the role of information sharing in trade credit distribution evidence from thailand

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The role of information sharing in trade creditdistribution: evidence from Thailandapel_1278 133..149

Rongrong Zhang*

Prior research has shown that information sharing among lenders facili-tates bank credit allocation and reduces default rates. We examine the roleof information sharing in trade credit allocation using a sample of publicly

traded firms in Thailand over the 1994–2005 period. Taking the establish-ment of a private credit bureau in 1999 as signalling improvement ininformation sharing among lenders, we obtain three main results in theimproved information sharing period: (1) Thai firms have become lessdependent on supplier credit; (2) financially constrained firms redistributemore funds via trade credit; and (3) the relationships between the use of trade credit and firm-specific factors such as liquidity, free cash flow,tangible assets, interest cost ratio, and firm size weaken as informationsharing improves. Our results are consistent with the view that betterinformation sharing facilitates credit allocation. Hence, policies aiming atfacilitating information exchange among financial intermediaries should

be supported. We also find support for the view that bank credit substi-tutes for trade credit. This substitution lowers firms’ cost of capital, giventhat trade credit is assumed to be more costly than bank loans.

Introduction

It was hypothesised that information sharingamong lenders overcomes adverse selectionproblems in credit markets, resulting inlenders becoming more willing to make credit

available (Jaffee and Russell 1976; Pagano and Jappelli 1993). This observation has receivedstrong empirical support [Jappelli and Pagano2002, 2006; Djankov et al. 2007 (henceforthDMS 2007); Brown et al. 2009].

The importance of trade credit as a source of short-term financing, both inside and outsidethe USA, is well documented (Mian and Smith1992, 1994; Petersen and Rajan 1997; Ng et al.1999; Guariglia and Mateut 2006). Researchers

have offered various explanations for whyfirms finance with trade credit, includingcapital market imperfections (Petersen andRajan 1997), information advantage of suppli-ers (Biais and Gollier 1997), product character-istics (Burkart and Ellingsen 2004), suppliers asinsurance providers (Cuñat 2007), trade credit

in bankruptcy renegotiations (Wilner 2000),and trade credit in inventory management(Bougheas et al. 2009).

To our knowledge, the effect of informationsharing among lenders on the availability of trade credit has not been empirically investi-gated. Our paper attempts to fill this void. Weanalyse use of trade credit using a sample of 2,076 publicly traded Thai firms operating

between 1994 and 2005. Prior research shows

* Rongrong Zhang, Assistant Professor of Finance, Georgia Southern University, USA.

doi: 10.1111/j.1467-8411.2011.01278.x

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© 2011 The Author Journal compilation © 2011 Crawford School of Economics and Government,The Australian National University and Blackwell Publishing Asia Pty Ltd.



that information sharing improves bank credit.Given that bank credit and trade credit can beviewed as substitutes, we expect firms to use

less trade credit in a better information sharingsetting. During the sample period, credit infor-mation sharing in Thailand improved as evi-denced by the establishment of a private credit

bureau in 1999 (DMS 2007).1 Therefore, we splitthe sample into two sub-periods, period I(1994~99) and period II (2000~05), and regardthe latter period as having better informationsharing.2 We show that, following the establish-ment of a private credit bureau, Thai firms

became less dependent on trade credit financ-

ing as evidenced by the significant decline indays in accounts payable.Given that trade credit is more important to

firms that are financially constrained, wehypothesise that financially constrained firmsuse more supplier credit and extend less netcredit than unconstrained firms, ceteris paribus.We use three approaches suggested by the lit-erature to classify the sample firms as beingunconstrained and constrained: (1) firm divi-dend policy; (2) an index measure derived fromresults in Kaplan and Zingales (1997) (the ‘KZ

index’); and (3) whether a firm is a net creditprovider or a net credit user.3 A firm is classifiedas being financially constrained if: (1) it makesno annual cash dividend payment; (2) it has ahigh KZ index; or (3) its days in accountspayable are greater than its days in accountsreceivable (that is, a net trade credit user). Weshow that: (1) financially constrained firms aremore dependent on supplier credit than uncon-strained firms; (2) both unconstrained and con-strained firms use less supplier credit in the

better information sharing period, consistentwith our view that demand for trade creditdecreases with the improvement in credit allo-cation; (3) constrained firms increase their nettrade credit in the better information sharingperiod, suggesting that information sharing

helps relieve financial constraints and results inmore fund redistribution via trade credit; and(4) trade credit use becomes less sensitive to

firm-specific factors such as liquidity, free cashflow, tangible assets, leverage, and the interestcost ratio in the better information sharingperiod. Taken together, our results suggest thatcredit information sharing plays an importantrole in corporate trade credit use.

We make several contributions to the extantliterature. First, our empirical results provideadditional support for the view that informa-tion sharing improves credit allocation andrelieves financial constraints. Second, we find

evidence suggesting that bank credit substi-tutes for trade credit. Third, we find evidencesuggesting that information sharing affects thedeterminants of trade credit.

The remainder of the paper is organised asfollows. Related Research and HypothesesDevelopment discusses the previous researchrelated to our work and develops the hypoth-eses. Data and Methodology describes thesample and our methodology. EmpiricalResults and Discussion presents the empiri-cal results and discussion. Summary and Con-

clusions provides the conclusions and theirimplications.

Related research and hypothesesdevelopment

Trade credit vs. bank credit

There is a vast amount of research on whyfirms use trade credit instead of bank credit.Below we discuss some of the explanations thatare not mutually exclusive. Trade credit is arelationship loan provided by suppliers to theirclients. Trade credit is commonly viewed as

1 A private credit bureau is defined as a private commercial firm or non-profit organisation that maintains a database on thestanding of borrowers in the financial system, and its primary role is to facilitate exchange of information among banksand financial institutions (see DMS 2007).

2 DMS data show that private credit bureau was established but not in operation in Thailand in 1999.3 Net credit providers refer to firms that have longer days receivable outstanding than days payable outstanding. These

firms collect their receivables more slowly than the time they take to pay back supplier credit. Net credit users refer tofirms that have shorter days receivable outstanding than days payable outstanding.

ASIAN-PACIFIC ECONOMIC LITERATURE

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© 2011 The Author Journal compilation © 2011 Crawford School of Economics and Government,

The Australian National University and Blackwell Publishing Asia Pty Ltd.



being more expensive than bank credit becausesuppliers often offer significant discounts todiscourage their customers from extending

short-term trade credit into intermediate oreven long-term financing. Forgoing the dis-count results in a high implicit borrowing rate.

Why do non-financial firms extend credit totheir trading partners when specialised finan-cial firms could provide financing? Priorresearch offers different explanations: Firmsmay not be able to raise funds using bank loansor security underwriting. Consequently, tradecredit becomes a financing means of the lastresort. It is well documented that this form of

financing is of particular importance to young,small, and/or opaque firms with ‘question-able’ credit worthiness (Petersen and Rajan1997). The monitoring advantage view of tradecredit suggests that suppliers might be able toobtain private information about their custom-ers (that is, the borrowers) at no or lower costscompared with other types of lenders (Biaisand Gollier 1997; Jain 2001). Meltzer (1960) firstproposed the redistribution view of tradecredit: Firms with better access to externalfunds could redistribute credit to their trading

partners via trade credit. Large (more liquid)firms pass funds via trade credit to their small(less liquid) customers during contractionarymoney periods (Nilsen 2002). Love et al. (2007)show that firms curtail trade credit to their cus-tomers post a financial crisis. They interpretthese results as being consistent with the redis-tribution view of trade credit: The financialcrisis results in a bank credit crunch, hamper-ing firms’ ability to redistribute funds via tradecredit.

Other researchers have examined the rolesof trade credit in bankruptcy negotiation,inventory management, and the collateralvalues of goods purchased on credit. Thesestudies do not focus on the relationship

between trade credit and bank credit. Giventhat information sharing affects bank credit, weare interested in literature relating trade creditto bank credit.

Information sharing and credit allocation

Pagano and Jappelli (1993) develop an adverse

selection model showing that credit informa-tion sharing reduces loan default rates and

borrowing costs by lessening the impact of adverse selection. Information sharing alsocreates incentives for borrowers to repay loanswhen enforcement of the credit contractthrough legal institutions is difficult Klein1982). In Klein’s model, borrowers repay theirloans because they know that defaulters will be

blacklisted, reducing future external finance.Vercammen (1995) and Padilla and Pagano(2000) also document a positive effect frominformation sharing on loan repayment. Jap-pelli and Pagano (2002) assemble survey dataon private and public information sharingarrangements, including credit bureaus andpublic credit registries, for 49 countries. Theydocument that total bank lending to the pri-vate sector is larger in markets with moreestablished information sharing mechanisms.DMS (2007) carry out a similar task with anexpanded sample, covering 129 countries, andshow that the presence of public credit regis-

tries and private credit bureaus facilitates theallocation of private credit.4 Brown et al. (2009)document similar effects of informationsharing on credit availability using a sample of transition economies.

In summary, prior research establishes thepositive effects of information sharing on bankcredit. Given that bank credit and trade creditare often considered substitutes and tradecredit financing is more costly, it follows thattrade credit use declines following improve-ment in credit information sharing. This leadsto our first testable hypothesis, formally statedas follows:

Hypothesis 1: Firms use less trade credit financingin a better information sharing environment.

Most firms receive supplier credit whenthey acquire inventory and extend customercredit when they make sales. The difference

4 Private credit measures claims on the private sector by commercial banks and other financial institutions (see DMS2007:302).

