Estimating the Return to Training and Occupational Experience: The Case of Female Immigrants

Do government provided training programmes benefit the participants and the society? We address this question in the context of female immigrants who first learn the new language and then choose between working or attending government provided training. Although theoretically training may have several outcomes, most evaluations have focused on only one outcome of training: the expected wage. Training might have no direct effect on wage, however, but it affects employment probability in higher paid jobs nevertheless. In order to measure the return to government provided training, and overcome the above reservations, we formulate an estimable stochastic dynamic discrete choice model of training and employment. Given the estimated model, the individual benefit is measured by the change in expected lifetime utility due to the effect of alternative training policy. The social return from training is measured by the expected increase in actual earnings minus the cost, due to a counterfactual policy. Our estimates imply that training has no significant impact on the mean offered wage in blue-collar occupation, but training increases the mean offered wage in white-collar occupation by 19%. Training also substantially increases the job offer rates in both occupations. Furthermore, counterfactual policy simulations show that free access to training programs relative to no training could cause an annual earnings growth of 31.3%. This large social gain (ignoring the cost of the programme) comes mainly from the impact of training on the job offer probabilities and, consequently, on unemployment, and not, as conventionally thought, from the impact of training on potential earnings. Moreover, the average ex-ante expected present value of utility for a female immigrant at arrival (individual benefit) increases by 50% using a counterfactual policy of fully available training relative to the estimated restricted level of training opportunity.