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Individual vs. group behavior: an empirical assessment of time preferences using experimental data

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Individual vs. group behavior: an empirical assessment of time preferences using experimental data

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Content Individual vs. Group Behavior: An Empirical Assessment
of Time Preferences Using Experimental Data
Karrar Hussain
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulllment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ECONOMICS)
December 2016
Table of Contents
List of Figures ii
List of Tables ii
A Acknowledgements iii
B Abstract iv
C Introduction 1
C.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
D Experiment Overview 4
E Experimental Design 7
E.1 Design Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
E.1.1 Training and Allocation Decisions . . . . . . . . . . . . . . . . . . . . . . 9
E.1.2 Experimental Timeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
E.1.3 Eort Allocations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
E.1.4 The Allocation-That-Counts . . . . . . . . . . . . . . . . . . . . . . . . . 10
F Results 11
F.1 Sample Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
F.2 Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
F.3 Structural Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
F.4 Theoretical Background: Individual vs. Group . . . . . . . . . . . . . . . . . . . 20
F.4.1 Collective Decision Functions . . . . . . . . . . . . . . . . . . . . . . . . 21
F.4.2 Individual vs. Group Analysis . . . . . . . . . . . . . . . . . . . . . . . . 23
F.4.3 Relationship Between Individual and Group Parameter Estimates . . . . 25
G Conclusion 30
Bibliography 31
H Appendix 36
H.1 A1: Nonlinear Least Squares Method . . . . . . . . . . . . . . . . . . . . . . . . 36
H.2 A2: Additional Individual and Group Preference Parameters Estimates . . . . . 39
H.3 A3: Additional Individual vs. Group Analysis . . . . . . . . . . . . . . . . . . . 43
H.4 A4: Experiment Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
i
List of Figures
1 Slider Bar Used to Capture Task Allocations . . . . . . . . . . . . . . . . . . . 8
2 Discounting Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3 Estimates Histogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4 Estimated CDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5 Timeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
List of Tables
1 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 Two-Limit Tobit Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . 15
3 Non-Linear Least Squares Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 18
4 Discounting, Present Bias, and Eort Cost Parameter Estimates . . . . . . . . 24
5 Summary of With-in Group Min and Max Parameter Estimates . . . . . . . . . 25
6 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
7 Individual (IND) vs. Group Regression Analysis . . . . . . . . . . . . . . . . . 29
8 Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
9 Group Vs. Individual Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
10 Group Vs. Individual Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
11 Group Vs. Individual Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
12 Group Vs. Individual Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
13 Additional Individual vs. Group Regression Analysis . . . . . . . . . . . . . . . 43
14 Individual (IND) vs. Group Regression Analysis . . . . . . . . . . . . . . . . . 44
ii
A Acknowledgements
I am grateful to Michael Callen (Harvard Kennedy School), Aprajit Mahajan (UC Berkeley),
Jerey B. Nugent (USC) and Charlie Sprenger (UC San Diego) for their advise and support.
I am grateful to the Center for Economic Research Pakistan (CERP), Sohaib Athar, Zara
Salman, Mehroz Alvi (IGC, Pakistan) for excellent logistical support.
I am indebted to Dr. Turab Hussain and Dr. Muhammad Hussain (Lahore University of
Management Sciences) for championing this project. Rao Hashim, Mubin Ahsan and Danish
Raza provided excellent research assistance. (Research is approved by the Institutional Review
Board at Harvard and at UC San Diego.)
iii
B Abstract
This paper studies individual and collective decisions through the preference elicitation method
over unpleasant task consumption. The study uses experimental data to analyze task consump-
tion decisions by groups of individuals, who have to reach a consensus regarding allocation of
tasks over time. For this purpose, a joint experimental elicitation of time preferences was per-
formed for the groups as well as for their individual members. Using an analysis of the elicited
choices of decision making over time, the study found that group/joint decision making resulted
in much higher elicited present-bias as compared to individual decision making. This nding is
robust to a variety of alternative specications. The results also suggest that the joint/group
aspect of the decision-making process exacerbates standard measures of present-bias due to
group members' discount rate heterogeneity. The study also concludes that group decisions are
more patient compared to its individual members.
JEL classication: C91, D12, D81
Keywords: Time Discounting, Preference Elicitation, Present-bias
iv
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C Introduction
Intertemporal choices { decisions in which costs and benets are spread out over time { are
both common and important aspects of life. Understanding the factors and dynamics associated
with these intertemporal choices is a valuable and important goal for any decision maker (policy
maker, household head, and managers etc.). In economics, the notion of intertemporal choices is
relevant in a wide range of issues such as consumption and savings, labor-leisure choice, building
reputations, education and health care, and investment decisions, because an economic agent's
choice often entails tradeos through time. This is why depicting such choices with structural
models of discounting has occupied the interest of economists for much of the last century
(leading contributions include Samuelson, 1937; Koopmans, 1960; Laibson, 1997; O'Donoghue
and Rabin, 2001).
The preference parameters { intertemporal in this case { governing these decisions and
associated models are of unique value in understanding a broad range of behaviors and have
received much empirical attention. In both eld and laboratory settings substantial eorts
have been made to structurally estimate the level and shape of discounting (examples include
Hausman, 1979; Lawrance, 1991; Warner, Pleeter, and Cagetti, 2003; Laibson, Repetto, and
Tobacman, 2005; Harrison, Lau, and Williams, 2002; Anderson, Harrison, Lau, and Rutstrom,
2008; Shapiro, 2005; Kuhn, 2013; Andreoni and Sprenger, 2012).
Research eorts (empirical and theoretical) have largely focused on modeling the sources
of time inconsistency at the level of the individual. This paper takes a dierent approach. It
considers how acknowledging the collective nature of choice can rationalize time inconsistencies
in revealed preferences settings. Many dimensions of intertemporal choice are better modeled as
an outcome of group, rather than individual, decision making. For instance, savings, education,
and health decisions are typically made at the collective household level, whereas, allocation
of budgets over time within rms and committees are products of multi-party deliberations.
Even in the context of individual choice, one can consider the existence of multiple selves with
distinct personalities rather than a single homogeneous decision making unit.
1
Acknowledgement of the collective nature of choice, and comparing it with its individual
members' choices, can help to rationalize the apparent time consistency or inconsistency in
the decision-making process. Theoretically, in a group context, inconsistencies can arise sim-
ply from the aggregation of heterogeneous preferences. Variations in individual discount rates
(Marglin,1963; Feldstein, 1964; Jackson and Yariv, 2015; Zuber, 2010) and innovations in the
Pareto weight summarizing the collective decision-making process, represent relevant consider-
1
Shefrin and Thaler (1981) contrasts the long-sighted \planner within us to the short-sighted \doer, while
Metcalfe and Mischel (1999) contrast our hot and cool systems. Such evidence supports the application of
collective choice models to characterize the behavior of individuals.
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ations in this regard. In fact, Jackson and Yariv (2015) show that for a uniform distribution
of discount rates in an otherwise homogeneous population, group utility maximization in a
non-dictatorial way generates aggregate behavior that corresponds to hyperbolic discounting.
As a result, if other things remain equal, it is optimal to favor impatient group members of the
group in early periods and patient members in later periods.
There is little empirical evidence on group decision making as an intertemporal choice. Even
so, numerous theoretical papers are devoted to group decision making and the aggregation of
time preferences. Under that aggregation, these papers predict that a collective decision process
will most likely result in inconsistent choices over time, even if group members are individually
consistent (Gollier and Zeckhauser, 2005; Jackson and Yariv, 2015). Empirical evidence on the
aggregation of time preferences supports this view. For instance, Jackson and Yariv (2014)
show that a large majority of subjects acting as social planners are present-biased and that
only 2% of them exhibit time consistent behavior. Mazzocco (2008) showed that intertemporal
decisions of couples are dierent from singles decisions. Using the Consumer Expenditure
Survey, he showed that the traditional non-durable consumption smoothing behavior under
expected utility was satised for singles but not for couples. According to Mazzocco (2008),
this suggests that together with spouse preferences, the relative decision power of each partner
plays an important role in this asymmetry. A consequence is that by ignoring the change in the
balance of power, the revealed behavior of individuals in intertemporal decisions can be biased.
This paper contributes to the literature on this topic by gathering evidence that compares
groups and individuals in the domain of task consumption/eort allocation over time. In par-
ticular, we compare individual decisions with those of groups, whose members are matched
randomly. Since every participant makes the decisions individually and collectively, the order
in which these kinds of decisions take place becomes important, i.e. making the individual deci-
sions rst, followed by the group decisions, or vice-versa. In the design part of the experiment,
we explicitly controlled for this order eect of decision making by selectively distributing the
ordering sequence among the groups.
The within-group parameters' estimates generated in this paper yield new empirical evi-
dence on the outcome resulting from individual and collective decisions over time. The group,
along with its individual (two) members intertemporal allocation decision were used to test the
theoretical predictions of the time preference models. As described above using Jackson and
Yariv (2015), a set of testable hypotheses were generated and tested subsequently. For this
using the groups' time preference estimates (specically focusing on groups' procrastination
tendency { related to present bias estimate) and their individual members' structural estimates
(including discount rate, present bias, and eort cost parameters' estimates), the roles and
eects of theory based heterogeneities are explored empirically on group procrastination ten-
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dency. These within group heterogeneities include the dierences in individual members' (i)
discount factor, (ii) parameter governing the cost of eort, and (iii) bargaining position of each
person in the group.
C.1 Literature Review
Following Samuelson (1937), and Fishburn and Rubinstein (1982), a large part of the theoretical
literature on time preferences builds on discounted utility and additively separable functional
forms that assume a separation between value and delay in assessing temporal sequences of
outcomes. A typical example is the exponential discounting utility model, which assumes
stationarity of time preferences and serves as the workhorse of many economic models. The
discounted utility model's representation of time preferences also facilitates the use of empirical
measurements. With an extra assumption on the linearity of utility, measures of discount factors
and discount rates can be carried out by way of simple experiments (Thaler, 1981; Coller and
Williams, 1999). If one instead assumes nonlinear utility, then measurements become more
sophisticated but also more complex (Andreoni and Sprenger, 2012).
Notably large body of laboratory research has focused on identifying the shape of time
preferences in the context of time-date monetary payments (Recent papers using time-dated
monetary payments include Ashraf, Karlan and Yin, 2006; Andersen, Harrison, Lau and Rut-
strom, 2008; Dohmen, Falk, Human and Sunde, 2010; Tanaka, Camerer and Nguyen, 2010;
Benjamin, Choi and Strickland, 2010; Voors, Nillesen, Verwimp, Bulte, Lensink and van Soest,
2012; Bauer, Chytilova and Morduch, 2012; Sutter, Kocher, Glatzle-Ruetzler and Trautmann,
2013; and Dupas and Robinson, 2013). The empirical literature on time preference using a time-
dated monetary payments has elicited an extremely wide variety of discount rates. Frederick
et al. (2002) report elicited discount rates ranging from less than 1% (Thaler, 1981) to more
than 1,000% (Holcomb and Nelson, 1992). Moreover, individuals often exhibit present-bias and
thereby violate stationarity (Benzion et al., 1989; Kirby and Marakovic, 1995; Bleichrodt and
Johannesson, 2001; DellaVigna, 2009).
Experiments involving time-dated monetary payments have several confounds to identify
the shape of time preferences from such monetary choices. Issues of payment reliability and risk
preference suggest that subject responses may be closely linked to their assessment of the ex-
perimenter's reliability rather than solely their time preferences. The main idea was originally
raised by Thaler (1981) who, when considering the possibility of using incentivized monetary
