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Lessons from previous quasi-experimental evaluations can assist the process of drawing regional EBSM comparison groups:

• To date, there is little evidence that the matching procedure used makes much difference to the accuracy of estimates from quasi-experiments. Thus, the simplest course would be to ensure the comparison group meets the eligibility requirements for the training program. At the very least the comparison group should be drawn from the same population as the treatment group. EBSM programs, which are primarily aimed at EI recipients, could use the HRDC longitudinal file for choice of comparison group subjects, for example. The NESS database may also be a useful source of comparison group members.

• Individual components of EBSM have specific eligibility criteria that should be taken into account when selecting a comparison group. For instance, targeted wage subsidies are largely focused on those who face particular obstacles to employment such as disabilities. The sample frame for the comparison group should come from the subset of the EI population who share these obstacles.

• Moving one step beyond simple eligibility matching is advisable in the case of regional EBSM programs: it seems clear that comparison groups should at least come from the same region. In the case of Transitional Jobs Fund, the comparison group should be chosen from the same communities as the treatment group (i.e., those with unemployment rates of 12% or higher). If desired, and assuming the requisite information is available from the database used, other potentially important matching variables are labour force status changes, age, education, marital status, family income, and time period.

• Sampling strategy depends on the matching procedure employed. The easiest approach -- and one that would be no less satisfactory than more complex ones judging by the literature -- would be to first limit the population to those in the region who were on EI during the period that the program was in operation (plus any other eligibility factors unique to each EBSM component). Then a simple random sample could be selected. A more precise matching strategy would be more difficult to implement, but by no means impossible. For example, if one wanted to match by sex, age and education, a series of dummy variables (0-1) could be set up: men=0, women =1; under 30 years old=0, 30+=1; less than high school=0, high school gradute=1. A three-digit variable could then be computed for each participant and non-participant with the first digit representing sex, the second age, the third education (e.g., a 29 year old female high school graduate = 101). The software program could then be used to select at random, a match (or more than one match) for each participant.

• As for sample size, standard formulas should be employed, keeping in mind that they calculate margins of error for final sample size rather than the original sample size. Appendix D summarizes how to choose an appropriate sample size.