HIAS-E-33

How do People Procrastinate to Meet a Deadline?
Yutaka Kayaba
Hitotsubashi Institute for Advanced Study, Hitotsubashi University

September 2016

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
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How do People Procrastinate to Meet a Deadline?⇤
Yutaka Kayaba
Hitotsubashi University
September 7, 2016

Abstract
Relying on e-learning data, I report here on an empirical investigation of daily homework progress to assess procrastination among high school students, whose behavior issusceptible to present-bias. The homework entails a non-binding goal for the students.
The main findings were as follows: First, the goal encouraged a considerable number ofstudents to study more to achieve it. Second, high achievers procrastinated until close
to the deadline, particularly females for Math homework. Finally, a considerable subset
of high achievers worked hard at the last minute to meet a non-binding deadline. Thesefindings imply that a non-binding goal strongly motivates such students’ self-control
in goal achievement; however, the process is one of procrastination, and the deadline
prevents further procrastination despite being non-binding.
Keywords: Procrastination, non-binding goal, deadline
JEL codes: D03, D91, I21⇤

Kayaba: Hitotsubashi University, Hitotsubashi Institute for Advanced Study, 2-1 Naka, Kunitachi-city,
Tokyo, 186-8601, Japan yutaka.kayaba@r.hit-u.ac.jp. I am extremely grateful to Surala Net for providing
the data for this study. My special thanks go to Takako Taguchi and Hiroyuki Suzuki, who took the time toanswer the questions about the data set and provided supplemental materials. This study does not reflect theviews of Surala Net. I am indebted to Akihiko Matsui for his encouragement and funding. I thank BotondKőszegi, Takeshi Murooka, Daisuke Nakajima, Kosuke Uetake, Ken Onishi, Daiji Kawaguchi and seminarand conference participants at the Otaru U. of Commerce, RIETI, the Association for Behavioral Economics
and Finance Annual Meeting 2014 at Keio U., the Economics of Education Conference 2014 at Keio U., theKyoto Summer Workshop on Applied Economics 2015 at Kyoto U., the Frontiers in Behavioral Economics
and Finance Conference at Osaka U., and the Labor Annual Conference 2015 at Hitotsubashi U. for theirvaluable comments. I also thank Tomomi Tanaka for thoughtful discussions through the entire project with
Surala Net. This research is supported by a Grant-In-Aid for Scientific Research (KAKENHI 24223002)
from the Japan Society for the Promotion of Science (JSPS) and the Ministry of Education, Culture, Sports,
Science, and Technology (MEXT). This study is also supported by the Seimeikai Foundation.

1

2

1

Introduction

Procrastination prevails in academic contexts. According to a study by Solomon and Rothblum (1984), slightly less than half (46%) of college students tend to procrastinate in writinga term paper, 27.6% in studying for exams, and 30.1% in reading assignments. Procrastination appears to be particularly problematic among adolescents, who have greater presentbias, according to several published studies. Decision-makers with present-biased preferences put o↵ costly activities that have delayed but significant rewards (such as studying),
often resorting to less significant immediate pleasure instead, indicating that such decisionmakers lack appreciation for the long-term perspective (O’Donoghue and Rabin (2001);
O’Donoghue and Rabin (1999a)). Predicated on recent findings in behavioral economics,
cognitive science, decision-neuroscience, and developmental neuroscience, Lavecchia, Liu,and Oreopoulos (2014) argue that teenagers are susceptible to greater present-bias, whichleads to their under-investment of educational input in their human capital.
Despite the consensus that the tendency of many people to procrastinate and to havepresent-bias is one of the major impediments to studying, behavior-based quantitative studies of procrastination are rare,1 perhaps due to a lack of longitudinal data recording thedetailed progress of a long-standing task. Employing unique log data in an e-learning context, which enabled me to directly observe the daily homework progress of high school
students, whose behavior is susceptible to present-bias, this study presents quantitative
empirical evidence of procrastination in terms of daily behaviors. Furthermore, this study
discusses implications for the role of deadlines in terms of the problem of procrastination.2
This study utilized log data that was retrieved from the e-learning service “Surala” provided
by Surala Net (Chiyoda-city, Tokyo, Japan). The e-learning service provides a web-based
educational environment in which elementary, middle, and high school students attend
lectures, solve problem sets, and receive feedback on their achievement in three fields ofstudy, namely Math, English, and Japanese. Unlike other e-learning services, “Surala”
provides all problem set material directly on the web-site (thus, no paper and pencil are
1

Traditionally, research in educational psychology has examined academic procrastination; however, moststudies rely on students’ self-reports in questionnaires called “PASS” (Solomon and Rothblum (1984)). Steel(2007) and Ferrari (1994) summarize recent findings.
2
O’Donoghue and Rabin (1999b) argued on a theoretical basis for the optimal use of deadlines to prevent
further procrastination among decision-makers with present-bias in their preferences. Ariely and Wertenbroch (2002) found on the basis of empirical data that people employ self-imposed, costly deadlines to deal
with their tendency to procrastinate.

3
required), which enables them to record all aspects of the studying behavior of each student
in fine detail. This allowed me to assess these students’ procrastination in terms of their
daily behaviors.
To the best of my knowledge, Burger, Charness, and Lynham (2011) is the only study
that employed daily behavioral data to assess procrastination quantitatively.3 Their study
required their subjects (college students) to complete 75 hours of studying during a period offive weeks in a university library. They recorded the duration of the students’ daily studying
time. However, they observed no significant procrastination in the average study time of 22
goal achievers, despite the treatment condition allowing the students to allocate their study
time freely over the five weeks, a condition in which they were expected to procrastinate. In
addition to several inconsistent behavioral results regarding procrastination, the researchersconcluded that procrastination is not easily reproduced in controlled experimental settings.
To overcome such shortcomings, the present study relied on non-experimental empirical
data, gathered among students at a high school (High School A) that actually assigned
long-term, time-consuming homework to all freshmen in the winter recess, on which they
worked as a real-world task. Nonetheless, the data satisfy certain desirable criteria ofexperimental settings, in that an identical task is assigned to a relatively uniform group of
subjects over an identical period of time. These conditions are indispensable prerequisites
for studying procrastination, which is sensitive to task structure and environment.
In addition to the advantage that the e-learning data allowed me to observe directly students’ daily progress on homework assignments, the homework assignments of High School
A possessed desirable properties to reveal students’ tendency to procrastinate in studying
with minimum confounds. The homework was not motivated by reward or punishment,
but was left to voluntary engagement by the students (thus, it was non-binding homework).
The environment in which procrastination matters is generally one with a “delay of gratification” paradigm; a large, significant benefit lies in the future, but in the short-term,
one has to engage in costly activities with no immediate benefit (Loewenstein, Read, andBaumeister (2003)), studying being a clear example. The non-binding goal setting of theHigh School A homework preserved the “delay of gratification” structure of studying, as
no immediate benefit of homework achievement was provided to the students. Indeed, a3

In addition to procrastination, Shapiro (2005) and Kaur, Kremer, and Mullainathan (2011) examined
self-control problems employing daily behavioral data. Shapiro (2005) examined empirical data on daily
food consumption, finding evidence for the existence of present-bias in the preferences of the people involvedin the study. Kaur, Kremer, and Mullainathan (2011) found evidence in a field experiment that labor
motivation was enhanced as payday approached.

