Nafziger Economic Development (4th ed)

capital input in increasing labor quality and economic growth. Theodore W. Schultz, in his presidential address to the American Economic Association in 1961, suggests that most of the residual can be attributed to investment in this input rather than to technical progress. He argues that

Studies of economic growth based on increases in man-hours worked and on increases in capital restricted to structures, equipment, and inventories, presumably with quality held constant, do not take account of the important changes that occur over time in the quality of labor and of material capital goods. The advance in knowledge and useful new factors based on such knowledge are all too frequently put aside as if they were not produced means of production but instead simply happened to occur over time. This view is as a rule implicit in the notion of technological change. (Schultz 1961:1–17; see also Schultz 1964)

Economists contending that output is explained by increases in input attribute the growth in total factor productivity to research, education, and other forms of human capital. Indeed Mankiw, Romer, and Weil’s (1992:407–437) empirical evidence (see Chapter 5) indicates that the overwhelming share of economic growth is explained by increases in inputs, human capital, physical capital, and labor. Dale W. Jorgenson and Zvi Griliches (1967:249–283) show that if quantities of output and input are measured accurately, the observed growth in total factor productivity in the United States is negligible, accounting for only 3.3 percent of economic growth. However, in reply to Denison’s careful analysis (1972:37–64), Jorgenson and Griliches (1972:65– 94) admit they erred in adjusting for changes in utilization of capital and land. Still, adjusting for the error leaves substantial scope for the importance of the growth of factor inputs. Moreover, for developing countries, a much larger growth share is explained by increased inputs.

From one perspective, capital includes anything that yields a stream of income over time. Investment is net addition to material, human, and intellectual capital. Improvements in people’s health, discipline, skill, and education; transfers of labor to more productive activities; and the discovery and application of knowledge constitute human and intellectual capital. Economic development, then, may be viewed as a generalized process of capital accumulation (Johnson 1976:542–547). This approach is valuable, as it emphasizes the relative return from alternative resource investments.

Zvi Griliches (1994:1–23), in his presidential address to the American Economic Association, lamented that measuring growth is difficult and data are scanty. Nevertheless, he noted that, thanks to some pioneering studies, economists know much more about the sources of input growth than they did in the 1960s.

The Cost of Technical Knowledge

Countries at different levels of technical learning use the same technology at widely varying levels of efficiency. The same steel mill costs three times as much to erect in Nigeria as in South Korea, and, once it operates, is only half as productive (Lall 1993:95–108).

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Choices among technologies, which continually change, are poorly defined. Technical knowledge, which is unevenly distributed internationally and intranationally, is acquired only at a cost and is almost always incomplete, so that any person’s knowledge is smaller than the total in existence. Less-developed areas can almost never acquire technical knowledge in its entirely, as blueprints, instructions, and technical assistance fail to include technology’s implicit steps.4 Learning and acquiring technology does not result automatically from buying, producing, selling, and using but requires an active search to evaluate current routines for possible changes. Search involves people gathering intelligence by purchasing licenses, doing joint research, experimenting with different processes and designs, improving engineering, and so forth. The LDC firms and governments obtain technical knowledge through transfer from abroad5 as well as internal innovation, adaptation, and modification. Paradoxically, LDCs can only buy information from abroad before its value is completely assessed, because this implies possessing the information.

The price of knowledge, determined in the wide range between the cost to the seller (often a monopolist) of producing knowledge and the cost to the buyer of doing without, depends on the respective resources, knowledge, alternatives, and bargaining strengths of both parties. Selling knowledge, like other public goods, does not reduce its availability to the seller but does decrease the seller’s monopoly rents (Nelson 1978:18; Fransman 1986).

Research, Invention, Development, and Innovation

Technical progress results from a combination of research, invention, development, and innovation. Basic research consists of systematic investigation aimed at fuller knowledge of the subject studied. Applied research is concerned with the potential applications of scientific knowledge, frequently to commercial products or processes. Development refers to technical activities that apply research or scientific knowledge to products or processes (Kennedy and Thirlwall 1972:11–72). Some research and development results in invention, devising new methods or products. At times, invention may require development. The commercial application of invention is innovation, discussed in Chapter 12.

According to one study, investment in agricultural research in the United States from 1940 to 1950 yielded a return of at least 35 percent per year, whereas that in hybrid corn research from 1910 to 1955 yielded at least 700 percent yearly (Griliches

4M. Bell and K. Pavitt (1995:74) recognize that “in fact technology consists of complex ‘bundles’ of information both codified and tacit. . . . Because tacit information is not readily transferable among firms and countries, technological blueprints do not contain inherent performance characteristics. . . . Instead, these blueprints have to be translated into specifications and procedures that are specific to particular applications – an uncertain creative process that can result in highly variable levels of performance.”

5Sports provide a model for understanding how technology gets disseminated. Cricket was invented in England in the 16th century, and soccer in England and baseball and basketball in the United States in the 19th century. Yet, through competition, clinics, learning in the inventors’ countries, and foreign coaching, the “technology” of shooting, batting, passing, and defending has been transferred around the world so that Latinos and Asians (eventually Africans) are dominant in many of these sports.

1958:419–431). In his 1968 presidential address to the American Economic Association, Kenneth E. Boulding (1970) speculated that the rate of return on the small investment in economic research was several hundred percent per year over the period from 1945 to 1965 (Boulding 1970:151).6

Despite these spectacular results, organized research and development (abbreviated as R&D) as a whole has had only a modest impact on the rate of economic growth, as much of it generates no new knowledge. Edward F. Denison (1962) estimates that only 7.5 percent of U.S. per capita growth from 1929 to 1957 can be attributed to organized R&D. Today, over half of these expenses are for defense and space programs, which have had only incidental benefits to civilian production. Furthermore, many economists are skeptical that creativity flourishes in the institutionalized R&D setting. Much technical progress results from on-the-job problem solving and performance improvement rather than from work done in R&D departments. Technical progress in DCs’ firms, regardless of the department of origin, can have substantial spillover effect in increasing LDC output through trade, the spread of multinational corporations, and the purchase or borrowing of technology from abroad (see Chapter 15). The impact of these spillovers may be greater than that of the R&D of developing countries, which is usually a smaller percentage of GNP than in DCs.

Yet, studies like Denison’s assume R&D spending is a flow cost used to produce output in a given year rather than an asset that accumulates through time. Thus, these studies, which assume current spending alone measures innovation, leave out accumulated knowledge (Kamien and Schwartz 1982:51).

A firm’s size, monopoly power, and product diversification will determine how much R&D it does. If it is large, monopolistic, and diverse, the enterprise is more likely to capture the benefits from R&D (Kennedy and Thirlwall 1972:61–62).

However, in competitive product markets like grain, the individual producer can appropriate only a small fraction of the benefits accruing from research. For example, Griliches indicates that consumers received almost all of the social returns of government-sponsored research on hybrid seed corn in the United States (Griliches 1958:419–437). Corn farmers, or even the hybrid seed corn industry, would probably not have undertaken the research, as private rates of return were far below social rates. When such a divergence in these rates exists, the case for government investment in research is strong.

In the 1950s and 1960s, U.S. and British technological leaders, with the highest ratio of R& D spending to GNP, had some of the lowest rates of productivity growth. How fast technology diffuses determines global inequality and LDCs’

6Boulding (1970:151) emphasizes the Great Depressions prevented by economic research. The rate of return on economic research has probably decreased, however, since Boulding wrote. In the late 1970s, an economic adviser to the Carter administration, Alfred Kahn, admitted that economists do not know how to reduce inflation substantially without increasing unemployment. A number of difficult economic problems exist in developing countries. However, in contrast to U.S. economic issues, few of these problems have been researched, suggesting that returns to economic research in the third world would be high.

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relative position. Alexander Gershenkron (1952) maintains the “advantage of relative backwardness.” There are potential advantages for countries that are technology followers, like early post–World War II Japan, and South Korea and Taiwan, in the last quarter of the 20th century. Although followership requires an early emergence of indigenous technological capacity, it may not require deep levels of knowledge.

From World War II to the 1980s, rapidly growing Japan and Germany were large net importers of technology, whereas slow-growing Britain and the United States were not, suggesting that part of the latters’ industrial problems is too little awareness of others’ inventions and development (Franko 1983:24).

However, the catch-up process is self-limiting because as a follower catches up, the possibility of making large leaps by acquiring best-practice technology becomes smaller and smaller. The potential for rapid growth by a follower, such as Japan and Germany, weakens as its technological level converges toward that of the leader, the United States (Abramovitz 1986:387–389). As Japan has shifted to leadership in numerous industrial sectors, it has had to orient its technological policies and educational system toward original research at the technical frontier, which is more expensive than followership. Indeed, as Chapter 3 argues, Japan had exhausted benefits from post–World War II technological catch-up, learning by doing, and internal and external economies of scale by the 1980s; Germany’s postwar “advantages from backwardness” also dissipated about the same time. Moreover, the United States increased its relative gap vis-a`-vis Germany and Japan in the 1990s by enjoying the world’s fastest growth in TFP (although, as indicated later, not necessarily the fastest growth in information technology TFP).

Computers, Electronics, and Information Technology

In the 1980s, economists studying the sources of growth noticed a productivity paradox, observing no positive relationship between information and communications technology (ICT) investments and productivity (Matambalya 2003:524). In 1987, the Nobel economist Robert Solow quipped that “You can see the computer age everywhere but in the productivity statistics” (Crafts 2001:2).

But history indicates a substantial time lag for major innovations. Like all enabling and general purpose technological innovations, the computer started as a crude specific-purpose technology, taking decades to be improved, embodied in reorganized workplaces, and diffused throughout the economy. The more demanding the technology is, the longer the learning curve (Lipsey 2001:4).

Although James Watt patented the first efficient steam engine in 1769, Richard Arkwright began using this new invention in his textile mill in 1783. But the economic historian Nicholas Crafts (2001:9, 21) indicates no effect of steam power on TFP until the beginning of the railway age, 1830, with the opening of the Liverpool and Manchester (U.K.) Railway. However, even in 1830–60, steam power only increased Britain’s TFP by 0.01 percentage points yearly. Moreover, the massive investment in railway construction after 1830 contributed only 0.21 percentage points annually to

Britain’s GDP, 1830–60. Overall, the railroad companies in the United States made no money from their investment, and railroad’s TFP was modest in the late 19th century (Crafts 2001; DeLong 2003b).

In 1871, Zenobe Theophile Gramme introduced the first electric motor of commercial significance. Paul David (1991:315–348) shows that it took four or more decades for the majority of U.S. industries to electrify. He speculates that a new technology needs 50-percent industry penetration before having a revolutionary impact.

ICT, just as steam, railroads, and the electric dynamo, took several decades to affect productivity.7 In the 1970s, when ICT’s effects first became visible, ICT’s productivity effect could only be captured at the micro level, not at the aggregate level of national income. However, by the late 1990s and the first years of the 21st century we see a socioeconomic transformation on par with the Industrial Revolution. Still, in LDCs, the low share of ICT in aggregate national investment obscures the high returns of the few enterprises adopting ICT (Matambalya 2003:526; Lipsey 2001:4).

For Paul David (2001:7), ICT requires three new directions to benefit productivity.

First, a growing range of purpose-built and task-specific information technologies, such as supermarket scanners and other data logging devices [must] become available. Second, networking capabilities and the emergence of a networked environment [must underpin] a re-configuration of work organization. Third, the development of Internet technology [must introduce] an entirely new class of organization-wide data processing applications.

Crafts (2001:20) estimates that the total contribution of ICT (computing equipment, communications equipment, and software) to GDP per-capita growth (from ICT capital and increased TFP) in the United States was 0.69 percentage points in 1974–90, 0.79 percentage points in 1991–95,8 and 1.86 percentage points in 1996– 2000. However, these estimates fail to include increased TFP of non-ICT sectors from ICT-facilitated work reorganization and knowledge spillovers.

By the 1990s, ICT was well integrated into production, showing up as a source of growth of GDP in DCs. ICT investment complemented human capital, physical infrastructure, and other private investment. With ICT established as a contributor to DC growth, some economists began to ask about ICT’s impact on the development of poor countries. Matti Pohjola (2001:1–2) asks: “Could IT [information technology] provide poor countries with the short-cut to prosperity by allowing them to bypass some phases of development in the conventional, long-lasting and belt-tightening process of structural change from an agrarian to an industrial and, ultimately, to a knowledge-based services economy?”

Low-cost information and communication technology improves allocative efficiency by choosing input–output combinations to minimize cost at prevailing factor

7According to Caselli (1999:78–102), these innovations were skill-biased, requiring fast learners and costly new skills that took time to develop, whereas Henry Ford’s assembly line was deskilling, increasing the demand of (and relative wages for) unskilled workers.

8Indeed, there was little correlation between ICT spending per capita and annual productivity growth, 1984–94 (Sichel 1997:119).

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prices, augments technical efficiency through cutting costs by better access to both factor and product markets, and facilitates economies of larger-scale production by breaking labor and capital constraints. Sub-Saharan Africa and South Asia, the two least computerized world regions (World Bank 2003h:298–301), have been hampered by a deficiency of infrastructure, including a lack of telephone mainlines, a long waiting list for subscribing to phones, expensive local telephone calls, and few mobile phones, personal computers, and Internet hosts (Matambalya 2003: 531–536).

India, with more than a billion people, had only 56 million cellular phone users at the end of 2004. However, since the 1990s’ economic reform and further deregulation of telecommunications in the first decade of the 21st century, the Indian government has opened telecommunications to an unprecedented degree of private and foreign participation. In 2003, the share of the Indian population with cellphones was 2 percent compared to 20 percent for China. With competitive expansion, “customers can get most [telecom] services they want dirt cheap” in India, making it “one of the world’s hottest markets for telecommunications handset makers, equipment suppliers, and investors,” according to the Wall Street Journal reporter Joanna Slater (2004b:A13). Subscribers to mobile phones, which just surpassed those to mainline phones in India in 2005, are expected to reach more than 130 million by 2008 (ibid.). China also has expanded its Internet phone service, hoping to leapfrog Western landline telephones (Ramstad and Brown 2004:B4).

Jeffrey Sachs (2000:81), Director of Columbia University’s Earth Institute contends: “Today’s world is divided not by ideology but by technology,” the digital divide between rich and poor. Fifteen percent of the world’s population, most of the OECD countries (including South Korea) plus Taiwan, are technological innovators, identified as those countries with 10 or more patents per million population. About 50 percent of the world, with at least 2 percent of GDP being high-tech exports, are technological adopters. Adopters include northern Mexico, Costa Rica, Argentina, Chile, Tunisia, South Africa, Israel, India (except the Ganges valley states), Singapore, Malaysia, Indonesia, Thailand, coastal China, the Baltic states, Russia (in a narrow strip near St. Petersberg), plus OECD countries New Zealand, Spain, Greece (northeastern), Poland, Czech Republic, Slovak Republic, Hungary, Slovenia, Romania, and Bulgaria. The rest of the world is technologically excluded, according to Sachs. Their greatest problems are “tropical infectious disease, low agricultural productivity and environmental degradation, . . . requiring technological solutions beyond their means” (ibid.).

Mancur Olson (1996:3–24) indicates the cheapness of borrowing and adopting foreign technology. South Korea was poorer than Ghana in 1950 but surpassed it in 1963 to 1979, partly through spending miniscule funds for royalties and all other payments for disembodied foreign technology, usually less than 1/1000th of GDP. Foreign direct investors in Korea, owners of productive knowledge, acquired, by Olson’s estimate, less than one-fiftieth of the gains from Korea’s rapid economic growth during these 16 years. South Korea’s path to becoming a leading generator of new technical knowledge supports the assumption that most of the world’s productive knowledge is available to poor countries at a relatively modest cost.

In 1965, Intel’s Gordon Moore coined Moore’s law, envisioning the doubling of computer capacity and the halving of computer and software prices every two years. The radical miniaturizing potential from nanotechnology, with millions of transistors on the head of a pin, could continue this trend throughout the early years of the 21st century. Pohjola (2001:13), who uses “hedonic price indices that take account of each year’s improvement in the performance of computing equipment,” shows that the price of personal computers declined 18 percent yearly from 1958 to 1994, necessitating a log scale to show these price reductions in a single graph. Indeed, according to Pohjola, if technical progress in the automobile had proceeded at an 18 percent annual rate after invention, the price of a car today would only be $5!

