But it’s hard to believe that the effect is really huge, precisely because the data tends to be so ambiguous. As Michael Cannon points out, this is the most vulnerable population–adults, many of them near-elderly, below the poverty line. This is where you’d expect to see the biggest effect of putting people on government insurance, because this group has very little recourse to alternative health care resources such as employer insurance, or paying out of pocket. (Though some of them seem to have found some anyway; as I make their somewhat confusing tables, 13% of the control group ended up on private insurance.)

I think this really points up the difficulty of finding good measures of “health”. We can come up with all sorts of objective measures, but we have to keep asking ourselves, relentlessly, whether what we’re actually measuring is good or bad: is higher utilization of services really improving peoples’ lives? Is lowering easily-measurable blood-cholesterol levels, at the risk of muscle atrophy, an improvement in health, and if so, how much? With some exceptions, the easiest things to measure are not necessarily the most important things to well-being.

This has implications for whether or not we should have a public health plan–is it worth the cost to make people feel happier about their insurance status, or protect them from uncollected medical bills rather than certain death? But it also has implications for how we’re going to structure the systems we have.