Does the Government That Governs Least Really Govern Best? A Quick Look at the Data.

Libertarians are fond of quoting Henry David Thoreau’s aphorism, “That government is best which governs least.” Thoreau was evidently paraphrasing his contemporary John O’Sullivan, but no matter who first said it, the quotation has become an axiom of those who love freedom. But is it true?
Let’s treat this aphorism as a hypothesis and test it against the data. To know what data we want, we first need to decide just what we mean by “best government” and “least government.”
“Best government” could mean one of three things: 
  1. It could mean the government that produces the best results in terms of human prosperity –  not just high GDP, but good health; access to food, clean water, shelter and education; safe communities; clean environment; and so on. To measure those things, we will use the Legatum Prosperity Index (LPI). 
  2. “Best government” could instead mean the government that allows the greatest degree of human freedom, including freedom of speech and religion, freedom in personal relationships, personal safety and security, security of property rights, freedom to trade, and so on. To measure those things, we will use the Human Freedom Index (HFI) from the Cato Institute. 
  3. Rather than a results-based measure, we could define “best government” in a procedural sense – a government that adheres to rule of law, maintains fair and impartial criminal justice, and is free from corruption. We can compile such a measure by extracting and combining relevant indicators from the LPI and HFI to construct a Quality of Government Index (QGOV). 

Libertarians typically interpret “least government” to mean “small government.” We will use two alternative measures of size:

  1. Cato’s HFI includes a “size of government” component – a composite indicator that includes measures of government spending, taxes, and government enterprise. It is measured on a scale of 0 to 10, with 10 indicating the smallest government. We will abbreviate this measure as SoG. 
  2. The SoG component of the HFI is subject to a number of methodological criticisms, so as a check, we can use a simpler measure, the ratio of government expenditures to GDP, taken from the IMF World Economic Outlook database. For easier comparison with SoG, we will convert this ratio, too, to a scale of 0 to 10, with 10 indicating the smallest government. We will abbreviate this measure of size of government as SGOV.

Now we are ready to test several versions of the hypothesis, “That government is best which governs least.” Let’s begin by interpreting this to mean that smaller government tends either to produce greater human prosperity as measured by Legatum’s LPI or greater human freedom as measured by Cato’s HFI. Here are the relevant scatterplots, based on a sample of 143 countries, using SGOV, the IMF measure, for size of government:

The plots do not support the hypothesis that small government produces either greater prosperity or greater freedom. (In reading the charts, remember that the SGOV index is constructed so that 0 indicates the largest government and 10 the smallest government.) Instead, smaller government tends to be associated with less prosperity and less freedom. Both relationships are statistically significant, with correlations of 0.43 for prosperity and 0.35 for freedom.
Using SoG, the Cato measure of size of government, instead of SGOV, the IMF measure, does not help. The correlations turn out still to be negative and statistically significant, although slightly weaker.
Let’s turn now to the alternative hypothesis that quality of government, rather than size, is what counts for prosperity and freedom. Here are those scatterplots:

