Statistical Insight: men’s employment more dependent on trade than women’s
by Fabienne Fortanier, OECD Statistics and Data Directorate
Concerns are growing that globalisation may have created a few big winners at the expense of many losers. This has stimulated efforts to analyse how trade can be made to Work for All, for example by focusing on the skills and occupations of affected workers. However, there has been less attention to the gender dimension of globalisation and global value chains, and in particular to whether they are having differing effects on men’s and women’s work.
New analysis shows that men’s employment is more dependent on international trade than women’s. On average across the countries studied, 37% of men’s jobs, but only 27% of women’s jobs, depend on exports – either because the firms they work for export directly, or because they indirectly supply other firms that subsequently export. This compares to only 27% of women’s employment (Figure 1).
Focusing on manufacturing exports only (which account for around 70% of international trade), Figure 2 illustrates that in nearly all countries, the share of women in employment that is indirectly sustained by manufacturing exports is higher than the share in employment that is directly sustained. For example, in Germany, women’s share of manufacturing jobs that is directly sustained was just over 20% in 2014 but close to 35% of indirect jobs (Figure 2).
Women’s jobs are thus both less dependent on trade overall, and less directly involved with manufacturing exports. These trends arise partly from differences in female labour participation across industries and partly from the relative contributions of these industries to total trade. Overall, women tend to work in business services and in other, mainly non-market, services, rather than in manufacturing, where on average they only account for a quarter of the workforce.
However, while men account for the lion’s share of the work involved in manufacturing exports, that work generates a substantial number of upstream jobs held by women. As Figure 3 shows, for each unit of labour input in direct manufacturing exports, an additional 0.5 unit of female labour input is generated in the companies that supply exporters, as well as an additional 0.9 unit of male labour inputs. Put differently, each job in manufacturing exports generates on average 1.4 additional jobs upstream, a third of which are jobs held by women.
The nature of the upstream participation also differs significantly between men and women. While the bulk of upstream jobs are in the services sector, this is particularly true for women’s jobs. Taking again Germany as an example, Figure 4 shows that less than 20% of women’s upstream jobs are in industrial and goods sectors (Agriculture, Utilities, Construction, Manufacturing and Mining), compared to 45% for men.
The detailed data compiled for this analysis on the export-dependency of men’s and women’s jobs provide an indication of the extent to which reducing gender wage gaps will depend on encouraging more women to seek employment in higher-paying sectors of the value chain.
How the indicators were constructed
The estimates of female employment in global value chains were produced by combining the TiVA ICIO (2008-2014) with data on labour input by industry, measured in hours worked as reported in the National Accounts, broken down by gender. The gender breakdown was derived from Labour Force Surveys, which is the only sufficiently detailed source to support this analysis, using a combination of total employees (male/female) broken down by industry, corrected for average weekly working hours to adjust for the fact that in many countries, women work fewer hours on average. For details on the calculations, as well as on the estimations made in case of missing data, see the accompanying background note.
- Gender in Global Value Chains: the impact of trade on male and female employment
- Background note on women in GVCs
- Gender in Global Value Chains: how does trade affect male and female employment?, OECD Statistics Newsletter (2018), Issue 68, July 2018