Online Appendix Unemployment, Participation and Worker Flows...

Online Appendix

Unemployment, Participation and Worker Flows

over the Life-Cycle

Sekyu Choi

Universitat Autonoma de Barcelona, Barcelona GSE and MOVE

Alexandre Janiak

Universidad de Chile, Department of Industrial Engineering,

Center for Applied Economics

Benjamın Villena-Roldan

Universidad de Chile, Department of Industrial Engineering,

Center for Applied Economics

1

A Conditional Analysis

We present results of different subsamples of interest. We analysis the relative importance of each

flow for several groups defined by educational attaintment, marital status, and child presence in

the household. The evidence shows a remarkable consistence of baseline results across different

samples.

A.1 Conditional Analysis by Educational Group and Gender

In this subsection, we consider two subsamples: individuals with at most a high school diploma

(ncol) and individuals with at least one year of college education (coll).

A.1.1 Estimated Flows and Stocks by Educational Group and Gender

Figure A1: Life-Cycle Unemployment and Participation Profiles: Males, Non-College vs College

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

U

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

P

ncol male coll male

Note: Unconditional life-cycle profiles estimated via weighted OLS.

2

Figure A2: Life-Cycle Unemployment and Participation Profiles: Females, Non-College vs College

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

U

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

P

ncol female coll female


Figure A3: Life-Cycle Profiles of Worker Flows Transitions: Males, Non-College vs College

.01

.02

.03

.04

.05

Pro

b

20 30 40 50 60 70

age

EU

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

EO.1

.15

.2.2

5.3

.35

Pro

b

20 30 40 50 60 70

age

UE

.1.2

.3.4

.5

Pro

b

20 30 40 50 60 70

age

UO

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OE

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OU

ncol male coll male


3

Figure A4: Life-Cycle Profiles of Worker Flows Transitions: Females, Non-College vs College

0.0

1.0

2.0

3.0

4

Pro

b

20 30 40 50 60 70

age

EU0

.05

.1.1

5.2

Pro

b

20 30 40 50 60 70

age

EO

.15

.2.2

5.3

.35

Pro

b

20 30 40 50 60 70

age

UE

.2.2

5.3

.35

.4.4

5

Pro

b

20 30 40 50 60 70

age

UO

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OE0

.02

.04

.06

.08

Pro

b

20 30 40 50 60 70

age

OU

ncol female coll female


4

A.1.2 Markovian Simulations by Educational Group and Gender

Figure A5: Markov-Chain Simulated Unemployment and Participation: Non-College

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=0.992

U males

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

R2=0.981

P males

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=0.988

U females

0.2

.4.6

.8

Pro

b20 30 40 50 60 70

age

R2=0.982

P females

actual Markov


5

Figure A6: Markov-Chain Simulated Unemployment and Participation: College

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

R2=0.980

U males

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

R2=0.988

P males

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

R2=0.949

U females

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

R2=0.986

P females

actual Markov


6

A.1.3 Importance Decomposition of Flows by Educational Group and Gender

Figure A7: AB1C Decomposition of the Importance of Flows: Unemployment, Males, Non-College

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.231

EU

0.0

5.1

.15

.2.2

5

Pro

b

20 30 40 50 60 70

age

1−R2=0.296

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.020

UE

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.074

UO

0.0

5.1

.15

.2.2

5

Pro

b

20 30 40 50 60 70

age

1−R2=0.030

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.810

OU

actual AB1C, mean


7

Figure A8: AB1C Decomposition of the Importance of Flows: Unemployment, Males, College

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.133

EU

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.688

EO

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.066

UE

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.061

UO

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.084

OE

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.644

OU

actual AB1C, mean


Figure A9: AB1C Decomposition of the Importance of Flows: Participation, Males, Non-College

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.013

EU

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.112

EO.2

.4.6

.81

Pro

b

20 30 40 50 60 70

age

1−R2=0.020

UE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.010

UO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.177

OE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.097

OU

actual AB1C, mean


8

Figure A10: AB1C Decomposition of the Importance of Flows: Participation, Males, College

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.007

EU

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.194

EO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.015

UE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.008

UO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.205

OE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.050

OU

actual AB1C, mean


Figure A11: AB1C Decomposition of the Importance of Flows: Unemployment, Females, Non-College

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.112

EU

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.177

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.010

UE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.038

UO

0.0

5.1

.15

.2.2

5

Pro

b

20 30 40 50 60 70

age

1−R2=0.027

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.953

OU

actual AB1C, mean


9

Figure A12: AB1C Decomposition of the Importance of Flows: Unemployment, Females, College

.04

.06

.08

.1.1

2.1

4

Pro

b

20 30 40 50 60 70

age

1−R2=0.169

EU

.02

.04

.06

.08

.1.1

2

Pro

b

20 30 40 50 60 70

age

1−R2=0.493

EO

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

1−R2=0.031

UE

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.089

UO

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.079

OE

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.844

OU

actual AB1C, mean


Figure A13: AB1C Decomposition of the Importance of Flows: Participation, Females, Non-College

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.013

EU

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.114

EO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.020

UE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.011

UO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.306

OE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.125

OU

actual AB1C, mean


10

Figure A14: AB1C Decomposition of the Importance of Flows: Participation, Females, College

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.010

EU.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.244

EO

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.017

UE

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.010

UO

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.332

OE.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.048

OU

actual AB1C, mean


11

A.2 Conditional Analysis by Marital Status and Gender

In this subsection, we consider married and non-married individuals.

A.2.1 Estimated Flows and Stocks by Marital Status and Gender

Figure A15: Life-Cycle Unemployment and Participation Profiles: Males, Non-Married vs Married.0

5.1

.15

.2.2

5

Pro

b

20 30 40 50 60 70

age

U

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

P

no married male married male


12

Figure A16: Life-Cycle Unemployment and Participation Profiles: Females, Non-Married vs Mar-ried

0.1

.2.3

.4

Pro

b

20 30 40 50 60 70

age

U

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

P

no married female married female


Figure A17: Life-Cycle Profiles of Worker Flows Transitions: Males, Non-Married vs Married

0.0

2.0

4.0

6

Pro

b

20 30 40 50 60 70

age

EU

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

EO

.15

.2.2

5.3

.35

Pro

b

20 30 40 50 60 70

age

UE

.1.2

.3.4

.5

Pro

b

20 30 40 50 60 70

age

UO

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

OE

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

OU

no married male married male


13

Figure A18: Life-Cycle Profiles of Worker Flows Transitions: Females, Non-Married vs Married