This time, both relationships are positive. High quality of government is strongly associated both with greater human prosperity and greater human freedom. Furthermore, the correlations are much stronger than those for the size of government.
But wait a minute. Before reaching any conclusions about the relative importance of size and quality of government, shouldn’t we should look at the relationship between those two variables? Could it be, for example, that quality matters, but also be true that small governments tend to be high quality governments? If so, then small government might still contribute to prosperity and freedom, but in a way that is statistically masked when we plot SGOV in isolation against either LPI or HFI.
However, that turns out that small governments do not, on the whole, tend to be of better quality. Here is what we get when we plot quality against size of government:
To help with interpretation, this figure ads labels to some of the data points. The red lines divide the figure into quadrants according the median values for size and quality of government. A trend line runs roughly from France to Nigeria. The correlation is definitely negative: Smaller governments (those to the right of the figure) tend to have lower quality. The relationship between the two variables is statistically significant, with a correlation of -0.48.
The governments with the highest quality scores are found among those in the northwest quadrant – the largest governments as measured by expenditures relative to GDP. It is no surprise to find a cluster of Scandinavian countries there, with some of their fellow EU members not far away. Farther to the right lie New Zealand and Switzerland (CHE), two countries with well-run governments that are close to the median size. The US government is also close to median size. Its quality score is a little lower, but still well above the trend line. In the same quadrant, but below the trend line—large but lower-quality governments—we find Kuwait and Saudi Arabia. Some of the other Gulf States are in the same neighborhood.
The northeast quadrant is more sparsely populated. The outliers here are Singapore and Hong Kong, countries with governments that are well run, but small in terms of expenditures. South Korea also falls into this quadrant, as does Ireland, which has a high-quality government that is relatively small by EU standards.
Moving to the southeast quadrant, we find countries that have small but low-quality governments, such as Nigeria, Democratic Republic of Congo, Chad, Central African Republic, Bangladesh, and others. These countries are also among the poorest on the planet in terms of per-capita GDP and indicators of human flourishing.
Finally, in the southwest quadrant, we find the sorry example of Venezuela – this decade’s poster child for socialism gone bad. North Korea would probably be nearby, but we don’t have enough data to include it in the sample. Russia and China are also in this quadrant, but much closer to the center, with both size and quality of government close to the median.
We conclude, then, that the hypothesis, “That government is best which governs least,” does not hold up well. The data suggest that Thoreau’s axiom should read, “That government is best which governs well.”
In short, quality of government, not size of government, is what matters for freedom and prosperity.
This post originally appeared in this form on Medium. For more details on data and methodology, read this two-part series: “Freedom, Prosperity, and Big Government,” and “Quality of Government, Not Size, Is the Key to Freedom and Prosperity.”

More on Work Requirements: A Response to AEI

An op-ed by Angela Rachidi and Robert Doar in Real Clear Policy, reprinted on the American Enterprise Institute’s blog, challenges some points I made about the effects of work requirements in my commentary on the topic last week. Rachidi and Doar’s critique focuses on the interpretation of data from the National Evaluation of Welfare to Work Strategies (NEWWS), which analyzed the results of 11 controlled experiments conducted around the country in the 1990s. Each experiment compared the experience of a group of welfare recipients who were subject to work requirements with a control group who did not face work requirements.

Rachidi and Doar point out that some of the programs were “jobs-first” programs that placed a primary emphasis on job placement, while others were “education-first” programs that emphasized human capital development before job placement. They claim that only the jobs-first variants are relevant to today’s debate over work requirements for noncash welfare such as SNAP, Medicaid, and housing assistance. Linking specifically to my commentary, they maintain that critics:

incorrectly cite the results from the education-focused programs, which were found to be largely ineffective compared to jobs-first programs, to suggest that work requirements would not be effective today in SNAP or Medicaid. The education-first programs are largely irrelevant to today’s discussion of extending work requirements to other safety-net programs — no one proposes that recipients be required to go to college in order to receive SNAP or Medicaid.

Fair enough. Let’s take a closer look at the results of the jobs-first experiments to see how effective they really were in the three cities (not four) where such experiments were conducted side-by-side with the education-first variant. (And, by the way, the education-first variants did not focus on “going to college,” but rather on basic education, high-school equivalency, and vocational training.)
Rachidi and Doar claim that:

The jobs-first programs in NEWWS most relevant for today’s discussions showed impressive gains in employment and earnings. In those programs, increases in the average number of quarters worked in a five-year follow-up period ranged from 8 percent to 21.1 percent, with similarly high increases for average total earnings.

That sounds impressive until you look at the actual numbers. Let’s start with the effects on employment. The NEWWS report measures success in finding work by the number of calendar quarters, out of the five-year experimental period, when participants did any paid work. Since that could mean just a few days of work over a three-month period, it is a considerably lower bar for success than stipulated by today’s work-to-welfare reformers. For example, the recent report on work requirements from the Council of Economic Advisers envisions a requirement of 20 to 30 hours per week, depending on the number of children in a participant’s family. Here are the results, taken from Table 4.1 of the NEWWS report and stated in quarters worked over the 20-quarter experimental period:

