Science Advances

Supplementary Materials

This PDF file includes:

  • Section S1. Exploring occupations and their constituent skills
  • Section S2. Skill complementarity propensities and clusters
  • Section S3. How educational requirements relate to skill requirements for occupations
  • Section S4. Validating skill polarization
  • Section S5. Projecting urban workforces onto the Skillscape
  • Section S6. Predicting economic well-being with sociocognitive skills
  • Section S7. Using Skillscape proximity to predict labor dynamics
  • Fig. S1. Transforming raw O*NET data with RCA.
  • Fig. S2. Distribution of aggregate skill importance by summing the raw O*NET values of each occupation.
  • Fig. S3. Projecting occupational skill requirements onto the polarized skill network.labelsep.
  • Fig. S4. A comparison of the raw O*NET data (left column) and the resulting Skillscape matrix (right column) for 2010, 2013, and 2015.
  • Fig. S5. The Skillscape network respects skill categorization from the experts.
  • Fig. S6. Complementarity scores for every individual skill (node in the network).
  • Fig. S7. The skill requirements of an occupation indicate the education required.
  • Fig. S8. Testing the significance of Skillscape polarization.
  • Fig. S9. Identifying the skill sets of urban workforces.
  • Fig. S10. Example cities projected onto the Skillscape according to the effective use of skills.
  • Fig. S11. Distribution of expected annual wages across occupations.
  • Fig. S12. Out-of-sample testing of model performance from Table 3.
  • Fig. S13. Out-of-sample testing of model performance from Table 4.
  • Fig. S14. Out-of-sample testing of model performance from Table 5.
  • Fig. S15. Out-of-sample testing of model performance from Table 6.
  • Fig. S16. A cartoon example of Area Under the Receiver Operating Characteristic curve (AUROC) calculation.
  • Fig. S17. Worker mobility and occupation redefinition are constrained by skill complementarity and polarization.
  • Fig. S18. Predicting changes in cognitive skill fraction of individual workers binning transitions by the magnitude of change.
  • Fig. S19. Predicting changes in cognitive skill fraction of individual workers binning transitions by their starting cognitive skill fraction.
  • Fig. S20. Predicting changes to the cognitive skill fraction of occupations.
  • Fig. S21. Predicting the effectively used skills of cities over time.
  • Fig. S22. Workers exhibit greater career mobility when leveraging exclusively sociocognitive or sensory-physical skills.
  • Fig. S23. Effects of randomly selecting cognitive skills as a null model alternative to Louvain community detection.
  • Fig. S24. Distribution of national employment and individual occupations as an inset, after binning by cognitivej.
  • Fig. S25. Distribution of national employment in 2015 and individual occupations as an inset, after binning by cognitivej while varying the number of bins.
  • Fig. S26. Binning employment according to cognitive skill fraction reveals a trimodal distribution across cities of all sizes.
  • Fig. S27. Skill proximity predicts skill acquisition for individual workers transitioning between occupations, for the skill requirements of occupations, and for labor markets of cities.
  • Table S1. Skills comprising each skill community on the Skillscape.
  • Table S2. Descriptions of each occupation type indicator variable used in regression models.
  • Table S3. Linear regression using standardized cognitivej for each occupation and occupation type indicator variables.
  • Table S4. Linear regression using cognitivej and employment in each occupation with a bachelor’s degree (denoted B.D. Employment) and without a bachelor’s degree (denoted No B.D. Employment).
  • Table S5. Linear regression using standardized cognitivec for each city and employment in that city of each occupation type.
  • Table S6. Linear regression using cognitivec and education variables.

Download PDF

Files in this Data Supplement: