Invisible Women: Data Bias in a World Designed for Men

Perez argues that the GDP, which is the “standard measure of a country’s economy,” has “a woman problem” (239). Created in 1934 to accommodate a war economy, GDP counted what governments and businesses produced. It did not include unpaid household labor, such as cooking and childcare, and therefore excluded women’s contributions in the interests of simplicity. Perez dubs this “the greatest gender data gap of all” (241). It is estimated that unpaid work constitutes as much as 50% of GDP in high-income countries and possibly 80% in low-income countries. Failure to count this data allows women’s unpaid work to be viewed as a costless resource to exploit. When cuts are made to social care budgets in contemporary times, the burden shifts primarily to unpaid women. The elderly and children must still receive care, and when women pick up more of these unpaid responsibilities, their participation in the paid labor force decreases. That development in turn lowers GDP and increases the wage gap between women and men. In the European Union, for example, 25% of women and only 3% of men attribute the reason for not being in the paid labor force to care work.

Perez concludes that the introduction of universal childcare in all countries would be the “best job-creation programme” (247).