  • Atlanta: Control group, 7.8; Jobs-first group, 8.5; education-first 8.3
  • Grand Rapids: Control group, 9.1; Jobs-first group, 9.8; education-first 9.5
  • Riverside: Control group, 4.7; Jobs-first group, 6.0; education-first 5.5

Two things stand out here. First, the absolute gains in employment were distinctly underwhelming in all cases. In Atlanta and Grand Rapids, the jobs-first approach produced less than one added quarter in which any work at all was done out of the 20 quarters of the experiment. In Riverside, the gain was less than two quarters out of the 20. Second, the differences between the jobs-first and education-first approaches were trivial. The jobs-first approach resulted in just 0.2 more quarters worked Atlanta, 0.3 in Grand Rapids, and 0.5 in Riverside.

The results for total earnings were similarly unimpressive. Here they are, stated as the average annual increase in earnings for participants in the two experimental groups compared with the control group:

  • Atlanta: Jobs-first group, $492; education-first $403
  • Grand Rapids: Jobs-first group, $310; education-first $169
  • Riverside: Jobs-first group, $456; education-first $272

Again, the absolute gains in income are small, and the difference in gains from jobs-first to education-first approaches is downright trivial. In Atlanta, the jobs-first group earned $1.71 more a week on average than the education-first group. In Grand Rapids, the difference was $2.71 a week, and in Riverside, $3.54 a week. Even allowing for the change in the cost of living since the 1990s, two or three extra dollars a week is not much progress toward self-sufficiency.

The greater success of the jobs-first approach in Riverside than in the other cities comes with an interesting twist. The Riverside experiment was different from the other two, in that it accepted only applicants without a high-school diploma or GED. Table 1 of the NEWWS report breaks out the earnings results for all three cities according to education. In those cities, too, the work-first approach showed relatively strong results for participants with less than high school, but not for those with more education. In Atlanta, the work-first approach had no significant advantage for participants with a high-school education or more, and in Grand Rapids, the education-first approach actually produced a greater gain in earnings than the work-first approach. Since any real-world work-to-welfare policy would cover people at any level of education, the overall results from Atlanta and Grand Rapids offer a more realistic idea of what such a policy could accomplish. Including results from Riverside is misleading.

The bottom line: Rachidi and Doar are technically correct to point out that the jobs-first variants of the NEWWS experiments were somewhat more successful and more similar to today’s work-requirement proposals than the education-first variants. However, the absolute differences in the effects of the two approaches were quite small, whether stated in terms of employment or of earnings. Most importantly, neither the jobs-first nor the education-first variants of work-requirements came close to producing the dramatic gains in self-sufficiency that today’s work-requirement advocates hope to achieve.

Reposted from NiskanenCenter.com

Is That Government Best Which Regulates Least?

In a recent post, I questioned Henry David Thoreau’s aphorism, “That government is best which governs least.” The data, it seems, show something different. Countries with small governments, as measured by the share of expenditures and taxes in GDP, tend actually to be somewhat less free and prosperous than those with larger governments. The quality of government, as measured by things like rule of law, independent judges, and integrity of government officials turns out to matter much more than the size of government. I concluded that Thoreau’s aphorism should be revised to read, “That government is best which governs well.
In response, several readers questioned whether the size of government, as measured by spending, was the right measure of “governs least.” Excessive regulation, they pointed out, may do more damage than spending and taxes. Maybe what we should say is, “That government is best which regulates least.”
Niskanen Center’s Will Wilkinson puts it this way:

Whether a country’s market economy is free — open, competitive, and relatively unmolested by government — is more a question of regulation than a question of taxation and redistribution. . .
If we want to encourage freedom and prosperity, we should pay more attention to easing the grip of the regulatory state.

