1. FACULTY OF ECONOMICS AND BUSINESS
Differences in Grit on Labor Outcomes
Grit, Wages and Productivity
Esther Kaufman
0768493
Thesis submitted to obtain
the degree of
M.S Economics
Promoter: Prof. Iris Kesternich ...
Tutor: Franziska Valder ...
Academic year: 2019-2020
5. Abstract
Leuven, May, 2020.
This study examines the relationship between the personality trait grit and labor outcomes by ana-
lyzing the results of an online experiment. The experiment was completed virtually over the Internet by
627 participating Cologne University students and administered by CenterERdata, Tillburg University.
The experiment elicited reservation wages of respondents for a one- hour job. These results are accom-
panied by a 12 point Grit Scale survey that allows for calculated grit scores to be tested on three distinct
outcomes; reservation wages, total job completed and duration expended. This study finds no significant
differences between gritty and not gritty students. However, a significant positive relationship between
faculty grit and reservation wage is observed. Analysis of grit on three distinct outcomes contributes to
the conversation of the explanatory role of non-cognitive skills in labor outcomes.
v
6.
7. Chapter 1
Introduction
As the world enters a global recession following the COVID-19 crisis, the labor markets as once under-
stood by business, politicians, and civil actors are faced with a tumultuous transformation. Undoubtedly,
the generations forward will be faced with new challenges and new uncertainty. There arises an urgency
to better understand what drives behavior of labor entrants and fuel creative solutions to seemingly
vast challenges ahead.This thesis seeks to analyze the relationship between the personality trait recently
identified as ”grit” with productivity and reservation wages of labor market entrants. Pioneer of this
psychological trait, Angela Duckworth explains,
”Define grit as perseverance and passion. Grit entails working strenuously toward challenges,
maintaining effort and interest over years despite failure, adversity, and plateaus in progress.
The gritty individual approaches achievement as a marathon; his or her advantage is stamina”
(Duckworth et al., 2007 p 1088).
Certainly, as there arises a desperate need to persevere for a better and more stable future, grit as a
non-cognitive skill becomes an enticing trait to explore. However, there exists an important distinction
between cognitive and non-cognitive skills that must be addressed. Cognitive skills are more easily
measurable as outcomes of aptitude and achievement tests. Measurements of cognitive skill assess the
rate at which individuals learn and their acquired knowledge (Almunld et al., 2011). These measurements
have been widely applied to the ”g” factor scheme of generalized intelligence, developed by Charles
Spearman in 1904. The ”g” factor develops hierarchical order that allows for ease of measurement in
analysis of outcome performance (Heckman & Rubinstein, 2001).
Subsequently, psychology has also developed taxonomy for testing non-cognitive skills as measures of
personality traits. The most common taxonomy in psychology is referred to as the Big Five that identifies
personality by measurements of Extraversion, Openness to Experience, Neuroticism, Agreeableness and
Conscientiousness (John & Srivastava, 1999).
Although in early literature on human capital Economist ignored non-cognitive traits (Heckman
& Rubinstein, 2001), the rise of behavioral economics has given weighted importance to these skills
in determining labor outcomes. The foundation for behavioral economics is attributed to economist
Herbert Simon, who departed from the limits set by the assumption of perfect rationality. Under perfect
rationality, unforeseeable outcomes are not considered and computations of unique solutions are required
to determine optimal choices. As this is not reflective of choice behaviors commonly observed in human
nature, Behavioral Economics lies in the framework of limited rationality. The assumption of limited
rationality necessitates an explicit definition of the hierarchy in mechanisms considered in decision-making
(Simon, 1955). This structure allows for economists to infer causal relationships in decision-making within
a given set of limits. Limited rationality allowed for the beginnings of a bridge between the psychology
of rationality and economic precision for modeling causality.
Since, economist have found increasing evidence to suggest that non-cognitive traits are significant
to explaining unobserved variance in labor outcomes of seemingly similar individuals. In order to make
this connection, economists firstly address the challenges of identification. Behavioral economics follows
the situationist approach that considers,”all traits can affect productivity in all tasks”(Almunld et al., p
27).This approach accepts personality traits as malleable to exogeneity posed by actions, self-knowledge
and contextual situations (Almunld et al., 2011).
This method allows for the adaptation of psychology findings constructed in the processes of op-
erationalization and construct validity. Operationalization is the process by which psychologist pick
1
8. 2 CHAPTER 1. INTRODUCTION
a certain task that will measure a particular trait.Construct validity tests the selection made during
operationalization by measuring correlation. Psychological measurements than identify productivity on
designated tasks to determine given personality traits (Almunld et al., 2011). In applying these findings
to economical models, Almunld et al. (2011) suggest setting productivity as a function of traits, efforts,
actions and situations to which an agent can optimize to. This allows for interpretation of measured
personality to be reflected as the performance and effort derived from the optimization choice of the
agent in respect to its productivity.
This approach has allowed for testing of the common assumption that qualifies rewards in a compet-
itive market solely to cognitive skills (Nyhus et al., 2004). Indeed, commonly used determinants of labor
outcomes as age, education, experience, occupation and income offer little explanation for differences in
earnings among homogeneous populations (Bowles et al., 2001).
The non-cognitive trait of grit, similar to the pre-existing taxonomy of conscientiousness, is distin-
guished by its narrow definition pertaining to longer-term stamina (Palczynska, 2018; Duckworth et al.,
2007). Although grit is a novel trait, Bowles et al. (2001) find support for the psychological associa-
tion determined between conscientiousness and job performance (Barrick & Mount, 1991). Bowles et
al. (2001) analyze employment as a repeated game with different types of effort. By modeling effort
as a probability of ”neglecting” an assigned task, they find that a distinction can be made between
incentive-enhancing and incentive-depressing traits. This finding rejects the assumption of productivity
as an exogenous measure set in contract (Bowles et al., 2001).
