Publication: Labor Demand in the Age of Generative AI: Early Evidence from the U.S. Job Posting Data
Loading...
Published
2025-11-18
ISSN
Date
2025-11-19
Editor(s)
Abstract
This paper examines the causal impact of generative artificial intelligence on U.S. labor demand using online job posting data. Exploiting ChatGPT’s release in November 2022 as an exogenous shock, the paper applies difference-in-differences and event study designs to estimate the job displacement effects of generative artificial intelligence. The identification strategy compares labor demand for occupations with high versus low artificial intelligence substitution vulnerability following ChatGPT’s launch, conditioning on similar generative artificial intelligence exposure levels to isolate substitution effects from complementary uses. The analysis uses 285 million job postings collected by Lightcast from the first quarter of 2018 to the second quarter of 2025Q2. The findings show that the number of postings for occupations with above-median artificial intelligence substitution scores fell by an average of 12 percent relative to those with below-median scores. The effect increased from 6 percent in the first year after the launch to 18 percent by the third year. Losses were particularly acute for entry-level positions that require neither advanced degrees (18 percent) nor extensive experience (20 percent), as well as those in administrative support (40 percent) and professional services (30 percent). Although generative artificial intelligence generates new occupations and enhances productivity, which may increase labor demand, early evidence suggests that some occupations may be less likely to be complemented by generative artificial intelligence than others.
Link to Data Set
Citation
“Liu, Yan; Wang, He; Yu, Shu. 2025. Labor Demand in the Age of Generative AI: Early Evidence from the U.S. Job Posting Data. Policy Research Working Paper; 11263. © World Bank. http://hdl.handle.net/10986/44004 License: CC BY 3.0 IGO.”
Digital Object Identifier
Associated URLs
Associated content
Other publications in this report series
Publication The Economic Value of Weather Forecasts: A Quantitative Systematic Literature Review(Washington, DC: World Bank, 2025-09-10)This study systematically reviews the literature that quantifies the economic benefits of weather observations and forecasts in four weather-dependent economic sectors: agriculture, energy, transport, and disaster-risk management. The review covers 175 peer-reviewed journal articles and 15 policy reports. Findings show that the literature is concentrated in high-income countries and most studies use theoretical models, followed by observational and then experimental research designs. Forecast horizons studied, meteorological variables and services, and monetization techniques vary markedly by sector. Estimated benefits even within specific subsectors span several orders of magnitude and broad uncertainty ranges. An econometric meta-analysis suggests that theoretical studies and studies in richer countries tend to report significantly larger values. Barriers that hinder value realization are identified on both the provider and user sides, with inadequate relevance, weak dissemination, and limited ability to act recurring across sectors. Policy reports rely heavily on back-of-the-envelope or recursive benefit-transfer estimates, rather than on the methods and results of the peer-reviewed literature, revealing a science-to-policy gap. These findings suggest substantial socioeconomic potential of hydrometeorological services around the world, but also knowledge gaps that require more valuation studies focusing on low- and middle-income countries, addressing provider- and user-side barriers and employing rigorous empirical valuation methods to complement and validate theoretical models.Publication Direct and Indirect Impacts of Transport Mobility on Access to Jobs: Evidence from South Africa(Washington, DC: World Bank, 2025-11-12)Access to jobs is essential for economic growth. In Africa, unemployment rates are notably high. This paper reexamines the relationship between transport mobility and labor market outcomes, with a particular focus on the direct and indirect effects of transport connectivity. As predicted by theory, wages are influenced by the level of commuting deterrence. Generally, higher earnings are associated with longer commute times and/or higher commuting costs. Local accessibility is also important, especially for individuals with time constraints. Both direct and indirect impacts are found to be significant in South Africa, where job accessibility has been challenging since the end of apartheid. For the direct impact, the wage elasticity associated with commuting costs is significant. Returns on commute are particularly high for women. Local accessibility to socioeconomic facilities, such as shops and health services, is also found to have a significant impact, consistent with the concept of mobility of care. To enhance employment, therefore, it is crucial to connect people not only to job locations but also to various socioeconomic points of interest, such as markets and hospitals, in an integrated