The new institutional economics and the theory of the firm

Journal of Economic Behavior & Organization Vol. 45 (2001) 133–153

The new institutional economics and the theory of the firm Eirik G. Furubotn a,b,∗ a

Private Enterprise Research Center, Texas A&M University, College Station, TX 77841, USA b University of the Saarland, Saarland, Germany

Received 23 February 1999; received in revised form 1 July 2000; accepted 7 August 2000

Abstract Concepts of importance in the literature of the New Institutional Economics, such as transaction costs and bounded rationality, have been used to extend the standard neoclassical model of the firm, but the hybrid models created have failed to provide adequate explanations of enterprise behavior. An alternative, “neoinstitutional,” model of the firm is developed in the paper that differs from the pure neoclassical model and hybrid models in respect to both the nature of the solution it yields and the process by which it reaches a solution. The neoinstitutional firm cannot be expected to achieve either the hypothetical allocative efficiency promised by the frictionless neoclassical model, or the relatively efficient (constrained Pareto optimal) solutions predicted by the largely frictionless hybrid models. The orthodox marginal rules of neoclassicism can only be used to solve lower-level problems that appear within the firm. © 2001 Elsevier Science B.V. All rights reserved. JEL classification: L20; D81 Keywords: New institutional economics; Bounded rationality

1. Introduction During the last three decades or so, certain concepts emphasized by “neoinstitutionalist” writers and other critics of contemporary neoclassicism have found their way into mainstream economic discussion. In consequence, it has now become commonplace for theorists to consider the roles that transaction costs and bounded rationality play in shaping economic behavior. The approach taken in the literature, however, often tends to follow a rather simple formula. That is, models are constructed that combine certain elements of the new thinking ∗ Fax: +1-979-845-6636. E-mail address: [email protected] (E.G. Furubotn).

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with conventional neoclassical analysis. 1 In short, there seems to be confidence in some quarters of the profession that significant gains in understanding can be achieved by extending or “generalizing” the standard neoclassical model through the expedient of introducing a few new or supplementary constraints into the analysis (e.g. De Alessi (1983, p. 66), Posner (1993), and Cheung (1998)). Thus, in the “extended” theory of the firm, there is acceptance of the idea that an entrepreneur or manager must take account of certain restrictions not present in the frictionless neoclassical environment (e.g. Stigler (1961) and Stiglitz (1985)). There is also agreement that, under the changed conditions of the neoinstitutional world, any search for essential information (concerning such things as the quality and price of inputs) will require the use of scarce resources, including cognitive capacity. But, at the same time, there is no feeling that a fundamental recasting of the optimization problem is needed. Analysis goes forward in a more or less orthodox manner. It follows, of course, that when the significance of newly recognized constraints is viewed in this way, the constraints seem to fit easily into the standard neoclassical framework. . . . Information becomes a valued but expensive factor of production and decisions, to be used efficiently like any other. In this sense, bounded rationality is quite rational, and the concept does not distinguish the New Institutional Economics (NIE) from contemporary neoclassical economics, save perhaps in terminology. (Scott, 1994, p. 316) At first blush, this line of argument may appear both plausible and encouraging. We are told, in effect, that progress can be achieved by making a few simple modifications to neoclassical theory while retaining its basic structure and familiar technical methods. It would certainly be convenient if neoclassical theory could be generalized in the manner suggested above. There are, however, good reasons to reject this optimistic interpretation. The “generalization” approach is based on the use of what may be called “hybrid” models (Furubotn and Richter, 1997, Chapter 10), or models constructed with assumptions drawn from the disparate neoclassical and neoinstitutional universes. And it is this procedure that creates difficulties. The mixture of what are, in fact, ill-matched assumptions leads to model structures that cannot maintain a consistent analytical viewpoint. For example, in the hybrid models encountered so frequently in the current literature, it is not unusual to find that the individual decision maker is assumed to be, simultaneously, perfectly informed about some matters while completely ignorant of others (Furubotn and Richter, 1997, p. 227), or that he must use scarce resources and incur costs to enforce his property rights in real assets but is capable of undertaking a complex optimization process instantly and without any cost whatsoever. Anomalies of this sort abound and cast doubt on the usefulness of solutions reached by hybrid models. Arguably, then, a closer connection with real-world conditions is needed. 1 Stiglitz, for example, has used the traditional tools of neoclassical analysis while seeking to overturn orthodox conclusions about market efficiency. He is quoted as saying: “I took the logic that had led people to be convinced that markets were efficient . . . I changed one assumption — that there was perfect information. I found a general way to model imperfect information. And when you plugged this in, you found markets to be almost always inefficient” (Uchitelle, 1998, p. 12). What is difficult to see here, however, is why the introduction of imperfect information (and transaction costs) does not have the effect of changing all of the key data in the orthodox neoclassical model. See also Putterman et al. (1998) and Myerson (1999, pp. 1068–1069).

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2. The neoinstitutional environment Observation of actual economic activity suggests that positive transaction costs are ubiquitous and unavoidable, and that human decision makers are, by their inherent nature, quite limited in their ability to acquire, store, retrieve, and process information. Inevitably, these special characteristics influence behavior and generate a distinctive environment — the “neoinstitutional” environment. What seems clear, then, is this. If neoclassical theory is to be extended so that it is able to explain the behavior that takes place in the more complex “neoinstitutional” environment, extensive reformulation of the standard frictionless theory is essential. As suggested above, it is not sufficient to follow the practice of hybrid models and change only a few elements of the basic neoclassical structure while leaving most of its other features intact. The introduction of critical new assumptions concerning positive transaction costs and bounded rationality has far-reaching consequences. Indeed, when a neoinstitutional environment rules, all of the things traditionally accepted as data in the neoclassical general equilibrium model undergo change simultaneously. That is, given the cognitive restrictions that constrain each individual and the costly nature of information, a decision maker can have only partial knowledge of the full range of options known to the society as a whole. He can no longer be assumed to know everything about existing technological alternatives, the characteristics and availability of all productive inputs, the existence and true properties of every commodity in the system, etc. Under these conditions, the character of the economic problem to be solved is necessarily transformed. The standard technical procedures of the orthodox neoclassical case cannot be applied meaningfully when (i) individual knowledge stocks are limited, differ widely from person to person, and tend to change from one period to the next; and (ii) the very process of decision making has significant costs and must be based on decision procedures other than “rational choice”. Quite simply, the neoinstitutional model differs from the pure neoclassical model, and from hybrid models, in respect to both the nature of the solution it yields and the process by which it reaches a solution. Although the neoinstitutional assumptions require that a radical departure be made from orthodox neoclassicism and in the way modeling is carried out, analysis is not totally divorced from neoclassical thinking. Thus, in the case of the theory of the firm, certain familiar preconceptions come through unchanged. Inter alia, decision makers are assumed to show purposive behavior and, despite uncertainty, a desire to undertake conscious design of the firm’s structure and policies. These individuals control enterprise behavior with a view to securing profits that are as large as possible given uncertainty and the numerous constraints extant (including the personal limitations of the decision makers themselves). From a motivational standpoint, the drive for profits is important. But, for reasons that will be explained more fully below, the neoinstitutional firm cannot be expected to achieve either the hypothetical allocative efficiency promised by the frictionless neoclassical model, or the relatively efficient (constrained Pareto optimal) solutions predicted by the largely frictionless hybrid models. The basic problem here arises from the difficulty of finding such “ideal” configurations for the firm when the array of technological/organizational options is very large, information is costly, and the cognitive capacity of decision makers is limited. In other words, given the latter conditions, transaction and deliberation costs will be so great as to rule out any exhaustive search for the theoretical optimum. Note, however, that while the firm’s overall structural and organizational solution cannot be determined with