Consequently, support for non-cognitive traits in explaining job performance has grown. The grow-
ing recognition of non-cognitive skills as measurable traits has not replaced effects of individual cognition.
Alternatively, individuals have been found to have different productivities respective to assigned tasks as
personality traits compliment or substitute cognitive skills (Almunld et al., 2011). A study by Heckman,
Strixrud & Urzua (2006) on log wages of the National Longitudinal Survey of Youth, 1979 (NLSY79)
supports an existing relationship between non-cognitive and cognitive skills. In modeling both skills as
latent variables, they find non-cognitive skills to explain between .4% and .9% of variance in log wages
(Heckman et al, 2006).In the same study by Heckman et al.(2006), cognitive skills further explained be-
tween 9% and 12% of differences in wages. Nyhus et al. (2004) acknowledge the abundance of literature
on the relationship of non-cognitive traits and labor outcomes that emerged from behavioral economics.
As a growing body of evidence points to non-cognitive skills as increasingly relevant, it is prudent to
begin the search for creative solutions here.
In further identifying grit independent to cognitive ability, Duckworth et al. (2007) laid the ground-
work for testing casual relationships between the non-cognitive trait and productivity. Although conclu-
sions of certainty can not be drawn amidst unprecedented times, identification of traits driving decision-
making is increasingly relevant. In a market encompassed by the discouragement of an infectious space
there persists a demand to understand the relevance of labor tenacity. The existing literature between
grit and labor outcomes allows for identification of actions and preferences that influence decision-making
of this trait.
Today grit has most extensively been studied in the context of education to meet a demand for the
amplification of non-cognitive skills perceived as assets. This pertains to the malleability of personality
that has been found to be most impressionable at young ages as it becomes increasingly stable with age
(Costa & McCrae, 2006). As education is a key determinant commonly used to study labor outcomes,
these findings allow for better modeling results of grit on productivity and earnings.
Through the lens of education, existing literature finds grit to relate to particular actions that
identify mechanisms of decision-making in respect to labor outcomes. In following outcomes of high school
students associated with varying degrees of grit, Mendolia et al. (2014) find those with higher grit scores
to be more likely to remain in both education and labor markets. Grit has also been found to be negatively
associated with wages when assessing its complementarity in determining wage inequality (Palcyznska,
2018). A finding further supported by (Lucas et al., 2015) that suggests gritty individuals persist on
tasks even when they face monetary losses. While personality traits alone only explain individual wage
variance of only one percent, interaction with cognitive skills accounted for 70% of additional variance
in wages unexplained by educational attainment alone (Palcysnka, 2018). Such findings continue to
reinforce the relationship between cognitive and non-cognitive skills.
Furthermore, strengthened work ethic attributable to grit shapes’ preferences in individual produc-
tivity. Alan et al. (2016) find that grit increases work ethic through the lens of classroom achievements of
students across a total of 37 schools in Turkey. Leaving 12 schools to serve as a pure control, a treatment
was applied to a sample in which teacher-training programs enhanced the underlying ideas of grit. In
9. 3
random selection, students were assigned a binary choice of an easy task versus a difficult task. Their
results find that the students enrolled in treatment schools were 15% more likely to attempt a difficult
task as compared to those studying in the designated control schools (Alan et al., 2016). As consideration
of personality traits by employers has become common knowledge, agent types with preferences for work
ethic can affect earnings.
This finds support in findings of Chetty et al (2011) in the study of long-term consequences of
personality on lifetime earnings. Classroom data gathered in Project STAR, an experiment to measure
the impact of class size and teacher experience, was matched to tax returns of 95 percent of student
participants. While the effects of class quality on test scores fade, gains in non-cognitive measures of
effort and initiative persisted and indicated positive lasting effects on accumulated earnings (Chetty et
al., 2011). This effect is reinforced by findings of Heckman & Kautz (2012) on the lasting social returns
of non-cognitive skills. Although a valid measurement for grit did not exist during the administration of
Project Star, the findings support that non-cognitive traits considered as sub factors of grit (effort and
tenacity) do not reverse over time. These results, in hand with the probable prospect of enhancing grit,
offer an attractive property of grit in formulating long-term predictions in the time of extreme volatility.
Furthermore, Alan et al. (2016) find that gritty students make pay-off maximizing choices. This was
secured by measuring expected payoffs based on accuracy and completion from both easy and difficult
tasks in respect to differentiated rewards. This allows for further interpretation of quality differentiation
in the productivity of gritty and non-gritty individuals.
This paper examines the relationship between the personality trait grit, reservation wage and pro-
ductivity by analyzing the results of an online experiment. The experiment was completed virtually
over the Internet by 627 participating Cologne University students and administered by CenterERdata,
Tillburg University. The experiment elicited reservation wages of respondents for a one- hour job. These
results are accompanied by a 12 point Grit Scale survey that allows for calculated grit scores to be tested
on three distinct outcome; reservation wage, total job completed and duration expended. This study
finds no significant differences between gritty and not gritty students. However, a positive significant
relationship between gritty academic faculties and reservation wage is observed. Contradicting findings
of Lucas et al (2015), it is possible these results are driven by other unobservable characteristics specific
to faculties. Analysis of grit on three distinct outcomes contributes to the conversation of the explanatory
role of non-cognitive skills in labor outcomes.
Albeit not significant, these findings observe the prevalence of substitution effects between cognitive
and non-cognitive skills (Almunld et al, 2011) in explaining variance of labor wages and productivity.
