Download Credit Rating and Short-Term Debt Financing: Empirical

Credit Rating and Short-Term Debt Financing:
Empirical Analysis of Listed Firms in Korea ‡
Byung-Uk Chong*, In-Deok Hwang**, Young Sang Kim***
November 24, 2014
Keywords: Asymmetric Information, Credit Rating, Financial Distress, Trade Credit, Short-Term
Debt Financing
JEL Classification: G14, G24, G28, G32
‡
We acknowledge that Korea Ratings Corp., Korea Investors Service Inc, and NICE Investors Service Co., Ltd.,
provided credit rating data and Korea Ratings Corp. provided financial data of listed firms in Korea
*
Corresponding author. College of Business Administration, University of Seoul, Tel: +82-2-6490-2250, E-mail:
[email protected]
**
Korea Ratings Corp., Tel: +82-2-368-5548, E-mail: [email protected]
***
Haile/US Bank College of Business, Northern Kentucky University, Tel:1-859-572-5160, E-mail:
[email protected]
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Credit Rating and Short-Term Debt Financing:
Empirical Analysis of Listed Firms in Korea
Abstract
This paper investigates how credit rating affects trade credit use in short-term debt financing. Empirical
results show that, under information asymmetry in debt market, credit rating plays a key role both as
screening device of lender and signaling device of borrower. Hence, credit rating system can mitigate
information asymmetry problem, resulting in the improvement of efficiency in allocation of funds in
short-term debt market of Korean economy. Interesting and unique empirical results of this paper is the
finding of non-linear relationship between credit rating level and trade credit use. Among low-rating firms, the
increase in credit rating reduces trade credit use, while, among high-rating firms, the increase in credit
rating raises trade credit use in Korean economy. Given that large firms have high credit ratings in Korean
economy, the positive relation between credit rating level and trade credit use reflects the possibility of
predatory transaction of trade credits. This paper also finds that various characteristics of borrowing firm
such as size, financial distress, product characteristics, and industry characteristics are key determinants
of trade credit use in short-term debt financing.
Keywords: Asymmetric Information, Credit Rating, Financial Distress, Trade Credit, Short-Term Debt
Financing
JEL Classification: G14, G24, G28, G32
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I. Introduction
This paper examines various determinants of trade credit use in firms’ short-term financing and
provides policy implications for diversifications of short-term financing structure, given that trade credits
are non-market-based bilateral debt contracts compared to bank revolving line and commercial paper,
both of which are market-based short-term financing instruments. Especially, this paper provides evidence
that credit rating system can function as a mechanism for mitigating information asymmetry problem so
that informationally opaque firms, e.g., small-medium firms, can expand the use of market-based shortterm financing instruments such as bank revolving line and commercial paper substituting trade credit use.
Expansion of credit rating coverage to more of informationally opaque firms can establish mechanism
where they can more efficiently select short-term financing instruments corresponding to their own risktype. Therefore, credit rating system can restrain excessive constraint of financial services to high-risk
borrowing firms, leading to vitalize real economic activities.
Firms need to purchase intermediate goods to produce final products and services and, for
purchasing the intermediate goods, they may use their own liquidity or need to finance short-term debts.
In short-term debt markets, there are alternative financing instruments with different economic and
financial characteristics, mainly classified into three types: bank revolving line, commercial paper, and
trade credit. Trade credits are offered by suppliers in the form of postponement of payment when selling
their products to buyers.1 Trade credits are non-market-based bilateral debt contracts between buyer and
seller, requiring relatively higher debt costs than bank revolving line and commercial paper, both of which
are market-based financing instruments. In the well-developed financial markets of industrialized
countries such as USA and Western European countries, trade credits are prevailingly used while they
service relatively high-risk firms. In Korean economy, the portion of trade credits is also quite high in
short-term financing among bank revolving line, commercial paper, and trade credit. 2
Since credit rating system provides information on the firm’s future cash flows and risk and hence
reflects firm’s intrinsic value, it can mitigate information asymmetry problem and improve efficiency in financial
markets. Moreover, as credit rating might be a device through which a borrowing firm can signal its own
risk-type, it can help the borrowing firm select efficient structure of short-term financing instruments
1
Trade credit is also known as account payable in general. Trade credit is bilateral debt contracts between nonfinancial companies and its key characteristic is that it is a short-term financing in the form of deferred-payment
and is operated through credit transaction account.
2
In our sample for year 2012, the ratio of trade credit to total asset was 8.65% for large firms (chaebol-affiliated
firms 9.76%), and 8.28% for small firms. Debt ratio such as short-term bank loan ratio was 13.97% for large firms
(chaebol-affiliated firms 9.13%) and 15.59% for small firms.
3
fitting in its own risk-type. Especially, given that the small-medium firms have more severe information
asymmetry problem than large firms, expansion of possession of credit rating among small-medium firms
can minimize distortion in selecting short-term financing structure. This paper investigates whether credit
rating plays a role as a screening device of lender and at the same time as a signaling device of a borrower
under the environments of information asymmetry. In particular, this paper focuses on the effects of credit
rating system on use of trade credit among alternative, but not perfectly substituting short-term financing
sources such as bank revolving line, commercial paper, and trade credit.
Empirical analysis examines various determinants of firms’ trade credit use. Variables controlling
for firms’ financial and operational characteristics are included in estimations. Since many economic
factors such as dichotomy between large-firm and small-medium-firm sectors, concentration of economic
power by chaebol-affiliated firms, and industry characteristics affect the firms’ trade credit use, the effects
of these factors are estimated. Because purchasing/borrowing firms’ trade credit use is closely related to
the characteristics of product/service selling/lending firm supplies, characteristics of product/service and
industry are also controlled in estimations.
Empirical results show that, under information asymmetry in debt market, credit rating plays a
key role both for screening device of lender and signaling device of borrower. Hence, credit rating system
can mitigate information asymmetry problem, resulting in the improvement of efficiency in allocation of
funds in short-term debt market of Korean economy. Interesting and unique empirical results of this paper is
the finding of non-linear relationship between credit rating level and trade credit use. Among low-rating firms,
the increase in credit rating reduces trade credit use, while, among high-rating firms, the increase in credit
rating raises trade credit use in Korean economy. The relation between the level of credit rating and the
level of trade credit use is non-linear and U-shaped. Given that large firms have high credit ratings in
Korean economy, the positive relation between credit rating level and trade credit use reflects the
possibility of predatory transaction of trade credits. This paper also finds that various characteristics of
borrowing firm such as size, financial distress, product characteristics, and industry characteristics are key
determinants of trade credit use in short-term debt financing.
The remainder of this paper is organized as follows. Section 2 reviews previous literature on trade
credits and short-term financing. Section 3 describes short-term debt and develops testing hypotheses.
Section 4 describes data and presents empirical results. Section 5 provides summary and conclusion.
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II. Literature Review
Research on trade credit use among alternative sources of short-term financing instruments can be
regarded as an extension and application of traditional study on selection of external financing sources in
corporate finance. The literature on trade credit falls into two main streams.
One stream of literature focuses on the “business” motivations for trade credit, seeing it as a way
to minimize transactions cost (Ferris, 1981), to allow firms to practice price discrimination (Brennan,
Maksimovic and Zechner, 1988), and to offer implicit quality guarantees (Lee and Stowe, 1993; Long,
Malitz and Ravid, 1993; Emery and Nayar, 1994; and Deloof and Jegers, 1995). Recently, Frank and
Maksimovic (2010) argue that the legal system, at least under common law, allows trade creditors to
repossess their collateral more easily than financial intermediaries.
Another stream of literature emphasizes with the “financial” aspects of trade credit, particularly
under information asymmetry in financial markets. Emery (1984) argues that trade credit can serve to
arbitrage the difference when the borrowing and lending rates faced by firms differ. As trade credit is an
important source of financing to firms running out of bank financing and capital market funding, many
theoretical models on trade credit assume that suppliers have certain advantages over banks. A
widespread notion in the trade credit literature is that trade credit(vendor financing) substitutes for bank
financing because suppliers have access to private information about the riskiness of their customers.
Smith (1987) argues that in the normal course of business, a seller obtains information about the true state
of a buyer’s business that is not known to financial intermediaries. The financial intermediaries may not
have detailed knowledge of industry conditions and are forced to rely excessively on accounting
information while seller can obtain more reliable information about buyer’s credit through frequent
transactions. Since this information is potentially valuable, seller acting on this information extend credit
to buyers on terms that they would not be able to receive from financial intermediaries. Mian and Smith
(1992) argue that the sales effort of suppliers gives them informational advantage in assessing their
customers’ credit risk. Biais and Gollier (1997) explain why suppliers are willing to offer credits to firms
that have exhausted their debt capacity with banks. They show that suppliers can more effectively identify
firms whose credit risk is overestimated by banks. Knowing that a firm’s credit line is unduly low based
on their business relationship with the firm, the suppliers are willing to extend a vendor financing.
Petersen and Rajan (1997) show that suppliers can extend more credit to firms with current losses but
positive growth of sales, interpreting this finding as evidence that suppliers have comparative advantage
in identifying firms with growth potential. They also argue that suppliers may have lower monitoring
costs and thus are able to provide vendor financing to firms constrained in their bank financing.
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Other previous studies on trade credit explain that the structure of the product market is the main
cause of the prominence of trade credit. Brennan et al. (1988) provide a model where a monopolist
finances the sale of its own product, setting the price of product and the interest rate of vendor financing
to maximize combined expected profits both from the sale of products and the offering of trade credit.
They argue that the monopolist will use differential interest rates to discriminate the buyers of the product
if the regulators force the monopolist to charge the same product price to all customers. In this setting,
they show that an elastic demand induces the monopolist to offer trade credit at subsidized interest rates
while the optimal interest rate varies with customers’ characteristics. In Brennan et al. (1988), the product
supplier internalizes the differential price elasticity of demand into debt costs by offering subsidized
interest rates to their customers. Other studies have adopted the product differentiation hypothesis for
trade credit that business managers use vendor financing to differentiate their products like advertising.
Nadiri (1969) argues that trade credit is a non-price factor that generates product differentiation
and thus optimal credit decisions for vendor financing reflect this aspect, which is termed as “product
differentiation hypothesis. In Nadiri (1969), manager maximizes firm value by choosing price of product
and size of trade credit. The optimal ratio of trade credit to revenues is directly proportional to the
elasticity of demand with respect to trade credit and inversely proportional to elasticity of demand with
respect to product price. The model predicts that the optimal receivables-to-sales ratio is positively related
to profit margin while the optimal profit margin is negatively related to price elasticity of demand. This
prediction suggests the intuitive notion that firms offer more generous credit terms when they can make
more profit margin from additional sales. Petersen and Rajan (1997) test the product differentiation
hypothesis along with other theories of vendor financing. They find a positive cross-sectional relation
between account receivables and profit margin, as predicted by the Nadiri (1969)’s model of the product
differentiation.
