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Policy and Future Research Recommendations

CHAPTER 5: CONCLUSION

5.4. Policy and Future Research Recommendations

The impact, scale, and scope of technological change on the labour market is multifaceted.

Furthermore, it is the function of several localised factors such as wage levels, capital investment levels, and other prevailing regulatory frameworks. Hitherto, technological change has both created and displaced a plethora of jobs, thereby presenting both opportunities and risks.

Like previous technological revolutions, there are those who stand to gain significantly more from modern (and future) technological developments than their fellow man. There are also those who stand to lose much more than their contemporaries. The introduction of a new technology thus presents great potential for the evolution of work and output; however, it remains important to first understand the implications of such technologies so as to design and implement inclusive policy responses that distribute accrued benefits appropriately and equitably.

As such, an appropriate response would be one that aims to mitigate the negative externalities and unintended consequences of newly introduced (labour replacing) technologies, e.g., rising inequality and unemployment.

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In order to implement targeted and timely policy interventions that aim to limit shock and maladjustment in the labour market, policy makers need to identify which occupational sectors are more likely to experience change as a result of newly introduced technologies, alongside the timeframe within which this change is expected to take place.

Education, upskilling and training policies would need to consider the degree of exposure different occupations and sectors experience along with whether these exposure levels are destructive or progressive.

An appropriate policy intervention would therefore identify where occupations are at risk of being replaced by technology and initiate measures to empower workers who occupy those occupations. This would ensure more favourable outcomes for employment growth and minimise skill mismatching.

However, such a response should take care not to suppress further technological innovation, which would serve as counterintuitive to any aspirations that involve economic growth.

In general, any proposed policy approach by the South African government should aim to predict and absorb the impact modern digital technologies are expected to have on the local (and international) labour market.

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BIBLIOGRAPHY

Accenture. 2018. Reworking the Revolution, https://www.accenture.com/_acnmedia/PDF- 69/Accenture-Reworking-the-Revolution-Jan-2018-POV.pdf. Date of access: 19 Jun.

2021.

Acemoglu, D. & Autor, D. 2011. Skills, Tasks and Technologies: Implications for Employment and Earnings. In: Ashenfelter, O. & Card, D. Handbook of Labor Economics, eds. 4B: Elsevier. pp. 1043-1171.

Acemoglu, D. & Restrepo, P. 2018a. Artificial Intelligence, Automation, and Work. In:

Agrawal, A., Gans, J. & Goldfarb, A. The Economics of Artificial Intelligence: An

Agenda: National Bureau of Economic Research, Inc. University of Chicago Press. pp.

197 – 236.

Acemoglu, D. & Restrepo, P. 2018b. The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment. American Economic Review, 108 (6): 1488-1542. http://dx.doi.org/10.1257/aer.20160696.

Acemoglu, D. & Restrepo, P. 2019. Automation and New Tasks: How Technology Displaces and Reinstates Labor. Journal of Economic Perspectives, 33 (2): 3-30.

http://dx.doi.org/1 10.1257/jep.33.2.3.

Acemoglu, D., Lelarge, C. & Restrepo, P. 2020. Competing with Robots: Firm-Level Evidence from France. AEA Papers and Proceedings, 110: 383-88.

http://dx.doi.org/10.1257/pandp.20201003.

95

Agrawal, A., Gans, JS. & Goldfarb A. 2019. Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction. Social Science Research Network, Rochester, NY. http://dx.doi.org/10.1257/jep.33.2.31.

Agrawal, A., McHale, J. & Oettl, A. 2018. Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth. (NBER Working Paper, 24541).

http://dx.doi.org/10.3386/w24541. Date of access: 9 Jun 2021.

Atkinson, R. 2019. Robotics and the Future of Production and Work.

https://itif.org/publications/2019/10/15/robotics-and-future-production-and-work. Date of access: 26 Jun 2021.

