Experimental Evidence on the Productivity Effects ofGenerative Artificial Intelligence
Shakked Noy

Whitney Zhang

MIT

MIT

March 2, 2023
Working Paper (not peer reviewed)

Abstract
We examine the productivity effects of a generative artificial intelligence technology—theassistive chatbot ChatGPT—in the context of mid-level professional writing tasks. In apreregistered online experiment, we assign occupation-specific, incentivized writing tasks
to 444 college-educated professionals, and randomly expose half of them to ChatGPT.
Our results show that ChatGPT substantially raises average productivity: time taken
decreases by 0.8 SDs and output quality rises by 0.4 SDs. Inequality between workers
decreases, as ChatGPT compresses the productivity distribution by benefiting low-ability
workers more. ChatGPT mostly substitutes for worker effort rather than complementing
worker skills, and restructures tasks towards idea-generation and editing and away fromrough-drafting. Exposure to ChatGPT increases job satisfaction and self-efficacy andheightens both concern and excitement about automation technologies.

We gratefully acknowledge financial support from an Emergent Ventures grant, the George and Obie Shultz
Fund, and the National Science Foundation Graduate Research Fellowship under Grant No. 1745302. The
research described in this article was approved by the MIT Committee on the Use of Humans as Experimental
Subjects, and was preregistered at the AEA RCT Registry (AEARCTR-0010882). We thank Daron Acemoglu,
Nikhil Agarwal, David Autor, Lucas Barros, Talia Benheim, Amy Finkelstein, John Horton, Simon Jäger, Ailidh
Leslie, Jackson Mejia, Ilan Noy, Liora Noy, Emily Partridge, Charlie Rafkin, Aakaash Rao, Nina Roussille, Chris
Roth, Frank Schilbach, Benjamin Schoefer, Lexi Schubert, Advik Shreekumar, Shine Wu, and participants at the
MIT Labor Lunch for helpful comments and conversations.

1Introduction

Recent advances in generative artificial intelligence may have widespread implications for
production and labor markets. New generative AI systems like ChatGPT or DALL-E, which
can be prompted to create novel text or visual outputs from large amounts of training data,
are qualitatively unlike most historical examples of automation technologies. Previous waves
of automation predominantly impacted “routine” tasks consisting of explicit sequences ofsteps that could be easily codified and programmed into a computer (Autor and Dorn, 2013;
Autor, 2015). Creative, difficult-to-codify tasks (such as writing and image generation) largely
avoided automation—a pattern that scholars noted might change with the advent of the deeplearning techniques that now underpin generative AI systems.
The emergence of powerful generative AI technologies reintroduces a host of classic
questions in a new context. Automation technologies—by definition—perform specific tasks
in place of humans. But, more broadly, they may either displace humans completely fromcertain occupations or complement existing human workers and increase their productivity(Acemoglu and Restrepo, 2020; Boustan et al., 2022; Kanazawa et al., 2022). Insofar as
automation technologies mostly displace human workers, they can increase unemployment,
while their impacts on aggregate productivity may be small or nonexistent to the degree thatthey mainly serve to redistribute income from workers to owners of capital (Acemoglu andRestrepo, 2018). Insofar as automation complements existing workers, it can simultaneously
benefit workers, capital owners, and consumers by raising productivity and wages andlowering prices (Kleinberg et al., 2018; Hoffman et al., 2018; Agrawal et al., 2019).
For example, a potent generative writing tool like ChatGPT might entirely replace certain
kinds of writers, such as grant writers or marketers, by letting companies directly automate
the creation of grant applications and press releases with minimal human oversight. Thismight not increase the quality of the resulting written output, but would let companies save on
wage costs by eliminating human labor. Alternatively, a tool like ChatGPT could substantiallyraise the productivity of grant writers and marketers, for example by automating relatively
routine, time-consuming subcomponents of their writing tasks, such as translating ideas into
an initial rough draft. In this case, demand for these services could expand, resulting in higher
employment and wages as well as greater productivity for companies and cheaper products
for consumers. Inequalities between workers could also be affected: inequality could decrease
if lower-ability workers are helped more by ChatGPT, or increase if higher-ability workers
have the skills necessary to take advantage of the new technology.
Which of these eventualities will generative AI systems bring about? The answer depends
on a host of questions: how do generative AI systems affect workers’ productivity in existingtasks? Do they affect productivity largely by substituting for worker effort or complementing
worker skills? Do they differentially affect low- or high-ability workers, or workers with1