0.0

2.0

4.0

6.0

8

Pro

b

20 30 40 50 60 70

age

EU0

.05

.1.1

5.2

Pro

b

20 30 40 50 60 70

age

EO

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

UE

.2.2

5.3

.35

.4.4

5

Pro

b

20 30 40 50 60 70

age

UO

.02

.04

.06

.08

.1.1

2

Pro

b

20 30 40 50 60 70

age

OE0

.02

.04

.06

.08

Pro

b

20 30 40 50 60 70

age

OU

no married female married female


14

A.2.2 Markovian Simulations by Marital Status and Gender

Figure A19: Markov-Chain Simulated Unemployment and Participation: Non-Married

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=0.972

U males

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

R2=0.986

P males

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=0.970

U females

.2.4

.6.8

Pro

b20 30 40 50 60 70

age

R2=0.982

P females

actual Markov


15

Figure A20: Markov-Chain Simulated Unemployment and Participation: Married

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

R2=0.963

U males

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

R2=0.966

P males

0.1

.2.3

.4

Pro

b

20 30 40 50 60 70

age

R2=0.985

U females

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

R2=0.975

P females

actual Markov


16

A.2.3 Importance decomposition of Flows by Marital Status and Gender

Figure A21: AB1C Decomposition of the Importance of Flows: Unemployment, Males, Non-Married

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.262

EU

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.439

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.051

UE

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.139

UO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.094

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.857

OU

actual AB1C, mean


A.3 Conditional Analysis by Child Presence and Gender

In this subsection, we consider individuals with and without at least a child in their households.

A.3.1 Estimated Flows and Stocks by Child Presence and Gender

17

Figure A22: AB1C Decomposition of the Importance of Flows: Unemployment, Males, Married

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.376

EU

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.227

EO

.05

.1.1

5.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.064

UE

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.077

UO

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.083

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.797

OU

actual AB1C, mean


Figure A23: AB1C Decomposition of the Importance of Flows: Participation, Males, Non-Married

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.009

EU

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.104

EO.2

.4.6

.81

Pro

b

20 30 40 50 60 70

age

1−R2=0.016

UE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.007

UO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.262

OE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.077

OU

actual AB1C, mean


18

Figure A24: AB1C Decomposition of the Importance of Flows: Participation, Males, Married

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.026

EU

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.085

EO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.038

UE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.029

UO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.171

OE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.089

OU

actual AB1C, mean


Figure A25: AB1C Decomposition of the Importance of Flows: Unemployment, Females, Non-Married

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.157

EU

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.404

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.036

UE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.091

UO

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.075

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.878

OU

actual AB1C, mean


19

Figure A26: AB1C Decomposition of the Importance of Flows: Unemployment, Females, Married

0.1

.2.3

.4

Pro

b

20 30 40 50 60 70

age

1−R2=0.043

EU

0.1

.2.3

.4

Pro

b

20 30 40 50 60 70

age

1−R2=0.070

EO

0.1

.2.3

.4

Pro

b

20 30 40 50 60 70

age

1−R2=0.019

UE

0.1

.2.3

.4

Pro

b

20 30 40 50 60 70

age

1−R2=0.023

UO

0.1

.2.3

.4

Pro

b

20 30 40 50 60 70

age

1−R2=0.031

OE

0.1

.2.3

.4

Pro

b

20 30 40 50 60 70

age

1−R2=0.498

OU

actual AB1C, mean


Figure A27: AB1C Decomposition of the Importance of Flows: Participation, Females, Non-Married

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.012

EU

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.163

EO

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.021

UE

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.011

UO

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.343

OE

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.106

OU

actual AB1C, mean


20

Figure A28: AB1C Decomposition of the Importance of Flows: Participation, Females, Married

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.014

EU

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.083

EO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.027

UE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.016

UO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.208

OE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.115

OU

actual AB1C, mean


Figure A29: Life-Cycle Unemployment and Participation Profiles: Males, No-Child vs Child

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

U

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

P

no child male child male


21

Figure A30: Life-Cycle Unemployment and Participation Profiles: Females, No-Child vs Child

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

U

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

P

no child female child female


Figure A31: Life-Cycle Profiles of Worker Flows Transitions: Males, No-Child vs Child

.01

.02

.03

.04

.05

Pro

b

20 30 40 50 60 70

age

EU

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

EO

.15

.2.2

5.3

.35

Pro

b

20 30 40 50 60 70

age

UE

.1.2

.3.4

.5

Pro

b

20 30 40 50 60 70

age

UO

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OE

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OU

no child male child male


22

Figure A32: Life-Cycle Profiles of Worker Flows Transitions: Females, No-Child vs Child

.01

.02

.03

.04

.05

Pro

b

20 30 40 50 60 70

age

EU0

.05

.1.1

5.2

Pro

b

20 30 40 50 60 70

age

EO

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

UE

.2.3

.4.5

Pro

b

20 30 40 50 60 70

age

UO

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OE0

.02

.04

.06

.08

Pro

b

20 30 40 50 60 70

age

OU

no child female child female


23

A.3.2 Markovian Simulations by Marital Status and Gender

Figure A33: Markov-Chain Simulated Unemployment and Participation: No-Child

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

R2=0.985

U males

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

R2=0.971

P males

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

R2=0.984

U females

.2.4

.6.8

Pro

b20 30 40 50 60 70

age

R2=0.967

P females

actual Markov


24

Figure A34: Markov-Chain Simulated Unemployment and Participation: Child

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=0.992

U males

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

R2=0.984

P males

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=0.979

U females

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

R2=0.983

P females

actual Markov


25

A.3.3 Importance Decomposition of Flows by Child Status and Gender

Figure A35: AB1C Decomposition of the Importance of Flows: Unemployment, Males, No-Child

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.100

EU

0.1

.2.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.159

EO

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.034

UE

0.1

.2.3

.4

Pro

b

20 30 40 50 60 70

age

1−R2=0.080

UO

0.1

.2.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.047

OE

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.527

OU

actual AB1C, mean


26

Figure A36: AB1C Decomposition of the Importance of Flows: Unemployment, Males, Child

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.157

EU

0.0

5.1

.15

.2.2

5

Pro

b

20 30 40 50 60 70

age

1−R2=0.288

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.017

UE

0.1

.2.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.055

UO

0.0

5.1

.15

.2.2

5

Pro

b

20 30 40 50 60 70

age

1−R2=0.041

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.478

OU

actual AB1C, mean


Figure A37: AB1C Decomposition of the Importance of Flows: Participation, Males, No-Child

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.020

EU

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.113

EO.2

.4.6

.81

Pro

b

20 30 40 50 60 70

age

1−R2=0.031

UE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.016

UO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.190

OE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.098

OU

actual AB1C, mean


27

Figure A38: AB1C Decomposition of the Importance of Flows: Participation, Males, Child

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.009

EU

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.135

EO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.017

UE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.008

UO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.194

OE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.081

OU

actual AB1C, mean


Figure A39: AB1C Decomposition of the Importance of Flows: Unemployment, Females, No-Child

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.047

EU

0.1

.2.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.147

EO.0

5.1

.15

.2.2

5.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.034

UE

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.045

UO

0.1

.2.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.058

OE

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.638

OU

actual AB1C, mean


28

Figure A40: AB1C Decomposition of the Importance of Flows: Unemployment, Females, Child

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.112

EU

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.195

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.016

UE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.058

UO

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.050

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.842

OU

actual AB1C, mean


Figure A41: AB1C Decomposition of the Importance of Flows: Participation, Females, No-Child

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.023

EU

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.069

EO.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.035

UE

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.021

UO

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.251

OE

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.113

OU

actual AB1C, mean


29

Figure A42: AB1C Decomposition of the Importance of Flows: Participation, Females, Child

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.013

EU0

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.191

EO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.019

UE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.012

UO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.326

OE0

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.085

OU

actual AB1C, mean


30

B Effects of the Great Recession

In this Section, we show in greater detail results for the analysis of the Great Recession. To do so,

we report results from our analysis for unconditional transition probabilities, unemployment, and

participation rates before and after January 2007.

B.1 Estimated Flows and Stocks, Before and After 2007

Figure A43: Life-Cycle Unemployment and Participation Profiles: Males, Before and After 2007

0.1

.2.3

Pro

b

20 30 40 50 60 70

age

u male

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

p male

2007−> <−2007


31

Figure A44: Life-Cycle Unemployment and Participation Profiles: Females, Before and After 2007

0.1

.2.3

Pro

b

20 30 40 50 60 70

age

U female

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

P female

2007−> <−2007


Figure A45: Life-Cycle Profiles of Worker Flows Transitions: Males, Before and After 2007

.01

.02

.03

.04

.05

Pro

b

20 30 40 50 60 70

age

EU male

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

EO male.1

.15

.2.2

5.3

Pro

b

20 30 40 50 60 70

age

UE male

.1.2

.3.4

.5

Pro

b

20 30 40 50 60 70

age

UO male

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OE male

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OU male

2007−> <−2007


32

Figure A46: Life-Cycle Profiles of Worker Flows Transitions: Males, Before and After 2007

0.0

1.0

2.0

3.0

4

Pro

b

20 30 40 50 60 70

age

EU female

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

EO female

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

UE female

.2.3

.4.5

Pro

b

20 30 40 50 60 70

age

UO female

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OE female

0.0

2.0

4.0

6.0

8

Pro

b

20 30 40 50 60 70

age

OU female

2007−> <−2007


B.2 Markov Chain Analysis

33

Figure A47: Markov-Chain Simulated Unemployment and Participation: Before 2007

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=0.994

U males

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

R2=0.985

P males

0.2

Pro

b

20 30 40 50 60 70

age

R2=0.987

U females

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

R2=0.985

P females

actual Markov


Figure A48: Markov-Chain Simulated Unemployment and Participation: After 2007

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

R2=0.980

U males

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

R2=0.980

P males

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=0.973

U females

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

R2=0.975

P females

actual Markov


34

B.3 Importance decomposition of Flows, before and after 2007

Figure A49: AB1C Decomposition of the Importance of Flows: Unemployment, Males, Before 2007

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.140

EU

0.0

5.1

.15

.2.2

5

Pro

b

20 30 40 50 60 70

age

1−R2=0.252

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.021

UE

0.1

.2.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.058

UO

0.0

5.1

.15

.2.2

5

Pro

b

20 30 40 50 60 70

age

1−R2=0.034

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.657

OU

actual AB1C, mean


35

Figure A50: AB1C Decomposition of the Importance of Flows: Unemployment, Males, After 2007

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.102

EU

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.308

EO

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.031

UE

0.1

.2.3

.4

Pro

b

20 30 40 50 60 70

age

1−R2=0.076

UO

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.104

OE

0.1

.2.3

.4

Pro

b

20 30 40 50 60 70

age

1−R2=0.424

OU

actual AB1C, mean


Figure A51: AB1C Decomposition of the Importance of Flows: Participation, Males, Before 2007

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.009

EU

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.142

EO.2

.4.6

.81

Pro

b

20 30 40 50 60 70

age

1−R2=0.017

UE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.008

UO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.195

OE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.073

OU

actual AB1C, mean


36

Figure A52: AB1C Decomposition of the Importance of Flows: Participation, Males, After 2007

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.012

EU

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.088

EO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.022

UE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.009

UO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.187

OE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.099

OU

actual AB1C, mean


B.4 Role of Inactivity, Before and After 2007

37

Figure A53: AB1C Decomposition of the Importance of Flows: Unemployment, Females, Before2007

0.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.082

EU

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.172

EO

0.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.017

UE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.044

UO

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.045

OE

0.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.874

OU

actual AB1C, mean


Figure A54: AB1C Decomposition of the Importance of Flows: Unemployment, Females, After2007

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.080

EU

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.288

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.028

UE

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.077

UO

0.1

.2.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.098

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.616

OU

actual AB1C, mean


38

Figure A55: AB1C Decomposition of the Importance of Flows: Participation, Females, Before 2007

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.010

EU

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.132

EO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.017

UE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.010

UO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.291

OE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.083

OU

actual AB1C, mean


Figure A56: AB1C Decomposition of the Importance of Flows: Participation, Females, After 2007

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.018

EU

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.144

EO

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.028

UE

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.014

UO

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.288

OE

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.099

OU

actual AB1C, mean


39

Figure A57: AB2 Decomposition Neglecting Inactivity for Life-Cycle Unemployment Rates: Males,Before 2007

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

actual AB2C, EU,UE mean

1−R2=0.079

.05

.1.1

5.2

.25

Pro

b20 30 40 50 60 70

age

actual AB2C, EU,UE zero

1−R2=0.036

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

actual AB2F mean, but EU,UE

R2=0.950

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

actual AB2F zero, but EU,UE

R2=0.985


40

Figure A58: AB2 Decomposition Neglecting Inactivity for Life-Cycle Unemployment Rates: Males,After 2007

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age


1−R2=0.063

.05

.1.1

5.2

.25

.3

Pro

b20 30 40 50 60 70

age


1−R2=0.045

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age


R2=0.921

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age


R2=0.977


41

Figure A59: AB2 Decomposition Neglecting Inactivity for Life-Cycle Unemployment Rates: Fe-males, Before 2007

0.2

Pro

b

20 30 40 50 60 70

age


1−R2=0.045

.05

.1.1

5.2

.25

Pro

b20 30 40 50 60 70

age


1−R2=0.043

0.2

Pro

b

20 30 40 50 60 70

age


R2=0.850

0.2

Pro

b

20 30 40 50 60 70

age


R2=0.965


42

Figure A60: AB2 Decomposition Neglecting Inactivity for Life-Cycle Unemployment Rates: Fe-males, After 2007

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age


1−R2=0.042

.05

.1.1

5.2

.25

.3

Pro

b20 30 40 50 60 70

age


1−R2=0.055

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age


R2=0.849

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age


R2=0.954


43

C Results for Ages 30 to 50

In this section, we present results restricted for individuals between 30 and 50 years old. Since

the common wisdom seems to be that inactivity matters mostly for younger and older individuals,

restricting our analysis to this population shows how robust our findings are. The fact that annual

probability transitions are close to limit ones (due to large transition probabilities, see section D,

a priori one expect that our results for ages 30-50 to be hardly different from those covering the

baseline range (ages 16-70). Figure A61 shows the results of simulating the unemployment profile

for ages 30 to 50, which gives us comparable accuracy to our baseline results. The results for

participation are slightly less successful.

