New IZA DPs -- AI & Labor Markets
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IZA DP Alerts
Jun 1, 2026, 9:09:22 AM (13 days ago) Jun 1
to it...@umich.edu
New IZA Discussion Papers brought to you by IZA@LISER
Dear Italo Gutierrez,
These new IZA Discussion Papers are now available online.
DP 18513 - Gallegos:
Guidance Over Adoption: Experimental Evidence on AI-Assisted Learning
DP 18515 - Pulito/Pytlikova/Schroeder/Lodefalk:
Who Adopts AI? Evidence on Firms, Technologies and Workers
DP 18517 - Irlenbusch/Rau/Rilke:
Human–AI Evaluation and Gender Transparency: Application Decisions in Competitive Hiring
DP 18521 - Bick/Blandin/Deming/Fuchs-Schündeln/Jessen:
Mind the Gap: AI Adoption in Europe and the US
DP 18522 - Milovanska-Farrington/Tomberlin:
AI-Enhanced Test Preparation and Student Performance: Evidence from an Introductory Economics Class
DP 18540 - Bryson/Kauhanen/Rouvinen:
AI and Worker Well-Being: Evidence from a Nationally Representative Study
DP 18545 - Polachek/Romano/Tonguc:
Strategic Reasoning and Sensitivity to Stakes in the Dictator and Ultimatum Games: LLMs vs. Human Proposers
DP 18565 - Freund/Mann:
Job Transformation, Specialization, and the Labor Market Effects of AI
DP 18574 - Fahn/Li/Sun:
Toward a Bad Job Economy: AI Adoption, Agency Costs, and Job Design
Please find the abstracts and download links below.
IZA DP No. 18513
Sebastian Gallegos:
Guidance Over Adoption: Experimental Evidence on AI-Assisted Learning
Abstract:
This paper estimates the causal effect of a large language model–based study assistant on student behavior and learning outcomes in a natural field setting with real academic stakes. I design and deploy a course-specific AI assistant (GPT-UAI) for undergraduate econometrics and evaluate it through two randomized interventions implemented across seven coordinated course sections at a selective university in Chile. The first intervention targets the extensive margin of use, encouraging GPT-UAI adoption prior to the midterm exam. The encouragement raises the GPT’s awareness and reported usage, but does not change its perceived value and does not improve midterm performance. The second intervention targets use at the intensive margin, providing guidance on learning-oriented usage for the final exam. Guidance shifts interactions with GPT-UAI toward tutor-style engagement, increases perceived usefulness by 0.38 standard deviations, improves final-exam performance by 0.21 standard d eviations, and raises the probability of earning a passing exam grade by 12 percentage points. The findings suggest that learning gains arise less from adoption than from guiding how students use course-specific AI assistants.
https://docs.iza.org/dp18513.pdf
IZA DP No. 18515
Giuseppe Pulito, Mariola Pytlikova, Sarah Schroeder, Magnus Lodefalk:
Who Adopts AI? Evidence on Firms, Technologies and Workers
Abstract:
Using surveys of Danish firms and individuals linked to employer–employee administrative data, we analyze AI adoption across technologies, business functions, and workers. We show that AI adoption is driven primarily by firm capacities rather than performance. Adoption is strongly associated with firm size, digital infrastructure, and workforce composition, particularly education and STEM intensity, while productivity and capital intensity explain little of the variation. Conditional on AI adoption, larger and more digitally mature firms deploy advanced technologies more broadly. Moreover, AI technologies diffuse across multiple business functions while other advanced technologies remain function-specific. Individual-level evidence mirrors these patterns and points towards workforce readiness as a key determinant of AI adoption. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict actual adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it.
https://docs.iza.org/dp18515.pdf
IZA DP No. 18517
Bernd Irlenbusch, Holger A. Rau, Rainer Michael Rilke:
Human–AI Evaluation and Gender Transparency: Application Decisions in Competitive Hiring
Abstract:
We study how human versus LLM-based evaluation and gender transparency shape entry into competitive jobs. In a preregistered online experiment, participants first complete a Niederle and Vesterlund (2007) tournament task to measure competitive preferences, then prepare text-based job applications and decide whether to apply under each of four evaluation regimes—human only, LLM only, and two hybrid human-in-the-loop configurations—while gender disclosure is randomized between subjects. LLM involvement reduces application rates, with stronger effects for women than men, including under hybrid designs. Effects are driven by non-competitive candidates; non-competitive women, the group most exposed to AI-induced deterrence, receive the strongest objective evaluations under pure AI assessment across all subgroups, yet are systematically underconfident and apply least often. Competitive men persistently apply and exhibit overconfidence-driven adverse selection, whereas competitive women show resilience to AI-induced deterrence while remaining well-calibrated under AI evaluation and exhibiting positive self-selection across regimes. We find no effects of gender transparency.
