Back in July, Treasury released an analytical note written by Harry Nicholls and Udayan Mukherjee on the implications of artificial intelligence for New Zealand. I read it last month, but didn't have time to write up my comments until now due to travelling. The analytical note is not a quantitative assessment on the economic or labour market or other impacts, but really just testing the waters and providing some broad overview of some of the issues. Fortunately (or unfortunately, depending on your perspective), this hasn't been superseded by subsequent quantitative analysis of the impacts, so it still provides a starting point for some discussion.
Nicholls and Mukherjee see three main issues: (1) the impacts of AI on productivity and investment; (2) the impacts on employment and the labour market; and (3) the development of regulatory approaches for AI. It may be my bias showing, but I think they miss the impact of AI on education and human capital development as a fourth main issue, but perhaps they see it as being subsumed under their first main issue (although they don't mention education at all, and mention human capital only once in the note).
The note discusses each of the three main issues in turn, but first it lays out frameworks for thinking through each issue. There are two (competing) approaches. The first is based on macroeconomic growth accounting framework, where:
AI can be thought of as a form of capital deepening, improving the quantity and quality of capital inputs. It can also be thought of as labour-augmenting technology that improves the quality of labour inputs. And it might be a form of technology that more effectively combines capital and labour, improving multi-factor productivity.
The second is a microeconomic approach, which sees:
generative AI as a form of workplace automation...
There are three types of effects [Acemoglu and Restrepo] identify in this framework:
• Displacement Effect: Automation makes it more efficient to produce some tasks by capital that were previously done by labour, so it reduces the share of labour in production.
• Reinstatement Effect: Automation can allow a more flexible allocation of tasks in production, and so can create a range of new tasks in which labour has a comparative advantage.
• Productivity Effect: Automation allows some of the tasks previously performed by labour to now be performed more cheaply by capital, and so increases the value-added in production and so potentially the overall demand for labour.
The benefit of this framework is that it helps to think through how the net impact of new automation technologies can be thought about as the result of the combination of the strength of these effects.
For example, the total effect of AI on labour demand is a combination of the Productivity Effect increasing the demand for labour and the Displacement Effect replacing labour from tasks it previously performed. Similarly, the impact of AI on wages could be thought of as the net change on marginal productivity from the Reinstatement Effect creating new tasks for labour demand and the Displacement Effect create new labour supply from replaced tasks.
I found the frameworks quite useful, but it is difficult to see how they can be reconciled (which, to be fair, is a common problem comparing macroeconomic and microeconomic frameworks). Nicholls and Mukherjee then turn to thinking through the three issues, but the frameworks seem to be a bit lost in those sections. However, they conclude by offering some potential areas for future research, which are:
• A deeper exploration of the policy levers that might accelerate the diffusion of AI, reducing the lag that characterises New Zealand’s diffusion of new technology.
• Investigating the likely impacts of AI on the productivity and competitiveness of particular sectors, or small-to-medium sized enterprises (SMEs), given their significance to the New Zealand economy.
• Taking a closer look at the implications of AI for employment and labour markets, with a particular focus on how these impacts should inform our policy settings around skills, immigration, and labour markets.
• Examining how AI intersects with our economic security, particularly if AI development is concentrated in a small number of large multinational overseas-based technology companies.
• Considering how AI could lift the productivity of New Zealand’s public sector, in order to maintain or enhance service levels in the face of pressures like an aging population.
Again, I would add the impacts on education and human capital development here, as well as research on the distributional consequences of AI. Those questions are important to understanding the future labour market impacts, as well as the need for changes to policy settings on taxes and transfers. Anyway, this is a good starting point as a think piece, and it is good to see that Treasury are actively thinking in this space, and hopefully there is more careful analytical work to come from them.
[HT: Inside Government, back in July]
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