Modeling trade credits based on contract theories is also prevailing in the literature. As in Stiglitz
and Weiss (1981), under asymmetric information about a borrowing firm’s risk-type, credit rationing
arises either when a lender cannot set an appropriate interest rate suiting the borrowing firm’s risk type or
when the lender is not willing to offer a loan at any interest rate. Schwartz and Whitcomb (1979) focus on
the credit rationing issue to explain why firms obtain trade credit. They find that firms under the credit
constraint in bank financing tend to use vendor financing. Smith (1987) shows that banks face an adverse
selection problem under private information on the borrowing firm’s risk-type. Banks offer financing as
much as a low-risk borrowing firm desires at lower rates and ration credit to high-risk borrowing firms. In
the meanwhile, product suppliers offer trade credit at high interest rates to high-risk borrowing firms.
Therefore, the corporate debt market is separated into low-risk-firm and high-risk-firm segmentations.
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That is, low-risk borrowing firms select bank revolving line at low interest rates while high-risk firms
select vendor financing at high interest rates, resulting in a separating equilibrium in corporate debt
market. Love et al. (2005) and Blasio (2005) argue that trade credits are useful short-term debt instrument
useful to the high-risk firms which face financial distress due to internal factors and credit constraint due
to external factors such as financial crisis and downturn in business cycles. In contrast, Giannetti et al.
(2011) find that firms can take advantage of trade credits to pay cheap debt costs: the firms with high
creditworthiness can reduce the debt costs by receiving discount on debt costs through early repayment of
trade credits.3
There exist other streams of previous studies on trade credit. Garcia-Appendini(2011) finds that
suppliers of intermediate goods have better skills in certifying creditworthiness of purchasing/borrowing
firms when offering trade credits. Financial institutions tend to offer credits to the firms which have
obtained trade credits and have paid them back on time. Such certification effect of trade credits is more
apparent when financial intermediaries have lack of information about borrowing firms.
Mian and Smith (1992) argue that the products sold on trade credits can be used as collateral with
more effective collection and liquidation. Frank and Maksimovic (2010) and Cunat (2007) find that
collateral value of the products transacted on trade credits increase when they are capital goods. This is
because the sellers are experts about the features and re-sale values of the products purchased on trade
credits, which are put as collateral. Deloof and Jerger(1999) find that supply of trade credits by sellers are
affected by the characteristics of the goods transacted on trade credits and is in proportion with the levels
of sales. Ng et al.(1999) and Fisman and Love(2003) examine how industry characteristics affect trade
credit use by including industry indicator variables in the estimations. They find that the firms in the
industries characterized by high growth potential and more investment opportunity tend to demand more
trade credits.
Berger and Udell(1998) find that firms’ financing sources become diversified as they grow and
expand. Especially, use of alternative short-term financing sources such as bank revolving lines and
commercial papers other than trade credits is expanded as the firm sizes become large and firm ages
become old. The firm characteristics such as size, age, and sales growth can proxy corporate transparency.
Berger and Udell(1998) and Fluck(1999) show that the borrowing firm can be considered to be
transparent on the basis of such characteristics incur less debt costs as lower information costs. Berger
and Udell (1998) and Fluck (1999) argue that such informationally transparent firms rely less on trade
3
Giannetti et al. (2011) categorized and assessed the ratings from 1(high credit risk) to 5 (low credit risk) and found
that the higher the credit risk is, the possibility of getting a discount for trade credit related contracts despite early
payment is lower. Giannetti et al. (2011) interpreted that the suppliers do not provide any discount benefits
considering that it would be difficult for financially distressed firms to make early payments.
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credits in their short-term financing than on other sources, including bank financing and capital market
funding.
Giannetti et al. (2011) find that large firms, which in general transact with more counterparties
than small-medium sized firms, have advantage in bargaining/negotiating power and thus can use more
trade credits offered by suppliers at cheaper debt costs (in the form of discount on early repayments of
trade credits). By analyzing National Survey of Small Business Finances (NSSBF) of 1998 and
COMPUSTAT data of 2001, Giannetti et al. (2011) find that the both sellers and buyers of differentiated
goods are inclined to use more trade credits. Fisman and Raturi (2004) also find that the more competitive
product market the more trade credits are offered by suppliers. Fabbri and Klapper (2009) provide the
evidence that the sellers with relatively weak market power tend to increase sales by offering trade credits.
This evidence is also in accordance with Wilner (2000).
In corporate finance and financial intermediation areas, the research on the selection of financing
sources by firms with differential risk types has been important issues under information asymmetry in
financial markets. In line with this traditional research stream, , this paper examines how borrowing firms
and lenders align short-term debt instruments, explaining the risk segmentation of short-term debt market
by alternative but not perfectly substituting debt instruments: bank revolving line, commercial paper, and
trade credits. In particular, this paper deals with the issue on the selection of short-term financing sources
by firms, risk of which are mainly characterized by credit ratings.
III. Development of Empirical Hypotheses
The existing literature on trade credit suggests two main views on trade credit, which one can
term “substitution view” versus “complementarity view.” The substitution view posits that trade credit is
a substitute for market-based short-term financing such as bank revolving line and commercial paper.
Reliance on trade credit is likely to be highest when borrowing firms are constrained from access to these
market-based short-term financing sources and then they will turn to substituting source of financing, in
particular trade credits. In this sense, trade credit and market-based financing are substitutes, and trade
credits are expected to be used more by firms under financial distress and in times of economic shocks,
e.g., financial crisis.
The complementarity view holds that the use of trade credit is greater in the environments which
have a large and efficient system of financial intermediaries. In such environments, non-financial
corporations act as ‘agents’ for financial intermediaries. That is, while it seems natural to conclude that
borrowers are likely to view market-based financing and trade credit as substitutes, the supply of trade
8
credit may be greater if supplying firms have easy access to bank financing and capital markets.
Following Biais and Gollier (1997) and Frank and Maksimovic (2010), the use of trade credits may be
viewed as complementary with financing by financial intermediaries. Firms obtain financing from
financial intermediaries and capital markets. For transactions where direct monitoring by financial
intermediaries is efficient, financial intermediaries and capital markets provide financing directly to firms.
However, in cases where suppliers are more efficient at monitoring, or in enforcing contracts, it may be
optimal for financial intermediaries to lend to suppliers, who then relend to the buying firms. Such
efficiencies may arise because suppliers have proprietary information about buyers, because they can
threaten to suspend future deliveries, or because they have a higher opportunity cost of any repossessed
inventory than do financial intermediaries.
This paper empirically examines firms’ selection of short-term debt financing instruments among
bank revolving line (intermediated loan/indirect financing), commercial paper (direct financing from
capital market), and trade credit (vendor financing). Since product suppliers have relatively limited access
to capital market compared to financial intermediaries, they incur higher cost of funds than financial
intermediaries, resulting in higher debt costs of trade credits. The main question of this paper is why
financially constrained firms rely more on trade credit even when seemingly cheaper bank revolving line
and commercial paper are available in the short-term debt market.
Even though long-term survival and growth of a firm is determined by competitive advantage, the
short-term operation is critically dependent upon management of working capital and short-term
financing. Management of working capital and short-term financing affects the financial strength and
creditworthiness of borrowing firm. Since the liquidity risk triggered by failure in short-term debt
management can generate firm-wide default, selection of short-term debt and the management of liquidity
are key factors that can maintain the firm as a going-concern entity.4
Theoretical background for short-term debt financing could be related to pecking order theory or
financing hierarchy theory of Myers (1984). Pecking order theory posits that, due to information
asymmetry between management and potential investors, external financing incurs additional debt costs.
Therefore, firms fund in the order of retained earnings, safe debt, risky debt, and equity.
Informationally opaque firms such as small-medium firms and high-growth firms usually have a
large portion of short-term financing in total debt. These firms have large portion of trade credits among
4
According to Gopalan et al.(2009)’s research on S&P’s ratings from 1980 to 2008, long-term corporate bonds
issued by companies with heavy dependence on short-term debt showed high return despite controlling of
variables including credit ratings, and that it is very likely for these companies to experience more than a two
notch drop in their ratings within a year.
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short-term debt instruments including bank revolving line and commercial paper. It is prevailing
perception that trade credits are “inferior alternative” to other short-term financing instruments such as
bank revolving line and commercial paper. Wilner (2000) argue that trade credits incur higher costs of
debt than bank revolving line and hence high-risk and financially constrained firms use more trade credits.
From the view point of pecking order theory, trade credit is useful short-term financing
instrument to the borrowing firms, which are constrained in funding through bank revolving line and
commercial paper. Financially sound firms can issue commercial papers solely based on its
creditworthiness to finance short-term funds for working capital management.5 Bank revolving line is
traditional indirect financing instrument for work capital. Similar to commercial paper, firms with high
creditworthiness and financial stability usually use bank revolving line for short-term financing. In sum,
trade credit, commercial paper, and bank revolving line have substitutability among themselves while
each of them respectively specializes in the borrower segment with different risk-type.
Excessive credit constraint on high-risk firms can restrain balanced economic growth and
development of financial markets. This paper examines the factors determining the structure of short-term
debt financing, i.e., mixture of bank revolving line, commercial paper, and trade credit. In particular, this
paper tests whether the trade credit use as a short-term financing instrument is complementary or
substituting to bank revolving line and commercial paper.
Optimal short-term financing structure minimizing debt costs can be obtained by removing information
asymmetry problem. Since credit rating system reflects the information on future cash flow and risk of a firm, it
can represent the intrinsic value of the firm and can play an important role as a screening device mitigating
information asymmetry problem. Because credit rating system may also function as a signaling device with
which a borrowing firm can reveal its own risk-type, it can select appropriate short-term debt financing
instrument corresponding to its own risk-type. Small-medium firms, which at large have poorer corporate
transparency than large firms, can minimize information asymmetry problem and diversity short-term
financing instruments by possessing credit rating. This paper estimates the effects of credit rating on the
structure of short-term debt financing by examining whether the possession of credit rating can mitigate
5
Unlike stocks and bonds, CP can be issued relatively easily without going through process such as BOD resolution,
issuer registration, registration of securities, and is exempted from duty to disclose. Also unlike bonds with long
term maturities, there are no additional costs such as underwriting fee and registration fee, and is subject to low
and flexible interest rate. Due to its ability to effectively cope with excess or[and] deficiency of short term fund,
CPs plays a pivotal role as short term debt tool despite CP’s refinancing risk amid credit crunch resulting from
sudden economic recession and financial shock. However, supply and demand within the CP market is very
volatile in accordance with the market’s short term liquidity. In times when the company faces poor performance,
its capacity to raise fund through CP becomes more vulnerable compared to trade credit or credit line. CP are
prone to liquidity risk due to refinancing risk triggered by short maturity, and large companies and blue-chip
companies tend to issue CP.
