The_Future_of_Employment

More documents

Recommendations

Info

TABLE II. Performance of various classifiers; best performances in bold. classifier model AUC log-likelihood exponentiated quadratic 0.894 −163.3 rational quadratic 0.893 −163.7 linear (logit regression) 0.827 −205.0 ing characteristic curve (AUC), which is equal to one for a perfect classifier, and one half for a completely random classifier, and the log-likelihood, which should ideally be high. This experiment was repeated for one hundred random selections of training set, and the average results tabulated in Table II. The exponentiated quadratic model returns (narrowly) the best performance of the three (clearly outperforming the linear model corresponding to logistic regression), and was hence selected for the remainder of our testing. Note that its AUC score of nearly 0.9 represents accurate classification: our algorithm successfully managed to reproduce our hand-labels specifying whether an occupation was computerisable. This means that our algorithm verified that our subjective judgements were systematically and consistently related to the O∗NET variables. Having validated our approach, we proceed to use classification to predict the probability of computerisation for all 702 occupations. For this purpose, we introduce a new label variable, z, denoting whether an occupation is truly computerisable or not: note that this can be judged only once an occupation is computerised, at some indeterminate point in the future. We take, again, a logistic likelihood, (8) P(z ∗ = 1 | f ∗ ) = 1 1+exp(−f ∗ ) . We implicitly assumed that our hand label, y, is a noise-corrupted version of the unknown true label, z. Our motivation is that our hand-labels of computerisability must necessarily be treated as such noisy measurements. We thus acknowledge that it is by no means certain that a job is computerisable given our labelling. We define X ∗ ∈ R 702×9 as the matrix of O∗NET variables for all 702 occupations; this matrix represents our test features. We perform a final experiment in which, given training data D, consisting 34
100 80 80 Assisting and caring for others Social perceptiveness Manual dexterity 50 100 0 0 0.5 1 Probability of Computerisation 50 0 0 0.5 1 Probability of Computerisation 80 60 40 20 0 0 0.5 1 Probability of Computerisation Persuasion Fine arts Finger dexterity 60 40 20 100 0 0 0.5 1 Probability of Computerisation 50 0 0 0.5 1 Probability of Computerisation 80 60 40 20 0 0 0.5 1 Probability of Computerisation Negotiation Originality Cramped work space 60 40 20 80 60 40 20 100 0 0 0.5 1 Probability of Computerisation 0 0 0.5 1 Probability of Computerisation 50 0 0 0.5 1 Probability of Computerisation FIGURE II. The distribution of occupational variables as a function of probability of computerisation; each occupation is a unique point. 35
Page 1 and 2: THE FUTURE OF EMPLOYMENT: HOW SUSCE
Page 3 and 4: dition to the computerisation of ro
Page 5 and 6: Titles (DOT), last revised in 1991,
Page 7 and 8: the Glorious Revolution of 1688, de
Page 9 and 10: a sequence of operations. 10 Yet wh
Page 11 and 12: of mechanisation, with dictaphones,
Page 13 and 14: How technological progress in the t
Page 15 and 16: can be accomplished by machines, wh
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: Williams, 2006). Such classifiers m
Page 37 and 38: 400M Management, Business, and Fina
Page 39 and 40: that sales occupations, which are l
Page 41 and 42: Average median wage (USD) 80k 60k 4
Page 43 and 44: do not account for future wage leve
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

The_Future_of_Employment

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?