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Table A.3 Results of wage regression to determine dominance regime

From: Separating wheat and chaff: age-specific staffing strategies and innovative performance at the firm levelDen Weizen von der Spreu trennen – Altersbezogene Personalpolitik und Innovationen auf der Betriebsebene

Variables

Innovation sample

Full sample

Workforce characteristics

 Mean age

1.73 ***

(0.58)

1.38 ***

(0.09)

 Mean age2

−0.02 ***

(0.01)

−0.01 ***

(0.00)

 Tenure

0.10

(0.21)

0.32 ***

(0.05)

 Tenure2

0.00

(0.01)

−0.01 ***

(0.00)

 White collar (%)

12.92 ***

(1.20)

6.60 ***

(0.27)

 Academic (%)

17.94 ***

(2.50)

18.42 ***

(0.68)

 Female (%)

−8.76 ***

(1.37)

−6.08 ***

(0.27)

 Part-time (%)

2.81 **

(1.26)

0.45 ***

(0.17)

Firm characteristics

 Investments

-0.00 *

(0.00)

0.00

(0.00)

 Good technical equipment

1.36 **

(0.59)

1.08 ***

(0.11)

 Collective agreement

1.00

(0.65)

1.86 ***

(0.13)

 Work council

5.04 ***

(0.66)

5.82

(0.15)

 Firm size

0.00 ***

(0.00)

0.00 *

(0.00)

 East

-8.60 ***

(0.56)

-6.05 ***

(0.12)

 Year 2003

0.85 *

(0.51)

0.04

(0.11)

Constant

- 13.39

(11.64)

-11.20 ***

(1.76)

N

585

15891

R 2

0.74

0.59

Wage residual (in 1,000 €)

N

Mean

St. dev

Min

Max

- Innovation sample

585

0.00

5.60

-19.4

20.0

- Full sample

15891

0.00

6.57

-38.6

78.2

  1. Notes: Pooled results for the years 2000 and 2003. Controls for sector of firms included (results not reported). Dependent variable: Average yearly per-worker salary in each establishment (in 1,000 €). Source: Elaborated for this study based on LIAB dataSignificance levels * 10%, ** 5%, *** 1% based on robust standard errors (in italics)