# AI exposure predicts unemployment risk

Morgan R. Frank  Department of Informatics and Networked Systems, University
of Pittsburgh, Pittsburgh, PA 15216 USA  Digital Economy Lab, Institute for
Human-Centered Artificial Intelligence, Stanford University, Stanford, CA
94305 USA  Media Laboratory, Massachusetts Institute of Technology, Cambridge,
MA, 02139 USA  Connection Science, Massachusetts Institute of Technology,
Cambridge, MA, USA  To whom correspondence should be addressed. E-mail:
<EMAIL_ADDRESS>Yong-Yeol Ahn  Connection Science, Massachusetts Institute
of Technology, Cambridge, MA, USA  Center for Complex Networks and Systems
Research, Luddy School of Informatics, Computing, and Engineering, Indiana
University, Bloomington, IN, USA  Indiana University Network Science
Institute, Indiana University, Bloomington, IN, USA  Esteban Moro  Media
Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139 USA
Department of Mathematics & GISC, Universidad Carlos III de Madrid, 28911
Leganes, Spain  Institute for Data, Systems, and Society, Massachusetts
Institute of Technology, Cambridge, MA, USA

> Is artificial intelligence (AI) disrupting jobs and creating unemployment?
> Despite many attempts to quantify occupations’ exposure to AI, inconsistent
> validation obfuscates the relative benefits of each approach. A lack of
> disaggregated labor outcome data, including unemployment data, further
> exacerbates the issue. Here, we assess which models of AI exposure predict
> job separations and unemployment risk using new occupation-level
> unemployment data by occupation from each US state’s unemployment insurance
> office spanning 2010 through 2020. Although these AI exposure scores have
> been used by governments and industry, we find that individual AI exposure
> models are not predictive of unemployment rates, unemployment risk, or job
> separation rates. However, an ensemble of those models exhibits substantial
> predictive power suggesting that competing models may capture different
> aspects of AI exposure that collectively account for AI’s variable impact
> across occupations, regions, and time. Our results also call for dynamic,
> context-aware, and validated methods for assessing AI exposure. Interactive
> visualizations for this study are available at
> https://sites.pitt.edu/~mrfrank/uiRiskDemo/.

## Introduction

Artificial intelligence (AI)—broadly defined as technology that learns
patterns from data—has revived fears of technological unemployment (?, ?, ?,
?, ?). Some argue that AI is similar to previous technologies that did not
result in massive unemployment (?, ?, ?). But others argue that AI is a
different class of technology because it can perform cognitive work and thus
warrants new consideration. This has prompted efforts to estimate occupation-
level AI exposure (?, ?, ?, ?, ?, ?) that can inform both economic research
and policy-making to support workers’ adaption to the future of work. For
example, the recent Biden Administration Blueprint for an AI Bill of Rights
(?) calls for “training, assessment, and oversight to combat automation bias.”
To achieve these goals, policymakers and workers need validated measures for
AI exposure (?, ?, ?, ?, ?).

However, validation is challenging because of the diverse outcomes that AI
exposure may produce and interactions between various occupations, locations,
and time. Existing studies have compared AI exposure scores to employment or
wage losses (?, ?) with mixed results. Government and industry analyses,
including from the Brookings Institution (?), the US Bureau of Labor
Statistics (BLS) (?) and the Organisation for Economic Co-operation and
Development (OECD) (?, ?, ?, ?), use AI exposure scores to analyze workers’
exposure to technology in order to predict potential _job losses_. However,
recent reports (?, ?, ?) from the US BLS find no noticeable decrease in
_employment_ for “high-exposure” occupations and suggest that new data is
needed to understand the labor effects of technology.

These mixed results may arise from focusing on the wrong symptoms of AI
exposure. For example, _employment_ data may be uncorrelated with _job
losses_. Unemployment or job losses for an occupation can increase or decrease
even as employment for that occupation increases (?). Technology alters an
occupation’s required workplace activities (i.e., within-occupation skill
change), which can displace workers who cannot adapt (i.e., job separations
from employer firing or worker quitting) (?, ?, ?). Finally, if displaced
workers cannot find new employment quickly, they may rely on unemployment
benefits while they continue job seeking. Thus, only examining detailed
employment data would overlook other major outcomes of AI exposure (?),
including skill change (?), job separations, and unemployment. Even when
unemployment is occurring, employment and wages may remain stable potentially
leading researchers to erroneously conclude that no detrimental outcomes had
occurred For instance, new workers may replace other workers if they can
complement new technology, thus boosting employment and/or wages for the
exposed occupation even though a disruption occurred (e.g., bank tellers and
ATMS (?, ?)).

Quantifying all of the consequences from AI exposure requires more than
employment and/or wage data. However, such data is typically available only on
aggregate (e.g., total unemployment or total job separations using LAUS from
the US Bureau of Labor Statistics (BLS)). For example, both total employment
and total unemployment can not elucidate all labor dynamics of specific
occupations by themselves. Breaking this barrier requires _high-resolution_
unemployment data that can be broken down by time, place, and occupation.

In this article, we break this barrier to detailed unemployment data by
building a high-resolution dataset from monthly unemployment counts by most-
recent occupation from each state’s unemployment insurance office. After
describing occupation AI exposure scores from existing studies, we see which
scores, if any, predict an increased probability of receiving unemployment
benefits (which we call _unemployment risk_), job separations (i.e., job
quitting or firing), or within-occupation skill change using multiple
regression analysis. Although individual scores are not predictive of
unemployment risk, job separations, or skill change, an ensemble model is
predictive even after controlling for regional fixed effects, temporal
effects, and occupations’ skill requirements. We conclude by analyzing which
AI exposure scores are most applicable in different parts of the US economy
(e.g., by state or occupation) and observe strong geographical heterogeneity
in the applicability of each AI exposure score suggesting that efforts to
predict AI job losses cannot rely on any one score. Combined, these results
demonstrate that employment and wage data may miss other detrimental labor
dynamics from AI technology and that efforts using only one AI exposure score
will misrepresent AI’s impact on the future of work.

Wave |  Study |  Year First Available |  Scores |  Description
---|---|---|---|---
1 |  O*NET Bachelors |  2003 |  (denoted %college) |  The fraction of workers in an occupation with a bachelor’s degree.
|  Acemoglu & Autor (?) |  2011 |  Computer Usage (Comp.Use), Routine Cognitive (R.Cog.), Routine Manual (R.Man.) |  Assess occupations on computer usage, routineness, and cognitive or manual requirements.
2 |  Frey & Osborne (?) |  preprint 2013 |  Probability of Computerization (auto) |  Combined a subset of occupation skills with subjective assessments of fully automatable or non-automatable occupations.
|  Arntz et al (?) |  2016 |  Probability of Computerization (auto2) |  Considered a complete set of occupations’ skills to assess automation risk in OECD countires.
|  O*NET Degree of Automation |  2016 |  (Deg.Auto.) |  The relative amounts of routine versus challenging work the worker will perform as part of a job.
3 |  Brynjolfsson et al (?) |  2018 |  Suitability for Machine Learning (SML) |  Surveyed ML experts in order to assess occupations’ task suitability for ML.
|  Felten et al (?) |  2018 |  (AI2) |  Crowdsource gig workers to establish connections between AI application capabilities and occupation abilities.
|  Webb (?) |  2019 |  % AI Exposure (AI), % Software Exposure (Software), % Robot Exposure (Robot) |  Uses NLP to compare technology patents to occupation tasks.
Table 1:  Waves of studies estimating AI exposure by occupation. Methodologies
have evolved from solely theoretical motivations (Wave 1) to greater
specificity into occupations’ skills (Wave 2) to connecting skills to the
capabilities of specific technologies (Wave 3). Scores are taken from each
study; short-hands for each score are provided in parentheses.

