Is it “Schlocky” to Compare Life Expectancies Between Countries?

Greg Mankiw writes:

The next time you hear someone cavalierly point to international comparisons in life expectancy as evidence against the U.S. healthcare system, you should be ready to explain how schlocky that argument really is.

He points to the following claim by Gary Becker:

National differences in life expectancies are a highly imperfect indicator of the effectiveness of health delivery systems.for example, life styles are important contributors to health, and the US fares poorly on many life style indicators, such as incidence of overweight and obese men, women, and teenagers. To get around such problems, some analysts compare not life expectancies but survival rates from different diseases. The US health system tends to look pretty good on these comparisons.

Becker cites a study that finds that the U.S. does better than Europe in cancer survival rates and in the availability of hip and knee replacements and cataract surgery.

It makes a lot of sense to think of health as multidimensional, so that some countries can do better in life expectancy while others do better in hip replacements and cancer survival.

But I disagree with Mankiw’s claim that it’s “schlocky” to compare life expectancy. If the U.S. really is spending lots more per person on health care and really getting less in life expectancy compared to other countries . . . that seems like relevant information.

To put it in statistical terms: much of our quantitative analyses are essentially comparisons. And, once you’re comparing, it makes sense to consider other factors (for example, Americans are less likely than Europeans to smoke, and more likely to be obese). But the overall outcome is important in its own right. Becker mentions cancer survival rates, and, cancer survival is definitely important–more important than all the research I’ve ever done, that’s for sure–but a large change in cancer survival rate does not necessarily correspond to a big increase in life expectancy. And the same can be said for joint replacements and cataract surgery. What’s missing in Mankiw’s discussion is the connection between the huge cost differences between the U.S. and other countries, and the very specific cases where our system works better.

The funny thing is, I think my former co-blogger Robin Hanson would probably agree that government-funded healthcare is a bad thing–but for an opposite reason from Mankiw’s! Hanson would oppose government health care, I think, because he would fear that it would lead to political pressure to spend even more on healthcare that, as he sees it, doesn’t actually do much of anything to improve net health outcomes. In contrast, I think Mankiw is opposing a government system because he fears it would lead to cost-cutting and a move to a European-style system with lower cancer survival rates, fewer hip replacements, etc.

In summary, I am sympathetic to Mankiw’s frustration with people who draw sweeping conclusions from raw comparisons. If policymakers are interested in moving the U.S. to a medical system more like France’s, or Taiwan’s, or whatever, they ultimately should be looking not at static comparisons but at how health and cost outcomes might change here under different proposed policies.

That said, life expectancy is important. If you’re going to make a raw comparison, I’d rather compare countries on life expectancy than on cancer survival rates or the availability of hip replacements and cataract surgery.

About Andrew Gelman 26 Articles

Affiliation: Columbia University

Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40.

His books include Bayesian Data Analysis (with John Carlin, Hal Stern, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), and, most recently, Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, Joe Bafumi, and Jeronimo Cortina).

Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.

Visit: Andrew Gelman's Website

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