For the past few years I have been
mulling a paradox: U.S. GDP keeps going up, yet it seems like we make
less stuff and that most of the smart people I know work fake jobs.
Growing up in the nineties, most of my toys and clothes had tags saying
“Made in Hong Kong” or “Made in Vietnam.” But the high-skill, high-tech
goods—the washing machine, the car, my computer—were often made in
America. Now? From my e-bike to my laptop, from my refrigerator to my
mattress, very few goods I own, high-tech or low-tech, were made in the
USA.
Meanwhile, I have heard arguments that America is actually making more things than ever. According to The Economist, lost jobs are due
to automation, not foreign competition, and it is a good thing that
machines have liberated us from factory work and enabled more service
jobs. Everyone from the American Enterprise Institute to The Wall Street Journal to Wikipedia agrees that U.S. manufacturing has not just not significantly fallen—but it has never been higher.
Sometimes there are discrepancies
between your real-world observations and the data. But this goes far
beyond just being a discrepancy: the data is saying the complete
opposite of what we see with our own eyes, hear from our acquaintances
in the job market, and deduce logically from our knowledge of
demographics, technology, industry, and trade. How is this possible? The
answer is actually very simple: the data is completely wrong. But you
can only figure this out if you go line-by-line into the hundreds of
pages of government GDP calculation methodology documentation. Which is
exactly what I did.
The most commonly
cited graph shared to demonstrate U.S. manufacturing strength is based
on the U.S. Bureau of Economic Analysis’s (BEA) manufacturing “real
value-added” data, which looks at manufacturing as a subset of total
GDP. This graph has been cited by Federal Reserve economists, Washington Post
columnists, professors—all claiming it refutes the idea that the U.S.
economy has been hollowed out. Adjusted for inflation, it shows
manufacturing is up 71% since the dataset began in 1997, and up a
healthy 37% per capita:
You might think that a measure of
manufacturing would in some way measure actual manufactured goods
emerging from U.S. factories, like tons of steel rolling out of mills,
number of CPUs coming out of chip fabs, and cars rolling off the
assembly line. But this is not the case. Despite the name, “real GDP” in
practice is the result of hundreds of arbitrary and subjective
decisions made by government-employed economists, such as “education
administrators are more productive than teachers” or that a 25% increase
in automobile “quality” can theoretically show up as a 166% increase in
“real GDP value added.”
It is remarkable how there is so much
commentary on GDP, yet so few people have truly wrestled with the
numbers and where they come from: an advanced MIT macroeconomics textbook
will reference GDP over sixty times, yet not once acknowledge the
decision-making that goes into making this number. Likewise, pro-market
public intellectuals are only too happy to cite GDP to make a point
without considering that the metric itself is the antithesis of market
capitalism: GDP is a very complicated statistical construct that is made
by government bureaucrats behind closed doors without any ability of
the public to replicate, audit, or verify assumptions. Sometimes, these
kinds of constructs can be useful for accurately representing real-world
phenomena, like manufacturing capacity. But a dive into how the sausage
is made makes clear that GDP is not one of them.
Where GDP Comes From
Back in the 1930s, U.S. policy-makers
and economists were facing two big problems. The first was that the
nation was suffering an economic crisis with millions of people and
businesses suffering loss of income, yet there was no existing way to
sum up incomes across the economy to get a sense of how the nation as a
whole was doing. The second problem was that previous economic
statistics focused on raw commodity outputs like bushels of wheat grown
or tons of steel produced, but now more of the economy was in services,
government work, and heterogeneous manufactured products.
The U.S. government commissioned
economists to create a comprehensive set of national income statistics.
The economists kept working and went on to develop statistics aimed to
measure the economic output by category of the economy and then,
ultimately, to sum it up into one number that eventually would be known
as “gross domestic product,” or GDP.
At first glance, summing up the
economy into a single number seems impossible. How do you add up apples
and oranges? Or, say, apples, cars, and dentist appointments? One’s
first inclination may be to add up total sales receipts in each
category, since dollar spending can be compared between products and
over time. But this fails because a rise in total spending for a product
category may be merely the result of a rise in price, not more
production. The clever solution was to combine expenditures or sales
receipts in dollar terms with measures of the average price of each
item. If spending on cars has doubled, but the price of the average car
has also doubled, then there was no real change in production of cars.
But if spending on cars doubled while the price only increased 50%, then
there was a substantial increase in cars purchased.
Statistical agencies built giant
databases of prices across all products, then matched the prices with
expenditures for each category. Now it seemed as if they had the ability
to do what seemed impossible, and add up changes in the economic
sectors into one number. The top economic textbooks have called this “truly among the great inventions of the twentieth century.”
