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Successful industrialization requires a long and protracted process of learning; new ideas from abroad are not enough. Poor countries also need to apply those ideas effectively, and such a capability can only be acquired via learning-by-doing. The very process of learning itself requires both the expansion of education and an extensive programme of investment in physical capital; many skills are acquired by a process of learning-by-doing in a factory environment. The prior development of an industrial capability is therefore a necessary condition for rapid rural industrialization in poor countries.

The foundations for the rapid growth of rural industry in China after 1978 were laid out during the Maoist era. Although much of China’s Maoist industry was inefficient, its workers acquired a vast array of skills via the process of learning-by-doing and by the diffusion of skills from urban areas. Policy changes after 1978 certainly added to the effectiveness of this inheritance. The Chinese evidence points inescapably to the conclusion that learning and skills development are essential pre-conditions for rapid rural industrialization in poor countries.

This chapter presents a unified framework for understanding the implication of ‘learning by doing’ in endogenous growth models. It considers three different ways in which the concept of ‘learning by doing’ has been exploited by the endogenous growth theory. The seminal work dates back to Kenneth Arrow (1962), who took a significant step towards offering a theory of labour augmentation and attributed productivity increases over time to learning. Arrow showed that technical progress amounts to ‘learning by doing’ and constructed an index for experience consisting of the value of cumulative gross investment at any point of time. Arrow's idea was extended by Antoine d'Autume and Philippe Michel (1993) and by Paul M. Romer (1986), the latter bringing out the importance of education and knowledge capital in determining the growth rates of economies. This chapter makes the three models comparable by utilizing a disembodied technical progress structure.

Starts with an illustration of the effect of growth on the terms of trade between a rich and a poor country, using a simple comparative‐static framework. Next, we study the pro‐competitive effects of trade liberalization in inputs with a model in which an import‐substituting upstream industry supplies an intermediate good to the producers of the final good. With imperfect competition and scale economies among upstream firms, the model has multiple equilibria, and trade liberalization may trigger an expansion to the higher output equilibrium. The next section is about the effects of trade on economic development as analyzed in endogenous growth theory, with its focus on learning‐by‐doing, dynamic learning spillovers, and trade‐induced technological patterns in growth. This section includes a model of North–South trade that endogenizes comparative advantage and the direction of technological specialization.

In early 2010 it will be five years since the Kyoto Protocol came into force. This short period has been truly astonishing for the new world of carbon trading. The market for trading emission rights and carbon credits has exploded — more than doubling each year. In 2005, the market was worth US$10 billion, in 2006 US$30 billion, in 2007 US$67 billion, and in 2008 more than US$120 billion. At the same time as the amounts of money involved have increased, so has the level of sophistication of those involved — investors, project proponents, host countries, and the international bodies responsible for overseeing these developments. However, rather than moving into a settled era of maturity and certainty, the future of the carbon market is still wracked with uncertainty. Uncertainty is in fact one of the key themes of this book. But so also is the spirit of experimentation and enterprise — of Innovation and of Learning by Doing. This chapter examines each of these three themes.

This chapter inquires into the different ways in which education can promote technological advancement and spur growth. The new brand of the growth theoretic model seeks to understand the process of growth on the basis of optimizing individual decisions to invest on education, research, and development. It is in this context that the economic characteristics of knowledge become crucial. Since generation of knowledge entails investment, knowledge ceases to be a pure public good. This chapter relates the concepts of education, knowledge, and human capital in the context of growth. The challenge is to identify and measures various forms of human capital to incorporate in the models. This chapter ends with a discussion on how quality of education matters for growth.

Technical progress may be viewed as the conscious result of research and development, or as an unplanned by-product of the process of privately conducted economic activities. This chapter looks at a series of models that avoid the complexities of alternative market structures. Except for the model of Romer (1986), the models have technical similarities. First, the chapter examines the notion of technical progress as a spillover or externality. It then discusses the learning by doing hypothesis, which views technical progress as an improvement in workers’ skills caused by exposure to capital resulting from continuing investment activity. It also explores command economy and private economy, as well as growth paths of capital and labour. Finally, the chapter considers the simplest type of endogenous growth model, the AK model, and shows how it is linked to the fixed coefficients model.

