I study the optimal taxation of robots, other capital, and labor income. I show that it is optimal to distort robot adoption. The robot tax (or subsidy) exploits general-equilibrium effects to compress wages, which reduces income-tax distortions of labor supply, thereby raising welfare. In the calibrated model, when robots are expensive, a robot subsidy is optimal. As robots get cheaper, it becomes optimal to tax them. Yet, when reforming the status-quo tax system, most welfare gains can be achieved by adjusting the income tax. The additional gains from taxing robots differently than other equipment capital are close to zero.
This paper studies how linear tax and education policy should optimally respond to skill-biased technical change (SBTC). SBTC affects optimal taxes and subsidies by changing (1) direct distributional benefits of each policy instrument, (2) indirect, general-equilibrium effects on wages, and (3) education distortions. Analytically, the effect of SBTC on these three components is shown to be ambiguous. In simulations for the US economy, SBTC makes the optimal tax system more progressive and lowers optimal education subsidies. This is because for both income taxes and education subsidies; their direct distributional effects become more important, which more than offsets the larger general-equilibrium effects and increased education distortions.