The structure of this paper is as follows. Section I provides an overview to the experiment and subsequent
data collected. Section II presents a discussion on observed differences between gritty and non-gritty
students. Section III presents regression analysis of differences in grit on outcomes of reservation wages
and productivity. Section IV presents robustness checks and relevance to existing literature. Section V
presents a conclusion on the relevance of findings and suggestions for further research.
10.
11. Chapter 2
Overview: Experimental Data and
Design
The analysis of this study utilizes data gathered from an online experiment issued to Cologne University
students solicited by online requests on University platforms. The experiment is completed virtually from
their personal computers and no particular skills or additional equipment are required. In total, responses
of 626 participants were collected. The experiment begins with the measurement of grit conducted by
the 12-point Grit Scale. The Grit Scale is a self-report survey that measures two distinct features; 1 =
not at all like me to 5 = very much like me and averaged to represent a measurement of grit (Duckworth
et al., 2007).
Experiment subjects are then offered a job to digitalize scanned PDF documents and are given one
of two job descriptions at random. Roughly half the sample receives a description of the job as one
that contributes to immediate medical research effort. The others are instructed to complete the job for
the purpose of digital archives with no significance. In this manner, the experiment acknowledges job
significance in outcome performance. In measuring the relationship between cognitive and non cognitive
skills captured by achievement tests, Heckman et al. (2012) raise the concern of the dependent nature
of behavior on rewards. To account for this in the subsequent analysis, a binary variable is used as a
control to ensure that it does not confound the variation posed by grit.
Given the job description, the reservation wage of participants is elicited following the standard tool
of the Becker-DeGroot-Marschak mechanism. Respondents are asked to indicate their reservation wage
following the job description as a value between 9 to 35 euros. This is compared to a random number
generator x, a value between 9.01 and 35. Those whose reservation wages were weakly above or below x
are admitted, otherwise the experiment ends. Participants are also given an option to quit. Those who
do begin the job are given a total of 76 text segments to digitalize. In testing productivity outcomes
pertaining to character counts, 9 observations are excluded for a technical fault in recorded durations.
2.1 Organization
The data is organized to best measure the effect of grit on reservation wage and productivity as measured
by character count completion and duration endured. Characteristics of age, gender and faculty of study
also control for the key variables of interest. Age differences among respondents are controlled for to
test differences found by Duckworth et al. (2007). In testing average grit scores between respondent
ages, Duckworth et al. (2007) find no significant changes in grit between age cohorts 25 - 34 and 35
- 44. However, those between 45 and 64 years old are found to have significantly higher levels of grit
(Duckworth et al., 2007). To account for grit that is attributed to age, a dummy variable is used to
indicate respondents between 25 and 44 years old. Furthermore, gender allows controlling for inherit
differences that may persists among participants. Differences in non-cognitive traits between genders
are supported by findings of Cooper (2018) in testing grit as a determinant of mismatch in college
attainment. Significant differences are also reported by Palczynska (2018) test of non-cognitive skills on
wages. Although the sample size of females and males in the study are similar, it does not provide for
a sufficient sample to analyze when distinguished between very gritty and not gritty. The breakdown of
the distribution is shown in Table 1 below.
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12. 2 CHAPTER 2. OVERVIEW: EXPERIMENTAL DATA AND DESIGN
Table 2.1: Table 1. Sample Descriptives
Female Male Age R.Wage Total Jobs Characters Duration (Hrs) Grit
—— Grit Level —
Very Gritty (75th PCTL) 37 28 25.62 15.20 12.48 8172.6 45.49 3.65
Not Very Gritty 168 107 25.57 15.36 12.56 8220.62 55.79 3.06
Gritty (50th PCTL) 87 71 25.22 15.06 12.63 8164.81 51.40 3.46
Not Gritty 118 64 25.89 15.55 12.49 8265.14 55.93 2.93
—— By Faculty —
Econ Social Sciences 157 133 24.5 15.71 12.32 8062.52 51.7 3.21
Law 20 17 26.24 15.33 13.91 9164.14 49.17 3.11
Medicine 45 17 26.27 14.49 12.78 8339.5 56.08 3.17
Arts and Humanities 20 7 28.52 14.47 12.89 8585.833 58.21 3.13
Mathematics 29 20 25.94 16 12.69 8293.034 49.42 3.28
Natural Sciences 29 8 25.84 13.5 13.15 8595.58 80.92 3.04
Other 35 23 29.64 15.133 12.02 7802.73 45.49 3.11
—— Total Sample —
Observations 330 224 554 542 36 340 340 617
Averages - - 25.67 17.47 12.54 8211.435 53.82 3.18
2.2 Grit
To identify the effect of grit, indicators are used to distinguish between gritty and non-gritty respondents
in the distribution. Indicators are chosen in reflection of the overall distribution of average grit scores
found among surveyed respondents. As seen above, the sampled average grit of 617 Grit Scale responses
is 3.18. In completion, the grit score distribution lies in range between 2.25 to 3.92 with a median
score of 3.1667. Very Gritty is used to indicate respondents whose grit scores are above or equal to the
75th percentile of the distribution, pertaining to a minimum score of 3.41. Corresponding are Not Very
Gritty scores of remaining respondents. The Gritty indicator is used to capture the median split of the
distribution that allows for robustness checks in further analysis.
2.3 Reservation Wage
In order to identify the effect of grit on reservation wages, the differences in wages elicited are also
observed. As shown in Table 1, reservation wages are recorded for 542 respondents. However, of these
231 do not begin the job as reflected by the total sample observed for character counts and total jobs
segments attempted. 25 of the 231 respondents chose the option to quit with reservation wages higher
than those randomly drawn. The remaining respondents who did not attempt jobs are attributed to
reservation wages that were lower than those randomly drawn. The reservation wages recorded provide
for a total sample of 311 reservation wages pertaining to participants that began the job.
