Évaluation et impact du sentiment de discrimination sur les trajectoires professionnelles
Session 2 Politiques de l’emploi
5. Evaluation of the impact of training
5.4. Impact of training on the duration of non employment and on reemployment
As expected, the probability of returning to employment increases with the level of education and decreases with the age of the individual. Family situation and nationality do not affect the length of the non employment spell. The estimates show the usual negative state and duration dependence of non employment. More interestingly, the more the unemployed worker spent time in training program while employed during the previous year, the higher her/his probability to return into employment. This reveals the necessity of allowing for long-term effects of training on the labor market history. Last, the more the individual has spent time in training out of employment during the past 12 months, the higher her/his probability to find a job quickly. This means that training while not employed reduces the non employment duration once the training is completed.
46
6. Conclusion
In this paper, we use a French survey to jointly estimate the effects of training programs dedicated to employed workers and to unemployed individuals on mobility on the labour market. Participation in training is taken as endogenous to account for selectivity phenomena allowing, for instance, those who are ex ante the most willing to participate in employment training programs may also be the ones who have a priori low exit rates from employment. We model the transitions between the states of the labor market using a multi-state multi-spell transition model. Participation in training is allowed to affect transitions up to one year after the program starts. We find that the conditional probability of reemployment is increasing with the proportion of the previous year the individual has spent in training, whatever the category of these programs. More surprisingly, past participation in employment training is associated with a greater hazard function for the transition from employment to non employment, indicating that firms use employment training programs in order to increase general human capital of workers when they anticipate a lower activity. It is interesting to note that the conditional probability of entering training when employed (resp., non employed) is increasing with the proportion of the last year the individual participated in training when non employed (resp., when employed). As we control for observed and unobserved characteristics of the worker, this result indicates that previous participation in these programs may reveal the willingness of the worker to participate in such programs and his ability to benefit from it. Consequently, the employer or the public services of employment are more likely to offer training to workers who have already been trained. A further research could consist to distinguish the impact of the training programs according to the characteristics of the workers and to the type of contract.
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Appendix
Figure 1 - Kaplan Meier estimates of the survival function of the employment (left) and unemployment (right) spells durations - stratification depending on whether there is participation in a training during the spell
Figure 2 - Kaplan Meier estimates of the survival function of the employment (left) and unemployment (right) spells durations - stratification depending on whether there is participation in a training before the spell starts
Table 2
% E training 0.1561 0.1622 0.5569 0.5332
(0.0267) (0.0221) (0.0688) (0.0536)
% NE training 0.3654 0.2903 0.0966 0.0384
(0.0428) (0.0314) (0.0520) (0.0430)
% Non-employment -0.0126 -0.0052 -0.0707 -0.0571
(0.0182) (0.0137) (0.0226) (0.0184)
% Employment training 0.1082 0.0831 -0.1074 -0.1661
(0.0215) (0.0161) (0.0611) (0.0544)
% Unemployment training -0.0453 -0.0103 0.0054 0.0294
(0.0575) (-0.236) (0.0532) (0.0519)
λ -0.6983 0.6960
(0.1677) (0.1419)
µ -0.8153 0.8365 -0.3745 -0.6804
(0.0793) (0.0493) (0.1608) (0.1871)
Standard errors are given in parentheses.
50
Parameters estimates (continued)
From E Training to E From NE Training to NE Parametric Non Parametric Parametric Non Parametric
Intercept -0.9824 -0.6298 -1.5791 -1.2780
(0.2962) (0.1999) (0.5303) (0.3542)
Female -0.5415 -0.5151
(0.1426) (0.1023)
Female with young children -0.0141 0.0463
(0.3727) (0.2470)
Male with young children -0.0580 -0.2321
(0.3485) (0.2347)
Female without young children -0.0113 -0.0781
(0.2328) (0.1596) Diploma
High School 0.5585 0.1303 0.0760 -0.0459
(0.2381) (0.1499) (0.2871) (0.1891)
Undergraduate 0.0175 -0.0554 -0.2059 -0.2347
(0.2526) (0.1740) (0.3311) (0.2273)
Graduate 0.1734 -0.1239 -0.8391 -0.7106
(0.2229) (0.1689) (0.3345) (0.2219)
Not French -0.9332 -0.5066 -0.3123 -0.1968
(0.4594) (0.3300) (0.4436) (0.2886)
Age
26-35 -0.1767 0.2174 0.2433 0.2745
(0.1981) (0.1393) (0.2669) (0.1993)
36-45 -0.1765 0.0386 0.0921 0.1443
(0.2091) (0.1520) (0.2790) (0.1941)
46-55 -0.2036 0.2003 0.5148 0.0421
(0.2638) (0.1766) (0.3626) (0.2603)
55+ 0.1711 -0.0194 -2.0348 -2.3971
(0.4890) (0.3949) (1.0756) (1.0307)
State Dependence
% NE -0.1357 -0.0794 -0.0190 -0.0353
(0.0482) (0.0353) (0.0400) (0.0294)
% E training -0.0909 -0.0881 0.2519 0.1516
(0.0263) (0.0197) (0.0803) (0.0514)
% NE training -0.0848 -0.0593 0.0017 -0.0218
(0.0766) (0.0510) (0.0459) (0.0290)
% NE -0.0038 -0.0012 -0.0205 -0.0175
(0.0300) (0.0179) (0.0241) (0.0167)
% E training 0.0143 -0.0210 -0.1356 -0.1377
(0.0232) (0.0170) (0.0666) (0.0480)
% NE training 0.1130 0.0550 0.0032 -0.0261
(0.0681) (0.0477) (0.0348) (0.0251)
λ -1.0802 1.5020 0.0420 -0.1463
(0.1275) (0.1578) (0.1983) (0.1743)
µ -0.5784 0.9537 -0.3489 -0.2865
Standard errors are given in parentheses.
Parameters estimates (continued)
Female with young children 0.0482 0.1777 0.0897 0.0229
(0.1418) (0.1146) (0.1600) (0.1210)
Male with young children 0.1653 0.1933 0.3132 0.1604
(0.1490) (0.1184) (0.1631) (0.1273)
Female without young children -0.0870 -0.0479 0.0864 0.0647
(0.0874) (0.0709) (0.0957) (0.0738)
State Dependence
% NE 0.0886 0.0578 -0.0849 -0.0897
(0.0175) (0.0148) (0.0144) (0.0116)
% E training 0.1435 0.1199 0.1979 0.1268
(0.0349) (0.0254) (0.0614) (0.0458)
% NE training -0.0188 -0.0722 0.1540 0.1082
(0.0866) (0.0678) (0.0265) (0.0204)
% NE in 1st year 0.0881 0.0964 -0.0529 -0.0418
(0.0125) (0.0102) (0.0108) (0.0083)
% E training in 1st year 0.0151 0.0295 -0.0201 0.0028
(0.0266) (0.0193) (0.0357) (0.0257)
% NE training in 1st year 0.0409 0.0652 -0.0566 -0.0189
(0.0429) (0.0339) (0.0311) (0.0236)
λ -0.0099 -0.5901 -0.3192 0.0539
(0.1675) (0.0953) (0.1090) (0.1080)
µ -0.8257 -1.1142 -0.5139 -0.6397
(0.0767) (0.0730) (0.0745) (0.0666)
Standard errors are given in parentheses.
Table 3
Parameters estimates for the unobserved heterogeneity
Probabilities c00 -0.5275 (0.2772) c01 2.0900 (0.1821) c10 0.2037 (0.2138)
Standard errors are given in parentheses.