Please use this identifier to cite or link to this item: http://hdl.handle.net/10071/12131
Author(s): Suleman, A.
Suleman, F.
Reis, E.
Date: 2016
Title: Fuzzy approach to discrete data reduction: an application in economics for assessing the skill premium
Volume: 17
Number: 3
Pages: 414 - 429
ISSN: 1611-1699
DOI (Digital Object Identifier): 10.3846/16111699.2014.978361
Keywords: Human capital
Skills
Earnings
Data reduction
Hierarchical cluster analysis
Fuzzy sets
Grade of membership model
Abstract: Measures of stock of skills alternative to human capital have raised fresh difficulties, especially in data managing. We propose to empirically compare the efficiency of a hierarchical cluster analysis and a fuzzy clustering in reducing discrete skill data. The outcomes of both methods are subsequently used to measure the impact of skills on earnings in addition to human capital. The proposed methodological comparison was made using an original dataset of retail bankers’ skills assessed by supervisors. Empirical evidence shows that the fuzzy approach is more efficient than the hierarchical clustering: the resulting clusters are fewer and easier to interpret. Furthermore, the earnings equation enriched with skill variables allowed us to correct the education premium, and provides information on monetary incentives related to individual skills. Our paper attempts to raise researchers’ and practitioners’ awareness of data reducing methods, and their implications for wage determinants.
Peerreviewed: yes
Access type: Open Access
Appears in Collections:BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica
DINÂMIA'CET-RI - Artigos em revistas internacionais com arbitragem científica

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