ZHANG — THE ROLE OF INFORMATION SHARING IN TRADE CREDIT FINANCING

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AP PB ConsPB Cons CashratioPB C

it i

it

360 0 1

2 3

4

= + × + ×

+ × × + ×

+ × ×

α β β β β β aashratio CashFlow

PB CashFlow Tangibleit itit it

+ ×

+ × × + ×

+

β β β β

5

6 7

88 9

10 11

× × + ×

+ × × + ×

PB Tangible LeveragePB Leverage Inter

it it

it

β β β eestRatio

PB InterestRatio SizePB Size

it

it it

i

+ × × + ×

+ × ×

β β β

12 13

14 tt it

it it

Log MLog GDP

+ ×

+ × +

β β ε

15

16

( )

( )

2(3)

NC PB ConsPB Cons CashratioPB C

it i

it

360 0 1

2 3

4

= + × + ×

+ × × + ×

+ × ×

α β β β β β aashratio CashFlow

PB CashFlow Tangibleit it

it it

+ ×

+ × × + ×

+

β β β

β

5

6 7

88 9

10 11

× × + ×

+ × × + ×

PB Tangible LeveragePB Leverage Inter

it it

it

β β β eestRatio

PB InterestRatio SizePB Size

it

it it

i

+ × × + ×

+ × ×

β β β

12 13

14 tt it

it it

Log MLog GDP

+ ×

+ × +

β β ε

15

16

( )

( )

2(4)

To account for unobservable cross-sectionaldifferences that may affect trade credit, we usefirm fixed effects models for all regressions.See Appendix 1 for variable definitions.

Trade credit ( AP360 and NC360)

Trade credit from suppliers is recorded asaccounts payable on the balance sheet of thecustomer. Customers that pay back their sup-pliers slowly would have higher accountspayable balances. In accounting, the accountspayable turnover ratio is a short-term liquiditymeasure used to quantify the rate at which acompany pays off its suppliers.5 A more directmeasure of how fast customers pay back their

suppliers is days payable outstanding ( AP360),computed as 360 divided by the accountspayable turnover ratio, which is the ratio of thecosts of goods sold over accounts payable.

Most firms simultaneously use suppliercredit and extend credit to their customers.Customer credit can be measured by daysreceivable outstanding ( AR360), which is 360

divided by the accounts receivable turnoverratio. The difference between AR360 and

AP360 reveals the net trade credit used. A posi-

tive (negative) net trade balance (NC360) sug-gests that a firm extends more (less) credit thanwhat it receives from its suppliers.

Information sharing dummy (PB)

The primary information sharing mechanismsinclude public credit registries and privatecredit bureaus. A public credit registry isdefined as a database owned by public authori-ties (usually the central bank or a banking

supervisory authority) that collects informa-tion on the standing of borrowers in the finan-cial system and makes it available to financialinstitutions. A private credit bureau is a privatefirm or non-profit organisation that maintains adatabase on the standing of borrowers in thefinancial system. Its primary role is to facilitateexchange of information among banks andfinancial institutions.

According to Kunvipusilkul (2009), twocredit reporting agencies were established in

Thailand in 1999 as a result of government ini-tiatives aiming at promoting stability in thefinancial system, increasing loan efficiency, andreducing risks. DMS (2007) data of Thailandshow that a public credit registry was notestablished during our sample period. Aprivate credit bureau was established in 1999,

but it was not operating in that year. Weassume that it began operating in 2000.

Our information sharing dummy variable(PB) is set to zero for firm-year observations

between 1994 and 1999, and to one between

2000 and 2005. Prior research shows that infor-mation sharing improves credit availability.Trade credit is often associated with higherimplicit costs than bank credit. Hence, we con-

jecture that firms use less trade credit in a better information sharing period (that is, PB =

one period). Even though access to bank creditimproves with information sharing, most firms

5 The accounts payable turnover ratio is calculated by taking the total credit purchases from suppliers and dividing it by theaverage accounts payable amount during the same period. Because credit purchase information data are not disclosed in

standard financial statements, in practice, researchers often substitute cost of goods sold for credit purchases in calcu-lating this turnover ratio.


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control for assets’ collaterability. Holding allelse constant, firms with more tangible assetshave easier access to bank loans. Hence, we

predict an inverse relationship between Tan- gible and AP360. The leverage ratio (Leverage),computed as the ratio of total debt over totalassets, might also affect trade credit financing.On the one hand, debt and trade credit can beviewed as substitutes; thus, a higher leverageratio leads to lower trade credit. On the otherhand, a high debt ratio may indicate that thefirm has already exhausted its debt capacity. Inthis case, trade credit becomes the alternativemeans of financing. This line of reasoning pre-

dicts a positive relationship between Leverageand AP360. We leave this relationship to bedetermined empirically.

The interest expense ratio (InterestRatio),computed as the ratio of total interest expensesto total debt, is included to control for the costof borrowing. Holding all else constant, tradecredit financing becomes more attractive whenthe interest cost on existing debt increases, thatis, a positive relationship between AP360 andInterestRatio.

We also control for the size effect. On the

one hand, larger firms have greater debt capac-ity and better access to the external capitalmarket, and therefore less demand for tradecredit, that is, a negative relationship betweenSize and AP360. On the other hand, large firmshave more buying power and thus are able tonegotiate better terms with their suppliers.Suppliers may also favour their large clients byoffering generous credit terms. If the cost of trade credit is lower for larger firms, we mayobserve a positive relationship between Size

and AP360. The sign on the coefficient of Sizeshall be empirically determined.

