Abstract
Over the last century life expectancy has increased substantially and so has the share of health care expenditures financed by governments. In cross-country comparisons, the US, which has the lowest government health expenditure share, often has the poorest health outcomes. Is there a plausible connection between health outcomes and government financing of health care? This paper addresses this question with panel data from 20 developed countries from 1950 to 2010. I review the history of government involvement in health care financing over this period. Then I use panel regression methods to examine whether a variety of mortality based outcome measures are correlated with the extent of government involvement. The answers are robustly negative.
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Notes
US Bureau of the Census, 1975, p. 74. This is the earliest year of official data.
See (Peltzman 2009).
See Cutler (2002) for a summary of some of these differences across the G-7 countries.
Norway, UK, New Zealand, Sweden, Finland, Denmark, Italy and Spain.
Germany, France, Japan, Canada and Australia. He also includes Belgium in this list, but there are no reliable 1980 data for this country.
US, UK, Australia, New Zealand, Canada, Germany.
This is one in a continuing series of similar studies by Davis and colleagues at the Commonwealth Fund with similar results and conclusions. These can be found at http://www.commonwealthfund.org/Publications/Health-System-Scorecards.aspx.
As will be seen in more detail later this kind of result is common in developed country cross-sections, particularly if they include the US. Indeed, the perceived “inefficiency” of US health care motivates some of the cross-sectional comparisons reviewed here. The perceived inefficiency comes from two sets of facts: the US spends much more on health care than other developed countries and it has below average health outcomes. The World Health Organization (2000) using such data plus subjective factors ranked the US system as 37th in overall health system performance. This is 19th out of the 20 developed countries in the sample used in this paper (New Zealand ranked 41st. Some pitfalls of cross-sectional comparisons of health outcomes and health spending are discussed in Atlas (2011).
Card et al. (2009) find that Medicare does reduce mortality for those over-65 who were admitted to hospitals following an unplanned emergency room visit. Here they compare mortality for those just over 65 with those just under 65 in order to exploit the more complete insurance coverage (on average) between the two groups.
See (OECD 2006), which estimates public health spending by age group. For the typical OECD country such spending is on the order of three or four times as high for a 75-year old as for a 35-year old.
For example, in Peltzman (2011) I show how the recent rise in obesity in the US is plausibly related to the reduced risk of heart disease mortality.
There may be little substantive difference between government expenditure for a specific health objective and a government requirement that private parties pay for the same objective. However, there is no practical way to measure such regulatory requirements over many countries and time periods. There is also ambiguity about the border between private and government sector spending. This is discussed below and much more extensively in the online data appendices for the individual countries in the sample.
I tried to minimize gaps in the OECD data by working back through successive OECD databases and judging if previous data could reasonably be used to fill the gap. Occasionally the OECD will drop data for methodological reasons, but the deleted data yield estimates of gov% reasonably similar to data arrived at by the preferred method. In such cases I would use the older data to fill in gaps in the newer data.
The spreadsheets may be obtained on request to the author. These spreadsheets also elaborate on my judgments in handling the various revisions and gaps in the post-1960 OECD data.
In general, I began with each country’s statistical abstract or historical statistics (if available) and then went to more specialized sources if necessary. For some countries some or all government and private health expenditures appear in a “health” section or in “national accounts.” More often, I had to use multiple sources, which included , in addition to the foregoing, consumer budget studies (for private expenditures), government fiscal accounts (for expenditures or receipts of social insurance funds, direct expenditures and similar specialized sources), consumer price indexes (for weights on health care items, which provide clues about expenditures), and so forth.
Where gov% expands from 41 % in 1980 to 48 by 2010.
The entries in column (3) of Table 3 come from implicitly differentiating the regression relationship, given that the government share \(=\) government spending/total spending and that total spending also appears on the left-hand side. Accordingly, the entries in column (3) show the value of dPrivate$/dGovernment$ that is consistent with the estimated b. For b\(<\)0 (i.e., the total declines when govshare increases) the implied dPrivate$/dGovernment$ would be \(<-1\). For 0\(<\)b\(<\)[1/1-govtshare] the implied derivative would be negative but above \(-\)1.
Specifically private insurance accounts for around 2/3 of private spending on average across my sample of 20 countries, out-of-pocket spending accounts for another 1/4 with non-profits, charities etc accounting for the remainder. (Data from http://stats.oecd.org/index.aspx?DataSetCode=HEALTH_STAT#).
This connection is likely to be especially important in Bismarckian systems, like Switzerland or Germany, that use subsidized and regulated private insurers to help implement their government health expenditures.
Allowing for different marginal effects of govshare also requires allowing for different intercepts (i.e., the country fixed effects ).
The panel estimator suggests the largest net addition—around 50 cents for each added government dollar.
These are accessible at http://www.mortality.org/.
The life tables used here are 1 x 1 period life tables. This means that they summarize mortality rates for a specific calendar year at 1 year age intervals from 0 to 110.
See (OECD 2006) figure 2.1, which shows per capita government health spending by age for developed OECD copuntries. For 75 year olds this is around five times the spending for 35 year olds. The comparable multiple for 0–4 year olds is around 1.5.
The standard deviations are those specific to the indicated identification strategy. For example, suppose the regression is identifying a relation within countries over time. Then the conceptual experiment summarized in column (4) is to raise govpc within a country over time by one standard deviation of govpc within country over time and then compare that to the resulting similarly measured outcome standard deviation.
It is below \(-\).9 either cross sectionally or in a typical country over time
For example in samples of US counties described in Peltzman (2011) regressions on cross-sections or differences over time between censuses of the standard deviation on mean life expectancy yield coefficients not too far from \(-\).5. In the current sample, the simple adjustment of adding half of life expectancy to the standard deviation mainly eliminates the negative correlation between the two moments.
According to OECD data for the latest year available at writing (usually 2011 or 2012) private out-of-pocket spending averages 16.3 % (standard deviation = 6.5) of total spending across my sample. The US figure is 11.6 % and ranks 16th of the 20 countries. Portugal and Switzerland have the highest out-of-pocket shares (31.7 and 31.3 % respectively). From http://stats.oecd.org/index.aspx?DataSetCode=HEALTH_STAT#).
For example, they live longer.
The more progressive results arise from using education rather than income as the standard for redistribution in Bhattacharya and Lakdawalla (2006) and by focusing on taxes paid and benefits received within cohorts over time rather than contemporary cross-sections of the population in (Rettenmaier 2012).
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I want to thank Ted Frech for comments on an earlier draft.
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Peltzman, S. Socialized medicine and mortality. Int J Health Care Finance Econ 14, 179–205 (2014). https://doi.org/10.1007/s10754-014-9151-z
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DOI: https://doi.org/10.1007/s10754-014-9151-z