## Quantifying Unemployment Risk by Occupation

We collect data from each US state’s unemployment benefits office detailing
the US unemployment benefit recipients by state, month, and their most recent
occupation. Summing unemployment counts across occupations in a given state
and month will correspond to the total unemployment reported by BLS LAUS.
However, unlike LAUS total unemployment, this data is stratified by
occupation, location, and time and thus offers better resolution with which to
study how labor disruptions differentially impact workers by occupation and
labor market. Combined with employment statistics from the US BLS, this data
enables a high-resolution measure of unemployment pressure
$p(u|soc,s,t)$—namely the unemployment risk of an occupation $soc$ given
location (i.e., state $s$) and time (i.e., year and month $t$). We explain
this methodology with more detail in SI Section 3. Unemployment risk varies by
occupation (e.g., construction workers versus transportation workers) while
controlling for states’ total labor market size and occupations’ local
employment share (e.g., more retail workers may receive unemployment benefits
in a state where retail workers are a larger share of employment). Data from
the BLS’s Job Openings and Labor Turnover Survey (JOLTS) and O*NET database
provide data on job separations and within-occupation changes to skill
demands. Taken together, our dataset offers a new opportunity to study the
comprehensive set of possible labor outcomes resulting from exposure to
technology across skill change, job separations, and unemployment.

We use unemployment risk to test the predictive power of existing models of AI
exposure, some of which have been used in policymaking (e.g., the OECD (?,
?)). Models for occupations’ exposure to AI and/or robotics have emerged over
the last decade while emphasizing different technologies and industries. But
it remains unclear which labor markets (e.g., states) or occupations are best
predicted by each approach, and how to combine their relative strengths. Here,
we will focus on AI exposure while leaving exposure to robotics mostly as
future work. Motivated by the skill-biased technological change theory (?, ?,
?), a first wave of studies argued that college-educated, high-skill workers,
who perform cognitive tasks, are complemented by technology while low-skill
workers, who perform manual tasks, are more frequently substituted by
technologies like robotics (?, ?). A second wave of studies used BLS O*NET
data to model occupations based on their skill requirements (?, ?). Most
recently, a third wave of studies compares technological capabilities to job
descriptions (i.e., a task-based approach (?)) by surveying machine learning
(ML) experts (?), surveying gig workers (?), or applying natural language
processing to technology patents (?). Table 1 summarizes these studies across
all three waves (see SI Section 1 for more information). These studies span
the previous decade and each raise the issue of technology reshaping US labor
which suggests that enough time has passed for some impact to have occurred.
While theory and methodology vary across these studies, none of them provide
empirical causal analysis to validate the role of technology in labor
outcomes.

## Automation Estimates and Unemployment Risk

Since each study shares the common goal of estimating AI exposure, we expect
that their estimates will mostly agree with each other. However, we find that
individual exposure scores across studies are not strongly correlated (see
Fig. 1A) and can even be anti-correlated when compared across occupations. For
example, Webb’s measure for Software usage is negatively correlated with
Acemoglu & Autor’s measure of Computer Usage and negatively correlated with
the national fraction of workers with a bachelor’s degree. The strongest
correlation among individual scores is between AI exposure and Software
exposure scores from the same study (?) ($R^{2}=0.494$), but the variance
explained from one score to the next is typically small (average $R^{2}=0.111$
and median $R^{2}=0.068$). This disagreement among exposure scores persists
across individual occupations and is present across all occupation categories
(see Fig. 1C).

Figure 1:  Technological exposure scores are not consistent with each other
and cannot individually predict unemployment risk well. (A) The Pearson
correlation of pairs of AI exposure scores across occupations. Scores are
ordered according to the study’s Wave (see Table 1). (B) The variation in
unemployment risk explained by each AI exposure score. Colors indicate the
score’s Wave. The inset reports the median variance explained ($R^{2}$) for
scores by Wave. (C) A heat map detailing occupations’ AI exposure scores
(color). Occupations are grouped according to their major occupation (x-axis).

Do any AI exposure scores predict unemployment risk and other labor outcomes?
If so, then technology may be detrimental to some US workers, and comparing
different AI exposure scores will reveal a clear winner that policymakers and
workers can use. But, if not, then several possible scenarios arise. On the
one hand, it may be that technology is not a major factor and does not shape
labor outcomes. In this case, no reasonable effort to measure AI exposure
would predict unemployment risk or job separations. On the other hand, if
technology indeed impacts workers, then the disagreement among exposure scores
(see Fig. 1A&C) may indicate that researchers have yet to converge on good
measures for AI exposure. Another possibility is that each exposure score
describes different types of AI exposure or performs well in different
regions; in this case, individual scores might predict only small shares of
unemployment risk individually but combine to explain a larger share.

Our analysis supports the last scenario. First, individual exposure scores do
not strongly predict unemployment risk (see Fig. 1B). For example,
occupations’ Computer Usage (?) explains less than 2% of the variation in
unemployment risk ($R^{2}=0.013$) across occupations, states, and time. The
national share of workers of an occupation with a bachelor’s degree explains
yields $R^{2}=0.018$. The most predictive model comes from Arntz et al. (?)
($R^{2}=0.107$), which was a response to another Wave 2 study from Frey and
Osborne (?) ($R^{2}=0.001$). On aggregate, the median variance explained
increased across the three waves of exposure scores (see Fig. 1B inset) from
$R^{2}=0.015$ for Wave 1 to $R^{2}=0.027$ for Wave 3. The most predictive
score explains only 10.7% of variation in unemployment risk across
occupations, states, and months.

Yet, taken together as an ensemble, AI exposure scores predict much more
variance in unemployment risk across occupations, states, and time. A single
linear model combining all exposure scores accounts for 29.1% of the variation
in unemployment risk (see Fig. 2B Model 1). However, this simple approach
misses several potential confounds which limits the causality that can be
inferred. For example, a worker’s skills or education may determine both their
AI exposure and employer’s likelihood to hire them. Or, automation may vary by
state and/or time depending on the local economy’s industrial composition. For
example, production line robotics would impact blue-collar rural economies
more than white-collar urban economies (?). Figure 2A provides a schematic
detailing how additional factors might shape technology’s impact on
unemployment risk. Note that wages need not be controlled for in our analysis
because wages are determined independently from unemployment risk; however,
both factors are shaped by employer dynamics during the hiring process and so
we expect them to be correlated (e.g., see SI Section 5).

While other potential confounds may exist, we expect that many of these
factors would affect the correlation between technology and unemployment
through skills or education. This observation informs our baseline model,
which controls for occupations’ O*NET skill requirements (i.e., using
principal component analysis. See SI Section4 for methodology), the national
fraction of workers in each occupation with a bachelor’s degree, and fixed
effects for year, month, and state. This baseline model accounts for 57.9% of
variation in unemployment risk across occupations. But, adding AI exposure
scores to the baseline model accounts for an additional 18.3 percentage points
of variation in unemployment risk (i.e., 76.2% variation explained. See Fig.
2B Models 2 & 3). We observe similar performance gains using out-of-sample
cross-validation (see Fig. 3A). We provide full regression tables in SI
Section 5. A large proportion of variation explained indicates that many
sources of unemployment risk affect workers through education, workers’
skills, seasonality, and regional factors, but the additional 18.3% of
variation explained with the inclusion of the ensemble model (i.e., Model 3)
suggests substantial influence from technology.