payments in intertemporal choice experiments noted `Real money experiments would be inter-
esting but seem to present enormous tactical problems. (Would subjects believe they would
get paid in ve years?)'. Further empirical work validates Thaler's (1981) suspicion. Andreoni
and Sprenger (2012a), Gine, Goldberg, Silverman and Yang (2010), and Andersen, Harrison,
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Lau and Rutstrom (2012) all document that when closely controlling transactions costs and
payment reliability, dynamic inconsistency in choices over monetary payments is virtually elim-
inated on aggregate. Further, when payment risk is added in an experimentally controlled way,
non-expected utility risk preferences deliver behavior observationally equivalent to present bias
as described above (Andreoni and Sprenger, 2012b). Furthermore, monetary payments may not
be suitable to identify parameters of models dened over time-dated consumption. Arbitrage
arguments imply that choices over monetary payments should only reveal subjects' borrowing
and lending opportunities (Cubitt and Read, 2007).
Evidence on group choice over time mainly concerns impatience. Available studies suggest
that groups are more patient than their individual members. For example, individuals are
more patient when making a joint decision with a partner than when making a decision for
themselves. This statement holds whether the group consists of a decision-making real-life
couple (Carlsson et al., 2012) or an experimental `articial' couple (Shapiro, 2010). Carlsson et
al. (2012) also nd that couple-made decisions violate stationarity. For larger groups, collective
patience has been reported in groups of three to seven people (Shapiro, 2010; Denant-Boemont
and Loheac, 2011).
There are two key contributions of this paper. First using the domain of eort task con-
sumption, it elicits the time preferences of groups and their individual members simultaneously.
Using these elicited preferences it estimates the time preference parameters for both groups and
their individual members. Secondly, it connects the groups' time preference parameter of time
inconsistency measures with theory based individual members' characteristics. The paper pro-
ceeds as follows. Section 2 presents the setting of the experiment overview and provides a
theoretical background on time preferences. Section 3 describes the experimental design. Sec-
tion 4 summarizes the experimental results, and Section 5 concludes.
D Experiment Overview
The duration of the experiment was three weeks in which 244 participants took part in the
study. All the participants were students from Lahore University of Management Sciences
(LUMS). The experiment was based on the premise of introducing high-resolution monitoring
technology. Each student in the sample was given a smart-phone equipped with a reporting
application. This application (discussed in detail in section 2) permitted precise observation of
when tasks were conducted, which in turn allowed for implementation of pay-for-performance
contracts. The task for participants was dened as taking pictures of pages of any book of their
choice using the high-resolution monitoring technology provided to them. However, this task
could only be completed in the ocial time window set for the dierent days of the experiment.
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All participants were informed of this at the start of the experiment.
The intertemporal nature of multi-day task performance allowed for the introduction of
intertemporal bonus contracts. Participants were oered a xed bonus of $15 for taking pictures
of either a total ofV = 200 pages over two-days in the individual setting or a total of V = 400
pages over two days in the group setting, depending on which category they were assigned.
Each group consisted of two members matched randomly. The total number of pictures for
the rst day was set as v
1
and for the second day as v
2
. The experiment had 3 active days of
participation spaced out with a gap of a week between them. On the rst day, the participants
were only expected to set their targets for v
1
and v
2
at three task rates R2f0:8; 1; 1:2g, both
as individuals and as a group, with v
1
being a minimum of 12. This was done to discourage
all the participants from allocating all of the task on a single day, aecting the intertemporal
nature of the experiment. Dierent venues had dierent ordering for preference selection i.e.
setting individual targets rst vs. group targets and vice versa to eliminate ordering eect. A
week later on day 2, participants were supposed to repeat the exercise, but were also allotted
work hours to complete v
1
targets of the task after they had made their choices. They were
also informed before the start of the experiment that they had a 20% chance of receiving the
preference schedule of v
1
and v
2
targets set on the rst day and 80% chance of decisions taken
on the second day right before the commencement of the task. They did not receive any bonus
if their specied targets of v
1
and v
2
were not met. Output exceeding v
1
targets on the rst
day was not transferable to v
2
. If either of the targets, v
1
or v
2
, were not met, the bonus was
not received. They had a 33% chance of getting tasks assigned as individuals and 66% chance
of getting work assigned as a group.
To motivate the intertemporal tradeos faced by participants, preference decisions regarding
v
1
andv
2
targets had to be set for 3 dierent `task rates.' This meant that relation between v
1
and v
2
varied depending on the task rate i.e. every task allocated to the later date v
2
reduced
the number of tasks allocated to the sooner datev
1
by a stated factor. For example, a task rate
of 1:0.8 implies that each task allocated to day 3v
2
reduces by 0.8 the number in day 2v
1
. The
3 dierent task rates wereR2f0:8; 1; 1:2g and the participants were informed that they could
be assigned the proposed targets for any one task rate randomly and the chances are equally
likely. The reason for choices of values of R is that besides having a value of 1 which is the
most natural one, they also cover the situations in which a task allocated to Week 3 reduces or
increases tasks allocated to Week 2. The participant's decision can be formulated as allocating
tasks (v
1
;v
2
) =f(R;V ) over time subject to the present-value budget constraint. The v
1
and
v
2
satised the intertemporal budget constraint
v
1
+Rv
2
=V:
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These intertemporal bonus contracts can be used to investigate intertemporal preferences.
The allocations participants make, (v
1
;v
2
), convey information on their discounting. Additional
experimental variation permits identication of an important behavioral aspect of intertemporal
choice: the existence of present-biased preferences.
In the sample of 244 participants, the study documented four principal results. First, on
aggregate, a present bias exists in participants behavior. In other words, the participants'
intertemporal allocation decisions exhibited time inconsistency. Participants on day 2 of the
experiment allocated signicantly fewer tasks tov
1
than on day 1. Second, the degree of present
bias was more pronounced in a group's task allocation decisions as compared to an individuals
task allocation setting. This justies the rationale for not forming the group for more or less
perfectly substitutable tasks. Third, the order in which decisions were made, whether making
the individual task allocation rst and then the group task allocation or vice versa had no
eect on the degree of present bias. Lastly, using within-groups estimates of present bias, the
variations in group's individual members' discount rates and bargaining power do explain group
procrastination tendencies as postulated by Jackson and Yariv (2015).
The results in this paper are important because it puts forward both nonparametric and
a parametric characterizations of individual and collective intertemporal choice for the same
set of participants, under experimentally controlled environments, based upon intertemporal
allocations of eort
2
. Starting with the an approach that was free of functional/structural
form using the experimentally induced exogenous variations of controlled factors, the study
moves to a theory based parametric analysis of time inconsistency issues on the individual
and group level. The reason for this approach is that our subsequent parametric estimates
are resulting from restrictive parametric assumptions rather than a failure of the underlying
theoretical framework that is free of functional form and related to an assessment of the degree
of dierences between these two kinds of decision environments. In the structural part of
empirical analysis the preference structure associated with the discounted utility approach is
applied to model group behavior without modication. This is in line with representative agent
modeling structure mostly used in macroeconomics literature. This unitary approach assumes
the collective acts as a single decision making unit and therefore, can be treated as a rational
individual.
2
which is in domain of consumption rather than domain of monetary choice. The monetary domain has
several confounding factors for the identication and estimation of the shape of time preferences. For more
details please see Andreoni and Sprenger (2013) (forthcoming QJE) paper
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E Experimental Design
The experiment focuses on allocations of eort for one kind of job/task. This task could be
completed over time. Some portions of the task might be completed sooner, and the remaining
portion of the task could be completed later, depending on their choices and chances.
This task consisted of taking a specic number of pictures of book pages through cell phones.
As mentioned above the total wasV = 200 pages for the individual setting andV = 400 pages
for the group setting. The aforementioned numbers of pictures were supposed to be taken
in one hour (9:30 pm to 10:30 pm, Pakistan Standard Time) on the second and third day of
the experiment. Participants were also instructed that their phone had a unique International
Mobile Equipment Identity (IMEI) number. Once they took a picture, they were also required
to upload the picture using the application on the phone by connecting their devices to LUMS
server's Wi-Fi. The participants were also given free Internet data package as backup by Telenor
Pakistan Limited, which has reliable and ecient cellphone data services in the Lahore area.
Participants were instructed to ensure that their pictures were clear and that the page numbers
were legible. If the page numbers were not legible, the picture would not be counted towards
their target for the bonus purposes.
In order to avoid corner solution scenarios in the allocation decisions, when a participant or
a group decides to allocate all the tasks to day 1 or day 2, we restricted the minimum number
of pictures for the day 1 task to be 12. In the subsequent statistical analysis we took care of
this issue explicitly. Only 2.50% of the total 2196 allocation decisions v
1
= 12 was observed.
On the rst day of the experiment the participants were asked to make allocation decisions
such that 3 decisions (for each R) were for each individual and 3 decisions (for each R) were
in a group of two formed randomly. On the second week, they were asked again to make the
same 9 allocation decisions (32 individuals+3 group). Towards the end of the second session,
all the participants were informed which allocation had been selected (randomly) for them out
of 18 total decisions that they recorded over the last two days of the experiment. We called
the selected task allocation decision as \decision-that-counts". In the second week, 2 hours
after the allocation decision session, participants were asked to complete their \decision-that-
counts" allocations in the specied timing, 9:30 pm to 10:30 pm, Pakistan Standard Time. All
the pictures taken through cellphones provided to them, were geo-stamped and time-stamped.
The allocation decisions across two weeks depended on the task rate. The task rate varied
across the decisions. On the target-setting page of the application (installed in the cell phones),
every slider bar corresponded to a specic task rate. For example in the rst slider bar the task
rate was 1:0.8, such that every task the participant allocated to the second day v
1
reduced the
number of tasks allocated to the third dayv
2
by 0.8. For simplicity, the task rates were always
represented as 1 :R, and the participants were fully informed of the value of R2f0:8; 1; 1:2g
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when making their decisions. An image of the main page of the application is provided as gure
1.
Figure 1: Slider Bar Used to Capture Task Allocations
Notes: The above slider bars show the individual task allocation decisions over the two weeks i.e. V = 200. The blue letters in
translate (literally) \set target". The next lines (from left to right) translates to \First day: 111; Second day: 111" for slider bar
0.8, \First day: 100; Second day: 100" for slider bar 1, and \First day: 90; Second day: 90" for slider bar 1.2. The text next to
the box translates to \nalize target" and the black letters on the bar translate to \set target."
Participants were informed about three important factors determining their \decision-that-
counts" allocated for them. First, the allocated task would be either from 13th March Decisions
(1st day of experiment) or 20th March Decisions (2nd day of experiment) according to a 20%
and 80% chance respectively. The chance of the \decision-that-counts" being from the set
of individual decisions was 33% and there was 66% chance that \decision-that-counts" would
come from their set of group allocation decisions. If the \decision-that-counts" turned out to
be a group decision, then as a group they would have to complete V = 400 pages on the 2nd
and 3rd day (27th March) of the experiment.
In the group setting, participants were also informed that there would be a group bonus
rather than individual bonuses for the members. If a participant was selected for group task
completion, her/his bonus would depend on the groups performance rather than their individual
performance within the group. We gave no instructions about the intra-group division of
workload i.e. who in that group will complete what fraction of total group task. If a group
