People who write about health insurance in the United States often cite statistics about what percentage of the population is insured. These statistics typically use point-in-time (PIT) measurements that indicate what percentage of the population is insured at any given point in time. These PIT measures are useful, but they can also lead people to believe that getting health insurance is a much rarer experience than it really is.
To illustrate this problem with PIT interventions, I used the Longitudinal Survey of Income and Program Participation (SIPP) to determine what percentage of the population was uninsured in a given month between January 2017 and December 2020 and what percentage of the Population were not insured at all point between January 2017 and December 2020.
The black line is the most commonly reported PIT measurement. It shows that in a typical month around 7 percent of the population is uninsured. The red line is the longitudinal measure and shows that in those four years, around 21 percent of the population was uninsured for at least one month. New non-insurance spells are most common in January of each month, which you would expect since health insurance plan years typically end in December.
Here’s the same chart, but for non-senior adults.
Among non-elderly adults, around 27 percent were uninsured for at least a month between January 2017 and December 2020.
Finally, here’s the same chart, but for people who were on Medicaid in January 2017. Initially, this group has a 0 percent non-insurance rate because they are all on Medicaid. But over time we can see that they are also often uninsured.
The Medicaid case is interesting because one hears again and again in the health insurance discourse that those insured with Medicaid are already insured and therefore do not benefit from various reform efforts. But as we can see above, that’s not true. Today’s Medicaid recipient is tomorrow’s uninsured. In fact, today’s Medicaid recipient will hold all types of insurance status in the future.
In general, policymakers and policy commentators often fall into the trap of looking at cross-sectional data and assuming that population categories are much more static than they really are. Yesterday’s uninsured are not today’s uninsured and today’s uninsured are not tomorrow’s uninsured. The same applies to the unemployed, the poor and many other similar groups.
When you patch the overall system together to reduce, eliminate, or improve certain economic statuses, the number of people that is helped is not the number of people who are in that status at any given time. It’s way bigger than that.