Macroeconomic factors

Corporate financing decisions are not onlydriven by firm-specific factors, such as liquid-ity and size, but are also affected by macroeco-nomic factors. We include two macroeconomicvariables to control for the effects of the eco-nomic environment on firms’ trade credit poli-cies: M2, to represent the money supply, and

GDP. A larger money supply (that is, higher

M2) indicates easier access to credit for corpo-rations and hence lower dependence on tradecredit, ceteris paribus. The effect of GDP on cor-

porate trade credit financing is less clear-cut.More rapid economic growth could lead toincreased demand for trade credit if othermeans of financing do not provide sufficientcapital. However, economic expansion could

be the result of economic stimulus, whichmight include accommodating monetary poli-cies that result in lower demand for tradecredit. We include GDP as a control variablewithout making a sign prediction.

Empirical results and discussion

Summary statistics

Table 1a presents summary statistics of the fullsample. Table 1b displays the Pearson correla-tion matrix. As predicted, cash flow (CashFlow)is negatively correlated with supplier credit( AP360). Asset tangibility (Tangible) is nega-tively correlated with net credit (NC360), sug-

gesting that firms with more tangible assetsextend less net credit. The leverage ratio (Lever-age) is positively related to supplier credit( AP360), but it is negatively correlated with netcredit (NC360), suggesting that debt and sup-plier credit are complements, and firms withhigh leverage extend less net credit. Firm size(TOTAL ASSETS) is positively related to sup-plier credit ( AP360) but is negatively correlatedwith net credit (NC360), that is, larger Thaifirms use more supplier credit but offer less net

credit.Petersen and Rajan (1997) show that suppli-ers favour customers with high credit ratings.Firms with a large asset base are commonlyviewed as more creditworthy. For suchreasons, suppliers may favour their large cus-tomers in extending credit. Suppliers may alsoprovide more generous credit to their biggestclients for sales reasons, which encourageuse of trade credit. Compared with smallerfirms, larger firms redistribute fewer funds viatrade credit. It might be that it is imperativefor smaller firms to extend credit to keep


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customers. The two macroeconomic variables( M2 and GDP) are both negatively correlatedwith supplier credit ( AP360), suggesting thatwhen monetary policy is accommodatingand/or the economy is accelerating, firmsdemand less trade credit. Taken together, thecorrelation matrix provides some preliminarysupport for our choices of the determinants of trade credit.

Trade credit use—constrained vs.unconstrained firms

As discussed earlier, we use three metrics,dividend policy, KZ index, and net creditextension, to identify financially constrainedand financially unconstrained firms. Wehypothesise that financially constrained firmsare more dependent on supplier credit ( AP360)and extend less net credit (NC360). Table 2 pro-

vides support for our view. On balance, finan-cially unconstrained firms, dividend payers,low KZ index firms, and net credit providersuse significantly less supplier credit ( AP360)and extend more net credit (NC360).

Table 3 examines the categorisation of thesample firms as financially constrained andfinancially unconstrained by these metrics. Itseems that the criteria dividend policy and KZindex are the most consistent in identifyingfinancially constrained firms. For example, 79per cent of firms that are classified as financiallyunconstrained based on dividend policy would

be classified as financially unconstrained usingthe KZ index. Sixty-five per cent of firms thatare classified as financially unconstrained

based on dividend policy would be classified asfinancially unconstrained based on the netcredit criterion. Only 9 per cent of firms that areclassified as financially constrained based ondividend policy would be classified as finan-cially unconstrained using the KZ index, but 58

per cent of firms classified as financially con-strained based on the dividend policy criterionwouldbe classified as financially unconstrainedusing net credit policy. Similarly, 92 per cent of firms that are classified as financially uncon-strained using the KZ index are dividendpayers, while 68 per cent of low KZ index firmsare also net credit providers. Among the threemethods, net credit policy is least reliable inclassifying firms as being financially con-strained or unconstrained.

The effect of information sharingon trade credit

To test the effect of information sharing ontrade credit, we examine the changes in thesupplier credit ( AP360) and net trade credit(NC360) before and after the establishment of the private credit bureau in Thailand. Table 4presents the results. For both the full sampleand the subsamples of financially constrainedand unconstrained firms, we find that firmsuse significantly less supplier credit in a better

Table 1aSummary statistics of the equation variables

Variables Mean Median Standard error Minimum Maximum

AP360 (in days) 56.71 46.63 47.95 1.79 448.34NC360 (in days) 7.22 10.18 57.10 -386.11 336.51Cashratio 0.05 0.03 0.06 -0.01 0.37CashFlow 0.08 0.09 0.10 -0.45 0.37Tangible 0.42 0.41 0.20 0.03 0.89Leverage 0.38 0.37 0.26 0.00 1.43InterestRatio 0.09 0.06 0.12 0.00 1.83TOTAL ASSETS (book value) 11,294 2,198 34,614 64 649,807

M2 (billions of baht) 5,189 5,244 855 2,829 6,439GDP (billions of baht) 5,451 5,123 932 3,629 7,103

Source: Author’s calculations.