completed the task allocated then both individuals in that group would get the bonus of $15
each, regardless of how the labor was divided.
The last factor determining their selected \decision-that-counts" was selection of three R2
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f0:8; 1; 1:2g. Participants were informed that all the Rs are equally likely, either in group or
in individual settings. The randomization process, which is inline with Augenblick, Niederle,
and Sprenger (2015) ensures incentive compatibility rather than cheap talk for all decisions.
The design choice which ensured that participants had a 20% chance of receiving preference
schedule of v
1
and v
2
targets from the rst day of decision making process was made to allow
and intimate them about their own potentially present-biased behavior.
E.1 Design Details
Our experiment consists of a single drive. This drive comprised of two sessions related to train-
ing/decision and two sessions related to task completion. For the training/decision sessions,
which began on 13th March and ended on 20th March, the physical presence of the participants
was compulsory. In these sessions, participants were taught the use of the application and the
method of sending the data to the main server. Participants were invited to eight dierent
venues within the LUMS campus. Each participant was randomly assigned to a particular
venue and group beforehand, and informed the night before via email to be present at that
particular venue. Questions about their demographics and big ve personality traits were also
recorded during this session. Participants also took part in dierent laboratory games related to
prediction behavior, risk behavior, and condence. All other activities, besides task allocation
decisions, were conducted individually i.e. participants played the games individually rather
than as a group.
E.1.1 Training and Allocation Decisions
Each session lasted 2 hours i.e. from 6 pm to 8 pm. In the last training/decision session, which
was conducted on 20th March 2015, all participants who had participated earlier in the 13th
March session were invited back. Training in all venues was identical except for one important
factor: Four out of a total of eight venues were randomly selected and in those training sessions
participants were asked to make individual task allocation decisions rst and group decisions
second. Similarly, in the remaining venues, participants were instructed to make individual
task allocation decisions after group decisions. Each training session was supervised by a venue
in-charge along with one or two assistants. A hotline number was provided if assistance was
required for those facing some problems while performing tasks or during its submission due to
hardware or software issues. Towards the end of every session and before making the allocation
decision, they were given some time to practice using the cellphone application.
Draft|Not Ready For Circulation 10
E.1.2 Experimental Timeline
An image of the experiment's timeline is provided in Appendix section A4 gure 5. We started
our study on Friday March 13th, 2015 with a training/decision session. 244 participants had
been recruited and were randomized into eight dierent venues. Participants making the in-
dividual task allocation decisions rst and the group decisions second, were assigned to four
venues; in the remaining four venues, we invited the participants who made the group decision
rst and individual second. During all the training sessions, venue in-charges were instructed to
make sure that participants within the same group exchanged their email addresses and shared
their contact numbers with each other for further communication.
Sessions regarding task completion were held on Friday March 20th and Friday March 27th.
Every participant was informed that these sessions were only for one hour starting from 9:30
pm, and ending sharply at 10:30 pm. Physical presence at these training sessions was not
required. Participants could perform the task from anywhere within the LUMS campus or even
at their homes (in case of day scholars), provided that they have Wi-Fi there.
E.1.3 Eort Allocations
In Week 1 (Friday 13th March), participants allocated tasks between Week 2 (Friday 20th
March) and week 3 (Friday 27th March), individually and in groups. In Week 2, participants
again allocated tasks between Weeks 2 and 3. Participants were not reminded of their initial
Week 1 allocations in Week 2. Note that in Week 1, participants were making decisions involving
two future work dates, whereas in Week 2, participants were making decisions involving a
present and a future work date. Before making decisions in Week 1, participants were told of
the Week 2 decisions and were aware that exactly one of all Week 1 and Week 2 allocation
decisions would be implemented.
E.1.4 The Allocation-That-Counts
Each group of two randomly-paired individuals made 18 decisions allocating work to Weeks 2
and 3: 12 decisions were made individually and 6 were group decisions. 12 individual decisions
constituted from 6 decisions of two individuals in the group and 6 group decisions were joint
decisions by both members. After Week 2 decisions, one of these 18 allocations was chosen at
random as the `allocation-that-counts,' and participants had to complete the allocated number
of tasks on the two work dates to ensure successful completion of the experiment.
Draft|Not Ready For Circulation 11
F Results
F.1 Sample Details
Table 1 summarizes our sample of participants regarding the important dimensions of demo-
graphics and behavior. The table shows that the mean age of the participants of this experiment
is approximately 20.3 years. In the overall sample, 39% were females. Most of the participants
had no access to formal savings accounts. In the behavioral economics empirical literature,
savings accounts have been used to predict the degrees of patience or present-bias. As men-
tioned earlier the individuals were randomly paired in a group. The mean of the group-mate
acquaintance index indicates that in most of the groups the individual members knew each
other at the start of the study. The duration of the acquaintances range from 0 { indicating
that the members have just met { up to 5 years of acquaintances. In the subsequent structural
analysis we explicitly control the time duration of acquaintance to explain group procrastina-
tion tendencies. In the context of this experiment, this variable tries to capture the eect of
coordination externality in the stage of intertemporal allocation of tasks.
The Big 5 personality index was rst developed by psychologists in the 1980s and has
subsequently become the standard and most widely used personality taxonomy in the eld.
3
The Big 5 personality index consists of ve traits, i.e. agreeableness, emotional stability (the
reverse of neuroticism), extraversion, conscientiousness, and openness.
Table 1: Summary Statistics
# of Obs Mean Standard Deviation Minimum Maximum
Demographic
Age (years) 244 20.32 1.75 17 27
Gender (Female = 0) 244 0.61 0.48 0 1
Has a On Campus Job (Yes =0) 244 0.91 0.28 0 1
Had a Savings Account (Yes =0) 244 0.73 0.44 0 1
Has a Savings Account (Yes =0) 244 0.62 0.48 0 1
Group-mate acquaintance Index (Least =1) 244 3.04 1.28 1 5
Acquaintance time duration (months) 244 13.84 21.56 0 60
Big 5
Openness 212 3.30 0.45 2.17 4.42
Conscientiousness 212 3.43 0.52 1.75 4.92
Extroversion 212 3.25 0.33 2.16 4.44
Agreeableness 212 3.44 0.46 2.33 4.58
Neuroticism 212 2.82 0.57 1.25 4.67
Notes: The Big 5 personality trait variables were recorded on a one to ve Likert scale.
3
See John et al. (2008) for a summary of the measures and its history. For a summary of empirical results
in psychology and economics, see Borghans et al. (2008).
Draft|Not Ready For Circulation 12
Following the methodology and the questionnaire by Callen et.al (2015), we measure these
traits using a 60 question survey developed specically in Urdu and validated for use in Pakistan
by the National Institute of Psychology at Quaid-i-Azam University, Islamabad. For each
participant the personality traits are measured separately, using a weighted (equal) average of
12 questions for each trait. The sample stats imply that the participants had high scores in
all dimensions of personality. Although there are studies which have explicitly investigated the
relationship between these traits and time preferences, this paper has not taken that approach.
80 85 90 95 100 105
Tasks Allocated to Sooner Date
v1
.8 .9 1 1.1 1.2
R
Panel A: Individual Decisions
160 180 200 220
Tasks Allocated to Sooner Date
v1
.8 .9 1 1.1 1.2
R
Panel B: Group Decisions
Immediate Choice Advance Choice
Figure 2: Discounting Behavior
Notes: Mean behavior in Individual and Group task allocated to sooner date combined.
F.2 Regression Analysis
Table 2 presents corresponding regression analysis for aggregate behavior focusing only on time
inconsistency. Given the experimental design, we regress the natural log of v
1
on the dummy
variable taking the value of 1 when the allocation decision is immediate and its interaction with
individual decision, and decision order dummies. We controlled for experimentally induced
variations, i.e. natural log of R, individual decision and decision order dummies. Using the
sample of decisions received during the training sessions, we estimated the two-limit Tobit
regression. This regression specication corrects for censoring around the minimum numbers
of task (i.e. minimum 12 tasks) to avoid corner solution scenarios in the allocation decisions.
The results present a non-parametric evidence for time inconsistency. Column (1) signies
the ndings from Figure 2, individual and groups] decisions combined, demonstrating that
Draft|Not Ready For Circulation 13
participants allocated signicantly fewer tasks as a percentage to v
1
as R increases and when
the allocation decision is immediate. In other words when the task rate is low, sooner tasks are
relatively cheap to complete, and participants allocated more tasks compared to when the task
rate is high, and sooner tasks are relatively expensive to complete. The estimate of immediate
decisions dummy is around -15% and this is statistically signicant. This indicates that on
average 15% fewer tasks were chosen on the 1st day of task completion and it is statistically
signiant (p = 3.58).
In column (2) we introduced the dummy for the individual decisions and its interaction
with immediate decision dummy variable, we observed that participants making immediate
choices in the individual setting allocate around 12% (s.e. around 4.4%) fewer tasks to v
1
than
making the same decision a week before the rst day of task completion. In the group setting,
the individuals allocate 21% fewer tasks on the rst day of task completion compared to a
week before. Comparing the estimate of immediate decisions dummy and its interaction with
the individual decision dummy estimate implies that on the individual level participants are
more time consistent and less present biased, on average, compared to a situation in which they
decide in groups. This is clear from the estimate of immediate decision dummy when interacted
with individual decision dummy, which increased by a factor of 9%, and is signicant at 5%
level. The estimateIndividualDecision(= 1) is negative and signicant due to the variation in
the total number of tasks over the two weeks period between individuals and their respective
groups. In the group setting it was xed at 400 and in the individual setting it was 200. The
negative estimate signies the fact that as an individual the participants were to allocate fewer
tasks compared to the group setting.
These ndings are contrary to recent few studies (examples include E. Carbonea, G. Infante,
2015; L. Boemont, E. Diecidue, and O. Haridon, 2015) in which authors have found or tried to
establish that groups are less present biased and more time consistent compared to individuals.
We mainly attribute this dierence to three factors. First, studies in which groups are less
present biased have used money. Several confounds exist for identifying the shape of time
preferences from such monetary choices. Issues of payment reliability and risk preference suggest
that subject responses may be closely linked to their assessment of the experimenter's reliability
rather than solely their time preferences. Also monetary payments may not be suitable in
identifying model parameters which are dened over time-dated consumption. The rst two
sections of the paper by Augenblick, Niederle, and Sprenger (2014) provides a detailed discussion
on this issue.
Second in all of those studies, the experimenter usually invited couples
4
, where, as in our
setting, groups were formed randomly. As evident from the sample summary statistics, the
4
where laboratory factors also play an important role, by virtue of structural arrangements
Draft|Not Ready For Circulation 14
group acquaintance index for the pool of people, participated in our experiment averages around
3.09 with the standard deviation of 1.27. This further suggests that since group members were
not fully aware of each other's individual characteristics, they opted for conservative targets
(to achieve the bonus amount) especially when making the immediate choice allocations. This
is also evident from the gure 2.
The last factor in which the group present bias estimates tend to be more than individual
decisions (for exactly the same people) is due to the underlying incentive structure for the bonus
achievement. Since the tasks were easy to perform and fundamentally perfectly substitutes in
nature, the eect of coordination externality could have become an important factor. In the
last part of the results section we will explore these issues in more detail by putting some
theory-based structure on the estimates.
In column (3) we analyze the eect of the order in which task allocation decisions were
taken. The ordering does not have any signicant eect on the behavior in the individual
or joint decisions. The estimates of individual Decision First (=1) dummy and its interaction
with immediate decision dummy are statistically insignicant. This along with the non changing
estimates of the estimate of immediate decision and its interaction with the individual decision
dummy estimates in the last column conrms the robustness of our nding.
Draft|Not Ready For Circulation 15
Table 2: Two-Limit Tobit Regression Analysis
Dependent variable: Log of Tasks Allocated to the 1st Day
(1) (2) (3) (4)

