What does CEA/CBA mean in COMPANIES & FIRMS

Cost-effectiveness analysis (CEA) is a quantitative technique used to compare the costs and effects of two or more options when there is uncertainty involved. It can evaluate interventions which produce multiple outputs and can help prioritize interventions within limited budgets.

Cost-benefit analysis (CBA) is a broader tool that emphasizes on monetary values by comparing the social benefits from a project with its estimated costs, taking into account all relevant costs and benefits over a specified period of time. The purpose of this technique is to determine whether a project should be implemented given its expected impacts on society as a whole are positive.

In order to conduct cost-effectiveness analysis, one needs to calculate the incremental cost effectiveness ratio (ICER), which compares the extra cost associated with using one intervention compared with another in terms of effect size per unit cost. The ICER then helps managers, organizations or governments decide which intervention should be adopted based on their goals and objectives as well as any budget constraints they may have.

The first step in conducting cost-benefit analysis involves calculating both tangible and intangible social benefits from an intervention, such as increases in productivity or quality of life for those involved in it. Costs associated with implementing such an intervention should also be taken into account, including capital costs and operating expenses as well as any opportunity costs incurred by switching from an existing option. Once all relevant information has been gathered, it can then be compared against each other and decisions made accordingly.

One limitation of these techniques relates to the fact that they rely heavily on assumptions regarding future events which may not necessarily turn out true in reality; thus creating discrepancies between expected results versus actual outcomes over time. Additionally, depending on how information is retrieved during the process, results may end up being biased due to personal preferences or interests introduced at any stage throughout data collection or calculations for example.