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<reference>
RAIS

August 7-8, 2025

RESEARCH
ASSOCIATION for
INTERDISCIPLINARY
STUDIES

DOI:10.5281/zenodo.17038880

On the Impact of Artificial Intelligence
on Employment:
Empirical Evidence from Selected Countries
Ahmet Koseoglu1, Ali Gokhan Yucel2
1

Department of Economics, Faculty of Economics and Business Administration, Erciyes University, Kayseri,
Turkiye, akoseoglu@erciyes.edu.tr
2
Department of Economics, Faculty of Economics and Business Administration, Erciyes University, Kayseri,
Turkiye, agyucel@erciyes.edu.tr

Abstract: This study investigates the impact of artificial intelligence (AI) on employment in a panel of
selected countries. Using a dynamic framework, we employ the two-step System Generalized Method
of Moments (System-GMM) estimator with Windmeijer correction to address endogeneity and account
for the persistence of labor market dynamics. High-quality AI publications are used as a proxy to
measure AI development. Employment is disaggregated by gender, skill level, and age groups to capture
heterogeneous effects across the labor force. The empirical results indicate that AI adoption exerts
differentiated effects on employment, with younger and low-skilled workers being more exposed to
displacement risks, while high-skilled groups show signs of complementarity. These findings suggest
that the labor market implications of AI are uneven and depend on demographic and skill characteristics.
Policy implications emphasize the importance of targeted education, skill upgrading, and adaptive labor
market policies to mitigate risks and harness the potential benefits of AI-driven technological change.
Keywords: Artificial Intelligence, employment, system GMM

Introduction
Although discussions on the effects of technological change on the labor market are considered
new, the debate has a long history. Say (1803), a prominent figure in classical economics, argued
that process innovation would not only displace workers employed in industries using newly
invented machines but could also positively affect the workforce by creating new lines of work in
the industries that produced them. Schumpeter (1912) also endorsed Say's perspective, arguing that
technological advances supported employment growth by leading not only to process innovation
but also to product innovation, which, by their nature, required the creation of new jobs. However,
some perspectives suggest that the benefits of process innovation may not entirely offset the initial
job losses (Feldmann, 2013). For instance, Ricardo (1821) stated that the laboring class's view that
using machinery in production is detrimental to their interests aligns more with fundamental
economic principles than mere prejudice. According to Marx (1992), technological innovations in
the capitalist system will cause an increase in capital accumulation, and as a result of this increase,
the current workforce will be forced to work at a lower wage level by choosing a production
technique that saves labor in the economy. In the labor market, at this new wage level below the
minimum subsistence wage level, some workers will choose not to work instead of working and
will become unemployed. As long as the increase in capital accumulation continues, this process
will continue and ultimately lead to the emergence of the “industrial reserve army” in Marxist
doctrine. Keynes (1930) also stated, in line with Marx, that rapidly developing technologies will
replace labor and create a new problem in the economy called “technological unemployment”.
Another prominent economist, Wassily Leontief, echoed a pessimistic perspective on this issue,
stating in an interview that labor would become increasingly less significant as machines replace
more and more workers (Brynjolfsson & McAfee, 2014).
The topic remains highly relevant today. Some research suggests that automation will not
only enhance production efficiency but also expand production scale and market demand,
thereby increasing the need for labor (Aghion et al., 2020; Autor, 2015; Zeira, 1998).
Conversely, other studies highlight that digital transformation and automation could pose
significant risks for workers who struggle to adapt, potentially having adverse effects on the
10

