REGIONAL ECONOMIC MODELING IN THE SOVIET UNION Daniel L. Bond International Research and Exchanges Board The National Council for Eurasian and East European Research 910 17th Street, N.W. Suite 300 Washington, D.C. 20006 TITLE VIII PROGRAM FINAL REPORT TO NATIONAL COUNCIL FOR SOVIET AND EAST EUROPEAN RESEARCH TITLE: Regional Economic. Modeling in the Soviet Union AUTHOR: Daniel L. Bond CONTRACTOR: International Research & Exchanges Board PRINCIPAL INVESTIGATOR: Daniel L. Bond COUNCIL CONTRACT NUMBER: 620-10 The work leading to this report was supported in whole or in part from funds provided by the National Council for Soviet and East European Research. REGIONAL ECONOMIC MODELING IN THE SOVIET UNION by Daniel L. Bond SUMMARY Because of the size and territorial diversity of the Soviet Union, regional considerations have always played an important role in Soviet economics and planning. In order to study and plan for regional development Soviet analysts have in recent years increasingly turned to the use of mathematical-economic models. This has been a part of a general "modeling" trend in Soviet economics which began in the late 1950s and is now a dominant approach in the discipline. Economic modeling in the Soviet Union is inextricably a part of economic planning, from whence it derives its purpose, support, and approach. The introduction of mathematical modeling in Soviet planning began, and has been most successful, at the lowest levels of planning —in problems more of an engineering than of an economic nature. Here relationships can be expressed in physical terms, evaluation criteria are more easily formulated and are less controversial, and the neces- sary data can be gathered rather easily. However the methodological vacuum at the higher levels, where questions of resource allocation on a society-wide scale are considered, quickly drew efforts to apply the modeling approach here also. But in every respect the problems multi- ply rapidly. In spite of many difficulties, a major effort has been placed on the development of models for macro-level (i.e., national or regional level) planning in the Soviet Union. This may be attributed to the decision taken by top party leaders to reduce the pressures for economic reforms of a decentralizing nature by seeking compensating improvements in increased efficiency of central planning. Not wanting to lose control of the economy, but needing to increase its efficiency, Soviet leaders have chosen to emphasize improvements in the planning process rather than attempt more fundamental reforms. An effort was begun in the 1960s to develop computer based econo- mic models for use in planning. Two objectives appear to have been of primary importance: (1) to allow plans to be constructed and modified more rapidly and with less manpower; and, (2) to improve the efficiency of resource allocation through the construction of better plans. These objectives have been pursued, to a great extent, independently of one another. A massive program to secure the first objective is now well underway in the creation of "automated systems of plan construction" at all levels of the planning hierarchy. The focus here is on the introduction of automated data management and plan calculation, with little concern so far given to changing the nature of the decision making process. To date most of the models proposed for this system have simply replicated the same proceedures employed in the past by the planning bureaucracy. -i- A second—and largely separate—effort has been underway to develop mathematical-economic models that would not just replicate the administrative planning process, but would provide planners with the means for improving the quality of their decisions. It is felt that for macro-level planning specific types of models can provide useful information for making what are termed "pre-plan calculations". As a first step in planning there is a need for extrapolations of trends in the economy, analysis of the implications of decisions concerning resource allocation, evaluation of the probable impact of technological progress, identification of possible imbalances between goals and resources, etc.. These need to be carried out at a high level of sectoral, regional and functional aggregation with a time horizon of five to fifteen years. They are intended to provide planners with the basic information necessary for making strategic decisions and to serve as a basis for subsequent physical planning calculations whcih are carried out in much greater detail. Some Soviet economists and plan- ners feel that one way this information can be provided quickly and at low cost is through the use of computer based modeling. One of the difficulties often encountered in formal mathematical- economic modeling is that each technique restricts the representation of the real world in some way. Input-output models are useful for understanding inter-sectoral relationships in production, but. they are not as useful as econometric models based on sectoral production functions when it comes to the analysis of substitution possibilities or temporal changes in productivity. Optimization models are useful tools for deciding on the, best allocation of resources to reach some goal, but they are not as helpful in understanding how system behavior constrains the planners' ability to make these allocations. Many other examples could be cited, as each formal technique has its limitations in regard to the nature of the specifications allowed, the type of data required for Its application, or the form of the solution process involved. Both balancing and optimization models have been extensively- studied by Soviet economists, and they now are also beginning to be used in the planning process. However an awareness of certain limi- tations of these models has recently become evident. ' It is being recognized that the economy is far too complex for any single objec- tive function to effectively reflect the goals of economic planning. Also, in order to be solvable with available algorithms and computers, balancing and optimization models must be specified in rather rigid formats. The limits this places on the flexibility for modeling certain economic processes has become increasingly obvious as Soviet economists have attempted to make their models more comprehensive. Some of the most interesting experiments of the last decade have involved models where global optimization has been foregone in order to introduce greater complexity in models' objective functions and struc- tural specifications. In particular, interest in the use of simulation models has became widespread during this period. Simulation models are designed to answer questions of the type "if epecificed actions are taken in period t, then what will be the results In period t+1, -ii- t+2, ... t+n?" They may be similiar in structure to balancing models (that is, they consist of a set of equations which when properly specified can be solved for the endogenous variables given values of the exogenous variables), but are qualitatively different in the degree of closure and flexibility of internal specification. The difference between simulation and optimization models is even more significant in that simulation models do not require an overall objective function for their solution. Instead simulation models are designed to perform calculations of an "if ... then" nature. That is, they allow examination of many alternative paths of development, where each path is assoicated with a particular set of government policies and external conditions. The choice of a "best" combination of poli- cies and results is left up to the model user. As a result of dropping the extremal value solution procedure, the flexibility of internal specification of simulation models is substantially greater, and computational problems less, than that of optimization models. However, the dropping of the objective function does raise questions as to the normative value of the solutions obtained from the use of these models. Although simulation models do not automatically provide a "best" solution (of the type obtained in optimization models) they can be used to generate information which can then be used in less formal decision making processes. Two broad classes of simulation models can be identified based on the manner in which their parameters used in the models are obtained, In one group parameters are set on the basis of expert opinion or planning "norms", in the second they are based on statistical estima- tion using time series or cross section data. The latter, know as econometric models, have shown a very rapid increase in popularity. One reason given for using the statistical approach to parameterization is very practical—it is often difficult to obtain from experts or higher authorities parameter values of precisely the type needed. Statistical estimation presents a quick and inexpensive substitute. In contrast to the situation in the West, in the Soviet Union there is very little basic economic research using statistical techni- ques designed to test hypotheses. Thus there is no substantial body of econometric research from which Soviet modelers can draw useful inputs for their work. (Although Western techniques can be imitated, it has been obvious that most Western specifications are not applicable to the Soviet economy.) Often the specifications used in the econometric models show little variation from those used in other simulation models. In only a few instances has the choice of econometric methods of parameterization been reflective of an experimental approach to model specification. This points up another characteristic of many modeling efforts in the Soviet Union. Often models are specified on the assumption that the relationships protrayed are deterministic and known. Thus in the initial stages of model construction there is no experimentation with alternative specifications that would require that data collection be carried out in the early stages of model construction. This leads to a separation of model building and empirical research which seems -iii- strange to Western analysts who normally pursue both tasks simulta- neously.