Tài liệu Do Firms Want to Borrow More? Testing Credit Constraints Using a Directed Lending Program doc

LINK DOWNLOAD MIỄN PHÍ TÀI LIỆU "Tài liệu Do Firms Want to Borrow More? Testing Credit Constraints Using a Directed Lending Program doc": http://123doc.vn/document/1038824-tai-lieu-do-firms-want-to-borrow-more-testing-credit-constraints-using-a-directed-lending-program-doc.htm

prospects of the firm. Lamont’s (1997) use of oil-price shocks to look at non-oil investment of
oil companies is an example of this strategy. Ho wever, it is not an accident that the companies
for which Lamont is able to have precise enough information about the nature of shocks tend t o
be very large companies and, as emphasized by Lamont and others,
3
cash flow shocks can have
very different effects on big, cash-rich firms than on small, cash-poor firms.
4
Here we take a different approach to t his question. We make use of a policy change that
affected the flow of directed credit to an identifiable subset of firms. Such policy changes are
common in many developing and developed countries–even the U.S. has the Community Rein-
vestment Act, which obliges banks to lend more to specific communities.
The advantage of our approach is t hat it gives us a specific exogenous shock to the supply of
credit to specific firms (as compared to a shift in the overall supply of credit). Its disadvantage
is that directed credit need not be priced at its true market price, and therefore a shock to the
supply of directed credit might lead t o more in vestment even if a firm is not credit constrained.
In this paper we develop a simple methodology based on ideas from elementary price theory
that allows us to deal with this problem. The methodology is based on two observations: First,
if a firm is not credit constrained, then an increase in t he supply of subsidized directed credit
to the firm must lead it to substitute directed credit for credit from the market. Second, while
investmentandthereforetotalproductionmaygoupevenifthefirm is not credit constrained,
it will only go up if the firm has already fully substituted market credit with directed credit.
We test these implications using firm-level data that we collected from a sample of small to
medium size firms in India. We m a ke use of a ch ange in the so-called priority sector regulation,
under which firms smaller than a certain limit are given priority access to bank lending.
5
The
first e xperiment we exploit is a 1998 reform which increased the maximum size below which a
firm is eligible to receiv e priority sector lending. Our basic empirical strategy is a difference-
3
Kaplan and Zingales (2000) make the same point.
4
The estimation of the effects of credit constraints on farmers is significantly more straightforward s ince
va riations in the weather provide a powerful source of exogeneous short-term variation in cash flow. Rosenzweig
and Wolpin (1993) use th is strategy to study the effect of credit constraints on investment in bullocks in rural
India.
5
Banks are p e nalized for failing to lend a certain frac tion of the portfolio to firms that are classified to b e in
the priority sector.
2
in-difference-in-difference approach, That is, we focus on the changes in the rate of change in
various firm outcomes before and after the reform for firms that were included in the priority
sector as a result of the new limit, using the corresponding changes for firms that were already
in the priority s e ctor as a control. We find that bank lending and firm revenues went up for the
newly targeted firms in the year of the reform. We find no evidenc e that this was accompanied
by substitution of bank credit for borrowing from the market and no evidence that revenue
growth was confined to firms that had fully substituted bank credit for market borrowing. As
already a rgued, the last two observations are inconsisten t with the firms being unconstrained in
their market borrowing. Our second experiment uses the fact that a subset of the firms that were
included in the priority sector in 1998 were excluded again in 2000. We find that bank lending
and firm revenues went down for these firms, both compared to th e firms that had always been
part of the priority sector and to firms that were included in 1998, and remained part of the
priority sector in 2000. This second experiment makes it unlikely that the results we obtain are
an artifact of differential trends for large, medium and small firms.
We also use this data to estimate parameters of the production function. We find no clear
evidence o f diminishing returns t o additional investment, which reinforces the idea that the firms
are not at the point where the marginal product is about to fall below the interest rate. Finally,
we try to estimate the effect of the program-induced additional inv estment on profits. While
the i nterpretation of this result relies on some additional assumptions, it suggests a very large
gap between the marginal product and the interest rate paid on the marginal dollar (the point
estimate is that Rs. 1 more in loans increased profits net of interest payment by Rs. 0.73, which
is much too large to be explained as just the effect of receiving a subsidized loan).