RAIS Conference Proceedings, August 7-8, 2025
labor market overall (Acemoglu & Restrepo, 2020; Aghion et al., 2019; Bertani et al., 2020; Ni
& Obashi, 2021). Also, Nguyen and Vo (2022) identify a non-linear relationship between AI
and employment.
A growing body of research on the labor market implications of technological change and
AI suggests that the heterogeneity of findings in the literature is largely due to differential
exposure of occupations and tasks to technological advancements. This discrepancy in exposure
is primarily attributed to the varying skill requirements associated with different types of jobs.
Two complementary hypotheses have emerged from this perspective: Skill-Biased
Technological Change (SBTC) and Routine-Biased Technological Change (RBTC). According
to the SBTC hypothesis, technological progress complements the skills of highly skilled
workers rather than displacing them, thereby boosting their productivity and increasing the
demand for skilled jobs relative to unskilled ones in the labor market (Acemoglu, 2002;
Acemoglu & Autor, 2011; Katz & Autor, 1999). This shift has also led to a rise in wage
inequality (Goldin & Katz, 2007). Although the SBTC hypothesis effectively accounts for skillbased income disparities driven by technological change at the macro level, supported by
decades of empirical evidence, it falls short in explaining the wage and job polarization that
became pronounced in the early 1990s, particularly with the displacement of many mediumskilled workers in routine-intensive jobs (Albanesi et al., 2025).
To address this gap, Autor et al. (2003) proposed the RBTC framework, arguing that
computerization displaces workers in routine cognitive and manual tasks while complementing
workers performing non-routine, problem-solving, and complex communication tasks.
Accordingly, technological advances have increased labor demand in non-routine cognitive
fields such as software development, research, management, and finance, which are typically
high wage and education intensive, as well as in non-routine manual occupations such as
cleaning, childcare, and construction, which are generally lower in both educational
requirements and wage (Goos & Manning, 2007). The decline in demand for routine tasks,
which medium-skilled workers predominantly carry out, has contributed to a U-shaped
employment structure characterized by growth in both high- and low-skilled jobs but a
contraction in middle-skill employment leading to what is commonly referred to as job
polarization (Autor & Dorn, 2013; Goos et al., 2014).
The remainder of this paper is structured as follows. The next section offers a
comprehensive review of the literature. Section III describes the data and presents the empirical
findings. The final section concludes with policy recommendations.
Literature Review
The literature on the net impact of AI and robotics on total employment highlights two opposing
perspectives: the displacement effect and the reinstatement effect. The displacement effect suggests
that AI and robotics reduce employment and wages by replacing workers in tasks they previously
performed. However, technologies that introduce new tasks where labor maintains a comparative
advantage can mitigate the adverse effects of automation. These new tasks not only boost
productivity but also trigger the reinstatement effect, re-integrating labor into a broader range of
activities and shifting the task composition of production in favor of labor. In contrast to the
displacement effect, the reinstatement effect positively influences labor demand and wages
(Acemoglu & Restrepo, 2019b; Fossen & Sorgner, 2022).
Crucially, the existence of the displacement effect does not necessarily imply that
automation will lead to a persistent decline in employment. Several countervailing forces can
mitigate its negative impact, indicating that automation, AI, and robotics may stimulate labor
demand. The first is the productivity effect, which arises when inexpensive machines replace
human labor (Autor, 2015). This effect reduces the production costs of automated tasks, drives
economic growth, and increases labor demand in non-automated sectors (Acemoglu & Restrepo,
2020). The second is capital accumulation, whereby the increase in capital accumulation
11

