Composition of public expenditure, effective demand, distribution and growth

Commendatore, Pasquale and Panico, Carlo and Pinto, Antonio (2007): Composition of public expenditure, effective demand, distribution and growth.

Abstract

We introduce public expenditure (PE) in a general post Keynesian framework characterized by a nonlinear investment function. Our aims are: 1) to provide a systematic analysis of the impact of PE (‘productive’ or ‘non productive’) and of the Government sector size on economic growth, allowing effective demand to play a crucial role. Our work fills a lacuna in the post Keynesian literature given that scant attention has been devoted to this topic. In our paper, ‘Productive’ PE affects the (fixed) coefficients of production similarly to Barro (1990); 2) to compare and contrast two different interpretations which assign a different meaning to the autonomous component of the investment function, corresponding to long run demand growth expectations: the Kaleckian interpretation assumes exogenous long run expectations; in the Classical or Harrodian interpretation, long-run expectations are linked to the ‘warranted rate of growth’; 3) to reproduce a variety of complex phenomena (multiple equilibria, hysteresis, low growth traps, regular and irregular growth cycles), by introducing a simple nonlinearity in the investment function in the spirit of Kalecki’s (1937) investment theory and Kaldor’s (1940) trade cycle model. A plethora of results emerge from our simple framework concerning comparative statics and dynamicbehaviour.

• Item Type: MPRA Paper
• Language: English
• Subjects: E - Macroeconomics and Monetary Economics > E6 - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook > E62 - Fiscal Policy; E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models > E12 - Keynes ; Keynesian ; Post-Keynesian; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis

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