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In this book, the authors develop a new and powerful methodology for macroeconomic modeling, based on a Kaleckian view of how things work. In the models, as in the real world, flow variables (like personal income) generate changes in stock variables (like personal wealth). A distinctive feature of Godley and Lavoie's models is that all relevant stock and flow variables are explicitly accounted for, so that they fit together as a complete system. This approach is in contrast to the partial equilibrium methods that are commonly used in economics, which allow some variables to change while others are ignored or assumed to remain constant. Godley and Lavoie (henceforth, G&L) convincingly argue that the partial equilibrium approach is logically incoherent and generates incorrect results. Of course, their alternative methodology, in which the dynamics of all stock and flow variables are fully accounted for, gives rise to much more complicated models (one of the models in Monetary Economics has over 100 equations). But by using computer simulations, one is able to get an understanding of the models and the properties of their solutions.
The first 300 or so pages of the book develop the individual building blocks of G&L's approach, which are then put together to form surprisingly realistic models in chapters 10-12. These models explicitly describe the interaction of the real and financial sides of the economy. The behavior of the financial institutions (especially the central bank) ends up playing a key role in determining the dynamics of the system as a whole.
Anyone who gives serious thought to the arguments in this book will probably want to challenge certain points, revise certain assumptions, and add features of the real world that the models ignore. But the aim of the book, I believe, is not to promote any one particular model, but rather to introduce and argue for a certain modeling *methodology*. (And judged on those terms, I would say the book is very successful). Nevertheless, there are still some specific propositions established by Godley and Lavoie which are interesting in their own right, and which are likely to survive reasonable revisions to G&L's models. The most important of these is: in modern capitalist economies there is no mechanism in place to automatically drive output toward its full employment level, even in the long run; to achieve full employment, active fiscal and monetary policy is required. Connected with this, the book provides a timely argument against the austerity policies that have been implemented in the US and Europe over the past few years.
In addition, in chapter 11 there are a some interesting results which are related to financialization. First, it is shown that if there is an increase in the ratio of consumer loans to income, this will stimulate aggregate demand in the short run, but diminish it in the long run. As G&L write, this "has interesting repercussions for the evolution of many OECD countries, most notably the United States and Canada, where aggregate demand over the last ten years or so has been sustained by a continuous expansion in the personal debt of households relative to their personal income." Second, G&L show that, in their model, if firms decide to finance a higher proportion of their investment through retained earnings rather than issuance of new equities, then this will also cause aggregate demand to diminish. This potentially sheds some light on the poor macroeconomic performance of the US over the past few decades, where the process of financialization (specifically the "shareholder value movement") has induced firms to finance a larger share of their investments with internal funds. There are many other insights like this scattered all over the book.
There is one last point I wanted to make about the book: if you are willing to learn a little bit of math and a little bit of computer programming, then you can simulate the models in this book (and recreate Godley and Lavoie's results) on your computer without buying any software. Here is what you do. In each model, the the vector x of values of the variables in any given period will satisfy an equation of the form F(x,y) = x, where y is a vector of values from previous periods. Since, in any given period, y is given by the past history of the model, this is a fixed point problem. In all of the models I have tried to simulate so far, this fixed point problem can be solved using a very simple and well-known numerical method called the "fixed point iteration". Using this idea, it is possible to write C programs that generate solutions to the models. To get the specific parameters values used by Godley and Lavoie in their simulations, go to the website referenced in the text (see footnote 24 on the bottom of page 19). I have recreated most of the simulations from chapter 10 using this technique. (I would be happy to share my code if you want to email me -- my contact information is on my amazon profile.)