RAIS Conference Proceedings, August 7-8, 2025
spurred by automation also boosts labor demand. The third is the deepening of automation: as
technology advances, machines become more efficient in already automated tasks, generating
additional productivity gains without causing further displacement, thus further supporting
labor demand (Acemoglu & Restrepo, 2019a).
In their seminal paper, Acemoglu and Restrepo (2018) explored concerns that automation,
driven by new technological advancements, might render labor redundant in tasks that
previously held a comparative advantage. Within their growth model with fixed capital and
exogenous technology, they propose that automation reduces employment, the labor share, and
wages, while introducing new tasks offsets these impacts. Additionally, in their growth model,
where both technology and capital accumulation are endogenous, they argue that if the longrun rental rate of capital relative to wages is sufficiently low, full automation of jobs will occur
in the long-run equilibrium. Conversely, if this rate is higher, automation lowers production
costs by employing labor, thereby limiting further automation and fostering the creation of new
jobs.
In another study, Acemoglu and Restrepo (2020) examined the effects of industrial robots
on the United States (US) labor market, demonstrating theoretically and empirically that
increased reliance on robots in production negatively impacts wages and employment.
Similarly, Frey and Osborne (2017) analyzed the susceptibility of 702 occupations in the US
labor market to computerization and reported that approximately 47% of US jobs fall into the
high-risk category, likely to be automated in the near future. They also provided evidence of a
strong negative relationship between wages, educational attainment, and the likelihood of
computerization.
Dengler and Matthes (2018) investigated the automation potential of tasks rather than
entire occupations, arguing that studies focusing solely on the automation of occupations tend
to overestimate the results. Analyzing approximately 8,000 tasks using a German occupational
database, they concluded that if all occupations were assumed to be replaceable, 47% of jobs
could be automated, consistent with other studies. However, when considering only certain
tasks as replaceable, they found that only 15% of German employees are at risk. Bertani et al.
(Bertani et al., 2020), in their empirical study using data from 15 developed countries between
1995 and 2016, concluded that intangible digital investments, such as software, AI, various web
services, and digital platforms, are likely to result in technological unemployment in the long
run.
Ni and Obashi (2021), based on their analysis of firms in the Japanese manufacturing
industry between 1995 and 2017, concluded that robot adoption at the industry level positively
influenced both the job creation rate and the job destruction rate at the firm level. However,
since the effect on job destruction was greater, robot adoption generally had a negative impact
on firms' net employment growth. Additionally, their study indicates that the displacement
effect due to robotic technology and the creation of new jobs driven by technological change
can coexist at the firm level.
Consistent with Acemoglu and Restrepo (2018), Fossen and Sorgner (2022) found in their
study on the US that labor-displacing digital technologies increase the risk of unemployment
for individuals and suppress wage growth. In contrast, labor-reinstituting digital technologies
have a positive impact on the workforce. In contrast to these studies, some research suggests
that AI will increase labor demand and wages through productivity, capital accumulation,
deepening automation, and reinstatement effects, which were discussed earlier.
Van Reenen (1997) conducted an empirical investigation employing fixed effects and
GMM estimation techniques on a panel dataset comprising 598 firms in the British
manufacturing sector over the period 1976–1982. The findings indicate that innovation exerted
a positive and statistically significant impact on employment, whereas industry-level wages and
union power were found to have no significant effect.
12

RAIS Conference Proceedings, August 7-8, 2025
Van Roy et al. (2018) employed the system-GMM method to analyze micro-level data on
European firms from 2003 to 2012. Their findings indicate that innovation, measured by
citation-weighted patents, had a positive and labor-friendly effect on employment, but only
within high-technology manufacturing sectors. No statistically significant relationship was
observed for low-technology manufacturing and service sector firms.
Focacci (2021) provided evidence that robots do not always lead to technological
unemployment in China and South Korea during the period 2008–2018. Similarly, Mutascu
(2021), through an econometric analysis of 23 advanced OECD and non-OECD countries with
high technology, found a non-linear relationship between the use of artificial intelligence and
unemployment. He concluded that accelerating AI, particularly in low-inflation environments,
can help reduce unemployment. Aligning with these findings, Dağlı (2021), using the metaanalysis method to investigate whether robots cause unemployment, concluded that using
robots in production increases employment. Yang (2022) examined the impact of AI technology
on firms' productivity and employee profiles in Taiwan and found that AI is positively
associated with both productivity and employment. He further noted that AI has significantly
altered the labor composition of firms, particularly by reducing the proportion of employees
with college-level or lower educational qualifications.
Data and findings
To examine the impact of AI on employment, we estimate the following model:
ln(𝐸𝑀𝑃!" ) = 𝛼! + 𝜏" + 𝜌 ln-𝐸𝑀𝑃!,"$% . + 𝛽% ln-𝐴𝐼&'(!,#$% . + 𝛽) ln(𝑃𝑎𝑡𝑒𝑛𝑡"$% ) +
𝛾% ln(𝑃𝑟𝑜𝑑!" ) + 𝛾) ln(𝑊𝑎𝑔𝑒!" ) + 𝛾* 𝐺𝐷𝑃+,-."/!# + 𝛾0 𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠!" + 𝛾1 𝑃𝑜𝑝!" + 𝜖!"

