What are the earnings and employment losses that workers suffer when demand for their occupations declines? To answer this question we combine forecasts on occupational employment changes, which allow us to identify unanticipated declines; administrative data on the population of Swedish workers, spanning several decades; and a highly detailed occupational classification. We find that, compared to similar workers, those facing occupational decline lost about 2-5 percent of mean cumulative earnings from 1986-2013. But workers at the bottom of their occupations’ initial earnings distributions suffered considerably larger losses. These earnings losses are partly accounted for by reduced employment, and increased unemployment and retraining.
We find that workers in the bottom tercile of their occupations’ earnings distributions suffered the largest losses (around 8-11 percent). Workers in the bottom tercile also lost more years of employment and spent more time in unemployment and retraining. We find that those in declining occupations were significantly more likely to leave their starting occupations. The propensity to exit declining occupations was U-shaped in initial occupational earnings rank, with those at the bottom (and to a lesser extent at the top) more likely to leave their starting occupations.
We show that our findings are consistent with a Roy model with negative occupational de- mand shocks, where workers may suffer displacement, and where finding reemployment takes time. In the model, those at the bottom of a declining occupation also have low earnings ca- pacity in other occupations, and therefore find it harder to find reemployment—whether in their own occupations or in other occupations. Hence they lose most from occupational decline. The model also rationalizes the U-shaped exit pattern that we describe above: those at the bottom of their occupations’ earnings distributions are more likely to leave their occupations when they are displaced, while those at the top are more likely to leave to avoid negative demand shocks.
Our findings suggest that the mean losses of occupational decline are lower than the losses suffered by displaced workers that have been reported in prior literature. This is likely because occupational decline is typically gradual, and can be partly managed through retirements, reduced entry into declining occupations, and increased job-to-job exits to other occupations.
Gradual occupational decline may also impose fewer negative spillovers on local economies compared to large, sudden shocks, such as plant closures.
At the same time, future occupational decline could still have substantial adverse consequences for workers’ outcomes, for the following three reasons. First, our paper studies occupational decline that—while unanticipated early in workers’ careers—was nevertheless fairly gradual. But if, for example, machine learning improves rapidly, occupational replacement may happen faster, and may be accompanied by an overall worsening of employment opportunities. Second, the occupational decline that we study largely spared the most skilled occupations, but this may change with new technologies. Many professionals made sizeable in- vestments in skills that are particularly useful in their occupations, and some may also benefit from economic rents. It is possible that for these workers the earnings losses from future occu- pational decline may be higher than those we estimate. Finally, and perhaps most importantly, our findings show that low-earning individuals are already suffering considerable (pre-tax) earnings losses, even in Sweden, where institutions are geared towards mitigating those losses and facilitating occupational transitions. Helping these workers stay productive when they face occupational decline remains an important challenge for governments.