How do the different AI exposure scores contribute to the ensemble models
predictions? The ensemble model is differentially leveraging the strengths of
each model to produce better predictions (e.g., see Fig. 3C). Comparing
individual scores to the ensemble model’s predictions highlights the states
and occupations where that methodology underperforms. For example, predictions
from Frey & Osborne’s (?) underestimate unemployment risk for Computer/math
occupations in California and overestimate unemployment risk for
Office/Administrative occupations in Connecticut in 2013 (i.e., the year that
scores became available. See Fig. 3D). As another example, predictions using
Webb’s AI score (?) underestimate unemployment risk for Food prep/service
occupations in California and overestimate unemployment risk for
Architecture/engineering occupations in Pennsylvania using 2019 data compared
to the predictions of the ensemble model (see Fig. 3E).

Figure 2:  Combining AI exposure scores from all studies substantially
improves predictions of unemployment risk. (A) A schematic for the theoretical
influence among education, skills, technology, and unemployment risk. (B)
Combined into a single linear model, AI exposure scores capture 29.1% of the
variation in unemployment risk (Model 1). Compared to a baseline model with
controls for year, seasonality, state, occupations’ educational requirements
and occupations’ O*NET skill requirements (Model 2), the combined model
accounts for 76.2% variation in unemployment risk (Model 3). All variables
were centered and standardized before LASSO regression. Unemployment risk is
calculated using monthly data on unemployment recipients from each US state’s
unemployment insurance office ($N=140,274$).  Figure 3:  Combining AI exposure
scores from all studies improves predictions of labor outcomes and reveals
shortcomings in individual scores. (A) Comparing out-of-sample variance
explained with and without AI exposure scores. Each analysis includes ten
independent trials of ten-fold cross-validation training a LASSO regression
model; this produces a distribution of 100 observations of each model’s
predictive performance. Horizontal lines represent the average of each
distribution. For each labor outcome, including the combined AI exposure model
significantly improves predictive performance (i.e., two-sample t-test
$p$-value $<10^{-3}$). (B) The average factor improvement in variance
explained for each labor outcome when the combined AI exposure model is added
to the baseline model. (C) Ensemble model’s predictions for unemployment risk
by occupation and state in 2018 are strongly correlated to actual unemployment
risk. Monthly unemployment data was averaged to produce annual estimates which
were used to calculate unemployment risk. (D) Compared to the ensemble model
(y-axis), predictions from Frey & Osborne’s (?) (x-axis) underestimate
unemployment risk (color) for Computer/math occupations in California and
overestimate unemployment risk for Maintenance occupations in Connecticut
using 2013 data. (E) Compared to the ensemble model, predictions using Webb’s
AI score (?) underestimate unemployment risk for Food prep/service occupations
in California and overestimate unemployment risk for Architecture/engineering
occupations in South Dakota using 2019 data.

## Technology, Job Separations, and Changing Skill Demands

What about other labor outcomes? For example, job separations may occur
without contributing to unemployment if displaced workers quickly find new
employment (?). Similar to unemployment risk, depending on the score used,
correlations varied between states’ AI exposure (see SI Section 2) and states’
job separation or total unemployment rate (see Fig. 4A&B, respectively). On
aggregate, Webb’s (?) measures for Robotics exposure were most predictive of
job separation rates while measures from Acemoglu & Autor (?) were most
predictive of unemployment rates (see Fig. 4D). But each individual score
contributed only a small amount of variance explained compared to the baseline
model. However, combining all scores into a single model yields a 16.8%
improvement in explained total unemployment rate variation by state and month
and a 20.2% improvement for job separation rates by state and month according
to out-of-sample cross-validation (see Fig. 3). Full regression tables are
provided in SI Sections 6 and 7.

Figure 4:  AI exposure scores individually predict small amounts of variation
states’ total unemployment, states’ total job separations, and within
occupation skill change. Linear regression coefficients reporting the
relationship between each exposure scores and (A) total unemployment rate, (B)
total job separation rates, and (C) within-occupation skill change. We provide
95% confidence intervals. Colors represent study waves. (D) For each labor
outcome, the additional predictive performance (i.e., adjusted $R^{2}$) when
exposure scores are added to the baseline model. In all plots, all variables
were centered and standardized. Full regression tables are available in the
SI.

Does technology shape labor outcomes by changing skill demands? Technology
rarely automates entire occupations wholesale but instead automates specific
workplace activities (?, ?). These changes may be subtle without producing job
separations or unemployment if workers adapt. Some studies hypothesize that AI
(?, ?) or machine learning (ML) (?) would change skill demands within
occupations slowly enough for workers to adapt their skills and/or find new
employment. However, re-skilling or up-skilling still imposes an adjustment
cost on both workers and employers (?, ?).

We use occupation skill profiles from the BLS O*NET database to track changes
to skill demands within about 700 different occupations (see SI Section 8).
Routine Manual work was the only Wave 1 AI exposure score positively
associated with skill change (see Fig. 4C). From Wave 2, both Frey & Osborne’s
(?) and Arntz et al.’s (?) scores were positively associated with skill
change. Technology-specific exposure scores from Wave 3 yielded mixed results.
Suitability for ML (?) predicts greater skill change, while AI exposure from
Felten et al. (?) predicts less change. This is especially surprising because
these studies employed similar methodologies; both studies surveyed Mechanical
Turk users about the specific capabilities of AI or ML to produce their
occupation scores. Ultimately, AI exposure according to Felten et al. (?)
yielded the largest gain in predictive performance, adding 5.7% variance
explained (i.e., adjusted $R^{2}$) compared to the baseline fixed effects
model (see Fig. 4D). However, adding the combined AI exposure model to a
baseline model controlling for year and occupation fixed effects produces a
58.8% improvement in variation explained (see Fig. 3). Summary skill change
statistics and full regression tables are provided in SI Section 8.

## Where and When Automation Estimates Work

Examining each AI exposure score’s performance across occupations, locations,
and time periods reveals how each score contributes to the combined model. For
example, unemployment risk for Sales workers across states was negatively
associated with the Routine Cognitive score (?) and positively associated with
Suitability for ML (?) (see Fig. 5A. Analysis included year and month fixed
effects). As an example of heterogeneous spatial performance, combined with
year and month fixed effects, % AI, Robotics, and Software scores from Webb
(?) are most predictive of unemployment risk by occupation in California
compared to other US states (see Fig. 5B). However, other AI exposure scores
yielded more even performance across states. For example, state-level scores
derived from Frey & Osborne (?) yielded roughly equal predictive performance
across states when predicting total job separation rates (see SI Section 7 for
similar maps of other exposure scores). Similarly, the performance of
individual exposure scores can vary over time. For example, Computer Usage (?)
was increasingly predictive of unemployment risk before 2016 but became less
strongly associated with risk thereafter—even becoming negatively associated
with unemployment risk in 2020 perhaps due to more work-from-home activities
during the COVID pandemic (see Fig. 5C). As another example, using scores from
Webb (?), percentage exposure to Robots indicated greater total job separation
rates in states except for the year 2017 when exposure to Software became
positively associated with separations (see SI Section 7). Thus, the best
model for predicting labor outcomes may vary depending on the spatial or
industrial context.

