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Table 11 Consumer credits and formal employment probability by education

From: Overlooked benefits of consumer credit growth: impact on formal employment

Panel A: paid employment, whole sample

 

Low skilled

High school degree

College degree

 

(OLS)

(IV)

(OLS)

(IV)

(OLS)

(IV)

Consumer credits

0.0707

0.120*

0.0930*

0.169***

− 0.0202

− 0.0430

 

(0.0517)

(0.0687)

(0.0480)

(0.0545)

(0.0226)

(0.0317)

Region FE

Yes

Yes

Yes

Yes

Yes

Yes

Year FE

Yes

Yes

Yes

Yes

Yes

Yes

Individual controls

Yes

Yes

Yes

Yes

Yes

Yes

Regional controls

Yes

Yes

Yes

Yes

Yes

Yes

Observations

177,534

177,534

90,969

90,969

89,977

89,977

First stage

 

− 3.780***

 

− 3.686***

 

− 3.824***

  

(0.505)

 

(0.487)

 

(0.422)

F-stat

 

55.9

 

57.25

 

82.25

Panel B: paid employment, men aged 30–64

 

Low skilled

High school degree

College degree

Consumer credits

0.0443

0.179**

0.107**

0.162***

− 0.0560**

− 0.0908*

 

(0.0589)

(0.0902)

(0.0519)

(0.0486)

(0.0254)

(0.0490)

2011 dummy

0.0402

− 0.0139

− 0.0334*

− 0.0550***

0.0301**

0.0442**

 

(0.0377)

(0.0508)

(0.0189)

(0.0195)

(0.0135)

(0.0183)

Region FE

Yes

Yes

Yes

Yes

Yes

Yes

Year FE

Yes

Yes

Yes

Yes

Yes

Yes

Individual controls

Yes

Yes

Yes

Yes

Yes

Yes

Regional controls

Yes

Yes

Yes

Yes

Yes

Yes

Observations

99,719

99,719

43,474

43,474

41,239

41,239

First stage

 

− 3.775***

 

− 3.686***

 

− 3.847***

  

(0.514)

 

(0.494)

 

(0.442)

F-stat

 

53.9

 

55.7

 

75.8

  1. (1) Robust standard errors are clustered at the household and Nuts2 levels, and ***, **, and * refer to 1, 5, and 10% significance levels. (2) Consumer credits are expressed in real per capita terms and are in logs. (3) Regional controls include real GDP per capita (in logs.), average real monthly earnings (in logs.) unemployment rate, average education, average firm size, net migration rate, and urban population (%) at Nuts2 level. (4) Individual controls include experience, experience squared, dummy variables for gender, education, age, civil status, part-time versus full-time employment status, temporary versus permanent employment status, firm size, urban-rural, sector (Nace Rev2.), and occupations (Isco 88). (5) Population weights are used