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information asymmetry problem, leading to the diversification of short-term financing from trade credit to
other market-based instruments such as bank revolving line and commercial paper. Empirical analysis of
this paper would show whether possession of credit rating helps borrowing firms expand the sources of
short-term financing.
<Table 3> shows the numbers of listed firms possessing credit ratings over the year 2000-2012 period
in Korea. As shown in <Table 2>, the observations are classified into large-firm sample and small-medium-firm
sample. During the estimation period, the proportion of listed firms possessing credit ratings kept declining and
this proportion stayed around 35% level. This low level of rating coverage may generate information asymmetry
problem in short-term debt markets, leading to possible adverse selection and moral hazard. Then, distortion in
allocation of financial resources can be further exacerbated.
Empirical analysis mainly tests hypotheses concerning the cross-sectional differences in trade credit use
under information asymmetry in short-term debt markets. This paper investigates how financial characteristics of
borrowing firms affect the selection of short-term debt financing, i.e., the level of trade credit use. The empirical
hypotheses concerning this issue are as following:
Hypothesis1: High-risk firms use more trade credit than bank revolving line and commercial paper in short-term
financing.
Hypothesis 2: Borrowing firms with more cash flows and current assets use more trade credits.
This paper mainly investigates the effects of credit rating on trade credit use in short-term debt financing.
In other words, this paper examines whether, by possessing credit ratings, borrowing firms can switch and
diversify from, “an inferior substitute”, trade credit to bank revolving line and commercial paper, in shortterm debt financing. Empirical hypotheses concerning this issue are as following:
Hypothesis 3: The relation between credit rating level and trade credit use is non-linear and U-shaped in Korean
economy.
Hypothesis 4: Firms possessing credit ratings lowers trade credits use.
The characteristics of product/service and industry can affect trade credit use. For example, the
industry where firms produce/sell standardized goods are competitive and hence bargaining/negotiating
power of firm purchasing intermediate good is weak. As a consequence, the trade credit use of such firms
could be low. In contrast, the industry where firms produce/sell differentiated goods in general have
bigger market power and hence intermediate goods purchased are usually customized for the purchasing
firm, resulting in the advantage in bargaining/negotiating power of purchasing firms. This may lead to
11
more use of trade credits by purchasing firms. In construction sector of Korean economy, subcontract is
prevailing and thus the level of trade credit use can be higher than in other industries. In service and
distribution sectors, competition in market and characteristics of goods/services can form specific
transactional relationship between sellers and buyers and then affects the levels of trade credit use. To
examine the effects of the characteristics of product/service and industry, this paper test following
hypothesis.
Hypothesis 5: The characteristics of product/service and industry generate different patterns of trade credit use.
The observations are the firms listed in KOSPI and KOSDAQ in Korea and are classified as following:
(i) firms with credit rating versus those without credit rating, high-rating firms versus low-rating firms, large
firms versus small-medium firms, chaebol-affiliated firms versus non-chaebol-affiliated firms.
Considering a structural feature of Korean economy, dominance of large firms (many of them are chaebolaffiliated), this paper tests whether the unfairly advantageous position of large firms in
bargaining/negotiating affects the structure of short-term financing across different types of firms, say
large versus small-medium firms. In particular, this paper examines whether large firms can take
advantage of their position in transaction and exploit sellers of intermediated goods and/or subcontractors
by receiving more trade credits.6
Main test variables are the variable measuring trade credit use in short-term debt financing and
the variables indicating information and certification effects of credit rating system. The estimations
include various control variables such as financial characteristics of borrowing firm, industry
characteristics of borrowing firm, financial distress, and financial crisis. In order to test the hypotheses on
short-term financing structure, i.e., level of trade credit use, this paper estimates the panel regression
models specified as following:
AP_DEBT = b0 + b1RATING_CONTENT + b2 ASSET + b3 EBIT + b4 CA + b5 CAPEX + b6 FIRM_RISK
+ b7 SALES_GROWTH + b8 LOAN_ASSET + e
(1)
In estimating panel regression model (1), the key test variable is RATING_CONTENT.
RATING_CONTENT represent dummy variables defined based on (i) whether a sample firm possesses credit
rating, (ii) level of credit rating, and (iii) The length of time over which a sample possess credit rating. The types
6
Chaebol-affiliated firm refers to the firm belonging a conglomerate that includes firms in a group which are
prohibited from mutual investment and share-holding. In Korea, chaebol-affiliated firm isannounced by the Fair
Trade Commission every April.
12
of credit ratings include Issuer Credit Rating (ICR), Bond Rating, Commercial Paper Rating. <Table 1> provides
the definitions of variables.
The empirical tests require the data of sample firms’ financing structure. Dependent variable,
AP_DEBT, is measured by Trade credit/(Trade Credit + Bank Revolving Line + Commercial Paper) and
indicates the degree of trade credit use in short-term debt financing.7
The level of a firm’s trade credit use may also be affected by borrowing firm’s characteristics
such as credit risk, growth pattern, external financing need, profitability, financial status, and business
cycles. Firm’s asset size (ASSET) controls the level of information asymmetry and a negative sign is
expected. ASSET is log-transformed value of total asset.
This paper includes the firm’s ability to generate internal funds by the firm’s profitability measure
EBIT/SALES. The firm’s ability to generate internal funds decreases its demand for trade credit financing,
and therefore, one can expect a negative sign on EBIT. As a firm’s ability to generate funds internally
decreases with its demand for trade credit financing, we also include a firm’s ability to generate internal
funds measured by EBIT.
Firm’s current assets (CA) representing short-term liquidity will decrease with its demand for
trade credit if trade credit financing takes a lower position in the firm’s pecking order structure than
internally generated liquidity. Borrowing firms tend to first rely on internally generated liquidity. If
internally generated liquidity is short, then the firms would seek external financing. In a firm’s pecking
order structure, there is a possibility that trade credit financing takes a lower position than other marketbased short-term financing sources such as bank revolving line and commercial paper (Myers, 1984). CA
is measured by a firm’s current asset divided by total asset. In contrast, maturity matching hypothesis
(Diamond, 1991; Hart and Moore, 1994) argues that firms minimize possible risk caused by mismatch
between current assets and current liabilities and thus tend to maintain balance of amounts and maturities
of current assets and current liabilities. Therefore, one can expect that the more the current assets the
more trade credits are used in the context of working capital management.
The level of trade credit is also affected by a borrowing firm’s external financing need as the
firm’s demand for trade credit will be larger if external financing need is bigger. External financing need
is measured by capital expenditure (CAPEX). CAPEX is defined by (Investment on fixed assets- Sale of
fixed assets)/Sales and a positive sign is expected.
7
The level of trade credit use can also be measured by ratio of trade credit to total asset. But, in this paper, the level
of trade credit use is measured standardized by the ratio of trade credit to short- term debt with an aim to indicate
the selection of trade credit among alternative short-term financing instruments.
13
Operational risk is controlled by the variable FIRM_RISK defined by standard deviation of
Sales/Average of Sales over past 5 years. Firm’s growth would affect its short-term financing as higher
growth in sales will need more intermediate goods purchased on more trade credits. Following Fisman
and Love (2003), firm’s growth potential proxied by SALES_GROWTH is included in the estimations. For
a firm’s growth, this paper employs the growth rate of sales (Sales Growth), measured by the percentage
change in sales from year t-1 to year t, i.e., (Salest/Salest-1)-1.
If the borrowing firm with trade credit has access to bank financing, it may reduce trade credit
financing because trade credit and bank financing are in general substitutes. That is, vendor financing is
most common among firms that face constraint in bank financing (Petersen and Rajan, 1997; Biais and
Gollier, 1997). To control for the substitutability between trade and bank financing, LOAN_ASSET
defined by bank financing divided by total assets is included in estimations and a negative sign on
LOAN_ASSET is expected.
IV. Empirical Analysis and Results
This paper investigates the sample firms listed in stock exchanges, KOSPI and KOSDAQ, in
Korea, total 18,369 firm-year observations over 2000-2012 period. In panel regression analyses, the
number of observations is reduced to 18,006 due to omitted variables. Following observations are
removed: (i) financial companies are removed in the sample, (ii) firms with missing financial statement(s)
for the past 5 years, (iii) holding companies and government-owned firms which have significantly
different accounting system are removed. To minimize survivorship bias in sampling, we include all of
newly listed firms, delisted firms, and firms with different fiscal year end. Financial data of listed firms
are obtained from corporate financial database of Korea Ratings Corporation.
<Table 1> provides the definitions of dependent and explanatory variables in more details. The
dependent variable, portion of trade credit of a borrowing firm, is defined by accounts payables, i.e., trade
credits, relative to total short-term financing, the sum of bank revolving line, commercial paper, and trade
credits.
<Table 2> summarizes the descriptive statistics of key variables. The observations are classified
into full sample, large-firm sample, and small-medium-firm sample. Compared to small-medium firms,
large firms can have advantage in financing since they can use internal capital market and hence can pay
lower debt costs. Moreover, large firms have less possibility of financial distress due to diversified
operations and the associated cash flows. Therefore, it is prevailing wisdom that large firms are less
inclined to use trade credit compared to bank revolving line and commercial paper for external short-term
debt financing. In Korean economy, it is observed that large firms use more trade credit than small-
14
medium firms, which is interesting but contradicting the prevailing patterns in industrialized economies.
This observation contradicts the perception that small-medium firms, which are constrained in access to
capital market and bank financing, would use more trade credit than large firms.
Credit rating data are obtained from four credit rating agencies in Korea: Korea Ratings Corp.,
Korea Investors Service Inc., NICE Investors Service Co., Ltd., and Seoul Credit Rating and Information
Inc. Bond rating and Issuer credit rating (ICR) are employed for long-term rating while CP rating is
employed for short-term rating. When the ratings for an identical firm are different across credit rating
agencies, i.e., split-rating, lower credit rating is used. In case a firm possesses both long-term rating (bond
rating, ICR) and short-term rating (CP rating), long-term rating is used as base rating. In case a firm
possesses only short-term rating (CP rating), CP rating A1 is matched to bond rating AA, A2 to A, A3 to
BBB, B to BB, respectively.
