Monthly Income As a Core Social Variable: Evidence From the Italian EU SILC Survey

Marco Di Marco ()
Additional contact information
Marco Di Marco: Italian National Institute of Statistics

Rivista di statistica ufficiale, 2008, vol. 10, issue 2, 13-31

Abstract: Though monthly income cannot be expected to be an accurate measure of annual income, it may nevertheless lead to an acceptable classification of the households if it ranks them properly, from the poorest to the richest. To explore this issue, a question about monthly household income has been added to the Italian EU SILC survey, in order to compare the results with the annual income. It turns out that, whilst the level of monthly income is a poor predictor of the level of yearly income, the rank of monthly income may lead to an acceptable classification of the households into quintiles, though some would be misplaced in the ‘wrong’ groups. Further improvements may come from econometric techniques. A quantile regression analysis shows that the ranks of monthly income could be used as predictors of the ranks of yearly incomes (rather than as substitutes). In the case of the Italian EU SILC, this permits to avoid a significant proportion of misplacements.

Keywords: income measurement; quantile regression (search for similar items in EconPapers)
JEL-codes: C21 D31 (search for similar items in EconPapers)
Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.istat.it/it/files/2011/05/2_3_20081.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:isa:journl:v:10:y:2008:i:2:p:13-31

Access Statistics for this article

More articles in Rivista di statistica ufficiale from ISTAT - Italian National Institute of Statistics - (Rome, ITALY) Contact information at EDIRC.
Bibliographic data for series maintained by Stefania Rossetti ().