Today, GDP numbers are calculated by
the Bureau of Economic Analysis, which is staffed by career economists
with no political appointees involved. It’s under the U.S. Department of
Commerce. However, the methodology is also an international effort.
U.S. economists join with their foreign counterparts at a United Nations
committee to define the “System of National Accounts.” These are
standards that nations around the world at least try to adhere
to—adherence can be a requirement for World Bank loans. The methodology
also changes over time. For instance, in 2012 there were major updates
to add development of intellectual property (IP) as being its own
contributing component to GDP.
Discourse over GDP is frequently confused because there are actually three different calculation approaches: the income approach, the expenditures approach, and the value-added
approach. In the textbooks, usually only the expenditures approach is
taught. In theory, each approach should sum to the same total number,
since everyone’s income must come from someone else’s spending. However,
when comparing sectors, such as government or healthcare, the totals
differ for each approach, and this can create a lot of confusion. On top
of that, each approach has a nominal and a real version. Thus when a news report refers to “GDP for healthcare”, this could be referencing one of six different numbers!
For the income approach to GDP, the
process is to add up every person’s compensation, plus corporate
retained earnings, plus some adjustments. For the expenditures approach,
the formula is to sum the final expenditures of private consumers, the
capital expenditures of businesses, the spending of the government, and
exports, then subtract imports. “Final expenditures” means that the
price of a car bought by a consumer is counted, but the money spent by
the car dealership on its electric bill, or the money spent by the car
factory on steel, is not counted. Counting non-final expenditures would
result in double-counting and would break the number. The expenditures
approach is most frequently taught in Economics 101.
Finally, we have the value-added
approach. Rather than counting just final sales, this approach counts
the sale value minus the input costs at each step. Imagine a gallon of
milk sold from a grocery store for $5. Rather than just counting that $5
as a “final expenditure,” the value-added approach counts the cow feed
sold to the farmer for $1, then the milk milked by the farmer for $4
(thus adding $3 in value), and then the gallon of milk sold by the store for $5 (adding $1 in value). You add up all the steps and get the same $5.
Each approach has its uses, but you
have to be careful with which you use. What percent of GDP is
healthcare? You get two different numbers depending on the approach.
With the expenditures approach, healthcare is 17% of GDP, but for the
value-added approach only 8%. Why? Because the value-added approach only
counts expenditures on hospital and clinic workers toward the
healthcare category. Money spent on manufacturing medical devices counts
as manufacturing; money spent building hospitals counts as
construction. For measuring healthcare’s share of the economy, it is
probably better to use the expenditures approach because it is
reasonable to include pharmaceutical production and hospital electricity
bills as part of healthcare.
What percent of GDP is government spending? When debating the value of government spending, Elon Musk was
fact-checked by his own platform and informed that “government was only
11.3% of GDP.” But this is using the value-added approach, which only
counts direct government employees. In the value-added approach, the
money the government spends on everything from constructing buildings to
software licenses does not count. Meanwhile, the expenditures approach
counts the government as 17% of GDP. This still excludes interest on
debt and transfers like social security, but it includes money the
government spends on grants or contracts with private businesses, such
as SpaceX.
If you want to know GDP by city or
region, the BEA uses the income approach. This is a practical decision.
The other approaches are too difficult to tie to specific locations, but
income tax data makes it easy to tie income to addresses. Since
regional GDP is just measuring income, it does not really tell you if
that region’s GDP is a result of actual useful market production, or if
it is from monopoly profits, rent-seeking, and government deals.
If you want to see what percent of
the economy is manufacturing, and how that has changed over time, you
can only use the value-added approach. Only the value-added approach
separates out each step in the economic chain: from mining the iron ore
to transporting it to the factory to manufacturing the product to
selling it at the store. The value-added approach categorizes each step,
so you can sum together just the increase in price from the
manufacturing step across all categories of spending.
There is a second major complexity: for each of these approaches, we have a real and a nominal version.
The nominal version is just based on adding up dollar sales or dollar
income. It does not even pretend to be a measure of product or
production. To get a measure that purports to measure changes in
production over time, economists created a statistic called “real” GDP.
Since the problem with nominal GDP is
that year-to-year changes are simply a result of money supply changes,
not production, the art of real GDP is to factor out price changes. This
is done by using price indexes. These price indexes are a joint effort
between the U.S. Department of Labor’s Bureau of Labor Statistics (BLS)
and the BEA. The BLS actually sends out thousands of agents to look at
prices in stores, browse websites, and survey producers. A price index
for, say, “cereals and bakery products” is thus created by selecting a
representative sample of what consumers actually buy, and then for each
item in the sample tracking changes in price and then calculating a
weighted average of these changes....
