who did not make multiple switches in the HL task nor chose the safe alternative in the last choice set. Out of the total sample of 187 subjects, 26 were inconsistent and thus dropped from the analysis in table 8. treatment effect. However, only when looking at the undiluted subjects do we see a significant treatment effect for both the positive and negative mood treatments. In fact, undiluted subjects in the positive and negative mood treatments made a lower number of safe choices compared with the control. This difference was significant at the 95% level for the positive mood treatment and 90% confidence level for the negative mood treatment. While the treatment effect is only marginally significant for the negative mood treatment, the results surrounding the undiluted subjects in the HL task indicate the both positive and negative moods result in a decrease in risk aversion. If one were to simply rely on the PANAS survey in the conventional three-stage experiment he would be led to the conclusion of no significant effect of mood on risk behavior.

However, incorporating facial expression analysis technology allows for a more accurate assessment of the true effect of positive and negative moods on risk preferences.

Table 8. Average Number of Safe Choices in HL Task by Treatment Neutral Positive Negative

Overall 5.577 4.964 5.380 (0.311) (0.345) (0.288) Dilution 4.846 5.065 5.464 (0.476) (0.508) (0.387) No Dilution 6.308 4.833 5.273 (0.354) (0.453) (0.442)

Significance tests (Versus Neutral) Overall - P=0.191 P=0.644

Dilution - P=0.758 P=0.315

No Dilution - P=0.013 P=0.071

Turning to the analysis of the EG task, table 9 presents a breakdown of the average choice made by diluted and undiluted subjects in each mood treatment. The choices were not significantly different across treatments for the diluted group, again suggesting no significant effect of positive or negative mood on risk preferences. However, only when we separately consider the undiluted subjects do we see signs of a significant treatment effect. Although undiluted subjects in the negative mood treatment are still not statistically different from the control, there is weak evidence of a lower risk aversion among the positive compared to neutral mood treatment. Interestingly, although results from the HL task indicated more risk-seeking behavior for both negative and positive mood treatments compared to the control, the EG task conforms to this result only for the positive mood treatment. This implies that the choice of risk preference elicitation method is important when considering the effect of incidental mood on risk-taking behavior. Moreover, it warrants further research to investigate the factors that can possibly lead to divergent results across risk tasks in this domain.

Table 9. Average Choice in EG Task by Treatment Neutral Positive Negative

Overall 3.270 3.651 3.016 (0.216) (0.195) (0.192) Dilution 3.625 3.636 3.419 (0.300) (0.249) (0.265) No Dilution 2.903 3.667 2.600 (0.302) (0.308) (0.261)

Significance tests (Versus Neutral) Overall - P=0.193 P=0.383

Dilution - P=0.977 P=0.611

No Dilution - P=0.08 P=0.451

The results presented so far provide strong evidence that the mood dilution caused by the intermediate mood measurement survey ripples through to attenuate the treatment effects obtained from the risk preference elicitation task. To further investigate this phenomenon, which we refer to as the dilution effect, we separately report the results from the HL and EG tasks for subjects who were significantly affected by the mood inducement video and those who weren’t. To achieve this, we calculated the median effect of the positive mood inducement video on the joy metric from

AFFDEX and median effect of the negative video on the anger and sadness metrics. Subjects in the positive and negative mood treatments were then split in half between those whose mood was affected above and below the median.

Table 10. Average Number of Safe Choices in HL Task by Treatment Responsiveness Neutral Positive Negative (anger) Negative (sadness) Above Median Below Median Above Median Below Median Above Median Below Median

Diluted 4.846 4.813 5.333 5.200 5.769 5.188 5.833 (0.476) (0.697) (0.760) (0.480) (0.632) (0.502) (0.613) Undiluted 6.308 4.250 5.417 6.100 4.583 5.545 5.000 (0.354) (0.708) (0.543) (0.526) (0.633) (0.474) (0.763)

Significance tests (Versus Neutral) Diluted - 0.967 0.570 0.629 0.261 0.640 0.235

Undiluted - 0.006 0.171 0.754 0.015 0.232 0.083

Table 11. Average Choices in EG Task by Treatment Responsiveness Neutral Positive Negative (anger) Negative (sadness) Above Median Below Median Above Median Below Median Above Median Below Median

Diluted 3.625 3.813 3.471 3.313 3.533 3.250 3.600 (0.300) (0.421) (0.286) (0.435) (0.307) (0.371) (0.388) Undiluted 2.903 4.267 3.067 2.333 2.867 2.600 2.600 (0.302) (0.371) (0.452) 0.374 (0.363) (0.363) (0.388)

Significance tests (Versus Neutral) Diluted - 0.719 0.740 0.554 0.852 0.457 0.961

Undiluted - 0.010 0.762 0.267 0.942 0.549 0.556

Tables 10 and 11 present the results for the HL and EG tasks, respectively, for each subject group. Looking at the positive mood treatment, we notice the treatment effect is only significant for the undiluted subjects above the median. In other words, positive mood decreases risk aversion only for subjects whose mood was significantly affected by the mood inducement video and who did not experience the mood dilution from the PANAS survey. The results for the negative mood treatment were noisy and counterintuitive. While the treatment effect was still not statistically significant in the EG task, results from the HL task indicate a stronger effect for subjects below median mood responsiveness to the video.

