Publications   Empirical Studies OLDRICH & LUDMILA KYN: TRENDS IN EAST EUROPEAN FACTOR PRODUCTIVITY 

 

INTRODUCTION

During the 1960s most of the countries of Eastern Europe experienced a visible retardation of economic growth. The investigation of this phenomenon lead many Eastern as well as Western economists to the conclusion that the retardation was caused primarily by declining rates of growth of the total factor productivity. Originally, the factor productivity was measured by traditional index methods, however, more recently several attempts were made to estimate the rate of growth of the total factor productivity from macroeconomic production functions (see Tlusty-Strnad , Hajek-Toms , Rychetnik , Brown­Neuberger-Licari , Desai , Weitzman , Toda , Hocke-Kyn-Wagener . Incidentally the production function approach lead some authors (Weitzman, Desai, Brown-Neuberger--Licari) to the suggestions that the low elasticity of substitution may be an alternative explanation of retardation.

Most of the work on the estimation of production functions was done for the Soviet Union. Those few studies which concentrated on the other East European countries came frequently to statistically insignificant results. The reasons are well known: small number of observations and an extremely high degree of multicollinearity. In one of our previous papers (Kyn-Kyn ) we tried to demonstrate, that this obstacles can be overcome by estimating the production functions from the pooled cross-section and time series data. By doing so we received economically reasonable and statistically significant estimates of the capital and labor elasticities, and of the rate of technical change for Poland, Czechoslovakia, Hungary, Bulgaria and Rumania. Our estimates did show a clear declining trend in the rate of technical change at least for some of the mentioned countries, but it did not unambiguously support the hypothesis of low elasticity of substitution.

 

DATA

The estimation from pooled cross-section and time series data has two obvious advantages: the number of observations is considerably increased and the multicollinearity is for all practical purposes avoided. However, one new problem arises: the traditional assumptions of homoscedastic and serially uncorrelated random disturbance cannot be maintained. In the above mentioned paper we did not pay any attention to this problem and applied the ordinary least squares method. In this paper we present the estimated macroeconomic production functions from the pooled data using the stepwise procedure in which the autocorrelation and heteroscedasticity is eliminated by transformation of variables.

Our intention to use the “combination approach” was facilitated by the fact that the official statistical sources in the East European countries provide almost all needed data in the similar classification into sectors and industrial branches1. Our basic observations are for each country cross-sectionally divided into 15—16 industrial branches and are available for approximately 20 post-war years as shown in Table 1.  

Table 1.

CountryPeriodNumber
of Years
Number of
Branches
Number of
Observat.
Czechoslovakia1948—19672015300
Poland1956—19681315195
Hungary1950—19681915285
Bulgaria1948—19682115315
Rumania1950—19692016320

The observations are:  

Yit   Gross value of industrial output in constant prices

Kit  Undepreciated stock of fixed capital (buildings, constructions,
         machines and equipment) in constant prices and

Lit  Average annual number of workers or alternatively
       number of man hours worked.2

 The subscripts i and t refer to branch and year respectively.

All the data are related only to the “socialist” or “state” sector and cover the industrial production in the East European definition, i. e. including mining but excluding the building industry. Naturally, agriculture, transportation, trade and services are also excluded.
 The above data were chosen solely on the basis of availability. Of course, it would be desirable to have data covering the whole economy, i. e. including private establishments and non-industrial sectors, and use GNP or national income as a measure of output. Such data are not readily available and especially not in the breakdown by sectors and industrial branches.

The data on gross value of industrial output and on labor could have been taken directly from the official East European statistical sources without great problems. Only the standard adjustments for organizational and price changes and estimation of few missing observations was needed. Much greater problem was with capital stock data, which in some cases had to be reconstructed from gross investment, without knowing the actual deterioration ratio. In the case of Poland and Czechoslovakia the capital stock was also adjusted for the degree of utilization.3 For these and other reasons we feel that the Czechoslovak and Polish capital stock data are relatively more reliable than those for the other three countries. The least we trust our estimates of Rumanian capital stock, because here we had very little information to start with. The labor data seem to be quite reliable, but the gross value of output data suffer apparently by the known disease of the East European statistics: the price bias due to the “inflation of constant prices”. This is likely to produce an upward bias in the estimate of the rate of technical change.

 

 

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