Autor, D & Dorn, D. 2013. The growth of low skill service jobs and the polarisation of the U.S. labour market. American Economic Review, 103(5): 1553–1597.

http://dx.doi.org/10.1257/aer.103.5.1553.

Autor, D. & Salomons, A. 2018. Is Automation Labor Share–Displacing? Productivity Growth, Employment, and the Labor Share. Brookings Papers on Economic Activity, 1–63.

http://www.jstor.org/stable/26506212.

Autor, D. H., Levy, F. & Murnane, R. J. 2003. The skill content of recent technological change: An empirical exploration. The Quarterly journal of economics, 118(4):1279- 1333. https://doi.org/10.1162/003355303322552801.

Autor, D.H. 2015. Why Are There Still So Many Jobs? The History and Future of Workplace Automation. Journal of Economic Perspectives, 29(3):3-30.

http://dx.doi.org/10.1257/jep.29.3.3

96

Au-Yong-Oliveira, M., Gonçalves, R., Oliveira, E., Oliveira, M., Silva, F. & Vieira, A.I. 2019.

The Role of Technologies: Creating a New Labour Market. Cham. Springer

International Publishing, 176-184. https://doi.org/10.1007/978-3-030-16181-1_17.

Beno, M. 2019. The Four Major Factors Impacting on the Future of Work. In: Rocha, Á., Adeli H., Reis, L., & Costanzo, S. eds. New Knowledge in Information Systems and Technologies. WorldCIST'19: vol. 930, pp. 12-24.

Bhorat, H. & Rooney, C. 2017. State of manufacturing in South Africa. (DPRU Working Paper, 2017/02).

http://www.dpru.uct.ac.za/sites/default/files/image_tool/images/36/Publications/Workin g_Papers/DPRU%20WP201702.pdf. Date of access: 07 Mar 2021.

Bhorat, H., Lilenstein, K., Oosthuizen, M. & Thornton, A. 2016. Vulnerability in Employment:

Evidence from South Africa. (DPRU Working Paper, 2016/04).

https://media.africaportal.org/documents/DPRU_WP201604.pdf. Date of access: 28 Feb 2021.

Bhorat, H., Lilenstein, K., Oosthuizen, M. & Thornton, A. 2020a. The Evolution of Earnings and Employment in South Africa: Tasks, Occupations and Gender, 2000-2015. UNU- WIDER ST Project Book.

http://conference.iza.org/conference_files/worldbank_2020/thornton_a26470.pdf.

Bhorat, H., Lilenstein, K., Oosthuizen, M. & Thornton, A. Wage polarisation in a high- inequality emerging economy: The case of South Africa. (WIDER Working Paper, 2020b/55). https://www.wider.unu.edu/sites/default/files/Publications/Working- paper/PDF/wp2020-55.pdf. Date of access: 25 Feb 2021.

97

Brynjolfsson, E. & McAfee, A. 2014. The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York, NY: W. W. Norton.

Brynjolfsson, E., Mitchell, T., & Rock, D. 2018. What Can Machines Learn, and What Does It Mean for Occupations and the Economy? AEA Papers and Proceedings, 108: 43-47.

https://doi.org/10.1257/pandp.20181019.

Brynjolfsson, E., Rock, D. & Syverson, C. 2017. Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics. (National Bureau of Economic Research Working Paper, 24001). http://www.nber.org/papers/w24001.

Date of Access: 01 Mar. 2021.

Bughin, J., Chui, M., Manyika, J. & Seong, J. 2018. Notes from the AI Frontier: Modeling the Impact of AI on the World Economy. https://www.mckinsey.com/featured-

insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on- the-world-economy. Date of access: 14 May 2021.

Bughin, J., Seong, J., Manyika, J., Chui, M. & Joshi, R. 2018. Modelling the Impact of AI on the World Economy. (McKinsey Global Institute, discussion paper).

https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai- frontier-modeling-the-impact-of-ai-on-the-world-economy. Date of access: 13 Jun 2021.

Byrne, D. & D. Sichel. 2017. The productivity slowdown is even more puzzling than you think. https://voxeu.org/article/productivity-slowdown-even-more-puzzling-you-think.

Date of access: 24 Apr 2021.

98

Choudhury P., Starr E., & Agarwal R. 2019. Machine Learning and Human Capital:

Experimental Evidence on Productivity Complementarities. Strategic Management Journal, 41(8): 1381-1411. https://doi.org/10.1002/smj.3152.

Comin, A. & Ferrer, M.M. 2013. If Technology Has Arrived Everywhere, Why has Income Diverged? American Economic Journal: Macroeconomics, 10(3): 137-178.

https://doi.org/10.3386/w19010.

Comin, D. & Mestieri, M. 2014. Technology Diffusion: Measurement, Causes, and Consequences. In: Handbook of Economic Growth. Elsevier: pp. 565-622.

Daniel B. le Roux. 2018. Automation and employment: The case of South Africa. African Journal of Science, Technology, Innovation and Development, 10(4): 507-517.

https://doi.org/10.1080/20421338.2018.1478482.

Das, M. & Hilgenstock, B. 2018. The Exposure to Routinization: Labor Market Implications for Developed and Developing Economies. (IMF Working Papers, 2018/135).

https://doi.org/10.5089/9781484361900.001. Date of Access: 17 May 2021.

Datta, S., Mahapatra, S.S., Sen, D.K. & Patel, S.K. Multi-criteria decision making towards selection of industrial robot. Benchmarking: An International Journal, 22(3): 465–

487. https://doi.org/10.1108/BIJ-05-2014-0046.

Davies, R., & Van Seventer, D. 2020. Labour Market Polarisation in South Africa: A Decomposition Analysis. (WIDER Working Paper, 2020/17).

https://www.wider.unu.edu/sites/default/files/Publications/Working-paper/PDF/wp2020- 17.pdf. Date of access: 03 Mar 2021

99

Edwards, L. & Lawrence, R. 2008. South African trade policy matters trade performance and trade policy. Economics of Transition, 16(4):585-608. https://doi.org/10.1111/j.1468- 0351.2008.00338.x.

Ernst, E., Merola, R. & Samaan, D. 2019. Economics of Artificial Intelligence: Implications for the Future of Work. IZA Journal of Labor Policy, 9(1):1-35.

https://doi.org/10.2478/izajolp-2019-0004.

Essers, D. 2016. South African Labour Market Transitions Since the Global Financial and Economic Crisis: Evidence from two Longitudinal Datasets. Journal of African Economies, 26(2): 192-222. https://doi.org/10.1093/jae/ejw024.

F, M., Manning, A. & Salomons, A. 2014. Explaining Job Polarisation: Routine-Biased Technological Change and Offshoring. American Economic Review, 104(8):2509- 2526. https://doi.org/10.1257/aer.104.8.2509.

Felten, E.W., Raj, M. & Seamans, R. 2019. The Variable Impact of Artificial Intelligence on Labor: The Role of Complementary Skills and Technologies. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3368605.

Fernández-Macías, E. 2018. Automation, digitalisation and platforms: Implications for work and employment. Eurofound. Luxembourg: Publications Office of the European Union.

https://doi.org/10.2806/13911.

Firpo, S., Fortin, N.M. & Lemieux, T. 2011. Occupational tasks and changes in the wage structure. (IZA Discussion Papers, 5542). https://ftp.iza.org/dp5542.pdf. Date of access: 02 May 2021

100

Fourie, F.C.v.N. 2012. The South African unemployment debate: Three worlds, three discourses? (REDI3x3 working paper, 1). https://doi.org/10.13140/RG.2.1.1554.0642.

Date of Access: 03 Apr 2021.

