Evaluation of the Australian Wage Subsidy Special Youth ...

20
This identifying assumption is valid if there is randomised assignment of individuals into
treatment and controls. Randomised assignment can ensure that the potential outcomes
are independent ( ╨ ) of the assignment to the treatment:
(18) Y t , Y c ╨ D
With conditional independence, or ignorable assignment (Rubin (1974)), then it is true
that:
(19) E (Y c | D=1 ) = E (Y c | D=0 ) and E (Y t | D=1 ) = E (Y t | D=0 )
And so the estimate of the causal effect of treatment can be estimated without bias while
using the mean outcome of non-participants to proxy for the counterfactual of
participants.
1.3.3 Selection
The economic model of the effect of the program on employment can be described in two
parts, participation in the program and the process of job entry. In summarizing the
process of these two parts, both observable and unobservable influences may play a role.
The main evaluation interest is whether the program influences job entry. Heckman and
Hotz (1989) pointed out that selection might occur on observables or unobservables. If
some observable or unobservable component that leads to program participation also has
a role in job entry then selection bias can arise. In this case, the assumption in equation
(19) does not hold, and instead
(20) E (Y c | D=1 ) ≠ E (Y c | D=0 ).
As a result, the use of non-participants as a comparison group in an evaluation of the
program is subject to selection bias.
An evaluation of the program effect must account for selection. A notable exception
might be where an experiment could assign eligible people randomly to program
treatment or no treatment and also measure all observable influences upon outcomes. In