The individual consequences of occupational decline

The individual consequences of occupational decline

The average person consequences of occupational decline

As new technologies replace human labour in an increasing number of tasks, employment in a few occupations invariably falls. This column compares outcomes for similar workers in similar occupations over 28 years to explore the results of large declines in occupational employment for workers’ careers. While mean losses in earnings and employment for all those initially employed in occupations that later declined are relatively moderate, low-earners lose a lot more.

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How costly could it be for workers when demand because of their occupation declines? As new technologies replace human labour in an increasing number of tasks, employment in a few occupations invariably falls. Until recently, technological change mostly automated routine production and clerical work (Autor et al. 2003). But machines’ capabilities are expanding, as recent developments include self-driving vehicles and software that outperforms professionals in a few tasks. Debates on the labour market implications of the new technologies are ongoing (e.g. Brynjolfsson and McAfee 2014, Acemoglu and Restrepo 2018). However in these debates, it is crucial to ask not merely “Will robots take my job?”, but also “What would eventually my career if robots took my job?”

Much reaches stake. Occupational decline may hurt workers and their own families, and may likewise have broader consequences for economic inequality, education, taxation, and redistribution. If it exacerbates differences in outcomes between economic winners and losers, populist forces may gain further momentum (Dal Bo et al. 2019).

In a fresh paper (Edin et al. 2019) we explore the results of large declines in occupational employment for workers’ careers. We assemble a dataset with forecasts of occupational employment changes that allow us to recognize unanticipated declines, population-level administrative data spanning several decades, and an extremely detailed occupational classification. These data allow us to compare outcomes for similar workers who perform similar tasks and also have similar expectations of future occupational employment trajectories, but experience different actual occupational changes.

Our approach is distinct from previous work that contrasts career outcomes of routine and non-routine workers (e.g. Cortes 2016), since we compare workers who perform similar tasks and whose careers may likely have followed similar paths were it not for occupational decline. Our work can be distinct from studies of mass layoffs (e.g. Jacobson et al. 1993), since workers who experience occupational decline might take action before losing their jobs.

Inside our analysis, we follow individual workers’ careers for nearly 30 years, and we find that workers in declining occupations lose typically 2-5% of cumulative earnings, in comparison to other similar workers. Workers with low initial earnings (in accordance with others within their occupations) lose more – about 8-11% of mean cumulative earnings. These earnings losses reflect both lost years of employment and lower earnings depending on employment; a number of the employment losses are because of increased time spent in unemployment and retraining, and low earners spend additional time in both unemployment and retraining.

Estimating the results of occupational decline

We start by assembling data from the Occupational Outlook Handbooks (OOH), published by the united states Bureau of Labor Statistics, which cover a lot more than 400 occupations. Inside our main analysis we define occupations as declining if their employment fell by at least 25% from 1984-2016, although we show our email address details are robust to using other cutoffs. The OOH also provides information on technological change affecting each occupation, and forecasts of employment as time passes. Using these data, we are able to separate technologically driven declines, and in addition unanticipated declines. Occupations that declined include typesetters, drafters, proof readers, and different machine operators.

We then match the OOH data to detailed Swedish occupations. This enables us to study the results of occupational decline for workers who, in 1985, worked in occupations that declined over the next decades. We verify that occupations that declined in america also declined in Sweden, and that the employment forecasts that the BLS designed for the united states have predictive power for employment changes in Sweden.

Detailed administrative micro-data, which cover all Swedish workers, allow us to handle two potential concerns for identifying the results of occupational decline: that workers in declining occupations may have differed from other workers, and that declining occupations may have differed even in lack of occupational decline. To handle the first concern, about individual sorting, we control for gender, age, education, and location, in addition to 1985 earnings. After we control for these characteristics, we find that workers in declining occupations were no not the same as others regarding their cognitive and non-cognitive test scores and their parents’ schooling and earnings. To handle the next concern, about occupational differences, we control for occupational earnings profiles (calculated using the 1985 data), the BLS forecasts, and other occupational and industry characteristics.

Assessing the losses and how their incidence varied

We find that prime age workers (those aged 25-36 in 1985) who were subjected to occupational decline lost about 2-6 months of employment over 28 years, in comparison to similar workers whose occupations didn’t decline. The bigger end of the number identifies our comparison between similar workers, as the lower end of the number compares similar workers in similar occupations. The employment loss corresponds to around 1-2% of mean cumulative employment. The corresponding earnings losses were larger, and amounted to around 2-5% of mean cumulative earnings. These mean losses might seem moderate given the large occupational declines, however the average outcomes usually do not tell the entire story. Underneath third of earners in each occupation fared worse, losing around 8-11% of mean earnings when their occupations declined.

The wages and employment losses that people document reflect increased time spent in unemployment and government-sponsored retraining – way more for workers with low initial earnings. We also find that older workers who faced occupational decline retired just a little earlier.

We also find that workers in occupations that declined after 1985 were less inclined to stay in their starting occupation. It really is quite likely that reduced supply to declining occupations contributed to mitigating the losses of the workers that remained there.

We show our main findings are essentially unchanged whenever we restrict our analysis to technology-related occupational declines.

Further, our discovering that mean earnings and employment losses from occupational decline are small isn’t unique to Sweden. We find similar results utilizing a smaller panel dataset on US workers, using the National Longitudinal Survey of Youth 1979.

Theoretical implications

Our paper also considers the implications of our findings for Roy’s (1951) model, that is a workhorse model for labour economists. We show that the frictionless Roy model predicts that losses are increasing in initial occupational earnings rank, under a wide selection of assumptions about the skill distribution. This prediction is inconsistent with this finding that the biggest earnings losses from occupational decline are incurred by those that earned minimal. To reconcile our findings, we add frictions to the model: we assume that workers who earn little in a single occupation incur larger time costs looking for jobs or retraining if indeed they make an effort to move occupations. This extension of the model, particularly when in conjunction with the addition of involuntary job displacement, we can reconcile many of our empirical findings.

Conclusions

There exists a vivid academic and public debate on whether we ought to fear the takeover of human jobs by machines. New technologies may replace not merely factory and workers in offices but also drivers plus some professional occupations. Our paper compares similar workers in similar occupations over 28 years. We show that although mean losses in earnings and employment for all those initially employed in occupations that later declined are relatively moderate (2-5% of earnings and 1-2% of employment), low-earners lose a lot more.

The losses that people find from occupational decline are smaller than those suffered by workers who experience mass layoffs, as reported in the prevailing literature. As the occupational decline we study took years as well as decades, its charges for individual workers were likely mitigated through retirements, reduced entry into declining occupations, and increased job-to-job exits to other occupations. In comparison to large, sudden shocks, such as for example plant closures, the decline we study could also have a less pronounced effect on local economies.

As the losses we find are normally moderate, there are many explanations why future occupational decline may have adverse impacts. First, while we study unanticipated declines, the declines were nevertheless fairly gradual. Costs could be larger for sudden shocks following, for instance, an instant evolution of machine learning. Second, the occupational decline that people study mainly affected low- and middle-skilled occupations, which require less human capital investment than those which may be impacted later on. Finally, and perhaps most of all, our findings show that low-earning folks are already suffering considerable (pre-tax) earnings losses, even in Sweden, where institutions are intended for mitigating those losses and facilitating occupational transitions. Helping these workers stay productive if they face occupational decline remains a significant challenge for governments.

References

Acemoglu, D and P Restrepo (2018), “The race between man and machine: Implications of technology for growth, factor shares, and employment,” American Economic Review 108(6): 1488-1542.

Autor, D, F Levy, and R J Murnane (2003), “The skill content of recent technological change: An empirical exploration,” Quarterly Journal of Economics 118(4): 1279-1333.

Brynjolfsson, E and A McAfee (2014), THE NEXT Machine Age: Work, Progress, and Prosperity in a period of Brilliant Technologies, W W Norton & Company

Cortes, G M (2016), “Where Have the Middle-Wage Workers Gone? A REPORT of Polarization Using Panel Data,” Journal of Labor Economics 34(1): 63-105.

Dal Bo, E, F Finan, O Folke, T Persson, and J Rickne (2019), “Economic Losers and Political Winners: Sweden’s Radical Right,” working paper.

Edin, P, T Evans, G Graetz, S Hernnäs, and G Michaels (2019), “Individual Consequences of Occupational Decline,” CEPR Discussion Paper 13808.

Jacobson, L S., R J LaLonde, and D G Sullivan (1993), “Earnings Losses of Displaced Workers,” American Economic Review 83(4): 685-709.

Roy, A D (1951): “Some applying for grants the distribution of earnings,” Oxford Economic Papers 3(2): 135-146.

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