enseignement de base au niger :quel bilan - CONFEMEN
enseignement de base au niger :quel bilan - CONFEMEN
enseignement de base au niger :quel bilan - CONFEMEN
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RURAL | -.6122861 .8451694 -0.72 0.470 -2.277667 1.053094<br />
_cons | 2.716843 1.731605 1.57 0.118 -.6952319 6.128918<br />
-------------+----------------------------------------------------------------<br />
_se | 4.911208 .3528661 (Ancillary parameter)<br />
------------------------------------------------------------------------------<br />
Modèle VI.10<br />
Tobit estimates Number of obs = 240<br />
LR chi2(12) = 20.61<br />
Prob > chi2 = 0.0564<br />
Log likelihood = -414.03703 Pseudo R2 = 0.0243<br />
------------------------------------------------------------------------------<br />
ABSMT | Coef. Std. Err. t P>|t| [95% Conf. Interval]<br />
-------------+----------------------------------------------------------------<br />
MTRESTENS | -1.415348 .7796223 -1.82 0.071 -2.951534 .1208379<br />
SERVICE | .0600599 .0513347 1.17 0.243 -.0410913 .161211<br />
DIPCYCLB | -1.949077 .982727 -1.98 0.049 -3.885465 -.0126889<br />
NIVCYCLB | -1.721071 .8680758 -1.98 0.049 -3.431548 -.0105946<br />
MULTIGRA | 2.167806 1.794282 1.21 0.228 -1.367689 5.703302<br />
MTAUTREACT | .0925492 .7832267 0.12 0.906 -1.450739 1.635837<br />
APEACTIV | -1.744343 .7394914 -2.36 0.019 -3.201454 -.2872316<br />
CONSULCOLL | .5846734 .9555708 0.61 0.541 -1.298205 2.467552<br />
INSPECTEUR | .4723793 1.011153 0.47 0.641 -1.520021 2.464779<br />
CONSPEDAG | -.3607586 .7728385 -0.47 0.641 -1.883577 1.16206<br />
MTCONCOUR | -.9720326 1.105613 -0.88 0.380 -3.150558 1.206493<br />
RURAL | -.659527 .8444917 -0.78 0.436 -2.323533 1.004479<br />
_cons | 1.878304 1.569621 1.20 0.233 -1.214513 4.971121<br />
-------------+----------------------------------------------------------------<br />
_se | 4.895915 .3513136 (Ancillary parameter)<br />
------------------------------------------------------------------------------<br />
Modèle VI.11<br />
Tobit estimates Number of obs = 240<br />
LR chi2(11) = 17.34<br />
Prob > chi2 = 0.0982<br />
Log likelihood = -415.67141 Pseudo R2 = 0.0204<br />
------------------------------------------------------------------------------<br />
ABSMT | Coef. Std. Err. t P>|t| [95% Conf. Interval]<br />
-------------+----------------------------------------------------------------<br />
SERVICE | .0860369 .0499515 1.72 0.086 -.0123865 .1844603<br />
DIPCYCLB | -1.680189 .9817822 -1.71 0.088 -3.61467 .2542924<br />
NIVCYCLB | -1.54752 .8706054 -1.78 0.077 -3.262941 .1679013<br />
MULTIGRA | 2.131877 1.821633 1.17 0.243 -1.457428 5.721183<br />
MTAUTREACT | .3290765 .782146 0.42 0.674 -1.212046 1.870199<br />
APEACTIV | -1.880088 .7447176 -2.52 0.012 -3.347462 -.4127132<br />
CONSULCOLL | .5252006 .9640768 0.54 0.586 -1.374394 2.424796<br />
INSPECTEUR | .5917284 1.016924 0.58 0.561 -1.411995 2.595452<br />
CONSPEDAG | -.5066542 .777528 -0.65 0.515 -2.038678 1.025369<br />
MTCONCOUR | -1.048313 1.118288 -0.94 0.350 -3.251762 1.155136<br />
RURAL | -.7957479 .8494009 -0.94 0.350 -2.469388 .8778923<br />
_cons | .8736487 1.489309 0.59 0.558 -2.060851 3.808148<br />
-------------+----------------------------------------------------------------<br />
_se | 4.954131 .3558218 (Ancillary parameter)<br />
------------------------------------------------------------------------------<br />
Le modèle suivant montre qu’en introduisant dans le modèle une variable indicatrice du<br />
nive<strong>au</strong> d’<strong>enseignement</strong>, on n’observe pas d’effet significatif <strong>de</strong> cette variable.<br />
130
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