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2016 On Inactivity in the Lab John Spaulding Jensenius III
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COLLEGE OF SOCIAL SCIENCES AND PUBLIC POLICY
ON INACTIVITY IN THE LAB
By
JOHN S. JENSENIUS III
A Dissertation submitted to the Department of Economics in partial fulfillment of the requirements for the degree of Doctor of Philosophy
2016 John S. Jensenius III defended this dissertation on March 29, 2016. The members of the supervisory committee were:
R. Mark Isaac Professor Directing Dissertation
Allen Blay University Representative
David J. Cooper Committee Member
Svetlana Pevnitskaya Committee Member
The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.
ii
Dedicated to my wife Emily.
iii ACKNOWLEDGMENTS
I would like to begin by thanking my advisor Dr. R. Mark Isaac, for his guidance and support over the course of this long journey. Mark’s patience and encouragement, especially since I began working many miles away, has been invaluable. I would also like to thank Dr. David Cooper, for inspiring me as a student, and taking me on as an undergraduate, and later graduate, research assistant. This is all your fault. I would like to thank Dr. Svetlana Pevnitskaya and Dr. Alan Blay for their comments and suggestions, both on this work and on future research that will expand upon it. I would like to thank the members, present and past, of XS/FS, the Experimental Social Sciences cluster at Florida State, for many years of discussions, feedback, guidance, and friendship. I would like to thank Dr. Akitaka Matsuo for his help learning R, and his guidance working with the data. Without his instruction and assistance, this document would have taken much longer to produce and would look uglier. I would like to thank the John and Hallie Quinn Foundation for their support of graduate students interested in the experimental social sciences, and in particular for providing partial funding of the research described in this dissertation. Lastly I would like to thank my family for their decades of support and encouragement.
iv TABLE OF CONTENTS
List of Tables ...... vii List of Figures ...... ix Abstract ...... xi
1. INTRODUCTION ...... 1
2. LITERATURE REVIEW...... 3
2.1 Literature on Inactivity ...... 3
2.2 Literature on Boredom ...... 4
2.3 Experimental Examples of Inactivity...... 5
3. BEHAVIORAL EFFECTS OF EXPERIMENTER-IMPOSED INACTIVITY...... 8
3.1 Introduction ...... 8
3.2 Hypotheses ...... 8
3.3 Experimental Design ...... 9
3.4 Results ...... 14
3.5 Conclusion ...... 17
4. BEHAVIORAL EFFECTS OF PEER-IMPOSED INACTIVITY ...... 31
4.1 Introduction ...... 31
4.2 Hypotheses ...... 31
4.3 Experimental Design ...... 32
4.4 Results ...... 36
4.5 Conclusion ...... 39
5. PAID TO SIT: MEASURING THE VALUE OF INACTIVITY ...... 58
5.1 Introduction ...... 58
5.2 Model and Hypotheses ...... 58
v 5.3 Experimental Design ...... 58
5.4 Results ...... 59
5.5 Conclusion ...... 60
6. ALTERNATIVES TO INACTIVITY ...... 64
6.1 Introduction ...... 64
6.2 Hypotheses ...... 64
6.3 Experimental Design ...... 64
6.4 Results ...... 68
6.5 Conclusion ...... 70
7. CONCLUDING REMARKS ...... 80
7.1 Conclusions ...... 80
7.2 Further Research ...... 80
APPENDICES ...... 82
A. EXPERIMENTAL INSTRUCTIONS ...... 82
A.1 Behavioral Effects of Experimenter Imposed Inactivity ...... 82
A.2 Behavioral Effects of Peer Imposed Inactivity ...... 88
A.3 Alternatives to Inactivity...... 92
B. HUMAN SUBJECTS COMITTEE APPROVALS ...... 96
B.1 Behavioral Effects of Inactivity (Chapters 3, 4, and 5) ...... 96
B.2 Alternatives to Inactivity (Chapter 6) ...... 105
References ...... 111
Biographical Sketch ...... 115
vi LIST OF TABLES
3.1 Gamble Choices ...... 23
3.2 Sessions Summary – Experimenter Imposed Inactivity ...... 24
3.3 The Dictator Game: Distribution of Decisions ...... 24
3.4 The Dictator Game: Amount Kept, with Experimenter Imposed Inactivity (Between Subjects, Periods 2 and 4) ...... 25
3.5 The Dictator Game: Amount Kept, with Experimenter Imposed Inactivity (Within Subjects, Periods 2 to 4) ...... 26
3.6 The Public Goods VCM: Distribution of Decisions ...... 26
3.7 The Public Goods VCM: Amount in Individual Account, with Experimenter Imposed Inactivity (Between Subjects, Periods 2 and 4) ...... 27
3.8 The Public Goods VCM: Amount in Individual Account, with Experimenter Imposed Inactivity (Within Subjects, Periods 2 - 4) ...... 28
3.9 The Gamble Choice: Choice Distributions Following Activity and Inactivity ...... 28
3.10 The Gamble Choice: Choice, with Experimenter Imposed Inactivity (Between Subjects, Periods 2 and 4) ...... 29
3.11 The Gamble Choice: Choice, with Experimenter Imposed Inactivity (Within Subjects, Periods 2 - 4) ...... 30
4.1 Sessions Summary – Peer Imposed Inactivity ...... 46
4.2 The Dictator Game: Amount Kept, with Peer Imposed Inactivity (Between Subjects, Periods 2 and 4) ...... 47
4.3 The Dictator Game: Distribution of Choices, with Peer-imposed Inactivity...... 47
4.4 The Dictator Game: Amount Kept, with Exogenous Number of Tasks (Between Subjects, Periods 2 and 4) ...... 48
4.5 The Dictator Game: Amount Kept, with Peer Imposed Inactivity (Within Subjects, Periods 2 - 4) ...... 49
4.6 The Public Goods VCM: Amount in Individual Account, with Peer Imposed Inactivity (Between Subjects, Periods 2 and 4)...... 50
vii 4.7 The Public Goods VCM: Distribution of Choices, with Peer-imposed Inactivity ...... 50
4.8 The Public Goods VCM: Amount in Individual Account, with Exogenous Number of Tasks (Between Subjects, Periods 2 and 4)...... 51
4.9 The Public Goods VCM: Amount in Individual Account, with Peer Imposed Inactivity (Within Subjects, Periods 2 - 4) ...... 52
4.10 The Gamble Choice: Choice, with Peer Imposed Inactivity (Between Subjects, Periods 2 and 4) ...... 53
4.11 The Gamble Choice: Distribution of Choices, with Peer-imposed Inactivity ...... 54
4.12 The Gamble Choice: Choice, with Exogenous Number of Tasks (Between Subjects, Periods 2 and 4) ...... 55
4.13 The Gamble Choice: Choice, with Peer Imposed Inactivity (Within Subjects, Periods 2 - 4) ...... 56
5.1 Wage at exit (Between Subjects) ...... 62
5.2 Predicting Wage > 0 (Between Subjects) ...... 63
6.1 Effect of Length on Bids (OLS)...... 77
6.2 Effect of Length on Bids (Random Effects) ...... 77
6.3 Effect of Log of Stage Length on Bids (Random Effects) ...... 78
6.4 Effect of Log of Stage Length on Bids/Time (Random Effects) ...... 79
6.5 Effect of Log of Stage Length on Bids/Time (Random Effects) ...... 79
viii LIST OF FIGURES
3.1 Experimental Diagram – Experimenter Imposed Inactivity ...... 18
3.2 Dictator Game Decision Screen ...... 18
3.3 Public Goods Game Decision Screen ...... 19
3.4 Gamble Choice Decision Screen ...... 19
3.5 Non-incentive stage – Inactive Screen ...... 20
3.6 Tic-Tac-Toe Activity Screen ...... 20
3.7 Rock-Paper-Scissors Activity Screen ...... 21
3.8 Picture Viewing Activity Screen ...... 21
3.9 Text search Activity Screen ...... 22
3.10 The Dictator Game: Choice Distributions Following Activity and Inactivity ...... 22
3.11 The Public Goods VCM: Choice Distributions Following Activity and Inactivity ...... 23
4.1 Experimental Diagram – Peer Imposed Inactivity ...... 41
4.2 No Assigned Tasks Screen ...... 41
4.3 Assigned Tasks Screen ...... 42
4.4 Text search Task Screen ...... 42
4.5 All Tasks Completed Screen ...... 43
4.6 The Dictator Game: Amount Kept versus Time Inactive ...... 43
4.7 The Dictator Game: Amount Kept versus Number of Tasks ...... 44
4.8 The Public Goods VCM: Amount in Individual Account versus Time Inactive ...... 44
4.9 The Public Goods VCM: Amount in Individual Account versus Number of Tasks ...... 45
4.10 The Gamble Choice: Choices versus Time Inactive ...... 45
4.11 The Gamble Choice: Choices versus Number of Tasks ...... 46 5.1 Final Inactive Stage...... 61
ix
5.2 Wage at Exit in the Final Inactive Stage ...... 61
6.1 Experimental Diagram – Alternatives to Inactivity ...... 71
6.2 Stage A Start Screen ...... 72
6.3 Stage A Bidding Screen ...... 72
6.4 Stage A Outcome Screen ...... 73
6.5 Restricted Internet Browsing ...... 73
6.6 Bids versus Stage Length ...... 74
6.7 Bids versus the Log of Stage Length ...... 74
6.8 Bids over Time versus Stage Length ...... 75
6.9 Bids over Time versus the Log of Stage Length ...... 75
6.10 Means and Variances of Bids ...... 76
6.11 Wages at Exit in the Final Stage ...... 76
x ABSTRACT
In this dissertation I explore the role of inactivity in the social science laboratory, looking at how it affects subjects and their behavior, and I look into some alternatives. In chapter 3, I look at how experimenter-imposed activity affects subject behavior in three simple economic environments: a Dictator Game, a Voluntary Contributions Public Good, and a Gamble Selection task. I find very little to no change in behavior in any of the environments as a result of the inactivity. In chapter 4, I look at the same three economic environments, but manipulate the source of the inactivity so that it is now partially the result of other subjects’ actions. In this situation, I find some evidence that inactivity affects subject behavior in the Dictator Game, and strong evidence that it affects behavior in the Public Goods environment, but no evidence that it affects risk preferences. In Chapter 5, I use a novel setup to take a direct measure of the value subjects have for inactivity. I find that subjects are willing to forgo positive wages to avoid inactivity. In Chapter 6, I look at possible alternatives to inactivity and elicit subjects’ valuations of each activity across different lengths of time. I identify two activities whose value I cannot show to be significantly different from zero, and whose value does not change significantly across different lengths of time.
xi CHAPTER 1
INTRODUCTION
Inactivity occurs when a person either has very little to do, or is choosing to do very little. This can occur in a number of occupations, such as closed-circuit television monitors, or security guards, where an employee spends a large amount of time waiting for an infrequent event, or for the end of their shift. It can also occur in smaller doses in daily life, such as waiting in traffic, or standing in lines at the supermarket or airport. Inactivity also happens to subjects in the lab. When discussing inactivity in the lab, this refers to the situation in which a subject has completed all available tasks and is waiting for the experiment to continue. Inactivity in the lab can be a purer form of inactivity, as by design experimental labs strive to reduce and eliminate sources of stimulation external to the experiment.
Inactivity in the lab can have a number of sources. Sometimes it is caused by the nature of the experiment. For example, sequential games by definition require one subject to wait for the other to make a decision. Thus unless the experimenter has compensated for this, any experiment featuring a sequential game will also feature an inactive subject.
Sometimes inactivity is caused by the heterogeneous nature of the subjects. Some subjects will make decisions faster than others, and may thus need to wait for the other subjects before a new period or round can begin. Likewise, for experiments featuring quizzes, some subjects will understand the instructions sooner, and will be inactive as they wait on others who have not yet completed the quiz. Sometimes an entire experiment will ground to a halt because someone has forgotten to confirm a decision, or push a continue button. In this case, everyone experiences a period of inactivity.
In spite of the existence these inactive periods during social science experiments, as well as in everyday life, economics as has largely ignored inactivity as a topic of study. This is somewhat troubling because if inactivity results in behavioral changes in subjects, and as we’ll see in the next chapter there is reason to suspect it might, then we’re potentially adding noise experimental results, or worse, biasing them. This dissertation will describe a series of
1 experiments designed to use a controlled laboratory environment to study inactivity and the effect it has on behavior, and to examine a few alternatives.
The following chapter will provide a review of relevant literature. This will include examples of inactivity in lab experiments as well as a brief overview of the related topic of boredom. Chapters 3 through 6 will present a series of experiments exploring inactivity in the lab. Chapter 3 will investigate how subjects change their behavior in simple economic settings after experiencing periods of inactivity exogenously created by the experimenter. Similarly, Chapter 4 will how subjects change their behavior in simple economic settings after experiencing periods of inactivity exogenously created by other subjects. Chapter 5 will look at the results of an incentivized measure of the value of inactivity. Chapter 6 will take a look at various alternatives to inactivity that may be implemented in an experimental setting. The final chapter will provide a summary of all results and suggestions for further research.
2 CHAPTER 2
LITERATURE REVIEW
2.1 Literature on Inactivity
To date, there has been little research conducted that focuses specifically on inactivity. Most of this research comes from one of three sources: the health sciences; labor economics; and psychology. Health studies tend to focus on long periods of reduced physical activity. Reimers, Knapp, and Reimers (2012), for example, looks at previous health literature, which finds physical activity reduces major mortality risk factors, such as heart disease, stroke, and cancer, and try to determine the resultant effect on life expectancy. For studies comparing physically active and inactive subjects, they find that on average physically active persons live approximately 3 years longer for men and 4 years longer for women than inactive persons. The timescales involved, however, make this study and similar studies less useful when discussing inactivity in the lab. Labor economics, on the other hand, tend to look at “economic inactivity” in terms of decreased production due to unemployment in the economy, or absenteeism at the firm level. Mikalachki and Gandz (1979), for example, propose an “inactivity index” which is the ratio of time missed by an employee relative to the amount of time they typically work. Neither the health nor labor research focuses on the immediate behavioral aspects of inactivity.
The psychology literature is more useful. A recent study by Wilson et al. (2014) observes subjects who have been placed in a room by themselves with little stimulus. They find “participants typically did not enjoy spending 6 to 15 minutes in a room by themselves with nothing to do but think [...] many preferred to administer electric shocks to themselves instead of being left alone with their thoughts.” This result holds true, even when looking at subjects who have rated the electrical shocks as unpleasant. “Most people seem to prefer to be doing something rather than nothing, even if that something is negative” (Wilson et al. 2014).
So what about inactivity is causing subjects to want to avoid it? One possibility is that subjects associate inactivity with boredom. “Doing nothing and boredom are closely intertwined, and both get a bad press” notes (de Vries 2015). Similarly, Conrad (1997) states “when people find themselves with ‘nothing to do’ in a situation where they wish they had something to do—if
3 only to occupy them enough so the time would seem to pass more quickly—the experience of boredom is likely”.
2.2 Literature on Boredom
One of the causes of boredom is called “quantitative underload” (Fisher 1987), which occurs when there is not enough work, or the tasks are too simple. Fisher notes “Students involved in retailing jobs were bored when there were no customers to wait on.” Additionally, “their boredom was often exacerbated by rules which constrained them to a particular location (behind the cash register) or posture (standing) and prohibited subsidiary behaviors such as reading or talking to coworkers.” This is particularly interesting as subjects in the lab are frequently constrained to a particular location (at the computer terminal) or posture (sitting) and prohibited subsidiary behaviors such as texting or talking to other subjects. Fisher (1993) further reports that “Respondents said they were bored on jobs which were simple, repetitive, had low mental demands, were not challenging, did not utilize their skills, or required watching for infrequent events” and that “some individuals noted that they were particularly bored when they had nothing to do following a long busy period.” If inactivity leads to boredom, then it follows that people experiencing periods of inactivity are likely also experiencing boredom.
Nineteenth-century German philosopher Arthur Schopenhauer observed that human unhappiness is the result of two causes, pain and boredom. The disutility of pain, which Schopenhauer associates with poverty, has been studied by economists, particularly in the form of financial incentives and disincentives, i.e. Andreoni, Harbaugh, and Vesterlund (2003)’s carrot and stick. Boredom, on the other hand, has been largely ignored as a topic of study by experimental economists. One recent exception is Markey (2014), a doctoral dissertation focused specifically on boredom. Markey provides an extensive review of the literature on boredom, as well as investigating boredom induction methods and manipulations in the laboratory. This, however, does not provide insight on if and how boredom might cause subjects to alter their behavior.
4 Fortunately, psychology again can provide additional information. From the psychology literature one finds that “available evidence indicates that boredom is associated mainly with negative individual and organizational outcomes” (Loukidou, Loan-Clarke, and Daniels 2009). It can be associated with lower performance (O’Hanlon 1981; Loukidou, Loan-Clarke, and Daniels 2009), emotional upset (Spector et al. 2006), stress (O’Hanlon 1981), deviant behavior (Wasson 1981), hostility and violence (Rupp and Vodanovich 1997; Dahlen et al. 2004; Scitovsky 2008; Kustermans and Ringmar 2011), increased preference for gambling (Barbalet 1999; Blaszczynski, McConaghy, and Frankova 1990) and thrill seeking (Hamilton 1983; Zuckerman 1979), and drug and alcohol use(Orcutt 1984; Weybright et al. 2015). If inactivity results in boredom, and boredom leads to the states and behaviors mentioned above, it is reasonable to assume that subjects in the lab who have recently been inactive may act differently than they would if they had not been subject to periods of inactivity.