4
recent field experimental study by Kaur, Kremer, and Mullainathan (2011) reported thatlabor motivation becomes significantly amplified as payday approaches, suggesting that the
influence of immediate rewards also matters in procrastination in studying, possibly to aneven greater extent among adolescents, who are susceptible to present-bias.4 However, thenon-binding setting in the present study was expected to minimize the e↵ect of immediate
rewards on the tendency for procrastination.
On the basis of these advantages of the daily behavioral e-learning data and the non-binding
goal setting of High School A, the present study provides empirical quantitative evidence of
procrastination and its implications for the role of deadlines as behavioral controls when a
non-binding goal is set, as well as an evaluation of the enhancement of studying motivation
by goal setting. The specific questions addressed by the study are as follows:
Question 1: Does non-binding goal setting encourage students to study more and achieve
the goal?
Question 2: How do students allocate their e↵ort to achieve the goal?
Question 3: Does a non-binding goal motivate students to study hard at the last minute
before the non-binding deadline to address the sizeable remaining task resulting from theirown procrastination?
The study is related primarily to the growing body of literature on behavioral control,
which discusses the role of non-binding goals in self-control problems, or more broadly, thatof commitment mechanisms without explicit reward or punishment (i.e., non-binding commitment).5 Bénabou and Tirole (2004) proposed a commitment mechanism by which people
exercise strong willpower to overcome present-biased preferences using internal commitment
mechanisms or “personal rules” that have no explicit punishment for violations. People withuncertainty in their self-control abilities struggle to follow the non-binding rules to avoid
a lapse and subsequent loss of confidence in their self-control abilities, which motivatesthem to perform further self-control, often excessively. Hsiaw (2013), Koch and Nafziger(2011) and Suvorov and Van de Ven (2008) focus specifically on the role of non-binding goal
setting, discussing how people with present-biased preferences overcome the bias with theassistance of non-binding goal setting. According to such theoretical works, non-binding
4

People with strong risk aversion are also expected to show less procrastination to obtain a certain reward,
which would be another concern in the case of immediate rewards.
5
Regarding the use of commitment devices with explicit punishment (i.e., binding commitment), Ariely
and Wertenbroch (2002) report from their experiment that people employ self-imposed, costly deadlines toavoid procrastination.

5
goals generate reference dependences or psychological dis-utilities for non-attainment, which
motivates the person to perform self-control. Fahn and Hakenes (2014) argued that motivations for reputation building act as a commitment mechanism for people to exercise strong
willpower to overcome the present-bias in their preferences in order to show colleagues theirself-control abilities. Although several theoretical models have been proposed, the dynamics of e↵ort allocation is not well discussed in the literature, and little empirical evidence
exists, perhaps due to data limitations. The present study provides empirical evidence ofdynamic e↵ort allocation in self-control problems for goal achievement in studying amonghigh school students susceptible to present-bias.
The first research question presented above concerns an evaluation of the policy of behavioral intervention with non-binding goal setting for self-control regarding goal achievement.
Heckman, Stixrud, and Urzua (2006) stressed the importance of non-cognitive abilities,
including self-control and motivation. The answer to the first question provides empiricalevidence on self-control abilities motivated by non-binding goal setting. The second question concerns e↵ort allocation and procrastination in the process by which self-control forgoal achievement is performed. The third question concerns the strength of a non-binding
deadline in motivation enhancement, by which certain students may be motivated to work
intensively immediately before a non-binding deadline. As the e↵ort in this case may beallocated even after the deadline, without any penalty, excessive intensive work immediately before the deadline appears unreasonable. This study provides evidence as to whether
certain students do so anyway.
The main findings of the present study are as follows: First the non-binding goal motivateda considerable number of students to study more to achieve it. Second, the majority of thehigh achievers procrastinated. This was especially the case among female high achieversin Math. Third, the non-binding goal motivated a considerable subset of high achievers to
study hard at the last minute before the deadline, despite it being non-binding, the work
resulting from their own procrastination in meeting the deadline. These findings jointly
suggest that a non-binging goal motivates a considerable number of students to exercise
strong self-control for goal achievement in studying; however in the majority, the e↵ort
exertion is put o↵ until immediately before the deadline, with the deadline, despite being
non-binding, playing a substantial role in preventing further procrastination.
The remainder of this paper is organized as follows: Section 2 describes the data. Section
3 reports on the extent to which the non-binding goal encouraged the students to study
more to achieve it. Section 4 discusses the students’ procrastination in the process of goal

6
achievement. Section 5 discusses the possible decision-making mechanism consistent withthe findings. Section 6 o↵ers concluding remarks.

2

Data

The e-learning log data retrieved for the present study comprise a longitudinal record of
the students’ studying behavior over the whole academic year (April 2013 to March 2014),
including the winter recess, during which non-binding homework is assigned. The auxiliary data include each student’s gender, academic course enrollment, and achievement testscores in November 2013, one month before the winter recess. The winter recess began on
December 21st, 2013, and ended on January 6th, 2014. The whole winter recess was set
as the time for the homework to be completed. With regard to the possible concern that
other binding homework assignments, if any, might “crowd out” the relevant non-binding
e-learning assignment, leading to spurious procrastination, anecdotal evidence from the instructors at High School A indicated that other homework assignments were minor andwould have had minimum e↵ect on in the e-learning homework.
Although there was no penalty for a delay in completion, the deadline was nominally set for
January 7th, 2014, the first day of the next academic term. The school authority announced
explicitly to the students that there was no penalty for non-completion of the homeworkand no direct impact on final grades, but encouraged their voluntary participation.6
Access to the homework was possible only through internet.7 As the public internet serviceof the school was not available to the students during the recess period, several students
without their own internet access were unable to work on the e-learning homework. Although the data on individual internet accessibility was not available, an informal survey
conducted by the school authority in September 2013, a few months prior to the intervention period, revealed that approximately 18% of the students had no private access to thee-learning service.
6

Regarding the possible concern that some students may not have believed the announcement, andthought that their achievement in the homework would impact on their final grades, this was not the case.
As I mention below, a non-negligible fraction of the students had no access to internet, putting them at aselective disadvantage for grading if the announcement were to be violated. Thus, reasonably, the school
authority had to strictly prohibit instructors from taking the e-learning achievement into consideration in
the final grades, and this ought to have been common knowledge among the students.
7
At the time, the e-learning service allowed access only from conventional desktop and laptop computers.
No access was possible from smart-phones or tablet-type devices.

7
The log data in the e-learning records for each student showed when they worked, for how
long, on what kind of unit, and whether or not they passed the unit successfully. In thise-learning service, almost all units consisted of one lecture and several problem sets. A
unit was marked as “passed” when a student successfully solved its problem sets. A unit
typically took approximately 15 minutes to complete, though several units required either
more or less time. To control for the heterogeneity in unit load, I employed “standard
required amount of time” reported by the e-learning system for each unit, according touser’s past attemps, as a measure of load for the unit to evaluate the cumulative progresswith the homework. These measures also allowed me to unify the progress in the di↵erent
fields of study, namely Math and English, in a single variable.
High School A o↵ered two academic courses, namely a General course and a Special Academic Advancement (SAA) course. The homework assignments across the two had almost
identical loads. The homework for the General course consisted of 60 English units and 14
Math units, with a total of 1,875 minutes of “standard required amount of time” (1,525 minutes for English and 350 minutes for Math), whereas SAA students are assigned the identical
60 English units and 13 units for Math (315 minutes), with a total of 1,840 minutes.
Table 1 presents the descriptive statistics of the students whose performance was consideredin the present study, namely the freshmen of the 2013 academic year in High School A. The
total number of freshmen was 446,8 including 312 males and 134 females. Among these,
53 students were enrolled in the SAA course, including 25 males and 28 females, and 393
students were enrolled in the General course, including 287 males and 106 females. The
proportion of female di↵ers remarkably across the two courses, with 0.4310 in the SAA
course and 0.2697 in the General course.
The auxiliary data document the students’ characteristics at the individual level, indicating
the academic course in which a student is enrolled (a dummy variable for the SAA course),
gender (a dummy variable for females), and achievement scores for a test administrated
by a private company, the Benesse Corporation (City of Okayama, Okayama-prefecture,
Japan). This achievement test (known as ”Shinken-moshi” in Japanese) is one of the leading
achievement tests for Japanese high school students who wish to assess their prospects foradvancing to higher education. All freshmen in High School A were required to take theachievement test in November 2013, one month before the intervention period. The scores8

As additional mandatory goals had been set by instructors at some point before the intervention period
for seven students who were struggling to follow in-class lectures, the data for these seven students were
excluded from the analysis.

8

Table 1: Descriptive statistics of the students
Upper panel: Special Academic Advancement students
Lower panel: General students

StatisticN

Mean

St. Dev.

