Department of Business Administration – Quantitative Marketing

University of Zurich

Spring Term 2011

Supervisor: Prof. Dr. Florian Stahl

The Effect of Emotions

on Individuals’ Inter-temporal Choices

Zurich, August 25th, 2011

Giacomelli Stefano

Via Sceredascia 3a, 6828 Balerna

giacomes@gmail.com

Field of study: Management and Economics

Student ID: 06-983-514

Abstract

The aim of this paper is to investigate the still unknown relationship

between emotions and intertemporal choices of individuals. We in-

duced Bachelor and Masters students to experience happy, sad, and

angry emotional states. Then, collecting their WTPs for Internet sub-

scriptions, we constructed the respective discount functions from av-

erage monthly discount rates. The results confirm that showing pic-

tures related to the target emotions in combination with the evocation

of a past memory significantly induces happiness, sadness, and anger.

Moreover, we found evidence of decreasing and inverse N-shape time

discounting for our discount functions. Last, as we hypothesized, the

graphical analysis reveals that happy individuals discount the future

more than sad and angry ones in the short-medium term (up to six

months in duration). Moreover, happiness seems to induce people to

subscribe for longer contract durations (eighteen months), than sad-

ness, and anger do (twelve months). This is one of the first studies that

try to shed light on the effect of emotions on individual’s inter-

temporal choice. We hope our results can raise interest in this field of

study.

“The emotions aren't always immediately subject to reason, but they

are always immediately subject to action”

William James

i

Table of Contents

List of Figures and Tables ..................................................................... I

List of Abbreviations .......................................................................... IV

1. Introduction......................................................................... 1

2. Theoretical Framework ....................................................... 6

2.1 Intertemporal choice Theory............................................... 6

2.1.1 Criticism to DU model and Alternatives .................. 9

2.2 Emotions and Economic Theory ...................................... 14

2.3 Hypotheses ........................................................................ 17

3. Data and Methods ............................................................. 20

3.1 Description of Data ........................................................... 20

3.2 Methods of investigation .................................................. 24

4. Results .............................................................................. 27

5. Conclusion ........................................................................ 43

6. Bibliography ...................................................................... V

7. Appendix............................................................................ X

I

List of Figures and Tables

Figure 1: Discount Functions

Figure 2: Question 1 on three months WTP for an Internet sub-

scription

Figure 3: Discount function for Happy Condition (N=57)

Figure 4: Discount function for Sad Condition (N=47)

Figure 5: Discount function for Angry Condition (N=41)

Figure 6: Discount functions and Average Discount Rates from

Bachelor Class

Figure 7: Discount Functions and Average Discount Rates from

Masters Class (Control)

Figure 8: Percentage Variations in r between & Ques-

tionnaire

Figure 9: List of Emotions’ Levels Collected

Table 1: Brain’s Region Involvement Depending on Emotions

Table 2: Number of Observations

Table 3: Average Levels of Emotions in Bachelor Class

Table 4: Average Monthly WTP and Discount Rate (r)

Table 5: Kruskal-Wallis analysis of variance for Cheerfulness

Table 6: Mann-Whitney-Wilcoxon for Cheerfulness

Table 7: Summary of Kruskal-Walliss analysis of variance for

Sad and Angry experiments’ pairs of target emotions

Table 8: Summary of Mann–Whitney–Wilcoxon for Gloomi-

ness, Sadness, Furiousness, and Anger

Table 9: Summary of Mann–Whitney–Wilcoxon for Cheerful-

ness, Happiness, Furiousness, and Anger in Master

class (Control)

II

Table 10: Kruskal-Wallis analysis of variance for Arousal

Table 11: Mann–Whitney–Wilcoxon for Arousal

Table 12: Percentage Monthly Variations in between &

Questionnaire for Bachelor and Master

Table 13: Kruskal–Wallis analysis of variance for Happiness

Table 14: Mann–Whitney–Wilcoxon for Happiness

Table 15: Kruskal–Wallis analysis of variance for Sadness

Table 16: Mann–Whitney–Wilcoxon for Sadness

Table 17: Kruskal–Wallis analysis of variance for Gloominess

Table 18: Mann–Whitney–Wilcoxon for Gloominess

Table 19: Kruskal–Wallis analysis of variance for Furiousness

Table 20: Mann–Whitney–Wilcoxon for Furiousness

Table 21: Kruskal–Wallis analysis of variance for Anger

Table 22: Mann–Whitney–Wilcoxon for Anger

Table 23: Mann–Whitney–Wilcoxon for Cheerfulness Master

class (Control)

Table 24: Mann–Whitney–Wilcoxon for Happiness Master class

(Control)

Table 25: Mann–Whitney–Wilcoxon for Furiousness Master class

(Control)

Table 26: Mann–Whitney–Wilcoxon for Anger Master class

(Control)

Table 27: Mann–Whitney–Wilcoxon for Arousal Master class

(Control)

Table 28: Kruskal–Wallis analysis of variance for 3 Months Du-

ration Discount Rates

Table 29: Kruskal–Wallis analysis of variance for 6 Months Du-

ration Discount Rates

Table 30: Kruskal–Wallis analysis of variance for 9 Months Du-

ration Discount Rates

III

Table 31: Kruskal–Wallis analysis of variance for 12 Months

Duration Discount Rates

Table 32: Mann–Whitney–Wilcoxon for Happy vs Sad 15

Months Duration Discount Rates

Table 33: Mann–Whitney–Wilcoxon for Happy vs Sad 18

Months Duration Discount Rates

Table 34: Mann–Whitney–Wilcoxon for Happy vs Sad 21

Months Duration Discount Rates

Table 35: Mann–Whitney–Wilcoxon for Happy vs Sad 24

Months Duration Discount Rates

IV

List of Abbreviations

DU model: Discounted Utility model

HD model: Hyperbolic Discounted model

Quasi-HD model: Quasi-Hyperbolic Discounted model

WTP: Willingness to pay

PET: Positron Emission Tomography

1

1. Introduction

In the past decades researcher’s interest in individual intertemporal

choice has gained more and more attention. A pioneer of this topic was

John Rae with his work The Sociological Theory of Capital1 in 1834.

With the intent to explain why nations differ in their wealth, Rae pro-

posed four psychological/sociological factors which can jointly explain

why a person should, or should not desire to accumulate during his/her

life. Other important studies were then conducted by Jevons2 (1888,

1905), and Eugen von Böhm-Bawerk (1890, 1891). All these authors

furthered Rae’s perspective that psychological determinants are a foun-

dation for individual time preference behavior.

In 1937 Samuelson introduced the concept of discount rate when formal-

izing his Discounted Utility Model. All the previous psychological fac-

tors involved in the intertemporal choice mechanisms were explained in

Samuelson’s model by that simple parameter. Despite Samuelson’s own

skepticism, economists from all over the world started to blindly trust in

the DU model mainly because its simplicity and elegance. Furthermore,

Koopmans (1960) provided a derivation of the model from a set of

proposition that pushed for an additional diffusion of the DU model.

In recent years economists as well as psychologists and neurobiologists

started to confute the DU model assumption using empirical evidence. In

particular Frederick et al. (2002) provided a critical review that exposes

the main criticism and major anomalies of the DU model. Moreover,

they enumerate alternative models such as Hyperbolic Discounting

1 Note that before Rae’s work Adam Smith already highlighted the determinant role of

intertemporal choice for the wealth of nations (Frederick et al. 2002).

2 Father and son.

2

Models, and Quasi-Hyperbolic Discounting Models. Among these mod-

els, the main area of interest is incorporating “visceral” influences on

behavior proposed by Loewenstein (1996), while a model that accounts

for the main anomalies reported to the DU model is the one of Loewen-

stein and Prelec (1992):

This section presents a model of intertemporal choice that accounts for

the anomalies just enumerated. Our model assumes that intertemporal

choice is defined with respect to deviations from an anticipated status

quo (or "reference") consumption plan; this is in explicit contrast to the

DU assumption that people integrate new consumption alternatives with

existing plans before making a choice.3

Several authors focus on other aspects of intertemporal choice such as

cognition in decisions (Ainslie 1975, O’Donoghue and Rabin 2000, and

Della Vigna and Malmendier 2006), reactions to cues (Mano 1992, Wil-

son and Daly 2004, and Winkielman et al. 2005), and delay of gratifica-

tion (Mischel and Ebbesen 1970, Mischel et al. 1972, and O’Donoghue

and Rabin 2001).

For this work, the publications of Elster (1998) and Loewenstein (2000)

are of interest. They analyze the impact of emotions in economic theory

shedding light on the important role played in decision-making and eco-

nomic behavior. What emerges from their results is that in recent year

little attention was paid by economists when considering emotion-

related arguments:

I think we need a better understanding of how emotions actually influ-

ence behavior before we can begin to think about how they may have

3 (Loewenstein and Prelec 1992, page 578)

3

evolved. … The more urgent task is to understand how emotions interact

with other motivations to produce behavior.4

Perhaps put off by their perceived unpredictability, economists have

only rarely incorporated visceral factors into their models of human

behavior.5

It seems reasonable that future studies of intertemporal choice should

consider to incorporating and trying to explain the impact of emotions

on individual’s behavior and decision-making process.

