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Evaluation of the Australian Wage Subsidy Special Youth ...

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118<br />

The treatment parameter, θ, cannot be correctly identified for values <strong>of</strong> X which do not<br />

satisfy this support condition. Matching operates by constructing, for those participants<br />

with support, a counterfactual from <strong>the</strong> non-participants. There are a number <strong>of</strong> ways <strong>of</strong><br />

defining this counterfactual. 74 Once <strong>the</strong> counterfactuals are identified, <strong>the</strong> mean impact <strong>of</strong><br />

<strong>the</strong> programme can be estimated as <strong>the</strong> mean difference in <strong>the</strong> outcomes <strong>of</strong> <strong>the</strong> matched<br />

pairs.<br />

A refinement to <strong>the</strong> matching approach was introduced by Rosenbaum and Rubin (1983).<br />

Rosenbaum and Rubin (1983) suggest <strong>the</strong> use <strong>of</strong> balancing scores which are functions <strong>of</strong><br />

<strong>the</strong> relevant observed covariates (here termed X). If assumptions (3) and (4) hold <strong>the</strong>n<br />

Rosenbaum and Rubin (1983) show that <strong>the</strong> conditional independence assumption (CIA)<br />

extends to <strong>the</strong> use <strong>of</strong> <strong>the</strong> propensity score:<br />

(5) Y c ╨ D | P(X) = p(x), ∀x∈ χ<br />

where ( ╨ ) symbolises independence, P(X) is <strong>the</strong> propensity score more fully termed<br />

P(D=1| X), <strong>the</strong> probability <strong>of</strong> participating in <strong>the</strong> programme. The important advantage <strong>of</strong><br />

Rosenbaum and Rubin’s (1983) innovation is that <strong>the</strong> dimensionality <strong>of</strong> <strong>the</strong> match can be<br />

reduced to one. 75 Ra<strong>the</strong>r than matching on a vector <strong>of</strong> characteristics, it is possible to<br />

match on just <strong>the</strong> propensity score. The information from <strong>the</strong> vector <strong>of</strong> characteristics is<br />

condensed within <strong>the</strong> estimated probability representing <strong>the</strong> propensity score. Having<br />

done so, <strong>the</strong> mean impact <strong>of</strong> <strong>the</strong> programme is again estimated as <strong>the</strong> mean difference in<br />

<strong>the</strong> outcomes <strong>of</strong> <strong>the</strong> matched pairs:<br />

(6) E[Y c | D=1, P(X)=P(x)] = E[Y c | D=0, P(X)=P(x)] = E(Y c | P(X)=P(x)]<br />

So <strong>the</strong>n <strong>the</strong> average treatment effect can be written:<br />

74 See, for example, Heckman et al. (1997) for a comparison <strong>of</strong> alternative matching schemes.<br />

75 Matching on <strong>the</strong> propensity score removes <strong>the</strong> dimensionality problem if <strong>the</strong> propensity scores are<br />

estimated parametrically, and relies on parametric estimation <strong>of</strong> <strong>the</strong> probability <strong>of</strong> participation providing<br />

an advantage over parametric estimation <strong>of</strong> <strong>the</strong> outcome.

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