different skill profiles? Do they affect workers’ enjoyment of their work (Schwabe andCastellacci, 2020)?
This paper takes the first step towards answering these questions.1 In an online experiment,
we recruit 444 experienced, college-educated professionals and assign each to complete two
occupation-specific, incentivized writing tasks. The occupations we draw on are marketers,
grant writers, consultants, data analysts, human resource professionals, and managers. The
tasks, which include writing press releases, short reports, analysis plans, and delicate emails,
comprise 20-to 30-minute assignments designed to resemble real tasks performed in theseoccupations; indeed, most of our participants report completing similar tasks before andrate the assigned tasks as realistic representations of their everyday work. Participants facehigh-powered incentives, in the form of large bonus payments, to produce high-quality work.
Quality is assessed by (blinded) experienced professionals working in the same occupations.
Evaluators are asked to treat the output as if encountered in a work setting and are incentivized
to grade outputs carefully. Evaluators assign an overall grade as well as separate grades
for writing quality, content quality, and originality. Each piece of output is seen by threeevaluators, with an average within-essay cross-evaluator correlation of 0.44.
A randomly-selected 50% of our participants—the treatment group—are instructed to sign
up for ChatGPT between the first and second task, are walked through how to use it, and aretold they are permitted to use it on the second task if they find it useful. The control group isinstead instructed to sign up for the LaTeX editor Overleaf. This design allows us to estimate
the causal effects of ChatGPT using a combination of within-person and between-person
variation, and performance on the first task serves as a measure of baseline ability that enables
our inequality analyses.
We collect participants’ output and elicit total time taken, time taken on various subcomponents of the task, job satisfaction, self-efficacy, and beliefs about automation. We also take asnapshot of each participant’s output each minute while they perform the task, to construct
an objective measure of time active on the task and to detect ChatGPT usage in the control
group and on the pre-treatment task.
A complete description of our experimental design, a copy of relevant survey questionnaires,and additional figures validating our central measures and extending our main results areincluded in the Online Appendix. Descriptive statistics about the sample, as well as balanceand selective attrition tests, are available in Table 1. The attrition rate is 5% in the control
group and 10% in the treatment group. Balance tests indicate that across 13 pre-treatment
characteristics, the treatment and control group exhibit a small significant difference only
for only two characteristic (employment status and being an HR professional). Our partly
within-person design, which controls for performance on the pre-treatment task, should
1A nascent literature has studied applications of machine learning to predictive tasks, but not generative tasks.

2

eliminate any influence of selective attrition on our results; in the Online Appendix, we
also report Lee (2009) bounds on our main results and versions of our results controllingfor employment status and occupation, which confirm that our results are highly robust to
selective attrition.

2

Results

2.1

Takeup of ChatGPTIn the treatment group, 92% of treated participants successfully sign up for ChatGPT,2 and
81% choose to use it on the second task, giving it an average self-assessed usefulness score of
4.4 out of 5.
Prior to treatment, about 70% of our participants had heard of ChatGPT and 30% had used
it before. Self-reported and objective measures indicate only 10-20% of the control group use
ChatGPT on the tasks, meaning there is at least a 60 percentage point experimentally-induced
gap in usage between our treatment and control groups on the second task. The fact thatsome control participants are using the tool means our estimates provide lower bounds on theeffects of ChatGPT usage on productivity.

2.2

Productivity

We measure productivity as earnings per minute. Figure 1 shows that the experimental
intervention increases this outcome dramatically. In the treatment group, time taken on the
post-treatment task drops by 10 minutes (37%) relative to the control group, who take anaverage of 27 minutes (p = 0.000). Average evaluator grades in the treatment group increaseby 0.45 standard deviations (p = 0.000), with roughly similar increases for overall grades andspecific grades for writing quality, content quality, and originality.
Figure 1 Panels (c) and (d) show that these effects are not limited to specific pockets of thetime or grade distributions: the entire time distribution shifts to the left (faster work) and theentire grade distribution shifts to the right (higher quality). At the individual worker level,
Figure 2 shows that treated workers who received a low grade on the first task experience bothincreases in grades and decreases in time spent, while workers who received a high grade
maintain their grade level while substantially reducing their time spent.
These results are virtually identical across our two main incentive schemes, covering 80%
of respondents: a “linear” scheme in which respondents are paid $1 for each point they receive
2Our survey was mostly active only after 5pm EST, when ChatGPT was almost always up, ensuring a very
high successful signup rate. About 8% of treated respondents nevertheless face idiosyncratic technical difficulties
that prevent them from signing up.

3

on each submission (each of which is graded on a 1-7 point scale), and a “convex” scheme inwhich respondents are additionally paid $3 for earning a grade of 6 or 7, giving them an extra
incentive to produce high-quality output.
Figure 1: Treatment Effects on Productivity(b) Average Grades Increase(a) Time Taken Decreases
5.2

Treatment Effect: -0.83 SDs
95% CI: [-0.63, -1.03]

30

25

20

Treatment Effect: 0.45 SDs
95% CI: [0.27, 0.63]

4.4

4

TreatedControl

15

3.6

Pre-Treatment

Post-Treatment

Pre-Treatment(c) Time Distribution (Second Task)
40

Post-Treatment(d) Grades (Second Task)
50

Control, Mean: 27
Treatment, Mean: 17

Percent of Essay-Grades

Percent of Respondents

TreatedControl

4.8

Mean Grade

Self-Reported Time Spent (Minutes)

35

30

20

10

0

Control: Mean 3.789
Treatment: Mean 4.54

40

30

20

10

0
0-5

6-10

11-15

16-20

21-25

26-30

31-35

36-40

Minutes Spent on Second Task

41-45

>45

1

2

3

4

5

Second-Task Grade67

Note: Panels (a) and (b) plot means (and 95% confidence intervals for those means) of self-reported time taken
and average grades in the first and second task, separately in the treatment and control groups. The results lookvery similar for the objective measure of time active; see Supplementary Materials. The panels also display thecoefficient on the treatment dummy from a person-evaluator-level OLS regression of the outcome variable (thewithin-person difference in the outcome between task 2 and task), on a treatment dummy, evaluator fixed effects,
incentive arm fixed effects, and occupation*task-order fixed effects, clustering at the worker level. The treatment
effect coefficient and standard error are normalized by dividing by the pre-treatment standard deviation of theoutcome in the relevant incentive group(s). Panels (c)-(d) display raw graphs of the outcome distribution in thetreatment versus control group on the second task.

Two supplementary interventions allow us to probe further. In one arm involving 20%
of participants, we require participants in both the treatment and control group to spend
exactly 15 minutes on each task. This holds effort fixed across the treatment and control
groups, allowing us to interpret any difference in grades as a pure effect of ChatGPT access on
productive capacity. In this arm, the treatment increases grades by a similar 0.39 standard
deviations (p = 0.13), albeit imprecisely estimated and with a slight imbalance in pre-treatment4

outcomes (see Online Appendix Figure A.7).
In another arm involving 30% of the treatment group, after completing the second task,
respondents are shown their first-task output and given the opportunity to edit or replace itusing ChatGPT if they wish. 23% choose to replace their response with ChatGPT’s output and
25% use ChatGPT to edit their original response, suggesting that participants view ChatGPT
as a way to improve output quality in addition to a convenient way to save time.

2.3

Productivity Inequality

The control group exhibits persistent productivity inequality: participants who score well on
the first task also tend to score well on the second task. As Figure 2 Panel (a) shows, there is acorrelation of 0.49 between a control participant’s average grade on the first task and theiraverage grade on the second task.
In the treatment group, initial inequalities are half-erased by the treatment: the correlation
between first-task and second-task grades is only 0.25 (p-value on difference in slopes = 0.004).
This reduction in inequality is driven by the fact that participants who scored lower on the firstround benefit more from ChatGPT access, as the figure shows: the gap between the treatment
and control lines is much larger at the left-hand end of the x-axis.