Economic growth is significantly slower in fragile environments and twice as volatile as in other emerging economies. Backwardness also shows in exports, which have remained stagnant and non-diversified since the 1980s. We revisit the role of exchange regimes in fostering exports and economic growth and, thereby, in reducing political fragility. We use a DSGE model tailored to replicate key features of fragile economies: presence of frictions in market adjustment and learning, influence of external shocks, and the crucial role of governments in providing public investment and delivering social transfers to the population. Our DSGE model allows us to track the response of variables associated with fragility to shocks that are likely to be important in fragile economies. The simulations we perform below illustrate the types of general-equilibrium interactions that may complicate the analysis of the effects of shocks that typically affect fragile economies on endogenous variables that may influence fragility.

Why do developing countries protect the intellectual property rights (IPR) of foreign inventors? Does this facilitate technology transfer from the industrial frontier? This chapter addresses those questions by telling the story of when and how technology was transferred through patent licensing to Spanish iron and steelmakers from the rest of Europe. It focuses on the period between 1850 and 1929, during which foreigners’ IPR were relatively well protected. Modern steelmaking was a quantum leap over previous techniques vis-à-vis scale and sophistication. In turn, this required a revolution in technology, knowledge, and skills. However, the transition to modern steelmaking was marked by a challenging process that has gone largely unrecognized by researchers: inventors had to find ways to transfer tacit knowledge to adopters that was inordinately difficult to codify, as it was arrived at via intuition and learning by doing. Patents played a key role in broadcasting new steelmaking techniques to Spaniards working in that industry. They also helped Spaniards connect with original, foreign inventors and establish enduring relationships with them. The latter shared their know-how and expertise with the former under the aegis of licensing agreements and brought them into their networks of suppliers and technicians. Moreover, when disruptive steelmaking innovations were patented in Spain, this spurred second generation foreign inventors to patent improvements. It also induced Spanish inventors to contribute their own add on innovations, which they themselves patented.

This volume is the result of the first in a series of lectures to honor Kenneth J. Arrow, one of Columbia University's most outstanding graduates, who received his Ph.D. from Columbia in 1951. This introductory chapter first describes the starting point of the present volume, namely Arrow's work on innovation, particularly his 1962 paper on learning by doing. The book approaches the question of free trade from Arrow's learning perspective. In so doing, it not only honors his legacy and challenges the conventional views, but also contributes to a key set of policy issues: how to increase the pace by which living standards increase, especially in developing countries. An overview of the subsequent chapters is also presented.

A quarter of a century ago, Alvin Weinberg offered one of the most insightful— and unsettling—observations anyone has made about modern technology. Speaking of the decision to use nuclear power, the long-time director of Oak Ridge National Laboratory warned that society had made a “Faustian bargain.” On the one hand, he said, the atom offers us a nearly limitless supply of energy which is cheaper than that from oil or coal and which is nearly nonpolluting. But on the other hand, the risk from nuclear power plants and nuclear-waste disposal sites demands “both a vigilance and a longevity of our social institutions that we are quite unaccustomed to.” We cannot afford, he said, to treat nuclear power as casually as we do some of our other technological servants—coal-fired power plants, for instance—but must instead commit ourselves to maintaining a close and steady control over it. Although Weinberg’s predictions about the cost of nuclear power may now seem naive, the larger issue he raised is even more relevant today than twenty-five years ago: Where should society draw the line in making these Faustian technological bargains? With each decade, technology becomes more powerful and more unforgiving of mistakes. Since Weinberg’s speech, we have witnessed major accidents at Three Mile Island, Chernobyl, and Bhopal, as well as the explosion of the Challenger and the wreck of the Exxon Valdez. And looking into the future, it’s easy to see new technological capabilities coming along that hold the potential for far greater disasters. In ten or twenty years, many of our computers and computer-controlled devices may be linked through a widespread network that dwarfs the current telecommunications system. A major breakdown like those that occasionally hit long-distance telephone systems could cost billions of dollars and perhaps kill some people, depending on what types of devices use the network. And if genetic engineering becomes a reality on a large scale, a mistake there could make the thalidomide debacle of the late 1950s and early 1960s look tame.

This chapter lays out the project of the book, namely, an investigation of the idea of popular political education as it appears in the work of Rousseau, Hegel, Tocqueville, and Mill. These theorists wrote either preceding the French Revolution (Rousseau) or in the tidal wake of that event (Hegel, Tocqueville, Mill). Despite their great differences, they all favored reforms hostile to the ancien régime (Hegel and Tocqueville's ambivalence towards the Revolution notwithstanding). Most obviously, they all wanted the public realm to be more far more inclusive than it had previously been. However, this desire was married to a shared anxiety about the lack of political experience and judgment to be found in ordinary people. This worry made them all centrally concerned with popular political education, and gave rise to aspirations to the role of "teachers of the people." Two main tensions emerge when we compare their respective approaches to this task. First, there is the tension between a moralizing model (Rousseau and Tocqueville) and a more intellectual or Enlightenment-inflected one (Hegel, Mill). Second, there is the tension between conceptions that emphasize "learning by doing" and those that stress "learning by understanding." General descriptions of the theorists' respective positions are offered.

