Career instability in a context of technological change

Career instability in a context of technological change
Career instability in a context of technological
change
Lucas Augusto van der Velde
Warsaw School of Economics
GRAPE
Application of Empirical Methods in Modern Economics
Poznan University of Economics and Business 2019
Lucas van der Velde Warsaw School of Economics GRAPE

Introduction
Motivation
Context
Over 5 million jobs expected to be automated worldwide
(World Economic Forum 2017)
New topic in economics
Most evidence is on aggregate data (net employment changes)
Exceptions: Cortes (2016), Bachmann, Cim and Green (2018).
Provide a new empirical test on models’ implications
Our contribution
Provide ﬁrst empirical analysis relating career patterns and
technological change using individual level data.

Introduction
Preview
H1 Workers in routine occupations experienced more career instability.
Conﬁrmed in UK, less so in Germany
Relation appears non-linear
H2 Workers leaving routine occupations experienced longer
unemployment spells.
Conﬁrmed in Germany, less so in UK
Unemployed or inactive?

Theoretical considerations
Context: Employment changes since 1970’s (US)
−.05
0
.05
.1
.15
.2
0 20 40 60 80 100
Skill Percentile (Ranked by Occupational Mean Wage)
1979−1989
100xChangeinEmploymentShare
Smoothed changes in employment by occupational skill percentile 1979−2007
Notes: Figure taken from Acemoglu and Autor (2011, pp. 1071)
.05
.1
.15
.2
angeinEmploymentShare
Smoothed changes in employment by occupational skill percentile 1979−2007

Routine biased technological change
Premise:
Analyze tasks → units of activity that produce output
Task classiﬁcation:
Manual Cognitive / interpersonal
Non-Routine Cleaning, repairing Managing, creating
Routine Assembling, packing Bookkeeping, spell checking

Routine biased technological change
Eﬀects of technological progress
(Autor et al. 2003, 2006, Acemoglu and Autor 2011)
Routine tasks
→ Substitution eﬀects dominate.
→ ↓ demand, ↓ price.
Non-routine cognitive tasks
→ Complementarity
→ ↑ demand, ↑ price.
Non-routine Manual tasks → neither complements nor substitutes
→ ↑ demand, ↑↓ price.

How do workers switch tasks
Main models → Not considered
(e.g. Autor et al. 2003, 2006, Acemoglu and Autor 2011, Goos et al. 2014, Jung
and Mercenier 2014)
Jaimovich and Siu (2012)
→ switching market with lower efficiency
→ Lower efficiency reflects learning skills
→ Non-routine is an absorbing state
Carrillo-Tudela and Visschers (2013)
→ Switch leads to occ. specific human capital loss
→ ↑ Pr(unemp) → ↑ Pr(switch)

Method & data
Data
1. German Socioeconomic Panel (GSOEP)
1984 - today (West Germany)
> 1500 individuals with balanced data (1991-2000)
2. British Household Panel Survey (BHPS)
1991 - 2008 → Discontinued
> 2500 individual with balanced data (1991-2000)
Descriptives
3. Occupation Network (O*NET)
Grouped data from US
Applied to EU before (e.g. Goos et al. 2014, Keister and Lewandowski 2016)
Can it be justiﬁed? (Hardy et al 2018)

Method & data
Measuring the task content of jobs
Deﬁnition follows Acemoglu and Autor (2011).
16 variables from 3 domains (O*NET)
Deﬁne 5 task types (all standardized)
a) Non-routine: Cognitive, Interpersonal, Manual
b) Routine: Cognitive, Manual
RTI = Non-routine − Routine (also standardized)
Discretization of the RTI variable → Quintiles

Method & data
Hypotheses
H1 Workers in routine occupations experienced more career
instability.

