data_country

<ul>
<li> country. Country name
  </li>
<li> code. World Bank country code
  </li>
<li> region. Macro-region to which the country belongs
</li>
</ul>

datasets

Easily compute education inequality measures and the distribution
of educational attainments for any group of countries, using the data set
developed in Jorda, V. and Alonso, JM. (2017) <DOI:10.1016/j.worlddev.2016.10.005>.
The package offers the possibility to compute not only the Gini index, but
also generalized entropy measures for different values of the sensitivity
parameter. In particular, the package includes functions to compute the
mean log deviation, which is more sensitive to the bottom part of the
distribution; the Theil’s entropy measure, equally sensitive to all parts
of the distribution; and finally, the GE measure when the sensitivity
parameter is set equal to 2, which gives more weight to differences in
higher education. The decomposition of these measures in the components
between-country and within-country inequality is also provided. Two
graphical tools are also provided, to analyse the evolution of the
distribution of educational attainments: The cumulative distribution
function and the Lorenz curve.

Vanesa Jorda

educineq

Compute and Decompose Inequality in Education

data_country function

A data frame with 142 rows and 3 variables

data_country: This dataset contains information about the available countries, their

Description

Usage

Arguments

Format