16 
Thus in our case the income is y = fC w -^-ye where we suppose 
that the distribution of income and wealth reflects in some way 
a growth process which has taken place in the past. 
W = Cj e 
In the ordinary formulation we should have 
act 
ßt 
Y = C2.e 
and after elimination of time 
ß 
y “ In c 2= — 
Z OC 
( w - In C| ) 
3 
(7) 
which corresponds to our regression with r— ^ 
Now in economics random elements or shocks play a large 
role and growth is influenced by them. We should therefore write, 
instead , of the above: 
W = c, 
Y = 
aft 
e + 
ßt 
0 % e 
£(t) 
+ y (t) 
(8) 
where £ (t) and VJ(t) are random variables. As a result we obtain 
ln ( Y - yj(t) ) -lnoj- jfci( W - £(t) ) - In c^. 
The random variables or "errors" ( not mere errors 
of observation^of course ) are in both variables. The common 
tactics of regression is to ascribe all the "errors" to one 
variable only, which yields two regression lines according to 
> whether you choose the one or the other variable as the 
repository of the errors. The equation (8) will not correspond 
to either of the two regression lines. -*-t may be guessed that it 
will lie somewhere between them. 
Let us now start from a different angle and consider 
the two kinds of relations that exist between income and wealth. 
One is the influence of wealth on income via the rate of return. 
It mainly affects unearned income. The other is the influence of 
past incomes on wealth via the propensity to save. Since 
present and past incomes are fairly strongly correlated it 
will appear as if current income acts on wealth via the 
propensity to save. 
At first sight it seems that the linear regression 
of income on wealth represents simply the influehce of wealth 
via the rate of return. There is however an alternative interpretation.