In spite of this, we have no knowledge of previous literature concerning

the effect of emotions on individuals’ intertemporal choices. Specifical-

ly, it remains unclear what mechanisms support the decision making

process and the consequent behavior when experiencing a given emo-

tion. Authors like Mano (1992) and Lewinsohn and Mano (1993) focus

on the role of unpleasantness and arousal in judgment formation, finding

them to have a strong impact. Winkielman et al. (2005) studied how

inducing emotions through showing emotional facial expressions can

cause aroused states, which have a direct impact on the willingness to

pay of the subjects. In addition to this evidence, physiological studies on

animals showed that there are dramatic changes in dopamine-associated

signals in rats’ nucleus accumbens during the development of self-

administration habit (Gratton and Wise 1994), and that reward-

associated cues alone can induce elevated extracellular dopamine

(Blackburn et al. 1989, and Pfaus et al. 1990). Similar study was con-

ducted on human beings using PET (positron emission tomography)

(Damasio et al. 2000). This leads to the conclusion that:

4 (Elster 1998, pages 72-73)

5 (Loewenstein 2000, page 427)

4

All emotions engaged structures related to the representation and/or

regulation of organism state, for example, the insular cortex, secondary

somatosensory cortex, cingulate cortex, and nuclei in brainstem tegmen-

tum and hypothalamus. These regions share a major feature in that they

are all direct and indirect recipients of signals from the internal milieu,

viscera and musculoskeletal frame.6

In addition, evidence for visceral responses can be found in Panksepp

(1998). Moreover, Davidson and Irwin (1999) proved that both in the

perception of emotional cue, and in the production of emotional re-

sponses, there is a main involvement of specific brain’s region.

It seems clear that not enough attention was given recently to the deter-

minant role that emotions can have in the individual’s decision-making

process for intertemporal choice.

The purpose of this paper is to investigate the effect that emotions play

on individual’s intertemporal choice through evocation of happy, sad,

and angry moments in one’s personal sphere. In particular, the research

wants to prove that when feeling one of the mentioned emotions, indi-

vidual’s willingness to pay for a wireless subscription is different de-

pending on the particular condition he/she is experiencing. Furthermore,

the average monthly discount rate for that service, in periods ranging

from three to twenty-four months, should have a proper pattern for each

of the three conditions submitted to the students participating the survey.

The results confirm the original hypotheses. It is shown that it is possible

to trigger basic emotions like happiness, sadness, and anger and the re-

lated arousal in individuals’ emotional sphere by self-evocation method

in combination with selected pictures. Furthermore, the results indicate a

6 (Damasio et al. 2000, page 1051)

5

clear matching between the three discounting functions patterns we cal-

culated and the recent evidence of decreasing discount rates found in

intertemporal choice literature. Finally, analysis of the role of emotions

on individuals’ intertemporal choice behavior shows that emotional

states actually impact on people’s discount rates in various way. In par-

ticular, evidence of higher time discounting during the happy condition

for three to twelve months subscription durations was found. For longer

time periods, the discounting functions share a similar pattern.

The results confirm the opinion that future studies should take the fun-

damental role played by emotions in intertemporal choice decisions

more into account. This paper argues that economists have neglected the

importance of emotional states in decision-making and their conse-

quences for too many years. This work attempts to help to show how

strong that impact can be, which would raise interest for further research

in this field. As a consequence, we believe that a better physiological,

psychological, and economical understanding of the determinants and

mechanisms underlying a certain behavior when feeling a specific emo-

tion, will be the key for novel and innovative marketing strategies.

The remainder of the paper is organized as follow. Chapter 2 provides a

wide theoretical framework to offer an insight into various disciplines

correlated to our study and to consequently construct hypotheses. The

data collection process is successfully described and some descriptive

statistics are presented in Chapter 3. Furthermore, the same section in-

troduces the methods used to investigate our hypotheses. Chapter 4 is

entirely dedicated to the interpretation of the results and to check their

robustness by some controls. The last chapter is left for conclusions and

further considerations and discussions.

6

2. Theoretical Framework

This section is entirely dedicated to a review of the relevant literature for

this work. In Subsection 2.1 we retrace the evolution of intertemporal

choice theory from its birth to the newest and most interesting approach-

es. Subsection 2.2 incorporates all of these elements that are related with

evocation of emotions, cue’s effect on individuals, and physiological

reactions interesting for intertemporal choice behavior. Finally, in Sub-

section 2.3, the hypotheses are stated supported by the preceding theo-

ries.

2.1 Intertemporal choice Theory

With the intent to explain why Nations differ in their wealth in his So-

ciological Theory of Capital (1834), John Rae first introduced inter-

temporal choice as a distinct topic. Rae identified six psychological and

sociological factors that were jointly assumed to be responsible for inter-

temporal choice behavior:

The desire to accumulate would then seem to derive strength, chiefly

from three circumstances.

1. The prevalence throughout the society of the social and benevolent

affections, or, of that principle, which, under whatever name it may be

known, leads us to derive happiness from the [future] good we com-

municate to others.

2. The extent of the intellectual powers, and the consequent prevalence

of habits of reflection, and prudence, in the minds of the members of the

society.

3. The stability of the condition of the affairs of the society, and the reign

of law and order throughout it.

7

It is weakened, and strength given to the desire of immediate enjoyment,

by three opposing circumstances.

1. The deficiency of strength in the social and benevolent affections, and

the prevalence of the opposite principle, a desire of mere selfish gratifi-

cation.

2. A deficiency in the intellectual powers, and the consequent want of

habits of reflection and forethought.

3. The instability of the affairs of the society, and the imperfect diffusion

of law and order throughout it.7

Frederick et al. (2002) identifies the first two factors as the most im-

portant in the individual’s desire for accumulation, while the last points

can be identified as limiting factors. The following discussions related to

this first psychological approach were made by N.W. Senior (1836),

W.S. Jevons (1888) and H.S. Jevons (1905). Senior believed that indi-

viduals judge the present and the future with the same weight but they

suffers the self-negotiation task during intertemporal choice. The other

two authors defend the thesis that people care only about immediate util-

ity (consumption) in their selfish delayed choices.8

In the second chapter of Capital and Interest (1890) E. von Böhm-

Bawerk challenges Senior’s Abstinence theory, and adds a new factor to

the Rae’s list in his next work The Positive Theory of Capital (1891):

IT is one of the most pregnant facts of experience that we attach a less

importance to future pleasures and pains simply because they are future,

and in the measure that they are future.9

7 (Rae 1834, pages 58-59)

8 See Frederick et al. (2002) for more details

9 (v. Böhm-Bawerk 1891, page 253)

8

Successively, according to the Austrian Economist’s previous publica-

tions, I. Fisher proposed a formal model of intertemporal allocation of

consumption in Part III of his work The Theory of Interest (1930).

Unfortunately, Rae’s first psychological approach, which lasted slightly

more than one century, was completely swept away from P.A. Samuel-

son’s DU model (1937):

( ) ∑ ( )

( ) ( ) (

)

The Greek letter in the above formula represents the amalgamation of

all previous psychological elements into a single parameter, that is, the

discount rate. Samuelson was skeptical at first about the strong and un-

realistic assumptions in his model:

Our task now is to indicate briefly the serious limitations of the previous

kind of analysis, which almost certainly vitiate it even from a theoretical

point of view.10

Unfortunately, the DU model had a wide diffusion among economists

for many years. The cause of this wide spread can be explained in two

different points in time. The model was immediately widely accepted

thanks to Samuelson’s elegant formulation; this was mainly because of

its simplicity. At a second time, T.C. Koopmans (1960) provided a for-

mal derivation of the model from a set of axioms, which definitely estab-

lished it as the standard model of intertemporal choice (Frederick et al.

2002). Important critics of the DU model are now presented along with

its main anomalies, and the most used alternatives such as Hyperbolic

Discounted models and Quasi-Hyperbolic discounting models.

10 (Samuelson 1937, page 159)

9

2.1.1 Criticism to DU model and Alternatives

The first serious challenge to the DU model was posed by G. Ainslie in

1975 (Berns et al. 2007). In fact, while studying why human beings are

often oriented to choose the worse of two alternative rewards, even

when they are conscious of it, Ainslie casts doubt directly on the expo-

nential discounting assumption:

Highly concave delay curves from some pairs of smaller-earlier and

larger-later alternative rewards can cross, predicting an initial prefer-

ence for the larger reward, which changes in favor of the smaller (spe-

cious) reward as the smaller reward becomes imminently available. This

description accords with an intuitive view of what temptation is like.11

Similar conclusions can be found in G.F. Loewenstein (1996). If

Ainslie’s explanation of individuals’ short-time preferences in inter-

temporal behavior can be attributed to impulsiveness, Loewenstein’s

interpretation focuses on visceral influences. The result is that at a suffi-

cient level of intensity, visceral factors can cause a present-time orienta-

tion behavior. This evidence, known as hyperbolic discounting, is well

documented in several other studies (e.g. Thaler 1981, O’Donoghue and

Rabin 1999, Carstensen et al. 1999, and Zauberman 2003).