<Table 3> summarizes the numbers of sample firms which possess CP Ratings and Bond Ratings
(including ICR) for 2000-2012 period. The observations are the firms listed in KOSPI and KOSDAQ and
they are classified into full sample, large-firm sample, and small-medium-firm sample. <Table 3> shows
that small-medium-firm sector has low proportion of rating possession. Possession ratio of CP rating is
extremely low. <Table 4> summarizes the distribution of sample firms across the levels of credit rating.
Especially, Panel B shows the distribution of samples grouped into large and small-medium firms. Panel
B in <Table 4> apparently shows that large firms are distributed at high ratings while small-medium firms
are at low ratings: polarization between large and small-medium firms occurs in the distribution of credit
ratings. 8 The statistics in <Table 2>, <Table 3>, and <Table 4> provides implication that estimations for
selection of short-term financing need to be conducted after controlling for firm characteristics carefully,
especially large versus small-medium firms. Due to dominant market power of large firms (many of them
are chaebol-affiliated) in Korean economy, it is needed to test whether unfairly advantageous position of
large firms in bargaining/negotiating affects the structure of short-term financing across different types of
firms, say large versus small-medium firms.9
<Table 5> provides the correlation coefficients between variables. The dependent variable
AP_DEBT has negative correlations with RATING_ALL and RATING_CP. AP_DEBT has negative
correlation with firm size (ASSET). These univariate tests show that the firm characteristics variables
mitigating information asymmetry have statistically significant correlation coefficients with AP_DEBT.
Current assets (CA), measure of firm’s liquidity holding, have positive correlation with AP_DEBT.
8
Number of large company samples with credit ratings (incl. CP, Bond, and ICR) is 2,981 (excluding overlaps) and
conglomerates accounts for 48.6% with 1,453.
9
In this research, conglomerates account for 20.1% of the total 10,707 sample, marking 2,157.
15
This univariate evidence supports the maturity matching hypothesis in the sense of Diamond (1991) and Hart
and Moore(1994). EBIT measuring firm’s profitability and ability to generate cash flow has positive
correlation with AP_DEBT, but magnitude is very low. The effect of EBIT on AP_DEBT can be ambivalent.
On one hand, the increase in EBIT (indicator of firm’s profitability) can decrease trade credit use since
trade credit is “inferior substitute” for bank revolving line and commercial paper. On the other hand, the
large EBIT generates more cash flow, leading to more use of trade credit since more current liabilities
can be used due to large ore liquidity buffer. Positive correlation between EBIT and AP_DEBT provides
univariate evidence supporting maturity matching hypothesis in the sense of Diamond (1991) and Hart
and Moore(1994). AP_DEBT has positive correlation with capital expenditure CAPEX, which measures the
demand for long-term financing for fixed assets such as equipment and plant. FIRM_RISK, measuring
business/operational risk, has positive correlation with AP_DEBT, but statistically insignificant. AP_DEBT
has statistically insignificant correlation with SALES_GROWTH. AP_DEBT has negative correlation with
LOAN_ASSET. The magnitude of correlation coefficient is large and very statistically significant. This
provides univariate evidence that trade credit and bank financing are substituting short-term debt
financing instruments.
In <Table 6>, credit rating dummy variable is defined based on the level of credit rating from low
to high rating and then panel regression of fixed effect model is conducted. Moreover, in <Table 6> the
sample firms possessing credit rating are classified into two groups: firms rated at speculative grades
(below BBB) and non-chaebol-affiliated versus firms rated above A and chaebol-affiliated. Each of these
groups is defined to be 1, otherwise 0, respectively and then panel estimations of fixed effect model are
conducted for each group to investigate how rating level affects trade credit use.10
<Table 6> shows the empirical results that, in low-rating samples, the increase in the credit rating
reduces the trade credit use, while, in high-rating samples, the increase in the credit rating raises the level
of trade credit use. These estimation results imply that the level of credit rating and trade credit usage
have non-linear and U-shaped relationship in Korean economy. That is, low-rating firms have more
likelihood of facing financial distress and credit constraint and hence trade credit use is larger. These
results support Petersen and Rajan (1997), Love et al.(2005), and Blasio (2005) in that financially
distressed firms are forced to use more trade credits since they are constrained in access to market-based
10
In panel data analysis, fixed effect model or random effect model is used in consideration of error term. As a
result of applying model 1 to total sample and conducting Hausman specification test, null hypothesis was
rejected and alternative hypothesis was adopted. Thus, we calculated the estimation by only applying fixed effect
model to all analysis results. Also White test was conducted as the company’s financial variables used in this
research can bring about heteroscedasticity issues by time series and cross-sectional data, and as a result, test
statistic p-value was smaller than 0.01, rejecting the null hypothesis. In order to control all heteroscedasticities for
all individual analysis results, robust standard errors were calculated and statistical significance was verified.
16
debt financing instruments such as bank revolving line and commercial paper.
In high-rating samples, firms might have advantageous position in bargaining and negotiating in
transactions and thus they can obtain more trade credits from suppliers. This result supports view of
Giannetti et al.(2011), who posit that firms with market power can exploit suppliers of intermediate
goods/services by obtaining trade credits under favorable debt terms and conditions. In Korean economy,
most of high-rating firms are large firms (many of them are also chaebol-affiliated), the increase in credit
rating among these sample firms may indicate larger firm size, more market power, and more
bargaining/negotiating power, resulting in the increase in the trade credit use. This evidence contradicts
the prevailing practice that trade credit is a short-term debt financing inferior to bank revolving line and
commercial paper especially in industrialized economies. That is, large firms, which are naturally highrated, can take advantage of their positions in transaction and exploit sellers by acquiring more trade
credits. Therefore, it might be the case that the levels of credit rating and trade credit use have non-linear
(U-shaped) relationship in Korean economy. Therefore, this paper defines dummy variables based on the
level of credit rating from low to high rating and then investigates the effect of credit rating at each level
on trade credit the use, AP_DEBT. The illustration below summarizes the results of <Table 6> by showing
the credit rating dummy variables and estimated coefficients. The shaded areas are level(s) credit ratings
over which dummy variables are defined in ascending order in <Table 6>. If credit rating belongs to the
shared area, dummy variable is 1, otherwise 0. As shown below, the first estimated coefficient is positive
at the lowest rating level. Then the estimated coefficients become and stay negative over the middle levels
of credit ratings. They become positive over the highest credit rating ranges.
Speculative
Rating:1
Investment
Rating:0
Below BBB:1
Above A: 0
BB-BBB:1
Above A & Below
B: 0
BBB:1
Speculative &
Above A: 0
BBB-A: 1
Speculative &
Above AA: 0
Above A: 1
Above AA:1,
Below BBB: 0
BBB:0
Excluding
Excluding Below
Below CCC
BB
Above AA
A
BBB
BB
Sample
Excluded
Below B
Sample
Excluded
Coefficient on
Credit Rating
0.0178
-0.0512***
-0.0513***
-0.0366***
-0.0420***
0.0512***
0.0773***
In <Table 6>, while the estimated coefficient of dummy variable for speculative-grade and nonchaebol-affiliated firm has insignificant positive sign, that of above-A-grade and chaebol-affiliated firm is
positive and statistically significant. This result implies that both groups of firms with opposite
17
characteristics use more trade credits and contradicts the empirical findings in the samples of
industrialized economies such US and Western European countries. The empirical finding provided in
<Table 6> shows that the Korean economy is polarized into extreme groups of firms: large firms versus
small-medium firms. Further, selection of short-term debt instruments between these two groups is based
on different economic incentive and motivation.
In <Table 7> the observations are grouped into full sample, large-firm sample, small-mediumfirm sample, and chaebol-affiliated firm sample. Key test variables are RATING_ALL and RATING_CP
and fixed effects models are estimated to investigate the effects of rating possession on trade credit
usage. 11 <In Table7>, credit rating dummy variables RATING_ALL and RATING_CP are consistently
estimated to be negative and statistically significant except for small-medium firms. These results imply
that borrowing firms can mitigate information asymmetry problem by possessing credit ratings, leading to
the less use of trade credits and more use of market-based short-term debt financing instruments, bank
revolving line and commercial paper.
The estimated coefficients on total asset (ASSET) are consistently negative and statistically
significant across all sub-samples and models. These results imply that large firms tend to have less
information asymmetry problem and hence they use more bank revolving line and commercial paper than
trade credit.
The estimated coefficients on current assets (CA) are consistently positive and statistically
significant. This result implies that firms maintain the balance between current assets and current
liabilities as an important aspect of working capital management and so naturally the increase in sales
accompany the increase in account receivables, simultaneously leading to the increase in account
receivables. These results are in accordance with Diamond (1991) and Hart and Moore (1994).
The effects of EBIT can be ambivalent: as an indicator of profitability, the increase in EBIT can
reduce the use of trade credit, an inferior short-term financing instruments while, as an indicator of ability
of generating cash flow, it can accompany the increased use of trade credit since EBIT can provides the
solvency for short-term debt. The estimated coefficient on EBIT is negative and statistically significant
only in full sample while they are all insignificant in other sub-groups of sample.
A higher capital expenditure requirement suggests that a firm is forced to demand more trade
credit when more capital is needed to fund investing activities, as shown by a positive sign on CAPEX. In
large firm sample, the estimated coefficient of capital expenditure CAPEX, a measure of long-term
demand for funds for fixed assets is positive. This result implies that, since the increase in long-term
11
For estimation of total sample firms, it is difficult to assume a linear relationship between variables as indicated in
<table 6>, and there could be limitations in estimating linear regression of total sample pooling large companies
and SMEs due to structural differences between large companies and SMEs in Korea.
18
financing for fixed assets can cause liquidity constraint and potentially financial distress, the firm with
large capital expenditure may need to rely more on trade credit than bank revolving line and commercial
paper. In small-medium firm sample, the estimated coefficient of CAPEX is not statistically significant.
This might be because financing need for fixed assets of small-medium firms is not substantial.
The estimated coefficient of FIRM_RISK, the measure of firm’s business risk, is not statistically
significant in all sub-groups of sample. LOAN_ASSET, the ratio of total bank financing to total assets,
indicates the accessibility to bank financing and controls the substitutability between bank financing and
trade credit. A negative sign on LOAN_ASSET is consistent with the argument that, when a firm can
easily access to bank financing, it uses less trade credit. This also supports the argument that bank
financing and trade credit are substituting short-term debt instruments.