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P e a r s o n c o

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T A L A S S E T S

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N o t e s : T h i s t a b l e g i v e s t h e s u m m a r y s t a t i s t i c s ( a ) a n d t h e P e a r s o n c o r r e l a t i o n m a t r

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p e

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p p e n d i x 1 ) .

S o

u r c e : A u t h o r ’ s c a l c u l a t i o n s .


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Table 2Trade credit use by financially constrained and financially unconstrained firms

Dividend payer Dividend non-payer t-Statistics P-value

AP360 50.57 65.29 -6.71 <0.0001NC360 9.76 3.66 2.34 0.0193No. of firms 1,210 866

Low KZ firms High KZ firms t-Statistics P-value

AP360 47.66 65.76 -8.75 <0.0001NC360 13.18 1.25 4.78 <0.0001No. of firms 1,038 1,038

Net credit provider Net credit user t-Statistics P-value

AP360 40.06 84.03 -19.23 <0.0001

NC360 35.15 -38.63 33.88 <0.0001No. of firms 786 1,290

Notes: This table presents mean difference test statistics of days in payable ( AP360) and net credit(NC360) between financiallyconstrained and financially unconstrained firms. Three categories are used to construct the financially unconstrained andfinancially constrained subsamples: (1) dividend policy; (2) an index measure derived from the results in Kaplan andZingales (1997) (the ‘KZ index’); and (3) net credit use. A firm is considered to be financially constrained if it does not makecash dividend distributions, or it has a KZ index above the median, or its days in receivables is shorter than its days in payable(that is, a net credit user). Conversely, a firm is considered financially unconstrained if it makes cash dividend distributions,or it has a KZ index below the median, or its days in receivables is longer than its days in payable (that is, a net creditprovider).Source: Author’s calculations.

Table 3Sample statistics for financially unconstrained and financially constrained firms

Unconstrained ConstrainedDividend payer Dividend non-payer

Low KZ index 0.79 0.09Net credit provider 0.65 0.58No. of firms 1,210 866

Low KZ index High KZ index

Dividend payer 0.92 0.24Net credit provider 0.68 0.56No. of firms 1,038 1,038

Net credit provider Net credit user

Dividend payer 0.61 0.54Low KZ index 0.55 0.42No. of firms 1,290 786

Notes: This table presents summary statistics of the firms that are classified as being financially unconstrained or financiallyconstrained using the three criteria for categorisation: (1) dividend policy; (2) an index measure derived from results inKaplan and Zingales (1997) (the ‘KZ index’); and (3) net credit use.Source: Author’s calculations.


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information sharing environment (that is, PB =

one period). Furthermore, the amount of netcredit declined significantly for financiallyunconstrained firms but increased significantlyfor financially constrained firms, supportingour view that information sharing has moresignificant impacts on financially constrainedfirms. Consequently, these firms increase creditdistribution via the trade channel. Theseresults provide preliminary support forHypotheses 1 and 2. The decline in suppliercredit and net credit for unconstrained firmsindicates that these firms curtail their customercredit. This could be an outcome of less

demand from their customers with the overall

improvement in credit allocation.

Regression analyses

Table 5a and b presents the results based on theestimation of the firm fixed effects models of Equations 1~4. We predicted a negative coeffi-cient on the PB dummy in the AP360 model,which suggests that all firms use less suppliercredit in a better information sharing period(that is, PB = 1). We predicted a positive coef-ficient on the Cons dummy in the AP360 model,which suggests that financially constrained

Table 4The effects of information sharing on trade credit

PB = 0 PB = 1 t-Statistics P-value

a. Full sample AP360 63.34 53.14 4.28 <0.0001NC360 11.87 10.06 -0.04 0.9671No. of firms 727 1349

b. By dividend policyDividend payer (unconstrained)

AP360 56.30 47.63 3.18 0.0015NC360 14.14 7.52 1.88 0.0601No. of firms 410 800

c. By KZ indexLow KZ firms (unconstrained)

AP360 49.78 46.68 1.16 0.2455NC360 18.94 10.52 2.43 0.0154No. of firms 328 710

High KZ firms (constrained) AP360 74.49 60.31 3.89 0.0001NC360 -2.56 3.63 -1.54 0.1249No. of firms 399 639

d. By net credit useNet credit provider (unconstrained)

AP360 44.59 37.47 4.36 <0.0001NC360 37.51 33.80 1.62 0.1049No. of observations 470 820

Net credit user (constrained)

AP360 97.628 77.429 4.45 <0.0001NC360 -48.4 -33.89 3.26 0.0012No. of observations 257 529

Notes: This table presents mean difference test statistics of days in payable ( AP360) and net credit (NC360) between period1 (PB = 0: 1994~99) and period 2 (PB = 1: 2000~05) for the full sample (a) and the subsamples of financially constrained andfinancially unconstrained firms (b, c, and d). See Table 2 for a description of the way in which the full sample was split intothe financially constrained and financially unconstrained subsamples.KZ = Kaplan and Zingales index.Source: Author’s calculations.