https://docs.iza.org/dp18517.pdf
IZA DP No. 18521
Alexander Bick, Adam Blandin, David Deming, Nicola Fuchs-Schündeln, Jonas Jessen:
Mind the Gap: AI Adoption in Europe and the US
Abstract:
This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI.
https://docs.iza.org/dp18521.pdf
IZA DP No. 18522
Stefani Milovanska-Farrington, Caleb Tomberlin:
AI-Enhanced Test Preparation and Student Performance: Evidence from an Introductory Economics Class
Abstract:
The emergence of artificial intelligence (AI) tools has offered new ways of teaching and learning. Businesses have also highlighted the importance of AI literacy in the workplace as AI is transforming operations and entire industries. Given the importance of obtaining AI skills and the opportunities it provides, it is useful for students to gain experience interacting with the emerging technology. Yet, the optimal ways to incorporate AI in each class so that students’ knowledge acquisition in coursework does not suffer are still unclear. This paper examines the causal effect of AI-enhanced test preparation on student performance in an economics class. In a difference-in-differences framework, we compare the changes in students’ test scores after relative to before utilizing AI to enhance learning between students who completed a guided AI assignment to prepare and those who did not. The findings provide evidence that learning through AI does not necessarily improve students’ performance on formal exams. This does not mean that students should not learn how to use AI tools, but rather that they may not prepare for exams in all courses while simultaneously improving their AI skill set.
https://docs.iza.org/dp18522.pdf
IZA DP No. 18540
Alex Bryson, Antti Kauhanen, Petri Rouvinen:
AI and Worker Well-Being: Evidence from a Nationally Representative Study
Abstract:
Utilizing nationally representative cross-sectional and longitudinal data from Finland (2018–2023), we provide a population-level assessment of the relationship between AI and worker well-being. Contrary to international evidence suggesting a positive or an inverted U-shaped relationship, we find no systematic association between AI use intensity and job satisfaction. However, we do find that work engagement is higher among employees who are personally involved with AI, with the strongest association among intensive users for whom AI is an essential part of their work. Furthermore, technology-replacement fears have remained stable despite rapid AI advancement and do not predict subsequent labour market transitions. An interpretation is that Finland’s high-trust institutional environment and robust social safety nets may effectively moderate the disruptive psychological and economic shocks typically associated with rapid technological change.
https://docs.iza.org/dp18540.pdf
IZA DP No. 18545
Solomon Polachek, Kenneth Romano, Ozlem Tonguc:
Strategic Reasoning and Sensitivity to Stakes in the Dictator and Ultimatum Games: LLMs vs. Human Proposers
Abstract:
This study examines how large language models (LLMs) respond to varying stake sizes in the Dictator and Ultimatum games using the high-stakes design introduced by Andersen et al. (2011). We test ten leading LLMs chosen for their accessibility, prominence, and differences in reasoning capabilities. Results reveal substantial variation across models: Only 5 of 10 models exhibit strategic behavior by offering more in the Ultimatum Game (UG) than in the Dictator Game (DG). Relative to humans, 4 models are consistently more generous, 2 consistently less, and 4 vary with stake size. Only 1 model shows a monotonic decline in UG offers as stakes increase; the remaining 9 are non-monotonic or stable. Unlike humans, most models reduce UG offers when endowed with wealth. Prompting for “human-like” decisions generally increases generosity in the UG. These findings are important for evaluating whether LLMs can serve as realistic proxies for human subjects in behavioral experiments and highlight key limitations and future directions for model development.
https://docs.iza.org/dp18545.pdf
IZA DP No. 18565
Lukas Freund, Lukas Mann:
Job Transformation, Specialization, and the Labor Market Effects of AI
Abstract:
A central effect of automation is to transform jobs - shifting their task content. We develop a general-equilibrium model of this process. Occupations bundle tasks; workers possess task-specific skills and sort by comparative advantage. When a task is automated, remaining tasks gain in importance, so wage effects depend on workers’ full skill profiles. We estimate the distribution of task-specific skills and project individual-level wage effects of generative AI automation. Moderate exposure benefits workers on average but high exposure harms them, with large dispersion within occupations; the return to social skills rises, that to analytical skills falls; and low-earners gain more than high-earners. Job transformation drives these results.
https://docs.iza.org/dp18565.pdf
IZA DP No. 18574
Matthias Fahn, Jin Li, Chang Sun:
Toward a Bad Job Economy: AI Adoption, Agency Costs, and Job Design
Abstract:
We study how AI affects compensation and job design when performance depends on workers’ non-contractible effort. In a principal–agent model with limited liability, AI reduces effort costs but disproportionately lowers the cost of achieving satisfactory performance. This raises the incentive cost of sustaining high effort and can induce firms to replace high-wage, high-effort good jobs with low-wage, low-effort bad jobs, even when good jobs create more total surplus. As a result, AI can lower wages, reduce worker welfare, and even depress profits. If workers can adopt AI unilaterally, adoption occurs even when the resulting equilibrium harms both parties; when adoption requires worker cooperation, resistance is strongest where AI erodes rents embodied in good jobs. In a search-and-matching extension, endogenous outside options amplify these forces, reinforcing a bad-job economy and potentially reducing employment.
https://docs.iza.org/dp18574.pdf
These new IZA Discussion Papers are now available online.
DP 18513 - Gallegos:
Guidance Over Adoption: Experimental Evidence on AI-Assisted Learning
DP 18515 - Pulito/Pytlikova/Schroeder/Lodefalk:
Who Adopts AI? Evidence on Firms, Technologies and Workers
DP 18517 - Irlenbusch/Rau/Rilke:
Human–AI Evaluation and Gender Transparency: Application Decisions in Competitive Hiring
DP 18521 - Bick/Blandin/Deming/Fuchs-Schündeln/Jessen:
Mind the Gap: AI Adoption in Europe and the US
DP 18522 - Milovanska-Farrington/Tomberlin:
AI-Enhanced Test Preparation and Student Performance: Evidence from an Introductory Economics Class
DP 18540 - Bryson/Kauhanen/Rouvinen:
AI and Worker Well-Being: Evidence from a Nationally Representative Study
DP 18545 - Polachek/Romano/Tonguc:
Strategic Reasoning and Sensitivity to Stakes in the Dictator and Ultimatum Games: LLMs vs. Human Proposers
DP 18565 - Freund/Mann:
Job Transformation, Specialization, and the Labor Market Effects of AI
DP 18574 - Fahn/Li/Sun:
Toward a Bad Job Economy: AI Adoption, Agency Costs, and Job Design
Please find the abstracts and download links below.
IZA DP No. 18513
Sebastian Gallegos:
Guidance Over Adoption: Experimental Evidence on AI-Assisted Learning
Abstract:
This paper estimates the causal effect of a large language model–based study assistant on student behavior and learning outcomes in a natural field setting with real academic stakes. I design and deploy a course-specific AI assistant (GPT-UAI) for undergraduate econometrics and evaluate it through two randomized interventions implemented across seven coordinated course sections at a selective university in Chile. The first intervention targets the extensive margin of use, encouraging GPT-UAI adoption prior to the midterm exam. The encouragement raises the GPT’s awareness and reported usage, but does not change its perceived value and does not improve midterm performance. The second intervention targets use at the intensive margin, providing guidance on learning-oriented usage for the final exam. Guidance shifts interactions with GPT-UAI toward tutor-style engagement, increases perceived usefulness by 0.38 standard deviations, improves final-exam performance by 0.21 standard d eviations, and raises the probability of earning a passing exam grade by 12 percentage points. The findings suggest that learning gains arise less from adoption than from guiding how students use course-specific AI assistants.
