Egypt, Cairo July 27, 2015 Labor Market Dynamics in the Middle East and North Africa

1. The Nature and Determinants of Labor Market
Dynamics in the MENA region
Estimates from Egypt, Jordan and Tunisa
Chaimaa Yassine 1
1Université Paris1 Panthéon-Sorbonne (CES), Paris School of Economics & Université du
Maine (GAINS-TEPP)
27 July 2015
ERF LABOR AND HUMAN RESOURCE DEVELOPMENT PROJECT 2014-50
«LABOR MARKET DYNAMICS IN THE MIDDLE EAST AND NORTH AFRICA»
C. Yassine (2014) ERF Workshop 27 July 2015 1 / 31

2. Motivation & Objectives
Motivation
Labor market dynamics remains an unexplored research topic
in the MENA region countries. No official Statistics on job
creations, destructions or mobility.
To guarantee productivity growth along with economic growth ⇒
Destruction of low productivity jobs, Creation of new high
productivity jobs & existing jobs get more productive (Job
Mobility).
Previous literature focused on aggregate stocks and flows (macro
level) - Yassine (2014), Langot and Yassine (2015, 2015b) or on
job creation only - Amer(2015), Kherfy(2015), Assaad and Krafft
(204).
C. Yassine (2014) ERF Workshop 27 July 2015 2 / 31

3. Motivation & Objectives
Objectives
1 With new waves and panel surveys available ⇒ Provide a
cross-country comparison between Egypt, Jordan and Tunisia
2 Highlight job destructions (JNE) and job mobility (JJ) patterns
and intensities
3 Explore the determinants, i.e. the role of observed workers’ and
jobs’ characteristics as well as macroeconomic factors like labor
market tightness.
4 Differences in JJ and JNE transitions can affect
1 Policymakers’ decisions such as private-sector targeted policies
and public employment strategies
2 Firms’ decisions such as offering trainings to the employee and
workers’ decisions to accept a job offer/ training offer
C. Yassine (2014) ERF Workshop 27 July 2015 3 / 31

4. Methodology
Methodology
For each Country, estimate labor market transitions (Job/Non-employment)
and (Private/Public/Self-employment/Non-employment) as a fuction of observed
workers’ and jobs’ characteristics and labor market tightness
1 Non-parametric Counting method
«Average transition probability over a 10 year panel»
2 Multinomial logit Regressions
«Pooled one year-lag transitions over a 10 year panel »
3 Survival analysis
«A multi-state multi spell semi-parametric Cox regression»
Recall and Design bias (Langot and Yassine (2015), Assaad, Krafft and
Yassine (2015) ) ⇒ Propose the creation of weights that attributes measurement
errors to individuals according to the type of transition encountered by an
individual in a certain year
For Policy implications: plug estimated transition probabilities in an equilibrium
job search model and simulate for policies
C. Yassine (2014) ERF Workshop 27 July 2015 4 / 31

5. Data
Data
Data from Egypt, Jordan, and Tunisia are used: ELMPS 2012,
JLMPS 2010 and TLMPS 2014.
All the surveys are nationally representative including both
detailed current employment and nonemployment information as
well as labor market histories ⇒ capture transitions and spells.
Following Yassine (2014): Extract a synthetic panel dataset for
each country, 2000-2011 for Egypt, 1999-2010 for Jordan and
2003-2014 for Tunisia
C. Yassine (2014) ERF Workshop 27 July 2015 5 / 31

6. Data
Samples
General Sample:Individuals between 15 and 54 years of age
Ever worked + young unexperienced new labor market entrants +
individuals who are permanently out of the labor force.
Males and females ⇒ capture gender differentials
Sub-sample: only male workers between the age of 15 and 49
years old used for the creation of “recall” weights and estimations
after bias correction.
C. Yassine (2014) ERF Workshop 27 July 2015 6 / 31

