Credit Rationing in Informal Markets:
The Case of Small Firms in India
Sankar De
Centre for Analytical Finance
Indian School of Business
Manpreet Singh
Centre for Analytical Finance
Indian School of Business
December 2010
1
Presentation scheme








Background
Summary of findings
Data and empirical variables
Methodology: identification issues
Results: Rationing of relationship-based credit
Results: Identification of credit-rationed firms
Significance of results
Conclusions
2
Background

This research is part of the research agenda on
the Role of Institutions in Emerging Capital
Markets at the Centre for Analytical Finance
(CAF) , Indian School of Business (ISB).
3
Main findings at a glance

We find evidence of rationing of credit within informal
relationships for the firms in our sample.

Credit rationing is correlated with firm size (assets)

Creditors resort to rationing to prevent involuntary
default by small firms in the presence of debt
overhang. Since direct monitoring is not feasible, the
creditors do not let the interest rate rise to an
arbitrarily high level and ration credit.

The bottom 20% - 30% of the firms in our sample by
asset size are at risk of credit rationing.

The critical interest rates are in 50% - 58% range.

Rationing triggers at higher rate for credit from
social than from business relationships.
4
Theoretical Support

Our findings are consistent with Moral Hazard model
of credit rationing (Ghosh, Mookherjee, and Ray,
1999).

They are not consistent with an alternative theory of
credit rationing to prevent voluntary default (in the
presence of outside options). Normally bigger firms
would have more outside options. We do not see that.
5
Significance of findings:
rationing of formal credit

This paper is the first to provide evidence of rationing of
informal credit.

Voluminous evidence exists on formal credit rationing in
India, especially for smaller firms (Banerjee and Duflo,
2001; Banerjee and Duflo, 2004; Banerjee, Cole, and Duflo,
2003; Gormley, 2007).

Similar evidence exists for other emerging countries.

Taken together, a firm may be excluded from formal and
informal credit markets at the same time.

Important policy
institutions.
implication:
strengthen
market
6
Significance of findings:
finance and growth

Our findings also throw light on the literature on financial
development and growth.

Rajan and Zingales (98): industries dependent on external
finance grow disproportionately faster in countries with
developed financial markets. RZ consider only formal
finance.

Fisman and Love (2003): industries with higher dependence
on trade credit financing achieve higher rates of growth in
countries with weaker financial institutions. They do not
consider informal finance.

AQQ (2005) suggest that informal finance can foster
economic growth.

Our findings indicate that informal finance is unlikely to
spur growth.
7
Significance of findings:
formal versus informal institutions
 The findings have implication for a much bigger
issue.
 Can informal private arrangements substitute for
formal public institutions, such as markets and
banks? Inter-firm credit is sometimes cited as an
example of such private arrangements.
 If yes, this would indeed be a very desirable
outcome, especially for countries with weak or
ineffective formal institutions.
 However, empirical studies are few and far
between. Studies with firm-level analysis are
even fewer.
8
Data

Unique dataset
Combines survey responses of a sample of Indian SMEs
with the panel data of corporate finance activities of the
same firms for five years (2001-2005) collected from CMIE
Prowess. The dataset permits
‐ Use of survey data for qualitative information and Prowess
data for hard quantitative information
‐ Partitioning the data in many different ways and
constructing a variety of indices for a given firm in the
sample
‐ Separate indices for credit for business relationship and
credit from social relationship s.
9
Data

Survey data
‐ Conducted in late 2006
‐ Survey administered in Personal interviews with company
owners and/or CEO/CFO
‐ Survey instrument had 108 questions in four parts
‐ Focused on company history, corporate financing, relations
with banks and financial institutions, informal relationships
and trade credit transactions, business and social networks,
and factors affecting corporate performance.
‐ Out of the Prowess population of 680 SMEs with complete
5-year financial data, after excluding firms with any kind of
financial business, we were able to survey 141 firms.
‐ The sample spans a variety of industries and all geographic
locations in India.
10
Sample representativeness

The sample firms account approximately 21% of the
population of 680 SMEs with complete 2001 – 2005
financial history in Prowess

For year 2005 (the last year before the survey was
conducted), we conduct large sample mean difference
tests between the sample firms and the Prowess SME
population for important firm-specific variables,
including total assets, sales, trade credit received and
extended.