(1)

where ln denotes the natural logarithm, 𝛼! represents country fixed effects, 𝜏" denotes time fixed
effects. In Eq. (1), employment (EMP) is the dependent variable, disaggregated by gender (male
and female); age group (15–24, 25–54, 55–64), and job skill level (low-, medium-, and high-skill
jobs). EMPt−1 captures the dynamic persistence of employment, reflecting the extent to which
current employment depends on its past level due to labor market frictions and gradual adjustment
processes. AIpub represents AI intensity, –the key independent variable– proxied by high-quality AI
publications, while Patent measures total patent applications and serves as an indicator of the
broader technological environment. Prod (Productivity), Wage (minimum wage), GDPgrowth (GDP
per capita growth rate), Openness (trade openness), Pop (working-age population) are the control
variables. The index 𝑖 = 1,2 … 78 denotes countries in the sample, 𝑡 = 2000 … 2023 indicates
years, and 𝜖!" is the idiosyncratic error term. The description of the variables along with the sources
are given in Table 1.
Table 1. Description and source of the variables
Var%able
𝐸𝑀𝑃
𝐴𝐼&'(
𝐴𝐼&)*
Prod
𝑊𝑎𝑔𝑒
𝐺𝐷𝑃+,-.*/
𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠
𝑃𝑜𝑝

Descr%pt%on
Employment by sex, age and, occupat%on
(thousands)
Number of h%gh-%mpact AI publ%cat%ons
Patent
publ%cat%ons
for
AI-related
technolog%es
Output per worker (GDP constant 2021
%nternat%onal $ at PPP)
M%n%mum wage, pr%vate sector (US$)
GDP per cap%ta growth (annual %)
The rat%o of the sum of exports and %mports to
GDP
Populat%on ages 15-64 (% of total populat%on)

13

Source
Internat%onal Labour Organ%zat%on (ILO, 2025)
Organ%zat%on for Econom%c Co-operat%on and
Development (OECD.AI, 2025)
World Intellectual Property Organ%zat%on (WIPO,
2025)
Internat%onal Labor Organ%zat%on (ILO, 2025)
Internat%onal Labor Organ%zat%on (ILO, 2025)
World Development Ind%cators (World Bank, 2025)
World Development Ind%cators (World Bank, 2025)
World Development Ind%cators (World Bank, 2025)

RAIS Conference Proceedings, August 7-8, 2025
Due to the short time dimension, possible endogeneity, dynamic structure (lagged
dependent variable) and heteroskedasticity/autocorrelation within units we applied two-step
system GMM approach. The estimated effects of high-impact total AI scientific publications
on employment, controlling for worker characteristics such as gender, skill level, and age, are
reported in Tables 2–4.
Table 2 presents results for different skill–age groups without gender differentiation.
According to these estimates, the focal variable of the analysis, 𝑙𝑛𝐴𝐼"$% , is positive and
statistically significant for all subgroups except medium-skilled workers (across all age
categories) and low-skilled prime-age workers (aged 25–54), implying that countries with
higher levels of AI research tend to experience stronger job creation or retention. The strongest
positive effects are observed for high-skilled workers aged 25–54 and 55–64, with coefficients
of 0.196 and 0.231, respectively.
Table 2. Employment-TOTAL: AI Publications
Variables

