Coexistence of Credit Markets and Speedy Payday Loans

credit marketThis section brings out the underlying factors that affect credit rationing in formal credit market and the determinants of entrepreneur’s choice between two sources of credit. This estimation also finds the incidence of formal sector rationing to be considerably higher than has been conventionally assumed. Here, the availability of credit or access to credit by borrowers has been explained in terms of the credit rationing behavior of lending institutions.

But theoretically, this should depend on whether the individual has a demand for credit. In this analysis, 279 out of310 (90%) respondents who had borrowed can therefore be considered as having had a demand for credit. Hence, the size of the formal loan as a proportion of the total loan is considered as rationed credit. Model I estimates the most basic regression effect focusing on the amount of loan borrowed from the formal sector with respect to the total loan borrowed by the entrepreneur with five different specifications as shown in the different columns in Table 5.

The specifications or different set of exogenous variables have been selected on the basis of the values of R2 and results of multi-collinearity by the trial-and-error method. The specification I indicates that the variables, like ‘number of earning members in a family’, ‘household industrial category’ and ‘owner of a pukka house’ are significant with positive coefficients. This means that these variables have a positive impact on borrowing of formal credit or are eventually negatively involved in credit rationing. Among all household industrial categories, those that are involved in sewing, weaving, or vermi-pit get more loans from the formal sector than the informal sector, perhaps because these house-based industries have a proper project design that eventually pulls formal credit toward them. Column II provides an interesting assessment about the interest rates in the formal credit market as well as the informal credit sector. The interest rate in the formal sector is significant at the 1% level with a negative coefficient. It is obvious that the higher the value of the interest rate, the lower is the demand for credit from the formal sector. Credits are the integral part of our lives but it is very important to take credits at favourable terms as via Speedy Payday Loans and speedy-payday-loans.com. We are waiting for applications!

Further, the coefficient for informal interest rate is positive and significant. This implies an interesting implicit horizontal linkage between the informal interest rate and formal sector credit demand. The interest rates in both the formal and informal sectors directly and/ or indirectly determine the demand for credit in the formal sector. On the other hand, the distance to the formal lender and that of the informal sector which proxy the transaction costs associated with the loan application from their respective sources are significant at the 1% and 10% levels, respectively, with negative and positive signs for the coefficients. Therefore, it can be said that these three variables are imperative determinants of credit rationing in the formal sector. It should be expressed that the insignificant results of household characteristics, like gender, age, educational qualifications, religion, and number of dependents suggest that, once other factors are controlled for, these factors do not have any role in credit rationing in the formal sector. Moreover, the variable ‘district dummy’ is included in order to get the region bias in this analysis. It shows significant impact. Since Murshidabad district is represented by 1 and Jalpaiguri is symbolized by 0 in the dataset, the people in Murshidabad are keener on formal sector credit demand than people in Jalpaiguri. Column III adds the political affiliation factor as a major driver of credit availability in the formal sector. Column IV includes membership in a group as a significant determinant in this case. Column V includes advantages in the informal sector and obstacles in the formal sector as a significant factor in demand for a loan in the formal sector. Further, this study argues that while loan demand is unobservable, it can be inferred under certain behavioral restrictions by aggregating individual loans received from various types of lenders. It therefore becomes important to take the determinants of interest rate differentiation between the two sectors. Another econometric framework is developed to estimate factors responsible for huge variation in interest rates between the two sectors as well as between the borrowers. Since in the formal sector interest rates are more or less within an equal range, this estimation, eventually, gives the results of variation in interest rates in the informal sector.

Models 2 and model 3 estimate the determinants of maximum likelihood of the major proportion of the loan going to the productive purposes for the formal and informal sectors respectively using binomial logistic regressions are shown in Table 6.