2.3 Experimental Examples of Inactivity
Although no one has directly studied inactivity, there are plenty of examples of inactivity in published laboratory experiments, and there is tangential evidence that subjects try to avoid it. The following sections discuss a few different ways that inactivity often plays a role in laboratory experiments.
2.3.1 Non-incentivized Labor
One common place to see inactivity to play a role is in the form of uncompensated labor, wherein subjects perform a costly task for which they do not receive additional payment, For example, (Cadsby, Song, and Tapon 2006) is an experimental test of compensation mechanisms. They provided subjects with a real effort task, in the form of an anagram word creation task, and either paid them a fixed salary, or paid them based on performance. Not surprisingly, they found that subjects performed more work under the paid-for-performance scheme. More surprising was that subjects performed significantly positive amounts of work under the fixed-salary scheme. Assuming any positive cost of effort, under a fixed salary scheme, subjects should not do any work at all. That is, of course, unless they would incur greater costs from being inactive.
5 2.3.2 Entry Decisions
Another common place to see inactivity affecting results is when subjects endogenously choose whether or not to participate in stage of the experiment. When the alternative is inactivity, it is often the case that subjects participate in the activity at a rate greater than one would expect from utility maximizing agents. For example Palfrey and Pevnitskaya (2008) investigates endogenous auction entry in first-price sealed-bid auctions. They find that given the choice between either entering an auction or receiving a fixed amount (but otherwise doing nothing until the auction was over) subjects had a strong tendency towards over-entry into the auction. In an attempt to explain some of this over-entry, they ran a treatment where instead of receiving a fixed amount and waiting for the auction to end, subjects who opted out of the auction instead played a low stakes game of rock-paper-scissors. Though this game had the same expected value as the fixed amount offered before, and very low variance in the monetary payouts of the potential outcomes, they found that over-entry was reduced by 60%. Again, subjects were willing to incur cost in order to avoid inactivity.
In addition to affecting entry rates, it is possible that inactivity could also have affected how subjects acted. For example, a previously inactive subject taking part in the auction might exhibit increased risk tolerance and bid more aggressively than they would otherwise. This could produce similar bidding behavior as seen in this study, which found that when given the choice between an auction and a fixed amount, subjects who entered the auction exhibited greater risk tolerance in their bidding behavior than subjects who weren’t given a choice. Given that this was a repeated game, it is possible that some of the participants in each auction had sat inactive in the previous round. Fortunately, but unlike many other studies, Palfrey and Pevnitskaya addressed this concern with the treatment described earlier.
2.3.3 Sequential Games
Another situation in which inactivity occurs by design is in the context of sequential games, where one player must wait for one or more players to make a decision before making one of their own. For example, this could take place in the trust game (Berg, Dickhaut and Mccabe 1995). In this game, the first player gives a monetary gift to the second player. This gift is multiplied before it reaches the second player, and the second player has the option of sending
6 a monetary gift back to the first. If subjects participating in this game have been waiting inactively for a period of time, waiting on other players in the room perhaps, it could affect how they play the game. If the first player is now feeling more risk tolerant, they may send a larger gift to the second player. If they are feeling more hostile, they might send a smaller gift to the second player. If the second player is feeling more hostile, they might return a smaller gift back to the first player. There are a number of ways subject behavior could be affected by inactivity in this game. This study could help establish what changes in behavior subjects exhibit when exposed to inactivity.
2.3.4 Inactivity Avoidance
Recently, experimenters have begun to recognize that inactivity may affect their results and have started to provide alternatives. Caria and Falco (2014) conduct a lab-in-field experiment looking at the relationship between employer expectations and employee performance in a real effort task. To mitigate concerns about subjects working simply to avoid boredom, a television was left running during the session and tuned to a popular television program. While subjects are never explicitly told that they can watch the program instead of working, should a subject choose to shirk, they would be aware that they could divert their attention to the show. (Cooper, Weber, and D’Adda 2016) uses and explicit choice instead. Subjects who decide to stop working on a real effort task are given the opportunity to browse a collection of twelve websites on the internet. The websites cover a broad range of topics such that a subject would be unlikely to need to work to avoid boredom.
7 CHAPTER 3
BEHAVIORAL EFFECTS OF EXPERIMENTER-IMPOSED INACTIVITY
3.1 Introduction
As discussed in the previous chapter, subjects in lab experiments often find themselves in a situation in which they have no actions to perform and must wait for the experiment to continue. The negative utility provided by such a situation has had obvious and documented effects in the lab on entry rates when it is the alternative to participation in an economic activity. The behavioral changes indicated by psychological research on boredom, however, indicates that it may also play a role in other decisions as well. In this chapter I will look at the situation where subjects are experiencing inactivity imposed on them solely by the experimental design. In the following experiment, a subject’s inactivity is not due to action, or inaction, by another subject, but is imposed on them exogenously, and is known to be so. This mirrors a situation in which a subject has to wait for a fixed period of time before making a decision. For example, a subject acting as a manager, may have to wait for a fixed period of time while other subjects, acting as employees, perform a real effort task. In this environment, we will use a between-subjects and within-subjects design to look at how subjects’ decisions differ following inactivity in three simple economics tasks: the dictator game, the public goods game, and a risk elicitation.
3.2 Hypotheses
Hypothesis 1: Following periods of experimenter-imposed inactivity, subjects will allocate more ECU to themselves when playing the dictator game.
Given that the literature reviewed in Chapter 2 indicates that inactive subjects often become bored subjects, and that bored subjects can exhibit emotional upset and hostility, I hypothesize that subjects’ other regarding preferences will be diminished, and that they will keep more for themselves.
Hypothesis 2: Following periods of experimenter-imposed inactivity, subjects will allocate more tokens to their personal account, and less to the group account when playing the public goods game.
8 Again, I expect inactive subjects to become bored and bored subjects to exhibit emotional upset and hostility. A subject who is upset with his or her peers is less likely to allocate tokens to the group account, and thus give up earnings (the group account has a marginal per capita return of 0.5) to increase total social welfare.
Hypothesis 3: Following periods of experimenter-imposed inactivity, subjects will exhibit less risk-averse behavior in the gamble choice task.
As noted in the literature review, boredom is associated with increased gambling and thrill seeking. In the gamble choice task, I expect to see that subjects who have just experienced a period of experimenter-imposed inactivity choosing riskier gambles than subjects who had not experienced inactivity.
3.3 Experimental Design
This experiment utilizes both a within-subjects and a between-subjects design. The experimental sessions were conducted at the xs/fs lab at Florida State University in November 2012 and October and November 2013, with subjects recruited from the student population using ORSEE (Greiner 2015). The experiment was programmed and conducted using the Zurich Toolbox for Ready-made Economics Experiments(Fischbacher 2007).
Prior to the beginning of the session, subjects were asked to place all of their belongings in a plastic bag which was then tightly shut with a zip tie. This was to insure that subjects did not have access to anything during the course of the experiment, other than what was provided for them. Subjects were then assigned seats, such that no two subjects who entered the lab were seated next to each other, and randomly assigned by the computer into two equal sized groups A and B.
Prior to the start of the experiment, and following some brief instructions, subjects were asked to input their name into their workstation the start of the experiment, to aid in the payment of subjects at the end of the experiment.
9 Each session was comprised of eight stages: four incentivized stages; three non- incentivized stages; and, following a summary of their earnings up until that point, the final stage. A generic session follows the format shown in Figure 3.1.
3.3.1 Incentivized stages
During incentivized stages, subjects are asked to perform an economic task. There are three different economic tasks that subjects could see, depending on the treatment: a dictator game task; a public good task; and a gamble selection task. These tasks were chosen for a few different reasons. First, as they do not involve waiting on another subject prior to making a decision, all inactivity immediately prior to the decision in each game comes from the experimental design. Second, all of the games are very easy to explain and simple to comprehend. This should reduce noise in the observations. Third, each task has different considerations. The dictator game reaches the social maximum earnings along the entirety of the game space, and has a clear equilibrium prediction. The public goods VCM is slightly more complicated, as the social optimum and the Nash equilibrium are on opposite sides of the decision space. Last, the gamble selection task is used to elicit risk preferences. Risk plays a role in many experiments and the psychology literature reviewed in Chapter 2 strongly suggests that an increase in risky behavior due to inactivity is likely. All subjects in a session will face the same task in an incentivized stage, and will perform that task during all four incentivized stages.
During incentivized stages in the dictator game treatment (DG), subjects take part in a one-shot dictator game, based on the setup described in Forsythe et al. (1994). In this “game” one subject, acting in the dictator role, is asked to divide a fixed amount of money between themselves and another subject, acting in the receiver role, whose sole action is to receive the money. Both subjects keep the money allocated to them by the dictator. In this experiment, subjects are randomly and anonymously paired and are informed that half of them will act as the dictator, and the other half will be the receiver, as determined randomly by the computer. They are not, however, informed of which role they have been assigned, and all subjects are asked to make decisions as if they are playing as the dictator. The dictator’s decision is used to allocate 100 experimental currency units (ECU) between the two subjects, and the receiver’s decision is
10 ignored. This mechanism is incentive compatible for subjects to report the same actions as they would if they were definitely the dictator. Prior to playing the game the first time, subjects receive instructions on the task and on how they will be paid for their decisions in this task and take a quiz to show they understand the game. While playing the game, subjects have text on their screen explaining the game, in case they need the reference. Subjects are not informed of their role or earnings from these stages until the summary stage. Subjects are matched with a new anonymous partner each time they play these stages. Partners are matched at random, so it is possible to play the same person twice, however the complete lack of information about your opponent and the outcome of the game renders repeated game considerations moot. The decision screen for the dictator game is shown in Figure 3.2. During incentivized stages in the public goods treatment (PG), subjects play a one shot public goods game, using a voluntary contributions mechanism (VCM), as in Isaac and Walker (1988) and many others. Subjects are randomly placed by the computer into groups of four. Then they are asked to allocate 20 tokens into a personal and a group account. Each subject earns 4 ECU for every token they allocate to their own personal account. The group account, on the other hand, pays out 2 ECU to every member of the group for every token submitted to it by any member of the group. Thus in this game, the Nash equilibrium is for all subjects to allocate all of their tokens to their personal account, and each earn 80 ECU. The social optimum, however, is for all subjects in a group to allocate all of their tokens to the group account. This would result in 80 tokens in the group account, and earnings of 160 ECU by every member of the group. Prior to the start of this stage, subjects receive instructions on this task and how they will be paid. They then need to complete a short quiz to show that they understand the game. Once again, subjects are not informed of the outcome of this stage until the summary stage, and again subjects are matched with a new anonymous set of partners each time they play these stages. Partners are matched at random, so while it is possible to play with the same person twice, the complete lack of information about your current and prior opponent and the outcome of the last game renders repeated game considerations moot. The decision screen for the public goods game is shown in Figure 3.3.
During incentivized stages in the gamble choice treatment (GC), subjects are asked to choose a gamble from a set of five gambles similar to those in Eckel and Grossman (2002).
11 Subjects are informed that over the course of the experiment they will make four of these decisions, but that only one of them will be selected at random to count toward their earnings. Only paying subjects for one decision eliminates the risk mitigation that occurs when multiple risky gambles are realized. The gambles used in the experiment are described in Table 3.1. A perturbation is added the payoffs each time to keep subjects from seeing the same choice every time and to mitigate anchoring on previous decisions. This perturbation is drawn from the set {0, 5, 10, 15} and subjects experienced each of these options in a random order as determined by the computer. These perturbations are reasonably small relative the payouts, and are added uniformly to each payoff, preserving the risk order the choices. Each gamble has a different expected value, and would be the optimal choice under different sets of risk preferences, thus each subject’s gamble choice provides an observation of their risk-preferences. The results of each gamble are computed, but are not immediately displayed to the subject. Subjects find out the results of each gamble selection and which one they were paid for in the summary stage. An example of the decision screen for the gamble choice treatment is shown in Figure 3.4. The gambles are presented in a visual format to aid in comparison and presented in a circular layout, which is reordered between rounds, to reduce the ability of subjects to easily repeat their previous decision without further though.
3.3.2 Non-incentivized stages
Unlike in the incentivized stages, where a subject’s actions determine their earnings, in non-incentivized stages, subjects will receive a fixed payment of 100 ECU. Each non- incentivized stage lasts exactly 5 minutes. Subjects will either receive a message informing them that they have no activities to perform in this stage, as seen in Figure 3.5, and will thus be inactive for 5 minutes, or they will receive a menu of simple activities that they may participate in. The following activities may be participated in by subjects who receive the menu:
Tic-Tac-Toe: This is a simple game of tic-tac-toe, which the subject chooses to play against either an “easy” or “harder” computer. The easy computer chooses at random and is very simple to beat. The harder computer will play the center square if it starts the game, and otherwise searches for potential winning moves by the subject. The computer will block and
12 winning move it finds, but otherwise it chooses randomly. The hard computer is thus beatable, however only when a random choice leaves it exposed. Subjects have the option of playing additional rounds of the game each time they finish. A simple scorecard counts wins, ties, and losses. A screenshot of this activity may be seen in Figure 3.6.
Rock-Paper-Scissors: This is a simple game of rock-paper-scissors played against the computer. Subjects choose rock, paper, or scissors by clicking on the appropriate button. Once they do so, a random number is drawn by the computer to determine its action. The winner of a match is determined following the standard rules of rock paper scissors. Subjects have the option of playing additional rounds of the game each time they finish. A simple scorecard will count wins, ties, and losses. A screenshot of this activity may be seen in Figure 3.7.
Pictures: Subjects are taken to a screen on which they may flip through a collection of images. The images are a selection of ten public domain landscape images. A screenshot of this activity may be seen in Figure 3.8
Text search: Subjects are taken to a screen showing a grid of letters in which they may search for specific nonsensical strings of letters. There are 6 available puzzles that subjects may flip between.
Half of the subjects will receive the menu of activities for all three non-incentivized stages. The other half of subjects will only receive the menu of activities in the second non- incentivized stage, and will receive the “no activities” message in the other non-incentivized stages. This will hopefully allow us to observe the persistence of any effect from the inactivity on subject choices. After four incentivized stages and three non-incentivized stages, subjects see the summary stage. In this stage subjects are provided with the outcomes and payoffs of the incentivized stages, and any information used to determine these amounts. By delaying this information until the end of the incentivized stages, I’ve decreased the amount of learning that can take place between stages. I’ve also reduced the possibility that subjects will take actions based upon the outcomes of previous stages. By reducing the role of learning and outcome
13 history in a subject’s decisions, I’ve increased the likelihood that changes in subject behavior are due to their experiences in the non-incentivized stages.
3.3.3 Final stage
In the seventh and final stage, subjects earn money by remaining in their seats. This stage was included to capture a measurement of subjects’ disutility from inactivity. This stage will be discussed further in Chapter 5.
3.4 Results
I conducted 10 sessions of this experiment, across the three treatments, with 177 subjects in total participating. A summary of the sessions and participants can be found in Table 3.2. Immediately prior to the decision in Period 1, subjects take a quiz to make sure they understand the game. There are large differences in the amount of time it takes subjects to complete the quiz, resulting in a rage of different inactivity levels prior to the first decision. This, combined potentially with uncertainty over how the experiment and interface works, results in an environment in the first period that is quite different than the other periods. For this reason, in this analysis I will be looking only at the subject decisions in Periods 2 – 4. I will review the results for each environmental treatment separately. The analysis of the data in this dissertation was conducted using the R programming language and environment (R Core Team 2015). Additional data manipulation was performed using the reshape2 package (Wickham 2007). Various regressions performed in this chapter and later chapters made use of the functions provided in the lme4 (Bates et al. 2015) and ordinal (Christensen 2015) packages. Tables were produced using the xtable (Dahl 2016) and texreg (Leifeld 2013) packages. Lastly, all graphs were produced using the ggplot2 package (Wickham 2009).
3.4.1 Dictator Game
The distribution of subject decisions following activity and inactivity in the Dictator Game is shown Figure 3.10 and described in Table 3.3. As one can see, there is little difference between subjects who have been inactive prior to their choice, and those that have not. I conduct
14 some basic regressions to confirm this, as seen in Table 3.4. From the OLS regressions, looking at Periods 2 and 4, I find only a significant effect from gender, with men keeping on average 10.5 units more for themselves. There is no effect from having been inactive prior to the decision, Running these regressions with random effects reduces the significance of the gender result, however it is still significant at the 5% level, and we still cannot find an effect from inactivity. This result is further confirmed with an Epps-Singleton Test (Epps and Singleton 1986; Forsythe et al. 1994; Eckel and Grossman 2002; Goerg and Kaiser 2009), where I fail to reject the hypothesis that the choices of inactive players and active players are drawn from the same distribution (Test statistic = 3.8641, p-value = 0.2765). I use the Epps-Singleton test rather than the more widely used Kolmogorov-Smirnov test, as it is typically the more powerful test (Goerg and Kaiser 2009) and additionally does not include the assumption that the data is continuous. This will be particularly important later when analyzing the Gamble decisions, which are discrete.
I also conduct a series of within-subjects regressions, which may be found in Table 3.5. I run another Epps-Singleton test on the decisions made by subjects the third time they play the dictator game, which come after both groups have just been active, and am unable to reject the claim that they are from the same distribution (Test statistic = 1.3303, p-value = 0.722). Using this result I look at the differences in behavior for subjects between the second and third time subjects play the dictator game, called periods 2 and 3, and between the third and fourth time subjects play the dictator game, called periods 3 and 4, for both active and inactive subjects. Again, I find no effect from inactivity on subject behavior. I conduct one more Epps-Singleton test on only the inactive subjects, looking at the behavior in Period 3 versus the behavior in Periods 2 and 4. Again, I fail to reject the claim that the data comes from the same distribution of decisions (Test statistic = 1.452, p-value = 0.6934). From these analyses, I find no differences in behavior in the Dictator Game resulting from experimenter-imposed inactivity.