1
: Log Task Rate -0.46*** -0.46*** -0.46*** -0.46***
(0.08) (0.08) (0.08) (0.08)

2
: Immediate Decision (=1) -0.15*** -0.21*** -0.14* -0.20**
(0.04) (0.05) (0.07) (0.08)

3
: Individual Decision (=1) -0.75*** -0.75***
(0.02) (0.02)

4
: Immediate Individual Decision (=1) 0.09** 0.09**
(0.04) (0.04)

5
: Individual Decision First (=1) -0.04 -0.04
(0.06) (0.06)

6
: Immediate Individual Decision First (=1) -0.02 -0.02
(0.08) (0.08)

0
: Constant 4.70*** 5.20*** 4.72*** 5.23***
(0.03) (0.03) (0.04) (0.04)
# of Obs 2196 2196 2196 2196
# of Groups 122 122 122 122
Adj R
2
0.01 0.17 0.01 0.17
F-stats 23.68 326.64 13.36 219.78
H
0
:
2
+
4
= 1
p-value 0.00
H
0
:
2
+
6
= 1
p-value 0.00
H
0
:
2
+
4
+
6
= 1
p-value 0.00
Notes: *p< 0:1, **p< 0:05, ***p< 0:01. Standard errors are clustered at group level. The table presents
the estimates of Immediate Decision (=1) and its interactions with other experimentally induced variations
using Two-Limit Tobit Regression technique. Column (1) presents aggregated decisions estimates. Column
(2) captures the estimates of Immediate Decision (=1) for the groups and individuals separately. Column
(3) represents the results of eect of decision ordering and its interaction with Immediate Decision (=1).
Draft|Not Ready For Circulation 16
F.3 Structural Analysis
Motivated by our non-parametric analysis, we proceed to estimate structural time preference
parameters. Subjects allocate eort to an earlier date, v
1
, and a later date, v
2
. We assume
quasi-hyperbolic discounting. Under the assumption of a quadratic cost of eort function and
that individuals/groups discount the future quasi-hyperbolically (Laibson, 1997; O'Donoghue
and Rabin, 1999), the participant's preferences can be written as:
b
1
v
2
1
+
1
d=1

k
b
2
v
2
2
Normalizing b
2
= 1 and therefore dividing the intertemporal eort cost function by b
2
(to
get rid of the scaling eect) the above cost function can be written as:

v
2
1
+
1
d=1

k
v
2
2
Here v represents a task performed on a given day.
> 0, and
= 1 will imply that the
eort cost function is stationarity over time. k captures delay length, which in this experiment
was xed at 7 days. The indicator 1
d=1
captures whether the decision is made in advance or
immediately on day 1 of the task performance. The parameters and summarize individual
or group discounting with capturing the degree of present bias, active for participants who
make immediate decisions, i.e. 1
d=1
= 1. If = 1, the model nests exponential discounting
with discount factor , while if < 1 the decision-maker exhibits a present bias, being less
patient in immediate relative to advance decisions.
Modeling the group decisions, we assume the group's members are characterized by indi-
vidual preferences and that the group acts as a specic decision maker similar to an individual
whose time preference parameters could be measured independently of its members' prefer-
ences. This modeling technique is in line with representative agent setup mostly used in macro
modeling. This unitary approach assumes that the collective acts as a single decision making
unit and therefore can be treated as a rational individual.
Minimizing discounted costs subject to the intertemporal budget constraint of the experi-
ment yields the intertemporal Euler equation:

v

1
R =
1
d=1

k
v

2
: (1)
Here v

1
and v

2
are the optimal tasks performed on a day 1 and day 2 of the experiment. This
tangency condition implies that when individual/group preferences are dynamically consistent,
the optimal (
v

1
v

2
) does not depend on the parameter
1
d=1
but only depends on the task rate
R, and the delay lengthk. Using the Euler equation with intertemporal budget constraint and
Draft|Not Ready For Circulation 17
rearranging yields the solution function for the optimal v

1
:
v

1
=

1
d=1

k
V

R
2
+
1
d=1

k
!
:
and
v

1
=
8
>
>
>
>
>
>
<
>
>
>
>
>
>
:

1
d=1
k
V

R
2
+
1
d=1
k

d = 1

k
V

R
2
+
k

d = 0
The above equation implies thatv

1
is a non-linear function of (R; 1
d=1
;k;V )
5
.If we assume the
allocation decisions satisfy the above equation subject to an additive error term, , we arrive
at the non-linear regression equation
v
1

it
=f(V;R
it
; 1
d=1
;k) +
it
: (2)
Using the above equation, the parameters (;;
) can be estimated with standard tech-
niques
6
. The non-linear least squares (NLS) procedures (see Appendix: A1) permitting the
estimation of preference parameters at the aggregate or individual level, are implemented in
many standard econometrics packages (in our case, Stata).
Before we start with the explanation of our results, it is important to recognize the strengths
and weaknesses of such an NLS preference estimator. Parameter
can be estimated as opposed
to assumed, which is an advantage. A potential disadvantage is that the NLS estimator is not
well-suited to the censored data issues inherent to potential corner solutions without additional
assumptions. In the context of this study, as mentioned above, the corner solution scenario
arose only in a few cases. For 2.50% of the total 2196 allocation decisions, v
1
= 12 was
observed. Although the NLS estimator can be adapted to account for possible corner solutions
by adapting the criterion function and making additional distributional assumptions, in this
paper we did not take this approach. Andreoni and Sprenger (2012) suggest a complementary
approach: taking logs of the Euler Equation allows one to estimate the model parameters in
a two-limit Tobit framework that corrects for censoring. As expected, given the total number
of corner points in the data, the results in Table 7 (Appendix: A1), which are based on the
method by Andreoni and Sprenger (2012), are nearly identical to the results in Table 3.
Table 3 columns present non-linear least squares regressions' estimates with standard errors
5
For this class of eort cost function, both relative risk aversion and intertemporal elasticity of substitution
are function of v.
6
please see the appendix for more detail
Draft|Not Ready For Circulation 18
Table 3: Non-Linear Least Squares Analysis
Dep. Var: v
1

it
Combined Ind Vs: Group Decision Order

c
0.78***
I
0.82***
IF
0.77***
(0.05) (0.06) (0.05)

c
0.98***
I
0.96***
IF
0.98***
(0.04) (0.03) (0.06)

c
1.07***
I
1.00***
IF
1.17***
(0.23) (0.22) (0.39)

G
0.71***
IS
0.0.79***
(0.06) (0.10)

G
1.01***
IS
0.95***
(0.04) (0.03)

G
1.23***
IS
0.85***
(0.29) (0.19)
# Observations 2196 # Observations 2196 # Observations 2196
# Groups 122 # Groups 122 # Groups 122
RMSE 0.54 RMSE 0.54 RMSE 0.54
LL -1781 LL -1199 LL -578
H
0
:
c
= 1

2
(1) = 16:01,
p-value=0.000
H
0
:
I
= 1

2
(1) = 9:240,
p-value=0.002
H
0
:
IF
= 1

2
(1) = 19:10,
p-value=0.000
H
0
:
c
= 1

2
(1) = 0:500,
p-value=0.481
H
0
:
G
= 1

2
(1) = 23:75,
p-value=0.000
H
0
:
IS
= 1

2
(1) = 4:920,
p-value=0.026
H
0
:
c
= 1

2
(1) = 0:100,
p-value=0.757
H
0
:
I
=
G

2
(1) = 4:530,
p-value=0.033
H
0
:
IF
=
IS

2
(1) = 0:010,
p-value=0.907
Notes: *p < 0:1, **p < 0:05, ***p < 0:01. Robust standard errors clustered at the group level.
2
(1)
0:05
= 3:84.
The table presents the structural estimates of intertemporal hyperbolic discounting model using Non-linear Least
Squares Regression technique. Column (1) presents the combine decisions estimates of , , and
. Column (2)
captures the structural estimates of the model for the groups and individuals separately. Column (3) represents the
structural estimates based on the eect of decision ordering.
Draft|Not Ready For Circulation 19
clustered at the group level. In column (1), we nd the estimated cost parameters' ratio

= 1:07(0:23). The null hypothesis of stationary cost of eort function could not be rejected,

2
(1) = 0:10< 3:84. The estimated weekly discount factor is averaged around 0.98 and under
the null hypothesis that H
0
: = 1
2
(1) = 0:50 < 3:84 implies that weekly discount factor
of 1 can not be rejected at 5% level of signicance. Regarding our main parameter of interest
, we estimate an aggregate = 0:78(0:05), and reject the null hypothesis of dynamic/time
consistency, H
0
: = 1
2
(1) = 16:01> 3:84. In column (1), our estimate of time consistency
is closer to the results of table 2, column (1) of non-parametric results.
In column (2) of the table the time preference parameters are estimated separately for
individuals and groups, using the full sample of decisions. One can observe that the estimate
of the individuals' present bias parameter is less than the groups' estimate. Both estimates are
statistically signicant at 5% level of signicance. Under the null hypothesis that H
0
:
I
= 1,

2
(1) has a value of 9.24>3.84 and H
0
:
G
= 1,
2
(1) has a value of 23.75>3.84.
2
(1) values
for both hypotheses imply that individuals and groups allocation decisions are present biased.
Similarly, the estimate of individuals' weekly discount parameter is less than groups' parameter
estimate. Both estimates are statistically signicant at 5% level of signicance. Under the
null hypothesis of dynamic/time consistency, H
0
:
I
= 1,
2
(1) has a value of 1.40<3.84 and
H
0
:
G
= 1,
2
(1) has a value of 0.05<3.84 implying that individual and group allocation
decisions can have a weekly discount factor of 1. This nding is in line with the studies'
estimates of discount factor in the task domain.
Testing the individual estimate of present biased estimate against the group estimate, the
null hypothesis test of H
0
:
I
=
G

2
(1) = 4:53 is rejected. Using Stata based lincom
command, which computes point estimates, standard errors, t-statistics, and p-values for a
specied linear combination of coecient estimates,
I

G
has a coecient of 0.11 with the
p-value of 0.03. On the other hand H
0
:
I
=
G

2
(1) = 5:12 is rejected and
I

G
has
a coecient of -0.05 with the p-value of 0.02. These results are similar to our non-parametric
analysis where the degree of dynamic/time inconsistency is, on average, more in group decisions
compared to individual decisions. In other words, this result implies that group task allocation
decisions are more present biased compared to individual settings.
Regarding discount factor estimates and their dierences, the results are consistent with
Milch et al. (2009) nding that participants discount more as individual decision makers than
in a group decision context. This nding that, the patient decisions taken by groups also show
that the discount factors for groups are more and in line with market interest rates than the
discount factor for individuals.
Comparing the cost parameter estimates' between the individual and group settings we
observe that while moving from individual to the group setting, the estimate of
increases but
Draft|Not Ready For Circulation 20

I

G
has a coecient of -0.23 with the p-value of 0.13 which indicates that the dierence
is not statistically signicant. The individual decisions' estimated cost parameters' ratio
I
=
1:00(0:21). The null hypothesis of stationary cost of eort function could not be rejected,
since
2
(1) test has a p-value of 0.99. Finally for the groups' estimated cost parameters' ratio

G
= 1:23(0:29). Under the null hypothesis of groups' stationary cost of eort function also
could not be rejected, since
2
(1) test has a p-value of 0.43. These results indicate that the
underlying cost of eort functions for both individuals and groups decisions are stationary.
In column (3) of the table, the time preference parameters are estimated for ordering eect
using the full sample of decisions. One can observe that the estimate of present bias in decision
setting, where individual task allocation decisions preceded the group task allocation decisions,
present bias estimate in the former is lower than in the later, but the dierence is statistically
insignicant. The discount factor in the former is higher than the later but again the dierence
is insignicant statistically. These results also conrm the results in table 2 of non-structural
estimates.
Two questions naturally arise in the current context, as discussed in the introduction part,
although the hyperbolic discount factor is microfounded in the misalignment of preferences
between two individual members. First, what are the relevant structural channels through
which one can pin down and connect the more pronounced (compared to individual) group
procrastination tendencies to its group members? Second, are there other non-theory based
important variables through which one can explain the group procrastination tendency beyond
the structural channels? The answers to these questions are important for understanding
the empirical validity of the aforementioned theories and other channels which can broaden
our knowledge of group based decisions and their dynamics. In the next section we start
discussing the theoretical background connecting the groups decision process to their individual
constituents.
F.4 Theoretical Background: Individual vs. Group
As mentioned in the introduction part of the paper that heterogeneity across members in a group
context is natural assumption for many applications. It is relevant for households, and prac-
tically any group/committee of agents making intertemporal decisions, including legislature,
management teams, and in the context of this experiment the task/eort allocations. Given
the sets of results obtained in the non-parametric and structural part (specically groups are
more present biased compared to their corresponding individual members) of the main results
section, here in this part of the paper we would like to introduce some theoretical background
for the group decisions. We will try to connect the individual decisions with the group alloca-
tion decisions observed in this experiment and using these theory based connections we test a
Draft|Not Ready For Circulation 21
set of hypothesis mentioned in the introduction part of the paper.
F.4.1 Collective Decision Functions
A collective cost of eort function can be thought of as providing a \planner's" cost of eort
function for a group. Examples include taking a weighted average of the agents' eort functions
(F [C](v) =
P
i
!
i
C
i
(v)) whereC is cost function andv is eort provision. This is an example of
utilitarian approach; other examples would include minimum of agents' cost of eort provision
(F [C](v) =min
i
C
i
(v)) which would be a Rawlsian approach. In the context of this experiment
we know C = (
1
;
1
;
1
;::;
n
;
n
;
n
). Here we consider an important class of collective cost of
eort functions: those thattimeseparable. Jackson and Yariv (2015) show that this class of
functions exhibit a particular sort of time inconsistency or intransitivity
7
: present bias, which
matches the empirical evidence presented in the paper.
As postulated by Jackson and Yariv (2015) given all the participants in the experiment are
time consistent for any prole (
1
;
1
;::;
n
;
n
)2C
n
, a time-separable collective/group cost of
eort function takes the form
F [
1
;
1
; ;:::
n
;
n
](v) =
X
t
~

