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(1994)

Productive Efficiency under Capitalism and State Socialism: An Empirical Inquiry Using Chance-Constrained Data Envelopment Analysis KENNETH

C. LAND,

C. A. KNOX

LOVELL,

and STEN THORE

ABSTRACT In this paper we undertake a comparison of the productive efficiency of a set of West European market economies and a set of East European planned economies. We employ the techniques of chance-constrained data envelopment analysis to conduct the comparison. These techniques are particularly appropriate when the performance of producers depends on their ability to make resource allocation decisions in the presence of technological and market uncertainties. We find the market economies to have been much more efficient in their allocation of resources.

Introduction To a large extent, the economic and political history of the 20th century has been the history of the evolution and competition of two alternative systems of organizing the production and distribution of goods and services in a nation: capitalism and state socialism. The Russion revolution of 1917 set the stage for an unparalleled social and economic experiment investing all decision-making in the arms of the government. As the century draws to its close, that system of state socialism has now mainly been dismantled. This paper deals with developing and applying tools that can be used to assess the relative merits of the economic performance of the two systems. Presumably, examining the record of capitalism and state socialism, it should by now be possible to arrive at some preliminary empirical conclusions about how the two systems have performed (see

11-m. For the purpose of the present investigation, it will be sufficient to define capitalism as a system where all (or almost all) productive entities are owned and managed by private KENNETH C. LAND is the John Franklin Crowell Professor and Chairman in the Department of Sociology and Senior Research Fellow in the Center for Demographic Studies, Duke University. C. A. KNOX LOVELL is Terry Professor of Economics, Department of Economics, University of Georgia, Athens. STEN THORE is the Gregory A. Kozmetsky Centennial Fellow at the IC* Institute (Innovation, Creativity, Capital), The University of Texas at Austin. Address reprint requests to Dr. Sten Thore, IC* Institute, The University of Texas at Austin, Austin, TX 78705-3596. 0

1994 Elsevier Science Inc.

0040-1625/94/$7.00

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individuals. Prices are set by markets. Under state socialism, the productive units are owned and managed by the government. Prices are set by the government. (For further characteristics of key economic mechanisms under state socialism, see [6-81). While in the political world a gulf has separated capitalism and state socialism, formal economic theory has been able to accord the two alternative systems a long and surprising history of friendly cohabitation. They have drawn on the same heritage of Western economic thinking, including the writings of Smith, Ricardo, Marx, and Marshall. Socialist economists have accepted many of the postulates of neoclassical economic theory, including that of an “economic man” maximizing consumer utility or minimizing producer cost (see [9], foreword). In line with this thinking, it has been common among socialist economists not only to assert that the microeconomic building-blocks of the two systems had the same mathematical form, but that the two systems would actually have identical mathematical solutions. (See Shubik [lo], who forcefully argued that the formalization of the neoclassical system in Debreu’s Theory of V&e [l l] could just as well be interpreted as a centralized economy with set and fixed prices.) But, whereas the capitalist economy would rely on the mechanism of markets to establish this solution, the socialist economy would calculate directly the optimal quantities of all goods and services, and enforce the production and distribution of these quantities by decree. These matters of communication and control under capitalism and state socialism were further explored by Kornai [ 12-141, who formulated a mathematical master program of decomposition type. Under capitalism, decentralization is carried out through the dissemination of prices (“price-directive” decentralization). Under state socialism, decentralization takes the form of direct quantity directives (“quantity-directive” decentralization). With suitable mathematical assumptions, the two methods of decentralization will lead to identical solutions. (See [ 1S- 181.) If the theoretical microeconomic building blocks under state socialism are the same as those under capitalism, then, presumably, it is also legitimate to assemble such building blocks into a conventional Western macroeconomic model and to estimate it with econometric methods. With the advent of the modern macroeconomic disequilibrium theory (see [19]) and the accompanying possibilities of modeling markets with administratively Iixed prices, a considerable literature arose estimating macroeconomic disequilibrium models for state socialist countries (see [20] for estimations relating to Poland, and [21] relating to Hungary). The main impression left by this literature is that economists have only made marginal headway in spelling out the real world gulf that has separated the two alternative systems of organizing production and distribution. In the mathematical representations these two systems are still astonishingly alike. Apparently the mathematical specifications have not been very successful in capturing their characteristics. In order to provide a new departure, we propose to examine critically two of the fundamentals of the (neoclassical) theory now reviewed: the assumption of efficiency and the assumption of certainty. Is it empirically true that the economic systems of capitalism and state socialism can both be described as if they had resulted from the solution of some general equilibrium model, involving neoclassical building blocks? Are real life economies efficient, i.e., Pareto optimal? And, second, is it empirically warranted to disregard the stochastic nature of most economic processes, representing them instead as known, deterministic, and available to the decision-makers to optimize at their discretion? While the results of our empirical work will be presented in the third section of this article, the a priori motivation for questioning these assumptions should be obvious. The nature and the strength of the economic forces propelling the economy toward efficiency

