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DISCUSSION PAPER SERIES

IZA DP No. 17554

AI, Task Changes in Jobs, and Worker
Reallocation

Christina Gathmann
Felix Grimm
Erwin Winkler

DECEMBER 2024

DISCUSSION PAPER SERIES

IZA DP No. 17554

AI, Task Changes in Jobs, and Worker
Reallocation
Christina Gathmann

LISER, University of Luxembourg, CEPR and IZA

Felix Grimm
LISER

Erwin Winkler

University of Erlangen-Nuremberg, IZA and LASER

DECEMBER 2024

Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may
include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA
Guiding Principles of Research Integrity.
The IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics
and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Foundation, IZA runs the
world’s largest network of economists, whose research aims to provide answers to the global labor market challenges of our
time. Our key objective is to build bridges between academic research, policymakers and society.
IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper
should account for its provisional character. A revised version may be available directly from the author.
ISSN: 2365-9793

IZA – Institute of Labor Economics
Schaumburg-Lippe-Straße 5–9
53113 Bonn, Germany

Phone: +49-228-3894-0
Email: publications@iza.org

www.iza.org

IZA DP No. 17554

DECEMBER 2024

ABSTRACT
AI, Task Changes in Jobs, and Worker
Reallocation*
How does Artificial Intelligence (AI) affect the task content of work, and how do workers
adjust to the diffusion of AI in the economy? To answer these important questions, we
combine novel patent-based measures of AI and robot exposure with individual survey
data on tasks performed on the job and administrative data on worker careers. Like prior
studies, we find that robots have reduced routine tasks. In sharp contrast, AI has reduced
non-routine abstract tasks like information gathering and increased the demand for ‘highlevel’ routine tasks like monitoring processes. These task shifts mainly occur within detailed
occupations and become stronger over time. While displacement effects are small, workers
have responded by switching jobs, often to less exposed industries. We also document
that low-skilled workers suffer some wage losses, while high-skilled incumbent workers
experience wage gains.
JEL Classification:

J23, J24, J31, J62

Keywords:

Artificial Intelligence, tasks, skills, reallocation, robots, patents

Corresponding author:
Erwin Winkler
University of Erlangen-Nuremberg
Lange Gasse 20
90403 Nuremberg
Germany
E-mail: erwin.winkler@fau.de

* We are grateful to Melanie Arntz, Matias Cortes, Sabrina Genz, Terry Gregory, Daniel Haanwinckel, Markus
Nagler, Regina Riphahn, and participants at SOLE, EALE, VfS, the Bank of Italy, DIW, Galatasaray University, University
of Trier, University of Duisburg-Essen, University of Kiel, University of Erlangen-Nuremberg, the ELMI conference on
Digital and Green Skills, the IZA/IfW conference on AI, and the Ifo Workshop on Digitalisation for helpful comments
and suggestions.

1

Introduction

Artificial Intelligence (AI) has seen stunning progress over the past years expanding the domains in
which AI is potentially applicable (Maslej et al., 2024). Its vast potential has sparked concerns that
AI could displace many workers and sharply increase inequality. An emerging literature has investigated the labor market e!ects of AI, focusing on worker employment and wages (e.g., Acemoglu
et al., 2022, Alekseeva et al., 2021, Bloom et al., 2024, Brynjolfsson et al., 2018, Bonfiglioli et al.,
2023, Engberg et al., 2024, Felten et al., 2018, Mann and Püttmann, 2023, Webb, 2020). Most
studies find no evidence for job displacement and a modest impact on wages or inequality among
workers.
Little is known about how AI changes jobs beyond displacement and whether and how workers
have adjusted to any such changes. This gap is all the more surprising because the theoretical
literature has moved beyond the notion that technological change raises marginal productivities of
inputs to include the idea that machines can take over some tasks traditionally performed by workers (Acemoglu and Autor, 2011, Acemoglu and Restrepo, 2019, Acemoglu et al., 2024, Autor et al.,
2024). In these task-based models, workers then have to switch to other, possibly complementary
tasks that are not yet automated, or perform new tasks that emerge in response to the new technology. As a result, the task content of jobs exposed to the new technology will change. Such a shift
might be accompanied by job displacement, but it needs not be. Moreover, technological change
might also change who performs a task if workers di!er in their comparative advantage (Acemoglu
and Restrepo, 2018). Technological advances like robots could perform ‘routine tasks’ that can be
codified into simple rules. AI, in contrast, does not need humans to program every step of their
work but can apply machine learning techniques without following human logic (Brynjolfsson et al.,
2018). Many therefore believe that AI has the capability to perform ‘non-routine’ tasks that require
tacit knowledge (Felten et al., 2018). The consequences of observed task shifts for employment,
worker productivity, and wages then depend crucially on the type of tasks that get automated;
whether workers can switch to complementary tasks; and how non-automated tasks are assigned to
di!erent types of workers.1
In this paper, we analyze how AI has a!ected the task content of jobs in Germany, and compare
it to the impact of robots. In particular, we study whether workers exposed to AI are less likely
1

A few recent field experiments show that generative AI has the potential to increase worker productivity (see
Brynjolfsson et al. (2023) for call centers, Hui et al. (2023), Noy and Zhang (2023) for writing tasks, Peng et al. (2023)
for IT developers, and Toner-Rodgers (2024) for scientists). Whether these results apply to other occupations is an
open question.

1

to perform certain tasks and switch to other tasks instead. Our analysis can distinguish between
task changes within and between occupations and track these changes over time. Moreover, workers
might respond to the new technologies by switching employers, occupations, or industries, as not
all jobs and sectors are equally exposed to AI or robots. Our paper provides the first evidence of
how workers might use their outside opportunities to adjust to the emergence of AI. Finally, we
also explore to what extent these adjustments a!ect workers’ wages.
A key challenge for our analysis is how to measure the new technological opportunities of AI.
We use a novel measure to characterize the evolution of AI and robots, which we developed by
applying NLP on the universe of patents from the European Patent O”ce (EPO) (Gathmann
and Grimm, 2024). We pool patents from all inventors filed at the EPO and match them to the
industries most likely to use them, thereby reducing concerns about reverse causality. We show
below that task shares cannot predict subsequent technology patents in using industries, and that
results are robust to dropping patents filed by German inventors. Our measures then capture the
evolving capabilities of AI and robots across detailed industries and within industries over time.
Reassuringly, the patent-based robot measure is highly correlated with actual robot installations in
manufacturing industries, which is commonly used in studies on robots (Acemoglu and Restrepo,
2020, Dauth et al., 2020, Graetz and Michaels, 2018). We further document that our patent-based
AI measure shows a strong association with job ads requiring AI skills and firms mentioning AI
on their website. A key advantage of our industry-level measures is that they enable us to analyze
AI’s impact on the task content of occupations, and the workers employed in those occupations,
and trace their dynamics over time.
We start out investigating how AI changes job tasks, both within and across occupations and
contrast the results with the better-known e!ects for robots. To do so, we combine our industry-level
technology measures with individual survey data on the tasks workers perform on their jobs. These
data have previously been used to study task human capital (Gathmann and Schönberg, 2010)
and technology-related changes in job tasks (Spitz-Oener, 2006). Our empirical analysis relies on
within-industry variation in AI and robot exposure over time between workers who have similar
socio-demographic characteristics and are employed in the same occupation.
Our first novel result is that AI has decreased abstract tasks in jobs, in particular in the category
of ’gathering information and investigating’. In contrast, AI has increased the need for ‘high-level’
routine tasks such as monitoring processes, which require the use of a computer or tablet. Most of
the changes occur within detailed occupations, indicating that AI shifts the assignment of workers
2

to tasks. The e!ects have become more pronounced as the capabilities of AI expanded over time.
We further show that the impact of AI on job tasks di!ers sharply from that of robots. Robots have
increased the demand for non-routine tasks and reduced the demand for routine tasks. The task
changes we find using robot patents are very similar to previous studies using robot installations
(e.g., Graetz and Michaels, 2018, Acemoglu and Restrepo, 2020, Dauth et al., 2021), providing
further support for the validity of our patent-based measures. The task shifts we document challenge
the view that AI might continue previous technological trends of automating mostly routine tasks.
Our second novel result is that the task changes associated with AI a!ect all workers but are not
skill-neutral. Both low- and high-skilled workers see a decline in the analytical task of ’gathering
information and investigating’, and an increase in the routine task of monitoring processes. Yet,
high-skilled workers also increase their activities in educating and training. Though both skill
groups are a!ected by the automation of some abstract tasks, high-skilled workers can leverage their
comparative advantage and expertise to switch to other abstract tasks like training and educating.
The possibility to perform other tasks thus shields high-skilled workers partially from the e!ects of
automation, while low-skilled workers see their abstract task share decline.
We then turn to administrative social security data to understand the consequences of the
observed task changes for worker careers. In line with earlier studies, we document that AI has so
far not destroyed many jobs in exposed industries. That does not mean that AI has not had an
imprint on the labor market, however. We do see sizable adjustments to AI at the worker level.
Our third novel result is that AI has resulted in worker reallocation across establishments. About
half of the job mobility is within the same detailed industry, while the other half is into similar, but
di!erent industries that are less exposed to AI. As for the task changes, the reallocation of workers
in response to AI di!ers sharply from the worker-level adjustments to robots. In line with prior
evidence from Germany (Dauth et al., 2021), we find that robots increased employment stability at
the initial establishment.
Our final novel result is that the reassignment of tasks across workers has reduced the wages
of low-skilled workers in exposed industries –irrespective of whether they switch jobs or not. The
modest decline in wages indicates that the loss from automation exceeds any productivity gains
for low-skilled workers in Germany. In contrast, high-skilled workers, though also a!ected by automation, do not experience wage declines on average. Incumbent workers actually benefit from
wage gains, possibly reflecting productivity gains and their comparative advantage in their job. Our
results thus confirm task-based models that AI can indeed result in wage losses for some workers if
3

