Trade Credit,Bank Credit and Financial Crisis

Trade Credit, Bank Credit andFinancial Crisisn

INESSA LOVEw

AND RIDA ZAIDI¼

wDevelopment Research Group, The World Bank and¼Fitch Ratings

ABSTRACT

This paper examines trade credit behavior in a sample of small and mediumenterprises in four East Asian countries before and after the financial crisis of1998. We use a unique dataset that contains detailed data on trade creditamount and contract terms along with data on access to finance. We findthat after the crisis, firms constrained in bank finance receive less trade creditin terms of percent of inputs bought on credit and shorter time ofrepayment. They also reduce credit extension to their customers in termsof quantity and length of time, due to a smaller pool of available finance.Discount terms rise on both receivables and payables. Our evidence does notsupport the hypothesis that trade credit can substitute for bank credit intimes of the crisis, and instead suggests that liquidity shocks are propagatedalong the supply chains.

I. INTRODUCTION

Around the world, many firms cite insufficient access to bank credit as one ofthe most important constraints to operation and growth of their business.These financing constraints are especially binding for small and mediumenterprises (SME). During the times of financial crisis, as the banks becomemore reluctant to lend, the financing constraints are likely to exacerbate,leading firms to cut investments in capital and R&D and bypass attractiveinvestment projects (Campello et al. 2009). An alternative source of finance iscredit provided by suppliers of raw materials and other inputs, referred to astrade credit. Trade credit has a potential to serve as an important source offinance to financially constrained firms because suppliers might be better able

n This paper’s findings, interpretations and conclusions are entirely those of the authors and donot necessarily represent the views of The World Bank and its Executive Directors or the countriesthey represent. The views expressed in this paper also remain those of the respective authors anddo not necessarily reflect those of the Fitch Group or any of its affiliated entities (‘‘Fitch’’). Fitch isnot responsible, in any way, for the views expressed in this paper and Fitch shall have no liabilityto any person or entity therefore.

r 2010 The Authors. Journal compilation r International Review of Finance Ltd. 2010. Published by BlackwellPublishing Ltd., 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

International Review of Finance, 10:1, 2010: pp. 125–147DOI: 10.1111/j.1468-2443.2009.01100.x

to overcome information and enforcement problems than financial institu-tions.1 These factors may allow suppliers to lend more liberally than banks,especially during the downturns.

Since trade credit is predominantly based on long-term relationships andlikely to involve sunk costs (see Cunat 2007), suppliers have an interest inkeeping their customers in operation. In order to maintain a product marketrelationship, trade creditors that are more dependent on their customer’sbusiness grant more credit to financially distressed customers than banks do(Wilner 2000). Nilsen (2002) shows that during monetary contractions smallfirms are more likely to rely on supplier credit. Petersen and Rajan (1997) findthat credit constrained firms extend less trade credit to their customers and takemore trade credit from their suppliers. Fisman and Love (2003) find that firms inindustries with greater reliance on trade credit exhibit faster growth in countrieswith low levels of financial development. Thus, theoretical arguments andempirical evidence suggest that suppliers might be able to lend a ‘helping hand’to financially constrained firms, mitigating the negative effects of financialcrisis.

However, in a systemic financial crisis it is likely that suppliers of financiallyconstrained firms may also suffer negative liquidity shocks. The existingtheoretical models (e.g. Cunat 2007; Wilner 2000) only deal with a singlecustomer’s distress event, rather than a systemic shock that might affect allsuppliers and customers alike. During such crisis times, the supply chains mightinstead propagate and amplify the liquidity shocks, consistent with indirectevidence in Raddatz (in press). This would also be in line with Boissay andGropp (2007) who find that liquidity shocks are passed down the supply chainfrom defaulting customers to firm’s suppliers, while firms with access to outsideliquidity absorb these shocks with their ‘deep pockets.’

During a financial crisis episode such ‘liquidity shock chains’ can operate inreverse: Firms that experience tightening financing constraints as a result ofbank credit contraction may withdraw credit from their customers, and pass theliquidity shock up the supply chain – i.e. their customers might cut the credit totheir customers, etc. The firms that normally have privileged access to outsidefinance, i.e. the ‘deep pockets’ (Boissay and Gropp 2007), might be the ones thatare most severely affected by the crisis: After all if a firm does not have bankfinance to start with, the banking crisis will have little direct effect on theirfinancial condition. The suppliers to financially constrained firms may alsoreduce the trade credit they extend either because they themselves arefinancially constrained (if the liquidity shocks are highly correlated) or becausethey choose to withdraw credit from their less creditworthy customers. Thus,the supply chains might propagate the liquidity shocks and exacerbate theimpact of the financial crisis.

