National Healthcare Expenditure: United States versus Other Countries: The US is not really an outlier.

Numerous people have asserted that the United States spends dramatically more on healthcare than other countries, presumably even more than countries of our level of wealth and affluence, and that this can only be explained by the fact that we do not have single-payer or some such.

Here are some examples graphs used to make this point

Above-expected-500x406 (1)


These appear to be very convincing at first blush, but i never found these arguments particularly convincing due primarily to:

  1. Imperfect comparability between the selected countries
  2. Issues relating to comparing countries of the “same” GDP
  3. cherry-picking of countries

I knew the proponents of single-payer were, at best, making an incomplete argument and that it invited an exaggerated impression of what we should likely expect from a country like ours, but, up until now, I lacked the data and the time to present these argument comprehensively.  I recently got in an argument with someone over this subject and found a treasure trove of data all in one place (mostly) to thoroughly debunk this overly simplistic argument.

To make my points, vis-a-vis fundamental issues with naive treatment of GDP per capita and sensitivity to comparison countries, here is a quick chart showing National Healthcare Expenditure (NHE) as a percent of GDP by GDP per capita


And here is another using all countries in the WorldBanks dataset with GDP per capita > 10K.  Here I am displaying both the WorldBank’s NHE (GDP component) and the WHO’s NHE data (note: this data is not complete and the numbers are slightly different).


Note: Regardless of the NHE figures used (i.e., World Bank vs WHO), neither is that well correlated with GDP per capita and these correlations are certainly not a simple linear relationship (as in the two charts at top).  This situation is even worse if you look at this in NHE at the percentage of GDP terms.  I excluded a lot of poor countries (<10K/yr GDP per capita) and I honestly did not make any attempt to manipulate this data. You might get a slightly different result with better matching of WHO figures (string matching/vlookup on different country naming conventions) but the essential conclusion is no different if you pick a reasonably broad array of countries and do not systematically exclude outliers.

The point here should be very clear: this sort of analysis is highly sensitive to which countries are selected for comparison.  A few petro states, financial centers, offshoring banking, tourism-driven economies, and other atypical countries will quickly blow this analysis up.  Moreover, it already looks as if there is not a simple linear relationship between NHE and GDP in per capita terms.


When KFF et al. say stuff like “While an increasing GDP per capita is associated with increased health spending, the United States is an outlier, spending more than similarly wealthy countries” they imply that GDP is a good measure of “wealth” here and that there is a linear relationship.  GDP dramatically and systematically understates US “wealth” (perhaps better phrased as material standard of living) relative to these and other countries (some not shown in their simple analysis).   Amongst other issues here, GDP per capita includes net exports (exports minus imports), tourism, banking, and other activity that does not directly contribute to the actual standard of living of the country.  There are significant differences between countries of the “same” GDP.   A country that derives, say, half of its GDP from exports or from tourism certainly gets economic benefit from that activity in the form of jobs and income, but that activity is not directly producing goods domestically, i.e., a country of the same GDP but that is not reliant of net exports, tourism (as in, tourists spending money on hotels, restaurants, etc), or the like is apt to enjoy a much higher standard of living and higher effective personal incomes than GDP alone would lead you to believe.

So, with that in mind, here are three graphs to illustrate some differences between the United States and the OECD countries that we are frequently compared to (including some other very high GDP countries to make this point very clear).

Per Capita Components of GDP (OECD)


Per Capita Individual Consumption Expenditures (OECD)


Core Components of GDP amongst top 16 countries by GDP per capita (PPP-adjusted)


Long story short, GDP ought to be interpreted with a huge grain of salt if we are claiming to predict material living conditions and/or prevailing wages (note: healthcare is man-power intensive and does not scale like, say, manufacturing can in rich countries).  To reinforce this point, and to highlight the double-standard of the nationalized healthcare proponents, vis-a-vis “it can only be because our system is (or is not) X”, here is a graph showing tertiary education expenditures in US versus other countries.   Seeing a pattern here yet?


