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NBER WORKING PAPER SERIES

AI AND JOBS: EVIDENCE FROM ONLINE VACANCIES
Daron Acemoglu
David Autor
Jonathon Hazell
Pascual Restrepo
Working Paper 28257
http://www.nber.org/papers/w28257

NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
December 2020, Revised February 2022

We thank Bledi Taska for detailed comments and providing access to Burning Glass data; Joshua
Angrist, Andreas Mueller, Rob Seamans, and Betsey Stevenson for very useful comments and
suggestions; Jose Velarde and Zhe Fredric Kong for expert research assistance; and David
Deming and Kadeem Noray for sharing their code and data. Acemoglu and Autor acknowledge
support from Accenture LLP, IBM Global Universities, Schmidt Futures, and the Smith
Richardson Foundation. Acemoglu acknowledges support from Google, the National Science
Foundation, the Sloan Foundation, and the Toulouse Network on Information Technology, and
Autor thanks the Carnegie Fellows Program, the Heinz Family Foundation, and the Washington
Center for Equitable Growth. The views expressed herein are those of the authors and do not
necessarily reflect the views of the National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
© 2020 by Daron Acemoglu, David Autor, Jonathon Hazell, and Pascual Restrepo. All rights
reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit
permission provided that full credit, including © notice, is given to the source.

AI and Jobs: Evidence from Online Vacancies
Daron Acemoglu, David Autor, Jonathon Hazell, and Pascual Restrepo
NBER Working Paper No. 28257
December 2020, Revised February 2022
JEL No. J23,O33
ABSTRACT
We study the impact of AI on labor markets using establishment-level data on vacancies with
detailed occupation and skill information comprising the near-universe of online vacancies in the
US from 2010 onwards. There is rapid growth in AI related vacancies over 2010-2018 that is
greater in AI-exposed establishments. AI-exposed establishments are reducing hiring in non-AI
positions. We find no discernible relationship between AI exposure and employment or wage
growth at the occupation or industry level, however, implying that AI is currently substituting for
humans in a subset of tasks but it is not yet having detectable aggregate labor market
consequences.
Daron Acemoglu
Department of Economics, E52-446
Massachusetts Institute of Technology
77 Massachusetts Avenue
Cambridge, MA 02139
and NBER
daron@mit.edu
David Autor
Department of Economics, E52-438
Massachusetts Institute of Technology
77 Massachusetts Avenue
Cambridge, MA 02139
and NBER
dautor@mit.edu

Jonathon Hazell
Department of Economics
London School of Economics
32 Lincoln's Inn Fields
United Kingdom
jadhazell@gmail.com
Pascual Restrepo
Department of Economics
Boston University
270 Bay State Road
Boston, MA 02215
and NBER
pascual@bu.edu

1

Introduction

The last decade has witnessed rapid advances in artificial intelligence (AI) based on new machine learning techniques and the availability of massive data sets.1 This change is expected
to accelerate in the years to come (e.g., Neapolitan and Jiang, 2018, Russell, 2019), and AI
applications have already started to impact businesses (e.g., Agarwal, Gans and Goldfarb,
2019). Some commentators see this as a harbinger of a jobless future (e.g., Ford, 2015;
West, 2018; Susskind, 2020), while others consider the oncoming AI revolution as enriching human productivity and work experience (e.g., McKinsey Global Institute, 2017). The
persistence of these contrasting visions is unsurprising given the limited evidence to date on
the labor market consequences of AI. Data collection efforts have only recently commenced
to determine the prevalence of commercial AI use, and we lack systematic evidence even on
whether there has been a major increase in AI adoption—as opposed to just extensive media
coverage.
This paper studies AI adoption in the US and its implications. Our starting point is
that AI adoption can be partially identified from the footprints it leaves at adopting establishments as they hire workers specializing in AI-related activities, such as supervised and
unsupervised learning, natural language processing, machine translation, or image recognition. To put this idea into practice, we build an establishment-level data set of AI activity
based on the near-universe of US online job vacancy postings and their detailed skill requirements from Burning Glass Technologies (Burning Glass or BG, hereafter) for the years 2007
and 2010 through 2018.2
We start with a task-based perspective, linking the adoption of AI and its possible implications to the task structure of an establishment. This perspective emphasizes that current
applications of AI are capable of performing specific tasks and predicts that firms engaged
in those tasks will be the ones that adopt artificial intelligence technologies.3 To identify
the tasks compatible with current AI technologies, we use three different but complementary
1

AI is a collection of algorithms that act intelligently by recognizing and responding to the environment
to achieve specified goals. AI algorithms process, identify, and act upon patterns in unstructured data (for
example, speech data, text, or images) to achieve specified goals.
2
The BG data have been used in several recent papers. Alekseeva et al. (2021) and Babina et al. (2020),
discussed below, use BG data to study AI use and its consequences. Papers using BG data to explore other
questions include Hershbein and Kahn (2016), Azar et al. (2018), Modestino, Shoag, and Ballance (2019),
Hazell and Taska (2019) and Deming and Noray (2020).
3
See Acemoglu and Autor (2011) and Acemoglu and Restrepo (2018, 2019a). This is not the only possible
approach to AI. One could also think of AI as complementing some business models (rather than performing
specific tasks within those models) or as allowing firms to generate and commercialize new products (see
Agarwal, Gans and Goldfarb, 2019, and Bresnahan 2019). We explain below why the task-based approach
is particularly well-suited to our empirical approach and how it receives support from our findings.

1

measures: Felten, Raj and Seamans’ (2018, 2019) AI Occupational Impact measure; Brynjolfsson, Mitchell and Rock’s (2018, 2019) Suitability for Machine Learning (SML) index;
and Webb’s (2020) AI Exposure score. These indices all identify sets of tasks and occupations that are most impacted by AI technologies, but each is computed based on different
assumptions on AI capabilities. We construct an establishment’s AI exposure from its baseline (2010-2012) occupational structure according to each one of these indices, and use these
baseline measures as proxies for AI exposure throughout our analysis.4 Since our goal is to
study the impact of AI on AI-using firms rather than AI-producing firms, we exclude firms
in the professional and business services and information technology sectors (NAICS 51 and
54), both of which are primary suppliers of AI services.
Our first result is that there is a rapid takeoff in AI vacancy postings starting in 2010 and
significantly accelerating around 2015-2016. Consistent with a task-based view of AI, this
activity is driven by establishments with task structures that are compatible with current
AI capabilities. For instance, a one standard deviation increase in our baseline measure of
AI exposure based on Felten et al.—approximately the difference in the average AI exposure
between finance and mining and oil extraction—is associated with 15% more AI vacancy
posting. The strong association between AI exposure and subsequent AI activity is robust
to numerous controls and specification checks when using the Felten et al. and the Webb
measures, but this is less apparent with the SML index. This leads us to place greater
emphasis on the Felten et al. and Webb measures when exploring the effects of AI exposure
on the demand for different types of skills and non-AI hiring.
Our second result establishes a strong association between AI exposure and changes in the
types of skills demanded by establishments. With the Felten et al. and Webb measures (and
to a lesser extent, with SML), we find that AI exposure is associated with both a significant
decline in some of the skills previously sought in posted vacancies and the emergence of new
skills. This evidence bolsters the case that AI is altering the task structure of jobs, replacing
some human-performed tasks while simultaneously generating new tasks accompanied by
new skill demands.
The finding that establishments with AI-suitable tasks hire workers into AI positions
and change their demand for certain types of skills does not, of course, tell us whether AI
is increasing or reducing overall or non-AI hiring in exposed establishments. In principle,
AI-exposed establishments may see an increase in (non-AI) hiring, if either AI directly
4

Figure 4 below shows that the relationship between mean wage of an occupation and the three AI
exposure measures is distinct, which is the basis of our claim that each one of these indices captures a
different aspect of AI exposure.

2

complements workers in some tasks, increasing their productivity and encouraging more
hiring, or AI substitutes for workers in some tasks, but increases total factor productivity
sufficiently to raise demand in non-automated tasks via a productivity effect (Acemoglu
and Restrepo, 2019a). Alternatively, AI adoption may reduce hiring if AI technologies are
replacing many tasks previously performed by workers and the additional hiring they spur
in non-automated tasks does not make up for this displacement.
Our third main result shows that AI exposure is associated with lower (non-AI and
overall) hiring. These results are robust in all of our specifications using the Felten et
al. measure and in most specifications with the Webb measure, but, as anticipated, not
with SML. The timing of these relationships is also plausible: substantial declines in hiring
take place in the time window during which AI activity surged—between 2014 and 2018.
This pattern of results, combined with the concentration of AI activity in more AI-exposed
tasks, suggests that the recent AI surge is driven in part by the automation of some of the
tasks formerly performed by labor. We find no evidence for either the view that there are
major human-AI complementarities in these establishments or the expectation that AI will
increase hiring because of its large productivity effects—though we cannot rule out that
other applications of AI that are not captured here could have such effects.
In contrast to the establishment-level patterns, we do not detect any relationship between
AI exposure and overall employment or wages at the industry or occupation level. There are
no significant employment impacts on industries with greater exposure to AI, and also no
employment or wages effects for occupations that are more exposed to AI. We conclude that
despite the notable surge in AI adoption, the impact of this new technology is still too small
relative to the scale of the US labor market to have had first-order impacts on employment
patterns outside of AI hiring itself. Nevertheless, our main findings—that AI adoption is
driven by establishments that have a task structure that is suitable for AI use and that
this has been associated with significant declines in establishment hiring—imply that any
positive productivity and complementarity effects from AI are at present small compared to
its displacement consequences.
Our paper builds on Alan Krueger’s seminal work on the effects of new digital technologies
on workers and wages (Krueger, 1993; Autor, Katz and Krueger, 1998). Subsequent literature
has investigated the implications of automation technologies, focusing on wages, employment
polarization and wage inequality (e.g., Autor, Levy and Murnane, 2003; Goos and Manning,
2007; Autor and Dorn, 2013; Goos, Manning, and Salomons, 2014; Michaels, Natraj and Van
Reenen, 2014; Gregory, Salomons and Zierahn, 2016). Recent work has studied the impact
of specific automation technologies, especially industrial robots, on employment and wages,
3

focusing either on industry-level variation (Graetz and Michaels, 2019), local labor market
effects (Acemoglu and Restrepo, 2020a), or firm-level variation (Dinlersoz and Wolf, 2018;
Bessen et al., 2019, Bonfiglioli et al., 2019; Dixon, Hong and Wu, 2019; Koch, Manuylov and
Smolka, 2021; Humlum 2019 and Acemoglu, Lelarge and Restrepo, 2020).
There are fewer studies of the effects of AI specifically, though this body of work is growing rapidly. Bessen et al. (2018) conduct a survey of AI startups and find that about 75% of
AI startups report their products help clients make better predictions, manage data better,
or provide higher-quality. Only 50% of startups report that their products help customers
automate routine tasks and reduce labor costs. Grennan and Michaely (2019) study how AI
algorithms have affected security analysts and find evidence of task substitution: analysts
are more likely to leave the profession when they cover stocks for which there are abundant
data available. Differently from these papers’ focus on AI-producing sectors and specific applications of AI such as finance, we study this technology’s effects on AI-using establishments
and non-AI workers throughout the economy.
Most closely related to our paper are a few recent works also investigating the effects
of AI on firm-level outcomes. Babina et al. (2020) study the relationship between AI
adoption and employment and sales at both the firm and the industry level. They document
that, consistent with Alekseeva et al. (2020), AI investment is stronger among firms with
higher cash reserves, higher mark-ups and higher R&D intensity, and moreover, these firms
grow more than non-adopters. A contrast between our approach and Babina et al.’s is
that we focus on AI suitability based on establishments’ occupational structures rather than
observed AI adoption, and this may explain why we arrive at different results for hiring. Also
related is Deming and Noray (2020), who use Burning Glass data to study the relationship
between wages, technical skills, and skills obsolescence. Though their focus is not AI, their
work demonstrates that Burning Glass data are suitable for detecting changes in job skill
requirements, an angle of inquiry we pursue below.
As noted above, our work exploits measures of AI-suitability developed by Felten, Raj and
Seamans (2018, 2019), Brynjolfsson, Mitchell and Rock (2018, 2019), and Webb (2020). Our
results are consistent with Felten, Raj and Seamans (2019), who find a positive relationship
between AI-suitability and AI vacancy posting, but no relationship with employment growth,
at the occupational level. We confirm that AI suitability is not at present associated with
greater hiring in more highly-exposed occupations or industries, but we find robust effects
on skill demand and a negative impact on establishment hiring.
The rest of the paper is organized as follows. Section 2 presents a model motivating our
empirical strategy and interpretation. Section 3 describes the data, and Section 4 presents
4

our empirical strategy. Section 5 presents our main results on AI exposure and AI hiring,
while Section 6 looks at changes in the types of skills AI-exposed establishments are looking
for. Section 7 explores the effects of AI on hiring at the establishment, industry and occupation levels. Section 8 concludes. Additional robustness checks and empirical results are
presented in the Appendix.

