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. 

Robots: Leibniz' dream is coming true in economics

Gottfried Leibniz, one of the legends in the history of mathematics, envisioned that human reasoning would one day be automated, thereby resolving a great many disputes among experts. He wrote (translated from German at WikiQuote from his 1688 "The characteristics of the art in order to make science fair"):

[...] if controversies were to arise, there would be be no more need of disputation between two philosophers than between two calculators. For it would suffice for them to take their pencils in their hands and to sit down at the abacus, and say to each other (and if they so wish also to a friend called to help): Let us calculate.

image credit: https://upload.wikimedia.org/wikipedia/commons/thumb/c/ce/Gottfried_Wilhelm_Leibniz%2C_Bernhard_Christoph_Francke.jpg/194px-Gottfried_Wilhelm_Leibniz%2C_Bernhard_Christoph_Francke.jpg Public domain.

More recently, Obama administration economists Furman and Summers claimed that only a fraction of the revenue loss from a corporate-income tax cut benefits labor. But the standard supply and demand model says the opposite.

Summers, as well as Nobel Laureate Paul Krugman, rejected this result, asserting that it depends on "what share of the capital stock is even affected by the corporate tax rate."

The supply and demand model readily accommodates the fact that the statutory corporate rate does not apply to much of the nation's capital. Now a machine has proven the supply-demand result, without assuming any functional form for the aggregate production function, and without restricting the share of capital that is subject to the tax (except that the share cannot be zero or negative).

You can view the proof in pdf here, or as an executable Mathematica notebook here.

For another economics dispute between Krugman and I that was resolved by machine, see here.

(They also incorrectly claim that "monopoly" overturns the result too. See here, and here. For some machine analysis of the issue, see the pdf here and the executable Mathematica notebook here.)

Your job cannot be automated? Then you need to worry!

Copyright, TheHill.com

One of the greatest labor force changes of the 20th century was the movement of workers out of farming. In 1900, more than two out of five workers were in agriculture. Now it is less than two workers out of every 100.

It's not that people stopped eating. Rather, farm machinery and innovation increased the amount of food that could be produced per farm worker by more than a factor of 10. Food got cheaper and that got people to buy more food, but not 10 times as much. The end result has been fewer jobs in agriculture.

Automation is expected to come to other industries and occupations, and it is tempting to forecast less employment for them too. A variety of studies are using engineering information to determine which jobs will be automated next.

While automation may be a question of engineering, job loss is even more a question of economics. A key part of the agriculture story is that people were unwilling to purchase all of the food that farmers were capable of producing, even though food was getting cheaper. But not all industries share this with agriculture. 

Suppose that the automation in agriculture had only been for chicken farming and not for any other food production. Chicken would have gotten cheaper relative to beef, fish, vegetables, fruit, etc., and that would have caused people to buy more chicken and less of other types of food.

Many -- even most -- of the extra chickens produced would have been purchased by consumers, and there would have been less need to reduce employment in chicken farming. 

The most dramatic job losses would have occurred in the food industries like beef and fish that were not automated and that compete with chicken. In other words, jobs that are difficult to automate from an engineering perspective may be exactly the jobs pushed to extinction by automation because they cannot compete.

It all depends on the competitive landscape and how willing are consumers, encouraged by lower prices, to absorb the extra output made possible by automation.

Trucking is a modern example, because engineers are predicting that machines will soon do a lot of the driving formerly done by trucking employees. But the result may be more jobs for people in trucking and fewer jobs for people in railroads, airlines and shipping that compete with trucking (unless they also get more productive at the same time that trucking does).

Another example has occurred in my own profession: Two or three generations ago, a large fraction of economists were employed manually performing the arithmetic of statistical analysis. Then, computers came along to automate that arithmetic, without really automating the tasks done by theoretical economists.

The result was an increase in the fraction of economists doing statistical work, because universities, businesses and government wanted more statistical analysis when computers made it became cheaper and more accurate. The fraction of economists doing theoretical work fell, precisely because their tasks were not automated.

So the more interesting economic question for a worker is not whether his job can be automated but whether he or she will miss out on automation to occur in the workplace of his or her primary competitors.

Robots and Property Values

Copyright, The New York Times Company

As robots begin to move goods and people from place to place, urban land might become more valuable.