Note: They did not use a linear projection here (unlike with healthcare) and still we are way above trend.

There are much better measures of “wealth” for our purposes here and this significantly tighten up these relationships.  No measure is perfect, but some of them are much less broken than others for our purposes here.   Because this data is readily available from the World Bank, let’s go with several measures used by the World Bank:

GDP and Actual Individual Consumption

GDP and GDP per capita are the measures most commonly used to compare the economic size of countries and the economic welfare of their populations (when economic welfare is measured by the volume of individual goods and services consumed). But they are summary measures. GDP, for example, says nothing about the distribution of income within a country, while GDP per head has limitations as a measure of economic welfare. Not only does it cover the goods and services that resident households consume to satisfy their individual needs, it also includes services, such as defense, police and fire protection, that governments produce to meet the collective needs of the community, as well as gross fixed capital formation and net exports neither of which constitute final consumption.

A possible alternative to GDP would be individual consumption expenditure of households, but for the different ways health and education services are financed in countries.

In some countries, governments or non-profit institutions serving households (NPISHs) provide the greater part of health and education services and these expenditures are included in the individual consumption expenditure of government or NPISHs. In other countries, households purchase nearly all health and education services from market producers and these expenditures are included in individual consumption expenditure of households. In these circumstances, individual consumption expenditure of households is not the correct measure with which to compare the volumes of individual goods and services actually consumed in different countries. Household in countries where government or NPISHs are the main providers of health and education services will appear to consume a smaller volume of goods and services than households in countries where households pay directly for the bulk of these services.

To overcome this problem, the ICP has, from the beginning, added the individual consumption expenditures of government and NPISHs to the individual consumption expenditure of households to derive the consumption expenditure of the population. On a per capita basis, this is a better measure of the economic welfare of households as it comprises only the individual goods and services that households actually consume. It covers all such goods irrespective of whether they are purchased by households themselves or are provided as social transfers in kind by government or NPISHs. Two decades later the concept was adopted by national accountants. It is referred to as actual individual consumption in the System of National Accounts 1993.

ICP comparisons are organized so that both GDP and actual individual consumption of countries can be compared.


Shorter version

  • Actual Individual Consumption (AIC) = Expenditures consumed by individuals (including private, government, and non-profit/NGO transfers, subsidies and the like).  This includes food, education, healthcare, housing, entertainment, transportation, and so on.
  • Individual consumption expenditure of households = the subset of the AIC paid for directly by individual households (as in after-tax and after-transfers)
  • Individual consumption expenditure of government = the subset of AIC paid for government (e.g., public education, public healthcare expenditures, etc)

These components (all adjusted for purchasing power parity, i.e., PPP) allow us to focus on that subset of GDP that most closely resembles the material standard of living of the country in question without injecting net exports, capital investment (as the sorts you’ll see in petro states to pay for future extraction), and so on.

Ok, with that out of the way…..

National Healthcare Expenditure (NHE) per capita by Actual Individual Consumption per capita


Observe how it accommodates a much broader array of countries here (e.g., Luxembourg, Kuwait, UAE, etc), generates a much tighter fit (r^2=~0.9), and actually does not increase linearly with income (that’s a 2nd order polynomial).

NHE as percent of AIC by AIC per capita


[Note: This isn’t a perfect fit and it’s not in GDP terms, but it captures the relative magnitude of NHE in consumptive terms than does the GDP per capita projection with a similarly large dataset.  The US certainly does not look like an outlier here!]

Here is another way to approach the “outlier” question….

NHE and Individual Consumption Expenditure by *households* by GDP per capita



[Note: I intentionally picked a very similar set of countries as the KFF analysis above.  The pattern of NHE and Household consumption are striking.  The US is a clear outlier in Individual Consumption Expenditure by Households per capita and in NHE, but the size of households consumption clearly swamps NHE.]

US disposable income is significantly higher in part because we have much lower taxes than most of Europe does (especially amongst the broad middle class), so let’s look at this by that component of AIC that’s actually spent by households directly (not government spending)!