1 See Smith (1987), Brennan et al. (1988), Biais and Gollier (1997), Petersen and Rajan (1997) and

Burkart and Ellingsen (2004) among others.

International Review of Finance

r 2010 The AuthorsJournal compilation r International Review of Finance Ltd. 2010126

In this paper we use the period of systemic financial crisis to test whethersuppliers can mitigate the impact of the banking crisis by increasing provisionof trade credit to their financially constrained customers, or whether theliquidity shocks are propagated along the supply chains, amplifying the impactof the crisis. We find evidence consistent with the latter.

We use a unique dataset based on a survey carried out after the 1998 financialcrisis in four East Asian countries: Thailand, Korea, Philippines and Indonesia.About 3000 firms were surveyed on the impact of crisis, access to various sources offinance before and after the crisis and their prospects for recovery. The dataset isperfectly suited for our purpose for several reasons. First, unlike most other cross-country datasets, it contains mainly small- and medium-sized firms, which aremost likely to be constrained in access to bank finance. Second, the survey containsvery detailed questions on the use of trade credit before and after the crisis.Importantly, we have data on the amount of credit (i.e. the percent of inputsbought on credit and percent of sales sold on credit) and the contract terms (i.e. thematurity of the credit and the price of credit in terms of the discount allowed oncash payments). Because we have these detailed data pre-crisis and post-crisis, wecan difference out any firm-specific level effects. Finally, the survey contains severalquestions that allow us to separate firms that are likely to be more constrained inthe access to bank finance. This allows us to focus on the heterogeneous responsesto crisis of firms with different degree of financing constraints.

We find that on average the use of trade credit declines after the crisis. This isevidenced in the decline in the percent of inputs our firms buy on credit fromtheir suppliers (i.e. accounts payables) and the percent of sales they extend oncredit to their customers (i.e. accounts receivables). We also find evidence thatthe length of payables declines in three out of four countries, but find mixedevidence on the length of receivables. Interestingly, we find that the cost ofcredit increases as firms offer higher discount on cash repayments after thecrisis. Thus, trade credit usage becomes more expensive and more restrictiveduring the crisis. Our aggregate results do not support the hypothesis that tradecredit is a viable substitute for contracted bank credit in times of financial crisis.

Our main interest is to study heterogeneous responses of financiallyconstrained firms. We use two key indicators for financially constrained firms.An objective measure separates firms that have applied for a bank loan but havebeen declined before or after the crisis. Firms that have submitted a loanapplication have revealed their demand for more bank finance, and thus therejection indicates the presence of financing constraints. The second measure isa subjective perception measure based on the survey response that access todomestic bank finance has become more restrictive during the crisis.

We have two main results. First, financially constrained firms extend lesscredit to their customers both in terms of percent of output sold on credit andlength of time they allow their customers and they charge higher cost for thetrade credit they offer. Second, financially constrained firms buy a smallerpercent of inputs on credit, have a shorter length of time to repay the credit totheir suppliers and have to pay a higher cost for trade credit.

Trade Credit, Bank Credit and Financial Crisis

r 2010 The AuthorsJournal compilation r International Review of Finance Ltd. 2010 127

The first finding has an easy interpretation – firms that face financialconstraints have to cut the credit they provide to their customers. Thus, theypass on their liquidity shock upstream to their customers, who in turn may cuttrade credit to their own customers. In this way the liquidity shock travels alongthe supply chain consistent with the previous evidence in Boissay and Gropp(2007) and Raddatz (in press).

The second finding is more difficult to disentangle because we do not haveany information on the financial position of the firm’s suppliers. There are twopossible cases that might affect the interpretation of this finding. First, thesuppliers of the financially constrained firms might themselves be financiallyconstrained. Note that on average we expect all firms to become moreconstrained during the crisis because of the systematic nature of financialcrisis. However, our focus is on heterogeneous responses as some firms are likelyto be more constrained than others, which we can isolate with our survey data.In other words, this case implies a high correlation between supplier’s andcustomer’s financing constraints, which is consistent with indirect evidence inRaddatz (in press). If this is the case, we can argue that our second result has thesame interpretation as the first one – that suppliers simply pass on theirliquidity shock up the supply chain to their customers.

