Using the production function suggested by Jones and Manuelli (1990), this article explores the consequences of introducing automation and artificial intelligence (A&AI) into a trade theoretic framework. An immediate implication is the possibility of a reversal of the trade patterns predicted by standard Heckscher–Ohlin theory, leading to Leontief paradox-type outcomes. We show that the Jones–Manuelli production function is capable of generating factor intensity reversals; consequently, our analysis suggests that factor intensity reversals may have a more prominent role to play in trade theory in the future when AI becomes prevalent.
AcemogluD., & RestrepoP. (2018). The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review, 108, 1488–1542.
2.
AcemogluD., & AutorD. (2011). Skills, tasks and technologies: Implications for employment and earnings. In AshenfelterO. & CardD. (Eds), Handbook of labor economics (Vol. 4, pp. 1043–1171). Elsevier-North Holland.
3.
AcemogluD., & RestrepoP. (2019). Artificial intelligence, automation and work. In AgarwalA., GansJ. & GoldfarbA. (Ed), The economics of artificial intelligence: An agenda (pp. 197–236). The University of Chicago Press.
4.
AghionP., JonesP. E., & JonesC. I. (2017). Artificial intelligence and economic growth(NBER Working Paper). https://www.nber.org/papers/w23928
5.
ArrowK. J., CheneryH. B., MinhasB. S., & SolowR. M. (1961). Capital-labor substitution and economic efficiency. Review of Economics and Statistics, 43, 225–250.
6.
ArtucE., BastosP., & RijkersB. (2018). Robots, tasks and trade(World Bank Policy Research Working Paper No 8674). https://openknowledge.worldbank.org/handle/10986/31076
7.
AutorD. H. (2013). The ‘task approach’ to labor markets: An overview. Journal for Labor Market Research, 46, 185–199.
8.
BrynjolfssonE., & McAfeeA. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
9.
ChakrabartiA. (2004). Asymmetric adjustment costs in simple general equilibrium models. European Economic Review, 48, 63–73.
10.
GoldfarbA., & TreflerD. (2018). AI and international trade(NBER Working Paper Series, #24254). https://www.nber.org/papers/w24254
11.
JonesL., & ManuelliR. (1990). A convex model of equilibrium growth: Theory and policy implications. The Journal of Political Economy, 98, 1008–1038.
12.
JonesR. W. (1965). The structure of simple general equilibrium models. The Journal of Political Economy, 73, 557–572.
13.
JonesR. W., & MarjitS. (1985). A simple production model with Stolper–Samuelson properties. International Economic Review, 26, 565–567.
14.
MarjitS. (1987). Trade in intermediates and the colonial pattern of trade. Economica, 54(214), 173–184.
15.
MeltzerJ. P. (2018). The impact of artificial intelligence for international trade (Brookings Institution Report). https://www.brookings.edu/research/the-impact-of-artificial-intelligence-on-international-trade/
16.
MuroM., MaximR., & WhitonJ. (2019). Automation and artificial intelligence: How machines are affection people and places. Brookings.
17.
World Trade Organization. (2018). The future of world trade: How digital technologies are transforming global trade. https://www.wto.org/english/res_e/publications_e/world_trade_report18_e.pdf
18.
ZeiraJ. (1998). Workers, machines, and economic growth. Quarterly Journal of Economics, 113, 1091–1117.