Total NHE expenditures per capita by Individual Consumption Expenditure by Households


and on a broader array of countries….


[Note: This fit is pretty tight and it is linear formula]

Here is another chart whereby I remove non-government NHE (WHO numbers) from household consumption figures to exaggerate the effect of US NHE here.


Comparing predictive skill amongst the same limited set of “comparable” countries

Even if we limit this analysis to the typical top ~40 OECD countries, thereby hiding the effects of large petro economies and the like with respect to very large systematic inaccuracies with GDP (for these purposes), we can still see that AIC significantly outperforms.  For starters, when they use NHE per capita that significantly masks the extent of proportional inaccuracies for less affluent countries (i.e., the “same” variance in Y is much greater for countries lower down in the X axis than it is for presumably high income countries on the right side of said distribution).

Even so, we can outperform GDP per capita:

  • GDP per capita: r**2=0.88 polynomial (0.87 linear) [part 1]
  • AIC per capita: r**2=0.96 polynomial (0.91linear) [part 2]

Part 1: NHE per capita by GDP per capita


Part 2: NHE per capita by AIC per capita


This effect is even more apparent when you actually look at this in proportional terms, i.e., NHE as percent of GDP by GDP per capita vs NHE as percent of AIC by AIC per capita! (see part 3 and 4 below)….

  • GDP per capita: r**2=0.45 2nd order polynomial (0.45 linear) [part 3]
  • AIC per capita: r**2=0.75 2nd order polynomial (0.71 linear) [part 4]

This difference is even more impressive when you consider that the latter, NHE / AIC, is against a smaller denominator, i.e., if exports, fixed capital formation, and the like we actually driving this incrementally, then we’d expect this to significantly underperform, not outperform.  Of course there is still significant unexplained variance, but that variance is much less than what GDP predicts….

Part 3: NHE as Percent of GDP by GDP per capita

Part 4: NHE as Percent of AIC by AIC per capita


Now let’s try the same thing on the entire dataset (all OECD NHE numbers I could match), excluding and illustrating a few exceptional outliers…..

[Excluding these outliers — my quick one-time pass at this]


NHE / GDP by GDP per capita


NHE / AIC by AIC per capita


And here, just for fun, I excluded the USA to demonstrate the trend w/o US NHE skewing it.  The polynomial trend still projects the USA at around 20% of AIC (which is surely repressed somewhat by Switzerland and Norway’s atypical behavior here). It’s still significantly non-linear and there is still significant variance from the trend (see the Netherlands at about 18% of AIC).


Here is another way to show how broken GDP per capita is here for this sort of NHE analysis

[Note: X-axis is GDP per capita]

college_grads_gross_income college_grads_net_income

[Note: Like household consumption and NHE, the US is way above trend, Norway is below, and so on (especially in net income terms).  Long story short: This is NOT just a by-product of GDP, NIPA accounting, or what have you.]

Another way….

Average Wage (OECD figures & formulation) by Average Individual Consumption by AIC.  That income concept is somewhat different, but nevertheless the data points in the same direction.


Projecting NHE versus other categories of consumption




[Note: Even when you exclude the US, a huge economy, from the calculation the trend is pretty clearly for the food, shelter, entertainment, and the like to plateau while healthcare and ‘misc’ spending grow.  Even if the NHE projection isn’t deemed sufficiently reliable here, the trends on the other major items implies growth of NHE.

Along a similar line here, we can look at compensation in healthcare.


physician_income_vs_college_labor (1) physician_income_vs_net_income (1)

Yes, US healthcare salaries are higher, but our salaries are higher across the board (the average, college grads, etc) because we truly are richer country and our people have higher real consumption expectations.  Again, see education and numerous other statistics.

Likewise, you can look at other measures of consumption….


Google Chrome 2

Google Chrome



We also have much lower tax burdens than Europe, especially the Scandinavian/Nordic Countries




Note: This includes all federal, city, state, and local taxes (averaged together nationally).