An alternative interpretation is possible if suppliers’ financing constraints arenot highly correlated with their customer’s financing constraints. In other words,suppliers of firms that we identify as financially constrained might on average beno different (at least in their degree of financing constraints) from suppliers offirms that we identify as unconstrained. In this case, our results would suggestthat suppliers of financially constrained firms are not willing to lend them a‘helping hand’ and they withdraw credit from less creditworthy firms.

While we cannot disentangle which of the two possibilities is indeed thecase, our results unambiguously show that negative shocks to the supply ofbank credit cannot be mitigated by an increase of trade credit, which has clearpolicy implications.

One potential concern with our results might arise because usage of tradecredit is likely to be correlated with firm’s growth opportunities. In the overallenvironment of financial crisis firms might find it optimal to fund less long-runinvestment and thus require less bank credit and trade credit to finance theirworking capital. In general it is difficult to control for firm’s growthopportunities; however, we can offer one robustness test because of the highproportion of exporting firms in our sample. Since the East Asian crisis wasassociated with large currency devaluations, all exporters found their growthopportunities expanding as their goods became more competitive. When welimit our sample only to exporting firms, we find very similar results. Thisevidence allows us to dismiss this alternative explanation.2

Our paper is closely related to Love et al. (2007) who study the effect of theAsian crisis on large publicly listed firms and, similar to our paper, find that

2 We appreciate an anonymous referee for bringing this point to our attention.



financially weaker firms extend less trade credit to their customers after thecrisis. Our paper extends the earlier work of Love et al. (2007) along twodimensions. First, we study trade credit behavior of small and medium firms,which have less access to bank finance and different trade credit behavior thanlarge public firms (Nilsen 2002; Boissay and Gropp 2007). The differences insample composition are important because we find different results on the tradecredit received by constrained firms.3 Second, and more importantly, we havedetailed data on trade credit terms – the length of payables and receivables andearly payment discounts, while Love et al. (2007) have only data on the amountof credit from firms’ balance sheets. While previous literature only studiedcredit terms in the cross section of firms (i.e. Ng et al. 1999; Giannetti et al.forthcoming), we find significant variation in trade credit terms over thebusiness cycle, which has not been documented previously.

The rest of the paper is as follows: Section II describes the dataset, Section IIIpresents the aggregate patterns in trade credit changes after the crisis, Section IVdescribes the empirical model and presents main regression results, Section Vpresents robustness checks and Section VI concludes.

II. DATA

We use a unique dataset based on a survey of 3160 manufacturing firms inThailand, Philippines, Korea and Indonesia between November 1998 andFebruary 1999.4 The survey was carried out by the World Bank and is describedin Dwor-Frecaut et al. (2000). There are two distinguishing features of thisdataset: first, it contains mostly SME and second, the survey was done aroundthe time of the Asian crisis and contains questions relating to pre-crisis and post-crisis environment. The survey comprises of quantitative indicators such asemployment levels and balance sheet data, along with qualitative questionsdealing with financing and capacity constraints that firms faced during and afterthe crisis. The qualitative questions were answered in an interview by a seniormanager of the firm that gauged their views on the difficulties confronted as aresult of the crisis and the sources of these difficulties. The sectors covered in thesurvey include food processing, auto parts, machinery, chemicals, garments andtextiles and electronics, although sectoral composition differs across countries.

3 Contrary to our results, Love et al. (2007) find that more constrained firms increase the reliance

on trade credit during the crisis. However, their sample consists of large publicly traded firms,

while our sample consists mainly of small and medium private firms. Large publicly traded

firms have more market power, which will increase their supplier’s willingness to extend them

extra trade credit (Brennan et al. 1988; Giannetti et al. 2008). Even in our sample larger firms

increase their reliance on trade credit during the crisis (in terms of increasing percent of inputs

bought on credit).

4 The final number of firms in our estimations varies, but is significantly reduced due to missing

observations in some variables.



As with all survey data, we have to keep in mind that it may be noisy,especially the answers to the pre-crisis quantities, which were collected after thecrisis. In addition, we need to be concerned with survival bias. However, thebias in this case goes against our results. For example, if we have found thattrade credit helps financially constrained firms during the crisis, one mightargue that firms that exited the sample were not helped by the trade credit,which would bias the results toward finding a substitution effect. In our case wedo not find this effect, and this is not due to the attrition bias.

