The policy cluster was computed in order to deal with the multicollinearity arising between policy variables.
According to this, start-up incentives would be more effective in reducing unemployment than both the policy cluster and training.
Start-up incentives and the policy cluster show the most favourable results, both in terms of reducing unemployment and also in terms of increasing employment.
In particular, the unemployment, employment and LFPR models were estimated instrumenting two of the four policy variables (training and policy cluster)17 and the implementation-related variable continuity through a two-stage least squares (2SLS) estimator.
According to the results, investing an additional US$1,000 [PPP] per unemployed in the policy cluster would reduce the overall unemployment rate by 1.6 percentage points and the low-skilled unemployment rate by around 2.2 percentage points.
The only exceptions are start-up incentives and the policy cluster, which seem to be fulfilling their activation role only among the low skilled, while for the overall population, they are mostly helping the unemployed move into employment.
Finally, the policy cluster is significant for both population groups, but while it appears to boost participation of the low skilled, it seems to be detrimental for the participation of the overall group.
Estimates relative to the unemployment rate show that the policy cluster, employment incentives and start-up incentives have a significant negative effect on the unemployment rates of the overall and low-skilled populations.
To provide an interpretation of the magnitude of the effects discussed above, I use the coefficients for the policy cluster and start-up incentives, which are particularly important for the low skilled, as basis for some calculations.
Following the same logic, this same country would need to spend around US$6,200 (PPP) in the policy cluster and US$3,600 (PPP) in start-up incentives for every fewer unemployed.