Source: OECD tax revenue database


Contrary to popular opinion, the bulk of this “extra” tax revenue in these European countries is raised on the broad middle class, not just the top few percent (which should be intuitively obvious if you think there is notably less pre-tax/pre-transfer income inequality there and understand these facts).









Long story short:

  1. Richer countries tend to spend more real money (volume) on lots of different things, albeit in somewhat different proportions.
  2. Richer countries tend to have higher labor costs (which is very important in labor-intensive industries like healthcare)
  3. Lower taxed people tend to have more money to spend and, especially in rich countries, they choose to spend more of it on healthcare than some people might like.




The above three clearly apply to the United States and to an appreciably larger degree than the countries we are typically compared to.  We do not need to invoke bizarre explanations about lack of single-payer healthcare (or what have you) to explain this situation because we should pretty well expect this and there are lots of other industries and sectors where we see this sort of international disparity besides healthcare and education.

Single payer systems might theoretically have some ability to cram even lower reimbursement (and other payments) rates down the throats of the healthcare industry (e.g.,physicians, nursing, hospital administration, medical devices, pharma, etc), but there are very real limits on what you can expect in a high income country like ours given prevailing wages in other industries.  Comparing our healthcare salaries to that of, say, France without taking into account other wages is just silly.

Likewise, single-payer might be theoretically be able to force patients to consume less, but those standards (in the real world) are going to be set according to the ability of people to pay and their already sated desires.  Why should we expect US consumers to make the same spending choices that they do in significantly poorer European countries?  I certainly believe that much of our NHE is subject to rapidly diminishing marginal returns (especially with respect to crude measures like life expectancy), but just where is the political will going to come from to support draconian restrictions, further reimbursement cuts, and the like?

It seems to me that that people have this entirely backwards: cost containment (if this is really our #1 priority) must start with budgeting.  So long as we are willing to pay (or borrow) for it, why should we expect the same choices here?

This argument is especially bizarre when you look the falling growth rates in actual NHE spending in the US:

Microsoft PowerPoint 2

Microsoft PowerPoint

Likewise, the data tends to suggest that, over the past ~20 years, Europe has experienced very real “inflation” of healthcare , too, no less, and sometimes more, than the United States.

The growth rate of healthcare inflation



If these single-payer systems have it all figured out, if real increases in healthcare demands have nothing to do with it, and if actual salary demands have nothing to do with it, then why have they not succeeded in keeping costs contained?  Obviously that’s just nonsense.  Moreover, given the vast differences between the actual per capita spending and NHE as percent of GDP between various regimes in Europe, why is there so little apparent curiosity for what explains these differences (specifics, please)?

I am not suggesting that there is no room for improvement here (obviously healthcare is very expensive) or that we cannot learn anything from other countries, but the analysis that has been presented thus far starts with the wrong premises (imho) and they present a very misleading picture.

Main data source:

World Bank International Comparison Program (ICP) 2011


More silliness related to corporate profits

I was pointed to this work by Hussman through Business Insider.

The implication here is that total dissaving is not only strongly correlated with corporate profits, but is directly causative.

Although he doesn’t fully specify this methods, it’s obvious that Corporate Profits is after-tax corporate profits (including foreign profits) and I was able to approximate his results using this FRED2 link.

Update: I re-charted this using the NIPA corporate profits inventory & capital adjusted data that he clearly used (CPROFIT).  It doesn’t really change the outcome here, but it matches his chart more precisely.

Corporate profits is, in other words, after-tax and including foreign profits.

Savings is approximately personal savings (PSAVE) + the Federal deficit/surplus (FGRECPT-FGEXPND) (multiplied by -1 to match to shape of the profit line)

There are many issues with this analysis

1: By showing after-tax profits he’s exposing his analysis to changes in the tax regime and business reporting (as I’ve alluded to in prior posts).   The corporate taxes paid has direct mechanical effect on after-tax profits and the tax regime has changed.  Further, in this most recent recession we have actually cut corporate taxes in the short term by allowing businesses to accelerate deprecation more rapidly.

2: Including foreign profits in this discussion also obscures the role that deficit spending (or consumer dissaving) might be playing here.

