By Robert E. Wright
America’s biggest problem is ignorance of proven precepts of political economy. I don’t mean fancy econometric techniques or models full of malarkey (i.e., much of professional economics), I mean the basic, irrefutable concepts and empirical regularities sketched below. If those had been widely understood and applied a year ago, states would not have locked down in response to a highly contagious virus deadly only to a relatively small and easily identified segment of the population. All the costs associated with lockdowns would have been avoided and the number of excess deaths would have been lower, if not nil.
If you think that not wearing a mask or attending a social event kills, you should consider the deadly effects of the perhaps well-intentioned but ultimately self-defeating government policies that defy the lessons adduced below.
In fact, Reason recently released a fun, short video of three deadly policies, including China’s infamous famine-inducing “kill the birds” campaign. Some brainiac in Mao’s CCP observed that birds ate grain and concluded that fewer birds would mean more grain for his comrades. Dutiful Chinese subjects killed birds, including insect-eating ones. The population of grain-eating insects therefore spiked, so grain production plummeted instead of rising. Because the Chinese did not engage in much trade then, famine ensued, i.e., people died from lack of food.
That prompts Lesson #1: Doing nothing is (often) better than doing something.
Many ostensible problems are homeostatic, i.e., self-correcting. Current trends up or down do not usually accurately predict infinite bad things (global temperatures; the number of contagious disease infections) or infinitesimal good things (economic output; the number of puppies) but rather fluctuations within stable systems. In other words, trends often are merely parts of a range-bound cycle rather than indications of impending Nirvana or Armageddon. Only exogenous shocks, like an asteroid strike or a panoply of ill-advised laws or executive orders, can destroy the dynamic equilibrium of such systems, which thankfully abound in both nature and society. In this case, birds and insects check each other’s numbers and trade based on market prices alleviates famines.
The Reason video also mocks New York’s abysmal “Covid-19 vaccine” rollout. The state compounded the negative effects of its poor website design with punitive sanctions on HCPs who were unable to “vaccinate” New Yorkers before the “vaccines” went bad, turning healthcare heroes into zeroes with the stroke of a bureaucratic pen. But what do you expect from a state that lied about how many nursing home residents its policies killed?
That prompts Lesson #2: Market competition beats government monopoly (almost) every time
A lone young woman was able to create a better “Covid-19 vaccination” website than the state of Massachusetts because she “only” had two small children at home to contend with instead of the bureaucratic morass in Baahstawn. That allowed her to be able somehow to figure out that putting “vaccination” locations and available times on the same webpage was both technologically possible and something that might induce people to sign up instead of to give up. In their defense, state website bureaucrats in both New York and Massachusetts were confused because they are usually instructed to make government websites as useless as possible, lest a single mom be able to collect unemployment or a teenager to obtain a driver’s license without a hassle.
When things go bad, it is natural to blame the status quo, or popular representations of it. When some 7 percent of the population of St. Louis (4,577 people) died of cholera in 1849, for example, the residents naturally blamed the sundry voluntary associations — including an impromptu, extralegal Committee of Public Health — that had taken responsibility for public health upon themselves.
While understandable, the reaction against voluntarism was unwarranted because what killed the Missourians was an as then unknown pathogen spread by drinking water infected with human sewage, not a particular institutional structure. Businesses and schools spontaneously shut themselves down as deaths mounted but of course people continued to drink water and to defecate in the usual places and ways, so there was no stopping the spread.
Knowledge of causes and cures was what was needed but something that governments rarely created, then or now. So instead of suggesting smart policies like a Vickrey-Clarke-Groves auction to efficiently and fairly distribute “Covid vaccines,” government officials offer inefficient, politicized, overly-complicated top-down plans that please almost no one.
That prompts Lesson #3: Everything comes at a cost, even when it appears to be free.
