The effect of a mixed passive and active labour market policy: Evidence from an Italian programme for dismissed workers

The Use of Administrative Data to Evaluate the Impact of Active Labor Market Policies: The Case of the Italian Liste di Mobilità

“…6 Causal effect of the "passive + transfer" component on employment rate at the 15employee threshold, from 36 months before to 36 months after enrollment in the Liste di Mobilità (LM), by age group and gender (point estimates and 95% confidence intervals). Source: Mazzarella et al (2014) nearly always negative over months 1 to 36 for each age-gender group, suggesting that the effect of the passive component prevails over the effect of the transfer component. The only exception is the group comprising men younger than 40.…”

Data-Driven Policy Impact Evaluation

“…To correct for this bias, it is sufficient to rescale the difference in outcome across the threshold by dividing it by the jump at the threshold in the probability of exposure to the treatment. Mazzarella et al (2014) consider it to be the jump documented in Fig. 6.…”

“…Indeed, the same uncertainty is faced with respect to the effect of the "passive + transfer" component, since it is apparent that this component provides two contrasting incentives: on the one hand, the monetary benefit to the worker increases his or her reservation wage, lengthening the unemployment spell; on the other hand, the benefit transfer to the hiring firm makes the worker more likely to receive job offers. Mazzarella et al (2014) exploit the discontinuity along the firm size dimension to identify the causal effect of the monetary benefit provided to workers dismissed by firms above the 15-employee cutoff. Because of errors in measuring the exact status with respect to eligibility for the monetary benefit, even if the probability of receiving the monetary benefit as a function of firm size is discontinuous at 15 employees, the size of the discontinuity is smaller than 1 (see the case of workers up to age 39 in Fig.…”

“…The fuzzy RDD used by Mazzarella et al (2014) arises when it is not the exposure to the treatment that jumps suddenly from 0 to 1 as the running variable crosses the threshold. Rather, it is the probability of being exposed that jumps suddenly but with the size of the jump smaller than 1.…”

“…Rather, it is the probability of being exposed that jumps suddenly but with the size of the jump smaller than 1. Mazzarella et al (2014) observe that this happens essentially because of a measurement error problem on the running variable (see the discussion in Sect. 4.3).…”