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Table 16 Robustness tests on unique variation explained by task measures: within-occupation task specialization

From: On the measurement of tasks: does expert data get it right?

Model: individual-level tasks & occupational FE

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Abstract (Ind.)

13.8%

13.5%

14.3%

13.2%

10.5%

12.7%

11.7%

Routine (Ind.)

5.4%

4.5%

5.2%

4.8%

0.7%

8.4%

5.3%

Total (Ind.)

19.1%

18.0%

19.4%

18.0%

11.2%

21.1%

17.0%

Occupation dummies

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\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

Wage/hr \(\ge 5\) & Empl. Hours \(\ge 15\)

\(\checkmark\)

      

Observations per occupation \(\ge 100\)

 

\(\checkmark\)

     

Occupational classification: 2-digit

  

\(\checkmark\)

    

Activities performed “often” OR “sometimes”

   

\(\checkmark\)

   

Task construction: exclude competencies

    

\(\checkmark\)

  

Task normalization a la Alda (2013)

     

\(\checkmark\)

 

2012 Sample only

      

\(\checkmark\)

Observations

26641

25468

27777

28026

27069

27777

13756

  1. The displayed values represent the unique variation in log wages associated with the task measure of interest, expressed relative to the R-squared of the full model. The output provides robustness checks on a model comprising individual-level and occupational FE, thereby accounting for task specialization within occupations. See Table 8 for reference. 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)