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Macroeconomics without IS-LM: A Counterfactual

  • Robert W. Dimand
Part of the Recent Economic Thought book series (RETH, volume 73)

Abstract

The teaching of intermediate macroeconomics, and until fairly recently of graduate macroeconomics, has been dominated by the IS-LM representation of the determination of aggregate demand by equilibrium conditions for the goods market and the money market. Robert Solow agreed with James Tobin that this framework in “the trained intuition of many of us” (Young 1987, 1). Its influence was so pervasive that even Milton Friedman used the IS-LM framework when he wished to explain to the economics profession at large the essence of the revived quantity theory of money and its differences from Keynesian economics (Gordon 1974). Robert Barro’s intermediate macroeconomics textbook (Barro 1993), which relegates IS-LM to the twentieth and final chapter on the Keynesian theory of fluctuations, remains an outlier even among textbooks with a strong New Classical flavour, as the habit of thinking in an IS-LM framework has become ingrained in successive generations of economists, even when they transform the model by assuming full employment. The IS-LM apparatus provides a convenient way of organizing analysis of income and interest determination, at the cost of diverting attention from formation of expectations of an uncertain future (stressed by Keynes 1937) to determination of current income given expectations. Macroeconomic argument was not always conducted in that framework, however, and IS-LM is absent from Keynes“s General Theory and from Hicks’s Economic Journal review (1936) of the General Theory. One can imagine macroeconomics advancing from the General Theory and from pre-Keynesian monetary and business cycle theory had Hicks never “done a Marshall” with his graph.

Keywords

Aggregate Demand Full Employment Aggregate Supply Business Cycle Theory Money Wage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media New York 2000

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  • Robert W. Dimand

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