An example of HD model, state the following (Zauberman et al. 2009):

( ) ∑ ( ) ( )

( )

It is evident from the functional form of ( ) that ( )

. This speci-

fication of HD model was first introduced by R. Herrnstein (1981) and

11 (Ainslie 1975, page 492)

10

later proposed by J. Mazur (1987). Other authors used different hyper-

bolic functional forms, however, all of them share the pattern of declin-

ing discount rates. For instance, G. Ainslie (1975) suggested ( )

,

while G. Loewenstein and D. Prelec (1992) proposed a more sophisti-

cated version, ( ) ( )

. All these models of

non-constant discounting allow individuals time-inconsistent behavior,

challenging one of the strongest assumption of the DU model. In fact,

Samuelson’s model assumes that once an agent choose an alterna-

tive/plan (of consumption), she will not change her preference over time;

the so called Dynamic Consistency. Despite this, there is empirical evi-

dence of preference reversals, which can be attributed to anything from

hyperbolic time discounting (Frederick et al. 2002), to visceral influ-

ences (Loewenstein 1996), and to consumers’ subjective perception of

time horizon per se (Zauberman et al. 2009). Time-inconsistent behavior

was already mentioned by R. H. Strotz (1955-56) when analyzing “the

deliberate regimenting of one’s future economic behavior” (p. 165):

To summarize, we have said that the optimal plan of future behaviour

chosen as of a given time

(A) may be a plan which will be followed under conditions of certainty

(the harmtiony case), or

(B) may be inconsistent with the optimizing future behaviour of the indi-

vidual (the intertemporal tussle).12

For instance, consumers naiveté, and the consequent overestimation of

future consumption frequency, when subscribing to a gym for a long

period (Della Vigna and Malmendier, 2006) demonstrate the Myopia

concept first introduced by Strotz (1955-56) well. Another interesting

12 (Strotz 1955-56, page 180)

11

example of preference reversals was found (and explained by hyperbolic

discounting curves) monitoring pregnant women pre, during, and post

childbirth (Christensen-Szalanski, 1984). He asked 18 pregnant women

to make a non-binding decision between having or not anesthesia during

parturition. Most of them opted for avoiding medicaments during birth.

However, when they started to experience pain, most of them exhibited

preference reversals by asking for anesthesia.

Hyperbolic discount is not the only way to explain such preference re-

versals, they can also be modeled in terms of quasi-hyperbolic discount

function. A formalization of Quasi-HD model is the following (Zauber-

man et al. 2009)13:

( ) ( ) ( ) ∑ ( ) ( )

( ) (

)

Again, as in the case of HD models, the discount rate decreases as the

time horizon gets longer. After comparing the shape of these alternative

models discount functions against the DU model one, it is evident from

Figure 1 that they successfully catch individuals’ present bias.

, -

Lowenstein and Prelec (1992) enumerated four anomalies of the DU

model. They first discuss the common difference effect, which is related

to the above explanation of time-inconsistent behavior and decreasing

13 For the first application of this Q-HD model see Laibson (1997).

12

discount rate over time. Second, critiques posed by empirical evidence

(Holcomb and Nelson, 1992) is the so called absolute magnitude effect,

meaning that large amounts of money suffer a smaller discounting if

proportioned with small ones. The third challenge to the DU model

comes from the gain-loss asymmetry. This shows that losses are strongly

discounted less than gains (Loewenstein, 1987). This evidence is in con-

flict with Samuelson’s assumption that the baseline consumption level is

constant across time periods. The last anomaly is the delay-speedup

asymmetry. Loewenstein (1988) found that when presenting people the

same option of speeding up and delaying consumption, in different for-

mats, the result is a framing effect. This directly conflicts any normative

theory, including DU model one.

Another Critical Review of intertemporal choice models is presented by

Frederick et al. (2002). This more recent analysis of the DU model as-

sumptions and anomalies takes into account all the previous critiques

proposed by Loewenstein and Prelec (1992). Furthermore, new empiri-

cal evidences, in contrast with Samuelson’s model, are illustrated. For

instance, applying hyperbolic functional forms to sets of data collected

from different studies leads to a significantly better fit than using the

exponential one (Myerson and Green 1995, Kirby 1997). Another im-

portant challenge to one of the flimsiest assumptions of the DU model is

the violation of consumptions independence. In fact, Loewenstein and

Prelec (1993) showed that individual’s preferences over profiles are af-

fected by the nature of consumption in periods in which consumption is

identical to the two profiles. Among the various recent critics to the DU

model assumptions, the authors highlight the weakness of the stationary

instantaneous utility function one. Generally, individuals’ preferences

change over years (Loewenstein and Angner 2003), as a consequence it

is unlikely that the cardinal instantaneous utility function can be constant

13

across time. Finally, the authors cast doubt on the independence of dis-

counting from consumption. According to them, it seems more reasona-

ble that people discount utility from food consumption and utility from

holiday using a different rate.

All the above literature and related empirical evidence against the DU

model explains the recent proliferation of alternative models such as

HD, Q-HD, and their variants (e.g. including visceral factors, reference

points, time orientation, etc.) well. Therefore, it is not surprising that

these models are more and more inclined to incorporate sophisticated

versions of those psychological mechanisms which underlay Rae’s first

analysis of intertemporal choice behavior.

14

2.2 Emotions and Economic Theory

The role of emotions in economic theory and economic behavior is not a

recent development. Already in 1789 J. Bentham, when he first intro-

duced the construct of utility, strongly believed in the prominent influ-

ence played by emotions in his theory:

Nature has placed mankind under the governance of two sovereign mas-

ters, pain and pleasure. It is for them alone to point out what we ought

to do, as well as to determine what we shall do. , - They govern us in

all we do, in all we say, in all we think: every effort we can make to

throw off our subjection, will serve but to demonstrate and confirm it.

, - The principle of utility recognizes this subjection, and assumes it for

the foundation of that system, the object of which is to rear the fabric of

felicity by the hands of reason and of law.14

As Rae’s (1834) first psychological approach to intertemporal choice

was swept away from Samuelson’s DU model (1937), so Bentham’s

(1789) attempt to explain the concept of utility involving emotions was

eclipsed by the Samuelson’s theory of revealed preference (1938).

Again, as the discount rate alone incorporated all previous psychological

factors, so did the index of preference substituted the emotional part

(happiness) in the construct of utility.

Fortunately, recent works (Elster 1998, Loewenstein 2000) revisited the

fundamental consequences of considering individual’s economic behav-

ior with respect to emotions. Moreover, not only economists but also

psychologists gave the above relation their attention. As a result, in re-

cent decades the emotions-related literature has experienced a dramatic

burst. Among the most important publications for investigation are the

14 (Bentham 1789: Chapter I1)

15

ones of Mano (1992), Loewenstein (1996), Damasio et al. (2000), and

Wilson and Daly (2004).

Economist H. Mano (1992) showed that pleasantness and arousal are

predominant dimensions in the individuals’ decision-making process and

its outcomes. In fact, his results lead to the conclusion that when experi-

encing a high level of pleasantness, individuals tend to spend more time

and to be more careful in decision strategies. Vice versa, high levels of

arousal cause people to behave in the opposite way15. Moreover, the rela-

tion between positive/negative affect and high/low levels of arousal is

still puzzling. What is known is that, for example, negative affect and

intermediate arousal (e.g. sadness) give rise to different judgmental pro-

cesses and outcomes than positive affect and middle arousal (e.g. happi-

ness).

Another of the most accurate and revealing studies is the analysis of

visceral influences on behavior proposed by G. Loewenstein (1996). The

main point of his work is that visceral factors (e.g. anger, fear) under-

mine individual’s rational behavior and cause them to behave contrary to

their long-term self-interest. This can happen with or without full aware-

ness of the subjects, depending on how strong their visceral influence is.

Recent empirical confirmations of the concepts explained above come

from psychological/physiological researches conducted by Damasio et

al. (2000), and Wilson and Daly (2004). Using PET, the Portuguese neu-

rologist and his team investigated the neural basis of emotion and feel-

ing. To trigger a determinate emotion, that is, sadness, happiness, anger,

and fear, they ask the subjects participating in the experiments to re-

experience personal life episodes through detailed mental evocations of

those moments. PET’s outputs provide the map of the physiological

15 Note that opposite conclusions were found to Isen and Means (1983).

16

functional process for each condition experienced by the individual. Ta-

ble 1 below shows their results in the detail.

, -

What is really interesting for this work is the fourth, second to last, and

last row. In fact, when processing a reward-related stimulus the ventral

tegmental area (VTA) of our brain is activated by a signal from the cor-

tex. Successively, the VTA releases dopamine into the nucleus accum-

bens, the septum, the amygdala, and the prefrontal cortex. The neuro-

transmitter dopamine reaches the nucleus accumbens, which is situated

in the forebrain, and allows for desire promotion. This implies that when

experiencing emotions like happiness, sadness and anger, distinct re-

gions of the brain are involved in the process of a reward leading to dif-

ferent individuals’ behaviors. For example, there are evident significant

reactions of the brain during happy and sad conditions, despite this, their

patterns show qualitative distinctions.

Later, Wilson and Daly (2004) obtain results from magnet resonance

imaging data, which confirm the activation of the nucleus accumbes in

middle aroused men. Furthermore, they found that those men discount

the future more when stimulated with cues of sexual opportunity.

What emerges from analyzing emotions and economic theory is that to

investigate their effect on individuals’ intertemporal choices, we need to

take into account not only mere variations of individuals’ preferences

across time, but also physiological and psychological components like

arousal and visceral factors.

17

2.3 Hypotheses

It seems so clear from past literature that a multitude of factors work in

concert to promote a series of mechanisms which drive individuals to

determinate behaviors in reward evaluation and intertemporal choice

when experiencing happy, sad, and angry emotions. Arousal levels and

visceral factors surely play a determinant role, as well as different emo-

tional states do.

Knowledge of studies that try to explain how emotional state like happi-

ness, sadness, and anger influence the discount rate that one applies in

intertemporal choice for a subscription are not known. Despite this,

while stating hypotheses, the most important previous findings that are

relevant to this study are reviewed.

Concerning the possibility to trigger the above-mentioned emotions in

an experimental setting, Wild et al. (2001) found additional evidence of

the emotional contagion hypothesis (Hsee et al. 1990, and Hatfield at al.

1992). In particular, individuals viewing pictures of happy faces feel

happiness and showing sad facial expression evoke sadness within the

viewer. This process is almost immediate, that is, it takes more or less

500 millisecond to take place. Damasio et al. (2000) asked subjects to

evoke sad, happy, and angry episodes through the production of detailed

mental pictures of those moments in order to map, via PET, which brain

regions were involved during the feeling of each emotion. Their results

show that each one of the selected self-inducted emotions produce a

proper mental pattern. For the empirical investigation this paper uses

both pictures and self-evocation methods to trigger happiness, sadness,

and anger in the subjects participating in the data collection process.

18

Moreover, Winkielman et al. (2005) find that in low/middle thirsty peo-

ple the arousal level is higher for happy people than for angry ones16.

H1 : Showing happy-, sad-, and angry-related pictures, in addition to

the individual’s self-evocation through writing a detailed person-

al life episode inherent to the target emotion, trigger as a conse-

quence happiness, sadness, and anger in the persons’ emotional

sphere.

H2 : The arousal level is similar for people during happy and sad

conditions but higher if those are compared with persons who

self-evoked angry emotional state.

Previous literature on intertemporal choice theory suggests hyperbolic

discounting pattern (Ainslie 1975, Thaler 1981, Loewenstein 1996, Zau-

berman et al. 2009, etc.). Furthermore, Stahl et al. (2010) provide evi-

dence of an inverse N-shaped pattern for discount rates of subscriptions,

where the point in which the discount rate increases correspond to the

maximum contract duration that consumers typically subscribe to a ser-

vice.

H3 : The discount rate for all happy, sad, and angry conditions de-

creases as the time horizon get longer showing inverse N-shaped

pattern.

H4 : The maximum contract duration for the wireless subscription

varies depending on the in-class triggered emotional state.

Finally, this paper’s main interest focuses on clarifying how happiness,

sadness, and anger directly impact individuals’ intertemporal choices.

Discount rate patterns for each emotional state directly from individual’s

WTP for an internet subscription will be attempted to be defined. The

16 A title of comparison, note that Mano (1992) defines sadness as middle arousing.

19

most related previous findings are presented in Winkielman et al.

(2005). They studied how WTP varies depending on the actual emotion-

al state (i.e. happiness and anger) and on the effect of a visceral factor

(i.e. thirst). However, their results do not confirm that happy people are

present oriented. In other words, the WTP of individuals experiencing

angry feelings is lower than for their happy feeling counterparts. This

result is believed to be influenced by the intensity of the visceral factor,

which strongly conditions individual’s behavior in the opposite way we

expect that it should be. Thus, the fifth and sixth hypotheses are in con-

trast with Winkielman et al. (2005), because happiness, sadness, and

anger are assumed to be basic emotions, thus not as influencing as vis-

ceral factors are.

H5 : The WTPs for the internet subscriptions of happy people is

higher in the short term (first six months) than for sad and angry

ones.17

H6 : The monthly discount rates of happy people are always higher

than for sad ones.18

17 This is equivalent to saying that the respective (to monthly WTP) discount rate is

higher in the short term (first six months) for happy people than for sad or angry

ones. 18

Apparently this can be partly in contrast with H2 in which happy and sad levels of

arousal were expected to be similar and higher than to angry ones. What plays in

favor of H6 is that similar levels of arousal do not directly imply same behavior

because of the contrasting impact of positive/negative affect (Mano 1992). Tak-

ing some insight from that two-dimensional model it is argued that, given H2 and

happiness present-time orientation effect (H5), the perfectly opposite affect in

happy versus sad conditions should leads to constantly higher discount rates for

self-inducted happiness.

20

3. Data and Methods

This section explains the data collection process and the methods that

were used to investigate our hypotheses. Subsection 3.1 explain how

happiness, sadness, and anger are triggered in subjects participating the

experiments, then summary statistics of the variables of main interest are

provide. Lastly, the data their reliability are discussed. Subsection 3.2 is

dedicated to the description of the methods used to investigate the six

hypotheses, and to motivate the them with respect to the previously de-

scripted data.

3.1 Description of Data

The final scope of the experiments is to obtain the data necessary to shed

light on the effect of emotions on individuals’ intertemporal choices. In

particular, we are interested in how three self-generated emotional states,

that is, happiness, sadness, and anger, influence the WTP for internet

subscriptions with variable durations ranging from three to twenty-four

months. The main challenge in this evocation process is to select an ap-

propriate strategy to actually trigger subjects’ emotions.

As a first step, three waves of questionnaires with bachelor’s students in

Business Administration and Management & Economics19 were per-

formed. In the first round of the experiment, they were asked to produce

a mental memory of a happy moment and to write a detailed summary of

that on a private sheet. Afterwards they respond to a questionnaire with

several questions about their feelings and emotions in that moment. Fur-

thermore, they express the own WTP for different durations of an inter-

19 During the lecture Introduction in Quantitative Marketing at the University of Zur-

ich.

21

net subscription contract (3 months, 6 months, …, 24 months). In the

following two weeks similar experiments were conducted for the two

other emotions, in chronological sequence, sad and angry. In contrast to

the first experiment (happy condition), they were asked to evoke the

emotions by writing a personal life episode, only after viewing a set of

pictures, related to the respective condition (sad or angry). During these

two waves, the students were not allowed to sit next to each other.20

Moreover, in weekly sequence, a control in a master class21 of Business

Administration and Management & Economics students was made, for

the angry and happy conditions using the same two procedure described

above for the bachelor class.22

Table 2 below shows the number of observations we left with for each

condition in each class.

Table 2: Number of Observations

Class

Experiment

Happy Sad Angry

Bachelor 57 47 41

Master 35 - 22

Notes: Recall that in the Master class we perform just two controls

20 This is justify by the fact that one can feel observed and consequently be shy when

writing a very private past feeling like sad or angry moments are.

21 During the lecture Innovation Marketing - Marketing of Innovations and Technology

Products at the University of Zurich.

22 Please note that all the treatments in the two class (bachelor and master) are per-

formed separately with at least one week of distance between each other. Moreo-

ver, to stimulate students to a reliable compilation of the questionnaires, a lottery

for winning an iPad2 was announced among participants who give serious an-

swers.

22

Table 3 provides some descriptive statistics relative to the average

arousal levels as well as to the average levels of experienced emotions

for the subjects in the bachelor class for each experiment, before and

after answering the questions related to their WTP for the subscriptions.

Table 3: Average Levels of Emotions in Bachelor class

Emotional

states

Happy Sad Angry

Before After Before After Before After

Arousal 3.61 3.04 3.72 3.53 2.71 2.95

Scared 1.74 1.84 2.21 1.89 1.73 1.54

Fearful 1.56 1.61 1.96 1.85 1.63 1.61

Disgusted 1.60 1.93 1.98 1.91 1.98 1.73

Hostile 1.84 2.21 2.15 2.34 3.93 3.46

Furious 1.68 2.23 2.64 2.47 4.51 3.98

Angry 2.26 2.68 3.02 2.74 4.80 4.20

Sad 2.02 1.81 4.19 3.19 2.71 2.15

Gloomy 2.04 1.93 3.94 3.06 2.78 2.29

Ashamed 1.47 1.49 1.87 1.70 1.51 1.54

Guilty 1.51 1.60 2.11 1.70 1.73 1.85

Cheerful 7.23 6.81 4.68 5.11 5.17 5.12

Happy 7.42 6.82 5.09 5.49 5.46 5.54

Notes: Arousal levels are measured on a scale ranging between 1 to 5, while the levels

of felt emotions on a scale ranging between 1 to 10. Bolded figures refer to target emo-

tions for each condition.

At first sight, it seems that the self-evocation process alone, as well as in

combination with viewing emotional pictures has the expected effect on

23

individuals’ emotional states. Moreover, the intensity of the feelings

seems to weaken very rapidly in all experiments.

Table 4 shows the average monthly WTP for each condition. Moreover,

the average monthly discount rate (r) is also displayed in a separate col-

umn.

Table 4: Average Monthly WTP and Discount Rate (r)

Duration

Happy Sad Angry

WTP r WTP r WTP r

3M 41.77 5.99% 43.18 4.89% 44.69 3.74%

6M 38.76 4.24% 39.88 3.77% 40.26 3.61%

9M 36.02 3.64% 36.64 3.45% 37.13 3.31%

12M 32.75 3.53% 35.07 2.96% 34.21 3.16%

15M 30.11 3.38% 31.41 3.10% 29.06 3.62%

18M 27.90 3.24% 28.96 3.03% 27.39 3.34%

21M 23.86 3.52% 26.60 3.01% 24.67 3.36%

24M 21.85 3.45% 25.05 2.88% 21.92 3.44%

Notes: The discounts rate above are calculated according to Stahl et al. (2010). The

relative formula is presented in Subsection 3.2..

Again, a raw analysis of the table above confirms the hypotheses that

happiness induces higher time discounting. Nevertheless, an accurate

data investigation is needed to obtain more meaningful results.

Overall, the data collected seem to be a very useful instrument for this

study’s final scope. We are also in possess of a small data set for further

controls and check of results’ reliability.

24

3.2 Methods of investigation

One of the main goals of this study is to clarify if and how individuals

experiencing different types of feelings change their intertemporal pref-

erences and consequently apply diverse discount rates depending on the

subscription’s duration. This information was collected indirectly by

WTP as follow:

Figure 2: Question 1 on three months WTP for an Internet subscription

Source: Zahlungsbereitschaft für ein Internet-Abonnement, Fragebogen Version

1: p.7.

The above example corresponds to Question 1. Furthermore, for each of

the three experiments, there were seven more identical questions. The

only difference being that they randomly increase by three months steps

in the duration of the hypothetical subscription up to twenty-four

months (e.g. Question 7 asks for 18 Months, and Question 5 for 24

Basic offer of the Internet Service Provider:

Price for a contract duration of 1 Month: 50.- CHF

Alternative offer of the Internet Service Provider:

Contract duration of 3 Months

For a Monthly Price of ____________ CHF/Month, I would accept

the Alternative offer of 3 Months contract duration instead of the

Basic offer (with monthly payment of the internet subscription charge)

25

Months).23 By completing the three waves of questionnaires in weekly

sequence, students provided their WTPs for an internet subscription with

durations ranging from three to twenty-four months, subject to evocation

of happiness, sadness, and anger.

To translate subjects’s WTPs to monthly discount rate we use the same

method of Stahl et al. (2010):

( )

In the above equation the discount factor ,

( ), and * +. Rearranging it with respect to , we

find the equation necessary to calculate the discount rate for a given du-

ration:

( .

/

)

Then, to obtain the discount function for each experiment the average

discount rates were calculated from observations for each of the dura-

tions, and then was simply plotted on a line chart.

All the average monthly discount rates, the average levels of arousal,

and the average intensity of feelings for each experiment, were collect-

ed, according to our hypotheses two main tasks were performed. The

first one is a graphical check of the three discount functions shapes for

testing H3 and H4. The second is slightly more sophisticated because of

the nature of H1, H2, H5, and H6. In fact, for these hypotheses the chart

23 Note that to randomize even more the results, students were randomly assigned to

two different versions of the questionnaires, which vary in order of the eight

questions.

26

analysis does not tell us if the differences, between happy, sad, and an-

gry conditions, are statistically significant. Furthermore, usual tests of

mean comparisons, like ANOVA or t-test, are not applicable to our data

because they do not satisfy the requested parametric assumptions.24 As a

consequence, we opted for non-parametric counterparts of the above-

mentioned tests. To check differences on a multivariate level the Krus-

kal–Wallis one-way analysis of variance was applied, then Mann–

Whitney–Wilcoxon tests were run as post-hoc analysis for differences in

groups.

The next section provides a deeper explanation of the results with re-

spect to the hypotheses. Moreover, we present some controls using the

observations collected in the masters class in which we performed the

angry, then the happy experiment.

24 In particular the sample data do not follow a normal distribution.

27

4. Results

The findings are presented following the hypotheses order. The first

claim is that the evocation technique we use for each of the three condi-

tions, that is, happy, sad, and angry, actually trigger happiness, sadness,

and anger in the students participating the experiment. The next tables

present the relative results. Table 5 introduces a multivariate level com-

parison of medians for the first condition tested in class. Note that

among the set of feelings collected for all experiments, just six of them

are useful for our investigation of H1, while the remaining are simply

controls.25 For the happy condition, the two relevant emotion levels to

compare against the ones collected in sad and angry experiments are

cheerfulness and happiness (target emotions).

Table 5: Kruskal–Wallis analysis of variance for Cheerfulness

Experiment Obs. Rank Sum

Happy 57 5570.00

Sad 47 2531.50

Angry 41 2438.50

Chi-squared with ties = 33.661 with 2 d.f.

Probability = .0001

Notes: Recall that we are testing

.

The table above clearly shows that there is a significant difference

among the medians of the three experiments for the variable cheerful-

ness.

25 Figure 9 in the Appendix displays all types of emotions we used.

28

To have more information about how the groups differ at univariate lev-

el, they are compared in pairs.

Table 6: Mann–Whitney–Wilcoxon for Cheerfulness

Pair 1 Obs. Rank Sum

Happy 57 3751

Sad 47 1709

z = 5.002

Probability = .0000

Pair 2 Obs. Rank Sum

Happy 57 3472

Angry 41 1379

z = 4.753

Probability = .0000

Pair 3 Obs. Rank Sum

Sad 47 1950.5

Angry 41 1965.5

z = -1.188

Probability = .2348

Notes: Recall that for each Conditions’ pair we are testing univariate differences in medians for Cherfulness’s levels.

The results in Table 6 perfectly reflect what was expected. The level of

intensity of the emotion cheerfulness is significantly higher during the

29

happy condition than for the other two. Moreover, during sad and angry

experiments those lower levels are not significantly different from each

other. This confirms that only during the self-evocation of happy memo-

ries students strongly relive emotional states involving happiness.

The results for the second target emotion are in line with our expecta-

tions. In fact, the Kruskal–Wallis test confirms the differences among

the three conditions with respect to happiness levels ( = 27.832, p <

.05), further evidence is provided by the Mann–Whitney–Wilcoxon test

for all the three comparisons at the univariate level (happy-sad z =

4.527, Prob > |z| = 0.0000 ; happy-angry z = 4.387, Prob > |z| = 0.0000 ;

sad-angry z = -0.733, Prob > |z| = 0.4635).26

The same tasks were performed for the second and third experiment. In

the sad experiment the target emotions are sadness and gloominess,

while we are interested in significant differences in the levels of furious-

ness and anger among conditions in the angry experiment. The results

can be interpreted as for the happy condition, thus, fully supporting H1.

Table 7 and 8 briefly summarize them.

26 For simplicity only the results are reported. The relative tables, if not presented di-

rectly, can be found in the Appendix (i.e. Tables 13 and 14).

30

Table 7: Summary of Kruskal-Walliss analysis of variance for Sad and

Angry experiments’ pairs of target emotions

Sad Angry

Gloominess Sadness Furiousness Anger

Chi-squared

with ties (2 d.f.) 14.173 17.183 33.670 21.408

Probability .0008 .0002 .0001 .0001

Notes: When presenting just the test’s results, the relative tables can be found in

the Appendix (i.e. Tables 15, 17, 19, and 21)

Table 8: Summary of Mann–Whitney–Wilcoxon for Gloominess, Sad-

ness, Furiousness, and Anger

Target

Emotions

Happy-Sad Happy-Angry Sad-Angry

z Prob. z Prob. z Prob.

Gloominess -3.667 .0002 -1.741 .0816 2.018 .0436

Sadness -4.041 .0001 -1.415 .1572 2.533 .0113

Furiousness -2.792 .005227 -5.650 .0000 -3.305 .0010

Anger -1.762 .781 -4.507 .0000 -2.968 .0030

Overall, evidence was found that the procedure used to trigger happi-

ness, sadness, and anger in student’s emotional sphere is valid. The re-

sults are particularly strong because when comparing the target emotions

of the two experiment which are not directly related to them, their levels

27 Despite the fact that there is no difference in the medians of furiousness levels be-

tween happy and sad experiments, when comparing these against the angry one

we find significant differences which lead to evidence of higher levels of furious-

ness during angry experiment.

31

are not significantly different from each other but lower than the one of

the tested condition. This suggests that the feeling level of a target emo-

tion really increases when being self-generated during the experiment.

Generally, all the results above confirm the first hypothesis that the lev-

els of self-inducted emotions are higher for those that are expected to be

higher which vary according to the weekly condition. Finally, when us-

ing data collected in the master class as a control, the results are still the

same (Table 9).

Table 9: Summary of Mann–Whitney–Wilcoxon for Cheerfulness,

Happiness, Furiousness, and Anger in Master class (Control)

Target

Emotions

Happy-Angry

z Prob.

Cheerfulness 3.153 .0016

Happiness 2.674 .0075

Furiousness -3.304 .0010

Anger -3.727 .0002

32

To investigate the second hypothesis the same methods were applied.

First, we look at multivariate differences in arousal levels among the

three conditions by Kruskal–Wallis one-way analysis of variance. Then

significant univariate dissimilarities in medians between pairs of exper-

iments by Mann–Whitney–Wilcoxon test were checked for. The claim is

that subject’s arousal levels when experiencing happiness and sadness

are similar, and both higher than the one registered during the angry

condition. Table 10 and Table 11 illustrate the results.

Table 10: Kruskal–Wallis analysis of variance for Arousal

Experiment Obs. Rank Sum

Happy 57 4650.00

Sad 47 4015.50

Angry 41 1919.50

Chi-squared with ties = 24.050 with 2 d.f.

Probability = .0001

33

Table 11: Mann–Whitney–Wilcoxon for Arousal

Pair 1 Obs. Rank Sum

Happy 57 2933.5

Sad 47 2526.5

z = -0.404

Probability = .6864

Pair 2 Obs. Rank Sum

Happy 57 3369.5

Angry 41 1481.5

z = 4.078

Probability = .0000

Pair 3 Obs. Rank Sum

Sad 47 2617

Angry 41 1299

z = 4.572

Probability = .0000

Notes: Recall that for each Condition we are testing univariate differences in medians

The evidence suggests that there is no statistically significant difference

between the arousal level registered during happy and sad conditions.

However, when comparing those levels to the one reached in the angry

experiment, both are significantly higher. The results are perfectly in

34

line with the second hypothesis. Unfortunately, using data collected in

the master class the null hypothesis of medians equality between the

level of arousal during happy and angry condition (z = 1.461, Prob > |z|

= 0.1440) cannot be rejected. This is probably due to the small number

of observations available.

The third and fourth hypotheses are directly implications of inter-

temporal choice theory for which a deep revision of such literature has

provided in Section 2.

The inverse N-shaped pattern (decrease-increase-decrease) of discount-

ing described in Stahl et al. (2010) was found by studying individual’s

WTP for membership plans in a health club. Given the similarities with

this study, we expect to find similar patterns for all the discount func-

tions we calculate with respect to average monthly discount rates. Fig-

ures 3, 4, and 5 reports them singularly.

Figure 3: Discount function for Happy Condition (N=57)

3.00%

3.50%

4.00%

4.50%

5.00%

5.50%

6.00%

6.50%

3M 6M 9M 12M 15M 18M 21M 24M

Ave

rage

Dis

cou

nt

Rat

e

Months

35

Figure 4: Discount function for Sad Condition (N=47)

Figure 5: Discount function for Angry Condition (N=41)

2.50%

3.00%

3.50%

4.00%

4.50%

5.00%

3M 6M 9M 12M 15M 18M 21M 24M

Ave

rage

Dis

cou

nt

Rat

e

Months

3.00%

3.20%

3.40%

3.60%

3.80%

4.00%

3M 6M 9M 12M 15M 18M 21M 24M

Ave

rage

Dis

cou

nt

Rat

e

Months

36

The graphical analysis reveals a clear inverse N-shape discounting with

respect to subscription’s duration for the happy and sad condition. Dif-

ferently, the discount function for the angry condition seems to initially

follow that pattern by decreasing-increasing however instead of restart-

ing to decrease, it suddenly has a new raise at 18 months duration.

Generally, the evidence found by Stahl et al. (2010) also applies for this

study and supports H3. We can motivate the slightly anomalous shape of

the discount function for anger by looking at the very small range of the

average discount rate.28

The figures above are also useful to test H4. According to Stahl et al.

(2010) the points at which the discount function switches from decreas-

ing to increasing pattern corresponds to the maximum contract durations

that consumers typically subscribe to a service. We believe that the ef-

fect of evoked emotions might lead to differences in duration prefer-

ences. Looking at Figure 3 it is evident that in the happy condition this

point is clearly at eighteen months, while Figure 4 and 5 show that for

sad and angry experiments the rise occurs at twelve months.

Because we are not completely able to confirm the inverse N-shape dis-

counting for anger from the graphical analysis, we avoid to overreach in

unreliable interpretations for that condition. On the other hand, the re-

sults support the hypothesis that the maximum duration for the subscrip-

tion vary depending on the other two, in-class triggered, emotional

states.

A possible explanation of these findings is that happy people surely dis-

count the future more because of their short-time preference, but it is

28 This can be ascribed to the experiment setting for the angry condition, which being

the last performed might have suffered by a stronger “order effect” in computing

the questionnaire.

37

also true that they are more optimistic about future events in general.29 In

this sense, it is not surprising that a lot of people pay for a long member-

ship duration after they enjoyed the one-week free of entry, during

which one can have fun with new friends and get in touch with amazing

exercise equipment. However, because of the same logic, it is even less

surprising that most of those people stop to go to the gym (exhibiting

preference reversals) when they start the first month of routinely train-

ings with its correlated physical effort. Overall, the results support H4,

showing a maximum contract duration for the internet subscription of

eighteen and twelve months for the happy and sad condition respective-

ly. This can most likely be explained by the happy emotional state itself,

given that there is also a big rise at twelve months in the angry condi-

tion. However, we do not have clear evidence for this, consequently fu-

ture studies should investigate the underlying reasons for these findings.

The last set of hypotheses, H5 and H6, focus completely on the mere

effect of emotions on individual’s WTP and the consequently discount

rates they apply, with respect to durations. First of all, discount functions

differences are analyzed among the three conditions graphically by look-

ing at Figure 6.

29 It is not necessary to have empirical evidence proving that bad things do not stop

happening when one is in a bad mood, nor vice versa sequence of good things

happen to happy people. Anyhow, Kaniel et al. (2010) found evidence that opti-

mists are more successful.

38

Figure 6: Discount functions and Average Discount Rates from Bache-

lor Class

Preliminary results from the graphical analysis are very interesting. First,

as expected, the monthly average discount rates resulting from self-

evoked happiness are always higher than the ones calculated for the sad

conditions for all of the contract durations proposed. Furthermore, we

hypothesized lower discounting in the first six months for sad and angry

experiments, the results suggest an even longer lasting period. In fact,

only for contracts of more than twelve months duration, the discount

function for the angry condition intersect the happy one. In a long time

point of view, over twelve months, the sad condition line is the lowest

while the other two follow a very similar trend.

One can argue that the discounting shapes presented above are the result

of an “order effect”. In other words, when first filling in the question-

naire during the happy experiment, students are more cautious in their

choice, while in the next two waves they get more and more familiar

3M 6M 9M 12M 15M 18M 21M 24M

HAPPY (N=57) 5.99% 4.24% 3.64% 3.53% 3.38% 3.24% 3.52% 3.45%

SAD (N=47) 4.89% 3.77% 3.45% 2.96% 3.10% 3.03% 3.01% 2.88%

ANGRY (N=41) 3.74% 3.61% 3.31% 3.16% 3.62% 3.34% 3.36% 3.44%

2.50%

3.00%

3.50%

4.00%

4.50%

5.00%

5.50%

6.00%

6.50%A

vera

ge D

isco

un

t R

ate

39

with it. This might result in quick and less accurate responses on their

WTPs, causing sad- and angry-related discount functions to be generally

lower compared with the happy one. To disprove this idea, it is enough

to look at Figure 7, which displays the discount functions calculated

from the data collected in the masters class where the angry condition

was the first to be performed and the happy one was second.

