This paper extends the Mirrlees (1971) model of optimal non-linear income taxation with a monitoring technology that allows the government to verify labor effort at a positive, but non-infinite cost. Monitored individuals receive a penalty, which increases if individuals earn a lower income...

The Netherlands has a unique tradition in which all major Dutch political parties provide CPB Netherlands Bureau for Economic Policy Analysis with highly detailed proposals for the tax benefit system in every national election. This information allows us to quantitatively measure the...

This paper studies the design of tax systems that implement a planner's second-best allocation in a market economy. An example shows that the widely used Mirrleesian (1976) tax system cannot implement all incentive-compatible allocations. Hammond's (1979) "principle of taxation" proves that any...

We analyze the optimal nonlinear income tax schedule when taxpayers earn multiple incomes and differ along many unobserved dimensions. We derive the necessary conditions for the government’s optimum using both a tax perturbation and a mechanism design approach, and show that both methods...

We characterize the second-best allocation in a Mirrleesian optimal tax model where agents differ in multiple dimensions and the planner can tax multiple goods non-linearly. We develop a new method that allows us to solve the partial differential equations that describe the optimum regardless of...

The aggregate capital stock in a nation can be overaccumulated for many different reasons. This paper studies which policy or policy mix is more effective in achieving the socially optimal (golden rule) level of aggregate capital stock in an infinite-horizon heterogeneous-agents...

The rapidly growing national debt in the U.S. since the 1970s has alarmed and intrigued the academic world. Consequently, the concept of dynamic (in)efficiency in an overlapping generations (OLG) world and the importance of the heterogeneous-agents and incomplete markets (HAIM) hypothesis to...