Figure A61: Markov-Chain Simulated Unemployment and Participation: Age 30-50

.04.

045.

05.0

55.0

6

Pro

b

30 35 40 45 50

age

R2=0.965

U males age 30−50

.88

.9.9

2.9

4

Pro

b

30 35 40 45 50

age

R2=0.981

P males age 30−50

.04.0

45.0

5.055

.06.0

65

Pro

b

30 35 40 45 50

age

R2=0.982

U females age 30−50

.7.7

2.7

4.7

6

Pro

b

30 35 40 45 50

age

R2=0.895

P females age 30−50

actual Markov 30−50


C.1 Decomposition Exercises, Ages 30 to 50

Below we perform the AB1C decomposition for the range 30 to 50. Figures 21 and 22 show the

results. In line with our previous analysis for the whole age range, the separation probability (EU)

is the most important factor for males, while the OU probability is still important, especially for

workers over 40. In contrast, the job finding probability does not affect the male profile by much.

For females, the most important flow is OU , seconded by the EU and EO probabilities. Hence,

although the importance of inactivity related flows decays in the prime-age group, they are still

important for shaping unemployment profiles. The belief that inactivity flows only matter for

44

younger and older workers is not supported by this evidence.

Figures A64 and A65 show the impact of each particular flow on the participation rate over

ages 30 to 50. For males, the most important flow is the OE, while for females, both EO and OE

are important to explain the profiles.

In sum, while the importance of inactivity related flows is decreased for prime-aged workers,

flows in and out of inactivity still have a substantial influence on unemployment and participation

over the life cycle for this group.

Figure A62: AB1C Decomposition of the Importance of Flows: Unemployment, Males, Age 30-50

.04

.045

.05

.055

.06

Pro

b

30 35 40 45 50

age

1−R2=0.862

EU

.04

.045

.05

.055

.06

Pro

b

30 35 40 45 50

age

1−R2=0.060

EO

.04

.045

.05

.055

.06

Pro

b

30 35 40 45 50

age

1−R2=0.085

UE

.04

.045

.05

.055

.06

Pro

b

30 35 40 45 50

age

1−R2=0.049

UO

.04

.045

.05

.055

.06

Pro

b

30 35 40 45 50

age

1−R2=0.102

OE

.04

.045

.05

.055

.06

Pro

b

30 35 40 45 50

age

1−R2=0.289

OU

actual AB1C, mean


45

Figure A63: AB1C Decomposition of the Importance of Flows: Unemployment, Females, Age 30-50

.04

.045

.05

.055

.06

.065

Pro

b

30 35 40 45 50

age

1−R2=0.120

EU

.04

.045

.05

.055

.06

.065

Pro

b

30 35 40 45 50

age

1−R2=0.094

EO

.04

.045

.05

.055

.06

.065

Pro

b

30 35 40 45 50

age

1−R2=0.030

UE

.04.

045.

05.0

55.0

6.06

5

Pro

b

30 35 40 45 50

age

1−R2=0.017

UO

.04.

045.

05.0

55.0

6.06

5

Pro

b

30 35 40 45 50

age

1−R2=0.036

OE

.04

.045

.05

.055

.06

.065

Pro

b

30 35 40 45 50

age

1−R2=0.140

OU

actual AB1C, mean


Figure A64: AB1C Decomposition of the Importance of Flows: Participation, Males, Age 30-50

.88

.9.9

2.9

4

Pro

b

30 35 40 45 50

age

1−R2=0.014

EU

.89

.9.9

1.9

2.9

3.9

4

Pro

b

30 35 40 45 50

age

1−R2=0.033

EO.8

9.9

.91

.92

.93

.94

Pro

b

30 35 40 45 50

age

1−R2=0.024

UE

.89

.9.9

1.9

2.9

3.9

4

Pro

b

30 35 40 45 50

age

1−R2=0.022

UO

.89

.9.9

1.9

2.9

3.9

4

Pro

b

30 35 40 45 50

age

1−R2=0.278

OE

.89

.9.9

1.9

2.9

3.9

4

Pro

b

30 35 40 45 50

age

1−R2=0.078

OU

actual AB1C, mean


46

Figure A65: AB1C Decomposition of the Importance of Flows: Participation, Females, Age 30-50

.71

.72

.73

.74

.75

.76

Pro

b

30 35 40 45 50

age

1−R2=0.061

EU.7

.72

.74

.76

Pro

b

30 35 40 45 50

age

1−R2=0.861

EO

.7.7

2.7

4.7

6

Pro

b

30 35 40 45 50

age

1−R2=0.125

UE

.7.7

2.7

4.7

6

Pro

b

30 35 40 45 50

age

1−R2=0.088

UO

.7.7

2.7

4.7

6

Pro

b

30 35 40 45 50

age

1−R2=0.447

OE.7

.72

.74

.76

Pro

b

30 35 40 45 50

age

1−R2=0.253

OU

actual AB1C, mean


47

D Alternative Decomposition Methods

Our decomposition method is similar to the one used by Pissarides (1986) and Shimer (2012).

More specifically, unemployment and labour force participation approximations in the latter are

the result of iterating the Markov chains an infinite number of times. Labour states obtained from

twelve months of transitions (to simulate one year in the life of a worker) with empirical transition

probabilities are not very different from the Markov chain limit. In most cases, the approximation is

accurate so that we can construct theoretical counterparts to the observed proportion of individuals

in each of the three considered states {e, u, o} at age a using the Markov chain limit. Therefore,

the approximation at any age a can be constructed by solving the following linear system1

(EUa + EOa) Ea = UEaUa +OEaOa

(UEa + UOa) Ua = EUaEa +OUaOa

(OEa +OUa) Oa = EOaEa + UOaUa

The interpretation of these equations is straightforward. The left hand side of these equations

represent the flow of individuals transiting away from states {e, u, o} respectively, at the end of age

a. The right hand side accounts for the number of individuals transiting into those same states.