Min

Max

Female (dummy variable)
Total score
Math score
English score

53
51
51
51

0.528
49.75151.60048.225

0.504
6.266
7.994
6.670

0
36.80036.50036.000163.70070.60069.500StatisticN

Mean

St. Dev.

Min

Max

Female (dummy variable)
Total score
Math score
English score

393
375
375
375

0.270
36.21737.54038.556

0.444
4.577
6.131
3.594

0
27.80030.70032.0001
51.00055.40053.500* The scores include a few missing entries due to students’ non-attendance.

9serve as a proxy for the students’ abilities, which were in this sense predetermined before theintervention period began. As seen in Table 1, the averaged total score of the SAA students(49.75) exceeds that of the General students (36.22), indicating that the SAA students
generally outperformed the General students on the test. Indeed, admission to the SAA
course is more selective, the aim being to provide e↵ective support for its students to advanceto higher education, which is not necessarily the case for the General course. The test scoresalso show that the SAA course predominantly includes middle-achieving students, as theirstandardized scores are roughly around 50 points, whereas the General course includes lowachieving students.9 Nonetheless, the two courses have overlap significantly in terms ofscores for any field of study.
The winter homework set by High School A was exogenously and uniformly assigned to
all freshmen with no selection bias, which allowed me to evaluate the causal e↵ect of theexogenous, non-binding goal setting on students’ studying behavior in a simple manner.
The e-learning was available to all freshmen well before the winter recess period, as ofApril 2013, to be precise, and the school authority encouraged their voluntary use of thee-learning service as supplemental to in-class lectures. Particularly in the winter recess,the authority additionally designated specific e-learning units, as well as a nominal, nonbinding deadline as a goal. Thus, the only di↵erence in the winter recess was the designationof target units and the nominal, non-binding deadline, which enabled me to evaluate thecausal, policy e↵ect of non-binding goal setting with minimum confounds. Moreover, theannouncement of the homework was made only a few days before the winter recess began,with students incapable of predicting it in advance.10 Taking advantage of this context, I
begin in Section 3 by evaluating the treatment e↵ect of the exogenous, non-binding goal
setting on the students’ studying behavior.

3

Increase in time spent on studying and goal achievement

This section addresses the first of the three questions posed in Section 1: Does non-binding
goal setting encourage students to study more and achieve the goal? Based on a range of
9

In Japan, the test scores are usually normalized with a mean of 50 and standard error of 10. The
population comprised all high school freshmen who took the same achievement test.
10
Nonetheless, some students had already unwittingly worked on several units set for the recess. Thesefour students are excluded from the analysis of procrastination in Section 4. Specifically, I excluded students
who had already “progressed” with more than 10% of the total homework on the day before the winterrecess began (December 20th, 2013).

10
evidence, I propose that the students were causally motivated to study more to achieve thegoal (perform self-control for the goal achievement), thereby evaluating the policy e↵ect of
non-binding goal setting.

3.1

Increase in time spent on studying

Did the non-binding goal setting in this context actually increase the students’ time spent
on studying? The upper panel of Figure 1 shows the average monthly studying time of the
freshmen in minutes. The studying time includes not only the time associated with the set
goal, but the entire time the students spent studying, irrespective of the goal having been
set. The data show that studying time increased dramatically during the winter recess (inDecember and January), the intervention period during which the non-binding goal wasestablished. The increase in January was particularly outstanding, rising to slightly below
150 minutes from around 20 minutes before December. The sharp and unique increase instudying time in the winter recess, and not in the summer recess, strongly supports the ideathat it was causally motivated by the non-binding goal setting.
The second noticeable change is the sudden drop in studying time after the winter recess,returning to baseline level in February and March. This decrease in studying time implies
that the studying was not an exciting activity in which students were motivated by fun
or intrinsic interest. Rather, it was a costly activity to which they were averse; they were
unwilling or reluctant to study, and studying required a certain level of self-control.
The non-binding goal was exogenously set uniformly for all the students by the school authority, which eliminated self-selection by the students. The non-existence of self-selectionin the goal setting allowed me to evaluate its average treatment e↵ects (ATE) in a primitive,
straightforward manner, by comparing the studying time before and after the goal was established. Specifically, the studying time in the two consecutive months of the winter recess(December and January) were compared to a twofold of the averaged value in the baselinemonths (May to October),11 revealing an increase in studying time of 214.81 minutes (s.d.
11

The baseline months were those before the winter recess, namely May to October. April was excluded
as it is the starting month of the Japanese academic year, during which instructive demonstrations andsoftware practice is conducted, which would be a nuisance for the analysis. I excluded November simply
for the conservative reason that the achievement test was administrated in November; however, including
November did not significantly alter the results.

11

Figure 1: Time spent on studying
Upper panel: Average studying time (minutes) in each month
Lower panels: Histogram of total studying time (minutes) in Dec. and Jan.
(Left: All students. Right: High achievers)

12
is 21.87).12 13 14 As to the variation in the increase in studying time, the histogram in thelower panels of Figure 1 show that the majority of students had an almost zero increase
and a small peak is seen around 1,200 minutes or 20 hours (left panel). Restricting thesample to the high achieving students whose final achievement was over 75%, the rightpanel demonstrates a single peak around 1,000 minutes, with a mean value of 1,183 minutes and a median value of 1,124 minutes, indicating that the non-binding homework wasa long-standing, time-consuming task that took approximately 20 hours to complete.
ATE could be heterogeneous due to students’ individual characteristics. Indeed a great
number of studies have reported that females are more subject to educational interventions
than males.15 To determine the heterogeneity of the policy e↵ects of the non-binding goal
12

One might argue that this calculation captures the increase in studying time only for e-learning, missing
any increase outside of e-learning, and thereby underestimating the policy e↵ect of the non-binding goal
setting. Particularly, those who did not prefer the e-learning presentation may have covered the corresponding content with other, conventional materials. Indeed, as the structure of the “Surala” e-learning
environment follows the unified curriculum established by the Japanese government, as well as including
other conventional, standard materials, students were able to switch easily to avenues other than e-learning.
Unfortunately, the data does not capture any study time outside of e-learning; thus, the observed increase instudy time could be regarded as the lower bound of the policy e↵ect of the non-binding goal. Nonetheless,the increase in studying time by the non-binding goal setting remained outstanding, suggesting that thepossibility of outside study does not undermine the significant causal e↵ects of the goal setting.
13
One might be concerned that, as December and January include more free time as a school recess thanthe baseline months, the calculation might allow overestimation, including spurious e↵ects of increasing
study time arising from a greater amount of free time, which occurs irrespective of the goal setting. To
address this issue quantitatively, I also report a conservative calculation, restricting the baseline months tothose in the summer period (July and August). As these two months include a longer recess period than
the two months in the winter, the modified calculation is expected to underestimate the policy e↵ect. This
amounts to 195.37 minutes (s.d. is 22.55), which does not di↵er significantly from the original, implyingthat the overestimation is minimal.
14
A further concern associated with the calculation is that the goal setting may have “crowded out”
studying time outside of e-learning, thereby overestimating the total increase in studying time. I do not deny
the possibility of such crowding out, but propose that its impact would be minimal, based on the following
two facts: First, as discuss in the footnote above, substitution with preferred material was straightforward
due to the unified guidelines of the Japanese government. Second, the use of e-learning had repeatedly beenencouraged by the school authority since April, with its use not prevailing until the winter recess. These
two facts jointly imply that students who chose preferred material over e-learning before the winter recess,regardless of repeated, persistent encouragement to use the e-learning service by the school authority, would
naturally continue to do so even during the winter recess.
15
Angrist and Lavy (2009); Anderson (2008); Angrist, Lang, and Oreopoulos (2009); Hastings, Kane,and Staiger (2006). Furthermore, a survey on gender di↵erences in economics (Croson and Gneezy (2009))
summarizes related findings.