Figure 5:  The effectiveness of AI exposure scores varies by occupation, year,
and state. (A) AI exposure scores’ correspondence with unemployment risk
across states and months. Individual regressions were run for each pair of
major occupation and AI exposure scores while controlling for skills, month,
year, and state. We report regression coefficient estimates with $p$-value
$<10^{-2}$. (B) The performance of scores from Webb (?) in combination with
year and month fixed effects for predicting unemployment risk by occupation.
Individual regressions were performed for each state. (C) For each year, we
regress scores from Acemoglu & Autor (?) against unemployment risk by
occupation with state and month-fixed effects. We provide 95% confidence
intervals for coefficient estimates. In all plots, all variables were centered
and standardized before analysis. Similar plots for other AI exposure scores
are provided in the Supplementary Materials.

## Discussion

How does technology shape labor outcomes? Previous AI exposure studies only
used occupations’ employment or wage trends to evaluate exposure measures.
However, other labor data should be considered if policy makers, workers, and
employers want to prepare for the future of work. This study validates AI
exposure scores against many more possible labor outcomes besides just
employment or wage changes. For instance, AI exposure studies (?, ?, ?, ?, ?,
?) claim that within-occupation skill change is the most likely outcome. Our
study supports this claim by showing that AI exposure scores yields a 59%
factor improvement in skill change predictions (see Fig. 3). However, in
disagreement with claims that technology does not impact unemployment (?), our
results also associate AI exposure with increased job separation rates in
states and unemployment risk across occupations. Thus, employment and wage
data are not enough to fully quantify the labor implications of technological
change. Future research on technology and automation can pursue a more
comprehensive study of technology and labor using this study’s data on
unemployment risk by occupation, state, and month (see
https://github.com/mrfrank8176/unemploymentRisk).

As federal and state policies aim to prepare for new technology and the future
of work (e.g., Illinois Future of Work Act 2021, 2021 IL H 645, and the
National Artificial Intelligence Initiative Act of 2020 (NAIIA)), how should
government and policy analysts quantify AI exposure? More than a decade of
research has produced multiple efforts estimating occupations’ AI exposure and
these exposure scores have been used in government and industry analyses,
including by the Brookings Institution (?), the US Bureau of Labor Statistics
(?), and the Organisation for Economic Co-operation and Development (OECD) (?,
?, ?, ?). The lack of agreement among different scores and their poor
individual ability to predict unemployment risk, job separation rates, or
within-occupation skill change (see Fig. 4) suggest that more research is
required to converge on good solutions with practical use in policy research.

How can we improve our understanding of work disruptions from technology? More
recent scores from Wave 3 were more predictive of occupations’ unemployment
risk (see Fig. 1), suggesting that focusing on technologies’ specific
capabilities may yield further improvements. But, on aggregate, the
significant predictive performance of the combined model (see Fig. 3)
demonstrates that existing scores identify key factors when taken together. A
better understanding of why AI exposure scores perform differently depending
on the state, year, and occupation (see Fig. 5) could yield progress. For
example, the high performance of scores from Webb’s study (?) in California
may be expected because of the study’s focus on information technology and the
technology industry’s presence in Silicon Valley. Similarly, the scores used
in this study were published across the past decade, and their association
with labor outcomes across states and/or occupations varies over time. That
is, we use scores from existing studies in the last decade to predict labor
outcomes throughout the entire time period regardless of the study’s
publication year (i.e., all scores are used for both prospective and
retrospective analysis). Combined, these results demonstrate a need for more
research and development in measuring AI exposure. Future work might produce
dynamic AI exposure scores that update annually based on occupations’ skill
requirements, new technological capabilities (e.g., according to recent
patents), or based on individual outcomes (?). More research may identify
composite AI exposure scores that leverage the methodological strengths of
studies across multiple waves.

Our study has limitations. First, our analysis of unemployment risk used
characteristic unemployment data for occupation groups (i.e., Standard
Occupation Classification Major Occupations) when more granular occupation
data would be preferred. Similarly, our analysis of job separation rates was
limited to the aggregation of states rather than by occupation, industry, or
firm. Despite these shortcomings, to the best of our knowledge, there are no
better comprehensive, nationally-representative data for describing
unemployment risk or job separations using federal data. Refining data from
the Department of Labor (?, ?) will spur dramatic improvements in predictions
of technology and the future of work. Second, similar to previous AI exposure
studies, this study does not establish causality between AI exposure and labor
outcomes because it is extremely difficult to identify natural experiments
around AI exposure at the national scale. Endogeneity exists because most
nascent technologies become mature enough to impact the national labor system
only if they fill some existing demand in the labor market. That is, new
technologies that are in demand will receive enough resources to evolve from
research and development to production and deployment. Although our study
fails to control for every potential confound, our analyses do control for
year (i.e., long-term trends), month (i.e., seasonality), and state (i.e.,
geographical) fixed effects where appropriate. For example, unlike previous
efforts to validate AI exposure scores, our analysis of unemployment risk by
occupation, state, and month controls for occupations’ skill requirements and
education requirements according to the US BLS (see Fig. 2A). Many factors,
such as workers’ self-selection into different types of work or increased
global competition (?, ?), will confound the relationship between AI exposure
and unemployment risk through skills or education requirements and, thus,
would be at least partially controlled for in our analysis. Third, there are
many other potential outcomes to AI exposure including changes to returns on
education (?) and retirement decisions (?). Fourth, our study is focused on
_digital_ AI and misses AI applications in other domains, including robotics
(?, ?). Fifth, our study does not account for recent AI applications whose
impact on the labor market are yet to materialize. However, even recent and
future AI applications and associated efforts to measure workers’ exposure (?,
?) require careful validation against detailed labor data.

In conclusion, we have demonstrated that many concerning labor outcomes can be
predicted with an ensemble model of AI exposure scores. Examples include
unemployment risk, skill change per occupation, and job separation rates by
state. While policymakers strive to prepare workers for artificial
intelligence and the future of work, new methods are required to holistically
quantify which workers have exposure to technology and the risk of detrimental
labor outcomes.

## Acknowledgements

This research is partly supported by the University of Pittsburgh Momentum
Fund and the Center for Research Computing. E.M. is in part supported by the
National Science Foundation under Grant No. 2218748, and by Ministerio de
Ciencia e Innovación/Agencia Española de Investigación
(MCIN/AEI/10.13039/501100011033), Spain through grant PID2019-106811GB-C32.
Y.Y.A. is in part supported by the National Science Foundation Grant No.
2241237. We thank Byungkyu Lee, Daniel Rock, Jaehyuk Park, Maria del Rio-
Chanona, and Dakota Murray for their helpful comments on the manuscript.

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##  1 Supplementary Materials: AI exposure predicts unemployment risk

###  1.1 Estimating Technology Exposure

Estimations of technology exposure have evolved over the last decade. The
first wave of theoretical studies adapted a production function (?, ?) to
worker productivity in the presence of automating technologies, arguing that
college-educated cognitive high-skill workers were complemented by technology,
including computers, while manual low-skill workers were substituted by
technologies like robotics. However, current AI technologies threaten
cognitive workers as well. As examples, consider that AI surpasses human
performance at predicting heart attacks (?) or computer vision applications in
radiology (?, ?, ?) and neighborhood safety (?). The modern, skill-biased
technological change framework (?) further argues that both routine manual and
cognitive work are ripe for automation—although cognitive workers will tend
towards greater productivity with technology while manual workers will tend
towards labor substitution (e.g., in manufacturing (?)).

The second wave of studies considered each occupation as a bundle of skill
requirements and job tasks. Beyond describing occupations as cognitive or
routine, occupations’ granular skill requirements are considered, for
instance, by leveraging the US Bureau of Labor Statistics (BLS) O*NET database
(?, ?, ?). An Oxford University study (?) subjectively identified “fully
automatable” and “not automatable” occupations combined with a subset of O*NET
variables representing perception, manipulation, creativity, and social
intelligence requirements of occupations. They employed a logistic regression
to assign a “probability of computerisation” to each remaining US occupation.
Alarmingly, they claimed that 47% of US employment had high risk of
computerization. However, the study only compared their estimates to
occupations’ education requirements and wages, thus leaving whether this
exposure creates unemployment or alters skill demands unclear. A competing
study directly estimated the automation risk of skills from the Programme for
the International Assessment of Adult Competencies (PIAAC) survey enabling a
direct assessment of technological exposure of occupations across OECD
countries (?), finding that only 9% of US workers had high automation risk.
Subsequent studies used these occupation estimates in a variety of contexts;
for example, finding that automation will affect 35% of employment in Finland
(?), 59% of employment in Germany (?), 45 to 60% of employment across Europe
(?), and that small US cities face greater impact from automation (?). To meet
the demand for occupation-level automation estimates, the BLS added a Degree
of Automation score to occupation profiles in the 2016 O*NET database.

The most-recent third wave of studies directly connects specific technological
capabilities to occupations’ job tasks to assess each occupation’s exposure
(i.e., a task-based approach (?)). One study surveyed machine learning (ML)
experts on the characteristics of tasks that are suitable for ML and produced
a Suitability for ML score for US occupations (?). Another study surveyed gig
workers to establish connections between AI application capabilities and
occupations (?). A more recent study used natural language processing to
connect technology patents to job tasks (?). Although motivated by the risk of
technological unemployment, these studies argue that AI exposure will mostly
result in labor reorganization through wealth inequality or changing skill
demands without major changes to unemployment.

###  1.2 Technology Exposure by State

We study monthly job separation and total unemployment rates by state using
the Job Openings and Labor Turnover Survey (JOLTS) and Local Area Unemployment
Statistics (LAUS) from the US Bureau of Labor Statistics (BLS). Unfortunately,
these data are not stratified by state and occupation, but they are
representative of each state’s monthly economy. Given a per-occupation
technology exposure score $exposure(j)$, we calculate the aggregate exposure
for state $s$ in year $y$ according to

$exposure(s,y)=\sum_{j\in SOC}exposure(j)\cdot share_{s,y}(j)$ (1)

where $j$ is a six-digit Standard Occupation Classification code and
$share_{s,y}(j)$ is the share of employment associated with occupation $j$ in
that state and year according to the BLS.

###  1.3 Quantifying Occupations’ Unemployment Risk and States’ Job
Separations

This study uses federal government data spanning 2010 through 2020 (except
where a different time frame is noted). The US Bureau of Labor Statistics
(BLS) Local Area Unemployment Statistics (LAUS) provides total unemployment
rates, and the BLS Job Openings and Labor Turnover Survey (JOLTS) provides
total job separation rates (i.e., fires and quits) by state and month. These
data capture the aggregate labor dynamics in each state but obfuscate the
heterogeneous dynamics of workers across occupations. Evaluating technology
exposure additionally requires high-resolution per-occupation data about
unemployment risk and changes to skill demands. We meet this requirement with
data describing occupations according to the Standard Occupation
Classification (SOC) system. The SOC is used by the US Department of Labor as
a standardized taxonomy of occupations. Each of about 700 occupations is
specified by a unique six-digit code where the first two digits also indicate
the occupation’s _major_ occupation group (hereafter, “major occupation”). For
example, the BLS Occupational Employment and Wage Statistics (OEWS) details
the employment distribution over six-digit occupations in each state each
year. The BLS also provides the O*NET database, which provides annual profiles
of the skills, knowledge, abilities, and work contexts required by each six-
digit SOC code.

These data include occupation employment and wages but lack insights into
unemployment by occupation. We overcome this barrier using data from the US
Department of Education and Training Administration (DETA). The DETA reports
characteristic unemployment data from each US state’s unemployment insurance
department detailing the most recent major occupation of every benefit
recipient in every state every month. Using this data, given the state $s$ and
month $m$, we directly compute the probability that an unemployment
recipient’s most recent major occupation was $soc$ (denoted $p_{s,m}(soc|u)$).
In combination with annual state employment distributions from OEWS, we use
Bayes’ Theorem to estimate each major occupation’s _unemployment risk_ in
state $s$ and month $m$ according to

$p_{s,m}(u|soc)=p_{s,m}(soc|u)\cdot p_{s,m}(u)/p_{s,m}(soc).$ (2)

$p_{s,m}(u)$ is the aggregate unemployment rate in each state according to
LAUS. $p_{s,m}(soc)$ is estimated according to the probability that a worker
in a state’s labor force is either actively employed in $soc$ according to
annual OEWS data or is unemployed and was most recently employed in $soc$
according to monthly DETA statistics. Note that $p_{s,m}(soc)$ is relative to
the state’s labor force rather than just employed workers. We provide
distributions of unemployment risk estimates in Figure 6.

Figure 6:  Annual distributions of occupation unemployment risk. The median of
monthly risk scores by Major SOC code and state within each year (i.e., median
taken over monthly values within each year).

###  1.4 O*NET Principal Component Analysis

We quantify occupation’s skill requirements using the US Bureau of Labor
Statistic’s (BLS) O*NET skills database. These occupation skill profiles
result from nationally-representative worker surveys covering over 700
different job titles from the Standard Occupation Classification (SOC) system
and details the importance of 120 to 230 examples of workplace knowledge,
abilities, work activities, and skills (henceforth, “skills”) in each year
from 2010 through 2020. Each occupation is identified by a unique six-digit
SOC code where the first two-digits describe the occupation’s Major Occupation
type. We use $onet_{y,j,s}\in[0,1]$ to denote the importance of skill $s\in S$
to occupation $j\in J$ in year $y$ such that $onet_{y,j,s}=1$ identifies an
essential skill and $onet_{y,j,s}=0$ indicates an irrelevant skill. We
simplify these annual occupation skill profiles using principal component
analysis (PCA). Figure 7 demonstrates the variation in skill requirements
captured by PCA as a function of the number of principal components included
in the analysis. Throughout this study, we use the first ten principal
components to control for occupations’ skill requirements. The first ten
principal components account for 96% of the total variation in occupation’s
skill requirements across all years of the O*NET data. These principal
components are used in our analysis of unemployment risk to control for
occupations’ skill requirements.

Figure 7:  O*NET variance explained by the number of principal components
included. In this study, we use the first ten principal components which
cumulatively account for 96% of the overall variation in occupations’ skill
requirements by year according to O*NET.

###  1.5 Unemployment Risk by Occupation, State, & Month

Figure 8:  Using occupation wage data, we observe a negative correlation
between unemployment risk and wages. BLS OEWS describes occupation wages for
each six-digit SOC code, state, and year. However, unemployment risk is only
calculated for each two-digit SOC code (i.e., Major Occupation), state, and
year. We sum six-digit SOC wage bill values by state to calculate wage bill
values for two-digit SOC codes in each state in each year.  Dependent
Variable: Log10 Unemployment Risk by Occupation, Month, & State
---
Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8
Acemoglu & Autor[24] Comp.Use | 0.000 |  |  |  |  |  |  |
Acemoglu & Autor[24] R.Cog. | -0.096∗∗∗ |  |  |  |  |  |  |
Acemoglu & Autor[24] R.Man. | 0.137∗∗∗ |  |  |  |  |  |  |
ONET Education %college |  | -0.134∗∗∗ |  |  |  |  |  |
Frey & Osborne[9] auto |  |  | 0.024∗∗∗ |  |  |  |  |
Arntz et al[10] auto2 |  |  |  | 0.327∗∗∗ |  |  |  |
ONET Automation Deg.Auto. |  |  |  |  | 0.152∗∗∗ |  |  |
Brynjolfsson et al[11] SML |  |  |  |  |  | 0.082∗∗∗ |  |
Felten et al[12] AI2 |  |  |  |  |  |  | -0.109∗∗∗ |
Webb[13] AI |  |  |  |  |  |  |  | 0.332∗∗∗
Webb[13] Robot |  |  |  |  |  |  |  | 0.412∗∗∗
Webb[13] Software |  |  |  |  |  |  |  | -0.294∗∗∗
$R^{2}$ | 0.028 | 0.018 | 0.001 | 0.107 | 0.023 | 0.007 | 0.012 | 0.089
adj. $R^{2}$ | 0.028 | 0.018 | 0.001 | 0.107 | 0.023 | 0.007 | 0.012 | 0.089
$p_{val}<0.1^{*}$, $p_{val}<0.01^{**}$, $p_{val}<0.001^{***}$
Table 2:  Linear regression analysis of occupations’ technology exposure and
unemployment risk. Data varies by Major SOC code, state, and month from
January 2010 through 2020 for a total of 140,274 data points. All variables
were centered and standardized before analysis, thus eliminating units from
the various exposure variables.  Dependent Variable: Log10 Unemployment Risk
by Occupation, Month, & State
---
Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | Model 9
Acemoglu & Autor[24] Comp.Use |  | 0.715∗∗∗ |  |  |  |  |  |  | 0.534∗∗∗
Acemoglu & Autor[24] R.Cog. |  | -0.510∗∗∗ |  |  |  |  |  |  | -0.241∗∗∗
Acemoglu & Autor[24] R.Man. |  | 0.000 |  |  |  |  |  |  | 0.000
Frey & Osborne[9] auto |  |  | -0.582∗∗∗ |  |  |  |  |  | -0.317∗∗∗
Arntz et al[10] auto2 |  |  |  | 0.245∗∗∗ |  |  |  |  | 0.377∗∗∗
ONET Automation Deg.Auto. |  |  |  |  | 0.054∗∗∗ |  |  |  | 0.000
Brynjolfsson et al[11] SML |  |  |  |  |  | -0.102∗∗∗ |  |  | -0.198∗∗∗
Felten et al[12] AI2 |  |  |  |  |  |  | -0.165∗∗∗ |  | 0.014∗∗∗
Webb[13] AI |  |  |  |  |  |  |  | 0.468∗∗∗ | 0.114∗∗∗
Webb[13] Robot |  |  |  |  |  |  |  | 0.291∗∗∗ | 0.647∗∗∗
Webb[13] Software |  |  |  |  |  |  |  | -0.000 | -0.098∗∗∗
ONET Education %college | 0.032∗∗∗ | -0.124∗∗∗ | -0.032∗∗∗ | 0.061∗∗∗ | 0.000 | 0.075∗∗∗ | 0.038∗∗∗ | -0.116∗∗∗ | -0.036∗∗∗
O*NET PCA | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
Year F.E. | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
Month F.E. | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
State F.E. | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
$R^{2}$ | 0.579 | 0.690 | 0.690 | 0.587 | 0.580 | 0.586 | 0.593 | 0.657 | 0.762
adj. $R^{2}$ | 0.579 | 0.689 | 0.690 | 0.587 | 0.580 | 0.586 | 0.592 | 0.657 | 0.762
$p_{val}<0.1^{*}$, $p_{val}<0.01^{**}$, $p_{val}<0.001^{***}$
Table 3:  LASSO regression analysis of occupations’ technology exposure and
unemployment risk. Data varies by Major SOC code, state, and month from
January 2010 through 2020 for a total of 140,274 data points. All variables
were centered and standardized before analysis, thus eliminating units from
the various AI exposure variables.

ct0¡ 8

Figure 9:  Variables’ regression coefficient estimate with $\log_{10}$
unemployment risk as the dependent variable. Bars represent 95% confidence
intervals. Independent regressions with month and state fixed effects were
performed for each year. All variables were centered and standardized before
analysis, thus eliminating units from the various AI exposure variables.

ct0¡ 8

Figure 10:  $\log_{10}$ unemployment risk variance explained by each model.
Independent regressions with month and state fixed effects were performed for
each year. All variables were centered and standardized before analysis, thus
eliminating units from the various AI exposure variables. Vertical dashed line
represents the year that AI exposure variables were made available.

ct0¡ 8

Figure 11:  $\log_{10}$ unemployment risk variance explained by each model.
Independent regressions with month and year fixed effects were performed for
each state.

###  1.6 State Unemployment Rates

Dependent Variable: Log10 Total Separations by State & Month
---
| Wave 1 | Wave 2 | Wave 3
Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8
Acemoglu & Autor[14] Computer Usage | 0.032∗ |  |  |  |  |  |  |
Acemoglu & Autor[14] Routine Cognitive | 0.458∗∗∗ |  |  |  |  |  |  |
Acemoglu & Autor[14] Routine Manual | -0.323∗∗∗ |  |  |  |  |  |  |
ONET Education % Workers w/ Bachelors |  | -0.073∗∗∗ |  |  |  |  |  |
Frey & Osborne[17] Prob. of Computerisation |  |  | 0.247∗∗∗ |  |  |  |  |
Arntz et al[18] Probability |  |  |  | 0.168∗∗∗ |  |  |  |
ONET Automation Degree of Automation |  |  |  |  | 0.312∗∗∗ |  |  |
Brynjolfsson et al[23] Suitability for ML |  |  |  |  |  | 0.185∗∗∗ |  |
Felten et al[24] AI Exposure |  |  |  |  |  |  | 0.264∗∗∗ |
Webb[25] % AI |  |  |  |  |  |  |  | -0.013
Webb[25] % Robot |  |  |  |  |  |  |  | 0.046
Webb[25] % Software |  |  |  |  |  |  |  | 0.101∗
$R^{2}$ | 0.176 | 0.005 | 0.061 | 0.028 | 0.097 | 0.034 | 0.069 | 0.018
adj. $R^{2}$ | 0.175 | 0.005 | 0.061 | 0.028 | 0.097 | 0.034 | 0.069 | 0.018
$p_{val}<0.1^{*}$, $p_{val}<0.01^{**}$, $p_{val}<0.001^{***}$
Table 4:  Linear regression analysis of states’ technology exposure and total
unemployment rates according to LAUS. Data varies by state, and month from
January 2010 through 2020 for a total of 6,600 data points. All variables were
centered and standardized before analysis thus eliminating units from the
various exposure variables.  Dependent Variable: Log10 Total Separations by
State & Month
---
| Wave 1 | Wave 2 | Wave 3 |
Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | Model 9 | Model 10
Acemoglu & Autor[14] Computer Usage |  | -0.108∗∗∗ |  |  |  |  |  |  |  | -0.006
Acemoglu & Autor[14] Routine Cognitive |  | 0.176∗∗∗ |  |  |  |  |  |  |  | 0.385∗∗∗
Acemoglu & Autor[14] Routine Manual |  | -0.194∗∗∗ |  |  |  |  |  |  |  | -0.618∗∗∗
ONET Education % Workers w/ Bachelors |  |  | -0.143∗∗∗ |  |  |  |  |  |  | -0.914∗∗∗
Frey & Osborne[17] Prob. of Computerisation |  |  |  | -0.020 |  |  |  |  |  | -0.106∗
Arntz et al[18] Probability |  |  |  |  | -0.018 |  |  |  |  | -0.196∗∗∗
ONET Automation Degree of Automation |  |  |  |  |  | -0.417∗∗∗ |  |  |  | -0.096
Brynjolfsson et al[23] Suitability for ML |  |  |  |  |  |  | -0.236∗∗∗ |  |  | -0.772∗∗∗
Felten et al[24] AI Exposure |  |  |  |  |  |  |  | -0.222∗∗∗ |  | -0.178∗∗∗
Webb[25] % AI |  |  |  |  |  |  |  |  | 0.111∗∗ | 1.345∗∗∗
Webb[25] % Robot |  |  |  |  |  |  |  |  | 0.210∗∗∗ | 0.983∗∗∗
Webb[25] % Software |  |  |  |  |  |  |  |  | -0.205∗∗∗ | -0.851∗∗∗
$\log_{10}$ Wage Bill ($) | 0.241∗∗∗ | 0.222∗∗∗ | 0.278∗∗∗ | 0.237∗∗∗ | 0.237∗∗∗ | 0.233∗∗∗ | 0.230∗∗∗ | 0.210∗∗∗ | 0.271∗∗∗ | 0.128∗∗∗
Year F.E. | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
Month F.E. | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
$R^{2}$ | 0.598 | 0.618 | 0.605 | 0.598 | 0.598 | 0.603 | 0.601 | 0.606 | 0.600 | 0.699
adj. $R^{2}$ | 0.597 | 0.616 | 0.603 | 0.597 | 0.597 | 0.601 | 0.599 | 0.604 | 0.599 | 0.698
$p_{val}<0.1^{*}$, $p_{val}<0.01^{**}$, $p_{val}<0.001^{***}$
Table 5:  Multiple linear regression analysis of states’ technology exposure
and total unemployment rates each month according to LAUS. Data varies by
state and month from January 2010 through 2020 for a total of 6,600 data
points. All variables were centered and standardized before analysis thus
eliminating units from the various AI exposure variables. We are unable to
control for state fixed effects in this analysis, so we instead control for
the size of each states’ economy according to their total wage bill (i.e.,
total wages paid).