2.4

Human-Machine Complementarity

ChatGPT could increase workers’ productivity in two ways. On the one hand, it could
substitute for worker effort by quickly producing output of satisfactory quality that workers
directly submit, letting them reduce the time they spend on the task. On the other hand, it
could complement workers’ skills: humans and ChatGPT working together could produce
more than the sum of their parts, for example if ChatGPT aids with the brainstorming process,or quickly produces a rough draft and humans then edit and improve on the draft. In our
experiment, evidence for the complementarity story could come in two forms: (a) we could
observe treatment-group participants choosing to expend significant time editing ChatGPT’s
output or repeatedly prompting ChatGPT in anticipation of earning higher grades, and (b) we
could observe that treatment participants’ essays receive higher grades than ChatGPT’s raw
output, suggesting that human input adds value.
We observe neither of these pieces of evidence, suggesting that ChatGPT is increasing
productivity primarily by substituting for worker effort. 68% of treated participants reportsubmitting ChatGPT’s initial output without editing it, and on average treated participants areactive on the task for only 3 minutes after we first observe them pasting in a large quantity oftext (presumably from ChatGPT). There is also no correlation between how long a participant
is active after pasting in the ChatGPT text and the grade they ultimately receive, and treated5respondents do not receive higher average grades than raw ChatGPT output that we giveto evaluators to grade, meaning we find no evidence that human editing is improving the
ChatGPT output. This is true even when participants are given strong pecuniary incentives to
do so, in the convex incentives group.
Figure 2: Effects on Grades and Time Across the Initial Grade Distribution(a) Grade Inequality Decreases
6

Mean Grade: Task 2

Change in95%
Slope:
CI: -0.999[0.62, -2.61]
5

4

3

Change in Slope: -0.243
95% CI: [-0.08, -0.41]

2
1

2

3

4
Mean Grade: Task 1

5

6

7

Control, Slope: 0.491 (SE 0.053)
Treatment, Slope: 0.248 (SE 0.065)(b) Time Taken Decreases Across Grade Distribution
40

Time Taken: Task 2

35
30
25
20
15
10

1

2

3

4
Mean Grade: Task 1

5

6

7

Control, Slope: 0.802 (SE 0.600)
Treatment, Slope: -0.198 (SE 0.564)

Note: this figure display scatterplots, binning responses in equal intervals, of respondents’ task-2 grade (Panel(a)) and task-2 time spent (Panel (b)) on their task-1 grade, separately by treatment and control group. Slopes arecalculated through a worker-level regression.

6

2.5

Task Structure

As suggested by the preceding discussion, ChatGPT substantially changes the structure ofwriting tasks. Figure 3 Panel A shows that prior to the treatment, participants spend about
25% of their time brainstorming, 50% writing a rough draft, and 25% editing. Post-treatment,
the share of time spent writing a rough draft falls by more than half and the share of timespent editing more than doubles.

2.6

Skill Demand

If ChatGPT is especially helpful to those with poor writing and communication skills relative
to their other skills, it could have major labor market implications by expanding the available
occupational choices and raise the earnings of individuals with strong idea-generation skills
who struggle to effectively get those ideas onto paper.
We perform several tests of this hypothesis. We construct two measures of a person’s
relative writing skills. First, at the beginning of the experiment, we ask participants to rank
from 1 to 3 their skills at communication (writing and speaking), problem solving, andcreativity. Second, in addition to assigning overall grades, evaluators separately assess eachpiece of output based on writing quality, content quality, and originality; the gap between aperson’s first-task overall score and their writing score affords another measure.
Similarly, we construct two measures of the individual-level benefits of ChatGPT. First,
at the end of the experiment, we ask treatment-group participants how much they would
be willing to pay on a monthly basis to access ChatGPT in their jobs. Second, we measure
how much each treatment participant’s grade increases from the first to the second task. We
find no clear evidence for the aforementioned hypothesis. Figure 3 Panel B show that average
willingness to pay for ChatGPT is flat across the terciles of both our measures of writing skill:
respondents, regardless of their writing skills, are willing to pay about 0.5% of their monthly
salary for a monthly subscription to ChatGPT. Grade gains from ChatGPT are also roughly
flat across both measures of relative writing skills: people with comparatively poor writing
skills do not experience unusually large grade gains.