2

Theory

In this section, we provide a model that motivates our empirical approach and interpretation.

2.1

Tasks, Algorithms and Production

Establishment e’s output, ye , is produced by combining the services, ye (x), of tasks x ∈ Te ⊂
T with unit elasticity (i.e., a Cobb-Douglas aggregator):
Z
ln ye = ln Ae +

α(x) ln ye (x)dx,

(1)

Te

where T is the set of feasible tasks, a subset Te of which is used in the production process
of establishment e, and α(x) ≥ 0 designates the importance or quality of task x in the
R
production process and are common across establishments. We impose Te α(x)dx = 1 for
all feasible Te , which ensures that all establishments have constant returns to scale.
Establishments differ in their productivity term Ae , and, more importantly, in the set of
tasks they perform (for example, because they produce different goods and services or use
distinct production processes). We also assume that each establishment faces a downwardsloping demand curve for its product and will set its price pe to maximize profits (and its
problem is separable from the profit-maximization problem of the firm’s other establishments
in case of multi-establishment firms). In this profit-maximization problem, we assume that
each establishment is small in the labor market and takes other prices and aggregate output
as given.
Tasks are produced by human labor, ℓe (x), or by services from AI-powered algorithms,
ae (x):

i σ
h
σ−1 σ−1
σ−1
,
ye (x) = (γℓ (x)ℓe (x)) σ + (γa (x)ae (x)) σ

(2)

where σ is the elasticity of substitution between labor and algorithms, and γℓ (x) and γa (x)
are assumed to be common across establishments. We assume that AI services are provided
by combining AI capital (machinery or algorithms) purchased from the outside, ke (x), and in5

house workers operating, programming, or maintaining this capital, ℓAI
e (x), with the following
technology:

ae (x) = min ke (x), ℓAI
e (x) ,

(3)

which implies that in-house AI workers need to be combined with capital in fixed proportions.5 We assume throughout that all establishments are price takers for production workers,
AI workers, and AI capital, whose respective prices are: w, wAI , and R.
We view recent advances in AI as increasing the ability of algorithms to perform certain tasks—corresponding to an increase γa (x) for some x. In what follows, we denote by
T A the subset of tasks that, due to these advances, can now be profitably performed by
algorithms/AI. These advances in AI technology will have heterogeneous impacts on establishments depending on their task structure. For example, an increase in γa (x) for text
recognition will impact establishments in which workers perform significant text recognition
tasks and will change the factor demands of these “exposed establishments”.
To make these ideas precise, we define establishment e’s exposure to AI as
R
Exposure to AIe =

ℓ (x)dx
x∈Te ∩T A e
R

ℓ (x)dx
x∈Te e

,

(4)

where the employment shares are measured before the advances in AI take place. This measure represents the share of tasks performed in an establishment that can now be performed
by AI-powered algorithms.6
We next explore how advances in AI impact AI activity and the demand for (non-AI)
workers.

2.2

Task Structure and AI Adoption

To illustrate how the task structure determines AI adoption, we follow Acemoglu and Autor
(2011) and Acemoglu and Restrepo (2018, 2019a) and assume that σ = ∞, so that algorithms
5

This assumption can be relaxed in various ways. First, the technology can be more general than Leontief,
so that factor prices affect how intensively AI workers are used. Second, establishments may be allowed to
substitute outsourced AI workers for in-house services. The first modification would not have any major
effect on our results, while the second would imply that our proxy for AI activity at the establishment level
may understate the extent of AI, potentially leading to attenuation of our estimates.
The common technology assumption in (2) and (3) can also be relaxed but is useful for simplifying the
exposition by ensuring that differences in factor demands across establishments are driven entirely by task
structures, making the link between the model and our empirical approach more transparent.
6
When σ = ∞ as
R in Propositions 1 and 2 below and the share of AI algorithms in cost is initially small,
exposure to AI is x∈Te ∩T A α(x)dx, which gives the share of tasks that can now be completed with AI in
total costs.

6

and labor are perfectly substitutable within a task. We also focus on the realistic case in
AI AI
which the initial cost share of AI, denoted by sA
e (= (Rke (x) + w ℓe (x))/total costs), is

small. Additionally, we consider the problem of a single establishment, holding the prices of
other establishments in the market as given.
Proposition 1 Suppose that σ = ∞ and the initial cost share of AI, sA
e , is small. Consider an improvement in AI technologies that increases γa (x) in T A and leads to the use
of AI algorithms in these tasks. Then the effects on the cost share of AI and in-house AI
employment are given by
dsA
e = Exposure to AIe ≥ 0,
and
d ln ℓAI
e =



1 − sA
e
+ (εe · ρe − 1) · (1 − sA
e ) · πe
sA
e


· Exposure to AIe ≥ 0,

where εe > 1 is the demand elasticity faced by the establishment, ρe > 0 its passthrough rate,
and πe ≥ 0 the average percent cost reduction in tasks performed by AI.
The proof of this proposition is provided in Appendix A, where we also provide the expressions for the passthrough rate, ρe , and average cost savings from the use of AI algorithms,
πe .
The proposition shows that changes in AI activity and hiring of AI workers are both
proportional to exposure to AI. Motivated by these results, in our empirical work we use
exposure to AI as the key right-hand side variable and identify greater use of AI with the
posting of more vacancies for in-house AI workers.
Although in this proposition we focused on the case where σ = ∞, a similar logic applies
when σ > 1 and AI does not fully replace workers in the tasks it is used. In this case,
AI advances still increase the cost share of AI and the hiring of AI workers in exposed
establishments. When σ < 1, however, technological advances will not raise the cost share of
AI because of strong complementarities between tasks produced by algorithms and humans.

2.3

AI, Task Displacement and Hiring

The next proposition characterizes the effects of AI advances on hiring of (non-AI) workers.
Its proof is also in Appendix A.
Proposition 2 Suppose that σ = ∞ and the initial AI share of costs, sA
e , is small. Consider
an improvement in AI technologies that increases γa (x) in T A and leads to the use of AI

7

algorithms in these tasks. The effects on on non-AI employment, ℓe , are:
d ln ℓe = (−1 + (εe · ρe − 1) · πe ) · Exposure to AIe ,

(5)

where εe > 1 is the demand elasticity faced by the establishment, ρe > 0 its passthrough rate,
and πe ≥ 0 the average percent cost reduction in tasks performed by AI.
Proposition 2 shows that the effects of AI advances on labor demand are proportional
to our exposure measure. More centrally, it clarifies the effects of AI advances on labor
demand. The direct consequence of such advances is to expand the set of tasks performed by
algorithms, T A , and to shrink the set of tasks allocated to workers in exposed establishments.
Because σ = ∞, this technological improvement displaces workers from tasks in T A . This
displacement effect is captured by the “−1” in the parentheses on the right-hand side of (5).
In addition, as emphasized in Acemoglu and Restrepo (2018), the reallocation of tasks from
workers to algorithms reduces costs and expands establishment output, ye (and this output
response depends on the demand elasticity and the passthrough rate). This “productivity
effect”, the magnitude of which is proportional to the cost reductions due to AI, πe ≥ 0,
increases hiring in non-automated tasks. If the second term on the right-hand side of (5),
(εe · ρe − 1) · πe , exceeds −1, the productivity effect dominates and AI technologies increase
hiring.7 Otherwise, AI advances will reduce (non-AI) hiring in exposed establishments.
We make two additional remarks. First, as with the results on AI activity, the main
conclusions of Proposition 2 can be generalized to the case in which σ > 1. In this case,
not all workers previously employed in AI-ex post tasks would be displaced, but the substitution away from them to algorithms would create a negative displacement and a positive
productivity effect, similar to those in the proposition.
Second, if different tasks require different skills, then the adoption of AI technologies may
also change the set of skills that exposed establishments demand (and list in their vacancies).
Skills relevant for tasks now performed by algorithms will be demanded less frequently, and
new skills necessary for working alongside AI algorithms may also start being included in
vacancies.
Our empirical work will be based on equation (5). We will explore the relationship between AI exposure, as defined in (4), and changes in the number and skill content of the
vacancies an establishment posts. Specifically, we will look at whether exposed establishments hire more AI workers, demand different sets of skills, and whether they increase or
7

This expression also clarifies that when the passthrough rate is less than 1/εe , the establishment’s price
increases sufficiently that output does not expand and thus employment always declines.

8

reduce their hiring of non-AI workers.

2.4

Human-Complementary AI

We have so far not considered human-complementary effects of AI. The possibility that AI
will complement workers engaged in exposed tasks can be captured by assuming that α(x)
increases for exposed tasks (see eq. 1) or, alternatively that σ < 1, so that algorithms and
human labor are complementary within a task (or both). This type of human-complementary
AI may increase labor demand because algorithms raise human productivity in exactly the
tasks in which AI is being adopted.
Evidence that AI is associated with greater establishment-level employment would be
consistent with the human-complementary view, but could also be consistent with task substitution associated with large productivity gains that nonetheless increase hiring at exposed
establishments. Conversely, evidence of negative, or even zero, effects would weigh against
both the human-complementary view and the possibility of large productivity gains from
AI—since both AI-human complementarity and large productivity effects boosting employment in non-automated tasks could generate a positive relationship between AI exposure and
establishments hiring. Our evidence below finds negative effects of AI exposure on (non-AI)
hiring, and thus suggests that the current generation of AI technologies is predominantly
task-replacing and generates only modest productivity gains.8 It remains possible that other
AI technologies than the ones we are proxying here could have different effects.

2.5

Measuring Exposure to AI

Propositions 1 and 2 show that we should see the effects of advances in AI in establishments
with task structures that make them highly exposed to AI. Differences in exposure are, in
turn, driven by the different task structures across establishments. In our empirical exercise,
we will use the occupational mix of an establishment prior to the major advances in AI to
infer its task structure and compute its exposure to AI. Formally, we assume that the set of
tasks in the economy, T , is partitioned into tasks performed by a set of distinct occupations
and denote the set of tasks performed in occupation o ∈ O by T o . Each establishment e’s
task structure is thus represented by the set of occupations that the establishment employs,
denoted by Oe ⊂ O, and so Te = ∪o∈Oe T o . For example, some establishments will employ
accountants and their production will use the set of tasks accountants perform, while others
8

Or that productivity gains, if present, have little effect on demand, potentially due to low passthrough
rates.

9

require the tasks performed by security analysts or retail clerks and thus hire workers into
these occupations. In our empirical work, we will use the occupational indices provided by
Felten, Raj and Seamans (2018, 2019), Webb (2020) and Brynjolfsson, Mitchell and Rock
(2018, 2019) to identify the set of occupations involving tasks where AI can (or could) be
deployed. We will then compute measures of AI exposure based on the occupational structure
of an establishment.9

3

Data

We next describe the BG data, document that it is broadly representative of employment
and hiring trends across occupations and industries, present our AI exposure indices, and
document their distribution across occupations and their evolution over time.

3.1

Burning Glass Data

Burning Glass (BG) collects data from roughly 40,000 company websites and online job
boards, with no more than 5% of vacancies from any one source. BG applies a deduplication
algorithm and converts the vacancies into a form amenable to data analysis. The coverage is
the near-universe of online vacancies from 2010 onwards in the United States, with somewhat
more limited coverage in 2007. Our primary sample comprises data from the start of 2010
until October 2018, though we also make use of the 2007 data. The vacancy data enumerate
occupation, industry and region information, firm identifiers, and detailed information on
occupations and skills required by vacancies, garnered from the text of job postings.
A key question concerns the representativeness of BG data given that the source of the
vacancies is online job postings. Figure 1 shows that BG data closely track the evolution of
overall vacancies in the US economy as recorded by the nationally representative Bureau of
Labor Statistics (BLS) Job Openings and Labor Turnover Survey (JOLTS). The exception
9

Using our notation, we can represent these indices as
R
o
A ℓ(x)dx
o
R ∩T
OExp = x∈T
,
ℓ(x)dx
x∈T o

where
ℓ(x) is average employment in task x and we denote average employment in occupation o by ℓo =
R
ℓ(x)dx. When ℓ(x) = ℓe (x), which follows from our common technology assumption, the exposure to
x∈T o
AI measure is equal to the employment weighted average of the occupation AI exposure measure:
P

P

o o

o∈Oe OExp ℓ
P
o
o∈Oe ℓ

=

o∈Oe

R

ℓ(x)dx o
ℓ
ℓ(x)dx

x∈T
R o ∩T A
x∈T o

P

o∈Oe

ℓo

R
=

x∈Te ∩T A

R
x∈Te

10

ℓ(x)dx

ℓ(x)dx

= Exposure to AIe .

is the downturn in BG postings data between 2015 and 2017.10 Appendix Figure A1 shows
that over the 2010-2018 period, the occupational and industry composition in BG is closely
aligned with both overall occupation employment shares from the OES and with industry
vacancy shares from JOLTS.11
We make use of Burning Glass’ detailed industry and establishment data. When this
information is available from the text of postings, vacancies are assigned a firm name and
a location, typically at the city level, as well as an industry code. We classify each firm
as belonging to the industry in which it posts the most vacancies over our sample period.
We define an establishment of a firm as the collection of vacancies pertaining to a firm
and commuting zone. Commuting zones are groups of counties that, due to their strong
commuting ties, approximate a local labor market (Tolbert and Sizer, 1996).
Of particular importance for our paper are BG’s detailed skill and occupation coding.
Vacancies in BG data contain information on skill requirements, scraped from the text of
the vacancy. The skills are organized according to several thousand standardized fields.
Groups of related skills are collected together into “skill clusters”. Over 95% of vacancies
are assigned a six-digit (SOC) occupation code.12
We use these skill data to construct two measures of AI vacancies, narrow and broad.
The narrow category includes a selection of skills relating to AI.13 The broad measure of AI
includes skills belonging to the broader skill clusters of machine learning, artificial intelligence, natural language processing, and data science. A concern with our broad AI measure
is that it may include various IT functions that are separate from core AI activities. For this
reason, we focus on the narrow AI measure in the text and show the robustness of our main
results with the broad occupation measure in the Appendix. Figure 2 shows the evolution
10

While JOLTS measures a snapshot of open vacancies posted by establishments during the last business
day of the month, BG counts new vacancies posted by the establishment during the entire month. We adjust
the numbers of job openings in JOLTS to match BG’s concept of vacancies, using the approach developed
by Carnevale et al. (2014). The difference in concept between JOLTS and Burning Glass vacancies likely
accounts for the downturn in BG postings data between 2015 and 2017.
11
We note that BG data represent vacancy flows, while the OES reports employment stocks, and thus we do
not expect the two data sources to align perfectly. Moreover, online vacancy postings tend to overrepresent
technical and professional jobs relative to blue collar and personal service jobs (Carnevale et al., 2014).
12
Six-digit occupation codes are highly granular, including occupations such as pest control worker, college
professor in physics, and home health aide.
13
The skills are Machine Learning, Computer Vision, Machine Vision, Deep Learning, Virtual Agents,
Image Recognition, Natural Language Processing, Speech Recognition, Pattern Recognition, Object Recognition, Neural Networks, AI ChatBot, Supervised Learning, Text Mining, Support Vector Machines, Unsupervised Learning, Image Processing, Mahout, Recommender Systems, Support Vector Machines (SVM),
Random Forests, Latent Semantic Analysis, Sentiment Analysis / Opinion Mining, Latent Dirichlet Allocation, Predictive Models, Kernel Methods, Keras, Gradient boosting, OpenCV, Xgboost, Libsvm, Word2Vec,
Chatbot, Machine Translation and Sentiment Classification.

11

of postings of narrow and broad AI vacancies in the BG data, highlighting the rapid takeoff
of AI vacancies after 2015, as noted in the Introduction. While a sharp uptick is visible in
all industries, the second panel of Figure 2 shows that the takeoff is particularly pronounced
in the information, professional and business services, finance, and manufacturing sectors.
In what follows, our primary focus is on “AI-using sectors”, and we drop establishments
belonging to sectors that are likely to be producing AI-related products, namely the information sector (NAICS sector 51) and the professional and business services sector (NAICS
sector 54). The former includes various information technology industries, likely to be selling
AI products, while the latter contains industries such as management consultancy, likely to
be integrating AI into other industries’ production processes.

3.2

AI Indices

We study three measures of AI exposure. Each is assigned at the six-digit SOC occupation
level, and each is designed to capture occupations concentrating in tasks that are compatible
with the current capabilities of AI technologies.
The first measure is from Felten et al. (2019). It is based on data from the AI Progress
Measurement project, from the Electronic Frontier Foundation. The Electronic Frontier data
identify a set of nine application areas in which artificial intelligence has made progress since
2010, such as image recognition or language modeling. Felten et al. use Amazon MTurk to
collect crowdsourced assessments of the relevance of each of these application areas to the 52
O*NET ability scales, e.g., depth perception, number facility, and written comprehension.
The authors then construct the AI Occupational Impact for each O*NET occupation as
the weighted sum of the 52 AI application-ability scores, where weights are equal to the
O*NET-reported prevalence and importance of each ability in the occupation.
The second measure is from Webb (2020). Webb’s analysis seeks to measure what tasks
artificial intelligence can perform by identifying overlaps between claims about capabilities
in AI patents and job descriptions in O*NET. Occupations that have a larger fraction of
such overlapping tasks are classified as more exposed.
The third measure is Suitability for Machine Learning (SML) from Brynjolfsson et al.
(2019). To build this measure, Brynjolfsson et al. (2019) develop a 23-item rubric that
enables the scoring of the suitability of any task for machine learning. They derive the SML
scores by applying this rubric to the textual description of the full set of O*NET occupations
using CrowdFlower, a crowdsourcing platform.
The three measures introduced above identify occupations that involve tasks in which AI

12

algorithms have made (or could make) significant advances. The measures differ in the way
they capture the applicability of AI to a task. Felten et al. and Webb focus on identifying
tasks that fall within existing capabilities, either by relying on the reports from the AI
Progress Measurement Project or based on the text of patents. The Brynjolfsson et al. SML
index is more forward-looking, and identifies tasks that could be performed by machine
learning/AI in the near term, even if outside the reach of existing capabilities. Given the
short period of time covered by the BG data, we expect, and in fact find, that Felten et
al.’s AI Occupational Impact and Webb’s measure should have greater explanatory power
for current adoption dynamics and establishment outcomes.
Figure 3 shows the distribution of our three indices by broad occupation categories and
by one-digit industry.14 Figure 4 relates this same information to wages by plotting average
AI exposure by occupational wage percentile for each index. The figures confirm that these
three measure capture different aspects of AI. The Felten et al. measure, for example,
is particularly high for managers, professionals and office and administrative staff and is
very low for service, production and construction workers, capturing the fact that these
occupations involve various manual tasks that cannot currently be performed by algorithms.
The Webb measure is not particularly high in sales occupations and shows a strong positive
relationship with occupational wage percentiles. In contrast, the SML measure is high for
office and administrative occupations, and for sales occupations, and (perhaps surprisingly)
above average for personal services, but is low for professional occupations and most bluecollar and service occupations. Consequently, SML has no systematic relationship with
occupational wage percentiles.15

4

Empirical Strategy

Our empirical strategy links measures of AI activity and job posting outcomes to AI exposure,
where both outcome and exposure variables are measured at the establishment level.
We estimate the following regression model:
∆ye,t−t0 = βAIe,t0 + x′e,t0 γ + εe,t−t0 ,

(6)

14
The broad occupational categories are those utilized by Autor (2019) and aggregate six-digit occupations
into 12 roughly one-digit categories.
15
Another notable difference is that the Webb index finds very little AI-suitability in either office or sales
occupations. Alongside his AI index, Webb (2020) creates a separate software exposure index, pertaining to
traditional non-AI software, that detects substantial software-suitability in office, administrative, and sales
occupations. We use this index as a control in our robustness checks.

13

where e denotes establishment, ∆ye,t−t0 denotes the change in one of our establishment-level
P
outcomes between 2010-12 and 2016-18, AIe,t0 = o share postingsoe,t0 ×AI scoreo is one of
our three measures of establishment AI exposure calculated using establishment data for
2010 through 2012, and xe,t0 is a vector of baseline controls, including industry dummies,
firm size decile dummies, a dummy for the commuting zone (CZ) in which the establishment
is located and, in some specifications, a set of firm fixed effects.16 Finally, εe,t−t0 is an error
term representing all omitted factors.
Our primary interest is in the coefficient β, which captures the relationship between AI
exposure and the outcome variable. We standardize the establishment AI exposure measure
by dividing it by its weighted standard deviation, with weights given by vacancies in 20102012. Hence, β is the change in the outcome variable associated with a standard deviation
difference in AI exposure.
The three main outcome measures we focus on for ∆ye,t−t0 are AI vacancies, changes in
job skill requirements of posted vacancies, and overall non-AI hiring, all measured at the
establishment level.

5

AI Exposure and AI Vacancies

We first document that AI exposure predicts establishment-level AI activity, as proxied by
our measure of narrow AI vacancies. Table 1 presents the main estimates. Panel A of this
table shows the relationship between AI exposure based on Felten et al.’s index and growth
in AI vacancies, while the subsequent two panels present results for our other measures of
AI exposure. We estimate regression models based on (6), with the left-hand side variable
defined as the change in the inverse hyperbolic sine of AI vacancies between 2010-12 and
2016-18.17 We focus on weighted specifications, using baseline establishment vacancies as
weights. In the text, we report heteroscedasticity-robust standard errors that allow for
arbitrary cross-sectional correlation across the establishments of each firm, and consider
alternative standard errors in the Appendix.
Column 1 is our most parsimonious specification and includes no covariates, thus depicting the unconditional bivariate relationship. The coefficient estimate in Panel A of
16
17

We pool 2010-12 data and, separately, 2016-18 data to improve precision.
The inverse hyperbolic sine transformation is given by:


p
ln x + (x2 + 1) .

For small values of x, this approximates a proportional change, but is well defined when x = 0, which is a
frequent occurrence in our sample of establishments.

14

β = 15.96 is precisely estimated (SE = 1.73) and shows a sizable association between AI
exposure and AI vacancies. This estimate implies that a one standard deviation increase
in AI exposure—which corresponds to the difference between finance and mining and all
extraction—is associated with approximately a 16% increase in AI vacancies.
The remaining columns explore the robustness of this relationship. Column 2 controls
for firm size decile and commuting zone fixed effects. The coefficient estimate of AI exposure
declines slightly to 13.82, but is now more precisely estimated. Column 3 additionally adds
three-digit (NAICS) industry fixed effects. Reflecting the sizable variation in AI exposure
across industries shown in Figure 3 above, these controls are more important for our regressions, and they reduce the magnitude of our estimate by about a third, to 9.19, but the
standard error of the estimate also declines (to 1.21).
Column 4 goes one step further and includes a full set of firm fixed effects, so that now
the comparison is among establishments of the same firm that differ in their AI exposure.
The estimate of β is similar to the bivariate relationship reported in column 1, 16.53, albeit
slightly less precise, since all of the cross-firm variation is now purged.
Figure 3 documented significant differences in AI exposure across occupations. This raises
the concern that our results may be confounded by secular trends across broad occupational
categories. Columns 5 and 6 additionally control for the baseline shares of vacancies that
are in sales and administration, two of the broad occupational categories that have been in
decline for other reasons (e.g., Autor and Dorn, 2013). These controls do not substantially
change the estimate in either column, which remain, respectively, at 9.75 (se = 1.20) and
16.87 (se = 1.86).18
The first panel of Figure 5 shows the specification from column 3 in the form of a bin
scatterplot, where each bin represents about 50,000 establishments. The relationship between AI exposure and AI vacancy postings is fairly close to linear across the distribution
and does not appear to be driven by outliers. The first panel of Figure 6 provides a complementary visualization, depicting the evolution of AI vacancies for the four quartiles of the
establishment AI exposure measure. It shows that the top two quartiles post significantly
more AI vacancies and drive the surge in AI vacancies after 2015.19
18

Table A1 in the Appendix shows that the results in Panel A are also robust if we include the baseline
shares of ten broad occupational categories. For example, the coefficients in the specifications that parallel
columns 5 and 6 are, respectively, 7.24 (se = 1.44) and 13.70 (se = 2.12). However, some of the results for
the Webb and SML AI exposure measures are sensitive to these controls.
19
Our exposure measure is a “shift-share” instrument, and the heteroscedasticity-robust standard errors
are not the most conservative ones because they do not recognize the additional correlation coming from
the covariation of these shares (Adao, Kolesar & Morales, 2019; Borusyak, Hull & Jaravel, 2021). Appendix
Table A2 repeats Table 1 with standard errors from Borusyak et al (2021), with similar results.

