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Table 1 Effect of interest on outcomes measured 6 years after graduation

From: Scars of early non-employment for low educated youth: evidence and policy lessons from Belgium

Panel A: Effect of early non-employment in the structural equation:

 
  

OLS

2SLS

Standard errors a

Robust

Cluster gp

Robust

Cluster gp

Outcomes:

 

(1)

(2)

(3)

(4)

Salaried empl.

coeff

–0.00169***

–0.00169***

–0.00256

–0.00256

 

se

(0.00034)

(0.00041)

(0.00375)

(0.00290)

 

P-val

 

0.00019

 

0.38202

 

Bootstrap P-val b

 

0

 

0.45646

 

Exogeneity test P-val c

   

0.767

Self-empl.

coeff

0.00054*

0.00054

0.00248

0.00248

 

se

(0.00030)

(0.00041)

(0.00338)

(0.00258)

 

P-val

 

0.19177

 

0.34175

 

Bootstrap P-val

 

0.18619

 

0.37437

 

Exogeneity test P-val

   

0.438

Overall empl.

coeff

–0.00115***

–0.00115***

–0.00008

–0.00008

 

se

(0.00021)

(0.00025)

(0.00207)

(0.00151)

 

P-val

 

0.00005

 

0.95655

 

Bootstrap P-val

 

0

 

0.96697

 

Exogeneity test P-val

   

0.467

Log earnings

coeff

–0.0269***

–0.0269***

–0.1002**

–0.1002***

 

se

(0.0033)

(0.0040)

(0.0419)

(0.0291)

 

P-val

 

2.51E-08

 

0.0013

 

Bootstrap P-val

 

0

 

0.0060

 

Exogeneity test P-val

   

0.00970

Log hours worked

coeff

–0.0203***

–0.0203***

–0.0723**

–0.0723***

 

se

(0.0024)

(0.0029)

(0.0307)

(0.0207)

 

P-val

 

9.35E-09

 

0.0011

 

Bootstrap P-val

 

0

 

0.0060

 

Exogeneity test P-val

   

0.0112

Panel B: Effect of the instrument in the first stage : OLS

 

Outcome:

Standard errors:

Robust

Cluster gp

  

Early non-empl.

coeff

5.4615***

5.4615***

  
 

se

(1.7273)

(1.6848)

  
 

P-val

 

0.00230

  
 

Bootstrap P-val

 

0.00400

  
 

F stat

 

10.51

  
 

Bootstrap F stat d

 

9.25

  
  1. Standard errors between parentheses. Panel A reports results from estimating β in Eq. (2). β is the effect of one pp increase in \(y^{0}_{it_{1}}\), i.e. the % of hours spent in non-employment in the first two and a half years after graduation relative to potential total hours if one would work full-time during the whole period. For clustered standard errors, we report the P-value and the wild bootstrap P-value. Column 1-2 (3-4) show OLS (2SLS). In 2SLS the provincial unemployment rate at graduation is used as instrument for \(y^{0}_{it_{1}}\). Panel B shows the effect of the instrument on \(y^{0}_{it_{1}}\) in the first stage and the corresponding F statistic
  2. *** p <0.01, ** p <0.05, * p <0.1
  3. aRobust accounts for heteroskedasticity. Clusters are defined by graduation year g and province of residence at graduation p (G=44 clusters)
  4. bComputed according to the wild bootstrap proposed by Davidson and MacKinnon (2010) for 999 repetitions
  5. cWith clustered standard errors, this test is defined as the difference between two Sargan-Hansen statistics: one for the equation where \(y^{0}_{it_{1}}\) is treated as endogenous and one for the equation where \(y^{0}_{it_{1}}\) is treated as exogenous. Under the null that \(y^{0}_{it_{1}}\) is exogenous, the statistic is distributed as χ 2(1)
  6. dBootstrap F statistic is the F statistic corresponding to the bootstrap P-value of the t statistic of the instrument: we rely on the equivalence between F and t distribution: for G=44, t 2(G−1)=F(1,G−1)