3.4.2 Public Goods Game
The distribution of subject decisions following activity and inactivity in the Public Goods Game is shown in Figure 3.11 and described in Table 3.6. Again one can see little difference between subjects who have just been inactive, and those that have not. The results of the
15 between subjects regressions for the Public Goods VCM may be found in Table 3.7. I find no effect from inactivity on subject behavior in any regression. This result is further strengthened with an Epps-Singleton Test, where I fail to reject the hypothesis that inactive players and active players’ choices are drawn from the same distribution (Test statistic = 3.0468, p-value = 0.55).
The within-subject regressions are shown in Table 3.8. Again an Epps-Singleton Test fails to reject that inactive and active players make decisions from the same distribution of decisions in period 3 (Test statistic = 3.1031, p-value = 0.5407). We then look at deviations from Period 3 behavior in periods 2 and 4 and find that in neither case may this be explained by inactivity. A final Epps-Singleton test on the inactive subjects only fails to conclude that they behave differently after inactivity (Test statistic = 4.4587, p-value = 0.3475). Across all tests conducted, I find no differences in behavior in the Public Goods environment as a result of experimenter-imposed inactivity.
3.4.3 Gamble Choice
Table 3.9 shows the distribution of subject decisions in the gamble choice environment. Again, there does not appear to be much difference in the decisions of subjects who have just been inactive, and those that have not. The results of the between-subjects regressions for the Gamble Choice environment may be found in Table 3.10. In the OLS and Random Effects models, we find the standard result that men tend to choose riskier gambles than women, however, we again find no effect from experiment-imposed inactivity. Since the decision space is discrete and limited, I also conduct a random effects ordered probit. Again I find the result that gender affects the choice of risk gamble, and I do not find an effect from inactivity. An Epps- Singleton Test fails to reject that the choices of inactive and active subjects come from the same distribution (Test statistic = 2.764, p-value = 0.5981).
The within-subject regressions, shown in Table 3.11, find no differences in behavior in the Gamble Choice environment. We cannot reject that subject decisions from the inactive group are distributed the same as subject decisions from the always active group in Period 3 (Test statistic = 1.4991, p-value = 0.8268). Using OLS and ordered probit regressions, we again fail to find that being inactive is the cause for differences in behavior between this period and Periods 2
16 and 4. An Epps-Singleton test on the inactive group cannot reject that their decision come from the same choice distribution (Test statistic = 2.2882, p-value = 0.5148). As with the Dictator Game and the Public Goods environment, I find no effect from experimenter-imposed inactivity on subjects’ decisions in the gamble choice task. . 3.5 Conclusion
Using a between-subjects and within-subjects design, I was not able to find a difference in behavior between subject decisions made following a period of experimenter-imposed inactivity and those that were made following an active period, in any of the environments I tested. This result is consistent in both between subjects and within-subjects analysis. This is good news as it means that inactivity caused directly by experimenters appears unlikely to bias results, at least in these three environments. Given that these environments are used in a multitude of different experiments, this is reassuring. The result, however, seems to be in contrast to what we would expect, given the psychological literature on boredom. One possibility is that a subject’s behavior, with regard to inactivity, depends on the source of that inactivity. I will investigate this further in Chapter 4.
17 Group A Group B Incentivized Stage Non-incentivized Stage: Non-incentivized Stage: No activities Menu of activities Incentivized Stage Non-incentivized Stage: Menu of activities Incentivized Stage Non-incentivized Stage: Non-incentivized Stage: No activities Menu of activities Incentivized Stage Summary of Earnings Final Stage
Figure 3.1: Experimental Diagram – Experimenter Imposed Inactivity
Figure 3.2: Dictator Game Decision Screen
18
Figure 3.3: Public Goods Game Decision Screen
Figure 3.4: Gamble Choice Decision Screen
19
Figure 3.5: Non-incentive stage – Inactive Screen
Figure 3.6: Tic-Tac-Toe Activity Screen
20
Figure 3.7: Rock-Paper-Scissors Activity Screen
Figure 3.8: Picture Viewing Activity Screen
21
Figure 3.9: Text search Activity Screen
Figure 3.10: The Dictator Game: Choice Distributions Following Activity and Inactivity
22
Figure 3.11: The Public Goods VCM: Choice Distributions Following Activity and Inactivity
Table 3.1: Gamble Choices
Gamble Choice Event Probability Payoff A 50% 240 + x ECU 1 B 50% 240 + x ECU A 50% 360 + x ECU 2 B 50% 180 + x ECU A 50% 480 + x ECU 3 B 50% 120 + x ECU A 50% 600 + x ECU 4 B 50% 60 + x ECU A 50% 720 + x ECU 5 B 50% 0 + x ECU where x {0, 5, 10, 15}
∈
23 Table 3.2: Sessions Summary – Experimenter Imposed Inactivity
Gamble Choice Dictator Game Public Goods VCM Gamble Choice Sessions 4 3 3 Total Subjects 82 56 39 Male 42 23 19 Female 40 33 20
Table 3.3: The Dictator Game: Distribution of Decisions Min. 1st Qu. Median Mean 3rd Qu. Max. Total 1.00 60.00 100.00 83.53 100.00 100.00 Following Activity 1.00 60.00 100.00 83.25 100.00 100.00 Following Inactivity 20.00 62.50 100.00 84.09 100.00 100.00
24 Table 3.4: The Dictator Game: Amount Kept, with Experimenter Imposed Inactivity (Between Subjects, Periods 2 and 4)
OLS OLS Random Effects Random Effects (Intercept) 82.9512*** 79.4810*** 82.9512*** 79.4810*** (2.4031) (4.0590) (3.2417) (5.4214) Inactive in Previous Stage 1.1341 0.1067 1.1341 0.1067 (3.3986) (3.3495) (4.4637) (4.4320) Male 10.5314*** 10.5314** (3.3674) (4.4556) Session 01 -1.6151 -1.6151 (4.5781) (6.0575) Session 02 -3.6333 -3.6333 (4.9035) (6.4880) Session 03 -0.5734 -0.5734 (4.7756) (6.3189) R2 0.0007 0.0624 Adj. R2 -0.0055 0.0327 Num. obs. 164 164 164 164 RMSE 21.7614 21.3441 AIC 1419.2226 1400.6554 BIC 1434.7219 1428.5542 Log Likelihood -704.6113 -691.3277 Num. groups: subj 82 82 Num. groups: Period.x 2 2 Var: subj (Intercept) 339.7114 330.0491 Var: Period.x (Intercept) 1.0932 1.0932 Var: Residual 137.4861 137.4861 ***p < 0.01, **p < 0.05, *p < 0.1
25 Table 3.5: The Dictator Game: Amount Kept, with Experimenter Imposed Inactivity (Within Subjects, Periods 2 to 4)
Period 2 - Period 3 Period 4 - Period 3 (OLS) (OLS) (Intercept) 38.0002*** 23.0307*** (10.6296) (6.4158) Inactive in previous stage -1.5545 -10.3982 (13.5616) (8.1856) Amount kept in Period 3 -0.4438*** -0.2160*** (0.1137) (0.0686) Male 5.8789 -0.6540 (3.7404) (2.2577) Session 01 -6.7550 -5.4753* (4.9949) (3.0148) Session 02 -9.9302* -3.0312 (5.2551) (3.1719) Session 03 -7.6120 -5.0010 (5.1682) (3.1195) Inactive in previous stage * 0.0396 0.1241 Amount kept in Period 3 (0.1574) (0.0950) R2 0.3304 0.1893 Adj. R2 0.2670 0.1126 Num. obs. 82 82 RMSE 16.0778 9.7043 ***p < 0.01, **p < 0.05, *p < 0.1
Table 3.6: The Public Goods VCM: Distribution of Decisions Min. 1st Qu. Median Mean 3rd Qu. Max. All 0.00 8.00 12.00 12.24 18.00 20.00 Following Activity 0.00 8.00 12.00 11.94 18.00 20.00 Following Inactivity 0.00 9.75 14.00 12.84 19.25 20.00
26 Table 3.7: The Public Goods VCM: Amount in Individual Account, with Experimenter Imposed Inactivity (Between Subjects, Periods 2 and 4)
OLS OLS Random Effects Random Effects (Intercept) 10.8750*** 10.1047*** 10.8750*** 10.1047*** (0.8182) (1.3014) (1.1092) (1.7193) Inactive in previous stage 1.9643* 1.9617* 1.9643 1.9617 (1.1571) (1.1696) (1.4685) (1.5049) Male -0.0736 -0.0736 (1.1983) (1.5418) Session 11 0.9476 0.9476 (1.4761) (1.8993) Session 12 1.2976 1.2976 (1.4761) (1.8993) R2 0.0255 0.0328 Adj. R2 0.0167 -0.0033 Num. obs. 112 112 112 112 RMSE 6.1227 6.1847 AIC 704.4574 701.4787 BIC 718.0499 723.2267 Log Likelihood -347.2287 -342.7394 Num. groups: subj 56 56 Num. groups: Period.x 2 2 Var: subj (Intercept) 22.6382 24.1114 Var: Period.x (Intercept) 0.3042 0.3042 Var: Residual 15.1065 15.1065 ***p < 0.01, **p < 0.05, *p < 0.1
27 Table 3.8: The Public Goods VCM: Amount in Individual Account, with Experimenter Imposed Inactivity (Within Subjects, Periods 2 - 4)
Period 2 - Period 3 Period 4 - Period 3 (OLS) (OLS) (Intercept) 2.8602 3.7941 (2.4540) (2.6351) Inactive in previous stage 1.5975 1.8150 (3.3604) (3.6083) Amount in Indv. Acct. in Choice 3 -0.4735*** -0.3108* (0.1626) (0.1746) Male 0.4572 -0.1811 (1.3794) (1.4812) Session 11 0.3277 -0.1318 (1.7023) (1.8278) Session 12 -0.9330 -0.8466 (1.7678) (1.8982) Inactive in previous stage * 0.1281 -0.1278 Amount in Indv. Acct. in Choice 3 (0.2372) (0.2547) R2 0.2912 0.1795 Adj. R2 0.2044 0.0790 Num. obs. 56 56 RMSE 5.0087 5.3782 ***p < 0.01, **p < 0.05, *p < 0.1
Table 3.9: The Gamble Choice: Choice Distributions Following Activity and Inactivity
Gamble Choice Following Activity Following Inactive 5 14 11 4 20 9 3 17 9 2 12 6 1 14 5
28 Table 3.10: The Gamble Choice: Choice, with Experimenter Imposed
Inactivity (Between Subjects, Periods2 and 4)
Num.groups: subj Male Var: Residual Var:Residual Var:Period (Intercept) Var: (Intercept) subj groups: Num. Period LogLikelihood BIC AIC RMSE obs. Num. R Adj. R Period 4 19 Session 17 Session Inactivestage previous in (Intercept)
***
2 p < < 0.01, p
2
**
p < p< 0.05,
*
p < p< 0.1
3.0000
(0.3046) (0.3046) (0.2181)
1.3445 1.3445 0.0067 0.3750
0.0196 0.0196
L OLS OLS
78 78
***
1.1273 1.7856
0.6567 (0.4194) (0.4194) (0.4021) 0.9574 (0.2908) (0.2898) (0.4187)
1.2731 1.2731 0.1094 0.4686
0.1556 0.1556
78 78
*** ***
** **
-121.0096
263.8028 263.8028 252.0193 3.0000
Random
(0.4009) (0.4009) (0.2871)
Effects
0.0000 0.0000 1.2902 0.3750
0.5513 0.5513
39 78 78
2 2
***
-117.4957
269.8451 269.8451 250.9914 1.7856
Random
(0.5560) (0.5560) 1.1273 (0.5330) (0.3855) (0.3841) (0.5549)
0.6567 0.9574
Effects
0.0000 0.0000 1.1480 0.4686
0.5513 0.5513
39 78 78
2 2
***
**
* *
Random Effects RandomEffects
Ordered Probit Probit Ordered
-109.9885
246.1173 246.1173 231.9770
(0.6386) (0.6386)
3.0589 3.0589 0.7257
39 78 78
Random Effects
Ordered Probit Probit Ordered
-106.0244
255.6159 255.6159 232.0489
(0.2684) (0.2684) (0.8880) 1.8777 (0.8468) (0.6069) (0.6037)
1.0762 -0.1948 1.6040
2.4762 2.4762 0.9228
39 78 78
**
* *
29 Table 3.11: The Gamble Choice: Choice, with Experimenter Imposed Inactivity (Within Subjects, Periods 2 - 4)
Gamble3 choice Period in
(Intercept) Log Likelihood LogLikelihood BIC AIC RMSE obs. Num. R Adj. R Inactive stage * previous in 19 Session 17 Session Male Inactive stage previous in Gamble3 choice Period in
***
2 p < < 0.01, p
2
**
p < p< 0.05,
*
p < p< 0.1
Period- 2
-0.4460
Period 3
(0.2086) (0.2086) (0.4492) (0.4153) (0.2952) (0.1556) (0.7315) (0.6135)
0.8128 0.1079 0.0785 0.2875 0.1509 0.1509 0.2875 0.8992 0.0785 0.1892 0.1079 0.4354 0.3276 0.3321 0.0366
(OLS) (OLS)
0.3172 0.3172
39 39
***
Period- 4
Period 3
(0.2007) (0.2007) (0.4323) (0.3997) (0.2841) (0.1498) -0.2691 (0.7040) (0.5904)
-0.1862 0.4973
0.3327 0.8654 0.8654 0.1315 0.2397 0.0704
(OLS) (OLS)
0.2687 0.2687
39 39
*
*
(Ordered Probit)
Period 2 - Period- 2
-1.1058
121.2203 121.2203 102.9211
Period 3
(0.4906) (0.4906) (0.9928) (0.9181) (0.7229) (0.3931) (1.7205)
-0.0584
-40.4606
1.0940 1.0940 0.9999 1.1386
39 39
***
(OrderedProbit)
Period 4 - Period- 4
122.9032 122.9032 104.6040 -0.7194
Period 3
(0.4457) (0.4457) (1.0128) (0.9364) (0.7164) 1.5225 (0.3537) (1.5772)
-0.2838
-41.3020
0.6789 0.6789 0.3540
39 39
**
**
30 CHAPTER 4
BEHAVIORAL EFFECTS OF PEER-IMPOSED INACTIVITY
4.1 Introduction
As discussed in chapter 2, subjects in lab experiments often find themselves in a situation in which they have no actions to perform and must wait for the experiment to continue. The negative utility provided by such a situation has had obvious and documented effects in the lab on entry rates when it is the alternative to participation in an economic activity. The behavioral changes indicated by psychological research on boredom, however, indicates that it may also play a role in other decisions as well. In this chapter I will look at the situation where subjects are experiencing inactivity imposed on them as a result of other subjects’ actions. In the following experiment, a subject’s inactivity is due, in part, to the decisions of another subject, and is known to be so. This mirrors a situation in which a subject has to wait for a variable period of time while other subjects make decisions before they can make a decision. For example, a subject acting as an employee, may have to wait for an unknown period of time while other subjects, acting as managers, decide on an incentive scheme. Similarly, a subject acting as a manager, may decide on a scheme quickly, but still have to wait for other subjects, also acting as managers, to make their decisions before the experiment can continue. In this environment, we will use a between-subjects and within-subjects design to look at how subjects’ decisions differ following inactivity in three simple economics tasks: the dictator game, the public goods game, and a risk elicitation.
4.2 Hypotheses
Hypothesis 1: Following periods of peer-imposed inactivity, subjects will allocate more ECU to themselves when playing the dictator game.
As in chapter 3, from the literature I expect inactive subjects to become bored and bored subjects to exhibit emotional upset and hostility, I thus hypothesize that subjects’ other regarding preferences will be diminished, and that they will keep more for themselves. Additionally, subjects who experience peer-imposed inactivity may be more likely direct his or her hostility
31 towards his or her peers, than in Chapter 3. I expect that this would result in a greater reduction in the strength of other regarding preferences, increasing the likelihood of behavioral changes.
Hypothesis 2: Following periods of peer-imposed inactivity, subjects will allocate more tokens to their personal account, and less to the group account when playing the public goods game.
Again, I expect inactive subjects to become bored and bored subjects to exhibit emotional upset and hostility. A subject who is upset with his or her peers is less likely to allocate tokens to the group account, and thus give up earnings (the group account has a marginal per capita return of 0.5) to increase total social welfare. Again, subjects who experience peer-imposed inactivity may be more likely direct his or her hostility towards his or her peers, than in Chapter 3. The more a subject is upset with his or her peers, the less likely he or she is to allocate tokens to the group account.
Hypothesis 3: Following periods of peer-imposed inactivity, subjects will exhibit less risk-averse behavior in the gamble choice task.
As noted in the literature review, boredom is associated with increased risky behavior and thrill seeking. In the gamble choice task, this would be evidenced by subjects who have just experienced longer periods of peer-imposed inactivity choosing riskier gambles than subjects who had experienced less inactivity.
4.3 Experimental Design
This experiment utilizes both a within-subjects and a between-subjects design, very similar to the design found in chapter 3. The experimental sessions were conducted at the xs/fs lab at Florida State University in October and November 2013, with subjects recruited from the student population using ORSEE (Greiner 2015). The experiment was programmed and conducted using the Zurich Toolbox for Ready-made Economics Experiments(Fischbacher 2007).
