The Personal Distribution of Income

Steindl, Josef

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Document type:: Works

Collection:: Josef Steindl Collection

Title:: The Personal Distribution of Income

Author:: Steindl, Josef

Scope:: Typoskript, 14 Seiten, mit handschriftlichen Anmerkungen

Year of publication:: 1990

Source material date:: [vermutlich um 1990]

Language:: English

Topic:: Stochastic processes and size distribution

Shelfmark:: S/M.52.10

Rights of use:: In Copyright

Access:: Free access

DOI:: https://doi.org/10.48671/nls.js.AC14446375

8 It would seem that in practice, in view of the value of hCj ,y must often be more or less high,so that the Pareto pattern will be confined to a rather narrow range of the income distribution while in the case of wealth it usually extends to almost the whole of the assessed wealth data. This, it is true, partly results from the fact that the wealth data are more truncated than the income data,in view of the underlying tax laws. Income and Wealth:Empirical Patterns The following illustrations are based on the cross classification of income and wealth available in Holland and in Sweden. These data share certain characteristics found also in other cross section data on size distributions given in official publications.The first feature is that the great bulk of the observations is concentrated in the corner of the first (the north-east quadrantIn other words the distributions are very skew. A great many of the units are small in either dimension. The second feature is that the wealth distribution is heavily truncated (in Sweden, for example, at 150.000 crowns) while the income distribution is given down to rather low levels. To put it in another way,only a small proportion of all income earners are assessed for wealth. If the mean income in the various wealth classes is calculated,a linear regression of a very regular pattern is obtained (fig.l). This "regression of the first kind" as it is called (Cramer 1946 p.270 )differs from the usual least squares regression in that it does not assume a priori a certain mathematical function for the regression.If the regression of the means turns out to be linear as is the case here then we should expect it to be the same as the result of a least squares regression on the basis of the full data (a difference may arise,however, in so far as we do not take into account the weights for the means corresponding to the frequencies in the various wealth classes). If the same regression of the first kind is calculated in the other dimension,that is , if we take the mean wealth for each of the various income classes,a completely different picture emerges:The mean wealth in the lowest income classes does not increase with income at all; for higher incomes it increases strongly so that a distinctly curved regression line results. The reason - or at least the most important of the reasons -for the peculiar shape of this regression line lies in the truncation of the wealth data. In the lower income classes we find only people who have something like the

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