15

Our simple measure of exposure to AI explains a significant fraction of the increase in AI
vacancy posting. To document this point, we calculate the adjusted R2 associated with the
specifications in Table 1. For comparison, in Appendix Table A3 we also compute the share
of the increase in AI activity associated with initial occupation composition by estimating
a version of our main regression equation (6), with the share of establishment vacancies in
each detailed occupation in 2010-2012 as regressors. The adjusted R2 of the Felten et al
measure of AI exposure is 0.0256. The adjusted R2 when initial occupation shares are used
as regressors is 0.10.20 Hence, our AI exposure measure accounts for more than one-quarter
of the AI adoption associated with baseline occupation structures.
Panels B and C of Table 1 repeat the Panel A regressions using the Webb and SML
indices of AI exposure. Estimates using the Webb measure, reported in Panel B, are similar
to those in Panel A in the first four specifications, though they are not fully robust to controls
for the baseline shares of sales and administration vacancies in columns 5 and 6. The second
panel of Figure 5 shows that the bin scatter plot with the Webb measure looks similar to—
though noisier than—the one in the first panel with the Felten et al. measure, and Figure
6 confirms that the surge in AI vacancies is again driven by the top two quartiles. Given
that the Felten et al. and Webb indices capture different components of AI exposure (recall
Figure 3), the broadly similar picture they depict is reassuring. However, from Appendix
Table A3, the partial R2 associated with the Webb measure is 0.0074, roughly one quarter
of the corresponding R2 for the Felten et al measure.
Results with the SML index in Panel C are broadly similar, but significantly weaker.
There is a positive association between the SML-based measure of AI exposure and AI
vacancy growth without any covariates, but when three-digit industry fixed effects are included, this relationship becomes negative. The proximate explanation for this pattern is
that the sales and administration occupations have a high SML score, as noted above, and
are negatively associated with AI adoption. When we control for the baseline shares of these
occupations in columns 5 and 6, the positive relationship in column 1 is restored. The third
panels of Figures 5 and 6 show a less clear pattern relative to the Felten et al. and Webb
measures as well. The bin scatterplot confirms the lack of a robust relationship between exposure to AI based on the SML measure and AI vacancies (from the specification in column
3), and the evolution of AI vacancy growth by exposure quartiles in Figure 6 no longer shows
a monotone pattern. These weaker results with SML motivate our greater emphasis on the
20

We estimate the regressions in Appendix Table A3 on a 10% sample, since there are many regressors in
the model with initial occupation shares. We have verified that the adjusted R2 is similar in the sample and
the full data when the regressors are our measures of AI exposure.

16

results using the Felten et al. and Webb measures in the remainder of the paper.
A concern with the estimates in Table 1 is that the AI measures may be proxying for
exposure to non-AI digital technologies. If so, this would cloud the interpretation of our
estimates as primarily capturing the impacts of AI exposure on establishment outcomes. We
check for this possibility in Table 2 by additionally controlling for Webb’s measure of exposure
to “software”, which is calculated analogously to his AI exposure measure, but focusing on
occupations and tasks suitable for traditional software and digital technologies. The inclusion
of the software exposure measure has little impact on the coefficients of interest, particularly
in the case of the Felten et al. index. For example, in the most loaded specification (column
6), the point estimate is now 17.47 with a standard error of 1.90, as compared to 16.87 and
a standard error of 1.86 in Table 1. The software exposure measure itself does not have
a consistent association with AI vacancy growth: it is positive and statistically significant
in some specifications, small and insignificant in others, and negative and significant in yet
others. This set of estimates bolsters our confidence that the AI exposure variable identifies
meaningful variation in the suitability of establishment task structure for AI, and that this
variation is distinct from exposure to traditional software and digital technologies.
We provide several robustness checks on these basic patterns in the Appendix. In Table
A4, we report estimates for AI vacancy growth using AI exposure calculated using establishments’ occupational structures in 2007 rather than 2010. Although this greatly reduces the
sample size as many establishments operating in both 2010 and 2018 are not present in the
2007 data, we obtain qualitatively similar results to those in Table 1.
In Table A5, we replace the narrow AI vacancy measures used in Table 1 with the broad
AI vacancy indices discussed above (see Figure 2), and in Table A6, we use the change in
the share of AI vacancies among all vacancy postings as the dependent variable. The results
in both tables corroborate our main findings in Table 1 and, if anything, are stronger and
more stable, especially with the Webb measure. The quantitative magnitudes implied by the
estimates in these tables are comparable to our baseline estimates. For example, with the
Felten et al. measure and the specification in column 6, an additional standard deviation of
AI exposure is associated with a 19 percentage point increase (se = 0.02) in the share of AI
vacancies between 2010 and 2018.
We also explored firm-level variants of the establishment-level models above. Because of
the many zeros in the data, the establishment-level estimates do not aggregate cleanly to
firm-level estimates. As shown in columns 1 and 2 of Table A7, these estimates are generally
imprecise and inconsistently signed. However, when we estimate models for the firm-level
mean of establishment AI vacancy growth (columns 3 and 4) or models with share of AI
17

vacancies (columns 5 and 6), the estimates are very similar to our main results in Table 1.
In summary, the data point to a recent surge in AI-related hiring, and our regression
evidence reveals that establishments whose task structures enable the use of artificial intelligence technologies have substantially increased their AI-related postings. This evidence
suggests that an important component of AI activity is linked to the types of tasks performed
in an establishment—though it does not preclude the possibility that AI activity has other
drivers, such as the development of new products or business models.

6

AI and New Skills

Having established the link between AI suitability and AI activity/hiring at the level of establishments, we now turn to the broader labor market implications of growing AI adoption.
AI is intended to supplement, replicate, and in some cases, exceed human-level intelligence
in a variety of tasks. We therefore anticipate that establishments with task structures that
are suitable for AI will tend to change the types of worker skills they demand.
To investigate whether AI exposure predicts skill demands in non-AI jobs, we build on
work by Deming and Noray (2020) who document such changes associated with broader
IT-related activity. We adapt their measure of change in skill demands to establishments
and separate their gross skill change measure into negative and positive changes, capturing
the disappearance of existing skills and the emergence of new skills:
negative skill changee,t2 ,t1 = − min

( S 
X
s=1

positive skill changee,t2 ,t1 = max

skillse,t2
vacanciese,t2

( S 
X
s=1

−

skillse,t1
vacanciese,t1







skillse,t2
vacanciese,t2



−

)



skillse,t1
vacanciese,t1

, 0 , and



)
,0 ,

Here, skillse,t is the number of times skill s is posted by establishment e in year t, which
we normalize by dividing it by the total number of vacancies posted by that establishment.
The negative skill change measure therefore represents a decline in the frequency with which
some of the skills that were formerly posted appear in vacancies, while the positive skill
change measure captures increases in the frequency with which other skills are posted in
vacancies—which may include the addition of skills that were not previously posted. We
calculate these measures for non-AI vacancies and, as before, for all establishments except
those in the professional and business services and information technology sectors (51 and
54).
18

Tables 3 and 4 show that establishment AI exposure is robustly associated with negative
and positive skill changes. For example, in the first panel of Table 3, which focuses on
negative skill changes, column 1 shows an estimate of 0.83, which indicates that a one
standard deviation in the Felten et al. exposure measure is associated with a 0.83 absolute
decline in the per-vacancy frequency with which skills previously demanded are posted (se =
0.09). This is a large change compared to the mean negative skill change in our sample, 4.70,
and suggests that the deployment of AI technologies goes hand-in-hand with significant skill
redundancies. Equally interesting is the pattern in Table 4, which shows that AI exposure
is associated with demand for new skills. Column 1 of this table shows that a one standard
deviation increase in the Felten et al. AI exposure measure is associated with 0.95 absolute
increase (se = 0.08) in the per-vacancy frequency of skills that were either demanded less
frequently previously or were not previously demanded. This too is a sizable impact as
compared to the mean positive skill change in our sample of 6.30.
These patterns are quite robust, as is shown in the remaining columns and panels of
Tables 3 and 4. Each of the three AI exposure measures—Felten et al., Webb, and SML—
predict both negative and positive establishment-level skill changes between 2010 and 2018.
Paralleling our findings at many points in the paper, the Felten et al. measure proves to have
the most stable and largest quantitative relationship to the outcome variable, followed by
Webb and SML. In particular, all measures prove robust to the inclusion of firm size deciles,
commuting zone dummies, and controls for initial establishment vacancy structures in sales
and administrative occupations. All three are also robust to the inclusion of firm fixed effects
when we look at negative skill changes. The association between AI exposure and positive
skill changes is no longer present when we include firm fixed effects, however, suggesting
that the addition of new skills may not be localized to highly-exposed establishments, but
rather occur throughout the firm or at the headquarters. Finally, Appendix Tables A8 and
A9 additionally confirm that controlling for Webb’s measure of exposure to software, as we
did in Table 2, has little effect on the relationship between AI exposure and changes in skill
demands. Akin to the Table 2 results, this pattern underscores that the predictive relationship between AI exposure and establishment outcomes is distinct from that for exposure to
traditional software.
To provide insight into what types of skills are affected by AI, we estimate the same models as in Tables 3 and 4 within 28 skill families created by Burning Glass. These families,
which are enumerated in Figures 7 and 8, cover major job activities and skill sets within
white-collar, blue-collar, and service occupations. We find that both positive and negative
skill changes concentrate in the same, relatively small subset of skills. This is shown in
19

Figure 7, which reports point estimates and 95% confidence intervals for a regression of
establishment negative skill change separately for each skill family on establishment AI exposure using each of our three measures. Using both the Felten et al and Webb measures, AI
exposure predicts increasing demands for skills relating to engineering, analysis, marketing,
finance, and information technology. Conversely, Figure 8 presents estimates for the relationship between AI exposure and positive skill change by skill family. For the Felten et al
and Webb measures, AI-exposed establishments have lower demands for skills in the same
families in which negative skill change is greatest.
The finding that AI exposure is associated with significant changes in the skills listed in
vacancies bolsters our confidence that AI adoption has real effects on the task content of
non-AI jobs—enabling firms to replace some of the tasks previously performed by workers,
making certain skills redundant, while simultaneously generating demand for new skills.
These results are also consonant with our theoretical model in Section 2, which suggests
that AI adoption will induce churn of tasks performed by workers as some tasks previously
performed by humans are taken over by algorithms.

7

AI and Jobs

Our theory leaves open the possibility that AI may increase or reduce overall (and non-AI)
hiring. We next investigate the effects of AI exposure on vacancies for non-AI positions.

7.1

AI Exposure and Establishment Hiring

Table 5 turns to the relationship between AI exposure and hiring. The structure of the table
is identical to that of Table 1 except that the left-hand side variable is now the change in the
inverse hyperbolic sine of total non-AI vacancies (and there are two extra columns, which
we describe below). The non-AI vacancy measure is chosen so as to focus on the effects of
AI activity on establishment hiring exclusive of already-reported impact on AI hiring itself.
As before, we drop professional and business services and information technology sectors
(NAICS 51 and 54).
In Panel A, where we focus on Felten et al.’s measure, we see a robust negative association
between AI exposure and subsequent non-AI hiring. The estimate in column 1 is -13.80 (se
= 4.22), indicating that a one standard deviation increase in AI exposure is associated with
a roughly 14% decline in overall non-AI vacancies. (We interpret the economic magnitudes
of these point estimates below.) This coefficient estimate remains stable when we control for

20

firm size decile, commuting zone and three-digit industry fixed effects in columns 2 and 3.
In column 4, we replace firm-level covariates with firm fixed effects while retaining the
commuting zone dummies from the prior column. This is a stringent specification since we
are now comparing across establishments of the same firm that differ in their AI exposure.
In this specification, the point estimate for AI exposure is -4.81, which is about half of the
magnitude in the preceding column. Simultaneously, the estimates become more precise as
the standard error falls from 4.08 to 1.44. The relationship between AI exposure and non-AI
vacancies remains comparable when we include the baseline shares of sales and administration
occupations in columns 5 and 6: -12.42 (se = 4.01) and -4.04 (SE = 1.47), respectively.21
We also investigated whether these estimates are driven by establishments that posted
jobs in 2010-2012 and then stopped posting in 2016-2018 (which may reflect either true
zero vacancy postings or establishment exits, perhaps for sampling reasons). Columns 7
and 8 limit the sample to establishments that posted in 2016-2018. The estimates are now
somewhat smaller, but still negative and statistically significant at 5%: -8.38 (se = 3.46) in
column 7, with three-digit industry fixed effects, and -3.56 (SE = 1.86) in column 8, with
firm fixed effects.22
How large are the effects reported in Panel A? The interpretation of the regression coefficients is not straightforward because our outcome variable is vacancy flows, which differs
from the stock of employment. To estimate the implied impact on employment, we cumulate
vacancies between 2010 and 2018 to create a measure of 2018 employment for each establishment. Then we regress our measure of cumulative hiring between 2010 and 2018 on AI
exposure in 2010 exactly as in Table 5.23 Table A11 in the Appendix reports the results of
this exercise. In Panel A, with Felten et al.’s measure of AI exposure, the regression coefficient in column 1 of -7.24 (se = 4.66) implies that a one standard deviation in AI exposure
is associated with a 7.2% decline in non-AI employment between 2010 and 2018. Since a one
standard deviation increase in AI exposure is quite large, this is a sizable but not implausible relationship. We also note that because this coefficient estimates the relative change in
21

Since AI exposure predicts an increase in AI vacancies, it is not self-evident whether the implied impact
on total vacancies (inclusive of AI hiring) is also negative. We show in Appendix Table A10 that the answer
is yes, as expected, since AI vacancies are a tiny share of total vacancies.
22
In Appendix Table A12 we calculate the standard errors from Borusyak et al (2012) for the specifications
in Table 5 to account for the shift-share structure of our AI exposure measure. The standard errors change
little.
23
More specifically, we assume that establishment employment, ℓe,t , follows the law of motion ℓe,t+1 =
f ve,t + (1 − s) ℓe,t , where ve,t denotes the establishment’s vacancies, f is the vacancy fill rate and s is the
separation rate. We calculate employment in 2010 by assuming that the establishment is in steady state
initially, and we compute employment in 2018 by iteratively solving the law of motion forward. We set
s = 0.4 to match the 2018 annual separation rate from JOLTS. We thank Andreas Mueller for suggesting
this exercise.