32 Prior to the beginning of the session, subjects were asked to place all of their belongings in a plastic bag which was then tightly shut with a zip tie. This was to insure that subjects did not have access to anything during the course of the experiment, other than what was provided for them. Subjects were then assigned seats, such that no two subjects who entered the lab were seated next to each other, and randomly assigned by the computer into two equal sized groups A and B.
Prior to the start of the experiment, and following some brief instructions, subjects were asked to input their name into their workstation the start of the experiment, to aid in the payment of subjects at the end of the experiment.
Each session was comprised of 9 stages: 4 Incentivized stages, 3 Non-incentivized stages, 1 summary stage, and the final stage. A generic session looks like the structure shown in figure 4.1
4.3.1 Incentivized stages
The incentivized stages for this experiment are exactly the same as in Chapter 3 above. During incentivized stages, subjects are asked to perform an economic task. There are three different economic tasks that subjects could see, depending on their treatment: a dictator game task; a public good task; and a gamble selection task. Again, these tasks were chosen as they are simple to explain and understand, they do not involve waiting on another subject prior to making a decision, and they provide three different decisions to examine. All subjects in a session will face the same task in an incentivized stage, and will perform that task during all four incentivized stages.
During incentivized stages in the dictator game treatment (DG), subjects take part in a one-shot dictator game. In this experiment, subjects are randomly and anonymously paired and are informed that half of them will act as the dictator, and the other half will be the receiver, as determined randomly by the computer, but they are not informed of which role they have been assigned, and all subjects are asked to make decisions as if they are playing as the dictator. The
33 dictator’s decision is used to allocate 100 ECU between the two subjects, and the receiver’s decision is ignored. Prior to playing the game the first time, subjects receive instructions on the task and on how they will be paid for their decisions in this task and take a quiz to show they understand the game. While playing the game, subjects have text on their screen explaining the game, in case they need the reference. Subjects are not informed of their role or earnings from these stages until the summary stage. Subjects are matched with a new anonymous partner each time they play these stages.
During incentivized stages in the public goods treatment (PG), subjects are randomly placed by the computer into groups of four. Then they are asked to make a single set of decisions, allocating 20 tokens between a personal and a group account. Each subject earns 4 ECU for every token they allocate to their own personal account. The group account, on the other hand, pays out 2 ECU to every member of the group for every token submitted to it by any member of the group. Prior to the start of this stage, subjects receive instructions on this task and how they will be paid. They then need to complete a short quiz to show that they understand the game. Once again, subjects are not informed of the outcome of this stage until the summary stage and are matched with a random set of partners each time they play these stages.
During incentivized stages in the gamble choice treatment (GC), subjects are asked to choose a gamble from a set of six gambles. The results of each gamble are computed, but are not immediately displayed to the subject, and subjects are only paid for one of their gamble choices, selected at random prior to the start of the experiment by the computer. Subjects find out the results of each gamble selection and which one they were paid for in the summary stage.
4.3.2 Non-incentivized stages
In non-incentivized stages, subjects will receive a fixed payment of 100 ECU. The second non-incentivized stage in this experiment is identical to the one described in Chapter 3, lasting 5 minutes, and providing the same menu of activities. The first and third non-incentivized stages differ substantially. Instead of lasting for a fixed period of time, the length of these stages will be endogenously determined. Each subject will be assigned a number of tasks to complete,
34 ranging from 0 to 3, and the stage does not continue until all subjects have completed their tasks. Subjects are free to choose which tasks to complete, so long as they complete the same number of tasks as they were assigned. Subjects are also free to abandon and return to tasks as they saw fit. This ability is included to more directly match the freedom of activity seen in chapter 3.The following activities may be performed by subjects to fulfil one of their tasks:
Tic-Tac-Toe: This is a simple game of tic-tac-toe, which the subject chooses to play against either an intermediately-skilled computer. A simple scorecard will count wins, ties, and losses. To complete a task in this activity, subjects must win twice.
Rock-Paper-Scissors: This is a simple game of rock-paper-scissors against a randomly choosing computer. Subjects have the option of playing additional rounds of the game each time they finish. A simple scorecard will count wins, ties, and losses. To complete a task in this activity, subjects must win nine times.
Pictures: Subjects are taken to a screen on which they may flip through a collection of ten images. To complete a task in this activity, subjects must view the pictures for 90 seconds.
Text search: Subjects are taken to a screen showing a grid of letters in which they may search for specific nonsensical strings of letters. To complete a task in this activity, subjects must solve a five word puzzle.
The tasks above were calibrated to take approximately 90 seconds each, on average. At least one subject always was assigned zero tasks and remained inactive through the entire stage. Conversely, one subject was never inactive during the stage, as the stage ended as soon as their tasks were complete.
After four incentivized stages and three non-incentivized stages, subjects will the summary stage. In this stage subjects are provided with the outcomes and payoffs of the incentivized stages, and any information used to determine these amounts. By delaying this information until the end of the incentivized stages, I’ve decreased the amount of learning that
35 can take place between stages. I’ve also reduced the possibility that subjects will take actions based upon the outcomes of previous stages. By reducing the role of learning and outcome history in a subject’s decisions, I’ve increased the likelihood that changes in subject behavior are due to their experiences in the non-incentivized stages.
4.3.3 Final stage
In the seventh and final stage, subjects earn money by remaining in their seats. As with the study in Chapter 3, this stage was included to capture a measurement of subjects’ disutility from inactivity. This stage will be discussed further in Chapter 5.
4.4 Results
A summary of the sessions and participants can be found in table 4.1. I will review the results for each environment separately.
4.4.1 Dictator Game
Figure 4.6 shows a plot of subject decisions in the Dictator Game versus the amount of time spent inactive prior to the decisions. Due to the variance in subject decisions, it is quite difficult to draw conclusions from this graph. I perform some basic regressions on this data, as seen in Table 4.2. While a simple OLS regression does not find any significant effects from time spent inactive, the Random Effects regression finds that each minute of inactivity results in a subject keeping 1 fewer ECU for him or herself. I conduct an Epps-Singleton test, comparing the least inactive third of subjects and the most inactive third of subjects. I am unable to reject that their decisions have the same distribution (Test statistic = 3.0366, p-value = 0.386).
As an alternative measure, I perform the same regressions using the number of activities assigned instead of time spent inactive. Table 4.3 shows The number of tasks is negatively correlated with the amount of time inactive, as subjects who are assigned zero tasks to perform will be the most inactive, and subjects who receive 3 tasks are more likely to be the least inactive, having had more to do before becoming inactive. While the number of activities assigned is a rough estimate of time inactive, it has the advantage of being completely
36 exogenous, and thus is in no way dependent on a subject’s ability level. The distributions of decisions with respect to number of tasks assigned are described in Table 4.3 and can be seen in Figure 4.7. The results of the between subjects regressions are in Table 4.4. In this case, I do not find the same results. In both the OLS and Random Effects regressions, I fail to find an effect from the number of tasks assigned on subject decisions. I conduct an Epps-Singleton test, comparing the decisions of subjects assigned zero tasks with subjects assigned 3 tasks. I am unable to reject that their decisions have the same distribution, although it is close (Test statistic = 6.235, p-value = 0.1007).
Similarly, in the within-subject regressions, shown in Table 4.5, I find no differences in behavior in the Dictator Game. I run another Epps-Singleton test on the period three decisions, which come after both groups have been active, and am unable to reject the claim that they are from the same distribution (Test statistic = 1.9388, p-value = 0.5852). I compare changes in behavior between period 3 and Periods 2 and 4 and I do not find that inactivity plays a role in explaining these differences. I also look at differences between Periods 2 and 4, and find that neither the difference in the amount of time a subject spends inactive, nor the difference in the number of tasks assigned to a subject affects their behavior in the game.
While I do find an effect from inactivity in the between subjects setting, the failure of this result to appear when using a different specification, and when looking at the within subjects data suggests that the result is not robust. While there may be an effect from peer-imposed inactivity on behavior in the dictator game, it has not been proven here.
4.4.2 Public Goods Game
Figure 4.8 shows a plot of subject decisions in the Public Goods Game versus the amount of time spent inactive prior to the decisions. There is a good deal of variance in subject decisions, so again it is difficult to draw conclusions from this graph. I perform some basic regressions on this data, as seen in Table 4.6. While a simple OLS regression does not find any significant effects from time spent inactive the Random Effects regression finds that each minute of inactivity results in a subject allocating roughly 0.4 additional tokens to his or her individual account. An Epps-Singleton test, comparing the least inactive third of subjects and the most
37 inactive third of subjects fails to reject that their decisions have the same distribution (Test statistic = 5.2066, p-value = 0.2667).
I also perform the same regressions using the number of activities assigned instead of time spent inactive. The distributions of decisions with respect to number of tasks assigned can be found in Table 4.7 and seen in Figure 4.9. These regressions are shown in Table 4.8. In both the OLS and Random Effects regressions, we find a significant effect from the number of tasks assigned on subject decisions. This strongly suggests that the effect is robust, however an Epps- Singleton test, comparing the decisions of subjects assigned zero tasks with subjects assigned 3 tasks is still unable to reject that their decisions have the same distribution (Test statistic = 6.1822, p-value = 0.1859).
The results of the within-subject regressions are shown in Table 4.9. The Epps-Singleton test on the period three decisions results in a failure to reject the claim that they are from the same distribution (Test statistic = 1.1062, p-value = 0.8933). I then compare changes in behavior between period 3 and Periods 2 and 4, and between Periods 2 and 4, and find that inactivity is involved in explaining these differences. Particularly, the change in inactivity plays a role when interacted with the level of contribution in period 3. This effect is still present when using the number of tasks a subject is assigned. In this case an increase of a minute in time inactive corresponds to an increase in amount allocated to the individual account of roughly seven or eight percent of a subjects period 3 contribution. In the case of tasks assigned, a decrease of 1 task assigned results in an increase in the amount assigned to the individual account of 18 percent of a subject’s period 3 contribution.
Peer-imposed inactivity appears to play a role in subjects decisions in a public goods environment. Through the various regressions presented, I find a significant positive relationship between the amounts are allocated by subjects to their personal accounts and the amount of inactivity they have recently experienced.
38 4.4.3 Gamble Choice
Figure 4.10 shows a plot of subject decisions in the gamble choice environment. The initial regressions, shown in Table 4.10, find no effect from peer-imposed inactivity. The result is confirmed by the Epps-Singleton Test, which fails to reject the claim that the least inactive third of subjects and the most inactive third of subjects draw their decisions from different distributions (Test statistic = 4.788, p-value = 0.3098).
The subject decisions are shown with respect to number of tasks assigned, in a violin plot, in Figure 4.11, and described in Table 4.11. I run the same set of regressions again using this measure of inactivity and again find no significant effect from inactivity. These regressions can be seen in Table 4.12. Unsurprising, the Epps-Singleton Test cannot distinguish between the distribution of decisions that subjects who are assigned 0 tasks and that subjects who are assigned three tasks (Test statistic = 5.203, p-value = 0.2671).
Lastly, the within-subject regressions, shown in Table 4.13, also find no significant differences due to inactivity in behavior in the Gamble Choice environment. This lack of change in risk behavior in both Chapters 3 and 4 is surprising because the literature shows strong ties between risky behavior and boredom. It is possible, however, that this relationship has more to do with boredom susceptibility, which is a characteristic of an individual, rather than state boredom, which is what we expect occurs during the experiment.
4.5 Conclusion
Using a within and between subjects design, I find some evidence that subjects behave differently in the dictator game following periods of peer-imposed inactivity, but using exogenous measures, this cannot be confirmed. In the Public Goods VCM environment, however, I do find results suggesting that subjects behave differently in the Public Goods environment. Subjects tend to allocate more money to their individual account, and less to the group account, as the amount of inactivity thy experience increases. This result can be seen both between and within subjects and can also be found when regressing decisions against the number of tasks assigned, an exogenous measure of inactivity which cannot depend on subject ability. In
39 the Gamble Choice environment, however, I find no changes in behavior due to inactivity, regardless of the regression model used for analysis.
40 Incentivized Stage Non-incentivized Stage: 0-3 Activities Assigned Incentivized Stage Non-incentivized Stage: Menu of activities Incentivized Stage Non-incentivized Stage: 0-3 Activities Assigned Incentivized Stage Summary of Earnings Final Stage
Figure 4.1: Experimental Diagram – Peer Imposed Inactivity
Figure 4.2: No Assigned Tasks Screen
41
Figure 4.3: Assigned Tasks Screen
Figure 4.4: Text search Task Screen
42
Figure 4.5: All Tasks Completed Screen
Figure 4.6: The Dictator Game: Amount Kept versus Time Inactive
43
Figure 4.7: The Dictator Game: Amount Kept versus Number of Tasks
Figure 4.8: The Public Goods VCM: Amount in Individual Account versus Time Inactive
44
Figure 4.9: The Public Goods VCM: Amount in Individual Account versus Number of Tasks
Figure 4.10: The Gamble Choice: Choices versus Time Inactive
45
Figure 4.11: The Gamble Choice: Choices versus Number of Tasks
Table 4.1: Sessions Summary – Peer Imposed Inactivity
Gamble Choice Dictator Game Public Goods VCM Gamble Choice Sessions 3 3 4 Total Subjects 48 60 64 Male 20 35 35 Female 28 25 29
46 Table 4.2: The Dictator Game: Amount Kept, with Peer Imposed Inactivity (Between Subjects, Periods 2 and 4)
