Just as cost-benefit and cost-effectiveness analysis methods can be applied to ex-ante analysis [link], they can be applied to ex-post policy evaluation. There are comparatively fewer data limitations and other restrictions than in the ex-ante application. In order to conduct a valid ex-post cost-benefit or cost-effectiveness strategy, the program must be able to be measured in quantitative terms. The quasi-experimental approaches to evaluation measure outcomes as scores, rates or similar indicators. But since policy impact is also measured in dollar terms, cost=based approaches should be included as measures in the quasi-experimental design approach.
The cost-oriented evaluation approach assumes that government agencies and other institutions have finite budgets with which to approach an given problem, and that the solution may have to be limited by such constraints regardless of the size or importance of the problem. As discussed in Chapter 7, there are two primary types of cost-oriented evaluation methods:
Cost-benefit analysis compares outcome to input with both stated in monetary values. Valuations can be made on such dimensions as rates of return on investment, net differences between discounted costs and benefits, and benefit-to-cost ratios.
Cost-effectiveness analysis identifies ways of achieving objectives at minimal costs. Instead of assigning monetary values to different objectives (as in cost-benefit analysis), this type of evaluation compares the costs of different ways of obtaining the same, measurable objective.
Having measured policy or program impact, we will want to estimate the cost or net benefit of the change in status detected. Following the principles of cost-benefit analysis, the analyst seeks to measure both tangible and intangible benefits and direct and indirect costs. One approach would be to convert these costs and benefits to dollars, and discount these costs and benefits back to a common date, usually the program start-up date. Another approach would be to convert costs and benefits to current dollars. These figures can then be used to estimate the costs and benefits of changes observed during the time of the program.
The analyst should not forget the earlier discussion of ex-ante cost-benefit analysis, including problems of measuring intangible benefits, measuring both direct and indirect costs, and considering distributional questions such as who gained and who lost as a result of the program. Remember, the program with the largest expected net benefit may not be chosen for implementation during ex-ante analysis because of political factors. Likewise, discovery of a high net benefit as part of an ex-post evaluation does not necessarily guarantee continuation of the program. For these reasons, assumptions underlying cost-based evaluations should be stated clearly, and the cost analysis should be combined with other types of analyses. It might be most usefully displayed as part of a Goeller scorecard. Since ex-post cost analyses are most often used to help determine costs for alternative future levels of service, a sensitivity analysis might also be part of the data displayed for decision makers.
Remember, cost data and impact data must be considered together. If the program has had no impact, cost data may only provide an indication of funds that might possibly be spent on another programmatic approach. If the program has had an impact, the cost-benefit data help us decide whether the impact was worth the cost and whether an alternative level of funding for future years might be efficient.