Much has been made here and elsewhere about how young people are subsidizing older people under the Affordable Care Act. While there is a substantial element of truth to this contention, at least young people generally get to become older people. So, if the ACA were to last for decades, one could drive a small bit of comfort by viewing the arguable inequity as instead amounting to younger purchasers under the ACA just financing the health care they will receive at subsidized rates as they enter their 50s and beyond. The analogy doesn’t work terribly well because unlike something like a long term life insurance policy in which a similar “subsidy” exists, there is nothing that forces those insured later in life to have insured earlier on. But at least youth is a “burden” that most of us share.
A closer look at the evidence, however, shows that the major determinant of whether someone is subsidizing another or being subsidized under the ACA is gender. As shown here, gender is more important than age for purposes of ACA subsidization. And, for most of their adult lives males subsidize women under the ACA. Since gender is largely immutable, males never get the money back. While there are many factors that bear on whether this system is fair, the extent of subsidization is large enough to be worth considering.
The graphic above shows the extent of subsidization. For each adult age (21-64) and each gender, I show the subsidy (positive or negative) the person receives under the ACA. The pink line shows the subsidy for women; the blue line shows the subsidy for men. Subsidization is the difference between the expected costs the person incurs and the person’s premiums under the ACA (without consideration of any government premium subsidies) normalized by dividing the difference by the person’s premiums. Expected costs are calculated based on research by the Society of Actuaries and available in Excel data format from this web site. Premiums are calculated based on data provided by the Kaiser Family Foundation following its study of the ACA. To make sure that the units of of cost used by Kaiser and the Society of Actuaries match up, I apply a multiplicative correction factor to the premiums to ensure that the total level of subsidization is zero assuming that the estimated distribution of uninsured all enroll in ACA plans at an age-independent rate. Use of more complicated assumptions about enrollment patterns, such as incorporation of the apparent fact that most of those purchasing policies in the individual Exchanges already had insurance, would result in a different correction factor but should not alter the basic conclusions of this post about cross-gender subsidization.
When one adds children into the mix, the picture becomes a bit more complex. As shown in the graphic below, insurers under the ACA appear heavily to subsidize children of both genders, although male children are subsidized somewhat more. The calculations here are based on an assumption that child-only policies cost 65.2% of the price for policies sold to 21 year olds. (The 3:1 constraint on the ratio of premiums under the ACA applies only to adults (42 U.S.C. § 300gg(a)(1)(A)(iii)). This assumption was based on my sampling actual policies sold in the individual Exchanges under the ACA.
What is curious and perhaps somewhat comforting to those wanting to see the ACA succeed is the fact that, notwithstanding the significant differences in subsidization, women have not enrolled at rates way higher than men. Overall, government statistics show that 54% of the enrollees are women and only 46% are men. Nor are children forming a large part of the group enrolling in the individual Exchanges notwithstanding the high subsidization rates; they amount to just 6% of the total enrollees as of January 1, 2014. Now, part of this relative equality in enrollment rates by gender could be due to the masking effects of aggregation. It might be that the female/male ratio is considerably higher among those ages 25-35, where the subsidization differential is quite large and the female/male ratio is much lower among those over age 60. Thus, even if the overall ratio of enrollees was quite even, we could conceivably be seeing unequal enrollment patterns within age brackets. As noted in an earlier post, neither the federal government nor any of the states have released data with the degree of detail that would be needed to confirm or refute this possibility and thus the actual joint distribution of enrollment by age and gender remains a matter for estimation using algebra and numeric methods rather than actual data. Still, it certainly appears that the rate of subsidization can not be the only factor affecting enrollment patterns; matters such as income, savings, risk aversion, as well as political, cultural and social factors are likely to be playing a role as well. How else can one, after all, explain the enormous differences in rates of enrollment across various states?
Now, is this “fair”? That’s a difficult question. Most serious questions about insurance underwriting justice are difficult. (I’m going to include a short bibliography at the end of this post). A large chunk of the difference between male and female healthcare expenses are based on the attribution of costs arising out of joint sexual activity to the female only. It is, after all, the female’s body that is primarily affected by pregnancy. That attribution is based mostly on convenience, however, and, in many cases, the difficulty that would be created in trying to collect from a biological father. Moreover, it may be that subsidization in this area is compensatory, addressing countervailing subsidies of men in other government programs.
Even if it is fair, however, to the extent potential enrollees are responding to the extent of subsidization, we need to be concerned that unisex rating is reducing the efficacy of the ACA in shrinking the number of uninsureds. Remember all the ills created by lack of insurance that substantially motivated the ACA? Charging men “too much” leaves many of those ills untreated. If men are not signing up because they are being asked to pay too high a price, the goals of the ACA in reducing the number of uninsureds and improving individual health are compromised. Let us not forget as various politicians attempt to diminish expectations about the achievements of the ACA that it was heavily advertised as a program to reduce the number of uninsureds. Don’t believe me? Look here (32 million), here (34 million by 2019) and here for examples.
There are two additional pictures that may be helpful to those graphically minded in considering this issue. The first, shown below, shows the expected costs of males (blue) by age, the expected costs of females (pink) by age, and the unisex ACA premium (green)(normalized so that the overall subsidization rate would be zero if enrollment rates were age-independent).
The second graphic lets one compare the degree of age subsidization under the ACA. The purple line (kind of a blend of blue and pink) shows the expected costs of enrollees assuming that 50% are male and 50% are female. The green line shows the unisex ACA premium, again normalized so that the overall subsidization rate would be zero if enrollment rates were age-independent among the previously uninsured population. (A different normalization metric should not dramatically change the picture). As one can see although there is a zone between ages 20 and 32 in which premiums are exceeding cost and a zone between ages 60 and 64 where costs are exceeding premiums, and, although as mentioned above, children are heavily subsidized, for most of adulthood, premiums track expected costs pretty closely. This may help explain why neither under my analysis nor under that of the Kaiser Family Foundation do departures of the age distribution from those originally foreseen have a gigantic affect on the profitability of the system. What might have a larger effect, if it were to occur, would be departures of the gender distribution of enrollees from those originally foreseen; but, as mentioned above, thus far this does not seem to be occurring.
I do need to add one critical note. All of this assumes that the expected costs for each age come in as predicted. This is hardly known for sure. There are many reasons, including adverse selection, moral hazard, and others why those costs might depart seriously from that which was projected.
A “starter set” bibliography on insurance underwriting justice
Kenneth S. Abraham, Distributing Risk (1986) (the starting point for thinking about this issue)
Tom Baker, Containing the Promise of Insurance: Adverse Selection and Risk Classification, 9 Conn. Ins. L.J. 371 (2002-2003), available online here.
Seth J. Chandler, Insurance Underwriting with Two Dimensional Justice, available here.
Seth J. Chandler, Insurance Regulation, in the Encyclopedia of Law and Economics, available here.
City of Los Angeles Department of Water & Power v. Manhart, 435 U.S. 702 (1978) (available here)
The Mathematica notebook that underlies the analysis and graphics presented in this blog entry is available on Dropbox here.