21

non-AI hiring at more versus less AI-exposed establishments, it does not imply an aggregate
reduction in total hiring.24
Panel B of Table 5 turns to Webb’s measure of AI exposure. The pattern is broadly
similar to the one we see in Panel A but less stable. The coefficient estimate without any
covariates in column 1 is -17.24 (se = 3.72). It is comparable in column 2 when we control
for CZ and three-digit industry fixed effects. However, the estimate declines substantially
to -2.22 (se = 0.93) in column 4 when we control for firm fixed effects, and is inconsistent
in sign and magnitude in columns 5 through 8. Finally, when we use SML in Panel C,
there is no consistent evidence for a negative association between AI exposure and non-AI
vacancy postings (negative and statistically significant in column 6, but positive in six of
eight columns and significantly so in two cases).
As in Table 2, we next control for Webb’s measure of exposure to software in order
to distinguish the effects of other (traditional) software applications from AI. The results
reported in Table 6 document that the software exposure measure itself has no consistent
association with non-AI hiring, while the effects of AI exposure remain very similar to our
baseline estimates in Table 5. For example, the estimate using Felten et al.’s measure in
column 1 is -14.62, compared to -13.80 for the same specification in Table 5.25
We showed in Figure 2 that AI adoption sharply accelerated around 2015 after having
grown comparatively slowly in the prior five years. This discontinuous growth provides an
opportunity to test whether any potential association between AI exposure and non-AI hiring
fits this timing. We perform this exercise in Table 7, where we break the outcome period
24

One can combine the reduced-form estimates in Tables 1 and 5 to obtain a Wald estimate of how AI
activity driven by differences in tasks structures across establishments affects non-AI hiring. For example,
the estimates in column 4 of Tables 1 and 5, using Felten et al.’s measure, yield an elasticity of −0.3—that
is, a 10% increase in AI adoption is associated with a 10% × 4.81/16.53 = 3% decline in non-AI hiring.
Between 2010 and 2018, the increase in AI vacancies ranged from 218 log points in finance to 198 log points
in manufacturing. Assuming that 1.6% of these increases—the partial R2 of our AI exposure measure in
Table 1—were driven by task-level substitution of algorithms for labor, one may infer that this type of AI
adoption led to a decline of 1% in non-AI hiring in finance and 0.92% in manufacturing. These estimates
should be interpreted with caution, since they ignore general equilibrium effects and spillovers, and the
partial R2 may over or understate the role of task substitution in AI activity. Indeed, the OLS relationship
between AI adoption and hiring, reported in Appendix Table A13, is positive. This underscores that other
sources of variation, including possible links between an establishment’s growth potential and its AI activity,
matter more for AI adoption than the baseline task structure captured by our AI exposure measure.
25
Another prediction of our conceptual framework is that hiring should decline particularly in occupations
that are themselves highly exposed to AI. Consistent with this prediction, all of our three AI exposure
measures predict a decline in “at-risk” vacancies. We are nevertheless cautious in interpreting these specifications and do not report them because they suffer from potential mean reversion. In particular, because
the exposure measure is equal to the share of establishment postings that are at-risk in 2010-2012, any mean
reversion in this measure will induce a spurious negative relationship between an establishment’s at-risk
vacancy share in 2010-2012 and its subsequent change.

22

of 2010-2018 into two subperiods, 2010-2014 and 2014-2018, and estimate a subset of the
specifications in Table 5 for these sub-intervals.
Focusing on Felten et al.’s AI exposure measure in Panel A, we see no economically or
statistically significant relationship between AI exposure in 2010 and non-AI hiring during
the years 2010-14, while there is a strong negative association for 2014-18, during the period
of rapid AI takeoff. In the baseline regression for 2014-18 (column 5), the point estimate
is -11.94 (se = 3.80), indicating that a one standard deviation increase in AI exposure is
associated with an approximately 12% decline in overall non-AI vacancies. This estimate remains stable and becomes more precise when we control for firm size deciles, commuting zone
controls, and three-digit industry fixed effects in column 6. The relationship also remains
comparable when we include the baseline shares of sales and administration occupations in
column 7—coefficient of -10.60 with a standard error of 2.82. This result is robust to firm
fixed effects, added in column 8, though, as before, their inclusions reduces the magnitude of
the relationship. Panels B and C report the same specifications for the Webb and SML measures, respectively. As in Table 5, the relationships between non-AI hiring and AI exposure
are less consistent and robust for these indexes.
Table A14 in the Appendix presents results at the firm level, which are, on the whole,
similar to the establishment-level results. Table A1, on the other han5d, depicts similar, even
if slightly smaller, estimates with average establishment size weights (rather than baseline
establishment size weights as in Table 5).
In sum, the evidence using the Felten et al. and Webb measures of AI exposure shows
statistically significant and economically meaningful negative effects, especially between 2015
and 2018, the period during which AI activity surged.

7.2

AI and Industry Employment

Associated with the surge in AI activity, there may also be industry-level changes that potentially offset or amplify the establishment-level consequences. To investigate whether more
exposed industries are contracting (or expanding), we aggregate our AI exposure measure
to the CZ-by-industry level and merge it with employment data. We proxy industry-level
AI activity using the mean occupation AI exposure across the six-digit occupations posted
in each sector by CZ cell during 2010-12, weighted by the number of vacancies posted in
each occupation. We measure the change in (the log of) industry-by-CZ employment using
County Business Patterns data (CBP) for 2000-2016. Because of increased suppression of
industry-by-location data in the CBP starting in 2017, our analysis of CBP data ends in

23

2016, thus (unfortunately) excluding the last several years of rapid AI expansion.26
The results are reported in the first three columns of Table 8, which again contins one
panel for each AI exposure measure. The outcome variable in these regressions is industryby-CZ employment. All models include industry fixed effects, commuting zone fixed effects,
and baseline occupational shares in sales and administration. The standard errors are robust
to heteroscedasticity and correlation within commuting zones.
Columns 1 and 2 examine trends in industry employment during 2003-2007 and 20072010, before the major AI advances that followed. These columns show that industry AI
exposure in 2010 does not predict differential employment behavior before 2010; thus, threedigit industries with different AI exposure were on roughly parallel trends before the pickup
in AI activity in the late 2010s. This pattern is essentially unchanged after 2010. We do
not see consistent positive or negative effects associated with AI exposure between 2010 and
2016. For example, the estimate in column 3 is -0.05 (se = 0.08). The point estimate implies
very small effects associated with industry AI exposure: a one standard deviation increase
in industry AI predicts an economically small and statistically insignificant 0.049% decline
in industry employment. Panels B and C of the table show similar results using the Webb
and SML measures in place of the Felten et al. index.
This set of null results may indicate that it is premature to detect AI’s impact on industry
reorganization or growth. Indeed, our calculations in footnote 24 suggest that the present
effects of AI adoption on even some highly-impacted sectors, such as finance, might still be
small. These results might also indicate that much of the effect of AI on employment, if
eventually present, will occur within industries.

7.3

Employment and Wages in AI-Exposed Occupations

As a second approach to measuring impacts that extend beyond firms, columns 4 through 9
of Table 8 assess whether occupations with greater AI exposure exhibit differential employment or wage trends after the onset of rapid AI hiring. For this analysis, we use occupational
employment and wage information from the US BLS Occupational Employment Statistics
(OES) data. This establishment-based data series provides more accurate estimates of employment and wages in occupations than is available from household surveys.
The observations in this table are at the six-digit occupation level, and the dependent
variable is the sum of employment in a six-digit occupation across all industries (excluding
sectors 51 and 54). In all columns, we control for three-digit occupation fixed effects and use
26

In processing the CBP data, we use the harmonization and imputation procedures developed by Fabian
Eckert, Teresa Fort, Peter Schott, and Natalie Yang, available at http://fpeckert.me/cbp/.

24

baseline employment as weights. The standard errors are robust against heteroscedasticity.
In columns 4 through 6, the dependent variable is change in employment, while in columns
7 though 9 it is change in the (log) average wage in the occupation.
The results for employment and wage growth using each of the AI exposure measures are
similar to the industry-level findings in earlier columns: we detect no differential employment
or wage behavior in more AI-exposed occupations after 2010.27
Our evidence is fairly clear that there is no systematic aggregate relationship between
AI exposure and industry and occupation-level outcomes. Our overall interpretation is that
while AI technologies appear to be changing task and skill composition at exposed establishments and firms, any aggregate effects of AI are too small to detect.28

8

Conclusion

There is much excitement and quite a bit of apprehension about AI and its labor market effects. In this paper, we explored the nature of AI activity in the US labor market
and its consequences for skill change, hiring, and industry and occupation level changes in
employment and earnings. We have three main findings:
1. We see a surge in AI activity, particularly after 2015, proxied by vacancies seeking
workers with AI skills, and this surge is driven by establishments with high exposure
to AI—meaning that their task structure in 2010 was suitable for the AI technologies that are subsequently introduced. This pattern is highly robust with two of our
three AI-exposure measures—those based on the indices constructed by Felten et al.
and Webb—and still present but less robust with our third measure, SML, based on
Brynjolfsson, Mitchell and Rock’s work.
2. We estimate consistent and robust changes in the skills demanded by high-exposure
establishments. In particular, establishments with task structures suitable for AI cease
to post vacancies that list a range of previously-sought skills and start posting additional skill requirements. This evidence suggests that some of the tasks that workers
27

Differently from our industry results, we detect a significantly faster increase in the employment of more
exposed occupations between 2007 and 2010 when using the Felten et al. AI measure. This may reflect fast
expansion in some IT-related occupations that have high Felten et al. scores, or it may be a chance finding
given the large number of point estimates reported in this table.
28
One alternative reading of these results is that AI is displacing and reinstating tasks at approximately
the same rate, yielding no net effect on labor demand. Our main results do not support this interpretation,
however, since we find significant declines in non-AI vacancies at exposed establishments.

25

used to perform in these establishments are no longer required, while new skills are
simultaneously being introduced.
3. With two of our three measures, we find that AI-exposed establishments reduce their
non-AI and overall hiring. These results are statistically significant, economically sizable, and robust with the Felten et al. measure, and robust in most specifications
with the Webb measure. We do not detect such negative employment effects with
SML, which is as expected, since the relationship between AI exposure and AI hiring
is much less robust and stable with SML as well.
In contrast to these three findings, we do not detect any relationship between AI exposure
and employment or wages at the occupation or industry level.
The totality of the results reported above on the labor market effects of AI convince us
that the AI is having real effects on establishments that are exposed to this new technology:
there is a significant surge in vacancies for AI workers in establishments with task structures
that are more suitable for AI; skill churn increases differentially at AI-exposed establishments,
with both greater retirement of previously-posted skills and greater introduction of new skills;
and finally, AI-exposed establishments appear to be reducing their non-AI hiring. These
patterns are consistent with the hypothesis that AI-powered algorithms are being used for
substituting for human skills. However, while AI technologies appear to be changing task
and skill composition at exposed establishments, any aggregate effects of AI, if present, are
not yet detectable—plausibly because AI technologies are still in their infancy and have
spread only to a limited part of the US economy.
Our results leave open important questions and have evident shortcomings. First, it will
be valuable to further explore and understand the juxtaposition of negative establishmentlevel impacts and zero aggregate effects. Second, our focus on AI adoption driven by the task
structure of establishments may exclude other types of AI impacts that are less related to
task structures, such as the use of AI to launch new products and services. These applications
could have different and possibly more positive effects on jobs. Naturally, our estimates are
not informative about AI applications that are missed by our AI exposure measures. Finally,
because the next generation of AI-enabled technologies will likely have different capabilities
from the current generation, our results do not foretell whether future AI technologies will
prove more complementary or more substitutable with human capabilities.

26

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30

Brookings

Figure 1: Vacancies in Burning Glass and JOLTS

●

JOLTS
Total

●

Burning Glass
Total

25000

●
●

31

Number of Vacancies (thousands)

20000

●

●
●

15000

●

●

●
●
●

●
●
●

10000
●

●

●

●

5000
●

2007

2010

2014
Year

2018

This figure plots the total number of vacancies in JOLTS and the total number of vacancies in Burning Glass by year. We multiply the
number of job openings in JOLTS by a constant factor, of 0.65, to arrive at a number of vacancies that matches the concept of a vacancy in
Burning Glass. This method follows Carnevale et al. (2014).

