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Table 5 Ordinary Least Squares estimates of the linear regression model for loan amount

From: Credit off-take from formal financial institutions in rural India: quantile regression results

Explanatory variables

Parameter estimates

Coefficient

SE

Intercept

−209997***

44913.00

State dummy 1

  

Chhattisgarh (CGARH)

−32785*

16763.00

Maharashtra (MAHA)

−44746***

15840.00

West Bengal (WB)

−34541

23433.00

Andhra Pradesh (AP)

−81708***

20033.00

Tamil Nadu (TN)

−109030***

22474.00

Village attribute

  

PIRRI

579.48*

328.02

PELECTRIC

1211.24***

312.82

AGRI

3075.69

4648.62

EDU

8582.52*

4590.04

FININS

−3606.29

4264.39

Household attribute

  

LANDD1

1342.84

15353.00

LANDD2

7719.08

6557.80

LANDD3

28154***

8522.69

IRRISTATD1

2773.70

9178.17

ALACDUM

26352**

10331.00

INFORDUM

−25485*

7113.22

DWELLD2

−9211.10

7991.39

SANITD1

11445

8177.80

FAMTOT

5151.78**

2239.92

CASTRDUM

15235*

8132.51

Loan attribute

  

PURPDUM

9801.21

11864.00

SECDUM

15093**

6910.41

TCOST

54.15***

7.91

RAINT

7745.93***

1234.58

R-square

 

0.5224

Number of observations

399

  1. ***Significant at a level of 1 percent; **significant at a level of 5 percent; *significant at a level of 10 percent significance.
  2. 1Gujarat (GUJ) state was used as the base state in this analysis.