The conditional distributions have all Pareto tails although the 
fit is bad ( only 4 values can be used ). The Pareto coefficient 
is between 3 and 4 in. all except the last two wealth classes, 
where it is very low, and it is 2.68 for the whole income distribution. 
It appears that the income distribution as a whole is - 
as far as its tail is concerned - decisively influenced by the 
last two wealth classes. This is due to the fact that most 
of the top income receivers are in the last two wealth classes, 
f 
where the income distribution is very unequal simply owing 
to the wide range of wealth in these two classes, as already 
mentioned before. In this way the peculiar result arises that 
the total income distribution is much more unequal than 
almost all the conditional income distributions. 
This in a way also answers the question which 
might well be asked: Why the pattern of income distribution 
could not be derived from the conditional distributions 
without reference to the wealth distribution. 
Allometric growth of income and wealth 
The discussion of the relations of income and ?»wealth will 
(/ 
now be extended to take accounyof influences in both directions. 
The starting point will be the regression of income 
on wealth which seems to be linear as far as the data go. 
This might be regarded as a case of allometry, in analogy to 
1) 
a "law" well known to biologists ' : Various parts of an organism 
grow at different but constant rates and as a result the proportions 
of their sizes ( on log scale ) remain constant in the growing body. 
1 ) Ludwig von ^ertalanffy, general System Theory. Penguin 1968 p.63. 
Devendra Sahal has used the allometric law in combination with 
the progress function in order to explain the Pareto distributio n 
£ in one dimension ); see A Formulation of the Pareto Distribution. 
HEM, Science ^entre, 1000 Berlin 33( mimeo ). Although the 
use I am trying to make of the law is different, I owe A to 
Devendra Sahal to have my attention drawn to it. '^