AI is mainly deployed for automation and workers cannot exploit some comparative advantage to
compensate for the automated tasks easily.
The findings in this paper have a number of important policy implications for the transition into
the age of AI. The fact that AI can substitute for non-routine tasks in all skill groups highlights
that AI will a!ect many more workers, including those high up the skill distribution. A second
implication is that workers are hit hardest if they cannot exploit some comparative advantage in
other tasks to substitute for automated tasks. Promoting on-the-job training and upskilling to
strengthen competencies in complementarity skills and workers’ comparative advantages becomes
then key to cushioning the potentially disruptive e!ects of AI in the labor market. Finally, our
findings document that the adjustments to evolving AI have so far occurred primarily through
worker reallocation. Policy-makers could then provide incentives for encouraging worker mobility
or job search assistance to identify suitable job opportunities in other industries, for instance.
Our paper contributes to several strands of the literature. In addition to studies on the labor
market e!ects of AI, our paper builds on the empirical task literature, which has documented
sizable changes in job tasks both within and between occupations in response to supply side changes
(Bittarello et al., 2018) or demand side changes (Consoli et al., 2023, Ross, 2017, Spitz-Oener, 2006).
The literature linking technological change to job tasks has largely focused on changes between
occupations (e.g., Autor et al., 2003, Gregory et al., 2022). We contribute to this literature by
showing that most observed changes in tasks occur within occupations rather than between them.
This result highlights that the labor market challenges of AI go well beyond job displacement and
worker reallocation across jobs.
Our study also relates to the literature on automation technologies. One strand of this literature is concerned with the di!usion of robots in the economy (e.g., Graetz and Michaels, 2018,
Acemoglu and Restrepo, 2020, Dauth et al., 2021, Koch et al., 2021, Humlum, 2019, Bonfiglioli
et al., forthcoming). Studies of robots have mostly focused on employment and wage e!ects, in
addition to adjustments at the worker and firm level. We contribute to this literature by analyzing
how robots shift job tasks. Furthermore, by comparing the e!ects of robots and AI, we show that
both technologies have very di!erent, often opposing e!ects on jobs, worker careers, and wages. A
second strand of the automation literature studies the reallocation e!ects of technology adoption at
the firm level (Bessen et al., 2019, Genz et al., 2021). We contribute to this literature by zooming in
on two specific technologies, AI and robots. Moreover, we focus on how incumbent workers respond
to the di!usion of AI and the impact on their wages.
4

The rest of this paper is structured as follows. The next section outlines our approach to
measuring advances in AI and robotics technologies using patent data. In sections 3 and 4, we
explore how AI has shifted the task content of jobs and compare it to the impact of robots. In
section 5, we analyze how workers adjust to the changes initiated by AI technologies and document
wage and employment e!ects. Finally, section 6 discusses the implications of our findings and
concludes.

2

Measuring Advances in AI and Robotics

2.1

Patent Data

A key challenge in assessing the impact of AI technologies on the content of jobs and the labor market
more broadly is to find a suitable measure of who is exposed to AI. Our measures of technological
progress in AI and, for comparison, robotics are based on patent data from the European Patent
O”ce (EPO). Patents are proxies for technological advances which have been heavily used in the
innovation literature. We use the universe of patents granted by the EPO between 1990 and
2018. These data include detailed bibliographical and technical information on all patents filed and
granted. In total, we use around 7 million patent documents, containing the title of the invention, an
abstract describing the invention as well as information on the inventor such as name, company, and
location. Many patents are filed by non-European inventors who want to protect their innovations
when selling on European markets. Importantly, each patent’s technical content is classified in the
Cooperative Patent Classification (CPC) and is assigned one or more codes by a specialized patent
examiner.

2.2

Develop New AI and Robot Measures

We create a measure of advances in AI and robotics in three steps (see Gathmann and Grimm,
2024, for more details). The first step is to classify patents as AI or robotics patents. For robots,
we identify them mostly by the CPC code B25J9 ‘Programme-controlled manipulators’. For AI, we
use a combination of a search based on AI-specific CPC codes and a keyword-based classification
that uses the patent’s title and abstract as inputs. AI is frequently embedded in other inventions
because algorithms and software may not be protected by patents on their own. Patent protection
is granted, however, if the algorithms or software are part of the solution for a technical problem like
image recognition, for example. To capture inventions that involve AI but are not classified by an
5

AI-specific CPC code, we use Natural Language Processing (NLP) to classify AI patents based on
keyword matches. We prepare the text input using NLP techniques, such as stemming, the removal
of stop words, and tokenization. We then use keyword matches to find AI-related inventions. This
approach yields around 7,000 AI patent applications and grants. Appendix Figure A.1 shows the
number of AI and robot patent grants per year during our sample period. The figure shows that
AI-related patenting in Europe has started to grow in the 2000s, but has taken o! after 2015. In
contrast, patenting in robots has already been sizable in the late 1990s, but shows a substantial
increase after 2005.
The second step is to identify the industries that make use of AI or robots patents in their
production of goods or services. It is important to stress that we do not want to identify the
producers of patents (‘innovators’); instead, we care to identify the industries that potentially use the
technological innovations protected by patents (‘firms using innovations’). Industries that produce
a patent need not be the same as the industries using a technological innovation. A patent on an AI
technology might be filed by a company in the IT sector but is later used in the manufacturing of
machinery or in agriculture, for instance. For the mapping from CPC codes to industries of use, we
employ a probabilistic walkover developed by Lybbert and Zolas (2014) and updated by Goldschlag
et al. (2019)2 . The walkover allows us to go from CPC codes to detailed (3-digit) industry codes.
More specifically, Lybbert and Zolas (2014) use the description of industries and the economic
activities performed in them to run a keyword search on the universe of patents in the PATSTAT
database. This approach identifies patents whose technological content is closely related to a given
industry. Using the CPC codes of the matches obtained in this search, they calculate the probability
that a patent belonging to a CPC code is linked to a specific industry. Based on the frequency of
patent-industry matches, they calculate a probabilistic weight using Bayes’ rule. They hereby take
into account the total number of possible codes and the number of times a code is matched to an
industry. This approach results in a list of patents with their CPC codes linked to industries that
produce goods and services with the knowledge embedded in the patent.
The final step is to construct a summary measure of the advances in AI. We consider patents as
the cumulative stock of knowledge on certain technologies that is available to firms for implementation in a given year. We therefore construct the following measures:
AIjt =

t
!

Log(1 + AIP atis ),

s=1990
2

See also Goldschlag et al. (2016) for applications of this probabilistic walkover from patents to industries.

6

(1)

and
Robotsjt =

t
!

Log(1 + RobP atis )

(2)

s=1990

where j denotes the industry, s the year of the patent grant and t denotes the period from
1990 to year t. We follow the literature and give each patent the same weight (e.g., Mann and
Püttmann, 2023).3 For both AI and robots, the measure varies by 3-digit industry and over time.
Appendix Table A.1 shows that AI exposure is highest in the industries ’Manufacture of computers
and peripheral equipment’, ’Manufacture of consumer electronics’, and ’Manufacture of communication equipment’. While AI exposure is highest in manufacturing during our sample period,
there are also exposed industries in the service sector such as ’Motion picture, video and television
programme activities’, ’Sound recording and music publishing activities’, or ’Medical and dental
practice activities’. In sharp contrast, robot exposure is highest in manufacturing and in particular,
industries ‘Manufacture of general-purpose machinery’, ‘Manufacture of special-purpose machinery’,
and ‘Manufacture of other fabricated metal products, metalworking activities’. The two technologies are also not independent of each other; on the contrary, our measures of AI and robot exposure
show a sizable positive correlation at the industry level (correlation coe”cient 0.53).

2.3

Validation of Measures

It is important to verify that our measures of AI and robot exposure also capture the actual
implementation of an AI or robot technology in exposed industries. To validate our robot measure,
we exploit the widely-used data on robot installations from the International Federation of Robotics
(IFR) (International Federation of Robotics, 2021). Unfortunately, the IFR data are available for
a much smaller set of industries than our patent-based measures of robots. After adjusting our
measure to the industry classification, Panel A of Appendix Figure A.2 shows a strong positive link
between our exposure measure and the actual installation of robots, suggesting that our measure
of robot exposure is a good predictor for actual robot installations.
Given the recent nature of AI technologies, it is much harder to find good proxies for the
implementation of AI. Our first proxy follows recent papers on AI using the number of online job
ads in Europe mentioning at least one AI skill. The second proxy uses information from firms’
webpages to classify firms as AI users based on data from Istar.ai.4 Panels B and C of Appendix
3
Weighting by forward patent citations to indicate the importance of a patent is not feasible given the recent
nature of patent activity we analyze.
4
Firms are classified as AI users if they mention investments in AI like data analytics, but also if they have a
chatbot on their webpage, for example.

7

Figure A.2 show that our AI exposure measures are strongly positively correlated with both proxies
for AI use at the industry level. We next discuss the data and strategy to analyze the impact of AI
and robots on job tasks.

3

AI and Job Tasks: Data and Empirical Strategy

3.1

Data on Tasks Performed on the Job

To analyze how AI a!ects the tasks workers perform in their jobs, we make use of the BIBB/BAuA
surveys (Hall and Tiemann, 2009, Hall et al., 2014, 2020). The data have previously been used
to analyze changes in job tasks over time (Spitz-Oener, 2006) and the impact of task distance on
worker mobility and earnings growth (Gathmann and Schönberg, 2010). The survey is a repeated
cross-section of employees that has been conducted roughly every six years since 1979. Each survey
consists of a representative sample of individuals ages 15 and older who work at least 10 hours per
week at the time of the interview.
We focus on the three most recent waves of 2006, 2012, and 2018, for which the task-related
questions are identical in each wave, allowing us to study changes over time in tasks performed on the
job. We restrict the sample to individuals between the ages of 18 and 65, working full-time (at least
35 hours per week) in dependent employment. The survey contains the socio-economic background
of the individual including educational background and age, but also the detailed occupation and
industry. Our analysis distinguishes between high-skilled workers with a university degree and
low-skilled workers without a university degree.
Most importantly, the survey elicits whether an individual performs any of seventeen di!erent
tasks. We analyze the detailed tasks and aggregate them into three broad categories: routine tasks,
non-routine abstract tasks, and non-routine manual tasks. The individual tasks and their classification into the three groups are as follows:
Routine tasks: Monitoring or operating machines or technical processes; manufacturing or producing of goods and products; transporting, storing or shipping; measuring or quality checks.
Non-routine abstract tasks: Developing, researching or constructing; gathering information,
investigating or documenting; working with computer or tablet; organizing, planning or preparing
work processes (of others); Buying, procuring or selling; teaching, training or educating; consulting
or informing; promoting, marketing, advertising or PR.

