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Table 9 Effect of interest excluding \(\overline {UR}_{p}\) and m i n U R pt from the specification

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:  
Standard errors a Robust Cluster gp Robust Cluster gp
Outcomes: b   (1) (2) (3) (4)
Salaried employment coeff –0.00169*** –0.00169*** –0.00199 –0.00199
  se (0.00034) (0.00041) (0.00387) (0.00348)
  P-val   0.00019   0.57020
  Bootstrap P-val c   0.00000   0.62462
  Exogeneity test P-val d    0.937 0.931
Self-employment coeff 0.00054* 0.00054 0.00219 0.00219
  se (0.00030) (0.00041) (0.00340) (0.00313)
  P-val   0.19253   0.48668
  Bootstrap P-val   0.19219   0.52853
  Exogeneity test P-val   0.619   0.587
Overall employment coeff –0.00115*** –0.00115*** 0.00020 0.00020
  se (0.00021) (0.00025) (0.00229) (0.00164)
  P-val   0.00005   0.90232
  Bootstrap P-val   0.00000   0.91291
  Exogeneity test P-val    0.540 0.386
Log earnings coeff –0.0269*** –0.0269*** –0.0947** –0.0947***
  se (0.0033) (0.0040) (0.0447) (0.0354)
  P-val   2.78E-08   0.01051
  Bootstrap P-val   0   0.03003
  Exogeneity test P-val     0.0361
Log hours worked coeff –0.0203*** –0.0203*** –0.0666** –0.0666***
  se (0.0024) (0.0029) (0.0326) (0.0251)
  P-val   1.05E-08   0.01105
  Bootstrap P-val   0   0.03403
  Exogeneity test P-val     0.0481
Panel B: Effect of the instrument from the first stage (OLS)   
Outcome: Standard errors: Robust Cluster (g*p)   
Early non-empl. Coeff 5.0319*** 5.0319***   
  se (1.6519) (1.7139)   
  P-val   0.0053   
  Bootstrap P-val   0.0120   
  F stat   8.620   
  Bootstrap F stat e   5.84   
  1. Standard errors between parentheses. Panel A reports results from estimating β in Eq. (2), excluding \(\overline {UR}_{p}\) and m i n U R pt . β is the effect of one pp increase in \(y^{0}_{it_{1}}\), i.e. the % of hours spent in non-employment at potential experience 0–2 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 reports 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 indicates heteroskedastic-robust standard errors. Clusters are defined by graduation year g and province of residence at graduation p (G =44 clusters)
  4. bThe outcomes are measured at potential experience 6. For continuous outcomes we add value one before taking the log, so that non-salaried employed at the moment of evaluation are included with outcomes equal to zero after the logarithmic transformation
  5. cComputed according to the wild bootstrap procedure proposed by Davidson and MacKinnon (2010) for 999 repetitions
  6. dWith 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)
  7. eBootstrap 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: with G=44, t 2(G−1)=F(1,G−1)