The effects of automation in the apparel and footwear industries and their gender dimensions: Indonesia case study

This paper addresses the drivers of and limitations to automation and technological upgrading in the apparel and footwear industries and their effects on men’s and women’s employment, quality of work and patterns of gender segregation. We do so through case studies of two apparel and two footwear factories in Indonesia, one of the world’s largest exporters in the industry, based on factory visits and interviews with employers, technology specialists, workers and worker representatives. These factories are all fairly large and modern and were selected with the expectation that they likely represent “best practice” in terms of technology adoption in supplier factories in an industry, which is generally a laggard in this regard, especially when compared to automobiles and electronics, two other industries organised through GSCs. Our paper sits at the intersection of two strands of literature, the first on automation and employment and the second on feminisation and defeminisation of employment in the face of technological upgrading and the linkages with gender segregation at the task and industry levels. Among our findings is that there is growing use of automation in fabric cutting and spreading and of semi-automation in footwear sewing, but that there is little automation in apparel sewing largely due to technical bottlenecks. While automation created labour displacement at the task level, it did not necessarily do so at the factory level, as affected workers shifted to different tasks. In terms of the gender division of labour, there is evidence that automation is integrating more men into production through the deskilling of sewing in footwear and in the operation of automatic and semi-automatic machines in both apparel and footwear production. While the reduction in the physical demands of certain shopfloor tasks has weakened the traditional rationale for the gender division of labour, we did not identify corresponding shifts on the shopfloor.