8

Non-routine manual tasks: Repairing; accommodating, hosting or preparing food; caring or
healing; cleaning, waste disposal or recycling; protecting, securing, guarding or regulating tra”c.
For each task, survey participants are asked whether they perform the respective task ’frequently’, ’occasionally’, or ’never’ in their job. Based on the answers, we compute task shares
for our three broad task categories. For each task category c, we compute the number of tasks
performed frequently or occasionally, which fall into category c, divided by the total number of all
tasks performed (frequently or occasionally) by worker i:
T askSharecit =

"
T askirt
"r→c
→ 100
r T askirt

(3)

where t denotes the survey wave and r is a specific task. T askirt is equal to one if the individual
performs task r frequently or occasionally in year t; and zero otherwise. The task share takes on
values between 0% and 100% and can be interpreted as the relative importance of category c in
worker i’s job. For example, if worker i performs a total of four tasks and two of them fall into
the routine category, then the routine task share equals 50%. Our findings do not depend on this
particular definition of task share as we discuss below. In our empirical analysis, we will analyze
both the aggregate task shares and the prevalence of each task individually to track which tasks
change in a job. It is important to stress that the survey does not add new tasks during our sample
period. Therefore, changes in the prevalence of performing a task are likely to include automation
of some aspects, productivity gains in others and the emergence of new aspects within the same
task.
Appendix Table A.2 shows descriptive statistics for each wave separately (2006, 2012, and 2018).
Panel A shows that the routine task share decreased by 0.8 percentage points and the manual task
share decreased by 1.3 percentage points between 2006 and 2018. Conversely, the abstract task
share increased by roughly 2 percentage points over the same period. Panel B tracks the share
of workers who perform one of the 17 tasks in the respective year. The decline in the routine
tasks between 2006 and 2018 is driven by the categories ’Monitoring/operating machines/technical
processes’, ’manufacturing/producing of goods/products’, and ’transporting, storing, shipping’. In
contrast, the increase in abstract tasks comes from the categories ’Developing, researching, constructing’, ’Gathering information, investigating, documenting’, ’Organizing/planning/preparing of
work processes (of others)’, and ’Working with computers/tablets’.5
5

Appendix Tables A.3 and A.4 show for each individual task the top three occupations which exhibit the largest
share of workers performing the respective task.

9

The task content of jobs di!ers substantially between skill groups. College-educated workers
mostly perform abstract tasks (72.8%), while the routine task share is only 15.8% and manual tasks
make up the remaining 11.4%. In contrast, workers without a university degree have higher routine
(26.3%) and manual (21.6%) task shares, but a lower abstract task share (52.1%). These di!erences
will be important when we analyze who is a!ected most by the observed task changes due to AI
and robots.

3.2

Empirical Strategy

To investigate task changes on the job and the role played by technological advances, we merge
our AI and robot exposure measures to the individual worker samples by two-digit industry and
period.6 We then estimate variants of the following model:
Yijot = ω1 AIjt + ω2 Robotsjt + Xit↑ ε + µj + ϑo + ϖt + ϱijot

(4)

Yijot denotes the outcome of worker i who is employed in 2-digit industry j and 2-digit occupation
o in survey year t (2006, 2012, or 2018). We start out with analyzing the three broad task shares
(routine, abstract, or manual), as defined in equation (3). We then proceed with an analysis of the
17 detailed tasks as outcome measures to understand how job contents have been changing. An
additional advantage of studying the detailed tasks performed by workers is that they are measured
as indicators, avoiding the adding-up constraint in the aggregate task shares.
The main explanatory variables of interest are AIjt and Robotsjt , the cumulative number of AI
or robot patents between 1990 and the survey wave (t) in using industry j (as defined in equation
1). Allowing for a time lag in the di!usion of technologies has little e!ect on our results as we show
below. We include a number of demographic characteristics as control variables Xit : the education
level, age groups (18-25, 26-35, 46-45, 46-55, 56-65), gender, and German nationality. We further
include fixed e!ects for 2-digit occupations (ϑo ) and 2-digit industries (µj ) as well as state and wave
fixed e!ects tt . All specifications use sample weights and cluster standard errors at the industry-year
level. The parameters of interest, ω1 and ω2 , are then identified from within-industry variation in
AI and robot exposure over time between workers with similar socio-demographic characteristics
and who are employed in the same 2-digit occupation.
6

More specifically, we aggregate up our patent measures to the level of 2-digit ISIC rev.3 industies. This corresponds
to the 2-digit industry classification in the BIBB/BAua data (WZ03). In the second part of the analysis, using
administrative data, we exploit our patent measures at the 3-digit industry level.

10

3.3

Balancing Test: Do Tasks Predict Future Exposure?

A potential concern with our empirical strategy in equation (4) is reverse causality. Rather than
technological change shifting job tasks, job tasks might a!ect future technological progress. Growing specialization or outsourcing of firms in response to globalization or other produce demand
shocks might shift the tasks performed in certain jobs, which in turn encourages firms to invest in
automation or AI adoption, for example. In such a case, the shift in tasks would be the cause rather
than the consequence of exposure to AI.
To address these concerns, we run a balancing test to determine whether initial task shares (in
2006) help to predict future exposure to AI or robotics technologies. In Table 1, we regress changes
in our patent measures between 2006 and 2018 on task shares in 2006 at the industry level. In Panel
A, we perform this analysis at the 2-digit level (56 industries) using our task data. In Panel B, we
perform the analysis at the 3-digit level (233 industries). Here, we make use of the occupational
employment structure within industries from administrative social security records (the data used
in Section 5 below). We first merge the task shares in 2006 to the administrative data at the 2-digit
occupation level; and then regress the task shares at the 3-digit industry level in 2006 on AI and
robot exposure at the 3-digit level.
Table 1 shows that conditional on broad sector dummies, initial task shares in 2006 do not
predict future patent exposure more than a decade later. The first specification (in columns (1) and
(3)) looks at the raw correlation at the industry level, while the second specification (in columns
(2) and (4)) controls for di!erences in job tasks between broad sectors (agriculture, manufacturing,
and services).
The raw correlations show a marginal positive correlation between AI technologies and routine
task shares at the 2-digit industry level (see column (1) of Panel A) and a positive correlation
between robots and routine task shares at the more detailed 3-digit industry level. Yet, once we
control for the substantial di!erences in tasks across broad sectors, the coe”cients get very small,
flip signs, and turn statistically insignificant. Moreover, the coe”cients are economically very small
both in absolute value (recall that the task shares range from 0 to 100) and also relative to the
large coe”cient for manufacturing. The main message to take away from Table 1 is that reverse
causality does not seem to be a major concern for our analysis.

11

Table 1: Balancing test
Panel A:
2-digit industries (BIBB)

# AI Exposure (2006-18)
(1)
(2)

# Robot Exposure (2006-18)
(3)
(4)

Routine task share 2006 (%)

0.75**
(0.35)
0.29
(0.23)

1.21
(0.73)
0.17
(0.42)

Abstract task share 2006 (%)
Manufacturing sector
Primary sector

-0.07
(0.46)
0.02
(0.26)
9.97***
(3.43)
-1.40
(1.45)
7.27
0.34
56

Std. Dev. of Y variable
R2
Observations

7.27
0.17
56

Panel B:
3-digit industries (SIAB)

# AI Exposure (2006-18)
(1)
(2)

# Robot Exposure (2006-18)
(3)
(4)

Routine task share 2006 (%)

0.21
(0.18)
0.05
(0.10)

0.85***
(0.38)
0.20
(0.18)

Abstract task share 2006 (%)
Manufacturing sector
Primary sector
Std. Dev. of Y variable
R2
Observations

3.87
0.04
233

-0.21
(0.18)
-0.07
(0.10)
5.24***
(1.13)
0.01
(0.40)
3.87
0.27
233

13.03
0.26
56

-0.76
(0.54)
-0.47
(0.29)
23.81***
(6.57)
-3.25
(2.06)
13.03
0.56
56

8.91
0.14
233

-0.21
(0.26)
-0.12
(0.14)
13.32***
(2.99)
-0.66
(0.57)
8.91
0.42
233

Notes: The table reports industry-level regressions of the change in AI exposure (columns (1) and (2)) and the
change in robot exposure (columns (3) and (4)) between 2006 and 2018 on task shares in 2006 and broad sector
dummies (agriculture, manufacturing and services). Regressions are weighted by the number of observations in
the respective industry. In Panel A, task shares are computed at the 2-digit industry level from the BIBB survey.
In Panel B, task shares are computed at the 3-digit industry-level from the administrative social security data
(SIAB), using occupational task shares from the BIBB data. Robust standard errors are shown in parentheses.
* p<0.10, ** p<0.05, *** p<0.01.

12

4

The Impact of AI on Tasks Performed on the Job

4.1

Industry-level Correlations

We start with simple descriptive evidence relating changes in job tasks to changes in our patentbased measures of AI and robot exposure. Figure 1 plots the change in the routine, abstract, and
manual task shares between 2006 and 2018 against the changes in the AI and robot exposure measures between 2006 and 2018. We first residualize task shares and changes in technology exposures
from demographics and broad sectors.7 The circle reflects the size of the industry and the regression
lines are based on a regression weighted by size.
Surprisingly, Panel A shows a positive correlation between AI technologies and the relative
importance of routine tasks. In sharp contrast, robot exposure (shown in Panel B of Figure 1)
shows a strong negative correlation with the routine task share, confirming prior evidence that
robots automate routine tasks and jobs (e.g., Acemoglu and Restrepo, 2020, Dauth et al., 2021,
Webb, 2020). It is reassuring that our robot measure shows very similar results to studies using
actual robot installations based on a smaller set of industries, increasing our confidence that our
measures indeed capture technological advances in the using industries.
Panel C and D in Figure 1 turn to the relationship between new technologies and abstract tasks.
AI technologies are associated with a decline in abstract tasks, while robots are slightly positive
correlated with abstract tasks. Finally, Panels E and F show that AI and robot technologies have
little association with changes in the manual task share. Overall, these industry-level correlations
provide a first hint that AI a!ects the task content of jobs in fundamentally di!erent ways than
robots.
7
Demographic controls include education groups, age groups, gender, and German nationality in the industry.
Sector controls are manufacturing, services, and the primary sector.

13

Figure 1: AI, Robots, and Task Changes at the Industry Level

(a) AI and Routine Tasks

(b) Robots and Routine Tasks

(c) AI and Abstract Tasks

(d) Robots and Abstract Tasks

(e) AI and Manual Tasks

(f) Robots and Manual Tasks

Notes: The figure shows the relationship between changes in task shares and changes in AI (left-hand side) or robot
exposure (right-hand side) between 2006 and 2018 at the industry level. All variables are residuals after adjusting for
demographic characteristics and broad sectors. Demographic controls include education groups, age groups, gender,
and workers with German nationality, measured in 2006. We also include dummies for three broad sectors (agriculture,
manufacturing and services). The size of the circle denotes the number of employees in the industry in 2006. The
figure is restricted to industries with at least thirty employees in our sample in 2006.

14

4.2

AI and Worker-Level Tasks

We now turn to the worker level and estimate how exposure to AI and robots a!ects individual
task shares by estimating equation (4). We control for 2-digit industry and year fixed e!ects,
socio-demographics, and federal state dummies. Figure 2 shows the results for overall task changes
and zooming in on task changes within occupations. The first specification (hollow diamonds)
shows overall task changes, which combines changes in the composition of occupations and task
changes occurring within occupations. The second specification zooms in on task changes within
detailed occupations by including (2-digit) occupation fixed e!ects. To facilitate the interpretation,
the figure shows the estimated impact of a one standard deviation increase in the AI and robot
exposure measure on the three broad task shares.
Panel (a) of Figure 2 confirms the stunning pattern that AI increases the routine task share
of workers and decreases their abstract task share, with no e!ect on their manual task share.
Interestingly, a comparison of the two specifications suggests that most of the overall task changes
occur within occupations, indicating that AI shifts the assignment of workers to tasks. Our finding
that the adjustments mostly occur within detailed occupations is important given that most of the
literature on technology and tasks has focused on changes between occupations. For robots, we find
the opposite pattern. In line with the idea that robots automated routine tasks (e.g., Acemoglu and
Restrepo, 2020, Dauth et al., 2021, Webb, 2020), we show that robot exposure is associated with a
decline in the routine task share with few changes in the abstract and manual task shares.
How large are the estimated e!ects of AI and robots on tasks? An increase in robot exposure
(by one standard deviation) reduces the routine task share by 0.9 percentage points in total and
by 0.6 percentage points within occupations. While these e!ects are small compared to the crosssection variation of task shares, they account for virtually all of the decline in the routine task share
between 2006 and 2018 (see Appendix Table A.2). Turning to AI, we find that an increase in AI
exposure (again by one standard deviation) increased the routine share by roughly 1 percentage
point and decreased the abstract task share by about 1 percentage point. The findings indicate
that the decline in routine task shares and the growth in abstract task shares would have been even
faster in the absence of AI.8
8

Appendix Table A.5 shows the actual point estimates for alternative specifications.

15

Figure 2: AI, Robots and Job Tasks at the Worker Level

(a) AI

(b) Robots
Notes: The figure shows point estimates and 95%-confidence intervals of a one standard deviation increase in exposure
to AI (Panel a) or robots (Panel b), respectively. The estimates are obtained from a regression of each task share on
AI and robot exposure controlling for 2-digit industry fixed e!ects and year fixed e!ects. In addition, we include
as demographic controls education, gender, age groups (18-25, 26-35, 36-45, 46-55, 56-65), German nationality and
federal state dummies. Number of observations: 37,415. Standard errors are clustered at the industry-year level.

16

If our results indeed reflect the di!usion of AI in the labor market, we would expect the e!ects
to become more pronounced over time as the capabilities of AI have vastly expanded since 2006.
Appendix Table A.6 shows in columns (2) to (4) separate estimates for each survey wave. The most
important finding is that the impact of AI on routine and abstract tasks is small and statistically
insignificant in 2006 but the e!ect more than doubles and becomes statistically significant by 2018.
The temporal pattern of the estimates provides additional support for our empirical strategy and
findings. In Appendix Figure A.3, we further demonstrate that our results are robust across a
variety of alternative specifications. First, we experiment with alternative ways of creating our AI
and robot exposure measures. The Figure shows that our results are very similar when we use
the absolute number of patents instead of the log transformation, when we drop German patents
from our measure (due to potential reverse causality), and when we lag our exposure measures
by three years. In addition, the results also hold when we construct the task shares in di!erent
ways. Remember that, in our baseline specification, we assign a value of 1 if the respective task is
performed ’frequently’ or ’occasionally’ and 0 otherwise. Figure A.3 shows that, when we assign a
value of 1 if the task is performed ’frequently’ and 0 otherwise, the point estimates become even
larger. Analogously, the point estimates become slightly larger when we account for intensive margin
changes in more detail by assigning a value of 2 for ’frequent’ task use, 1 for ’occasional’ task use,
and 0 otherwise.
The surprising finding that AI technologies appear to lower the abstract and increase the routine
task share of jobs raises the question of which specific job tasks change with AI. To investigate this,
we use information on the seventeen detailed tasks available in the survey. We then re-estimate
equation (4) where we now have the probability of performing a detailed task as the dependent
variable. The results are conditional on occupation fixed e!ects and thus focus on changes within
2-digit occupations.
Figure 3 shows that the decline in abstract tasks is largely accounted for by a decline in the
task of ‘gathering information, investigating, and documenting’. AI-assisted tools like data analytics, screening tools, or predictive maintenance technologies provide di!erent layers of information,
therefore reducing the need to collect and gather information manually.9 The decline in the need to
collect information indicates that AI can partially automate such tasks – even prior to generative AI.
The e!ect is sizable: an increase in AI exposure (by one standard deviation) reduces the probability
9

One example would be a malfunctioning machine where AI can diagnose the problem, whereas before, the responsible person had to call the service provider or study a handbook to fix the machine.

17

of performing this task by around 3 percentage points. The point estimates for other abstract tasks
like ‘Buying, procuring, selling’, and ‘Promoting, marketing, advertising, PR’ are negative, but not
statistically significant. Importantly, however, there are virtually no changes in the probability
of working with a computer or tablet and in the probability of doing high-end abstract tasks like
‘Developing, researching, constructing’.
Figure 3 further shows that the AI-induced rise in the routine task share exclusively comes from
an increased likelihood of ‘monitoring or operating machines and technical processes’. This e!ect
might reflect the fact that AI-assisted tools help workers monitor other machines or technical processes, but also require the worker to oversee the output of the technical processes of AI themselves.
We now investigate this link between AI and routine tasks in more detail.10
Figure 3: AI and Detailed Job Tasks

Notes: The figure shows the e!ect of AI exposure (by one standard deviation) and the 95% confidence intervals
on the probability to perform a single task. The dependent variable is a dummy variable equal to one if a worker
performs a task on the job. Regressions control for 2-digit industry, 2-digit occupation and year fixed e!ects as well
as demographic characteristics (education, gender, age groups (18-25, 26-35, 36-45, 46-55, 56-65), German nationality
and federal state dummies. Regressions employ sample weights. Standard errors are clustered at industry-year level.
10

Appendix Figure A.7 shows the corresponding task-level results for robots.

18

4.3

High-level Versus Low-level Routine Tasks

The result that industries exposed to AI saw an increase in the relative importance of routine tasks
is novel and might seem surprising. The traditional view of routine tasks is that they can be easily
codified into rules and eventually taken over by a machine. One example would be a repetitive task
that a worker used to perform in an assembly line. We do not think that workers now switch (back)
to tasks that are easily codified in response to AI. Instead, we think of the corresponding shift in
task as a switch towards what we call ‘high-level’ routine tasks. High-level routine tasks are those
that require humans to monitor and evaluate the process and results of algorithms, including the
machines that algorithms might monitor and diagnose (see Figure 3).
To shed more light on the nature of routine tasks that complement AI, we define routine tasks
as ‘high-level’ if a worker also reports working with a computer or laptop. In contrast, we define
a routine task as ‘low-level’ if a worker reports not working with a computer or tablet. Appendix
Table A.7 shows that high-level routine tasks are most prevalent among aircraft pilots, laboratory
occupations in medicine, and occupations in physics. Individuals in these occupations are working
in an environment with advanced technologies and are frequently required to analyze and interpret
the output of technical processes or algorithms. In sharp contrast, the low-level version is most
prevalent among occupations in gardening, occupations in building construction, and occupations
in civil engineering.
We then re-estimate equation (4) where the dependent variable is now whether a worker performs
a specific routine task at the high-level or the low-level. Otherwise, the specification is the same as
before and includes all controls. The results in Figure 4 clearly show that the rise in routine tasks
is concentrated among ‘high-level’ routine tasks, in particular in the task ’monitoring or operating
machines and technical processes’. We see few changes in ‘low-level’ routine tasks, in turn. The
findings that AI requires more ‘high-level’ routine tasks is not specific to the particular split into
high- or low-level tasks. To demonstrate that, we use the following question: ’How often is the
execution of your work prescribed down to the last detail?’ We consider a given routine task as
’low-level’ if the worker answers this question with ’often’. In contrast, we consider a given routine
task as ’high-level’ if the worker answers this question with either ’occasionally’, ’seldom’, or ’never’.
Appendix Figure A.4 shows a very similar increase in ‘high-level’ routine tasks using this alternative
definition of task complexity compared to the baseline. Together, they clearly show that AI raises
the need for complex tasks that belong to the broad category of routine tasks.

19

Figure 4: AI and Low- versus High-Level Routine Tasks

Notes: The figure shows the impact of AI exposure (by one standard deviation) on the probability of performing a
specific routine task. We define a routine task as ‘high-level’ if a worker also reports working with a computer or
laptop. In contrast, we define a routine task as ‘low-level’ if a worker reports not working with a computer or tablet.
The estimation is based on equation (4) including all controls.