13
setting, I performed Ordinary Least Square (OLS) regressions, explaining the increase instudying time in terms of pre-intervention, predetermined variables of students’ characteristics, namely a female dummy, an SAA dummy, and the total score in the achievement test
as a proxy for the students’ abilities. Table 2 presents the regression results. Consistent
with the literature, I found significantly larger policy e↵ects among females (Columns (1)to (3)). Given that this selective e↵ect on females was not observed in the studying time
during the baseline months (Column (4)), it may be concluded that the females’ larger
increase in studying time was causally motivated by the non-binding goal setting.
Table 2: OLS regression for increase in studying time (minutes)
Dependent variable
Total increase in Dec. and Jan.

FemaleSAA
Total score
Constant(Baseline)

(1)

(2)

(3)

(4)

287.966⇤⇤⇤(59.821)127.927(107.131)
9.326⇤(4.764)209.475(169.623)

299.408⇤⇤⇤(59.202)15.156(68.314)284.565⇤⇤⇤(59.527)

126.658⇤⇤⇤(19.644)5.060(3.109)62.347(115.009)

2.475(4.602)40.431⇤⇤⇤(14.981)0.230(0.412)0.168(15.905)

426
0.097
0.090

446
0.087
0.083

426
0.092
0.088

426
0.085
0.078

Observations
R2
Adjusted R2

Note: Robust standard errors are in parentheses. ⇤ p<0.1; ⇤⇤ p<0.05; ⇤⇤⇤ p<0.01

3.2

Goal achievement

How many students did ultimately achieve the non-binding goal? Table 3 presents the descriptive statistics of the high achievers who had completed more than 75% of the homework

14

Table 3: Descriptive statistics of high achievers
StatisticN

Mean

St. Dev.

Min

Max

Female (dummy variable)
SAA (dummy variable)
Total score
Achievement rate by Jan. 7
Achievement rate by Jan. 31
Achievement rate by Feb. 28

56
56
55
56
56
56

0.571
0.232
41.349
0.693
0.925
0.952

0.499
0.426
6.960
0.303
0.109
0.074

0
0
29.400
0.027
0.437
0.752

1
1
58.500
1.000
1.000
1.000

Figure 2: Histogram of final achievement rates

15

Figure 3: Histogram of high achievers’ baseline monthly studying time (minutes)
by the last day of February.16 The high achievers included 56 students, i.e., 11.9% of theentire freshman group, whereas 57 students made some progress more than 10%, but failed
to complete the homework. The abilities of the high achievers identified varied widely, as
their total scores varied from 29.4 to 58.5, indicating that low-achieving students also successfully completed the homework. In terms of progress, most students finished working on
the homework by the end of January, with further progress even after the deadline (January7th), which appears reasonable, given that the non-binding goal allowed them to delay the
homework without penalty.
To consider the variation in final achievement, I present a histogram of the final achievement
rates of the entire freshman group in Figure 2. The distribution has two obvious peaks
around zero and one, indicating that the non-binding homework motivated students in
two diametrically di↵erent ways: Most students were not motivated at all, perhaps evenreluctant to try to work on the homework, whereas several students were motivated wellenough to complete it, with a few intermediate drop-outs. This finding provides a further
implication for the role of non-binding goal setting in motivation enhancement, namely thatthe goal does not simply motivate students to study more, but also urges them to complete
16

The high achievers are defined here are those who had achieved the goal of more than 75% at the endof February. However, the discussion here and below holds true even if the final achievement were to beevaluated at the end of January or March. Furthermore, the discussion here and below remains robust as
to the choice of the threshold value above 75%.

16
the task.
Additionally, to determine whether the high achievers had chronic studying habits, I present
a histogram of average studying time in the baseline months in Figure 3. The histogramshows that most students are clustered around zero, indicating that most of the high achievers did not have chronic studying habits, but rather were motivated specifically by thenon-binding goal setting.
As seen in Table 3, the 56 high achievers include 32 females and 13 SAA students. Whereas
these high achievers comprised 11.9% of the entire freshman group, the female high achievers comprised 23.9% of all the female students, and the SAA high achievers comprised
24.5% of all the SAA students, implying that, in addition to the ATE on studying time,goal achievement also di↵ered significantly depending on students’ characteristics. To dissociate distinct contributions of various student characteristics, I once again conducted OLS
regressions, explaining the final achievement rates at the end of February in terms of predetermined variables of students’ characteristics (Column (1), Table 4). The results indicatethat achievement was higher among female than male students, as was the case for studying
time. Moreover, the higher achievement among SAA students may be explained in termsof their better abilities, rather than the course design, as the regression coefficient for theirabilities di↵ered significantly from zero, whereas that for the dummy variable course did
not. The result remains robust even if the final achievement rates are measured on theoriginal deadline of January 7th (Column (2), Table 4).
However, it is worth mentioning that, although the SAA students performed well, a considerable number of General students also achieved the goal despite their disadvantage interms of ability. The majority of the high achievers, specifically 43 of the 56 (76.8%), wereenrolled in the General course, suggesting that even low-achieving students may be considerably motivated by non-binding goal setting. Heckman, Stixrud, and Urzua (2006) points
out that non-cognitive factors, such as self-control and motivation, play a substantial rolein social success, especially for disadvantaged students. The present results show that selfcontrol for goal achievement is not exclusive to high- and middle-achieving students, butalso occur among low-achieving students.17
To summarize the results presented in this section, I report several findings on the policye↵ects of the non-binding goal setting in the present context. First, the non-binding goal
17

Appendix I presents a histogram showing the (cognitive) abilities of the high achievers measured interms of the total score in the achievement test. This shows that the majority of the high achievers (75%)
were low-achieving students whose scores were below 45.

17

Table 4: OLS estimates for goal achievement rates
Dependent variable

FemaleSAA
Total score
Constant

Observations
R2
Adjusted R2

Until Feb. 28(Until Jan. 7)

(1)

(2)

0.178⇤⇤⇤(0.038)0.011(0.077)0.009⇤⇤⇤(0.003)0.237⇤⇤(0.112)0.128⇤⇤⇤(0.031)
0.025(0.068)0.007⇤⇤(0.003)0.177⇤(0.101)

426
0.120
0.113

426
0.105
0.099

Note: Robust standard errors are in parentheses. ⇤ p<0.1; ⇤⇤ p<0.05; ⇤⇤⇤ p<0.0118
setting encouraged a considerable number of students, leading them to study more and toachieve the goal. Second, students who worked on the homework were strongly motivated tocomplete it, with few intermediate drop-outs. Third, those high achievers were not students
who habitually spent time studying, but rather those who did not study chronically. Jointly,
these three findings strongly suggest that a non-binding goal causally motivates students
to study more, helping them to exercise self-control for goal achievement that they would
not otherwise perform voluntarily. This provides an answer to the first question posed in
Section 1. An additional finding was that, although the policy e↵ect was greater amongfemales and high-achieving (SAA) students, a substantial number of low-achieving students
were also well motivated to achieve the goal.
Result 1: Motivated by a non-binding goal, a considerable number of students, including
a substantial portion of low-achieving students, studied more and achieved the goal, as theywould not have done voluntarily.

4

Procrastination

In this section, I address the second and third questions posed in Section 1: How do students
allocate their e↵ort to achieve a goal, and does a non-binding goal motivate them to study
hard at the last minute before the non-binding deadline to address the sizeable remaining
task resulting from their own procrastination? I address these questions on the basis of daily
behavioral data, focusing particularly on the behavior of the General course high achievers,
who comprised the majority (approximately 90%) of all the high achievers.
The upper panel in Figure 4 shows the students’ averaged cumulative progress with thehomework, distinguishing between the SAA high achievers, the General high achievers, andthe low achievers.18 The red line plots the average cumulative progress of the SAA highachievers, the blue line of the General high achievers, and the gray line of the low achievers.
The outstanding feature of all three curves is the kink that emerges a day before the deadline(January 7th, 2014, denoted by the green vertical line), following rapid growth during the
few days immediately before the deadline. The rapid growth indicates that they worked
intensively immediately before the deadline, and the almost flat lines of the high achievers
after the kinks at the deadline indicate that they suddenly stopped working intensively oncethe deadline had passed. These features are echoed in the lower panel, which displays the18

Low achievers were those whose final achievement rate was between 10% and 75%.

