This paper frames growth incidence analysis within the logic of social impact evaluation
understood as an assessment of variations in individual and social outcomes attributable to
shocks and policies. It uses recentered influence function (RIF) regression to link the
growth incidence curve (GIC) to household characteristics and perform counterfactual
decomposition à la Oaxaca-Blinder to identify sources of variation in the distribution of
consumption expenditure in Cameroon in 2001-2007. We find that the structural effect is
driven mostly by the sector of employment and geography and is the main driver of the
observed pattern of growth. The composition effect accounts for the lion’s share of the
observed variation in the social impact of growth. In particular, that effect tends to reduce
poverty while the structural effect tends to increase it. This conclusion is robust with
respect to the choice of poverty measures and RIF regression models.