4.4

AI and Job Tasks by Worker Skill

The finding that AI increases ‘high-level’ routine tasks might indicate that some workers are more
a!ected than others. We focus here on di!erences between high-skilled and low-skilled workers.
Our dependent variables are again the detailed routine and abstract tasks. The specification is the
same as before.
Figure 5 shows several novel patterns. The first noteworthy pattern is that the AI-induced
decline in performing abstract tasks is stronger among the low-skilled. More specifically, for lowskilled workers, six out of eight abstract tasks exhibit a negative point estimate though several
are marginally insignificant. A second important result is that high-skilled workers in AI-exposed
industries experience a large increase in the probability of ‘teaching, training, and educating’ as
well as an increase in ’consulting, informing’. These positive e!ects fully o!set the decline in other
abstract tasks, such that high-skilled workers do not experience a decline in their abstract tasks
20

overall. The point estimate for ‘teaching, training, and educating’ suggests that increasing AI
exposure (by one standard deviation) raises the probability by a sizable 6 percentage points, which
is larger than the raw change in the probability of performing the task between 2006 and 2018 (see
Appendix Table A.2). The rise in teaching and training activities of high-skilled workers suggests
positive complementarities between the di!usion of AI technologies, the need for learning-by-doing,
and the expertise and comparative advantage of high-skilled workers.

21

Figure 5: AI and Detailed Job Tasks by Worker Skill

(a) Low skilled

(b) High skilled
Notes: The figure shows the e!ect of AI exposure (by one standard deviation) and the 95% confidence intervals on
the probability to perform a single task, separately for low-skilled (Panel A) and high-skilled workers (Panel B).

22

The final noteworthy pattern we observe in Figure 5 is that the AI-induced shift towards routine
tasks, in particular towards ‘monitoring or operating machines and technical processes’, is strongest
for high-skilled workers. We might expect this increase to be concentrated in the ‘high-level’ tasks
discussed before; some of these might be even completely new monitoring tasks. To investigate
this, we re-estimate our baseline specification in equation (4), focusing on ‘high-level’ routine tasks
separately for high- and low-skilled workers. Figure 6 shows that low-skilled workers (Panel A)
experience an increase ‘high-level’ routine task of ’monitoring/operating’ and, to a lesser extent,
’manufacturing/producing’ and ’measuring, quality checks’. For high-skilled workers, in contrast,
Panel B shows a much larger increase in the ‘high-level’ routine task of monitoring machines and
technical processes as well as a larger increase in ’measuring, quality checks’ compared to low-skilled
workers.
Overall then, our findings show that task automation a!ects all workers, even those with a
college education. Yet, two countervailing forces benefit high-skilled workers: the first one is that
they have the expertise and comparative advantage to switch to other tasks in response to AI.
Moreover, they also benefit by seeing the demand for high-level routine tasks expand, which might
be complementary to AI or could be entirely new specific tasks within the broader task captured in
the survey.
Figure 6: AI and Routine Tasks for Low- and High-skilled Workers

(a) Low skilled

(b) High skilled

Notes: The figure shows the e!ect of AI exposure (by one standard deviation) and the 95% confidence intervals
on the probability to perform a single task, separately for low-skilled (Panel A) and high-skilled workers (Panel B).
We define a routine task as ‘high-level’ if a worker also reports working with a computer or laptop. In contrast, we
define a routine task as ‘low-level’ if a worker reports not working with a computer or tablet.

23

5

AI and Labor Market Outcomes

Our findings thus far show that AI has begun to change the task content of jobs. The impact
has mainly occurred within detailed occupations. Yet, how do these observed shifts in job tasks
impact overall employment, worker mobility, and earnings of workers exposed to AI? One channel
would work through automation: if, as the evidence in the last section indicates, AI automates
some abstract tasks, and if there are no strong productivity gains working in the opposite direction,
we might observe a decline in employment in exposed industries. The observed change in the task
content of jobs could also reflect a reshu$ing of workers, where workers remain in their occupation
but leave exposed firms and industries to seek opportunities elsewhere. Whether and which workers
see their earnings decline or increase because of AI will then depend on the relative size of the
automation and productivity e!ects, as well as the outside options of workers in exposed industries.
We now turn to administrative data on worker careers and earnings to investigate these important
questions.

5.1

Administrative Labor Market Data

To study worker adjustments to AI, we use the ‘Sample of Integrated Labour Market Biographies’
(SIAB), a 2% random sample of the administrative social security records. The data cover about
80% of the German workforce, excluding self-employed, civil servants, and military personnel. We
observe each individual’s exact employment status, i.e., whether the person is employed, registered
as unemployed, or non-employed; the data further records the daily wage. The detailed information
enables us to analyze the employment and wage e!ects of AI and robot exposure. Moreover, we
observe the occupation, firm, and industry of the worker, which we use to study worker reallocation
in response to AI exposure. We further have detailed information on socio-demographic control
variables such as education, age, gender, nationality, and plant location.
To match the data to our analysis of task changes, we aggregate the social security data at the
worker level into three periods: 2004-2009, 2010-2015, and 2016-2021. The periods cover several
years before and after the survey waves (in 2006, 2012, and 2018). Given the large sample in the
social security data, we can merge our exposure measures to AI and robotics technologies at the
even more detailed 3-digit industry level and period.

24

5.2

Estimation Approach

Our analysis of the impact of AI and robot exposure estimates variants of the following model:
Yijot = ω AI AIjt + ω Rob Robjt + ϑo + ςt + µj + εXit + ϱijot

(5)

where Yijot is the labor market outcome of worker i employed in (2-digit) occupation o and (3-digit)
industry j in period t (2004-2009, 2010-15 and 2016-21).
AIjt and Robjt denote the exposure to AI and robots in 3-digit industry j and period t. The
measures are calculated from equation (1) as the cumulative number of AI and robot patents
between 1990 and 2006, 2012, or 2018 (standardized with zero mean and a standard deviation of
one). Control variables include 2-digit occupation fixed e!ects (ϑo ), 2-digit industry fixed e!ects
(µj ) and time period fixed e!ects (ςt ). We further control for the following worker characteristics
(Xit ): worker’s education, gender, age, German nationality, firm tenure (0-2 years, 3-5 years, 6-10
years, more than 10 years), firm size (0-9 employees, 10-99, 100-499, 500-999, 1000-9999, more than
10,000), and federal state dummies. To rule out that our results are biased by trade-related product
demand shocks, we include changes in net exports during the time period in the industry in which
the worker is employed in the base year. All control variables in equation (5) are measured in the
base year of each period (i.e., 2004, 2010, or 2016). Standard errors are clustered at the 3-digit
industry x period level.
Our main outcomes of interest are employment measured as the cumulative days employed
(in logs), earnings measured as cumulative earnings (in logs), and job, occupational, or industry
mobility measured by an indicator for switching employer, occupation, or industry during the fiveyear period. Equation (5) then compares workers who are at the start of the period employed
in the same 2-digit occupation and 2-digit industry with similar demographics, region, and labor
market history. The parameters of interest (ω AI and ω Rob ) are identified from variation in AI and
robot exposure between 3-digit industries within the same 2-digit industry, as well as within 3-digit
industries over time among workers with similar observable characteristics.

5.3

Displacement E!ects of AI and Robots

We start by assessing the impact of AI and robot exposure on overall employment at the individual
worker level. To do so, we estimate equation (5), where the dependent variable is the cumulative
days employed during the five-year period following the base year.
25

Figure 7 plots the e!ect of AI and robot exposure (by one standard deviation). We find that
AI exposure leads to a modest decline in overall employment by about 0.5 percentage points. This
displacement e!ect is about 1/7 of a standard deviation. In contrast, we find no displacement e!ect
of robots, which is again fully in line with previous evidence for Germany. We next distinguish
between employment e!ects for high- and low-skilled workers. AI displaces workers across the skill
distribution, with the e!ect being slightly stronger for low-skilled workers, i.e. those without a
university degree. This finding fits the patterns on job tasks, which indicate that AI automates
some abstract tasks for all workers, but that low-skilled workers have fewer opportunities to switch
to other tasks. The evidence here indicates that AI automation has caused some job losses. Yet,
our estimates also find that the adjustment to AI has so far not resulted in large-scale displacement
at the worker level.
Figure 7: Employment e!ects of AI and Robots

Notes: The dependent variable is log cumulative days employed over a period of five years. Regressions control for
2-digit industry fixed e!ects, 2-digit occupation fixed e!ects, period fixed e!ects, state fixed e!ects, demographics
(education, age, gender, nationality), tenure, firm size, and the industry-level change in net exports. AI exposure
and robot exposure are standardized. High-skilled workers are those with a university degree. Standard errors are
clustered at the 3-digit industry x period level.

26

5.4

AI and Worker Reallocation

Rather than through displacement, a reorganization of production and work processes might require
workers to switch jobs within firms. Moreover, the di!usion of AI and robot technologies might
require an inflow of new workers with skills that are able to implement or make productive use of the
new technologies. Alternatively, workers might leave exposed industries to seek new opportunities
in industries that require tasks that are partially automated through AI or robots
We investigate the reallocation of workers in response to exposure to AI and robots in Table
2. Our dependent variables are now whether an individual moves to a new employer or switches
the industry of employment or occupation between the base year and the end of the period. The
first finding in Table 2 is that AI induces more workers to switch employers (see column (1)). The
point estimate indicates that a one standard deviation increase in AI exposure raises the probability
of moving to a di!erent firm by 1.4 percentage points. Exposure to robots, in contrast, has the
opposite e!ect, actually reducing worker mobility across firms. The job-stabilizing e!ect of robots on
employment relationships is in line with previous studies for manufacturing industries in Germany
(Dauth et al., 2021). Does the higher job mobility also imply that workers switch occupations?
Column (2) indicates that the answer is no: workers are not more likely to switch to a di!erent
2-digit occupation if exposed to AI. This result is in line with our findings from the survey data
that most of the task changes occur within the same 2- and even 3-digit occupation.
We next ask whether the documented increase in firm mobility occurs mostly within the same
industry, e.g. from non-adopting firms to adopting firms, or to firms in non-exposed industries. A
second noteworthy result in Table 2 is that workers move mostly to a di!erent employer within the
same 2-digit industry (see columns (3) to (5) of Table 2). About half of this mobility occurs between
3-digit industries and the other half within 3-digit industries. Interestingly, column (6) suggests
that cross-industry mobility is primarily out of exposed industries and into similar industries that
are not (yet) exposed to AI.
Finally, we explore whether workers switching jobs move to ‘better-paying’ firms. We measure better-paying firms by pre-estimated AKM fixed e!ects (Abowd et al., 1999), which capture
unobserved di!erences like management quality, e”ciency, or market position while holding the
composition of the workforce constant. These regressions are based on fewer observations because
we do not have AKM firm fixed e!ects for recently founded firms. It is noteworthy that job mobility
occurs in equal shares to higher-paying and lower-paying firms (columns (7) and (8) of Table 2. This
last finding suggests that some workers benefit from moving jobs, while others might actually lose
27

a good job match.
Table 2: AI and Job Reallocation

AI
Robots
Mean Y
Observations

Firm
Change

Occupation
Change
(2)

Di!erent
Industry
(3-digit)
(3)

Di!erent
Industry
(2-digit)
(4)

Same
Industry
(2-digit)
(5)

Lower AI
Exposure
Industry
(6)

Higher
AKM
Firm
(7)

Lower
AKM
Firm
(8)

(1)
0.014***
(0.005)
-0.018***
(0.004)

0.001
(0.008)
-0.090
(0.007)

0.007
(0.005)
-0.019***
(0.004)

0.002
(0.004)
-0.017***
(0.004)

0.013**
(0.005)
0.002
(0.003)

0.039***
(0.008)
0.000
(0.001)

0.007
(0.004)
-0.015***
(0.003)

0.007*
(0.004)
-0.004
(0.003)

0.358
952,750

0.312
952,750

0.241
952,750

0.218
952,750

0.141
952,750

0.087
952,750

0.181
894,582

0.155
894,582

Notes: The dependent variables are indicators for moving to a di!erent firm (1), occupation (2), 3-digit industry (3), 2-digit
industry (4), a di!erent firm within the same 2-digit industry (5), an industry with lower AI exposure (6), a firm with higher
AKM e!ect (7), and a firm with lower AKM e!ect (8). Regressions control for 2-digit industry fixed e!ects, 2-digit occupation
fixed e!ects, period fixed e!ects, state fixed e!ects, demographics (education, age, gender, nationality), tenure, firm size, and the
industry-level change in net exports. AI exposure and robot exposure are standardized. Standard errors are clustered at the 3-digit
industry x period level in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

5.5

Impact of AI and Robots on Earnings

The results in the previous subsections indicate that AI has resulted in worker reallocation in
addition to some job displacement. We find little evidence that AI exposure increases occupational
mobility. These patterns support our conclusion from the task data that adjustments to the new
technologies have occurred primarily through changes in job tasks within the same occupation.
Workers in exposed industries thus see the content of their jobs change, while workers in the same
occupation in non-exposed industries have not seen much pressure to adapt. The e!ects on either
margin are still modest but have real consequences for the exposed workers. We now explore whether
the observed adjustments to AI are also reflected in worker earnings.
To do so, we re-estimate equation (5) where the dependent variable is now the cumulative
earnings of the worker over the 5-year period. Figure 8 indicates that earnings slightly decrease
for workers exposed in their job to AI. The estimate indicates that increasing AI exposure by one
standard deviation reduces earnings by about 1.2 percentage points over a five-year period. While
the overall e!ect is modest, Figure 8 further shows that AI hits the earnings of low-skilled workers
harder than those of high-skilled workers. The estimates indicate that the earnings of low-skilled
workers decline by 2.5 percentage points if exposure to AI increases by a standard deviation, while
we see no statistically significant e!ect for high-skilled earnings.
28

Based on the job reallocation results in the previous section, we next ask whether the observed
changes in earnings are related to worker mobility. These estimates are obtained by comparing
job movers in exposed industries to job movers in non-exposed industries controlling for a large set
of other confounding factors. Yet, we consider these estimates to be suggestive, as unobservable
determinants of job mobility may di!er across industries for reasons other than AI exposure. Nevertheless, we find the exercise insightful for understanding which workers might lose from the di!usion
of AI. The bottom part in Figure 8 indicates that low-skilled workers see their earnings decline when
they remain in their job, but also if they move to a di!erent job. One possible interpretation of this
pattern is that low-skilled workers have mostly been a!ected by automation with few gains from
productivity gains, switching to di!erent tasks or seeking outside options in a di!erent job. The
fact that job mobility is associated with lower earnings likely indicates that job mobility among
low-skilled workers is, at least in part, involuntary.
The picture looks very di!erent for high-skilled workers. High-skilled workers who remain in
their jobs in exposed industries see their earnings increase by around 1.5 percentage points. Unlike
low-skilled workers, high-skilled workers might thus benefit from productivity gains or even new
tasks associated with AI. The average e!ect on movers is zero suggesting that some high-skilled
workers manage to improve their job match while others lose a good match. Even if high-skilled
workers switch jobs, they do not su!er earnings losses, while low-skilled workers lose. Overall,
our findings suggest that high-skilled workers are more likely to benefit from changes in AI than
low-skilled workers.
We demonstrate that these findings on employment, reallocation, and earnings are highly robust
to alternative specifications of the exposure measures or the timing of the e!ects in Appendix Figures
A.5 and A.6 as well as Appendix Table A.9. The patterns are qualitatively and quantitatively very
similar to the baseline results.

6

Conclusion

This paper shows that AI has already shifted the task content of jobs in Germany. Using a new
measure of industry-level exposure to AI and robot technologies based on patent data, we have
five main findings. First, we document that AI has decreased the abstract task share in jobs,
in particular in the category of gathering information and documentation. In contrast, AI has
increased the need for high-level routine tasks in monitoring processes, which require the use of
29

Figure 8: Impact of AI on Earnings

Notes: The dependent variable is log cumulative earnings over a period of five years. Regressions control for 2digit industry fixed e!ects, 2-digit occupation fixed e!ects, period fixed e!ects, state fixed e!ects, demographics
(education, age, gender, nationality), tenure, firm size, and the industry-level change in net exports. AI exposure and
robot exposure are standardized. High-skilled workers are those with a university degree. Movers are those who leave
their initial firm (in the base year) during the following 5-year-period; stayers remain employed at the initial firm
throughout the period. Standard errors are clustered at the 3-digit industry x period level.

30

a computer or tablet. Most of the changes occur within detailed occupations, indicating that AI
shifts the assignment of tasks to workers. These e!ects become more pronounced as the capabilities
of AI expand over time.
Second, AI’s impact on job tasks di!ers sharply from that of robots, which increased the demand
for abstract tasks and reduced the demand for routine tasks. This observation challenges the view
that AI continues previous technological trends of automating mostly routine tasks. Indeed, the
evidence indicates that AI can take over non-routine tasks that push the boundary of what machines
can do further toward complex tasks. Third, the changes brought about by AI are not skill-neutral.
Low-skilled workers in AI-exposed industries switch from abstract to routine tasks, mirroring the
overall changes. High-skilled worker, in contrast, can leverage their comparative advantage by
switching from information gathering to educating and training as well. Hence, while both skill
groups are a!ected by the automation of abstract tasks (information gathering), high-skilled workers
can more easily switch to performing other abstract tasks like training and educating.
We then turn to administrative social security data to understand the consequences for worker
careers. Our fourth result is that AI has so far not displaced many workers. Instead, workers have
responded to AI by switching employers. About half of the job mobility is within industry, while
the other half is into similar, but di!erent industries that are less exposed to AI. The AI-induced
reallocation di!ers from that of robots, which stabilized jobs with the same employer but increased
occupational switches. Finally, we document that the reassignment of tasks across workers reduces
the wages of low-skilled workers in exposed industries –irrespective of whether they switch jobs or
not. This modest decline in wages suggests that for low-skilled workers, the loss from automation
exceeds any productivity gains in Germany at the dawn of the AI age. In contrast, high-skilled
workers benefit on average from productivity gains and their comparative advantage in tasks if they
remain in their jobs. Our results highlight that if the di!usion of AI is dominated by automation
concerns, this can lead to wage losses for some workers.
While the magnitude of the employment and wage changes are still modest, the patterns suggest
that AI has profound impacts on job content, task assignment, and the productivity of di!erent
skill groups. Most importantly, the observed changes benefit some workers but reduce the earnings
of others. Moreover, the observed patterns di!er a lot from those of earlier waves of technology, in
particular the di!usion of robots. As AI continues to evolve and di!use into more industries and
occupations, policy-makers need to direct training and re-skilling measures to strengthen labor’s
comparative advantage and encourage worker mobility to increase benefits from AI advancements.
31

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34

A

Appendix
Figure A.1: Evolution of AI and Robot Patents over Time

Notes: The figure shows the number of AI and robot patent grants per year between 1990 and 2018.

35

Figure A.2: Link between patents and other measures
A: Robot patents and robot stock (2-digit)

B: AI patents and AI firms (3-digit)

C: AI patents and AI vacancies (2-digit)

Notes: The figures show the correlation of our AI and robot exposure measures with other proxies for AI and robots.
The exposure measures are based on patents in the specific technologies and measured as cumulative sum of log
patent grants until 2018. Panel A shows the correlation between our robot patent measure and the stock of industrial
robots in 2018 (based on International Federation of Robotics (2021)). Panel B exhibits the correlation between our
AI patent measure and the share of firms using or developing AI in Germany in 2022 (based on data from company
websites from Istari.ai). Panel C shows the correlation between our AI patent measure and the number of vacancies
that require at least one AI skill (based on online job vacancy data for Germany in 2021).

36

Figure A.3: Robustness: AI and Robots and Job Tasks

(a) AI

(b) Robots
Notes: The figure shows estimates of robustness tests in which we use the absolute number of patents (square),
drop German patents (circle), use a 5-year lag of patent exposure (x), code tasks as 1 if performed frequently and 0
otherwise (+), assign a value of 2 if a task is performed frequently, 1 if it is performed occasionally, and 0 otherwise
(triangle).