Another major advantage of this survey is very detailed information on tradecredit behavior of the firms before and after the crisis. On the payables side, thesurvey contains percent of inputs bought from suppliers on credit (percent),length of time for which credit is received, measured in days (length) anddiscount terms that suppliers give, measured as a percent discount on early orcash payments (discount), both before and after the crisis. We have a similar setof responses for receivables: percent of output sold on credit to customers(percent), length of time for which credit is given (length) and discount termsthat firms offer to customers (discount). Questions relating to discount termsare somewhat ambiguous, since the questions do not require respondents toidentify particular forms of discount they offer and are only required toquantify a single ‘discount price.’ However, we do find some responses thatdetail types and terms of discounts firms offer, which vary from discounts on:cash payment, prior customer relationships, size of order; defects in the productand (or) on early payments. We only analyze discounts on cash payments andearly repayments of credit, both of which reflect a cost of financing, and alsocomprise a sizeable portion of the sample of observations on discount terms.The other discount terms are either non-quantifiable or not comparable acrossthe board and are therefore excluded.5

To analyze the relationship between availability of bank finance and tradecredit finance we choose variables that reflect the degree of financingconstraints. The survey has detailed questions on the declined loan applica-tions, reliance on bank loans, restrictiveness in bank credit, sustainability inloan repayments and subjective measures of perceptions of constraints inavailability of domestic bank finance and short-term working capital.

We use two main indicators for financially constrained firms. Our firstmeasure is constructed from a question on whether a bank or financialinstitution declined a loan to the firm during a specified period. We argue thatfirms that were declined a loan have a revealed demand for more loans becausethey have applied for additional loans. The loan decline question covers threetimes periods: decline in the first half of 1997, in the second half of 1997 and in1998. We construct a dummy variable named ‘anydecline,’ which equals to oneif the firm was denied loans in any of the three periods. We consider anydecline

5 Discount terms on both payables and receivables were also corrected by removing observations

where discount terms were higher than 40%. This procedure eliminated 52 observations on

both payables and receivables side.



to be an objective measure of financing constraints since it indicates firms thathave an unmet demand for more bank credit.6

Our second measure is a subjective perception measure based on the surveyresponse that asks respondents whether the access to domestic bank finance hasbecome more restrictive during the crisis. Specifically, the survey contains thequestion: ‘Did credit from domestic banks become more restrictive after thecrisis?’ This question required respondents to rank whether bank credit becameless restrictive, more restrictive or did not change after the crisis. We create adummy called ‘dombank’ that takes on a value of one if the availability ofdomestic bank finance became more restrictive after the crisis.7 Note that ourmain measures focus on ability of firms to access bank finance, not the cost offinance, which clearly has increased during the Asian crisis.8

While we use our two measures – anydecline and dombank – as our key measuresof financing constraints, the survey allows us to construct several additionalmeasures, which we use for robustness checks. First, we use questions in whichfirms were asked to quantify percentage of bank finance they would use beforeand after the crisis for short-term working capital and long-term expansionpurposes.9 These responses are used to understand changes in reliance on bankloans and their implications for trade credit. We expect the crisis to have a largeradverse effect on the set of firms that rely on bank loans as compared with firmsthat rely on their own funds to finance investments and working capital. Forinstance, an overall financial sector credit crunch will increase financialconstraints for firms relying on bank loans as the cost of debt servicing mayincrease in the face of rising interest rates. In addition, firms that have a highproportion of short-term debt might face difficulties in rolling it over. Since firmsin our dataset have a high proportion of short-term debt, reliance on loans can bean indicator of a weak financial position after the crisis. In contrast, firms thatrely on internal finance will not be as severely affected by a bank credit crunch.

Thus, we create the following additional variables: ‘anyloan’ is a dummy thattakes on the value one if firms report relying on bank loans either before and/orafter the crisis; ‘loan10’ is a dummy for firms that relied on loans before but notafter crisis and ‘loan11’ is a dummy for those that relied on loans before and afterthe crisis. While firms that rely on loans before the crisis are more disadvantaged

6 We cannot say if these firms are the most constrained, since the least creditworthy firms may not

even have applied for a bank loan. Petersen and Rajan (1997) use a similar variable, which has

the same limitation.

