Biden-Harris policies and their consequences were no surprise to those paying attention

Milton Friedman used to advise researchers to focus on large policy changes rather than attempting to separate a small change’s signal from the noise. In this sense, the “ambitious” policy agenda of the Biden-Harris administration was expected to be a gift to the research community.

Accepting this gift, since 2020 I have been making forecasts of some of the consequences of those policies. Now is a good time to assess the accuracy of those forecasts, which relate to aggregate labor markets, insurance price controls, and drug price controls.

As promised, Biden and Harris redistribute income with health insurance expansions and student-loan forgiveness, although not necessarily in the Robin Hood direction. They give union bosses more tools for reducing competition in the labor market. They try to regulate the internet as a public utility. They distort healthcare markets in many ways, including a new ban on short-term health insurance plans, and granting selected companies a monopoly on a generic drug. They go significantly further than the Obama administration in terms of requiring the private sector to change behavior in ways the bureaucrats expect to reduce carbon emissions. At great human-capital expense, they enabled teacher unions and blue-state governments to maintain “social distancing” far longer than warranted.

The exhaustive list would have more than 1,000 entries.  Overall, even the federal agencies’ own low-ball estimates of the costs of the regulations finalized 2021-24 are almost $2 trillion.

1. Macro Performance

Four years ago, I released a study with Kevin Hassett, Tim Fitzgerald, and Cody Kallen of the economic effects of candidate Biden’s agenda compared to President Trump’s. Knowing that campaign promises do not necessarily turn into policy, we analyzed several policy scenarios. The scenario closest to the portfolio of policy changes over the past four years we called “capital taxation constant” (CTC). Biden-Harris climate regulations proved to be somewhat more aggressive than represented by the CTC scenario including, for example, a requirement that manufacturers of medical inhalers (sic) either cease production or convince the Environmental Protection Agency that they are earnestly seeking lower-emission technologies. On the other hand, as nonlawyers we did not account for such a high failure rate of Biden-Harris rules in federal courts.

Under the CTC scenario, labor and capital would be 5.0 percent below the Trump baseline in the long run. In tomorrow's Wall Street Journal, we show that a single trend fits the data well from 2017-Q1 through 2021-Q4, except for the first full pandemic quarter. Then inflation hit and employee compensation—and national income more broadly, which isn’t shown in the chart—fell 5 percent behind. To be more precise, the latest data (2024-Q2) show inflation-adjusted employee compensation per adult to be 4.6 percent below the trend.

Arguably, human capital would have fallen somewhat below its trajectory after 2020 due to pandemic behaviors unrelated to Biden-Harris policies. By itself, this would have pulled labor income somewhat below its prior trend for several years. On the other hand, with some of the regulations not taking effect until 2025 and beyond, we have not yet seen the full effect of the Biden-Harris policies. Large language models and other “AI” technologies have been a positive growth effect that was unanticipated in 2020 when we made the forecasts.

We used a closed-economy model (tariffs were modeled like other excise taxes) that imposes a constant labor’s share, a constant depreciation rate, no statistical discrepancy, and equality between GNP and GDP. In reality, labor’s share of national income has been pretty constant, but the national income’s share of GNP has fallen a bit. More significant has been a fall in the ratio of GNP to GDP. Conversely, a real GDP per capita chart would look “better” than our compensation chart, which of course is no consolation for workers.

Economic Theory in the White House: An Index of 67 Instances in One Year

It is difficult to exaggerate the usefulness of Chicago Price Theory for economic analysis in the White House.  Below is an index of 67 instances that I can remember where Chicago Price Theory was directly and specifically applied to analysis (usually publicly released) of economic issues over a one year time frame.  As an example of what I mean by "directly and specifically," compare Chicago Price Theory's Figure 19-3 to Figure 7-2 in the 2019 Economic Report of the President.

Figure 19-3 from Chicago Price Theory

From the 2019 Economic Report of the President

(the second derivative of the after-AI demand curve is part of the discussion in both sources).

Economic issue analyzed by CEA	CPT pages
ACA employer mandate	75	-	75
Agency Compliance with Circular A-4	131	-	132
Artificial Intelligence and the labor market	120	-	121
Artificial Intelligence and the labor market	132	-	133
Artificial Intelligence and the labor market	147	-	148
Artificial Intelligence and the labor market	176	-	179
Artificial Intelligence and the labor market	182	-	183
Artificial Intelligence and the labor market	186	-	186
Artificial Intelligence and the labor market	189	-	191
Artificial Intelligence and the labor market	195	-	196
CEA's sample of 20 deregulatory actions	7	-	9
CEA's sample of 20 deregulatory actions	59	-	61
CEA's sample of 20 deregulatory actions	131	-	132
CEA's sample of 20 deregulatory actions	135	-	138
CEA's sample of 20 deregulatory actions	140	-	144
Corporate-income taxation	184	-	185
Corporate-income taxation	185	-	186
Corporate-income taxation	210	-	210
Green New Deal	116	-	119
Green New Deal	131	-	132
Health insurance deregulation	59	-	61
Health insurance deregulation	102	-	103
Health insurance deregulation	131	-	132
Health insurance deregulation	150	-	150
HHS Removal of Safe Harbor for Rebates	66	-	72
HHS Removal of Safe Harbor for Rebates	140	-	144
Highly socialist countries	135	-	138
Highly socialist countries	150	-	150
Macro effects of trade policy	32	-	32
Macro effects of trade policy	176	-	179
Measuring Rx drug prices	48	-	55
Measuring Rx drug prices	55	-	57
Medicare for All	157	-	159
Medicare for All	160	-	161
Medicare for All	168	-	170
Medicare for All	176	-	179
Medicare for All	209	-	209
Opportunity Zones	98	-	99
Pandemic Innovation Values	206	-	206
Telecommunications deregulation	7	-	9
Telecommunications deregulation	79	-	81
Telecommunications deregulation	102	-	103
Telecommunications deregulation	140	-	144
Telecommunications deregulation	150	-	150
The "doubling effect" of switching from reg to dereg	106	-	107
The cumulative impact of regulation (conceptual)	116	-	119
The cumulative impact of regulation (conceptual)	120	-	121
The cumulative impact of regulation (conceptual)	131	-	132
The cumulative impact of regulation (conceptual)	132	-	133
The cumulative impact of regulation (conceptual)	135	-	138
The cumulative impact of regulation (conceptual)	147	-	148
The cumulative impact of regulation (conceptual)	176	-	179
The opioid epidemic	7	-	9
The opioid epidemic	44	-	45
The opioid epidemic	66	-	72
The opioid epidemic	74	-	75
The opioid epidemic	128	-	129
The opioid epidemic	131	-	132
The opioid epidemic	135	-	138
The opioid epidemic	204	-	206
USMCA	96	-	98
Wage growth	48	-	55
[redacted regulatory impact analysis]	48	-	55
[redacted regulatory impact analysis]	157	-	159
[redacted regulatory impact analysis]	186	-	188
[redacted trade deregulation]	131	-	132
[redacted trade deregulation]	135	-	138

I suspect that this is historically unusual.  For example, the neoclassical growth model (standard training in Economics PhD programs and on pages 176-196 of Chicago Price Theory) had never been mentioned in an Economic Report of the President until 2018.  In 2018 and 2019 that model was used to address several policy questions, especially those cited above. 

Do Recessions Save Lives?

Copyright, The New York Times Company

More people die in economic expansions, and fewer die in recessions.  Whether and how policy makers should heed this pattern depends on the hitherto unknown links between mortality and economic activity.

Recessions can be stressful and depressing, especially for the people who lose their jobs.  Suicide rates spike during recessions, and for that reason alone recessions have been called deadly.  Two researchers, David Stuckler and Sanjay Basu, noted that suicides and binge drinking are positively correlated with unemployment and concluded that “Austerity kills” by adding to unemployment.