https://docs.iza.org/dp18513.pdf
IZA DP No. 18515
Giuseppe Pulito, Mariola Pytlikova, Sarah Schroeder, Magnus Lodefalk:
Who Adopts AI? Evidence on Firms, Technologies and Workers
Abstract:
Using surveys of Danish firms and individuals linked to employer–employee administrative data, we analyze AI adoption across technologies, business functions, and workers. We show that AI adoption is driven primarily by firm capacities rather than performance. Adoption is strongly associated with firm size, digital infrastructure, and workforce composition, particularly education and STEM intensity, while productivity and capital intensity explain little of the variation. Conditional on AI adoption, larger and more digitally mature firms deploy advanced technologies more broadly. Moreover, AI technologies diffuse across multiple business functions while other advanced technologies remain function-specific. Individual-level evidence mirrors these patterns and points towards workforce readiness as a key determinant of AI adoption. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict actual adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it.
https://docs.iza.org/dp18515.pdf
IZA DP No. 18517
Bernd Irlenbusch, Holger A. Rau, Rainer Michael Rilke:
Human–AI Evaluation and Gender Transparency: Application Decisions in Competitive Hiring
Abstract:
We study how human versus LLM-based evaluation and gender transparency shape entry into competitive jobs. In a preregistered online experiment, participants first complete a Niederle and Vesterlund (2007) tournament task to measure competitive preferences, then prepare text-based job applications and decide whether to apply under each of four evaluation regimes—human only, LLM only, and two hybrid human-in-the-loop configurations—while gender disclosure is randomized between subjects. LLM involvement reduces application rates, with stronger effects for women than men, including under hybrid designs. Effects are driven by non-competitive candidates; non-competitive women, the group most exposed to AI-induced deterrence, receive the strongest objective evaluations under pure AI assessment across all subgroups, yet are systematically underconfident and apply least often. Competitive men persistently apply and exhibit overconfidence-driven adverse selection, whereas competitive women show resilience to AI-induced deterrence while remaining well-calibrated under AI evaluation and exhibiting positive self-selection across regimes. We find no effects of gender transparency.
https://docs.iza.org/dp18517.pdf
IZA DP No. 18521
Alexander Bick, Adam Blandin, David Deming, Nicola Fuchs-Schündeln, Jonas Jessen:
Mind the Gap: AI Adoption in Europe and the US
Abstract:
This paper combines international evidence from worker and firm surveys conducted in 2025 and 2026 to document large gaps in AI adoption, both between the US and Europe and across European countries. Cross-country differences in worker demographics and firm composition account for an important share of these gaps. AI adoption, within and across countries, is also closely linked to firm personnel management practices and whether firms actively encourage AI use by workers. Micro-level evidence suggests that AI generates meaningful time savings for many workers. At the macro level, in recent years industries with higher AI adoption rates have experienced faster productivity growth. While we do not establish causality, this relationship is statistically significant and similar in magnitude in Europe and the US. We do not find clear evidence that industry-level AI adoption is associated with employment changes. We discuss limitations of existing data and outline priorities for future data collection to better assess the productivity and labor market effects of AI.
https://docs.iza.org/dp18521.pdf
IZA DP No. 18522
Stefani Milovanska-Farrington, Caleb Tomberlin:
AI-Enhanced Test Preparation and Student Performance: Evidence from an Introductory Economics Class
Abstract:
The emergence of artificial intelligence (AI) tools has offered new ways of teaching and learning. Businesses have also highlighted the importance of AI literacy in the workplace as AI is transforming operations and entire industries. Given the importance of obtaining AI skills and the opportunities it provides, it is useful for students to gain experience interacting with the emerging technology. Yet, the optimal ways to incorporate AI in each class so that students’ knowledge acquisition in coursework does not suffer are still unclear. This paper examines the causal effect of AI-enhanced test preparation on student performance in an economics class. In a difference-in-differences framework, we compare the changes in students’ test scores after relative to before utilizing AI to enhance learning between students who completed a guided AI assignment to prepare and those who did not. The findings provide evidence that learning through AI does not necessarily improve students’ performance on formal exams. This does not mean that students should not learn how to use AI tools, but rather that they may not prepare for exams in all courses while simultaneously improving their AI skill set.