7. Weights correcting recall bias
Recall Weights
1 Using auxiliary information on true stocks and/or transitions from
ELMPS 2006 (for Egypt), Employment and Unemployment
Surveys (for Jordan), TLFS (for Tunisia) we correct labor market
transitions between states E, U and I on the aggregate level
(Langot and Yassine, 2015)
2 An adjustment factor rjt : a weight for the micro-data observations
of transitions j in a way such that the aggregate transition rate
obtained from the micro data would be equal to the matrix of
aggregate corrected transition rates. ⇒ Transition recall weights
3 Create longitudinal panel weights for a given spell n of individual i
that starts in year t and end in year T
win =
T
t=t
rjti
C. Yassine (2014) ERF Workshop 27 July 2015 7 / 31

8. Weights correcting recall bias
Figure: Unemployment Rates
.02
.03
.04
.05
.06
.07
2000 2002 2004 2006 2008 2010
Unemp rate (raw) Unemp rate (corrected)
National estimate of unemp rate (CAPMAS)
(a) Egypt
.02
.04
.06
.08
.1
.12
.14
2000 2002 2004 2006 2008 2010
Unemp rate (raw) Unemp rate (corrected)
National estimate of unemp rate
(b) Jordan
.02
.04
.06
.08
.1
.12
.14
2000 2002 2004 2006 2008 2010 2012
Unemp rate (raw)
National estimate of unemp rate
(c) Tunisia
C. Yassine (2014) ERF Workshop 27 July 2015 8 / 31

9. Weights correcting recall bias
Figure: Separations - Employment to Non-employment
0
.02
.04
.06
2000 2002 2004 2006 2008 2010
Job−to−Job (raw) Job−to−Job (corrected)
(a) Egypt
0
.02
.04
.06
2000 2002 2004 2006 2008 2010
Job−to−Job (raw) Job−to−Job (corrected)
(b) Jordan
0
.02
.04
.06
2000 2002 2004 2006 2008 2010 2012
(c) TunisiaC. Yassine (2014) ERF Workshop 27 July 2015 9 / 31

10. Weights correcting recall bias
Figure: Job Findings - Non-employment to Employment
0
.05
.1
.15
.2
2000 2002 2004 2006 2008 2010
Job−to−Job (raw) Job−to−Job (corrected)
(a) Egypt
0
.05
.1
.15
.2
2000 2002 2004 2006 2008 2010
Job−to−Job (raw) Job−to−Job (corrected)
(b) Jordan
0
.05
.1
.15
.2
.25
.3
.35
.4
.45
.5
2000 2002 2004 2006 2008 2010 2012
(c) Tunisia
C. Yassine (2014) ERF Workshop 27 July 2015 10 / 31

11. Weights correcting recall bias
Figure: Job-to-Job Transitions
0
.02
.04
.06
2000 2002 2004 2006 2008 2010
Job−to−Job (raw) Job−to−Job (corrected)
(a) Egypt
0
.02
.04
.06
2000 2002 2004 2006 2008 2010
Job−to−Job (raw) Job−to−Job (corrected)
(b) Jordan
0
.02
.04
.06
2000 2002 2004 2006 2008 2010 2012
(c) Tunisia
C. Yassine (2014) ERF Workshop 27 July 2015 11 / 31

12. Counting
Counting Method
Table: Count of Transition Probabilities: Males Vs. Females, Age 15-54,
Egypt 2000-2010, Jordan 1999-2009, Tunisia 2003-2013
Males Females
EGYPT
same job new job NE Total same job new job NE Total
E 95.16% 3.83% 1.01% 100.00% E 95.15% 1.54% 3.32% 100.00%
NE 14.81% 85.19% 100.00% NE 1.44% 98.56% 100.00%
Total 81.19% 18.81% 100.00% Total 18.27% 81.73% 100.00%
JORDAN
same job new job NE Total same job new job NE Total
E 95.39% 3.56% 1.05% 100.00% E 94.01% 2.46% 3.53% 100.00%
NE 6.27% 93.73% 100.00% NE 0.87% 99.13% 100.00%
Total 72.22% 27.78% 100.00% Total 13.50% 86.50% 100.00%
TUNISIA
same job new job NE Total same job new job NE Total
E 97.93% 2.03% 0.04% 100.00% E 99.30% 0.67% 0.02% 100.00%
NE 14.88% 85.12% 100.00% NE 13.25% 86.75% 100.00%
Total 90.23% 9.77% 100.00% Total 93.75% 6.25% 100.00%
C. Yassine (2014) ERF Workshop 27 July 2015 12 / 31
Females have lower probabilities of job-
to-job transitions but higher probabilities
of job-to-non-emp transitions

13. Counting
Males Females
EGY
G F I NW NE G F I NW NE
G 98.92% 0.18% 0.21% 0.16% 0.53% 98.22% 0.14% 0.09% 0.06% 1.49%
F 0.91% 95.97% 1.09% 1.21% 0.82% 0.78% 90.13% 0.23% 0.08% 8.77%
I 0.94% 1.10% 95.31% 1.10% 1.55% 0.73% 0.50% 86.24% 1.02% 11.51%
NW 0.58% 0.41% 0.90% 97.39% 0.73% 0.05% 0.00% 0.19% 97.89% 1.88%
NE 1.92% 2.06% 8.20% 2.62% 85.19% 0.59% 0.17% 0.38% 0.30% 98.56%
Total 20.60% 10.03% 31.25% 19.28% 18.84% 9.25% 1.09% 2.29% 5.62% 81.74%
JOR
G F I NW NE G F I NW NE
G 97.95% 0.29% 0.33% 0.34% 1.08% 97.51% 0.07% 0.16% 0.02% 2.24%
F 1.53% 97.41% 0.18% 0.13% 0.74% 1.13% 95.35% 0.19% 0.00% 3.33%
I 0.13% 0.25% 96.90% 1.34% 1.37% 0.16% 0.36% 92.35% 0.29% 6.83%
NW 0.48% 0.39% 0.92% 97.41% 0.80% 0.38% 0.17% 0.72% 96.05% 2.68%
NE 1.87% 1.54% 2.17% 0.69% 93.73% 0.27% 0.29% 0.24% 0.07% 99.13%
Total 23.45% 13.19% 21.83% 13.76% 27.78% 6.02% 3.58% 2.81% 1.09% 86.50%
TUN
G F I NW NE G F I NW NE
G 99.51% 0.17% 0.19% 0.12% 0.01% 99.76% 0.15% 0.00% 0.00% 0.08%
F 0.32% 98.76% 0.17% 0.66% 0.09% 0.38% 99.04% 0.14% 0.03% 0.42%
I 0.61% 0.59% 97.69% 0.94% 0.17% 0.38% 0.23% 98.85% 0.49% 0.05%
NW 0.28% 0.12% 0.18% 99.29% 0.13% 0.20% 0.00% 0.03% 99.73% 0.03%
NE 2.79% 3.06% 4.42% 2.83% 86.90% 3.32% 4.21% 2.08% 2.47% 87.92%
Total 22.41% 20.46% 19.09% 23.95% 14.08% 20.91% 19.87% 10.85% 16.42% 31.95%
C. Yassine (2014) ERF Workshop 27 July 2015 13 / 31

14. Counting
Adding Weights
Table: Count of Transition Probabilities, Males, Age 15-49, Egypt, 2000-2011
and Jordan 1999-2010
With Weights No Weights
EGY
same job new job NE Total same job new job NE Total
E 94.91% 4.04% 1.05% 100.00% E 94.17% 4.00% 1.83% 100.00%
NE 14.73% 85.27% 100.00% NE 10.45% 89.55% 100.00%
Total 80.33% 19.67% 100.00% Total 78.82% 21.18% 100.00%
JOR
same job new job NE Total same job new job NE Total
E 90.48% 7.36% 2.16% 100.00% E 86.83% 7.06% 6.11% 100.00%
NE 11.68% 88.32% 100.00% NE 8.46% 91.54% 100.00%
Total 70.88% 29.12% 100.00% Total 67.01% 32.99% 100.00%
C. Yassine (2014) ERF Workshop 27 July 2015 14 / 31

15. Counting
EGY
With Weights Without Weights
G F I NW NE G F I NW NE
G 98.93% 0.20% 0.25% 0.16% 0.46% 98.60% 0.20% 0.25% 0.16% 0.79%
F 0.97% 95.89% 1.25% 1.16% 0.73% 0.96% 95.34% 1.24% 1.15% 1.31%
I 0.95% 1.12% 95.22% 1.09% 1.63% 0.94% 1.10% 94.02% 1.08% 2.86%
NW 0.61% 0.42% 0.96% 97.24% 0.78% 0.42% 0.95% 96.58% 1.33% 0.73%
NE 1.87% 2.03% 8.18% 2.64% 85.27% 1.34% 1.46% 5.81% 1.85% 89.55%
Total 18.82% 10.15% 32.58% 18.78% 19.67% 18.53% 10.08% 49.17% 1.14% 21.09%
JOR
G F I NW NE G F I NW NE
G 95.89% 0.62% 0.66% 0.66% 2.17% 91.65% 0.59% 0.63% 0.63% 6.49%
F 3.17% 94.79% 0.37% 0.23% 1.44% 3.08% 92.14% 0.36% 0.23% 4.20%
I 0.26% 0.50% 93.70% 2.63% 2.91% 0.25% 0.47% 88.90% 2.50% 7.88%
NW 1.07% 0.80% 2.00% 94.53% 1.61% 1.04% 0.77% 1.95% 91.96% 4.27%
NE 3.57% 2.82% 4.07% 1.22% 88.32% 2.60% 2.05% 2.95% 0.86% 91.54%
Total 23.20% 13.28% 21.60% 12.80% 29.12% 21.95% 12.52% 20.38% 12.16% 32.99%
C. Yassine (2014) ERF Workshop 27 July 2015 15 / 31

16. MNL Regressions
Workers’ Characteristics
Table: Marginal Effects (Gender and Age) from MNL regressions, All, 15-54
years old
Egypt Jordan Tunisia
SJ J–>J J–>NE SJ J–>J J–>NE SJ J–>J J–>NE
Male Ommit.
Female -0.016*** -0.017*** 0.033*** -0.029*** -0.025*** 0.054*** -0.002 0.001 0.001
(0.003) (0.002) (0.002) (0.006) (0.003) (0.005) (0.003) (0.003) (0.001)
15-24 Ommit.
Age 25-34 0.019*** -0.002 -0.017*** 0.024*** -0.018*** -0.006 0.008 -0.007 -0.001
(0.003) (0.002) (0.002) (0.006) (0.005) (0.004) (0.005) (0.005) (0.001)
Age 35-54 0.032*** -0.012*** -0.020*** 0.019* -0.021** 0.002 0.013** -0.012* -0.001
(0.004) (0.003) (0.003) (0.008) (0.007) (0.005) (0.005) (0.005) (0.001)
Single Ommit.
Married -0.010*** 0.010*** 0.000 -0.013** 0.009* 0.004 0.004 -0.004 -0.000
(0.002) (0.002) (0.001) (0.004) (0.004) (0.002) (0.004) (0.004) (0.000)
C. Yassine (2014) ERF Workshop 27 July 2015 16 / 31

