tip: TIP curve
In AngelBerihuete/rtip: Inequality, Welfare and Poverty Indices and Curves using the EU-SILC Data

Description Usage Arguments Details Value Author(s) References See Also Examples

Estimates TIP curve ordinates. The TIP curve is defined by plotting the cumulated proportion of population on the x-axis and the cumulated per capita poverty gap (the distance between each income and the poverty threshold) on the y-axis from the biggest one downwards.

1 2	tip(dataset, ipuc = "ipuc", hhcsw = "DB090", hhsize = "HX040", arpt.value = NULL, samplesize = 50, norm = FALSE, plot = FALSE)

`dataset`	a data.frame containing the variables.
`ipuc`	a character string indicating the variable name of the income per unit of consumption. Default is "ipuc".
`hhcsw`	a character string indicating the variable name of the household cross-sectional weight. Default is "DB090".
`hhsize`	a character string indicating the variable name of the household size. Default is "HX040".
`arpt.value`	the at-risk-of-poverty threshold to be used (see arpt). Default is NULL which calculates arpt with default parameters.
`samplesize`	an integer which specifies the number of (equally spaced) percentiles to be used in the estimation of the TIP ordinates The default is 50. If samplesize is set to ”complete”, ordinates are computed in each value along the whole distribution.
`norm`	logical; if TRUE, the normalised TIP curve ordinates are computed using the normalised poverty gaps (poverty gaps divided by the poverty threshold).
`plot`	logical; if TRUE plots the TIP curve.

The TIP (Three I's of Poverty) curve ordinates are computed using the equivalised disposable income. The equivalence scales that can be employed are the modified OECD scale or the parametric scale of Buhmann et al. (1988). The default is the modified OECD scale (see setupDataset).

A data.frame with the following components:

x.tip vector of cumulated proportion of population.

y.tip vector with values of tip curve ordinates.

A. Berihuete, C.D. Ramos and M.A. Sordo

B. Buhmann et al. (1988) Equivalence scales, well-being, inequality and poverty: sensitivity estimates across ten countries using the Luxembourg Income Study (LIS) database, Review of Income and Wealth, 34, 115–142.

S.P. Jenkins and P.J. Lambert (1997) Three I's of poverty curves, with an analysis of UK poverty trends, Oxford Economic Papers, 49, 317–327.

setupDataset, arpt

data(eusilc2)
ATdataset <- setupDataset(eusilc2, country = "AT")
tip.curve <- tip(ATdataset, arpt.value = arpt(ATdataset), norm = TRUE)
str(tip.curve)