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The Trifurcation of the Labor Markets in the Networked, Knowledge-Driven, Global Economy

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Abstract

This conceptual, interdisciplinary paper will start with an introduction to the new, networked, knowledge-driven, global economy. The objective of this paper, using a narrative review of the economic, business, and management literature of labor markets and human capital, will be to negate the notion that we have, at present, one labor market for human capital, and will conjecture that we currently have (or are about to have) three autonomous markets for labor that are driven by different market dynamics and mechanisms. The three markets are identified as follows: routine labor, skilled labor, and talent. Each one of the markets will then be discussed, including future trends, issues, and remedies. This trifurcation of the labor markets is mostly the combined result of phase transition resulting from three major impetuses. The first is the effect(s) that technological revolutions have on the supply and demand of/for multidimensional skills of human capital. Second is the “winner take all” market structure enabled by the “industrial” economy’s framed legislation and social norms. The third impetus is the context (for the technological revolutions and the nature of the markets) of a networked global economy that is driven by knowledge developing at an accelerated pace. The narrative multidisciplinary literature review used was a modified version of the integrative literature review. The review confirmed that the three labor markets’ dynamics are to a considerable degree dissimilar and that legislating, conducting monetary, and fiscal policies that treat them as one labor market could (and probably already does) cause more harm than good, resulting in destabilizing the labor markets (as indicated by the growing unemployment rate of the young generation worldwide) and by extension, the social fabric of the new economy (as indicated by the growing economic and educational inequality worldwide). The paper concludes with a framework of the three labor markets, followed by a summary, including the need for a new legal and social paradigm regarding labor and the need for a new formal model for value creation. Finally, the limitations of the study as well as potential research gaps are identified.

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Notes

  1. Referenced in Rifkin, 2014, p. 47; Abramovitz, 1989, p. 133.

  2. See also Goldman Sachs concerns at http://www.bloomberg.com/news/articles/2016-02-03/goldman-sachs-says-it-may-be-forced-to-fundamentally-question-how-capitalism-is-working

  3. See also Thompson, 2015.

  4. See also Rogerson et al., 2005; McGuinness, 2006; Neumark & Wascher, 2007; Okkerse, 2008; Crinò, 2009.

  5. A similar approach can be seen at Greenaway and Nelson 2001; Menger 2006; Fields 2007; Neumark and Wascher 2007.

  6. Open time frame; no limits for years.

  7. Thanks to Laura Albareda for bringing this to our attention.

  8. See an example in Iñigo and Albareda 2016.

  9. See Torraco 2005 (p. 358) for examples in Human Resource studies.

  10. Including the time-dependent bifurcation (e.g., Barnett and Chen 2015; Bianca et al., 2013); and a two-dimensional bifurcation of a labor model which includes human capital and production of new ideas (Barnett and Ghosh 2013).

  11. cf. with Gregory 2002 (p. 230).

  12. See also the literature on skill biased technological change (e.g., Bound and Johnson 1992; Fowler and Fowler 2012; Buera et al., 2015) and the pervasiveness of job polarization (Snower and Goerlich 2013; Goos et al. 2014).

  13. Please note the difference between skills (used here in Fig. 2) and human capital (traditionally referred as education) not used here. This distinction is important as in some industries and/or economic segments the two are highly related, while in others (unique markets, e.g., arts), they are not (see example in Rengers 2002; chapter two).

  14. Consider as an illustration a billion dollar machine in a paper mill operated by a few machine operators with a master’s degree in engineering and computer sciences and the possibility of them being replaced by high school graduates. See also the findings in Fowler and Fowler (2012) and Guvenen et al. (2015).

  15. See Guvenen et al. (2015) for a model of multidimensional learning about abilities to acquire skills.

  16. See example in Regev and Zoabi (2014) proposing a model of entrepreneurial talent enabling the entrepreneur to search for an appropriate technology and to match that technology within the most profitable segment, despite being faced with information friction resulting from the inability to know how well the talent matches the needs of the technology and/or the segments (assuming investments in search are equal between individuals). This paper is suggesting that above and beyond such differences between individuals as listed above (and between countries as their model suggests), the talent’s costs of investments of the search are different between individuals, since the more talented are more effective, efficient, and/or lucky by being located in (or close by) a winner-take-all market, which is shortening their time for search, and also increasing the probability for a successful match due to smaller information asymmetry (see partial support in Jung and Subramanian 2013). All this is adding to and accelerating the non-linear response proposed in their model (small differences are amplified by talent utilization).

  17. How the talent market is different.

  18. Over 20% of Fortune 500 CEOs are dismissed annually (in recent years) while the average tenure went up in 2013 to 9.7 years (see http://blogs.wsj.com/atwork/2014/04/09/study-ceo-tenure-on-the-rise/ ).

  19. e.g., Bidwell 2013, including what Standing (2011) called “precariat”; see also a review in Munck 2013; and direct evidence in Webber 2015.

  20. Mostly due to minimal individual level absorptive capacity (e.g., ter Wal et al., 2011) and inability to learn new skills (e.g., Nembhard and Uzumeri 2000) at times when the half-life of knowledge is short and shrinking (e.g., Russ 2016).

  21. A plausible explanation could be the relatively high cost of housing and financing which make mobility impractical in many cases in the USA, while in other countries, geographical mobility is not a commonly accepted practice and norm (see for example Florida 2014; and cf. with Molloy et al., 2016).

  22. Since the majority of the jobs discussed here are in the service sector (see also Autor and Dorn 2013), this discussion of “value” goes above and beyond the credence attribute aspects of the value of a service as a product (Yadev and Berry 1996;Walker et al., 2006) and the public good aspect (Stiglitz 1999), which were always inherent in the discussion of service valuation and have become even more entangled with the attributes and characteristics of knowledge as a product and/or production factor (Grant 1996; Gherardi 2000).

  23. See example in. Krueger et al., 2014, and discussion in Kotler 2015, pp. 68–77.

  24. See also review by Neumark and Wascher 2007.

  25. See interesting discussions in Cappelli and Keller 2013; Leoni and Gritti 2013.

  26. A similar pattern was found in the Swedish economy for the 1985–95 timeframe (Savvidou 2006) for embodied technical change.

  27. The study covers the time frame between 1980 and 2005.

  28. For example, see http://www.nytimes.com/2015/04/28/us/politics/being-less-tough-on-crime-is-2016-consensus.html?_r=0 ; http://webapp.urban.org/reducing-mass-incarceration/index.html ; http://www.vera.org/sites/default/files/resources/downloads/incarcerations-front-door-report_02.pdf

  29. See for example Mittelstadt 2015.

  30. When high school or vocational education might be sufficient for the tasks at hand (Autor and Dorn 2013). See also Basu 2015, where in many countries education, at different levels, has none, or negative impact on economic growth (p. 17).

  31. For example, higher than 40% for young college graduates in the USA since 2009 (see http://www.theatlantic.com/business/archive/2015/05/the-new-normal-for-young-workers/393560/).

  32. Lack of access to capital, e.g., Ferguson 2009, p. 15.

  33. See Anonymous 2015, a recent survey at The Economist (May 30, 2015, Vol. 415, No. 8940, pp. 11, 21–26).

  34. See example at. Schulz et al., 2006.

  35. See Frase 2015 as referenced in Thompson 2015; also at http://www.versobooks.com/books/1847-four-futures

  36. About 48% of employed US college graduates are in jobs that the Bureau of Labor Statistics (BLS) suggests require less than a 4-year college education; see Vedder et al. 2013.

  37. See for example, drive-through supermarkets or grocery stores (see http://www.hngn.com/articles/169778/20160114/drive-through-supermarkets-revolutionize-grocery-shopping.htm or http://fortune.com/2015/07/27/amazon-drivethrough-grocery-stores/) and self-service kiosks (for examples, see http://www.businessinsider.com/what-self-serve-kiosks-at-mcdonalds-mean-for-cashiers-2015-8 ).

  38. Vardi estimated 50% of all the global workforce by 2045 (Vardi as cited in Zillman 2016).

  39. See also Cahuc and Laroque 2014; Colin and Palier 2015; Thompson 2015.

  40. See an interesting recent discussion in Freedman 2016.

  41. See for example, http://www.slate.com/blogs/browbeat/2014/10/10/germany_college_is_free_there_even_for_foreign_students_why.html

  42. e.g., Acemoglu 2011.

  43. See for example Frey 2015; Kotler 2015.

  44. This notion of the trap is supported by the diminishing probabilities of social and economic mobility of the majority of employees regardless of their demographics, etc. (e.g., Molloy et al. 2016).