Trade credit is operational debt which needs to be maintained at some optimal level by taking into
account firm-wide total leverage including both short-term and long-term debt financing. Thus, it is
interesting to estimate the effects of financial distress generated by excessive leverage on short-term
financing selection. Firm’s credit risk would also affect the level of trade credit use as more severe
financial distress could affect firm’s short-term financing structure.
In the estimations of <Table 8>, in addition to the base models in <Table 7>, the dummy
variables indicating borrowing firm’s financial distress are included: LEVERAGE_DISTRESS and
INTEREST_COVERAGE12. LEVERAGE_DISTRESS is a dummy variable defined to be 1 if debt-equity
ratio is above 200%, otherwise 0. INTEREST_COVERAGE is a dummy variable defined to be 1 if
interest coverage ratio is below 1, otherwise 0. Furthermore, this paper also employs indicator variable of
global financial crisis to control for the effect of extreme credit constraint on short-term financing;
FINANCIAL_CRISIS is equal to 1 if observation year is 2008-2009, otherwise 0.
The estimated coefficients of the variables which are in common in <Table 7> and <Table 8> are
similar in signs and statistical significance. 13 At large, there is no substantial difference in estimations
between large firm sample and small-medium firm sample. As in <Table 7>, large firms are inclined to
use more market-based short-term financing instruments (bank revolving line and commercial paper) than
trade credits.
12
Debt-to-equity ratio of 200% was set as principle for Corporate Restructuring during the 1997 Korean Financial
Crisis, and is not commonly used since 2001 with limited use in regulating holding companies and drawing up
innovative guideline for public institutions. However, in this research, debt-to-equity ratio of 200% was applied
considering following factors. First, the market practice that Bank of Korea and Korea Finance Institute are using
debt-to-equity ratio of 200% as main criteria of financial constraint proxy was considered. Second, debt-to-equity
ratio of 200% is being used when Financial Supervisory Service and Banks evaluate credit risk of Main Debtor
Groups since early 2000s every year.
13
<Table 9> only indicates estimation figure of total sample. Separately from this, specific estimation figures are not
listed as no differences were found between estimation figures of large companies, SMEs and conglomerates.
19
The estimated coefficient of LEVERAGE_DISTRESS is positive and statistically significant. This
result indicates that highly leveraged firms, i.e., firms with debt-equity ratio above 200%, use more trade
credits since high leverage can potentially trigger financial distress, leading to constraint in access to
market-based short-term debt instruments, bank revolving line and commercial paper
The estimated coefficient of INTEREST_COVERAGE is negative and statistically significant.
That is, low interest coverage ratio reduces trade credit use. Interest coverage ratio, EBIT/Financing Costs,
is a measure of ability of paying debt costs, which is in nature based on the firm’s ability of generating
operating cash flow. Thus, low interest coverage ratio may indicate that a borrowing firm faces liquidity
constraint due to insufficient cash flow. Then, in the sense of maturity matching theory (Diamond, 1991;
Hart and Moore, 1994), low interest coverage ratio constrains trade credit use. Liquidity constraint of
purchasing/borrowing firm generates economic incentive for selling/lending firm to reduce the supply of
trade credit as it has advantage in promptly acquiring the information on purchasing/borrowing firm’s
financial status due to long-term repeated transactions with purchasing/borrowing firm.
While LEVERAGE_DISTRESS indicates long-term perspective of financial distress on capital
structure, INTEREST_COVERAGE indicates short-term perspective of financial distress on operating cash
flow. Thus, the estimated coefficients of LEVERAGE_DISTRESS and INTEREST_COVERAGE are
opposite,
positive
and
negative
respectively.
Especially,
the
negative
estimated
sign
on
INTEREST_COVERAGE is in accordance with maturity matching argument in the sense of Diamond
(1991) and Hart and Moore (1994).
The estimated coefficients of FINANCIAL_CRISIS, dummy variable equal to 1 when observation
year is 2008-2009, are all negative and statistically significant. This result implies that trade credit is more
pro-cyclical and sensitive to economic shock than bank revolving line and commercial paper, resulting in
sharp reduction of trade credit use under extreme credit constraint caused by global financial crisis. The
decrease in trade credit use over the period of global financial crisis may be the evidence that severe
financial constraint caused by crisis restrains the available liquidity of selling/lending firms, resulting in
the reduction in the supply of trade credit. This evidence is in accordance with Love and Zaidi (2010) and
supports the argument that transaction of trade credits is pro-cyclical especially during the period of
economic and financial shock and the supply of trade credit is sensitive to business cycles. Therefore,
trade credit is not perfect substitute for bank revolving line and commercial paper in the phase of
economic shock and during financial crisis from a macroeconomic point of view.
Giannetti et al. (2011) argues that the characteristics of industry and the associated characteristics
of good/service affect the pattern of trade credit use. Following Giannetti et al. (2011), <Table 9>
provides the estimation results of effects of industry and good/service characteristics on the selection of
20
short-term debt instruments, i.e., trade credit use. This paper defines dummy variables equal to 1 if
product/service sold are classified to standardized goods, differentiated goods, service based on SIC.
Moreover, construction and distribution, which are at large highly leveraged sectors in Korea, are also
defined to be dummy variable 1, otherwise 0.14 Estimation models are classified across product/service
and industry dummy variables. Panel A and Panel B of <Table 9> show the estimation results with
RATING_ALL and RATING_CP respectively. The estimation results in <Table 9> are similar to those in
<Table 7> and <Table 8>, the estimated coefficients of RATING_ALL and RATING_CP are negative and
statistically significant, implying that the possession of credit ratings can mitigate the information
asymmetry problem, leading to less use of trade credits, i.e., more use of market-based short-term
financing instruments such as bank revolving line and commercial paper.
In <Table 9>, the estimated coefficient of dummy variable for standardized goods
(STANDARDIZED_GOOD) is negative while that of differentiated good (DIFFERENTIATED_GOOD) is
positive. This evidence is in accordance with Giannetti et al. (2011) in that the firms selling differentiated
products receive more trade credits from intermediate good suppliers than those in other sectors. The
estimated coefficients of industry dummy variables for service industry (SERVICE), distribution industry
(DISTRIBUTION), and construction industry (CONSTRUCTION) are all positive and statistically
significant. The positive estimated coefficient of dummy variable for distribution industry may indicate
the circumstances where large buyers purchasing various commodities from small-medium suppliers in
general have advantageous bargaining/negotiating power and thus can obtain trade credits in favorable
terms.
The
positive
estimated
coefficient
of
dummy
variable
for
construction
industry
(CONSTRUCTION) may be due to specific industry characteristics of construction industry. The final
product of construction industry is a kind of differentiated product customized to the need of buyer, while
the construction is not reversible and substitutable, and the price for the final product is paid after the
construction is completed and the product is delivered. All of these characteristics of construction
industry generate circumstances where the construction companies receive account payables as much as
they can and delay the payment of them as late as possible (Smith, 1987; Lee and Stowe, 1993; Long et
al., 1993). Moreover, the estimation result also reflects downstream subcontracting quite prevailing in
construction industry in Korea.
Overall summary of the results shown in <Table 6> through <Table 9> shows that (i) borrowing
firms with high credit risk are inclined to use more trade credits than bank revolving line and commercial
paper in short-term debt financing, supporting [Hypothesis 1], (ii) firms with more cash flows and current
14
Since the dummy variables for industry classification cannot be estimated by fixed effects model of panel analysis
due to multicollinearity, estimations were conducted by OLS.
21
assets (cash holding and account receivables) use more trade credits, supporting [Hypothesis 2].
The estimation result in <Table 6> show that, among low-rating sample firms, the increase in credit
rating reduces trade credit use while, among high-rating sample firms, the increase in credit rating raises
trade credit use in Korean economy. That is, the relationship between credit rating level and trade credit
use is non-linear. Given that large firms (many of them are also chaebol-affiliated) have high credit
ratings in Korean economy, the positive relation between credit rating level and trade credit use reflects
the possibility of predatory/exploiting transaction of trade credits. That is, large firms (with high credit
rating and chaebol-affiliated) can enforce the suppliers (with low credit rating and small-medium size) of
intermediate goods to provide trade credits taking advantage of their bargaining/negotiating power. These
results could support [Hypothesis 3].
In all estimation results of <Table 6> through <Table 9>, the estimated coefficient of the dummy
variables indicating possession of credit rating, RATING_ALL and RATING_CP, are negative and
statistically significant. These results support [Hypothesis 4] that credit rating mitigates the information
asymmetry problem and thus the possession of credit rating can reduce the use of trade credits, non-market
bilateral debt contracts which are inferior substitutes for market-based short-term financing instruments, bank
revolving line and commercial paper.
In the estimation results of <Table 9>, the characteristics industry in which selling firms operate
and the characteristics of final product/service which firms sell form the business environment of sectors
and then this generates differential pattern of trade credit use across different industries and types of final
products/services. These results support [Hypothesis 5].
V. Summary and Conclusion
The main goal of this paper is to examine whether borrowing firms use credit ratings to improve
corporate transparency and to certify their own risk-type and whether this role of credit rating in turn
provides the mechanism where lenders, suppliers of trade credits, commercial bank (providers of bank
revolving line), and investors in capital market (purchasers of commercial papers), can design appropriate
debt contracts corresponding to borrowers’ risk-type based on the information credit rating transfers. In
particular, this paper tests whether the observations classified into large and small-medium firms reduce
trade credit use while increasing other market-based short-term debt financing instruments, bank
revolving line and commercial paper by possessing credit ratings. In sum, this paper tests whether credit
rating can play a role as a screening device of lender and signaling device of borrower, leading to
informationally efficient allocation of short-term debts across trade credits, bank revolving line, and
22
commercial paper, while mitigating information asymmetry problem.
The empirical results provide evidence that high-risk borrowing firms are inclined to use more
trade credits than bank revolving line and commercial paper in short-term financing and that those with
more cash flows and current assets (cash holding and account receivables) raises trade credit use, supporting
maturity matching hypothesis.
