Credit Access in
India
Abhijit V. Banerjee
How well is capital
allocated?
Prima Facie Evidence
Figure 1. Average Firm Size in India and Comparator
Countries in 1990
Value
added
(in US$
millions
) per
establis
hment
in 1990
10.00
9.00
8.00
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
Textiles
Iron and steel
Transport
equipment
Food products
Machinery
Machinery,
Industrial
Other
Other non-
except electric
electric
chemicals
chemicals
metallic
mineral
products
Top nine ISIC 3 digit industries by value added for India in 1990
India
Comparator countries
All industries
More…

TFPR dispersion within 4 digit industries (from
Hsieh and Klenow)
U.S.
China
India
90th/10th
1.9
5.6
5.7
75th/25th
1.3
2.5
2.4
A natural experiment on credit access:
Priority Sector Lending in India



Based on Banerjee-Duflo (2004)
Banks need to lend 40% of their portfolio to the
priority sector, including SSI.
Changes in the priority sector rules:
 January
1998: inclusion in the SSI sector of firms with
investment in plant and machinery between 0.65 and
3 crores rupees.
 Early 2000: firms with ipm between 3 crores rupees
and 1 crore rupees taken away from the priority
sector.
Results
We firms level data from 1 very wellregarded public bank.
 We estimates the effect on credit, sales,
and profits, using firms that were always
in the priority sector as controls.
 The firms that were included in the
priority sector and then excluded grew
much faster in all 3 measures when
included, and fell behind on all 3 when
excluded.

Average change in limit
1997
1998
Year
1999
small
0.111
0.076
0.076
0.048
0.085
0.080
big
0.054
0.113
0.085
0.018
0.003
0.041
-0.057
0.036
0.009
-0.030
-0.082
-0.039
difference
2000
2001
2002
Effect on sales and profit
Dependent variables
Log(sales)t+1
Log(profit)t+1
Log(sales)t+1
Log(profit)t+1
-log(sales)t
-log(profit)t
-log(sales)t
-log(profit)t
1996-1999
1998-2001
post
big
post*big
-0.007
(.048)
-0.121
(.074)
0.164
(.08)
452
-0.078
(.082)
-0.255
(.288)
0.289
(.33)
389
0.011
(.052)
0.109
(.065)
-0.193
(.106)
454
-0.118
(.152)
0.014
(.163)
-0.155
(.236)
376
Implications



1 percent increase in credit generates 1 percent
increase in sales and 3 percent increase in
profits
Even after taking default rates (NPA) into
account, the implied marginal product of capital
is close to 100%! Average rates of NPA do not
explain under-lending.
Evidence of systemic failure: Neither private
lenders nor public banks are supplying these
largish firms with the credit they need.
Direct evidence from interest rates





There is a large body evidence showing that many
smaller firms are also extremely short of credit.
In Banerjee (2002) I review evidence showing that small
firms often pay interest rates of over 50% of which at
most 5% is explained by default.
This tells us that their marginal product must be above
50% whereas the cost of capital is of the order of 5-6%
at most.
On the other hand, from the ICOR for India (4.5), the
average marginal product in India cannot be more than
22%.
So a lot of firms must be earning a lot less than 22%.
Evidence from within industry
allocation



Evidence from the knitted garment industry in
Tirupur.
The firms that are associated with a cash-rich
community start out almost three times larger
than those started by other people.
These firms very soon fall behind in terms of
output, but continue to have significantly more
fixed capital.
What are lenders
(not) doing?
Lending rules for public sector
banks



Maximum Permissible Bank finance: Since the Nayak
Committee, banks can set their own rule (turnover
based, or based on working capital gap)
For example in the bank we study, MPBF is the
maximum of turnover based limit and the limit based on
the working capital gap
Comments:



Rules set for limited growth.
Profitability does not enter in the official rule.
Inventories are not a very good collateral in practice
However the problem goes
beyond the rules


Banerjee and Duflo (2001) look at the actual
lending decisions of a bank, and compare it with
the actual limit.
Main results:
 The
lending limit changes very infrequently (in 64% of
the case, it does not change) despite growth
 Increases are not very responsive to firms’
characteristics and performance.
 Lending in smaller than MPBF in 68% of the cases.

Systematic deviation from the rule in the
direction of inertia.
Inertia in Lending
1997 1998 1999
(1)
(2)
(3)
proportions of cases in which
Granted limit remained the same
Limit was attained by the borrower
Granted limit from banking system remained the same
Maximum authorized limit has increased
Predicted sales have increased
Granted limit <maximum authorized limit
Granted limit <0.20*predicted sales
0.66
0.80
0.66
0.63
0.72
0.60
0.85
0.64
0.69
0.63
0.74
0.67
0.63
0.85
0.65
0.72
0.63
0.73
0.73
0.60
0.80
Inertia in Lending Decisions
proportion of cases
where limit was not changed
A- PAST UTILIZATION
C. PROFIT OVER SALES
REACHED LIMIT
INCREASED
Yes
0.60
0.65
No
0.66
0.61
Difference
-0.06
0.04
(.064)
(.048)
B-CURRENT SALES
INCREASED
Yes
No
Difference
D. CURRENT RATIO
INCREASED
0.62
0.68
-0.06
(.048)
0.62
0.65
-0.03
(.044)
Why are they not
lending?
Understanding why banks do not
lend?
Lack of positive incentives
 Fear of lending
 Lending to the government and the easy
life
 The risk of (marginal) default

Fear of Lending
Employees of public bank are subject to
anti-corruption legislation: widespread
concerns about the legal proceedings.
 No incentive to lend more: it is easier to do
nothing.
 Using monthly data on lending combined
with public data on CVC investigation, we
examine whether there is a decrease in
lending in a bank following CVC activity.