The rest of the paper is organized as follows: The next section describes the institutional
environment and our data sources, provides some descriptive evidence and informally argues that
firms may be expected to be credit constrained in this environment. The next section develops
our empirical strat egy, starting with the theory and ending with the equations we estimate.
The penultimate section reports the results. We conclude with some admittedly speculative
discussion of what our results imply for credit policy in India.
3
2 Ins titutio n s, D a ta a nd Some Descr ip tive Ev id en ce
2.1 The Banking Sector in I ndia
Despite the emergence of a number of dynamic private sector banks and entry by a large number
of foreign banks, the biggest banks in India are all in the public sector, i.e., they are corporatized
banks with the government as the controlling share-holder. The 27 public sector banks collect
over 77% of deposits and comprise over 90% of all branches.
The particular bank we study is a public sector bank. While we are bound b y confidentiality
requirements not to reveal t he name of the bank, we note it was rated among the top five public
sector banks for several of the past few years by Business Today, a major business magazine.
While banks in India occasionally provide longer-term loans, financing fixed capital is primar-
ily the responsibility of specialized long-term lending institutions such as the Industrial Finance
Corporation of India. Banks typically provide s hort-term working capital to firms. These l oans
are given as a credit line with a pre-specified limit and an interest rate that is set a few per-
centage points above prime. The spread between the interest rate and the prime rate is fixed in
advance based on the firm’s credit rating and other characteristics, but cannot be more than 4%.
Credit lines in India charge interest only on the part that is used and, given that the interest
rate is pre-specified, many borrowers want as large a credit line as they can get.
2.2 Priority Sector R egulation
All banks (public and private) are required to lend at least 40% of their net credit to the “priority
sector”, which includes agriculture, agricultural processing, transport industry, and small scale
industry (SSI). If banks do not satisfy the priority sector target, they are required to lend money
to specific government agencies at very low rates of interest.
In January 1998, there was a change in the definition of the small scale industry sector.
Before this date, only firms with total investment in plant and machinery below Rs. 6.5 million
were included. The reform extended the definition to include firms with investment in plants
and machinery up to Rs. 30 million. In January 2000, the reform was partially undone by a
new change: Firms with investment in plants and machinery between Rs. 10 million and Rs. 30
million were excluded from the priority sector.
4
The priority sector targets seem to be binding for the bank we study (as well as for most
banks): Every year, the bank’s share lent to the priority sector is very close to 40% (it was 42% in
2000-2001). It is plausible that the bank had to go some distance down the client quality ladder
to achiev e this target. Moreover, there is the issue of the physical cost of lending. Banerjee and
Duflo (2000) calculated that, for four Indian public banks, the labor and administrativ e costs
associated with lending to the SSI sector were 22 P aisa per Rupee lent, or about 1.5 Paisa higher
than that of lending in the unreserved sector. This is consistent with the common view that
lending to smaller clients is more costly.
Two things changed when the priority sector limit was raised: First, the bank could draw
from a larger pool and therefore could be more exacting in its standards for clients. Second, it
could save on the cost of lending by focusing on slightly larger clients. For both these reasons
the bank would like to switch its lending towards the newly inducted members of the priority
sector. If these firms were constrained in their demand for credit before the policy change, one
would expect to see an expansion of lending to these firms relative t o firms that were already in
the priority sector.
6
When firms with investment in plant and machinery above 10 million Rs.
were excluded again from the priority sector, loans to these firms no longer counted towards the
priority sector target. The bank had to go back to the smaller clients to fulfill i t s priority sector
obligation. One therefore expects that loans to those firms declined relative to the smaller firms.
2.3 Data Collection
The data for this study were obtained from one of the better-performing Indian public sector
banks. This bank, like other public sector banks, routinely collects balance sheet and profit
and loss account data from all firms that borrow from it and compiles the data in t he firm’s
loan folder. Every year the firm also must apply for renewal/extension of its credit line, and
the paperwork for this is also stored in the folder, along with the firm’s initial application, even
when there is no formal review of the file. The folder is typically stored in the branch until it is
6
The increase in lending to larger firms may come entirely at the expen se of sm aller firms (without affecting
total len ding to the priority sec tor), or the reform cou ld cause an incre a se in the amou nt lent to the priority
sector. We will focus on the comparison between firm s that were newly labelled as priority sector and smaller
firms.
5
physically impossible to put more documents in it.
With the help of employees from this bank, as well as a former bank officer, we first extracted
data from the loan folders in the spring of 2000. We collected general information about the
client (product description, i nvestment in plant and machinery, date of incorporation of units,
length or the relationship with the bank, current limits for term loans, working capital, and letter
of credit). We also recorded a summary of the balance sheet and profit and loss information
collected by the bank, as well as information about the bank’s decision regarding the amount of
credit to extend to the firm and the interest rate charged.
As we discuss in more detail below, part of our empirical strategy called for a comparison
between accounts that have always been a part of the priority sector and accounts t hat became
part of the priority sector in 1998. We first selected all the branches that handle business
accounts in the six major regions of the bank’s operation (including New Delhi and Mumbai).
In each of these branches, we collected information on all the accounts that were included in
the priority sector after January 1998 (these are the accounts for which the investment in plant
and machinery is between 6.5 and 30 million Rupees). We collected data on a total of 249 firms,
including 93 firms with investment in plants and m achinery be tween 6.5 and 30 million Rupees.
We aim ed t o collect data for the years 1996-1999, but when a folder is full, older information
is not alw ays kept in the branch. Every year, there are a few firms from which the data was
not collected. We have 1996 data on lending for 120 accounts (of the 166 firms that had started
their relationship with the bank by 1996), 1997 data for 175 accounts (of 191 possible accounts),
1998 data for 217 accounts (of 238), and 1999 data for 213 accounts. In the winter 2002-2003,
we collected a new wave of data on the same firms in order to study the impact of the priority
sector contraction on loans, sales and pro fits. We have 2000 data for 175 accounts, 2001 data
for 163 accounts, and 2002 data for 124 accounts.
7
Table 1 presents the summary stat istics for all data used in the analysis of credit constraint
7
The reason why we have less data in 2000, 2001 and 2002 than in 1999 is that some firms had not had their
2002 review when we re-surveyed them late 2002, and 43 accounts were closed between 2000 and 2002. The
prop ortion of accounts closed is balanced: It is 15% am ong firmswithinvestmentinplantandmachineryabove
10 million, 20% among firmswithinvestmentinplantandmachinerybetween 6.5 and 10 m illion, and 20% among
firms with investment in plant an d machinery below 6.5 million. Thus, it doe s not app ear that sample selection
bias would emerge from the closing of those accounts.
6
and credit r ationing (in the full sample, and in the sample for which we have information on the
change in lending between the previous period and that period, which is the sample of interest
for the analysis).
2.4 Descriptive Eviden ce on Lend ing De cisio n s
In this subsection, we provide some description of lending decisions in the banking sector. We use
this evidence to argue that this is an environment where credit constraints arise quite naturally.
Tables 2 and 3 show descriptive statistics regarding the loans in the sample. The first row
of table 2 shows t hat, in a majority of cases, the loan limit does not change from year to year:
In 1999, the limit was not updated ev en in nominal terms for 65% of the loans. This is not
because the limit is set so high that it is essentially non-binding: row 2 shows that in the six
years in the sample, 63% to 80% of the accounts reached or exceeded the credit limit at least
once in the year.
This lack of growth in the credit l imit granted by t he bank is particularly striking given that
the Indian economy registered nominal growth rates of over 12% per year. This wo uld suggest
that the demand for bank credit should have increased from year to y ear over the period, unless
the firms have increasing access to another source of finance. There is no evidence that they
were using any other formal source of credit. On average, 98% of the working capital loans
provided to firms in our sample come from this one bank, and, in any case, the same kind of
inertia shows up in the data on total bank loans t o the firm.
That the demand for formal sector credit increased from year to year is suggested by rows 3
to 5 in table 2. The bank’s official guidelines for lending explicitly state that the bank should try
to meet the legitimate needs of the borrower. For this reason, the m aximum lending limits that
can be authorized by the bank for working capital loans are explicitly linked to the projected
sales of the borrower—the maximum limit is supposed to be one-fifth of the predicted sales for
the year. Every y ear, a bank officer must approve a sales projection for the firm and calculate
a maximum lending limit on the basis of t he turnover.
8
Projected sales therefore pro v ide a
measure of the credit needs of the firm. Row 3 shows that actual sales have increased from
8
The exact rule is that the limit on turnover basis should be the minimum of 20% of the projected sales and
25% of th e p rojected sales minus the fin ances available to the firm from other sources.
7
year to year for most firms. Rows 4 and 5 show that both projected sales and the maximum
authorized lending also increased from year to year in a large majorit y of cases. Yet there was
no corresponding change in lending from the bank. The change in the credit limit that was
actually sanctioned systematically fell short of what the bank determined to be the firm’s needs
as determined by the bank. In 1999, 80% of the actual limits granted were below 20% of the
predicted sales, and 60% were below the maximu m limit calculated by the bank. On average,
the granted limit was 89% of the recommended lim it, and 67% of what following the rule based
on 20% of predicted sales would give. It is possible that some of the shortfall was covered by
informal credit, including trade credit: According to the balance sheet, total current liabilities
excluding bank credit increased b y 3.8% every year on average. H owever, some expenses ( such
as wages) are typically not covered by trade credit and, moreover, trade credit could be rationed
as well. The question that is at the heart of this paper is whether such substitution operates to
the point where a firm is not credit constrained.
In table 3, we examine in more detail whether this tendency could be explained by other
factors that might have affected a firm’s need for credit. Column (3) shows that no variable we
observe seems to explain why a firm’s credit limit was changed: Firms are not more lik ely to get
an increase in limit if they reached the maximum limit in t he previous year, if their projected
sales (according to the bank itself ) have increased, if their current sales have increased, if the
ratio of profits to sales has increased, or if the current ratio (the ratio of current assets to current
liabilities, a traditional indicator of how secure a working capital loan is, in India as well as in
the U.S.) has increased. Turning to the direction or the magnitude of changes, only an increase
in projected sales or current sales predicts an increaseingrantedlimit,andonlyanincreasein
projected sales predict the level of increase. This could well be due to reverse causality, however:
The bank o fficer could be more l ikely to predict an increase in sales when he is willing to give a
larger credit extension to the firm.
One r eason the granted limit may not change is that the previous year’s limit already incor-
porated all information relevant to the lending decision: The limit is not responsive to what is
currently going on in the firm, because these are just short-run fluctuations which tell us little
about the future of the firm. If this were the case, we should observe that granted limits are
much more responsive to these factors for young firms than for old firms. Columns 5 and 6
8
in table 3 repeat the analysis, breaking the sample into recent and older clients. Changes in
limits are more frequent for younger clients, but they do not seem to be more sensitive to past
utilization, increases in projected sales, or profits.
The fact that the probability of a limit’s change is uncorrelated with observable firm char-
acteristics is striking. One plausible theory relates this to the fact, noted above, that changes in
the limit are surprisingly rare. If bank officials are reluctant to change the limit, a large fraction
of the observed changes may reflect effective lobbying or something purely procedural (“it has
been five years since the limit was raised”) rather than economic rationality.
What explains the reluctance of loan officers to do what is, palpably, their job? A recent
report on banking policies commissioned by the Reserve Bank of India suggests one potential
explanation: “The [working group] observed that it has received representations from the man-
agement and the unions of the bank complaining about the diffidence in taking credit decisions
with which the banks are beset at present. This is due to investigations by outside agencies
on the accountability of staff in respect to Non-Performing Assets.” (Tannan (2001)). In other
words, the problem is that changing the limit (in either direction) involves sticking one’s neck
out—if one cuts the limit the firm may complain, and if one raises it, there is a possibility one
would be held responsible if the loan goes bad: The Central Vigilance C ommission (a govern-
ment body entrusted with monitoring the probity of public officials) is formally notified of every
instance of a bad loan in a public sector bank, and investigates a fraction of them.
9
Consistent
with this “fear of lending” explanation, Banerjee, Cole and Duflo (2004) show that lending slows
down whenever there is an inve stigation against an credit o fficer in a given ban k .
Simply renewing a loan without changing the amount is one easy way to avoid such re-
sponsibilit y, especially if the original decision was someone else’s (loan officers are frequently
transferred). The problem is likely exacerbated by the fact that the link bet ween the prof-
itability of t he bank and the career prospects of an individual loan officer, is, at best, rather
weak.
It should be emphasized, however, that while the fact that our bank is in the Indian public
sector may have exacerbated t he problem, the core tension here is quite universal. All banks of
9
There were 1,380 investigations of bank officers in 2000 for credit related frauds, 55% of which resulted in
major sanctions.
9
any size deal with the problem that the officer who decides whether or not make a loan does
not have very much to lose if the loan goes bad, while the bank could stand to lose a lot. They
deal with it by limiting the discretion that the officer has (by requiring that he use a scoring
model, for example) and by penalizing officers whose loans go bad, who in turn respond by not
taking any more chances than they have to. For both these reasons, certain firms will not be
able to get the credit that they want from the bank (see Stein ( 2002) for a model that makes
this point).
The fact that the bank in our data does not seem to be responding to changes in firms’ credit
needs, suggests that some firms would have an unmet demand for credit from this particular bank.
It does not prove that the firm will be credit constrained: After all, there are other banks, and
other sources of credit (such as trade credit). Nevertheless, it does make it more plausible.
3 Establishing C redit C onstr aints
3.1 Theory
Consider a firm with the follo wing fairly standard production technology: The firm must pay
a fixed cost C before starting production (say the cost of setting up a factory and installing
machinery). The firm then invests in labor and o ther variable inputs. k rupe es of working
capital invested in variable inputs yield R = F(k) rupees of revenu e after a suitable period.
F (k) has the usual shape–it is increasing and concave.
As ment ioned abo ve, we need to consider the case where the firms have multiple sources of
credit. We will say that a firm is credit rationed with respect to a particular lender if there is no
interest rate r suchthattheamountthefirm wants t o borrow at that rate is strictly positive and
equal to an amount that the lender is willing to lend at that rate.
10
Essentiallythissaysthat
the supply curve of loans from that lender to the firm is not h orizontal at some fixed in terest
rate.
We will say the firm is credit constrained if there is no interest rate r such that the amount
that the firm wants to borrow at that rate is equal to an a mount that a ll the lenders taken
10
The am ount the firm wants to borrow at a given rate is assumed to be an amount that would maxim ize the
firm’s pro fit if it cou ld borrow as much ( or as little) as it wa nts at that rate.
10
together are willing to lend at that rate. This says t hat the aggregate supply curve of capital to
the firm is not horizontal at some fixed interest rate.
Note that a firm could be credit rationed with respect to every l ender without being credit
constrained in our sense. This can be the case, for example, when there is an infinite supply o f
lenders, each willing to lend to no more than $10 at an interest rate of 10%.
It is convenient to begin with the simple case where there are only two lenders, which we
willcallthe“market"andthebank. Denotethemarketrateofinterestbyr
m
and the interest
rate that the bank charges by r
b
. Given that the bank is statutorily required t o lend a certain
amount to the priority sector, there is reason to believe that the bank lending rate is below the
market rate: r
b
≤ r
m
.
The policy change we analyze involves the firmsinquestionbeingoffered additional bank
credit. We will show in the next section that there was no corresponding change in the interest
rate. To the extent that firms accepted the additional credit being offered to them, this is direct
evidence of credit rationing with respect to the bank. However this in itself does not imply that
they would ha ve borrowed more at the market interest rat e. A possible scenario is depicted in
figure 1. The horizontal axis in the figure measures k while the vertical axis represents output.
The downward sloping curve in the figure represents the marginal p roduct of capital, F
0
(k).The
step function represents the supply of capital. In the case represented in the figure, we assume
that the firm has access to k
b0
units of capital at the bank rate r
b
but was free to borrow as
much as it wanted at the higher market rate r
m
. As a result, it borrowed a dditional resources
at the market rate until the point where the marginal product of capital is equal to r
m
.Its
total outlay in this equilibrium is k
0
. Now consider what happens if the firm is now allow ed to
borrow a greater amount, k
b1
, at the bank rate. Since at k
b1
the marginal product of capital is
higher than r
b
, the firm will borrow the entire additional amount offered to it . Moreover, it will
continue to borro w at the market interest rate, though the amount is now reduced . The total
outlay, however, is unchanged at k
0
. This will remain the case as long as k
b1
<k
0
:Theeffect of
the policy will be to substitute market borrowing with bank loans. The firms profits will go up
because of the additional subsidies, but its total outlay and output will remain unchanged.
The expansion of bank credit will have output effects in this setting if k
b1
>k
0
.Inthiscase,
the firm will stop borrowing from the market a nd the marginal cost of credit it faces will be
11