I mean that beyond the Econ 101 concept of “opportunity cost” or “guns or butter,” i.e., time or other resources expended to achieve goal X are no longer available to achieve Y. Even policies that seem like a “free lunch” can be very costly indeed. For example, if a government exempts a producer from liability for its product, people will assume that the risks the product poses are substantial. Otherwise, why would liability exemption be asked for, or granted? If the goal is to induce people to use the product, a wiser policy might be to mandate triple liability. Of course that too comes at a cost, maybe a monetary one (like higher “Covid-19 vaccine” prices), or maybe a safer product brought out later, after more thorough testing.
Still, millions of people have calculated that the expected benefits of receiving a “Covid-19 vaccine” outweigh the expected costs. Bully for them, but they should not bully others, socially or through government policy, to do likewise. Millions are already immune, millions prefer natural immunity to vaccination, and millions are at higher risk from vaccines than from viruses. And growing numbers accept the (let’s face it for most slim) chance of dying rather than continue living in the midst of a confused majority.
That prompts Lesson #4: Popular ideas are not necessarily right, morally or empirically.
AIER’s own Ethan Yang just won a major international award for espousing a similar point, which has been lost in the mists of time, and the misty eyes of reputed advocates of democracy. I am sure you have heard the joke about democracy being two wolves and a sheep voting on what is for dinner. According to the wolves, it’s in the “public interest” that the sheep sacrifice itself for the “greater good.” The sheep knows it’s all hogwash (as in why not wash off the hog and eat it instead?), which is why sheep “vote with their feet” when wolves are afoot.
Unfortunately, human wolves are more difficult to spot than the real thing. Instead of hiding in the weeds, they lurk in the swampy crevices of legislation and regulations, waiting to trick unsuspecting voters into supporting policies not in their best interests through various subterfuges, including larding the titles of laws with popular words like “affordable,” “patriot,” and “safe.” When those tricks prove insufficient, human wolves claim “scientific” backing because few voters dare to question the (wo)men in the white lab coats though, ironically, they are the people whose claims should be interrogated the most thoroughly.
That prompts Lesson #5: Scientists are usually wrong, by design.
Science isn’t about “proving” theories; it is about not rejecting hypotheses. Scientific theories spawn predictions that (good) scientists empirically test. If the predictions hold up, the theory can stand as is, for now. If they do not — if a theory’s hypotheses can be rejected in the real world — the theory needs to be modified until it collapses entirely and must be replaced. Only through that method are theories with low predictive power jettisoned and ones with higher predictive power taught to students, used to guide public policy, and subjected to increasingly sophisticated testing.
Americans’ lodestar should be that scientific method, not blind obedience to somebody in a white lab coat, even (especially) those cloaked with the authority of government. Scientists, you see, are subject to the same public choice critique as government officials: they may work in their own self-interests rather than those of you, me, or some vague “public.”
Even more disturbingly, government public health experts do not face significant checks. Scientists outside of government may challenge the conclusions of government scientists but cannot prevent politicians from using their theories, even ones that do not make accurate real world predictions to justify sweeping public policies.
As a consequence, many Americans suffer the ill effects of social distancing policies even though the scientific method rejects the hypothesis that lockdowns and mandatory masking help to stop the spread of Covid-19. Ironically, scientists have not rejected the hypothesis that social distancing mandates are downright counterproductive.
Putting all five lessons of political economy together (especially along with some elementary knowledge of statistical tricks*) would have allowed Americans to better assess policymakers’ claims about Covid-19 transmission and the best ways to mitigate it and hence peacefully to resist unprecedented policies of dubious constitutionality and little scientific merit.
*Example: This chart from the CDC invokes fear by showing how many times more likely older people are to die from Covid-19 than younger people. The problem isn’t with the calculations per se, though like all Covid-19 figures they are based on dubious data, it is that the chart fails to show the absolute likelihood of death, which is quite low until the highest age group, and even then more akin to cholera in antebellum St. Louis than smallpox among American Indians. Statistical illiterates see the high multiples without understanding that they stem from very low base probabilities. The chart is akin to claiming that somebody’s income increased 13,000 percent in one year without revealing that the worker earned only $1 in the base year.
Originally published by the American Institute for Economic Research. Republished with permission under a Creative Commons Attribution 4.0 International License.