Hypothesis 1: Career instability
Hypothesis 1: Measuring career instability
What is instability?
Instability: distance from a perfectly stable career.
What is a perfectly stable career?
Continuous employment in “similar” occupations
How does a career look like?
Are careers consistent with RBTC?
How to measure distance?
Optimal matching

Optimal matching
Imagine two workers with careers:
W1 E - U - E - E
W2 U - E - E - E
How to make these careers equal?
1. Substitution → W2: E - U - E - E
2. Insert and Delete (INDEL) → W2: E - U - E - E - ¡E
Minimum number of steps ⇒ Optimal matching

Optimal matching: Finding the right costs
Distance depends on how costs are operationalized
Bug or a feature? (Wu (2000) & Levine (2000)
Minimum criteria
“Costs should be symmetric, fulﬁll the inequality triangle and be 0 only
for the substtution of an element with itself”
Studer and Ritschard (2016)

Optimal matching: Finding the right costs
Setting substitution costs
1. All costs equal to 1
2. Costs =1/ transition probability (Lesnard 2010)
(period speciﬁc, computationally intensive, may be nonsensical)
3. Use theory derived measures (Hollister 2009)
Against: arbitrary, which dimension is relevant?
Setting INDEL costs
1. 0.5 ×maximum substitution cost
2. Costs should be prohibitely high (Lesnard 2010)
3. Costs should depend on context (Hollister 2009)
→ 0.5 might be optimal
→ Computationally intensive

Optimal matching: our approach
Deﬁnitions Proposals
Career elements RTI quintiles+ NE
Substitution costs
One
Diﬀerences in RTI + one to/from NE
Indel costs Half of max. substitution costs
Reference sequence Continuous employment in same element

Results
Measures of instability
Group 1 2 3 4 5 6 (NE)
Germany
OM1 - unit cost 0.20 0.32 0.39 0.35 0.33 0.64 ***
OM2 - RTI costs 0.13 0.16 0.20 0.18 0.22 0.64 ***
Great Britain
OM1 - unit cost 0.28 0.36 0.46 0.42 0.39 0.54 ***
OM2 - RTI costs 0.22 0.19 0.36 0.25 0.29 0.54 ***

Results
Uncovering effect of RTI
Specification
yt,t+1 = β0 + β1RTIt + controls + t
where
yt,t+1 is a measure of instability.
β1 is coefficient of interest → Hypothesis: β1 > 0.
Other controls: year of birth, gender, educational attainment, city.

Results
Results
Speciﬁcation: yt,t+1 = β0 + β1RTIt + controls + t
Germany Great Britain
OM-Unit OM-RTI OM-Unit OM-RTI
RTI 0.01 0.01 0.03*** 0.02**
(0.02) -0.01 (0.01) (0.01)
R2
0.04 0.06 0.03 0.03
N 1593 1593 1985 1985

Robustness checks
Non-linearities
Quintile Germany Great Britain
OM-Unit OM-RTI OM-Unit OM-RTI
1 Baselevel
2 0.10* 0.01 0.07** -0.03
(0.05) (0.03) (0.03) (0.02)
3 0.13*** 0.03 0.16*** 0.13***
(0.04) (0.03) (0.04) (0.02)
4 0.10** 0.02 0.12*** 0.02
(0.05) (0.03) (0.03) (0.02)
5 0.07 0.05 0.09** 0.06**
(0.05) (0.03) (0.04) (0.03)
R2
0.05 0.06 0.05 0.08
N 1593 1593 1985 1985

Robustness checks
Robustness checks
1. Alternative measures of instability
% not in mode, # elements, # jobs
2. Alternative career elements
Occupation grouped based on distance of tasks, employment status
Results not aﬀected by these choices
Follow our expectations
Resilient to robustness checks
Statistically signiﬁcant..., but economically relevant?

Robustness checks
Hypotheses
H1 Workers in routine occupations experienced more career instability.

Hypothesis 2: Unemployment spells
Method
Speciﬁcation
timeNE,t = f (RTIt−1, controls)
where
timeNE,i → lenght of non-employment spell i starting in t.
f (·) → log-logistic hazard rate.
RTIt−1 → RTI last occupation → H0: βRTI > 0.
other controls: year of birth, educational level, gender and spell
number.