7 The scale is from 1–5, where 1–2 was less restrictive; 3 is no change and 4–5 was more restrictive.

We used responses 4–5 as measure of constrained firms.

8 While the overall cost of funds has increased, it is the difference in costs of internal funds and

external funds that determines firm’s preferences for using internal or external funds, see

Fazzari, Hubbard and Petersen (1988) among others.

9 Reliance on bank loans is an aggregate of two questions in the survey: (1) If this firm is going to

undertake a substantial investment for building or machinery, estimate what percentage of

financing would come from loan from local bank, before and after the crisis. (2) If this firm

needed short-term financing, estimate what percentage would come from loan from local bank,

before and after the crisis.



during the crisis, the firms that suspend reliance on loans after the crisis (i.e.those for which loan10 equals to one) are likely to be the most constrained, asthey consider that loans become less accessible during the crisis.10

Although a small percentage of firms stopped relying on bank loansaltogether, a much larger proportion reduced its reliance on loans. Therefore,we construct an additional variable called ‘loandown,’ which equals one ifreliance on loans (in terms of percent of investment or working capital financedwith loans) has decreased after the crisis. We consider those firms that reducedreliance on loans after the crisis as becoming more constrained in access to thebank finance.

A reduction in reliance on loans could be due to same factors thatcontributed to firms discontinuing reliance on loans (e.g., an increased costof servicing loans or restrictions from banks to roll-over debt, which wouldforce firms to look for alternative sources). We include loandown measure ofdecreased reliance on loans together with anyloan variable to isolate the effect ofcrisis on the firms with reliance on loans (proxied by anyloan) and on the firmsthat reduced the reliance on loans, i.e. those that become more constrainedafter the crisis.

We also use firms’ responses to questions about loan repayments.11 Thequestion asked about the perceived ability of firms to make loan payments overa set of time intervals (less than 3 months, 3–5 months, 6 months to a year,greater than a year and indefinitely). We create two dummy variables for firmsthat can sustain their interest payments for a year or more, ‘sustain1,’ and thosewho cannot, ‘unsustain1.’ The control group comprises firms that do not havean outstanding loan after the crisis. It is not clear that firms that cannot sustaintheir loan payments are more constrained than those who cannot even obtain aloan. However, it is still interesting to study the trade credit policy of firms thatfind themselves in an unsustainable loan repayment position. Therefore, weinclude this variable in our robustness checks.

Lastly, we have another perception measure of financing constraints whichwe construct using firms’ responses to the following question: ‘Did firms face adecline in output due to insufficient bank credit for working capital?’ This is aqualitative question in which firms were asked to rank on a scale of 1(unimportant) to 5 (very important) factors that contributed to an outputdecline (with lack of working capital finance from banks being one of thefactors). We constructed a dummy variable workcap, which equals to one if thefirm responded in the range of 3–5, to reflect firms that were highly constrained

10 Note that these loan variables do not indicate the presence or absence of bank loans because the

responses are to a question on percentage of financing that firms will take from banks in case of

a hypothetical investment need. However, it is plausible that those firms who can potentially

access bank loan are more likely to answer that they will use this source of finance in these

hypothetical examples. Therefore, we consider these proxies to measure reliance on bank loans

instead of indicating any actual bank loans, which firms may or may not have.

11 The exact question is ‘How long will you be able to sustain meeting the loan payments if the

interest rates remain at current levels?’ This variable is only observed after the crisis.



by lack of working capital finance, and zero otherwise (i.e. not constrained bylack of bank finance for working capital).12

The next section presents descriptive statistics and discusses aggregatechanges in trade credit and financing constraints around the time of the crisis.

III. AGGREGATE CHANGES AROUND THE CRISIS

A. Trade credit

We first examine changes in trade credit around the crisis time. Table 1 shows thebehavior of trade credit for our entire set of trade credit variables. We compareaverage values for each of the trade credit variables before and after the crisis andindicate whether the differences are significant. For each variable, we carry out apaired t-test of each firm before the crisis with the same firm after the crisis.

We notice a strong pattern of decline in both percent receivables and percentpayables after the crisis. The percent of payables declines for all four countries inour sample, while percent of receivables declines for all, but Philippines. Thereis also a significant decline in duration of payables of 1 week (observed in threeout of four countries), although there is no similar decline in duration ofreceivables, for the whole sample.13 Thus, overall, we observe contraction intrade credit provided and received by the firms in our sample.

Since bank credit has declined on aggregate after the crisis, this evidence doesnot support the hypothesis that trade credit can fully substitute for contractedbank credit. However, this behavior is consistent with Burkart and Ellingsen(2004) who predict a procyclical movement in trade credit with respect towealth shocks in credit constrained firms. They argue that this effect would bemost pronounced in developing countries. On the other side, Ramey (1992)finds countercyclical movements of trade credit for US data, comprising ofunconstrained firms. Nilsen (2002) also finds small and large firms withoutbond ratings, use greater amounts of trade credit during monetary contractionsto supports their diminished bank borrowing.

Interestingly, we also observe a significant increase in discount on both payablesand receivables. As the discount term that we use is a percentage discount on cashpayments, it is proportional to the cost of trade credit. Thus, the data reflect thatthe cost of trade credit moved along with the interest rates, which rose sharply

12 Scale 3–5 is a medium to very high contribution of working capital problems in output decline.

We also created another dummy that uses ranks 2–5 for firms that were constrained and 1 for

firms that were unconstrained. There was no significant difference in the results.

13 Breaking the results by country, we notice some interesting differences. In the case of length of

receivables, evidence is contradictory and inconclusive. For instance for Thailand there is a

shortening of length of receivables by 2 days and in Indonesia, there is a much sharper decline of

approximately 12 days, although this change is not significant. However, for Korea and

Philippines, there is a statistically significant rise in length of receivables. The evidence is more

consistent on the payables side as average length of payables decline for three out of four

countries, except Korea, where there is no significant change.



Table 1 Trade credit: pre-crisis and post-crisis

Observations Before After Significance

All countriesReceivables

Percent 2203 78.59 73.82 nnn

Length 1908 60.51 60.95 NSDiscount 763 8.77 11.71 nnn

PayablesPercent 1818 77.82 63.95 nnn

Length 1682 69.23 62.55 nnn

Discount 604 8.18 11.03 nnn

ThailandReceivables

Percent 507 80.34 77.40 nnn

Length 513 54.33 52.75 nn

Discount 156 4.39 4.21 NSPayables

Percent 545 80.23 77.00 nnn

Length 557 62.58 55.73 nnn

Discount 167 4.20 3.89 NSKorea

ReceivablesPercent 728 76.83 71.62 nnn

Length 717 87.65 92.47 nnn

Discount 459 10.74 11.33 nnn


Length 722 85.42 85.95 NSDiscount 324 10.51 11.64 nnn

PhilippinesReceivables

Percent 467 82.29 82.90 NSLength 180 12.16 35.45 nnn



Length 54 46.98 14.44 nnn


IndonesiaReceivables

Percent 501 75.90 64.92 nnn

Length 498 45.29 33.24 NSDiscount 8 18.75 10.88 n


Length 349 49.80 32.45 nnn

Discount 5 11.00 10.60 NS

The table gives average of the percentage of goods sold to customers on credit (receivables,percent) or percent of inputs bought from suppliers on credit (payables, percent), length forwhich credit was given (receivables) or received (payables), in number of days, and discount termsoffered to customers or offered by suppliers, in percentage discount given on cash payment. Alldata are from the World Bank survey of establishments on impact of East Asian Crisis (1999).nSignificant at 10%.nnSignificant at 5%.nnnSignificant at 1%.NS is not significant.



after the crisis. Previous literature relied on cross-sectional data to study contractterms (i.e. Ng et al. 1999; Giannetti et al. forthcoming). Thus our paper providesnovel results on changes in trade credit contract terms over the business cycle.

B. Financing constraints

Panel A of Table 2 presents summary statistics for the set of financing constraintvariables. We highlight two of the main constraints measures – anydecline anddombank – on the top of the table, followed by the additional measures that weuse for robustness checks. A significant proportion of firms (though clearly not amajority) are defined as constrained according to our measures. We find thatnearly 30% of firms have faced a decline in their loan applications (i.e.anydecline), while over 45% of firms say that domestic bank finance havebecome more restrictive during the crisis (i.e. dombank).

Our additional measures paint a similar picture. For instance, 36% of firmscan service loan repayments for more than a year, but there is a significantproportion (24%) that foresees problems in loan repayments. Over 30% of firmsclaimed that inability to obtain bank financing for working capital hadcontributed to decline in output.

