Growth incidence curve — iq_growth_incidence • inequality

Computes the growth incidence curve (GIC), showing the annualised or total growth rate at each quantile of the distribution between two time periods.

Usage

iq_growth_incidence(
  x_t0,
  x_t1,
  weights_t0 = NULL,
  weights_t1 = NULL,
  n_quantiles = 20L,
  na.rm = FALSE
)

Arguments

x_t0: Numeric vector of incomes in period 0.
x_t1: Numeric vector of incomes in period 1. Must be the same length as x_t0.
weights_t0: Optional weights for period 0.
weights_t1: Optional weights for period 1.
n_quantiles: Integer. Number of quantile bins. Default 20 (ventiles).
na.rm: Logical. Remove NA values? Default FALSE.

Value

An S3 object of class "iq_growth_incidence" with elements:

gic: data.frame with columns quantile (midpoint), growth (proportional growth rate at that quantile).
mean_growth: Numeric. Mean growth across all quantiles.
median_growth: Numeric. Median growth rate.
n_quantiles: Integer.

Details

If the GIC is upward-sloping, the rich grew faster and inequality increased. If downward-sloping, growth was pro-poor.

References

Ravallion, M. and Chen, S. (2003). "Measuring Pro-Poor Growth." Economics Letters, 78(1), 93–99.

Examples

d <- iq_sample_data("panel")
gic <- iq_growth_incidence(d$income_t0, d$income_t1)
plot(gic)