Even if we could be sure that austerity and related fiscal policies create recessions, it would be premature to conclude that they literally kill people.  Industrial and construction accidents are more common in economic expansions, and less common in recessions because those industries’ activities follow the business cycle.  Overtime hours may be more dangerous than average, and overtime is more common at the peak of the business cycle.  Moreover, the share of people working in construction – one of the most hazardous industries – increases during expansions and falls during recessions.

Highway accidents also follow the business cycle because more vehicles are on the road during economic expansions, and fewer on the road during recessions. (Mr. Stuckley and Mr. Basu noted that the United States’ Great Depression of the 1930s also had abnormally low rates of fatalities due to traffic accidents.)

With more accidents at work and on the road during expansions, expansions have more deaths by such accidents, and recessions have fewer.

It turns out that the business cycle for suicides is more than offset by the business cycle for other deaths.  Mortality and the unemployment rate are negatively correlated.  Christopher J. Ruhm, a professor of public policy and economics at the University of Virginia, has looked at all causes of death and found that most of them – suicide was the exception – occur less frequently at the depths of the business cycle.

Perhaps most surprising is that the business cycle for overall deaths is dominated by the business cycle for deaths among elderly people, perhaps especially elderly women.  Because so many elderly people are retired, they are especially unlikely to have recently been laid off from their job (which can lead to suicide), to drive their car to work hurriedly, or to take part in a dangerous construction project.

We don’t really know how the business cycle for economic activity is connected to the cycle for elderly deaths.  One hypothesis is that economic expansions create air pollution, and air pollution kills elderly people.  Another hypothesis is that nursing homes have more trouble retaining their staffs during expansions because they have to compete with other businesses.  Perhaps family members who are busy at work during expansions spend less time helping their elderly relatives.

Life is valuable, so it may be at least as important to understand what determines mortality and its cycles as it is to understand what causes recessions.

The iPhone5 and Consumer Spending

Copyright, The New York Times Company

That the introduction of iPhone 5 increases consumer spending is no triumph for Keynesian economics, but merely an advertisement for plans by the Bureau of Economic Analysis to improve national accounting.

Suppose there were an island economy with 100 able-bodied adults, all of whom work as fisherman, each catching a fish a day. The 100 fish are eaten by the island people, which makes inflation-adjusted daily consumer spending in the economy – or consumption, as economists call it – equal to 100.

(In order to clarify concepts and measurement practices, I have deliberately kept this model economy simple, with no unemployment, liquidity traps and the like.)

Now suppose that 10 of the fisherman decide to quit fishing to build, nurture and tend to an apple orchard. In the beginning, the orchard produces no apples, so consumer spending drops to 90 fish a day, because only 90 adults are out fishing while the other 10 are developing an orchard.

Time passes, and a bountiful 100-apple harvest occurs. Because the apples taste good, the island fishermen obtain all 100 apples from 10 orchard owners in exchange for fish. The apple harvest has by itself boosted consumption in the economy, which suddenly jumped from 90 fish a day to 90 fish a day plus 100 apples.

It would be ridiculous to proclaim that the apple harvest and its effect on consumer spending prove that the economy is plagued by a liquidity trap, or to insist that the immediate effect of the harvest on consumer spending is independent of the quality of the apples. By construction, this model economy has no Keynesian features, yet nonetheless a harvest has a large effect on measured consumption.

Consider now the American economy, in which Apple has just released its iPhone 5, and some economists say they believe that the release itself will noticeably increase aggregate consumer spending. Paul Krugman has gone even further, to assert that the increase is a proof for Keynesian economics and that the “short-run benefits from the new phone have almost nothing to do with how good it is.”