17. MNL Regressions
Table: Marginal Effects (Education) from MNL regressions, All, 15-54 years
old
Egypt Jordan Tunisia
SJ J–>J J–>NE SJ J–>J J–>NE SJ J–>J J–>NE
Illit.
Ommit.
R&W -0.005 0.008** -0.002 0.007 -0.008 0.000 -0.006 0.006 0.000
(0.003) (0.003) (0.002) (0.009) (0.008) (0.005) (0.004) (0.004) (0.001)
Below -0.008** 0.003 0.005** 0.001 -0.002 0.002 -0.004 0.004 0.001
Interm. (0.002) (0.002) (0.002) (0.008) (0.007) (0.005) (0.003) (0.003) (0.000)
Interm. -0.012*** 0.012*** -0.001 0.003 -0.003 0.000 -0.011* 0.011* -0.000
& above (0.002) (0.002) (0.001) (0.008) (0.007) (0.005) (0.005) (0.005) (0.000)
Univ -0.019*** 0.021*** -0.002 -0.007 0.012 -0.005 -0.021** 0.021** -0.000
. & above (0.003) (0.003) (0.002) (0.009) (0.008) (0.005) (0.008) (0.008) (0.000)
C. Yassine (2014) ERF Workshop 27 July 2015 17 / 31
Education is an important determinant
only for Egypt. High education levels for
job-to-job in Tunisia

18. MNL Regressions
Jobs’ Characteristics
Table: Marginal Effects (Job of Origin) from MNL regressions, ALL, 15-54
years of age.
Egypt Jordan Tunisia
SJ J–>J J–>NE SJ J–>J J–>NE SJ J–>J J–>NE
Public
Ommit.
Formal -0.026*** 0.019*** 0.006*** -0.054*** 0.053*** 0.001 -0.020*** 0.020*** 0.001
PRWW (0.003) (0.002) (0.002) (0.005) (0.004) (0.002) (0.004) (0.004) (0.000)
Informal -0.037*** 0.027*** 0.009*** -0.065*** 0.046*** 0.019*** -0.042*** 0.042*** 0.000
PRWW (0.003) (0.002) (0.002) (0.005) (0.004) (0.003) (0.006) (0.006) (0.000)
Non-wage -0.032*** 0.029*** 0.003 -0.017*** 0.022*** -0.005* -0.006* 0.006* 0.000
(0.003) (0.003) (0.002) (0.004) (0.004) (0.003) (0.003) (0.003) (0.000)
C. Yassine (2014) ERF Workshop 27 July 2015 18 / 31

19. MNL Regressions
Table: Marginal Effects of Unemployment and Overall Model’s Predicted
Probabilities
Egypt Jordan Tunisia
SJ J–>J J–>NE SJ J–>J J–>NE SJ J–>J J–>NE
UR 0.604*** -0.424*** -0.180** 0.622*** -0.436*** -0.186*** 0.046 0.068 -0.102*
(0.103) (0.088) (0.058) (0.098) (0.087) (0.052) (0.081) (0.067) (0.043)
Pr 0.954*** 0.0325*** 0.0138*** 0.906*** 0.0667*** 0.0275*** 0.976*** 0.0236*** 0.000603***
C. Yassine (2014) ERF Workshop 27 July 2015 19 / 31
Unemployment rate (i.e. increased
labor market tightness) decreases
job destructions and mobility

20. MNL Regressions
Do we get different resuts
as we add the transitions’ weights?
C. Yassine (2014) ERF Workshop 27 July 2015 21 / 31

