Anglia Ruskin Research Online (ARRO)
Browse
Drydakis_2018_2.docx (38.22 kB)

Economic Pluralism in the Study of Wage Discrimination: A Note

Download (38.22 kB)
journal contribution
posted on 2023-08-30, 15:35 authored by Nick Drydakis
Economic pluralism proposes that economists and social planners should consider alternative theories to establish a range of policy actions. Neoclassical, Feminist and Marxian theories evaluate well-grounded causes of wage discrimination. However, a reluctance to consider less-dominant theories among different schools of economic thought restricts analysis and proposed policies, resulting in a monism method. In considering Neoclassical, Feminist and Marxian theories, racist attitudes, uncertainties regarding minority workers’ productivity and power relations in lower-status sectors might generate discriminatory wages. Each cause is observed in contemporary labour markets and deserves corresponding policy action. Given pluralism, wage discrimination might be reduced by implementing equality campaigns, creating low-cost tests to predict workers’ productivity and abolishing power relations towards minority workers. Time is needed to provide a pluralistic evaluation of wage discrimination. In addition, pluralism requires rigorous investigations to avoid incoherencies. Pluralism might be jeopardised if there is a limited desire to engage with less-dominant theoretical frameworks. Also, pluralism might be misled with rejection of dominant theories.

History

Refereed

  • Yes

Volume

39

Issue number

4

Page range

631-636

Number of pages

7

Publication title

International Journal of Manpower

ISSN

1758-6577

Publisher

Emerald

File version

  • Accepted version

Language

  • eng

Legacy posted date

2018-08-31

Legacy creation date

2018-08-31

Legacy Faculty/School/Department

ARCHIVED Lord Ashcroft International Business School (until September 2018)

Usage metrics

    ARU Outputs

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC