The Nature and Determinants of Labor MarketDynamics in the MENA region

Estimates from Egypt, Jordan and Tunisa

Chaimaa Yassine 1

1Université Paris1 Panthéon-Sorbonne (CES), Paris School of Economics & Université duMaine (GAINS-TEPP)

27 July 2015ERF 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

Motivation & Objectives

Motivation

Labor market dynamics remains an unexplored research topicin the MENA region countries. No official Statistics on jobcreations, destructions or mobility.

To guarantee productivity growth along with economic growth ⇒Destruction of low productivity jobs, Creation of new highproductivity jobs & existing jobs get more productive (JobMobility).

Previous literature focused on aggregate stocks and flows (macrolevel) - Yassine (2014), Langot and Yassine (2015, 2015b) or onjob creation only - Amer(2015), Kherfy(2015), Assaad and Krafft(204).

C. Yassine (2014) ERF Workshop 27 July 2015 2 / 31

Motivation & Objectives

Objectives

1 With new waves and panel surveys available ⇒ Provide across-country comparison between Egypt, Jordan and Tunisia

2 Highlight job destructions (JNE) and job mobility (JJ) patternsand intensities

3 Explore the determinants, i.e. the role of observed workers’ andjobs’ characteristics as well as macroeconomic factors like labormarket tightness.

4 Differences in JJ and JNE transitions can affect1 Policymakers’ decisions such as private-sector targeted policies

and public employment strategies2 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

Methodology

Methodology

For each Country, estimate labor market transitions (Job/Non-employment)and (Private/Public/Self-employment/Non-employment) as a fuction of observedworkers’ 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 andYassine (2015) ) ⇒ Propose the creation of weights that attributes measurementerrors to individuals according to the type of transition encountered by anindividual in a certain year

For Policy implications: plug estimated transition probabilities in an equilibriumjob search model and simulate for policies

C. Yassine (2014) ERF Workshop 27 July 2015 4 / 31

Data

Data

Data from Egypt, Jordan, and Tunisia are used: ELMPS 2012,JLMPS 2010 and TLMPS 2014.

All the surveys are nationally representative including bothdetailed current employment and nonemployment information aswell as labor market histories ⇒ capture transitions and spells.

Following Yassine (2014): Extract a synthetic panel dataset foreach country, 2000-2011 for Egypt, 1999-2010 for Jordan and2003-2014 for Tunisia

C. Yassine (2014) ERF Workshop 27 July 2015 5 / 31

Data

Samples

General Sample:Individuals between 15 and 54 years of ageEver 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 49years old used for the creation of “recall” weights and estimationsafter bias correction.

C. Yassine (2014) ERF Workshop 27 July 2015 6 / 31

Weights correcting recall bias

Recall Weights

1 Using auxiliary information on true stocks and/or transitions fromELMPS 2006 (for Egypt), Employment and UnemploymentSurveys (for Jordan), TLFS (for Tunisia) we correct labor markettransitions 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 observationsof transitions j in a way such that the aggregate transition rateobtained from the micro data would be equal to the matrix ofaggregate corrected transition rates. ⇒ Transition recall weights

3 Create longitudinal panel weights for a given spell n of individual ithat starts in year t and end in year T

win =T∏

t=t

rjti

C. Yassine (2014) ERF Workshop 27 July 2015 7 / 31

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) TunisiaC. Yassine (2014) ERF Workshop 27 July 2015 8 / 31

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

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) TunisiaC. Yassine (2014) ERF Workshop 27 July 2015 10 / 31

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) TunisiaC. Yassine (2014) ERF Workshop 27 July 2015 11 / 31

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

EGYPTsame 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%

JORDANsame 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%

TUNISIAsame 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

Counting

Males Females

EGYG 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%

JORG 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%

TUNG 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

Counting

Adding Weights

Table: Count of Transition Probabilities, Males, Age 15-49, Egypt, 2000-2011and Jordan 1999-2010

With Weights No Weights

EGYsame 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%

JORsame 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

Counting

EGY

With Weights Without WeightsG 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%

JORG 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

MNL Regressions

Workers’ Characteristics

Table: Marginal Effects (Gender and Age) from MNL regressions, All, 15-54years 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

MNL Regressions

Table: Marginal Effects (Education) from MNL regressions, All, 15-54 yearsold

Egypt Jordan Tunisia

SJ J–>J J–>NE SJ J–>J J–>NE SJ J–>J J–>NEIllit.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 0.001Interm. (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

MNL Regressions

Jobs’ Characteristics

Table: Marginal Effects (Job of Origin) from MNL regressions, ALL, 15-54years of age.

