EconPapers    
Economics at your fingertips  
 

Matching and clustering in square contingency tables. Who matches with whom in the Spanish labour market

Pablo Álvarez de Toledo, Fernando Núñez and Carlos Usabiaga ()

Computational Statistics & Data Analysis, 2018, vol. 127, issue C, 135-159

Abstract: The general framework of contingency tables is used to develop previous methodological contributions on labour matching data. A contingency table is generated by the combination of the multiple characteristics that define each row and column category (worker and job categories in our field). In this context, a dimension problem arises that has to be addressed. Two key concepts related to the labour matching process are defined: propensity to match and similarity in the matching. Both measures can be divided into partial components which allow a better understanding of the underlying structure of the data. On the basis of the methodological contribution proposed, an application to the Spanish labour market is conducted, which relies on a large database of administrative microdata (Continuous Working Life Sample, MCVL). A scenario in which each worker category and each job category is defined by the combination of two attributes (location and occupational level) is displayed.

Keywords: Contingency tables; Propensity to match; Factor decomposition; Clustering; Labour matching data; Spanish labour market (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S016794731830121X
Full text for ScienceDirect subscribers only.

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:eee:csdana:v:127:y:2018:i:c:p:135-159

DOI: 10.1016/j.csda.2018.05.012

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2022-05-17
Handle: RePEc:eee:csdana:v:127:y:2018:i:c:p:135-159