Technological change and labor market inequality

Technological change and labor market inequality

Technological change and labor market inequality:A microeconometric perspective on selected issues

Lucas Augusto van der VeldePhD candidate

University of WarsawFaculty of Economic Sciences

February 2017

Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality


Context

Technology and inequality

Skill Biased Technological Change

−.05

0

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0 20 40 60 80 100Skill Percentile (Ranked by Occupational Mean Wage)

1979−1989

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Smoothed changes in employment by occupational skill percentile 1979−2007

Notes: Figure taken from Acemoglu and Autor (2011, pp. 1071)



Context



−.05

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1979−1989 1989−1999

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Context



−.05

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1979−1989 1989−1999 1999−2007

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t Sha

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Context

Routine Biased Technological Change

Task classification

I Manual vs Abstract (cognitive + interpersonal)

I Routine vs Non-routine

Previous results

I Changes in task content(Autor et al. 2003, Spitz-Oener 2006, Green 2012, Akcomak et al. 2015)

I Wage and demand polarization.(Autor et al. 2003, 2006, Goos and Manning 2007, Acemoglu and Autor 2011, Goos et al.

2014, Jaimovich and Siu 2012, Cortes 2016, Beaudry et al. 2016)

But... this is still new literature

I Few countries and only aggregate data

⇒ Many model micro-foundations not tested



Context

Our work

General Hypothesis: Wage and career patterns in Europe provideempirical support to the assumptions implicit in the Routine BiasTechnological Change (RBTC) hypothesis

Contribution: empirical analysis of RBTC model assumptions

I Three topics new to the literature

I Technology (routine intensity) and ...

1. ... within occupation wage dispersion2. ... career patterns3. ... early retirement



Technology and wage dispersion

Study 1: Technology and wage dispersion

Motivation

A First test on models’ assumption(Autor et al. 2003, 2006, Acemoglu and Autor 2011, Jung and Mercenier 2014)

B Wage dispersion within occupation is greater than between.

C Within occupation wage dispersion can increase in future

Hypothesis 1Within occupation wage dispersion is positively correlated with the

non-routine task content of the occupation, ceteris paribus




Data & Empirical strategy

Data

I Task content of jobs → Routine task intensity (RTI)

I Wages and occupations → Matched employee-employer database

Specification

wage dispersionj = α0 + βRTIj + γD + ε

where

I Within occupation wage dispersion → e.g. 90th to 10th percentiles

I β is the coefficient of interest → H1: β is negative and significant

I D represents a set of controls




Results

Measure of dispersion 90/10 90/50 50/10Unconditional β -0.09*** -0.04*** -0.04***Wages R2 0.48 0.42 0.39

Conditional β -0.07*** -0.04*** -0.03**Wages R2 0.54 0.48 0.47

Results

1. confirm assumption from the theory

2. are robust to several robustness tests



Technology and employment

Study 2: Technology and career patterns

Motivation

A Test transition mechanisms developed in models of RBTC(Jaimovich and Siu 2012, Carrillo-Tudela and Visschers 2013, Wiczer 2015)

B Over 5 million people expected to lose routine jobs worldwide

Hypothesis 2

Individuals working in routine intensive jobs are more likely to haveunstable careers and to experience longer non-employment spells




Data & Method

Data

I Long panel data from Europe

I Countries: (West) Germany and Great Britain

Career instability

I 6= Time out of employment

I Use several measures → optimal matching

I Specification1⇒ Instability[t,t+1] = α0 + βRTIt + γD + εH2 : β is positive and significant

Non-employment spells

I non-employment = unemployment + inactivity

I Specification 2 ⇒ Spell duration = α0 + βRTIt−1 + γD + ε

H2 : β is positive and significant




Main results

Career instability Non-employment spellsOM1 OM2 NE U I

GermanyRTI 0.01 0.01 0.09*** 0.10*** 0.06

(0.02) (0.01) (0.03) (0.03) (0.04)Great BritainRTI 0.03*** 0.02** 0.05 -0.06 0.18***

(0.01) (0.01) (0.04) (0.04) (0.05)

Results

1. country specificity

2. relation is statistically significant, but economically small




Technology and retirement decisions

Motivation

A Test transition mechanisms developed in models of RBTC(Jaimovich and Siu 2012, Carrillo-Tudela and Visschers 2013, Wiczer 2015)