3: Cutting it off at 1970 obscures what is obviously a much worse correlation in earlier years, i.e., when savings were much higher and corporate profits were much higher.

4: Lagging quarterly data by a full year just doesn’t make much sense to me.

Same method, i.e., 4Q lead, using PRE-tax profits

Same method over a longer time span (1947-2011)

Scatter chart savings vs after-tax


Scatter chart: savings vs pre-tax profits

Observe: When you remove taxation from the equation the slope and correlation virtually disappears.

After tax profits vs Corporate Profits 

It’s almost as-if corporate taxes actually has a direct impact on after-tax profits (and thus accounts for most of this apparent correlation)!  Who would have thought???




Note: This includes both foreign and S-Corporation profits.


Comparing domestic pre-tax profits with savings  (notice: pre-tax corporate profits are NOT at record highs)

Quick Scatter chart with annual domestic corporate profits vs savings (this is annual, not quarterly, since I don’t have historic quarterly data)

If anything, this would suggest that higher savings are correlated with higher profits.

I am including the CBO chart to show that:

1: the taxation of domestic corporate profits hasn’t changed that much. If anything, this under-states the recent  burden, relative to the the 80s, since a large and growing share of profits are being taxed at the individual level (S-Corporations), i.e., they are being factored in the denominator (profits) but not the numerator (corporate taxes paid).

2: my analysis of the NIPA corporate profit data matches their analysis almost exactly for the period they produced this data.

In short, the analysis conducted by BI and company is extremely misleading.  Deficit spending and dissaving may have some short term stimulative impact, but it’s ultimately a net negative.  These funds will have to be repaid in the future, both by tax payers and individual consumers, which will slow future economic growth.


A look at college board data as it relates to affirmative action

The college board provides some summary data regarding the distribution of SAT scores by ethnicity and other measures.

Reading | Math | Writing
Black: 428 (98) | 427 (97) | 417 (94)
White: 528 (103) | 535 (102) | 516 (103)
Asian: 517 (125) | 595 (125) | 528 (127)

Mean (Standard Deviation)

With this data we can produce some quick analysis using Excel like so:

Note: This is only an estimate, assuming a normal distribution.  The left and right tails appear to be non-continuous because the SAT top codes the very highest and very lowest results (< 200 and > 800).

You might be able to tell, if you look carefully, that there are some pronounced differences to the right-hand side of the distributions.

One thing you might infer here is that if the elite universities decide to pick the top part of any distribution with preferences for various ethnic groups, i.e., to ensure that, say, 12% of the class is black, no more than 30% asian, this actually will tend to vastly amplify these differences amongst the ethnic groups within each institution.

If the “elite” schools constitute, say, 15K entering students total and the schools collectively pick the highest scoring students across these this is what it will look like (roughly).  [Yes, I know Harvard and the like will theoretically reject a non-trivial number of “perfect” scoring students, but this is largely along ethnic lines (against asians) and other better schools tend to pick them up]

Top 15K students with racial preferences


At 30K students

As a direct result of the preferences driving relatively poorly qualified black people into these schools there is relatively little overlap amongst black people and whites/asians.

It’s worth noting, too, that the 25th percentile of most of the elite schools (for all groups) is about 660-700 across all categories.

So what essentially happens is that more elite schools need to poach blacks and hispanics/latinos from schools several tiers below if they want to make their targets and this has a clear ripple effect through the entire system (i.e., those lower tier schools also need to reach further down to meet their targets too).

It is thus not very surprising when Richard Sander and others find very similar results when they look at law school class rank data when some handful of these students move on to law school several years later and similar race-based preferences are exercised so that the law schools can meet their own numbers.