Interesting and unique empirical results of this paper is the finding of non-linear relationship between
credit rating level and trade credit use. Among low-rating firms, the increase in credit rating reduces trade
credit use, while, among high-rating firms, the increase in credit rating raises trade credit use in Korean
economy. That is, the relation between the level of credit rating and the level of trade credit use is nonlinear and U-shaped. Given that large firms (also in many cases chaebol-affiliated firms) have high credit
ratings in Korea, the positive relation between credit rating and trade credit use reflects the possibility of
predatory transaction of trade credits. Large firms (high-rating, chaebol-affiliated) can enforce the
suppliers (low-rating, small-medium sized, non-chaebol-affiliated) of intermediate goods to provide trade
credits taking advantage of their bargaining/negotiating power.
Empirical results of this paper imply that, under information asymmetry in short-term debt market,
credit rating plays a key role as screening device of lender and signaling device of borrower. Credit rating
system can mitigate information asymmetry problem resulting in the improvement of efficiency in
allocation of funds in short-term debt market of Korean economy. Since trade credit in nature has more
likelihood of distortion in allocation of funds due to its characteristics of non-market-based bilateral debt
contract, the expansion of credit ratings to small-medium firms can diversify their short-term financing
alternatives to market-based financing sources such as bank revolving line and commercial paper in
general with lower debt costs and more favorable contractual terms. Furthermore, the expansion of credit
rating and the associated diversification of short-term financing sources can provide the buffers that can
absorb the financial distress and economic shock while preventing excessive contraction of financing in
times of crisis.
23
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Nilsen, J., “Trade Credit and the Bank Lending Channel,” Journal of Money, Credit and Banking, 34(1), 2002, 226–
253.
Petersen, M. and R. Rajan, “The Benefit of Lending Relationships: Evidience form Small Business Data” Journal of
Finance, 49, 1994, 3-37.
Petersen, M. and R. Rajan, “Trade Credit: Theories and Evidence,” Review of Financial Studies, 10, 1997, 661-691.
Rajan, R. and L. Zingales, “What do we know about capital structure? Some evidence from international data,”
Journal of Finance, 50, 1995,. 1421–1460.
Rajan, R. and L. Zingales, “Financial Dependence and Growth,” American Economic Review, 88(3), 1998, 559-586.
Schwartz, R., “An Economic Model of Trade Credit,” Journal of Financial and Quantitative Analysis, 9, 1974, 643657.
Schwartz, R. and D. Whitcomb, “The Trade Credit Decision,” in J. Bicksler (ed.), Handbook of Financial
Economics, North-Holland, Amsterdam, 1979.
Smith, J., “Trade Credit and Information Asymmetry,” Journal of Finance, 4, 1987, 863-869.
Stiglitz, J. and A. Weiss, “Credit Rationing in Markets with Imperfect Information,” American Economic Review,
71(3), 1981, 393-410.
Wilner, B., “The Exploitation of Relationships in Financial Distress: The Case of Trade Credit,” Journal of Finance,
55, 2000, 153-178.
25
<Table 1> Definitions of Variables
Dependent Variable
AP_DEBT
Trade Credit/short-Term Debt:
where Trade Credit = (Account Payables + Note Payables) and
Short-Term Debt = (Trade Credit + Bank Revolving Line + Commercial Paper)
Explanatory/Control Variables
RATING_ALL
1 if borrowing firm possesses at least one of ICR, Bond Rating, and CP Rating,
otherwise 0
1 if borrowing firm possesses at least one of ICR, Bond Rating, and CP Rating,
RATING_CP
otherwise 0
ASSET
EBIT
CA
CAPEX
FIRM_RISK
SALES_GROWTH
LOAN_ASSET
Dummy variable
Bond rating & ICR are long-term credit rating
Dummy variable
CP rating is short-term credit rating
Log of book value of total asset
Earnings Before Interest and Taxes/Book Value of Sales
Book Value of Current Assets/Book Value of Total Assets
(Purchase of Fixed Assets-Sale of Fixed Assets)/Book Value of Sales
(Standard Deviation of Sales)/Average of Sales
Average and standard deviations estimated
over past 5 years
(Salest/Salest-1)-1
Bank Borrowing/Book Value of Total Assets
LEVERRAGE_DISTRESS
1 if debt-equity ratio> 200%, otherwise 0
INTEREST_COVERAGE
1 if interest coverage ratio< 1, otherwise 0
FINANCIAL_CRISIS
Measure of portion of trade credit in shortterm debt: level of use of trade credit relative
to bank revolving line and commercial paper
1 if observation year is in the period of global financial crisis
(2008.1~2009.12) , otherwise 0
26
Dummy variable:
Indicating long-term financial distress
Dummy variable:
Indicating short-term financial Distress
Dummy variable
Indicating extreme economic/financial shock
and credit constraint
<Table 2> Descriptive Statistics
Panel A: Descriptive Statistics of Key Variables across Full Sample, Large Firm Sample, Small-Medium Firm Sample
Full Sample
Variables
Mean
25%
percentile
Median
Large Firm Sample
75%
percentile
Mean
25%
percentile
Median
Small-Medium Firm Sample
75%
percentile
Mean
25%
percentile
Median
75%
percentile
AP_DEBT
0.4515
0.1788
0.3686
0.7143
0.4624
0.1876
0.3810
0.7320
0.4365
0.1669
0.3517
0.6791
ASSET
11.5792
10.6066
11.3280
12.2650
12.1342
11.0917
11.9819
12.9462
10.8035
10.3252
10.8117
11.3061
EBIT
-0.0520
-0.0045
0.0406
0.0859
-0.0161
0.0072
0.0446
0.0885
-0.1023
-0.0324
0.0346
0.0820
CA
0.5125
0.3659
0.5078
0.6478
0.4959
0.3413
0.4824
0.6322
0.5357
0.4038
0.5385
0.6661
0.0104
0.0334
0.0855
0.0771
0.0109
0.0345
0.0845
0.1217
0.0096
0.0322
0.0870
CAPEX
0.0957
FIRM_RISK
0.2923
0.1397
0.2310
0.3771
0.2811
0.1306
0.2173
0.3634
0.3079
0.1547
0.2481
0.4000
SALES_GROWTH
0.1865
-0.0706
0.0644
0.2059
0.1593
-0.0515
0.0708
0.2020
0.2245
-0.1023
0.0527
0.2129
LOAN_ASSET
0.1970
0.0410
0.1626
0.3101
0.1822
0.0323
0.1440
0.2848
0.2179
0.0545
0.1895
0.3428
27
<Table 2> Descriptive Statistics
Panel B: Structure of Short-Term Debt Financing over 2000-2012 Period
Structure of Short-Term Debt Financing:
Trade Credit/short-Term Debt
Possession of Credit Rating
Non-Possession of Credit Rating
Year
Mean
Median
Mean
Median
2000
0.3308
0.2680
0.4329
0.3809
2001
0.3500
0.2822
0.4324
0.3460
2002
0.3810
0.3115
0.4509
0.3695
2003
0.4245
0.3649
0.4636
0.3795
2004
0.4547
0.3770
0.4726
0.3840
2005
0.4757
0.4275
0.5047
0.4344
2006
0.4690
0.4108
0.5017
0.4299
2007
0.4319
0.3903
0.4841
0.4020
2008
0.3742
0.3283
0.4186
0.3081
2009
0.3994
0.3525
0.4381
0.3458
2010
0.4223
0.3670
0.4554
0.3759
2011
0.4006
0.3584
0.4603
0.3811
2012
0.3788
0.3082
0.4739
0.3761
28
<Table 3> Number of Listed Firms Possessing Credit Rating
CP Rating
Large Firm
Bond Rating
Small-Medium Firm
NonPossession
NonPossession
Possession
Large Firm
Total
ICR
Small-Medium Firm
NonNonPossession
Possession
Possession
Possession
Large Firm
Total
Small-Medium Firm
Possession
NonPossession
Possession
NonPossession
Total
Year
Possession
2000
171
410
20
354
955
225
356
48
326
955
0
581
0
374
955
2001
160
502
15
428
1,105
224
438
54
389
1,105
0
662
0
443
1,105
2002
160
578
8
514
1,260
176
562
41
481
1,260
0
738
0
522
1,260
2003
146
657
6
567
1,376
157
646
28
545
1,376
2
801
1
572
1,376
2004
147
684
3
585
1,419
165
666
16
572
1,419
5
826
0
588
1,419
2005
147
714
3
575
1,439
162
699
20
558
1,439
14
847
1
577
1,439
2006
143
755
5
605
1,508
155
743
22
588
1,508
27
871
3
607
1,508
2007
143
777
4
630
1,554
167
753
46
588
1,554
63
857
22
612
1,554
2008
140
786
2
652
1,580
176
750
41
613
1,580
59
867
11
643
1,580
2009
127
771
0
637
1,535
176
722
22
615
1,535
120
778
6
631
1,535
2010
130
749
0
663
1,542
173
706
18
645
1,542
93
786
10
653
1,542
2011
118
748
0
684
1,550
179
687
13
671
1,550
69
797
8
676
1,550
2012
114
730
0
702
1,546
191
653
15
687
1,546
60
784
11
691
1,546
Total
1,846
8,861
66
7,596 18,369
2,326
8,381
384
7,278 18,369
512
10,195
73
7,589
18,369
Note: 49 firms with rating D (5 firms for CP ratings, 45 firms for bond rating, and 1 firm for ICR) are classified to non-possession since these
firms cannot raise funds on its creditworthiness.
29
<Table 4> Distribution of Credit Ratings: Number of Listed Firms across Levels of Credit Rating
Samples are classified based on effective credit rating, i.e., the lowest credit rating, across credit rating agencies. Bond ratings include ICR
which is long-term credit rating system. In Panel A, the figures in parentheses of “Below B” rating are the numbers of sample firms defaulted.
