2013_11_28_ICS_The NCID organizes a seminar on indices to measure poverty.
NCID organizes a seminar on the indices for measuring poverty
Casilda Lasso de la Vega, from the University of the Basque Country, stated that "what is important is not how we identify the poor, but how we group them together".
Casilda Lasso de la Vega, Professor at the University of the Basque Country, gave a seminar on how to measure poverty, organized by the Navarra Center for International Development of Institute for Culture and Society.
To introduce it, he exposed Amartya Sen's work from 1976, in which methodology is considered one of the most important elements. "Although in recent years there is a agreement about poverty being a multidimensional phenomenon, the approach provided by Sen should still be taken into account," said Casilda, who added that "cut-off points should be established."
According to her, the first cut-off point has to do with the identification of the poor within each dimension, while a second step requires a minimum issue of private dimensions for a person to be considered poor. "How we identify the poor does not matter, what really matters is how we group them," explained Professor Lasso de la Vega.
Indices for measuring multidimensional poverty
The expert from the University of the Basque Country pointed out that many indexes have been introduced to measure multidimensional poverty, but only those that include cardinal variables, that is, those that have dimensions of a quantitative nature, produce good results. "However, most of the data available to measure the dimensions of poverty are ordinal or categorical in form," she said.
Professor Lasso de la Vega concluded her presentation by showing the deprivation curves developed using a approach based on the issue count of deprivations suffered by the poor. The application of this methodology consisted of a selection of a minimum issue of deprivations required for an individual to be identified as poor.
"Not only did the method provide a suitable framework for measuring multidimensional poverty with data ordinal or categorical, but it also ensured unanimous ranking of deprivation count vectors when they did not cross. In the case where the curves did cross, the results led to conclusive verdicts," he concluded.