Interesting Changes in Industry Concentration in San Antonio

One common indicator used to get a sense of the structure of a local economy is the location quotient. Specifically, it measures the concentration of an industry in a local economy, such as a metropolitan area economy or a state economy, relative to the concentration of the same industry in some base area, typically the national economy. The most often used data to calculate the location quotient is employment, but income or wages is also used. The location quotient for industry i in region r is calculated using the following formula:

LQir = (Employmentir/Total Employmentir)/(EmploymentUS/Total EmploymentUS)

I did these calculations for the San Antonio metropolitan area economy using this formula. I calculated the location quotients for the NAICS 2-digit level industries. The names of these industries and the location quotients as of January 1990 and April 2017 are shown in the following table. April 2017 was used because it was the most current data available at the time I made the calculations.

Four industries in San Antonio have seen increases in their concentration levels since January 1990 (highlighted in yellow). The construction, mining, and logging industry saw the largest increase in relative concentration followed by financial activities, professional and business services, and manufacturing.

The largest declines in the location quotients were in the government sector followed by other services. The hospitality and education and health industries also saw smaller declines in their relative concentrations, and while the trade, transportation, and utilities and the information industries both saw declines so small one should probably just treat these as being inconsequential.

It is also interesting to note that a location quotient greater than 1.00 indicates that the concentration of the industry in the region is greater than the concentration at the level of the national economy.

As of April 2017, the industries with such location quotients were construction, mining, and logging; information; financial activities; education and health; hospitality; and government. The highest location quotient as of April 2017 was the financial activities industry; it had the second highest location quotient in January 1990. The industry with the highest location quotient in January 1990 was government.


These changes highlight two interesting characteristics of the San Antonio economy.

First, it is an economy with a broad base of industries with relatively high concentration levels. Second, the relative base of employment has shifted away from government. This is not to say that government activities and funding are not still a vital component of the San Antonio economy because they are. The military has a big impact on the local economy, and it is worth noting that the military does not have to report employment levels, so I do not believe they are captured in these calculations.

Additionally, government funding of healthcare is very important to the San Antonio economy due to the size of the healthcare industry in the region. That said, the government sector still has a location quotient of 1.08. This fact combined with the diversity of the industry base in San Antonio is why the economy also tends to be somewhat stable relative to regional economies with more focused industry bases.



Why Study Economic History

I just finished reading Paul Ormerod’s book, Positive Linking: How Networks Can Revolutionise the World. It is a great book, in which he clearly makes the case that if we truly want to understand how the economy functions then we have to understand the role of networks. In the course of part of this discussion, he also provides a great illustration of why we should study economic history. This is a long quote that spans several pages in the book, but I think it is worth it.

     In the week of 15 September 2008 capitalism nearly ground to a halt, Share prices collapsed. Credit markets froze. And we were within hours of cash machines, ATMs, being closed to the public.

     It was the American authorities who really saved the world in that terrifying week. And they did so not by the manipulation of elegant rational expectations models and theories, but by experiment and by relying on their knowledge of what had gone wrong in the Great Depression of the 1930s. Faced with a wholly uncertain immediate future, the authorities reacted by trying rules of thumb, by seeing what worked and what did not. They reacted exactly as Herb Simon said humans behaved all those years ago. They knew it was impossible to work out the optimal strategy. So they tried things which seemed reasonable and, quite literally, hoped for the best.

     It was fortuitous – and an important illustration of the role of chance and contingency in human affairs – that the chairman of the Federal Reserve at the time, Ben Bernanke, was a leading academic authority on the Great Depression. He knew that, above all, the banks had to be protected. It may seem monstrously unfair that the bankers themselves escaped penalties – indeed, it is unfair – but the abiding lesson of the 1930s is that in a financial crisis the banks have to be defended. Money is the blood which flows through the economy to keep it alive. If the chairman instead had been, say, a world expert on dynamic stochastic general equilibrium models, we would almost certainly now be in the throes of the second Great Depression.

     Bernanke had already restored a concept which is absent from the rational behavior rule book, that of ‘moral suasion’. Moral suasion, the central bank ‘persuading’ bankers to make particular decisions, is how banks used to operate before the complicated, rule-based, hugely expensive bureaucratic control systems based on concepts of ‘market failure’ were introduced…

     The key point about all these actions is that the American authorities paid no attention to academic macroeconomic theory of the past thirty years. RBC theory, DSGE models, rational expectations – all the myriad erudite papers on these topics might just as well have never been written. Instead, the authorities acted. They acted imperfectly, in conditions of huge uncertainty, drawing on the lessons of the 1930s and hoping that the mistakes of that period could be avoided (p. 183-184 and 190, electronic version).

To me, this is a clear (and dramatic) example of why it is important to study and understand the lessons of economic history.