Joao Guerreiro

We study the immigration policy that maximizes the welfare of the native population in an economy where the government designs an optimal redistributive welfare system and supplies public goods. We show that when the government can design different tax systems for immigrants and natives, free immigration is optimal. It is also optimal to use the tax system to encourage the immigration of high-skill workers and discourage that of low-skill workers. When immigrants and natives must be treated alike, banning low-skill immigration and allowing free immigration for high-skill workers is optimal. However, there might be no high-skill immigration when heavy taxes are levied on all high-skill workers, both natives and immigrants.

Published: Joao Guerreiro & Sergio Rebelo & Pedro Teles, 2020. "What Is the Optimal Immigration Policy? Migration, Jobs, and Welfare," Journal of Monetary Economics, .

Using a quantitative model that features technical progress in automation and endogenous skill choice, we show that, given the current U.S. tax system, a sustained fall in automation costs can lead to a massive rise in income inequality. We characterize the optimal tax system in this model. We find that it is optimal to tax robots while the current generations of routine workers, who can no longer move to non-routine occupations, are active in the labor force. Once these workers retire, optimal robot taxes are zero.