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the aid of orthodox technical analysis, it is possible that the neoclassical approach can still be employed fruitfully to solve lower-level problems that appear within the firm. When the secondary problem is not too complex, the less will be the required outlays on transaction and deliberation costs, and the more closely will the assumptions of neoclassical analysis be approximated (Thaler, 1994, pp. 3–5). If, as some economists suggest, it is highly desirable to extend the basic neoclassical model so that it can have applicability to a neoinstitutional world, it would seem essential for the extension to be carried out in a consistent and systematic way. Ideally, all of the implications of the new set of assumptions should be accounted for. Thus, in considering the theory of the firm, the objective of the paper is to explain, in some detail, why a capitalist enterprise faces a special type of optimization problem when operating in a neoinstitutional environment, and why it must exhibit a behavioral pattern that is significantly different from that described by the neoclassical model or hybrid models. In this connection, it should also be noted that while the solutions yielded by neoinstitutional enterprise models have an evolutionary character, the behavior portrayed is somewhat distinct from that of other current evolutionary models. 2 In particular, there seems to be no reason to believe that change in society’s existing knowledge stock must necessarily occur in order for an endogenously generated transformation of a system to take place over time, or that natural selection processes will lead to an evolutionary path that can be characterized as “efficient” or optimal.

3. A comparison of models To facilitate exposition, a simple diagram can be used to contrast the different conceptions of enterprise behavior offered, respectively, by the neoclassical model, the hybrid model, and the neoinstitutional model. Thus, Fig. 1 is introduced to deal with the familiar problem of how a firm proceeds when attempting to obtain the greatest possible output (q) from a given outlay (B0 ). We have: (A) In the case of the frictionless neoclassical model, it is assumed that the entrepreneur is fully informed about all existing technological options (via the orthodox production function), and that he knows accurately the prices of the inputs (x1 , x2 ). Then, since all optimization and deliberation costs are taken as zero, the total sum expended on inputs (y) can be equal to the total budgeted outlay (B0 ), and the entrepreneur is able to determine, instantly and costlessly, the operating position that maximizes output. The firm moves immediately to this “ideal” equilibrium point (␣) — where it will remain until one of the givens changes. The possibility of the firm considering innovation as a strategy in its adjustment process is not given attention in this type of analysis. (B) The situation of the hybrid model is similar to that of the neoclassical model in the sense that the entrepreneur is assumed to possess far-reaching cognitive capabilities and information. Thus, inter alia, he knows everything about existing technology. What is recognized, though, is that the firm may face certain difficulties not considered in the pure neoclassical case (Demsetz, 1969). For example, it may be assumed that price dispersion 2 There are, of course, numbers of different models that can be said to fall into the general category of evolutionary-theoretic constructs. See Nelson (1995).

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Fig. 1. Output solutions under different model structures.

holds in factor markets and that the entrepreneur must use time and resources to determine the most favorable prices at which to acquire needed inputs. As a result of such search costs (S1 ), the actual expenditure on factors x1 and x2 can be no more than y . This sum is less than the total budgeted outlay, or y  = B 0 − S1 . Hence, Fig. 1 indicates that the equilibrium solution shifts from the neoclassical position at ␣ to point ␤. The latter solution is interpreted in the literature as a constrained Pareto optimum and, consequently, as efficient (De Alessi, 1983, p. 69; Leibenstein, 1985). Since this “ideal” solution emerges from a calculus-based maximization procedure, the implication is that the entrepreneur was able to undertake an exhaustive search over all of the myriad technological options extant costlessly, and reach a stable equilibrium end point (␤) instantly. Again, innovation is ruled out as an adjustment procedure for the firm. This case is characteristic of hybrid models — which seek to move closer to real-world conditions by introducing supplementary constraints into an otherwise unchanged orthodox model. (C) The entrepreneur in the neoinstitutional model is distinct in that he has no capacity for performing miraculous feats of calculation. Neither does he possess any more than superficial general knowledge, initially, of the technological alternatives available for producing the commodity. These influences on decision making are crucial, of course, and affect the behavior of the firm. The diagram is unable to represent the full complexity of the neoinstitutional case but its essential features can be conveyed by Fig. 1. Thus, assuming that the entrepreneur does not know the production function, he is forced to examine conditions at various points in input space and secure detailed information on particular technical processes. In doing this, he may be guided by what other firms in the industry have done, and by what is currently considered to be “best practice”. In any event, such exploratory activity is costly in terms of both cognitive and material resources. Precisely how far the search for

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technical (and price) information should be pushed represents a difficult judgment problem for the decision maker. More information about alternatives and more careful deliberation may permit more promising solutions to be discovered. But, then, given a fixed budget, more resources allocated to the optimization process means that fewer can be directed to actual production. For example, the entrepreneur may decide that the monetary value of resources devoted to learning about technical options, plus the monetary value of all other transaction and deliberation costs associated with the firm’s activities, should equal S2 . Then, the sum y , or (B 0 − S2 ), remains for the purchase of the productive inputs (x1 , x2 ). In Fig. 1, the relatively few (socially known) technical processes that are studied by the entrepreneur in detail are indicated by points a, b, c, and ␪. It is from the alternatives represented in this set that he must choose his ultimate operating arrangement. Since the choice set is rather small, each option it contains can be evaluated carefully and compared with the other possibilities without extremely large deliberation costs being incurred. And, given the constraint y , it follows that, as far as current technology is concerned, ␪ turns out to be the best attainable option of which the entrepreneur is aware. It leads to greater output than the other options and requires no greater expenditure on inputs than y . Note, however, that even though the neoinstitutional model follows a procedure that economizes on transaction and deliberation costs, it does take explicit account of a whole range of costs linked to optimization and production. From this standpoint, it contrasts strikingly with the neoclassical model and the hybrid model — which either ignore all such costs completely, or understate them by focusing on only certain cost elements. The situation explains why Fig. 1 specifies that y = B 0 > y  > y  , and that, with respect to outputs, ␣ > ␤ > ␪. The justification for believing that a firm will be led to a solution such as ␪, rather than to an outcome like ␣ or ␤, rests fundamentally on the understanding that the computational complexity of achieving these classic idealized positions is simply too great. In effect, the information that must be acquired and evaluated is so extensive, and the corresponding costs to be incurred so great, as to rule out deliberate action on the part of decision makers to find conventional optima (␣, ␤). Even the predisposition of decision makers to carry on the search for superior productive arrangements period after period will not do much to increase the possibility of their reaching these classic positions. Moreover, the problem of search and deliberation is seen to be compounded when it is recognized that an entrepreneur, in seeking an advantageous solution, can consider the possibility of innovation. That is, in addition to thinking about current technology, he can speculate on the desirability of developing and utilizing some novel technological/organizational arrangement of his own design. Then, of course, the set of potential alternatives is open ended, and the idea of finding a definitive optimum (comparable to a classic Pareto equilibrium point) has no clear meaning. To say all this, however, is not to suggest that the conventional marginal calculus and programming methods cannot be applied effectively in the solution of certain types of problems connected with the firm’s operations. As noted earlier, the orthodox approach can be utilized to secure efficient treatment for some lower-level problems that arise within the general framework of a firm. Decision making is a costly process and thus the extent to which resources are used to find desirable arrangements is determined on the basis of perceived costs and benefits. When the matter to be resolved is not too complex, so that the extent of the information that must be collected and assessed is well defined and manageable, the associated costs will be acceptable. Then, the usual marginal costs and returns can indeed be

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calculated accurately or approximated. 3 It is also true that, as a practical matter, firms will always find workable solutions (such as point ␪ in the diagram) and, relative to a structure actually in place, it will be possible to know marginal costs and returns, and to use the information in making subsequent adjustments of position. What marginalism cannot do, though, is guide the firm to “ideal” solutions such as ␣ and ␤. Despite its simplicity, the analysis based on Fig. 1 is sufficient to show that when firms operate in an environment characterized by positive transaction costs and bounded rationality, economic behavior changes significantly from the neoclassical pattern. Specifically, we find that: 1. The neoinstitutional firm, which shapes its policies with the aid of decision rules designed to economize on search and deliberation costs, cannot be expected to achieve the classic efficiency results suggested by the neoclassical model or the hybrid model. Moreover, different firms in the industry will tend to reach different solutions. 2. The neoinstitutional firm does not move instantly and costlessly to a stable equilibrium position when it finds a workable production arrangement. Rather, the firm tends to undertake a continuing search for superior configurations using a trial and error process. 3. Since the firm is forced to invest time and resources in any adjustment effort attempted, it can consider whether to seek a better production arrangement from among the set of existing (socially known) options, or to invest in the development of new knowledge and a novel arrangement. 4. Given the uncertainty surrounding decision making, the individual devising the firm’s policies has to act as a true entrepreneur rather than as a manager routinely implementing clear-cut marginal rules for allocation. 5. A firm functioning in a neoinstitutional environment may still employ orthodox neoclassical optimization procedures to solve some (secondary) problems it encounters, provided the problems in question are not too complex and do not involve high costs of solution. Finally, it should be emphasized that the findings described in (1)–(5) above emerge, quite logically, as a consequence of the new assumptions concerning transaction costs and bounded rationality that are introduced to extend the basic neoclassical model.

4. The firm’s technological options In order to examine the neoinstitutional firm in greater depth than was done in Section 2 where the simplified diagram shaped discussion, it will be useful, as a first step, to look more closely at the subject of technology. According to neoclassical theory, the “state of the arts” is a datum and defines a society’s current knowledge of the set of alternative technological/organizational arrangements available to produce any given commodity (as h). In a neoinstitutional environment, however, a decision maker interested in producing 3 Precise answers to second-level problems do not necessarily contribute much to the overall efficiency of a firm. Success with secondary matters can even be counterproductive if the secondary solutions reached reinforce a basically ill-conceived structure that has already been established for the firm.

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commodity h can never be assumed to have information about more than a small fraction of the total knowledge stock possessed by society. Moreover, mainstream theory argues that any technical information the entrepreneur has will necessarily be drawn from the subset of “efficient” processes (as in Fig. 1). The explanation given for this behavior rests on the distinction neoclassical analysis makes between technology (or the production set) and the production function. The idea is that the production set lists all combinations of inputs and outputs associated with technologically feasible ways of producing a commodity, while the production function represents a subset of the production set that contains the “efficient” or output-maximizing technological/organizational options open. In other words, any specified input combination, as (x10 , x20 , . . . , xn0 ), is understood to be linked to a number of distinct technical processes, with each process associated with some given level of output. Entrepreneurs, however, are supposed to be concerned only with the output-maximizing processes defined by the production function because the “best” utilization of any particular input combination is said to be a technical, not an economic, problem. But, even in a purely neoclassical context, the exclusive focus on the production function is misleading. If each technical process associated with a given input mix is defined in terms of the way in which the given inputs are utilized, we are forced to consider the likelihood that different modes of input utilization exert different physical and/or psychological effects on the inputs. And if such effects do occur, there will normally be some impact of utilization conditions on factor prices. A competitive firm secures inputs from independent factor markets, and must pay the going rate established in any market if it is to be able to attract inputs. In the case of labor, for example, there tend to be numbers of distinct markets differentiated on the basis of the relative pleasantness or unpleasantness of the employment conditions promised. Arduous or hazardous conditions of work, such as occur when “crash” output programs are undertaken, must be rewarded with higher wage rates. Similarly, when the operation of machinery is speeded up to increase the rate of output from a given input combination (x10 , x20 , . . . , xn0 ), there is an inevitable tendency for user cost (or else the market price of machine services obtained via leased equipment) to rise. In general, then, if given productive factors are pressed to the limit in an effort to maximize commodity output over the flow period defined for the production function, the firm’s operations will be quite costly. What seems clear is that so-called “efficient” processes that maximize commodity output per period need not constitute the best choice for the profit oriented firm. If, as is characteristic, input prices rise as increased pressure is placed on inputs to yield effectively larger service flows per period, it will often be preferable for the firm to sacrifice some commodity output (and potential revenue) for relatively greater savings on input costs. Forced draft technological/organizational arrangements can actually diminish profits. It is useful to push given factors harder only as long as the marginal outlay associated with such action is less than the corresponding marginal revenue product. To the extent that larger profits are preferred to smaller profits, the unvarying output maximization required by the neoclassical production function cannot be ideal for the firm. What is best in purely physical terms is not necessarily best in economic terms. The conclusion to be reached then is that, in principle, the production set rather than the production function is the relevant technological datum for the firm. And, since many different technical organizations or processes can be associated with each combination in input space, it follows that the number of operating arrangements that the firm may conceivably use is much larger than the usual neoclassical analysis admits.

