Alex Tabarrok of Marginal Revolution linked to my primer and other research on health care recently. This brought a lot of extra attention to my work. Most of the response was positive, I think, but several people angrily lambasted my analysis. The adverse reactions generally amounted to little more than name-calling, and much low-quality material was amplified by the usual suspects.
I will not respond to name-calling. I will, however, reply to the few seemingly substantive critiques for the benefit of those that have trouble recognizing how weak these arguments are. I will also use this opportunity to drive home the point that health spending growth has clearly been increasingly non-linearly as a function of income, with the slope increasing, increasingly, in global cross-section and OECD panel data analysis. Indeed, I will show that the constant rate of relative increase implied by log-log regression specification, which triggered certain people, tends to underestimate the slope at higher income levels significantly.
On Twitter, the other day, Noah Smith (@Noahpinion) argued the mere fact my model shows the income elasticity of health expenditure is greater than one implies these higher-income regimes are “starving themselves to death” to pay for healthcare.
Noah does not seem to appreciate that (1) my independent variable, income, is measured in real terms and (2) the relative price of expenditures changes systematically with incomes. Real income levels are overwhelmingly determined by productivity. As incomes rise, it generally takes less input to obtain the same quality-adjusted quantity. It is quite possible to allocate less input (especially human resources) to a given expenditure category and, simultaneously, to consume a larger quantity of that category per capita. The rise in productivity leaves room to increase real consumption.
Above and beyond the real income growth (i.e., the decline in average prices relative to nominal income levels), prices move relative to each other. Relative prices are systematically related to real income levels (spatially and temporally). Some prices rise relative to other prices due in large part to inherent differences in potential productivity growth and their exposure to the domestic labor costs.
Relative prices are clearly very much related to income growth, but relative prices are nonetheless an important partial conceptual explanation for the changing consumption share. Broad price indices are useful statistical tools, however, they hide significant systematic differences in underlying prices that are necessary to understand differences in material living conditions over time and between countries.
In the popular telling, there is a strong and reasonably constant relationship between health spending and life expectancy. We can presumably compare countries of “similar” development with eyeball regressions well enough to make strong inferences about the efficacy of a particular country’s health care system1. I have doubts.
In reality, healthcare is surely subject to rapidly diminishing returns and other factors shape outcomes largely independent of actual health inputs. Striking as such plots may be, the apparent slope for the United States can be readily predicted based on the relationship observed in other high-income countries with nothing more than time series for health expenditure and life expectancy. Truth be told, the average marginal effect of health expenditure for high-income countries in recent years is likely pretty close to zero (particularly as pertains macro-level indicators like life expectancy).
The US intercept, meanwhile, can be explained quite well by obesity. Obesity is likely to have very large negative effects on all manner of health outcomes. It is actually much more predictive of outcomes in the developed world than health spending. Indeed, the effects of obesity and related western diseases may be such that the apparent slope on health spending turns unambiguously negative in the developed world in the future (cross-sectionally, even if not in time series…).
Income is a double-edged sword. Income growth stronglypredicts health spending growth, but income growth also predicts rising obesity, diabetes, prescription opioid use, illicit drugs, and likely several other “western” illnesses. While there probably are some idiosyncratic US factors not explained by current income levels (e.g., deep roots), many of these “American” issues are more widespread and more linked to income than most people appreciate. The equilibrium between the positive side (esp. healthcare) and the negative side (overeating, drugs, etc) is likely to seriously confound extrapolation from prior experience because of the non-linearities and tipping effects involved with these different processes.
Disclaimer: The title of this post is somewhat tongue-in-cheek and I fell slightly short of one million charts. Also, Noah probably isn’t wrong about everything he’s ever said on this topic, just the central points under contention, and he has tons of company.
Noah Smith (Noahpinion) recently published a column in Bloomberg with the title “Health-Care Costs Are Still Eating the U.S. Economy.” This came to my attention because he was kind enough to link to my blog when he alluded to my arguments on health spending. Though there are obvious areas of agreement (e.g., that health spending has risen faster than income; that the so-called “cost curve” hasn’t been bent; that rising health spending puts a very real crimp on take-home paychecks amongst the working class), I strongly disagree with the central arguments he advanced and feel that he did not quite accurately characterize my views.
“In sum, health care is still eating the economy, and that’s still cause for alarm….Why is this happening? Some argue that the U.S. is just very rich, and that prosperous countries choose to spend more on health care, which drives up prices. They note, for example, that although U.S. health-care spending is unusually high as a percent of gross domestic product, it’s not th at high as a percent of individual consumption. But this isn’t very convincing, because consumption is a result of high health-care prices as well as an effect — high health care prices force Americans to use more of their income than people in other rich countries.”
He seems to misunderstand the argument and appears to be insufficiently aware of the evidence against his assertions. Since he is surely not alone in these thoughts, I am going to put some time into weaving together statistical evidence, supporting data, and thoughts I have strewn over a good number of blog posts, tweets, etc. over the years. My goal in this post is to parsimoniously explain the evidence at multiple levels, clear up misperceptions, and address probable objections for the benefit of those that have invested considerably less time into assessing the evidence for themselves.
(warning: this is going to be even longer than usual).
He believes that a distributionally-adjusted measure is a more relevant way of understanding health spending and that higher US income inequality implies much lower spending. Several others have also raised similar objections in response to my arguments on US health care over the past year or two.