𝑙𝑛𝑒𝑚𝑝*01
𝑙𝑛𝐴𝐼*01
𝑙𝑛𝑝𝑎𝑡𝑒𝑛𝑡*01
𝑙𝑛𝑝𝑟𝑜𝑑*
𝑙𝑛𝑤𝑎𝑔𝑒*
𝐺𝐷𝑃𝑔𝑟𝑜𝑤𝑡ℎ*
𝑜𝑝𝑒𝑛𝑛𝑒𝑠𝑠*
𝑝𝑜𝑝*
𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡

Low-skilled
15-24
0.627***
(0.199)
0.162*
(0.096)
0.082
(0.077)
-1.033*
(0.620)
0.138*
(0.085)
0.011***
(0.003)
-0.001
(0.001)
-0.049*
(0.029)
15.467*
(8.770)
Included
0.000***

25-54
0.821***
(0.109)
0.069
(0.044)
0.045*
(0.027)
-0.387*
(0.231)
0.022
(0.018)
0.003**
(0.001)
-0.0007
(0.0005)
-0.006*
(0.004)
5.675*
(3.275)
Included
0.000***

Medium-skilled
55-64
0.558***
(0.184)
0.130*
(0.071)
0.150***
(0.054)
-0.739**
(0.321)
0.028
(0.049)
0.007**
(0.003)
-0.001
(0.001)
-0.018
(0.014)
10.779***
(4.119)
Included
0.000***

15-24
0.920***
(0.136)
0.027
(0.048)
0.024
(0.044)
-0.172
(0.265)
0.013
(0.021)
0.007***
(0.002)
-0.0004
(0.0009)
-0.010
(0.013)
2.927
(4.379)
Included
0.000***

25-54
0.865***
(0.287)
0.044
(0.116)
0.038
(0.077)
-0.233
(0.487)
-0.003
(0.034)
0.002
(0.003)
-0.0009
(0.001)
-0.008
(0.015)
4.137
(8.294)
Included
0.000***

High-skilled
55-64
0.897***
(0.293)
0.026
(0.106)
0.039
(0.093)
-0.178
(0.469)
-0.003
(0.038)
0.003
(0.003)
-0.0001
(0.001)
-0.007
(0.019)
2.963
(7.866)
Included
0.000***

15-24
0.491***
(0.176)
0.132*
(0.078)
0.179***
(0.049)
-0.683**
(0.285)
0.047
(0.049)
0.009**
(0.003)
-0.002
(0.001)
-0.042**
(0.021)
11.604***
(4.360)
Included
0.000***

25-54
0.443*
(0.248)
0.196**
(0.097)
0.173*
(0.099)
-0.608*
(0.343)
0.009
(0.058)
0.005
(0.004)
-0.001
(0.001)
-0.015
(0.022)
11.147**
(5.284)
Included
0.000***

55-64
0.375*
(0.224)
0.231**
(0.095)
0.173**
(0.068)
-0.485**
(0.201)
-0.011
(0.044)
0.005*
(0.003)
-0.002*
(0.001)
-0.036*
(0.020)
10.836**
(4.245)
Included
0.000***

𝑦𝑒𝑎𝑟_𝑑𝑢𝑚𝑚𝑦
Wald test pvalue
0.004*** 0.071*
0.014**
0.065*
0.179
0.088*
0.025**
0.049**
0.009***
AR(1) p-value
0.534
0.729
0.642
0.164
0.212
0.166
0.131
0.126
0.390
AR(2) p-value
0.331
0.168
0.809
0.116
0.193
0.602
0.245
0.142
0.421
Hansen test pvalue
1086
1086
1086
1086
1086
1086
1086
1086
Number of obs 1086
34
34
52
34
34
36
53
53
33
No. of
instruments
Notes: Robust standard errors in parentheses. The instrumental variables consist of one- and two-year lags.
*** p < 0.01, ** p < 0.05, * p < 0.1.