Frank, M.R., Autor, D., Bessen, J.E., Brynjolfsson, E., Cebrian, M., Deming, D.J., Feldman, M., Groh, M., Lobo, J., Moro, E., Wang, D., Youn, H., Rahwan, I. 2019. Toward

understanding the impact of artificial intelligence on labor. Proceedings of the National Academy of Sciences of the United States of America, 116(14):6531-6539.

https://doi.org/10.1073/pnas.1900949116.

Goos M., Manning A. & Salomons A. 2009. Job Polarisation in Europe. American Economic Review, 99(2):58–63. https://doi.org/10.1257/aer.99.2.58.

Goos M., Manning A. & Salomons A. 2014. Explaining Job Polarization: Routine-Biased Technological Change and Offshoring. American Economic Review, 104(8):2509- 2526. https://doi.org/10.1257/aer.104.8.2509.

Goos, M. & Manning, A .2007. Lousy and Lovely Jobs: The Rising Polarisation of Work in Britain. Review of Economics and Statistics, 89(1), 118–

133. https://doi.org/10.1162/rest.89.1.118.

Gordon, R. 2018. Why has Economic Growth Slowed When Innovation Appears to be Accelerating? (NBER working paper series, 24554).

http://www.nber.org/papers/w24554. Date of Access: 16 Mar 2021.

Gravina, A.F. & Foster-McGregor, N. 2020. Automation, globalisation and relative wages: An empirical analysis of winners and losers. (MERIT working papers, 2020/40).

https://www.merit.unu.edu/publications/wppdf/2020/wp2020-040.pdf. Date of access:

13 Apr 2021.

101

Harari, Y.N. 2018. Lessons for the 21st Century. 1st ed. New York: Spiegel & Grau.

Hoogeveen, J.G. & B. Özler. 2006. Poverty and inequality in post-apartheid South Africa:

1995 to 2000. In: Bhorat, H. & Kanbur, R. eds. Poverty and policy in post-apartheid South Africa. Human Sciences Research Council: Cape Town: pp. 59-94.

Hundenborn, J., Leibbrandt, M. & Woolard, I. 2018. Drivers of inequality in South Africa.

(UNU-WIDER working paper, 2018/162). https://doi.org/10.35188/UNU- WIDER/2018/604-3. Date of access: 17 Apr 2021.

Jaimovich, N & Siu, H.E. 2012. The trend is the cycle: job polarisation and jobless

recoveries. (NBER working paper, 18334). http://www.nber.org/papers/w18334. Date of access: 03 Mar 2021.

Jung, J .2020. The fourth industrial revolution, knowledge production and higher education in South Korea. Journal of Higher Education Policy and Management, 42(2):134-156.

https://doi.org/10.1080/1360080X.2019.1660047.

Kapeliushnikov, R. 2019. The phantom of technological unemployment. Russian Journal of Economics, 5(1):88-116. https://doi.org/10.32609/j.ruje.5.35507.

Kerr, A. Lam, D. & M. Wittenberg. 2019. Post-Apartheid Labour Market Series 1993-2019 [dataset]. Version 3.3. Cape Town: DataFirst [] produce and distributor].

https://doi.org/10.25828/gtr1-8r20.

Kerr, A., & M. Wittenberg. 2019. A Guide to PALMS version 3.3. Available at:

https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/434/download/10286. Date of access: 13 Apr 2021.

102

Keynes, J.M. 1930. Economic Possibilities for our Grandchildren. In: Johnson, E., &

Moggridge, D., eds. The Collected Writings of John Maynard Keynes, Vol. IX, Essays in Persuasion. New York: W.W. Norton. pp. 358–73.

Krugman, P. 2013. Sympathy for the Luddites. New York Times, 13 Jun.

https://www.nytimes.com/2013/06/14/opinion/krugman-sympathy-for-the-luddites.html.

Date of access: 09 March 2021.

Lane, M. & Saint-Martin, A. 2021. The impact of Artificial Intelligence on the labour

market: What do we know so far? (OECD Social, Employment and Migration Working Papers, 256). https://doi.org/10.1787/7c895724-en. Date of Access: 03 Jun 2021.