The point is a good one — worth putting to the same kind of statistical test used in the previous post. Here we go:

Data sources for prosperity: The Legatum Prosperity Index, a composite measure of but good health; access to food, clean water, shelter and education; safe communities; clean environment; and related indicators. The Social Progress Index, a similarly broad measure of human flourishing.
Data sources for freedom: The Human Freedom Index from the Cato Institute and the Personal Freedom Index from the same source, a subset of indicators that leaves out purely economic aspects of freedom like freedom of trade and sound money.
Data sources for regulation: The measure of regulatory freedom from the Cato data base, and the similar regulatory component of the Index of Economic Freedom from the Heritage Foundation. Both indicators are scaled in a way that assigns higher scores to countries with more “regulatory freedom,” that is, less regulation.
A first look at the data does show a positive association between regulatory freedom and prosperity. For example, data from 131 countries for the Heritage index of regulation and the Social Progress Index indicate that countries with higher regulation scores (less regulation) are in fact more prosperous. As measured by the statistic R², about 46 percent of differences in prosperity are statistically associated with differences in regulation. Similar findings are obtained by substituting the Cato measure of regulation or the Legatum measure of prosperity.
However, statistical association is not the same as causation. After all, we would find a similar positive relationship between the heights of all children in a grade school and their scores on a standardized math test. It would be wrong, though, to conclude that being tall makes people better at math. The apparent relationship would be a statistical illusion caused by the fact that sixth-graders are better at math than first-graders, and also happen to be taller. If we corrected for age, the correlation between height and math scores would disappear.
Much the same is true for the relationship between regulation and social prosperity. It turns out that most of the apparent statistical association between the two variables is a result of their level of development, as measured by per capita GDP. At any given level of development, whether high or low, countries with high scores for regulatory freedom do not score much better on noneconomic aspects of prosperity (population health, education, environmental quality, access to information, and so on) than do countries at similar levels of development but with low regulation scores. Similarly, once we control for differences in GDP per capita, regulation scores have little explanatory power for between-country differences in personal freedom.
However, it would be wrong to interpret these findings as proof that regulation does not matter. I could be, instead, that the Cato and Heritage indicators are just bad measures of those aspects of regulation that are important to prosperity and freedom.
A further comment by Wilkinson gives a clue as to why this might be the case:

Free markets require the presence of good regulations, which define and protect property rights and facilitate market processes through the consistent application of clear law, and an absence of bad regulation, which interferes with productive economic activity. A government can tax and spend very little — yet still stomp all over markets.


Wilkinson is exactly right. Bad regulation is a drag on freedom and prosperity but good regulation facilitates them. The problem with the Cato and Heritage indexes of regulation is that they indiscriminately jumble together regulations of all kinds, the good with the bad. The result is statistical mush that is incapable of explaining anything.
To distinguish the good from the bad, we need to look both at the aims of a regulation and the way it is implemented. Good regulations are those that have benign aims and are efficiently implemented. Bad regulations are those that have bad intentions, or good intentions that are badly implemented, or both.
Here are some guidelines, that can help distinguish good regulations from bad ones:
Retain regulations that support the basic rules of a market economy. Those include regulations that protect property rights, ensure that contracts are honored, and protect against common law harms like fraud, negligence, and nuisance. In principle, such rules can be enforced through civil litigation, but courts can be slow and costly. It may, for example, make more sense to send inspectors to ensure that nightclubs keep their fire exits unlocked than to wait for relatives of the deceased to sue a club’s owners for negligence after a fire occurs.
Replace regulations that have legitimate aims but are ineffective or have harmful unintended consequences. For example, in an effort to reduce CO2 emissions, CAFE standards set minimum gas mileage for new cars. Meeting the standards raises the price of cars, but better fuel economy lowers the cost per mile of driving. The unintended consequence is that people drive more, roads are more congested, and more accidents occur. A carbon tax would be a more efficient way to reduce emissions, since it would encourage people both to buy efficient cars and to drive them less.
Repeal regulations that are motivated primarily by the manipulation of public policy for private gain — what economists call rent seeking. Regulations that restrict competition or impose price controls rarely serve any purpose other than enriching special interests at the expense of the public. Restrictive occupational licensing provides many examples.
Following these “Three R’s” would do more to promote prosperity and freedom than mindlessly cutting regulations across the board. It is not really true that that government is best which regulates least. Rather, that government is best which regulates well.

Previously posted on Medium. For further information on methodology and data sources behind the statistical results, see “The Way Economic Freedom Indexes Measure Regulation is Deeply Flawed.”