OLS OLS Random Effects Random Effects (Intercept) 75.4575*** 72.1210*** 75.9942*** 72.4205*** (4.4835) (5.8163) (3.7744) (6.4382) Minutes inactive in -0.7884 -0.8408 -0.8913** -0.9011** previous stage (0.7361) (0.7450) (0.4137) (0.4148) Male 0.9427 0.9188 (4.8153) (6.5346) Session 06 2.4341 2.4821 (5.3996) (7.3020) Session 07 9.2170 9.2456 (6.1225) (8.3099) R2 0.0121 0.0369 Adj. R2 0.0015 -0.0055 Num. obs. 96 96 96 96 RMSE 22.6623 22.7419 AIC 828.6518 816.1936 BIC 841.4736 836.7084 Log Likelihood -409.3259 -400.0968 Num. groups: subj 48 48 Num. groups: Period.x 2 2 Var: subj (Intercept) 406.1417 423.6438 Var: Period.x (Intercept) 0.0000 0.0000 Var: Residual 108.1198 108.1270 ***p < 0.01, **p < 0.05, *p < 0.1
Table 4.3: The Dictator Game: Distribution of Choices, with Peer-imposed Inactivity
Min. 1st Qu. Median Mean 3rd Qu. Max. Total 20.00 50.00 67.50 71.88 100.00 100.00 Assigned 0 Activities 20.00 50.00 60.00 69.85 100.00 100.00 Assigned 1 Activity 50.00 50.00 60.00 71.10 100.00 100.00 Assigned 2 Activities 50.00 50.00 70.00 73.00 100.00 100.00 Assigned 3 Activities 50.00 50.00 65.00 73.41 100.00 100.00
47 Table 4.4: The Dictator Game: Amount Kept, with Exogenous Number of Tasks (Between Subjects, Periods 2 and 4)
OLS OLS Random Effects Random Effects (Intercept) 71.8310*** 68.3283*** 70.5406*** 67.0902*** (3.9651) (5.5936) (3.6861) (6.4494) # of tasks assigned in -0.3161 -0.2429 0.5209 0.5430 previous period (2.0826) (2.0926) (1.2545) (1.2567) Male 1.2658 1.2970 (4.8402) (6.5550) Session 06 1.7648 1.7665 (5.4040) (7.3194) Session 07 8.8030 8.8544 (6.1560) (8.3363) R2 0.0002 0.0235 Adj. R2 -0.0104 -0.0194 Num. obs. 96 96 96 96 RMSE 22.7974 22.8988 AIC 830.7625 818.3695 BIC 843.5843 838.8843 Log Likelihood -410.3813 -401.1848 Num. groups: subj 48 48 Num. groups: Period.x 2 2 Var: subj (Intercept) 403.7897 422.2948 Var: Period.x (Intercept) 0.4249 0.4250 Var: Residual 117.3641 117.3591 ***p < 0.01, **p < 0.05, *p < 0.1
48 Table 4.5: The Dictator Game: Amount Kept, with Peer Imposed Inactivity (Within Subjects, Periods 2 - 4)
Period 2 - Period 4 - Period 4 - Period 4 - Period 3 Period 3 Period 2 Period 2 (OLS) (OLS) (OLS) (OLS) ** ** ** ** (Intercept) 39.9496 23.0277 -18.5490 -15.5274 (14.8899) (10.3753) (8.0796) (7.4272) * ** ** ** Amount kept in Period 3 -0.4129 -0.3154 0.2638 0.2335 (0.2126) (0.1251) (0.1114) (0.0940) Male -0.2773 -2.5489 -2.5576 -2.3663 (5.1304) (3.1459) (4.3926) (4.3753) Session 06 -3.2058 -2.4984 3.4312 4.6476 (6.0884) (3.5476) (5.2592) (4.9886) Session 07 2.2581 3.4546 2.3356 2.2747 (6.6031) (3.9534) (5.6210) (5.6158) ∆ minutes inactive -3.0130 -3.5805 -1.8235 (2.1484) (2.3675) (1.5728) ∆ minutes inactive * * 0.0159 0.0187 Amount kept in Period 3 0.0524 (0.0290) (0.0304) (0.0216) ∆ # of tasks 5.1449 (4.1885) ∆ # of tasks * -0.0696 Amount kept in Period 3 (0.0568) R2 0.3410 0.2053 0.2021 0.1990 Adj. R2 0.2446 0.0891 0.0853 0.0818 Num. obs. 48 48 48 48 RMSE 17.0805 10.2866 14.5550 14.5827 ***p < 0.01, **p < 0.05, *p < 0.1
49 Table 4.6: The Public Goods VCM: Amount in Individual Account, with Peer Imposed Inactivity (Between Subjects, Periods 2 and 4)
OLS OLS Random Effects Random Effects (Intercept) 9.2225*** 10.0357*** 8.8828*** 9.9285*** (1.3083) (2.1048) (1.2551) (2.2203) Minutes inactive in 0.2970 0.3966 0.3698* 0.4141** previous stage (0.2443) (0.2515) (0.2054) (0.2094) Male -3.2458** -3.2394** (1.2636) (1.5934) Session 08 2.6118 2.6522 (1.7117) (2.0903) Session 09 -0.6387 -0.6181 (1.5807) (1.9764) R2 0.0124 0.1016 Adj. R2 0.0040 0.0704 Num. obs. 120 120 120 120 RMSE 7.0264 6.7882 AIC 791.0062 781.0351 BIC 804.9437 803.3350 Log Likelihood -390.5031 -382.5175 Num. groups: subj 60 60 Num. groups: Period.x 2 2 Var: subj (Intercept) 29.2643 26.6198 Var: Period.x (Intercept) 0.0000 0.0000 Var: Residual 20.2668 20.2579 ***p < 0.01, **p < 0.05, *p < 0.1
Table 4.7: The Public Goods VCM: Distribution of Choices, with Peer-imposed Inactivity
Min. 1st Qu. Median Mean 3rd Qu. Max. Total 0.00 5.00 10.50 10.88 15.25 20.00 Assigned 0 Activities 0.00 7.00 13.00 11.93 17.00 20.00 Assigned 1 Activity 0.00 10.00 11.00 11.89 15.00 20.00 Assigned 2 Activities 0.00 5.00 10.00 10.51 15.00 20.00 Assigned 3 Activities 0.00 2.00 10.00 9.20 15.00 20.00
50 Table 4.8: The Public Goods VCM: Amount in Individual Account, with Exogenous Number of Tasks (Between Subjects, Periods 2 and 4)
OLS OLS Random Effects Random Effects (Intercept) 12.1433*** 13.6905*** 11.8867*** 13.5583*** (1.0655) (1.6248) (1.0707) (1.9182) # of tasks assigned in -1.0233* -0.8951 -0.8522* -0.7987* previous period (0.5695) (0.5563) (0.4742) (0.4721) Male -3.2066** -3.2266** (1.2649) (1.5883) Session 08 1.7011 1.7006 (1.6096) (2.0258) Session 09 -1.0901 -1.0918 (1.5521) (1.9534) R2 0.0266 0.1024 Adj. R2 0.0184 0.0712 Num. obs. 120 120 120 120 RMSE 6.9754 6.7853 AIC 789.3166 780.4213 BIC 803.2541 802.7212 Log Likelihood -389.6583 -382.2106 Num. groups: subj 60 60 Num. groups: Period.x 2 2 Var: subj (Intercept) 28.0355 26.0879 Var: Period.x (Intercept) 0.0000 0.0000 Var: Residual 20.7684 20.7524 ***p < 0.01, **p < 0.05, *p < 0.1
51 Table 4.9: The Public Goods VCM: Amount in Individual Account, with Peer Imposed Inactivity (Within Subjects, Periods 2 - 4)
Period 2 - Period 4 - Period 4 - Period 4 - Period 3 Period 3 Period 2 Period 2 (OLS) (OLS) (OLS) (OLS) (Intercept) 4.2903 3.4214 1.3812 1.3034 (3.0854) (3.9159) (2.6143) (2.6335) *** * Amount in Indiv. Acct. in Period 3 -0.6936 -0.4412 0.0537 0.0355 (0.1878) (0.2585) (0.1371) (0.1386) Male 0.7055 -1.6037 -2.4098 -2.3398 (1.4103) (1.6951) (1.7972) (1.8168) Session 08 1.0060 0.4527 -0.5152 -0.7943 (1.8017) (2.2453) (2.1706) (2.1804) Session 09 0.2302 -0.0769 -0.5194 -0.6187 (1.6623) (2.0035) (2.0712) (2.0766) ∆ minutes inactive -0.4288 0.0893 -0.4984 (0.4515) (0.6342) (0.4794) ∆ minutes inactive * ** ** 0.0272 Amount in Indiv. Acct. in Period 3 0.0739 0.0801 (0.0342) (0.0520) (0.0376) ∆ # of tasks 1.6297 (1.0821) ∆ # of tasks * **
Amount in Indiv. Acct. in Period 3 -0.1832 (0.0807) R2 0.2927 0.1351 0.1588 0.1461 Adj. R2 0.2127 0.0372 0.0636 0.0495 Num. obs. 60 60 60 60 RMSE 5.0682 5.9680 6.3334 6.3808 ***p < 0.01, **p < 0.05, *p < 0.1
52 Table 4.10: The Gamble Choice: Choice, with Peer Imposed Inactivity (Between Subjects, Periods 2 and 4)
Random Random Random Random Effects Effects OLS OLS Effects Effects Ordered Ordered Probit Probit (Intercept) 3.0468*** 2.6962*** 3.0575*** 2.7508*** (0.2258) (0.4407) (0.2332) (0.4808) Minutes inactive -0.0255 -0.0171 -0.0273 -0.0234 -0.0497 -0.0262 in previous stage (0.0302) (0.0347) (0.0245) (0.0261) (0.0307) (0.0371) Male 0.1672 0.1709 0.2021 (0.2763) (0.3572) (0.4496) Session 14 0.5044 0.4714 0.7161 (0.4108) (0.4965) (0.6414) Session 15 -0.0160 -0.0436 -0.0001 (0.4119) (0.5088) (0.6422) Session 16 0.3413 0.3295 0.5111 (0.3778) (0.4845) (0.6077) Period 4 0.2696 (0.2446) R2 0.0057 0.0338 Adj. R2 -0.0022 -0.0058 Num. obs. 128 128 128 128 128 128 RMSE 1.4717 1.4744 AIC 445.0294 450.7127 381.4001 387.8154 BIC 459.2896 476.3810 398.5123 419.1878 Log Likelihood -217.5147 -216.3563 -184.7001 -182.9077 Num. groups: 64 64 64 64 subj Num. groups: 2 2 Period.x Var: subj 1.3761 1.4278 2.1742 2.1419 (Intercept) Var: Period.x 0.0087 0.0118 (Intercept) Var: Residual 0.7906 0.7908 ***p < 0.01, **p < 0.05, *p < 0.1
53 Table 4.11: The Gamble Choice: Distribution of Choices, with Peer-imposed Inactivity
Min. 1st Qu. Median Mean 3rd Qu. Max. Total 0.00 5.00 10.50 10.88 15.25 20.00 Assigned 0 Activities 0.00 7.00 13.00 11.93 17.00 20.00 Assigned 1 Activity 0.00 10.00 11.00 11.89 15.00 20.00 Assigned 2 Activities 0.00 5.00 10.00 10.51 15.00 20.00 Assigned 3 Activities 0.00 2.00 10.00 9.20 15.00 20.00
54 Table 4.12: The Gamble Choice: Choice, with Exogenous Number of Tasks (Between Subjects, Periods 2 and 4)
Random Random Random Random Effects Effects OLS OLS Effects Effects Ordered Ordered Probit Probit (Intercept) 2.9000*** 2.5588*** 2.9386*** 2.5963*** (0.2183) (0.3656) (0.2417) (0.4516) # of tasks assigned in -0.0062 -0.0072 -0.0320 -0.0326 0.0087 0.0079 previous period (0.1167) (0.1167) (0.0873) (0.0875) (0.1102) (0.1108) Male 0.1575 0.1583 0.1851 (0.2759) (0.3564) (0.4451) Session 14 0.5938 0.5938 0.8461 (0.3690) (0.4767) (0.6098) Session 15 0.0590 0.0594 0.1107 (0.3832) (0.4951) (0.6176) Session 16 0.3733 0.3734 0.5539 (0.3726) (0.4814) (0.5995) Period 4 0.3574* (0.2108) R2 0.0000 0.0319 Adj. R2 -0.0079 -0.0078 Num. obs. 128 128 128 128 128 128 RMSE 1.4759 1.4758 AIC 443.3510 448.8153 384.0876 388.3124 BIC 457.6111 474.4835 401.1998 419.6847 Log Likelihood -216.676 -215.408 -186.044 -183.156 Num. groups: subj 64 64 64 64 Num. groups: Period.x 2 2 Var: subj (Intercept) 1.3755 1.4215 2.0322 2.0968 Var: Period.x (Intercept) 0.0272 0.0272 Var: Residual 0.7932 0.7932 ***p < 0.01, **p < 0.05, *p < 0.1
55 Table 4.13: The Gamble Choice: Choice, with Peer Imposed Inactivity (Within Subjects, Periods 2 - 4)
Period 2 - Period 4 - Period 4 - Period 4 - Period 3 Period 3 Period 2 Period 2 (OLS) (OLS) (OLS) (OLS) (Intercept) 0.5398 0.9173 0.6526 0.5522 (0.6084) (0.6933) (0.5029) (0.4175) *** * Gamble choice in Period 3 -0.4675 -0.3585 0.0208 -0.0356 (0.1572) (0.1854) (0.1241) (0.1006) Male -0.1074 0.2348 0.3469 0.3552 (0.2770) (0.2932) (0.3300) (0.3268) ** ** * Session 14 1.0499 0.1299 -0.9522 -0.7111 (0.3974) (0.3594) (0.4261) (0.3856) ** * Session 15 0.4984 -0.4652 -1.0057 -0.7473 (0.3888) (0.3565) (0.4414) (0.3895) ∆ minutes inactive -0.0127 0.0446 -0.0101 (0.0591) (0.1287) (0.0749) ∆ minutes inactive * 0.0171 -0.0029 0.0170 Gamble choice in Period 3 (0.0172) (0.0372) (0.0216) ∆ # of tasks 0.0235 (0.2335) ∆ # of tasks * -0.0203 Gamble choice in Period 3 (0.0675) R2 0.3070 0.2683 0.1449 0.1203 Adj. R2 0.2341 0.1913 0.0549 0.0277 Num. obs. 64 64 64 64 RMSE 1.0195 1.1151 1.2183 1.2357 ***p < 0.01, **p < 0.05, *p < 0.1
56 Table 4.13 - Continued
Period 2 - Period 4 - Period 4 - Period 4 - Period 3 Period 3 Period 2 Period 2 (Ordered (Ordered (Ordered (Ordered Probit) Probit) Probit) Probit) Gamble choice in Period 3 -0.9198*** -0.8667** 0.0379 -0.0366 (0.3401) (0.3520) (0.1954) (0.1514) Male -0.5144 0.3468 0.4553 0.5278 (0.5528) (0.5349) (0.5233) (0.5024) Session 14 2.2333*** 0.8234 -0.9759 -0.8016 (0.8473) (0.6563) (0.6837) (0.5974) Session 15 0.8418 -0.8595 -1.0559 -0.8618 (0.7891) (0.6646) (0.7172) (0.5996) ∆ minutes inactive -0.0141 -0.0512 -0.0411 (0.1200) (0.2321) (0.1116) ∆ minutes inactive * 0.0282 0.0361 0.0205 Gamble choice in Period 3 (0.0342) (0.0670) (0.0323) ∆ # of tasks 0.0449 (0.3494) ∆ # of tasks * -0.0037 Gamble choice in Period 3 (0.1017) AIC 173.8235 177.8294 207.9516 208.4367 BIC 199.7301 205.8948 233.8582 234.3433 Log Likelihood -74.9117 -75.9147 -91.9758 -92.2183 Num. obs. 64 64 64 64 ***p < 0.01, **p < 0.05, *p < 0.1
57 CHAPTER 5
PAID TO SIT: MEASURING THE VALUE OF INACTIVITY
5.1 Introduction
As discussed in chapter 2, anecdotal evidence from other experiments has demonstrated that subjects often are willing to take costly actions to avoid inactivity. In this chapter I will use a novel elicitation mechanism to take a direct measurement subjects’ distaste for inactivity: subjects are paid a decreasing wage to remain inactive at their station, and choose when to end the experiment and leave the lab.
5.2 Model and Hypotheses
To help identify the utility of inactivity, I present a simple equation for modeling the utility of an unpaid activity:
where is a monotonically��������� � =increasing �������� function+ �������� of ∗the ��������� time spent � doing the particular activity.��������� Prior theory � and literature has treated and as 0, such that will always be zero. ���������� ���������� ����������� � Hypothesis 1: Subjects will forgo positive earnings in order to avoid additional inactivity.
Evidence from the experiments I discussed in Chapter 2 has shown that subjects will try to avoid inactivity. In this experiment, that would be expressed by subjects who leave the experiment while still receiving a positive wage.
5.3 Experimental Design
The experiments in chapters 3 and 4 both concluded with a final stage designed to provide a direct measure of the value each subject associates with inactivity. The subjects in this had already participated in one of the two previously described experiments prior to seeing this final stage.
58 5.3.1 Final stage
In this stage subjects are paid to remain inactive at their computer. At any time they may elect to end the stage and go get paid, at which point they are free to leave the experimental session. Their earnings for the final stage are determined by the length of time they spend inactive. During this stage, subjects view a screen that is blank, except for text displaying their current wage rate, as seen in Figure 5.1. This wage begins at $0.020/s and decreases by $0.001/s every 15 seconds. These values are listed in United States Dollars to make the wage rate as salient to subjects as possible. This is a truth revealing mechanism. A subject maximizes value by remaining in the experiment while the wage rate is above his or her per second value of not being inactive. When the wage rate goes below his or her per second value of not being inactive, the subject maximizes value by choosing to leave. For example, if a subject chooses to leave after 45 seconds, when the wage rate changes from $0.018 to $0.017, this would indicate that he or she values not being inactive at somewhere between these two rates.
Subjects are paid in a separate room so that the subjects still in the experiment will not see any queuing that may occur. To aid in the rapid payment of subjects, an observer role is built into the game to make real-time payment data available to the experimenters. When a subject clicks their button to end the experiment and get paid, their name and payment information are highlighted so that the subjects payment can be prepared by the time they make it to the payment area. 5.4 Results
A histogram of subject exit decisions is shown in Figure 5.2. Immediately it is apparent that the modal decision is for subjects to leave at a wage of $0.000 per second. In fact, a full 38% of subjects exit at a wage of $0.000 per second, and 47% leave at a rate between •$0.001 and $0.001. In spite of this, most subjects actual leave the experiment while still receiving a positive wage. The mean wage at time of exit can be seen as the value of the intercept in the first regression in Table 5.1. This value, $0.0048 per second, corresponds to an hourly wage of around $17/hour and in is significantly different from zero, if we assume a normal distribution. Looking at the histogram of subject exit decisions in Figure 5.2, however this seems like a poor assumption. I perform a nonparametric test of the mean of these decisions and confirm that the
59 mean is significantly different from zero. I compute a 99% confidence interval for the mean between $0.00391 per second and $0.00581 per second.
In Table 5.1 and Table 5.2, I attempt to identify other contributing factors to subjects’ decisions to leave. In Table 5.1, I perform OLS regressions on the exit wage and find that males and participants in the peer-imposed inactivity sessions are both more likely to leave at a higher wage rate, however recent inactivity and total inactivity do not seem to have an effect. This is an interesting result as it means that the cause of inactivity appears to affect subjects more than the amount of it. In table 5.2 I perform probit regressions, looking at a binary outcome for whether or not subjects leave prior to the wage rate equalling zero. Only the gender of the subject appears to affect the likelihood that they will leave at a positive wage rate, with men being more likely to leave at a positive rate. This gender result is consistent with previous literature, which has found that men on average are more susceptible to boredom than women (Eckel and Grossman 2002; Wasson 1981), as boredom prone individuals would be expected to leave earlier.
5.5 Conclusion
This experiment clearly demonstrated that subjects are willing to forgo a positive amount of money in order to avoid inactivity. This concurs with our expectations from the anecdotal evidence seen in prior experiments. If subjects incur negative utility from inactivity, then this has obvious implications for any instance in which subjects are choosing between performing a task or remaining inactive. That said, slightly less than half of the subjects extracted all or nearly all of the available earnings from this stage, indicating that for many subjects there is a near zero value for inactivity. The segregation of subjects into those affected and those unaffected dependes on the gender of the subject, but does not depend on subjects earnings prior to the stage, nor the amount of inactivity experienced during the session.
60
Figure 5.1: Final Inactive Stage
Figure 5.2: Wage at Exit in the Final Inactive Stage
61 Table 5.1: Wage at exit (Between Subjects)
OLS OLS OLS (Intercept) 0.0048*** 0.0017 0.0022 (0.0004) (0.0020) (0.0020) Inactive in previous stage (EII) 0.0011 (0.0010) Minutes inactive in previous stage (PII) -0.0002 (0.0002) PII 0.0030** 0.0015* (0.0013) (0.0008) Profit earned prior to the stage in USD 0.0001 0.0001 (0.0001) (0.0001) Male 0.0014** 0.0015** (0.0007) (0.0007) Total Minutes inactive during the experiment 0.0000 (0.0001) R2 0.0000 0.0345 0.0285 Adj. R2 0.0000 0.0204 0.0172 Num. obs. 349 349 349 RMSE 0.0068 0.0067 0.0067 ***p < 0.01, **p < 0.05, *p < 0.1
62 Table 5.2: Predicting Wage > 0 (Between Subjects)
Probit Probit (Intercept) -0.4887 -0.4343 (0.3805) (0.3741) Inactive in previous stage (EII) 0.1778 (0.1894) Minutes inactive in previous stage (PII) -0.0064 (0.0409) PII 0.2564 0.1141 (0.2392) (0.1436) Profit earned prior to the stage in USD 0.0141 0.0137 (0.0190) (0.0190) Male 0.2362* 0.2412* (0.1357) (0.1355) Total Minutes inactive during the experiment 0.0162 (0.0278) AIC 488.6190 487.1872 BIC 511.7494 506.4625 Log Likelihood -238.3095 -238.5936 Deviance 476.6190 477.1872 Num. obs. 349 349 ***p < 0.01, **p < 0.05, *p < 0.1
63 CHAPTER 6
ALTERNATIVES TO INACTIVITY
6.1 Introduction
The behavioral changes discussed in chapters 3 and 4, and the value loss measured in chapter 5, make inactivity a poor choice of activity for subjects waiting during experiments. In this chapter I will look at a few different alternatives that could be used in place of inactivity. I will use a willingness-to-pay elicitation to gather subjects’ valuations for various activities across several different lengths of time. These values will be compared against each subject’s willingness to pay to leave the experiment early to create a valuation for each subject of each activity for several different lengths of time. This will allow me look at each activities value profile with respect to time. A well behaved activity is one whose value has very little variance across subjects, while also being indistinguishable from zero.
6.2 Hypotheses
Hypothesis 1-6: Each activity has a time-invariant value that is not significantly different from the value of skipping the stage.
I set up the null hypothesis that each activity is well behaved. That is, each activity will have a value equivalent to the value of simply skipping the stage, and this will not change with respect to time. This would mean that subjects are indifferent between non-incentivized tasks and their own free time.
6.3 Experimental Design
The sessions for this experiment were conducted at the xs/fs lab at Florida State University in November 2015, with subjects recruited from the student population using ORSEE (Greiner 2015). The experiment was programmed and conducted using the Zurich Toolbox for Ready-made Economics Experiments (Fischbacher 2007).
64 Prior to the beginning of the session, subjects were asked to place all of their belongings in a plastic bag which was then tightly shut with a zip tie. This was to insure that subjects did not have access to anything during the course of the experiment, other than what was provided for them. Subjects were then assigned seats, such that no two subjects who entered the lab were seated next to each other.
Prior to the start of the experiment, and following some brief instructions, subjects were asked to input their name into their workstation the start of the experiment, to aid in the payment of subjects at the end of the experiment.
In the experiment subjects will participate in eight incentivized rounds. Each of the first seven rounds will consist of two stages: an elicitation stage; and an actualization stage, which are described in sections 6.3.1 and 6.3.2 respectively. The final round will be a single unique stage and is described in section 6.3.3. A generic session looks like Figure 6.1. Subjects receive instructions covering the all of the sessions and answer quiz questions to ensure they understand how the experiment will work.
Following the quiz, before beginning the first stage, subjects received a 60 second preview of each of the seven options they may see during the game. This is to expose subjects to each option so that they may make more accurate choices about how much their willing to pay. The order in which subjects are presented with each option is randomized so that order effects should wash out as noise.
6.3.1 Elicitation stages
In the elicitation stages, we use the truth-telling compatible mechanism described by Becker, Degroot, and Marschak (1964) to elicit the willingness of subjects to pay to switch between different options to occur during the actualization stage. Specifically, at the start of each of the first seven rounds, subjects will be informed of the length of that round’s actualization stage, endowed with $1, and will be assigned one of seven options to participate in during the actualization stage. In the elicitation stage, subjects will be given the opportunity to report how
65 much they are willing to pay, or would need to receive (up to $1 in either direction) to switch to each of the six other options. One of these six other options will be selected at random by the computer, which will then generate a price for switching from the assigned option to this alternative option. The price will be drawn at random from a uniform distribution between -$1 and $1. If the price drawn by the computer is below the price the subject is willing to pay, the subject will pay the computer-drawn price and will switch to the alternative option for the actualization stage.
The screen as initially seen by the subjects looks like Figure 6.2. The randomly select option is shown at the top of the screen along with the length of the stage. Each of the non- assigned options is shown in a randomized order along with a slider bar, used by the subjects to input their decisions. The slider bar initially does not contain a marker so as to avoid subjects using it as an anchor. When a subject makes an initial decision, by clicking on the slider, text appears on the right, describing in text what their decision means, as seen in Figure 6.3. This is done to make the mechanism more understandable, and to avoid confusion when dealing with negative numbers. After a subject has made a decision for each option, a “submit” button appears. Once the subject clicks the button they learn which option has been randomly selected as the alternative and what the randomly drawn price for switching is. They are also provided with an explanation in text, describing their decision, whether or not they are switching and why, as shown in Figure 6.4.
6.3.2 Actualization stages
The actualization stage lasts for the length of time mentioned during their decisions in the elicitation stage. These lengths of time are drawn from the set {30, 60, 60, 120, 150, 180, 210}, denoted in seconds. Every subject will face each stage length, however the order in which they are presented is random, and varies from subject to subject. During this assigned length of time, subjects will participate in the option determined in the elicitation stage. The potential options are:
Blank Screen: The screen is empty.
66 Landscape Photos: Subjects are provided with a set of landscape images on the screen which they may peruse. This is the same activity as in Chapters 3 and 4.
Tic-Tac-Toe: This is the game of tic-tac-toe, played against the computer. Subjects can choose between an easy and harder difficulty level. This also is the same activity as in Chapters 3 and 4.
Rock-Paper-Scissors: This is the game of Rock-Paper-Scissors, played against a computer which chooses randomly. This is the same activity as in Chapters 3 and 4 as well.
Text Search: This is a word search puzzle, where subjects may search for specific sequences of letters hidden among a grid of letters. This too is the same activity as in Chapters 3 and 4.
Restricted Internet Browsing: This is a collection of 12 websites that may be viewed on the computer screen. The sites used for this experiment were sportsillustrated.com, gocomics.com, weather.gov, wikipedia.com, tmz.com, movieweb.com, miniclip.com, washingtonpost.com, mtv.com, radio.com, eonline.com, and usatoday.com. This is the same setup used by Cooper, Weber, and D’Adda (2016). A screenshot of the interface is available in Figure 6.5.
Skip stage: If this option occurs, the subject will simply skip the actualization stage in this period and will move directly to the elicitation stage of next period. For the preview of this stage, subjects simple skip to the next option description.
6.3.3 Final stage
After completing the first seven rounds, subjects will participate in one final round. In the final round, subjects will be randomly assigned one of six options seen in the previous rounds (the “skip stage” option will not be assigned), and will be paid a decreasing rate to remain seated at their computer. They can leave this stage and end the experiment at any point once it has
67 begun. This stage acts very similarly to the final stage described in Chapter 5, with the exception that some subjects are given an activity to perform instead of remaining inactive.
6.4 Results
I normalize each bid for each activity with respect to each subjects bid for skipping the stage. Skipping the stage should have a consistent absolute value, as it is a time invariant activity. That is, skipping to the next stage is the same activity and takes the same amount of time regardless of the length of time assigned for the actualization stage. This makes it a useful baseline which we can use to compare the other activities. Thus the value for skipping the stage is always zero, and all bids are interpreted with respect to this.
6.4.1 Stages 1 - 7
The means of subjects’ bids for each activity with respect to length of time may be seen in Figures 6.6 through 6.9. I conduct a basic OLS regression for each activity with respect to its length in Table 6.1. In these results, we can see that the blank screen and the Photo browsing perform poorly, being significantly different from skipping the stage, and varying in value with respect to time, respectively. This holds up in the regressions shown in table 6.2, where I regress using a random effects model, with the blank screen now also fails the time invariance goal as well. Additionally, we can see that Rock Paper Scissors and Tic Tac Toe also have diminishing value over time in the random effects model. The regressions in table 6.3, using the log of period length, finds consistent results. The only two well performing activities, which are not statistically different in value from skipping the stage and which do not vary in value with respect to stage length are the text search and internet browsing. Figure 6.10 shows the means and distributions in an alternative manner, and shows that these two activities have comparatively low variance in value across subjects as well. I also perform the same random effects regressions on bids/time, a per second value, for each activity, as seen in Figures 6.4 and 6.5. As with the previous regressions, the blank screen activity and the landscape photos both perform poorly. In these regressions, both activities have a per second value intercept significantly less than zero, and this rate varies with respect to time. No other activities are found to behave significantly different than skipping the stage.
68 6.4.2 The Final Stage
The results of the final stage are shown in Figure 6.11. Subjects were assigned completely at random to the treatments, resulting in different numbers of observations for each activity. In no case, however, do we have enough observations to perform a proper statistical analysis of the data. We can, however, perform a visual comparison against what we would hope to see in this stage. An ideal task would have all exit observations at an exit rate of $0.000. The two best performing tasks from the previous portion of this experiment, which did not have values significantly different than leaving the stage, and whose values did not vary significantly with respect to time, do not look like this at all. For the limited internet browsing, subjects left at a mean wage of $0.065 per second and only one of the eight subjects assigned to that activity left at a rate of $0.000 per second. Similarly two of the 10 subjects assigned to the text search left at $0.001 per second, and none left at $0.000 per second. The mean exit wage for subjects assigned to the text search was $0.0096 per second.
I’m not certain why these two options fail to look anything like an ideal activity in this final stage, though I can guess It would be easy to blame insufficient observations and bad luck, however the distribution of observations so far looks nothing like how it should. More likely, subjects left the internet stage early because it became more salient relative to the earlier stages that they were choosing between the restricted internet and leaving the experiment earlier and likely having unrestricted access to the full internet through their phone or their own computer. Following the assumption of free disposal, unrestricted browsing should be worth at least as much as restricted browsing. In particular, the exclusion of any social media sites from the restricted access, due to concerns about communication, likely leads to a substantial difference in value between restricted and unrestricted internet access for some subjects.
For the text search, an issue with the experimental implementation is likely the culprit. The puzzles available in the final stage are the same puzzles that have been available throughout the experiment. It is entirely possible that by this point of the experiment, subjects have already looked at all of the available puzzles and either completed them, or completed as much of them as they felt they were able, leaving them with nothing to do. If this is the case, then we would expect to see a distribution for text search that looks like the distribution for the blank screen. In
69 fact, they do not appear dissimilar. While the blank screen does have more observations at $0.000, it still has a mean exit wage of $0.0068.
6.5 Conclusion
In this chapter, we looked at several different activities that could be used in place of inactivity in the lab and compared their performance with respect to subject valuations. We find that most of the activities are either valued at a nonzero amount, or are shown to vary in value over with respet to stage length. The two best performing activities are the text search and browsing the internet, in that they are not shown to have a different value from skipping the stage, and do not seem to change value across different lengths of stages.
70
Round Stage
- Preview Stage
Elicitation Stage 1 Actualization Stage
Elicitation Stage 2 Actualization Stage
Elicitation Stage 3 Actualization Stage
Elicitation Stage 4 Actualization Stage
Elicitation Stage 5 Actualization Stage
Elicitation Stage 6 Actualization Stage
Elicitation Stage 7 Actualization Stage
8 Final stage
Figure 6.1: Experimental Diagram – Alternatives to Inactivity
71
Figure 6.2: Stage A Initial Screen
Figure 6.3: Stage A Bidding Screen
72
Figure 6.4: Stage A Outcome Screen
Figure 6.5: Restricted Internet Browsing
73
Figure 6.6: Bids versus Stage Length
Figure 6.7: Bids versus the Log of Stage Length
74
Figure 6.8: Bids over Time versus Stage Length
Figure 6.9: Bids over Time versus the Log of Stage Length
75
Figure 6.10: Means and Variances of Bids
Figure 6.11: Wages at Exit in the Final Stage
76 Table 6.1: Effect of Length on Bids (OLS) Blank Landscape Tic Tac Rock Paper Text Internet Screen Photos Toe Scissors Search (Intercept) -16.6362*** -7.6763 2.3527 0.3839 1.3594 3.0112 (5.4976) (5.0601) (5.0557) (5.0482) (4.3132) (4.5485) Length of stage -0.0663 -0.0627* -0.0600 -0.0608 -0.0399 -0.0269 (0.0410) (0.0377) (0.0377) (0.0376) (0.0321) (0.0339) R2 0.0058 0.0062 0.0056 0.0058 0.0034 0.0014 Adj. R2 0.0036 0.0039 0.0034 0.0036 0.0012 -0.0008 Num. obs. 448 448 448 448 448 448 RMSE 52.0391 47.8976 47.8559 47.7851 40.8271 43.0546 ***p < 0.01, **p < 0.05, *p < 0.1
Table 6.2: Effect of Length on Bids (Random Effects) Rock Blank Landscape Tic Tac Text Paper Internet Screen Photos Toe Search Scissors (Intercept) -16.6362*** -7.6229 2.5629 0.2891 1.3443 3.0848 (5.9323) (5.6353) (6.0836) (5.7749) (4.5714) (5.0875) Length of stage -0.0663** -0.0631** -0.0617** -0.0600** -0.0398 -0.0275 (0.0319) (0.0298) (0.0293) (0.0301) (0.0283) (0.0281) AIC 4705.7977 4641.5548 4633.3945 4648.9155 4561.0629 4576.0288 BIC 4726.3217 4662.0788 4653.9184 4669.4394 4581.5868 4596.5528 Log Likelihood -2347.899 -2315.777 -2311.697 -2319.458 -2275.531 -2283.014 Num. obs. 448 448 448 448 448 448 Num. groups: 64 64 64 64 64 64 subj Num. groups: 7 7 7 7 7 7 round Var: subj 1082.4967 870.2439 878.9870 814.1232 381.6222 580.2795 (Intercept) Var: round 0.0000 15.6179 54.6830 30.1763 3.8754 18.3997 (Intercept) Var: Residual 1637.7107 1420.2686 1374.0146 1452.4648 1286.1820 1264.1032 ***p < 0.01, **p < 0.05, *p < 0.1
77 Table 6.3: Effect of Log of Stage Length on Bids (Random Effects) Rock Blank Landscape Tic Tac Text Paper Internet Screen Photos Toe Search Scissors (Intercept) 9.6879 11.7744 18.9107 19.4342 15.1737 10.3021 (14.6193) (13.7154) (13.7514) (13.9095) (12.7241) (12.8520) log( Length of -7.4213** -5.8395** -5.1427* -5.7040** -4.0282 -2.2762 stage ) (3.0088) (2.8159) (2.7775) (2.8492) (2.6741) (2.6577) AIC 4694.9657 4632.6510 4625.2820 4639.7764 4551.6799 4567.1522 4587.676 BIC 4715.4896 4653.1750 4645.8060 4660.3004 4572.2039 2 Log Likelihood -2342.483 -2311.326 -2307.641 -2314.888 -2270.840 -2278.576 Num. obs. 448 448 448 448 448 448 Num. groups: 64 64 64 64 64 64 subj Num. groups: 7 7 7 7 7 7 round Var: subj 1083.5523 870.1018 878.4446 814.1431 381.7150 580.1482 (Intercept) Var: round 0.0000 15.1715 53.9298 30.1380 3.5165 18.0566 (Intercept) Var: Residual 1630.3215 1421.2625 1377.8117 1452.3258 1285.5324 1265.0220 ***p < 0.01, **p < 0.05, *p < 0.1
78 Table 6.4: Effect of Length on Bids/Time (Random Effects)) Rock Blank Landscape Tic Tac Text Paper Internet Screen Photos Toe Search Scissors (Intercept) -0.4664*** -0.2670*** -0.0036 -0.0395 0.0254 0.0604 (0.0781) (0.0797) (0.0846) (0.0818) (0.0788) (0.0727) Length of stage 0.0017*** 0.0009* -0.0003 -0.0001 -0.0004 -0.0004 (0.0005) (0.0005) (0.0005) (0.0005) (0.0005) (0.0004) AIC 899.5331 941.9511 931.2429 939.7605 944.1334 791.2743 BIC 920.0571 962.4750 951.7668 960.2844 964.6574 811.7983 Log Likelihood -444.767 -465.976 -460.621 -464.880 -467.067 -390.637 Num. obs. 448 448 448 448 448 448 Num. groups: subj 64 64 64 64 64 64 Num. groups: round 7 7 7 7 7 7 Var: subj (Intercept) 0.1499 0.1172 0.1194 0.1157 0.0695 0.0656 Var: round (Intercept) 0.0000 0.0016 0.0080 0.0043 0.0042 0.0080 Var: Residual 0.3365 0.3833 0.3691 0.3800 0.4034 0.2769 ***p < 0.01, **p < 0.05, *p < 0.1
Table 6.5: Effect of Log of Stage Length on Bids/Time (Random Effects) Rock Blank Landscape Tic Tac Text Paper Internet Screen Photos Toe Search Scissors (Intercept) -1.0390*** -0.5890*** 0.0808 0.0135 0.1871 0.2178 (0.2071) (0.2200) (0.2187) (0.2202) (0.2248) (0.1892) log( Length of stage ) 0.1693*** 0.0940** -0.0248 -0.0146 -0.0444 -0.0450 (0.0432) (0.0462) (0.0454) (0.0460) (0.0474) (0.0393) AIC 889.6703 932.3655 922.1224 930.6213 934.6807 781.8908 BIC 910.1942 952.8895 942.6464 951.1453 955.2047 802.4148 Log Likelihood -439.835 -461.183 -456.061 -460.311 -462.340 -385.945 Num. obs. 448 448 448 448 448 448 Num. groups: subj 64 64 64 64 64 64 Num. groups: round 7 7 7 7 7 7 Var: subj (Intercept) 0.1500 0.1172 0.1194 0.1158 0.0696 0.0656 Var: round (Intercept) 0.0000 0.0017 0.0079 0.0043 0.0042 0.0079 Var: Residual 0.3359 0.3828 0.3690 0.3800 0.4031 0.2767 ***p < 0.01, **p < 0.05, *p < 0.1
79 CHAPTER 7
CONCLUDING REMARKS
7.1 Conclusions
In this dissertation I explored the topic of inactivity in the lab. In Chapter 3, I looked at experimenter-imposed inactivity and did not find that it had any effect on subject behavior in the Dictator Game, a Public Goods VCM, or a risk elicitation Gamble choice task. This is a good result as it means that delays from technical issues or other causes that are not related to other subjects do not seem to alter subject behavior. In Chapter 4 I looked at the same three environments under peer-imposed inactivity. With regard to the gamble choice task, I again failed to identify any change in behavior due to inactivity. This is a particularly reassuring result, as risk preferences play a role in many different experiments in the lab. I found mixed results with regard to the effect of peer-imposed inactivity on the Dictator game. While a relationship could be found between the amount of time spent inactive and the dictator choice, it was not present when looking at the number of tasks assigned, an exogenous measure of inactivity. Additionally, I found strong results that a subject’s behavior in the Public Goods VCM is influenced by the amount of time that subject had just spend inactive. Inactive subjects kept more in their individual account and gave less to the group. This suggests that inactivity may play a role in situations whether the equilibria prediction and the social optimum pull in different directions. In Chapter 5, I used a novel setup to test how much subjects valued inactivity. I observed that on average, subjects were willing to leave the experiment at positive wage rates, indicating that subjects have a negative valuation for inactivity, but also that nearly half of all subjects left with $0.001/s of the profit maximizing wage. Having established that inactivity has negative properties in the lab, in Chapter 6 we looked at alternatives activities and found two potential activities, browsing the internet and a text search game, that do not cause subjects to lose utility. 7.2 Further Research
The next step in this research is to test the two activities identified in chapter 6, to confirm that they have no behavioral effects, neither in the experimenter-imposed inactivity environment nor the peer-imposed inactivity environment. If no behavioral effects can be found
80 in this research, these activities can be implemented, when possible, in place of inactivity. This will improve the reliability of results as well as subject well fare.
