From Palladium Magazine, October 3:
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:
Patrick Fitzsimmons/U.S. real value-added for manufacturing according to the U.S. Bureau of Economic Analysis, 1997-2024
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....
....MUCH MORE
