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Capital Accumulation, Technological Progress, and Knowledge

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The American Economy from Roosevelt to Trump

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Abstract

The chapter presents the main determinants of economic growth and their relevance in the US economy in the period 1950–2017. A rapid capital accumulation, accompanied by substantial technological progress and a strong rise in knowledge, can determine a good economic performance. There is, however, a complex feedback between these three determinants. In the 1950s, the US started from very good levels in the stocks of physical capital, technology, and knowledge, but gradually lost part of its advantages, especially as regards manufacturing production, vis-à-vis Western European countries, Japan, and the four Asian tigers, and more recently also in relation to large emerging countries, such as China and India. The US tried to react by delocalizing a large part of its production abroad and developing new lucrative services, especially in the financial sphere, in ICT, and in the internet economy. The US could do so because of its innovative drive, but also because it was able to attract massive flows of capitals and a large number of scientists and experts from other countries. However, in the last decades, there has been a powerful backlash, that is, the rapid growth in competitiveness of some of the advanced and emerging countries, such as Germany and China, and the consequent deindustrialization of the US economy and its large structural deficit in the balance of current accounts.

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Notes

  1. 1.

    See, for the useful distinction between extractive and inclusive institutions , Acemoglu and Robinson (2012).

  2. 2.

    This is a modified and extended version of the chart presented in Valli (2015), p. 111.

  3. 3.

    See Kaldor (1957), Kaldor and Mirllees (1962), Arrow (1962).

  4. 4.

    See Romer (1986). See also Lucas (1988), Grossman and Helpman (1994).

  5. 5.

    See Schumpeter (1911, 1939).

  6. 6.

    See Solow (1956, 1957, 1970).

  7. 7.

    See Valli (2005), p. 74.

  8. 8.

    See the already cited works by Romer, Lucas, Grossman and Helpman. See also Romer (1990) and Barro (1990).

  9. 9.

    See, for example, David (1975), Aghion et al. (2009), and Acemoglu and Robinson (2012).

  10. 10.

    See Chandler (1977).

  11. 11.

    See Galbraith (1952, 1958, 1967).

  12. 12.

    See Gordon (2012, 2014, 2016).

  13. 13.

    Several authors, such as Klaus Schwab (2016), prefer to distinguish between the Third Industrial Revolution and the Fourth industrial revolution or Industry 4.0. The former, occurring from the 1970s to the first decade of the 2000s, was principally based on the first wave of the digital revolution, while the latter is characterized by extensive use of clouds, big data, and blockchain; the ascent of internet giants, such as Google, Amazon and Alibaba; 3-D printing, the internet of things, robots and androids, nanotechnology, artificial intelligence, green energy and, in the very near future, self-driving cars and trucks, quantum computers, and possible great improvements in the medical, biological, and genetic fields. Some of these problems will be further examined in paragraph 5.6.

  14. 14.

    See Brynjolfsson and McAfee (2014).

  15. 15.

    See Gordon (2014), pp. 8–19 and Gordon (2016).

  16. 16.

    See, for example, Foray (2004), Arena et al. (2012).

  17. 17.

    See UNDP (2016). Expected years of education are the number of years of schooling that a child of school entrance age can expect to receive if prevailing patterns of age-specific enrolment rates persist throughout the child’s life.

  18. 18.

    See, for example, Kuznets (1965, 1966), Chenery (1960), Chenery and Syrquin (1975), and the more complex structural approach of Pasinetti (1977, 1993).

  19. 19.

    It is interesting to note that from 2014 to 2017, the percentage of employment in agriculture has begun to slightly grow after the preceding secular decline. There has also been a rising interest of several young people in organic agriculture and the gradual expansion of farmers’ markets.

  20. 20.

    See Bureau of Labour Statistics (2018).

  21. 21.

    See OECD (2018b).

  22. 22.

    See Bureau of Labour Statistics (2018).

  23. 23.

    See, for example, McKinsey (2017).

  24. 24.

    McKinsey (2017), pp. 102–103. In the rapid automation scenario, the loss of employment and the need to shift to other occupational categories would be much larger.

  25. 25.

    See Gordon (2014, 2016).

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  1. Department of Economics and Statistics “Cognetti De Martiis”, University of Torino, Torino, Italy

    Vittorio Valli

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Valli, V. (2018). Capital Accumulation, Technological Progress, and Knowledge. In: The American Economy from Roosevelt to Trump. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-96953-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-96953-4_5

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