GTAP Resources: Resource Display
| GTAP Resource #7737 |
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"Generative AI and Occupational Change in Australia: A Task-Based CGE Analysis" by Lennox, James and Janine Dixon Abstract This paper analyses the long-run labour-market and macroeconomic implications of generative AI (GenAI) using a task- and occupation-based computable general equilibrium (CGE) model of the Australian economy. Unlike partial-equilibrium task-based approaches or CGE studies representing automation as coarse labour–capital substitution, our framework models augmentation and automation as endogenous, competing task technologies and embeds this mechanism in a 45-industry, 97-occupation general-equilibrium structure. We calibrate the model using separate occupational exposure scores for augmentation and automation potential, and report results from a reference simulation in which GenAI is fully and broadly adopted. Real GDP rises by 29.8%, but welfare rises by only 16.2%, because the new equilibrium is substantially more capital intensive—investment’s share of GDP rises from 34.5% to 40.7%. Of the GDP gain, roughly one third is attributable to direct task-level productivity improvements; the remainder reflects capital deepening and general-equilibrium reallocation. The dominant labour-market effect is a reconfiguration of task composition within occupations rather than large-scale displacement between them: approximately 46.7% of tasks are augmented and 9.1% automated, affecting roughly two thirds of all work. Net occupational employment shifts are moderate, with losses concentrated in lower-skilled white-collar roles. The broad patterns of these results are robust to alternative assumptions about the level of global export demand. |
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- Technological change - Model extension/development - Oceania |
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Public Access  GTAP Resource 7737 (1.9 MB) Replicated: 0 time(s)Restricted Access No documents have been attached. Special Instructions No instructions have been specified. |
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Last Modified: 4/13/2026 1:32:49 PM
