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2. Explaining Job Polarization: Routine-Biased Technological Change & Offshoring Alan Manning London School of Economics Maarten Goos University of Leuven Anna Salomons Utrecht University School of Economics

3. Job polarization in Europe

4. Occupation groups • High-paying: Physical, mathematical and engineering professionals; Corporate managers and managers of small enterprises; Life science and health professionals; Other professionals; Physical, mathematical and engineering associate professionals; Other associate professionals; Life science and health associate professionals. • Middling: Metal, machinery and related trade work; Drivers and mobile plant operators; Stationary plant and related operators; Precision, handicraft, craft printing and related trade workers; Office clerks; Customer service clerks; Extraction and building trades workers; Machine operators and assemblers; Other craft and related trade workers. • Low-paying: Personal and protective service workers; Laborers in mining, construction, manufacturing and transport; Models, salespersons and demonstrators; Sales and service elementary occupations.

5. Job polarization by occupation group Notes: Employment share changes are long differences for the period 1993-2010 and averaged across our sample of 16 EU countries. Occupations are ordered by their mean wage rank across the 16 European countries. The occupations are grouped into the 4 lowest-paid, 8 middling and 9 highest-paid occupation groups.

6. Pervasiveness of job polarization

7. The canonical model • Labor of different skill types (L and H) combined to produce one final good (Y) according to a CES production function: • An increase in the relative labor productivity of high-educated labor leads to an increasing demand for high-educated labor. This could be due to SBTC. • Under some strong identification assumptions, the canonical framework provides support for SBTC and estimates of key structural parameters(e.g.elasticity of substitution between high- & low-skilled workers ()), (Katz & Murphy (92), Card & Lemieux (02)).

8. The canonical model • Additional evidence in support of SBTC came from shift-share analyses.Decomposing the change over time in the employment share of skill group jwithin and between industries i: • Large part of skill-upgrading was found to take place within industries (Katz & Murphy (92), Bound & Johnson (92), Berman, Bound & Griliches (93)). Change in the skill group composition within industries. Change in the relative importance of skill group jbetween industries.

9. Job polarization vs. the canonical model withinindustries withinandbetweenindustries

10. Rethinking the canonical model This paper is based on two main modifications of the canonical model: 1. The nature of technology: task- rather than skill-biased 2. Allowing the industry output mix to change over time following technological progress  Both modifications together will allow us to better understand the phenomenon of job polarization within and between industries

11. Rethinking the canonical model This paper is based on two main modifications of the canonical model: 1. The nature of technology: task- rather than skill-biased 2. Allowing the industry output mix to change over time following technological progress  Both modifications together will allow us to better understand the phenomenon of job polarization within and between industries

12. The nature of technology • More nuanced view on technological progress by Autor, Levy & Murnane (03): routine tasks can be automated leading to routine-biased technological progress • What exactly these routine tasks are that can be automated depends on the episode of technological progress • Today (machine learning, mobile robotics): • Routine tasks are those that follow a set protocol and are codifiable in software language • Non-routine tasks require mental flexibility or physical adaptability

13. Routine and non-routine occupations

14. The nature of technology • Routine tasks located in the middle of the wage distribution is consistent with job polarization • “Trade in middling tasks”: some middling tasks are easier to offshore than others which is also consistent with job polarization • But we do not want to hold a horse race between both • We would expect routine-biased technological change and task offshoring to lead to job polarization within industries

15. Rethinking the canonical model This paper is based on two main modifications of the canonical model: 1. The nature of technology: task- rather than skill-biased 2. Allowing the industry output mix to change over time following technological progress  Both modifications together will allow us to better understand the phenomenon of job polarization within and between industries

16. Allowing industry output mix to change • The canonical model assumes industry output mix unaffected by routine-biased technological progress and task offshoring • But industries use occupational tasks in different intensities: likely to lead to a change in the industry output mix and a between-industry effect of routine-biased technological progress and task offshoring  A shift-share analysis of occupational employment shares shows that there is job polarization also between industries

17. A shift-share analysis of occupations Notes: Averaged across all 16 countries. All numbers are percentage point changes in occupational employment shares over the 1993-2010 long difference where employment shares in 1993 and 2010 are imputed on the basis of average annual growth rates for countries with shorter data spans.

