Model | (1) | (2) | (3) | (4) | (5) | (6) |
|---|
Abstract (Occ.) | 14.4% | | | 4.2% | | |
Routine (Occ.) | 6.5% | | | 1.7% | | |
Abstract (Ind.) | | 14.5% | | 3.8% | 4.8% | 15.5% |
Routine (Ind.) | | 7.0% | | 1.3% | 2.0% | 7.5% |
Abstract (Exp.) | | | 15.8% | | 6.2% | |
Routine (Exp.) | | | 6.0% | | 2.5% | |
Total (Occ.) | 20.9% | | 21.7% | 5.9% | 8.7% | |
Total (Ind.) | | 21.5% | | 5.1% | 6.9% | 23.0% |
N = 27,777 | | | | | | |
- The displayed values represent the percentage drop-off in R-squared after removing task measures and are relative to the R-squared of the full model. Results are based on computing the squared partial correlation between log wages and the task measure of interest. The model description for specifications (1)-(6) along with controls included is the same as in Table 8 described above. The two bottom rows summarize the importance of different dimensions of task measures by adding up the decrease in R-squared after removing individual- and occupation-level tasks, respectively, from the model. I use BIBB/BAuA data that has been collected in 2011–12 and 2017–18, and data from BERUFENET, covering the years 2011–13. For the BIBB/BAuA data, see Hall et al. (2020b) and (Hall et al. (2020a), respectively. For the BERUFENET data, see Dengler et al. (2014)