 credit demandModel 2 explains the determinants of likelihood that the major portion (above 50%) of the money borrowed from the formal sector would be used for productive purposes. On the basis of the entrepreneur’s estimation about the expenditure of the loan borrowed in the year 2007-08, this dependent variable has been generated. Similarly, Model 3 estimates the factors responsible for the probability that the major portion (above 50%) of the money borrowed from the informal sector would be used for productive purposes. Concerning the nature of the dependent variables, the monthly average income has been classified into five categories according to the distribution named income class. Another variable, viz., ‘dependent ratio’ (estimated as the total number of dependents in the family divided by the total number of earning members) is included in this analysis. In order to get the effect of loan size, the loan from the formal sector with respect to the total loan has been considered as a new exogenous variable. The first two columns belong to model 2 and the last two columns belong to model 3, expressing the coefficients, standard errors of the variables in respective of the first column and marginal effects after logit in the second column. The specification of the model has been decided by the trial-and-error method on the basis of pseudo-R2 (greater than 0.21) and probability greater than Chi2. The results show, as a whole, that income class of the household, loan borrowed from the formal sector as a proportion of the total loan borrowed by the entrepreneur, interest rates in both the formal and informal sectors, distance of the formal lender, political affiliation to the local panchayat party, and associated other obstacles to getting a loan from the formal sector are the major determinants (highly significant) that the loan from the formal sector can be invested in productive purposes. The significant determinants for the informal sector are income class, amount of loan from the formal source with respect to the total loan, loan duration, interest rates in both the formal and informal sectors, advantages associated in the borrowing process in the informal sector, and membership in an SHG. The respective signs of the coefficients signify the relationship with the dependent variable. The positive sign in the coefficient of the ‘income class’ variable (‘0’ is considered as lowest income, 5 is used for the highest class) shows that the higher the income class, the higher is the probability that the formal loan will be used as working capital. It is not enough to have only a wage for living, Speedy Payday Loans offers such a service as approval of loans online.

The interesting fact is that this income class follows the same relationship with considerable effect for the informal sector too. This shows that people in a higher income class prefer to invest in business from the loan irrespective of its source. Another result shows that if the entrepreneur gets more money from formal lenders he/she prefers to use more for business purposes. Both transaction cost and interest rates matter in these cases. The interest rate in the formal sector poses a negative relation for the formal sector and also lies in a negative relation for the informal sector. This leads to the conclusion that if the formal sector interest rate increases, the money used for business purposes from the informal sector will also decrease. This may be because whenever the interest rate in the formal sector increases, informal lenders deliberately raise their interest rates. Eventually, it follows the same dimension. Another result reveals that membership in a self-help group does not have a significant result in the case of formal credit but it has a significant negative impact for informal credit. The reason (gathered from field experience) is that if the entrepreneur becomes a member of a group (availing of a group lending program), the informal lender avoids giving him/her a loan during that period. However, the empirical results, in broader structure, support the theoretical propositions laid out in Section 2. The first proposition claims that interest rate alone is not responsible for credit demand from the concerned source. The empirical findings also show that although the interest rate is much lower in the formal sector, the producer prefers to use the informal sector because of the lower effective rate of interest (actual interest rate and transaction cost). The second proposition claims that the effort of taking a loan from the formal sector depends on the profitability of the project or business using that loan. Since the study does not have data on the profit of the business in 2008-09, it was compelled to use income level. As Model 2 states that the likelihood of the formal loan used as working capital depends on income class, the second proposition is satisfied. Similarly, the third proposition claimed that the effort of taking a loan from informal sector depends on whether the social net present value of the project is positive. The distribution of the data supports the probability that informal loan used as working capital in the business depends on the advantages associated with the informal sector. Therefore, the third proposition is also satisfied from the empirical analysis.

Table 5: Results of Model1 with 5 Specifications

Formal Loan Coefficient Coefficient Coefficient Coefficient Coefficient
/Total Loan (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.) (Std. Err.)
I II III IV V
Sex 0.0562

(0.043)

0.00085

(0.0009)

Age -0.0016

(0.002)

0.002

(0.0192)

Years of -0.0035 .0012
Schooling (0.0069) (0.00839)
Number of 0.0383* -.008
Earning Members (0.023) (0.011)
Number of 0.0006 -.00103
Dependents in the Household (0.019) (0.003)
Monthly Average 0.00002 0.0000243* 0.000026* 0.000019
Family Income (0.00003) (0.00001) (0.00001) (0.000015)
Religion, Hindu = -0.0582 -.01239
1; Otherwise = 0 (0.047) (0.024)
‘Pakka’ House 0.0990* 0.007
Dummy (0.056) (0.025
Alternative 0.0202 .012
Occupation