Le Roux, J. 2015. Industrial robot population density and the neoclassical growth model.

Pretoria: Gordan Institute of Business Science, University of Pretoria. (Mini Dissertation – MBA).

Lechman, E. 2015. ICT Diffusion in Developing Countries: Towards a New Concept of Technological Takeoff. Berlin, Germany: Springer, Cham.

Leontief, W. 1952. Machines and Man. Scientific American, 187(3): 150-164.

https://doi.org/10.1038/scientificamerican0952-150.

Levy, F. & R J Murnane. 1992. United-States Earnings Levels and Earnings Inequality – a Review of Recent Trends and Proposed Explanations. Journal of Economic Literature, 30(3): 1333-81. http://www.jstor.org/fcgi-bin/jstor/listjournal.fcg/00220515/.21-.30.

103

Marengo, L. 2019. Is this time different? A note on automation and labour in the fourth industrial revolution. J. Ind. Bus. Econ. 46:323–331. https://doi.org/10.1007/s40812- 019-00123-z.

Maxim, R., Muro, M & Whiton, J. 2019. What jobs are affected by AI? Better-paid, better- educated workers face the most exposure. Brookings.

https://www.brookings.edu/research/what-jobs-are-affected-by-ai-better-paid-better- educated-workers-face-the-most-exposure/. Date of Access: 21 Feb 2021.

Milanovic, B. 2014. The Return of Patrimonial Capitalism: A Review of Thomas Piketty's Capital in the Twenty-First Century. Journal of Economic Literature, 52 (2): 519-34.

https://doi.org/10.1257/jel.52.2.519.

Mokyr, J., Vickers, C. & Ziebarth, N.L. 2015. The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different? Journal of Economic Perspectives, 29(3):31–50. https://doi.org/10.1257/jep.29.3.31.

O*NET Resource Center. 2021. O*NET 26.0 Database at O*NET Resource Center [Dataset]. https://www.onetcenter.org/dl_files/database/db_26_0_excel.zip. Date of access: 01 Apr 2021.

OECD. 2017. How technology and globalisation are transforming the labour market. Paris, France. https://doi.org/10.1787/empl_outlook-2017-7-en. Date of access: 20 Feb 2021.

Oldenski, L. 2014. Offshoring and the Polarisation of the U.S. Labor Market. ILR Review, 67(3):734-761. https://doi.org/10.1177/00197939140670S311.

104

Piketty, T. 2014. Capital in the twenty-first century. Cambridge MA: The Belknap Press of Harvard University Press.

Rao, R.V., Patel, B.K., & Parnichkun, M. 2011. Industrial robot selection using a novel decision-making method considering objective and subjective preferences. Robotics and Autonomous Systems, 59(6):367-375 https://doi.org/10.1016/j.robot.2011.01.005.

Schwab, K. 2017. The Fourth Industrial Revolution. New York, NY: Random House USA Inc.

Spencer D., Cole M., Joyce S., Whittaker X. & Stuart M. 2021. Digital Automation and the Future of Work. Brussels. Publications Office of the EU.

https://doi.org/10.2861/826116.

Statistics South Africa (SSA). 2012. South African Standard Classification of Occupations (SASCO) 2012. Pretoria: South Africa.

http://www.statssa.gov.za/classifications/codelists/SASCO_2012.pdf. Date of access:

05 Mar 2021.

Statistics South Africa (SSA). 2021. Gini coefficient in South Africa 2006-2015, by area.

Pretoria: South Africa. https://www.statista.com/statistics/1127890/gini-coefficient-in- south-africa-by-

area/#:~:text=According%20to%20the%20latest%20governmental,most%20southern

%20country%20of%20Africa. Date of access: 05 Mar 2021.

Steuart, James. 1767. An Inquiry into the Principles of Political Economy. London: Millar, A.

& Cadell, T.