soviet military and civilian resource allocation 1951-1980

Soviet Military and Civilian Resource Allocation 1951-1980Author(s): Paul M. Johnson and Robert A. WellsSource: The Journal of Conflict Resolution, Vol. 30, No. 2 (Jun., 1986), pp. 195-219Published by: Sage Publications, Inc.Stable URL: http://www.jstor.org/stable/174252 .

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Soviet Military and Civilian Resource Allocation 1951-1980

PAUL M. JOHNSON Department of Political Science Florida State University

ROBERT A. WELLS Department of Political Science University of New Orleans

The "guns versus butter" literature focuses mainly on the industrialized capitalist democracies and has reported little success in empirically substantiating the existence of durable trade-off relationships between military spending and particular civilian programs during peacetime. This article identifies structural features of Soviet-type politicoeconomic systems that make them more likely to display such durable trade-offs and then demonstrates the point by multiple regression time-series estimation of the effects of changing rates of growth in Soviet military spending on 13 major civilian programs during the period 1951-1980, controlling for demographic, economic growth, and leadership succession effects. Despite considerable noise in the Soviet military spending data, substantial and robust trade-off phenomena are demonstrated only for housing construction and the production of durable consumer goods, whereas ideologically favored programs for productive investment and social spending (except perhaps old-age pensions) appear to have been much less affected by marginal changes in military spending.

The human and material resources useful for military purposes have other productive uses. In economies with high levels of employment, large increases in the resources devoted to the military can be accommodated only by increasing total production of resources or by compensating with downward adjustments in the resources allocated to civilian uses. Ever since our remote forebears invented the institution of the territorial state, the obligation of subject populations to bear substantial economic costs to maintain permanent military establishments has been a nearly universal feature of political life, but the precise

JOURNAL OF CONFLICT RESOLUTION, Vol. 30 No. 2, June 1986 195-219 o 1986 Sage Publications, Inc.

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196 JOURNAL OF CONFLICT RESOLUTION

way in which the military burden is planned and shared by the citizenry in particular countries at particular times is highly variable and often politically contentious. Persistent patterns in the outcomes when a polity confronts the issue of "guns versus butter" may enlighten us a good deal about such diverse but interrelated matters as the values of its incumbent governmental elite, the relative influence of the various social and economic interests represented within the population, and the developmental prospects of the system.

The contemporary literature of empirical research on the consequences of varying levels of military spending for civilian resource use focuses primarily on the industrialized capitalist countries of the West, with secondary attention being paid to the less developed countries of the Third World. That is, it deals with countries that have very strong market-regulated elements in their national economies. Because of the "public choice" conceptual framework of most investigators and their consequent preoccupation with the hypothesis that central government decision makers more or less consciously confront and resolve "trade- offs" between competing values in a rational planning process, the civilian sectors chosen for investigation as the military's probable competitors for economic resources are normally those over which prevailing institutional arrangements permit the central government to exercise a large measure of direct control. Most commonly, this has entailed studies examining variations on the basic hypothesis of an inverse trade-off relationship between the growth rates of military spending and national budgetary provisions for health care, education, and income maintenance programs. (For excellent brief reviews of this literature, see Russett, 1982, and Domke et al., 1983.)

The focus of researchers' attention on categories of civilian resource allocation that are dominated by central government budgetary allocations is clearly calculated to improve the chances of verifying the inverse trade-off hypothesis. Even so, robust trade-off relationships between defense and other categories of public or private spending have proved surprisingly difficult to demonstrate statistically with longitudinal data for the industrialized capitalist countries, except when the data sets included periods of truly massive mobilization occasioned by involve- ment in major wars (Russett, 1982).

A plausible interpretation of these persistently rather weak findings is that basic features of the specific economic and political institutions of the countries most studied may militate against any regular exercise of synoptic decision making on a scale capable of imposing a consistent set



Johnson, Wells / SOVIETRESOURCEALLOCATION 197

of priorities on macroeconomic resource allocation for any prolonged period of time.

From an economic point of view, the institutional separation of economic from political authority in capitalist societies implies a substantial limitation on the government's responsibility for, and practical control over, the resource allocation process outside the state sector. Rather than confront the agonizingly detailed decisions and divisive bureaucratic infighting entailed in financing military expansion internally by cutting budgets of other state agencies, governmental leaders frequently resort to tax increases or deficit spending as a means of passing all or most of the increased burden along to the private economy. There the ultimate impact on civilian resource allocations will be the largely unplanned result of the spontaneous reactions and interactions of countless private decision makers, responding indi- vidually through the marketplace as the government procurement officers bid up the prices (or expropriate part of the supply) of those particular multipurpose resources currently required in greater quantities by the military while taxpayers strive to compensate for their lightened pocketbooks by reducing their purchases of whatever com- modities happen to be lowest on their current priority lists. There is no obvious reason any pattern of consistently winning or losing sectors should emerge over time in the private economy, and the military budget's trade-off relationships with those of other state agencies should at least be considerably attenuated by the infusion of resources from "outside."

Political institutions prevalent in the industrialized capitalist countries that entail a pluralistic diffusion of power and influence also undermine the kind of synoptic decision making that might impose a systematic and enduring set of priorities in budget construction. Even in the more executive-dominated parliamentary systems of Western Europe's unitary states, budgeting "is less a systematic comparison of alternatives than a disjointed aggregation of spending decisions reached largely in isolation" (Domke et al., 1983: 20). The periodic transfer of executive and legislative power among competing political parties with differing constituencies, programs, and priorities no doubt also works against the statistical identification of clear-cut and long-persisting patterns of trade-off between military spending changes and any specific civilian economic sectors. Such political institutions seem particularly conducive to outcomes in which changes in the military burden are both widely diffused and erratically shifted from year to year and hence




undermine the polimetricians' indefatigable efforts to discover clear-cut and recurrent short-term trade-off relationships in the budgetary outcomes.

