Of Machines and Men: Optimal Redistributive Policies under Technological Change

This dissertation consists of three studies in public economics. It investigates how governments should optimally design redistributive policies in light of technology-driven inequality. Chapters 2 and 3 analyze how in an economy in which inequality is driven by skill-biased technical change (SBTC), a welfare-maximizing government should optimally set education and tax policies. Income taxes should become more progressive with technical change. Whether education should be subsidized more or less depends on the interplay of distributional benefits and distortions and on the availability of tax instruments. With non-linear education-dependent income taxes, we find that education is subsidized more as inequality rises. With a linear income tax and education subsidy, we find that the subsidy rate falls with SBTC. Chapter 4 studies the optimal taxation of robots and labor income. In the model, robots substitute for routine labor and complement non-routine labor. It is shown that while it is optimal to distort the use of robots, robots may be either taxed or subsidized. The robot tax exploits general-equilibrium effects to compress the wage distribution. Wage compression reduces income-tax distortions of labor supply, thereby raising welfare. Quantitatively, for the US, the optimal robot tax is positive, but its welfare impact is negligible.

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