Customer Reviews

On Economic Inequality (Radcliffe Lectures)

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Despite the fact that the discipline of economic inequality measurement is thriving in the last twenty or so years (may be thirty something if you count from Atkinson (1970) paper), the textbook format publications of the general character are rare; I can only think of Lambert (2002) as another major source. Amartya Sen is a Nobel prize winner in the area of inequality, poverty and famines; and James Foster who wrote an extensive commentary featured in this second edition of the original Sen's lectures is arguably the most often cited author in inequality and poverty measurement due to his principal contributions on the classes of decomposable measures and poverty orderings.

It should be noted that Sen is kinda difficult reading; his language is a little bit archaic, and not very typical for the modern highly mathematized economic publications (don't worry, you'll see enough abstract algebra once you start going into the details of the derivation of the properties of inequality/poverty indices...)

I don't think that the other review gives anything close to the real merit of this book, as it seems to be written by an information econometrician rather than somebody doing substantive distribution research. I would comment that generalized entropy measures are important, if not central, in the inequality measurement due to their nice properties, but are not the only measures possible. The authors rather wanted to give a big picture, and, as I said, you can get all sorts of details in the article publications they refer to.

I learned a lot from Sen's and Foster's book. It is an important key to the understanding of inequality measures. Especially the welfare funcion seems to be a much better measure to describe income than the average per capita income.

I, however, have one objection. Sen is careful enough not to completely reject Theil's formula (see formula 2.11 in "On Economic Inequality"). And although Sen and James E. Foster are puzzled by the application of entropy to economics, they seemingly also feel, that it is interesting enough to be discussed.

Unfortunately though, Sen called Theil's formula not only "interesting", but also "arbitrary". Here is an example for how Sen's further comments on Theil's measure successfully inhibited other researchers to develop an understanding for entropy measures. "Sen ... describes the major flaw in T very nicely when he states that it 'is not a measure that is exactly overflowing with intuitive sense'. Why then would econometricians - or anyone else - want to use T?" This response (from a participant from Macquarie University, 1991 Conference of the Australian Association for Research in Education) is regrettable. I wish, Sen and Foster would reevaluate entropy measures and their application to the measurement of inequality.

"Intuitive" understanding of entropy is rare. (That is why confusing entropy problems with energy problems is common.) The major flaw in evaluating entropy measures often is lack of common knowledge in physics and/or information theory as well as lack of intuition. If sociologists and economists don't trust information theory they at least should observe how the "Shannon index" is used in statistical ecology.

As for economics, you find excellent examples for how to use Theil's measure in James K. Galbraith's "Created Unequal" (1998, ISBN 0-684-84988-7).