Here’s an admission: there is a lot we don’t know.
Although the gendered impacts of tax are becoming better and better understood, lack of data is (in most jurisdictions) a major hurdle not only to analyzing the scope of the problem, but also to creating solutions.
For example, tax filers are usually not identified by sex; many women taxpayers are hidden behind joint filing systems (in which their earning is often considered to be supplemental to that of a male breadwinner); expenditure and often income data are collected at household level. In general, governments simply do not collect the sex-disaggregated data needed for tax incidence analysis, and the data that do exist do not have sufficient information to do a full gender analysis.
We do know that:
- Women overall earn less than men, and are more affected by poverty and disadvantage.
- Women are more likely to work in low-paying, precarious jobs (often with no benefits, and/or in the informal sector) and they often work longer hours than men when you take unpaid care work into account.
- In many countries, we have good data and information about how the poorest sectors of society are negatively impacted by tax policy. Because women are over-represented among the poor in most-countries, we can therefore safely say that a disproportionate number of women suffer these impacts.
- Women do most of the unpaid care work, which includes shopping for groceries and household goods – and therefore will bear the brunt of VAT, which we know is a regressive tax. (Yes, in some households it will be joint income or money earned by a male head of household that will be be used to pay for these goods; but many women are single mothers, and many poor women in two-parent households use their own meagre income to pay for goods that benefit the children and the household, having no access to their male partners’ earnings.)
- Many policies and tax codes implicitly or explicitly stereotype women as wives and mothers, reliant on a male breadwinner.
- Women are more reliant on public services, both because of their higher likelihood of poverty and their gender itself (pregnancy, maternity and childcare make it hard for even healthy women to avoid hospitals and health clinics, for example).
- Women are also more likely than men to work in the public sector.
- So… we know women are disproportionately affected when government budgets and services and cut. Even as many women are laid off or their benefits cut, their work in the home is increased at these times, as they substitute for the health and care services cut.
However, although some excellent empirical studies have been done (please get in touch/comment below if you have good examples to share!), in most jurisdictions there is a significant lack of disaggregated data to conclusively show the gender distribution and impact of tax – with concrete improvements in recent years very limited in scale.
We know from long experience that without hard quantitative data, legislators and policy-makers are likely to drag their feet or deny there is a problem. Although we hope progress can begin without waiting years for data that will likely back up what we already know and can infer, more and better data will likely be a key persuasion tool moving forward.