Thus, an increase in the trend growth rate will lead to a decrease in unemployment, while a decrease in the trend growth rate will lead to an increase in unemployment. This result is sensitive to changes in certain assumptions underlying the model. For instance, Aghion and Howitt point out that technological progress does not occur evenly across sectors and that it tends to destroy old jobs at the same time that it creates new ones.
If an increase in the pace of innovation actually increases the rate of job destruction more than it increases the rate of job creation, the equilibrium unemployment rate may actually go up. Mortensen and Pissarides look at how technology affects unemployment in a model in which firms are assumed to lock in the existing technology when they create a new job. Because of technical progress, the technology embodied in a particular job becomes obsolete over time.
The firm then has a choice of whether to spend the money to update the technology in the existing job and this may involve retraining the worker or to destroy the job. In their model, the cost of updating the technology is the key determinant of the relationship between productivity and unemployment. To take one example, if updating costs are prohibitively high, faster technical progress which makes existing capital obsolete faster leads to greater job destruction.
Note that because job creation and destruction depend upon job updating costs which are likely to vary by firm and by industry, the model does not provide an unambiguous prediction about the relationship between economy-wide productivity growth and unemployment in the data.
The model by Manuelli provides perhaps the most direct link between the s and the s. In his model, an anticipated but not yet realized improvement in technology reduces the market value of existing firms, which causes firms to cut back on investment and job creation.
Thus, the unemployment rate goes up. Once the new technology becomes available, firms begin to increase investment and create more jobs, causing the unemployment rate to fall. Manuelli argues that stock markets fell and unemployment rose in the mids partly because markets realized that new technologies were coming that would make existing ones obsolete. These new technologies relating to computers and information technology began to mature sometime in the s, causing unemployment to fall and productivity to rise over time.
His model does not predict a productivity slowdown in the s, though others have proposed similar models that do. Economic theory provides us with a number of reasons why the unemployment rate might be affected by a surge or a fall in the rate of productivity growth that is due to technological developments. However, at this point, we do not have a lot of evidence on the relative importance of the different links emphasized by different models. It will take further research to determine the relevant empirical magnitudes.
It is likely, though, that part of the decrease in unemployment during the second half of the s represents a temporary response to the surge in productivity and the associated boom in the economy.
To the extent that this is true, one should expect to see the unemployment rate stabilize above the lows seen during this expansion—even if productivity continues to grow at rates comparable to those achieved during the second half of the s. The development of the Internet as a tool for job search, on the other hand, argues that the level of unemployment at which the economy settles—the equilibrium level—is likely to be lower than before.
Once again, at this point it is hard to say how much lower. Aghion, Phillipe, and Peter Howitt. Endogenous Growth Theory. Cambridge: MIT Press. Gomme, Paul. Manuelli, Rodolfo E. Mortensen, Dale T. Saving, Jason L. This publication is edited by Sam Zuckerman and Anita Todd.
Permission to reprint must be obtained in writing. Box San Francisco, CA Skip to content Readability Tools. Reader View. Dark Mode. High Contrast. Reset All. Economic Research. More Economic Letters. Unemployment and Productivity Bharat Trehan.
The Atlantic Crossword. Sign In Subscribe. Nonfarm business sector labor productivity increased at a 9. Bureau of Labor Statistics reported today. This was the largest gain in productivity since the third quarter of , when it rose 9.
Labor productivity, or output per hour, is calculated by dividing an index of real output by an index of hours of all persons, including employees, proprietors, and unpaid family workers.
Output increased 4. This isn't good news for unemployment. What you're seeing here is employers squeezing more output out of workers putting in fewer hours. This makes total sense, considering third-quarter GDP grew by 3.
In order for output to have increased, fewer workers must have been producing more. A demand for more output is what generally drives employment to increase.
Yet, in this case, that demand is being satisfied with fewer hours worked. As a result, it's pretty clear that employers have decided to simply get more out of their current workers, rather than turn to the labor market to ramp up hiring.
The fear, then, is that this trend will continue. Output may continue to increase, but employers may simply require current workers pick up the slack, rather than look to the giant pool of unemployed Americans. But how does this productivity trend look from a historical perspective? Here's a chart that has productivity blue versus unemployment rate red , since , per BLS data: I wouldn't get too concerned about the lines crossing, or specific values, but I think the trends are telling.
As you can see, a productivity spike around peak in unemployment isn't uncommon. The last time unemployment was this high was in At that time, productivity spiked as well, but not until after unemployment began to decrease. That makes sense, because once growth started, employers started hiring, but not as quickly as the growth implied.
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