An additional branch of research to consider is expanding this study to look at how other basic economic games are affected by inactivity. Trust games (Berg, Dickhaut, and McCabe 1995) and other simple sequential games would be excellent candidates, as the very nature of sequential games tends to cause inactivity. Also, coordination games such as the battle of the sexes game (Luce and Raiffa 1957) could behave differently, as unlike the simple games used in this study, these games either contain no pure strategy Nash equilibria, or contain multiple pure strategy equilibria. Lastly, it would be interesting to look at inactivity in a repeated game environment. If subjects believe that inactivity is costly for others, it could be used as a punishment mechanism, even when no monetary punishment is possible. The structure provided here could be used to study these and other economic environments.
81 APPENDIX A
EXPERIMENTAL INSTRUCTIONS
A.1 Behavioral Effects of Experimenter Imposed Inactivity
Experiment Instructions This is an experiment on the economics of decision making. In addition to your participation fee, you will have the chance to earn money based on your decisions in this experiment. It is extremely important that you put away all materials including external reading material and turn off your cell phones and any other electronic devices. If you have a question, please raise your hand and I will come by and answer your question privately. In the experiment you will earn Experimental Currency Units (or ECU). At the end of the experiment, your ECU from all periods will be summed and converted to dollars where 1 ECU = $0.02. You will be paid with a check today, and your total earnings will be rounded to the nearest cent. Today’s experiment is comprised of 9 stages. Prior to the start of each stage, additional instructions will be given.
Stage 1: [Dictator Game Treatment] At the beginning of this first stage, you have been randomly and anonymously grouped with another subject. One member of each group will be assigned the role A, and the other member will be assigned the role B. You will not be informed of your role. You will then be asked to allocate 100 ECU between yourself and the other member of your group. If your role is A, then the allocation you specified will occur: You will receive the amount you allocated to yourself, and the other member of your group will receive the amount you allocated to them. If your role is B, then the allocation the other member of your group specified will occur: You will receive the amount the other member of your group allocated to you, and the other member of the group will receive the amount they allocated to themself. There will only be one round in this stage. You will now take a brief quiz to make sure you understand how this stage works, and how you will be paid for your actions in this stage.
82
[Public Goods Treatment] At the beginning of this stage, you will be randomly and anonymously assigned to a group with 3 other participants. You will be given 20 tokens. Your task is to decide how many tokens to allocate to an Individual account and how many to allocate to a Group account. You can allocate anywhere from 0 to 20 tokens to each account, but the total allocated to both must sum to 20. Negative allocations or fractional allocations are not allowed. Tokens invested in the individual account earn 4 ECU per token, while tokens invested in the group account earn profits in a different way. The experimenter adds up all the tokens allocated to the group account by members of your group and doubles this amount. This new amount is then split evenly among the members of your group and converted to ECU at 4 ECU per token. You earnings for this stage are determined by the equation Earnings = 4*(Tokens allocated to the individual account) + 4*(2*Total tokens contributed by your group to the group account)
You will now take a brief quiz to make sure you understand how this stage works, and how you will be paid for your actions in this stage.
[Risk Elicitation Treatment] The task for this stage occurs in stages 1, 3, 5, and 7. One of these 4 stages will be randomly selected to count toward your earnings. In this stage you will be presented with five gambles, and asked to choose one, and only one, of these gambles to play. Each gamble will contain two possible outcomes each occurring with some probability. If this stage is selected to count toward your earnings, your compensation will be determined by which of the five gambles you select and which of the two possible outcomes occurs. If you look at your screen you will see an example set of gambles. If this stage were selected to count towards your earnings, and you were to select gamble 4, you would have a 50% chance of earning 700 ECU and a 50% chance of earning 160 ECU.
83 You will now take a brief quiz to make sure you understand how this stage works, and how you will be paid for your actions in this stage.
Stage 2: In this stage, you subjects have been randomly and anonymously divided into two groups. This stage will last 5 minutes and you will receive a fixed rate of 100 ECU for having participated in this stage. You will receive further instructions for this stage on your computer screen. You will now take a short quiz about this stage.
Stage 3: Stage 3 is a repeat of stage 1. [Stage 3 includes all of the instructions from stage 1, except it excludes the sentence covering the quiz.] Stage 4: In this stage, you subjects have been assigned into the same two groups you were assigned to in stage 2. This stage will last 5 minutes and you will receive a fixed rate of 100 ECU for having participated in this stage. You will receive further instructions for this stage on your computer screen.
Stage 5: Stage 5 is a repeat of stage 1. [Stage 5 includes all of the instructions from stage 1, except it excludes the sentence covering the quiz.]
Stage 6: In this stage, you subjects have been assigned into the same two groups you were assigned to in stage 2. This stage will last 5 minutes and you will receive a fixed rate of 100 ECU for having participated in this stage. You will receive further instructions for this stage on your computer screen.
Stage 7:
84 Stage 7 is a repeat of stage 1. [Stage 7 includes all of the instructions from stage 1, except it excludes the sentence covering the quiz.]
Stage 8: This stage shows your results from stages 1 through 7, including all decisions, roles, outcomes, and stage earnings that are relevant to your final payout. If you have any questions about your payout, please raise your hand and I will come around to answer them. Otherwise, once you have finished reviewing your results up to this point, please click the continue button. Once everyone has clicked continue, we will move on to the final stage of today’s experiment.
Stage 9: This is the final stage of the experiment. All numerical values in this stage will be expressed in terms of US Dollars, rather than ECU. In this stage you will earn money by remaining at your station. Once this stage begins, a number will appear on your screen reflecting the rate at which you are currently being paid. This rate will begin at $0.02 per second. Every 15 seconds thereafter, the rate will decrease by $0.001 per second. Thus 15 seconds into this stage, the rate will decrease from $0.02 per second to $0.019 per second. At any point during this stage you may click the button marked “Leave Experiment”. Once you have done that, you will stop accumulating any additional earnings in this stage. You may then gather your belongings and move to the designated payment area, where you will receive a check for your earnings in today’s experiment. Multiple people may choose to leave the experiment at the same time. I anticipate an average wait of about 5 seconds between your arrival in the payment area and the receipt of your payment. Let’s work through an example. Let’s say you pushed the “Leave Experiment” button 47 seconds into the stage. You would have spent 15 seconds earning $0.02 per second, 15 seconds earning $0.019 per second, 15 seconds earning $0.018 per second, and 2 seconds at the rate currently displaying on your screen, $0.017 per second. In the first 15 seconds you earned $0.02 ECU per second, times 15 seconds, or $0.30. In the next 15 seconds, you earned $0.019 ECU per second, times 15 seconds, or $0.285. In the third set of 15 seconds, you earned $0.018 per second, times 15 seconds, or $0.27. In the final 2 seconds before you clicked the “Leave Experiment” button, you earned $0.017 per second, times 2 seconds, or $0.034. This puts your
85 total earnings for stage 8 at $0.30 + $0.285 + $0.27 + $0.034 = $0.889, which would be rounded up to the nearest cent: $0.89. You will now take a brief quiz to make sure you understand how this stage works, and how you will be paid for your actions in this stage. -----
Stage 1, 3, 5, 7 – Public Goods Treatment – Other stages are the same. [Stage 3,5,7 only] This stage is a repeat of Stage 1. [Stage 1,3,5,7] At the beginning of this stage, you will be randomly and anonymously assigned to a group with 3 other participants. You will be given 20 tokens. Your task is to decide how many tokens to allocate to an Individual account and how many to allocate to a Group account. You can allocate anywhere from 0 to 20 tokens to each account, but the total allocated to both must sum to 20. Negative allocations or fractional allocations are not allowed. Tokens invested in the individual account earn 4 ECU per token, while tokens invested in the group account earn profits in a different way. The experimenter adds up all the tokens allocated to the group account by members of your group and doubles this amount. This new amount is then split evenly among the members of your group and converted to ECU at 4 ECU per token. You earnings for this stage are determined by the equation Earnings = 4*(Tokens allocated to the individual account) + 4*(2*Total tokens contributed by your group to the group account)
[Stage 1 only] You will now take a brief quiz to make sure you understand how this stage works, and how you will be paid for your actions in this stage. -----
Stage 1, 3, 5, 7 – EGC treatment – Other stages are the same. The task for this stage occurs in stages 1, 3, 5, and 7. One of these 4 stages will be randomly selected to count toward your earnings. In this stage you will be presented with five gambles, and asked to choose one, and only one, of these gambles to play. Each gamble will contain two possible outcomes each occurring with
86 some probability. If this stage is selected to count toward your earnings, your compensation will be determined by which of the five gambles you select and which of the two possible outcomes occurs. [Stage 1 only] If you look at your screen you will see an example set of gambles. If this stage were selected to count towards your earnings, and you were to select gamble 4, you would have a 50% chance of earning 800 ECU and a 50% chance of earning 100 ECU. You will now take a brief quiz to make sure you understand how this stage works, and how you will be paid for your actions in this stage. -----
Onscreen instructions during stages 2, 4, and 6 for Experimenter-Imposed Inactivity treatment: These instructions will not be read out loud, but will be presented on the subjects screen. There are two possible sets of instructions that subjects may receive. Set 1: [Half of the subjects will see this set during stages 2 and 6.] You have no activities to perform in this stage. Please wait for the experiment to continue. Set 2: [Half of the subjects will see this set during stages 2 and 6. All subjects will see this set during stage 4. ] You have four activities you may choose to perform during this stage. You may participate in as many activities as you like, and as often as you like until the end of the stage. You may leave or restart an activity at any time. Participation in these activities is not required and will not affect your earnings for this round. [Subjects are presented with buttons that launch the four activities: Tic-tac-toe (vs a computer); Rock-Paper-Scissors (vs a computer); a controllable slideshow featuring paintings of landscapes; and a text search game, where subjects search for specific words among a grid of letters].
87 A.2 Behavioral Effects of Peer Imposed Inactivity
Experiment Instructions This is an experiment on the economics of decision making. In addition to your participation fee, you will have the chance to earn money based on your decisions in this experiment. It is extremely important that you put away all materials including external reading material and turn off your cell phones and any other electronic devices. If you have a question, please raise your hand and I will come by and answer your question privately. In the experiment you will earn Experimental Currency Units (or ECU). At the end of the experiment, your ECU from all periods will be summed and converted to dollars where 1 ECU = $0.02. You will be paid with a check today, and your total earnings will be rounded to the nearest cent. Today’s experiment is comprised of 9 stages. Prior to the start of each stage, additional instructions will be given.
Stage 1: [Dictator Game Treatment] At the beginning of this first stage, you have been randomly and anonymously grouped with another subject. One member of each group will be assigned the role A, and the other member will be assigned the role B. You will not be informed of your role. You will then be asked to allocate 100 ECU between yourself and the other member of your group. If your role is A, then the allocation you specified will occur: You will receive the amount you allocated to yourself, and the other member of your group will receive the amount you allocated to them. If your role is B, then the allocation the other member of your group specified will occur: You will receive the amount the other member of your group allocated to you, and the other member of the group will receive the amount they allocated to themself. There will only be one round in this stage. You will now take a brief quiz to make sure you understand how this stage works, and how you will be paid for your actions in this stage.
[Public Goods Treatment]
88 At the beginning of this stage, you will be randomly and anonymously assigned to a group with 3 other participants. You will be given 20 tokens. Your task is to decide how many tokens to allocate to an Individual account and how many to allocate to a Group account. You can allocate anywhere from 0 to 20 tokens to each account, but the total allocated to both must sum to 20. Negative allocations or fractional allocations are not allowed. Tokens invested in the individual account earn 4 ECU per token, while tokens invested in the group account earn profits in a different way. The experimenter adds up all the tokens allocated to the group account by members of your group and doubles this amount. This new amount is then split evenly among the members of your group and converted to ECU at 4 ECU per token. You earnings for this stage are determined by the equation Earnings = 4*(Tokens allocated to the individual account) + 4*(2*Total tokens contributed by your group to the group account)
You will now take a brief quiz to make sure you understand how this stage works, and how you will be paid for your actions in this stage.
[Risk Elicitation Treatment] The task for this stage occurs in stages 1, 3, 5, and 7. One of these 4 stages will be randomly selected to count toward your earnings. In this stage you will be presented with five gambles, and asked to choose one, and only one, of these gambles to play. Each gamble will contain two possible outcomes each occurring with some probability. If this stage is selected to count toward your earnings, your compensation will be determined by which of the five gambles you select and which of the two possible outcomes occurs. If you look at your screen you will see an example set of gambles. If this stage were selected to count towards your earnings, and you were to select gamble 4, you would have a 50% chance of earning 700 ECU and a 50% chance of earning 160 ECU. You will now take a brief quiz to make sure you understand how this stage works, and how you will be paid for your actions in this stage.
89 Stage 2: In this stage, you subjects have been assigned a series of tasks to complete. You will receive a fixed rate of 100 ECU for having participated in this stage. You will receive your list of tasks and further instructions for each task on your computer screen. The experiment will continue once everyone has completed his or her assigned tasks. You will now take a brief quiz to make sure you understand how this stage works, and how you will be paid for your actions in this stage.
Stage 3: Stage 3 is a repeat of stage 1. [Stage 3 includes all of the instructions from stage 1, except it excludes the sentence covering the quiz.] Stage 4: In this stage, you may participate in the same four activities as in Stage 2. This stage will last 5 minutes and you will receive a fixed rate of 100 ECU for having participated in this stage. You will receive further instructions for this stage and the activities on your computer screen.
Stage 5: Stage 5 is a repeat of stage 1. [Stage 5 includes all of the instructions from stage 1, except it excludes the sentence covering the quiz.]
Stage 6: In this stage, you subjects have been assigned a series of tasks to complete. You will receive a fixed rate of 100 ECU for having participated in this stage. You will receive your list of tasks and further instructions for each task on your computer screen. The experiment will continue once everyone has completed his or her assigned tasks.
Stage 7: Stage 7 is a repeat of stage 1. [Stage 7 includes all of the instructions from stage 1, except it excludes the sentence covering the quiz.]
Stage 8:
90 This stage shows your results from stages 1 through 7, including all decisions, roles, outcomes, and stage earnings that are relevant to your final payout. If you have any questions about your payout, please raise your hand and I will come around to answer them. Otherwise, once you have finished reviewing your results up to this point, please click the continue button. Once everyone has clicked continue, we will move on to the final stage of today’s experiment.
Stage 9: This is the final stage of the experiment. All numerical values in this stage will be expressed in terms of US Dollars, rather than ECU. In this stage you will earn money by remaining at your station. Once this stage begins, a number will appear on your screen reflecting the rate at which you are currently being paid. This rate will begin at $0.02 per second. Every 15 seconds thereafter, the rate will decrease by $0.001 per second. Thus 15 seconds into this stage, the rate will decrease from $0.02 per second to $0.019 per second. At any point during this stage you may click the button marked “Leave Experiment”. Once you have done that, you will stop accumulating any additional earnings in this stage. You may then gather your belongings and move to the designated payment area, where you will receive a check for your earnings in today’s experiment. Multiple people may choose to leave the experiment at the same time. I anticipate an average wait of about 5 seconds between your arrival in the payment area and the receipt of your payment. Let’s work through an example. Let’s say you pushed the “Leave Experiment” button 47 seconds into the stage. You would have spent 15 seconds earning $0.02 per second, 15 seconds earning $0.019 per second, 15 seconds earning $0.018 per second, and 2 seconds at the rate currently displaying on your screen, $0.017 per second. In the first 15 seconds you earned $0.02 ECU per second, times 15 seconds, or $0.30. In the next 15 seconds, you earned $0.019 ECU per second, times 15 seconds, or $0.285. In the third set of 15 seconds, you earned $0.018 per second, times 15 seconds, or $0.27. In the final 2 seconds before you clicked the “Leave Experiment” button, you earned $0.017 per second, times 2 seconds, or $0.034. This puts your total earnings for stage 8 at $0.30 + $0.285 + $0.27 + $0.034 = $0.889, which would be rounded up to the nearest cent: $0.89. You will now take a brief quiz to make sure you understand how this stage works, and how you will be paid for your actions in this stage.