7

Figure 3: Effects on Task Structure and Skill Demand(a) Effects on Task Structure
60

Control
Treatment

Percent of Time Spent

50

40

30

20

10

Brainstorming

Rough-Drafting

Editing(b) No Clear Heterogeneity in Benefits of ChatGPT by Relative Writing Skills
Outcome: Willingness to Pay for ChatGPT (% Salary)
.8
.6
.4
.2

Outcome: Grade Gain from ChatGPT
1.5
1
.5
0

Self-Rated
Communication Skills

Bad Writer

Overall versus Writing
Grade on First Task

MediumGood Writer

Note: Panel (A) shows self-reported time allocation on three separate task components, separately in the treatment and control group on the
post-treatment task. Panel (B) shows that ChatGPT does not have greater benefits for treated respondents whose writing skills are poor
relative to their other skills. Specifically, in the left-hand column, respondents are sorted by their self-assessed skill rankings: at the start ofthe survey, they rank their communication, problem-solving, and creativity skills. Those who rank communication 1st are defined as “Good”
communicators, similarly for those who rank it 2nd and 3rd. In the right column, participants are sorted according to their grades:
evaluators give each essay a separate overall, writing quality, content quality, and originality score. We define people as “Good”
communicators if their average writing score exceeds their overall score, (about 30% of respondents), “Medium” if the two are equal (50%),and “Bad” if overall exceeds writing (20%). In the top row, the outcome is how much the participant is willing to pay to access ChatGPT intheir job (elicited hypothetically). In the bottom row, the outcome is the participant’s grade gain between the first and second task
(everywhere we restrict to treated participants). Flat slopes within each of the

8

2.7

Job Satisfaction and Self-Efficacy

Access to ChatGPT could affect job satisfaction. For example, it could make participants
happier by automating tedious or annoying components of the task or allowing them to finish
more quickly. Alternatively, it could make the experience less enjoyable by quickly automating
the most fun parts of the task. It could similarly either boost self-efficacy by giving participants
access to a complex and powerful tool that enhances their capabilities, or it could lower it bymaking participants feel superfluous. We measure job satisfaction with a question, after eachtask, about how much participants enjoyed the task, and self-efficacy with a question about
how skilled/effective they felt while completing the task, both on 1-10 Likert scales.
Figure 4 Panel (a) shows that ChatGPT substantially increases job satisfaction, by about
0.40 standard deviations (p = 0.000). Panel B shows that it mildly and imprecisely increases
self-efficacy, by 0.20 standard deviations (p = 0.060), despite the fact that participants aremostly using it to substitute for their own effort. Qualitative feedback from participants (in an
open-text box at the end of the survey) suggests that many enjoy discovering and working
with this tool.

2.8

Beliefs About Automation

Many of our treated participants had never heard of (30%) or never used (70%) ChatGPT
before participating in the experiment. Hence, most essentially encounter the technology forthe first time and receive a crash course on its usefulness for writing tasks. How are theirbeliefs about future waves of automation affected by this encounter?
After respondents complete their second task, we elicit three beliefs, each on a 1-10 scale:
how worried they are about workers in their occupation being replaced by AI; how optimistic
they are that AI will make workers in their occupation more productive; and, overall, how
optimistic or pessimistic they feel about future advances in AI. The effects of treatment onthese outcomes are displayed in Figure 4 Panel (c); worry about automation increases by 0.26
standard deviations (p = 0.006), excitement by 0.39 (p = 0.000), and net optimism increases by
about 0.20 (p = 0.037).

