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Table 11 Robustness tests on task measures as wage predictors: survey vs expert data

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

Dependent Variable: log hourly real wage

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Abstract (Ind.)

0.37***

0.34***

0.42***

0.68***

0.38***

0.62***

0.37***

(0.04)

(0.03)

(0.03)

(0.06)

(0.03)

(0.05)

(0.05)

Routine (Ind.)

0.19***

0.18***

0.22***

0.40***

0.16**

0.44***

0.22***

(0.04)

(0.03)

(0.03)

(0.06)

(0.03)

(0.05)

(0.04)

Abstract (Exp., Occ.)

0.33***

0.35***

0.26***

0.29***

0.31***

0.27***

0.35***

(0.03)

(0.03)

(0.03)

(0.03)

(0.03)

(0.03)

(0.04)

Routine (Exp., Occ.)

0.16***

0.19***

0.14***

0.10***

0.15***

0.08**

0.10**

(0.03)

(0.03)

(0.03)

(0.03)

(0.03)

(0.03)

(0.04)

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

      

\(\checkmark\)

Expert tasks (Occupational)

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

\(\checkmark\)

Adj. \(R^{2}\)

0.20

0.20

0.19

0.20

0.20

0.22

0.19

Observations

26641

25468

27777

28026

27069

27777

13756

  1. Robust standard errors in parentheses
  2. * \(p<0.10\), ** \(p<0.05\), *** \(p<0.01\)
  3. This output provides robustness tests on the baseline estimates of the model described in Eq. (10) and displayed in Table 6. Recall that this model regresses log wages on individual- and occupation-level tasks. For brevity, I only focus on the model of interest comprising individual- and expert data on tasks, i.e. column (5) of Table 6. 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)