Turning to the results for female workers in Table 3, lagged AI publications positively and
significantly affect all categories except for low- and medium-skilled prime-age women (aged 25–
54). Notably, for high-skilled women aged 25–54 and 55–64, the coefficients on 𝑙𝑛𝐴𝐼"$% are 0.386
and 0.303, respectively, the largest values across all female subgroups. These evidences suggest
that AI technologies are especially compatible with the occupational structure of high-skilled,
prime-age female workers, such as those in healthcare, education, and administrative services. AI
tends to play a strongly complementary rather than a displacing role in this labor force segment.

14

RAIS Conference Proceedings, August 7-8, 2025
Table 3. Employment-FEMALE: AI Publications
Var6ables
𝑙𝑛𝑒𝑚𝑝!"#
𝑙𝑛𝐴𝐼!"#
𝑙𝑛𝑝𝑎𝑡𝑒𝑛𝑡!"#
𝑙𝑛𝑝𝑟𝑜𝑑!
𝑙𝑛𝑤𝑎𝑔𝑒!
𝐺𝐷𝑃𝑔𝑟𝑜𝑤𝑡ℎ!
𝑜𝑝𝑒𝑛𝑛𝑒𝑠𝑠!
𝑝𝑜𝑝!
𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡
𝑦𝑒𝑎𝑟_𝑑𝑢𝑚𝑚𝑦
Wald test p-value
AR(1) p-value
AR(2) p-value
Hansen test p-value
Number of obs
No. of Gnstruments

Low-sk6lled
15-24
0.594***
(0.139)
0.208**
(0.094)
0.070
(0.067)
-1.063**
(0.487)
0.147*
(0.083)
0.008
(0.005)
-0.001
(0.001)
-0.044**
(0.022)
16.403***
(6.256)
Included
0.000***
0.000***
0.430
0.472
1079
35

25-54
1.132***
(0.192)
-0.053
(0.078)
-0.027
(0.042)
0.253
(0.403)
-0.017
(0.039)
0.005**
(0.002)
0.0004
(0.0007)
0.0003
(0.004)
-3.386
(5.256)
Included
0.000***
0.043**
0.730
0.216
1086
33

Med6um-sk6lled
55-64
0.478***
(0.170)
0.142**
(0.069)
0.156***
(0.056)
-0.706**
(0.279)
0.045
(0.049)
0.007
(0.005)
-0.002
(0.002)
-0.005
(0.020)
9.647***
(3.550)
Included
0.000***
0.007***
0.597
0.571
1085
52

15-24
0.896***
(0.062)
0.062**
(0.031)
0.023
(0.025)
-0.265*
(0.138)
0.015
(0.016)
0.010***
(0.002)
0.0004
(0.0004)
-0.009
(0.007)
3.913*
(2.021)
Included
0.000***
0.028**
0.143
0.864
1086
36

25-54
0.763***
(0.207)
0.093
(0.076)
0.059
(0.059)
-0.389
(0.316)
-0.008
(0.025)
0.003
(0.002)
-0.001
(0.001)
-0.009
(0.009)
6.592
(5.338)
Included
0.000***
0.086*
0.147
0.141
1086
32

H6gh-sk6lled
55-64
0.817***
(0.081)
0.060*
(0.031)
0.061**
(0.027)
-0.287**
(0.119)
-0.004
(0.016)
0.004**
(0.001)
-0.0008
(0.0006)
-0.007
(0.005)
4.486**
(1.873)
Included
0.000***
0.053*
0.236
0.268
1086
52

15-24
0.409**
(0.166)
0.120*
(0.070)
0.250***
(0.074)
-0.797***
(0.295)
0.056
(0.054)
0.007*
(0.004)
-0.002
(0.001)
-0.046*
(0.023)
12.587***
(3.921)
Included
0.000***
0.007***
0.106
0.130
1086
53

25-54
-0.102
(0.485)
0.386*
(0.208)
0.317**
(0.155)
-1.214**
(0.592)
0.084
(0.090)
0.010*
(0.006)
-0.003
(0.002)
-0.033
(0.039)
26.448**
(10.195)
Included
0.000***
0.003***
0.147
0.570
1086
37

55-64
-0.592
(0.325)
0.303**
(0.135)
0.286**
(0.116)
-0.171
(0.287)
-0.157
(0.141)
0.004
(0.007)
-0.005**
(0.002)
-0.033
(0.038)
9.287*
(4.919)
Included
0.000***
0.055*
0.562
0.689
1086
53

Notes: Robust standard errors in parentheses. The instrumental variables consist of one- and two-year lags.
*** p < 0.01, ** p < 0.05, * p < 0.1.