21. MNL Regressions
Table: Marginal Effects of some determinant variables with and without
weights, Egypt, Male workers 15-49 years old, 2000-2011
Egypt
With transition recall weights No weights
SJ J–>J J–>NE SJ J–>J J–>NE
15-24
ommit.
25-34 0.022*** 0.002 -0.024*** 0.012*** 0.002 -0.014***
(0.004) (0.002) (0.004) (0.003) (0.002) (0.002)
35-54 0.036*** -0.012** -0.025*** 0.026*** -0.012*** -0.014***
(0.005) (0.004) (0.004) (0.004) (0.004) (0.002)
Single ommit.
Married -0.005 0.012*** -0.007*** -0.007** 0.012*** -0.005***
(0.003) (0.002) (0.002) (0.002) (0.002) (0.001)
Public ommit.
FPRWW -0.027*** 0.026*** 0.001 -0.026*** 0.026*** 0.000
(0.004) (0.002) (0.003) (0.003) (0.002) (0.002)
IPRWW -0.036*** 0.031*** 0.005 -0.033*** 0.032*** 0.002
(0.004) (0.002) (0.003) (0.003) (0.002) (0.002)
NW -0.040*** 0.037*** 0.003 -0.038*** 0.037*** 0.001
(0.004) (0.003) (0.004) (0.004) (0.003) (0.002)
UR 0.241** -0.251*** 0.010 0.166* -0.256*** 0.090*
(0.090) (0.073) (0.054) (0.082) (0.073) (0.037)
C. Yassine (2014) ERF Workshop 27 July 2015 20 / 1

22. MNL Regressions
Table: Marginal Effects of some determinant variables with and without
weights, Jordan, Male workers 15-49 years old, 1999-2010
Jordan
With transition recall weights No weights
SJ J–>J J–>NE SJ J–>J J–>NE
15-24
ommit.
25-34 0.028** -0.020*** -0.007 0.023*** -0.022*** -0.001
(0.009) (0.006) (0.009) (0.006) (0.006) (0.003)
35-54 0.001 -0.022** 0.021 0.013 -0.022** 0.009
(0.015) (0.008) (0.015) (0.009) (0.008) (0.005)
Single ommit.
Married 0.024* 0.013** -0.037*** 0.006 0.012** -0.019***
(0.011) (0.004) (0.011) (0.006) (0.004) (0.004)
Public ommit.
FPRWW -0.043*** 0.062*** -0.019* -0.057*** 0.063*** -0.006*
(0.008) (0.005) (0.007) (0.005) (0.005) (0.002)
IPRWW -0.063*** 0.049*** 0.014 -0.059*** 0.052*** 0.007**
(0.008) (0.004) (0.008) (0.005) (0.004) (0.003)
NW -0.002 0.027*** -0.025*** -0.020*** 0.027*** -0.007**
(0.007) (0.004) (0.007) (0.005) (0.004) (0.002)
UR 0.378* -0.605*** 0.227 0.834*** -0.596*** -0.237***
(0.161) (0.096) (0.140) (0.109) (0.099) (0.051)
C. Yassine (2014) ERF Workshop 27 July 2015 22 / 33

23. Survival Analysis
Drawing the Sample for survival analysis:
Employed/Non-employed individuals in 2000 for Egypt and 1999
for Jordan
Left censored observations ⇒ Define an elapsed duration as the
duration from the moment a spell started until the sampling date.
For a job/unemployment spells: this is fairly direct since we have
the date of start of an ongoing job/unemployment spell.
For inactivity: if not available from retrospective accounts, then we
assume it’s the duration from the moment the individual turned 15.
C. Yassine (2014) ERF Workshop 27 July 2015 23 / 33

24. Survival Analysis Non-Parametric KM and CI curves
Same Job
Job to Non−employment
Job to Job
0.2.4.6.81
0 10 20 30 40 50
Years
Transitions of initially employed workers by years since appointment, Egypt Males
Same Job
Job to Non−employment
Job to Job
0.2.4.6.81
0 10 20 30 40 50
Years
Transitions of initially employed workers by years since appointment, Egypt Females
]
Same Job
Job to Non−employment
Job to Job
0.2.4.6.81
0 10 20 30 40 50
Years
Transitions of initially employed workers by years since appointment, Jordan Males
Same Job
Job to Non−employment
Job to Job
0.2.4.6.81
0 10 20 30 40
Years
Transitions of initially employed workers by years since appointment, Jordan Females
C. Yassine (2014) ERF Workshop 27 July 2015 24 / 33

