Measuring compliance with minimum wages
Measuring compliance with minimum wages
Identifying genuine underpayment of minimum wages is not straightforward. Some well-known statistical issues affect the measurement of compliance rates, but factors such as processing or behavioural influences amongst respondents can also have an impact. We study the quantitative measurement of non-compliance with the minimum wage, using UK apprentices (who have particularly high non-compliance rates) as a case study. We show that understanding the institutional and behavioural context can be invaluable, as can triangulation of different sources. While the binary nature of compliance makes such problems easier to identify and evaluate, this analysis holds wider lessons for the understanding of the characteristics of large and complex datasets.
249-270
Veliziotis, Michail
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Ritchie, F.
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Drew, H.
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Whittard, Damian
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9 May 2018
Veliziotis, Michail
e43806b3-fdb5-494b-a624-04a5227d2fad
Ritchie, F.
8c7bd826-280b-4f2c-bb1d-568c23a9ce8f
Drew, H.
8083f694-6f00-455e-9fc7-d2532cc5ab0f
Whittard, Damian
14c81ad7-edd7-4727-845c-f920fd59a1f5
Veliziotis, Michail, Ritchie, F., Drew, H. and Whittard, Damian
(2018)
Measuring compliance with minimum wages.
Journal of Economic and Social Measurement, 42 (3-4), .
(doi:10.3233/JEM-180448).
Abstract
Identifying genuine underpayment of minimum wages is not straightforward. Some well-known statistical issues affect the measurement of compliance rates, but factors such as processing or behavioural influences amongst respondents can also have an impact. We study the quantitative measurement of non-compliance with the minimum wage, using UK apprentices (who have particularly high non-compliance rates) as a case study. We show that understanding the institutional and behavioural context can be invaluable, as can triangulation of different sources. While the binary nature of compliance makes such problems easier to identify and evaluate, this analysis holds wider lessons for the understanding of the characteristics of large and complex datasets.
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Accepted/In Press date: 25 February 2018
Published date: 9 May 2018
Identifiers
Local EPrints ID: 421336
URI: http://eprints.soton.ac.uk/id/eprint/421336
ISSN: 0747-9662
PURE UUID: 56e193af-92a7-4676-99f5-d5e66c1cb40b
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Date deposited: 01 Jun 2018 16:30
Last modified: 16 Mar 2024 04:24
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Author:
F. Ritchie
Author:
H. Drew
Author:
Damian Whittard
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