The EPA's SAFE Vehicle Rule Poses a False Choice. We Can Have Safe Vehicles AND Clean Air.

The EPA has released a proposed rule that would freeze corporate average fuel economy (CAFE) standards at 37 miles per gallon, rather than allowing them to rise to the Obama administration’s target of 54 MPG, as currently scheduled. The administration’s  proposal has the cute name of Safer and Affordable Fuel-Efficient Vehicles Rule, or “SAFE Vehicle Rule,” for short.

The proposed rule has been widely panned by environmentalists, and rightly so. However, the critics of the rule are wrong simply to defend the existing CAFE standards. The EPA’s analysis of the flaws of those standards is justified. But neither the EPA nor its critics are reaching the right conclusion, which is that we should repeal CAFE standards and replace them with a carbon tax — one tough enough to reduce carbon emissions by as much, or more, without the unintended consequences.

Let’s go straight to the heart of the EPA’s argument. Brad Plumer summarizes the issues in a recent article for the New York Times. Here are the key points he raises.

The rebound effect

The biggest problem with CAFE standards as a tool for reducing greenhouse gas emissions is that they encourage fuel saving only at the dealership, not at the pump. Once a consumer buys a low-mileage vehicle, the cost of driving an extra mile goes down, thereby reducing the incentive for fuel-saving measures like moving closer to work, working at home, riding the bus to work, or consolidating errands. The tendency of more fuel-efficient vehicles to induce additional driving is known as the rebound effect. More vehicles on the road, in turn, means an increase in accidents – one of the safety issues raised by the EPA in defense of the SAFE Vehicle Rule.

The variable that is most critical to the size of the rebound effect is the how much more people will drive when it costs them less to do so – what economists call the elasticity of demand. For example, if a 10 percent improvement in fuel efficiency (assuming no change in the price of fuel) would cause a 3 percent increase in driving, the elasticity would be -0.3. The increased miles driven would partly offset the improvement in miles per gallon, so that total fuel consumption would decrease by only about 7 percent. If the elasticity were only -0.1, then fuel consumption would fall by 9 percent in response to a 10 percent improvement in fuel efficiency.

The fact is, we don’t really know what that critical elasticity is — only that averaged over all drivers, it is negative. Numerical estimates differ widely. Plumer draws our attention to some small estimates, while the EPA cherry-picks the largest ones (I have covered this debate more extensively at Economonitor). But both sides miss the point: We know for sure that the number of miles driven goes up when the cost per mile goes down, and vice versa. That means that the number of miles driven goes up when CAFE standards are tightened.

Elasticity also matters when  the cost of fuel rises, as it would with a carbon tax. However, using a tax to raise fuel prices at the pump has a different set of impacts on vehicle choice and driving behavior. If we take -0.3 as our estimate of elasticity, a tax that raised the price of fuel by 10 percent would still reduce total fuel use by 3 percent. But now consumers would respond to the tax partly by buying moderately more efficient vehicles and partly by driving them less, rather than by buying super-efficient vehicles and then driving them more, as would happens with a 54 MPG CAFE standard. We don’t know with certainty how large a tax would be needed to save the same amount of fuel as the Obama-era CAFE standards, but economist Noah Kaufman makes a rough estimate of $50 per ton, or about 50 cents per gallon of gasoline.

Vehicle turnover

The EPA’s second safety-related objection to CAFE standards is that by raising the price of new vehicles, they encourage people to hold on to their old vehicles longer. If so, that means it would take longer for innovations like automatic braking and electronic stability control to work their way into the national vehicle fleet.

Superficially, that seems consistent with the fact that both the average age of vehicles on the road and the average cost of new cars have risen in recent years. But wait a minute — is there really a causal relationship there?

The global information firm IHS Markit reports that the average age of vehicles on the road has, in fact, risen by a full two years since 2002, but the trend in price is another matter, Data from the Bureau of Economic Analysis and AxelGeeks (reported here from WGNTV.com) show that the inflation-adjusted price of new cars actually fell by some 13 percent over the same period. Why, then, are original owners hanging on to their cars longer and why are used cars being kept in service longer before they are junked? IHS Markit points to improvements in vehicle quality, not price, as the main reason.