25. Survival Analysis Non-Parametric KM and CI curves
A Multi-state multi-spell Cox Proportional Hazard
Model (Male Worekers Only)
Without Weights With weights
Pr –> Pub Pr –>NW Pr –>Unemp Pr –> Inac Pr –> Pub Pr –>NW Pr –>Unemp Pr –> Inac
Age 0.358*** 0.073 -0.379** -1.015*** 0.340** 0.060 -0.288 -1.130***
(0.102) (0.076) (0.130) (0.137) (0.108) (0.081) (0.161) (0.139)
Ageˆ2 -0.006*** -0.001 0.005* 0.013*** -0.006** -0.001 0.004 0.015***
(0.002) (0.001) (0.002) (0.002) (0.002) (0.001) (0.002) (0.002)
Illit.
ommit
R&W 1.618*** 0.329 1.463* 0.299 1.631*** 0.415 1.342* 0.292
(0.430) (0.270) (0.571) (0.350) (0.434) (0.272) (0.640) (0.355)
Below 0.699 0.335 1.848*** 0.421 0.826* 0.388 1.578*** 0.583*
Int. (0.359) (0.196) (0.434) (0.250) (0.363) (0.201) (0.471) (0.249)
Int. 2.031*** 0.720*** 2.075*** 0.516* 2.072*** 0.772*** 1.880*** 0.371
& Above (0.302) (0.177) (0.432) (0.246) (0.303) (0.181) (0.473) (0.258)
Uni 3.023*** 0.992*** 2.622*** 0.393 3.022*** 0.962*** 2.043** 0.526
& above (0.375) (0.279) (0.587) (0.507) (0.387) (0.290) (0.642) (0.509)
Single
Ommit .
Married 0.770* 0.459 -0.746* -0.878*** 0.581 0.547 -1.127*** -0.839***
(0.355) (0.322) (0.308) (0.219) (0.417) (0.343) (0.311) (0.227)
C. Yassine (2014) ERF Workshop 27 July 2015 25 / 33

26. Survival Analysis Non-Parametric KM and CI curves
Without Weights With weights
Pr –> Pub Pr –>NW Pr –>Unemp Pr –> Inac Pr –> Pub Pr –>NW Pr –>Unemp Pr –> Inac
Rural
Ommit.
Cairo -0.561 -0.124 0.268 0.256 -0.599 -0.140 0.285 0.036
(0.308) (0.221) (0.363) (0.297) (0.328) (0.224) (0.394) (0.298)
Alx 0.176 -0.382 0.533 -0.444 0.019 -0.410 0.628 -0.239
_Suez (0.261) (0.237) (0.348) (0.356) (0.277) (0.249) (0.355) (0.365)
Urban -0.105 -0.206 0.001 -0.072 -0.065 -0.162 0.017 -0.135
(0.173) (0.164) (0.289) (0.211) (0.183) (0.168) (0.332) (0.221)
Manuf.
Ommit.
Agric 0.246 -0.487* -0.229 -0.423 0.120 -0.535* 0.212 -0.415
(0.294) (0.234) (0.524) (0.260) (0.294) (0.239) (0.534) (0.258)
Services 0.288 -0.208 0.498 -0.328 0.127 -0.261 0.795* -0.378
(0.232) (0.205) (0.370) (0.250) (0.239) (0.211) (0.374) (0.243)
Constr. 0.423 -0.171 0.431 -0.454 0.225 -0.205 0.847 -0.544*
(0.274) (0.212) (0.409) (0.269) (0.280) (0.218) (0.471) (0.273)
(1-5)
Ommit.
Size 0.099 -0.493** -0.019 0.066 0.151 -0.559*** -0.069 0.055
(6-49) (0.168) (0.152) (0.282) (0.198) (0.172) (0.150) (0.303) (0.192)
Size 0.020 -1.172*** 0.210 -0.563 -0.036 -1.205*** 0.690 -0.671
(50+) (0.247) (0.279) (0.330) (0.331) (0.259) (0.289) (0.418) (0.352)
Informal
Formal -0.453 0.016 -0.722* -0.466 -0.532* -0.016 -0.816 -0.283
(0.237) (0.211) (0.354) (0.290) (0.263) (0.216) (0.425) (0.279)
N(Obs.) 56486 56486 56486 56486 56486 56486 56486 56486
C. Yassine (2014) ERF Workshop 27 July 2015 26 / 33