Results



Following vigilance activity in a particular bank,
total lending by the bank drops by 3-5%,
compared to other banks and stay low for
several years.
This understates the overall impact since there
might be some anticipatory reaction as well (we
have only data on the date at which the CVC
advice was given).
This could imply a sizeable reduction in the
credit supply in the economy.
What is going on?

The attraction of easy life
 The
combination of high interest rates on government
borrowing and a boom in consumer finance as the
economy transitions to credit financing of durables
reduces the pressure to lend to industry.



Test: Are banks in slow growing states more
responsive to variation in government interest
rates then banks in fast growing states ?
Data: yearly data on C/D ratio for 45 banks
Results: in high growth environment, banks are
less elastic to the spread in their lending
decisions
Results
Time Period
Growth
Spread * Growth, when spread > 0
Spread * Growth, when spread < 0
Year Fixed Effects
Bank Fixed Effects
Synthetic Growth Index
1985-2000 1992-2000
(3)
(4)
2.195
(0.970)
-0.257
(0.104)
-0.079
(0.791)
2.634
(1.165)
-0.219
(0.103)
0.473
(0.562)
Yes
Yes
Yes
Yes
The Risk of Default
Are firms more likely to default because
they are in the priority sector?
 According to the data from our bank, 2.5%
of loans in the priority sector become NPA
every year
 For firms included in 1998, the rates of
default was and remained lower till 2002
(but climbs sharply in 2001, after they get
excluded from the priority sector).

1997
1998
1999
2000
2001
2002
Cumulative fraction NPA
Size of the firm
Small
Big
(1)
(2)
0
0.026
0.052
0.078
0.118
0.125
0.011
0.011
0.0229
0.057
0.0919
0.137
Is public ownership
the problem?
Is public ownership the problem?




Cole (2004) exploits a natural experiment the
nationalization of banks in 1980 to answer this question.
The 1980 nationalization took place according to a strict
policy rule: all private banks whose deposits were above
a certain cutoff were nationalized.
He compares banks that were just above the 1980 cutoff
to those that were just below the 1980 cutoff, while
controlling for bank size in 1980.
The idea behind this comparison is that the relationship
between size and behavior should not change
dramatically around the cutoff, unless nationalization
itself causes changes in behavior.
Figure 1: Rural and Agricultural Credit
-.05
-.2
-.15
-.15
-.1
-.1
Share of Credit
-.05
0
0
.05
Credit to Rural
.05
Credit to Agriculture
12
13
14
15
16
Log Deposits in 1980
17
12
13
14
15
16
Log Deposits in 1980
17
Table 7: Deposit, Credit, and
Branch Growth
Log Real Growth of:
Credit
Deposits
Growth of:
Branches Rural Branches
Post (1980-1990)
-0.085 *** -0.078 *** -0.114 *** -0.181 ***
(0.024)
(0.017)
(0.015)
(0.014)
Post*Nationalization
-0.026
(0.033)
Nineties (1990-2000)
-0.040 *** -0.027
(0.017)
(0.014)
Nineties * Nationalization
-0.073 *
(0.039)
-0.012
(0.036)
-0.088 **
(0.041)
-0.044
(0.033)
-0.066 **
(0.031)
-0.122 *** -0.219 ***
(0.022)
(0.018)
-0.053
(0.034)
-0.086 ***
(0.028)
Table 9: Causal Effect of Nationalization on
Lending
Estimate of Discontinuity
1992
2000
Average loan size:
Share of bank's credit to:
Agriculture
-24.753 **
(10.332)
-143.867 **
(69.784)
0.082 ***
(0.030)
0.031
(0.021)
Rural areas
0.073 ***
(0.027)
0.021
(0.023)
Small scale industry
0.009
(0.017)
0.020
(0.026)
Trade, transport and finance
-0.073 *
(0.040)
-0.037
(0.031)
Government credit
0.020 *
(0.011)
Intere rate (residual)
-0.007
(0.008)
-0.007
(0.006)
Not much of a demonstrable gap
between public and private banks



Overall public lending grew at the same rate as
private lending in the 80s; lagged behind in 90s
No differences in lending patterns today.
Historically public banks favored agriculture/rural
borrowers, while neglecting trade/transportation.
No differences in lending to small-scale industry.
Closing thoughts
What is happening right now: some
speculation


Over the last couple of years, there may well be a shift.
Lots of innovation going on in the financial sector.






Big banks like ICICI Bank and SBI are becoming increasingly
aggressive
Private finance is growing fast
The stock market is booming.
Boom in retained earnings in the corporate sectors
Large corporates are borrowing heavily on the world markets
My prediction is that we will look back on this period and
conclude that the recent growth acceleration had to do
with a sharp inflow of capital into undercapitalized firms.