manner. This integration will enable individuals to spend more time working and commuting longer distances.Publication The Macroeconomic Implications of Climate Change Impacts and Adaptation Options(Washington, DC: World Bank, 2025-05-29)Estimating the macroeconomic implications of climate change impacts and adaptation options is a topic of intense research. This paper presents a framework in the World Bank's macrostructural model to assess climate-related damages. This approach has been used in many Country Climate and Development Reports, a World Bank diagnostic that identifies priorities to ensure continued development in spite of climate change and climate policy objectives. The methodology captures a set of impact channels through which climate change affects the economy by (1) connecting a set of biophysical models to the macroeconomic model and (2) exploring a set of development and climate scenarios. The paper summarizes the results for five countries, highlighting the sources and magnitudes of their vulnerability --- with estimated gross domestic product losses in 2050 exceeding 10 percent of gross domestic product in some countries and scenarios, although only a small set of impact channels is included. The paper also presents estimates of the macroeconomic gains from sector-level adaptation interventions, considering their upfront costs and avoided climate impacts and finding significant net gross domestic product gains from adaptation opportunities identified in the Country Climate and Development Reports. Finally, the paper discusses the limits of current modeling approaches, and their complementarity with empirical approaches based on historical data series. The integrated modeling approach proposed in this paper can inform policymakers as they make proactive decisions on climate change adaptation and resilience.Publication From Policy to Practice: Lessons from the Implementation of the Refugee Work Rights Policy in Ethiopia(Washington, DC: World Bank, 2025-11-10)This paper examines the early implementation of Ethiopia’s refugee work rights policy, with a focus on the issuance of permits that enable refugees to engage in economic activities. Building on significant legal and institutional advances under the 2019 Refugee Proclamation and subsequent directives, the analysis explores how these reforms are being operationalized in practice. Using a mixed-methods approach, combining document review, administrative data analysis, and semi-structured interviews, the paper identifies both progress and remaining challenges. Permit issuance has increased since the adoption of detailed operational guidance in 2024, reflecting the Government of Ethiopia’s commitment to operationalizing its progressive legal framework and ensuring that refugees can exercise their right to work. However, take-up remains modest, with about 5.2 percent of the working-age population holding a permit. Preliminary evidence suggests that coordination gaps, limited subnational capacity, low awareness among refugees and employers, and disincentives to formalize in a largely informal labor market are contributing to the low take-up. The paper offers policy suggestions, grounded in the Ethiopian context and emerging evidence, to help translate legal commitments into improved labor market outcomes for refugees.Publication Monitoring Global Aid Flows: A Novel Approach Using Large Language Models(Washington, DC: World Bank, 2025-11-04)Effective monitoring of development aid is the foundation for assessing the alignment of flows with their intended development objectives. Existing reporting systems, such as the Organisation for Economic Co-operation and Development’s Creditor Reporting System, provide standardized classification of aid activities but have limitations when it comes to capturing new areas like climate change, digitalization, and other cross-cutting themes. This paper proposes a bottom-up, unsupervised machine learning framework that leverages textual descriptions of aid projects to generate highly granular activity clusters. Using the 2021 Creditor Reporting System data set of nearly 400,000 records, the model produces 841 clusters, which are then grouped into 80 subsectors. These clusters reveal 36 emerging aid areas not tracked in the current Creditor Reporting System taxonomy, allow unpacking of “multi-sectoral” and “sector not specified” classifications, and enable estimation of flows to new themes, including World Bank Global Challenge Programs, International Development Association–20 Special Themes, and Cross-Cutting Issues. Validation against both Creditor Reporting System benchmarks and International Development Association commitment data demonstrates robustness. This approach illustrates how machine learning and the new advances in large language models can enhance the monitoring of global aid flows and inform future improvements in aid classification and reporting. It offers a useful tool that can support more responsive and evidence-based decision-making, helping to better align resources with evolving development priorities.
Journal
Journal Volume
Journal Issue
Collections
Related items
Showing items related by metadata.