Similar patterns emerge in Table 4, which reports results for male workers disaggregated by age
and skill. Specifically, lagged high-impact AI publications have no significant effect on mediumskilled male workers across all age groups, potentially reflecting that automation risks and skill
polarization are more concentrated in middle-skill routine occupations. On the other hand, they
positively and significantly affect low-skilled male workers, except those aged 25–54. Moreover,
for high-skilled male workers across all age categories, lagged AI activity significantly increases
employment, with the largest effects again observed for the 25–54 and 55–64 age groups (0.215
and 0.224, respectively), echoing Blanas et al. (2019) on high-skilled older men. These groups may
benefit from AI-related productivity enhancements in manufacturing, engineering, and technical
services. These findings are broadly consistent with the RBTC hypothesis, whereby technology
disproportionately complements high-skilled and non-routine work and creates complementary
opportunities for some low-skilled groups, especially at early career stages.
Table 4. Employment-MALE: AI Publications
Var6ables
𝑙𝑛𝑒𝑚𝑝!"#
𝑙𝑛𝐴𝐼!"#
𝑙𝑛𝑝𝑎𝑡𝑒𝑛𝑡!"#
𝑙𝑛𝑝𝑟𝑜𝑑!
𝑙𝑛𝑤𝑎𝑔𝑒!
𝐺𝐷𝑃𝑔𝑟𝑜𝑤𝑡ℎ!
𝑜𝑝𝑒𝑛𝑛𝑒𝑠𝑠!
𝑝𝑜𝑝!
𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡
𝑦𝑒𝑎𝑟_𝑑𝑢𝑚𝑚𝑦

Low-sk6lled
15-24
0.527***
(0.189)
0.184**
(0.084)
0.122
(0.085)
-1.239**
(0.564)
0.127*
(0.075)
0.011***
(0.004)
-0.002*
(0.001)
-0.057**
(0.026)
18.549**
(7.809)
Included

25-54
0.678***
(0.238)
0.106
(0.081)
0.098
(0.089)
-0.698
(0.548)
0.035
(0.045)
0.005*
(0.003)
-0.001
(0.001)
-0.017
(0.015)
10.196
(7.500)
Included

Med6um-sk6lled
55-64
0.690***
(0.125)
0.092*
(0.050)
0.117**
(0.052)
-0.614**
(0.258)
0.026
(0.031)
0.009***
(0.003)
-0.001
(0.001)
-0.008
(0.012)
8.015**
(3.150)
Included

15-24
0.875***
(0.107)
0.039
(0.036)
0.040
(0.042)
-0.251
(0.214)
0.010
(0.021)
0.008***
(0.002)
-0.0008
(0.0009)
-0.010
(0.010)
3.868
(3.161)
Included

15

25-54
0.769***
(0.297)
0.067
(0.112)
0.072
(0.083)
-0.397
(0.528)
-0.004
(0.037)
0.003
(0.003)
-0.001
(0.001)
-0.009
(0.016)
6.518
(8.526)
Included

H6gh-sk6lled
55-64
0.685***
(0.178)
0.105
(0.069)
0.096*
(0.057)
-0.444
(0.308)
-0.017
(0.044)
0.007**
(0.003)
-0.001
(0.001)
-0.011
(0.015)
7.340
(4.493)
Included