###  1.7 State Job Separation Rates

Dependent Variable: Log10 Total Separations by State & Month
---
| Wave 1 | Wave 2 | Wave 3
Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8
Acemoglu & Autor[14] Computer Usage | 0.077∗∗∗ |  |  |  |  |  |  |
Acemoglu & Autor[14] Routine Cognitive | -0.143∗∗∗ |  |  |  |  |  |  |
Acemoglu & Autor[14] Routine Manual | 0.399∗∗∗ |  |  |  |  |  |  |
ONET Education % Workers w/ Bachelors |  | -0.212∗∗∗ |  |  |  |  |  |
Frey & Osborne[17] Prob. of Computerisation |  |  | -0.088∗∗∗ |  |  |  |  |
Arntz et al[18] Probability |  |  |  | -0.076∗∗∗ |  |  |  |
ONET Automation Degree of Automation |  |  |  |  | -0.157∗∗∗ |  |  |
Brynjolfsson et al[23] Suitability for ML |  |  |  |  |  | -0.170∗∗∗ |  |
Felten et al[24] AI Exposure |  |  |  |  |  |  | -0.156∗∗∗ |
Webb[25] % AI |  |  |  |  |  |  |  | -0.257∗∗∗
Webb[25] % Robot |  |  |  |  |  |  |  | 0.023
Webb[25] % Software |  |  |  |  |  |  |  | 0.127∗∗
$R^{2}$ | 0.097 | 0.045 | 0.008 | 0.006 | 0.025 | 0.029 | 0.024 | 0.029
adj. $R^{2}$ | 0.096 | 0.045 | 0.008 | 0.006 | 0.024 | 0.029 | 0.024 | 0.028
$p_{val}<0.1^{*}$, $p_{val}<0.01^{**}$, $p_{val}<0.001^{***}$
Table 6:  Linear regression analysis of states’ technology exposure and total
job separation rate according to JOLTS. Data varies by state, and month from
January 2010 through 2020 for a total of 6,600 data points. All variables were
centered and standardized before analysis thus eliminating units from the
various exposure variables.  Dependent Variable: Log10 Total Separations by
State & Month
---
| Wave 1 | Wave 2 | Wave 3 |
Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | Model 9 | Model 10
Acemoglu & Autor[14] Computer Usage |  | 0.003 |  |  |  |  |  |  |  | 0.406∗∗∗
Acemoglu & Autor[14] Routine Cognitive |  | 0.068∗∗∗ |  |  |  |  |  |  |  | 0.105∗∗∗
Acemoglu & Autor[14] Routine Manual |  | 0.116∗∗∗ |  |  |  |  |  |  |  | -0.072∗
ONET Education % Workers w/ Bachelors |  |  | -0.218∗∗∗ |  |  |  |  |  |  | -0.272∗∗∗
Frey & Osborne[17] Prob. of Computerisation |  |  |  | 0.270∗∗∗ |  |  |  |  |  | 0.654∗∗∗
Arntz et al[18] Probability |  |  |  |  | 0.222∗∗∗ |  |  |  |  | -0.247∗∗∗
ONET Automation Degree of Automation |  |  |  |  |  | 0.116∗ |  |  |  | -0.212
Brynjolfsson et al[23] Suitability for ML |  |  |  |  |  |  | 0.213∗∗∗ |  |  | -1.013∗∗∗
Felten et al[24] AI Exposure |  |  |  |  |  |  |  | 0.352∗∗∗ |  | 0.518∗∗∗
Webb[25] % AI |  |  |  |  |  |  |  |  | -0.001 | 0.221∗∗
Webb[25] % Robot |  |  |  |  |  |  |  |  | 0.280∗∗∗ | 0.672∗∗∗
Webb[25] % Software |  |  |  |  |  |  |  |  | -0.002 | -0.320∗∗∗
$\log_{10}$ Wage Bill ($) | -0.259∗∗∗ | -0.220∗∗∗ | -0.203∗∗∗ | -0.197∗∗∗ | -0.202∗∗∗ | -0.257∗∗∗ | -0.249∗∗∗ | -0.210∗∗∗ | -0.189∗∗∗ | -0.159∗∗∗
Year F.E. | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
Month F.E. | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
$R^{2}$ | 0.379 | 0.401 | 0.394 | 0.402 | 0.394 | 0.379 | 0.381 | 0.398 | 0.404 | 0.457
adj. $R^{2}$ | 0.376 | 0.399 | 0.392 | 0.400 | 0.391 | 0.377 | 0.378 | 0.395 | 0.402 | 0.454
$p_{val}<0.1^{*}$, $p_{val}<0.01^{**}$, $p_{val}<0.001^{***}$
Table 7:  Multiple linear regression analysis of states’ technology exposure
and total job separation rates each month according to JOLTS. Data varies by
state and month from January 2010 through 2020 for a total of 6,600 data
points. All variables were centered and standardized before analysis thus
eliminating units from the various AI exposure variables. We are unable to
control for state fixed effects in this analysis, so we instead control for
the size of each states’ economy according to their total wage bill (i.e.,
total wages paid).

ct0¡ 8

Figure 12:  Variables’ regression coefficient estimate with monthly
$\log_{10}$ total job separations as the dependent variable. Bars represent
95% confidence intervals. Independent regressions with month fixed effects and
were performed for each year while controlling for states’ total wage bill
(i.e., total wages paid). All variables were centered and standardized before
analysis thus eliminating units from the various AI exposure variables.

ct0¡ 8

Figure 13:  Monthly $\log_{10}$ total job separations variance explained by
each model. Independent regressions with month fixed effects and were
performed for each year while controlling for states’ total wage bill (i.e.,
total wages paid). All variables were centered and standardized before
analysis thus eliminating units from the various AI exposure variables.

ct0¡ 8

Figure 14:  Monthly $\log_{10}$ total separations variance explained by each
model. Independent regressions with month fixed effects were performed for
each state while controlling for states’ total wage bill (i.e., total wages
paid).