Some data on IRS changing income concept

IRS report on changing AGI income definitions

The Tax Reform Act of 1986 (TRA 86) made extensive changes to the calculation of AGI beginning with 1987. These changes made necessary a revision of the calculation of the 1979 Income Concept, in order to make tax years beginning with 1987 comparable to the base years, 1979 through 1986. The law changes limited the deduction of passive losses and eliminated unreimbursed employee business expenses and moving expenses as “adjustments” (moving expenses changed back for 1994) in figuring AGI beginning with Tax Year 1987. Since passive losses had been fully deductible for both income measures prior to 1987, the disallowed passive losses had to be deducted in the 1979 Income Concept calculation for tax years after 1986. Some income items, such as capital gains, that had been partially excluded from AGI under prior law were fully included. The new law also eliminated or restricted some deductions. Therefore, if AGI is used to measure income, comparisons between 1986 income and tax data with that for years after 1986 are misleading. A more accurate comparison can be made using the 1979 Income Concept because it measures income in the same way for all years. Table B shows total income and selected tax items for 2009 using AGI and the 1979 Income Concept, classified by size of 2009 income. Before TRA 86 became effective, a comparison of income measured by AGI with that measured by the 1979 Income Concept showed significant differences at income levels of $200,000 or more.

But, with the elimination of preferential treatment of various income items by TRA 86, such as the exclusion of a portion of capital gains, much of the difference disappeared. Under tax law prior to 1987, the capital gains exclusion accounted for the largest difference at the higher income levels between the two income measures. For 2009, 1979 Concept income was 2.2 percent higher than income as calculated using AGI. This difference was primarily attributed to the inclusion of more than $343.4 billion in nontaxable pensions and annuities (including IRA distributions) in the 1979 Income Concept. Income for all returns, using the 1979 Income Concept, decreased 8.2 percent for 2009; income for the $200,000 and above group decreased 20.0 percent. Total income tax for all returns decreased 16.1 percent in 2009 after an decrease of 7.5 percent in 2008; and total income tax reported for the $200,000 and above income group decreased 19.3 percent for 2009, down from the 12.0 percent decrease for 2008. The average tax rates (income tax as a percentage of total income) for each income class and both income concepts for years 1986 through 2009 are shown in Figure 4. For the population as a whole, average tax rates for 2009 (based on the 1979 Income Concept) were 1.1 percentage points lower than those for 2008. Between 1986 and 2009, the average tax rates declined in all income categories except the $1 million or more category.

Average income tax rates using consistent 1979 Income Concept (direct from IRS data table)

Observation: The very top income groups are paying roughly similar taxes as they paid in 1986 when we actually use a consistent methodology like this.  Lower to middle income groups are paying substantially less and the methodology makes much less difference for them (AGI and TIC render similar results).

Comparison between AGI and Total Income Concept tax rate for selected groups

Notice how AGI and TIC lines actually converge much more closely after-tax reform.

Total income tax as percentage of AGI

Note: This is taxes paid as a percentage of the constantly changing Adjusted-Gross-Income concept that the IRS warns against.  It’s also, not coincidentally, usually the one used by advocates of going back to the supposedly golden era when high marginal tax rates presumably collected much more taxes from the very rich [despite all the evidence].

Notice how similar the high income lines look here to the data reported by this CRS chart

Although he doesn’t spell out his methods or even which taxes he’s including (payroll, CIT,…?), it’s highly likely that his analysis is simply naively dividing taxes paid by the AGI.

Some other charts produced directly by the IRS for the top 0.2%

Note: The first chart (figure I, 1951-1986) is the income tax paid as a percentage of AGI [which changed significantly, but not nearly to the same extent that it did between 86 and 87].  The second chart (figure H, 1916-1950) is “net income” — which isn’t perfectly analogous to AGI.   Nevertheless, you might observe that between the mid-40s and 1986 the average rate of the 0.2% averaged around 40% of AGI, which roughly corresponds to the more recent AGI chart above [and which, the data shows, AGI way over reported the effective tax rates in economic terms ( at least it did by 1986 )

Piketty and Saez  — see table A3 (individual + payroll)

IRS data on effective rates.  See figure E…

More IRS: see 1979 income concept, etc

IRS calculations of AETR for top 0.2% 1916-1950 and 1951-1986 @ Figure E, pg 46