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are certainly quite different under capitalism and state socialism. In the one case, they fall under the heading of “competition,” in the other under “planning.” And the nature of uncertainty under the two systems is also different. Under capitalism, new technological uncertainties and new market uncertainties are continuously encountered as producers are engaged in product development and innovation. These risks are spread among a large number of competing producers. The system pays a premium to those managers who are able to reduce the risks; it penalizes those who manage risk poorly. Under state socialism, production of mainly known technologies is delegated to state monopolies. But state socialism generates its own uncertainties [2, 8,22,23]. Managers learn to reckon with abrupt changes in production targets, and with uncertainties in the allocations of raw materials, labor, and other inputs. Allocation of scarce resources may be redirected from one plant to another, as planners try to adjust to changing circumstances. Long-run output targets may be maintained, but insufficient resources may be allocated, making the realization of these targets hypothetical at best. Long-run targets may be upgraded-or downgraded-by an overlay of intermediate plans. Managers will thus often receive conflicting and mutually inconsistent directives. Their job is to make the best out of an often chaotic situation. In brief, whereas managers under capitalism face market uncertainty, managers of state socialist firms face bureaucratic uncertainty. To accomplish our task of comparing the performance of the two competing systems, we shall develop an analytical format that permits us to measure the degree of efficiency of production achieved by managers in the face of stochastic uncertainties. That is, the concept of efficiency is related to how the decision-makers deal with uncertainty. Inefficient use of inputs is not just excessive use. Efficiency should be conceived of as an ex ante concept. Accordingly, it would be useful to modify the standard definition of efficiency to incorporate the degree of preparedness that management has established to handle stochastic variation in production relationships. The standard technique for estimating productive efficiency is so-called data envelopment analysis. (The original reference for DEA is [24]. For surveys, see [25-271). The particular approach to be used here is chance-constrained DEA (for theoretical developments, see [28, 291). The calculations proceed by constructing an enveloping efficiency frontier leaving 95% (or some other suitable threshold probability level laid down a priori) of all producers on the frontier itself or located on one side of it only. That is, violations of the efficiency frontier are required not to occur more often than in 5% of all cases. Producers located on the frontier are assigned the efficiency rating 1.OO. Producers located below the frontier are subefficient. Their efficiency measure is less than 1 .OO. The next (second) section briefly reviews the basic chance-constrained envelopment format. The input and output observations of each producer to be examined are compared with so-called “best practice,” defined as the greatest possible radial contraction of the inputs, while still obtaining the considered vector of outputs in 95% (or some other given threshold fraction) of all cases. The radial contraction factor is the desired chance-constrained envelopment measure of efficiency. The third section reports on our empirical work, which is based on a three-year panel of aggregate input-output data taken from 17 market-oriented West European economies and seven planned East European economies. There we show, using conventional DEA techniques, that the capitalist economies have allocated resources much more efficiently than the state socialist economies have. When we use chance-constrained DEA to incorporate the impact of uncertainties on resource allocation decisions, the performance differential between the two systems increases. This illustrates convincingly our

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thesis that when comparing the performance of two such different systems of resource allocation, it is important to allow for differences in the way managers deal with uncertainty. The fourth section argues that a rapid rate of product development in the Western world during the 1970s and 1980s increased the efficiency gap between capitalism and state socialism. A simple numerical experiment involving an increased technological uncertainty in the capitalist countries (mirroring the proliferation of new industrial processes and new products) is shown to push the planned economies even deeper into inefficiency and shortages. Our analytical techniques thus contribute to the understanding of the recent collapse of the state socialist system of resource allocation in the formerly planned economies. Mathematical Developments: Chance-Constrained Data Envelopment Analysis (DEA) and Incentives for Efficiency Whereas conventional economic analysis is based on the assumption of known and deterministic production relationships, we shall here assume a stochastic setting formalized as follows. Let there be i = 1,.. .,I producers and use the symbol x,, to denote the inputs employed (m = 1,...,M) and the symbol yin to denote the outputs then received (n = 1 ,...,N). The inputs are taken to be predetermined, but the outputs are stochastic and are defined by a joint probability distribution. No restriction need be placed on the particular distribution to be used, other than that it should have a well-defined cumulative density function. To simplify the presentation, we henceforth assume that the joint distribution is normal. (Another mathematically tractable alternative is that the distribution is jointly lognormal.) The distribution will then be characterized by the mathematical expectation&+” and the covariance matrix Cov (ym,yinJ), n,n’ = l,..., N. Clearly, the objective risk for production delays and breakdowns in the capitalist and state socialist settings is an empirical question, and there is no doubt that different economies vary in this respect, both cross-sectionally and over time. On the other hand, there is a difference in the way the two systems handle adverse developments when they do occur. In the competitive sectors of a capitalist economy, a production shortage in one firm sets the stage for competitors to secure additional orders and to increase their market shares. In a state socialist economy, bureaucratic restrictions to the effect that a given firm may obtain supplies from only one or a limited number of suppliers may substantially lengthen the adaptive response times. The basic idea of the Charnes-Cooper efficiency measure is to compare the producer being rated with “best practice.” If the producer does more poorly than best practice, it is rated inefficient. If the producer does as well as best practice, it is efficient. Best practice is formed as a weighted composite or amalgam of the performance of all producers. The weight of producer i is written hi. Best practice outputs are then X, y,,,hi, best practice inputs are C, x,,h,. The comparison with best practice involves an attempt at radial contraction of all inputs xom of the producer under examination (currently indexed “O”), while seeing to it that the same outputs yo,, or more, are being obtained. That is, denoting the degree of radial contraction by 8, the efficiency measure aims at minimizing the contraction 8 subject to the constraints &y,X, Lyon,

n =

8x0, - X:,x&, 20, hitO,i

l,..., N m = l,..., M

= 1, . . . ,I, and 0 unrestricted

(1) in sign

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If the optimal value of 0 is less than unity, so that some radial contraction is possible, the producer under examination is said to be inefficient. But if the optimal 8 is unity and all constraints (1) are binding, the producer is efficient. When the outputs are stochastic, the corresponding chance-constrained efficiency measure is calculated from (see 128, 291) min 8 subject to Prob (Zfy,hi >_Yon)2 0.95, 8x0,

h,aO,

-

Z:,XiJzi&O~

n = l,..., N

(2)

m = l,...,M

i = 1, . . . ,I, and 8 unrestricted

in sign

The input observations are all treated as predetermined. Formulation (2) contains the chance constraints Prob (Z, yin?, 2 _YO,)> 0.95, n = 1,. ..,N. The meaning of these constraints is as follows: The procedure of contracting inputs must still leave best practice, i.,e., the desired frontier point, at the cutting edge of technology. Best practice represents the practice of the best performing producers. Only at most a small fraction of them, say 5010,are assumed to perform better. (Any other suitable threshold fraction may be chosen; the figure 5% is used here just as an example.) The chance-constrained efficiency frontier is a “soft” frontier. At most, 5% of the observations are permitted to cross it. Observed outputs then exceed best practice. But the bulk of the observations are still required to fall beneath the frontier. Converting the chance-constraints in (2) into their certainty equivalents yields: E(EJinlLi - JJO~)- 1.645 s.d. (ZJin& - JJO,)20,

n = l,...,N

(3)

Constraints (3) spell out the nature of the comparison to be established between the producer currently being rated and best practice. They require the best practice outputs of the producer to be at least at par with observed outputs. Since outputs are stochastic, this involves the build-up of a buffer of outputs equal to 1.645 s.d. (Ei_Yinhi - ~0,). The buffer serves as a contingency against uncertainty. If the optimal measure 0 = 0* equals unity and if all constraints are tight, the producer under consideration is rated as efficient. It lies on the chance-constrained efficiency frontier. But if the optimal 8 = 0* is less than unity so that the observed point lies below the frontier, the producer is inefficient and O* provides a measure of the degree of inefficiency. The threshold level of 5% has been chosen here as the acceptable level of output failures in a capitalist economy. While the precise numerical value might be difficult to assess empirically, there can be no doubt that the capitalist system, at least in its competitive sectors, deals much more harshly with output failures than the state socialist system does. Under capitalism, output failure for a single firm means that some potential market share is snapped up by existing competitors. Output failure for an entire market causes the price to rise and new producers will be drawn into the market. In either case, the consequences are lost profit opportunities. But the frustrations of unsatisfied consumers in the state socialist economy do not bother the manufacturer. The chronic shortages of consumer goods in socialist countries seem to indicate that the central planning agency does not even attempt to meet demand. Why is the command economy not able to produce in sufficient amounts a conventional low-technology product like soap and to distribute it to consumers? In the capitalist

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economy a firm needs to keep a keen eye on the market. It needs to protect and to develop its market appeal. Its success is measured by its market share. It stays in touch with current developments in the market through various means of marketing. Under state socialism, the manufacturer has no contact with the market. The manufacturer is often a monopolist-i.e., the market share is 100%. Once the output leaves the factory door, the manufacturer is absolved of responsibility with respect to the master plan. Whether the goods meet with the approval of the consumers or not is of no concern to the manufacturer. There may thus arise large supply deficiencies that remain uncorrected. The acceptable level of output failures in the state socialist economy is therefore certainly much higher than in the capitalist economy. If, for instance, the acceptable level is set at 60%, the chance-constraints appearing in program (2) become Prob (Eainhi > ~0,) > 0.40, n = 1,. ..,N and the corresponding E(&Y&

certainty

equivalents

(4) are now

- ~0,) + 0.253 s.d. (&yi,,hi - ~0,) 2 0,

n = l,.. .,N

(5)

This time best practice output of the producer currently being rated falls below observed output most of the time. The chronic shortages of consumer goods in socialist countries seem to indicate that the central planning agency does not even attempt to meet demand. The constraints (5) imply that state socialism actually leads to a negative buffer of consumer goods-a net exhaustion of available stocks reflected by empty shelves and prospective customers lining up outside the store waiting for the next truck arriving from the warehouse. But these are only theoretical speculations. We now turn to an empirical examination of the same issue. Empirical Results Ideally, one would need firm-level data to estimate the models outlined in the preceding section, the index i = 1, . . .,I denoting, as stated, a representative selection of firms, both in capitalist countries and in state socialist countries. Perhaps such a data set can be constructed, or reconstructed, in the future. In the lack of such data, we have been forced to use economy-wide cross-section data. That is, rather than comparing firms we shall compare countries. In so doing, we shall leave untouched the entire problem of aggregation-in particular, the matter of the aggregation of expectational variables. Our empirical analysis is conducted on a panel consisting of 17 West European market economies and 7 East European planned economies observed annually for each of three years over the 1978-80 period. Although the data are dated, they are the most recent reliable data available for comparison purposes, and they have the virtue of capturing the performance of the planned economies at full maturity, long after they were established and long before their demise. The single output indicator is real GDP per worker. The two input indicators are aggregate energy usage per worker and aggregate net capital stock per worker. For details on the construction of the variables, see [30, 3 11. Observations were enumerated as follows: i = 1,...,17 i = 18,...,24 i = 25,...,41 i = 42,...,48 i = 49,...,65 i = 66,...,72

market economies, 1978 planned economies, 1978 market economies, 1979 planned economies, 1979 market economies, 1980 planned economies, 1980

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TABLE 1 Productive Efficiency Estimates, by Country and Year (Estimation Method: Deterministic DEA) country Market economies Austria Belgium France West Germany Netherlands Switzerland United Kingdom Denmark Finland Iceland Norway Sweden Italy Malta Portugal Spain Turkey Average all market economies Planned economies Bulgaria Czechoslovakia East Germany Hungary Poland Romania USSR Average all planned economies

The conventional

deterministic

1978

1979

1980

0.930 1.000 1.000 0.900 0.944 1.000 0.852 0.899 0.710 0.772 1.000 1.000 0.954 1.000 0.919 0.982 1.000

0.863 0.963 0.995 0.884 0.942 0.921 0.884 0.902 0.678 0.774 0.931 0.953 0.849 0.995 0.908 0.986 1.000

0.778 0.933 0.976 0.838 0.927 0.858 0.878 0.876 0.574 0.764 0.835 0.885 0.866 0.948 0.977 l.CGO 1.000

All years 0.827 0.520 0.570 0.461 0.570 0.491 0.405

0.906 0.926 0.532 0.555 0.483 0.631 0.544 0.421

All years

0.950 0.555 0.531 0.510 0.693 0.564 0.440 0.580

DEA model becomes

min 8 subject to Z

YJd~_YO,

0x0,

-

Xi

XiJbi 2 0,

(6)

m = 1,2

Xihj = 1, h, >o,

i = 1, . . . ,I, and Clunrestricted

in sign

where y = GDP per worker, xr = aggregate energy usage per worker, and x2 = aggregate net capital stock per worker. Note the condition Ci iii = 1, which requires that only convex combinations of the observed country data are permitted as best practice. This condition corresponds to an assumption of variable returns to scale in production, which seems appropriate in this case in light of the great size disparity among the economies being analyzed. Table 1 reports the results of conventional deterministic DEA. The average productive efficiency rating in the market economies exceeds that of the planned economies by over 50%. There is only one instance of a market economy not reaching the planned economy mean (Finland in 1980), and only two instances of a planned economy exceeding

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the market economy mean (Bulgaria in 1979 and 1980). (We also carried out the conventional deterministic DEA calculations using the logarithms of all variables. In regression analysis and related parametric econometric techniques, the logarithms of macroeconomic production data usually give better statistical fit than the raw data themselves. But for the DEA calculations, the choice does not seem to be important. Logarithmic data produced the same efficient observations throughout, and similar-although not quite identical - relative rankings of observations.) Turning next to the chance-constrained calculations, the model is min 8 subject to Prob (Z, y,,h, > yen) 2 0.95 for the market economies Prob (Ciy,,hi >_Y& B 0.40 for the planned economies 8x0, - &X,,h, z 0,

(7)

m = 1,2

Xjh, = 1, h,>O,

i=l

Converting

, . . . ,I, and 8 unrestricted

in sign

the two chance-constraints

into certainty

equivalents

yields

E(Xiyih< - yo) - 1.645 s.d. (C,y,h, - yo) > 0 for the market economies

(8a)

E(Xiyih, - yO) + 0.253 s.d. (C,y,h, - yO) Z 0 for the planned economies

(8b)

In order to carry out the calculations of chance-constrained DEA, we need information about the joint probability distribution of GDP per worker for each observation, y,, i = 1,...,72. As a first approximation, one may aim at inferring such information from the frequency distributions of the underlying data. Since GDP per worker is an arithmetic average, standard distribution theory implies that it is normally distributed. In particular, we shall need information about var y,. One approach to measuring this entity is var y, Ix,,, (m = 1,2), that is, as a conditional variance, conditional upon as a the observed and predetermined inputs x,,,,, m = 1,2. Taking the conditionality standard least squares linear regression varyi (xi,, Cm = 12) = ICicV, - PIX,I - i2xd*ll/I - 2) where b, and (j2 are the regression coefficients of y on x, and x2, respectively, we found, calculating (9) separately for the market economies (i = 1,. . . ,17; 25,. . . ,41; 49,. . . ,65) and for the planned economies (i = 18,. . .,24; 42,. . .,48; 66,. . .,72) var yl = 90.696 for the market economies

(lOa)

var yi = 37.083 for the planned economies

(lob)

But was this really the variability that entered the mind of the planners? Was it not rather the subjectively felt variability that mattered-the expected potential variability, rather than the historical record? The record for state socialism may have been stability. Yet, at the same time, was there not a creeping anxiety that at a certain point things could no longer continue in the old autocratic manner ? We acknowledge the relevance of these questions, but in the absence of hard data, we decided to stick with (lOa-b). We now make assumptions as follows. First, we assume that all observed outputs coincide with their mathematical expectations. That is, the observed GDP per worker

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TABLE 2 (Estimation Method: Chance-ConstrainedDEA)

Productive Efficiency Estimates, by Country and Year

Countrv Market economies Austria Belgium France West Germany Netherlands Switzerland United Kingdom Denmark Finland Iceland Norway Sweden Italy Malta Portugal Spain Turkey Average all market economies Planned economies Bulgaria Czechoslovakia East Germany Hungary Poland Romania USSR Average all planned economies

1978

1979

1980

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.934 0.997 1.000 1.000 l.OOLl 1.000 1.000 1.000 1.000

1.000 1.000 l.ooo 1.000 1.000 1.000 1.000 1.000 0.900 0.984 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1.000 1.000 1.000 1.000 1.000 1.000 l.OOLl 1.000 0.796 0.972 1.000 1.000 1.000 1.000 1.000 1.000 1.OOO 0.992

All years 0.785 0.489 0.531 0.444 0.541 0.454 0.390

0.883 0.508 0.512 0.465 0.601 0.499 0.405 All years

0.902 0.532 0.505 0.491 0.662 0.519 0.423 0.550

in each country serves as an unbiased estimate of the true GDP per worker. Second, we assume that all outputs are stochastically independent: the GDP per worker in one country is independent of the GDP per worker in any other country. Taken as a statement of historical record, this, of course, is patently untrue. But, as a statement of the subjective expectations of the planner, it is to say that the planner disregards disturbances arising from abroad. Third, we assume that the within-country variability of GDP per worker in Western countries is given by (lOa), and the within-country variability in Eastern countries is given by (lob). The certainty equivalents (8a) and (8b) then simplify into: (Xjyiki - JQ - 1.645J(&ALfvaryJ

- 2&vary.

+ vary0

2 0 for the market economies (Ziyihi - ~0) + 0.253&

(lla)

xf varyi) - 2& vary0 + vary0 b 0 for the planned economies

(llb)

where the variances var yi are given by (lOa) and (lob). Inserting into (7) and solving, we found the results exhibited in Table 2. Relative to the conventional deterministic DEA results reported in Table 1, the chance-constrained DEA results show a wider gap in production efficiency between the market and the planned economies. Recall that the very purpose of the chance-constrained formulation of DEA is to replace the “hard” frontier of deterministic DEA with a “soft frontier” that the

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TABLE 3 DEA, by Country and Year (Optimal buffers of GDP per worker) (A neaative entrv means an exaected shortace)

Countrv Market economies Finland Iceland Planned economies Bulgaria Czechoslovakia East Germany Hungary Poland Romania USSR

1978

1979

1980

17.43 17.96

17.39 17.84

17.29 17.88

-2.49 -2.89 -2.81 - 2.42 - 2.48 - 2.77 -2.39

- 2.43 - 2.86 - 2.90 - 2.42 -2.44 -2.90 -2.38

- 2.47 - 2.85 - 2.87 - 2.42 -2.42 -2.85 -2.38

output observations are permitted to cross, but not too often. All formerly inefficient market economies are now located closer to the envelope than before, or they are actually pushed onto it. Their efficiency ratings have increased. For example, consider the case of the United Kingdom in 1980. According to the deterministic calculation it had an efficiency rating of 0.878 (Table 1). The reference set of efficient observations defining “best practice” consisted of Belgium 1978 and Malta 1978. Best practice energy use was 49.5 and best practice capital use was 110.2, to be compared with actual inputs of 104.3 and 125.5 respectively. With the chance-constrained calculation, the efficiency rating was raised to 1.0. Best practice inputs now coincided with actual inputs. But for the planned economies, the effects are in the opposite direction, Their efficiency ratings have now all fallen. The case of the USSR in 1980 may serve as an example. The deterministic calculation had produced an efficiency rating of 0.440 (Table 1). The reference set consisted of Malta 1978 and Turkey 1979. Best practice energy use was 28.4 and best practice capital use 67.2 (actual inputs were several times larger, 107.1 and 152.7 respectively). With chanceconstrained DEA, the efficiency rating drops to 0.423 (Table 2). The reference set remains unchanged. But this time, best practice inputs are even smaller, 27.5 and 64.6 respectively. Perhaps the most characteristic feature of chance-constrained DEA is the accumulation of optimal buffers of output, held as a contingency against stochastic variability. For any market economy, the buffer of GDP per worker is given by the formula 1.645 s.d. (X,yih,* - yO). (If the country whose efficiency is currently calculated happens to be rated as efficient, so that the reference set actually is the country itself, then Ci y,hi* = yo and no buffer is needed.) But for the planned economies, the buffer is negative, equalling - 0.253 s.d. (XIy,hi* yo). There is an expected shortage of goods. Optimal buffers are reported in Table 3. Only Iceland and Finland among the market economies hold such buffers. By contrast, the planned economies experience shortages every year. These effects reinforce the productive efficiency differentials already reported. The Crisis and the Collapse of State Socialism The analysis presented so far has explored some of the characteristic traits of capitalism and state socialism, arising from the different nature of incentives and the different

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nature of uncertainty present in the two systems. Capitalism is driven by competition; it generates market uncertainty. State socialism is planned; it feeds bureaucratic uncertainty. At a more basic level, we believe that the divergence between the two systems during the final decades of the 20th century was reinforced by the pace of rapid technological change. As late as in the 196Os, both capitalist and state socialist economies were still dominated by standardized and uniform products, like power generation, steel and other metals, fertilizers, cotton and synthetic rayons, and plastics. There were increasing returns to scale; many industries were dominated by a few large national manufacturers. But a subtle change in the nature of technology occurred. Today, most products are endowed with a broad array of attributes in terms of technological specifications, design, and appearance. They are multidimensional. Each product targets a narrow market niche. There are no longer any obvious advantages in being big. In each industry, small and large firms exist side by side. It is more important to be nimble than big. The emerging great variety in the marketplace in the capitalist world is difficult to represent mathematically, and hence to formalize in economic models. One can still make a list of output categories n = 1,...,N. But the mathematics does not show the breadth of design varieties inside each such category. Nor does it show the rapid turnover of individual designs over time, older design vintages becoming obsolete and new designs being introduced in the marketplace at an accelerating rate. Indirectly, the turbulent regeneration and upgrading of individual products in the capitalist world is mirrored by an increased system uncertainty. At the time, many argued that the increased uncertainty in the Western world was to be ascribed to the two oil price shocks in 1973 and 1979. Others pointed at the new emphasis in monetary policy on controlling the money stock rather than interest rates, leading to volatile interest rates and added financial uncertainty. But with the benefit of hindsight, we now realize that many expressions of increased uncertainty were but manifestations of an underlying change in the nature of basic technological relationships. This uncertainty related not only to the direction of future developments in electronics, biochemistry, medicine, and other highly visible fields of high technology, but also to the great mass of gradual changes in the performance and quality of ordinary consumer and producer goods sold in the capitalist world. State socialism was never successful in manufacturing complex high technology products. Intermittently, updated technology directives would be issued by the central planning directorate. New technology, if achieved at all, was imposed from above. It introduced new challenges in the tenuous relationship between the directorate and plant managers. But the challenge of manufacturing products that would appeal to the consumers was not one of them. In order to illustrate the increasing divergence between the two systems, we can simulate the effects of increasing technological uncertainty in the capitalist world by a simple numerical experiment. Replace the assumptions (lOa-b) by varyi = 2 x 90.696 = 181.392 for the market economies

(12a)

var yi = 37.083 for the planned economies

(12b)

That is, the technological uncertainty in the market economies as measured by var y, is doubled. The results are shown in Table 4 and Table 5. For the planned economies, the efficiency ratings are now even lower than before. Furthermore, the expected shortages of goods have increased. In brief: the technological development in the West, and the

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TABLE 4 Productive Efficiency Estimates, by Country and Year (Estimation Method: Chance-Constrained (Increased Uncertainty in Market Economies; see Assumptions (12a-b)) country Market economies Finland Iceland Average all market economies Planned economies Bulgaria Czechoslovakia East Germany Hungary Poland Romania USSR Average all planned economies

1978

1979

1980

1.000 1.000

0.995 l.ooO

0.888

All years 0.773 0.484 0.519 0.440 0.534 0.451 0.386

1.ooo 0.998

0.872 0.503 0.499 0.461 0.593 0.485 0.401 All years

DEA)

0.890 0.526 0.500 0.486 0.654 0.516 0.419 0.542

TABLE 5 Chance-Constrained DEA, by Country and Year (Optimal buffers of GDP per worker) (A negative entry means an expected shortage. Increased Uncertainty in Market Economies: see Assumutions (12a-b)) Country Market economies Finland Planned economies Bulgaria Czechoslovakia East Germany Hungary Poland Romania USSR

1978

1979

1980

0.0

24.35

24.20

- 3.14 -3.70 -3.67 -3.00 -3.14 - 3.27 - 2.96

-3.04 - 3.65 -3.82 -3.00 -3.06 -3.83 -2.95

-3.12 -3.64 - 3.67 -3.00 - 3.01 - 3.37 - 2.95

accompanying increasing uncertainty there pushed the planned economies even deeper into inefficiency and shortages. In addition, strong psychological mechanisms were at work. Through Western magazines, books, radio, and TV, the citizens of the planned economies could fathom the abyss that separated them from the ever-expanding plethora of new consumer goods in the capitalist world. These signals fed a mounting feeling of rejection and frustration among the consumers in the socialist countries-of a gap between what was and what could have been. But the system did not respond to that gap. The pressure just grew on the existing inflexible social and technological structure. In our view, state socialism collapsed (a) because it could not generate from inside the flow of new technologies and new consumer goods that alone could have relieved the mounting uncertainty and frustration; (b) because of the system’s growing inability to meet even basic consumer demand; and (c) because the system had no mechanism for adjusting to change. References 1. Bergson, A., Comparative

Productivity and Efficiency in the USA and the USSR, in Comparisons nomic Systems, A. Eckstein, ed., University of California Press, Berkeley, CA, 161-218, 1971.

of Eco-

PRODUCTIVE

EFFICIENCY

UNDER

CAPITALISM

AND STATE

SOCIALISM

151

2. Berliner, J., Managerial Incentives and Decision Making: A Comparison of the United States and the Soviet Union, in Comparative Economic Systems, M. Bornstein, ed., Irwin, Homewood, IL, 1974. 3. Burawoy, M., and Lukacs, J., Mythologies of Work: A Comparison of Firms in State Socialism and Advanced Capitalism, American Sociological Review 50, 723-737 (1985). North-Holland, Amsterdam, 1980. 4. Kornai, J., Economics of Shortage, 5. Kornai, J., The Road to a Free Economy, W. W. Norton, New York, 1991. 6. Berliner, J., Factory and Manager in the USSR, Harvard University Press, Cambridge, MA, 1957. I. Berliner, J., The Static Efficiency of the Soviet Economy, American Economic Review 54, 480-489 (1964). 8. Berliner, J., The Innovation Decision in Soviet Industry, MIT Press, Cambridge, MA, 1976. 9. Lange, O., Introduction to Econometrics, Pergamon Press, Warszawa, Poland, 1st ed. 1957, 2nd ed. 1962. 10. Shubik, M., Competitive and Controlled Price Economies: The Arrow-Debreu Model Revisited, in Equihbrium and Disequilibrium in Economic Theory, G. Schwodiauer, ed., D. Reidel, Dordrecht, Holland, 1977. 11. Debreu, G., Theory of Value, Wiley, New York, 1959. Amsterdam, 1975. 12. Kornai, J., Mathematical Planning of Structural Decisions, 2nd ed., North-Holland, Amsterdam, 1971. 13. Kornai, J., Anti-Equiiibrium, North-Holland, 14. Kornai, J., Thoughts on Multi-Level Planning Systems, in Multi-Level Planning: Case Studies in Mexico, M. Goreux and A. Manne, eds., North-Holland, Amsterdam, 1973. 15. Charnes, A., Clower, R. W., and Kortanek, K. O., Effective Control through Coherent Decentralization with Preemptive Goals, Econometrica 35, 294-320 (1967). versus Demand-Constrained Systems, Econometrica 47, 801-821 (1979). 16. Kornai, J., Resource-Constrained 17. Kornai, J., and Liptak, Th., Two-level Planning, Econometrica 33, 141-169 (1965). 18. Liptak, Th., The General Model of “Two-Level Planning,” in Multi-Level Planning: Case Studies in Mexico, M. Goreux and A. Manne, eds., North-Holland, Amsterdam, 1973. 19. Barro, R. J., and Grossman, H. I., A General Disequilibrium Model of Income and Employment, American Economic Review 61, 82-92 (1971). 20. Charemza, W., and Gronicki, M., Plan andDisequilibria in Centrally PlannedEconomies: Empiricallnvestigation for Poland, North-Holland, Amsterdam, 1988. 21. Marer, P., The Mechanism and Performance of Hungary’s Foreign Trade 1968-79, in Hungary: A Decade of Economic Reform, P. Hare et al., eds., Allen & Unwin, London 1981. 22. Laki, M., Year-End Rush Work in Hungarian Industry and Foreign Trade, Acta Oeconomica 25, 3765 (1980). 23. Stark, D., Rethinking Internal Labor Markets: New Insights from a Comparative Perspective, American Sociological Review 51, 492-504 (1986). 24. Charnes, A., Cooper, W. W., and Rhodes, E., Measuring the Efficiency of Decision Making Units, European Journal of Operational Research 2(6), 429-443 (1978). 25. Banker, R., Charnes, A., Cooper, W. W., Swarm, J., and Thomas, D. A., An Introduction to Data Envelopment Analysis with some of its Models and their Uses, Research in GovernmentalandNonprofit Accounting 5, 125-163 (1989). 26. Charnes, A., and Cooper, W. W., Preface to Topics in Data Envelopment Analysis, Annals of Operations Research 2, 59-94 (1985). 27. Seiford, L., and Thrall, R. M., Recent Developments in DEA: The Mathematical Programming Approach to Frontier Analysis, Journal of Econometrics 46, 7-38 (1990). 28. Land, K. C., Lovell, C. A. K., and Thore, S., Productive Efficiency Under Capitalism and State Socialism: The Chance-Constrained Programming Approach, in Public Finance in a World of Transition, Pierre Pestieau, ed., supplement to Public Finances 47, 109-121 (1992). 29. Land, K. C., Lovell, C. A. K., and Thore, S., Chance-Constrained Data Envelopment Analysis, Managerial and Decision Economics, 14, 541-554 (1993). 30. Moroney, J. R., Energy Consumption, Capital, and Real Capital: A Comparison of Market and Planned Economies, Journal of Comparative Economics 14(2), 199-220 (1990). 31. Moroney, J. R., and Lovell, C. A. K., The Performance of Market and Planned Economies Revisited, Working Paper, Department of Economics, University of North Carolina at Chapel Hill, 1992. Revised 21 October 1993

For Further Reading Charnes, A., and Cooper, W. W., Chance-Constrained Programming, Management Science 15, 73-79 (1959). Charnes, A., and Cooper, W. W., Chance Constraints and Normal Deviates, Journalof theAmerican Statistical Association 57, 134-148 (1962).

152

K.G.

LAND,

C.A.K.

LOVELL,

AND S. THORE

Charnes, A., and Cooper, W. W., Deterministic Equivalents for Optimizing and Satisficing under Chance Constraints, Operations Research 11, 18-39 (1963). Charnes, A., Cooper, W. W., Golany, B., Seiford, L., and Stutz, .I., Foundations of DataEnvelopment Analysis for Pareto-Koopmans Efficient Empirical Production Functions, JournalofEconometrics 30,91-107 (1985). Charnes, A., Cooper, W. W., and Symonds, G. H., Cost Horizons and Certainty Equivalents: An Approach to Stochastic Programming of Heating Oil, Munogement Science 4(3), 235-263 (1958). Land, K. C., Stark, D., and Thore, S., On the Technical Efficiency of Capitalist and State Socialist Firms: A Chance-Constrained Activity Analysis, presented at the Fall Meeting of the American Sociological Association, Chicago, 1987. Thore, S., Chance-Constrained Activity Analysis, European Journalof OperationalResearch 30,267-269 (1987). Thore, S., Economic Logistics: The Optimization of Spatial and SectorialResource, Production, and Distribution Systems, Quorum Books, Westport, CT, 1991.