The PaF income polarization index
paf(y, a, ncores = 1)
pafF(y, a, ncores = 1)The paf() function, for a single value of \(\alpha\), returns a vector with the PaF index, the alienation (twice the Gini index) and identification components and 1 + the normalized covariance. If a range of values of \(\alpha\) are given, it will return a matrix with the same components, where each row corresponds to a specific value of \(\alpha\).
The pafF() function returns only the PaF index for either one or more values of \(\alpha\).
A numeric vector with income data.
The value of \(\alpha\). This can either be a number or a vector with many values. In any case, the \(\alpha\) may take values between 0.25 and 1.
The number of cores to use. If greater than 1, parallel computing will take place. It is advisable to use it if you have many observations and or many variables, otherwise it will slow down the process. The default is 1, meaning that code is executed serially.
Michail Tsagris and Christos Adam.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Christos Adam econp266@econ.soc.uoc.gr.
The functions compute the PaF index of Duclos, Esteban and Ray (2004) for either a specific value, or for a range of values, of \(\alpha\). The pafF() estimates the index using Eq. (8) and (9) in the paper, whereas paf() is faster as it uses Eq. (3) of the paper.
Duclos J. Y., Esteban, J. and Ray D. (2006). Polarization: concepts, measurement, estimation. In The Social Economics of Poverty (pp. 54--102). Routledge.
Duclos J. Y., Esteban, J. and Ray D. (2004). Polarization: concepts, measurement, estimation. Econometrica, 72(6): 1737--1772.
paf.boot
y <- rgamma(100, 10, 0.01)
paf(y, 0.25)
paf( y, c(0.25, 0.5, 0.75, 1) )
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