Career instability in a context of technological change

Career instability in a context of technological change

Career instability in a context of technologicalchange

Lucas Augusto van der Velde

University of WarsawFaculty of Economic Sciences

March 2017

Lucas van der Velde University of Warsaw Faculty of Economic SciencesCareer instability in a context of technological change


Introduction

Motivation

Context

I Over 5 million jobs expected to be automated worldwide

I New topic in economics

I Most evidence is on aggregate data (net employment changes)I Models’ assumptions are largely untested

Our contribution

I Test models assumptions.

I Provide first empirical analysis relating career patterns andtechnological change using individual level data.



Theoretical considerations

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)

I Routine tasks→ Substitution effects dominate.→ ↓ demand, ↓ price.

I Non-routine cognitive tasks→ Complementarity→ ↑ demand, ↑ price.

I Non-routine Manual tasks → neither complements nor substitutes→ ↑ demand, ↑↓ price.




How do workers switch tasks

I Main models → Not considered(e.g. Autor et al. 2003, 2006, Acemoglu and Autor 2011, Goos et al. 2014, Jung

and Mercenier 2014)

I Jaimovich and Siu (2012)→ switching market with lower efficiency→ Lower efficiency reflects learning skills→ Non-routine is an absorving state

I Carrillo-Tudela and Visschers (2013)→ Switch leads human capital loses→ > Pr(unemp) → > Pr(switch)




Hypotheses

H1 Workers in routine occupations experienced more career instability.

H2 Workers leaving routine occupations experienced longerunemployment spells.



Data

Data

1. German Socioeconomic Panel (GSOEP)I 1984 - today (West Germany)I > 1500 individuals with balanced data (1991-2000)

2. British Household Panel Survey (BHPS)I 1991 - 2008 → DiscontinuedI > 2500 individual with balanced data (1991-2000)

3. Occupation Network (O*NET)I Grouped data from USI Applied to EU before (e.g. Goos et al. 2014)I Routine task intensity =

∑routine tasks−

∑non-routine tasks



Data

Hypotheses

H1 Workers in routine occupations experienced more careerinstability.




Hypothesis 1

Hypothesis 1: Measuring career instability

I Imagine two workers with careers:

W1 E - U - E - E

W2 U - E - E - E

I How to make them equal?

1. Substitution → W2: E - U - E - E

2. Insert and Delete (INDEL) → W2: E - U - E - E - �E

I Minimum number of steps ⇒ Optimal matching



Hypothesis 1

Optimal matching

Definitions Proposals

Career elementsQuintiles of RTI + NE

Labor market status (FT PT SE NE)

Substitution costsOne

Differences in RTI + one to/from NE

Indel costs Half of substitution costs

Reference sequence Continuous employment in same element



Hypothesis 1

Sample career

0

100

200

300

Indi

vidu

als

1081 1105 1129 1153 1177 1201Months since 01/1900

RTI Quintiles 1 2 3 4 5 NE

Group 1 - Germany

0

100

200

300

400

Indi

vidu

als

1081 1105 1129 1153 1177 1201Months since 01/1900

RTI Quintiles 1 2 3 4 5 NE

Group 1 - Great Britain



Hypothesis 1

Method

Specification

yt,t+1 = β0 + β1RTIt + controls + εt

where

I yt,t+1 is a measure of instability.

I β1 is coefficient of interest → Hypothesis: β1 > 0.

I Other controls: year of birth, gender, educational attainment, city.



Hypothesis 1

Results

Specification: yt,t+1 = β0 + β1RTIt + controls + εt

Germany Great BritainCosts Unit RTI Unit 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 0.03N 1593 1593 1985 1985

Results

I Follow our expectations

I Resilient to robustness checks

I Statistically significant..., but economically relevant?



Hypothesis 1

Hypotheses

H1 Workers in routine occupations experienced more career instability.




Hypothesis 1

Method

SpecificationtimeNE ,t = f (RTIt−1, controls)

where

I timeNE ,i → lenght of non-employment spell i .

I f (·) → log-logistic hazard rate.

I RTIt−1 → RTI last occupation → H0: βRTI > 0.

I other controls: year of birth, educational level, gender and spellnumber.



Hypothesis 1

Results

Specification: timeNE ,t = f (RTIt−1, controls)

Germany Great BritainNE 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)

LL -5875 -4610 -766.1 -4179 -1507 -2198AIC 11775 9246 1552 8390 3047 4425

Results

I Follow our expectations

I Resilient to robustness checks

I Statistically significant..., but economically relevant?



Hypothesis 1

Results: predicted survival curves

Specification: timeNE ,t = f (RTIt−1, controls)

0

.2

.4

.6

.8

1

Sur

viva

l

0 10 20 30 40 50Months in non−employment

RTI Quintile: 1 2 5

Predicted survival curves

(a) Germany

.2

.4

.6

.8

1

Sur

viva

l0 10 20 30 40 50

Months in non−employment

RTI Quintile: 1 2 3 4 5

Predicted survival curves

(b) Great Britain



Conclusions

Conclusions

I Weak link between career patterns and RTI

I Link is country specific

1. Longer unemployment spells in Germany.2. More unstable careers in Great Britain.

I How to reconcile empirical results and theory

1. Embedded technological progress.2. Link human capital loss to differences in task content.



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.

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.

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

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

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.


Economy & Finance

Career instability in a context of technological change