Results
Speciﬁcation: timeNE,t = f (RTIt−1, controls)
NE U I NE U I
RTIt−1 0.09*** 0.10*** 0.06 0.05 -0.06 0.18***
(0.03) (0.03) (0.04) (0.04) (0.04) (0.05)
N 3215 2636 597 2286 966 1284
Mean dur. 22.94 13.40 48.60 39.09 12.25 52.54
Median dur. 8 4 27 11 5 28

Results: predicted survival curves
Speciﬁcation: timeNE,t = f (RTIt−1, controls)
0
.2
.4
.6
.8
1
Survival
0 10 20 30 40 50
Months in non−employment
RTI Quintile: 1 2 5
Predicted survival curves
Germany
.2
.4
.6
.8
1
Survival
0 10 20 30 40 50
Months in non−employment
RTI Quintile: 1 2 3 4 5
Predicted survival curves
Great Britain

Robustness checks
1. Non-linear relations
2. Alternative survival functions
3. Non-parametrict estimation
Results are consistent
Follow our expectations
Statistically signiﬁcant..., but economically relevant?

Conclusions
Conclusions
Weak link between career patterns and RTI
Link is country speciﬁc
1. Longer unemployment spells in Germany.
2. More unstable careers in Great Britain.
How to reconcile empirical results and theory
1. Embedded technological progress.
2. Link human capital loss to diﬀerences in task content.

Conclusions
Other potential application of Optimal Matching
Life-cycle sequences
Retirement patterns, school-to-work transitions, etc.
Old idea with new data (SHARE, GGP, etc.)
Reactions to events
Female labor supply around childbirth
Death of a relative...
Experiments (?)
where actions can take place in diﬀerent order and order matters
Evolution of cooperation

Conclusions
Thank you for your attention

Bibliography
Bibliography I
Acemoglu, D. and Autor, D.: 2011, Skills, tasks and technologies: Implications for
employment and earnings, Handbook of Labor Economics 4, 1043–1171.
Autor, D., Katz, L. F. and Kearney, M. S.: 2006, The polarization of the US labor
market, American Economic Review 96(2), 189–194.
Autor, D., Levy, F. and Murnane, R. J.: 2003, The skill content of recent
technological change: An empirical exploration, Quarterly Journal of Economics
118(4), 1279–1333.
Carrillo-Tudela, C. and Visschers, L.: 2013, Unemployment and endogenous
reallocation over the business cycle, Discussion Papers 7124, Institute for Study of
Labor (IZA).
Goos, M., Manning, A. and Salomons, A.: 2014, Explaining job polarization:
Routine-biased technological change and oﬀshoring, American Economic Review
104(8), 2509–2526.
Hollister, M.: 2009, Is optimal matching suboptimal?, Sociological Methods &
Research 38(2), 235–264.
Jaimovich, N. and Siu, H. E.: 2012, The trend is the cycle: Job polarization and
jobless recoveries, Working paper 18 334, National Bureau of Economic Research.

Bibliography
Bibliography II
Jung, J. and Mercenier, J.: 2014, Routinization-biased technical change and
globalization: Understanding labor market polarization, Economic Inquiry
52(4), 1446–1465.
Keister, R. and Lewandowski, P.: 2016, A routine transition? Causes and consequences
of the changing content of jobs in Central and Eastern Europe, (05/2016).
Lesnard, L.: 2010, Setting cost in optimal matching to uncover contemporaneous
socio-temporal patterns, Sociological Methods & Research 38(3), 389–419.
Studer, M. and Ritschard, G.: 2016, What matters in diﬀerences between life
trajectories: A comparative review of sequence dissimilarity measures, Journal of
the Royal Statistical Society: Series A (Statistics in Society) 179(2), 481–511.

Bibliography
Sample selection
In Out In Out
Age 38.36 38.44 37.64 36.37 ***
Female 0.4 0.56 *** 0.55 0.48 ***
Education
Prim. 0.72 0.74 *** 0.59 0.64 ***
Sec. 0.14 0.12 0.28 0.27
Univ. 0.14 0.14 0.12 0.1
Employed 0.94 0.76 *** 0.85 0.84
Occ. codes
(1-3) 0.42 0.41 *** 0.44 0.39 ***
(4-6) 0.25 0.24 0.3 0.29
(7-9) 0.33 0.35 0.26 0.32
N 1818 2427 2512 2191
Notes: In/out refers to whether observations were included in the final sample (10 year balanced panel with at least one employment
spell). *** denotes significant differences at the 1% level from t-test (age) and χ2 tests (all remaining variables).
Back

Bibliography
Variables used to derive the task content
Non-routine Routine
Cognitive
Analyzing data/information (A) Importance of repeating the same tasks
(C)
Thinking creatively (A) Importance of being exact or accurate
(C)
Interpreting information for others (A) Structured v. Unstructured work (re-
verse) (C)
Interpersonal
Establishing and maintaining personal
relationship (A)s
Guiding, directing and motivating sub-
ordinates (A)
Coaching/developing others (A)
Manual
Operating vehicles, mechanized devices,
or equipment (A)
Pace determined by speed of equipment
(C)
Spend time using hands to handle, con-
trol or feel objects, tools or controls (C)
Controlling machines and processes (A)
Manual dexterity (Ab) Spend time making repetitive motions
(C)
Spatial orientation (Ab)
Domains: (A): activities , (C): context , (Ab): abilities
Back

Bibliography
Routine task intensity
−3 −2 −1 0 1 2
Routine task intensity (RTI)
Germany
Examples
Min 348 – Religious associate professional
p25 610 – Market oriented skilled agricultural worker
p50 344 – Government oﬃcial
p75 419 – Oﬃce clerk
Max 829 – Machine operator
Back

Bibliography
From RTI to quintiles
Germany
−3−1.501.53
RTIindex
0 20 40 60 80 100
Skill percentile
RTI quintiles: 1 2 3 4 5
Great Britain
3

Bibliography
Sample careers: Germany
0
100
200
300
Individuals
1081 1105 1129 1153 1177 1201
Months since 01/1900
RTI Quintiles 1 2 3 4 5 NE
Group 1: Most Non Routine
0
100
200
300
400
Individuals
1081 1105 1129 1153 1177 1201
Group 5: Most Routine

Bibliography
Sample careers: Great Britain
0
100
200
300
400
Individuals
1081 1105 1129 1153 1177 1201
Group 1: Most non-routine
0
100
200
300
400
Individuals
1081 1105 1129 1153 1177 1201
Group 5: Most routine
Back

Bibliography
Do we capture the features of RBTC?
Over a 10-year period
Individuals tend to remain in their initial element
More so in Germany
More so in Non-routine occupations
Movements to non-employment
Higher in more routine occupations
Higher in Great Britain
Transitions to non-routine occupations
Common (GB)
Movements among neigbouring groups (DE)
Back

Bibliography
10 year transition matrix: Germany
Year 2001
1990 1 2 3 4 5 NE
1 0.69 0.07 0.05 0.06 0.03 0.09
2 0.17 0.57 0.07 0.06 0.03 0.09
3 0.13 0.06 0.47 0.10 0.08 0.15
4 0.07 0.08 0.07 0.53 0.09 0.16
5 0.04 0.03 0.10 0.09 0.54 0.20
NE 0.19 0.08 0.25 0.15 0.14 0.20
Messages
Main diagonal presents largest values
Movements to NE higher for more routine
Hirings occured in routine and non-routine

Bibliography
10 year transition matrix: Great Britain
Year 2001
1990 1 2 3 4 5 NE
1 0.60 0.08 0.06 0.07 0.06 0.13
2 0.18 0.50 0.05 0.08 0.05 0.14
3 0.12 0.08 0.44 0.11 0.06 0.20
4 0.16 0.11 0.07 0.39 0.12 0.14
5 0.12 0.07 0.05 0.16 0.43 0.17
NE 0.14 0.13 0.14 0.17 0.14 0.27
Messages
Main diagonal still larger...
.. but less so than in Germany
Inverse U shape for moves to NE
Back