t
C(v
t
):
such that
~

t
=
P
i
!
i

t
i
. Time-separable group/collective functions are often used in the liter-
ature. For instance, standard utilitarian aggregation of individual utilities (or one that puts
dierent weights on dierent individuals) is a special case. According to Jackson and Yariv
(2015) for any prole (
1
;
1
;::;
n
;
n
)2 C
n
such that for some k, j,
k
6=
k
, a collective
function of the form
F [
1
;
1
; ;:::
n
;
n
](v) =
X
t
~

t
C(v
t
):
such that
~

t
=
P
i
!
i

t
i
for each t, and so
F [
1
;
1
; ;:::
n
;
n
](v) =
X
i
!
i
C
i
(v):
F is either dictatorial or present biased
8
. It is pertinent to mention that before taking the
group decisions every participant was asked to make unanimous decisions in group setting
since the share of bonus was xed for each group member, therefore the collective discount
7
The intransitivity here is quite dierent from Condorcet's (1785) description of the voting paradox and
Arrow's impossibility Theorem (1963) because our collective decision structure are quite dierent from the
voting setting mentioned in these papers.
8
for the detail of proof please see Jackson and Yariv (2012) and Jackson and Yariv (2015) papers
Draft|Not Ready For Circulation 22
factor must be a weighted sum of the participants, and so must correspond to a weighted
utilitarian collective/group cost of eort function.
The proposition encompasses many of the formulations of time inconsistent preferences. In
the structural analysis part of the paper we assumed quasi-hyperbolic formulation which in
this case corresponds to
~

1
= 1 and
~

t
=
t1
for all t> 1. As long as behavior has separable
structure and satises unanimity, the proposition shows that a present bias is to be expected.
Using this proposition, a set of testable hypotheses can be generated and tested subse-
quently. In this section using the groups' time preference estimates (specically focusing on
groups' procrastination tendency related to present bias estimate) and their individual mem-
bers' structural estimates (including discount rate, present bias, and eort cost parameters'
estimates), the roles and eects of theory based heterogeneities are explored empirically on
group procrastination tendency. These within group heterogeneities include the dierences in
individual members' (i) discount factor, (ii) parameter governing the cost of eort, and (iii)
bargaining position of each person in the group.
Regarding bargaining powers we know that in a utilitarian formulation, group decisions
depend on the preferences of the individual members, and the relative strengths of the individual
members' weights in the group decisions captured by Pareto/bargaining weights. Empirically
the introduction of a bargaining mechanism into the group decision making process by Manser
and Brown (1980) and McElroy and Horney (1981), there has been a development of so-called
collective models, which assume that groups can achieve ecient decisions (Chiappori, 1992;
Browning and Chiappori, 1998). According to Browning and Chiappori (1998), in the context
of this experiment the group's intertemporal eort cost function can be expressed as:
C
G
=!
i
C
i
+!
j
C
j
:
where
!
i
+!
j
= 1: and (!
i
;!
j
)> 0
These restrictions satisfy the unanimity condition in the group/collective decision making pro-
cess.
C
l
=
l
v
2
1l
+
l
1
d=1

l
k
v
2
2l
and l =

G;i;j

where C
G
is the group group's intertemporal eort cost function, C
i
and C
j
represent the
individual membersi andj intertemporal eort cost function respectively, and!
i
and!
j
denote
membersi andj bargaining power respectively, which measures how individual preferences are
aggregated into groups' joint decisions. In our experimental setting since we observe both
individual and joint decisions, it means that we can measure/estimate to what extent each
Draft|Not Ready For Circulation 23
member in
uences the joint/group decisions. Using a multinomial logit model, also known
as polytomous logistic regression !
i
and !
j
are estimated by running the following regression
equation, for each group and summary statistics are presented in Table 6.
v
1Gp
=!
i
v
1ip
+!
j
v
1jp
+
p
:
s:t: !
i
+!
j
= 1: and p =

1; 2; 3;:::; 122

After estimating (!
i
;!
j
) for each group we contructedj(^ !
IND
)j which is the absolute
dierence between the groups' individual members' bargaining/Pareto weights. Using these
absolute dierence between the individual members we further constructed (j^ !j 1)
G
(= 1)
which is the dummy indicator for the group in which one of the member has a approximately
all the bargaining power. The summary statistics of these dummy indicators are presented
in Table 6. The roles and eects of these variables on group procrastination tendency are
described in Table 7.
F.4.2 Individual vs. Group Analysis
Table 4 presents within group estimates of discounting, present bias, and eort cost parameters
at the individual and group level. For each group and its corresponding individuals, we estimate
the parameters of equation (2). These parameters are estimated by nonlinear least squares as
in Table 3. Time preferences and eort cost parameters are estimable for 122 groups and their
244 individual participants. The results are broadly consistent with those estimated at the
aggregate level. The median estimated weekly discount rate is 0.92, close to the aggregate
values obtained in Table 3. In line with the aggregate results, the median individual present
bias estimate is less than the median estimated group present bias parameter. The median
estimated cost of eort parameter is 0.69, suggesting that individual decisions' parameter is
very similar to group parameter. In addition to median values, Table 4 reports the fth to
ninety-fth percentile range for group estimates, and its corresponding individuals members'
estimates of the annual discount rate
^
,
^
, and ^
along with the minimum and maximum values.
Draft|Not Ready For Circulation 24
Table 4: Discounting, Present Bias, and Eort Cost Parameter Estimates
N Median 5th Percentile 95th Percentile Minimum Maximum
Group
^
122 0.92 0.75 0.99 0.55 0.99
^
122 0.90 0.18 2.32 0.02 10.8
^
122 0.69 0.19 1.33 0.14 2.80
Individual
^
244 0.92 0.76 0.97 0.66 1.00
^
244 0.97 0.13 2.79 0.02 15.1
^
244 0.69 0.18 1.51 0.06 2.80
Notes: NLS estimators as in Table 3.
For the majority of individuals and groups, the employed estimation strategy generates
reasonable parameter estimates. However, extreme observations do exist. Figure 3 presents
histograms of time preference,
^
and discounting estimates
^
. The histograms demonstrate that
a large proportion of subjects have low discount rates and maximum present bias. Estimation
results for all subjects are presented in Appendix Tables A2.
0 5 10 15 20
0 1 2 3 0 1 2 3
Group Individual
Percent
Estimated present bias
0 10 20 30
.6 .8 1 .6 .8 1
Group Individual
Percent
Estimated weekly discount factor
Histogram of estimated time preference parameters
Figure 3: Estimates Histogram
For further systematic analysis presented in the subsequent part, and since each group
consisted of two individuals paired randomly, every group that can be considered estimates in
terms of minimum, and maximum. Table 5 presents across groups' estimates of minimum and
maximum discounting, present bias, and eort cost parameters, focusing only on individual
decisions. Comparing the results in Table 5 with group parameter estimates presented in Table
4, it is clear that the group median parameter estimates in Table 4 is always greater than the
Draft|Not Ready For Circulation 25
minimum and less than the maximum of the median of these estimates presented in Table 5.
The same observation is also true for the 5th Percentile and 95th Percentile estimates of groups
compared with their minimum and maximum counterparts. In other words the median, 5th,
and 95th Percentile group estimates are in between the minimum and maximum of individuals
estimates. This nding is inline with Gollier and Zechhausers (2005) model of aggregation of
time preferences, in which the rate of impatience of the representative agent is a weighted mean
of individual rates of impatience, although at the extreme points this may not hold.
9
Table 5: Summary of With-in Group Min and Max Parameter Estimates
N Median 5th Percentile 95th Percentile Minimum Maximum
Minimum
^
122 0.88 0.72 0.95 0.66 0.95
^
122 0.81 0.09 1.23 0.03 11.5
^
122 0.49 0.15 1.12 0.57 2.80
Maximum
^
122 0.95 0.79 0.99 0.75 1.00
^
122 1.07 0.39 3.93 0.03 15.1
^
122 0.79 0.28 1.74 0.14 2.80
Notes: Based on NLS estimators as in Table 3.
Figure 4 shows that the distribution of time preference parameters i.e. present bias and
weekly discount factor for the minimum and maximum in the group, along with its correspond-
ing collective estimates. The gure highlights two important results visually. First groups'
time preference estimates are bounded by their individual members' estimates and secondly,
the group present bias estimates tend to be closer to within group minimum estimate. In other
words the person who has more procrastination tendency in the group dominates the overall
group procrastination tendency.
F.4.3 Relationship Between Individual and Group Parameter Estimates
In order to investigate the reasons behind/as to why groups' task allocation decisions are
more present biased compared to their individual counterparts, we carried out a theory-based
systematic inquiry. In this last part of our empirical analysis, we put dierent theories to the
test. As mentioned earlier theoretically, in a group context, inconsistencies can arise simply
from the aggregation of heterogeneous preferences. Variations in individual discount rates and
cost functions' parameters represent relevant considerations in this regard. In this part, using
9
The authors also showed that heterogeneous individual exponential discounting yields collective hyperbolic
discounting
Draft|Not Ready For Circulation 26
0 .2 .4 .6 .8 1
(Prob <= Value)
0 5 10 15
Estimated present bias
0 .2 .4 .6 .8 1
.5 .6 .7 .8 .9 1
Estimated weekly discount factor
Cumulative Distribution Function
Group Within group max
Within group min
Figure 4: Estimated CDF
the NLS based individual and group estimates of time preference parameters' we test Jackson
and Yariv (2015)'s main idea.
In this section we explicitly test how variations in individual discount rates, eort cost pa-
rameters, and dierences in (within) groups' bargaining weights are related and could generate
aggregate behavior which is more present biased. We also test other important individual fac-
tors shape the group behavior along with their interconnection. As discussed earlier, this paper
also explores the role of coordination externality and its eect on the group procrastination
tendencies. The mean of the group-mate acquaintance index mentioned in Table 1 indicates
that in most of the groups, the individual members knew each other at the start of the study.
The duration of the acquaintance ranges from 0 (indicating that the members have just met) up
to 5 years of acquaintance. In this section, we explicitly control the time duration of acquain-
tance to explain group procrastination tendencies, in order to see the eect of coordination
externality.
Table 6 summarizes the NLS based estimates in which we are interested and tries to establish
and test the theory-based relationship to group procrastination tendency. j(
^

IND
)j is the
absolute dierence between the groups' individual members' weekly discount rate and it has a
mean value of 6% with the standard deviation of 0.07. The variablej(^
IND
)j is the absolute
dierence between the groups' individual members' cost of eort parameters and it has a mean
of 0.36 with standard deviation equal to 0.41. j(^ !
IND
)j is the absolute dierence between
the groups' individual members' bargaining/Pareto weights. This variable has mean of 0.85
indicating that within groups the members do have dierent bargaining power and for most of
the groups the chances of having a non-dictatorial setup is quite high.
Draft|Not Ready For Circulation 27
Table 6: Summary Statistics
Variables Mean Standard Deviation Minimum Maximum
j(
^