Figure 7: Discount Functions and Average Discount Rates from Master

Class (Control)

Despite the order of the experiment being inverted, the discount function

for the happy condition is still the highest one. Moreover, the discount

rates converge for longer subscription periods using data from the mas-

ter class, such as for bachelor ones.

A last control was performed for the “order effect” by calculating the

percentage variations among the average discount rates of the first and

second condition in both bachelor and masters class. The idea is the fol-

lowing. For the bachelor class the happy condition was performed first

3M 6M 9M 12M 15M 18M 21M 24M

HAPPY (N=35) 5.61% 4.12% 3.16% 2.89% 3.21% 2.67% 2.72% 2.58%

ANGRY (N=22) 4.37% 3.25% 2.86% 2.73% 2.86% 2.53% 2.57% 2.43%

2.00%

2.50%

3.00%

3.50%

4.00%

4.50%

5.00%

5.50%

6.00%

Ave

rage

Dis

cou

nt

Rat

e

40

and afterwards the sad one, Figure 6 shows that there is a decrease in the

average monthly discount rate for all durations from the first (happy) to

the second (sad) condition in that class. If there is “order effect” with

respect to our questionnaires, one would expect that in the masters class,

where angry one was performed first, then the happy condition, it will

comes up that the average monthly discount rate also decrease from the

first (angry) to the second (happy) condition. This formula was used to

calculate the percentage variations:

.

/

There is no evidence from the results that the “order effect” takes place.

Table 12 and Figure 8 clearly challenge this possible confounding factor.

Table 12: Percentage Monthly Variations in between & Ques-

tionnaire for Bachelor and Master

Duration

Class

Bachelor Master

3M -18.4% 28.5%

6M -11.1% 26.9%

9M -5.2% 10.7%

12M -16.2% 5.7%

15M -8.3% 12.4%

18M -6.4% 5.2%

21M -14.7% 6%

24M -16.5% 6%

41

Figure 8: Percentage Variations in between & Questionnaire

It is evident from the graph that the order in which students fill in the

questionnaires does not influence the individua’s intertemporal choice.

The controls we made allow to ascribe the different discounting patterns

of the three experiments to the momentary self-triggered emotional state.

Using graphical and descriptive comparisons, these findings confirm H5

and H6.30 Unfortunately, when applying Kruskal–Wallis one-way analy-

sis of variance and Mann–Whitney–Wilcoxon test, all the results are not

statistically significant. This is not particularly surprising because of the

restricted number of observations and the small range of differences that

are being tested. However, in both bachelor and masters classes almost

30 Note that we define in H5 as short term a subscription duration of six months. The

results confirm the hypothesized pattern even for a longer time period (twelve

months).

-30.00%

-20.00%

-10.00%

0.00%

10.00%

20.00%

30.00%

40.00%

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42

the same discounting pattern was observed tested conditions. This plays

in favor of further research in this field using broader experimental set-

tings or collecting the data directly from a real setting.

Overall, the results fully or partly confirm the six hypotheses. In particu-

lar, strong evidence was found for the way used to trigger happiness,

sadness, and anger. This is perfectly in line with earlier literature on ev-

ocation of emotions in an individual’s emotional sphere (Hsee et al

1990, Hatfield et al. 1992, Wild et al 2001). Moreover, the arousal level

for each of the three experiments respect the theoretical framework

(Damasio et al. 2000, Winkielman et al. 2005), suggesting that it plays a

determinant role in emotional states. The findings also support the mid-

dle hypotheses, which belong to the classical literature on intertemporal

choice (Ainslie 1975, Thaler 1981, Loewenstein 1996, O’Donoghue and

Rabin 1999, Carstensen et al. 1999, Zauberman 2003, Berns 2007, Stahl

et. al. 2010, etc.). In fact, the discounting patterns resulting from the

calculations do not deviate from the most recent studies. Thus, it is safe

to say that even when experiencing distinct memories and the related

emotions, that is, happiness, sadness, and anger, individuals apply de-

creasing discount rate over time. Finally, raw data support the view that

for each emotional state it is possible to track a proper discounting

shape, in particular that happiness decreases one’s WTPs and reversely

increases, as a consequence, the related discount rates if it is compared

with sadness. This last consideration is directly in contrast with scarce

previous studies, in fact Winkielman et al. (2005) obtained opposite re-

sults when studying thirsty individual’s WTP for happy and angry emo-

tional states.

43

5. Conclusion

The main goal of this paper is to clarify the effect of emotions on indi-

viduals’ intertemporal choices. The empirical evidence suggests that

their involvement cause people to behave differently depending on their

momentary emotional state. In particular, happy people showed a lower

WTP for durations ranging from three to twenty-four months when

compared with individuals who evoked sadness and anger. The results

can be summarized as follows.

Emotional states were successfully triggered in students by showing

them pictures inherent to the three target emotions and afterwards asking

them to mentally evoke a past memory and writing it on a private sheet.

This result is fundamental for further research on intertemporal choice

involving emotions. In fact, the possibility to significantly trigger basic

feelings like happiness, sadness, and anger in an experimental setting

really simplifies studies in this area. To reinforce the first finding even

more, differences in arousal levels of individuals experiencing the three

self-evoked emotions were tested. Perfectly in line with previous litera-

ture (Mano 1992, Winkielman et al. 2005), the results indicate that hap-

piness and sadness are moderately arousing and not significantly differ-

ent from each other, while anger produces significant lower arousal lev-

el. Again, this analysis is useful for further studies. As social and politi-

cal theorist John Elster (1998) wrote: “emotions without arousal are a

bit like Hamlet without the Prince of Denmark”31. For this reason, future

studies should focus more on the strong linkage between arousal, as well

as the relation between positive and negative affect, and emotions which

then influence intertemporal decision behavior.

31 (Elster 1998, page 50)

44

The results obtained by investigating the third and fourth hypotheses

allow us to get some interesting insight on intertemporal choice theory

but also, on a broader perspective, on marketing selling strategy. First of

all, evidence of the inverse N-shape discount pattern with respect to sub-

scription duration described by Stahl et al. (2010) was found. More pre-

cisely, the discount function presents a hyperbolic shape until the maxi-

mum contract duration that a consumer is willing to subscribe to a ser-

vice is reached. Then, there it suddenly increases before it starts to de-

crease again. What makes this result important is that once more it cast

doubt on Samuelson’s DU model. In fact, in the last decades several

authors challenged the DU model assumption of constant discounting in

time (Ainslie 1975, Thaler 1981, Loewenstein 1996, O’Donoghue and

Rabin 1999, Carstensen et al. 1999, Zauberman 2003, Berns 2007, Stahl

et. al. 2010, etc.) providing various explanations for a decreasing dis-

counting pattern. Furthermore, our results allow us to get an idea of how

emotional states affect the maximum contract duration. For sadness and

anger that point is at twelve months, while for happiness it is at eighteen

months. This should be useful for psychological marketing strategies, in

fact, one can use emotional states as a driver for shorter or longer mem-

bership offers.

Finally and most importantly, it was investigated how happiness, sad-

ness, and anger directly impact individual’s intertemporal choice behav-

ior. The main interest here is in understanding the differences in subjects

discount rates for each duration depending on the above emotions.

Shedding light on these mechanisms gets raise attention mainly because

of the scarce consideration this argument has received. In fact there is no

previous knowledge of similar studies, except for a small section dedi-

cated in their analysis of the effect of visceral factors on individuals’

WTP by Winkielman et al. (2005). Probably because visceral factors

45

differ in intensity from basic emotional states like the ones we tested,

our results are opposite of theirs. In fact, by using descriptive statistics

and graphical comparison we found that individual’s WTP actually var-

ies depending on the three experimental settings. The evidence shows

that during the happy condition the collected discount rates are higher

than the ones in the sad experiment for all the durations proposed. This

is also true with the angry condition for the first twelve months. After

this membership time length, the three discount functions share a very

similar trend. The consequence of these results, if confirmed, can have

deep impact on both future studies involving intertemporal choice, and

for business in general. It is unquestionable that knowing exactly how a

happy, sad, angry person will behave in his choice over periods of time

will allow sellers to prepare ad-hoc contracts, or to discriminate among

consumers. Furthermore, the today’s non-existent literature would prob-

ably experience a strong rise in interest for establishing how different

emotional states impact on individals’ WTP and discount rates over

time, and consequently develop innovative marketing strategies.

Despite the evidence found, this study suffers from remarkable limita-

tions. It is the first time that an economical empirical study directly ap-

proaches such a niche argument as the effect of emotions on individuals’

intertemporal choice. As one can guess from the theoretical framework,

this study not only incorporates economic factors, but also psychologi-

cal, biological, physiological, and neurological ones. Given this unusual

mix of disciplines, it would be arrogant to pretend that these results are

enough to causally explain the mere effect of emotions on people’s be-

havior within this puzzling set of inter-correlated mechanisms, which is

still almost unknown. The main limitation of this paper is identifiable in

the not fully satisfactory completeness of the data set, in particular in the

number of observations. In fact, the results regarding the direct effect of

46

the three emotions tested on individual’s intertemporal choice are not

statistically significantly different when applying Mann–Whitney–

Wilcoxon tests among conditions. However, the clear pattern that

emerges in favor of higher discounts rates for the happiness experiment

is undeniable after looking at the descriptive statistic. Another weakness

of this study is the experimental setting from which the data were col-

lected. Student’s effort in answering about their WTPs for a hypothetical

subscription is quite far away from the complex process that is beneath a

real decision. Thus, it is desirable that future studies evaluate the option

of collecting the data directly from subscription services providers, or

offering stronger incentives (e.g. monetary rewards).

Finally, we want to specify that this study does not intend to immediate-

ly clarify all the mechanisms among emotions and intertemporal choice.

However, we clearly found evidence of several guidelines that can serve

for further studies. In fact, we suggest that future research should focus

more on the interconnections between emotional states and arousal.

Moreover, it could be interesting to better investigate the causes that

lead to different maximum contract duration from a real setting. Lastly,

further studies should be conducted to investigate the psychological

mechanisms underneath these first revelatory findings regarding emo-

tions and intertemporal choice discounting.

For too many years economists neglected the importance of emotional

states in intertemporal choice decision-making and their consequences.

This paper is meant to show how strong that impact can be, raising in-

terest for further research in this area.

V

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X

7. Appendix

Figure 1: Discount Functions

Source: Berns et al. (2007), p. 483

XI

Figure 9: List of Emotions’ Levels Collected

Source: Fragebogen Version 1, p. 5

XII

Table 1: Brain’s Regions Involvement Depending on Emotions

Source: Damasio et al. (2000), p. 1052

XIII

Table 13: Kruskal–Wallis analysis of variance for Happiness

Experiment Obs. Rank Sum

Happy 57 5448.00

Sad 47 2658.00

Angry 41 2479.00

Chi-squared with ties = 27.832 with 2 d.f.

Probability = .0001

XIV

Table 14: Mann–Whitney–Wilcoxon for Happiness

Pair 1 Obs. Rank Sum

Happy 57 3678.5

Sad 47 1781.5

z = 4.527

Probability = .0000

Pair 2 Obs. Rank Sum

Happy 57 3422.5

Angry 41 1428.5

z = 4.387

Probability = .0000

Pair 3 Obs. Rank Sum

Sad 47 2004.5

Angry 41 1911.5

z = -0.733

Probability = .4635

XV

Table 15: Kruskal–Wallis analysis of variance for Sadness

Experiment Obs. Rank Sum

Happy 57 3389.50

Sad 47 4316.00

Angry 41 2879.50

Chi-squared with ties = 17.183 with 2 d.f.

Probability = .0002

XVI

Table 16: Mann–Whitney–Wilcoxon for Sadness

Pair 1 Obs. Rank Sum

Happy 57 2401.5

Sad 47 3058.5

z = -4.041

Probability = .0001

Pair 2 Obs. Rank Sum

Happy 57 2641

Angry 41 2210

z = -1.415

Probability = .1572

Pair 3 Obs. Rank Sum

Sad 47 2385.5

Angry 41 1530.5

z = 2.533

Probability = .0113

XVII

Table 17: Kruskal–Wallis analysis of variance for Gloominess

Experiment Obs. Rank Sum

Happy 57 3400.50

Sad 47 4202.00

Angry 41 2982.50

Chi-squared with ties = 14.173 with 2 d.f.

Probability = .0008

XVIII

Table 18: Mann–Whitney–Wilcoxon for Gloominess

Pair 1 Obs. Rank Sum

Happy 57 2456.5

Sad 47 3003.5

z = -3.667

Probability = .0002

Pair 2 Obs. Rank Sum

Happy 57 2597

Angry 41 2254

z = -1.741

Probability = .0816

Pair 3 Obs. Rank Sum

Sad 47 2326.5

Angry 41 1589.5

z = 2.018

Probability = .0436

XIX

Table 19: Kruskal–Wallis analysis of variance for Furiousness

Experiment Obs. Rank Sum

Happy 57 3027.00

Sad 47 3432.50

Angry 41 4125.50

Chi-squared with ties = 33.670 with 2 d.f.

Probability = .0001

XX

Table 20: Mann–Whitney–Wilcoxon for Furiousness

Pair 1 Obs. Rank Sum

Happy 57 2603.5

Sad 47 2856.5

z = -2.792

Probability = .0052

Pair 2 Obs. Rank Sum

Happy 57 2076.5

Angry 41 2774.5

z = -5.650

Probability = .0000

Pair 3 Obs. Rank Sum

Sad 47 1704

Angry 41 2212

z = -3.305

Probability = .0010

XXI

Table 21: Kruskal–Wallis analysis of variance for Anger

Experiment Obs. Rank Sum

Happy 57 3291.00

Sad 47 3340.00

Angry 41 3954.00

Chi-squared with ties = 21.408 with 2 d.f.

Probability = .0001

XXII

Table 22: Mann–Whitney–Wilcoxon for Anger

Pair 1 Obs. Rank Sum

Happy 57 2733.5

Sad 47 2726.5

z = -1.762

Probability = .0781

Pair 2 Obs. Rank Sum

Happy 57 2210.5

Angry 41 2640.5

z = -4.507

Probability = .0000

Pair 3 Obs. Rank Sum

Sad 47 1741.5

Angry 41 2174.5

z = -2.968

Probability = .0030

XXIII

Table 23: Mann–Whitney–Wilcoxon for Cheerfulness Master class

(Control)

Single Check Obs. Rank Sum

Happy 22 828

Angry 35 825

z = 3.153

Probability = .0016

XXIV

Table 24: Mann–Whitney–Wilcoxon for Happiness Master class

(Control)

Single Check Obs. Rank Sum

Happy 22 800

Angry 35 853

z = 2.674

Probability = .0075

XXV

Table 25: Mann–Whitney–Wilcoxon for Furiousness Master class

(Control)

Single Check Obs. Rank Sum

Happy 22 451

Angry 35 1202

z = -3.304

Probability = .0010

XXVI

Table 26: Mann–Whitney–Wilcoxon for Anger Master class (Con-

trol)

Single Check Obs. Rank Sum

Happy 22 417

Angry 35 1236

z = -3.727

Probability = .0002

XXVII

Table 27: Mann–Whitney–Wilcoxon for Arousal Master class (Con-

trol)

Single Check Obs. Rank Sum

Happy 22 721.5

Angry 35 931.5

z = 1.461

Probability = .1440

XXVIII

Table 28: Kruskal–Wallis analysis of variance for 3 Months Duration

Discount Rates

Experiment Obs. Rank Sum

Happy 57 4619.00

Sad 47 3245.50

Angry 41 2720.50

Chi-squared with ties = 3.682 with 2 d.f.

Probability = .1586

XXIX

Table 29: Kruskal–Wallis analysis of variance for 6 Months Duration

Discount Rates

Experiment Obs. Rank Sum

Happy 57 4400.00

Sad 47 3297.00

Angry 41 2888.00

Chi-squared with ties = .975 with 2 d.f.

Probability = .6141

XXX

Table 30: Kruskal–Wallis analysis of variance for 9 Months Duration

Discount Rates

Experiment Obs. Rank Sum

Happy 57 4314.50

Sad 47 3308.00

Angry 41 2962.50

Chi-squared with ties = .439 with 2 d.f.

Probability = .8027

XXXI

Table 31: Kruskal–Wallis analysis of variance for 12 Months Dura-

tion Discount Rates

Experiment Obs. Rank Sum

Happy 57 4501.00

Sad 47 3169.50

Angry 41 2914.50

Chi-squared with ties = 2.106 with 2 d.f.

Probability = .3488

XXXII

Table 32: Mann–Whitney–Wilcoxon for Happy vs Sad 15 Months

Duration Discount Rates

Experiment Obs. Rank Sum

Happy 57 3034.5

Sad 47 2425.5

Chi-squared with ties = .276 with 2 d.f.

Probability = .7827

XXXIII

Table 33: Mann–Whitney–Wilcoxon for Happy vs Sad 18 Months

Duration Discount Rates

Experiment Obs. Rank Sum

Happy 57 3039

Sad 47 2421

Chi-squared with ties = .306 with 2 d.f.

Probability = .7599

XXXIV

Table 34: Mann–Whitney–Wilcoxon for Happy vs Sad 21 Months

Duration Discount Rates

Experiment Obs. Rank Sum

Happy 57 3134.5

Sad 47 2325.5

Chi-squared with ties = .932 with 2 d.f.

Probability = .3511

XXXV

Table 35: Mann–Whitney–Wilcoxon for Happy vs Sad 24 Months

Duration Discount Rates

Experiment Obs. Rank Sum

Happy 57 3171

Sad 47 2289

Chi-squared with ties = 1.176 with 2 d.f.

Probability = .2397