Optimal fiscal policy under learning |
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| Institution: | 1. Department of Economics, University of Pretoria, Pretoria 0002, South Africa;2. Department of Economics, Northeastern University, 301 Lake Hall, 360 Huntington Avenue, Boston, MA 02115 USA;3. Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany;4. College of Business Administration, University of Nebraska at Omaha, 6708 Pine Street, Omaha, NE 68182, USA;5. School of Business and Economics, Loughborough University, Leicestershire LE11 3TU, UK;1. Department of Economics, Fudan University, China;2. Department of Economics, Chinese University of Hong Kong, Hong Kong;3. Department of Economics, Hong Kong University of Science and Technology, Hong Kong;1. Hoover Institution, Stanford University, 434 Galvez Mall, Stanford, CA 94305, United States;2. NBER, United States |
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| Abstract: | This paper characterizes optimal fiscal policy when agents learn about future taxation. A benevolent and fully rational government chooses taxes on labor income and state-contingent bonds to finance public spending, considering that private agents form their expectations through a learning algorithm. Facing a trade-off between distortionary taxes and distorted expectations, the Ramsey planner chooses the policy that minimizes the total cost of distortions. The analysis produces two main results. First, the government will use fiscal variables to manipulate expectations, reducing taxes and issuing debt at times of pessimism and doing the opposite at times of optimism. This speeds up learning. Second, the expectation-dependent fiscal plan is also history-dependent, and it prescribes taxes that are not as smooth and more persistent than under rational expectations. These findings are robust to alternative learning algorithms. |
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| Keywords: | Optimal fiscal policy Adaptive learning |
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