Panel A: Full Sample
CP Rating
Bond Rating (Including ICR)
Year
A1
A2
A3
Below B
Total
AAA
AA
A
BBB
BB
Below B
Total
2000
15
37
86
55(2)
193
4
12
41
112
83
34(13)
286
2001
18
44
65
51(2)
178
5
11
50
88
106
29(11)
289
2002
21
41
61
45
168
7
11
45
64
67
28(5)
222
2003
22
35
63
32
152
7
11
42
59
37
33(3)
189
2004
30
35
63
22
150
6
12
48
73
23
24(2)
186
2005
28
34
70
18
150
5
18
47
75
21
22
188
2006
37
36
61
14
148
5
22
49
65
18
27
186
2007
35
47
51
14
147
5
21
60
59
25
64
234
2008
38
43
49
12
142
5
25
63
45
27
78(3)
243
2009
42
37
37
11
127
6
43
59
38
20
53(1)
219
2010
49
38
37
6
130
6
47
70
44
26
37(1)
230
2011
53
36
26
3
118
6
53
77
40
32
24(4)
232
2012
49
35
27
3
114
6
60
71
45
40
14(2)
236
Total
437
498
696
286(5)
1917
73
346
722
807
525
467(45)
2940
30
Panel B: Classified Samples: Large Firm and Small-Medium Firm
Large Firm
Small-Medium Firm
Short-Term Credit Rating:
Long-Term Credit Rating:
Short-Term Credit Rating:
Long-Term Credit Rating:
CP Rating
Bond Rating (Including ICR)
CP Rating
Bond Rating (Including ICR)
Year
A1
A2
A3
Below
B
2000
15
37
79
41
2001
18
44
61
38
2002
21
41
59
39
2003
22
35
62
27
2004
30
35
61
21
2005
28
34
68
17
2006
37
36
58
12
2007
35
47
48
13
2008
38
43
48
11
2009
42
37
37
11
2010
49
38
37
6
2011
53
36
26
3
2012
49
35
27
3
Total
437
498
671
242
Total
172
161
160
146
147
147
143
143
140
127
130
118
114
1848
AAA
AA
A
BBB
BB
Below
B
4
12
41
102
55
17
5
11
50
82
69
13
7
11
45
60
43
13
7
11
42
57
26
17
6
12
48
71
19
13
5
18
47
71
16
11
5
22
49
62
15
11
5
21
60
56
16
28
5
25
63
44
22
39
6
43
58
37
16
34
6
47
70
43
15
25
6
53
77
39
20
18
6
60
71
42
29
7
73
346
721
766
361
246
Total
231
230
179
160
169
168
164
186
198
194
206
213
215
31
2513
A1
A2
A3
Below
B
0
0
7
14
0
0
4
13
0
0
2
6
0
0
1
5
0
0
2
1
0
0
2
1
0
0
3
2
0
0
3
1
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
25
44
Total
21
17
8
6
3
3
5
4
2
0
0
0
0
69
AAA
AA
A
BBB
0
0
0
10
0
0
0
6
0
0
0
4
0
0
0
2
0
0
0
2
0
0
0
4
0
0
0
3
0
0
0
3
0
0
0
1
0
0
1
1
0
0
0
1
0
0
0
1
0
0
0
3
0
0
1
41
BB
Below
B
Total
28
17
55
37
16
59
24
15
43
11
16
29
4
11
17
5
11
20
3
16
22
9
36
48
5
39
45
4
19
25
11
12
24
12
6
19
11
7
21
164
221
427
<Table 5> Correlation Analysis
The sample includes the firms listed in Stock Exchange in Korea over 2000-2012 period. See <Table 1> for the definitions of variables. Figures in parentheses
are p-values of Pearson correlation coefficients.
AP_DEBT
AP_DEBT
RATING_ALL
RATING_CP
ASSET
EBIT
CA
CAPEX
FIRM_RISK
SALES_
GROWTH
LOAN_ASSET
RATING_ALL
RATING_CP
ASSET
EBIT
CA
CAPEX
FIRM_RISK
SALES_
GROWTH
LOAN_
ASSET
1
-0.0694
(<.0001)
1
-0.0107
0.7097
(0.1500)
(<.0001)
1
-0.0114
0.547
0.5406
(0.1252)
(<.0001)
(<.0001)
1
0.042
0.0265
0.0332
0.1183
(<.0001)
(0.0003)
(<.0001)
(<.0001)
1
0.0658
-0.053
-0.0432
-0.1019
0.0114
<.0001
(<.0001)
(<.0001)
(<.0001)
(0.1236)
0.0053
-0.0058
-0.005
-0.0095
-0.0655
-0.0013
(0.4788)
(0.4307)
(0.4980)
(0.1996)
(<.0001)
(0.8623)
1
1
0.008
-0.0989
-0.1202
-0.2227
-0.1467
0.0366
0.0302
(0.2793)
(<.0001)
(<.0001)
(<.0001)
(<.0001)
(<.0001)
(<.0001)
1
-0.0033
-0.0025
-0.0013
-0.0026
0.0151
-0.0023
0.0005
0.104
(0.6534)
(0.7361)
(0.8611)
(0.7294)
(0.0413)
(0.7549)
(0.9408)
(<.0001)
-0.6284
(<.0001)
-0.0215
(0.0036)
-0.0641
(<.0001)
-0.0466
(<.0001)
-0.0511
(<.0001)
-0.0539
(<.0001)
-0.008
(0.2824)
0.0002
(0.9835)
32
1
-0.0028
(0.7053)
1
<Table 6> Effect of Level of Credit Rating on Trade Credit Use: All Observations with Credit Ratings
The sample includes the firms listed in Stock Exchange in Korea over 2000-2012 period. See < Table 1> for the definitions of variables. The dependent variable is
AP_DEBT. Fixed effects models are estimated. Figures in in parentheses are t-values. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Dependent
Variable
AP_DEBT
Sample
Classification
All Observations with Credit Rating Classified by Rating Level
Dummy Variable
by Rating Level
Speculative Rating:1
Investment Rating:0
Below BBB:1
Above A: 0
Credit Rating
Dummy Variable
0.0178
(1.08)
-0.0218*
(-1.73)
0.0447**
(2.31)
0.1960***
(4.30)
0.0032
(0.21)
0.0580*
(1.65)
0.0008
(0.57)
-0.8893***
(-15.31)
0.7503***
(4.43)
0.2299
34.09
3385
698
-0.0512***
(-2.89)
-0.0330***
(-2.67)
0.0464**
(2.37)
0.1837***
(4.02)
0.0025
(0.16)
0.0599*
(1.70)
0.0007
(0.52)
-0.8593***
(-15.00)
0.9353***
(5.57)
0.2343
35.32
3385
698
ASSET
EBIT
CA
CAPEX
FIRM_RISK
SALES_GROWTH
LOAN_ASSET
CONSTANT
R2
F-Statistics
Number of Obs
Number of Groups
BBB:1
BBB-A: 1
BB-BBB:1
Speculative & Above Speculative & Above
Above A & Below B: 0
A: 0
AA: 0
-0.0513***
(-3.79)
-0.0320***
(-2.64)
0.0491**
(2.51)
0.1886***
(4.18)
0.0033
(0.22)
0.0593*
(1.69)
0.0007
(0.53)
-0.8701***
(-15.38)
0.9151***
(5.62)
0.2363
35.68
3385
698
-0.0366***
(-2.94)
-0.0271**
(-2.24)
0.0465**
(2.39)
0.1912***
(4.20)
0.0037
(0.25)
0.0618*
(1.76)
0.0008
(0.61)
-0.8793***
(-15.48)
0.8366***
(5.15)
0.2340
35.61
3384
698
-0.0420***
(-3.33)
-0.0272**
(-2.28)
0.0464**
(2.39)
0.1907***
(4.21)
0.0043
(0.29)
0.0640*
(1.82)
0.0008
(0.61)
-0.8882***
(-15.48)
0.8506***
(5.30)
0.2350
34.53
3384
698
33
Non-Chaebol-Affiliated versus Chaebol-Affiliated
Above A: 1
Below BBB: 0
Excluding Below CCC
Above AA:1,
BBB:0
Excluding Below BB
Speculative & NonChaebol-Affiliated: 1
Others: 0
Above A & ChaebolAffiliated Large Firm: 1
Others: 0
0.0512***
(2.89)
-0.0330***
(-2.67)
0.0464**
(2.37)
0.1837***
(4.02)
0.0025
(0.16)
0.0599*
(1.70)
0.0007
(0.52)
-0.8593***
(-15.00)
0.8841***
(5.37)
0.2343
35.32
3385
698
0.0773***
(4.11)
-0.0442***
(-2.86)
-0.1472
(-1.19)
0.2242***
(3.96)
0.0196
(0.64)
0.0872*
(1.75)
-0.0002
(-0.24)
-1.1204***
(-14.47)
1.1047***
(5.05)
0.2727
32.09
2355
397
0.0189
(1.22)
-0.0219*
(-1.75)
0.0445**
(2.30)
0.1971***
(4.28)
0.0030
(0.20)
0.0575
(1.63)
0.0007
(0.51)
-0.8871***
(-15.27)
0.7518***
(4.44)
0.2299
33.80
3385
698
0.0364**
(2.03)
-0.0294**
(-2.42)
0.0459**
(2.36)
0.1860***
(4.08)
0.0029
(0.19)
0.0607*
(1.72)
0.0007
(0.50)
-0.8710***
(-15.26)
0.8461***
(5.22)
0.2319
34.77
3385
698
<Table 7> Effects of Possession of Credit Rating on Trade Credit:
Base Model of Panel Analysis
The sample includes the firms listed in Stock Exchange (KOSPI and KOSDAQ) in Korea over
2000-2012 period. The observations are classified into Full Sample, Large Firm, Small-Medium
Firm, and Chaebol-Affiliated Firm. See < Table 1> for the definitions of variables. The dependent
variable is AP_DEBT. Fixed effects models are estimated. Figures in in parentheses are t-values. ***,
**, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Panel A: Possession of Credit Rating Indicated by RATING_ALL
Dependent
Variable
Possession of
Credit Rating
Sample
Classification
RATING_ALL
ASSET
EBIT
CA
CAPEX
FIRM_RISK
SALES_GROWTH
LOAN_ASSET
CONSTANT
R2
F-Statistics
Number of Obs
Number of Groups
AP_DEBT
RATING_ALL
Full Sample
Large Firm
Small-Medium Firm
-0.0672***
(-8.53)
-0.0290***
(-5.20)
0.0160***
(3.11)
0.2409***
(11.73)
0.0073
(1.25)
0.0380**
(2.48)
0.0001
(0.15)
-0.9266***
(-35.52)
0.8535***
(12.72)
-0.0781***
(-8.16)
-0.0295***
(-4.23)
0.0314***
(3.75)
0.2367***
(8.32)
0.0311***
(4.53)
0.0402**
(1.97)
0.0023
(1.22)
-0.8897***
(-25.55)
0.8806***
(10.12)
-0.0385***
(-2.90)
-0.0313***
(-3.30)
0.0193***
(3.59)
0.2361***
(7.95)
-0.0045
(-1.62)
0.0273
(1.16)
-0.0001
(-0.37)
