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Gender, Migration and Domestic Work: An Introduction

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Part of the Migration, Diasporas and Citizenship Series book series (MDC)

Abstract

As the rich have got richer and households have become busier, demand for commoditized household services has risen, with the supply chain becoming increasingly transnational. The emergence of this international market in commoditized services is important for two reasons. First, it shows how reproduction, as well as production, has become part of the global economy. Second, it shows that individuals and households, as well as states and markets, both contribute to and are directly affected by the large-scale social processes of globalization, rising inequalities and migration that are characteristic of contemporary society. A wealth of literature has emerged on female maids and nannies, many of whom are migrants.1 In this book we consider a parallel trend — the re-emergence of male household workers, that is, men who are paid to do traditionally masculine domestic jobs in and around the houses and gardens of generally wealthier people.2 Many of these workers too are migrants, though the proportion of migrants in handyman work remains lower than the proportion of migrants among maids and nannies. Handyman work involves a wide range of small-scale jobs such as fixing shelves, decorating and small-scale repair on an occasional basis, while gardening can involve more regular work including lawn mowing and leaf blowing in extensive suburban gardens. This book investigates the experiences of suppliers and consumers of stereotypically masculine household services at the micro level, but, through its emphasis on contemporary masculinities and the gendering of paid work, the book contributes to wider-scale debates relating to globalization, migration and social reproduction.

Keywords

European Union Domestic Work Citizenship Status Gender Division Domestic Service 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Majella Kilkey, Diane Perrons and Ania Plomien 2013

Authors and Affiliations

  1. 1.University of SheffieldUK
  2. 2.London School of Economics and Political ScienceUK
  3. 3.University of Southern CaliforniaUSA
  4. 4.Florida State UniversityUSA

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