Credit Access in India Abhijit V. Banerjee. How well is capital allocated?

Credit Access in India

Abhijit V. Banerjee

How well is capital allocated?

Figure 1. Average Firm Size in India and Comparator Countries in 1990

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Textiles Iron and steel Transport

equipment

Food products Machinery

except electric

Machinery,

electric

Industrial

chemicals

Other

chemicals

Other non-

metallic

mineral

products

All industries

Top nine ISIC 3 digit industries by value added for India in 1990

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India Comparator countries

Prima Facie Evidence

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 well-

regarded 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.

1997 1998 1999 2000 2001 2002

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

difference -0.057 0.036 0.009 -0.030 -0.082 -0.039

Year

Average change in limit

Effect on sales and profit

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

post -0.007 -0.078 0.011 -0.118(.048) (.082) (.052) (.152)

big -0.121 -0.255 0.109 0.014(.074) (.288) (.065) (.163)

post*big 0.164 0.289 -0.193 -0.155(.08) (.33) (.106) (.236)452 389 454 376

1996-1999 1998-2001

Dependent variables

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 whichGranted limit remained the same 0.66 0.64 0.65Limit was attained by the borrower 0.80 0.69 0.72Granted limit from banking system remained the same 0.66 0.63 0.63Maximum authorized limit has increased 0.63 0.74 0.73Predicted sales have increased 0.72 0.67 0.73Granted limit <maximum authorized limit 0.60 0.63 0.60Granted limit <0.20*predicted sales 0.85 0.85 0.80

Inertia in Lending Decisions

A- PAST UTILIZATION C. PROFIT OVER SALESREACHED LIMIT INCREASED

Yes 0.60 0.65No 0.66 0.61Difference -0.06 0.04

(.064) (.048)

B-CURRENT SALES D. CURRENT RATIOINCREASED INCREASED

Yes 0.62 0.62No 0.68 0.65Difference -0.06 -0.03

(.048) (.044)

proportion of cases

where limit was not changed

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 1985-2000 1992-2000(3) (4)

Growth 2.195 2.634(0.970) (1.165)

Spread * Growth, when spread > 0 -0.257 -0.219(0.104) (0.103)

Spread * Growth, when spread < 0 -0.079 0.473(0.791) (0.562)

Year Fixed Effects Yes YesBank Fixed Effects Yes Yes

Synthetic Growth Index

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).

Small Big(1) (2)

1997 0 0.0111998 0.026 0.0111999 0.052 0.02292000 0.078 0.0572001 0.118 0.09192002 0.125 0.137

Cumulative fraction NPASize of the firm

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.

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5S

har

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12 13 14 15 16 17Log Deposits in 1980

Credit to Agriculture

-.15

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-.05

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12 13 14 15 16 17Log Deposits in 1980

Credit to Rural

Figure 1: Rural and Agricultural Credit

Deposits Credit Branches Rural Branches

Post (1980-1990) -0.085 *** -0.078 *** -0.114 *** -0.181 ***(0.014) (0.015) (0.017) (0.024)

Post*Nationalization -0.026 -0.012 -0.044 -0.066 **(0.033) (0.036) (0.033) (0.031)

Nineties (1990-2000) -0.040 *** -0.027 -0.122 *** -0.219 ***(0.014) (0.017) (0.018) (0.022)

Nineties * Nationalization -0.073 * -0.088 ** -0.053 -0.086 ***(0.039) (0.041) (0.034) (0.028)

Log Real Growth of: Growth of:

Table 7: Deposit, Credit, and Branch Growth

Table 9: Causal Effect of Nationalization on Lending

1992 2000

Average loan size: -24.753 ** -143.867 **(10.332) (69.784)

Share of bank's credit to:Agriculture 0.082 *** 0.031

(0.030) (0.021)

Rural areas 0.073 *** 0.021(0.027) (0.023)

Small scale industry 0.009 0.020(0.017) (0.026)

Trade, transport and finance -0.073 * -0.037(0.040) (0.031)

Government credit 0.020 *(0.011)

Intere rate (residual) -0.007 -0.007(0.008) (0.006)

Estimate of Discontinuity

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.