Egypt Jordan Tunisia

SJ J–>J J–>NE SJ J–>J J–>NE SJ J–>J J–>NE

PublicOmmit.

Formal -0.026*** 0.019*** 0.006*** -0.054*** 0.053*** 0.001 -0.020*** 0.020*** 0.001PRWW (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*** 0.000PRWW (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

MNL Regressions

Table: Marginal Effects of Unemployment and Overall Model’s PredictedProbabilities

Egypt Jordan Tunisia

SJ J–>J J–>NE SJ J–>J J–>NE SJ J–>J J–>NEUR 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

MNL Regressions

Do we get different resutsas we add the transitions’ weights?

C. Yassine (2014) ERF Workshop 27 July 2015 21 / 31

MNL Regressions

Table: Marginal Effects of some determinant variables with and withoutweights, Egypt, Male workers 15-49 years old, 2000-2011

Egypt

With transition recall weights No weightsSJ J–>J J–>NE SJ J–>J J–>NE

15-24ommit.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

MNL Regressions

Table: Marginal Effects of some determinant variables with and withoutweights, Jordan, Male workers 15-49 years old, 1999-2010

Jordan

With transition recall weights No weightsSJ J–>J J–>NE SJ J–>J J–>NE

15-24ommit.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

Survival Analysis

Drawing the Sample for survival analysis:

Employed/Non-employed individuals in 2000 for Egypt and 1999for JordanLeft censored observations ⇒ Define an elapsed duration as theduration from the moment a spell started until the sampling date.

For a job/unemployment spells: this is fairly direct since we havethe date of start of an ongoing job/unemployment spell.

For inactivity: if not available from retrospective accounts, then weassume it’s the duration from the moment the individual turned 15.

C. Yassine (2014) ERF Workshop 27 July 2015 23 / 33

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 50Years

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 50Years

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 50Years

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 40Years

Transitions of initially employed workers by years since appointment, Jordan Females

C. Yassine (2014) ERF Workshop 27 July 2015 24 / 33

Survival Analysis Non-Parametric KM and CI curves

A Multi-state multi-spell Cox Proportional HazardModel (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)

Age2̂ -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.ommitR&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)SingleOmmit .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

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 –> InacRuralOmmit.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

Survival Analysis Non-Parametric KM and CI curves

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

Survival Analysis Non-Parametric KM and CI curves

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C. Yassine (2014) ERF Workshop 27 July 2015 28 / 33

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

Policy Implications

The idea is to plug the transition probabilities we estimate aboveand plug it in an extended Mortensen-Pissarides equilibriumsearch model (Langot and Yassine, 2015).The aim is to contextualize for each country

1 To be able to model labor market outcomes given the estimatedlabor market transitions

2 Simulate for potential or existing labor market policies

C. Yassine (2014) ERF Workshop 27 July 2015 30 / 33

Concluding Remarks

1 Three stagnant/rigid labor markets - Jordanian at the lead withhigher turnover and mobility rates than Egypt and Tunisia.

2 Focus on determinants of job destructions and job mobilityGender is a very important predictor for both Egypt and JordanAge is a crucial determinant of all job transitions for Egypt, whileonly job-to-job transitions for JordanMarriage decreases significantly the willingness to move from job tonon-employment for both Egyptian and Jordaian men andincreases their openess to move to new jobs.Education is significant as a determinant of labor market transitionsonly in Egypt.Job-to-job transitions are more encouraged in Informal andnon-wage work for Egypt while interestingly for Jordan a lot ofchurning takes place in the formal and informal wage work than inthe non-wage work.Labor market tightness determines job mobility and job destructionwhen considering both males and females, but only job mobilitywhen considering males only.

C. Yassine (2014) ERF Workshop 27 July 2015 31 / 33

Concluding Remarks

Can we trust these results? Are we over-exploiting the data?

Weights do actually make a difference in some of the results andestimations.

For the Policy implications, ideas on what policies to directpolicymakers to.

1 Hiring subsidies2 Minimum wage3 Wages/Package offerd by the public sector

C. Yassine (2014) ERF Workshop 27 July 2015 32 / 33

Concluding Remarks

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