27. Survival Analysis Non-Parametric KM and CI curves
.002.003.004.005.006.007
Smoothedhazardfunction
5 10 15 20 25
analysis time
Pr−−>Pub
(h) Raw
.001.002.003.004.005.006
Smoothedhazardfunction
5 10 15 20 25
analysis time
Pr−−>Pub
(i) With Weights
.004.0045.005.0055.006.0065
Smoothedhazardfunction
5 10 15 20 25 30
analysis time
Pr−−>NW
(j) Raw
.003.004.005.006
Smoothedhazardfunction
5 10 15 20 25 30
analysis time
Pr−−>NW
(k) With Weights
C. Yassine (2014) ERF Workshop 27 July 2015 27 / 33

28. Survival Analysis Non-Parametric KM and CI curves
.0005.001.0015.002.0025
Smoothedhazardfunction
5 10 15 20 25
analysis time
Pr−−>Unemp
(l) Raw
.001.002.003.004.005
Smoothedhazardfunction
5 10 15 20 25
analysis time
Pr−−>Unemp
(m) With Weights
.6.7.8.91
Smoothedhazardfunction
5 10 15 20 25 30
analysis time
Pr−−>Inact
(n) Raw
.6.7.8.91
Smoothedhazardfunction
5 10 15 20 25 30
analysis time
Pr−−>Inact
(o) With Weights
C. Yassine (2014) ERF Workshop 27 July 2015 28 / 33

29. Survival Analysis Non-Parametric KM and CI curves
Why should we be sceptical about duration analysis?
C. Yassine (2014) ERF Workshop 27 July 2015 29 / 33

30. Policy Implications
The idea is to plug the transition probabilities we estimate above
and plug it in an extended Mortensen-Pissarides equilibrium
search model (Langot and Yassine, 2015).
The aim is to contextualize for each country
1 To be able to model labor market outcomes given the estimated
labor market transitions
2 Simulate for potential or existing labor market policies
C. Yassine (2014) ERF Workshop 27 July 2015 30 / 33

31. Concluding Remarks
1 Three stagnant/rigid labor markets - Jordanian at the lead with
higher turnover and mobility rates than Egypt and Tunisia.
2 Focus on determinants of job destructions and job mobility
Gender is a very important predictor for both Egypt and Jordan
Age is a crucial determinant of all job transitions for Egypt, while
only job-to-job transitions for Jordan
Marriage decreases significantly the willingness to move from job to
non-employment for both Egyptian and Jordaian men and
increases their openess to move to new jobs.
Education is significant as a determinant of labor market transitions
only in Egypt.
Job-to-job transitions are more encouraged in Informal and
non-wage work for Egypt while interestingly for Jordan a lot of
churning takes place in the formal and informal wage work than in
the non-wage work.
Labor market tightness determines job mobility and job destruction
when considering both males and females, but only job mobility
when considering males only.
C. Yassine (2014) ERF Workshop 27 July 2015 31 / 33

32. Concluding Remarks
Can we trust these results? Are we over-exploiting the data?
Weights do actually make a difference in some of the results and
estimations.
For the Policy implications, ideas on what policies to direct
policymakers to.
1 Hiring subsidies
2 Minimum wage
3 Wages/Package offerd by the public sector
C. Yassine (2014) ERF Workshop 27 July 2015 32 / 33

33. Concluding Remarks
C. Yassine (2014) ERF Workshop 27 July 2015 33 / 33