Subsidized Health Insurance May Lower Poverty, But We Don’t Measure It
Brendan Saloner, PhD, is a Robert Wood Johnson Foundation (RWJF) Health & Society Scholar in residence at the University of Pennsylvania and a senior fellow at the Leonard Davis Institute of Health Economics.
In these early days of the Affordable Care Act, the uninsured rate has begun to sharply decrease. One recent estimate suggests 5.4 million adults gained insurance coverage in the first quarter of 2014. The Congressional Budget Office projects that enrollment in Medicaid and the health insurance Marketplaces will increase even more rapidly over the next two years.
The importance of increased health insurance coverage for improved access to health care justifiably receive much of the public’s attention, but the impact of coverage on the financial health of families may be equally important. Subsidized health insurance can increase the disposable income of families by freeing up money that was previously used to pay out-of-pocket for doctor’s visits and prescription drugs. Newly insured individuals also benefit from the risk-protection of health insurance since even people who use little or no health care are at risk of unexpected accidents or newly diagnosed diseases.
A recent study in Oregon that compared adults who received free health insurance through a lottery to those who applied but did not receive the free care found that the “winners” were much less likely to say that they needed to cut back on necessities to pay for health care. They also had much less medical debt and a lower likelihood of receiving a notice from a collection agency.
The Oregon study, and other studies like it, suggests that subsidized health insurance reduces the incidence of poverty—defined as the inability of an individual or household to meet basic needs with resources available on hand. Unfortunately, the official poverty statistic used by the US Census Bureau does not account for health care spending or health care needs in its calculation of poverty. As a result, poverty measurement does not account for the burden of health care spending on low-income families nor does it account for the impact of public insurance programs such as Medicaid that provide government-subsidized health insurance to households.
Bringing health care spending into the measurement of poverty is not straightforward, however. Consider a family consisting of a single parent with a child. The parent earned $15,000 last year. Last year, the parent was uninsured and the child received Medicaid coverage. The parent spent $500 last year on her own health care (mainly for medication and visits to a community clinic), but they spent nothing out-of-pocket on the child’s health care. The family also received $1,000 from the Supplemental Nutrition Assistance Program (better known as food stamps).
Using the official poverty measure (OPM), this family would narrowly be classified as poor in 2013. The OPM is based exclusively on the amount of cash income (essentially wages plus cash transfers) received by a household adjusted for the size of the household. Depending on the household, focusing exclusively on cash income can either overstate or understate the resources that a family has available to meet their daily needs. Food stamps are one government form of assistance that work effectively like cash income – every dollar of food stamps that families receive equates to roughly one additional dollar of consumption for that family. That being the case, we understate family resources by not looking at food stamps.
Health insurance also has monetary value for families, but nobody can agree on how much. The fact that the child in our example has Medicaid means that the parent will not have to dip into her pocket to pay for a doctor’s visit if the child needs one. If the adult who is uninsured now, picks up subsidized Medicaid coverage this year that will further increase her resources available to pay for other items in the family budget.
The “fungible” value of health insurance—the amount of money that a family would be willing to pay for a dollar of health insurance protection—could range anywhere from zero to more than one dollar. This issue is nicely summarized by the New York Times, and I also previously addressed it here and here. To briefly summarize: if we take the consumption decisions of different households as an indicator of how much they value health care versus other goods, we would conclude that health insurance has no fungible value for the poorest families (absent government programs, the very poorest spend virtually nothing on health care). However, as family income increases, so does willingness to spend money on health insurance protection and to consume health care. Most individuals are risk averse—i.e. they would pay more than one dollar to avoid a one-in-thousand chance of an event that costs one thousand dollars. Thus, one dollar of health insurance would be valued at a rate higher than one dollar. Further problems arise since demand for health care varies not only by income but also by age, disability status, and personal taste.
Partially because it is so very difficult to estimate the fungible value of health care, the Census Bureau has tried a different approach to including health care spending into poverty. Since 2011, the Census Bureau has published a Supplemental Poverty Measure (SPM), which incorporates a wider array of family resources such as food stamps into poverty income, but also subtracts from family income expenses that might reduce family’s ability to meet basic needs. Under the SPM, out-of-pocket health expenses including monthly insurance premiums are subtracted from a family’s income. The rationale is that considering out-of-pocket spending will do more to account for the greater burden that health care needs have on the disabled and uninsured. Conversely, if public programs like Medicaid reduce out-of-pocket spending, then more families will be drawn out of poverty at least insofar as their disposable income increases.
In practice, however, adding medical out-of-pocket costs to the SPM seems to exaggerate the resources of some families and undercount the resources of others. Korenman and Remler use measures of resource deprivation as a test for the sensitivity of the SPM measure and find that the SPM measure with out-of-pocket spending dramatically overstates the number of elderly individuals who are “poor” and understates the number of younger families with children. They argue we would be better off building into our poverty measure the cost of a “basic capped plan,” a universally available, community-rated insurance plan that provides everyone with only those health care benefits that are deemed socially essential. If we can calculate the cost of the basic health plan, we can determine how much to adjust family income in order to ensure every family has enough resources to afford minimally adequate health insurance.
Measuring the cost of the “basic capped plan” focuses our attention where it should go—defining the cost of the minimally adequate bundle of health insurance protection required for families to escape poverty. Even if scholars could agree about how to measure the value of health insurance to different poor families, there would remain deeply contentious questions about how to draw the line between the allotment of health care that is socially required for those families to not be poor. Just as we might disagree about whether a budget that allows a family to only purchase processed and canned foods provides sufficient resources to not be poor, we can expect debates about how much health care, and what kind of health care, is required for a family to have a minimally adequate health care share. Philosophical principles as much as good measurement will be needed to untangle the health care poverty conundrum.