IND
)j 0.06 0.07 0 0.26
j(^
IND
)j 0.36 0.41 0 2.15
j(^ !
IND
)j 0.85 0.29 0 1
j(^ ! 1)j
G
(= 1) 0.72 0.44 0 1
(
^
1)
both
(= 1) 0.05 0.21 0 1
Acquaintance Time Duration (months) 13.84 21.56 0 60
Notes: No of observations is 244. j(
^

IND
)j is the absolute dierence between the groups' individual members'
weekly discount rate. j(^
IND
)j is the absolute dierence between the groups' individual members' cost of eort
parameters. j(^ !
IND
)j is the absolute dierence between the groups' individual members' bargaining/Pareto
weights. (j^ !j 1)
G
(= 1) is the dummy indicator for the group in which one of the member has a approximately
all the bargaining power. (
^
1)
both
(= 1) is the dummy indicator which takes the value of 1 for those groups in
which both members are time consistent approximately.
Using ^ ! we can construct a dummy indicator (j^ !j 1)
G
(= 1) for the group in which
one of the member has approximately all the bargaining power (withj(^ !
IND
)j> 0:98). This
variable has mean of 0.72 signifying the fact that in majority of the groups the chances of
having a dictatorial member (borrowing the terminology from Jackson and Yariv; 2015) is high
i.e. they ignore the preferences of all but one agent.
Lastly it is pertinent to mention that, in the context of our design, the possibility that
in groups both participants are individually time consistent does not poses any additional
challenge for our main experimental ndings since we observe both group and individual deci-
sions. Focusing on how aggregation relates to time inconsistency, we explicitly controlled for
the underlying individual preferences to isolate the eects of aggregation. In the case where
both participants are individually time consistent we constructed (
^
1)
both
(= 1) which is
the dummy indicator taking the value of 1 for those groups in which both members are time
consistent approximately. It has a mean of 0.05 indicating that in the overall sample 5% of
groups have both members who are nearly time consistent i.e. both members have
^
between
0.95 and 1.05.
Table 7 presents the results of theory based inter-connections. The estimates in column
(1), and (4) of Table 7 signies the fact that weekly discount factor heterogeneities of the
individual do explain group procrastination tendencies. These columns also show that the
presence of a dictator or dominant individual based on his/her bargaining power improves the
procrastination tendency in group. These results are inline or a direct test of Jackson and
Yariv (2015) in which they showed that for a uniform distribution of discount rates in an
otherwise homogeneous population, group utility maximization generates aggregate behavior
that corresponds to hyperbolic discounting and if there is some fundamental heterogeneity in
temporal preferences by way of diering discount factors, then the only well-behaved collective
Draft|Not Ready For Circulation 28
utility functions that are both time consistent and respect unanimity are dictatorial. For these
columns under H
0
:Constant = 1, F (1; 121) have pvalues of 0.87 and 0.65 respectively, we
see that, given there are no variations in individual discount rates, and in eort cost parameters
the group's allocation decisions would represent time consistent pattern given the individuals
are exponential discounters.
Column (2), and (5) of the results signies the eect of coordination externality on group
present bias estimate. As the Acquaintance Duration between the individual members in-
creases, it is natural to think that the individual's coordination problems lessens. This in
turn decreases the group present bias tendency. In both columns, its estimate is marginally
signicant at 10%. Under the H
0
: Constant = 1, F (1; 121) has a pvalues shows that,
given that there are no variations in individual discount rates and in eort cost parameters,
the group's allocation decisions would represent time consistent patterns even if the individuals
are exponential discounters with no Acquaintance Duration. Lastly, in column (3), and (6)
the association ofBig5 personality traits with group procrastination tendency are investigated.
These columns show thatBig5 personality traits do not have the power to explain the groups'
tendency for procrastination. The eect of presence of within group dictator is not signicant
in these columns and this may be attributable to
uctuation of overall sample size.
Draft|Not Ready For Circulation 29
Table 7: Individual (IND) vs. Group Regression Analysis
Dependent variable:
^

G
(1) (2) (3) (4) (5) (6)
j(
^

IND
)j -3.84** -3.07** -3.16** -3.86** -3.00** -3.09**
(1.25) (1.19) (1.28) (1.26) (1.23) (1.32)
j(^
IND
)j 0.14 0.21 0.18 0.13 0.24 0.19
(0.26) (0.24) (0.25) (0.28) (0.24) (0.25)
j(^ !
IND
)j -0.25 -0.17 0.07 -0.21 -0.21 0.06
(0.23) (0.27) (0.31) (0.27) (0.26) (0.28)
(j^ !j 1)
G
(= 1) 0.58** 0.43** 0.30 0.57** 0.43** 0.30
(0.20) (0.19) (0.20) (0.21) (0.18) (0.18)
(
^
1)
both
(= 1) -0.27 -0.36 -0.38 -0.30 -0.30 -0.35
(0.23) (0.30) (0.32) (0.30) (0.30) (0.32)
Acquaintance Duration 0.01* 0.01* 0.01* 0.01*
(0.01) (0.01) (0.01) (0.01)
j(Big 5)j -0.08 -0.05
(0.14) (0.15)
j(Age (in years))j -0.04 0.02 0.06
(0.06) (0.05) (0.07)
j(Gender)j -0.09 0.17 0.11
(0.22) (0.18) (0.20)
Constant 1.02*** 0.76*** 0.72** 1.11*** 0.65** 0.56
(0.14) (0.19) (0.25) (0.24) (0.24) (0.34)
# of Groups 122 122 109 122 122 109
Adj R
2
0.06 0.30 0.30 0.07 0.31 0.31
RMSE 1.13 0.98 1.02 1.13 0.97 1.02
H
0
:Constant = 1
p-value 0.87 0.19 0.27 0.65 0.15 0.19
Notes: *p < 0:1, **p < 0:05, ***p < 0:01. Standard errors are clustered at group level. Column
(1) presents the estimates of variations in group members' weekly discount factors, cost of eort
parameters, bargaining power estimates, presence of dictator, and both time consistent members
dummy . Column (2) presents the estimates of eect of acquaintance duration on group procras-
tination tendency controlling for the variables in column (1). Column (3) captures the estimates
of eect of within group dierences in Big5 personality traits controlling for the variables in col-
umn (2). Column (4), (5), and (6) represent the estimates of theory and non-theory based factors
controlling for within group dierences in age and gender.
To summarize, the main message of Table 7 is that groups whose members have misaligned
discount rates will be present biased, and the presence of a dictator within group improves
Draft|Not Ready For Circulation 30
the group present biased estimate. To emphasize that divergent preferences within the group
are sucient to render time inconsistency, even controlling for time consistent individuals and
presence of a dictator within the group, the variation in discount factors has signicantly
aected group procrastination tendency. Similarly, the eect of coordination externality, which
is captured by Acquaintance Duration variable, is also important in understanding the group
procrastination tendency.
G Conclusion
This paper proposed an analysis of individual and collective decisions through the preference
elicitation method over unpleasant task consumption. The study uses experimental data to
analyze task consumption decisions by groups of individuals who have to reach a consensus
regarding allocation of tasks over time. For this purpose a joint experimental elicitation of time
preferences was performed for the groups as well as for their individual members.
The main results of the paper are the following. First ,on aggregate, a present bias exists
in participants behavior i.e. the participants' intertemporal allocation decisions exhibited time
inconsistency. Participants on day 2 of the experiment allocated signicantly fewer tasks to v
1
than on day 1 suggesting that the decisions are present biased. Second, the degree of present
bias was more pronounced in a group's task allocation decisions as compared to an individuals
task allocation setting. This justies the rationale for not forming the group for more or less
perfectly substitutable tasks. Third, the order in which decisions were made, whether making
the individual task allocation rst and then the group task allocation or vice versa had no
eect on the degree of present bias. Lastly, using within-groups estimates of present bias and
discount factor, the variations in group's individual members discount rates do explain group
procrastination tendencies as postulated by Jackson and Yariv (2015).
This paper acknowledges that the results could be partly explained, by a selection bias. In
our experiment, as in any experiment involving longitudinal measures, subjects were supposed
to commit to three sessions over a time span of three weeks. Here, a specicity of our subjects
is probably their ability to commit and schedule (Frederick, 2005; Perez-Arce, 2011; Dohmen
et al., 2010). The estimates of present bias and discount rates for individual choices we found
are no higher than that found in the literature, although the empirical literature on task
consumption is very limited. Moreover, we were mainly interested in comparisons. It is plausible
that the selection bias impacted all decisions to a similar extent, thus we have no big eect on
our comparisons. Finally, our coordinating device allowed groups to quickly converge towards a
given decision. In this respect, our results have implications of the way boards and committees
can achieve consistent decisions.
Draft|Not Ready For Circulation 31
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Draft|Not Ready For Circulation 36
H Appendix
H.1 A1: Nonlinear Least Squares Method
Let there be N experimental subjects and P Convex Time Budget (CTB) budgets. Assume
that each subject j makes her v
1tij
;i = 1; 2;:::;P , decisions (individual and group both)
according to the non-linear Euler equation mentioned above but that these decisions are made
with some mean-zero, potentially correlated error. That is let
f(V;R; 1
d=1
;k;b
1
;;;b
2
) =

b
2

1
d=1

k
V
b
1
R
2
+b
2

1
d=1

k
!
:
then
v
1t

ij
=f(V;R; 1
d=1
;k;b
1
;;;b
2
) +e
tij
:
Stacking the P observations for individual j making her individual and group decisions, we
have
v
1t

j
=f(V;R; 1
d=1
;k;b
1
;;;b
2
) +e
j
:
The vector e
j
is zero in expectation with variance covariance matrix V
j
, a (PP ) matrix,
allowing for arbitrary correlation in the errorse
ij
. We stack over theN experimental subjects
to obtain
v

1t
=f(V;R; 1
d=1
;k;b
1
;;;b
2
) +e:
We assume that the terms e
ij
may be correlated within groups (or individuals within the
same group) but that the errors are uncorrelated across groups (or individuals within the
same group), E(e
0
j
e
g
) = 0 for j6=g. And so e is zero in expectation with covariance matrix

, a block diagonal (NPNP ) matrix of clusters, with groups covariance matrices, V
j
. We
dene the usual criterion function S(V;R; 1
d=1
;k;b
1
;;;b
2
) as the sum of squared residuals,
S(V;R; 1
d=1
;k;b
1
;;;b
2
) =
N
X
j=1
P
X
i=1
(v
1t

ij
f(V;R; 1
d=1
;k;b
1
;;;b
2
))
2
;
and minimize S(:) using non-linear least squares with standard errors clustered on the group
level to obtain
^
,
^
, and (
^ b
1
b
2
). NLS procedures permitting the estimation of preference pa-
rameters at the aggregate or individual level are implemented in many standard econometrics
packages (in our case, Stata).
It is important to recognize the strengths and weaknesses of such an NLS preference estima-
tor. Parameters b
1
and b
2
can be estimated as opposed to assumed, which is an advantage. A
potential disadvantage is that the NLS estimator is not well-suited to the censored data issues
Draft|Not Ready For Circulation 37
inherent to potential corner solutions. Although the NLS estimator can be adapted to account
for possible corner solutions by adapting the criterion function and making additional distri-
butional assumptions but in this paper we did not take this approach. Andreoni and Sprenger
(2012) suggest a complementary approach: taking logs of the Euler Equation allows one to
estimate the model parameters in a two-limit Tobit framework that corrects for censoring. As
expected given the total number of corner points in the data the Table 7 results based on the
method by Andreoni and Sprenger (2012) are very close to the results in Table 3.
In the Euler Equation Approach the parameters of interest can be recovered from non-linear
combinations of regression coecients with standard errors calculated via the delta method. As
discussed in Andreoni and Sprenger (2012) one important issue to consider in estimation is the
potential presence of corner solutions. We provide estimates from two-limit tobit regressions
designed to account for the possibility that the tangency condition implied by task's Euler
Equation does not hold with equality (Wooldridge, 2002).
As a robustness check for our main Table 3 estimates Table 7 is presented. In the table
comparing the estimates of column (1) with (4), column (2) with (5), and column (3) with
(6) one can observe that they are very close. The values of
2
(1) = under the specied null
hypothesis are also not that dierent signifying the estimates obtained and presented in Table
3 of the main part of the paper are robust to the problems of censoring.
Draft|Not Ready For Circulation 38
Table 8: Regression Analysis
Dependent variable: Tasks Allocated to the First Day (lnv
1

it
)
Euler Equation Approach NLS Approach
(1) (2) (3) (4) (5) (6)
0.776*** 0.813*** 0.706*** 0.782*** 0.819*** 0.712***
(0.049) (0.053) (0.057) (0.054) (0.060) (0.059)
0.997*** 0.977*** 1.038*** 0.975*** 0.959*** 1.009***
(0.031) (0.031) (0.037) (0.035) (0.035) (0.040)
(
b
1
b
2
) 1.085*** 0.994*** 1.295*** 1.072*** 1.001*** 1.231***
(0.208) (0.191) (0.277) (0.233) (0.217) (0.294)
# of Obs 2196 1464 732 2196 1464 732
# of Clusters 122 122 122 122 122 122
Pse-LL -2817 -1893 -922 -1781 -1199 -578
BIC 5672 3822 1876 3585 2421 1176
H
0
: = 1

2
(1) = 16.01 9.25 23.74 21.08 12.63 26.67
p-value= 0.000 0.002 0.000 0.000 0.000 0.000
H
0
: = 1

2
(1) = 0.50 1.40 0.05 0.01 0.55 1.08
p-value= 0.481 0.237 0.816 0.917 0.456 0.299
H
0
: (
b
1
b
2
) = 1

2
(1) = 0.10 0.00 0.62 0.17 0.00 1.14
p-value= 0.757 0.997 0.431 0.682 0.973 0.286
Notes: *p< 0:1, **p< 0:05, ***p< 0:01. Standard errors are clustered at group level.
2
(1)
0:05
= 3:84.
The table presents the comparison of structural estimates of intertemporal hyperbolic discounting model
using Non-linear Least Squares Regression technique and Euler Equation approach. Table also shows
the p-values of dierent tests for the two proposed methods. Column (1) and (4) compare and present
the combine decisions estimates of , , and (
b1
b2
). Column (2) and (5) captures the structural estimates
of the model for the sub sample of individual decisions only. Column (3) and (6) represents the groups'
structural estimates based on the two proposed methods.
Draft|Not Ready For Circulation 39
H.2 A2: Additional Individual and Group Preference Parameters
Estimates
In this section of appendix we provide four tables of additional group and its individual
members' estimates of present bias, discount rate, and cost of eort parameters. The estimates
are based on NLS.
Table 9: Group Vs. Individual Estimates
G:No (
d
v
I
1
v
A
2
v
A
1
v
I
2
)
G
^

G
(
d
v
I
1
v
A
2
v
A
1
v
I
2
)
i1
^

i1
(
d
v
I
1
v
A
2
v
A
1
v
I
2
)
i2
^

i2
1 1.08 1.10 0.55 0.97 1.13 1.17
3 0.02 0.28 0.02 0.49 0.73 3.95
4 0.40 0.40 0.35 0.33 0.53 0.52
5 1.01 1.00 1.74 1.90 0.71 0.72
7 1.02 1.04 1.03 1.05 1.11 1.35
8 1.01 1.03 0.68 0.67 0.86 0.83
9 5.68 0.81 0.97 0.52 3.88 0.37
10 0.26 0.29 1.00 1.20 0.11 0.11
11 0.35 0.36 0.09 0.09 0.11 0.52
12 0.66 0.75 0.26 0.34 0.35 0.30
13 1.54 1.57 0.41 0.43 0.98 0.99
14 0.29 0.29 0.60 0.59 0.32 0.34
15 0.43 0.95 0.41 0.92 0.21 0.21
16 0.33 0.37 0.10 0.10 0.07 0.07
17 0.89 0.86 1.06 1.07 1.00 1.00
18 0.98 0.97 0.66 0.84 0.97 0.97
19 0.90 0.90 1.00 0.90 2.46 2.49
20 0.91 0.91 0.64 0.66 0.68 0.70
23 0.87 0.90 0.70 0.96 1.07 1.06
24 1.01 1.00 0.82 0.80 0.98 0.97
25 0.78 0.77 1.99 2.08 0.83 0.82
26 1.06 1.10 1.37 1.35 1.06 1.10
27 0.42 5.32 0.23 2.79 0.23 2.79
28 0.85 0.85 0.93 0.95 1.11 1.15
29 1.00 1.00 0.20 0.19 1.00 1.00
30 1.00 1.17 1.26 0.42 0.14 0.15
31 0.90 0.86 0.48 0.47 1.29 1.33
32 1.07 1.10 0.98 1.08 1.00 1.05
Notes: Tabulates the mean ratio of average subsequent/immediate (I) (
v1
v2
) to advance (A) (
v1
v2
) for eort
and corresponding time preference estimates for groups and its individual members.
Draft|Not Ready For Circulation 40
Table 10: Group Vs. Individual Estimates
G:No (
d
v
I
1
v
A
2
v
A
1
v
I
2
)
G
^

G
(
d
v
I
1
v
A
2
v
A
1
v
I
2
)
i1
^

i1
(
d
v
I
1
v
A
2
v
A
1
v
I
2
)
i2
^

i2
33 0.84 0.83 0.78 0.79 0.78 0.79
34 0.86 0.91 0.86 0.78 0.87 0.71
35 0.99 1.05 1.29 1.88 1.00 1.07
36 0.79 0.78 0.87 0.87 1.25 1.28
37 0.87 0.82 1.15 1.15 1.14 1.14
39 1.00 1.27 0.88 0.93 0.88 0.93
40 1.18 1.21 1.00 1.00 1.31 1.32
41 1.00 1.00 1.00 1.00 0.87 0.98
43 1.06 1.08 1.01 1.01 6.11 6.30
44 0.83 0.86 1.89 2.22 0.57 0.55
45 0.54 0.54 1.01 1.01 1.01 1.01
46 0.94 0.96 1.01 1.00 0.94 0.96
47 0.83 0.80 0.66 0.64 0.85 0.85
49 0.94 0.92 1.15 1.13 0.35 0.34
51 0.75 0.77 1.00 1.00 1.02 0.83
52 1.01 1.03 0.81 0.83 0.08 0.08
53 4.07 4.18 2.48 2.80 1.56 1.82
54 0.91 0.92 0.98 0.99 0.69 0.70
55 0.53 0.53 0.16 0.26 0.66 0.66
57 1.13 1.14 0.92 0.91 1.13 1.14
58 1.55 1.60 1.26 1.29 0.82 0.80
59 0.58 0.67 0.85 0.85 1.01 1.01
60 0.73 0.65 3.67 3.80 0.62 0.60
61 0.98 0.99 1.00 1.00 0.76 0.83
62 1.01 1.00 1.01 1.02 1.00 0.99
63 0.44 0.43 1.09 1.12 1.06 1.08
64 0.91 0.93 1.17 1.19 1.63 1.66
65 0.75 0.76 0.62 0.61 1.00 1.00
66 0.94 0.90 0.75 0.86 2.53 2.78
67 0.99 0.97 4.72 4.96 0.16 0.16
68 0.34 0.34 1.20 1.25 0.35 0.33
69 1.10 1.10 0.72 0.70 1.12 1.14
70 0.57 0.57 0.18 0.18 1.00 0.18
71 0.04 0.14 0.86 0.80 1.35 1.11
72 1.41 1.43 1.20 1.21 1.00 1.21
79 1.04 1.03 0.99 0.99 0.99 0.99
80 0.98 0.97 0.96 0.95 0.83 0.82
Notes: Tabulates the mean ratio of average subsequent/immediate (I) (
v1
v2
) to advance (A) (
v1
v2
) for eort
and corresponding time preference estimates for groups and its individual members.
Draft|Not Ready For Circulation 41
Table 11: Group Vs. Individual Estimates
G:No (
d
v
I
1
v
A
2
v
A
1
v
I
2
)
G
^

G
(
d
v
I
1
v
A
2
v
A
1
v
I
2
)
i1
^

i1
(
d
v
I
1
v
A
2
v
A
1
v
I
2
)
i2
^

i2
81 0.59 0.58 1.00 0.86 1.00 1.00
82 0.77 0.51 0.10 0.10 1.09 1.10
84 0.03 0.03 0.03 0.03 0.03 0.03
88 1.01 1.01 1.03 1.02 1.11 1.14
89 0.69 0.67 4.54 3.93 1.69 1.71
90 4.02 4.49 1.02 1.02 1.92 1.95
92 0.58 0.57 1.24 1.23 3.33 2.56
93 0.69 0.70 0.90 1.15 0.70 0.71
95 0.40 0.40 0.50 0.52 1.00 0.52
96 1.22 1.23 2.55 2.82 0.98 0.98
97 2.85 2.98 0.87 0.84 1.83 1.87
102 0.31 0.29 0.64 0.63 1.24 1.29
103 0.89 1.00 0.15 1.94 0.82 0.81
104 1.03 1.13 2.40 2.35 0.90 0.89
105 0.77 0.75 0.65 0.65 0.97 0.99
106 0.93 0.94 0.60 0.59 0.98 0.98
107 0.84 0.82 1.63 1.65 1.19 1.19
108 0.27 0.26 0.75 1.20 1.10 1.11
109 0.27 0.29 0.83 0.85 0.89 0.88
111 1.83 2.04 1.29 1.32 1.07 1.25
112 0.78 1.01 0.88 0.92 0.82 0.89
113 0.98 0.79 0.05 0.05 1.42 1.24
114 0.09 0.08 2.06 2.22 0.19 0.12
115 0.50 0.71 0.12 0.12 0.55 0.62
116 0.99 1.02 0.53 0.65 0.92 0.95
117 0.39 0.32 0.50 0.42 0.31 0.18
118 0.20 0.18 0.38 0.34 0.49 0.48
119 0.96 0.68 1.46 1.58 1.02 1.02
122 1.46 1.48 1.40 1.36 0.58 0.61
124 0.08 0.08 1.76 1.72 0.06 0.15
125 0.07 0.07 0.12 0.13 0.23 0.24
126 1.06 1.05 0.95 0.94 0.99 0.98
128 0.73 0.72 0.70 0.67 0.74 0.72
129 0.75 0.76 0.87 0.87 0.15 0.10
130 0.27 0.25 0.27 0.25 0.34 0.38
131 1.11 1.11 0.83 0.81 0.99 1.04
133 0.64 0.19 1.91 1.90 2.18 2.26
Notes: Tabulates the mean ratio of average subsequent/immediate (I) (
v1
v2
) to advance (A) (
v1
v2
) for eort
and corresponding time preference estimates for groups and its individual members.
Draft|Not Ready For Circulation 42
Table 12: Group Vs. Individual Estimates
G:No (
d
v
I
1
v
A
2
v
A
1
v
I
2
)
G
^