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Actually, a movement toward reliance on the production set is only one of numerous possible changes that have the effect of expanding the firm’s array of feasible production options. For example, if it is assumed, quite reasonably, that there are n general types of productive factors needed for production of the firm’s output (qh ) and that each such factor can appear in a number of different quality forms (1, 2, . . . , v, in each case) it is clear that there are effectively more “inputs” available for use, and that the firm has more distinct input combinations capable of generating commodity output. A simple model can be constructed to show precisely how the few changes noted so far can lead to a great many more production options than those identified in orthodox neoclassical theory. In the latter case, society’s knowledge of the various “efficient” ways of producing commodity h is given by the production function qh = f (z∗ , x1 , x2 )

(1)

Here, two general inputs exist (x1 , x2 ), and each appears in only one quality. Since each combination of these inputs must be used “efficiently,” the technological parameter (z∗ ) indicates that both inputs will be utilized at the high level of intensity required to generate maximum outputs. Although Eq. (1) is normally assumed to be continuous, it will now be convenient to say that Eq. (1) represents a set of N discrete input combinations and their associated outputs. The number of alternative operating points (N) is very large but finite. Next, consider a production model based on two general inputs, but, now, each general input exists in two distinct quality forms (x1 , x1 , x2 , x2 ). For simplicity, assume further that production conditions are such that: (i) only two inputs are used in any given process, (ii) two variant forms of the same general input cannot be employed to secure output, and (iii) production is not undertaken with combinations of different processes. It is also accepted that any input mix of the eligible inputs can be utilized in production at one or another of three different levels of intensity zα , α = 1, 2, 3. The respective intensity levels suggest the tautness of the production environment and the pressure placed on inputs to yield services. Given this structure, there are four subfunctions associated with each intensity level. For example (1)

qh = f (1) (z1 , x1 , x2 ) (2)

qh = f (2) (z1 , x1 , x2 ) (3)

qh = f (3) (z1 , x1 , x2 )

(2)

(4)

qh = f (4) (z1 , x1 , x2 ) Arrays similar to Eq. (2) are linked to intensity levels z2 and z3 . Consequently, there are 12 subfunctions in total. In general, the number of subfunctions that may appear depends on the number of general input types employed (n), the number of quality forms in which each such input exists (v), and the number of intensity levels (α) that can be used in production. The relation is v n α. Further, if it is said that none of the possible subfunctions is continuous, and that each subfunction has N distinct input combinations as conceivable operating points, the total (finite) number of alternatives, or processes, relevant for potential production (A) is given by the formula: A = (v n α)N, where v ≥ 2, n ≥ 1, α ≥ 1

(3)

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While this analysis is based on a series of major simplifying assumptions, it is sufficient to show how easily the magnitude of A (the firm’s choice set) can become enormously large. Merely by changing the parameters of the model modestly and saying that n = 10, v = 5, and α = 6, we go from the 12 subfunctions of the earlier example to 58,593,750. Since each of these subfunctions has N relevant points, and since N itself is very large, it is obvious that the “state of the arts” implies the existence of a vast menu of production alternatives (or processes). The literature has long recognized that the firm’s choice set can be overwhelming (Simon, 1979), but what must be emphasized is that this understanding is entirely consistent with the neoclassical logic. That is, without violating the spirit of neoclassical analysis, or inquiring too deeply into the actual complications of technology and the firm’s production process (Nelson and Winter, 1982, pp. 59–65), the model based on Eqs. (2) and (3) emerges. Clearly, the situation would become even more extreme if attempts were made to further reduce the level of abstraction and consider the effects of other features of technology and organization having relevance to the firm’s choice of an operating position — as, e.g. the firm’s planning horizon, its internal property-rights structure, its prevailing corporate culture, etc. In short, technological/organizational complexity is an element that has to be faced by any adequate theory of the firm.

5. The optimization process Given a frictionless economy in which all relevant information is available costlessly, and decision makers are able to deal easily with problems of enormous complexity, the existence of a multitude of technological/organizational alternatives would present no difficulty to the firm. It would be possible for an entrepreneur to survey the known situation with respect to technology and prices, and find instantly the optimal (profit-maximizing) arrangement for conducting business. However, once the idealized world of zero transaction costs and unbounded rationality is abandoned, the (extended) theory of the firm is forced to consider the role of optimization costs (and their relation to the acquisition and processing of information about existing technology or innovative activity). Under the new conditions, each firm in the system discovers that the general process of learning about existing technological opportunities and prices, and of choosing a preferred operating position, becomes a costly activity — an activity that involves major expenditures of time, human effort, and material resources (Conlisk, 1996). In this connection, Göttinger’s remarks on optimization are of particular interest. The calculation of the optimal decision by the agent is generally assumed to be costless in [the neoclassical] approach. However, certain results in the theory of computation indicate that a useful requirement for a function to be ‘computable’ is that is can be realized by a step-by-step procedure that can be implemented mechanically (Minsky, 1967). Since implementation of each step in the procedure requires the services of some human or mechanical agent, any computation requires the use of scarce resources: and agents may not perform the computation required for the continuous optimization of their criterion functions. Therefore, the optimal decisions of agents in an economy are

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determined after the cost of reaching the decision is taken into consideration. (Göttinger, 1982, pp. 223–224) It follows from this understanding, of course, that “rational-choice” procedure, as defined in mainstream theory, cannot be used to discover the optimal configuration for the firm. Even the highly simplified model of Section 3 is sufficient to show, via Eq. (3), that the number of alternative operating points that require consideration is so large as to make “rational choice” prohibitively costly. Specifically, the approach is ruled out because it presupposes that each of the feasible production options known to the society as a whole is also known to the firm’s entrepreneur, and that he is able to compare each option with each of the other possibilities in order to determine the ideal organizational/technological choice for the firm. But we find that, in a neoinstitutional environment, quite different behavior must be followed by the profit-seeking entrepreneur attempting to enter an industry. Given his initial ignorance, he is forced to make significant expenditures of time and resources even to learn about a small subset of the production alternatives known to society. In effect, he has to investigate the conditions at different points in the existing production set that he believes may promise efficient operation, and thus “construct” his own partial map of technology. By limiting himself to a modest number of points, he can keep search costs within acceptable bounds, and also ensure that the costs of evaluating the options in the subset (to determine the most favorable option) will not be excessive. Of course, to accomplish these ends, the decision maker must forsake “rational choice” and employ relatively crude, cost-saving choice methods. Deciding on particular choice methods, then, involves, inter alia, a tradeoff between: (i) the possible advantages of having more extensive information about technology, and (ii) the benefits that the firm can enjoy by maintaining search and evaluation costs at low levels. Similar remarks can be made about the firm’s problem in obtaining data concerning existing factor and commodity prices.

6. An elementary theory of the neoinstitutional firm The objective of this section is to describe the behavior of a simple, owner-managed firm that is operating within a neoinstitutional environment. Thus, only one individual controls the firm’s activities, and he is assumed to have exclusive claim on the firm’s residual, and to enjoy other property rights in the organization of the type said to exist in the “classical” capitalist enterprise (Alchian and Demsetz, 1972). To begin discussion, analytic attention will be focused on a firm that is seeking to enter a competitive industry characterized by numerous firms producing a single, standard commodity. Since the orthodox marginal rules for maximizing profit (De Alessi, 1983, p. 69) are too costly to follow when transaction costs and bounded rationality hold, a firm concerned with economic survival must seek some other guidelines for directing its behavior. Effectively, less exacting methods than those derived from the marginal calculus must be substituted to determine resource allocation. We know, of course, that alternative decision methods (such as rules-of-thumb) do exist, and that these expedients are able to economize on optimization costs. The alternatives open include random choice, imitation, obeying an authority, and convention (Pingle, 1992, p. 8; Conlisk, 1996; Leibenstein, 1985, pp. 5–8). An important

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implication of the use of such “short-cut” procedures is that firms need not be driven immediately to find the very best of all possible operating arrangements. Moreover, despite free entry and a large number of competing firms, the elusive and costly “ideal solution” of the frictionless model will not be a pressing objective at any stage. In short, a firm may function as a viable member of an industry even though it does not follow conventional profit maximization. As Alchian has noted, the pertinent requirement is positive profits through relative efficiency (Alchian, 1950, p. 20). What counts is the position of a firm relative to its actual competitors — who, themselves, must be operating quite far from any “ideal” position of profit maximization. Experience in the real world suggests that individuals are willing to undertake investment projects despite the impossibility of forming accurate expectations about future outcomes. Thus, in setting up a production unit initially, and at each subsequent period, the owner-manager of a neoinstitutional firm is assumed to act with some boldness. He faces difficult problems because he must decide the extent to which he should allocate time and resources to the task of acquiring economically relevant information and assessing its significance for the firm. He may decide, at some stage, that the costs of securing and processing additional information exceed the potential gain from such action and do nothing. Nevertheless, we can assume that, at the outset, an entrepreneur contemplating entrance into an industry has limited familiarity with key data, and needs to make expenditures to achieve more precise understanding of some of the opportunities and pitfalls that exist in this sector. In order to estimate what the firm’s profits will be under different possible configurations for his new venture, an entrepreneur must seek knowledge of certain data. Specifically, he requires information on: (i) some of the technological/organizational alternatives for the production of the chosen commodity that are currently known to society as a whole, and (ii) some of the prices at which inputs and the commodity in question are selling. Prices in a neoinstitutional world may tend to be sticky (Furubotn and Richter, 1997, pp. 285–287), but price dispersion can also be anticipated and hence there is a need to know at least some of the market prices extant. What should be emphasized is that knowledge of current technical and market data is essential even if the entrepreneur intends to develop an innovative approach to production. Given information costs, it is inevitable that the entrepreneur’s stock of acquired knowledge will be far from complete, yet this fact does not prevent decision making from going forward. As suggested earlier, the information costs relevant to the firm’s optimization process are of two basic types. Thus, for example, in the case of technological/organizational options, the entrepreneur must: (i) commit resources and time to select a particular decision procedure to guide his activities in gathering information about feasible productive arrangements; and (ii) bear the cost of actually applying the method chosen to the task of securing data on technological options. Insofar as a relatively inexpensive decision method has to be used, the “rational-choice” approach is, of course, ruled out. But the matter of how a particular “less costly” decision method is to be found poses some difficulty. What is required is the use of higher-level decision method, i.e. a rule to chose the rule. But, unfortunately, the selection of the higher-level rule demands a still higher-level rule, and the problem of infinite regress is encountered. The general result is that:

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. . . decision-costs act to limit the extent to which rationality can be displaced to higher levels. There must come a point where, as Knight says, the “rational thing to do is to be irrational” and simply choose a choice method without reason. Otherwise, all resources would be used in decision-making. (Pingle, 1992, p. 11) It should be noted in this connection that while the decision method may have to be chosen arbitrarily, it has great importance because it is used to establish the set of technological/organizational alternatives which (along with price data) will act to limit the entrepreneur’s choice of the ultimate operating arrangement for the firm. The procedure followed to obtain needed price information concerning the relevant inputs and the commodity being produced is the same, mutatis mutandis, as that just described for technology. Once samples of these price data are collected, however, some decision rule (presumably exhaustive search over the limited entries) must be chosen and applied to determine the particular price estimates to be introduced into the firm’s production plan. Obviously, all of this information-related activity generates costs, and the costs will be larger or smaller depending on how much information is collected and how intensively it is assessed. The entrepreneur, after having accumulated what he regards as a “satisfactory” knowledge stock, is in a position to formulate (at some additional cost) a set of alternative profit projections. A representative profit estimate, relating to one particular technological/organizational configuration for the firm, takes the following form: (1)

(1)

(1)

Πtz = pˆ ftz (αz , x1b , x2b , . . . , xnb )

   s n n+1


(1) − rˆib xib − Tj − Hz(1) − a m Ck i=1

j =1

(4)

k=1

Eq. (4) represents one estimate of the amount of profit (Π tz ) that the entrepreneur expects (1) (1) (1) to see when certain quantities of inputs (x1b , x2b , . . . , xnb ) are employed with a particular technical process (t) and a particular form of internal organization for the firm (α z ). In other words, given the information previously acquired about production options, a specific technological/organizational relation (ftz ) is known to exist at this point of possible operation, and its potentialities are being explored. To simplify notation, it is assumed in Eq. (4) that the respective inputs are all of the same relatively quality or grade level (b). The commodity price (p) ˆ and the input prices (ˆrib , i = 1, 2, . . . , n) have also been established by the earlier search activity, and these market parameters permit the calculation of total revenue and the total remuneration going to productive factors per period. Since transaction costs must be incurred in acquiring the specified quantities of inputs and in selling output, these costs n+1 4 j =1 Tj have to be subtracted from total revenue along with direct factor cost. Another (1)

type of cost is represented by the term Hz . It is intended to show the aggregate cost of administering the particular organizational plan (α z ) under which the firm is operating (and includes such elements as the costs involved in monitoring the inputs). 4 To simplify exposition, it is assumed here that all of the inputs are obtained, and the output sold, through straightforward market transactions. We know, however, that with different organization than α z , transactions could be accomplished by other means (Williamson, 1985, 1996; Masten, 1996).

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The last expression in Eq. (4) relates to the outlays made by the entrepreneur in acquiring the information needed to make decisions. Specifically, a certain set of decision procedures (1, 2, . . . , s) was chosen and applied in order to obtain the information that permitted the formulation of Eq. (4). And we assume that a similar approach was used in formulating m−1 other profit projections. These m profit estimates established by the entrepreneur represent only a small sample of the enormous number of operating possibilities available in the multidimensional technological/organizational space, securing this level of detailed but

even  s knowledge has a significant aggregate cost — viz. m . The latter assumes, for C k k=1 simplicity, that the decision-cost outlay is the same in each of the m cases investigated. The aggregate decision cost just mentioned is a necessary part of the start-up procedure of a decision maker contemplating entry into an industry and, thus, must be considered in determining the profitability of whatever enterprise configuration is ultimately chosen from among the m alternatives. The aggregate decision outlay, however, has the character of an investment because the benefits of the information accumulated may carry over for a number of periods into the future. Therefore, depending on the entrepreneur’s calculation of how long his initial arrangements for the firm will prove usable (profitable), the effective decision cost allocated to the first period will  be greater  or smaller, but no more than a s fraction (a) of the aggregate decision cost m k=1 Ck . The last term in Eq. (4) reflects the fact that the total cost can be spread over a number of periods; and the other m − 1 profit projections will include comparable terms. In short, optimization costs count, and can be highly influential in determining: (i) how much information is collected before decisions are made, and (ii) what particular production options are actually considered and implemented. If the entrepreneur has responded to his initial lack of specific knowledge about existing technological/organizational options and has made a sizeable outlay to gather information, the total number of alternative profit projections (m) can be moderately large. Nevertheless, at this stage of the optimization process, he should be willing to examine the alternatives exhaustively, i.e. consider each alternative, compare it with all others, and determine the most favorable possibility open. Such an assessment will involve a cost. Therefore, if, e.g. Π tz is found to be the best choice, some fraction (say g) of the assessment cost must be subtracted from Π tz in order to obtain an accurate indication of the residual that is expected to accrue to the firm. The corrected value of the residual that emerges (Πtz ) is important, of course, because unless it is greater than or equal to zero, no action will be taken to initiate production with the technological/organizational arrangement associated with Πtz . In other words, if none of the m alternatives considered is able to yield what is perceived to be a normal profit, the entrepreneur will be forced to search for more extensive information about existing production alternatives, consider innovation, or abandon his plans to enter this particular industry. The optimization process just outlined is distinctive because it leaves room for a wide variety of behaviors to be undertaken by decision makers. 5 The precision of the strict 5 It is important to distinguish between the different meanings that can be given to the term “optimization”. In its usual definition, the assumption is that a solution is secured through the use of calculus methods. This approach suggests the existence of a large set of options which must be subjected to an exhaustive search over all possibilities. Consequently, the choice process is very costly. By contrast, the use of simplified decision rules can reduce the set of options to a small number. Then, even exhaustive search can be undertaken at relative low cost.

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neoclassical approach is lost, but this has to be the case given the special conditions of the neoinstitutional environment. We know that, in such an environment, the optimization process must be conducted with choice methods that are themselves chosen without the use of strictly rational procedures. Complexity forces decision makers to establish essentially arbitrary rules for choosing a suitable configuration for the firm. And, in the case of entrepreneurs contemplating entry into a competitive industry composed of relatively small firms, the first impulse may well be to imitate existing units that appear to be profitable. In other words, it seems likely that the typical approach will not be to seek novelty and try innovative technology but, rather, to adopt an arrangement that represents the “best current practice” and seems to be working. In this way, costly investment in the origination and implementation of fundamentally new production options can be avoided. Nevertheless, even when imitation is the objective, precise duplication of a currently profitable enterprise is not so easily accomplished. Since profitability depends on a firm’s total technological/organizational configuration, and not on technical arrangements in the narrow sense, the numerous enterprise characteristics that affect performance have to be considered. This means, however, that mistakes can easily be made because uncertainty exists about the structural details and actual profit positions of existing firms. Indeed, there can be no assurance that any firm chosen for imitation is the best model possible since the search conducted by an entering firm for a model will not be exhaustive. In effect, transaction costs and bounded rationality will lead entering firms to select different models to follow, and “noise” will cause deviations from the patterns chosen for duplication. The general result is that firms following the imitation strategy will tend to scatter within a neighborhood representing technological/organizational options that have proved profitable under current conditions. Depending on the relative success of their search and adjustment activities, firms can secure greater or lesser profits. The least well adapted firms may be forced to leave the industry as superior units enter, but there is no basis for believing that the remaining firms have achieved or even approximated classic Pareto optimal solutions. That is, solutions achieved on the assumption of a frictionless system or, in the hybrid case, a largely frictionless system. No entrepreneur is able to compare his actual position with a hypothetical “ideal” position and make the necessary corrections to reach the optimum. In practice, he can only obtain understanding of how well his arrangements are adapted to the going economic conditions by the indirect method of observing his profit position. The situation described implies that relatively inefficient and marginally profitable firms can remain as active members of the industry. These firms, and others in the industry, do face some significant choice problems, however. In an ongoing system, each firm must decide on a strategy for future action in order to preserve its viability. The basic choices open are as follows: (i) A firm may decide to keep its existing structure unchanged, shun further experimentation, and thus avoid making costly additional investment in information and adjustment. Presumably, such a firm hopes that no major shifts in economic conditions will come about in the near future, and that it will be able to maintain an acceptable profit position. If most industry members adhere to this strategy, a relatively stable situation can emerge in the industry, a situation in which industry prices, outputs, and profits are not uniform among firms but fall into a broad pattern that can be sustained over some time period.

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(ii) Since a decision maker has no definite knowledge that his firm’s present structure is well adapted to future economic circumstances, or that adjustments in the structure could not bring about still larger profits, he may decide to increase his information about the existing knowledge stock possessed by society, and search further through the options present in multi-dimensional technological/organizational space. In other words, he may replicate the general procedure he followed initially in attempting to achieve the “best current practice” — using the same or different decision rules to explore options. Such investment in renewed search will be undertaken if the entrepreneur believes that the prospective gains from corrective action are greater than the costs of action. Judgments concerning whether to take action and on the extent of the investment to be made are, of course, subjective and will depend, inter alia, on the willingness of the entrepreneur to assume risk, and on the urgency of his need to sustain or improve profits. Since search has a cost, and since the benefits derivable from incremental pieces of information are impossible to estimate accurately ex ante (Hirshleifer and Riley, 1979, p. 1395), ambitions for improvement normally have to be tempered. Indeed, to strike a balance between cost and return, the firm may find it expedient to limit adjustment to secondary characteristics while keeping such things as durable capital unchanged. In any event, those firms that are able to move to superior technological/organizational configurations will enhance their productivity and tend to squeeze less adroit firms from the industry. It follows, then, that even if no fundamentally new knowledge is created by society, and firms merely seek to exploit existing possibilities more effectively, entrepreneurs concerned with profits are able to bring about growth in real output over time. In orthodox neoclassical terms, the process described here is analogous to one in which firms approach an existing production possibility frontier, rather than move to a new frontier that has been shifted outward by technological advance. A question can arise, of course, concerning the definition of “innovation”. At one extreme, innovation can be said to take place if an entrepreneur merely observes and copies the structure of an existing firm that is operating successfully in the economy. The rationale for this position is that the entrepreneur has learned something previously unknown to him and, as a result of this “new knowledge”, has been able to launch a useful productive enterprise. Or, alternately, it might be argued that firms functioning in a neoinstitutional environment are almost certain to differ among themselves in some respects, and that the unique features indicate the presence of innovative activity. But interpretations of this sort would seem to drain much of the significance from the term innovation by focusing more on routine operations than on major creative acts. Although its limits and content may be hard to specify precisely, there is such a thing as society’s accumulated knowledge, and there is reason to distinguish between entrepreneurs who search through technical/organizational options that have been discovered previously, and those who seek basically new and untried options. Moreover, from a practical standpoint, the costs and risks associated with attempts to find basically new opportunities tend to be much greater than the costs and risks linked to routine replication or minor variations on existing themes. (iii) Instead of accepting all of the existing constraints in the system as fixed and unchangeable, an entrepreneur may consider the possibility of taking political action to alter some of the constraints. That is, acting alone or in association with other interested parties, the entrepreneur can endeavor to change the “rules of the game” in directions favorable to himself. Obviously, the profitability of his firm can be ensured (or increased) if the

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government revises certain of its policies with respect to such matters as foreign trade, taxation, regulation, industry subsidies, etc. Thus, by diverting time and resources to rent-seeking projects, the entrepreneur can, conceivably, project himself into a superior position without any need to undertake innovation or search for more information about socially known technological/organizational options. In short, investment in lobbying may appear to represent the most “productive” use of the firm’s limited investment funds. Whether this is so, however, depends in good part on the nature of the existing institutional structure and, in particular, on the manner in which government and the legal arrangements function. What this situation suggests, in turn, is that the theory of the firm is linked, ultimately, to an understanding of the theory of the state. (iv) In initiating production de novo, or in subsequent adjustments of the firm to the neoinstitutional environment, most decision makers are likely to behave somewhat conservatively and avoid very bold strategies requiring the discovery and application of fundamentally new knowledge. Nevertheless, it can be expected that a certain number of daring entrepreneurs will appear over time, and these individuals are the ones who will often make large profits and bring about major economic change. As Alchian has noted: “. . . the greater the uncertainties of the world, the greater is the possibility that profits [will] go to venturesome and lucky rather than to logical, careful, fact-gathering individuals” (Alchian, 1950, p. 20). This point raises the question of the motivation of investors who seek “novelty”. It is well known that when future outcomes are unknowable, as in the case of true innovation, it is not possible to establish probabilities that permit meaningful maximization of expected profit (Wiseman, 1991, pp. 151–152; De Vany, 1996). And we also know that the neoinstitutional scene does not provide anything like a comprehensive set of futures markets to guide decision making. Presumably, each would be innovator must make his projections of future events using his own model of economic reality, and, as Keynes suggested, it may be that: “. . . it is our innate urge to activity which makes the wheels go round, our rational selves choosing between the alternatives as best we are able, calculating where we can, but often falling back to our motive on whim or sentiment or chance (Keynes, 1936, p. 162). In one fashion or another, people do seem to have faith that, despite Knightian uncertainty, they can learn and make reasonable investment choices. 6 In the case of the firm’s activities, it is also true, of course, that innovation may involve only modest (and less costly) changes in procedure — such as the provision of better-serviced goods, more quickly delivered goods, etc. (Blaug, 1998, pp. 6–7). But whether innovation is fundamental and sweeping or minor, it has the effect of eliminating unadaptive and high-cost producers, and thus of improving the technological/organizational forms employed in the industry. The latter tendency is clearly desirable for economic progress — unless innovation happens to result in the entrenchment of monopolistic elements and barriers to entry. In general, imperfect competition tends to insulate firms from market discipline, leads to waste, and may reduce the industry’s thrust toward continuing (non-trivial) innovation (Marris and Mueller, 1980, p. 57). The discussion of points (i)–(iv) above suggests that the firm’s dynamic adjustment problem reduces, in large part, to one of allocating resources over time among a number of alternative investment possibilities. And since choice is complicated by the existence 6 Random selection of stocks may produce better returns than those generated on the basis of the advice given by stockbrokers and fund managers, but the latter individuals are still able to find credulous customers.

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of numerous options and Knightian uncertainty, the investment problem has to be solved with the aid of simplified decision rules that are themselves not selected by strictly rational procedures. Thus, in considering how best to survive and prosper in the neoinstitutional environment, different entrepreneurs, employing subjective standards, will select different rules and achieve different results. Given many trials conducted by competing firms, with some firms following the conservative strategy of imitation and others the venturesome strategy of innovation, a system may show relatively rapid movement toward more growth-effective organization. There can be no assurance, however, that endogenously generated transformation will lead inevitably and quickly to beneficial outcomes. A variety of factors, including pre-existing institutional arrangements, imperfect competition, perceived search costs, etc. can act to check growth-producing evolution. Certainly, classic Pareto efficiency is not to be anticipated in a neoinstitutional context.

7. Conclusions In considering the operation of the “neoinstitutional” firm, it does not seem either necessary or desirable to reject completely the explanatory insights provided by neoclassical economics. The frictionless neoclassical model can, in fact, be extended to take account of behavior that unfolds in a system exhibiting positive transaction costs and bounded rationality (Simon, 1991). It is true, of course, that when reinterpretation of the orthodox model is carried out systematically, and the far-reaching consequences of the new assumptions are understood, the theory of the firm changes profoundly. Some indication of the behavior to be expected from the neoinstitutional firm has been offered in the preceding sections. It can be observed, however, that the differences that exist between traditional theory and the new approach are revealed with particular clarity by the way in which the concept of economic efficiency is interpreted in the respective models. Orthodox analysis places heavy emphasis on the idea that a firm operating in an idealized competitive system will be guided by market forces to what can be termed a classic Pareto optimal equilibrium. Similarly, the hybrid firm is said to move to a constrained Pareto optimal position (Stiglitz, 1985, p. 26). These “efficient” solutions are best thought of as hypothetical because they cannot be attained in the real world of optimization costs and boundedly rational decision makers. By contrast, the neoinstitutional firm promises more “realistic,” if less encouraging, results. Different neoinstitutional firms will show different productivities, but none can be expected to achieve the efficiency levels presupposed by the frictionless neoclassical model or the largely frictionless hybrid model. From a purely formal standpoint, it is true that all three types of firms considered here attain constrained optimal solutions — in the sense that each is assumed to move to the most advantageous position permitted by the particular set of constraints it faces. Nevertheless, it is possible to draw a distinction between the optimum reached by the neoclassical firm, on the one hand, and that of the neoinstitutional firm, on the other. In a world of complete information and unbounded rationality, a decision maker who achieves a Pareto optimum position knows with certainty that he has wrung the last shred of advantage from the circumstances that confront him, and that he cannot make any further adjustment that will improve his situation. Thus, until the data change, he has no incentive to experiment and find a new

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solution. With small modification, the hybrid case is analogous. Things are quite different, though, for the neoinstitutional firm. There, the decision maker who is securing some profit does not know whether the position he has attained is capable of improvement or not. Thus, while search for a superior solution in any one period may be limited (e.g. by resource restrictions), there is incentive for the decision maker to renew the search for promising alternatives in subsequent periods. Various strategies are open. Consistent with the general neoclassical logic, he can look for a superior option by exploring the socially known technological/organizational possibilities extant. But, operating in an uncertain world, he may also consider devoting resources to innovation, or to rent-seeking activities. What seems apparent, then, is that the constrained optimal solution of the neoinstitutional case has a transient character. And, lacking the stability of the orthodox Pareto optimum, it is not only different but appears less significant. Of course, as Leibenstein has noted, “any decision procedure that does not permit nonoptimal choices denies the essential meaning of the word optimization, that is, the necessarily comparative element involved” (Leibenstein, 1985, p. 11). Efficiency defined as constrained maximization means that every equilibrium reached is “efficient,” and this convention does not push discussion in a very useful direction. Hence, it seems necessary to select some independent standard for assessing outcomes in a neoinstitutional universe. One way to proceed is to interpret an efficiency criterion as a device that establishes a dichotomy and separates relatively more desirable activities from less desirable ones. It is plausible to say that, from a one-period or microstatic standpoint, efficient arrangements can be differentiated from inefficient arrangements on the basis of whether a firm is making positive economic profits or not. The minimum requirement is that a firm must be earning (what are perceived to be) normal profits so that opportunity costs can be covered. This conception of efficiency is obviously crude — and sufficiently elastic to permit firms having various levels of productivity to fall into the category of efficient organizations. Nevertheless, it is capable of saying, at any cross-section of time, that, e.g. a firm earning profits is “efficient” relative to one that operates with a soft-budget constraint and is being subsidized by government. The effective message conveyed is that when individuals, who are boundedly rational and who possess only very limited economic information, are required to make allocative decisions, a system can do no better than to ensure that resources flow to those firms that are capable of producing outputs that sell for prices that cover (or more than cover) production and other legitimate costs, and to deny resources to firms that lose money (Furubotn, 1999). The positive profit criterion is strictly a one-period concept and tells us nothing about dynamic efficiency but, in this respect, it matches the formulation of most New Institutional Economics models. Decision makers functioning in a neoinstitutional environment have no detailed allocational rules to guide them and, in consequence, are likely to change their behavior from the simple pattern indicated by neoclassical analysis. Instead of placing primary emphasis on adjusting inputs and outputs while accepting all of the technological and other constraints as fixed, major attention can be given to the possibility of changing some of the constraints (North, 1990, pp. 80–81). This approach does not require a careful consideration of Lagrangian multipliers as neoclassical theory might suggest. Rather, since irriducible uncertainty rules, the focus must be on broad changes in the firm’s environment brought about by an altered pattern of constraints. In adjusting, entrepreneurs can be conceived as

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following some type of investment strategy along the lines noted in points (i)–(iv) of Section 5. Of course, by experimenting with what appear to be promising avenues for investment in change, including legal and institutional change, the hope is to enhance enterprise profitability. Any given firm may stop searching for improvements and, thus, can reach something akin to an equilibrium situation. How long the firm can remain passive, however, depends on the actions of other firms in the industry, and on the intensity of competition. Contending firms using trial and error methods will put pressure on the least productive units and cause some to exit. Yet evolutionary change does not necessarily lead to ever more productive firms (Ricketts, 1994, pp. 346–348). In principle, the industry can freeze in some configuration that maintains an uneasy status quo. In this state, individual units will tend to differ in structure, but insofar as they are earning profits they all meet the loose definition of one-period efficiency noted above. However, as far as the dynamic efficiency of an economic system is concerned, little can be said. There seems to be a basis for arguing that decentralized organization and competition are more conducive to growth then centralism and monopoly. But, under neoinstitutional conditions, “. . . the very notion of optimization may be incoherent in a setting where the range of possibilities is not well defined, even if the issue of different interests could be resolved in this terminology. . . ” (Nelson, 1995, p. 83). Certainly, theory is carried a long way from the nice regularities of orthodox welfare economics.

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