On a variety of counts, the USSR would seem to be a more likely candidate than advanced capitalist democracies for displaying systematic trade-offs between military and other forms of spending. In the first place, centrally planned and administered socialist economies of the Soviet type afford the central political leadership much greater and more direct authority and responsibilities in the macro- and micro- level allocation of societal resources. Soviet society is characterized by the structural fusion of economic and political authority and its concen- tration in the hands of the top leaders of the Communist Party, who maintain a permanent monopoly on appointments to significant decision-making posts in all spheres of community life. Autonomous market institutions are systematically and effectively repressed (or encapsulated) and replaced by a unitary bureaucratic chain of command that directs all large-scale production and distribution of economic resources according to legally binding planners' directives rather than supply-and-demand criteria. The non-Communist government's escape hatch of passing on rises in government spending to be absorbed in haphazard fashion by the private economy is no longer available when the private economy has already been reduced to a practical minimum. The same small, ideologically homogeneous group of top Communist leaders who initially decide to accelerate military spending in their role as guardians of the state's security are also obliged in their roles as supreme economic planners to make good on their decision by issuing directives to withdraw the necessary physical resources from civilian use by trimming, canceling, or delaying specific projects or programs in the perennially ultrataut national economic plan.

Another characteristic of the Soviet political order possibly conducive to the emergence of the regular and recurrent trade-off patterns is the permanent domination of the government by a single, highly disciplined political party with a remarkably stable set of explicit economic planning principles enshrined since the early 1930s in a highly codified official ideology: (1) more rapid growth of investment than of consumption; (2) priority of investment in industry over investment in agriculture and the "nonproductive" (service) sectors of public transit, housing, and social programs; (3) within industry, priority of heavy industry over light industry; (4) identification of progress toward the ideal social order of "full Communism" with the gradual but steady




increase in the ratio of free or subsidized "collective consumption" to "individual consumption" financed from the population's money incomes.

Regular and recurrent trade-off patterns are probably also fostered by institutionalized norms of political behavior that regard all spontaneous forms of pressure group activity by nonelites as not only improper but presumptively criminal and by the conservative, self- reproducing propensities of an aging, cooptively recruited ruling oligarchy of men who first achieved national prominence more than 30 years ago and jealously monopolized the reins of power into the decade of the 1980s.

We might also expect any trade-offs engendered by fluctuations in military spending to be more readily detectable in the Soviet case simply by virtue of the exceptional scale of their military spending. The steady and massive growth of Soviet military power has been one of the most significant and unsettling long-term trends in the evolution of the post- World War II international order. Since the 1950s, the Soviet leaders have had to contend with a number of adverse developments beyond their control that might most logically have led to at least some backsliding toward their former position of military inferiority: the postwar recovery and economic boom in Western Europe and Japan and the historically unprecedented degree of political cooperation between these former rivals, the emergence of about a dozen newly successful industrial societies (such as Norway and South Korea) firmly tied to the American alliance systems, a serious secular decline in the trend of Soviet and East European economic growth rates beginning in the early 1960s, and the transformation of the Sino-Soviet alliance into a bitter enmity. Yet so extraordinary have been the magnitude and persistence of the Soviet military buildup that it has nevertheless today become a matter of serious dispute among informed observers whether the USSR's present strategic situation represents mere parity with the United States and its allies or conceivably some form of practical superiority.

None of this has come about without the payment of a stiff price. A crude measure of the general magnitude of that burden often referred to by both journalists and scholars is the proportion of Soviet gross national product accounted for by military spending. Although foreign experts' estimates of Soviet military spending levels and GNP differ substantially according to the techniques employed in their calculation, it is widely accepted in the Western literature that the annual totals have




seldom, if ever, dropped very far below 10% of GNP during the past 20 years, while the upper range of estimates for certain years run as high as 18% (Becker, 1981; Lee, 1977). Because the postwar Soviet economy has been characterized by "full employment" (at least in the loose sense implied by ultrataut planning and recurring complaints of serious labor and material shortages), a military buildup of this scale could occur only by diverting very large quantities of scarce resources that the planners might otherwise have profitably employed to advance their purposes in the civilian economy.

Quantitative estimation of the total direct and indirect military burden on the Soviet economy over any lengthy period would involve intransigent conceptual, theoretical, and methodological problems quite apart from the well-known scarcity of appropriate and reliable economic data. For starters, estimating the opportunity costs of their entire military effort would depend upon our ability to discern not only precisely what sectoral distribution Soviet planners would have made of better than 10% of their GNP if they had completely disarmed themselves a generation ago, but also what the secondary consequences of such decisions might have been for the subsequent growth rate and structure of GNP until today. (For an excellent clarification of the theoretical and methodological issues involved, see Becker, 1981.)

We attempt no such hypothetical global cost assessment here, but instead pursue the more modest task of examining short-term trade-offs that have been manifested "at the margin" when the growth rates of military spending have fluctuated from year to year. Our interest is not even in chronicling from year to year what programs the resources for the increment in military spending were diverted from (or where the extra went, in years of decelerating growth), but rather in determining whether over a substantial period of years there were identifiable civilian programs that were repeatedly and disproportionately affected by variations in the pace of the Soviets military buildup.

The principal technique we use to address this question is linear regression of time-series data on annual growth rates of resources devoted to the Soviet military effort and of those allocated to a wide variety of competing civilian programs.' We specify and estimate simple

1. Both conceptual and methodological considerations led us to transform the data to annual percentage rates of change. Conceptually, because we are interested in short-term trade-offs that occur at the margin, an indicator of rate of change is more easily interpreted than absolute ruble spending levels would be, and, in any case, some of our measures of civilian sector outputs are in the form of index numbers rather than ruble figures in the



Johnson, Wells / SOVIET RESOURCEALLOCATION 201

linear models explaining variations in the growth rates of allocations for each of the various civilian programs, introducing the growth rate of military spending into each model as one of the independent variables. The estimating technique routinely employed is ordinary least squares; however, when preliminary examination of the Durbin-Watson statistic indicates a better than 5% chance of autocorrelation problems, the affected equations have been reestimated applying the Cochrane-Orcutt iterative least squares procedure. To avoid distortions being introduced into the analysis by the peculiarities of Soviet planning policies that feature relatively infrequent but quantitatively large adjustments of relative prices, data originally expressed in value terms have been adjusted by the best available price indices in order to produce an approximation of "constant ruble" rather than "established prices" series.

The hypothesis to be tested in each estimation is that there is a competitive trade-off relationship between the growth rate of the particular civilian program and that of the military. Significant negative regression coefficients associated with the military spending variables support the hypothesis, whereas regression coefficients that are positive or not significantly different from zero imply its disconfirmation.

PROBLEMS OF MEASUREMENT

SOVIET MILITARY SPENDING

The well-known propensity of the Soviet regime for secretiveness nowhere finds greater expression than in its attitude toward information bearing upon the size and composition of its military establishment. The only official information released to the public is a single lump-sum entry labeled "defense" that appears in the state budget presented each

original data sources and hence would not be expressed in comparable units. From a methodological point of view, annual percentage rates appear preferable because of a familiar problem frequently encountered in time-series analysis of budgetary allocations: expansion of budgetary outlays over time results in greater absolute changes occurring in the later years of the data set. Because regression analysis gives more weight to those years in which absolute increases or decreases are largest, the use of absolute change data would weight recent years more heavily than earlier years. Use of annual percentage changes in this article helps us to avoid what could otherwise be troublesome problems of heteroscedasticity.




year for the formal approval of the Supreme Soviet. No official Soviet source has ever indicated just what is supposed to be included in this figure, but it is quite clear to all serious foreign students of the subject that many, and perhaps most, of the funds expended by the USSR for military purposes (even very narrowly construed-that is, only per- sonnel costs, operations and maintenance expenses, military construction, procurement of weaponry, and military research and development) must derive from other sources. Consequently, a great deal of effort has been expended by Western analysts to derive more realistic estimates of the level of Soviet military spending. (For a detailed and critical examination of the principal rival methodologies, see Cockle, 1978.)

For the purposes of the present analysis, which is focused on ques- tions of resource allocation within the Soviet economy rather than on making international comparisons of the relative magnitudes or capabil- ities of competing military establishments, we need "domestic ruble" estimates of Soviet military spending rather than cost estimates based upon Western price relations and speculative foreign exchange rates. We also need series of sufficient length to allow for meaningful multivariate analysis. We have been able to uncover only three credible data sets on Soviet military spending that meet these criteria and have elected to conduct parallel analyses using all of them, having first transformed the data by replacing absolute levels of spending with annual percentage rates of change in estimated spending. The first data set, SIPRIDEF, is derived from the "constant price" spending estimates compiled by the Stockholm International Peace Research Institute and published in annual issues of the SIPRI Yearbook. The SIPRI data involve only modest adjustments to the official Soviet budget figures (with the current ruble figures then deflated by an official Soviet index of retail consumer prices), and the percentage growth rates accordingly rather closely resemble those of the official Soviet series.

CIADEF, the second set of data on Soviet military spending we employ, is based on the U.S. Central Intelligence Agency's 1982 "Estimated Soviet Defense Expenditures, 1951-1980" denominated in 1970 rubles at factor cost prices (JEC, 1982: 123). The original annual estimates are reported as a range, and in computing the annual rates of change we have utilized the midpoints of the ranges. The CIA estimating procedure disregards the official Soviet defense budget entirely and instead is based directly on a methodical process of "costing out" individual items in the inventories of human and material military assets




that the agency's intelligence-gathering activities show the Soviets to have been employing year by year.

LEEDEF, our third Soviet military spending series, is derived by linking two constant ruble price series estimated by former CIA analyst William T. Lee for the years 1955 to 1980 (Lee, 1977: 65-67; 1980: 22). Lee's estimates were computed by a particularly complex version of the "budgetary residuals" method, which involves augmenting the official Soviet defense budget figures with ingeniously deduced estimates of supplemental military funding concealed in the "science" line item and in the unitemized residuals of the state budget. Data for the years 1951 to 1954 were independently computed by Paul M. Johnson, replicating as closely as possible the method exhaustively detailed in Lee (1977). Annual growth rates were calculated using the midpoints of Lee's ranges.2

The three military spending series are estimated using sharply differing methods by analysts who also adhere to rather different policy views, and, not surprisingly, produce somewhat differing results. The SIPRI estimates rather consistently show the lowest absolute figures of the three and also by far the lowest cumulative growth rate. The Lee- Johnson estimates show the highest absolute spending levels for most years and the highest cumulative growth rate. The CIA figures generally fall between the other two. Nevertheless, despite these differences, the three series converge in showing somewhat similar patterns of year-to- year acceleration and deceleration of the growth rates. That is, there seems to be fairly consistent agreement among the estimators as to which years featured the most and the least rapid growth of military spending during any given subperiod. Encouraged by the significant intercorrelations in the growth rates of these data series and hoping to tap the strengths of all the varying methodologies by which they were derived, we also constructed a fourth composite indicator of growth in Soviet military spending based on a simple average of the first three. (The intercorrelations of our various defense spending measures are shown in Table 1.)

MEASURES OF RESOURCE ALLOCATIONS TO MAJOR CIVILIAN PROGRAMS

The dependent variables in our various regressions are estimates of the annual percentage real growth rates in certain sizable sectors of the civilian economy hypothesized to be in competition with the military for

2. The LEEDEF data points estimated by the senior author were as follows: 1952, 19.0%; 1953, 8.0%; 1954, -5.0%; 1955, 15.0%. Although these figures were estimated




TABLE 1 Intercorrelations of Military Spending Growth Estimates

(Pearson correlation coefficients)

LEEDEF CIADEF SIPRIDEF

CIADEF .42 p= .01

SIPRIDEF .77 .37 p <.01 p = .02

AVGDEF .89 .69 .89 p<.Ol p<.01 p<.01

NOTE: All significance levels refer to single-tailed test.

scarce productive resources. Although official Soviet sources have been much more forthcoming in the publication of useful statistical material pertaining to the civilian economy than they have been with respect to military-related matters, there are, of course, considerable problems in deriving suitable indices of Soviet resource allocation to the various civilian sectors hypothetically in competition with the military for resources. Official data series are often incomplete. Definitions of statistical categories are often vague and sometimes change significantly with little notice or explanation. Indices of price increases used to transform current ruble data into "real" terms often contain a downward bias.

In our efforts to devise suitable macroeconomic indicators of the growth rates of the various economic sectors of interest in this study, we have leaned heavily upon the published work of several leading Western students of the Soviet economy who have made it their business to sort out and deal with as many as possible of the often knotty problems involved in constructing valid and reliable data series from the raw materials made available by the Soviet authorities. Although we are satisfied that we have utilized the best data available, it should be borne in mind that the inherent shortcomings of the underlying statistical raw materials inevitably degrade the quality of the data to an unknown but probably significant degree, no matter how sophisticated the processes of manipulation and adjustment employed to clean them up. The "noise" in the data in turn suggests the need for caution in assessing the degree of confidence to be placed in the results of the econometric

following techniques used in Lee (1977), Dr. William Lee was not consulted personally regarding this extension of his data set, and consequently he bears no responsibility for its accuracy.



Johnson, Wells / SOVIET RESOURCE ALLOCATION 205

analysis of macroeconomic relationships. Brief descriptions of the individual variables follow.

SOCSEC: "Social security" allocations for old age-pensions of workers in the state sector (excludes state subsidies to the pension funds of collective farms, which began in 1965; Hutchings, 1983: 115). This indicator does not include other social insurance programs such as family allowances or aid to the disabled, but our experimentation with broader definitions of this variable clearly indicates that inclusion of such expenditures produces virtually the same annual rates of growth.

HEALTH: Estimated budgetary and nonbudgetary outlays in 1970 prices for the provision of health services, excluding investment (JEC, 1982: 395-401).

EDUCAT: Estimated expenditures in 1970 prices on general, specialized, vocational, and higher education, excluding investment (JEC, 1982: 394-401).

CONSERV: Volume index of provision of consumer services valued at 1970 prices, excluding housing, education, and health (calculated from data in JEC, 1982: 65-67).

FOOD: Volume index of food consumption weighted by 1970 prices (JEC, 1982: 65-67).

SOFTGDS: Volume index of consumption of selected soft goods, weighted by 1970 prices (clothing and certain nondurable household products; JEC, 1982: 65-67).

CONSDUR: Volume index of retail sales of principal consumer durable and miscellaneous goods, including private automobiles, motorcycles, bicycles, TV sets, household furniture, domestic appliances, and a wide variety of smaller personal items in common use, weighted by 1970 prices (JEC, 1982: 65-67).

CONGOODS: Composite index of supplies of consumer goods, derived from the three preceding indices, weighted according to 1970 prices (JEC, 1982: 65-67).

CONSUMP: Composite index of total consumption of consumer goods and services, including, CONGOODS, CONSERV, HEALTH, and EDUCAT above, as well as an index of the current value of housing services, weighted according to 1970 prices (JEC, 1982: 65-67).

TTLINVS: Total state investment at constant prices; links several official Soviet series compiled in Clarke and Matko (1983: 17-18).

HOUSCON: Housing construction as measured by square meters of usable living space reported completed annually (Clarke and Matko, 1983; 31-32; Smith, 1973: 424; our own estimates for 1951-1954).

AGINVS: Total investments in agriculture at constant prices, linking several (somewhat inconsistent) official Soviet series compiled in Clarke and Matko (1983: 17-18).

NHINVEST: Total investments exclusive of housing construction (our own estimates, derived from data underlying HOUSCON and TTLINVS above, using additional Soviet data in Narkhoz, 1970: 483).




RESULTS

We began our search for sectors in the Soviet economy most regularly held hostage over the years to the growth rate of the military budget by examining the bivariate relationships between their respective growth rates over the entire period 1951-1980. Table 2 displays the simple correlation coefficients between the growth rates of our military spending variables and the various civilian programs hypothesized to be in competition with the military for resources. Inspection of the table reveals that 6 of the 13 civilian programs display significant negative correlations with at least one of the measures of Soviet military spending, suggesting persistent substitution relationships: social security, foodstuffs, soft goods, consumer durables, total state investment, and housing construction. Of these, consumer durables, housing construction, social security, and soft goods seem to be involved in the sharpest trade-offs and display the most consistent results across the multiple indicators of military spending. Two indicators, education and agricultural investment, obstinately enough not only fail to support the trade-off hypothesis but even show rather strong positive correlations with one or two measures of military spending.3

The generally positive preliminary results are encouraging, but the highly simplified assumptions involved in such two-variable models of the determination of growth of resources allocated to the various civilian uses demand caution against drawing premature conclusions. There are other constraints potentially affecting the available supply of resources for these programs besides the competition of the military, and presumably still other variables affect the planners' sense of the relative urgency of demand for each individual civilian good or service. Trade-off relationships revealed by the bivariate analysis could well be

3. Subsequent investigations showed that the apparent complementarity of agricultural investment and CIADEF disappeared completely when any of a number of plausible control variables were introduced into the regression model. However, the anomalous finding for education not only held up but was strengthened when we further elaborated the model by controlling for the growth in the school-age population (5 to 19 years), showing significant positive regression coefficients for three out of four military spending variables. Because the estimates for spending on education were deliberately constructed with an eye toward excluding spending on scientific research (much of which is known to be for military applications), it is hard to come up with a good explanation for why education should so frequently seem to benefit from increases in the military budget. Interestingly enough, the same counterintuitive relationship between education and military spending also turned up in the analogous equations for U.S. education spending reported in Russett (1982).




TABLE 2 Relating Growth Rates of Military and Civilian Programs

(Pearson correlation coefficients)

A VGDEF LEEDEF CIADEF SIPRIDEF

CONSUMP -.1031 -.055 6 -.2247 -.0004 p=.297 p=.387 p=.121 p=.499

SOC SEC -.3905 -.3997 -.5043 -.1172 p=.018 p=.016 p=.003 p=.272

HEALTH -.1177 -.2180 .0083 -.0798 p=.272 p=.128 p=.483 p=.340

EDUCAT .3554 .1383 .4723 .2870 p=.029 p=.237 p=.005 p=.066

CONSERV .1932 .2016 .2985 .0157 p =.158 p =.147 p= .058 p = .468

CONGOODS -.1460 -.0782 --.2933 -.0210 p = .225 p = .343 p = .061 p = .475

FOOD -.0526 .0454 -.3570 .1294 p = .393 p = .408 p = .029 p = .252

SOFTGDS -.3505 -.4086 -.1264 -.3218 p=.031 p=.014 p=.257 p=.044

CONSDUR -.4668 -.3568 -.3887 -.4122 p= .005 p=.029 p=.019 p=.013

TTLINVS -.3595 -.4612 -.1193 -.3024 p=.028 p=.006 p=.269 p=.055

HOUSCON -.5565 -.5061 -.6527 -.2730 p=.001 p=.003 p=.001 p=.076

NHINVEST -.0070 .1195 .1833 .0595 p = .486 p = .269 p =.171 p = .380

AGINVS .0653 -.1647 .5455 -.1501 p=.368 p=.197 p=.001 p-.218

NOTE: All significance levels shown refer to single-tailed test.

spurious, and genuine trade-off relationships with the military spending might be masked by the effects of additional factors working in an opposite direction.

The small number of cases we had to work with (N = 29) severely limited the number of control variables it was practical to introduce, but the literatures on trade-offs in Western systems and on Soviet budgeting readily suggested several plausible hypotheses about other determinants




of these categories of Soviet spending the possible confounding effects of which we felt ought to be investigated and allowed for before taking very seriously the trade-off effects of military spending suggested by our preliminary analysis.

The first competing hypothesis is often associated with the notion that structurally determined imperatives of maintaining bureaucratic cohesion and integration support budgeting procedures that assign a customary "fair share" of new revenues to established programs. According to such models of resource allocation, incremental income effects may be expected to dominate the outcomes, and growth rates in planners' allocation of resources to any individual program will tend to move in rough synchronization with the fluctuating growth rates of total resources available for bureaucratic allocation. Because a centrally planned socialist economy of the Soviet type excludes market allocation of resources on principle and virtually the entire national income is thus subject to bureaucratic allocation, we hypothesize a direct, positive relationship between the growth rates of each of the various civilian programs and the growth of the overall Soviet economy. To test for the income effect, we calculated annual percentage growth rates of officially reported Soviet national net material product (NMP) (Clarke and Matko, 1983: 7). (We also experimented using U.S. intelligence estimates of Soviet GNP from JEC, 1982, but found such high correlations with NMP growth rates that presentation of the near identical results is unjustified.)

A second approach to modeling the determinants of resource allocations to at least certain kinds of "entitlement programs" emphasizes the importance of demographic effects. If the usual patterns uncovered in studies of other industrial economies are any guide, we would expect changes in Soviet spending for such programs as education, old-age pensions, health care, and housing to be positively related to the rates of increase or decrease in the target populations-the number of young people to be schooled, old folks to be supported and cared for after retirement, people to be housed, and so forth. Accord- ingly, we located annual data on the size and estimated age distribution of the Soviet population (U.S. Dept of Commerce, 1973, 1979; Clarke and Matko, 1983) and computed percentage growth rates of both total population and selected age cohorts deemed to be potentially useful in specifying the demand for particular programs we wished to model.

Liberal democratic theory emphasizes elite competition and the occasional replacement of top political leadership as an important




mechanism for altering a country's policy priorities, and investigators constructing statistical models for changes in budgetary allocations in Western industrial democracies routinely specify party-in-power variables as explanatory factors. The rigorously enforced monopoly of the CPSU in Soviet politics obviously renders "party" a nonvariable, but interrupted time-series analysis by Bunce (1981) suggests convincingly that transitional leadership periods following changes in the incumbent Party first secretaries in both Eastern Europe and the Soviet Union have been characterized by substantial proconsumer budgetary innovations. Accordingly, we added to our data set a dummy variable TLEADER, which is scored 1 for the years 1953-1957 and 1965-1968 (based on the "Kremlinological" periodization of the succession struggles following the death of Stalin and the removal of Khruschev in Breslauer, 1982) and 0 for other years.

Our efforts to specify more powerful multivariate models to explain variations in the growth rates of the various civilian programs have been much too extensive to detail in full here, although they may provide the subject matter for other papers. Suffice it to say that we experimented extensively not only with the variables already described but with others as well, including place in the hypothetical five-year plan cycle and identity of the incumbent first secretary, all with various lag structures. Here we have space to report only selected findings of most immediate relevance to the subject at hand, the question of trade-offs with military spending.

The first point to be noted is that our extensive efforts to respecify and elaborate the models for those civilian programs originally revealing no significant trade-offs in the bivariate correlation analysis proved unsuccessful in uncovering any clearer relationships to military spending by adding additional theoretically justifiable independent variables. Although the signs of the regression coefficients for the various measures of military spending in this group of multivariate models were almost uniformly negative, they did not often or consistently across the alternative measures reach conventionally accept- able levels of statistical significance and so will not be further discussed in this article.

Second, further preliminary experimentation with those variables that showed significant bivariate correlations with only one or two of the four military spending measures (FOOD, TTLINVS, AGINVS) produced no more consistent results for the multivariate regression models that we were able to devise. Accordingly, their capacity to




provide support for any hypothetical trade-off relationships with military spending is entirely dependent upon difficult and highly contentious prior judgments as to which experts' estimates of Soviet military spending are to be considered most dependable. Because we do not wish to become unnecessarily embroiled in this controversy at this stage in our research project, these more ambiguous results also will not be further discussed in this article.

What we will concentrate on for the remainder of this article are those programs that seem to present evidence of more reliable trade-off relationships by virtue of the strength and direction of their bivariate association with at least three of the four military spending series: durable consumer goods (CONSDUR), housing construction (HOUS- CON), social security (SOCSEC), and soft goods (SOFTGDS). Our approach was to experiment with the available data in a number of separate but theoretically plausible specifications to model the determinants of the growth of resources allocated to these various programs and then to present the models that explain the greatest total variance. We were then in a position to assess whether the impact of military spending remains significant after these additional variables have been taken into account.

CONSUMER DURABLES

Our approach to specifying a model for the growth in production of durable consumer goods hypothesized a positive income effect of NMP on the planners' supply function and a positive demographic effect on their perceptions of demand due to increases in the total population (POPGRRT). We also borrowed from Bunce (1981) the hypothesis that planners would be particularly likely to favor the interests of consumers during the transitional years following a leadership succession and used the dummy variable TLEADER to test for this relationship. All three independent variables proved to be positively and significantly related to CONSDUR in our simple bivariate regressions. Unfortunately, NMP, POPGRRT, and TLEADER proved to be rather highly intercorrelated (correlation coefficients in the general vicinity of .5), and the effects of this multicollinearity made it impractical to identify with much exactness in our multiple regression models the relative contributions of these three variables to the determination not only of




TABLE 3

Models for Consumer Durables

CONSDUR = -.50* AVGDEF + 6.6* TLEADER -.1 NMP + 1.2 POPGRRT + 9.9* (.18) (2.6) (.43) (4.6) (3.8)

R2 = .48 Durbin-Watson = 2.03a

CONSDUR = -.31* LEEDEI - .09 NMP - .05 POPGRRT + 7.05* TLLADER + 11.3* (.17) (.47) (5.1) ((6.2)

R' = .39 Durbin-Watson = 1.993

CONSDUR = -.36* CIADEI + .28 NMP +1.45 POPGRRT + 5.86* TLL ADLR + 6.2 (.19) (.51) (4.3) (2.8) (4.7)


CONSDUR = -.40* SIPRIDEI -.45 NMP +2.16 POPGRRT + 7.78* TLLADLR +9.2 (.12) (.41) (4.9) (2.5) (5.6)

R2 = .53 Durbin-Watson = 2.04z'

a. Equation estimated using Cochrane-Orcutt procedure (all others estimated by ordinary least squares). *Significant at p < .05, single-tailed test.

CONSDUR but also of a number of other programs.4 Fortunately, none of these three control variables is more than modestly correlated with the military spending variables (the worst is TLEADER, with simple R ranging from .1 to .3), so we can safely place much more confidence in our estimates of those regression coefficients that are most central to the subject of this article.

Table 3 displays one of the longer versions of our specification of the models for consumer durables using our various estimates of military spending along with NMP, population growth rate, and TLEADER as

4. The relatively high(R = .49) correlation of the TLEADER variable with the growth rate of Soviet net material product also suggests to us the need for greater caution in interpreting at least some of the findings in Bunce (1981). Bunce's findings of strong relationships between changes in leadership and budgetary innovations in European Communist countries may be partially spurious because her research design does not provide any control for the fortuitous effects of any unusually rapid economic growth rates during the transitional periods. Although we have checked the precise statistics only for the case of the USSR, impressionistic eyeballing of Bunce's tables and the relevant NMP growth figures suggest that a good many of the USSR's leadership transition periods in other Eastern European countries were also periods of accelerating economic growth. (Of course, if there were a direct causal relationship between the ascension of new leaders and revitalization of the economy, then perhaps we would not want to describe the resulting impact on the budget as "spurious" after all.)




independent variables. Because of multicollinearity, several of the regression coefficients for the nonmilitary independent variables fail to attain conventional levels of statistical significance when entered together (even though all three do so when entered separately along with the military spending variables in shorter versions of the model), nevertheless the coefficients for AVGDEF remain significant and negative, as hypothesized. (Although the variable TLEADER appears from these estimates to be the "best" of the nonmilitary variables, this appearance is, of course, deceiving, given that multicollinearity of these variables precludes the reliable discrimination of their respective contributions to the explanation of the variance in the dependent variable.)

Based on these models, one might expect a one percentage point increase in the growth of military spending to result, other things being equal, in a decrease in the growth rate for durable consumer goods of roughly one-third to one-half a percentage point.

HOUSING CONSTRUCTION

Our principal efforts toward a more complex model of the growth rate in housing construction examined the same three effects explored in modeling consumer durables above-the income effect (hypothesizing a positive relationship of housing construction with the growth rate of NMP), the demographic effect (hypothesizing a positive relationship with the growth rate in the population), and the political succession effect (hypothesizing greater than normal attention to housing construction in the early years of a new administration before the consolidation of power by the new first secretary). Analysis quickly demonstrated that there was no statistical support for the existence of any income effect worth mentioning, either in bivariate models or in any of a great variety of multivariate models with which we experimented. Models employing both population growth rate and the leadership succession dummy variable again encountered problems of multicollinearity, resulting in difficulties in untangling the relative contributions of the two variables to the model and often resulting in one or the other showing up with statistically insignificant regression coefficients despite the moderately strong and statistically significant positive bivariate relationships each displayed with housing construction. Table 4 displays results for a representative long-form specification of the housing construction model (that for AVGDEF once again) as well as the complete results for




the essentially equally performing and more parsimonious two-variable specifications using only the various military spending series and population growth rate as independent variables.

Both in the long-form specification and in the two-variable versions of our models for the growth rate in housing construction, the regression coefficients for all of the military spending variables are significant and negative, as is consistent with the existence of a genuine trade-off relationship. According to these models, a one percentage point increase in the growth rate of military spending might be expected to entail one-third to a full percentage point decrease in housing construction.

SOCIAL SECURITY

Our efforts to model the determinants of growth in Soviet social security outlays (SOCSEC) other than defense expenditures encountered

TABLE 4 Models for Housing Construction

HOUSCON = -.81* AVGDEF + 8.67* POPGRRT + 3.01 TLEADER -2.0 (.24) (3.22) (2.77) (4.4)

R2 =.46 Durbin-Watson = 1.75

HOUSCON = -.87* AVGDEF + 9.61* POPGRRT - 1.9 (.24) (3.11) (4.4)

RI =.46 Durbin-Watson = 1.90

HOUSCON = -.59* LEEDEF + 8.23* POPGRRT +.39 (.24) (3.57) (5.7)

RI =.34 Durbin-Watson 1.79

HOUSCON -1.01* CIADEF + 11.82* POPGRRT -4.8 (.16) (2.58) (3.5)


HOUSCON = -.37* SIPRIDEF + 11.04* POPGRRT - 7.2 (.20) (3.59) (4.8)

R2 =.27 Durbin-Watson = 1.70

a. Equation estimated using Cochrane-Orcutt procedure (all others estimated by ordinary least squares). *Significant at p < .05, single-tailed test.




unexpected problems. Our initial hypothesis, derived from our reading of the corresponding literature on Western industrial countries, was that SOCSEC would be largely accounted for by some combination of the growth in total national resources available to the leadership for allocation (the income effect) and the growth in the size of the elderly population (the demographic effect). NMP and SOCSEC were hypothesized to be positively related on the theory that a rapidly rising national income would lessen the planners' sense of resource scarcity and encourage them to loosen the purse strings, whereas more sluggish periods in the economy would lead to a more penurious attitude toward expenditures not immediately linked to stimulating worker productivity.

To test for the demographic effect, we calculated growth rates of estimated Soviet population 65 years of age and older (POP65). Here our hypothesis was that POP65 and SOCSEC would be positively related on the expectation that pensions would be treated as an entitlement program automatically providing stipulated benefits to those who achieve the requisite age. We knew that the Soviet population structure had undergone pronounced shifts over the period of our study due to the demographic effects of World War II. To our considerable surprise, NMP and POP65 (both singly and in various combinations, lagged and unlagged, with and without a wide variety of plausible control variables) proved to be essentially worthless as predictors of growth in Soviet pensions, and this conclusion continued to apply when we experimented with several redefinitions of the dependent variable both including and excluding state subsidies to the pension funds of collective farms.

The least unsuccessful specification of the model for social security we were able to come up with after quite extensive experimentation involved only two variables-defense spending and an index of total real output of consumer goods and services (CONSUMP) estimated by Gertrude Schroeder and Elizabeth Denton, as presented in JEC (1982: 317-401). The regression equations are presented in Table 5. This specification of the model works satisfactorily only for the versions employing the Lee estimates of military spending and our composite estimate AVGDEF, and even these two equations account for only about 20% of the variance. The beta for the CIA military spending estimates remains significant when we introduce CONSUMP into the equation, but the beta for the control variable falls just short of customary significance levels (p = .074). The SIPRI estimates of military spending remain essentially unrelated to social security outlays in this model, as in all others with which we experimented.




TABLE 5 Models for Social Security Outlays

SOCSEC = -.795* AVGDEF + 2.033* CONSUMP + 2.488 (.38) (1.13) (6.45)

R2 = .19 Durbin-Watson= 1.96

SOCSEC = -.729* LEEDEF +2.137* CONSUMP +4.177 (.32) (1.1 1) (6.63)

R2 = .21 Durbin-Watson = 2.09

SOCSEC = -.917* CIADEF + 1.609 CONSUMP +3.873 (.34) (1.10) (6.19)

R2 = .26 Durbin-Watson= 1.64

SOCSEC = -.189 SIPRIDEF + 2.28* CONSUMP - 2.34 (.30) (1.21) (6.40)


*Significant at p < .05, single-tailed test.

Detailed examination of the data reveals that the most significant changes in Soviet social security outlays were the result of several spectacular annual increases in per capita benefit levels in 1956 to 1958. These coincided with several of the very few absolute declines of Soviet military spending in the period under study and also with moderately rapid improvements in the production of consumer goods. In the remaining years of the study, social security spending still does not seem to respond to the demographic changes to any significant extent, but rather to changes in the overall level of consumption goods and services made available to the total population. Our speculative interpretation is that in more normal years the top leadership in their roles as planners determine first the share of the national income to be alloted to consumption rather than investment and only then allot benefit increases for pensioners, perhaps based on some notion of parity with the increases in living standard being given the working age population in the form of wage and salary increases. But when large increments in available resources are occasionally available (as in years of military spending reductions), the planners may feel able to do something more to alleviate the plight of Soviet pensioners (whose incomes have perennially been stuck at near-poverty levels despite relative gains in the post-Stalin era) by giving them disproportionate raises. Except for the SIPRI data, our measures of changes in military spending formally




support the hypothesis of a short-term trade-off relationship with the material living standards of Soviet pensioners, but the effects are sufficiently localized in time to put the overall value of the generalization in serious question.

SOFT GOODS

In modeling the determinants of growth in Soviet production of soft goods, income and transitional leadership effects proved to be the most potent predictors. Introduction of the military spending variables into the model produced the results displayed in Table 6. The signs of the regression coefficients for the military spending variables are negative, as the trade-off hypothesis predicts, and in three of the four cases they are statistically significant. The fact that the Durbin-Watson statistics remain in the "indeterminant" zone even after application of the Cochrane-Orcutt procedure raises the possibility that distortive autocorrelation effects may be responsible for the coefficients achieving statistical significance, yet the relative consistency of the estimated betas across alternative measures of military spending perhaps lends some additional credence to the estimates. At best, these results weakly support the generalization that, on average, a 1% increase in the growth rate of military spending has entailed only about two-tenths of 1% decrease in the growth rate of soft goods made available to the Soviet population-scarcely a dramatic effect.

CONCLUSION

The question of whether there have been identifiable sectors of the Soviet civilian economy that have been repeatedly and disproportionately affected by annual fluctuations in the growth rates of military spending seems to have been answered fairly convincingly in the affirmative. The output of durable consumer goods and housing construction have both been shown to display rather clear inverse trade-off relationships with military spending in the USSR during the period 1951 to 1980, whereas outlays for social security appear to be involved in a more episodic, less reliable trade-off. There is somewhat ambiguous confirmation for a weak but measurable impact on the availability of soft goods. It is worth noting that the magnitudes of the




TABLE 6 Models for Soft Goods

SOFTGDS =-.24* AVGDEF + .33* NMP + 2.43* TLEADER + 2.86* (.07) (-16) (1.11) (1.41)

R2 = .58 Durbin-Watson= 1.57a

SOFTGDS = -.22* LEEDEF + .30* NMP + 2.39* TLEADER + 5.00* (.06) (.16) (1.09) (1.50)

R2 = .60 Durbin-Watson = 1.5 1a

SOFTGDS = -.04 CIADEF + .39* NMP + 3.17* TLEADER + 2.32* (.08) (.18) (1.06) (1.24)


SOFTGDS = -.19* SIPRIDEF + .19 NMP + 2.88* TLEADER + 4.23* (.05) (.16) (1.06) (1.40)

R2 =.62 Durbin-Watson = 1 48a

a. Equation estimated using Cochrane-Orcutt procedure (all others estimated using ordinary least squares). *Significant at p < .05, single-tailed test.

trade-off relationships detected compare rather favorably with the weaker findings generally reported in the corresponding literature on military trade-offs in advanced capitalist democracies during peacetime, despite the fact that the amount of "noise" in our data on military spending is surely at least an order of magnitude higher than what students of these less secretive political orders have to contend with. This is consistent with our earlier theoretical speculations on the structural features of the Soviet-type political economy that seem conducive to the establishment of relatively durable patterns in the priorities imposed upon the economy.

Apart from their more abstract comparative significance, the specific content of the empirical findings should be of some interest for students of the Soviet economy as well. It is noteworthy that the stronger trade- off relationships detected involved economic sectors relating to forms of individualized consumption affecting the material living standards enjoyed by the population. Our two indicators of major forms of "collective consumption" seem to have been either relatively little affected by changes in military spending (health) or somewhat positively affected (education). Although we would want to supplement these measures based on current operating expenses with measures of the




more volatile and potentially more transferable investment outlays for these sectors before trumpeting this conclusion with too much confidence, this finding casts at least some doubt on recent speculation by Eberstadt (1981) that stinting on the health budget necessitated by the pressures of military spending might be one important reason for the adverse turn in Soviet life expectancy figures now known to have begun in the early 1970s.

The most negatively affected sectors relate only very indirectly to the prospects for the Soviet leaders' traditional top economic policy goal- promoting accelerated growth in "the production of the means of production." Although our initial screening in Table 2 seems to suggest an ominous growth-threatening trade-off between total state investment and military spending like that earlier found by Cohn (1973), the short- term relationship disappears entirely when we refine the variable by deducting estimated "nonproductive" investment in housing construction from total investment to yield the variable NHINVEST. (And this negative finding continues to hold up in all of the more complex models with which we experimented.) This may have some policy implications, given that hawkish American policy advocates sometimes seem to be arguing that continuation or feasible acceleration of the East-West arms race might drive the Soviet economy into stagnation and collapse by depriving it of needed investment. There is surely some level of military effort that would have this effect on the Soviet economy. But the experience of these three postwar decades as summarized in our admittedly rather simplified models suggest that Soviet planners have quite remarkable abilities to insulate their priority investment programs and instead to impose the bulk of the burden of increases in military spending on the long-suffering Soviet consumer. Although a more sophisticated and integrated multiequation model of the Soviet economy might conceivably turn up subtler or longer-term trade-offs of military spending for productive investment than our cruder approach is capable of detecting, it seems prudent to consider closely whether it is not apt to be rather more expensive for us to spend the Soviets into bankruptcy than the hawks imagine.

REFERENCES

BECKER, A. S. (1981) The Burden of Soviet Defense: A Political-Economic Essay (R-2752-AF). Santa Monica, CA: Rand Corporation.




BRESLAUER, G. W. (1982) Khrushchev and Brezhnev as Leaders: Building Authority in Soviet Politics. London: Allen & Unwin.

BUNCE, V. (1981) Do New Leaders Make a Difference? Executive Succession and Public Policy Under Capitalism and Socialism. Princeton, NJ: Princeton Univ. Press.

CLARKE, R. A. and D.J.I. MATKO (1983) Soviet Economic Facts, 1917-8 1. New York: St. Martin's.

COCKLE, P. (1978) "Analyzing Soviet defense spending: the debate in perspective." Survival 20, 5: 209-219.

COHN, S. H. (1973) "Economic burden of defense expenditures," pp. 147-162 in U.S. Congress, Joint Economic Committee, Soviet Economic Prospects for the Seventies. Washington, DC: Government Printing Office.

DOMKE, W. K., R. C. EICHENBERG, and C. M. KELLEHER (1983) "The illusions of choice: defense and welfare in advanced industrial democracies, 1948-1978." Amer. Pol. Sci. Rev. 77, 1: 19-35.

EBERSTADT, N. (1981) "The health crisis in the USSR." New York Review of Books (February 19): 23-31.

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Prepared for the Use of the Joint Economic Committee, Congress of the United States. Washington, DC: Government Printing Office.

LEE, W. T. (1977) The Estimation of Soviet Defense Expenditures 1955-1975: An Unconventional Approach. New York: Praeger.

---(1980) "Prepared statement on estimates of Soviet defense spending," in Hearings Before the Subcommittee on Oversight of the Permanent Select Committee on Intelligence, House of Representatives, September 3, 1980. Washington, DC: Govern- ment Printing Office.

NARKHOZ (various years) Narodnoye khozyaystvo SSSR v.... g. [The National Economy of the USSR in the year .. .] Moscow: Central Statistical Administration.

RUSSETT, B. (1982) "Defense expenditures and national well-being." Amer. Pol. Sci. Rev. 76, 4: 767-777.

SMITH, W. S. (1973) "Housing in the Soviet Union-big plans, little action," in U.S. Congress, Joint Economic Committee, Soviet Economic Prospects for the 1970s. Washington, DC: Government Printing Office.

Stockholm International Peace Research Institute. (n.d.) SIPRI Yearbook: World Armaments and Disarmaments. Cambridge: MIT Press.

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soviet military and civilian resource allocation 1951-1980

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