G
(
d
v
I
1
v
A
2
v
A
1
v
I
2
)
i1
^

i1
(
d
v
I
1
v
A
2
v
A
1
v
I
2
)
i2
^

i2
135 0.99 0.99 1.53 1.54 1.00 1.00
137 1.13 1.12 0.81 0.83 0.99 1.04
138 0.44 0.43 0.77 0.76 0.71 0.70
139 0.43 0.43 2.34 2.44 1.03 0.99
143 1.18 0.92 0.98 0.98 1.06 1.10
144 0.66 0.67 0.89 0.87 1.69 1.70
146 0.22 0.19 1.30 1.36 0.29 0.28
147 0.99 0.97 0.97 0.95 0.99 0.99
148 0.13 0.89 1.50 0.94 1.13 1.07
149 1.18 1.22 0.80 0.87 1.80 1.36
150 0.58 0.58 0.33 0.32 1.10 1.10
151 0.95 0.93 0.93 0.89 1.01 0.98
152 1.06 1.03 1.06 1.04 4.07 4.30
153 10.51 10.87 15.75 15.09 16.17 11.50
155 1.36 2.32 0.96 1.04 0.40 0.40
156 3.48 3.14 1.00 1.00 16.60 13.04
157 0.82 0.63 1.05 1.00 0.84 0.67
158 0.02 0.02 0.18 0.17 0.41 0.39
161 0.30 0.49 1.00 0.49 0.90 0.97
162 1.22 1.53 1.09 0.95 2.13 0.90
Notes: Tabulates the mean ratio of average subsequent/immediate (I) (
v1
v2
) to advance (A) (
v1
v2
) for eort
and corresponding time preference estimates for groups and its individual members.
Draft|Not Ready For Circulation 43
H.3 A3: Additional Individual vs. Group Analysis
Table 13: Additional Individual vs. Group Regression Analysis
Dependent variable:
^

G
(1) (2) (3) (4) (5) (6) (7) (8)
j(Age (in years))j -0.06 -0.06
(0.14) (0.14)
j(Gender)j -0.10 -0.20
(0.41) (0.45)
j(Outside Class Study Hrs)j -0.12 -0.12
(0.08) (0.07)
j(On Campus Job)j -0.61 -0.82
(0.37) (0.51)
j(Family Income in Log)j -0.17 -0.18
(0.21) (0.24)
j(Past Savings Acc:)j 0.22 0.19
(0.49) (0.92)
j(Curr:Savings Acc:)j -0.59 1.05
(0.37) (0.76)
Constant 1.08*** 1.04*** 1.212*** 1.06*** 1.11*** 0.97*** 1.14*** 1.42***
(0.19) (0.18) (0.23) (0.12) (0.18) (0.10) (0.17) (0.38)
# of Groups 122 120 120 120 110 122 122 110
R
2
0.00 0.00 0.01 0.01 0.00 0.00 0.01 0.06
Notes: *p < 0:1, **p < 0:05, ***p < 0:01. Standard errors are clustered at group level. The table presents the estimates of other
important demographic variables' dierences on groups' present biased estimated variable.
Table 12 shows the association of additional individual characteristics (mentioned in the demo-
graphic section) with the group estimated present bias parameter. The results signify the fact
that beyond discount factor heterogeneity there is no association between group procrastina-
tion tendency and dierences in groups' individual members' characteristics per say. Table 13
of this section presents the robustness test of the point estimates obtained in Table 7. Control-
ling for the other important demographic variables mentioned in the empirical literature, the
point estimates of variation in individual member discount factors andAcquaintanceDuration
between them, remain the same.F stats also indicate that given there are no variations in in-
dividual members' discount rates and in eort cost parameters the groups allocation decisions
would represent time consistent pattern even if the individuals are exponential discounters
with no Acquaintance Duration.
Draft|Not Ready For Circulation 44
Table 14: Individual (IND) vs. Group Regression Analysis
Dependent variable:
^

G
(1) (2) (3) (4) (5) (6)
j(
^

IND
)j -3.84** -3.47*** -3.76*** -3.86*** -3.26*** -3.53***
(1.28) (1.18) (1.12) (1.20) (1.16) (1.27)
j((
^ b1
b2
)
IND
)j 0.20 0.24 0.23 0.32 0.23 0.37
(0.22) (0.30) (0.22) (0.25) (0.22) (0.25)
(
^

min
1)
IND
(= 1) 0.16 0.11 0.16 0.11
(0.30) (0.45) (0.36) (0.54)
(
^

max
1)
IND
(= 1) -0.23 -0.34 -0.06 -0.04
(0.13) (0.23) (0.12) (0.16)
Acquaintance Duration 0.63** 0.63*** 0.64** 0.66**
(0.27) (0.23) (0.27) (0.27)
j(
^

IND
)jAcq Duration -6.29** -6.64** -6.37* -6.60**
(2.70) (2.89) (2.75) (2.94)
Constant 1.23*** 1.54*** 1.13*** 1.29*** 1.05*** 1.11
(0.21) (0.49) (0.14) (0.30) (0.14) (0.30)
Control for Demographic Variables No Yes No Yes No Yes
# of Groups 122 110 122 110 122 110
R
2
0.04 0.10 0.23 0.28 0.34 0.39
H
0
:Constant = 1
p-value= 0.26 0.35 0.71
p-value= 0.27 0.32 0.69
Notes: *p< 0:1, **p< 0:05, ***p< 0:01. Standard errors are clustered at group level. Column (1) again represents
the formal test of Jackson and Yariv (2015)'s main hypothesis. Column (5) represents the robustness of the results
obtained in column(1) controlling for dierences in demographic variables mentioned in Table 13. Column (3), and
(5) is the same as in Table 7, and column (4), and (6) represents the corresponding robustness check of the results
obtained.
Draft|Not Ready For Circulation 45
H.4 A4: Experiment Protocol
Instructions
Thank you for participating in our experiment. We will begin shortly.
Eligibility:
To be in this study, you need to meet the following criteria:
You must be willing to participate for three consecutive Fridays. Participation will require
your presence on specic days as outlined.
You will need at least one hour and at max three hours on Friday 13th March, Friday
20th March and Friday 27th March.
Informed Consent:
Placed in front of you is an informed consent form to protect your rights as a subject. Please
read it. If you choose not to participate in the study you are free to leave at this point,
deciding to leave later would seriously harm our resources allocated to this study. If you have
any questions, we can address those now. We will collect the forms after the main points of
the study are discussed.
Anonymity:
Your anonymity in this study is assured. All the information that we acquired, will only be
used for the purpose of communication with you. After the study, your email information will
be destroyed and will not be connected to your responses in the experiment.
Venue:
Venue for Friday 20th March will be the same.
For Friday 27th March, you dont have to be present physically. You can work from
anywhere remotely given that you have internet connection.
Rules:
Please turn your own cell phones o.
If you have a question at any point, just raise your hand.
There will be a short survey once we are nished with the instructions.
During the process of reviewing your answers in your survey if we nd your responses in
violation of any of the instructions, you might get removed from the experiment.
Draft|Not Ready For Circulation 46
You will get Rs.500 as participation fee. Participation means showing up on rst two
Fridays.
If you complete the assigned tasks on all required days of participation as instructed, a
completion payment of Rs.1500 will be provided.
You may receive additional earnings during the experiment if you participate in potential
survey games.
If you choose to end your participation before the completion of the experiment, please
report this to study administrators at the mentioned email address.
All payments will be made on 1st April in IGC oce room 161. You will return the
phones given to you for experiment purposes to IGC to receive this payment.
Task:
In this study there is only one task. This task will be completed over time. Some portion
of the tasks may be completed sooner, and some portion of the task can be completed later
depending on your choices and chance.
This task will consist of taking specic number of pictures of books through cell phones.
Remember your phone has a unique IMEI number. Once you take a picture, you need to
upload the picture using the application on the phone. Make sure your pictures are clear
and the numbers are legible. If the numbers are not legible, they will not be counted. Some
portion of the tasks may be completed on second Friday, and some portion of the task can
be completed on third Friday. You will practice the working of phone application before the
actual task starts.
Task Rates:
The allocation decisions across two weeks depend on the task rate. The task rate will vary
across your decisions. On the target-setting page of the application (installed in the cell phones
given to you), every slider bar corresponds to a specic task rate. For example in the rst
slider bar the task rate is 1:0.8, such that every task you allocate to second Friday reduces
the number of tasks allocated to third Friday by 0.8. For simplicity, the task rates will always
be represented as 1:X, and you will be fully informed of the value of X when making your
decisions.
The Experiment Timeline:
Before explaining the activities to be done on each Friday you need to have an overall picture
of the timeline.
First Friday (13th March 2015):
Draft|Not Ready For Circulation 47
• Physical
Presence

• Alloca&on
Decision

Day
1

Friday
13th

March

• Physical
Presence

• Alloca&on
Decision

Day
2

• Task
Comple:on
Day
1

Friday
20th

March

• Task
Comple:on
Day
2

Friday
27th

March

Figure 5: Timeline
Notes: Three weeks experimental timeline gure provided to all participants
First of all, all of the Subjects will be required to ll out dierent survey forms.
After the Survey forms have been completed and collected, you will be asked to make a
series of 3 decisions for task distribution as an individual.
Once you have made decisions for individual task distribution, again you will be required
to make 3 decisions and distribute the task as a group.
Keep in mind that your decision today is for the task you will be doing on 20th and 27th.
This applies to both Individual and Group decisions.
In each decision you are free to allocate your tasks as you choose.
Second Friday (20th March 2015):
During our second session here in the very same venue, again you will be asked to make
3 Decisions (both individually and as a group) as you did during rst session.
By this time we will have 12 decisions from every subject. (3 Individual + 3 Group on
13th and 3 Individual + 3 Group on 20th)
Exactly one of your 12 total decisions will be implemented randomly.
We will discuss how this allocation decision is chosen during our training session.
We refer to this allocation decision as the "decision-that-counts." The tasks you allocated
yourself for 20th and 27th in the decision-that-counts must be completed.
If you do not complete the tasks according to the decision-that-counts, you will not
receive completion amount of Rs. 1500 and will only receive participation fee of Rs. 500.
In order for your tasks on second or third Friday to be counted, they must be completed
between 9:30 pm and 10:30 pm of that Friday.
Surveys will be conducted which will give you a chance to earn more money.
Draft|Not Ready For Circulation 48
Third Friday (27th March 2015):
You will have to complete your tasks for this day according to your decision-that-counts
You can do this remotely from anywhere.
How we will choose the Decision-That-Counts:
The process of selecting the Decision-that-Counts is simple probability. Three stages to deter-
mine the decision-that-counts:
1. First you will be allocated either 13th March Decisions or 20th March Decisions according
to a 20% and 80% chance respectively.
2. Once you have been allocated to a specic date (13th or 20th) either you will be given
an Individual Task or a Group Task with 33% and 66% chance respectively.
3. After both the steps given above are complete, you will receive one of the 3 decisions you
made for that specic date and specic task type with equal chance.
EACH DECISION COULD BE THE DECISION-THAT-COUNTS SO TREAT EACH DE-
CISION AS IF IT WAS THE ONE DETERMINING YOUR TASKS.
Short Survey: Please answer the following questions:
1. How many weeks do we require you to participate?
2. In which of the three weeks are you asked to participate remotely and not come to this
venue?
3. What is the percent chance that one of your 20th March allocations will be implemented?
4. If you face a 1:2 task rate for allocations between Weeks 2 and 3, every task you allocate
to Week 2 decreases by how many number of tasks you allocate to Week 3?

Abstract (if available)

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Asset Metadata

Creator Hussain, Karrar (author)

Core Title Individual vs. group behavior: an empirical assessment of time preferences using experimental data

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Economics

Publication Date 09/26/2016

Defense Date 03/25/2016

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag OAI-PMH Harvest,preference elicitation,present-bias,time discounting

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Pesaran, M. Hashem (committee chair), Callen, Michael (committee member), Khwaja, Asim Ijaz (committee member), Nugent, Jeffrey B. (committee member), Sprenger, Charles D. (committee member)

Creator Email karrar.jaffar@gmail.com,karrarhu@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-305809

Unique identifier UC11279540

Identifier etd-HussainKar-4808.pdf (filename),usctheses-c40-305809 (legacy record id)

Legacy Identifier etd-HussainKar-4808.pdf

Dmrecord 305809

Document Type Dissertation

Format application/pdf (imt)

Rights Hussain, Karrar

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA