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«Industrial Policies, the Creation of a Learning Society, and Economic Development1 B. Greenwald and J. E. Stiglitz Industrial policies—meaning ...»

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In light of the pervasive market failures associated with innovation and learning, the commonly heard objection to industrial policies—the mantra that government should not be involved in “picking winners”10--is beside the point: the objective of the government is to identify, and “correct” externalities and other market failures. While it is now widely accepted that there can be large negative externalities (e.g. from pollution, or from excessive risk taking in the financial sector), we are concerned here with an equally important set of positive externalities.

While government may not be perfect in identifying negative externalities, there is by now consensus (except among polluters) that environmental regulations have been very beneficial;

so too for positive externalities: even if government identifies such externalities imperfectly, it is wrong to assume that they are “zero”: government can improve upon the market allocation. The best way of doing so is a matter of controversy, upon which we comment in the concluding section. But it is clear that many governments (both in developed and developing countries) have a credible record of industrial policy interventions.11 A closer look at learning spillovers We emphasized earlier that there are important positive externalities from learning. Such spillovers are pervasive and large, and they are larger in some industries than in others. And obviously, markets will not take into account these externalities.

Inappropriately designed intellectual property regimes can actually inhibit innovation. (See the references cited earlier in footnote 6.) In this view, it makes no difference whether the economy produces potato chips or computer chips. Let the market make the decision—not some government bureaucrat.

The returns on US government investments in technology and science are even higher than those of the private sector (which in turn are far higher than private sector returns elsewhere.) See Council of Economic Advisers 1997.

Spillovers occur even in the presence of a patent system. Many advances cannot be patented (advances in mathematics, for example); and the benefits of much of what is learned in the process of research cannot be appropriated. Indeed, the disclosure requirements of a patent are intended to enhance these societal benefits. We’ll provide further illustration below.

There are many aspects of learning spill-overs. There are direct technological spillovers: the production of any good involves many stages, and some of the stages may involve processes that are similar to those used in another seemingly distinct sector. Atkinson and Stiglitz (1969) noted, learning is localized: it affects production processes that are similar to those for which there has been learning.12 But the learning is not limited to a single process and related processes for a particular product. Innovations in one sector may benefit other sectors that look markedly different, but use similar processes. Sectors that are, in one way or another, more similar may, of course, benefit more. (Indeed, the same argument holds within a sector.

An innovation in one technology in a given sector may have limited spillovers for other technologies—the spillovers may be greater to other products using analogous technologies.) There are especially important spill-overs in methods of production. Inventory control and cash management techniques affect virtually every firm in an economy. Just-in-time production or assembly lines are examples of production processes that affect many industries.13 Improvements in skills (techniques) in one sector have spillover benefits to other sectors in which analogous skills are employed. Hidalgo and colleagues (2007) characterized the product space, attempting to identify the “capabilities” that different sectors have in common. Presumably, if two Because countries differ, too, some learning that may be relevant in one country may be of limited benefit in other countries. Most changes in technology, however, could confer benefits across borders.

The extent to which that is the case may depend on the level of skills (human capital) and the institutional arrangements.

They are also examples of ideas that are hard to be protected by patents, though in some cases, America’s business process patents attempt to do.

products entail similar capabilities, learning that enhances a particular capability in one sector will have spillover benefits to related sectors for which that same capability is relevant.14 It is, as we have suggested, impossible to appropriate the benefits of much of this learning. An idea like just-in-time production, replaceable parts, or assembly lines spreads quickly throughout the economy, and can't be protected by intellectual property. Learning what grows well in a particular climate with a particular soil is information that is not patentable. The result, as we noted earlier, is that there will be insufficient investment in exploration. There are equally important economy-wide “technologies,”and improvements in these have society wide benefits. These include those that arise out of the development of institutions. A financial system developed to serve the manufacturing sector may equally serve the rural sector.

Improvements in the education system, necessary for an effective industrial sector, too can have benefits for the service sector or the agricultural sector.

Knowledge is embodied in people. This is especially relevant for what is called tacit knowledge, understandings that are hard to codify, to articulate as simple prescriptions, that could easily be conveyed through textbooks or classroom learning. Workers move from firm to firm, and thus convey some of the learning that has occurred in one firm to those in others. But knowledge is also embodied in firms that supply inputs to multiple firms. What they learn in dealing with one firm in one industry may be relevant for another firm. There can be backward, forward, and horizontal linkages (Hirschman, 1958).

Technological knowledge is also embodied in machines, and a machine constructed for one purpose can often be adapted for quite another. It is not an accident that the Ohio Valley (stretching up to Michigan) gave rise to innovations in bicycles, airplanes, and cars: while the products were distinct, the development of these products shared some of the same technological know-how. This illustrates the principle that it may be difficult to identify ex ante what are “nearby” products, products such that advances in learning in one affects the other.

We do not comment here whether their empirical approach really does capture fully the set of related capabilities. The effects of an improvement in one sector on other sectors depends not just on the similarity of those sectors, but on the institutional arrangements, e.g. providing scope for exploiting linkages. Thus, the fact that natural resource sectors have traditionally not been closely linked to other sectors may be partly a result of the absence of effective industrial policies, and the exploitive relationships often evidenced in that sector.

Knowledge, in this sense, is like a (good) disease: it can spread upon contact. But some kinds of contact are more likely to lead to the transmission of knowledge than others. Some of the people who might possibly come into contact with the knowledge are “susceptible,” i.e. they are more likely to learn, to use the knowledge, and perhaps even develop it further. Firms, realizing that knowledge is power (or at least money), seek to limit the transmission of knowledge—it might help one’s rivals, who might be able to build on it, putting oneself at a disadvantage. Thus, firms go to great lengths to maintain secrecy. While for the advancement of society, it is desirable that knowledge, once created, be transmitted as broadly and efficiently as possible, profit maximizing firms have traditionally sought to limit to the extent possible the transmission of knowledge.

The architecture of the economy--including all the rules concerning intellectual property-affects the speed and extent of transmission of knowledge.

There is, in this, however a trade-off that is fully analogous to that in the design of patents and that is at the root of the critique of the efficient markets hypothesis: if knowledge were perfectly transmitted, there would be no incentive to expend resources on gathering and producing knowledge. There would be underinvestment in knowledge creation (and in the case of developing countries, gathering knowledge from others). Hence, an optimally designed learning society does not entail the perfect transmission of knowledge (except for knowledge that is publicly provided.)15 There are, however, natural impediments to the perfect transmission of knowledge. It is plausible that a market economy engages in excessive secrecy (relative to the social optimum).

This, of course, has been the contention of the open source movement. Collaborative research in the open source movement is still economically viable, both because there are still economic returns (e.g. because of the tacit knowledge that is created by the learning/innovation process itself) and because there are important non-economic returns to and incentives for innovation.

(Dasgupta and David, 1994).

And indeed, this is one of the advantages of public support for the creation of knowledge.

We can thus think of the economy as a complex network of individuals interacting directly with each other and via institutions (like corporations, schools) of which they are a member, ideas (knowledge) being created at various nodes in this network, being transmitted to others with whom there is a connection, being amplified, and re-transmitted, a complex dynamic process the outcomes of which can be affected by the topography of the network, which, together with the rules of the game, affect the incentives to gather, transmit (or not to transmit), and amplify knowledge.

A sub-problem within this systemic problem is the design of the component institutions (e.g.

corporations). For within the institution, there may be incentives to develop knowledge and to hoard or to transmit it. The issue of the architecture of a learning firm is parallel to that of the architecture of a learning economy. In some ways, the two cannot be separated: Traditional discussions of the boundary of firms (Coase, 1937) focused on transactions costs; but equally important is the structure of learning. It may be easier to transmit information (knowledge) within a firm than across enterprises, partly because the “exchange” of knowledge is not wellmediated by prices and contracts16. If so, and if learning is at the heart of a successful economy, it would suggest that firms might be larger than they would be in a world in which learning is less important.17 (On the other hand, the difficulties of developing appropriate incentives for the reward of innovation may militate against large enterprises. There is an ongoing debate over whether large or small enterprises are most conducive to innovation.

Large firms may have the resources to finance innovation, typically lacking in smaller enterprises, but there is an impressive record of large firms not recognizing the value of path breaking innovations, including Microsoft being too wedded to the keyboard, and Xerox not recognizing the important of a user friendly interface, like Windows.) That is, it is hard to write good incentive compatible innovation contracts, to know, for instance, when a firm fails to produce a promised innovation whether it was because of lack of effort or because of the intrinsic difficulty of the task. Cost plus contracts, designed to share the risk of the unknown costs required to make an innovation, have their own problems. See, e.g. Nalebuff and Stiglitz, 1983 An alleged major disadvantage of firms is that transactions within firms are typically not mediated by prices, with all of the benefits that accrue from the use of a price system. But if the benefits of using prices exceeded the costs, firms presumably could use prices to guide internal resource allocations, and some enterprises do so, at least to some extent. There is another perspective on these issues, related to accountability and control. See Stiglitz 1994.

In the discussion below, we mostly


from microeconomic structures, focusing on broader policies, on the principles which should guide government intervention, and on alternative instruments. Section I summarizes key results on the implications of learning externalities. Section II discusses how, in the presence of capital constraints, access to finance may be an important instrument of industrial policy. Section III discusses other instruments.

Section IV focuses on the role of government investment policy. We conclude, in Section V, with a general set of remarks about industrial policy, especially as it relates to the promotion of a learning economy and society.

II. Learning Externalities

A central thesis of this paper is that government should encourage industries in which there are large learning externalities. A simple two-period model in which labor is the only input to production suffices to bring out the major issues.18 We show that government should encourage (i) the production of goods in which there is more learning; (ii) the production of goods which generate more learning externalities; and (iii) the production of goods which enhance learning capabilities.

Assume (for simplicity) that utility is separable between goods in the two periods and between

goods and labor:

(1) W = U(xt) – v(Lt) + [ U(xt+1) – v(Lt+1)], where xt is the vector of consumption {xkt} at time t and Lt is aggregate labor supply at time t.

The disutility of work is the same in all sectors, and Lt is aggregate labor input in period t:

Lt = Ltk and Lt+1 = Lt+1k, where Lik is the input of labor in sector k in period i.

Production is described by (in the appropriate choice of units) (2) xtk = Ltk.

In this simple model, the more output of good j in period t, the lower the production costs in period t+1. We assume (3) xt+1k = Lt+1k Hk[Lt], where Lt is the vector of labor inputs at time t {Lkt}.

The learning functions Hk and their properties are at the center of this analysis. In the

following analysis, two properties of these learning functions will play a central role:

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