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Growth and Inequality: Dependence of the Time Path of Productivity Increases (and other Structural Changes) Author info | Abstract | Publisher info | Download info | Related research | Statistics Manoj Atolia () (Department of Economics, Florida State University)
Santanu Chatterjee () (Department of Economics, Terry College of Business, University of Georgia)
Stephen J. Turnovsky () (Department of Economics, University of Washington)
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registered author(s):
This paper examines the significance of the time path of a given productivity increase on growth and inequality. We show that whereas the time path impacts only the transitional path of aggregate quantities and has no effect on their ultimate steady-state levels, it has both transitional and permanent consequences for wealth and income distribution. As a result, the growth-inequality trade-off generated by a given discrete increase in productivity contrasts sharply with that obtained when the same ultimate productivity increase is acquired gradually. This is true both in transition and across steady states. We show that a gradual productivity increase can generate a Kuznets-type inverted U-shaped relationship between inequality and per-capita income. The distance from the technology frontier is also shown to have important implications for both the magnitude and persistence of inequality. Finally, our results suggest that economies with similar aggregate structural characteristics may have very different outcomes for income and wealth inequality, depending on the intrinsic nature of the productivity growth path.
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Paper provided by Department of Economics, Florida State University in its series Working Papers with number
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Keywords: Reverse shooting ; global saddle path ; distance mapping ; Find related papers by JEL classification: C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques C61 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Optimization Techniques; Programming Models; Dynamic Analysis
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