###  1.8 Within-Occupation Skill Change

We explore within-occupation changes to skill demand using annual occupation
skill profiles from the BLS O*NET database. After normalizing the varying
Likert scales used across O*NET surveys, we obtain a skill vector for each
occupation in each from 2010 through 2017. Let $onet_{j_{y},s}\in[0,1]$
represent the real-valued importance of skill $s$ to occupation $j$ in year
$y$ such that $onet_{j_{y},s}=1$ identifies an essential skill and
$onet_{j_{y},s}=0$ identifies an irrelevant skill. Inspired by earlier work
(?), this data allows us to model each occupation as a vector of O*NET
variables. O*NET is updated annually, but each occupation is only updated
every five years through a rolling survey. That is, occupations are not
updated each year. Therefore, for each six-digit Standard Occupation
Classification (SOC) code $j$, we capture the within-occupation skill change
in the year that $j$ was updated in O*NET and compare the updated skill
profile to $j$’s skill requirements in 2010 according to

$\Delta skill(j_{y},j_{2010})=1-\frac{\sum_{s\in
S}\min(onet_{j_{y},s},onet_{j_{2010},s})}{\sum_{s\in
S}\max(onet_{j_{y},s},onet_{j_{2010},s})}.$ (3)

Notice that eq (3) is one minus the Jaccard similarity and $\Delta
skill\in[0,1]$. We consider O*NET data only from 2010 through 2017 because the
SOC taxonomy was updated in 2018. Figure 15 displays the distribution of skill
change scores for the occupations that were updated in each year. Although we
provide distributions for 2018, 2019, and 2020 in Fig. 15, all other analyses
of skill change in this study are restricted to O*NET data from 2010 through
2017 because the SOC taxonomy was updated from 2018 on-wards (i.e., the list
of occupation codes changed) which introduces the potential for skill changes
to result from the merging or separation of 2010 SOC codes.

Figure 15:  The annual distribution of within-occupation skill change compared
to 2010. We consider only the occupations with skill profiles that were
updated in each year. That is, each occupation contributes to the regression
in only one year. We use 2011 through 2017 for the main analysis because the
SOC taxonomy used to distinguish occupations was updated in 2018 thus
introducing a potential confound to any longitudinal analysis.  Dependent
Variable: Within-Occupation Skill Change compared to 2010 by Detailed
Occupation
---
| Wave 1 | Wave 2 | Wave 3
Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8
Acemoglu & Autor[14] Computer Usage | -0.275∗∗∗ |  |  |  |  |  |  |
Acemoglu & Autor[14] Routine Cognitive | 0.062 |  |  |  |  |  |  |
Acemoglu & Autor[14] Routine Manual | 0.069 |  |  |  |  |  |  |
ONET Education % Workers w/ Bachelors |  | -0.216∗∗∗ |  |  |  |  |  |
Frey & Osborne[17] Prob. of Computerisation |  |  | 0.345∗∗∗ |  |  |  |  |
Arntz et al[18] Probability |  |  |  | 0.245∗∗∗ |  |  |  |
ONET Automation Degree of Automation |  |  |  |  | 0.050 |  |  |
Brynjolfsson et al[23] Suitability for ML |  |  |  |  |  | 0.363∗∗∗ |  |
Felten et al[24] AI Exposure |  |  |  |  |  |  | -0.256∗∗∗ |
Webb[25] % AI |  |  |  |  |  |  |  | -0.203∗∗∗
Webb[25] % Robot |  |  |  |  |  |  |  | 0.261∗∗∗
Webb[25] % Software |  |  |  |  |  |  |  | 0.016
Year F.E. | No | No | No | No | No | No | No | No
Major SOC F.E. | No | No | No | No | No | No | No | No
$R^{2}$ | 0.108 | 0.047 | 0.119 | 0.060 | 0.002 | 0.132 | 0.065 | 0.081
adj. $R^{2}$ | 0.103 | 0.045 | 0.117 | 0.058 | 0.001 | 0.130 | 0.064 | 0.076
$p_{val}<0.1^{*}$, $p_{val}<0.01^{**}$, $p_{val}<0.001^{***}$
Table 8:  Multiple linear regression analysis of within-occupation skill
change compared to 2010 O*NET skill profiles. Data varies by six-digit SOC
code from 2011 through 2017 for a total of 479 data points. All variables were
centered and standardized before analysis thus eliminating potentially
different units from the technology exposure scores.  Dependent Variable:
Within-Occupation Skill Change compared to 2010 by Detailed Occupation
---
| Wave 1 | Wave 2 | Wave 3 |
Variable | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | Model 9 | Model 10
Acemoglu & Autor[14] Computer Usage |  | -0.214∗∗∗ |  |  |  |  |  |  |  | -0.118∗
Acemoglu & Autor[14] Routine Cognitive |  | -0.035 |  |  |  |  |  |  |  | -0.079
Acemoglu & Autor[14] Routine Manual |  | 0.096 |  |  |  |  |  |  |  | 0.034
ONET Education % Workers w/ Bachelors |  |  | -0.103∗ |  |  |  |  |  |  | 0.051
Frey & Osborne[17] Prob. of Computerisation |  |  |  | 0.220∗∗∗ |  |  |  |  |  | 0.075
Arntz et al[18] Probability |  |  |  |  | 0.075 |  |  |  |  | 0.032
ONET Automation Degree of Automation |  |  |  |  |  | -0.057 |  |  |  | -0.066
Brynjolfsson et al[23] Suitability for ML |  |  |  |  |  |  | 0.390∗∗∗ |  |  | 0.330∗∗∗
Felten et al[24] AI Exposure |  |  |  |  |  |  |  | -0.343∗∗∗ |  | -0.269∗∗∗
Webb[25] % AI |  |  |  |  |  |  |  |  | -0.107 | 0.008
Webb[25] % Robot |  |  |  |  |  |  |  |  | 0.242∗∗ | 0.120
Webb[25] % Software |  |  |  |  |  |  |  |  | -0.022 | -0.027
Year F.E. | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
Major SOC F.E. | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes
$R^{2}$ | 0.251 | 0.274 | 0.257 | 0.272 | 0.255 | 0.253 | 0.292 | 0.306 | 0.268 | 0.357
adj. $R^{2}$ | 0.207 | 0.228 | 0.212 | 0.228 | 0.209 | 0.208 | 0.249 | 0.264 | 0.221 | 0.304
$p_{val}<0.1^{*}$, $p_{val}<0.01^{**}$, $p_{val}<0.001^{***}$
Table 9:  Multiple linear regression analysis of within-occupation skill
change compared to 2010 O*NET skill profiles. Data varies by six-digit SOC
code from 2011 through 2017 for a total of 479 data points. All variables were
centered and standardized before analysis, thus eliminating potentially
different units from the technology exposure scores.