Benefit Incidence Analysis

Spending is progressive in absolute terms if poorer groups receive a larger share of the benefit than richer groups, and progressive in relative terms if the benefit makes up a larger share of poorer groups' income even when the absolute share is not pro-poor. Benefit incidence analysis assesses this by comparing each income group's share of the subsidy with its share of the population or income, often summarised with concentration curves and indices. A regressive result means richer groups capture a disproportionate share, signalling weak targeting.

Standard benefit incidence analysis attributes to each user the government's unit cost of providing the service, because that cost is measurable from budgets and administrative data, whereas the subjective value a household places on the service is not. This is a known limitation: a poorly run clinic may cost the same to provide as a good one but be worth far less to patients. Analysts mitigate it by using disaggregated unit costs and, where possible, complementing BIA with measures of service quality and demand.

Benefit incidence analysis is typically a static, accounting exercise: it takes observed patterns of service use and distributes current spending across groups, without modelling how behaviour would respond to a policy change. Microsimulation models, by contrast, apply tax-benefit rules to detailed household data and can simulate the distributional effects of hypothetical reforms, sometimes incorporating behavioural responses. BIA answers 'who benefits from spending as it is now?'; microsimulation can answer 'who would gain or lose if we changed the policy?'.