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Compute gini index from Lorenz Quadratic fit

Source: R/gd_compute_pip_stats_lq.R
gd_compute_gini_lq.Rd

gd_compute_gini_lq() computes the gini index from a Lorenz Quadratic fit. Key values is a vector that can be set to fit the follwing formulas: e = -(A + B + C + 1): condition for the curve to go through (1, 1). m = (B^2) - (4 * A). m < 0: condition for the curve to be an ellipse (m is called alpha in paper). n = (2 * B * e) - (4 * C). n is called Beta in paper r = (n^2) - (4 * m * e^2). r is called K in paper.

Usage

gd_compute_gini_lq(A, B, C, key_values)

Arguments

A

numeric: Lorenz curve coefficient. Output of regres_lq()$coef[1].

B

numeric: Lorenz curve coefficient. Output of regres_lq()$coef[2].

C

numeric: Lorenz curve coefficient. Output of regres_lq()$coef[3].

key_values

vector with (e,m,n,r)

Value

numeric

References

Datt, G. 1998. "Computational Tools For Poverty Measurement And Analysis". FCND Discussion Paper 50. World Bank, Washington, DC.