Growth of per-capita income is associated with (i) significant shifts in the sectoral economic structure, (ii) systematic changes in relative prices and (iii) the Kaldor facts. Moreover, (iv) cross-sectional data shows systematic expenditure structure difference between rich and poor households. Ngai and Pissarides (2006) and Acemoglu and Guerrieri (2008) are consistent with observation (i)-(iii) but abstract form non-homotheticities of preferences. However, they cannot replicate the structural change between the U.S. goods and service sector quantitatively. This paper presents a growth model, which reconciles both forces of structural change -relative price and income effectswith balanced growth on the aggregate. The theory is simple and parsimonious and contains an analytical solution. The model can replicate shape and magnitude of the nonbalanced sectoral facts as well as the balanced nature of growth on the aggregate. In a structural estimation, the model's functional form is exploited to obtain estimates for the relative importance of income and price effects as determinants of the structural change.Keywords: Structural change, relative price effect, non-Gorman preferences, Kaldor facts. JEL classification: O14, O30, O41, D90. * I would like to thank Josef Falkinger, Reto Föllmi, Volker Grossmann, Marcus Hagedorn, Danyang Xie, Fabrizio Zilibotti and Josef Zweimüller for illuminating discussions. Moreover, I would like to thank Gregori Baetschmann, Victoria Galsband, Sandra Hanslin, Andreas Müller, Nick Netzer, Iryna Stewen, Raphael Studer and Franziska Weiss for many valuable comments and suggestions.
We propose a new method for computing equilibria in heterogeneous-agent models with aggregate uncertainty. The idea relies on an assumption that linearization offers a good approximation; we share this assumption with existing linearization methods. However, unlike those methods, the approach here does not rely on direct derivation of first-order Taylor terms. It also does not use recursive methods, whereby aggregates and prices would be expressed as linear functions of the state, usually a very high-dimensional object (such as the wealth distribution). Rather, we rely merely on solving nonlinearly for a deterministic transition path: we study the equilibrium response to a single, small "MIT shock'' carefully. We then regard this impulse response path as a numerical derivative in sequence space and hence provide our linearized solution directly using this path. The method can easily be extended to the case of many shocks and computation time rises linearly in the number of shocks. We also propose a set of checks on whether linearization is a good approximation. We assert that our method is the simplest and most transparent linearization technique among currently known methods. The key numerical tool required to implement it is value-function iteration, using a very limited set of state variables.
Growth of per-capita income is associated with (i) significant shifts in the sectoral economic structure, (ii) systematic changes in relative prices and (iii) the Kaldor facts. Moreover, (iv) cross-sectional data shows systematic expenditure structure difference between rich and poor households. Ngai and Pissarides (2006) and Acemoglu and Guerrieri (2008) are consistent with observation (i)-(iii) but abstract form non-homotheticities of preferences. However, they cannot replicate the structural change between the U.S. goods and service sector quantitatively. This paper presents a growth model, which reconciles both forces of structural change -relative price and income effectswith balanced growth on the aggregate. The theory is simple and parsimonious and contains an analytical solution. The model can replicate shape and magnitude of the nonbalanced sectoral facts as well as the balanced nature of growth on the aggregate. In a structural estimation, the model's functional form is exploited to obtain estimates for the relative importance of income and price effects as determinants of the structural change.Keywords: Structural change, relative price effect, non-Gorman preferences, Kaldor facts. JEL classification: O14, O30, O41, D90. * I would like to thank Josef Falkinger, Reto Föllmi, Volker Grossmann, Marcus Hagedorn, Danyang Xie, Fabrizio Zilibotti and Josef Zweimüller for illuminating discussions. Moreover, I would like to thank Gregori Baetschmann, Victoria Galsband, Sandra Hanslin, Andreas Müller, Nick Netzer, Iryna Stewen, Raphael Studer and Franziska Weiss for many valuable comments and suggestions.
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