18. Allowing industry output mix to change • Shift-share analysis shows job polarization is occurring both within and between industries • Need to model the impact of technology and offshoring allowing the industry output mix to change  Contribution: model & empirically identify the channels through which routine-biased technological progress & offshoring impact the demand for occupations both within and between industries

19. Agenda 1. A general task model capturing the impacts of routine- biased technological change and task-offshoring on labor demand 2. Taking the model to the data 3. Extent to which the model can explain job polarization in Europe

20. A general task model of labor demand

21. Effects of RBTC and offshoring Routine-biased technical progress (RBTC) and task-offshoring are relative decreases in the price of “other inputs” in production of routine and offshorable tasks respectively. Impacts of on the demand for labor for routine / offshorable occupations: Displacement effect within industries in the elasticity of substitution between inputs in task production; Attenuation effect within industries in the elasticity of substitution between tasks in goods production; Displacement effect between industries in the elasticity of substitution between tasks in goods production; Attenuation effect between industries in the elasticity of substitution between goods in consumption.

22. A summary of the general task model • A general framework capturing the employment impacts of routine-biased technological progress and task offshoring • Relative employment in routineand offshorable occupations decreases within and between industries if displacement effects dominateattenuation effects • Can account for job polarization within and between industries since routine and offshorable tasks are performed by medium-skillworkers

23. Agenda 1. A general task model capturing the impacts of routine- biased technological change and task-offshoring on labor demand 2. Taking the model to the data 3. Extent to which the model can explain job polarization in Europe

24. Empirical specification • We had: • Assume the followinggoods production technology: • Assume the following task production technology: • Assume that preferences are homothetic and product demand is iso-elastic with

25. Structural labor demand equation • We had: • Log linearization of functional forms gives:

26. Structural labor demand equation: data • Labor demand equation (adding country & year subscripts): hourly wages (EHCP, EU-SILC, UKLFS, OECD) hours worked (ELFS, SIAB) coefficient*routine*tt + coefficient*offshorability* tt industry-occupation-country fixed effect industry marginal costs (STAN) population (STAN) income/capita (STAN) price index (STAN)

27. Measuring technological progress and offshoring at the task level • Occupation specific measures of: • Routine Task Intensity (Rj) from Autor, Levy & Murnane (03), based on Dictionary of Occupational Titles (DOT) data. • Offshorability index (Fj) from Blinder & Krueger (13), based on Princeton Data Improvement Initiative (PDII) data. • Occupation specific measures are interacted with a time-trend to capture routine-biased technological change and task-offshoring respectively (e.g. RTIj*time-trend).

28. Some examples • Most routine occupations: • Metal, machinery & related trade workers, Machine operators & assemblers, Office clerks • Least routine occupations: • Managers, Drivers, Other professionals, Personal & protective service workers • Most offshorable occupations: • Stationary plant & related operators, Machine operators & assemblers, Physical, mathematical and engineering professionals • Least offshorable occupations: • Life science & health (associate) professionals, Managers of small enterprises, Personal & protective service workers

29. Structural labor demand equation: estimating parameters • Labor demand equation: hourly wages (EHCP, EU-SILC, UKLFS, OECD) hours worked (ELFS) coefficient*routine*tt+ coefficient*offshorability* tt industry-occupation-country fixed effect industry marginal costs (STAN) population (STAN) income/capita (STAN) price index (STAN)

30. Estimates of structural parameters

31. Agenda 1. A structural labor demand model capturing the impacts of routine-biased technological change and task-offshoring 2. Taking the model to the data 3. Extent to which the model can explain job polarization in Europe

32. A shift-share analysis of occupations Notes: Averaged across all 16 countries. All numbers are percentage point changes in occupational employment shares over the 1993-2010 long difference where employment shares in 1993 and 2010 are imputed on the basis of average annual growth rates for countries with shorter data spans.

33. A shift-share rooted in our model • Employment share of occupation j in time t: • Differentiating wrt time:

34. A shift-share rooted in our model

35. A shift-share rooted in our model • Result for technological change (similarly for offshoring): where

36. Actual and predicted job polarization Notes: Employment share changes are long differences for the period 1993-2010 and averaged across our sample of 16 EU countries. Results are pervasive across our sample of countries.

38. Job polarization within and between industries Notes: Averaged across all 16 countries, all changes in percentage points.

41. Between industry effects Notes: The first column gives the predicted employment share. The second column gives the predicted changes conditional on industry output. The third column gives the predicted changes due to consequent changes in relative industry output prices.

42. Conclusions • Canonical model of the labor market cannot account for job polarization within & between industries • We provide an empirically identifiable task-based framework to model technological progress and offshoring accounting for within and between industry effects on the structure of occupational employment • Routine-biased technological change and task-offshoring can explain job polarization in Europe

44. Labor demand equation – task production function CES • Labor demand equation: hourly wages (EHCP, EU-SILC, UKLFS, OECD) hours worked (ELFS) coefficient*routine*tt + coefficient*offshorability* tt industry-occupation-country fixed effect industry marginal costs (STAN) population (STAN) income/capita (STAN) price index (STAN)