These two numbers must be the same, assuming a stationary age-specific population structure and

stationary transition probabilities xza. Solving for the states, we get functional forms that relate

them to age specific transition rates only.

Ea = E(UEa, UOa, OEa, OUa)

Ua = U(EUa, EOa, OEa, OUa)

Oa = O(EUa, EOa, UEa, UOa)

accordingly, we can construct these “theoretical” counterparts for participation (pa = 1− Oa) and

unemployment rates (ua = Ua/(Ea + Ua)) using the above equations and our estimates of {XZa}:

ua ≈ ua =Ua

Ua + Ea

=OEaEUa +OUa(EUa +EOa)

OEa(UOa + EUa) + UEa(OEa +OUa) +OUa(EUa + EOa)

pa ≈ pa

= 1− Oa =UEa(OEa +OUa) +OEa(UOa + EUa) +OUa(EUa + EOa)

UEaEOa + EOaUOa + UOaEUa + UEa(OEa +OUa) +OEa(UOa + EUa) +OUa(EUa + EOa)

In Figure A66 we plot the observed versus theoretical (constructed) rates, for both men and women.

As seen from the Figure, the theoretical rates follow closely their observed counterparts and pose

a reasonable approximation to the observed profiles. Notice that in order to calculate stocks

1The limiting labour states e, u, o are just the normalized eigenvector (so that its components add up to 1)associated to an eigenvalue of value 1.

48

Figure A66: Limit Markov-Chain Simulated Unemployment and Participation

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=.995

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

R2=.991

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=.99

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

R2=.994

actual lim Markov


of unemployed, employed and inactive workers, the method above does not rely on initial condi-

tions/distribution of workers across employment states but only age-specific transition probabilities.

The goodness of fit of the theoretical rates is due to high monthly transition probabilities, which

dwarfs the effect of initial conditions.

Given that theoretical participation and unemployment rates depend only on age-specific transi-

tion probabilities, we can assess their relative importance in explaining aggregate life-cycle profiles.

Using the same logic as in the “all but one change” (AB1C) method,2 we compute the limiting

states at each age by using our estimates XZa. However, we keep fixed a particular transition

probability at its mean life-cycle value, one at a time, and we allow the rest of them to change

according to age. We present these decompositions for unemployment and participation in Figures

A67 to A70 below.

When comparing the results from this “limit” method to the ones we see in the Markov chain

analysis, we get roughly identical results. In terms of participation, the most important transition

2This is in contrast to what Shimer (2012) does in the context of a business cycle decomposition. He fixes alltransition probabilities at their mean and changes only one, what we labeled the AB1F method above.

49

Figure A67: Limit AB1C Decomposition of the Importance of Flows: Unemployment, Males

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

actual EU at mean

1−R2=0.196

0.0

5.1

.15

.2.2

5

Pro

b

20 30 40 50 60 70

age

actual EO at mean

1−R2=0.445

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

actual UE at mean

1−R2=0.017

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

actual UO at mean

1−R2=0.060

0.0

5.1

.15

.2.2

5

Pro

b

20 30 40 50 60 70

age

actual OE at mean

1−R2=0.044

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

actual OU at mean

1−R2=0.821


probability is the one from employment to inactivity EO. If this transition probability were to be

constant throughout the life-cycle, the participation profile would be flatter. The EO probability is

very important to determine early and late life employment status. Also, movements from inactivity

into the labour force (both OE and OU probabilities) determine to a great extent unemployment

after the age of 60.

As for the life-cycle profile of the unemployment rate, again the EO probability plays an impor-

tant role, followed by the EU as well as the OU transition probabilities. The job finding probability

(UE) does not affect differences in life-cycle participation and unemployment significantly. It turns

out that the transitions into and out from the labour force are quite important in shaping unem-

ployment and participation rates life-cycle profiles.

These results contrasts to Shimer (2012) and Fujita and Ramey (2009) findings in relation to the

business cycle. These authors show that UE and EU flows are enough to account for the cyclical

fluctuations of the unemployment rate. For life-cycle analysis, our evidence shows that inactivity

transitions are key to understand the unemployment and participation by age.

50

Figure A68: Limit AB1C Decomposition of the Importance of Flows: Unemployment, Females

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

actual EU at mean

1−R2=0.077

0.2

Pro

b

20 30 40 50 60 70

age

actual EO at mean

1−R2=0.338

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

actual UE at mean

1−R2=0.012

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

actual UO at mean

1−R2=0.039

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

actual OE at mean

1−R2=0.032

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

actual OU at mean

1−R2=0.984


51

Figure A69: Limit AB1C Decomposition of the Importance of Flows: Participation, Males

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

actual EU at mean

1−R2=0.018

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

actual EO at mean

1−R2=0.238

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

actual UE at mean

1−R2=0.007

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

actual UO at mean

1−R2=0.016

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

actual OE at mean

1−R2=0.072

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

actual OU at mean

1−R2=0.009


52

Figure A70: Limit AB1C Decomposition of the Importance of Flows: Participation, Females

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

actual EU at mean

1−R2=0.013

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

actual EO at mean

1−R2=0.249

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

actual UE at mean

1−R2=0.005

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

actual UO at mean

1−R2=0.011

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

actual OE at mean

1−R2=0.143

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

actual OU at mean

1−R2=0.013


53

E Introducing Controls

In this appendix, we present the same figures as in the body of the paper, but here our estimates

include different sets of controls in matrix D below:

fXZatc

√NX

atc =

A∑

a=1

XZaDatc

√NX

atc + βWatc

√NX

atc + ǫatc

√NX

atc (A1)

When we control for time, cohort and state effects, our conclusions remain as in the main body

of the paper. Adding educational attainment dummies to our estimations do not change our results

either, as hinted by the exercise in the paper where we separate samples by educational group.

It is important to consider the interaction between cohort and time effects. In particular, we find

it crucial for isolating the life-cycle component for the post Great Recession period (2007 onwards).

This suggests potentially different effects of the business cycle on different cohorts of the population,

possible due to schooling quality or vintage human capital differences across generations. Once we

properly take these issues into account, our estimated life-cycle profiles are very similar to the

results using unconditional estimates reported in the body of the paper.

E.1 Controlling for Cohort, Time, and State (CTS)

Figure A71: Life-Cycle Unemployment and Participation Profiles, Control CTS

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

U

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

P

male female

Note: Life-cycle profiles controlled for 5-year cohort effects, 4th-order polynominal cohort-specific time trends, seasonalmonthly dummies, and state effects, estimated via weighted OLS.