-0.9751***
(-25.18)
0.8594***
(7.91)
Chaebol-Affiliated
Firm
-0.1032***
(-4.73)
-0.0061
(0.45)
-0.0035
(-0.18)
0.1682***
(2.59)
0.0566
(1.13)
0.0127
(0.28)
-0.0001
(-0.17)
-0.8630***
(-10.10)
0.7104***
(3.71)
0.2885
187.18
18006
2081
0.2705
103.02
10446
1108
0.3187
96.15
7560
973
0.2270
20.87
2165
311
34
Panel B: Possession of Credit Rating Indicated by RATING _CP
Dependent
Variable
Possession of
Credit Rating
Sample
Classification
RATING_CP
ASSET
EBIT
CA
CAPEX
FIRM_RISK
SALES_GROWTH
LOAN_ASSET
CONSTANT
R2
F-Statistics
Number of Obs
Number of Groups
AP_DEBT
RATING_CP
-0.0638***
(-5.53)
-0.0335***
(-4.82)
0.0326***
(3.86)
0.2399***
(8.38)
0.0317***
(4.66)
0.0410**
(2.01)
0.0023
(1.21)
-0.8884***
(-25.37)
0.9168***
(10.52)
Small-Medium
Firm
-0.0432
(-1.24)
-0.0324***
(-3.43)
0.0190***
(3.41)
0.2374***
(7.99)
-0.0044
(-1.57)
0.0274
(1.17)
-0.0001
(-0.36)
-0.9753***
(-25.14)
0.8684***
(8.01)
Chaebol-Affiliated
Firm
-0.0909***
(-5.71)
-0.0165
(-1.23)
-0.0011
(-0.06)
0.1623**
(2.55)
0.0574
(1.15)
0.0134
(0.31)
0.0000
(-0.05)
-0.8505***
(-9.95)
0.8315***
(4.41)
0.2649
97.78
10446
1108
0.3178
95.57
7560
973
0.2291
22.93
2165
311
Full Sample
Large Firm
-0.0611***
(-5.54)
-0.0319***
(-5.74)
0.0161***
(3.03)
0.2433***
(11.80)
0.0073
(1.26)
0.0384**
(2.52)
0.0001
(0.15)
-0.9261***
(-35.37)
0.8793***
(13.11)
0.2852
182.59
18006
2081
35
<Table 8> Effects of Possession of Credit Rating on Trade Credit Use:
Panel Analysis with Controls for Financial Distress and Financial Crisis
The sample includes the firms listed in Stock Exchange in Korea over 2000-2012 period. The
dependent variable is AP_DEBT. Fixed effects models are estimated. Additional control variables for longterm
financial
distress
(LEVERAGE_DISTRESS),
short-term
financial
distress
(INTEREST_COVERAGE), and economic/financial shock (FINANCIAL_CRISIS) are included in
the estimations. See < Table 1> for the definitions of variables. Figures in in parentheses are tvalues. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Dependent
Variable
Possession of
Credit Rating
Financial Distress
& Crisis
RATING_ALL
AP_DEBT
Possession of at least one of CP Rating, Bond
Rating, and ICR: RATING_ALL
Interest
Financial
Leverage
Coverage
Crisis
-0.0701***
-0.0662***
-0.0676***
(-8.98)
(-8.42)
(-8.60)
RATING _CP
ASSET
EBIT
CA
CAPEX
FIRM_RISK
SALES_
GROWTH
LOAN_ASSET
LEVERAGE_
DISTRESS
INTEREST_
COVERAGE
FINANCIAL_
CRISIS
CONSTANT
R2
F-Statistics
Number of Obs
Number of Groups
-0.0297***
(-5.32)
0.0189***
(3.28)
0.2478***
(12.07)
0.0082
(1.28)
0.0347***
(2.29)
0.0000
(0.12)
-1.0053***
(-37.91)
0.0820***
(10.21)
-0.0294***
(-5.29)
0.0136***
(2.95)
0.2255***
(10.96)
0.0066
(1.30)
0.0327**
(2.12)
0.0001
(0.20)
-0.9078***
(-34.89)
-0.0263***
(-4.73)
0.0150***
(3.01)
0.2387***
(11.68)
0.0072
(1.28)
0.0374**
(2.44)
0.0001
(0.13)
-0.9228***
(-35.36)
Possession of CP Rating:
RATING _CP
Interest
Financial
Leverage
Coverage
Crisis
-0.0615***
(-5.61)
-0.0327***
(-5.89)
0.0188***
(3.19)
0.2501***
(12.13)
0.0083
(1.29)
0.0352**
(2.34)
0.0001
(0.12)
-1.0028***
(-37.71)
0.0799***
(9.96)
-0.0316***
(-6.31)
0.8627
(12.88)
0.2980
184.14
18006
2081
0.8743
(13.03)
0.2913
169.13
18006
2081
-0.0627***
(-5.69)
-0.0322***
(-5.82)
0.0135***
(2.87)
0.2271***
(11.00)
0.0066
(1.32)
0.0329**
(2.14)
0.0001
(0.21)
-0.9064***
(-34.72)
-0.0619***
(-5.62)
-0.0292***
(-5.27)
0.0151***
(2.94)
0.2412***
(11.75)
0.0072
(1.29)
0.0379**
(2.48)
0.0001
(0.14)
-0.9223***
(-35.20)
-0.0332***
(-6.64)
-0.0225***
(-5.46)
0.8271
(12.32)
0.2901
175.34
18006
2081
36
0.8892
(13.29)
0.2942
180.39
18006
2081
0.9009
(13.44)
0.2882
164.49
18006
2081
-0.0224***
(-5.42)
0.8532
(12.72)
0.2868
171.63
18006
2081
<Table 9> Effects of Possession of Credit Rating on Trade Credit Use:
Panel Analysis with Controls for Product and Industry Characteristics
The sample includes the firms listed in Stock Exchange (KOSPI and KOSDAQ) in Korea over
2000-2012 period. The dependent variable is AP_DEBT. Fixed effects models are estimated.
Characteristics of industries where sellers operate are included in the form of dummy variables as
additional control variables. Those industries include standardized good, differentiated good service,
construction, and retail. See < Table 1> for the definitions of variables. Figures in in parentheses are
t-values. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Panel A: Possession of Credit Rating Indicated by RATING_ALL
Dependent
Variable
Possession of
Credit Rating
Industry
Classification
RATING_ALL
ASSET
EBIT
CA
CAPEX
FIRM_RISK
SALES_GROWTH
LOAN_ASSET
STANDARDIZED_
GOODS
AP_DEBT
Possession of at least one of CP Rating, Bond Rating, and ICR: RATING_ALL
Standardized
Good
-0.0861***
(-15.60)
-0.0145***
(-8.27)
0.0182***
(2.79)
0.2983***
(28.54)
0.0001
(0.01)
-0.0175*
(-1.70)
-0.0002
(-0.55)
-1.1087***
(-88.38)
-0.0730***
(-18.33)
Differentiated
Good
-0.0760***
(-13.55)
-0.0133***
(-7.61)
0.0169***
(2.73)
0.3029***
(29.05)
0.0008
(0.14)
0.0035
(0.34)
-0.0003
(-0.95)
-1.1353***
(-90.09)
Service
Construction
-0.0835***
(-14.82)
-0.0124***
(-7.06)
0.0181***
(2.73)
0.3008***
(28.62)
0.0005
(0.08)
0.0142
(1.40)
-0.0004
(-1.16)
-1.1216***
(-88.26)
-0.0859***
(-15.14)
-0.0110***
(-6.27)
0.0183***
(2.77)
0.2873***
(26.55)
0.0009
(0.14)
0.0181*
(1.79)
-0.0004
(-1.20)
-1.1280***
(-89.91)
0.0449***
(12.20)
DIFFERENTIATED_
GOODS
0.0199***
(4.25)
SERVICE
0.0640***
(6.91)
CONSTRUCTION
DISTRIBUTION
CONSTANT
R2
F-Statistics
Number of Obs
Retail
Distribution
-0.0805***
(-14.41)
-0.0121***
(-6.93)
0.0178***
(2.73)
0.3026***
(28.94)
0.0009
(0.14)
0.0169*
(1.68)
-0.0004
(-1.25)
-1.1293***
(-90.04)
0.4343***
(18.70)
0.4481
523.01
18006
0.3980***
(16.98)
0.4428
496.04
18006
0.4188***
(17.96)
0.4390
493.10
18006
37
0.4433***
(18.83)
0.4397
498.70
18006
0.0230***
(2.78)
0.4243***
(18.21)
0.4386
491.93
18006
Panel B: Possession of Credit Rating Indicated by RATING _CP
Dependent
Variable
Possession of
Credit Rating
Industry
Classification
RATING_CP
ASSET
EBIT
CA
CAPEX
FIRM_RISK
SALES_GROWTH
LOAN_ASSET
STANDARDIZED_
GOOD
AP_DEBT
Possession of CP Rating: RATING_CP
Standardized
Good
-0.0682***
(-9.79)
-0.0092***
(-5.28)
0.0197***
(2.89)
0.2996***
(28.54)
0.0005
(0.07)
-0.0208**
(-2.01)
-0.0002
(-0.50)
-1.1122***
(-87.98)
-0.0731***
(-18.17)
Differentiated
Good
-0.0516***
(-7.35)
-0.0077***
(-4.41)
0.0185***
(2.85)
0.3037***
(29.03)
0.0013
(0.20)
0.0002
(0.02)
-0.0003
(-0.91)
-1.1379***
(-89.60)
Service
Construction
-0.0601***
(-8.47)
-0.0066***
(-3.78)
0.0197***
(2.84)
0.3020***
(28.63)
0.0010
(0.14)
0.0116
(1.14)
-0.0004
(-1.13)
-1.1256***
(-87.94)
-0.0665***
(-9.14)
-0.0057***
(-3.24)
0.0199***
(2.87)
0.2891***
(26.60)
0.0013
(0.19)
0.0149
(1.47)
-0.0004
(-1.16)
-1.1315***
(-89.54)
0.0467***
(12.65)
DIFFERENTIATED_
GOOD
0.0172***
(3.65)
SERVICE
0.0620***
(6.61)
CONSTRUCTION
DISTRIBUTION
CONSTANT
R2
F-Statistics
Number of Obs
Retail
Distribution
-0.0567***
(-8.11)
-0.0063***
(-3.64)
0.0194***
(2.85)
0.3035***
(28.92)
0.0013
(0.19)
0.0140
(1.38)
-0.0004
(-1.22)
-1.1320***
(-89.57)
0.4821***
(20.67)
0.4437
498.68
18006
0.4495***
(19.06)
0.4388
473.84
18006
0.4726***
(20.17)
0.4344
470.89
18006
38
0.4920***
(20.89)
0.4351
478.09
18006
0.0232***
(2.80)
0.4776***
(20.40)
0.4342
470.25
18006