2.4 Productivity
The effect of grit on productivity is also analyzed in this study by considering character counts. Figure
1 below provides a visual of the distribution between Very Gritty and Not Very Gritty participants.
In observing the total jobs segments attempted by respondents, the experiment finds that that the
maximum job segments attempted was 36. However, as the averages remain significantly lower among
the distribution, this maximum reflects an outlier as can be seen in Figure 1. Character counts are
summed for all individuals to account for the experiment technicality that allowed respondents to jump
through job segments with minimum characters entered. The distribution of character count allows for
better analysis of productivity in terms of quality and quantity achieved by participants. As seen, when
taking into account outliers, Very Gritty participants follow a similar clustering in average character
13. 2.5. FACULTIES 3
Figure 2.1: Character Count Distribution
counts achieved as non-gritty participants. This is further confirmed by t-test results of differences
presented in Table 2.
Productivity is also captured as a measurement of duration endured over the span of the experiment.
Although respondents were limited to one hour for the completion of the job, it was not required to be
consecutive. For this reason, the duration captured in the data corresponds to the total time spent on
the experiment. In this manner, the outcome of duration signifies the extent to which participants may
have postponed the completion of the jobs assigned. The common assumption that procrastination is
negatively associated with productivity is applied in interpretation of analysis. As seen in Figure 2,
significant variance in duration ranges from several minutes upward of two weeks. The frequency of
extremely low duration can be most attributed to the 286 participants who did not participate in the
job assigned.
Figure 2.2: Character Count By Grit
2.5 Faculties
Although no significant findings are determined between the general distributions of grit among partici-
pants, the study finds significant difference in grit among faculties. The surveyed respondents represent
students from 26 different majors of study. The majors are represented in Table 1 as they pertain to their
respective faculty of orientation at Cologne University. To the best knowledge of this study, there is no
existent literature pertaining to differences of grit between fields of study or among industry sectors. As
grit pertains to an elevated perseverance and determination, this trait has been most commonly tested
among high achievers.
14. 4 CHAPTER 2. OVERVIEW: EXPERIMENTAL DATA AND DESIGN
Duckworth et al. (2007) introduced the identification of grit as a distinct personality trait analyzing
survey data on 1,545 participants aged 25 and older. In analyzing responses, Duckworth et al. (2007)
generate a two-factor solution that identifies consistency of interests and perseverance of effort as two
sub-factors that allow for the scaling of an individual grit scores. The presence of grit is than tested
for significance through studies conducted on academic achievement of IV league university students,
retention rates of cadets enrolled in the U.S. West Point academy, and finalists of a global Spelling Bee
competition (Duckworth et al., 2007). These studies and those similar, provide insight on the prevalence
of grit among samples of especially competitive individuals. However, there persists a gap in understand-
ing grit among inherently less competitive individuals and as they pertain to different labor sectors. This
limitation is persists in this study, as all respondents observed are enrolled in higher education. Ranking
as the 18th of 66 ranked Universities in Germany (U.S News), a number of educational biases may pertain
to the Cologne University students observed.
The need for further research in the prevalence of grit among differing sectors is pertinent to studying
differences in innovation and performance. In a study of Australian entrepreneurs, Mooradian et al.
(2016) test the two sub-factors of grit, perseverance of effort and consistency of interest. Their study
confirms that perseverance of effort is positively related to innovation success, while consistency in
effort is found to improve firm performance (Mooradian et al., 2016). In a time of shifting markets,
the relationship between grit as it pertains to unique markets could be used to support investment
opportunities.
This study finds support that grittier faculties have a reservation wage 1.6 times larger than those
in less gritty studies . The positive relationship observed between reservation wages and grit contradicts
earlier findings by Lucas et al. (2015) and (Palcyznska, 2018). However, the difference in results may be
driven by unobservable heterogeneity between faculties and suggests cause for further research. As this
finding does not pertain directly to industry, it is presented as support for further investigation.
15. Chapter 3
Differences in Grit
To test for significant differences between identified grit distributions, a t-test is applied to reservation
wages, duration and productivity captured as mean values of character counts. Despite slight skewness in
character count distributions of respondents; the overall distribution as presented in Figure 1 is assumed
to converge to a normal distribution in accordance to the central limit theorem. The character count
distribution irrespective of grit levels as shown by Figure 3 offers support to t-test findings reported in
Table 2.
Figure 3.1: Character Count Distribution
Although no significant findings between grit distributions are observed, differences in quality of
productivity persist within the sample. To identify quality of productivity, job text segments completed
by respondents are summed to represent roughly 20%, 60%, 80% and 100% completion. This method
allows for a test in mean differences to be drawn on differing levels of productivity achieved between
grit distributions. This is observed for those who completed up to 7, 21, 28, and 36 jobs respectively.
Additionally, the differences observed in distributions of these job segments support the relevance of
quality associated with gritty students (Alan et al., 2016). Although the average character counts are
lower for gritty individuals, this does not imply worse productivity. In comparing the minimum values of
character counts, it is observed that they are considerably higher than for Not Very Gritty individuals.
The variation in character counts within identified segments reflects work of respondents who did not
complete the entire text segment required. For example, the minimum character count of 350 is observed
for a Not Very Gritty respondent whose work lies among those who completed between 1 to 7 text
segments. This corresponds to an entry in which only a few sentences of the paragraph assigned were
completed.