54

Figure A72: Life-Cycle Profiles of Worker Flows Transitions, Males, Control CTS

0.0

1.0

2.0

3.0

4.0

5

Pro

b

20 30 40 50 60 70

age

EU male

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

EO male

.15

.2.2

5.3

.35

Pro

b

20 30 40 50 60 70

age

UE male

.1.2

.3.4

.5

Pro

b

20 30 40 50 60 70

age

UO male

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OE male

0.0

5.1

Pro

b

20 30 40 50 60 70

age

OU male

CI 95% Prob


Figure A73: Life-Cycle Profiles of Worker Flows Transitions, Females, Control CTS

0.0

1.0

2.0

3.0

4

Pro

b

20 30 40 50 60 70

age

EU female

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

EO female

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

UE female

.2.3

.4.5

Pro

b

20 30 40 50 60 70

age

UO female

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OE female

0.0

2.0

4.0

6.0

8

Pro

b

20 30 40 50 60 70

age

OU female

CI 95% Prob


55

Figure A74: Markov-Chain Simulated Unemployment and Participation, Control CTS

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=0.938

U males

.2.4

.6.8

1

Pro

b20 30 40 50 60 70

age

R2=0.956

P males

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=0.984

U females

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

R2=0.984

P females

actual Markov


56

Figure A75: AB1C Decomposition of the Importance of Flows: Unemployment, Males, ControlCTS

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.329

EU

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.251

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.045

UE

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.144

UO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.026

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.509

OU

actual AB1C, mean


57

Figure A76: AB1C Decomposition of the Importance of Flows: Unemployment, Females, ControlCTS

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.066

EU

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.201

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.017

UE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.049

UO

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.060

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.876

OU

actual AB1C, mean


Figure A77: AB1C Decomposition of the Importance of Flows: Participation, Males, Control CTS

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.032

EU

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.163

EO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.046

UE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.031

UO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.220

OE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.105

OU

actual AB1C, mean


58

Figure A78: AB1C Decomposition of the Importance of Flows: Participation, Females, ControlCTS

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.011

EU0

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.127

EO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.016

UE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.008

UO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.312

OE0

.2.4

.6.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.084

OU

actual AB1C, mean


59

F Time-Aggregation Results

F.1 Method

Following Shimer (2013) and Elsby, Hobijn, and Sahin (2013), we perform a simple transformation

for each one of matrices Γi, which contain monthly transition probabilities between labour mar-

ket states for each age i. As shown by Shimer (2013), if all eigenvalues of Γi are real, positive

and distinct (as is the case for each considered age in our data), then matrix Γi, containing the

instantaneous transition rates between each labour market state, can be recovered by a simple

eigenvalue/eigenvector transformation: Γi = PiΛiP−1i

, where Pi is the matrix of eigenvectors of

matrix Γi; Λi is the diagonal matrix with eigenvalues of Γi; finally, Λi is the same as Λi, but its

elements are replaced by natural logarithms of the original eigenvalues in Λi. Finally, we use tran-

sition probabilities instead of transition rates, by computing XZ = 1 − exp(−fXZ) where fXZ is

element (X,Z) of matrix Γi. We report monthly time-aggregation corrected transition probabilities

in Figures A79 to A80.

F.2 Robustness of AB1C method:

Given the eigenvalues Λi and the relationship between instantaneous and discrete time transition

probabilities, we can construct transition probabilities for any arbitrary length of time: instead of

month-to-month transitions, we can compute transitions for weeks, days, hours, etc. Note that the

monthly transition probability between states j and k is given by

XZ = 1− exp(−fXZ∆t)

where ∆t = 1 represents one month. Thus, to obtain a weekly, daily or hourly transition probability,

we just have to make ∆t = 7/30, ∆t = 1/30 or ∆t = 1/(24 ∗ 30) respectively. We report the

AB1C decomposition for monthly adjusted transition probabilities in Figures A86 to A85. The

corrected transition probabilities are used to simulate the life-cycle profiles of Unemployment and

Participation of a cohort. While some corrected flows are very different from the unadjusted ones,

the Markovian process accurately predicts stocks of labour statuses only using flows (see Figure

A81).

As we shorten the frequency of transitions, the resulting transition probability matrices (for

example, one per every hour of a 55 year life-cycle in the case of hourly transitions between 16 and

70 years of age) become almost diagonal, since the probability of transiting out of the current labour

force status during the next hour is close to zero. Thus, performing our AB1C decomposition on

these higher frequency transitions gives us an almost ceteris paribus decomposition exercise: since

the relative perturbation to diagonal elements of the matrix when replacing some off-diagonal and

age-specific transition probability with its life-cycle mean becomes minimal. These results are

60

shown in figures A86 to A89 for the hourly case.

Finally, we replicate the AB2 decompositions to understand the effects of omitting inactivity

in the unemployment and participation life-cycle profiles. Using both monthly and hourly time-

aggregation corrected probabilities, we obtain very similar results to those of Section 5 in the main

text. We show the robustness of the AB1C decomposition and confirm the results in the main body

of the paper.

F.3 Monthly Corrected Flows and Simulations

Figure A79: Life-Cycle Profiles of Worker Flows Transitions: Males, Corrected for Time-Aggregation

.01

.02

.03

.04

.05

.06

Pro

b

20 30 40 50 60 70

age

EU male

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

EO male

.15

.2.2

5.3

.35

Pro

b

20 30 40 50 60 70

age

UE male

.1.2

.3.4

.5

Pro

b

20 30 40 50 60 70

age

UO male

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OE male

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OU male

raw ta

Note: Unconditional life-cycle profiles estimated via weighted OLS.Corrected for Time-Aggregation following Shimer (2013).

61

Figure A80: Life-Cycle Profiles of Worker Flows Transitions: Females, Corrected for Time-Aggregation

.01

.02

.03

.04

.05

Pro

b

20 30 40 50 60 70

age

EU female

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

EO female

.15

.2.2

5.3

.35

Pro

b

20 30 40 50 60 70

age

UE female

.2.3

.4.5

.6

Pro

b

20 30 40 50 60 70

age

UO female

0.0

5.1

Pro

b

20 30 40 50 60 70

age

OE female

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OU female

raw ta


Figure A81: Markov-Chain Simulated Unemployment and Participation: Corrected for Time-Aggregation

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=0.981

U males

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

R2=0.987

P males

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=0.972

U females

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

R2=0.988

P females

actual Markov


62

F.4 AB1C Decomposition with TA Corrected Probabilities in Various Frequencies

Figure A82: AB1C Decomposition of the Importance of Flows: Unemployment, Males, TA Monthly

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.125

EU

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.251

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.039

UE.0

5.1

.15

.2.2

5.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.064

UO0

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.055

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.591

OU

actual AB1C, mean


63

Figure A83: AB1C Decomposition of the Importance of Flows: Unemployment, Females, TAMonthly

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.063

EU

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.161

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.049

UE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.060

UO

0.0

5.1

.15

.2.2

5

Pro

b

20 30 40 50 60 70

age

1−R2=0.072

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.822

OU

actual AB1C, mean


64

Figure A84: AB1C Decomposition of the Importance of Flows: Participation, Males, TA Monthly

