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.

Why I have an OpenAI subscription

 It's $20 per month.  Here are the features I value and use:

• Others' custom GPTs
• My own custom GPTs
• Integration into email (Thunderbird) and word processing (MS Word for Mac OS)

In many cases, a custom GPT is nothing more than somebody uploading their documents that become the primary information source.  The creator can also train the custom GPT by giving it queries and then pointing out mistakes or room for improvement.  In some cases, the creator may also work with OpenAI to provide additional capabilities like Wolfram has done.  Regardless, the OpenAI subscription gives access to custom GPTs created by self and, if shared publicly, others.

Wolfram's Custom GPT

The Wolfram GPT, for instance, combines the "verbal skills" of Large Language Models with the analytical capabilities of computational AI.  As needed, Wolfram's GPT automatically calls an algebra engine or other computational engine and then processes the response.  Learn more here.

Since 2016, my coding has been primarily in the Wolfram language. While ChatGPT Plus can be a helpful aid in this coding, my own capabilities surpass it. The Wolfram GPT, however, operates at an entirely different level. This is demonstrated by a time-aggregation function it wrote for me. 

I have used @, /@, @@, @@@, #, #1, #2, :>, ->, but had no idea &-> was a thing!

Here's another example from Wolfram:

My own custom GPTs

I have several of my own custom GPTs, which are created merely by uploading my own documents.  Those docs become the LLM's primary knowledge source.  One of them is "Chicago Price Theory Tutor" which is amazing (but not yet shared with the public :)).  How smart is this?

Custom GPTs like this can understand and repeat back algebra (see the previous Wolfram screenshot).  I did not have to train it in this regard -- just upload a document that had algebra in it.  It is terrible at charts or graphs, so I instruct my custom GPTs not to even attempt to make a chart.

Once the Chicago Price Theory Tutor gave a wrong answer.  But then I realized that the document I provided was correct but confusing on that topic.  The 2nd edition of Chicago Price Theory will explain better.

Integration into email and word processing

The subscription allows you to interact with the LLM via API.  You could write your own code to call the LLM, but all I have done so far is use others' code that I prime with my OpenAI API key.  The Aify add-on for Thunderbird email adds a button to email-compose windows to perform various LLM tasks on the text therein, such as summarize it, check grammar, recommend edits, etc.  "GPT for Excel Word" is an add-on that performs similar tasks in MS Word, and presumably in Excel as well, though I haven't yet tried Excel.

There is an extra charge (beyond the $20/month) for API usage.  The extra charge is proportional to the amount of text sent back and forth to Open AI.  A busy day for me in this regard generates charges of less than $1.  "GPT for Excel Word" levies its own charge of the same order of magnitude.

Accessing the API this way, or other ways, also provides quite a large "context window" that improves results for certain tasks.  I think the custom GPTs also, in effect, have a large context window through uploading sizable documents.