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The Economic Burden of the “Tampon Tax” on Low-Income Households
Specifically, this research examines the price elasticity of demand for menstrual products among low-income households (calculated as 185% of the poverty line) as compared to their higher-income counterparts. We use the 185% threshold because it is used for many U.S. federal food assistance programs on the basis that food is also a necessity. We use the Nielsen Consumer Panel Dataset, which tracks retail purchases of 40,000-60,000 households over 2004-2015. As expected, our simple demand model finds low-income households are more own-price inelastic than higher-income households for both tampons (-0.45 vs. -0.52) and pads/pantyliners (-0.41 vs. -0.45); when facing the same supply curve and tax rate, this implies a menstrual product tax is regressive.
To examine the magnitude of the burden, we apply state-level tax rates to purchases in our data, and we calculate the burden as the ratio of tax expenditure to household income. We find that the tax burden on low-income households is about 3 times larger on average than that on higher-income households in 2004-06. The relative magnitude of the burden increases steadily over 2006-2011 to 3.5 times before decreasing over 2012-2015 back to roughly 3 times. We suspect that there may be a correlation with the passing of the Affordable Care Act (ACA) and the decreased burden on low-income households: as of August 2011, the ACA eliminated out-of-pocket charges on contraceptives. This mandate effectively lowered the cost of highly effective (and expensive) contraceptives, such as intrauterine devices (IUD), which can substantially regulate (or even mitigate) menstruation. While future research is needed to examine the linkage between the tax burden and the ACA, our study is the first to estimate both the regressivity and the burden of the so-called tampon tax.
Researcher(s) own analyses calculated (or derived) based in part on data from The Nielsen Company (US), LLC and marketing databases provided through the Nielsen Datasets at the Kilts Center for Marketing Data Center at The University of Chicago Booth School of Business.
The conclusions drawn from the Nielsen data are those of the researcher(s) and do not reflect the views of Nielsen. Nielsen is not responsible for, had no role in, and was not involved in analyzing and preparing the results reported herein.