Figure 2: Share of AI Vacancies in Burning Glass

Share of AI Vacancies in Burning Glass Share of AI Vacancies by Broad Industry
●

Broad AI

●

Information

●

Prof & Business
Svcs

2.0

0.8
●

●

1.5
●
●

Narrow AI
●

Finance

●

Manufacturing

●

1.0
●

0.4

●
●

●
●

●
●

●
●

●

●
●

●

●

●

●
●

●

●
●
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●
●

2010

●
●

●
●
●

2015
Year

2020

●
●
●

●
●

●

●
●
●

0.5

●

●
●

●

●
●
●

●
●

Retail Trade
Wholesale Trade

●
●

Other Sectors
Services

●
●

●

●

●
●
●

●

●

●

●

0.2

●
●
●

●

●

●

●

●
●
●

●
●

●
●
●

Share of AI Vacancies (pp)

32

Share of AI Vacancies (pp)

0.6

●

●

●

●

●

0.0
2010

2015
Year

2020

The left panel plots the share of vacancies in Burning Glass that post a skill in the Broad or Narrow AI categories, as defined in the main
text. The right panel plots the share of narrow AI vacancies in Burning Glass, by year, in each industry sector.

Figure 3: AI Exposure by Broad Occupation and Sector
AI By Broad Occupation

AI By Sector
Finance/Insurance

Managers

Prof & Business Svcs
Professionals

Education Svcs
Information

Office/Admin

Real Estate
Technicians

Government
Manufacturing

Sales

Utilities
Personal Svcs

Mining
Other Svcs

Health Svcs

33

Healthcare & Social Asst
Production

Agriculture
Wholesale Trade

Clean + Protect Svcs

Arts/Entertainment
Transportation

Construction
Accomodation/Food Svcs

Construction

Retail Trade
Farm

Transportation/Warehousing
−1.0

−0.5
0.0
0.5
1.0
Occupation AI Exposure
Measure

−2

Felten et al

−1
0
1
Industry AI Exposure
Webb

2

SML

The left panel plots the average of the standardized measures of AI exposure across broad occupations. The right panel plots the average of
the standardized measures of AI exposure across 2-digit NAICS sectors, by taking the mean across the 6 digit SOC occupations posted in
each 2 digit NAICS sector, weighted by the number of vacancies posted by each sector in each occupation.

Figure 4: AI Exposure by Occupation Wages

34

Occupation AI Exposure

1.0

0.5

0.0

−0.5

0

25

50

75

100

Occupation Wage Percentile
Measure

Felten et al

SML

Webb

The figure plots a smoothed polynomial regression of the (standardized) measures of AI exposure in each 6-digit SOC occupation against
its rank in the wage distribution. We rank occupations according to their mean hourly wage for 2010-2018, obtained from the Occupational
Employment Statistics.

Figure 5: Binscatter of AI Growth and Establishment AI Exposure
(a) Felten et al Measure

(b) Webb Measure

10
0
-10
-20

-20

-10

0

10

20

Growth of AI Vacancies (%)

20

Growth of AI Vacancies (%)

-2

-1
0
1
Establishment AI Exposure in 2010 (Felten et al Measure)

2

Coefficient is 9.19, SE is 1.21, regressor is standardized

-2

-1
0
1
Establishment AI Exposure in 2010 (Webb Measure)

2

Coefficient is 3.21, SE is .81, regressor is standardized

35

(c) SML Measure

-20

-10

0

10

20

Growth of AI Vacancies (%)

-2

-1
0
1
Establishment AI Exposure in 2010 (SML Measure)

2

Coefficient is -2.21, SE is .96, regressor is standardized

The figure presents binned scatter plots that summarize the relationship between establishment AI Exposure in 2010 and the growth of AI establishment vacancies
between 2010 and 2018. Panel A uses the measure of AI exposure from Felten et al. (2019). Panel B uses the Webb (2020) measure. Panel C uses the SML measure,
from Brynjolfsson et al. (2019). In all panels, the covariates from column 3 of Table 1 are partialled out. The solid line corresponds to a regression with 2010
establishment vacancies as the weight. The corresponding point estimates and standard errors are reported at the bottom of each panel. We exclude vacancies in
industry sectors 51 (information) and 54 (business services).

Figure 6: Establishment Share of AI Vacancies by Quartile of AI Exposure
Felten et al Measure
1.5
●

Webb Measure

Top Quartile
of AI Exposure

●

1.00

SML Measure

Top Quartile
of AI Exposure

●

Third Quartile

●

Top Quartile
of AI Exposure

●

Second Quartile

●

Bottom Quartile
of AI Exposure

●

0.75
●
●

●

●

Third Quartile

●
●
●

●

0.5

●
●

●

0.50

●

●

●
●

●
●

●

0.25

●

●

●
●
●

●

●

0.0

●
●

●

●
●

2010

●
●

●
●

●

●

0.50
●

●
●
●

●
●
●
●

●

0.25

●

●
●

●
●

●

●

●

●

Second Quartile

●

●

●

●

●
●

●
●

●

●

Third Quartile

Share of AI Vacancies (pp)

1.0

Share of AI Vacancies (pp)

36

Share of AI Vacancies (pp)

0.75

●
●

●

●

●

●

Second Quartile

●

2015
Year

●

●

●
●

Bottom Quartile
●
of AI Exposure

●
●

●

Bottom Quartile
of AI Exposure

2020

●

●
●

●
●

●

●

●

●

●

●

●
●
●

●

●

●

●
●

●

●

●

●

●
●

●

●

●

●

●

●

●

●

●

0.00

0.00
2010

2015
Year

2020

2010

2015
Year

2020

This figure plots establishments’ share of AI vacancies in Burning Glass, for each quartile of the distribution of 2010 establishment AI
exposure, after partialling out their 2010-2012 share of vacancies in sales and administration. In the first panel, the measure of occupation
AI exposure is from Felten et al. (2019). In the second panel, the measure is SML, from Brynjolfsson et al. (2019). In the third panel, the
measure is from Webb (2020). We exclude vacancies in industry sectors 51 (information) and 54 (business services).

Figure 7: Effect of Establishment AI Exposure on Negative Skill Change, by Skill Family

Customer
Services

Maintenance

Logistics

Sales

Agriculture

Education

National
Security

Industry
Knowledge

Media

Science and
Research

Design

Engineering

Environment Architecture

Personal
Care

Energy

Religion

Finance

Business

Information
Technology

.1
0
-.1
-.2

Point estimate for skill change

.2

.3

Health

37

Economics Manufacturing

Analysis

Marketing

-.1

0

.1

.2

.3

Legal

-.2

Point estimate for skill change

Human
Administration Resources

Felten et al AI Exposure
SML AI Exposure

Webb AI Exposure

Notes: this figure plots the effect of establishment AI exposure on negative skill change by skill family. We construct measures of negative skill change at the
establishment level as defined in the main text, separately for each skill family in Burning Glass. Then we regress establishment negative skill change on establishment
AI exposure, using the specification of column (3), Table 3, separately for each skill family. We plot the resulting point estimates and 95% confidence intervals for our
three measures of AI exposure. We list the results separately for each skill family, as described at the top of the graph. We report estimates using our AI exposure
measures based on Felten et al. Webb and SML with bars of different shades.

Figure 8: Effect of Establishment AI Exposure on Positive Skill Change, by Skill Family

Customer
Services

Maintenance

Logistics

Sales

Agriculture

Education

National
Security

Industry
Knowledge

Media

Science and
Research

Design

Engineering

Environment Architecture

Personal
Care

Energy

Religion

Finance

Business

Information
Technology

.1
0
-.1
-.2

Point estimate for skill change

.2

.3

Health

38

Legal

Economics Manufacturing

Analysis

Marketing

.2
.1
0
-.1
-.2

Point estimate for skill change

.3

Human
Administration Resources

Felten et al AI Exposure
SML AI Exposure

Webb AI Exposure

Notes: this figure plots the effect of establishment AI exposure on positive skill change by skill family. We construct measures of positive skill change, at the
establishment level as defined in the main text, separately for each skill family in Burning Glass. Then we regress establishment positive skill change on establishment
AI exposure, using the specification of column (3), Table 3, separately for each skill family. We plot the resulting point estimates and 95% confidence intervals for our
three measures of AI exposure. We list the results separately for each skill family, as described at the top of the graph. We report estimates using our AI exposure
measures based on Felten et al. Webb and SML with bars of different shades.

Table 1: Effects of AI Exposure on Establishment AI Vacancy Growth, 2010-2018

Growth of Establishment AI Vacancies, 2010-2018
(1)
Establishment AI
Exposure, 2010
Observations
Establishment AI
Exposure, 2010
Observations

39

Establishment AI
Exposure, 2010
Observations
Covariates:
Share of Vacancies in
sales & admin, 2010
Fixed Effects:
Firm Size Decile
Commuting Zone
3-digit Industry
Firm

(2)

(3)

(4)

(5)

(6)

15.96
(1.73)
1,075,474

Panel A: Felten et al. Measure of AI Exposure
13.82
9.19
16.53
9.75
(1.43)
(1.21)
(1.89)
(1.20)
1,075,474
954,519
1,075,474
954,518

16.87
(1.86)
762,672

6.59
(1.13)
1,159,789

Panel B: Webb Measure of AI Exposure
5.08
3.21
5.91
0.42
(0.96)
(0.81)
(1.27)
(0.82)
1,159,789
1,021,673
1,159,789
1,021,673

1.14
(1.08)
824,803

3.76
(1.19)
1,159,789

Panel C: SML Measure of AI Exposure
2.30
-2.21
-3.04
1.95
(1.04)
(0.96)
(1.38)
(0.89)
1,159,789
1,021,673
1,159,789
1,021,673

4.47
(1.34)
824,803

✓
✓

✓
✓
✓

✓
✓

✓

✓

✓
✓
✓

✓
✓

This table presents estimates of the effects of establishment AI exposure on establishment AI vacancy growth. The sample is establishments posting vacancies in
2010-12 or 2016-18, outside NAICS sectors 51 (information) and 54 (business services). The outcome variable, constructed from Burning Glass data, is the growth
rate of AI vacancies between 2010 and 2018, multiplied by 100. We approximate this growth rate with the change in the inverse hyperbolic sine of the number of
vacancies posted by the establishment in 2010-12 and 2016-18. The regressor, establishment AI exposure in 2010, is the standardized mean of occupation AI exposure,
over the 6 digit SOC occupations for which the establishment posts vacancies in 2010-2012, weighted by the number of vacancies posted per occupation. In Panel
A, the measure of occupation AI exposure is from Felten et al. (2019). In Panel B, the measure of occupation AI exposure is SML, from Brynjolfsson et al. (2019).
In Panel C, the measure of occupation AI exposure is from Webb (2020). The covariates included in each model are reported at the bottom of the table. Column 1
contains only establishment AI exposure. Columns 2-3 and 5 include fixed effects for the decile of firm size (defined as the total vacancies posted by an establishment’s
firm in 2010-2012). Columns 2-6 include commuting zone fixed effects. Columns 3 and 5 include 3-digit NAICS industry fixed effects. Columns 4 and 6 include firm
fixed effects. Columns 5 and 6 control for the share of 2010-12 vacancies in each establishment, belonging to the broad occupations of either sales or administration.
In each regression, observations are weighted by total establishment vacancies in 2010-2012. Standard errors are clustered by firm.

Table 2: Effects of AI Exposure on Establishment AI Vacancy Growth, Controlling for Software Exposure
Growth of Establishment AI Vacancies, 2010-2018
(1)

(2)

(3)

(4)

(5)

(6)

40

Establishment AI
Exposure, 2010
Estab. Software
Exposure
Observations

16.28
(1.74)
2.36
(0.76)
1,059,620

Panel A: Felten et al. Measure of AI Exposure
14.10
9.63
17.43
9.96
(1.44)
(1.23)
(1.95)
(1.24)
2.24
2.62
4.83
0.66
(0.71)
(0.76)
(1.23)
(0.82)
1,059,620
941,046
1,059,620
941,046

Establishment AI
Exposure, 2010
Estab. Software
Exposure
Observations

10.64
(1.83)
-6.28
(1.51)
1,159,789

Panel B: Webb Measure of AI Exposure
7.88
3.85
6.81
1.50
(1.50)
(1.10)
(1.52)
(1.07)
-4.27
-0.96
-1.44
-1.81
(1.26)
(0.96)
(1.34)
(1.00)
1,159,789
1,021,673
1,159,789
1,021,673

2.57
(1.41)
-2.54
(1.49)
1,159,789

4.14
(1.18)
1.56
(0.80)
1,159,789

Panel C: SML Measure of AI Exposure
2.64
-1.96
-2.42
1.90
(1.04)
(0.96)
(1.28)
(0.88)
1.44
1.09
2.40
-0.90
(0.77)
(0.69)
(1.04)
(0.78)
1,159,789
1,021,673
1,159,789
1,021,673

4.37
(1.28)
-0.76
(1.11)
1,159,789

Establishment AI
Exposure, 2010
Estab. Software
Exposure
Observations
Covariates:
Share of Vacancies in
sales & admin, 2010
Fixed Effects:
Firm Size Decile
Commuting Zone
3-digit Industry
Firm

✓
✓

✓
✓
✓

✓
✓

17.47
(1.90)
1.83
(1.19)
1,059,620

✓

✓

✓
✓
✓

✓
✓

This table presents estimates of the effects of establishment AI exposure on establishment AI vacancy growth, controlling for establishment software exposure. Our measure of software exposure
is from Webb (2020). Establishment software exposure is the standardized mean of occupation software exposure, over the 6 digit SOC occupations for which the establishment posts vacancies in
2010-2012, weighted by the number of vacancies posted per occupation. The sample is establishments posting vacancies in 2010-12 and 2016-18, outside NAICS sectors 51 (information) and 54
(business services). The outcome variable, constructed from Burning Glass data, is the growth rate of AI vacancies between 2010 and 2018, multiplied by 100. We approximate this growth rate
with the change in the inverse hyperbolic sine of the number of vacancies posted by the establishment in 2010-12 and 2016-18. The regressor, establishment AI exposure in 2010, is the standardized
mean of occupation AI exposure, over the 6 digit SOC occupations for which the establishment posts vacancies in 2010-2012, weighted by the number of vacancies posted per occupation. In Panel
A, the measure of occupation AI exposure is from Felten et al. (2019). In Panel B, the measure of occupation AI exposure is SML, from Brynjolfsson et al. (2019). In Panel C, the measure of
occupation AI exposure is from Webb (2020). The covariates included in each model are reported at the bottom of the table. Column 1 contains only establishment AI exposure. Columns 2-3
and 5 include fixed effects for the decile of firm size (defined as the total vacancies posted by an establishment’s firm in 2010-2012). Columns 2-6 include commuting zone fixed effects. Columns 3
and 5 include 3-digit NAICS industry fixed effects. Columns 4 and 6 include firm fixed effects. Columns 5 and 6 control for the share of 2010-12 vacancies in each establishment, belonging to the
broad occupations of either sales or administration. In each regression, observations are weighted by total establishment vacancies in 2010-2012. Standard errors are clustered by firm.

Table 3: Effects of AI Exposure on Establishment Negative Skill Change, 2010-2018

Establishment Negative Skill Change, 2010-2018
(1)
Establishment AI
Exposure, 2010
Observations
Establishment AI
Exposure, 2010
Observations

41

Establishment AI
Exposure, 2010
Observations
Covariates:
Share of Vacancies in
sales & admin, 2010
Fixed Effects:
Firm Size Decile
Commuting Zone
3-digit Industry
Firm

(2)

(3)

(4)

(5)

(6)

0.83
(0.09)
339,282

Panel A: Felten et al. Measure of AI Exposure
0.83
0.97
0.50
1.00
(0.09)
(0.07)
(0.05)
(0.07)
339,282
322,901
339,282
322,901

0.54
(0.05)
339,282

0.62
(0.11)
353,107

Panel B: Webb Measure of AI Exposure
0.60
0.45
0.20
0.68
(0.11)
(0.06)
(0.04)
(0.11)
353,107
335,589
353,107
335,589

0.34
(0.04)
353,107

0.53
(0.08)
353,107

Panel C: SML Measure of AI Exposure
0.52
0.32
0.26
0.46
(0.07)
(0.07)
(0.04)
(0.09)
353,107
335,589
353,107
335,589

0.36
(0.04)
353,107

✓
✓

✓
✓
✓

✓
✓

✓

✓

✓
✓
✓

✓
✓

This table presents estimates of the effects of establishment AI exposure on establishment negative skill change. The sample is establishments posting in both 2010
and 2018, outside NAICS sectors 51 (information) and 54 (business services). The outcome variable, constructed from Burning Glass data, is establishment negative
skill change for 2010-2018, as defined in the main text. The regressor, establishment AI exposure in 2010, is the standardized mean of occupation AI exposure, over
the 6 digit SOC occupations for which the establishment posts vacancies in 2010-2012, weighted by the number of vacancies posted per occupation. In Panel A, the
measure of occupation AI exposure is from Felten et al. (2019). In Panel B, the measure of occupation AI exposure is SML, from Brynjolfsson et al. (2019). In Panel
C, the measure of occupation AI exposure is from Webb (2020). The covariates included in each model are reported at the bottom of the table. Column 1 contains
only establishment AI exposure. Columns 2-3 and 5 include fixed effects for the decile of firm size (defined as the total vacancies posted by an establishment’s firm
in 2010-2012). Columns 2-6 include commuting zone fixed effects. Columns 3 and 5 include 3-digit NAICS industry fixed effects. Columns 4 and 6 include firm fixed
effects. Columns 5 and 6 control for the share of 2010-12 vacancies in each establishment, belonging to the broad occupations of either sales or administration. In
each regression, observations are weighted by total establishment vacancies in 2010-2012. Standard errors are clustered by firm.

Table 4: Effects of AI Exposure on Establishment Positive Skill Change, 2010-2018

Establishment Positive Skill Change, 2010-2018
(1)
Establishment AI
Exposure, 2010
Observations
Establishment AI
Exposure, 2010
Observations

42

Establishment AI
Exposure, 2010
Observations
Covariates:
Share of Vacancies in
sales & admin, 2010
Fixed Effects:
Firm Size Decile
Commuting Zone
3-digit Industry
Firm

(2)

(3)

(4)

(5)

(6)

0.95
(0.08)
339,282

Panel A: Felten et al. Measure of AI Exposure
0.94
0.58
0.02
0.62
(0.09)
(0.09)
(0.04)
(0.09)
339,282
322,901
339,282
322,901

0.05
(0.04)
339,282

0.69
(0.09)
353,107

Panel B: Webb Measure of AI Exposure
0.66
0.26
-0.01
0.43
(0.09)
(0.08)
(0.03)
(0.08)
353,107
335,589
353,107
335,589

0.13
(0.04)
353,107

0.62
(0.09)
353,107

Panel C: SML Measure of AI Exposure
0.59
0.19
0.10
0.26
(0.09)
(0.09)
(0.04)
(0.09)
353,107
335,589
353,107
335,589

0.03
(0.04)
353,107

✓
✓

✓
✓
✓

✓
✓

✓

✓

✓
✓
✓

✓
✓

This table presents estimates of the effects of establishment AI exposure on establishment positive skill change. The sample is establishments posting vacancies in
2010-12 and 2016-18, outside NAICS sectors 51 (information) and 54 (business services). The outcome variable, constructed from Burning Glass data, is establishment
positive skill change for 2010-2018, as defined in the main text. The sample is establishments posting in both 2010 and 2019. The regressor, establishment AI exposure
in 2010, is the standardized mean of occupation AI exposure, over the 6 digit SOC occupations for which the establishment posts vacancies in 2010-2012, weighted
by the number of vacancies posted per occupation. In Panel A, the measure of occupation AI exposure is from Felten et al. (2019). In Panel B, the measure of
occupation AI exposure is SML, from Brynjolfsson et al. (2019). In Panel C, the measure of occupation AI exposure is from Webb (2020). The covariates included
in each model are reported at the bottom of the table. Column 1 contains only establishment AI exposure. Columns 2-3 and 5 include fixed effects for the decile of
firm size (defined as the total vacancies posted by an establishment’s firm in 2010-2012). Columns 2-6 include commuting zone fixed effects. Columns 3 and 5 include
3-digit NAICS industry fixed effects. Columns 4 and 6 include firm fixed effects. Columns 5 and 6 control for the share of 2010-12 vacancies in each establishment,
belonging to the broad occupations of either sales or administration. In each regression, observations are weighted by total establishment vacancies in 2010-2012.
Standard errors are clustered by firm.

Table 5: Effects of AI Exposure on Establishment Non-AI Vacancy Growth, 2010-2018
Growth of Establishment Non-AI Vacancies, 2010-2018
Full Sample
(1)

(2)

Establishment AI
Exposure, 2010
Observations

-13.80
(4.22)
1,075,474

-16.36
(4.11)
1,075,474

Establishment AI
Exposure, 2010
Observations

-17.24
(3.72)
1,159,789

-18.21
(3.63)
1,159,789

Establishment AI
Exposure, 2010
Observations

7.02
(3.13)
1,159,789

5.74
(3.01)
1,159,789

43

Covariates:
Share of Vacancies in
Sales, Admin. in 2010
Fixed Effects:
Firm Size Decile
Commuting Zone
3-digit Industry
Firm

(3)

Establishments Posting in 2018
(4)

(5)

(7)

(8)

Panel A: Felten et al. Measure of AI Exposure
-11.90
-4.81
-12.42
-4.04
(4.08)
(1.44)
(4.01)
(1.47)
954,519
1,075,474
954,519
1,075,474

-8.38
(3.46)
324,901

-3.56
(1.86)
341,525

-6.73
(3.01)
1,021,673

Panel B: Webb Measure of AI Exposure
-2.22
-8.30
1.51
(0.93)
(3.70)
(0.98)
1,159,789
1,021,673
1,159,789

-4.70
(2.66)
337,758

-1.44
(1.36)
355,529

2.05
(2.92)
1,021,673

Panel C: SML Measure of AI Exposure
0.95
2.21
-3.01
(1.16)
(3.61)
(1.22)
1,159,789
1,021,673
1,159,789

0.01
(2.94)
337,758

-0.91
(1.38)
355,529

✓
✓
✓

✓

✓

✓
✓

✓
✓
✓

✓
✓

✓
✓
✓

(6)

✓

✓
✓

✓

This table presents estimates of the effects of establishment AI exposure on establishment non-AI vacancy growth. The sample is establishments posting vacancies in
2010-12 and 2016-18, outside NAICS sectors 51 (information) and 54 (business services). The outcome variable, constructed from Burning Glass data, is the growth
rate of non-AI vacancies between 2010 and 2018, multiplied by 100. We approximate this growth rate with the change in the inverse hyperbolic sine of the number of
vacancies posted by the establishment in 2010-12 and 2016-18. The regressor, establishment AI exposure in 2010, is the standardized mean of occupation AI exposure,
over the 6 digit SOC occupations for which the establishment posts vacancies in 2010-2012, weighted by the number of vacancies posted per occupation. In Panel A,
the measure of occupation AI exposure is from Felten et al. (2019). In Panel B, the measure of occupation AI exposure is SML, from Brynjolfsson et al. (2019). In
Panel C, the measure of occupation AI exposure is from Webb (2020). The final two columns exclude establishments that do not post positive vacancies in 2018. The
covariates included in each model are reported at the bottom of the table. Column 1 contains only establishment AI exposure. Columns 2, 3, 5 and 7 include fixed
effects for the decile of firm size (defined as the total vacancies posted by an establishment’s firm in 2010-2012). Columns 2-8 include commuting zone fixed effects.
Columns 3, 5 and 7 include 3-digit NAICS industry fixed effects. Columns 4, 6 and 8 include firm fixed effects. Columns 5 and 6 control for the share of 2010-12
vacancies in each establishment, belonging to the broad occupations of either sales or administration. In each regression, observations are weighted by total vacancies
in 2010-2012. Standard errors are clustered by firm.

