No, US school funding is actually somewhat progressive.

Note: I have already touched on these issues in a much longer, much broader post on education and on twitter at some length, however I thought it’d be useful to zoom in on this issue and marshal the evidence in one place for future reference.

It is still commonly 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modest inequalities associated with local income levels (several decades earlier), this information is generally wildly out of date today.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).

The explanatory power of drugs, car accidents, and homicides on US life expectancy gaps

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.


Towards a general factor of consumption




Health, consumption, and household disposable income outside of the OECD




On popular health utilization metrics

This Commonwealth Fund report has been widely cited for explaining why US health expenditures are so high.

The analysis finds that the U.S. spends more than all other countries on health care, but this higher spending cannot be attributed to higher income, an aging population,
or greater supply or utilization of hospitals and doctors. Instead, it is more likely that higher spending is largely due to higher prices and perhaps more readily
accessible technology and greater obesity.

Since I have already spoken to the incomes argument at some length and explained why I find overall “high prices” to be unpersuasive as it pertains to NHE in general and the US specifically, I will instead focus narrowly on this utilization argument since there are a number of similar analyses with identical/similar indicators.

The report proffers this table as an explanation for why high utilization cannot explain high US health expenditures.


Similar analyses are found elsewhere:



My problem with these sorts of analyses is that these sorts of indicators do not themselves account for enough NHE directly or correlate with NHE well enough to claim to account meaningfully for utilization and other major non-price drivers of NHE (if you wish to remove quantities of technology, prescription medicines, etc from “utilization” for semantic reasons).  When (1) your utilization measures can only account for maybe 10-15% of the variance (2) only relates to a modest proportion of NHE in most developed countries and (3) one ought to know there are other major cost drivers to account for, it’s pretty silly to claim that your half-hearted attempt to explain the variance honestly means it cannot be utilization and that it must be (mostly) the result of some US specific prices.

Some useful data on the dispersion characteristics of US health expenditures

Some people (1, 2, 3, 4)  have made hay out of data showing total health spending in the United States is heavily concentrated on a small fraction of the population:



My intuition and knowledge of the health care industry has long led me believe this likely not too dissimilar from what goes on in other developed countries (the young and/or reasonably healthy simply do not need or want much in the way of health care).   If nothing else it seems unreasonable to use this data to argue the US is an outlier without at least going through the exercise of comparing it to other countries.  Unfortunately the OECD and related entities provide little in the way of public data along these lines, so I have not been able to do this analysis myself.

However, I recently stumbled across a blog post from IFS regarding a study they published that speaks directly to these and related topics, so I thought I would briefly share this and related information in a quick-and-dirty blog post (full-text copy here).

Disposable income also explains US health expenditures quite well

few months ago I argued consumption, specifically Actual Individual Consumption, is an exceptionally strong predictor of national health expenditures (NHE) and largely explains high US health expenditures.  I found AIC to be a much more robust predictor of NHE than GDP and at least an order of magnitude stronger than other components of GDP when disaggregated (collectively and separately) in multiple regression analysis.

However, because some people are inherently suspicious of consumption per se and because others are under the impression this is primarily about financing (health) consumption out of savings/debt, I think it useful to also demonstrate these patterns as it relates to household disposable income (tl;dr they’re very well correlated and produce very similar results in the long term)

US life expectancy is below naive expectations mostly because it economically outperforms

In my prior few posts I made a strong case that the United States’ exceptionally high health care expenditures are well explained by its unusually high material standard of living.   In response to this several people I have interacted with have fallen back to the position that something still must obviously be uniquely wrong with the US health care system because US outcomes are significantly below what one might expect given its level of spending:

Life expectancy in OECD

They believe it cannot be a coincidence that the country that spends so much more than expected (according to naive expectations) also gets worse outcomes than expected and generally gets worse outcomes than the most developed countries of predominantly European and Asian origin.

In this blog post I will address the so-called “outcomes” dimension and explain why these apparently sub-par outcomes are not only not otherwise inexplicable, but can actually be explained in a fairly straight forward and parsimonious fashion  For the moment, I will narrow my focus on the subset of factors that drive US health outcomes significantly below naive expectations (not necessarily the full residual) and that I have good reason to suspect are significantly causally related to the expenditures issue.  Later, perhaps in another lengthy blog post, I will address other factors that are mostly orthogonal to expenditures and that further affect US health outcomes.

Predicting health care expenditures in OECD panel data as a non-linear relationship

In my prior post, wherein I argued at length that US health care expenditures are reasonably well explained by Actual Individual Consumption (AIC) and that GDP is an inferior predictor, I pointed out toward the end that the linear specification I used is likely to significantly overstate US residuals because there is good evidence for non-linearity and because the US is far out on the frontier vis-a-vis consumption.

This non-linearity can be seen pretty clearly if you look at the 2011 data derived from the World Bank (for AIC) and WHO (for HCE).

In per capita terms
In percentage terms

Since some people may (1) doubt the accuracy of these statistics outside of the few highly developed countries (2) imagine that these poor countries are somehow qualitatively different in a way that’s not well correlated with their level of economic development or (3) are particularly reluctant to accept non-linearity as a potential partial explanation for the US here, I thought I’d approach this from a somewhat different angle.



Edit (12/12/18): I recommend reading this newer post instead if you’re haven’t heard from me before on this topic.




These plots and the arguments that usually go with them give the strong impression that US spends about twice as much as it should.  However, these are misleading for several reasons, namely:

  1. GDP is a substantially weaker proxy for “wealth” and a substantially weaker predictor of health care expenditures than other available measures.
  2. The US is much wealthier than other countries in these plots in reality.
  3. The arbitrary selection of a handful of countries tends to hide the problems with GDP in this context and, oddly enough, simultaneously downplay the strength of the relationship between wealth and health care spending
  4. Comparing these two quantities with a linear scale tends to substantially overstate the apparent magnitude of the residuals from trend amongst the richer economies when what we’re implicitly concerned with is the percentage spent on healthcare.

When properly analyzed with better data and closer attention to detail, it becomes quite clear that US healthcare spending is not astronomically high for a country of its wealth.  Below I will layout these arguments in much greater detail and provide data, plots, and some statistical analysis to prove my point.