In the first installment of this series on the structure of the American economy, I explained my motives for writing about this rather dry and perhaps a bit intimidating topic. Economists, I noted, are professionally guilty of increasingly bad economic analysis. Less and less of what our profession cranks out is actually useful:
The consequences of bad analysis are formidable. Our economy is in the shape it is – struggling to grow and staggering under an unsustainable government debt burden – in large part because economists have derelicted on their duties to provide clear, concise and well-researched information to policy makers. That, of course, is not to say politicians are innocent: there is a widespread tendency among our political leaders to let the interests of their constituents be overrun by the interests of their benefactors. While we economists can’t do anything about the intellectual honesty (such as it is) of our politicians, we can do something about the integrity of our own discipline and profession. Sadly, in the past 30 years economics has descended into a dungeon of econometric masturbation, where increasingly technical analysis produces increasingly irrelevant outcomes. The standard for good research among academic economists today is to crank out eclectic flea-killing standardized under the template “The influence of X on Y given Z”.
Efforts at trying to understand the economy as a structure of institutions, relations of gainful trade, behavioral patterns, intentions and ideological influences are frowned upon at best. More often than not they are ridiculed and the student or scholar is advised to go get a degree in history instead.
As economists close ranks around their castle of multivariate regressions, their ability to make a difference outside the computer lab at their economics department is rapidly dwindling. That is their choice. Fortunately, those of us who learned political economy and chose to pursue it as a scholarly career, can still be out there and make a difference.
One of those differences is to explain the economy as a complex system of human behavior that for the most part does not lend itself to correlative (let alone non-correlative) statistics. Economic policy making takes place in the real world, where decisions are almost always marred by limited information and uncertainty about the future. Then, of course, we have the ideological considerations, which drive a lot of policy making to a much larger degree than even many politicians are willing to recognize.
A future article will discuss the relationship between ideology and economic policy in more detail. For now, let us add one more piece of information to our knowledge of what the American economy actually looks like.
Let us start with a picture of the industries in which Americans find work. Figure 1, which goes back to 1939, shows the long-term decline in manufacturing as a major source of employment: the decline, which tapered off about ten years ago and has since been at least marginally reversed, gave way to the rise of, primarily, two services industries. The first is Education and Health (light blue), which of course has grown mostly on the health-care side. The second is PBS, which stands for “professional and business services” (light green). This industry includes everything from engineering consultants and computer systems developers to corporate managers, lawyers, even administration and waste-management services.
The next question is: how does this employment share match up against the contribution that each industry makes to our economy? The latter variable is measured as the value that an industry produces.** Table 1 compares the two, based on numbers for 2019. It is interesting to note that the financial-services industry adds more to GDP than any other industry, but it is also noteworthy that manufacturing contributes more to our economy than education and health:
|L share||V.A. share||Ratio|
Bureau of Labor Statistics (Employment);
Bureau of Economic Analysis (GDP Value Added)
Table 1 is good to keep in mind whenever we debate economic differences (also known as income inequality). For example, the 8.7 million people who work in the financial-services industry only represent 6.8 percent of the workforce, but they are responsible for more than one fifth of our total economic output. Therefore, it is also reasonable that many people in this industry make a lot of money.
By the same token, retail employees contribute a comparatively small share of our economy. It is only reasonable to expect that those jobs pay a bit less than jobs in other industries.
We can apply the same reasoning to taxes. Sadly, we have high taxes in America (though not as high as in most European countries) which means that those taxes also affect the ability of our economy to grow – and the allocation of economic resources. The bigger the differences are between industries, in terms of tax burden, the more skewed is the allocation of resources in the private sector.
Figure 2 reports the net tax burden on private-sector industries. The burden is defined as taxes on production, less subsidies, divided by the value added in each industry. If taxes are aligned with the value added – and if we don’t have any corporate welfare being paid out under the guise of “subsidies” – then the tax burden is the same regardless of what industry you operate in. To put it mildly, that is not the case:
Now for the inevitable: is there a correlation between, on the one hand, Figure 1 and Table 1 and, on the other hand, Figure 2? Yes, of course there is. It is a complex relationship that does not lend itself to a simple blog article or two, but there are two variables in that relationship that deserve closer scrutiny: profits and lobbying.
We will be looking at both of them in coming articles. Stay tuned.
*) TTU is a misspelled abbreviation for Transportation, Warehousing and Utilities.
**) Simply put, value added is measured as the value of production less the cost of inputs. It is one of three methods for measuring GDP, the other two being absorption (spending) and income earned. All three measurements must always add up to the same amount. Using all three is a good way to make sure you got your numbers right. It is also a good way to learn what our economy really looks like.