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.006

EU

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.132

EO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.017

UE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.005

UO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.168

OE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.115

OU

actual AB1C, mean


65

Figure A85: AB1C Decomposition of the Importance of Flows: Participation, Females, TA Monthly

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.006

EU

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.146

EO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.016

UE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.004

UO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.235

OE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.137

OU

actual AB1C, mean


66

Figure A86: AB1C Decomposition of the Importance of Flows: Unemployment, Males, TA Hourly

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.156

EU

0.0

5.1

.15

.2.2

5

Pro

b

20 30 40 50 60 70

age

1−R2=0.289

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.025

UE

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.055

UO

0.0

5.1

.15

.2.2

5

Pro

b

20 30 40 50 60 70

age

1−R2=0.038

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.599

OU

actual AB1C, mean


67

Figure A87: AB1C Decomposition of the Importance of Flows: Unemployment, Females, TA Hourly

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.092

EU

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.212

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.028

UE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.047

UO

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.047

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.829

OU

actual AB1C, mean


68

Figure A88: AB1C Decomposition of the Importance of Flows: Participation, Males, TA Hourly

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.008

EU

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.126

EO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.022

UE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.005

UO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.182

OE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.119

OU

actual AB1C, mean


69

Figure A89: AB1C Decomposition of the Importance of Flows: Participation, Females, TA Hourly

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.010

EU

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.139

EO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.024

UE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.006

UO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.265

OE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.144

OU

actual AB1C, mean


70

G Misclassification (MC) Results

G.1 Method

It is well known in the literature that the estimation of transition probabilities from flow data

is sensitive to misclassification (MC) error in recorded labour market states. Given our main

results (i.e., importance of the participation margin for explaining life-cycle unemployment and

participation) any evidence of serious MC error might put in doubt our results, especially since

Unemployment (U) and Out of the labour force (O) are states more likely to be coded with error.3

In this section we analyze the effects of MC error by using two alternative corrections proposed

by the literature: The first approach, follows closely Feng and Hu (2013) (henceforth FH), who use

a latent variable approach to estimate the probability of misclassification of the current true labour

force state, given observed individual histories. The method in FH requires the use of individual

longitudinal information on labour force states, which is available from the CPS since individuals

are followed a total of 8 months (two sets of four consecutive months, separated by an eight month

hiatus). We apply the FH method using labour force histories of three consecutive months, from

where we extract the age-specific joint probability of transiting through specific paths: For example,

given observed information for individuals aged i during months t− 1, t and t+1, we can compute

the set of joint probabilities

Pr(st−1 = j, st = k, st+1 = l)

with {j, k, l} ∈ {E,U,O}. In words, for each age group, we can calculate the fraction who, for

example, transited from employment in period t− 1, to employment in period t and eventually to

unemployment in period t+ 1; i.e., history EEU has a related probability Pr(Et−1, Et, Ut+1) over

all those individuals who have non-missing information for those three consecutive months. The

FH method uses a combination of these probabilities to compute Pr(s∗t |st) with s ∈ {E,U,O} (the

probability of the true labour force state being s∗t given reported state st) through an eigenvalue-

eigenvector decomposition of a suitable arrangement of the above mentioned probabilities into

matrices. Then, using results from Poterba and Summers (1986), one can compute worker flows and

transition probabilities using the true state probabilities. We depart from the FH methodology in

two ways: we use information from three consecutive months, while they use information on months

t− 9, t and t+1. We make this choice in order to minimize data requirements, since we need to be

able to compute these probabilities for each age group: requiring a match between months t− 9, t

and t+1, might produce too much attrition due to the added difficulty of matching workers across

the eight month hiatus in the CPS survey. Our second departure from the FH methodology is also

related to the issue of sample size: we average the resulting age-specific probabilities arising from

3This has been pointed out by Abowd and Zellner (1985) and Poterba and Summers (1986).

71

the FH procedure across 3 ages, in order to minimize the effect of outliers due to low sample size

(specially for younger and older workers). That is, age i corrected stocks and transition probabilities

reflect the average of ages i− 1 to i+ 1.

Second, we follow Elsby, Hobijn, and Sahin (2013) (henceforth EHS), who perform a mechani-

cal recoding of unemployment-nonparticipation cyclers. By matching individuals along the entire

longitudinal dimension of the CPS,4 EHS recode ”obvious” cases of MC: for example, a worker who

in four consecutive months is observed as OOUO (spends the first two months out of the labour

force, the next month as unemployed and the last month out of the labour force) is then recoded

as OOOO. A similar recoding takes place for histories OUOO, UUOU, UOUU and so on.5

Two main differences exist between this procedure and the one from FH: (i) FH require less

data (three matched months instead of four) and (ii) the FH method provides ”corrected” transition

probabilities between all states, not only for those between the unemployment (U) and the out of

the labour (O) states.

Figures A90 and A91 present life-cycle profiles for each transition probability, as they appear

in the main body of the paper (raw), corrected as in Feng and Hu (2013) (FH adj) and corrected

as in Elsby, Hobijn, and Sahin (2013) (EHS adj). Notice that the EHS correction shows differences

for the UO and OU profiles mostly, while the FH adjusts all probabilities downwards. More

importantly for our results, the MC error seems to have a very mild life-cycle component: besides

the last years for OE and OU , the difference between raw and corrected transition probabilities

doesn’t move systematically with age. This can also be seen from figures A94 to A96, where the

probability of being recorded in labour state X given true state X∗ are shown to be very stable over

the life-cycle with one exception: The probability of not being coded as unemployed when truly

unemployed is higher when the worker has less than 20 and more than 65 years of age (inverted

u-shaped of Pr(U |U∗)) which is intuitive, if we think of these stages as the ones where workers are

more ambivalent between participating or not, for example, due to schooling choice decisions for

the young and retirement decisions for the older workers. On the other hand, Pr(O|U∗) is higher

at the beginning and end of the life-cycle, which is the flip side of the previous pattern.

However, when we compute unemployment and participation profiles using the corrected data,

our results differ marginally from those obtained using the raw transition probabilities. The same

applies for our AB1C decomposition exercises for both unemployment and participation, for both

genders. Our interpretation of these results is similar to the one found usually in the literature: at

the aggregate level, these MC errors tend to cancel each other, producing insignificant effects on

flows and rates.

4The method requires four consecutive months of information per worker, as opposed to only two months neededfor the standard flow estimations in the body of the paper.

5See table 2 of Elsby, Hobijn, and Sahin (2013) for a complete list of cycles being recoded.

72

G.2 Monthly Corrected Flows and Stocks

Figure A90: Life-Cycle Profiles of Worker Flows Transitions: Males, Corrected for Misclassification.

