Automation and the current challenges of the labor market


Today, many businesses are struggling to hire the workers they need. Whatever the causes, this current challenge will surely bring about widespread change on the part of employers. This type of adjustment is not a new phenomenon, but economists spend very little time explaining the mechanisms of change. We also don’t explain that these types of fixes are normal and generally, if not always, improve society. This is true for many types of changes in the labor market. But, even as the world improves, there are winners and losers, or rather each of us knows the benefits and costs. This too deserves to be explained, with a few examples.

When companies cannot hire enough workers at the wages they deem appropriate, they call it a labor shortage. Of course, workers also have a voice at work, and a company that thinks a paycheck is fair doesn’t matter if a worker disagrees. This process of matching workers with employers is messy and slow, and there is little the government can do about it. We of course try, and the states are all federally funded to create an online help database. It is even possible that in a few years, with a few million dollars more, some states will have a system as good as in 2004.

More from Michael Hicks:What is happening in the labor markets

The big challenge in matching workers is not information on job availability, but rather information on job quality and wages. High turnover is a sign of inconsistent information about the job and the worker. At a time when there is more demand than supply of workers, wages are expected to rise. This increases the relative cost of workers and makes automation more cost effective.

In the decade and a half leading up to the Great Recession, the United States experienced considerable labor market disruption due to inexpensive technology. This technology ranged from the digitization of many machines to the control and monitoring of stocks, including robotics. Whenever the cost of a labor-saving technology is lower than the cost of labor, companies automate some of the work. This experience should shed light on what we are now seeing in labor markets. As it becomes expensive to hire new workers, companies will automate some tasks.

As technology improves and more tasks can be automated, workers tend to fall into two categories: those who are primarily add-ons to automation and those who are primarily substitutes for automation. Complementary workers keep their jobs and are often better paid. The workers who are for the most part substitutable find themselves unemployed.

Of course, most of us are on a continuum in between. Technology helps us do some things better and replaces other tasks. Here, formal education seems to play a huge role. More formal education makes it easier for workers to learn new tasks in the wake of new technologies. Education, especially higher education, plays a vital role in making a worker more ‘automation resistant’. Let me give an example.

The field of economics has been almost unrecognizable from the time I was an undergraduate student. Today, the Internet contains a lot of data, and desktop computers allow us to perform surprisingly complex analyzes. This new technology allows an economist to do in one day what an entire team of researchers might have needed months or years to do in 1980. This has made the economy cheaper, while improving quality. It also added an impressive number of research questions to ask and answer. This has resulted in a sharp increase in the demand for economic research and economists.

This is a largely successful example, but it should be noted that economists who could not adapt to new techniques and technologies have largely disappeared. There are many examples of workers who do not adapt well.

This weekend my family ordered groceries through a well-known delivery service. We had purchased two shipments during COVID and the second was about to expire. The experience was horrible. The scanned products, like cereals, were good, but the purchase of fruit was a disaster. For Granny Smith apples, we received Gala apples. Instead of plums, we received nectarines, and instead of bananas, we received plantains.

I don’t mean to be cruel because there are people I know and respect who maybe eat plantains. But, it must be said that plantains are to bananas, what firewood is to cantaloupe. They are not even sold side by side in order to avoid such a macabre mistake.

Strangely enough, this wasn’t even the worst part of the delivery. Almost all of the bad fruit we received was either bruised and spoiled or so immature that it wouldn’t be edible for about a week. This experience highlights precisely the kind of human skills that machines are very unlikely to replace. Almost all the fruits were labeled, so the personal shopper should not have made such mistakes. But, if you haven’t actually seen a plantain, you’d be easily fooled into thinking it is a gloriously tall banana. If you’ve eaten it before, you’ll never make this mistake again. It’s the culinary equivalent of petting a skunk you think is a cat.

Technology may be able to distinguish between bananas and plantains, but we are a long way from inexpensive methods of choosing perfectly ripe cantaloupe or watermelon. Humans are particularly adept at making such distinctions. It is clear that 200,000 years of evolution and a tightly integrated sense of smell, sight and touch give us a unique ability to judge such questions. I find it unlikely that a robotic personal shopper will replace human-chosen fruit anytime soon. This personal shopper did not have a robotics problem, but rather a carefree problem.

These two skills vary across the field of formal education. Formulating and testing a scientific hypothesis typically requires nearly eight years of college education. Distinguishing ripe fruit from unripe fruit requires some human senses and some experience of shopping with a discerning parent. Either way, technology can combine to make these jobs productive, the adjustment period can be long and require substantial changes for consumers and businesses.

Despite the many skills we are gifted as humans, the best proof is that humans who are exposed through more formal education will be more productive. This higher productivity will translate into higher wages. There is a natural limit to hand picking fruit, but there are no such limits to many other tasks that combine with automation. There will be opportunities for these particularly human tasks. But, it has always been true that more formal education is the key to isolating individual workers and the places where they live from the risk of automation.

Michael J. Hicks, PhD, is director of the Center for Business and Economic Research and George and Frances Ball Distinguished Professor of Economics at Miller College of Business at Ball State University.

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