Category

(0.0198) (0.008)
Specific 0.0201** -.0031
Industrial

Category

(0.009) (0.004)
Interest rate/ -0.00618*** -.0068766*** -0.0071*** -.0066***
formal (0.00084) (0.00078) (0.001) (0.00067)
Formal Loan /Total Loan Coefficient (Std. Err.) Coefficient (Std. Err.) Coefficient (Std. Err.) Coefficient (Std. Err.) Coefficient (Std. Err.)
I II III IV V
Interest rate/ 0.04583*** 0.02052*** 0.02061*** .02134***
Informal (0.00225) (0.0042) (0.0044) (0.004)
Duration -0.0029366 -.0038
Category (0.005) (0.0055)
Poverty (BPL) 0.01793 0.01311 .01445
(0.01647) (0.017) (0.017)
District 0.04020* 0.03601* .0182
(0.02009) (0.02) (0.025)
Political 0.36373*** 0.3301*** .323227***
Affiliation (0.05239) (0.055) (0.056)
Collateral -0.0017
Formal (0.013)
Purpose of 0.01461 0.0112 .01036
Formal sector (0.01402) (0.014) (0.014)
Loan
Purpose of 0.0215**
Informal Loan (0.011)
Obstacles for 0.0111 -.01115*
Formal Loan (0.007) (0.0075)
Advantages in -0.0150* -.01427**
Informal Sector (0.008) (0.008)
SHG Dummy -0.01159 0.0024
(0.0198) (0.02)
Distance to 0.00101*** -.00058 0.0009**
Formal Lender (0.00077) (0.0007) (0.0002)
_cons 0.3953** 0.0784** 0.09313* 0.2475*** .18095*
(0.143) (0.04345) (0.04086) (0.059) (0.0856)

Table 6: Binomial Logistic Regressions

Variables Model 2 Model 3
Coefficient Marginal Coefficient (Std. Marginal
(Std. Err.) Effects Err.) Effects
Sex -0.1559

(0.409)

*-0.0165

(0.068)

0.345*

(0.461)

* 0.0217 (0.023)
Age -0.0167

(0.018)

-0.004

(0.003)

0.001

(0.023)

-0.0003

(0.001)

Years of Schooling 0.0549

(0.057)

0.008

(0.102)

0.066

(0.061)

0.004

(0.003)

Dependency Ratio 0.363

(0.356)

0.0639

(0.063)

-0.026

(0.395)

-0.0056

(0.021)

Income class 3.33**

(1.461)

0.532

(0.128)

0.182**

(2.170)

0.008

(0.115)

Formal Loan /Total Loan 5.885***

(1.405)

0.608

(0.259)

-8.692***

(2.230)

-0.48

(0.118)

Loan Duration -0.128

(0.206)

0.037

(0.021)

0.242**

(0.268)

0.0047

(.0068)

District Dummy -0.104*

(0.394)

*-0.0432

(0.086)

-0.583*

(0.469)

*-0.007

(0.025)

BPL; Yes=1. 0.185

(0.352)

*0.022

(0.063)

-0.057

(0.375)

*0.0046

(0.02)

Interest rate/formal sector -0.151**

(0.071)

0.001

(0.018)

-0.116**

(0.0108)

-0.005

(0.006)

Interest rate in Informal sector 0.078***

(0.025)

0.0084

(0.0041)

0.038**

(0.030)

0.0025

(.001)

Distance of the formal Lender -5.623***

0.603

(0.248)

0.394**

(0.101)

0.031

(0.121)

Advantage of the informal -0.1054

(0.171)

-0.0137

(0.031)

3.394***

(1.324)

0.82

(0.172)

Variables Model 2 Model 3
Coefficient Marginal Coefficient (Std. Marginal
(Std. Err.) Effects Err.) Effects
SHG Dummy 0.106

(0.13)

*0.013

(0.068)

-1.763**

(0.438)

*-0.042

(0.024)

Political Affiliation 5 719*** (1.696) *0.282

(0.115)

Dropped
Obstacles to get loan from

Formal Sector

0 799*** (0.099) 0.012

(.023)

-0.189

(0.163)

-0.0103

(0.009)

_cons -5.265** -0.763*
1.815 (0.438)

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