I have several lines of response to these sorts of concerns.
As I mentioned in two (long) posts on how comprehensive measures of household consumption or disposable income explain high US national health expenditures (NHE) rather well, I believe US health expenditures are overwhelming accounted for by higher volume of health consumption (the quantities of goods and services consumed in the health care sector). I reject the popular notion that idiosyncratically high prices are what causes the US to spend so much more than other OECD countries and I reject the notion that rising health prices explain why US health expenditures have grown so much faster than income over the past few decades (at least).
This does not mean that prices play no role whatsoever in international comparisons of NHE. It does not mean, for instance, that if we could somehow force U.S. physicians, nurses, and various other workers in the health care sector to work for the real remuneration paid to their counterparts in, say, Brazil with equal productivity that the price of medical care, especially more labor intensive categories, would not plummet (presumably real NHE could decrease if volume were hold constant…. not a very safe assumption imo). It means that the reason health expenditures have accounted for an increasing share of our consumption (or income) over time has little to do with prices increasing relative to income and a great deal to do with the volume of health goods & services consumed rising at a faster rate than overall consumption (or income) per capita.
Much the same goes for international price comparisons in cross-sectional analysis. The cost of an internationally equivalent basket of health care (both goods and services) rises a little faster than the cost of a broader, economy-wide/GDP, basket of goods and services with rising income levels, but it does not rise all that much faster with respect to income (actually somewhat less overall in the long run).
In order to significantly explain the presumably large US residual in NHE per capita (obviously I don’t quite agree with this perspective) there must be a large residual in the actual relative price levels; we simply do not find any evidence of this with the best available evidence. With better data US residual is either slightly above trend (by no means an “outlier”) or below trend, depending on the precise predictor (e.g., GDP, AIC, average wages, etc) and the basket of goods and services employed. In no case do I find evidence of a large departure from trend in any broad, highly impactful, index of health price levels that would suggest that high US NHE isn’t overwhelmingly explained by high volumes of (real) health care consumption. More generally, the cross-sectional and time series data are actually quite consistent with each other, i.e., the issues that drive (US) expenditures up in time series are quite consistent with and related to the what we observe in cross sectional analysis between countries.
It is stillcommonly supposed by much of the public that school funding is terribly unequal due to reliance on local funding mechanisms (especially property tax). Although there were once modest inequalities associated with local income levels (several decades earlier), this information is generally wildly out of date today. Within the vast majority of states districts with less advantaged students (read: higher poverty, lower income, fewer parents with college degrees, minority, etc) actually spend at least as much money per pupil (often more), both overall and in the narrower instructional expenditure category, and where there are inequalities these differences are usually quite modest and fleeting.
Though school funding is still significantly a local affair in most states there is substantial progressive redistribution of state and federal funds that effectively offset these potential inequalities (and then some). Some districts may choose to spend more controlling for income/wealth (tax effort) and there is some variance (mostly poorly explained by any SES measure), so that malcontents can always find isolated examples to complain about, but various formulas employed at the state and federal level sets a floor and effectively acts to prevent there being substantial positive correlations between school spending and district median income (or low poverty rates, percent minority, school free lunch percentage, and so on …. this holds across multiple measures).
Although I discussed similar issues in a prior post on US health outcomes, I recently stumbled across a JAMA article authored by several CDC researchers (h/t @bswud) which points out that drug poisonings, fire-arm homicides, and motor vehicle accidents can directly explain a large part of the US life expectancy gap with several major comparison countries. By “directly” I mean that which can be mathematically estimated through the actual causes of death instead of its statistical association with life expectancy more broadly. The actual causal effect from statistical estimates are likely to be inflated by other factors that are associated with it (though I personally believe there is still a meaningful signal in the difference between the two estimates insofar as it can act as a proxy for other lifestyle differences as well and that these sorts of differences are far more important than modest differences in how health care is provisioned between different developed countries at present)
In 2012, the all-cause, age-adjusted death rate per 100 000 population was 865.1 among US men vs 772.0 among men in the comparison countries (Table 1), and 624.7 among US women and 494.3 among women in the comparison countries. Men in the comparison countries had a life expectancy advantage of 2.2 years over US men (78.6 years vs 76.4 years), as did women (83.4 years vs 81.2 years). The injury causes of death accounted for 48% (1.02 years) of the life expectancy gap among men. Firearm-related injuries accounted for 21% of the gap, drug poisonings 14%, and MVT crashes 13%. Among women, these causes accounted for 19% (0.42 years) of the gap, with 4% from firearm-related injuries, 9% from drug poisonings, and 6% from MVT crashes. The 3 injury causes accounted for 6% of deaths among US men and 3% among US women.
The US death rates from injuries exceeded those in each comparison country (Table 2). Among men, these injuries accounted for more than 50% of the life expectancy gap with Austria, Denmark, Finland, Germany, and Portugal. Among women, they accounted for more than 30% of the gap with Denmark, the Netherlands, and the United Kingdom. The country-specific comparisons depend partly on the actual size of the gap in life expectancy between the United States and each country. For example, men in Portugal have lower injury mortality than US men, but a small life expectancy advantage, which results in the 3 injury causes accounting for more than 100% of the gap.
They didn’t provide any visualizations so I thought I’d share some using their estimates.