91 A.3 Alternatives to Inactivity
Experiment Instructions This is an experiment on the economics of decision making. In addition to your participation fee, you will have the chance to earn money based on your decisions in this experiment. It is extremely important that you put away all materials including external reading material and turn off your cell phones and any other electronic devices. If you have a question, please raise your hand and I will come by and answer your question privately.
In today’s experiment all amounts will be denoted in US Dollars. You will be paid with a check today, and your total earnings will be rounded up to the nearest cent.
Today’s experiment is comprised of 8 periods. Prior to the start of the experiment, instructions for all periods will be given. This experiment is self-paced, so after all instructions and quizes are complete, you will go through the rest of the experiment on your own.
Once you have finished the final period, you will be able to get paid and leave the experiment. This means you should collect your belongings and exit the lab through the door you used to enter the lab. You may collect your payment from the desk that was used to check you in to the experiment. To aid us in this process, we will ask you to type your name on the next screen. This will help your payment occur as quickly as possible.
Final Period Instructions
These instructions are for the final period of the experiment. You will be shown them again immediately prior to the last period. Again, all numerical values in this period will be expressed in terms of US Dollars. At the beginning of this stage, you will be assigned an option, from the set of options you saw in the first seven periods, with the exception that you will not be assigned the skip stage option. In this stage you will earn money by remaining at your station. Once this stage begins, a number will appear on your screen in the upper right reflecting the rate at which you are currently being paid. This rate will begin at $0.020 per second. Every 15 seconds thereafter, the rate will decrease by $0.001 per second. Thus 15 seconds into this stage,
92 the rate will decrease from $0.020 per second to $0.019 per second. At any point during this stage you may click the button marked "Leave Experiment". Once you have done that, you will stop participating in your assigned option, and you will stop accumulating any additional earnings in this stage. You may then gather your belongings and move to the designated payment area, where you will receive a check for your earnings in today's experiment. Multiple people may choose to leave the experiment at the same time. I anticipate an average wait of about 15 seconds between your arrival in the payment area and the receipt of your payment. This amount of time may be longer if many people leave the experiment at the same time.
Let’s work through an example. Let’s say you pushed the “Leave Experiment” button 47 seconds into the stage. You would have spent 15 seconds earning $0.02 per second, 15 seconds earning $0.019 per second, 15 seconds earning $0.018 per second, and 2 seconds at the rate currently displaying on your screen, $0.017 per second. In the first 15 seconds you earned $0.02 ECU per second, times 15 seconds, or $0.30. In the next 15 seconds, you earned $0.019 ECU per second, times 15 seconds, or $0.285. In the third set of 15 seconds, you earned $0.018 per second, times 15 seconds, or $0.27. In the final 2 seconds before you clicked the “Leave Experiment” button, you earned $0.017 per second, times 2 seconds, or $0.034. This puts your total earnings for stage 8 at $0.30 + $0.285 + $0.27 + $0.034 = $0.889, which would be rounded up to the nearest cent: $0.89
You will now take a brief quiz to make sure you understand how this stage works and how you will be paid for your actions in this stage.
Periods 1-7:
Each of the first seven periods will be comprised of two parts, called Stage A and Stage B.
At the start of each Stage A, you will receive a $1 endowment and will be randomly assigned one of seven different options to occur during Stage B. You will also be told how long this Stage B will last. You will then be given the possibility of switching tasks.
93
For each of the six options you are not assigned to, you will be asked how much money you would be willing to pay, or would need to receive, in order to experience that option during Stage B in place of your assigned option. The computer will randomly choose an alternative option, and randomly choose a cost of switching to this alternative option. The cost will be between -$1 and $1. If the amount you are willing to pay is above the cost chosen by the computer, you will pay the amount chosen by the computer and switch to the alternative option. Likewise, if you submit a negative willingness to pay (that is you would need to receive money to switch), and the price is below (more negative) or equal to this amount, you will receive the amount determined by the computer and will switch to the alternative option.
For example, let's say you are willing to pay $0.45 to switch to the alternative option and that the computer randomly chooses a price of $0.67. In this case the $0.45 you are willing to pay is less than the $0.67 price chosen by the computer, so you would keep your endowment and you would participate in your originally assigned option.
Likewise, let's say you are willing to pay -$0.15 to switch to the alternative option (that is, you would need to be paid $0.15 to switch to the alternative option), and that the computer randomly chooses a price of -$0.25. In this case the -$0.15 you are willing to pay is greater than the -$0.25 price chosen by the computer, so you would pay the price chosen by the computer (that is, you would receive $0.25) and you would switch to the alternative option.
Another example: Let's say you are willing to pay $0.15 to switch to the alternative option and that the computer randomly chooses a price of -$0.25. In this case the $0.15 you are willing to pay is greater than the -$0.25 price chosen by the computer, so you would pay the price chosen by the computer (that is, you would be paid $0.25) and you will switch to the alternative option.
At this point Stage A is complete and you will move on to Stage B. In Stage B you will participate in the option determined in Stage A for the amount of time specified in Stage A.
94 There are 7 different options you may experience as Stage B: Blank Screen: The screen will be empty except for the text labeling this as Stage B. Pictures: You will be provided a set of landscape images on the screen which you may peruse. Tic-Tac-Toe: This is the game of tic-tac-toe, played against the computer. Rock-Paper-Scissors: This is the game of Rock-Paper-Scissors, played against a computer. Text Search: This is a word search puzzle, where you search for specific sequences of letters hidden among a grid of letters. Restricted Internet: This is a collection of websites that may be viewed on your computer screen. Skip stage: If this option occurs, you will simply skip Stage B in this period and will move directly to Stage A of next period.
Stage B will end automatically once the time specified in the current period's Stage A has elapsed and you will move on to the next period's Stage A.
Before we begin the experiment, you will be shown each of these activities for 60 seconds to get you better acquainted with these options. First, you will take a short quiz to make sure you understand this part of the experiment.
95 APPENDIX B
HUMAN SUBJECTS COMMITTEE APPROVALS
B.1 Behavioral Effects of Inactivity (Chapters 3, 4, and 5)
The Florida State University Office of the Vice President For Research Human Subjects Committee Tallahassee, Florida 32306-2742 (850) 644-8673, FAX (850) 644-4392
APPROVAL MEMORANDUM
Date: 10/30/2012
To: John Jensenius [ [removed] ]
Address: 2180 Dept.: ECONOMICS
From: Thomas L. Jacobson, Chair
Re: Use of Human Subjects in Research Behavioral Effects of Inactivity
The application that you submitted to this office in regard to the use of human subjects in the proposal referenced above have been reviewed by the Secretary, the Chair, and one member of the Human Subjects Committee. Your project is determined to be Expedited per 45 CFR § 46.110(7) and has been approved by an expedited review process.
The Human Subjects Committee has not evaluated your proposal for scientific merit, except to weigh the risk to the human participants and the aspects of the proposal related to potential risk
96 and benefit. This approval does not replace any departmental or other approvals, which may be required.
If you submitted a proposed consent form with your application, the approved stamped consent form is attached to this approval notice. Only the stamped version of the consent form may be used in recruiting research subjects.
If the project has not been completed by 10/28/2013 you must request a renewal of approval for continuation of the project. As a courtesy, a renewal notice will be sent to you prior to your expiration date; however, it is your responsibility as the Principal Investigator to timely request renewal of your approval from the Committee.
You are advised that any change in protocol for this project must be reviewed and approved by the Committee prior to implementation of the proposed change in the protocol. A protocol change/amendment form is required to be submitted for approval by the Committee. In addition, federal regulations require that the Principal Investigator promptly report, in writing any unanticipated problems or adverse events involving risks to research subjects or others.
By copy of this memorandum, the Chair of your department and/or your major professor is reminded that he/she is responsible for being informed concerning research projects involving human subjects in the department, and should review protocols as often as needed to insure that the project is being conducted in compliance with our institution and with DHHS regulations.
This institution has an Assurance on file with the Office for Human Research Protection. The Assurance Number is FWA00000168/IRB number IRB00000446.
Cc: Mark Isaac, Advisor HSC No. 2012.9058
97 The Florida State University Office of the Vice President For Research Human Subjects Committee Tallahassee, Florida 32306-2742 (850) 644-8673, FAX (850) 644-4392
RE-APPROVAL MEMORANDUM
Date: 10/30/2013
To: John Jensenius [[removed]]
Address: 2180 Dept.: ECONOMICS
From: Thomas L. Jacobson, Chair
Re: Re-approval of Use of Human subjects in Research Behavioral Effects of Inactivity
Your request to continue the research project listed above involving human subjects has been approved by the Human Subjects Committee. If your project has not been completed by 10/29/2014, you must request renewed approval by the Committee.
If you submitted a proposed consent form with your renewal request, the approved stamped consent form is attached to this re-approval notice. Only the stamped version of the consent form may be used in recruiting of research subjects. You are reminded that any change in protocol for this project must be reviewed and approved by the Committee prior to implementation of the proposed change in the protocol. A protocol change/amendment form is required to be submitted for approval by the Committee. In addition, federal regulations require that the Principal Investigator promptly report in writing, any unanticipated problems or adverse events involving
98 risks to research subjects or others.
By copy of this memorandum, the Chair of your department and/or your major professor are reminded of their responsibility for being informed concerning research projects involving human subjects in their department. They are advised to review the protocols as often as necessary to insure that the project is being conducted in compliance with our institution and with DHHS regulations.
Cc: Mark Isaac, Advisor [[removed]] HSC No. 2013.11430
99 The Florida State University Office of the Vice President For Research Human Subjects Committee Tallahassee, Florida 32306-2742 (850) 644-8673, FAX (850) 644-4392
RE-APPROVAL MEMORANDUM
Date: 9/2/2014
To: John Jensenius [[removed]]
Address: 2180 Dept.: ECONOMICS
From: Thomas L. Jacobson, Chair
Re: Re-approval of Use of Human subjects in Research Behavioral Effects of Inactivity
Your request to continue the research project listed above involving human subjects has been approved by the Human Subjects Committee. If your project has not been completed by 9/1/2015, you must request renewed approval by the Committee.
If you submitted a proposed consent form with your renewal request, the approved stamped consent form is attached to this re-approval notice. Only the stamped version of the consent form may be used in recruiting of research subjects. You are reminded that any change in protocol for this project must be reviewed and approved by the Committee prior to implementation of the proposed change in the protocol. A protocol change/amendment form is required to be submitted for approval by the Committee. In addition, federal regulations require that the Principal Investigator promptly report in writing, any unanticipated problems or adverse events involving
100 risks to research subjects or others.
By copy of this memorandum, the Chair of your department and/or your major professor are reminded of their responsibility for being informed concerning research projects involving human subjects in their department. They are advised to review the protocols as often as necessary to insure that the project is being conducted in compliance with our institution and with DHHS regulations.
Cc: [] HSC No. 2014.13477
101 The Florida State University Office of the Vice President For Research Human Subjects Committee Tallahassee, Florida 32306-2742 (850) 644-8673, FAX (850) 644-4392
APPROVAL MEMORANDUM
Date: 1/12/2016
To: John Jensenius [[removed]]
Address: 2180 Dept.: ECONOMICS
From: Thomas L. Jacobson, Chair
Re: Use of Human Subjects in Research Behavioral Effects of Inactivity (Data analysis)
The application that you submitted to this office in regard to the use of human subjects in the proposal referenced above have been reviewed by the Secretary, the Chair, and one member of the Human Subjects Committee. Your project is determined to be Expedited per 45 CFR § 46.110(7) and has been approved by an expedited review process.
The Human Subjects Committee has not evaluated your proposal for scientific merit, except to weigh the risk to the human participants and the aspects of the proposal related to potential risk and benefit. This approval does not replace any departmental or other approvals, which may be required.
If you submitted a proposed consent form with your application, the approved stamped consent
102 form is attached to this approval notice. Only the stamped version of the consent form may be used in recruiting research subjects.
If the project has not been completed by 1/10/2017 you must request a renewal of approval for continuation of the project. As a courtesy, a renewal notice will be sent to you prior to your expiration date; however, it is your responsibility as the Principal Investigator to timely request renewal of your approval from the Committee.
You are advised that any change in protocol for this project must be reviewed and approved by the Committee prior to implementation of the proposed change in the protocol. A protocol change/amendment form is required to be submitted for approval by the Committee. In addition, federal regulations require that the Principal Investigator promptly report, in writing any unanticipated problems or adverse events involving risks to research subjects or others.
By copy of this memorandum, the Chair of your department and/or your major professor is reminded that he/she is responsible for being informed concerning research projects involving human subjects in the department, and should review protocols as often as needed to insure that the project is being conducted in compliance with our institution and with DHHS regulations.
This institution has an Assurance on file with the Office for Human Research Protection. The Assurance Number is FWA00000168/IRB number IRB00000446.
Cc: Mark Isaac, Advisor HSC No. 2015.17155
103
104 B.2 Alternatives to Inactivity (Chapter 6)
The Florida State University Office of the Vice President For Research Human Subjects Committee Tallahassee, Florida 32306-2742 (850) 644-8673, FAX (850) 644-4392
APPROVAL MEMORANDUM
Date: 11/12/2014
To: John Jensenius [[removed]]
Address: 2180 Dept.: ECONOMICS
From: Thomas L. Jacobson, Chair
Re: Use of Human Subjects in Research Alternatives to Inactivity
The application that you submitted to this office in regard to the use of human subjects in the proposal referenced above have been reviewed by the Secretary, the Chair, and one member of the Human Subjects Committee. Your project is determined to be Expedited per 45 CFR § 46.110(7) and has been approved by an expedited review process.
The Human Subjects Committee has not evaluated your proposal for scientific merit, except to weigh the risk to the human participants and the aspects of the proposal related to potential risk and benefit. This approval does not replace any departmental or other approvals, which may be required.
105
If you submitted a proposed consent form with your application, the approved stamped consent form is attached to this approval notice. Only the stamped version of the consent form may be used in recruiting research subjects.
If the project has not been completed by 11/11/2015 you must request a renewal of approval for continuation of the project. As a courtesy, a renewal notice will be sent to you prior to your expiration date; however, it is your responsibility as the Principal Investigator to timely request renewal of your approval from the Committee.
You are advised that any change in protocol for this project must be reviewed and approved by the Committee prior to implementation of the proposed change in the protocol. A protocol change/amendment form is required to be submitted for approval by the Committee. In addition, federal regulations require that the Principal Investigator promptly report, in writing any unanticipated problems or adverse events involving risks to research subjects or others.
By copy of this memorandum, the Chair of your department and/or your major professor is reminded that he/she is responsible for being informed concerning research projects involving human subjects in the department, and should review protocols as often as needed to insure that the project is being conducted in compliance with our institution and with DHHS regulations.
This institution has an Assurance on file with the Office for Human Research Protection. The Assurance Number is FWA00000168/IRB number IRB00000446.
Cc: Mark Isaac, Advisor HSC No. 2014.13478
106
The Florida State University Office of the Vice President For Research Human Subjects Committee Tallahassee, Florida 32306-2742 (850) 644-8673, FAX (850) 644-4392
RE-APPROVAL MEMORANDUM
Date: 10/30/2015
To: John Jensenius [[removed]]
Address: 2180 Dept.: ECONOMICS
From: Thomas L. Jacobson, Chair
Re: Re-approval of Use of Human subjects in Research Alternatives to Inactivity
Your request to continue the research project listed above involving human subjects has been approved by the Human Subjects Committee. If your project has not been completed by 10/28/2016, you must request renewed approval by the Committee.
If you submitted a proposed consent form with your renewal request, the approved stamped consent form is attached to this re-approval notice. Only the stamped version of the consent form may be used in recruiting of research subjects. You are reminded that any change in protocol for this project must be reviewed and approved by the Committee prior to implementation of the proposed change in the protocol. A protocol change/amendment form is required to be submitted for approval by the Committee. In addition, federal regulations require that the Principal
107 Investigator promptly report in writing, any unanticipated problems or adverse events involving risks to research subjects or others.
By copy of this memorandum, the Chair of your department and/or your major professor are reminded of their responsibility for being informed concerning research projects involving human subjects in their department. They are advised to review the protocols as often as necessary to insure that the project is being conducted in compliance with our institution and with DHHS regulations.
Cc: Mark Isaac, Advisor [[removed]] HSC No. 2015.16881
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114 BIOGRAPHICAL SKETCH
John Spaulding Jensenius III was born January 26, 1986 in Washington, DC. He received a Bachelor of Arts in Computer Science and Economics, with honors, from Case Western Reserve University in May 2008. He received a Master of Science in Economics from Florida State University in December of 2010. In December 2013, John began working as the lab manager for the Nuffield College Centre for Experimental Social Science at the University of Oxford in Oxford, United Kingdom, where he currently lives with his wife Emily.
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