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Table 5Regression results on financial constraints, information sharing, and trade credit distribution

a. The effects of information sharing and financial constraint

Dependent variable AP360 Dependent variable NC360

Independent variablesPredicted

sign Estimate P-valuePredicted

sign Estimate P-value

Classifying financially constrainedand financially unconstrainedfirms by dividend policyIntercept 69.47 0.3764 41.25 <0.0001***PB dummy - -14.42 <0.0001*** - 7.68 0.0466**Cons dummy + 9.67 0.0101** - -12.12 0.0037***PB dummy ¥ Cons dummy - -5.94 0.1787 + 11.54 0.0222**

Cashratio - -12.93 0.4406 + -27.79 0.1714CashFlow - -54.89 <0.0001*** + 4.99 0.713Tangible - -0.13 0.9818 + -42.90 <0.0001***Leverage +/- 0.76 0.8758 +/- 4.23 0.4757InterestRatio + -1.61 0.8608 - -11.40 0.3031LOG (TOTAL ASSETS) - 0.93 0.2486 + -4.46 <0.0001***Log ( M2) - -2.06 0.8864 + -3.21 0.855Log (GDP) +/- 3.30 0.8205 +/- -10.28 0.5621R2 0.192 0.137

Classifying financially constrainedand financially unconstrainedfirms by the KZ indexIntercept 70.49 <0.0001*** 53.21 <0.0001***PB dummy - -5.56 0.0669* - -4.35 0.2403Cons dummy + 15.13 <0.0001*** - -13.93 0.0018***PB dummy ¥ Cons dummy - -9.94 0.0149** + 10.38 0.0373**Cashratio - -9.75 0.5584 + -31.32 0.1237CashFlow - -48.94 <0.0001*** + 0.17 0.9902Tangible - -1.32 0.8176 + -42.05 <0.0001***Leverage +/- -2.50 0.6213 +/- 7.20 0.2438InterestRatio + -0.58 0.9488 - -12.35 0.2627LOG (TOTAL ASSETS) - 0.54 0.4888 + -4.26 <0.0001***Log ( M2) - -3.38 0.8065 + -7.24 0.6671Log (GDP) +/- 2.88 0.839 +/- -7.28 0.6735R2 0.196 0.137

Classifying financially constrainedand financially unconstrained

firms by net credit useIntercept 64.69 <0.0001*** 63.91 <0.0001***PB dummy - -6.86 0.0037*** - -3.46 0.1775Cons dummy + 50.28 <0.0001*** - -81.02 <0.0001***PB dummy ¥ Cons dummy - -14.25 0.0002*** + 19.18 <0.0001***Cashratio - -14.00 0.3515 + -26.52 0.1042CashFlow - -50.71 <0.0001*** + -5.35 0.6189Tangible - -12.49 0.0155** + -20.83 0.0002***Leverage +/- 4.66 0.2669 +/- 0.90 0.8441InterestRatio + -0.87 0.9144 - -11.05 0.2105LOG (TOTAL ASSETS) - -0.12 0.8689 + -3.04 <0.0001***Log ( M2) - -0.03 0.9979 + -0.49 0.97Log (GDP) +/- -1.06 0.9325 +/- -9.71 0.4758R2 0.345 0.444


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Table 5(Continued)

b. The sensitivity of trade credit use to firm-specific factors

Classifying financially constrainedand financially unconstrained firms

by dividend policyIntercept 78.59 0.3167 152.16 0.1129PB dummy - 1.21 0.9289 +/- 10.89 0.5111Cons dummy + 7.88 0.0417** - -9.80 0.0381**PB dummy ¥ Cons dummy - -2.12 0.6566 + 7.26 0.213Cashratio - -47.93 0.0645* + 11.59 0.7146PB dummy ¥ Cashratio + 63.21 0.0615* - -71.96 0.0816*CashFlow - -106.59 <0.0001*** + 4 5.77 0.0367**PB dummy ¥ CashFlow + 83.59 0.0002*** - -65.20 0.0189**

Tangible - -

10.63 0.2305 + -

51.03 <

0.0001***PB dummy ¥ Tangible + 16.53 0.1153 - 12.83 0.3171Leverage +/- -8.36 0.3106 +/- 16.21 0.1077PB dummy ¥ Leverage +/- 12.40 0.2223 +/- -18.90 0.1281InterestRatio + 23.73 0.0825* - -33.29 0.0463**PB dummy ¥ InterestRatio - -47.22 0.0106** + 41.05 0.0689*LOG (TOTAL ASSETS) - 3.39 0.0128** + -4.36 0.0089***PB dummy ¥ LOG (TOTAL

