Fig 1.
Duda-hart index and points of local maxima for 8 and 17-group splits (in red).
Sources: Ward hierarchical clustering method based on 72 principal components derived from 220 occupational descriptors from O*NET.
Table 1.
Wage sums of squares within clusters and goodness of fit.
Table 2.
Reasons behind bureau of labor statistics employment projections for change in jobs by occupations.
Table 3.
Clustering results based on ward’s method.
Table 4.
The average education, experience and job training.
Table 5.
The average scores for work values and interests.
Fig 2.
The mean and the standard deviation in wages by occupational groupings.
Sources: Based on wage data from the Occupation Employment Statistics survey (BLS), occupations are clustered using the Ward hierarchical method based on 72 principal components derived from 220 occupational descriptors from O*NET. The dots represent the mean wage for each sub-group, and the bars reflect the standard deviations in wages for the given sub-group.
Fig 3.
Job losses and gains by occupational groupings (in thousands).
Sources: Based on BLS Employment Projections 2019–2029, Table 1.2 “Employment by detailed occupation, 2019 and projected 2029”. Occupations are clustered using Ward hierarchical method based on 72 principal components derived from 220 occupational descriptors from O*NET. Gains are shown in blue, and losses–in orange.
Fig 4.
Job losses and gains by occupational groupings, including technological factors.
Sources: Based on BLS Employment Projections 2019–2029, Table 1.2 “Employment by detailed occupation, 2019 and projected 2029” and Table 1.12 “Factors affecting occupational utilization, projected 2019–29”. Occupations are clustered using the Ward hierarchical method based on 72 principal components derived from 220 occupational descriptors from O*NET. Total change is shown in blue, change that has a reason is shown in orange, and change due to technology is shown in black.
Fig 5.
Job losses and gains by occupational groupings—By wage and education level.
Sources: Authors calculations based on BLS Employment Projections 2019–2029, Table 1.2 “Employment by detailed occupation, 2019 and projected 2029”. Occupations are clustered using the Ward hierarchical method based on 72 principal components derived from 220 occupational descriptors from O*NET.