From 1960 to 1995, productivity growth in the Group of 7 (G7) economies – Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States – was slow, especially in the United States.9 Economists have thoroughly documented the resurgence of GDP growth in the United States in the late 1990s, fueled by ICT investment.10 Trends in the other G7 economies are more difficult to detect, “partly because of discrepancies among official price indexes for IT equipment and software” (Jorgenson 2003:1). However, using internationally harmonized IT prices that eliminate many discrepancies, Harvard’s Dale Jorgenson found that all G7 economies, not just the United States, enjoyed an IT investment that boosted growth in the late 1990s. Whereas in other G7 economies, IT investment growth was partly countered by weaker growth in non-IT investment, TFP growth accelerated from the early to late 1990s in all except Italy (ibid., pp. 1–24 and Appendix; DeLong 2003a).

In 2001, the world information technology expenditures (computer hardware and software, data communications equipment, and computer services) were about $2,000 billion, about 1/20th of 1 percent of world gross investment. In the same year, high-income countries had 396.9 Internet users per 1,000 people, with middleincome countries 36.8 and low-income countries 6.4 (U.N. Development Program 2003:277). The proportion of people with computers that same year showed somewhat comparable ratios: 416.3 per 1,000 in high-income economies, 35.4 in middleincome economies and 6.1 in low-income countries (World Bank 2003h:300). Figure 11-2 shows that, among LDC regions, Latin America and the Caribbean leads with 59.3 personal computers per 1,000 people, while at the bottom are sub-Saharan Africa, with 9.9, and South Asia, with 5.3, per 1,000. Table 11-1 indicates that, in 2001, Japan spent more on information and communications technology (ICT) per capita, $3,256, than any other country, with the United States second with $2,923, Denmark third with $2,912, and Sweden fourth with $2,804. Among those listed,

9 The fastest productivity growth during this period was in Japan.

10 ICT increases the services sector’s productivity through such substitutions as: (1) digital bugle taps instead of coronets played live at funerals of fallen U.S. service men and women and (2) the Opera Company of Brooklyn, New York performing Mozart’s opera, “The Marriage of Figaro,” with 12 musicians and a technician overseeing a computer program playing other parts instead of a full orchestra (Hilsenrath 2003:A1). But is the computerized output the same service?

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FIGURE 11-2. Personal Computers per 1,000 People (by LDC regions).

Source: World Bank 2003i:301.

low-income economies such as Indonesia (17), India (19), and Vietnam (26), spent least on ICT per capita.

In 1990, the world had 98 mainline phones and 2 mobile phones per 1,000 people; in 2001, 169 mainline and 153 mobile per 1,000 (UNDP 2003:277). Projections suggest that the number of mobile phones surpassed mainline phone by the middle of the first decade of the 21st century. Travelers to major cities in middle-income economies such as China (the largest cellphone market in the world, with about 174 million users in 2003 [Ramstad 2003:B7]), Malaysia, and Mexico and even lowincome economies such as India, Bangladesh, Nigeria, Vietnam, and Cambodia are likely to be impressed by the ubiquity of the mobile telephone. Mobile phones, based on satellite technology, do not require the massive infrastructure investment that mainline telephones do. In Senegal, allowing local entrepreneurs to offer telecommunications services, with private telecenters with a telephone and perhaps a fax machine, has increased public access (World Bank 2001h:87). Moreover, once government allows competitive markets for mobile providers, these firms soon outstrip public-sector telecommunications firms previously considered “natural monopolies.” The World Bank (2003f:98) indicates that in Africa in 2001, mobile subscribers were 3.5 per 100 inhabitants in competitive markets compared to only 0.8 in monopoly markets.

In 1998, almost half ICT imports by OECD countries were from non-OECD countries, primarily in Asia (OECD 2000). A number of LDCs have high ratios of high technology (products with high R&D intensity, such as in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery) exports (much from foreign investment) to total manufactured exports, 2001: the Philippines