19
average daily studying time during the winter recess, together with the extended periods
until the end of February. The time spent on studying rose dramatically immediately before
the deadline, reaching a peak in the final three days, but uniformly dropping to almost to thezero level after the deadline. These informal observations suggest that, many high achievers
relied on the deadline for their homework progress, even though it was non-binding.
Furthermore, the upper panel of Figure 4 shows distinct, heterogeneous patterns of e↵ort
allocation across the students from di↵erent academic courses. Despite the almost identicalcumulative achievements at the exact moment of the deadline and after it,19 in the time up
until the deadline, the General high achievers progressed far less, or procrastinated morethan the SAA high achievers. The General high achievers’ progress on January 1st reachedonly below 20%, while that of the SAA high achievers reached 40%. The high achievers in
the General course disproportionately allocated their e↵ort immediately before the deadline,
in contrast to the SAA high achievers.20
To test the above informal observations in a more rigorous manner by controlling the distinct
contributions of heterogeneous observable characteristics of the students, I performed OLS
regressions, explaining the progress in terms of the predetermined variables on students’characteristics. I extracted three mutually exclusive periods of equal length (six days) fromthe winter recess, named the First, Second, and Third periods. The First period was from
December 21st to December 26th 2013, the Second from December 27th 2013 to January
1st 2014, and the Third from January 2nd to January 7th 2014.
The statistical model for the regression is as follows:
yt,i = ct +1t F emalei +2t SAAi +3t Abilityi + ✏t,i(1)

where the dependent variable y stands for the progress. The subscript i stands for individual,
and t = {1, 2, 3} denotes the period, First, Second, and Third respectively. I used theachievement test scores as a proxy for students’ abilities, as for the regressions in Section 3.
Table 5 shows the regression results. Somewhat consistently with the informal insightso↵ered above, the SAA high achievers progressed marginally significantly more by 22.6%
19

The Appendix discusses the identical progress of students from the two academic courses.
A concern here may be that the SAA high achievers progressed earlier because they are simply able
to perform the same task more quickly, implying that this result is not evidence of earlier e↵ort exertion.
However, the lower panel of Figure 4 shows that the SAA high achievers actually spent more time studyingin the earlier stage of the winter recess than did the General high achievers, indicating that the SAA highachievers actually expended more e↵ort at an earlier stage.
20

20

Figure 4: Daily progress
Upper panel: Averaged cumulative progress
Lower panel: Averaged studying time (minutes)

21in the First and Second periods irrespective of the contribution of their abilities (p < 0.10,
Column (4)), while they progressed less in the final three days by 21.4% (not significant),irrespective of the contribution of their abilities (Column (5)). As to the e↵ect of theirabilities, none di↵ered significantly from zero individually; however, the signs of the point
calculation suggest that the high-achieving students who earned higher scores tended toprogress more in the first two periods, and less in the Third period.
Although I found only marginally significant heterogeneity of the progress in terms of theindividual e↵ects in the coefficients of the OLS regressions, the joint e↵ects of students’characteristics revealed a significant di↵erence in terms of heterogeneous patterns in theirprogress. Figure 5 presents the conditional expectations for progress conditioned on thestudents’ characteristics, Ê[yt |X], which is the fitted value implied by the OLS calculations,where X denotes the set of regressors. The red curve in the left panel shows the progressof the SAA high achievers, and the blue curve displays that of the General high achievers
after controlling for gender ratio di↵erences. In the right panel, the red curve shows the
progress of the females in the General course, and the blue curve that of the males.

Figure 5: Progress in each period
Left panel: General (blue) and SAA (red)Right panel: Male (blue) and Female (red) in General course
X axis: Period (First, Second, and Third)

As can be observed in the left panel, the General high achievers, who indeed comprised
the majority of all high achievers (41 of 51 students), showed severe procrastination. Their

22

Table 5: OLS estimates for the total progress in each period
Dependent variable

FemaleSAA
Total score
Constant

Observations
R2
Adjusted R2

First

SecondThirdFirst and Second

Final three days

(1)

(2)

(3)

(4)

(5)

0.019
(0.015)0.068(0.041)
0.002(0.002)0.053(0.083)0.081(0.070)0.159(0.121)
0.004(0.006)0.011(0.213)0.166⇤(0.090)0.228(0.139)0.004(0.009)0.591⇤(0.348)0.099(0.076)0.226⇤(0.134)0.006(0.007)0.042(0.280)0.155⇤⇤(0.080)0.214(0.136)0.0003(0.008)0.335(0.326)

51
0.233
0.184

51
0.115
0.059

51
0.118
0.062

51
0.183
0.131

51
0.111
0.054

Note: Robust standard errors are in parentheses. ⇤ p<0.1; ⇤⇤ p<0.05; ⇤⇤⇤ p<0.0123
averaged cumulative progress in the First and Second periods was small, reaching merely
13.6%, whereas their final progress was substantially greater, reaching 52.9% (p < 0.001).21
Indeed the final six-day progress of the General high achievers was a factor of 3.54 comparedto the cumulative progress of twelve days in the first two periods. In contrast, the SAA
high achievers’ aggregate progress was almost identical in the Second and Third periods,
namely 31.7% and 27.0%, respectively. Indeed, there was no significant di↵erence in the
progress during the two periods (p = 0.7887).22 Moreover, the direct comparison between
the students on the two academic courses shows that the General high achievers progressed
less in the First and Second periods than did the SAA high achievers (p < 0.05),23 whereas
they progressed more in the Third period (p < 0.05).24
As to the gender di↵erence in this subsample (i.e., the high achievers), the females progressed slightly less in the First and Second periods, but this di↵erence was not significant(p = 0.2055), and the females progressed more in the Third period, which was statisticallymarginally significant (p < 0.1).25 26 Whereas we saw in Section 3 that self-control forgoal achievement was outstanding among both SAA students and females, their procrastination appears diametrical as the SAA students procrastinated less, whereas the females
procrastinated marginally more. This seemingly inconsistent result is partly addressed in
the next subsection, which focuses on the heterogeneity of Math and English, providing theobservation that the female high achievers showed substantial and peculiar procrastinationin Math.
21

The hypothesis that the progress in the Third period and the cumulative progress in the First and Second
periods were identical is rejected, as the di↵erence is 0.3930 (s.d. is 0.0702) and p-value is 2.1177 ⇤ 10 8 . The
standard deviation is cluster-bootstrapped between the two academic courses. Hereafter, hypothesis tests
are performed by the cluster-bootstrap. Additionally, I performed a nonparametric test (Wilcoxon pair-wisetest) for the General students and the hypothesis that the progress in the Third period and the cumulative
progress in the First and Second periods was identical is rejected (p < 0.001).
22
The hypothesis that the progress in the two periods was identical is not rejected, as the di↵erence is
0.0477 (s.d. is 0.1780) and p-value is 0.7266.
23
The hypothesis that the cumulative progress in the First and Second periods was identical across thetwo courses is rejected, as the di↵erence is 0.2803 (s.d. is 0.1162) and p-value is 0.0178.
24
The hypothesis that the progress in the Third period was identical across the two courses is rejected, as
the di↵erence is 0.2593 (s.d. is 0.1161) and p-value is 0.0256.
25
The hypothesis that the progress in the Third period was identical is rejected, as the di↵erence is 0.1579(s.d. is 0.0792) and p-value is 0.0807.
26
Note that the observation that the females amongst the high achievers procrastinated marginally moredoes not necessarily indicate that females tend to procrastinate more in general, due to the possibility ofself-selection. As seen in Section 3, females are generally more encouraged to study; thus, females with astronger tendency to procrastinate may have been included in this subsample.

24
The progress in the final three days of the recess (January 4th, 5th, and 6th) demonstrates
the distinct, intensive progress of the General students more clearly. Figure 6 shows thehistogram of the General high achievers’ progress (left panel) and that of the SAA highachievers (right panel). Whereas a sizable number of students in both courses progressedalmost not at all in the three days, more than half of the General high achievers progressedwith no less than 45% of all the homework in just those three days (the median value ofthe distribution is 46.80%).27
The finding that the students who aimed to advance to higher education showed less procrastination do not conflict with the existing findings of a field experiment by Burger, Charness,and Lynham (2011), in which they found no significant aggregate procrastination amongcollege students. Rather, the present findings suggest that procrastination matters among
low-achieving students who do not necessarily aim to advance to higher education (i.e.,
General students), at least in the case of non-binding goal setting.28
Given that the General high achievers comprised the majority in the present sample, it
may be concluded in answer to the second research question that the majority of the highachievers allocated their e↵ort disproportionately, most intensively immediately before thedeadline, or simply procrastinated.
Result 2: The majority of high achievers, namely General students, procrastinated.
Next, to answer to the third question posed in Section 1, the left panel in Figure 6 shows an
outstanding peak around 75%. This peak implies that a considerable subset of the Generalhigh achievers progressed intensively by completing 75% of the homework in the final threedays. Recall from Section 3 that the homework took approximately 20 hours for averagehigh achieving students; thus, this 75% progress was by no means an easy task for General
27

Kolmogorov-Smirnov test marginally rejects the hypothesis that the two samples are realized from an
identical distribution, with p-value of 0.0631.
28
Unfortunately, due to data limitations, I cannot propose a reason for the SAA students not procrastinating. One possibility is that, as the SAA course aims to advance students to higher education, theirstronger will to advance might have led to less procrastination. This stronger will might have encouraged
such students to exercise their willpower at an earlier stage. However, other explanations may also apply. For example, earlier progress by the SAA students might have been due to parental intervention, bywhich parents persuaded such students to work earlier. Another potential explanation is that the “delayed
gratification” di↵ered across the courses. The SAA students might have had more to gain from studying,advancing to higher education in the future, which further motivated them and led to less procrastinations.
Alternatively, the SAA students might simply have been less exposed to temptations that prevented themfrom studying. The present data is unable to identify the precise mechanism.

25
students, who fall in the category of low achievers in terms of their studying ability. Indeed,
the remaining tasks would have been a heavy burden. Recall further that the students
were allowed to allocate their e↵ort after the deadline without any penalty (as it was a
non-binding deadline), and so could have avoided intensive hard work at the last minute.
Nonetheless, the empirical data show that these students did indeed make the last-minutee↵ort to complete the sizeable remaining task resulting from their own procrastination,
aiming to meet the non-binding deadline.
Result 3: Motivated by a non-binding goal, a considerable subset of high achievers worked
hard at the last minute to complete the sizeable remaining task resulting from their own
procrastination.

Figure 6: Histogram of progress in the final three days
Left panel: General students. Right panel: SAA students.

Females’ peculiar procrastination in MathFigure 7 shows the heterogeneous progress in each field of study, namely Math (left panel)and English (right panel) among the high achievers in each field. The mean progress is thefitted value implied by the OLS calculations explaining the progress of each subject in thesame way as previously.29 The mean progress of the male General students is represented by
the blue curve, of the female General students by the red curve, and of the SAA students by
29

The OLS calculations are provided in the Appendix.

26

Figure 7: Progress in Math and English in each period
Left panel: Math, Right panel: English
Blue: Male, Red: Female, Cyan: SAA students

the cyan curve. Interestingly, there was no significant di↵erence in the pattern of progress
across Math and English for either the SAA students or the male General students. However,the female General students showed a distinctly peculiar procrastination in Math. The
female General students showed almost zero progress in the First and Second periods, andthen progressed dramatically by 69.8% in the final period, this progress being significantly
greater (p < 0.001).30 The direct comparison across genders shows that the females’ progressin the first two periods was significantly less than the males’ progress (p < 0.05);31 however,their progress in the final period was significantly greater (p < 0.05).32 Existing studies haveshown that females have a tendency to avoid working on Math due to their aversive attitudes
to Math.33 Furthermore, I provide additional evidence in the Appendix that supports the30

The hypothesis that the progress in the Third period was identical to the cumulative progress in the firsttwo periods is rejected, as the di↵erence is 0.6880 (s.d. is 0.0957) and p-value is 6.4371 ⇤ 10 13 . Additionally,
I performed a nonparametric test (Wilcoxon pair-wise test) and the hypothesis is rejected (p < 0.001).
31
The hypothesis that the progress in the first two periods was identical across genders is rejected, as thedi↵erence is 0.1809 (s.d. is 0.0774) and p-value is 0.0194.
32
The hypothesis that the progress in the Third period was identical across genders is rejected, as thedi↵erence is 0.2248 (s.d. is 0.1093) and p-value is 0.0398.
33
Wilder and Powell (1989) provide a comprehensive survey on the issue.

27
notion of females’ Math avoidance,34 which jointly suggest that females’ Math aversion is
the most convincing theory behind the selective procrastination in Math observed in thepresent study.
This finding provides a further implication for the role of non-binding goal setting, reinforcing the answers to the second and third questions posed in Section 1: Females in thisstudy procrastinated in Math, possibly due to Math aversion; however, their motivation,enhanced by the non-binding goal setting, strongly encouraged them to work hard at thelast minute to meet the deadline, despite it being non-binding, perhaps helping them toovercome their aversion to Math.
To summarize this section, the present study found that the majority of high achieversin the General course procrastinated until the non-binding deadline but worked intensively
immediately before it. Specifically, a sizable subset of the General high achievers progressed
intensively only in the final three days, indicating that the non-binding deadline motivatedthem to complete the sizeable remaining task resulting from their own procrastination at thelast minute. Moreover, the female students’ procrastination was particularly outstanding inMath, as they progressed almost not at all in the first two periods, perhaps due to their Mathaversion, but they overcame this aversion in the final period, progressing dramatically tomeet the deadline, despite it being non-binding. Jointly, these findings suggest that even a
non-binding deadline plays a substantial role in motivating students to work and preventing
further procrastination.

5

Possible explanations by economic decision-making models

Why were these students motivated to meet the non-binding deadline despite the sizeableremaining task and/or the mental stress of their task aversion? Several theoretical studies on implicit commitment in the absence of reward or punishment have shown that themechanisms of implicit commitment actually encourages people to exert strong willpower toovercome their present-bias and pursue a long-term benefit, as briefly discussed in Section 1(Bénabou and Tirole (2004); Hsiaw (2013); Koch and Nafziger (2011); Suvorov and Van deVen (2008); Fahn and Hakenes (2014)).
34

In the Appendix, I discuss two further possible explanations; however, I found no positive evidence tosupport these. Moreover, I provide positive evidence that females in another institution using the samee-learning service actually showed avoidance for Math.

28
The results of the present study broadly agree with this theoretical work on implicit commitment mechanisms. The non-binding goal-setting in this case may have acted as animplicit commitment, causally encouraging the students, who regarded the difficulty of thegoal as appropriate for them, and therefore enacted self-control to overcome their presentbias in studying. However, due to data limitations, the exact decision-making mechanismthat occurred here cannot be fully identified, and the psychological factors generating thedis-utility of non-attainment of the goal remain hidden.
Nonetheless, the present study empirically and quantitatively reveals the timing of the e↵ortbeing exercised immediately before the deadline, which has been elusive in the empiricalliterature. Although this was a case of a non-binding deadline, the delay of e↵ort exertionor procrastination observed here is consistent with theoretical models of procrastinationin which agents with present-biased preferences avoid immediate costs and put o↵ costly
activities in delayed gratification paradigms (O’Donoghue and Rabin (2001); O’Donoghue
and Rabin (1999a); O’Donoghue and Rabin (1999b)). However, in contrast to the binding
deadlines in such literature, and perhaps motivated to avoid the psychological dis-utilityof the non-attainment of the goal, the students in this study tried to meet the deadline,
despite it being non-binding, performing self-control to study at the last minute.
In terms of this explanation, two distinct types of self-control are included in the decisionmaking process, namely self-control for goal achievement and self-control for preventing
procrastination in the process. The explanation above implies that these two forms of selfcontrol are not necessarily exercised at the same time. Possibly, they are exercised throughdissociated mechanisms. At the very least, self-control for goal achievement motivated by a
non-binding deadline does not appear to necessarily prevent procrastination in the process.
The findings here point to the need for future theoretical work modeling self-control and theassociated dynamic allocation of e↵ort, and on the role of deadlines in behavioral control
in procrastination, or more broadly on the commitment mechanism.

6

Concluding remarks

Utilizing the advantages of unique, real-world data available from an e-learning service inwhich students’ daily homework progress was directly observable, and of a non-binding
homework context that straightforwardly revealed students’ procrastination, this study empirically evaluated procrastination among high school students, recognizing that students’behavior is known to be susceptible to present-bias, and the associated influence of a dead-29
line. The main findings of the study are as follows: First, the non-binding goal setting
successfully encouraged a considerable number of students to spend more time studying
to achieve the goal. The extent of goal achievement was remarkable among females and
SAA students; however, many low-achieving students also successfully achieved the goal.
Second, the high achievers in the General course, who indeed comprised the majority of allhigh achievers, procrastinated until close to the deadline and worked intensively immediately before the deadline. This was particularly true for the Math homework among thefemale high achievers. Finally, a sizable subset of the high achievers worked hard at thelast minute before the deadline, trying to complete the sizeable remaining task resulting
from their own procrastination and/or mental stress from their task aversion to meet thedeadline, despite it being non-binding.
These findings jointly suggest that a non-binging goal may motivate considerable numbers ofstudents to exercise strong self-control for goal achievement. However, for the majority, thee↵ort exertion is put o↵ until immediately before the deadline, and the deadline, even if it isnon-binding, plays a substantial role in preventing further procrastination. These findings
may inform future theoretical work on dynamic decision-making involving self-control.
This study represents just one small step toward an understanding of procrastination. Further research is required to determine whether and how the manner in which the allocationof e↵ort di↵ers according to task structure, environment, and commitment mechanisms. Intuitively, e↵ort allocation should depend on a range of factors, primarily on task structure.
Indeed, theoretical works cited in Bénabou and Tirole (2004) and Hsiaw (2013) employedtask structures in which people had to continue to exercise their willpower to achieve a goal,such as abstaining from smoking, which does not permit the decision-maker’s last-minuteintensive work to achieve the goal, as was the case in the present study. Further research,
including theoretical and empirical work, is required to enrich decision-making models to
describe procrastination in detail.

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Suvorov, A., and J. Van de Ven (2008): “Goal setting as a self-regulation mechanism,” Discussion paper, Center for Economic and Financial Research (CEFIR).
Wilder, G. Z., and K. Powell (1989): Sex di↵erences in test performance: A survey of literature,
no. 89. College Entrance Examination Board New York.

32

AppendicesA

Test scores and academic courses
Table 6: Correlation matrix of SAA course and test scores
Statistic

SAA

Total

Math

English

SAA
Total
Math
English1-

0.67551-

0.58280.86991-0.61070.83720.6045
1

Table 6 displays the correlation matrix for achievement test scores and academic courses(a dummy variable for the SAA course). These data show that the SAA dummy and testscores have strong correlations, namely 0.6755 for total score (the cumulative score forMath, English, and Japanese), 0.5828 for Math, and 0.6107 for English, respectively.

B

Discussion of habit formation

In this section I discuss the possibility that the studying habits in the winter recess continued even in the post-intervention period. Charness and Gneezy (2009) and Levitt, List,
Neckermann, and Sado↵ (2012) report that their intervention successfully led to students’habituation of desirable activities, namely exercising in a gym and studying, respectively. I
discuss here whether this was also the case in the present context.
The upper panel of Figure 1 suggests that no persistent increase in aggregate studying
time occurred. However, studying habits might have been formed selectively by the highachievers. To determine whether this was the case, I performed OLS regressions (Table7), explaining the increase in studying time in February (Columns (1) and (2)) and March(Column (3)), with achievement rates evaluated at the end of January (Column (1)), thoseon January 7th (Column (2)), which was the original deadline for the homework, and thoseat the end of February (Column (3)).

33

Table 7: OLS estimates for increase in studying time post-intervention
Dependent variable
Increase in Feb.
(1)
Achievement until Jan. 31(2)

12.462(21.529)1.655(9.127)29.273(18.315)0.109(0.696)
6.106(27.596)10.036(9.668)28.624(17.818)
0.540(0.746)17.401(28.826)

30.084⇤⇤(13.278)
10.332⇤(6.347)3.825(14.237)
0.567(0.445)20.154(16.908)

426
0.030
0.020

426
0.011
0.002

426
0.037
0.028

Achievement until Feb. 28

SAA
Total score
Constant

Observations
R2
Adjusted R2(3)

38.640(25.900)

Achievement until Jan. 7

FemaleIncrease in Mar.

Note: Robust standard errors are in parentheses. ⇤ p<0.1; ⇤⇤ p<0.05; ⇤⇤⇤ p<0.01

34
The regression results are mixed: The studying time in February was larger among the students who had achieved more by January 31st, as the regression coefficient for the achievement rates is positive (but not significant, Column (1)). However, inconsistent with thisthe coefficient becomes negative when the achievement rates are evaluated on January 7th(Column (2)). Moreover, also inconsistently, the result in Column (3) demonstrates thatthe high achievers tended to study less in March. These regression results jointly provide no consistent or convincing evidence that particular studying habits emerged in the
post-intervention period.

C

Histogram of high achievers’ abilities

Figure 8: Histogram of high achievers’ (cognitive) abilities
Figure 8 displays the histogram of the high achievers’ cognitive abilities, measured in termsof the total score on the achievement test. Not only the middle- to high-achieving students,
who achieved around 50 or more, but also many low-achieving students, managed to achieve
the goal, and these latter students comprised the majority. The proportion of low-achieving
students whose score was below 45 is 75%.

35

D

Discussion of progress at the deadline
Table 8: OLS estimates for progress until the deadline
Dependent variable
Until Jan. 7
FemaleSAA
Total score
Constant

Observations
R2
Adjusted R2

0.060(0.090)0.002(0.112)0.002(0.008)0.558(0.333)
51
0.015
0.048

Note: Robust standard errors are in parentheses. ⇤ p<0.1; ⇤⇤ p<0.05; ⇤⇤⇤ p<0.01
In this section, I provide evidence that progress at the exact time of the deadline (January
7th, 2014) was almost identical among high achievers, regardless of their academic course,
gender, and abilities. The upper panel of Figure 4 shows that the progress at the deadline
was almost identical between the General and the SAA students. This first impression is
confirmed in the results of the following OLS regressions, explaining the progress in termsof the students’ observable heterogeneous characteristics (Table 8), in which no explanatory
variable significantly di↵ers from zero. The progress at the deadline was almost identicalacross academic courses and genders, in contrast to the distinct progress until close to thedeadline, as reported in Section 4.

36

EDiscussion of progress after the deadline

In this section, I discuss progress after the deadline. Figure 9 shows the histogram of highachievers’ progress after the deadline. Most of the students are clustered around zero,
indicating that most completed the homework by the deadline. With respect to the heterogeneity, Table 9 displays the results of OLS regressions, explaining the progress after thedeadline in terms of students’ characteristics. No regression coefficient di↵ers significantly
from zero, suggesting that the high achievers’ progress after the deadline was almost identical regardless of academic course, gender, and abilities, which contrasts significantly withthe distinct progress until close to the deadline, as reported in Section 4.

Figure 9: Histogram of high achievers’ progress after the deadline

F

Respective progress in Math and English

In this section, I provide the OLS estimates for the progress in Math and English, respectively, which were used to draw the plots in Figure 7. Table 10 presents the summary
statistics for the subsample (upper panel for Math and lower panel for English). The subsample includes the students whose final achievement rate evaluated on February 28th,
2014, was no less than 75% (and whose progress on December 20th was less than 10% for
excluding students who progress substantially before the intervension period). Table 11
demonstrates the estimates from the OLS regressions for Math progress. The regression
model is identical to that in Table 5, which underlies the plots in the left panel of Figure

37

Table 9: OLS estimates for progress after the deadline
Dependent variable
Progress
FemaleSAA
Total score
Constant

0.037 (0.098)
0.034 (0.117)
0.005 (0.009)
0.499 (0.361)

Observations
R2
Adjusted R2

51
0.014
0.049

Note: Robust standard errors are in parentheses. ⇤ p<0.1; ⇤⇤ p<0.05; ⇤⇤⇤ p<0.01
7. Similarly, Table 12 show the OLS regressions for English progress, which underlies theplots in the right panel of Figure 7.

