Since early 2020, global stakeholders have highlighted the significant gendered consequences of the COVID-19 pandemic, including increases in the risk of gender-based violence (GBV). Researchers have sought to inform the pandemic response through a diverse set of methodologies, including early efforts modelling anticipated increases in GBV. For example, in April 2020, a highly cited modelling effort by the United Nations Population Fund (UNFPA) and partners projected headline global figures of 31 million additional cases of intimate partner violence due to 6 months of lockdown, and an additional 13 million child marriages by 2030. In this paper, we discuss the rationale for using modelling to make projections about GBV, and use the projections released by UNFPA to draw attention to the assumptions and biases underlying model-based projections. We raise five key critiques: (1) reducing complex issues to simplified, linear cause-effect relationships, (2) reliance on a small number of studies to generate global estimates, (3) assuming that the pandemic results in the complete service disruption for existing interventions, (4) lack of clarity in indicators used and sources of estimates, and (5) failure to account for margins of uncertainty. We argue that there is a need to consider the motivations and consequences of using modelling data as a planning tool for complex issues like GBV, and conclude by suggesting key considerations for policymakers and practitioners in using and commissioning such projections.
TOSCAdata: Modelling data pipeline applications in TOSCA
