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Table 5 Regression results

From: Having a bad attitude? The relationship between attitudes and sickness absence

 

(1)

(2)

(3)

(4)

 

Self-certified

Self-certified

GP certified

GP certified

 

coef.

coef.

coef.

coef.

 

(se)

(se)

(se)

(se)

Female

−0.311

−0.142

2.330*

2.460*

 

(0.19)

(.018)

(1.28)

(1.48)

Immigrant

0.001

−0.096

0.736

1.087

 

(0.20)

(0.21)

(1.25)

(1.30)

Lenient attitudes towards sick leave

 

0.406**

 

0.657

  

(0.16)

 

(1.10)

Lenient norms regarding sick leave

 

−0.045

 

−0.030

  

(0.12)

 

(0.99)

Being stressed

 

−0.082

 

1.037

  

(0.13)

 

(0.88)

Low motivation

 

0.178

 

−1.117

  

(0.14)

 

(0.91)

Low work satisfaction

 

0.084

 

2.127*

  

(0.12)

 

(1.18)

Low work ethic

 

0.339**

 

0.609

  

(0.14)

 

(1.16)

Gender equal attitudes

 

−0.253*

 

0.064

  

(0.14)

 

(1.16)

Lenient towards cheating

 

−0.267**

 

1.854

  

(0.13)

 

(1.63)

Negative towards welfare state

 

0.223

 

0.760

  

(0.16)

 

(1.17)

Risk-averse preferences

 

−0.230

 

1.028

  

(0.18)

 

(1.34)

_cons

5.402***

5.825***

18.001*

22.818**

Controls a

Yes

Yes

Yes

Yes

Attitudes

No

Yes

No

Yes

R-squared

0.157

0.214

0.133

0.176

Low education

0.703***

0.674***

1.211

0.649

 

(0.22)

(0.21)

(1.20)

(1.22)

Controls b

Yes

Yes

Yes

Yes

Attitudes

No

Yes

No

Yes

R-squared

0.154

0.213

0.135

0.176

Old

−0.075

−0.032

2.617*

2.776*

 

(0.22)

(0.23)

(1.37)

(1.60)

Controls c

Yes

Yes

Yes

Yes

Attitudes

No

Yes

No

Yes

R-squared

0.141

0.194

0.107

0.140

N d

226

226

226

226

  1. *p < 0.10, **p < 0.05, ***p < 0.01
  2. aage, age 2, education, na_educ, single_hh, na_singlehh, child_cust, na_custody, immig_sec, na_immig, student, secondjob, director, shifts, district_a, ass_living, mental_health
  3. bfemale, age, age 2, na_educ, single_hh, na_singlehh, child_cust, na_custody, immig_sec, immigrant, na_immig, student, secondjob, director, shifts, district_a, ass_living, mental_health
  4. cfemale, education, na_educ, single_hh, na_singlehh, child_cust, na_custody, immig_sec, immigrant, na_immig, student, secondjob, director, shifts, district_a, ass_living, mental_health
  5. dWe use frequency weights in the regressions due to the fact that we observe the employees for varying amounts of time, putting more weight on the employees that we have more information about. The frequency weights duplicate the observations according to the employees’ number of working days. This means that the total number of observations, if we take the frequency weights into account, is 104,229. We use cluster robust standard errors to count for the serial correlation that occurs due to the frequency weights
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