https://docs.iza.org/dp18522.pdf
IZA DP No. 18540
Alex Bryson, Antti Kauhanen, Petri Rouvinen:
AI and Worker Well-Being: Evidence from a Nationally Representative Study
Abstract:
Utilizing nationally representative cross-sectional and longitudinal data from Finland (2018–2023), we provide a population-level assessment of the relationship between AI and worker well-being. Contrary to international evidence suggesting a positive or an inverted U-shaped relationship, we find no systematic association between AI use intensity and job satisfaction. However, we do find that work engagement is higher among employees who are personally involved with AI, with the strongest association among intensive users for whom AI is an essential part of their work. Furthermore, technology-replacement fears have remained stable despite rapid AI advancement and do not predict subsequent labour market transitions. An interpretation is that Finland’s high-trust institutional environment and robust social safety nets may effectively moderate the disruptive psychological and economic shocks typically associated with rapid technological change.
https://docs.iza.org/dp18540.pdf
IZA DP No. 18545
Solomon Polachek, Kenneth Romano, Ozlem Tonguc:
Strategic Reasoning and Sensitivity to Stakes in the Dictator and Ultimatum Games: LLMs vs. Human Proposers
Abstract:
This study examines how large language models (LLMs) respond to varying stake sizes in the Dictator and Ultimatum games using the high-stakes design introduced by Andersen et al. (2011). We test ten leading LLMs chosen for their accessibility, prominence, and differences in reasoning capabilities. Results reveal substantial variation across models: Only 5 of 10 models exhibit strategic behavior by offering more in the Ultimatum Game (UG) than in the Dictator Game (DG). Relative to humans, 4 models are consistently more generous, 2 consistently less, and 4 vary with stake size. Only 1 model shows a monotonic decline in UG offers as stakes increase; the remaining 9 are non-monotonic or stable. Unlike humans, most models reduce UG offers when endowed with wealth. Prompting for “human-like” decisions generally increases generosity in the UG. These findings are important for evaluating whether LLMs can serve as realistic proxies for human subjects in behavioral experiments and highlight key limitations and future directions for model development.
https://docs.iza.org/dp18545.pdf
IZA DP No. 18565
Lukas Freund, Lukas Mann:
Job Transformation, Specialization, and the Labor Market Effects of AI
Abstract:
A central effect of automation is to transform jobs - shifting their task content. We develop a general-equilibrium model of this process. Occupations bundle tasks; workers possess task-specific skills and sort by comparative advantage. When a task is automated, remaining tasks gain in importance, so wage effects depend on workers’ full skill profiles. We estimate the distribution of task-specific skills and project individual-level wage effects of generative AI automation. Moderate exposure benefits workers on average but high exposure harms them, with large dispersion within occupations; the return to social skills rises, that to analytical skills falls; and low-earners gain more than high-earners. Job transformation drives these results.
https://docs.iza.org/dp18565.pdf
IZA DP No. 18574
Matthias Fahn, Jin Li, Chang Sun:
Toward a Bad Job Economy: AI Adoption, Agency Costs, and Job Design
Abstract:
We study how AI affects compensation and job design when performance depends on workers’ non-contractible effort. In a principal–agent model with limited liability, AI reduces effort costs but disproportionately lowers the cost of achieving satisfactory performance. This raises the incentive cost of sustaining high effort and can induce firms to replace high-wage, high-effort good jobs with low-wage, low-effort bad jobs, even when good jobs create more total surplus. As a result, AI can lower wages, reduce worker welfare, and even depress profits. If workers can adopt AI unilaterally, adoption occurs even when the resulting equilibrium harms both parties; when adoption requires worker cooperation, resistance is strongest where AI erodes rents embodied in good jobs. In a search-and-matching extension, endogenous outside options amplify these forces, reinforcing a bad-job economy and potentially reducing employment.
https://docs.iza.org/dp18574.pdf
IZA DP Alerts | www.iza.org
If you have trouble downloading the papers, or for any other questions regarding the IZA Discussion Paper Series, please contact iza...@liser.lu.
IZA@LISER Network
Luxembourg Institute of Socio-Economic Research (LISER)
11, Porte des Sciences, L-4366 Esch-sur-Alzette / Belval
Privacy Policy | Manage your subscriptions
If you have trouble downloading the papers, or for any other questions regarding the IZA Discussion Paper Series, please contact iza...@liser.lu.
IZA@LISER Network
Luxembourg Institute of Socio-Economic Research (LISER)
11, Porte des Sciences, L-4366 Esch-sur-Alzette / Belval
Privacy Policy | Manage your subscriptions
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