Wage expectation, information and the decision to become a nurse - Philipp Kugler (Institut für Angewandte Wirtschaftsforschung (IAW), 2021)
In light of skilled-labor shortage in nursing, the effect of a change in the wage of nurses on their labor supply is intensely discussed in recent literature. However, most results show a wage elasticity close to zero. Using extensive data of former German 9th graders, I analyze the role of the expected wage as an incentive to become a nurse. To estimate a causal effect, I select controls and their functional form using post-double-selection, which is a data driven selection method based on regression shrinkage via the lasso. Contrary to common perceptions, the expected wage plays a positive and statistically significant role in the decision to become a nurse. Further, understating a nurse's wage decreases the probability of becoming one. Concerning omitted variable bias, I assess the sensitivity of the results using a novel approach. It evaluates the minimum strength that unobserved confounders would need to change the conclusion. The sensitivity analysis shows that potential unobserved confounders would have to be very strong to overrule the conclusions. The empirical results lead to two important policy implications. First, increasing the wage may help to overcome the shortage observed in many countries. Second, providing information on the (relative) wage may be a successful strategy to attract more individuals into this profession.
Keywords: I11; I21; J24; J31; 330; health professional; expected wage; wage information; machine learning; sensitivity analysis