  45. See also “knowledge trap” (Jones 2010).

  46. See also the “Concept of Ba” by Nonaka and Konno 1998.

  47. Resulting in a higher potential for value appropriation (see Nahuis and Smulders 2002).

  48. Such markets have the potential for scalability (see example in Kaplan and Rauh 2013, p. 48).

  49. See for example, the exuberant incomes of CEOs: Bertrand and Mullainathan 2001.

  50. e.g., “butterfly effect,” see example from the art market in. Salganik et al., 2006; high scalability (Kaplan and Rauh 2013, pp. 48); criticality due to the pace of innovation (Kim et al., 2016).

  51. What Hidalgo 2015 calls “knowhow.”

  52. See the extremely high earnings of star performers, athletes, etc.: Ertug and Castellucci 2013; Rosen 1981; or the impact of the nature of externalities involved, and the size and scalability of the network (e.g., Torrent-Sellens 2015).

  53. See example in Rengers 2002; chapter 3.

  54. See “knowledge trap” as well (Jones 2010).

  55. See also George et al., 2016 (p. 8) estimations of between less than 1% to up to 8%.

  56. Similar to how governments should regulate monopolies; see for example, Dewatripont et al., 2010; Alexander et al., 2012.

  57. See an interesting discussion in Martin 2014.

  58. See also Kotler 2015, pp. 29–61.

  59. See also Gorz 1982; Levy and Murnane 2012; Davenport and Kirby 2015.

  60. Even though IBM’s Watson is getting smarter and cheaper; Kroeker 2011.

  61. See Anonymous 2014, a survey at The Economist, January 18, 2014; Kotler 2015.

  62. See Kümmel 2011, p. 212, Table 4.5—from a different perspective.

  63. For Germany, Japan, and the United States, for the second half of the last century.

  64. See examples in Cohen and Broschak 2013; McGinn and Milkman 2013.

  65. See also Bolton and Dewatripont 1994 for a model of an organization as an information processing entity where efficiency of information processing is a result of organizational structure.

  66. See additional discussion on employer learning in Habermalz 2010.

  67. e.g., by using feedback or implementing information systems.

  68. See additional discussion on leadership and coordination in Bolton et al. 2013.

  69. Think fast food or retail service occupations.

  70. See example of such multidimensional space of skills at Lise and Postel-Vinay 2015.

  71. See additional discussion in Handscombe and Patterson 2004.

  72. See “Niche construction and evolution of leadership” Spisak, et al., 2015.

  73. The referencing in section is minimized since the different aspects were already discussed earlier in this paper.

  74. See for example Finland, at http://www.independent.co.uk/news/world/europe/finland-to-consider-introducing-universal-basic-income-in-2017-a6963321.html

  75. See for example Elon Musk, at http://mashable.com/2016/11/05/elon-musk-universal-basic-income/#xK0XU0uTmmqZ

  76. See example in Williamson et al., 1975.

  77. An additional layer of complexity is added by the variability of the unique markets; same talent might be valued significantly differently by two different markets; see example in the art markets (Ertug et al. 2016).

  78. See an example in Fields 2007.

  79. Lucas, 2009, p. 5.

  80. Lucas, 2009, pp. 5, 6; Alvarez et al., 2008, p. 4.

  81. Number of dimensions, scale and scope.

  82. See also the concept of profit pool (Gadiesh & Gilbert, 1998).

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Acknowledgements

I would like to thank the seminar participants at the School of Business at the University of Jordan; Universita degli Studi Roma Tre; LUISS University in Rome; ORT Braude College of Engineering in Karmiel, Israel; the Ben Gurion University of the Negev, Israel; the School of Management of University of Silesia, Chorzów, Poland; the Department of Education and Psychology, University of Silesia, Katowice, Poland; Link Campus University in Rome; School of Administrative Studies at York University in Toronto; and Brian Silverman, Loet Leydesdorff, David Autor, Robert Reich, Carsten Herrmann-Pillath, Reiner Kümmel and two anonymous reviewers for helpful comments and suggestions. The author also thanks Kelly Anklam and Eric B. Russ Comeras for their assistance in editing this paper and wishes to thank the Frederick E. Baer Professorship in Business and the Philip J. and Elizabeth Hendrickson Professorship in Business at UW-Green Bay for partial financial support. As always, all mistakes are his.

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  1. Austin E. Cofrin School of Business, University of Wisconsin -, Green Bay, WI, USA

    Meir Russ

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Correspondence to Meir Russ.

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This is a modified and significantly enhanced version of a chapter by Russ (2014a).

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Russ, M. The Trifurcation of the Labor Markets in the Networked, Knowledge-Driven, Global Economy. J Knowl Econ 8, 672–703 (2017). https://doi.org/10.1007/s13132-016-0434-0

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