In order to ensure viability of t-test results applied to observed differences, respondents who made
no viable effort to begin a job segment are excluded in the calculation presented above. An assumption
of no viable effort is made on text entries that included single characters or an incoherent combination
of letters. An explanation for these types of entries relates to the experimental structure that allowed
respondents to skip through sections after entering only a minimum character. To avoid imposing
1
16. 2 CHAPTER 3. DIFFERENCES IN GRIT
stringent assumptions on effort, all other job entries, regardless of level of completion, are included in
the calculations of character counts.
Table 3.1: Differences between Grit Score Averages
N Mean Med Min Max Duration (Hours) *T-Test
—— Very Gritty——
Reservation Wage 61 15.20 15 9 30 -
Duration (Hours) 65 45.49 21.56 1.12 396.30 -
Total Jobs 65 12.48 12 1 34 -
Character Count 65 8172.6 7878 707 23515 -
7 Jobs 11 2742.18 2651 707 4825 38.73
21 jobs 25 10457.16 9929 8776 13075 50.39
28 jobs 3 16033.33 15290 15145 17665 59.012
36 jobs 1 - - - 23515 119.18
— Not Very Gritty—
Reservation Wage 250 15.36 15 9 30 - 0.829
Duration (Hours) 275 55.79 23.64 1.10 402.16 - 0.270
Total Jobs 275 12.56 13 1 28 - 0.912
Character Count 275 8220.62 8112 350 18688 - 0.938
7 Jobs 49 2898.18 2656 350 4882 58.89 0.735
21 jobs 97 10648.32 10215 8374 144339 60.29 0.553
28 jobs 25 15786.2 15283 13944 18688 37.03 0.797
36 jobs 0 - - - - - -
Despite insignificant findings, the differences in duration expended between gritty distributions
support the substitution effects discussed by the situationist approach of behavioral economist. In
testing for comparative advantage, this approach considers the factor of effort as one that may reflect
compensating cognitive skills (Almunld et al, 2016). This reflects the assumption that all traits can
affect productivity as lacking traits are compensated for. As effort can be a vector of time (Almunld et
al, 2016), duration spent on job segments captures this difference. In Table 2, Not Gritty students are
observed to have spent more time on the job and produced higher mean character counts. Interpretation
of the results through the situationist lens allows concluding that those with less grit compensate through
higher levels of effort. In other words, grit is substituted by effort. In considering duration as levels of
procrastination, the same interpretation holds. Those more inclined to procrastinate require exerting
higher levels of effort in order to complete a task.
To test for differences between educational faculties, an indicator variable is constructed to differen-
tiate between ”More gritty” and ”Less Gritty”. This identification is constructed by grouping faculties
according to average grit scores. Construction of more versus less gritty faculties allows for an ample
sample size to be used in application of a t-test. Table 3 shows that students of economics and social sci-
ences, medicine and mathematics had higher levels of grit than those in law, arts and humanities, natural
sciences or those who indicated other. T-test results find significant difference in levels of grit, but no
difference in reservation wages, character count or duration of respondents. Although not significant in
differences, students of grittier faculties are observed to have lower character counts and higher reserva-
tion wages. Nonetheless, a significant positive effect between students of gritty faculties and reservation
wages is observed in regression analysis presented in Table 4. However, this finding could be influenced
by expectation of wages.
17. 3
Table 3.2: Differences between Grit Score Averages
N Avg Grit R.Wage Character Count Duration
—— More Gritty——
Econ Social Sciences 282 3.21 15.71 8062.52 51.70
Medicine 62 3.17 14.49 8339.5 56.08
Mathematics 49 3.28 16 8293.03 49.42
Total : Econ Med Math 393 3.21 17.92 8116.23 52.25
— Less Gritty —
Law 37 3.11 15.33 9164.14 49.17
Arts and Humanities 27 3.13 14.47 8585.83 58.21
Natural Sciences 37 3.04 13.5 8595.57 80.92
Other 58 3.11 15.13 7802.73 45.49
Total : Law AH NS
Other
122 3.14 17.07 8231.25 59.68
T-Test by Total - 0.011** 0.134 0.819 0.487
A survey study of European students enrolled in business and economic studies, finds significant
determinants in explaining differing wage expectations among students (Brunello et al., 2004). As the
sample analyzed was comprehensive of German students among other European countries, its findings
are relevant to the experiment of Cologne University participants. Findings support that perceptions of
individual abilities and formal university education are significant determinants in higher wage expecta-
tions (Brunello et al., 2004). Furthermore, Betts (1996) finds significant difference in wage expectations
between disciplines of study in a survey of undergraduate students at the University of California, San
Diego. Findings of Betts (1996) support differences in wage expectations driven by year of study and
corresponding field. Overall, students surveyed across University of California majors finds that stu-
dents specialize in acquiring information on earnings respective to their specialized disciplines (Brett,
1996). Existing literature on expectation of wages could offer explanation for the differences observed
and contradictory results of previously established relationships between wages and grit.
Limitations of the present data do not allow for control of wage expectations. However, the sig-
nificant positive relationship observed between gritty faculties and reservation wages suggest cause for
further analysis.
18.
19. Chapter 4
Regression Analysis
4.1 Grit and Reservation Wages
The differences observed between grit distributions are further analyzed in regression framework. In
testing grit on Reservation wage, the first specification follows;
R Wagei = Brandomiβ1 + V eryGrittyiβ2 + i
Where R Wagei is the reservation wage subject i states in the experiment; Brandom controls for the
difference in job significance; V eryGritty remains the grit indicator as assigned by percentile distribution.
To account for censoring in reservation wages both Tobit and OLS regressions are conducted. This
method follows in line with a study by (Kesternich et al., 2020) that test similar experimental results for
effects on job significance. Figure 4 presents the distribution of reservation wages found in the data. As
the random number generator had a minimum value of 9.01, the Tobit model captures potential bias of
57 censored respondents.