Publication The Role of Social Media and User-Generated Content in Post-Conflict Peacebuilding(World Bank, Washington, DC, 2013-06)There is a growing body of practice and literature on the role of information and communication technologies (ICTs) in preventing and responding to violence. There is also a lot of excitement and corresponding literature about the role of the internet in non-violent change and democratization. The use of mobile phones, social networks such as Facebook and Twitter, and user-generated content (UGC) like blogs and YouTube videos in the protests in Tunisia and Egypt, as well as throughout the wider middle-east and North Africa (MENA) region have shown how ICTs can complement and augment the exercise of rights to freedom of expression, freedom of association, and freedom of peaceful assembly. This literature focuses on the use of ICTs before and during conflict, for example in conflict prevention and early warning. What about the use of ICTs in post-conflict situations; after the negotiation of peace agreements? How can ICTs be used in post-conflict interventions; more specifically in post-conflict peacebuilding and post-conflict reconstruction and recovery? What role of can be played here by social media and user-generated content?Publication Who on Earth Is Using Generative AI? Global Trends and Shifts in 2025(Washington, DC: World Bank, 2025-10-15)Nearly three years after ChatGPT’s launch, the generative artificial intelligence landscape remains in rapid flux. Using high-frequency website traffic data from Semrush, this paper tracks global adoption patterns for the 60 most-visited consumer-facing generative artificial intelligence tools through mid-2025. Five key findings emerge. First, fierce competition drives continuous innovation: two of 2025’s top five tools—DeepSeek and Grok—are new entrants, and development is rapidly diversifying into multi-modal capabilities, reasoning, and specialized applications. Second, ChatGPT maintains dominance despite competition, accounting for 77 percent of traffic to the top 60 tools in April 2025. Third, usage of generative artificial intelligence has exploded since mid-2024: ChatGPT traffic grew 113 percent year-over-year, driven by 42 percent user growth and 50 percent increased visits per user, with session duration doubling. Fourth, high-income countries are pulling decisively ahead, creating stark global divides. While 24 percent of internet users in high-income countries use ChatGPT, penetration drops to 5.8 percent in upper-middle-income countries, 4.7 percent in lower-middle-income countries, and just 0.7 percent in low-income countries. Regression analysis confirms that gross domestic product per capita strongly predicts adoption growth. Fifth, localization shapes competitive advantage: non-U.S. tools concentrate heavily in home markets, with Le Chat drawing 69 percent of traffic from Europe and several Chinese tools exceeding 90 percent domestic usage. These patterns reveal an artificial intelligence landscape characterized by intense innovation, persistent market leadership, accelerating growth, and deepening global inequality, underscoring the need for inclusive policies as generative artificial intelligence becomes central to economic participation.Publication International Economic Activities and the Demand for Skilled labor: Evidence from Brazil and China(World Bank, Washington, D.C., 2004-10)Increases in international economic integration can lead to greater specialization according to comparative advantage, but also to the diffusion of skill-biased technologies. In developing countries characterized by relative abundance of unskilled labor, these factors can have opposite effects on the relative demand for skilled labor. This paper investigates the impact of the use of imported inputs, exports and foreign direct investment on the demand for skilled workers of Brazilian and Chinese manufacturing plants. We find that while in Brazil increased levels of international integration are associated with an increased demand for skilled labor, the opposite is true in China.Publication From Technological Catch-up to Innovation : The Future of China’s GDP Growth(World Bank, Washington, DC, 2012-01-02)This report stats that income gaps among countries are largely explained by differences in productivity. By raising the capital/labor ratio and rapidly assimilating technologies across a wide range of activities, China has increased factor productivity manifold since 1980 and joined the ranks of middle income countries. With the launch of the 12th FYP, China has set its sights on becoming a high income country by 2030 through a strategy combining high levels of investment with rapid advances in technology comparable to that of Japan from the 1960s through the 1970s, and Korea s from the 1980s through the end of the century. The report concludes that the best bet is an innovation system anchored to and drawing its energy from a competitive national economy. Technological progress and the flourishing of innovation in China will be the function of a competitive, globally networked ecosystem constructed in two stages during 2011- 2030. Government technology cum competition policies will provide impetus in the first stage, but success will hinge on the quality of the workforce, the initiative and policies of firms, the emergence of supporting services.Publication Do Capital Incentives Distort Technology Diffusion? Evidence on Cloud, Big Data and AI(Washington, DC: World Bank, 2024-09-19)The arrival of cloud computing provides firms a new way to access digital technologies as digital services. Yet, capital incentive policies present in every OECD country are still targeted towards investments in information technology (IT) capital. If cloud services are partial substitutes for IT investments, the presence of capital incentive policies by unintentionally discourage the adoption of cloud and technologies that rely on the cloud, such as artificial intelligence (AI) and big data analytics. This paper exploits a tax incentive in the UK for capital investment as a quasi-natural experiment to examine the impact on firm adoption of loud computing, big data analytics and AI. The empirical results find that the policy increased investment in IT capital as would be expected; but it slowed firm adoption of cloud, big data and AI. Matched employer-employee data shows that the policy also led firms to reduce their demand for workers that perform data analytics, but not other types of workers
Users also downloaded
Showing related downloaded files
No results found.