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Table 8 Estimates for the entire sample, seven educational categories

From: Does education raise productivity and wages equally? The moderating role of age and gender

 

GMM-SYS

LP

Value added per hour worked (ln)

Wage cost per hour worked (ln)

Value added-wage cost gapb

Value added per hour worked (ln)

(1)

(2)

(3)

(4)

Lagged dependent variable (ln)

0.664***

(0.056)

0.471***

(0.137)

0.466***

(0.031)

0.785***

(0.044)

Shares of workers

 Primary education (E1)

Reference

Reference

Reference

Reference

 Lower secondary (E2)

0.018

(0.028)

−0.015

(0.023)

0.030

(0.025)

0.004

(0.018)

 General upper secondary education (E3)

0.068**

(0.030)

0.001

(0.022)

0.064**

(0.026)

0.037**

(0.017)

 Technical and professional upper secondary education (E4)

0.068**

(0.030)

0.023

(0.023)

0.041*

(0.023)

0.014

(0.015)

 Bachelor’s or equivalent level (E5)

0.122***

(0.047)

0.056

(0.040)

0.085**

(0.035)

0.071***

(0.022)

 Master’s or equivalent level (E6)

0.199***

(0.061)

0.243***

(0.074)

0.082*

(0.042)

0.205***

(0.055)

 Post-Master’s level or PhD (E7)

0.366**

(0.161)

0.295*

(0.163)

0.235*

(0.141)

0.392***

(0.142)

Hansen over-identification test, p value

0.487

0.154

0.686

 

Arellano-Bond test for AR(2), p value

0.129

0.284

0.217

 

Number of observations

6714

6714

6714

6691

Number of firms

1844

1844

1844

1844

Chi-squared statistic for equality of regression coefficients, H0

 E2 = E3

3.63*

1.36

1.78

4.72**

 E2 = E4

4.40**

5.13**

0.28

0.55

 E2 = E5

5.21**

2.81*

2.84*

11.67***

 E2 = E6

8.48***

10.61***

1.41

14.22***

 E2 = E7

4.65**

3.47*

2.18

7.62***

 E3 = E4

0.00

2.14

1.34

3.35*

 E3 = E5

1.35

1.73

0.41

2.59

 E3 = E6

5.25**

9.73*

0.20

12.78***

 E3 = E7

3.51*

3.14*

1.55

6.20**

 E4 = E5

1.33

0.57

1.94

6.64***

 E4 = E6

4.74**

7.81***

1.02

14.60***

 E4 = E7

3.48*

2.60

1.94

7.31***

 E5 = E6

1.14

8.75***

0.00

6.50***

 E5 = E7

2.37

2.64

1.10

5.78**

 E6 = E7

1.16

0.15

1.22

1.39

Interpretationa:

E1 < E(3,4,5,6,7)

E2 < E(3,4,5,6,7)

E3 < E(4,5,6,7)

E4 < E(5,6,7)

E5 < E(6,7)

E6 < E7

E1 < E(6,7)

E2 < E(4,5,6,7)

E3 < E(6,7)

E4 < E6

E5 < E6

E1 < E(3,4,5,6,7)

E2 < E5

E1 < E(3,5,6,7)

E2 < E(3,5,6,7)

E3 < E(4,6,7)

E4 < E(5,6,7)

E5 < E(6,7)

Education

increases

productivity

Education

increases

wage costs

Low-educated less profitable than more educated

Education increases productivity

  1. Notes: Standard errors, that are robust to heteroskedasticity and autocorrelation, are reported between parentheses. Regressions also control for: % of workers with 10 years of tenure or more; % workers younger than 30 and older than 49 years, respectively; % women; % part-time workers; % blue-collar workers; % workers with fixed term employment contract; % apprentices; % temporary agency workers; ln of firm size; ln of capital stock per worker; level of collective wage bargaining; region where the firm is located (2 dummies); industries (8 dummies), and years dummies (11). AR (2) refers to second-order autocorrelation in first-differenced errors. GMM-SYS specifications include first and second lags of explanatory variables (except time dummies) as instruments
  2. ***p < 0.01, **p < 0.05, *p < 0.1
  3. ‘<’ indicates when regression coefficients are statistically different at the 10% level
  4. b Value added-wage cost gap = ln(value added per hour) − ln(wage cost per hour)