15-24
0.747***
(0.125)
0.090*
(0.052)
0.076**
(0.036)
-0.394**
(0.183)
0.026
(0.021)
0.012***
(0.001)
-0.0008
(0.0007)
-0.023**
(0.010)
6.424**
(2.886)
Included

25-54
0.483**
(0.191)
0.215**
(0.085)
0.125**
(0.062)
-0.556*
(0.291)
0.025
(0.048)
0.005
(0.004)
-0.001
(0.001)
-0.016
(0.015)
10.263**
(4.096)
Included

55-64
0.453**
(0.184)
0.224***
(0.082)
0.145***
(0.055)
-0.495***
(0.193)
0.010
(0.028)
0.004*
(0.002)
-0.002*
(0.001)
-0.026*
(0.015)
9.645***
(3.501)
Included

RAIS Conference Proceedings, August 7-8, 2025
Wald test p-value
AR(1) p-value
AR(2) p-value
Hansen test p-value
Number of obs
No. of Gnstruments

0.000***
0.004***
0.420
0.326
1086
33

0.000***
0.023**
0.944
0.402
1086
33

0.000***
0.007***
0.870
0.813
1086
52

0.000***
0.059*
0.189
0.474
1086
53

0.000***
0.182
0.226
0.177
1086
35

0.000***
0.098*
0.150
0.682
1086
53

0.000***
0.004***
0.109
0.407
1086
23

0.000***
0.033**
0.106
0.249
1086
53

0.000***
0.006***
0.263
0.300
1086
33

Notes: Robust standard errors in parentheses. The instrumental variables consist of one- and two-year lags.
*** p < 0.01, ** p < 0.05, * p < 0.1.

Conclusion
The results from our model highlight that artificial intelligence, measured by high-quality AI
publications, has a significant and heterogeneous impact on employment. While AI adoption poses
displacement risks for younger and low-skilled workers, it appears to complement high-skilled
employment, supporting the notion that technological progress generates uneven labor market
outcomes. These findings underscore the need for policies that promote skill upgrading and
adaptability to ensure that the benefits of AI are broadly shared across the workforce.
Acknowledgments
The authors gratefully acknowledge the support of the Scientific and Technological Research
Council of Türkiye (TÜBİTAK) through the 2224-A Grant Program (Application No.
1919B022501379).
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17
</reference>

<statements>
1. The deepening of automation occurs when technological improvements enhance capital efficiency in tasks that have already been fully automated.
2. Crucially, deepening automation generates productivity gains without causing further worker displacement, thereby weakly elevating labor demand across surviving human-centric tasks.
3. The conceptual evolution of technological change in labor economics has advanced through three distinct phases: Skill-Biased Technological Change (SBTC), Routine-Biased Technological Change (RBTC), and generative cognitive equalization.
4. The original SBTC hypothesis posited a monotonic complementarity between new computing technology and tertiary-educated labor, which linearly widened the college-versus-high-school wage premium.
5. Subsequent empirical shifts revealed that computerization did not simply bias demand toward higher skill levels, but rather targeted codifiable, repetitive operations.
6. This RBTC mechanism systematically automated routine cognitive tasks in clerical, administrative, and accounting professions, alongside routine manual tasks in assembly and fabrication.
7. In the theoretical paradigm table, the dominant mechanism for Skill-Biased Technological Change (SBTC) is Monotonic capital-skill complementarity.
8. In the theoretical paradigm table, the targeted worker/task cohorts for Skill-Biased Technological Change (SBTC) complement tertiary education and substitute for basic manual labor.
9. In the theoretical paradigm table, the structural labor market impact of Skill-Biased Technological Change (SBTC) is that it linearly expands the college wage premium and between-group wage inequality.
10. In the theoretical paradigm table, the dominant mechanism for Routine-Biased Technological Change (RBTC) is Algorithmic substitution of codifiable, rule-based operations.
11. In the theoretical paradigm table, the targeted worker/task cohorts for Routine-Biased Technological Change (RBTC) displace middle-skill routine cognitive and fabrication roles.
</statements>

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