This chapter examines policies in support of protecting infant industry and the local sector. In doing so, it considers arguments regarding the traditional infant industry and presents an innovation-based model of economic growth to show the impact of protection on domestic innovation and growth. In the event of trade liberalization, to invest in learning-by-doing by the domestic infant industry will be a costly affair. Governments can protect the infant industry by adopting targeted policies of subsidizing production or restricting import. They may also focus on nontargeted policies such as maintaining undervalued exchange rates that will enable the industry to get benefits as long as it does not import too many inputs from abroad itself.

In January 1975, the magazine Popular Electronics trumpeted the beginnings of a revolution. “Project Breakthrough,” the cover said: “World’s First Minicomputer Kit to Rival Commercial Models.” Inside, a six-page article described the Altair, an unassembled computer that could be ordered from MITS, a company in Albuquerque originally founded to sell radio transmitters for controlling model airplanes. To the uninitiated, it didn’t look like much of a revolution. For $397 plus shipping, a hobbyist or computer buff could get a power supply, a metal case with lights and switches on the front panel, and a set of integrated circuit chips and other components that had to be soldered into place. When everything was assembled, a user gave the computer instructions by flipping the panel’s seventeen switches one at a time in a carefully calculated order; loading a relatively simple program might involve thousands of flips. MITS had promised that the Altair could be hooked up to a Teletype machine for its input, but the circuit boards needed for the hookup wouldn’t be available for a number of months. To read the computer’s output, a user had to interpret the on/off pattern of flashing lights; it would be more than a year before MITS would offer an interface board to transform the output into text or figures on a television screen. And the computer had no software. A user had to write the programs himself in arcane computer code or else borrow the efforts of other enthusiasts. One observer of the early computer industry summed up the experience like this: “You buy the Altair, you have to build it, then you have to build other things to plug into it to make it work. You are a weird-type person. Because only weird-type people sit in kitchens and basements and places all hours of the night, soldering things to boards to make machines go flickety-flock.” But despite its shortcomings, several thousand weird-type people bought the Altair within a few months of its appearance. What inspired and intrigued them was the semiconductor chip at the heart of the computer.

The discussion of the role of technology during the period of Agricultural Enlightenment follows naturally from the preceding chapter. In the context of Chapter 5 ‘technology’ is understood to embrace practices (e.g. improved crop rotations) and skilled farm labour and management, as well as hardware (more effective ploughs, threshing machines etc.). It is demonstrated that the very notion of technology as opposed to natural knowledge or ‘science’ was largely a product of the late Enlightenment, and one which came to prominence, whether in agriculture or in industry, during the period of acute geo-political rivalry marking the close of the eighteenth century in Europe. This chapter focuses particularly on the issue of the transferability or mobility of technology and on the ways in which landlords and governments contrived to shift skills and productive techniques from one location to another.

The Malthusian theory hypothesizes that the natural environment imposes various capacity constraints on human population growth and that population size has been and will be checked by these constraints. In such a classical theory, which was presumably motivated by observations of the ancient world, population might be the most important dynamic variable, although its role is rather passive: population is a variable that would be affected by, but would not affect, the environment. Boserup (1981), however, sees the role of population in the development of human economy as more consequential. She gave many persuasive examples that showed that, at least for the period up to the mid-twentieth century, population size might be a variable which actively spurred technological progress. This is also the viewpoint held by Lee (1986) and Pryor and Maurer (1982). After the Industrial Revolution, the role of population in economic dynamics, along with the reduction of mortality fluctuations and the increasing control of female fertility, evidently became secondary. The key variable that dominates the analysis of economic dynamics in the neoclassical growth theory along the lines of Solow (1956) is capital (or per capita capital). In Solow’s growth model, the role of population is minimal in the steady state: neither the level nor the growth rate of the steady-state per capita consumption has anything to do with the size of a population; only the steady-state per capita income level will be affected by the population growth rate. The growth pattern in the latter half of the twentieth century is markedly different. A key feature of our recent growth experience is the rapid innovation of new technologies. Modern growth theory has embraced the concept of increasing returns to explain such a unique growth pattern. However, various versions of the theory of increasing returns turn out to be necessarily linked to population. The hypothesis of learning by doing implies that growth in productivity is an increasing function of aggregate production, which is itself positively related to the size of population.