37

Figure A.4: Robustness: AI and Low- versus High-Level Routine Tasks

Notes: The figure shows the impact of AI exposure (by one standard deviation) on the probability of performing a
specific routine task. We define a routine task as ‘low-level’ if a worker also reports that the execution of his work is
often prescribed down to the last detail. In contrast, we define a routine task as ‘high-level’ if a worker reports that
the execution of his work is not often prescribed down to the last detail. The estimation is based on equation (4)
including all controls.

38

Figure A.5: Robustness: Employment e!ects of AI and Robots

Notes: The dependent variable is log cumulative days employed over a period of five years. Regressions control for
2-digit industry fixed e!ects, 2-digit occupation fixed e!ects, period fixed e!ects, state fixed e!ects, demographics
(education, age, gender, nationality), tenure, firm size, and the industry-level change in net exports. AI exposure
and robot exposure are standardized. High-skilled workers are those with a university degree. Standard errors are
clustered at the 3-digit industry x period level.

39

Figure A.6: Robustness: Impact of AI on Earnings

Notes: The dependent variable is log cumulative earnings over a period of five years. Regressions control for 2digit industry fixed e!ects, 2-digit occupation fixed e!ects, period fixed e!ects, state fixed e!ects, demographics
(education, age, gender, nationality), tenure, firm size, and the industry-level change in net exports. AI exposure and
robot exposure are standardized. High-skilled workers are those with a university degree. Movers are those who leave
their initial firm (in the base year) during the following 5-year-period; stayers remain employed at the initial firm
throughout the period. Standard errors are clustered at the 3-digit industry x period level.

40

Figure A.7: Robots and Detailed Job Tasks

Notes: The figure shows the relationship between robot exposure and the probability to perform a single task (measured in percent). Regressions include 2-digit industry, 2-digit occupation and period dummies, demographic controls
(education, gender, age groups (18-25, 26-35, 36-45, 46-55, 56-65), German nationality) and federal state dummies.
Regressions employ sample weights. Standard errors are clustered at industry-year level. the lines reflect 95% confidence intervals.

41

Table A.1: Industries with the Highest Number of AI and Robot Patents
Number of Patents (1990-2018)
Panel A: AI Patents
Manufacture of computers and peripheral equipment
Manufacture of consumer electronics
Manufacture of communication equipment
Manufacture of measuring, testing, navigating and control equipment
Manufacture of optical instruments and photographic equipment
Manufacture of general-purpose machinery
Motion picture, video and television program activities
Sound recording and music publishing activities
Manufacture of special-purpose machinery
Medical and dental practice activities

643.0
405.2
147.1
92.2
75.26
72.00
69.7
65.7
52.4
46.5

Panel B: Robot Patents
Manufacture of general-purpose machinery
Manufacture of special-purpose machinery
Manufacture of other fabricated metal products, metalworking activities
Manufacture of domestic appliances
Manufacture of measuring, testing, navigating and control equipment
Manufacture of computers and peripheral equipment
Manufacture of optical instruments and photographic equipment
Manufacture of dairy products
Medical and dental practice activities
Manufacture of basic iron and steel

718.4
275.5
167.9
161.5
157.6
147.5
131.0
130.5
100.5
99.2

Notes: The table shows the industries (ISIC rev.4, 3-digit level) with the largest cumulative number of AI (Panel A) and robot
(Panel B) patents from 1990 to 2018, respectively.

42

Table A.2: Descriptives of Task Data

Panel A: Task Groups
Routine task share (%)
Abstract task share (%)
Manual task share (%)

Panel B: Detailed Tasks
Monitoring/operating machines/technical processes
Manufacturing/producing of goods/products
Transporting, storing, shipping
Measuring, quality checks
Developing, researching, constructing
Gathering information, investigating, documenting
Promoting, marketing, advertising, PR
Organizing/planning/preparing of work processes (of others)
Teaching, training, educating
Consulting, informing
Buying, procuring, selling
Working with computer/tablet
Repairing
Accomodating, hosting, preparing food
Caring, healing
Protecting, securing, guarding, regulating tra”c
Cleaning, waste disposal, recycling

Observations

All years
(1)

2006
(2)

2012
(3)

2018
(4)

mean/sd
23.70
(15.09)
57.21
(22.22)
19.10
(14.36)

mean/sd
23.89
(15.69)
56.27
(22.97)
19.84
(14.86)

mean/sd
24.11
(14.91)
57.01
(21.92)
18.88
(14.09)

mean/sd
23.08
(14.62)
58.38
(21.66)
18.55
(14.06)

mean/sd
0.50
(0.50)
0.28
(0.45)
0.54
(0.50)
0.75
(0.43)
0.37
(0.48)
0.81
(0.39)
0.35
(0.48)
0.71
(0.45)
0.59
(0.49)
0.85
(0.35)
0.41
(0.49)
0.82
(0.38)
0.49
(0.50)
0.16
(0.37)
0.22
(0.41)
0.40
(0.49)
0.52
(0.50)

mean/sd
0.50
(0.50)
0.28
(0.45)
0.55
(0.50)
0.73
(0.44)
0.36
(0.48)
0.79
(0.41)
0.36
(0.48)
0.67
(0.47)
0.57
(0.50)
0.86
(0.35)
0.41
(0.49)
0.79
(0.40)
0.50
(0.50)
0.16
(0.36)
0.23
(0.42)
0.41
(0.49)
0.51
(0.50)

mean/sd
0.51
(0.50)
0.30
(0.46)
0.55
(0.50)
0.77
(0.42)
0.37
(0.48)
0.81
(0.39)
0.35
(0.48)
0.70
(0.46)
0.60
(0.49)
0.85
(0.35)
0.41
(0.49)
0.83
(0.37)
0.49
(0.50)
0.15
(0.36)
0.21
(0.41)
0.39
(0.49)
0.53
(0.50)

mean/sd
0.49
(0.50)
0.26
(0.44)
0.53
(0.50)
0.76
(0.43)
0.39
(0.49)
0.83
(0.37)
0.34
(0.47)
0.76
(0.43)
0.60
(0.49)
0.85
(0.35)
0.41
(0.49)
0.85
(0.36)
0.48
(0.50)
0.17
(0.38)
0.21
(0.41)
0.40
(0.49)
0.51
(0.50)

37,415

12,754

12,229

12,432

Notes: The table shows the mean and standard deviation (in brackets) for task shares of the three task groups
(Panel A) and detailed tasks (Panel B). Column (1) shows the descriptives for all years, columns (2) to (4) for each
survey wave (2006, 2012, and 2018) separately.

43

Table A.3: Top occupations performing individual tasks (part 1)
Occupation
Manufacturing/producing of goods/products
Occupations in leather- and fur-making and -processing
Occupations in metalworking
Occupations in plastic- and rubber-making and -processing
Measuring, quality checks
Occupations in metalworking
Occupations in physics
Occupations in mechatronics, automation and control technology
Monitoring/operating machines/technical processes
Aircraft pilots
Drivers of vehicles in railway tra”c
Occupations in metalworking
Transporting, storing, shipping
Driver of vehicles in road tra”c
Occupations in warehousing and logistics, delivery services, cargo handling
Occupations in animal care
Developing, researching, constructing
Teachers and researcher at universities and colleges
Draftspersons, technical designers, and model makers
Occupations in software development and programming
Gathering information, investigating, documenting
Occupations in pharmacy
Occupations providing nutritional advice or health counselling, wellness
Occupations in biology
Working with Computer/Tablet
Occupations in human resources management and personnel service
Driving, flying and sports instructors at educational institutions other than schools
Occupations in accounting, controlling and auditing
Buying, procuring, selling
Occupations in body care
Occupations in floristry
Sales occupations (retail) selling drugstore products, pharmaceuticals, medical supplies
Organizing/planning/preparing work processes (of others)
Occupations in theatre, film and television productions
Occupations in event organisation and management
Managing directors and executive board members
Teaching, training, educating
Teachers in schools of general education
Teachers for occupation-specific subjects in-company instructors
Driving, flying and sports instructors at other educational institutions

Share
.979
.897
.863
.974
.974
.972
.980
.973
.946
.960
.932
.885
.940
.926
.924
1.000
1.000
1.000
1.000
1.000
1.000
.961
.933
.928
1.000
.974
.951
.999
.994
.978

Notes: The table shows for each individual task the top three occupations which exhibit the highest share of workers
performing the respective task.

44

Table A.4: Top occupations performing individual tasks (part 2)
Occupation
Consulting, informing
Legislators and senior o”cials of special interest organisations
Occupations in public relations
Occupations in non-medical therapy and alternative medicine
Promoting, marketing, advertising, PR
Occupations in public relations
Legislators and senior o”cials of special interest organisations
Managing directors and executive board members
Repairing
Occupations in building services engineering
Occupations in plumping, sanitation, heating, ventilating, and air conditioning
Conditioning and processing of natural stone and minerals, production of building materials
Accomodating, hosting, preparing food
Cooking occupations
Gastronomy occupations
Occupations in geriatric care
Caring, healing
Occupations in geriatric care
Occupations in nursing, emergency medical services and obstetrics
Occupations in human medicine and dentistry
Cleaning, waste disposal, recycling
Occupations in animal care
Occupations in housekeeping and consumer counselling
Sales occupations (retail) selling foodstu!s
Protecting, securing, guarding, regulating tra!c
Occupations in the inspection and maintenance of tra”c infrastructure
Occupations in physical security, personal protection, fire protection and workplace safety
Armed forces personnel in other ranks

Share
1.000
1.000
1.000
.995
.904
.869
.960
.940
.939
.935
.889
.872
.988
.970
.951
.954
.921
.906
.894
.881
.850

Notes: The table shows for each individual task the top three occupations which exhibit the highest share of workers performing
the respective task.

45

Table A.5: Technology and Worker-level Task Shares
Panel A: Routine tasks (%)

(1)

(2)

(3)

1.266***
(0.303)
-0.882**
(0.354)
23.695
0.197

1.185***
(0.278)
-0.889***
(0.305)
23.695
0.244

0.998***
(0.351)
-0.606**
(0.256)
23.695
0.370

(1)

(2)

(3)

-1.436***
(0.435)
0.545
(0.580)
57.207
0.201

-1.278***
(0.393)
0.555
(0.511)
57.207
0.311

-0.964**
(0.488)
0.266
(0.401)
57.207
0.499

(1)

(2)

(3)

0.170
(0.287)
0.337
(0.305)
19.097
0.146

0.092
(0.274)
0.334
(0.296)
19.097
0.233

-0.033
(0.285)
0.340
(0.240)
19.097
0.367

X

X
X
X

X
X
X
X

AI
Robots
Mean of Y variable
R2
Panel B: Abstract tasks (%)
AI
Robots
Mean of Y variable
R2
Panel C: Manual tasks (%)
AI
Robots
Mean of Y variable
R2
Industry & year FE
Demographic controls
State FE
2-digit occupation FE

Notes: The dependent variables are the task shares in year t measured in percent:
routine tasks (Panel A), abstract tasks (Panel B) and manual tasks (Panel C). The
AI and robot exposure measures are defined in equation (1). All columns control for
2-digit industry and year dummies. Column (2) adds demographic controls, which
include education, gender, age groups (18-25, 26-35, 36-45, 46-55, 56-65), German
nationality, and federal state dummies. Column (3) adds 2-digit occupation fixed
e!ects. Number of observations: 37,415. Standard errors clustered at industry-year
level are shown in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

46

Table A.6: Comparing the E!ects across Time
Panel A: Routine tasks

2006
(1)

2012
(2)

2018
(3)

AI

0.412
(0.418)
-0.456
(0.451)
23.892
0.358

0.616**
(0.296)
-0.510
(0.317)
24.112
0.374

0.913***
(0.203)
-0.913***
(0.205)
23.077
0.371

2006
(1)
-0.297
(0.610)
0.925
(0.604)
56.272
0.498

2012
(2)
-0.323
(0.585)
0.434
(0.507)
57.006
0.501

2018
(3)
-0.882***
(0.231)
0.899***
(0.235)
58.378
0.494

2006
(1)
-0.115
(0.263)
-0.469**
(0.228)
19.837
0.358

2012
(2)
-0.294
(0.365)
0.075
(0.262)
18.882
0.370

2018
(3)
-0.031
(0.119)
0.014
(0.145)
18.545
0.373

Robots
Mean Y
R2
Panel B: Abstract tasks
AI
Robots
Mean Y
R2
Panel C: Manual tasks
AI
Robots
Mean Y
R2

Notes: The dependent variables are routine tasks (Panel A), abstract tasks
(Panel B) and manual tasks (Panel C) in year t measured in percent. The
AI and robot exposure measures are defined in equation (??). All columns
control for 2-digit industry, 2-digit occupation and year fixed e!ects, demographics (education, gender, age groups, German nationality) and federal
state dummies. Standard errors are clustered at industry-level in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

47

Table A.7: Top occupations performing high-level and low-level routine tasks
Occupation
High-level Monitoring/operating machines/technical processes
Aircraft pilots
Laboratory occupations in medicine
Occupations in physics
Low-level Monitoring/operating machines/technical processes
Occupations in gardening
Occupations in building construction
Occupations in civil engineering
High-level Measuring, quality checks
Occupations in physics
Aircraft pilots
Occupations in mechatronics, automation and control technology
Low-level Measuring, quality checks
Occupations in building construction
Floor layers
Painters and varnishers, plasterers, occupations in the waterproofing of buildings
High-level Manufacturing/producing of goods/products
Occupations in metalworking
Occupations in industrial glass-making and -processing
Occupations in precision mechanics and tool making
Low-level Manufacturing/producing of goods/products
Occupations in leather- and fur-making and -processing
Occupations in the production of clothing and other textile products
Occupations in floristry
High-level Transporting, storing, shipping
Sales occupations (retail) selling drugstore products, pharmaceuticals
Occupations in pharmacy
Doctors’ receptionists and assistants
Low-level Transporting, storing, shipping
Occupations in building construction
Painters and varnishers, plasterers, occupations in the waterproofing of buildings
Floor layers

Share
.980
.884
.882
.523
.505
.500
.974
.966
.957
.750
.622
.618
.778
.726
.726
.525
.480
.402
.817
.720
.713
.615
.611
.608

Notes: The table shows for each individual (high-level and low-level) routine task the top three occupations
which exhibit the highest share of workers performing the respective task.

48

Table A.8: Descriptive Statistics of Social Security Data (SIAB)

Cumulative earnings
Log cumulative earnings
Cumulative days employed
Log cumulative days employed
University degree
Vocational degree
Low-skilled
Female
German nationality
Age
Tenure 0-2 years
Tenure 3-5 years
Tenure 6-10 years
Tenure > 10 years
Plant size 0-9
Plant size 10-99
Plant size 100-499
Plant size 500-999
plant size 1,000-9,999
plant size >= 10,000
# Net Exports

Mean

Std. Dev.

25th Perc.

75th Perc.

207,332.31
12.01
1,648.78
7.37
0.18
0.75
0.07
0.34
0.93
39.62
0.29
0.24
0.21
0.26
0.13
0.37
0.27
0.08
0.12
0.02
0.00

141,146.81
0.74
349.28
0.35
0.38
0.43
0.26
0.47
0.25
9.49
0.45
0.43
0.41
0.44
0.33
0.48
0.44
0.28
0.33
0.15
1.00

119,514.76
11.69
1665.00
7.42
0.00
1.00
0.00
0.00
1.00
32.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
-0.12

251,035.69
12.43
1826.00
7.51
0.00
1.00
0.00
0.00
1.00
48.00
1.00
0.00
0.00
1.00
0.00
1.00
1.00
0.00
0.00
0.00
0.22

Notes: The table shows selected descriptive statistics of our sample using Social Security Data.
Cumulative earnings and employment are computed over 5 years following the base years 2004,
2010, and 2016. # Net Exports denotes the change in net exports during the time window in
the 3-digit industry in which the worker is employed in the base year. All other variables are
measured in the base year. N=952,750.

49

Table A.9: Robustness: AI and Job Reallocation
Firm
Change

Occupation
Change
(2)

Di!erent
Industry
(3-digit)
(3)

Di!erent
Industry
(2-digit)
(4)

Same
Industry
(2-digit)
(5)

Lower AI
Exposure
Industry
(6)

Higher
AKM
Firm
(7)

Lower
AKM
Firm
(8)

(1)
Baseline

0.014***
(0.005)

0.001
(0.008)

0.007
(0.005)

0.002
(0.004)

0.013**
(0.005)

0.039***
(0.008)

0.007
(0.004)

0.007*
(0.004)

Absolute number

0.007**
(0.003)

-0.0015
(0.003)

0.006*
(0.003)

0.002
(0.002)

0.005*
(0.003)

0.015***
(0.004)

0.003
(0.003)

0.004**
(0.002)

3-year-lag

0.013***
(0.004)

0.002
(0.008)

0.006
(0.004)

0.001
(0.004)

0.012**
(0.005)

0.033***
(0.007)

0.007*
(0.004)

0.006*
(0.003)

Drop German patents

0.014***
(0.005)

0.002
(0.008)

0.007
(0.005)

0.002
(0.004)

0.012**
(0.005)

0.037***
(0.007)

0.007*
(0.004)

0.006*
(0.003)

Mean Y
Observations

0.358
952,750

0.312
952,750

0.241
952,750

0.218
952,750

0.141
952,750

0.087
952,750

0.181
894,582

0.155
894,582

Notes: The dependent variables are indicators for moving to a di!erent firm (1), occupation (2), 3-digit industry (3), 2-digit industry
(4), a di!erent firm within the same 2-digit industry (5), an industry with lower AI exposure (6), a firm with higher AKM e!ect (7),
and a firm with lower AKM e!ect (8). Regressions control for 2-digit industry FEs, 2-digit occupation FEs, period FEs, state FEs,
demographics (education, age, gender, nationality), tenure, firm size, and the industry-level change in net exports. AI exposure and
robot exposure are standardized. Standard errors are clustered at the 3-digit industry x period level in parentheses. * p<0.10, ** p<0.05,
*** p<0.01.

50
</reference>

<statements>
1. Recent working-paper evidence, which is not journal evidence, points to early-career employment declines and task reallocation, but remains provisional.
2. The ADP paper is a recent paper in the supplied pool rather than a journal article, and the German paper is explicitly an IZA Discussion Paper with provisional status.
3. Gathmann, Grimm, and Winkler combine patent-based measures of AI and robot exposure with German survey task data and administrative career data.
4. They find that robots reduce routine tasks, while AI reduces non-routine abstract tasks such as information gathering and increases demand for “high-level” routine tasks such as monitoring processes.
5. These task shifts occur mainly within detailed occupations and strengthen over time.
6. Displacement effects are small, but workers respond by switching jobs, often to less exposed industries.
7. Low-skilled workers suffer some wage losses, while high-skilled incumbent workers experience wage gains; high-skilled workers who remain in exposed industries see earnings increase by around 1.5 percentage points over five years.
8. High-skilled workers also increase activities in educating and training, with a one-standard-deviation increase in AI exposure raising the probability of teaching, training, and educating by about 6 percentage points.
9. The patent-based measures are designed to reduce reverse causality: initial task shares do not predict later patent exposure, and results are robust to dropping German-inventor patents.
10. The authors also note that the survey’s static task categories mean observed task changes may combine automation, productivity gains, and new sub-tasks within the same category.
11. They do not overturn the journal evidence’s finding of no detectable aggregate employment or wage effects in the 2010–2018 vacancy data, because they cover different periods, countries, and post-generative-AI adoption windows.
12. The German working paper finds that robot exposure is concentrated in manufacturing machinery and metalworking, while AI patents are concentrated in computers, consumer electronics, communication equipment, and related high-tech industries.
13. Working papers — What the sources establish: Early-career employment declines and task reallocation appear in ADP and German administrative/survey data
14. Working papers — What the sources do not establish: Settled journal evidence; the ADP paper cautions other factors may matter, and the German paper is provisional
15. The German working paper suggests that task changes and worker reallocation can occur even when displacement effects are small.
16. These results are important because they point to where future journal evidence should look, but they are not journal evidence and should not be treated as settled.
</statements>

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