Technological change and labor market inequality
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Transcript of 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
Technological change and labor market inequality
Context
Technology and inequality
Skill Biased Technological Change
−.05
0
.05
.1
.15
.2
0 20 40 60 80 100Skill Percentile (Ranked by Occupational Mean Wage)
1979−1989
100
x C
hang
e in
Em
ploy
men
t Sha
re
Smoothed changes in employment by occupational skill percentile 1979−2007
Notes: Figure taken from Acemoglu and Autor (2011, pp. 1071)
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
Context
Technology and inequality
Skill Biased Technological Change
−.05
0
.05
.1
.15
.2
0 20 40 60 80 100Skill Percentile (Ranked by Occupational Mean Wage)
1979−1989 1989−1999
100
x C
hang
e in
Em
ploy
men
t Sha
re
Smoothed changes in employment by occupational skill percentile 1979−2007
Notes: Figure taken from Acemoglu and Autor (2011, pp. 1071)
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
Context
Technology and inequality
Skill Biased Technological Change
−.05
0
.05
.1
.15
.2
0 20 40 60 80 100Skill Percentile (Ranked by Occupational Mean Wage)
1979−1989 1989−1999 1999−2007
100
x C
hang
e in
Em
ploy
men
t Sha
re
Smoothed changes in employment by occupational skill percentile 1979−2007
Notes: Figure taken from Acemoglu and Autor (2011, pp. 1071)
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
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
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
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
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
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
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
Technology and wage dispersion
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
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
Technology and wage dispersion
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
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
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
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
Technology and employment
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
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
Technology and employment
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
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
Technology and employment
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
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
Technology and employment
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
Technological change and labor market inequality
Technology and employment
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
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
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
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
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
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
Conclusions
Thank you for your attention
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
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.
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality
Technological change and labor market inequality
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.
Lucas van der Velde University of Warsaw Faculty of Economic SciencesTechnological change and labor market inequality