Figure 4.1: Reservation Wages
Table 4 shows results from four specifications tested by Tobit and OLS. In addition to the first
specification presented, (2) and (3) shows results when controlling for age and gender.
1
20. 2 CHAPTER 4. REGRESSION ANALYSIS
Table 4.1: Reservation Wage and Grit
(1) (2) (3) (4) (5) (6) (7) (8)
R Wage R Wage R Wage R Wage R Wage R Wage R Wage R Wage
Brandom -1.030 -1.046 -1.019 -1.091 -0.720 -0.733 -0.712 -0.801
(0.152) (0.146) (0.157) (0.135) (0.230) (0.222) (0.236) (0.191)
Very Gritty -0.210 -0.200 -0.230 -0.447 -0.199 -0.194 -0.215 -0.439
(0.816) (0.824) (0.799) (0.623) (0.791) (0.797) (0.776) (0.565)
Gender -0.439 -0.460 -0.399 -0.411
(0.548) (0.530) (0.514) (0.503)
Age 0.0527 0.0441
(0.404) (0.406)
Age 25 44 -0.216 -0.123
(0.769) (0.841)
Gritty Major 1.361 1.270
(0.114) (0.079)
cons 15.23∗∗∗ 14.62∗∗∗ 16.05∗∗∗ 14.30∗∗∗ 15.70∗∗∗ 15.24∗∗∗ 16.41∗∗∗ 14.82∗∗∗
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
/
var(e.R Wage) 38.24∗∗∗ 38.09∗∗∗ 38.18∗∗∗ 36.60∗∗∗
(0.000) (0.000) (0.000) (0.000)
N 311 311 311 290 311 311 311 290
R2 0.0048 0.0085 0.0064 0.0163
p-values in parentheses
∗
p < 0.05, ∗∗
p < 0.01, ∗∗∗
p < 0.001
The results find that difference in age can reverse the relationship to reservation wages. Yet, no
evidence is observed to support that the inverse change be attributable to growth in grit. Those below
25 years of age had an average grit score of 3.18 (s.d = .38), those above 44 had a mean grit score of 3.09
(s.d= .38), and those in between had scores of 3.18 (s.d = .36). This suggests that when lower levels
of grit are excluded, a negative relationship between a grittier sample and reservation wage persists.
As no significant relationship is determined, the difference may also be driven by unobserved wage
expectations. Those who return to education at a significantly older age could be driven by previous
or current employment experience. Returning to education to further career development could explain
differences in wage expectations.
Specification (4) presents regression results based on the differences among faculties discussed in
section 2. As discussed in section 2, the positive relationship may be attributable to unobserved hetero-
geneity within faculties. OLS regressions on the outcome of reservation wage are observed in specifications
(5) through (8) and suggest that censored wages do not significantly change the relationships observed.
Overall, findings found in Table 4 indicate that grit has a largely negative association with reser-
vation wages, supporting findings of (Palcysnka, 2018) and (Lucas et al., 2015). However, the results
of this outcome are limited to the data collected. Missing observations exist for both reservation wages
elicited and random wages drawn for both students that did and did not begin the job. Of the missing
observation, 29 begin the job while 55 do not. As respondents were given the option to quit the job,
it is possible that students had reservation wages higher than 35 euros. This limitation does not allow
accounting for potential censoring from above that could result in underestimation. However, the ample
sample of recorded observations and multiple regression methods applied are supported in robustness
checks discussed in section 4.
21. 4.2. GRIT AND PRODUCTIVITY 3
4.2 Grit and Productivity
To test the effect of grit on productivity, methodology proposed by Kesternich et al. (2020) is followed.
OLS and Heckman selection model are applied to test character count and duration measurements as a
dependent variables. A two-step Heckman Selection model is used to account for potential selection bias
among participants who begin the job (Kesternich et al., 2020). The first step estimates the probability of
beginning a job. The second step estimates the productivity specification with consideration of selection
bias from reservation wages and jobs significance. The first specification is;
CharacterCounti = Brandomiβ1 + V eryGrittyiβ2 + i
Additional specifications follow those made in Table 4. The results in Table 5 present the first four
specifications as tested by a Heckman selection model. Results (5) through (8) present the findings of an
OLS regression. No significant findings are found in either method but a significant mills ratio confirms
a negative bias by the exclusion restriction. Additionally, job significance is found to be insignificant
in selection. This allows for inference to be drawn on a basis that job meaning does not confound grit
estimates.
Findings from the experimental data present a negative relationship between grit and productivity.
Despite a positive estimate of very gritty respondents in (4), students in gritty studies likewise have
a negative relationship to productivity. Similarly, a negative relationship is observed between grit and
duration.
To test the effect of grit on productivity as measured by duration, analysis follows the same speci-
fications as measured for character count. The first specification remains;
Durationi = Brandomiβ1 + V eryGrittyiβ2 + i
where Duration is the log transformation of duration hours captured in the experiment. A log transfor-
mation is applied in consideration of the variance observed in Figure 2. A Heckman selection model is
run to test for similar selection bias observed for those who began the job. However, as the mills ratio
is found to be insignificant in pertaining to duration as depicted in (5), OLS is used to test duration.
Overall, only age is found to have a positive effect on prolonging duration. This could suggest that
levels of procrastination change with age, but is not significant to the effect of grit on productivity. The
negative relationship observed between grit and duration does support the substitution effects discussed
by (Almund et al, 2011). As suspected, through a situationist lens, those with higher levels of grit exert
less effort. Similarly, higher levels of grit are observed to associate with lower levels of procrastination.
