The_Future_of_Employment

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of our 70 hand-labelled occupations, we aim to predict z ∗ for our test features X ∗ . This approach firstly allows us to use the features of the 70 occupations about which we are most certain to predict for the remaining 632. Further, our algorithm uses the trends and patterns it has learned from bulk data to correct for what are likely to be mistaken labels. More precisely, the algorithm provides a smoothly varying probabilistic assessment of automatability as a function of the variables. For our Gaussian process classifier, this function is non-linear, meaning that it flexibly adapts to the patterns inherent in the training data. Our approach thus allows for more complex, non-linear, interactions between variables: for example, perhaps one variable is not of importance unless the value of another variable is sufficiently large. We report P(z ∗ | X ∗ ,D) as the probability of computerisation henceforth (for a detailed probability ranking, see Appendix). Figure II illustrates that this probability is non-linearly related to the nine O∗NET variables selected. V. EMPLOYMENT IN THE TWENTY-FIRST CENTURY In this section, we examine the possible future extent of at-risk job computerisation, and related labour market outcomes. The task model predicts that recent developments in ML will reduce aggregate demand for labour input in tasks that can be routinised by means of pattern recognition, while increasing the demand for labour performing tasks that are not susceptible to computerisation. However, we make no attempt to forecast future changes in the occupational composition of the labour market. While the 2010-2020 BLS occupational employment projections predict US net employment growth across major occupations, based on historical staffing patterns, we speculate about technology that is in only the early stages of development. This means that historical data on the impact of the technological developments we observe is unavailable. 21 We therefore focus on the impact of computerisation on the mix of jobs that existed in 2010. Our analysis is thus limited to the substitution effect of future computerisation. Turning first to the expected employment impact, reported in Figure III, we distinguish between high, medium and low risk occupations, depending on their 21 It shall be noted that the BLS projections are based on what can be referred to as changes in normal technological progress, and not on any breakthrough technologies that may be seen as conjectural. 36
400M Management, Business, and Financial Computer, Engineering, and Science Education, Legal, Community Service, Arts, and Media Healthcare Practitioners and Technical Service Sales and Related Office and Administrative Support Farming, Fishing, and Forestry Construction and Extraction Installation, Maintenance, and Repair Production Transportation and Material Moving 300M ←−−− Low−−−→ 33% Employment ←−−− Medium−−−→ 19% Employment ←−−− High−−−→ 47% Employment Employment 200M 100M 0M 0 0.2 0.4 0.6 0.8 1 Probability of Computerisation FIGURE III. The distribution of BLS 2010 occupational employment over the probability of computerisation, along with the share in low, medium and high probability categories. Note that the total area under all curves is equal to total US employment. 37
Page 1 and 2: THE FUTURE OF EMPLOYMENT: HOW SUSCE
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Page 11 and 12: of mechanisation, with dictaphones,
Page 13 and 14: How technological progress in the t
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Page 17 and 18: pletely automated (Phua, et al., 20
Page 19 and 20: challenging or critical application
Page 21 and 22: e compared (Churchill and Newman, 2
Page 23 and 24: monly defined as non-routine (Brynj
Page 25 and 26: easonably mature, enabled by the ra
Page 27 and 28: obots can now reproduce some aspect
Page 29 and 30: the O∗NET aggregate. In addition,
Page 31 and 32: TABLE I. O∗NET variables that ser
Page 33 and 34: Williams, 2006). Such classifiers m
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Page 45 and 46: 2013), are highly susceptible to co
Page 47 and 48: Autor, D., Levy, F. and Murnane, R.
Page 49 and 50: Clifford, G.D. and Clifton, D. (201
Page 51 and 52: Hangal, S. and Lam, M.S. (2002). Tr
Page 53 and 54: Louis, P. and Paterson, G. (1986).
Page 55 and 56: Oldenski, L. (2012). Offshoring and
Page 57 and 58: APPENDIX The table below ranks occu
Page 59 and 60: Computerisable Rank Probability Lab

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