ASSETS)+ -3.45 0.0286** - -0.35 0.8543

Log ( M2) - -5.25 0.7172 +/- -3.18 0.8574Log (GDP) +/- 3.31 0.8197 +/- -9.28 0.6014R2 0.206 0.143


by the KZ indexIntercept 100.68 0.1954 152.96 0.109PB dummy - -6.16 0.6438 +/- 19.11 0.2429Cons dummy + 19.94 <0.0001*** - -14.06 0.0063***PB dummy ¥ Cons dummy - -11.20 0.033** + 7.97 0.2164Cashratio - -46.11 0.0738* + 11.27 0.7217PB dummy ¥ Cashratio + 60.65 0.0713* - -71.45 0.0833*CashFlow - -90.00 <0.0001*** + 3 7.80 0.0902*PB dummy ¥ CashFlow + 69.81 0.0022*** - -60.91 0.0297**Tangible - -16.03 0.0716* + -48.16 <0.0001***PB dummy ¥ Tangible + 20.32 0.0538* - 11.38 0.3786Leverage +/- -17.31 0.0407** +/- 21.19 0.0412**PB dummy ¥ Leverage +/- 16.77 0.1134 +/- -19.74 0.129

InterestRatio +

23.69 0.0813* - -

33.62 0.0439**PB dummy ¥ InterestRatio - -45.16 0.0137** + 40.77 0.0698*LOG (TOTAL ASSETS) - 1.96 0.1536 + -3.33 0.0489**PB dummy ¥ LOG (TOTAL

ASSETS)+ -2.36 0.1364 - -1.24 0.5243

Log ( M2) - -8.27 0.5431 +/- -7.04 0.6733Log (GDP) +/- 5.02 0.7195 +/- -6.63 0.6994R2 0.212 0.145


by net credit useIntercept 99.10 0.1552 129.93 0.0876*PB dummy - 14.14 0.2395 +/- -8.09 0.5374Cons dummy + 49.83 <.0001*** - -80.13 <0.0001***


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firms are more dependent on supplier creditthan unconstrained firms. The interaction term

between the PB dummy and the Cons dummytests the incremental effect on constrainedfirms with the change in information sharing.We predicted a negative coefficient of the inter-action term in the AP360 model and a positivecoefficient of the interaction term in the NC360model.

Table 5a gives the regression results for the

models in Equations 1 and 2, and these are gen-erally consistent with our predictions. Finan-cially constrained firms are more dependent ontrade credit financing than unconstrainedfirms. If we assume that information sharingimproves credit availability, it should havethe most impact on the use of trade credit byconstrained firms. We predicted that (1) con-strained firms extendless net creditthan uncon-strained firms; and (2) information sharingrelieves financial constraints and thus encour-ages credit redistribution via trade credit. If information sharing results in the substitution

of bank credit for trade credit, we may observe adecline in trade credit for all firms. However,firms use trade credit even when they have easyaccess to other means of credit. Therefore, wepredict that the positive effect of informationsharing on credit should result in a narrowingof thegap in net credit between constrained andunconstrained firms, that is, constrained firmsincrease their net credit distribution relative tounconstrained firms with the improvement in

information sharing. The coefficients on PB,Cons, and their interaction terms have theexpected signs, although they are less statisti-cally significant in some cases. We also find thatfirms with more cash flow use less suppliercredit and that firms with more tangible assetsand/or a larger asset base extend less net credit.

Table 5b shows the results of the regressionmodels in Equations 3 and 4, which test howfirm-specific factors affect trade credit use fol-lowing the improvement in informationsharing. The three classification schemes, thatis, dividend policy, KZ index, and net credit

Table 5(Continued)

PB dummy ¥ Cons dummy - -13.78 0.0004*** + 18.22 <0.0001***Cashratio - -37.41 0.1092 + -6.76 0.7907PB dummy ¥ Cashratio + 42.39 0.164 - -37.16 0.2634CashFlow - -83.55 <0.0001*** + 8.65 0.6206PB dummy ¥ CashFlow + 52.45 0.009*** - -21.88 0.3173Tangible - -26.70 0.0009*** + -23.90 0.0063***PB dummy ¥ Tangible + 22.99 0.0156** - 4.05 0.6961Leverage +/- 1.53 0.8353 +/- 1.67 0.8351PB dummy ¥ Leverage +/- 3.94 0.6602 +/- -2.15 0.8257InterestRatio + 22.27 0.0705* - -30.72 0.0223**PB dummy ¥ InterestRatio - -41.29 0.0129** + 33.70 0.0627*LOG (TOTAL ASSETS) - 2.70 0.0278** + -3.57 0.0077***PB dummy ¥ LOG (TOTAL

ASSETS)

+ -4.14 0.0036*** - 0.76 0.6221

Log ( M2) - -5.55 0.6446 +/- 2.31 0.8605Log (GDP) +/- -0.22 0.9858 +/- -9.51 0.4858R2 0.353 0.446

* Indicates statistical significance at the 10 per cent level; ** indicates statistical significance at the 5 per cent level; *** indicatesstatistical significance at the 1 per cent level.Notes: This table presents the regression results of the fixed effects models for the sample of 2,076 Thai firm-year observationsover the 1994~2005 period. Panel (a) presents the regression results for Equations 1 and 2. Panel (b) presents the regressionresults for Equations 3 and 4.KZ = Kaplan and Zingales index.Source: Author’s calculations.


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use, produce similar results, although the sig-nificance of some coefficients varies. In the

AP360 regression, we find a positive and sig-

nificant coefficient for the constrained dummy(Cons), consistent with the view that con-strained firms are more dependent on suppliercredit. The coefficients on the firm-specificfactors and their interaction terms with the PBvariable all carry the expected signs. Forexample, in the AP360 regression, the coeffi-cient on cash holdings is negative and signifi-cant, and the coefficient on its interaction termwith the PB variable is positive and significant,suggesting that firms with more liquidity are

less dependent on supplier credit ( AP360).However, the liquidity effect diminishes withthe increase in information sharing.

Similarly, the cash flow variable has a nega-tive and significant coefficient, but its interac-tion term with the PB variable has a positiveand significant coefficient, suggesting thatfirms with a greater cash flow use less suppliercredit. However, this relationship weakens fol-lowing the establishment of the private credit

bureau. We also find opposite signs for thecoefficients on tangible assets, leverage, inter-

est ratio, and firm size along with their respec-tive interaction terms. Taken together, theseresults suggest that information sharing at leastpartially offsets the effects of firm-specificdeterminants of trade credit.

The results of the NC360 regression are gen-erally consistent with our predictions. Informa-tion sharing seems to weaken the effects of cashholding, cash flow, tangible assets, and interestratio. The coefficients on the variables Tangibleand LOG (TOTAL ASSETS) have opposite signs

to our predictions. It is possible that Thai firmswith a larger asset base and/or more tangibleassets are more likely to receive generous creditterms from their suppliers; consequently, theyuse more trade credit without incurring highcosts as compared with smaller firms with less

tangible assets. Taken together, we find strongempirical support for our hypotheses.

Thailand was the first country affected by

the Asian financial crisis of 1997–98.7 Becauseour sample period overlaps with this crisisperiod, we conducted some robustness checksto ensure our results are not skewed by thecrisis. Our main results still hold (not reportedhere) after we exclude observations from thecrisis years (1997–98).8

Summary and conclusions

Earlier literature shows that informationexchange among creditors overcomes theadverse selection problem in lending, reducesdefault rates, and increases credit volume. Thispaper examines the role of information sharingin trade credit distribution. Using the establish-ment of a private credit bureau in Thailand assignalling an improvement of informationsharing, we obtain three main results: (1) firmsare less dependent on trade credit financing ina better information sharing setting; (2) finan-cially constrained firms are more dependent on

supplier credit than financially unconstrainedfirms and less likely to redistribute funds viatrade credit. However, following the establish-ment of the private credit bureau, financiallyconstrained firms increased fund redistribu-tion via trade credit; and (3) the improvementin information sharing weakens the effects ontrade credit use of firm-level factors such asliquidity, cash flow, asset tangibility, leverage,interest costs, and firm size.

Taken together, our research contributes to

two strands of literature: the role of informationsharing in credit distribution and trade creditresearch. Our results are consistent with theview that better information sharing facilitatescredit allocation. We also find support for theview that bank credit substitutes for trade

7 The Asian financial crisis began with a 20 per cent devaluation of the Thai baht in July 1997. It soon has severe adverseimpacts on the economies of other Asian countries such as Indonesia, Malaysia, Singapore, and Hong Kong.

8 This crisis had a significant impact on Thai firms’ credit policies. The percentage of firms that were net credit usersincreased significantly from an average of 28 per cent before the crisis to 38 per cent during the 1997~98 period, suggestingthat firms switch to trade credit when bank liquidity dries up. We also find that firms that are unable to obtain more credit

from their suppliers cut back on customer credit. Hence, credit redistribution becomes more difficult during a credit crisisperiod. However, Love et al. (2007) show that trade credit only provides temporary relief during periods of credit crises.


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credit. This substitution lowers firms’ cost of capital, given that trade credit is assumed to bemore costly than bank loans. Hence, policies

aiming at facilitating information exchangeamong financial intermediaries should besupported.

Trade credit research suggests that suppliersmay have a monitoring advantage over banks. Itwould be interesting to examine whether such a

monitoring advantage diminishes in a betterinformation sharing environment.

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Appendix 1

Variables Definitions AP360 Accounts payable in days, computed as 360 divided by the accounts payable turnover

ratio, which is the ratio of the cost of goods sold over accounts payable.NC360 Difference between the receivable collection period and accounts payable in 360 days.

The receivable collection period is computed as 360 divided by the accountsreceivable turnover.

PB A binary variable set equal to zero if the firm-year observation is prior to 2000 and setequal to one between 2000 and 2005. This measure was based on the year the privatecredit bureau began operations in Thailand. See DMS (2007) for details.

Cashratio The ratio of cash and marketable securities over total assets.CashFlow The ratio of the sum of net income before extraordinary items plus depreciation minus

net dividends over lagged total assets.Tangible The ratio of net property, plant, and equipment over total assets.

Leverage Total debts to total assets ratio.InterestRatio Total interest expenses to total debt ratio.TOTAL ASSETS The book value of total assets.

M2 Represents money and ‘close substitutes’ for money. Economists use M2 when lookingto quantify the amount of money in circulation.

GDP Gross domestic product. The total market value of all final goods and services producedin a country in a given year.

Source: Author’s calculations.


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the role of information sharing in trade credit distribution evidence from thailand

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