Even if Keynesian economics were completely wrong, economists would expect the iPhone 5 release to cause consumer spending and gross domestic product to be greater than it was before the release, to a degree related to the phone’s overall value to consumers. Note that, as in my island economy, the development of Apple products reduces consumption before the release, because the people working on the coming products are not available to produce consumer goods during that time.

Much of the development work on the iPhone 5 did not, before its release, count as investment or G.D.P. (G.D.P. is the sum of public and private consumption and public and private investment). The national accounts treat research and development activities as intermediate inputs, which means that they are subtracted from revenue for the purpose of determining a corporation’s contribution to national production.

This same is true for, say, Apple’s legal expenses in developing patents (many of which are discussed on the Mactech Web site) and license terms for their new product.

These development activities appear as G.D.P. only when the product is completed and sold. If the product is not valuable, it will not sell and will not count for much, although national consumption could still rise if upon project completion the developers move out of development and into the production of consumer goods.

(Research and development employees usually receive wages during the development phase, but their prerelease compensation comes out of corporate profits).

For the purposes of understanding the timing of economic activity, the national accounts’ treatment of research and development is a weakness, because it recognizes the developers’ activity at the wrong time – only after the product is released. Economists at the Bureau of Economic Analysis (the agency producing our nation’s accounts) are aware of this weakness, how it has become acute with the rise of the technology sector and steps they might take to improve the accounts. They are doing the best they can with the data and economic research results that are available.

Measured consumption rises in the quarter when people buy their new iPhones, not when they actually use them (in my island model, the consumption would be measured when the apples are bought, not when they are eaten). IPhones are durable goods that are used for years after Apple sells them as new, including by second and third owners.

Curiously, the timing of measured iPhone consumption would be markedly different if Apple or cellular carriers had chosen to rent the phones rather than sell them, because the measured consumption would occur each month when users paid their rental fees (the Bureau of Economic Analysis is aware of this weakness and rectifies it in sectors where it is most acute, like the housing sector).

The iPhone 5 release is no occasion to cheer for wasteful government spending, but perhaps does help make the case for a larger budget at the Bureau of Economic Analysis, so that it can continue its progress on measuring the amount and timing of economic activity.

Pictures from Seneca, SC

Earlier I posted about a construction project in Seneca, SC. Here is a picture of the site:

Stimulus Spending reaches Seneca SC

I was a happy resident of Seneca, SC for a short time. Current resident Professor Matt Lindsey describes some stimulus impact there; the good people of Seneca deserve better.

"It has taken a while, but the stimulus package has reached [Seneca]. Over the last weekend Walnut Street was resurfaced and widened with a center lane for turning (into???). The important aspect is the widening which was undertaken with such zeal that the sidewalks had to be moved and repaved leaving the power line poles in the middle of the outside lanes. It is this latter aspect that provides the much vaunted multiplier effect of the spending. Now the power company will have to come move the poles out of harms way. Very soon, too, because it is only a matter of time before someone takes the outside lane and mows down one or two poles. More multiplication of stimulus effects will result when the state has to come back and resurface the holes left by the pole movement. This Obama team is full of clever tricks!"

It is easy to forget that "GDP" stands for "Domestic product, GROSS of depreciation." Thus, one shortcoming of GDP as a measure of economic value is that measured GDP can go up merely because the government pays workers to destroy something (or render it less useful, like those sidewalks and telephone poles in Seneca). The destruction is not subtracted, but every dollar of the government salaries are counted in GDP (regardless of whether anyone ever turns left in that new left turn lane!).

You might hope that NDP (net domestic product) would correct for this, but it does not. NDP does subtract depreciation, but only anticipated depreciation (with an occassional exception when there is mass destruction like a hurricane). Thus, those telephone poles and sidewalks in Seneca are assumed to depreciate at a fixed rate regardless of whether the government actually destroys them or makes them less useful.

The national GDP accounts are a wonderful tool and are thoughtfully and diligently maintained by the Bureau of Economic Analysis. But sometimes they need some tweaking, and can potentially be exploited by those who would like to exaggerate the value of public spending.