The Impact of Automation on the Unemployed Maarten Goos 1 Emilie Rademakers 2 Anna Salomons 1 Bert Willekens 2 1 Utrecht University 2 University of Leuven May 29, 2018 Work in progress 1 / 39
Introduction Table of Contents Introduction 1 Data 2 Job search in labor markets with task overlap 3 Empirical approach Estimates without task overlap across jobs Estimates with task overlap across jobs Robustness tests The impact of automation on the unemployed 4 Conclusion 5 2 / 39
Introduction Modeling automation Katz&Murphy (92): Technological progress is factor-augmenting such that labor demand increases (under realistic parameter values). Autor&Acemoglu (11): Technological progress is task-replacing such that the automation of labor tasks leads to a decrease in labor demand and workers reallocate to different tasks based on comparative advantage. Acemoglu&Restrepo(16;18a,b): There are several countervailing effects that increase labor demand, especially the creation of new labor tasks. 3 / 39
Introduction Adjustment costs from automation? If automation changes the demand for jobs and tasks, the reallocation of workers to new jobs and tasks can be a complex and slow process . Effects are visible in recent studies that focus on the adjustment of local labor markets to negative shocks in labor demand (e.g. Acemoglu&Restrepo (17); Autor, Dorn&Hanson (15)). However, little is known about a mismatch between workers’ task competencies and automation leading to slowdown in the adjustment of employment and wages and in productivity gains. 4 / 39
Introduction Results preview We build on Manning&Petrongolo’s (17) geographic search model to illustrate search in markets for detailed jobs that are linked by their task contents . We assume a negative shock to routine-task vacancies to capture automation. We find significantly longer unemployment durations for job seekers with routine-task competencies because an unemployed job seeker’s labor market is restricted to jobs for which she has all or most of the required task competencies. 5 / 39
Data Table of Contents Introduction 1 Data 2 Job search in labor markets with task overlap 3 Empirical approach Estimates without task overlap across jobs Estimates with task overlap across jobs Robustness tests The impact of automation on the unemployed 4 Conclusion 5 6 / 39
Data Data Main VDAB sample from an online job platform introduced by VDAB (Flemish PES) containing information about: Unemployed job seekers. details Job vacancies. details 8 cross-sections from every quarter of 2013-2014. Auxiliary Social security records of unemployed job seekers in the VDAB sample. Bi-weekly (un)employment spells for 2010-2015. Gender, nationality, location, education. 7 / 39
Data Measuring tasks Unemployed job seekers have to complete a task-competency profile by indicating one or more occupation-experience cells (i.e. “jobs”) Use ROME-v3 (comparable to e.g. US O*NET): 3 experiences x 676 occupations or 2028 jobs which can be aggregated into ISCO88 occupation groups. ROME-v3 links 3489 tasks to these jobs. Vacancies are linked to ROME-v3 by employers or VDAB. 8 / 39
Table 1: Shares of job seekers and vacancies across occupation groups % first listed % all listed % vacancies (1) (2) (3) 01: armed forces 0.03 0.03 0.04 11: legislators and senior officials 0.18 0.22 0.43 12: corporate managers 5.56 5.55 18.97 13: general managers 0.08 0.10 0.11 21: physical, mathematical and engineering science professionals 0.83 0.82 3.38 22: life science and health professionals 0.18 0.17 0.49 23: teaching professionals 2.96 3.11 2.13 24: other professionals 4.95 4.54 5.05 31: physical and engineering science associate professionals 2.17 2.13 7.69 32: life science and health associate professionals 1.68 1.56 1.67 33: teaching associate professionals 0.00 0.01 0.01 34: other associate professionals 8.91 8.54 15.46 41: office clerks 9.43 9.28 5.92 42: customer services clerks 2.93 3.31 1.88 51: personal and protective services workers 7.82 7.94 3.69 52: models, salespersons and demonstrators 8.14 8.24 5.30 61: market-oriented skilled agricultural and fishery workers 1.35 1.41 0.35 71: extraction and building trades workers 5.21 5.26 5.08 72: metal, machinery and related trades workers 3.26 3.07 6.16 73: precision, handicraft, printing and related trades workers 0.59 0.52 0.19 74: other craft and related trades workers 0.86 0.87 1.11 81: stationary-plant and related operators 0.29 0.31 0.32 82: machine operators and assemblers 3.18 3.37 2.99 83: drivers and mobile-plant operators 5.53 5.94 3.55 91: sales and services elementary occupations 7.56 8.81 6.40 92: agricultural, fishery and related labourers 0.64 0.70 0.10 93: labourers in mining, construction, manufacturing and transport 15.69 14.21 1.52 N occupation-experience cells 1158 1460 877 N sample 17 493 17 493 11 228 N platform 229 535 229 535 70 407