Specifications (6) through (9) in Table 5 correspond to robustness checks conducted using the median
split (Gritty) as an indicator for average grit.
22. 4 CHAPTER 4. REGRESSION ANALYSIS
Table 4.2: Character Count and Grit
(1) (2) (3) (4) (5) (6) (7) (8)
CC CC CC CC CC CC CC CC
Very Gritty -62.65 -49.08 -42.79 103.7 -43.70 -16.00 2.107 111.7
(0.908) (0.928) (0.937) (0.852) (0.934) (0.976) (0.997) (0.836)
Brandom -27.77 -33.78 -39.06 86.53 94.53 85.77 77.60 206.7
(0.952) (0.941) (0.932) (0.852) (0.821) (0.837) (0.852) (0.630)
Gender 348.9 385.3 662.2 708.2
(0.428) (0.382) (0.121) (0.098)
Age 3.935 -11.27
(0.918) (0.713)
Age 25 44 467.3 554.7
(0.291) (0.194)
Gritty Major -53.24 -136.1
(0.919) (0.782)
cons 9701.9∗∗∗ 9032.5∗∗∗ 8884.2∗∗∗ 9426.3∗∗∗ 8175.6∗∗∗ 7401.1∗∗∗ 6821.0∗∗∗ 8129.3∗∗∗
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
select
Reservation Wage -0.0899∗∗∗ -0.0899∗∗∗ -0.0899∗∗∗ -0.0911∗∗∗
(0.000) (0.000) (0.000) (0.000)
Brandom 0.0130 0.0130 0.0130 0.00229
(0.910) (0.910) (0.910) (0.984)
cons 1.765∗∗∗ 1.765∗∗∗ 1.765∗∗∗ 1.749∗∗∗
(0.000) (0.000) (0.000) (0.000)
/mills
lambda -2461.6∗∗ -2441.7∗∗ -2427.5∗∗ -2133.8∗
(0.003) (0.003) (0.003) (0.010)
N 542 542 542 521 340 340 340 318
R2 0.0002 0.0077 0.0123 0.0010
p-values in parentheses
∗
p < 0.05, ∗∗
p < 0.01, ∗∗∗
p < 0.001
23. 4.2. GRIT AND PRODUCTIVITY 5
Table 4.3: Table 5. Duration and Grit (Median Split)
(1) (2) (3) (4) (5) (6) (7) (8) (9)
D D D D D D D D D
Very Gritty -0.0685 -0.0606 -0.0581 -0.0966 -0.147
(0.784) (0.809) (0.817) (0.706) (0.564)
Brandom 0.0263 0.0240 0.0228 0.0740 0.0645 0.0253 0.0232 0.0221 0.0740
(0.894) (0.903) (0.908) (0.717) (0.752) (0.898) (0.906) (0.911) (0.717)
Gender 0.193 0.199 0.182 0.189
(0.339) (0.325) (0.367) (0.353)
Age -0.0000462 -0.000479
(0.997) (0.974)
Age 25 44 0.0729 0.0714
(0.719) (0.724)
Gritty Major -0.119 -0.116
(0.612) (0.623)
Gritty -0.134 -0.117 -0.115 -0.0729
(0.496) (0.557) (0.563) (0.723)
cons 2.791∗∗∗ 2.483∗∗∗ 2.443∗∗∗ 2.885∗∗∗ 2.639∗∗∗ 2.841∗∗∗ 2.553∗∗∗ 2.503∗∗∗ 2.897∗∗∗
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
select
Reservation Wage -0.0899∗∗∗
(0.000)
Brandom 0.0130
(0.910)
cons 1.765∗∗∗
(0.000)
/mills
lambda 0.203
(0.588)
N 340 340 340 318 542 340 340 340 318
R2 0.0003 0.0030 0.0034 0.0018 0.0014 0.0039 0.0042 0.0018
p-values in parentheses
∗
p < 0.05, ∗∗
p < 0.01, ∗∗∗
p < 0.001
24.
25. Chapter 5
Robustness Check
A Gritty indicator is used to identify grit scores in respect to the median value of the distribution. This
indicator corresponds to about 54% of respondents who have higher grit scores as compared the 46%
of less gritty respondents. The use of this measure tests a larger distribution of grit scores and their
respective variance on outcomes of reservation wages and productivity. This allows for a robustness check
to be made against those with significantly higher levels of grit. As seen in results reported in Table 5
and Table 6, when testing with mild distinction between grit scores there remain no significant results
between grit and labor outcomes. Robustness checks support the overall negative relationship between
grit and reservation wage as observed in this study and supported by literature presented. A robust
measure of grit suggests roughly .49lessinreservationwageswhileverygrittyscorescorrespondtoonly.21
less. This suggests that higher grit contributes to a lesser negative effects on reservation wages. Although
educational biases may influence these results, the findings support literature collected among high
achievers thus far.
As selection bias was found to be significant for those who began the job, consequent robustness
checks follow the Heckman selection model presented in Table 5. The results support that grit has a
negative relationship to productivity as captured by character counts. Those with robust measures of grit
are likely to complete roughly 25 less characters while very gritty respondents complete roughly 62 less
characters. Despite, the lesser estimate captured in a robust measure the negative relationship between
grit and productivity is confirmed. The lesser positive relationship observed when gritty disciplines are
considered further supports this relationship. The positive relationship observed in specification (4),
an inverse to all other specifications is most likely attributed to unobservable characteristics between
orientations of study. Still these findings do not capture the quality of productivity associated with
differences in grit. There persists room for further research in the efficiency differences that may exist
between different levels of grit.
In respect to productivity as a measure of duration, the slightly larger negative effect of the robust
measure supports the interpretation of substitution effects previously discussed. Nevertheless, as distri-
bution of duration does not follow a normal distribution the models remain an imperfect fit for the data.
Future research could aim to capture duration spent on individual job segments to allow for additional
testing in the causal relationship under assumptions of normality. Alternatively, future research of non-
parametric and dynamic methodology as they pertain to behavioral economics could improve estimation
of underlying causal relationships.
1
27. Chapter 6
Conclusion
As labor markets shift under economic strain, a demand to understand the decision-making mechanisms
of labor entrants rises. This study finds no significant relationship between the non-cognitive trait of
grit and labor outcomes measured in experimental data of Cologne University students. In both sample
observations and regression analysis, higher levels of grit are found to correspond to lower estimates
in reservation wages. Although not significant these findings align with previous literature regarding
preferences of gritty individuals.
However, further research on the relationship between grit and productivity is needed to enhance
the results of this study. The analysis conducted in this study observes a generally negative relationship
between grit and productivity. Yet, as the findings in the study are insignificant, there persists need
for further research on the causal relationship. The analysis of literature provided as it pertains to the
relationship of non-cognitive and cognitive skills provide potential avenues for further research.
Since (Bowles et al., 2001) first highlighted the need to investigate non-cognitive skills in labor
outcomes, growing literature [Heckman, et al., (2006); Nyhus (2004); Chetty et al (2011); Palczynska
(2018)] has found evidence for non-cognitive skills as key determinants. Thus far, the analysis pertain-
ing to the significance of non-cognitive traits has been exclusively tested as complementary to either
cognitive skills or additional non-cognitive traits. Pioneer of grit, Angela Duckworth, collaborates with
Economist in providing methodology for modeling causality through identification of actions, preferences,
and situations (Almunld et al., 2016). For this reason, the results of this study beg further research on
the relationship between grit and productivity as influenced by additional characteristics.
In regards to grit, the particular trait of locust of control has been found to have significant inter-
action. Locust of control refers to the perception of control an individual believes to hold in decision-
making. In their work on grit and labor outcomes Palcyska (2018) and Mendolia et al. (2014), find
locust of control to have a significant influence on outcomes between gritty and non-gritty individuals.
Complementary to this study, Araujo et al. (2013) finds additional support for the relationship between
locust of control and wages. In testing the trait of self- esteem on wages, Araujo et al. (2013) find direct
a positive effect on wages, but an indirect effect when controlled for locust of control. Evidence of signifi-
cant effects of interacting traits on wages allows for similar adaptation to the study of grit. Additionally,
the established relationship between cognitive and non-cognitive skills suggests that additional measures
of cognitive skills could affect estimates derived in this study. The limitations discussed in the findings
of this study intend to offer support for future investigation of grit as it pertains to wages, productivity
and disciplines of study.
As the abundance of literature on grit and education suggests grit to be a trait that can be amplified,
the need to understand its true effect on labor outcomes remains vital. As this is the first study on effects
of differences in grit scores on labor outcomes it serves as a foundation.
1
28.
29. Appendix A
Survey Questions
Translated Grit Scale Survey
1. I have mastered an important challenge even after setbacks.
2. New ideas and projects sometimes distract me from ongoing projects.
3. My interests are changing from year to year.
4. Setbacks do not discourage me.
5. I was obsessed with an idea or a project, but I have a short time later lost interest in it.
6. I work hard.
7. I often set a goal, but later I pursue another.
8. I’m having trouble concentrating on projects that are more than takes a few months.
9. I always finish what I start.
10. I have achieved a goal that has taken several years of work.
11. Every few months, I pursue new interests.
12. I am careful.
Categories: 1. Very inapplicable 1 2 3 4 5. Very applicable 5
Majors Listed
1 Biochemie (Biochemistry)
2 Biologie (Biology)
3 Chemie (Chemistry)
4 Erziehungswissenschaften, Bildungswissenschaften (Educational Sciences)
5 Geowissenschaften (Earth Sciences)
6 Geographie (Geography)
7 Geschichtswissenschaften (History)
8 Gesellschaftswissenschaften (Social Sciences)
9 Informatik (Computer Science)
10 Kunst (Art)
11 Lehramt (Teaching)
12 Mathematik (Mathematics)
13 Medienwissenschaften (Media Studies)
14 Medizin (medicine)
15 Neuere Philologien (Newer Philosophy)
16 Pharmazie (Pharmacy)
17 Philosophie, Arch¨aologie, Ethnologie (Philosophies, Archeology, Ethnology)
18 Physik (physics)
19 Psychologie (Psychology)
20 Rechtswissenschaft (Law)
21 Sportwissenschaften (sports science)
22 Sprachwissenschaften (Linguistics)
23 Sozialwissenschaften (Social Sciences)
24 Theologie (Theology)
25 Wirtschaftswissenschaften (Economics)
(BWL, VWL, Wirtschaftsp¨adagogik) (Business Administration, Economics, Business Education)
26 Sonstiges (Other)
3
30. 4 APPENDIX A. SURVEY QUESTIONS
Alan et al. (2019); Almlund et al. (2011); Barrick and Mount (1991); de Araujo and Lagos (2013);
Bowles et al. (2001); Betts (1996); Brunello et al. (2004); Chetty et al. (2011); Cooper (2016); Costa Jr
and McCrae (2006); Duckworth et al. (2007); Heckman et al. (2006); Heckman and Rubinstein (2001);
Heckman and Kautz (2012); John et al. (1999); Lucas et al. (2015); Mendolia and Walker (2014); Moora-
dian et al. (2016); Nyhus and Pons (2005); Palczy´nska (2018); ?
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