Table 6: Effects of AI Exposure on Establishment Non-AI Vacancy Growth, Controlling for Software Exposure
Growth of Establishment Non-AI Vacancies, 2010-2018
Full Sample

44

(1)

(2)

Establishment AI
Exposure, 2010
Estab. Software
Exposure
Observations

-14.62
(4.28)
-7.50
(3.68)
1,059,620

-17.21
(4.17)
-7.35
(3.60)
1,059,620

Establishment AI
Exposure, 2010
Estab. Software
Exposure
Observations

-23.04
(4.61)
9.01
(4.32)
1,159,789

-25.36
(4.47)
10.88
(4.22)
1,159,789

-14.04
(4.68)
10.98
(4.79)
1,021,673

4.70
(3.03)
-4.37
(3.60)
1,159,789

Establishment AI
Exposure, 2010
Estab. Software
Exposure
Observations
Covariates:
Share of Vacancies in
sales & admin, 2010
Fixed Effects:
Firm Size Decile
Commuting Zone
3-digit Industry
Firm

5.97
(3.15)
-4.41
(3.66)
1,159,789

✓
✓

(3)

Establishments Posting in 2018
(7)

(8)

Panel A: Felten et al. Measure of AI Exposure
-12.02
-5.43
-12.47
-3.93
(4.00)
(1.49)
(3.94)
(1.54)
0.66
-1.77
1.07
2.03
(3.09)
(1.08)
(3.28)
(1.17)
941,046
1,059,620
941,046
1,059,620

-8.68
(3.36)
-1.67
(3.41)
322,187

-3.73
(1.90)
-0.41
(1.40)
338,645

Panel B: Webb Measure of AI Exposure
-3.06
-14.95
-0.29
(1.05)
(5.59)
(1.09)
1.36
11.16
3.19
(1.14)
(5.11)
(1.18)
1,159,789
1,021,673
1,159,789

-7.30
(4.51)
3.80
(5.26)
337,758

-2.56
(1.56)
1.74
(1.54)
355,529

Panel C: SML Measure of AI Exposure
2.62
0.84
2.40
-2.67
(2.93)
(1.12)
(3.63)
(1.20)
2.46
-0.43
3.04
2.80
(3.10)
(0.95)
(3.30)
(1.03)
1,021,673
1,159,789
1,021,673
1,159,789

-0.17
(2.89)
-0.85
(3.36)
337,758

-0.94
(1.39)
-0.13
(1.34)
355,529

✓
✓
✓

✓

✓
✓
✓

(4)

✓
✓

(5)

(6)

✓

✓

✓
✓
✓

✓
✓

✓

This table presents estimates of the effects of establishment AI exposure on establishment non-AI vacancy growth, controlling for establishment software exposure. Our measure of software
exposure is from Webb (2020). Establishment software exposure is the standardized mean of occupation software exposure, over the 6 digit SOC occupations for which the establishment posts
vacancies in 2010-2012, weighted by the number of vacancies posted per occupation. The sample is establishments posting vacancies in 2010-12 and 2016-18, outside NAICS sectors 51 (information)
and 54 (business services). The outcome variable, constructed from Burning Glass data, is the growth rate of non-AI vacancies between 2010 and 2018, multiplied by 100. We approximate this
growth rate with the change in the inverse hyperbolic sine of the number of vacancies posted by the establishment in 2010-12 and 2016-18. The regressor, establishment AI exposure in 2010, is
the standardized mean of occupation AI exposure, over the 6 digit SOC occupations for which the establishment posts vacancies in 2010-2012, weighted by the number of vacancies posted per
occupation. In Panel A, the measure of occupation AI exposure is from Felten et al. (2019). In Panel B, the measure of occupation AI exposure is SML, from Brynjolfsson et al. (2019). In Panel
C, the measure of occupation AI exposure is from Webb (2020). The final two columns exclude establishments that do not post positive vacancies in 2018. The covariates included in each model
are reported at the bottom of the table. Column 1 contains only establishment AI exposure. Columns 2, 3, 5 and 7 include fixed effects for the decile of firm size (defined as the total vacancies
posted by an establishment’s firm in 2010-2012). Columns 2-8 include commuting zone fixed effects. Columns 3, 5 and 7 include 3-digit NAICS industry fixed effects. Columns 4, 6 and 8 include
firm fixed effects. Columns 5 and 6 control for the share of 2010-12 vacancies in each establishment, belonging to the broad occupations of either sales or administration. In each regression,
observations are weighted by total vacancies in 2010-2012. Standard errors are clustered by firm.

Table 7: Effects of AI Exposure on Establishment Non-AI Vacancy Growth, 2010-2014 and 2014-2018
Growth of Establishment Non-AI Vacancies
2010-2014 Growth
(1)
Establishment AI
Exposure, 2010
Observations

-1.86
(4.77)
1,075,474

(2)

(3)

2014-2018 Growth
(4)

(5)

(6)

(7)

(8)

-0.59
(3.52)
954,519

Panel A: Felten et al. Measure of AI Exposure
-1.82
0.39
-11.94
-11.32
(3.46)
(1.11)
(3.80)
(2.93)
954,519
1,075,474
1,075,474
954,519

-10.60
(2.82)
954,519

-5.21
(1.02)
1,075,474

-2.26
(2.16)
1,021,673

-1.86
(0.71)
1,159,789

-0.58
(2.75)
1,021,673

-0.95
(0.91)
1,159,789

Establishment AI
Exposure, 2010
Observations

-7.51
(3.38)
1,159,789

-2.57
(2.17)
1,021,673

Panel B: Webb Measure of AI Exposure
-6.04
-0.35
-9.73
-4.16
(2.66)
(0.64)
(2.42)
(1.83)
1,021,673
1,159,789
1,159,789
1,021,673

Establishment AI
Exposure, 2010
Observations

3.73
(2.66)
1,159,789

1.17
(2.46)
1,021,673

Panel C: SML Measure of AI Exposure
2.79
1.90
3.30
0.88
(3.08)
(0.73)
(2.09)
(2.30)
1,021,673
1,159,789
1,159,789
1,021,673

45

Covariates:
Share of Vacancies in
Sales, Admin. in 2010
Fixed Effects:
Firm Size Decile
Commuting Zone
3-digit Industry
Firm

✓

✓
✓
✓

✓
✓
✓

✓

✓
✓

✓
✓
✓

✓
✓
✓

✓
✓

This table presents estimates of the effects of establishment AI exposure on establishment non-AI vacancy growth, separately for 2010-2014 and 2014-2018. The
sample is establishments posting vacancies in 2010-12 and 2016-18, outside NAICS sectors 51 (information) and 54 (business services). In columns 1-4, the outcome
variable, constructed from Burning Glass data, is the growth rate of non-AI vacancies between 2010 and 2014, multiplied by 100. We approximate this growth rate
with the change in the inverse hyperbolic sine of the number of vacancies posted by the establishment in 2010-12 and 2013-15. In columns 5-8, the outcome variable
is the change in the inverse hyperbolic sine of the number of vacancies posted by the establishment in 2013-15 and 2016-18. The regressor, establishment AI exposure
in 2010, is the standardized mean of occupation AI exposure, over the 6 digit SOC occupations for which the establishment posts vacancies in 2010-2012, weighted
by the number of vacancies posted per occupation. In Panel A, the measure of occupation AI exposure is from Felten et al. (2019). In Panel B, the measure of
occupation AI exposure is SML, from Brynjolfsson et al. (2019). In Panel C, the measure of occupation AI exposure is from Webb (2020). The covariates included
in each model are reported at the bottom of the table. Columns 1 and 5 contains only establishment AI exposure. Columns 2, 3, 6 and 7 include fixed effects for the
decile of firm size (defined as the total vacancies posted by an establishment’s firm in 2010-2012); and also include 3-digit NAICS industry fixed effects. All columns
other than 1 and 5 include commuting zone fixed effects. Columns 4 and 8 include firm fixed effects. Columns 3 and 7 control for the share of 2010-12 vacancies in
each establishment, belonging to the broad occupations of either sales or administration. In each regression, observations are weighted by total vacancies in 2010-2012.
Standard errors are clustered by firm.

Table 8: Effects of AI Exposure on Market Employment and Wage Growth
Industry by CZ Employment Growth (CBP)
2003-2007
(1)

2007-2010
(2)

2010-2016
(3)

Market AI Exposure,
2010
Observations

0.03
(0.17)
10,937

0.10
(0.20)
10,926

-0.05
(0.08)
10,929

Market AI Exposure,
2010
Observations

0.10
(0.15)
10,981

0.18
(0.17)
10,968

Market AI Exposure,
2010
Observations

-0.14
(0.17)
10,981

46

Covariates:
Share of Vacancies in
Sales, Admin. in 2010
Fixed Effects:
Commuting Zone
Sector
3-digit Occupation

Occupation Employment Growth (OES)
2004-2007
(4)

2004-2007
(7)

2007-2010
(8)

2010-2018
(9)

Panel A: Felten et al. Measure of AI Exposure
0.34
0.86
0.51
(0.34)
(0.32)
(0.35)
736
700
680

-0.00
(0.17)
680

0.02
(0.20)
648

-0.17
(0.06)
629

0.11
(0.09)
10,968

Panel B: Webb Measure of AI Exposure
0.00
0.11
-0.17
(0.17)
(0.21)
(0.29)
713
704
717

0.11
(0.08)
660

-0.05
(0.10)
653

-0.02
(0.04)
663

0.37
(0.18)
10,968

-0.01
(0.08)
10,968

Panel C: SML Measure of AI Exposure
0.00
-0.17
-0.37
(0.25)
(0.29)
(0.25)
713
704
717

-0.03
(0.08)
660

0.18
(0.12)
653

0.04
(0.05)
663

✓

✓

✓

✓
✓

✓
✓

✓
✓
✓

✓

✓

✓

2007-2010
(5)

✓

2010-2018
(6)

Occupation Wage Growth (OES)

✓

This table presents estimates of the effects of market AI exposure on market employment and wage growth. In columns 1-3, the outcome is the growth rate of sector
(i.e. 2 digit NAICS industry) by commuting zone employment, measured in percentage points per year (i.e. 100 x the log change divided by number of years),
from the County Business Patterns; for 2003-2007, 2007-2010 and 2010-2016, respectively. The sample excludes industry sectors 51 (information) and 54 (business
services). In columns 4-6, the outcome is the growth rate of 6 digit SOC occupation employment outside sectors 51 and 54, measured in percentage points per year,
from the Occupation Employment Statistics; for 2004-2007, 2007-2010 and 2010-2018, respectively. In columns 7-9, the outcome is the growth of 6 digit SOC median
hourly wages outside sectors 51 and 54, measured in percentage points per year, also from the Occupational Employment Statistics. In columns 1-3, the regressor is
the standardized mean occupation AI exposure, across the 6 digit occupations posted in each sector by commuting zone cell, based on the distribution of vacancies
by detailed occupation in each zone and industry in 2010-2012. The regressions are weighted by baseline employment in each sector by commuting zone. In columns
4-9, the regressor is standardized occupation AI exposure by 6 digit SOC occupation. In panel A, the measure of occupation AI exposure is from Felten et al. (2019);
in panel B the measure is SML from Brynjolfsson et al. (2019); in panel C the measure is from Webb (2020). All regressions are weighted by baseline employment.
The covariates included in each model are reported at the bottom of the table. Columns 1-3 contain sector and commuting zone fixed effects, and controls for the
share of 2010-2012 vacancies in either sales or administration in each sector by commuting zone, measured from Burning Glass. Columns 4-9 control for 3-digit SOC
occupation fixed effects. Standard errors are clustered by commuting zone in columns 1-3, and robust against heteroskedasticity in columns 4-9.

AI and Jobs: Evidence from Online
Vacancies (Online Appendix)
Daron Acemoglu

David Autor

Jonathon Hazell

MIT and NBER

MIT and NBER

LSE

Pascual Restrepo
Boston University and NBER
October 2021

Appendix A: Proofs
Proofs of Propositions 1 and 2:. Consider an establishment e that initially allocates
tasks TeN to (non-AI) labor and TeAI to AI algorithms. The unit cost of production for this
establishment is
Z
Z
Z


`
AI
K
a
ln Ce =
α(x) ln w/γ (x) dx+
α(x) ln (w + w )/γ (x) dx−
α(x) ln(α(x))dx.
TeN

TeAI

Te

Moreover, its total demand for non-AI workers can be computed as
`N
e =

1
· y e · Ce ·
wN

Z
α(x)dx,
TeN

and the total demand for AI workers can be computed as
`AI
e =

1
· ye · Ce ·
AI
w + wK

Z
α(x)dx
TeAI

Consider a (discrete) improvement in the productivity of algorithms for an infinitesimal
set of tasks T A , so that it becomes cheaper to perform these tasks by algorithms rather than
workers.
As stated in both propositions, we assume that the share of tasks allocated to AI is
initially small, so that
Z
α(x)dx
Exposure to AIe ≈

T A ∩TeN

Z
α(x)dx
TeN

AI thus reduces unit costs by
Z


α(x)dx · πe ,

d ln Ce = −
T A ∩TeN

where

Z




α(x) · ln w/γ ` (x) − ln (wAI + wK )/γ a (x)
T A ∩TeN
Z
πe =
>0
α(x)dx
T A ∩TeN

is the average cost savings from substituting AI algorithms for workers in the set of tasks
given by T A .
1

Then, the change in total cost can be expressed as
Z


α(x)dx · πe ,

d ln(ye · Ce ) = (εe · ρe − 1) ·
T A ∩TeN

where εe > 1 is the demand elasticity faced by the firm and ρe > 0 the establishment’s
passthrough rate, summarizing how its price responds to the change in total costs; formally,
ρe =

d ln pe
.
d ln Ce

Rewriting this expression,

α(x)dx · πe · Exposure to AIe ,

Z
d ln(ye · Ce ) = (εe · ρe − 1) ·
TeN

The demand for non-AI workers 

[The evaluation harness truncated this reference: showing the first 120000 of 160201 characters.]
</reference>

<statements>
1. Using the Felten et al. exposure measure, a one-standard-deviation increase in AI exposure—approximately the difference between finance and mining/oil extraction—is associated with 15% more AI vacancy posting.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.