B Occupational choice reasonably exogenous to technological change

C Ageing phenomenon in Europe

Hypothesis 3

Older workers in routine intensive jobs reduced labor supply more thanthose working in non-routine occupations




Data & Method

DataI Long panel data from EuropeI Countries: (West) Germany and Great Britain

Analyzing retirement decisionsI Two margins: intensive (hours) and extensive (employment)I Differential effect of RTI

SpecificationsHours worked

Hours = α0 + α1RTI + α2(age > a) + βRTI ∗ (age > a) + γD + ε

H3: β is negative and significantRetirement decisions

Pr(retire | age > a) = α0 + βRTI + γD + ε

H3: β is positive and significantLucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality



Main results

Germany Great BritainIntensive Margin (a = 55) (a = 60) (a = 55) (a = 60)

RTI *(Age ≥ a) 0.16 0.23 0.42* -0.11

RTI -0.43** -0.41** -1.10*** -1.07***

(Age ≥ a) 0.04 2.24*** -1.52*** -2.34***

Extensive Margin FE RTI const. FE RTI const.

RTI 0.002 0.026* 0.001 0.029

Results

1. do not confirm expectations from theory

2. similar across specifications



Conclusions

Summary of findings

1. RTI and wage dispersion within occupationI New test of RBTC hypothesis predictionsI Confirmed and robust

Implications for theory

→ Data confirm characterization of routine/ non-routine tasks



Conclusions

Conclusions for RBTC

2. RTI and career stabilityI Weak link + country specificity

I Longer unemployment spells in GermanyI More unstable careers in Great Britain

3. RTI and early retirementI No relation between RTI & retirement decisions

Implications for theory → Data do not confirm proposed mechanisms

I Suggestion 1: embedded technological progress on non-routine jobs

I Suggestion 2: link human capital losses to differences in taskcontent



Conclusions

Thank you for your attention



Bibliography

Bibliography I

Acemoglu, D. and Autor, D.: 2011, Skills, tasks and technologies: Implications foremployment and earnings, Handbook of Labor Economics 4, 1043–1171.

Akcomak, S., Kok, S. and Rojas-Romagosa, H.: 2015, Technology, offshoring and thetask-content of occupations: Evidence from the United Kingdom, InternationalLabour Review 115(2).

Autor, D., Katz, L. F. and Kearney, M. S.: 2006, The polarization of the US labormarket, American Economic Review 96(2), 189–194.

Autor, D., Levy, F. and Murnane, R. J.: 2003, The skill content of recenttechnological change: An empirical exploration, Quarterly Journal of Economics118(4), 1279–1333.

Beaudry, P., Green, D. A. and Sand, B. M.: 2016, The great reversal in the demandfor skill and cognitive tasks, Journal of Labor Economics 34(S1), S199–S247.

Carrillo-Tudela, C. and Visschers, L.: 2013, Unemployment and endogenousreallocation over the business cycle, Discussion Papers 7124, Institute for Study ofLabor (IZA).

Cortes, G. M.: 2016, Where have the middle-wage workers gone? A study ofpolarization using panel data, Journal of Labor Economics 34(1), 63–105.



Bibliography

Bibliography II

Goos, M. and Manning, A.: 2007, Lousy and lovely jobs: The rising polarization ofwork in Britain, Review of Economics and Statistics 89(1), 118–133.

Goos, M., Manning, A. and Salomons, A.: 2014, Explaining job polarization:Routine-biased technological change and offshoring, American Economic Review104(8), 2509–2526.

Green, F.: 2012, Employee involvement, technology and evolution in job skills: Atask-based analysis, Industrial & Labor Relations Review 65(1), 36–67.

Jaimovich, N. and Siu, H. E.: 2012, The trend is the cycle: Job polarization andjobless recoveries, Working paper 18 334, National Bureau of Economic Research.

Jung, J. and Mercenier, J.: 2014, Routinization-biased technical change andglobalization: Understanding labor market polarization, Economic Inquiry52(4), 1446–1465.

Spitz-Oener, A.: 2006, Technical change, job tasks, and rising educational demands:Looking outside the wage structure, Journal of Labor Economics 24(2), 235–270.

Wiczer, D.: 2015, Long-term unemployment: Attached and mismatched?, Workingpaper 2015-42, FRB St Louis.


Technological change and labor market inequality

Economy & Finance

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