0.0

1.0

2.0

3.0

4.0

5

Pro

b

20 30 40 50 60 70

age

EU male

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

EO male

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

UE male

0.1

.2.3

.4

Pro

b

20 30 40 50 60 70

age

UO male

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OE male

0.0

5.1

Pro

b

20 30 40 50 60 70

age

OU male

raw FH adj EHS adj

Note: Unconditional life-cycle profiles estimated via weighted OLS., Corrected for Misclassification.

73

Figure A91: Life-Cycle Profiles of Worker Flows Transitions: Females, Corrected for Misclassifica-tion.

0.0

1.0

2.0

3.0

4

Pro

b

20 30 40 50 60 70

age

EU female

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

EO female

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

UE female

.1.2

.3.4

.5

Pro

b

20 30 40 50 60 70

age

UO female

0.0

5.1

.15

Pro

b

20 30 40 50 60 70

age

OE female

0.0

2.0

4.0

6.0

8

Pro

b

20 30 40 50 60 70

age

OU female

raw FH adj EHS adj


Figure A92: Life-cycle Unemployment Rate Profiles, Corrected for Misclassification.

0.1

.2.3

Pro

b

20 30 40 50 60 70

age

U male

0.1

.2.3

Pro

b

20 30 40 50 60 70

age

U female

raw FH adj EHS adj


74

Figure A93: Life-cycle Participation Rate Profiles, Corrected for Misclassification.

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

P male

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

P female

raw FH adj EHS adj


Figure A94: Life-Cycle Estimated Misclassification Errors for Employment

0.2

.4.6

.81

Pro

b

20 30 40 50 60 70age

Pr(E|E*)

0.2

.4.6

.81

Pro

b

20 30 40 50 60 70age

Pr(E|U*)

0.2

.4.6

.81

Pro

b

20 30 40 50 60 70age

Pr(E|O*)

male female

Note: Computed on monthly unconditional estimates following Feng and Hu (2013).

75

Figure A95: Life-Cycle Estimated Misclassification Errors for Unemployment

0.2

.4.6

.8P

rob

20 30 40 50 60 70age

Pr(U|E*)

0.2

.4.6

.8P

rob

20 30 40 50 60 70age

Pr(U|U*)

0.2

.4.6

.8P

rob

20 30 40 50 60 70age

Pr(U|O*)

male female


Figure A96: Life-Cycle Estimated Misclassification Errors for Inactivity

0.2

.4.6

.81

Pro

b

20 30 40 50 60 70age

Pr(O|E*)

0.2

.4.6

.81

Pro

b

20 30 40 50 60 70age

Pr(O|U*)

0.2

.4.6

.81

Pro

b

20 30 40 50 60 70age

Pr(O|O*)

male female


76

G.3 Markovian Simulations with MC Corrected Probabilities

Figure A97: Life-Cycle Unemployment and Participation Profiles, Corrected for Misclassificationfollowing Feng and Hu (2013).

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

R2=0.992

U males

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

R2=0.990

P males

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

R2=0.980

U females

0.2

.4.6

.8

Pro

b20 30 40 50 60 70

age

R2=0.989

P females

actual Markov

Note: Unconditional life-cycle profiles estimated via weighted OLS., Corrected for Misclassification following Feng and Hu(2013).

77

Figure A98: Life-Cycle Unemployment and Participation Profiles, Corrected for Misclassificationfollowing Elsby, Hobijn, and Sahin (2013).

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

R2=0.992

U males

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

R2=0.984

P males

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

R2=0.985

U females

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

R2=0.983

P females

actual Markov

Note: Unconditional life-cycle profiles estimated via weighted OLS., Corrected for Misclassification following Elsby, Hobijn,and Sahin (2013).

G.4 AB1C Method with MC Corrected Probabilities

78

Figure A99: AB1C Decomposition of the Importance of Flows: Unemployment, Males, Correctedfor Misclassification following Feng and Hu (2013).

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.130

EU

0.1

.2.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.234

EO

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.030

UE

0.1

.2.3

.4

Pro

b

20 30 40 50 60 70

age

1−R2=0.055

UO

0.1

.2.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.042

OE

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.660

OU

actual AB1C, mean


79

Figure A100: AB1C Decomposition of the Importance of Flows: Unemployment, Females, Cor-rected for Misclassification following Feng and Hu (2013).

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.103

EU

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.211

EO

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.034

UE

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.052

UO

0.1

.2.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.059

OE

.05

.1.1

5.2

.25

.3

Pro

b

20 30 40 50 60 70

age

1−R2=0.829

OU

actual AB1C, mean


80

Figure A101: AB1C Decomposition of the Importance of Flows: Participation, Males, Correctedfor Misclassification following Feng and Hu (2013).

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.010

EU

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.371

EO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.010

UE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.015

UO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.172

OE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.157

OU

actual AB1C, mean


81

Figure A102: AB1C Decomposition of the Importance of Flows: Participation, Females, Correctedfor Misclassification following Feng and Hu (2013).

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.008

EU

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.271

EO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.015

UE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.007

UO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.254

OE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.238

OU

actual AB1C, mean


82

Figure A103: AB1C Decomposition of the Importance of Flows: Unemployment, Males, Correctedfor Misclassification following Elsby, Hobijn, and Sahin (2013).

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.184

EU

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.224

EO

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.025

UE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.047

UO

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.036

OE

.05

.1.1

5.2

.25

Pro

b

20 30 40 50 60 70

age

1−R2=0.622

OU

actual AB1C, mean


83

Figure A104: AB1C Decomposition of the Importance of Flows: Unemployment, Females, Cor-rected for Misclassification following Elsby, Hobijn, and Sahin (2013).

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.122

EU

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.158

EO

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.022

UE

.05

.1.1

5.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.040

UO

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.044

OE

0.0

5.1

.15

.2

Pro

b

20 30 40 50 60 70

age

1−R2=0.848

OU

actual AB1C, mean


84

Figure A105: AB1C Decomposition of the Importance of Flows: Participation, Males, Correctedfor Misclassification following Elsby, Hobijn, and Sahin (2013).

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.011

EU

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.157

EO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.017

UE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.009

UO

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.203

OE

.2.4

.6.8

1

Pro

b

20 30 40 50 60 70

age

1−R2=0.066

OU

actual AB1C, mean


85

Figure A106: AB1C Decomposition of the Importance of Flows: Participation, Females, Correctedfor Misclassification following Elsby, Hobijn, and Sahin (2013).

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.012

EU

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.153

EO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.018

UE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.011

UO

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.333

OE

0.2

.4.6

.8

Pro

b

20 30 40 50 60 70

age

1−R2=0.075

OU

actual AB1C, mean


86

Online Appendix Unemployment, Participation and Worker Flows...

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Transcript of Online Appendix Unemployment, Participation and Worker Flows...