Lighter vehicles

The EPA argues that strict CAFE standards further hurt safety by encouraging manufacturers to make lighter vehicles, even though, it claims, heavier vehicles are safer. It is true that in a head-to-head between a Ford Expedition Max (5,700 pounds) and a Ford Fiesta (2,500 pounds), you definitely want to be driving the Max. But, as Plumer points out, the Max makes driving safer only for its own occupants, while reducing the safety of those in the Fiesta. If the administration really cared about safety, it seems that the best way would be to reduce the total range in vehicle size.

Higher fuel prices induced by a carbon tax ought to do just that, since they would discourage the purchase of gas-hungry SUVs. A study from Resources for the Future found that between 2003 and 2007, gasoline prices accounted for about half of changes in market share between SUVs and smaller cars. However, that study also found that over time, CAFE standards have blunted the effect of fuel prices, so that SUVs have remained popular no matter what.

Of course, some people would always want an SUV to tow the family boat to the lake or a pickup to haul lumber to the construction site. Even so, a carbon tax would help. For one thing, it would increase the incentive for manufacturers to make such large vehicles lighter to save on fuel, for example, by replacing steel components with aluminum. In addition, for families that have both a Max and a Fiesta in their driveways, higher fuel prices would increase the incentive to leave the Max parked when there is no need to tow a boat or haul a whole soccer team to a game.

But, wouldn’t a carbon tax also encourage people to buy more of the smallest vehicles? Wouldn’t that offset any benefits from a reduction in the chance of being T-boned by a monster SUV? Not necessarily, since, CAFE standards already encourage the purchase of the smallest vehicles, despite low gas prices. Wards Auto, another leading source of industry information, asks why car companies keep making small cars at all, when all the glamor is with larger vehicles. Number one on their list of explanations: Selling small cars, even at a slim profit margin, or none, lets carmakers comply with the CAFE system and sell more Maxes.

On balance, then, the best way to improve safety would be to eliminate CAFE standards and replace them with a carbon tax. Doing so would make for fewer ultra-heavy vehicles while eliminating the incentive to manufacture ultra-light vehicles and sell them at a loss. Meanwhile, all vehicles, especially the largest ones, would be driven fewer miles, for a net gain in highway safety.

Electric vehicles

Although it is not strictly a safety issue, Trump administration officials also object to the way CAFE standards favor electric cars. Writing in the Wall Street Journal, Transportation Secretary Elaine Chao and EPA administrator Andrew Wheeler argue that “to meet the previous administration’s fuel-economy and greenhouse-gas standards, manufacturers would have to produce vehicle lineups that are 30 percent electric or more over the next seven years — far more vehicles than buyers are likely to want.”

It may very well be that people don’t want that many electric vehicles given today’s low gasoline prices, although they would probably want more if a carbon tax raised the price of gasoline. However, that is only part of the story. Replacing CAFE standards with a carbon tax would not only have an impact on how many electric vehicles were sold, but also on who buys those vehicles and where.

The problem with electric vehicles, as even their fans point out, is that they are only as efficient as the power you use to charge them. As the Sierra Club notes in a consumer guide to buying electric vehicles, “When coal plants supply the majority of the power in a given area, electric vehicles may emit more CO2 and SO2 pollution than hybrid electric vehicles. Learn where your electricity comes from” before you buy, they warn.

Electric vehicles are a case where a carbon tax would make a triple play. First, it would encourage people to buy efficient vehicles in general, electric vehicles included. Second, it would establish incentives that would concentrate electric vehicle purchases in the areas where renewable power is abundant, since where the electric supply relies heavily on coal, electric vehicle buyers would have to pay for the carbon emissions at charge-up. Third, over time, a carbon tax would put pressure on utilities to switch away from carbon-intensive electricity, at which point electric cars would make sense everywhere.

The bottom line

Where does all this leave us with regard to the coyly named SAFE Vehicle Rule? On the one hand, CAFE standards arguably do have unintended consequences for vehicle safety. But critics of the SAFE Vehicle Rule are also right.

There is no reason why we can’t have safe highways and a clean environment. The solution is obvious: Get rid of CAFE standards and replace them with a carbon tax of equivalent rigor. That would give us less pollution, fewer miles driven, and a selection of safe vehicles driven by economic realities and consumer choice rather than by bureaucratic whim.

Repeal and replace — but don’t do the repeal until you have the replacement firmly locked in.

Previously posted at NiskanenCenter.com

No, Health Care for All Would Not Turn Us into a Venezuela

In a recent editorial for the Wall Street Journal, James Freeman plays the red scare card against Bernie Sanders’ Medicare for All (M4A) plan, which he compares to the policies of “Venezuela’s Nicolás Maduro, whose mania for wealth redistribution has brought a country to its knees.”
Freeman gets the causation backwards. Venezuela is not in crisis because Maduro has been too generous with socialist goodies like health care. Quite the opposite. The Lancet reports that from 2010 to 2014, Maduro slashed health care spending by a third — and then stopped the publication of official statistics. Since then things have only gotten worse. Collapse of the health care system is one of the major reasons Venezuelans are fleeing their country by the tens of thousands every day.
But enough of Venezuela. Freeman misses the point about Sanders’ health care plan in more important ways, as well.
It is true that M4A would be expensive. Freeman relies on a study by Charles Blahous for the Mercatus Center, which estimates that M4A would cost taxpayers $32.6 trillion over ten years, even after positing substantial savings in total national health care spending. A more detailed study by Jodi Liu for RAND (of which Blahous appears not to have been aware when he wrote his Mercatus paper) comes in a significantly lower. It estimates that a baseline version of M4A would add about $1.8 trillion a year to net federal healthcare spending without cost savings and $1.7 trillion a year with. (Those numbers assume that current revenue sources for health care remain in place but do not include any proposed new revenue sources.) A trillion here, a trillion there — either way, it’s real money.

But what are the implications and what are the alternatives? Freeman first tries framing the issue in terms of the debt and deficit:

Remember, before a single nickel is spent on a Sanders-style makeover of American health care, the Congressional Budget Office is already forecasting that U.S. government debt held by the public will surge over the next two decades to 118% of GDP from its current 78%.

That’s nonsense. Sanders’ plan, if implemented in full, would add nothing to the debt since he proposes paying for it in full with new taxes, mostly on high-income taxpayers. Freeman also neglects to mention that the added tax burden would be offset, at least to a substantial degree, by the elimination of the financial burden of insurance premiums and out-of-pocket costs that those same taxpayers now pay.
The real issue, then, is whether the sharp increases in federal taxes that Sanders’ plan calls for are the best way to finance the universal, affordable access to health care that both Republicans and Democrats profess to want.
In my view, it is not. A better way to make health care affordable would be to implement a system of universal catastrophic coverage (UCC). As I have explained in detail elsewhere, UCC would use income-based deductibles to ensure that everyone pays their fair share of health care costs, but not more than their fair share. Under UCC, the poorest families would get full coverage without deductibles, middle-class families would face deductibles similar to those they pay under the ACA, and high-income families would be responsible for all but truly catastrophic medical expenses.
Using deductibles instead of taxes would reduce the administrative costs and deadweight losses of first levying taxes on upper-income families and then returning that money in the form of health care benefits. It would also mean that all but the poorest and sickest families would have “skin in the game.” To the extent at least a major part of their health care expenses would come out of their own pockets, middle- and upper income consumers would have an incentive not only to shop carefully but also to pressure their political leaders for reforms leading to greater transparency and competition in healthcare markets.

Proper scaling of the parameters of a UCC plan could make it revenue neutral, as Liu’s RAND analysis confirms. With further tweaking, the financial responsibility of households in various income categories could be made as “progressive,” (in the distributional sense) as Sanders’ plan, or less progressive, or more so, as desired by the collective political will of Congress and the electorate.
In short, Freeman poses a false dilemma. We do not have to choose between universal, affordable healthcare or a crushing burden of taxation. The quest for health care for all need not turn us into a Venezuela — a country where an especially noxious brand of authoritarian socialism has ruined both the economy and the health care system.
Reposted from Medium.