Amazon.com has announced that it is testing package delivery by drones — small, unmanned helicopters that would bring a purchase from Amazon’s fulfillment center to the customer’s front porch. Driverless cars are being developed to help move goods and people from place to place.

“Location, location, location” is the saying in real estate: a property’s value is determined primarily by its location. An apartment in central Illinois might be worth 20 times as much in Manhattan, because a Manhattan apartment gives its resident access to many more goods, activities and high-paying jobs.

This is not to say that urban living is always the best, or that all urban properties are created equal. Locations involve trade-offs, and rural areas offer amenities that big cities cannot. But for centuries, real estate markets have shown that people and businesses are willing to pay more for urban properties.

As technology helps with moving goods and people more cheaply, it might seem that urban real estate would give up some of its price premium because distance becomes less of an obstacle to economic transactions. Wouldn’t a driverless car cause some workers to sell their Manhattan apartments and commute to their jobs from more spacious homes in the suburbs or even rural New York State?

But don’t forget that many people and businesses currently avoid urban areas because of the monthly expense of owning or renting urban property. New technologies might allow them to use urban properties on a part-time basis, or use less urban property to accomplish the same tasks, which would make urban property more valuable.

A restaurant may need less refrigeration and storage space because it takes multiple food deliveries per day. Grocery stores may save on shelf space by having a greater fraction of their items delivered directly to customers without being shelved in the store. Households may opt for less storage space or parking, for example — and more room for people — when they can get items and transportation cheaply and on time.

For every Manhattan resident who leaves his apartment for the suburbs, there could be many others for whom technology induces them to use a Manhattan property on a part-time basis.

New technologies are more likely to emerge in urban areas, because that’s where the innovators expect to find the most customers. Amazon said that it planned to start its drone service in urban areas, and I wouldn’t be surprised if the first commercial uses of driverless cars were in big cities like San Francisco or Los Angeles.

Thus, while cities already give their residents access to more goods and services, technology may further shift that advantage and thereby increase urban property values.

Robotic-Task Economics

Copyright, The New York Times Company

The forces of supply and demand suggest that the machines of the future will continue to be significantly different from people.

Anthropomorphic robots are prominent in science fiction. Novels like Mary Shelley’s “Frankenstein,” and films like “E.T.,” “Star Wars” and “The Terminator” feature machines with one head, two eyes and many other features of humans. But those robots were created by authors to entertain audiences, and not by investors to produce some other kind of economic value.

At first glance, it would seem that the state of technological progress is all that limits the creation of machines closely resembling humans. Especially after the recent recession, people are concerned that technological progress is moving at a pace that will soon permit machines to put wide swaths of the human population out of work, and not just displace workers from one industry to another.

I disagree. Powerful economic forces will push the machines of the future to be different from people, and to complement workers rather than mimic what they do.

Machines are expensive to design and manufacture, and most of the people directing the creation of machines have an eye on the rate of return: the economic value of a finished machine as compared to the costs of creating it. The more profitable investments will, by definition, be in machines with a higher rate of return.

The earth is already occupied by quite a number of people, and a machine like Frankenstein or C-3PO might find itself with seven billion competitors.

Take my example last week of baby-sitter robots. A baby-sitting machine with a high rate of return might, as one commenter suggested, be one designed to help children during fires and other disasters. Or a machine to care for children at night. These are examples of child care tasks with less competition from people than the ordinary baby-sitting tasks.

Human help is, of course, not free, and economizing on the cost of a baby sitter or an automobile driver is a reward for creating a machine to do those tasks. But the amount of the reward is commensurate with the amount of wages that people earn in the task.

Machines that drive human workers into unemployment, rather than into another industry where the human workers will be productive, will serve only to drive down workers’ wages and thereby drive down the machine’s value. Machines that, instead, help people to be more productive will find it much harder to saturate their own market.

Rather than finding an intelligent and tireless robot in your office chair, expect the machines of the future to help workers, not harm them.

Nanny Robots and Population Growth

Copyright, The New York Times Company

As any parent can attest, caring for young children is time-intensive. As a result, child care is one of the largest segments of the economy, at least when the nonmarket household sector is included.

Many parents treasure their children and feel the benefits outweigh the time and costs of having children. Many other adults decide not to have their own children, and the time costs are sometimes a factor in that decision. (To be sure, the influences are complex; a study by Satoshi Kanazawa of the London School of Economics, which suggests that women with higher IQs are less likely to have children, made waves in the blogosphere in recent days.)

The time costs of child care are also a factor limiting teenage pregnancy. Teenagers are encouraged to complete high school and higher levels of schooling, and students’ parents, teachers and counselors – if not the teenagers themselves – understand that teenage motherhood takes time away from schoolwork and thereby makes academic success less likely.

The world would be very different if children did not need so much time. More people, perhaps especially teenagers, would have children if children did not require so much time and attention, especially from their mothers. People who already have children despite the cost might have more of them if they expected each child to require less time.

As the time costs of children limit population growth, the population would be likely to grow more rapidly if those costs were somehow reduced, whether you think that such growth is a good thing or a bad one.

If each child required less parental time, you might expect parents – especially mothers – to use their time on other things, like work more outside the home, pursue their own schooling or leisure activities. But it is possible that people would spend more of their lives caring for children and less time on those other things because they would be having more children.

Wealthy people have already had some of these opportunities, because they can afford numerous baby sitters, nurses and tutors. But technological progress may one day reduce child-care costs for the general population.

Because robots and other machines take on a number of tasks formerly done by people – even playing chess – and are expected to do others like drive cars, perhaps we should expect that robots will some day take care of children, too.

People today may believe that it would be inhuman or immoral to leave young children at home alone with a robot or to drop them off at a day care center staffed by machines. But economic and technological changes of the past have already transformed child-rearing attitudes and practices: take test tube babies, working mothers, screen time, fast food or children with their own telephones.

There is little need to worry that machines will take over all aspects of child rearing. People will always have a comparative advantage over machines, even if machines could in principle be better at just about anything. For the same economic reason that the world can produce more by assigning some tasks to unskilled people and other tasks to talented people, people will be doing tasks that are difficult for machines relative to other tasks.

But perhaps robots will make parenting easier and thus more popular.

The Future of Driving

Copyright, The New York Times Company

Driverless vehicles would be a windfall for households and businesses that acquire them but would probably increase traffic and nationwide fuel usage.

Google and other innovators are working on vehicles that someday might drive themselves with little or no attention from human passengers. The vehicles of the future will have fast, observant computers that automatically communicate position and road conditions with other vehicles on the road.

Driverless vehicles are expected to help children, the blind, the elderly and others who currently cannot safely drive themselves. Helped by their huge amounts of data and computing power, driverless cars are also purported to reduce traffic congestion and nationwide fuel consumption by driving smarter.

But smarter driving will lead to more driving, because smarter driving reduces the cost per mile of vehicle usage. The end result of additional driving could be more traffic and more aggregate fuel consumption.

These days, a driver has three main costs of the trip to consider: fuel consumption, vehicle wear and tear, and time and attention devoted to driving that could be for something else. (Drivers also need to consider other costs of vehicle ownership, such as the purchase price and the cost of insuring the vehicle.)

Fuel and wear and tear cost roughly 50 cents a mile, which is why employers reimburse employees for job-related personal vehicle usage at about that rate. At an average speed of 30 miles an hour (including stops, traffic conditions and so on), each mile takes two minutes of driver time. For those who value their time at more than $15 an hour, the time cost of the trip exceeds the combined fuel and wear and tear costs.

Research has shown that cutting travel costs through reduced gas prices causes people to drive more, for example by eschewing carpools and public transportation. A driverless car should also cause people to use their vehicles for more miles, because they could use their time in the car to sleep, work, watch television, read a book and do other things they might normally do at home.

Households and business may also begin to use vehicles with no human passengers or drivers in order to move goods from one place to another and, by economizing on the human driver costs, they may want to move more goods than they do today.

As people take on additional activities in their personal vehicles, they may also demand larger vehicles that necessarily require more fuel per mile.

Before driverless cars are adopted, a number of hurdles must be cleared. Some refinements in vehicle technology need to be resolved; insurance companies and state regulators must also figure out liability issues.

Even if driverless vehicles led to more congestion and more aggregate fuel consumption, driverless vehicles would be a welcome technological advance, because the billions of hours that people already devote to driving could be put to alternative uses.

But expect new driving technologies to increase the number of vehicles on the road.