Table 1: Shares of job seekers and vacancies across occupation groups % first listed % all listed % vacancies (1) (2) (3) 01: armed forces 0.03 0.03 0.04 11: legislators and senior officials 0.18 0.22 0.43 12: corporate managers 5.56 5.55 18.97 13: general managers 0.08 0.10 0.11 21: physical, mathematical and engineering science professionals 0.83 0.82 3.38 22: life science and health professionals 0.18 0.17 0.49 23: teaching professionals 2.96 3.11 2.13 24: other professionals 4.95 4.54 5.05 31: physical and engineering science associate professionals 2.17 2.13 7.69 32: life science and health associate professionals 1.68 1.56 1.67 33: teaching associate professionals 0.00 0.01 0.01 34: other associate professionals 8.91 8.54 15.46 41: office clerks 9.43 9.28 5.92 42: customer services clerks 2.93 3.31 1.88 51: personal and protective services workers 7.82 7.94 3.69 52: models, salespersons and demonstrators 8.14 8.24 5.30 61: market-oriented skilled agricultural and fishery workers 1.35 1.41 0.35 71: extraction and building trades workers 5.21 5.26 5.08 72: metal, machinery and related trades workers 3.26 3.07 6.16 73: precision, handicraft, printing and related trades workers 0.59 0.52 0.19 74: other craft and related trades workers 0.86 0.87 1.11 81: stationary-plant and related operators 0.29 0.31 0.32 82: machine operators and assemblers 3.18 3.37 2.99 83: drivers and mobile-plant operators 5.53 5.94 3.55 91: sales and services elementary occupations 7.56 8.81 6.40 92: agricultural, fishery and related labourers 0.64 0.70 0.10 93: labourers in mining, construction, manufacturing and transport 15.69 14.21 1.52 N occupation-experience cells 1158 1460 877 N sample 17 493 17 493 11 228 N platform 229 535 229 535 70 407
Figure 1: Number of jobs in which the same task occurs
Data Examples of tasks Examples of most frequent tasks: Basic maintenance and repair of machines or other equipment. Coordinating a team. Registration and dissemination of information related to production processes. Examples of least frequent tasks: Dismounting the equipment, structures (cells, ...) and hydraulic, pneumatic and electrical circuits of an aircraft. Developing the hospital policy for nursing. Performing a medical examination of the animal and assessing the therapeutic needs (medication, surgery, ...). 12 / 39
Table 2: Defining task overlap (1) (2) occupation-exp cell task overlap = 8/ 11 Production worker, < 2 years (total n. add. tasks Packer, >5 years of tasks=8 ) ISCO88=93 ISCO88=93 1. Logging activity data (number of 9. Check the products upon re- pieces,...) ceipt, when completing the order or upon shipment 2. Transporting the products or waste 10. Labelling the product, to the storage, shipping or recycling branding and checking the infor- zone mation (expiration date,...) 3. Providing the workstation with ma- 11. Preventive or corrective ba- terials and products or checking the sic maintenance of machines or stock equipment 4. Clearing and cleaning the work area (materials, fittings,...) 5. Packaging products according to characteristics, orders and mode of transport 6. Fitting, assembling and attach- ment of pieces. Check that the as- sembly has been correct (use, view) 7. Monitoring the flow and progress of products on a production or transport line 8. Detect and locate visible defect and sort them accordingly (surface, color,...)
Figure 2: Heatmap of task overlap across jobs
Data Summary so far Collected data for workers’ task competencies and job vacancies and defined task overlap across jobs . However, this is not informative about the importance of jobs and their task overlap for labor market outcomes . Given an unemployed job seeker’s task competencies, relate her job finding probability to tightness in job cells for which she has all or some of the required task competencies . 15 / 39
Job search in labor markets with task overlap Table of Contents Introduction 1 Data 2 Job search in labor markets with task overlap 3 Empirical approach Estimates without task overlap across jobs Estimates with task overlap across jobs Robustness tests The impact of automation on the unemployed 4 Conclusion 5 16 / 39
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