Bibliography
Common career patterns
Germany
Group 1 Group 5
Sequence Frequency Sequence Frequency
1 46.44 5 35.01
161 8.81 56 12.47
16 5.76 545 5.28
14 2.71 565 4.08
141 2.37 53 3.12
Great Britain
Group 1 Group 5
Sequence Frequency Sequence Frequency
1 28.47 5 19.86
16 5.32 56 6.31
161 4.4 565 4.21
121 3.94 54 3.04
1616 2.08 545 2.34
Back

Bibliography
Alternative measures of instability
RTI Quintiles NE
1 2 3 4 5
Germany
% not-mode 0.20 0.32 0.39 0.35 0.33 0.64 ***
# Elements 1.76 2.05 2.22 2.20 2.01 2.51 ***
# Jobs 3.06 3.20 3.37 3.64 3.41 3.76 ***
Great Britain
% not-mode 0.26 0.36 0.42 0.42 0.39 0.55 ***
# Elements 2.31 2.42 2.50 2.61 2.51 2.94 ***
# Jobs 4.85 4.67 4.87 4.96 4.80 5.05 ***
Notes: Table presents ANOVA tests for diﬀerences in means between people in
diﬀerent groups

Bibliography
Alternative measure of instability – regression
% not mode # elements # Jobs
Germany
RTI -0.00 0.00 0.08
SE (0.01) (0.04) (0.09)
R2
0.03 0.02 0.02
Great Britain
RTI 0.01 0.05 0.00
SE (0.01) (0.05) (0.09)
R2
0.03 0.02 0.02
Notes: Table presents regressions of instability measures on task content of jobs .
Robust standard errors in parentheses. Controls include gender, age, residence, and
education
Back

Bibliography
Optimal matching based on Labor market status
Country RTI quintiles NE
1 2 3 4 5
Germany 0.63 0.75 0.64 0.54 0.46 1.34 ***
Great Britain 0.68 0.78 0.92 0.70 0.68 1.48 ***
Notes: Table presents ANOVA tests for diﬀerences in means between people in
diﬀerent groups
Back

Bibliography
Non-employment duration – non-linear RTI eﬀects
NE U I NE U I
RTI Quint.
2 -0.17* -0.07 -0.05 0.31*** 0.17 0.26
(0.10) (0.09) (0.14) (0.12) (0.12) (0.16)
3 0.12 0.14 0.18 0.27** 0.08 0.30*
(0.10) (0.10) (0.12) (0.12) (0.13) (0.16)
4 0.08 0.14 0.06 0.10 -0.19* 0.44***
(0.09) (0.09) (0.12) (0.12) (0.11) (0.17)
5 0.18* 0.18* 0.16 0.24** -0.06 0.51***
(0.10) (0.10) (0.13) (0.11) (0.12) (0.16)
N 3215 2636 597 2286 966 1284
Notes: Log-logistic survival function. Additional controls include gender, education,
age, urban and spell number
Back

Bibliography
Alternative parametric speciﬁcation
NE U I
Germany
RTI 0.09*** 0.08** 0.10*** 0.08** 0.06 0.10*
(0.03) (0.04) (0.03) (0.03) (0.04) (0.05)
LL -5880 -5722 -4629 -4488 -766.8 -745.9
AIC 11776 11487 9274 9020 1550 1530
Great Britain
RTI 0.07* 0.07* -0.06* -0.04 0.22*** 0.16***
(0.04) (0.04) (0.04) (0.04) (0.05) (0.05)
LL -4231 -4129 -1514 -1489 -2221 -2171
AIC 8477 8298 3043 3018 4457 4379
Notes: Log-logistic survival function. Additional controls include gender, education,
age, urban and spell number. Second columns includes controls for frailty at the
individual level.
Back

Bibliography
Alternative non parametric speciﬁcations
NE U I
Germany
RTI 0.96* 0.94*** 0.95
SE (0.02) (0.02) (0.05)
LL -20947 -17016 -2570
AIC 41914 34053 5153
Great Britain
RTI 0.95* 1.02 0.90***
SE (0.02) (0.03) (0.03)
LL -14104 -5626 -6700
AIC 2,320 11277 13425
Notes: Non-parametric Cox model. Additional controls include gender, education, age,
urban and spell number.
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Career instability in a context of technological change

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