To ensure robustness of the findings presented above, we now turn to estimating the model described in the methodology section. This allows us to generate point estimates of the relative risk aversion coefficient, and to model this parameter as a function of treatments, risk tasks, dilution groups, and individual characteristics. Using this model, we can make more definitive conclusions regarding the true effect of positive and negative moods on risk preferences as well as the significance of the dilution effect inherent in the standard three-stage experimental design. Table 12 reports the results with different specifications of the relative risk aversion coefficient. The model in column 1 used as covariates indicator variables for the HL task, undiluted group, positive and negative mood treatments, as well as the interaction between mood treatment and undiluted group. Column 2 included the demographic variables gender, race, and school year, while column 3 included income. Finally, column 4 was a saturated model that combined all the regressors in the first three specifications.

As can be seen, the coefficient on the indicator variable for the HL task was negative and significant across all specification, proving that the choice of risk preference elicitation method can indeed alter the results. The negative sign on this coefficient indicates that subjects were more risk-seeking in the HL compared to the EG task. More importantly, while the coefficients on the positive and negative mood dummies were not significant across all specifications, the interaction between those variables and the undiluted group was significant at the 99% and 90% level for the positive and negative mood treatments respectively. This provides strong evidence supporting the dilution effect discussed in the descriptive results presented above. Thus, relying solely on survey- based mood measurement methods does significantly bias the results by attenuating the treatment effects of . The negative magnitude on the coefficient implies a lower level of risk aversion for the undiluted subjects in both the positive and negative mood treatments compared with the control (neutral mood).

Table 12. Relative Risk Aversion Coefficient Estimate by Treatment and Subject Characteristics [1] [2] [3] [4] Parameter Parameter Parameter Parameter Variable (r) (Std. Error) (Std. Error) (Std. Error) (Std. Error) Constant 1.299 *** 1.597 *** 1.396 *** 1.672 *** (0.150) (0.526) (0.167) (0.531) Holt-Laury -1.503 *** -1.526 *** -1.507 *** -1.531 *** (0.108) (0.108) (0.108) (0.108) Undiluted 0.537 *** 0.559 *** 0.536 *** 0.542 *** (0.162) (0.159) (0.164) (0.161) Positive 0.087 0.072 0.078 0.071 (0.178) (0.174) (0.177) (0.174) Negative 0.229 0.195 0.213 0.195 (0.171) (0.168) (0.172) (0.169) Positive x Undiluted -0.657 *** -0.655 *** -0.646 *** -0.647 *** (0.242) (0.238) (0.241) (0.238) Negative x Undiluted -0.379 * -0.400 * -0.391 * -0.399 * (0.230) (0.231) (0.231) (0.233) Male - -0.221 ** - -0.212 ** (0.104) (0.104) School Year - -0.020 - -0.021 (0.024) (0.024) White - 0.207 - 0.240 * (0.133) (0.136) African American - 0.436 ** - 0.441 ** (0.169) (0.171) Hispanic - 0.292 ** - 0.282 * (0.148) (0.150) Medium Income - - -0.100 -0.037 (0.126) (0.127) High Income - - -0.146 -0.124 (0.111) (0.117)

Observations 2,355 2,355 2,355 2,355 Log Likelihood -1394.91 -1387.09 -1394.04 -1386.48 Notes: Single (*), double (**), and triple (***) asterisks are used to denote significance at the 0.1, 0.05, and 0.01 levels respectively. Regarding the demographic and socioeconomic effects, we find males to be more risk- seeking than females and upper school year students more risk-seeking compared with lower school year students. This is apparent in the fact that the coefficients on the associated indicator variables were negative and significant. Also, the coefficients on the indicator variables for White,

African American, and Hispanic individuals were positive and significant implying that they are generally more risk-averse compared to other individuals (e.g., Asians, Pacific Islanders, etc.).

6. Conclusion

The literature addressing the effect of incidental positive and negative mood on risk preferences reports contradictory results, which might be stemming from key differences in particular elements of the experimental design. This study is aimed at examining difference in the observed treatment effect arriving from the choice of mood manipulation check and risk preference elicitation task. In doing so, facial expression analysis technology and pupil dilation measures are utilized as an alternative to the standard survey-based mood measurement techniques to assess the success of mood inducement. Moreover, the effect of induced mood on risk preferences is measured using two popular risk preference elicitation tasks in the experimental economics literature to assess the sensitivity of treatment effects to the choice of elicitation mechanism.

When investigating the effect of induced positive and negative mood on risk preferences, we split subjects in each mood treatment into two groups. The diluted group followed the conventional three-stage experimental design, which includes the PANAS survey as the instrument for mood measurement. Conversely, the undiluted group skipped this intermediate survey and went straight through from mood inducement to risk preference elicitation using the HL and EG tasks. We find strong evidence of a mood dilution resulting from the use of survey-based mood manipulation checks, which leads to a significant bias by attenuating the observed effect of positive and negative moods on risk preferences. In fact, the treatment effect was statistically significant only for the undiluted group, whose mood was not diluted as a result of sitting through the intermediate PANAS survey. While the results indicated that positive mood stimulates more risk- seeking behavior, the effect of negative mood on risk preferences was more task dependent.

Our analysis revealed a very important issue that needs to be considered when trying to test the effect of incidental mood on risk preferences and individual behavior in general. Moreover, we demonstrate a useful application of biometric tools (i.e., facial expression analysis technology and pupil dilation measures) in experimental and research. Using psychophysiological data, we are able to circumvent the problem of mood dilution by generating accurate measures of subjects’ moods without the need for an intermediate task between mood inducement and preference elicitation. We this work encourages further research on the topic that can provide an even deeper understanding of the mechanism through which mood influences the individual decision-making process.

7. References

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