In each case, the difference is insignificant.
11
Summary statistics

Summary of survey data
‐
‐
‐
‐
‐
‐

Chemicals and chemical products-15%
Construction companies- 9%
Basic metals-8%
Food products & beverages-7%
For 2/3rd of the firms’ manager belongs to founding family
For 63% of the firms owners are actively involved in day-today management
Summary statistics of firm characteristics from
panel data (Median Firm-year)
‐
‐
‐
‐
Assets: 3.16 Mn. $
Trade Credit Received: 0.41 Mn. $
Average payment period : 87 days
Bank Credit Received: 0.43 Mn. $
14
Inter-firm credit classification:
our approach
Inter-Firm
Credit
Relationshipbased Credit
BusinessRelationship
based credit
Other Credit
Socialrelationship
based credit
15
Empirical measures


Proportion of credit from relationships (ranges from 0 to 1)
Business Relationships
‐
‐
‐

Reliable Industry Sources
Met in Professional Setting
Location in same City/Proximity
0.069
0.064
0.067
Credit from
Business
Relationships
Social Relationships
‐
‐
‐
‐

Sample Average
Extended Family
Social Acquaintances
Same Caste
Same Native Language
0.041
0.054
0.051
0.055
Credit from
Social
Relationships
Credit from
All
Relationships
We use two approaches:
‐
‐
Simple addition, with equal weights
PCA to calculate the weights
16
Summary statistics of relationship-based
inter-firm credit
17
Methodology
 Creditit = Trade Credit from relationship-based sources
scaled by firm assets; for firm i in year t
 Costi= Annualized cost of credit (using discount rate and
free credit period reported by survey firms)
 Controls
- Financing Sources: Bank Credit and Internal Sources , scaled by
firm assets
- Firm Characteristics: Total Assets, Net Sales, Age (all log
transformed)
- Industry fixed effects to control for heterogeneity in use of
trade credit across industries
- Time fixed effects to control for any change in macroeconomic
environment
 We estimate equation (1) for credit from all relations,
business relations, and social relations.
18
Identification strategy
 The observed level of relationship-based credit for a
given firm is determined simultaneously by the both
the credit extended to the firm by its suppliers as
well as the firm’s demand for credit.
 We use Cost of Goods Sold by the firms as a proxy
for its demand for trade credit after adjusting for
labor cost. It is free credit during a typical trade
credit contract period (equation 2).
 Analytically, our procedure estimates the firm’s true
demand for credit independently of any supply-side
factors. This demand estimate serves as an
instrument for credit demand when estimating the
credit supply function (equation 1).
19
Evidence of credit rationing
Credita from
Independent Variables
Trade Credit Terms
Cost
Cost 2
Cost at maximum credit
Proportion of firms paying higher cost
Firm-year Observations
No. of Firms
R
2
Transformed Credita from
Creditb from
All
Business Social
All
Business Social
All
Business Social
Relations Relations Relations Relations Relations Relations Relations Relations Relations
0.217*** 0.086*** 0.147*** 0.216*** 0.184*** 0.234*** 0.332*** 0.119** 0.219***
[0.060]
[0.030]
[0.037]
[0.059]
[0.064]
[0.063]
[0.107]
[0.060]
[0.065]
-0.198*** -0.086*** -0.126*** -0.196*** -0.182*** -0.200*** -0.402*** -0.173*** -0.227***
[0.064]
[0.033]
[0.039]
[0.063]
[0.069]
[0.067]
[0.108]
[0.059]
[0.066]
55%
14%
455
91
50%
14%
455
91
58%
14%
460
92
55%
14%
455
91
50%
14%
455
91
58%
14%
460
92
41%
24%
452
91
34%
45%
452
91
48%
19%
457
92
0.52
0.53
0.48
0.52
0.53
0.5
0.5
0.49
0.45
Robust Standard errors in brackets; *: significant at 10%; **: significant at 5%; ***: significant at 1%; a Scaled by Total Assets; b Scaled by Total Borrowings;
We use Log (1+Total Sales), Log (Total Assets) and Log (1+ Age),
c
20
Robustness checks

We recognize the overlap between different types of
business and social relationships in survey questions.
‐ Use PCA to correct for over-weighting of the proportions of
credit received from a particular relationship-based source.
‐ All results continue to hold (Table 4, Panel B)

We also scale credit by total borrowings instead of
total assets.
‐ Results continue to hold (Table 4, Panel C)

We also do the analysis for various lags of total assets.
‐ Results are robust to such changes (Table 5)
21
Economic significance

Credit/Total Assets from All Relations
‐ Regression Coefficient of
• Cost: 0.22
• Cost2: (-) 0.20
‐ Median cost of credit : 22%
‐ Cost at Maximum Credit: 55%
‐ Credit at Median Cost (in Mn. $): 0.43
‐ Maximum Credit (in Mn. $): 0.88
‐ Credit at higher cost (in Mn. $): 0.67

Similar results for Credit/Total Borrowings
22
Identifying prospective credit-rationed firms

How to identify the likely candidates for credit
rationing?

Demand for collateralizable assets is the fundamental
cost of financing in many existing models of financial
constraints (Bernanke and Gertler, 89; Banerjee and
Newman, 93; Liberti and Mian (JF, 10)

In our tests, the dependent variable Credit from
Relationship-based Sources is scaled by assets.

We classify the firms in our sample by their assets and
run the tests for each class.
23
Identifying credit-rationed firms

We augment the Price variables in the previous
model with TOP(j) dummy where
‐ TOP(j) is a dummy variable taking value 1 if the firm
belongs to top j percentile in terms of average assets and
zero otherwise, j=10 to 90

Using this model we identify firms which are most
likely to face credit rationing

To run this test, we use two different types of
asset distributions:
‐ Average assets over the sample period 2001-5
‐ Assets in each year (dynamic classification)
24
Results
Panel A: Percentiles based on average Panel B: Percentiles based on assets
assets during 2001-05
distribution each year 2001-05
All
Relations
Business
Relations
Social
Relations
0.152**
[0.030]
-0.114
[0.054]
0.062*
[0.018]
-0.055
[0.025]
0.099**
[0.017]
-0.064
[0.038]
0.160**
[0.029]
-0.127
[0.047]
0.058*
[0.019]
-0.053
[0.027]
0.115***
[0.015]
-0.086*
[0.028]
0.161**
[0.028]
-0.141*
[0.041]
0.052
[0.022]
-0.051
[0.028]
0.119***
[0.015]
-0.096**
[0.024]
0.170**
[0.026]
-0.150* *
[0.037]
0.056*
[0.019]
-0.057
[0.025]
0.127***
[0.013]
-0.120**
[0.024]
0.227***
[0.021]
-0.210***
[0.026]
0.092***
[0.013]
-0.093**
[0.014]
0.143***
[0.013]
-0.123***
[0.018]
0.185***
[0.024]
-0.169**
[0.030]
0.069**
[0.016]
-0.070**
[0.017]
0.128***
[0.014]
-0.108**
[0.019]
0.219***
[0.064]
2
-0.201***
Cost
[0.068]
Firm year Observations
455
0.087***
[0.032]
-0.087**
[0.035]
455
0.148***
[0.039]
-0.128***
[0.042]
460
0.219***
[0.064]
-0.201***
[0.068]
455
0.087***
[0.032]
-0.087**
[0.035]
455
0.148***
[0.039]
-0.128***
[0.042]
460
Credit costs
Top 70 percentile
Cost
Cost 2
Top 80 percentile
Cost
Cost 2 (
Top 90 percentile
Cost (
Cost 2
All
Cost
All
Business
Social
Relations Relations Relations
25
Results

We find that the bottom 20/30 percent of firms by
asset size are at risk of credit-rationing.

They are firms with assets less than $1.8-2 mn.

Size of a median firm in our full sample is $3.15
mn.

The results for the two types of asset distributions
are very similar (Table 7)
27
Industry classification of firms
at risk of credit rationing

Credit rationing is not endemic to particular
industries.

For example, manufacturing of chemicals and
chemical products industry

-
Accounts for 3.3% of the bottom 20% and 5.1% of the
bottom 30%,
-
Accounts for 65 firm-year observations in our full sample
of 455.
Hence the reasons must be firm-specific.
28
Industry Classification of Firms at Risk
of Credit Rationing
29
Further analysis

Firms at risk of rationing vis-à-vis other sample
firms
‐
‐
‐
‐
‐
‐
‐
Have lower assets (by construction )
Receive less trade credit from all sources and
relationship-based sources
Have higher average payment period
Receive less bank credit
Are of same age (as on 2005)
Have lower profitability
Have more outstanding debt in relation to assets (debt
overhang)
30
Conclusions

Informal credit is rationed.

Overall, we find that firm assets play an important
role in credit decisions of the lenders.

Creditors appear to ration credit to contain moral
hazard problems on the part of borrowers.
31