Publications 
2. Data
2.1 In our estimations, we used either the aggregate time series (19511970) for the total industry or the sectoral time series for ten major industrial branches (energy, fuels, ferrous metals, nonferrous metals, machinebuilding, construction materials, wood and paper, chemicals, light industry, food) of the following data^{2)}:
(i) The gross value of output (excluding turnover tax) (Y),
(ii) The stock of fixed assets at the beginning of the year (K),
(iii) The number of employees (L) or alternatively the number of manhours (L*)
The choice of gross value of output as the left hand variable may not be fully satisfactory from the theoretical point of view, however, it was inevitable because the data on net output were not available for industrial branches^{3) }
The fact that the intermediate inputs  including the intrabranch turnover  are part of the gross value of output, but do not appear on the righthand side of the regression equation would not really matter if the share of intermediate product in the gross value of output would remain constant over the whole observed period. It is, however, very unlikely that this would be the case.^{4}^{)}
2.2. We have decided to use the official Soviet Statistics whenever possible and therefore we have accepted also the official Soviet prices as weights and the price indices as conversion factors to constant prices. This adds an additional grave problem, namely that part of inflation which is disguised as improvements in the quality of goods and therefore hidden in the official Soviet price indices.^{5)}
Another difficult problem was created by variations in the degree of utilization of input factors. It has been recognized that the Soviet type economies undergo periodical fluctuations in their economic activities. The proper approach would be to subtract the unutilized portion of a factor before regression. Unfortunately we have not had enough information to do that. In the capitalist countries with the flexible labor market it can be assumed that the prevailing part of unutilized labor appears in the category of “unemployed” so that the data on employment do not contain any large unutilized labor. Under Soviet socialism the situation is different: the rigid central planning and the strict labor laws make firing of workers almost impossible so that the downswings in economic activity results in a lower degree of utilization of employed labor rather than in increased unemployment.
2.3. Probably the main obstacle is a very high degree of multicolinearity in data. The following correlation matrices very clearly indicate this difficulty.
Table 1 Correlation matrix of the variables
Y  K  L  t  
Y  1.000  .999  .984  .980 
K  1.000  .982  .976  
L  1.000  .998 
lnY  lnK  lnL  t  t2  
lnY  1.000  .989  .998  .999  .957 
lnK  1.000  .999  .999  .960  
lnL  1.000  .998  .957  
t  1.000  .970 
Multicolinearity is particularly severe in Soviettype economies because centralized planning makes the economic growth relatively smooth so that the variables ln K and ln L are highly correlated with time. When the number of employees (L) is replaced by the number of manhours worked (L* ) the correlation coefficients are slightly diminished:
lnY  lnK  lnL  t  t2  
lnL*  1.000  .976  .973  .978  .959 
It may be interesting to note here that our set of data is almost identical with that of Weitzman (70, p.677),



