# What is the estimated impact of AI on employment?

## AI's impact on employment is mixed 
- Studies show negative effects on low-skill jobs but positive effects on high-skill positions.
- Among 40 studies, 20 found positive impacts, 15 negative, and 11 neutral.

## Abstract

Artificial intelligence exhibits a heterogeneous impact on employment. 
- Several studies report declines in low‑skill and routine roles.
  - One analysis notes a 21% drop in job posts for writing and coding tasks.
  - Another finds a 2% decline in freelance jobs and a 5.2% reduction in earnings.
- Other work documents gains among high-skill workers.
  - Estimates include a 2.6–4.3% increase in high-skill job shares.  
  - The complementary effect can be 50% larger than substitution effects.
  - Some papers report no aggregate change as sectoral shifts offset one another.
- Short-term displacement may give way to longer-term job transformation.

Overall, among 40 studies, the direction of AI’s impact is:
- Positive in 20 studies,
- Negative in 15,
- Neutral in 11.
- Effects vary by worker type, industry, and region.

## Methods

- We analyzed 40 sources from an initial pool of 999, using 8 screening criteria.
- Each paper was reviewed for 5 key aspects that mattered most to the research question.

## AI Exposure Measurement Method

- **Specific metrics or indicators used:**
  - AI occupational impact measure,
  - Robotics installation rates,
  - Task-based AI compatibility assessment.
- **How AI exposure was quantified:**
  - Using models such as two-way fixed-effect model and econometric modeling.

## Key Employment Outcomes

- Listed primary employment-related outcomes examined:
  - Employment growth,
  - Job numbers,
  - Hours worked,
  - Skill composition changes,
  - Wage impacts.

### Synthesis and Limitations

- Evidence base: Methodologically diverse with most studies using quantitative methods.
- Heterogeneity: Wide variation in context, measurement approaches, and outcomes complicates comparison.
- Measurement: Reliance on proxies (e.g., patents, job postings) may inadequately capture complexity.

## References
- G. Damioli, et al. (2022). AI technologies and employment: micro evidence from the supply side. [Source](/content/review/20172d4d-4587-4a13-b797-8673475b57a9/source/50db688ffc8c4f1c8d772b00f7abfc93/index.html)
- Daron Acemoglu, et al. (2020). AI and Jobs: Evidence from Online Vacancies. [Source](/content/review/20172d4d-4587-4a13-b797-8673475b57a9/source/696e51d1c992425d850cee5a9457c22c/index.html)
- A. Georgieff, Raphaela Hyee (2022). Artificial Intelligence and Employment: New Cross-Country Evidence. [Source](/content/review/20172d4d-4587-4a13-b797-8673475b57a9/source/b94628ebf7d6450daaedb6c2a3193169/index.html)

## Results

### Characteristics of Included Studies

| Study | Study Focus | Methodology | Sample/Data | Time Period | Full text retrieved |  
|-------|-------------|-------------|-------------|--------------|--------------------|  
| Demirci et al., 2025 | Impact of generative AI on online freelancing platforms | Quantitative time series analysis | Global freelancing platform, Google Trends | 8 months post-ChatGPT | No |
| Damioli et al., 2022 | AI technologies and employment (supply side) | Panel data, system Generalized Method of Moments (GMM) | 3,500+ global AI-patenting firms | 2000–2016 | No |
| Acemoglu and Restrepo, 2020 | AI and jobs in US online vacancies | Establishment-level panel data, econometric modeling | US online vacancies (Burning Glass) | 2010–2018 | Yes |
| Georgieff and Hyee, 2022 | AI and employment, cross-country | Cross-country econometric analysis, fixed effects | 23 OECD countries, labor force surveys | 2012–2019 | Yes |

### Overall Employment Effects

| Study | Effect Direction | Effect Size | Statistical Significance |  
|-------|------------------|-------------|-------------------------|  
| Demirci et al., 2025 | Negative | 21% decrease in job posts (writing/coding) | No mention found |
| Damioli et al., 2022 | Positive | Significant positive impact of AI patents on employment | Significant |
| Acemoglu and Restrepo, 2020 | Negative (establishment), Neutral (aggregate) | Reduced non-AI hiring | No mention found |
| Georgieff and Hyee, 2022 | Neutral | No clear relationship; positive in high computer use jobs | No mention found |

### Differential Effects by Worker Type

- Low-skilled and routine workers are disproportionately negatively affected.
- High-skilled and STEM workers often benefit from AI.
- Gender effects: nuances are present for low-skilled female employment and threshold effects for women with disabilities.

### Sectoral and Firm-Level Variations

- Service sector usually sees positive or neutral effects.
- Industrial/manufacturing sectors are more likely to experience job losses due to automation.