9

Figure 4: Effects on Subjective Outcomes(a) Job Satisfaction(b) Self-Efficacy

.4
.2

.2

Self-Efficacy (SDs)Job Satisfaction (SDs)Treatment Effect: 0.5 SDs
95% CI: [0.32, 0.68]

0-.2TreatedControl

-.4

.1

0

-.1

-.2

Pre-Treatment

Treatment Effect: 0.2 SDs
95% CI: [-0.02, 0.42]

Post-Treatment

TreatedControl
Pre-Treatment

Post-Treatment

diff: 0.389 SDs
p-value: 0.000

diff: 0.2 SDs
p-value: 0.037

Excited About
Enhancement

Overall
Optimism

diff: 0.264 SDs
p-value: 0.006

4

Average Response (1-10 Scale)
5
6
7

8

(c) Automation Beliefs

Worried About
Replacement

Control

Treatment

Note: Panels (a) and (b) show job satisfaction and self-efficacy (originally elicited on scales of 1-10, normalized tohave mean 0 and standard deviation 1) pre- and post-treatment in the treatment and control group. Dots aremeans and error bars are 95% confidence intervals for means. The figures also print the coefficient on thetreatment effect of a regression specified as in Figure 1. Panel (c) cross-sectionally compares beliefs about
automation in the treatment and control group, all on 1-10 scales; the first question is “How worried are you
about workers in your occupation being replaced by AI?” The second is “How optimistic are you that AI may
make workers in your occupation more productive?” The third question is “How do you feel about the impactsof future advances in AI (1 = Very pessimistic, 10 = Very optimistic)”.

10

2.9

Two-Week Followup Survey

One indication of the value of ChatGPT to participants is whether they continue to use it after
the experiment. To track whether participants are subsequently using ChatGPT in their realjobs, we resurvey them two weeks after their completion of the initial survey. This followup is
still in progress, with an 82% response rate among the 423 respondents who have been invited
so far, and no evidence of differential response rates by treatment status.
33% of former treatment group participants have used ChatGPT in their job in the past
week, relative to 18% of control group participants. Restricting to workers who had notpreviously used ChatGPT when they participated in our main experiment, 26% of treated and
9% of control workers are now using ChatGPT in their jobs (p-value on difference 0.048). Users
give it an average usefulness score of 3.65/5.00, somewhat lower than in our main experiment,
likely owing to the greater length and complexity of real-world tasks. The range of tasks theyreport using it for is broad: generating recommendation letters for employees, responding tocustomer service requests, brainstorming, search-engine requests, rough-drafting emails, andso on.
Respondents who are not using ChatGPT in their jobs mostly report that this is because
the chatbot lacks context-specific knowledge that forms an important part of their writing.
For example, they report that their writing is “very specifically tailored to [their] customers
and involves real time information” or “unique [and] specific to [their] company products.”
These comments point to an important (and inherent) limitation of our experiment: it
involves relatively small, self-contained tasks that lack much context-specific knowledge
beyond what we stipulate in the task prompts. However, our core result, that ChatGPT can
increase productivity on many mid-level professional writing tasks, is supported by the factthat many respondents choose to use it in their real jobs. The fact that the treatment groupis substantially more likely to use ChatGPT than the control group also suggests that thedissemination of ChatGPT into real professional activity is still in its very early stages, withmany people not using it due to a lack of knowledge about or experience with the technology.
There is no difference between the former treatment and control in their overall satisfaction
with their job in the followup survey. That said, many of our respondents only started using
ChatGPT in their job in the past week or two, and it may take longer for access to ChatGPT toaffect overall job satisfaction.

3

Discussion

College-educated professionals performing mid-level professional writing tasks experience
substantial increases in productivity when given access to ChatGPT. The generative writing
tool increases the output quality of low-ability workers while reducing their time spent, and it
11allows high-ability workers to maintain their quality standards while becoming significantly
faster. At the aggregate level, ChatGPT substantially compresses the productivity distribution,
reducing inequality. It is also already being used by many workers in their real jobs. The
experimental evidence suggests that ChatGPT largely substitutes for worker effort rather thancomplementing workers’ skills, potentially causing a decrease in demand for workers, withadverse distributional effects as capital owners gain at the expense of workers.
The experiment has several important limitations worth enumerating. First, the tasks are
relatively short, self-contained, and lack a dimension of context-specific knowledge, which may
inflate our estimates of ChatGPT’s usefulness. The results on job satisfaction and self-efficacy
are similarly limited, reflecting enjoyment of a small task rather than feelings about a worker’s
whole job, as evidenced by the fact that there is no difference between the treatment andcontrol groups in real job satisfaction after two weeks. Second, an experiment, by its nature,
captures only direct, immediate effects of ChatGPT on the selected occupations. There will bemany indirect, reinforcing, or counteracting “general-equilibrium” effects as labor markets
and production systems adapt to the advent of technologies like ChatGPT. The effects of
ChatGPT will also likely vary by occupation, task, and skill level.
Only time and future research will fully reveal how ChatGPT and its successors will affectlabor markets. For now, the evidence we provide suggests that generative AI technologies
will—and have already begun—to noticeably impact workers.

12Bibliography
Acemoglu, Daron and Pascual Restrepo, “The Race between Man and Machine: Implicationsof Technology for Growth, Factor Shares, and Employment,” American Economic Review,
2018, 108 (6), 1488–1542.
and , “Robots and Jobs: Evidence from US Labor Markets,” Journal of Political Economy,
2020, 128 (6), 2188–2244.
Agrawal, Ajay, Joshua S Gans, and Avi Goldfarb, “Artificial Intelligence: The Ambiguous
Labor Market Impact of Automating Prediction,” Journal of Economic Perspectives, 2019, 33(2), 31–50.
Autor, David, “Why Are There Still So Many Jobs? The History and Future of Workplace
Automation,” Journal of Economic Perspectives, 2015, 29 (3), 3–30.
and David Dorn, “The Growth of Low-Skill Service Jobs and the Polarization of the US
Labor Market,” American Economic Review, 2013, 103 (5), 1553–1597.
Boustan, Leah Platt, Jiwon Choi, and David Clingingsmith, “Automation After the Assembly
Line: Computerized Machine Tools, Employment and Productivity in the United States,”
NBER Working Paper, 2022.
Hoffman, Mitchell, Lisa B Kahn, and Danielle Li, “Discretion in Hiring,” Quarterly Journal of
Economics, 2018, 133 (2), 765–800.
Kanazawa, Kyogo, Daĳi Kawaguchi, Hitoshi Shigeoka, and Yasutora Watanabe, “AI, Skill,
and Productivity: The Case of Taxi Drivers,” NBER Working Paper, 2022.
Kleinberg, Jon, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan, “Human Decisions and Machine Predictions,” Quarterly Journal of Economics,
2018, 133 (1), 237–293.
Lee, David S, “Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment
Effects,” The Review of Economic Studies, 2009, 76 (3), 1071–1102.
Schwabe, Henrik and Fulvio Castellacci, “Automation, Workers’ Skills and Job Satisfaction,”
Plos one, 2020, 15 (11), e0242929.

13

Table 1: Descriptive Statistics and Balance Tests
Control

Treatment

N

Mean

SD

N

Mean

SD

Difference

Demographics
Annual Salary ($)
Years of Tenure in Occupation
Employed
College Degree

215
228
220
229

71,808
10.63
0.90
1.00

47,648
9.28
0.29
0.07

197
212
207
215

76,267
10.07
0.96
1.00

51,271
8.48
0.20
0.07

4,458
-0.5640.052**-0.000Occupation
HR Professional
Consultant
Data Analyst
Grant Writer
ManagerMarketer

229
229
229
229
229
229

0.06
0.13
0.11
0.16
0.42
0.12

0.24
0.33
0.32
0.36
0.50
0.32

215
215
215
215
215
215

0.11
0.11
0.11
0.17
0.41
0.09

0.31
0.32
0.31
0.38
0.49
0.29

0.046*-0.015-0.0070.015-0.014-0.025First-Task Performance
Time Spent Task 1 (minutes)
Grade Task 1 (1-7)
Job Satisfaction Task 1 (1-10)
Self-Efficacy Task 1 (1-10)

221
228
228
228

26.22
3.72
6.32
6.89

13.20
1.34
2.54
2.11

209
208
212
212

26.59
3.89
6.34
6.91

15.43
1.31
2.36
2.14

0.374
0.172
0.019
0.020

Note: This table plots means and standard deviations of descriptive characteristics for our sample, separately bytreatment and control group. The right-hand side column shows the difference in means between treatment andcontrol (levels of significance: ∗ 10%, ∗∗ 5%, and ∗∗∗ 1%).

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