G

Discussion of alternative explanations for female procrastination in MathIn the following two sections, I pursue explanations for the females’ selective procrastination in Math, which contrasted with the males, who showed no tendency to procrastinate
selectively in Math. In this section, I first explore the possibility of explanations other thanfemales’ aversion to Math, showing that none of these is positively supported by the data.
The first potential explanation to be discussed is that, in this particular subsample, females
were weaker than males in Math, therefore taking more time to pass each Math unit, regardless of whether or not they had Math-aversion.35 To determine this, I performed an
OLS regression, explaining the achievement test scores in Math in terms of students’ char35

In fact, many studies have documented that females are weaker in Math than males. An incomplete list
includes Guiso, Monte, Sapienza, and Zingales (2008), Ellison and Swanson (2010), Fryer and Levitt (2010),
Niederle and Vesterlund (2010), and Joensen and Nielsen (2014)).

38

Table 10: Summary statistics for high achievers in Math and English
Upper panel: Math. Lower panel: English

StatisticN

Mean

St. Dev.

Min

Max

FemaleSAA
Math score
Achievement by Jan. 7
Achievement by Feb. 28

64
64
62
64
64

0.578
0.219
41.748
0.743
0.990

0.498
0.417
8.104
0.389
0.037

0
0
30.700
0.000
0.800

1
1
60.6001.000
1.000

StatisticN

Mean

St. Dev.

Min

Max

FemaleSAA
English score
Achievement by Jan. 7
Achievement by Feb. 28

53
53
50
53
53

0.547
0.208
41.560
0.702
0.979

0.503
0.409
5.577
0.366
0.042

0
0
34.000
0.000
0.810

1
1
57.000
1.000
1.000

39

Table 11: OLS estimates for Math progress in each period
Dependent variable

FemaleSAA
Math score
Constant

Observations
R2
Adjusted R2

First

Second

Third

(1)

(2)

(3)

0.017
(0.041)0.141(0.085)0.001(0.001)
0.021(0.047)0.179⇤⇤(0.077)0.227⇤⇤(0.108)0.004(0.005)0.032(0.207)0.234⇤⇤(0.112)0.169(0.153)0.003(0.008)0.579⇤(0.329)

62
0.144
0.100

62
0.193
0.151

62
0.092
0.045

Note: Robust standard errors are in parentheses. ⇤ p<0.1; ⇤⇤ p<0.05; ⇤⇤⇤ p<0.0140

Table 12: OLS estimates for English progress in each period
Dependent variable

FemaleSAA
English score
Constant

Observations
R2
Adjusted R2

First

Second

Third

(1)

(2)

(3)

0.012
(0.013)0.060(0.038)0.004(0.003)0.127(0.104)0.065(0.086)0.160(0.155)0.004(0.012)0.015(0.459)0.145(0.110)0.273⇤(0.140)0.001(0.011)0.505(0.452)

50
0.224
0.173

50
0.070
0.009

50
0.090
0.031

Note: Robust standard errors are in parentheses. ⇤ p<0.1; ⇤⇤ p<0.05; ⇤⇤⇤ p<0.01
Table 13: OLS estimates for Math scores
Dependent variable
Math score
FemaleSAA
Constant
Observations
R2
Adjusted R2

2.004 (1.710)
10.478⇤⇤⇤ (1.910)
38.219⇤⇤⇤ (1.346)
62
0.332
0.309

Note: Robust standard errors are in parentheses. ⇤ p<0.1; ⇤⇤ p<0.05; ⇤⇤⇤ p<0.0141
acteristics (Table 13). The coefficient for the female dummy would be negatively significant
if the explanation holds. However, the coefficient for the female dummy does not di↵er significantly from zero. Moreover, the point estimate is positive, suggesting that the females
in this subsample may even be better than males in Math.
Table 14: OLS estimates for Math progress until January 1st
Dependent variable
Math progress until Jan. 1st
FemaleSAA
Math score
Eng. progress until Jan. 1
Constant

Observations
R2
Adjusted R2

0.197⇤⇤(0.077)0.352⇤⇤(0.133)0.004(0.005)0.142(0.172)0.026(0.214)
62
0.303
0.254

Note: Robust standard errors are in parentheses. ⇤ p<0.1; ⇤⇤ p<0.05; ⇤⇤⇤ p<0.01
The second possible explanation to be discussed is that females made English progress earlier
and Math progress was simply “crowded out” until the end of second period (January 1st).
To explore the possibility of such “crowding out” of Math, I added a conditional variable,
namely progress in English until January 1st, to the regression specification (4) in Table
11. The coefficient would be negative if the “crowding out” had occurred. The regression
result is presented in Table 14. Contrary to this potential explanation, the coefficient is not
significantly di↵erent from zero, and the sign is positive, with other coefficients remaining
almost una↵ected. This result contradicts the explanation that “crowding out” by English
progress occurred in Math progress among the present females, leading to the Math progress

42
being delayed until the end of the second period.

H

Additional discussion of females’ Math aversion

In this section, I provide positive evidence for females’ Math-aversion in further data fromthe same e-learning service in another educational institution, namely College B. College
B used the Surala e-learning system until March 2014 to allow students to review thethree-year middle school curriculum e↵ectively and promptly within a year, as requiredby the authority to prepare for incoming internships in middle schools in the following
years. Specifically, the college establish a required course for almost all freshmen using thee-learning system, in which they required the students to freely choose 400 units out of 722
possible units to work on during the academic year. The units included Math, English, andJapanese. There were 472 freshmen in the 2013 academic year (April 2013 to March 2014),
including 313 males and 159 females. Examining the di↵erent choice patterns for Mathunits across genders, I discuss whether or not the females in this sample showed aversion
to Math.

Figure 10: Fractions of choices for Math units in each month
Figure 10 displays the number of Math units passed monthly by the students as a fractionof all the units passed. Consistent with expectations, the females chose to complete fewer
Math units than the males, suggesting their tendency to avoid Math. To examine the fe-43

Table 15: OLS estimates for Math choices
Dependent variable
Fraction of Math

FemaleConstant

Observations
R2
Adjusted R2

(1)

(2)

(3)

(4)

0.030⇤⇤⇤(0.011)0.150⇤⇤⇤(0.007)0.046⇤⇤(0.019)0.172⇤⇤⇤(0.012)0.038⇤⇤⇤(0.013)0.160⇤⇤⇤(0.009)0.062⇤⇤⇤(0.021)0.181⇤⇤⇤(0.015)

2,569
0.004
0.003

1,136
0.007
0.006

1,980
0.006
0.005

897
0.013
0.012

Note 1: Cluster-robust (individual-level) standard errors are in parentheses. ⇤ p<0.1; ⇤⇤ p<0.05; ⇤⇤⇤ p<0.01
Note 2: (1) Pooled OLS on the entire data
(2) Until September 1st(3) Those who finally almost achieved the requirement (more than 97.5%)(4) Conditioned on both (2) and (3)44males’ lesser selection of Math units in more detail, I performed pooled OLS regressions,explaining individual monthly proportions of Math choices in terms of a female dummy(Table 15). The OLS regression for the whole sample (Column (1)) reveals that the regression coefficient for the female dummy is significantly negative, implying that females wereinclined to avoid Math. To check the robustness of the result, I restricted the subsample
until the end of August, the earlier period in which the students’ voluntary choice may be
more directly revealed, as they had a larger set of incomplete units from which to select(Column (2)). Again, the regression coefficient di↵ers significantly from zero in the negative direction. Moreover, I also examined another subsample, including only students who
eventually completed 400 units successfully to achieve the goal (Column (3)), and confirmedthat the regression coefficient remained significantly negative. Finally, I examined the casethat fulfills both conditions above, namely that for high achievers’ choice in the earlier stage(Column (4)), and the regression coefficient was even more negative than before. These results jointly and robustly suggest that females tend to have a stronger aversion to Maththan males.