Panel B reports changes in constraint variables over the crisis period. Ascompared with 10% firms that were declined a loan before the crisis, 28% weredeclined a loan after the crisis. Loandown variable provides a sharper increase inthe extent of credit constraints than loan10 measure. About 140 firms in oursample completely stopped relying on bank loans after the crisis (about 7% ofnon-missing observations), while we observe a larger proportion, approximately30%, reduced their reliance on loans. These patterns suggest that financialconstraints became more binding after the crisis.

Correlation between our various financing constraints variables is shown inPanel C. As expected, all constraints measures are positively correlated,although the strength and significance of correlation varies across the differentmeasures. The variables are not perfectly correlated, which means that theycapture different aspects of financing constraints that firms face. Hencedifferent measures isolate different subsets of firms. Not surprisingly firms thatreduced reliance on loans after the crisis (i.e. loandown) and firms that stoppedrelying on loans (i.e. loan10) exhibit a high and significant correlation of 0.4.Firms that were rejected a loan (anydecline) were also more likely to report beingconstrained in bank finance (dombank) or claim that lack of working capitalfrom banks contributed to a decline in their output (workcap).

IV. REGRESSION ANALYSIS

A. Empirical methodology

Our empirical estimation focuses on heterogeneous responses to financial crisisfor firms with different financial positions. Since our trade credit variables are



Table 2 Descriptive Statistics

Panel A: Summary statistics on financing constraints variablesa

No. Observations Mean SD

Main measuresAnydecline 2697 0.288 0.453Dombank 2842 0.455 0.498

Additional measuresAnyloan 1868 0.770 0.421Loandown 1868 0.330 0.471Sustain1 2942 0.357 0.479Unsustain1 2942 0.237 0.425Workcap 2887 0.311 0.463

Panel B: Frequency distribution of selected constraints variablesb

Declined loan before Declined loan after

No Yes TotalNo 1919 508 2427Yes 26 254 280Total 1945 762 2707

Reliance on loan before Reliance on loan after


Anyloan Loandown


Panel C: Correlation matrix of financing constraints measuresc

Anydecline Loan10 Loandown Unsustain Workcap Dombank

Anydecline 1.0000Loan10 0.1046n 1.0000

0.0000Loandown 0.1704n 0.4001n 1.0000

0.0000 0.0000Unsustain1 0.0744n 0.0122 0.0200 1.0000

0.0002 0.6049 0.3957Workcap 0.2649n 0.0689n 0.0351 0.1636n 1.0000

0.0000 0.0037 0.1398 0.0000



available for the same firm both before and after the crisis, we focus on thedifference – i.e. the change in trade credit over the period of the crisis (defined asafter crisis value minus before crisis value). Using change in trade credit as ourdependent variable implicitly controls for firm-specific factors that affect thelevel of trade credit. Thus, we examine whether financing constraints andchanges in availability of bank finance affected trade credit behavior during thecrisis.

We estimate the following model using ordinary least squares:

DTCi ¼ ai þ b1 � FINi þ b2 � Xi þ b3 � Ci þ b4 � Si þ ei ð1Þ

where TC is one of the six dependent variables: percent payables, lengthpayables, discount terms on payables, percent receivables, length receivablesand discount terms on receivables. The explanatory variable FIN is one of thevariables measuring financing constraints, described above; X is a vector of firm-specific variables, which includes a measure of size (log of sales in 1996 in USdollars) and dummy variables for exporters and importers. We also includedummies to control for country and industry effects: C is a vector of countrydummies and S is a vector of sector dummies. Industry dummies are important

Panel C: Correlation matrix of financing constraints measuresc

Anydecline Loan10 Loandown Unsustain Workcap Dombank

Dombank 0.3285n 0.1790n 0.0904n 0.1207n 0.2993n 1.00000.0000 0.0000 0.0002 0.0000 0.0000

aPanel A gives summary statistics on the set of dummy variables that indicate financialconstraints. Anydecline is a dummy for firms that were declined a bank loan before or after thecrisis or in both periods, anyloan is a dummy for firms that relied on loans either before or afterthe crisis or in both periods, loandown is a dummy for firms that reduced reliance on loans afterthe crisis, sustain1 is a dummy for firms that can sustain loan repayments for a year or more ifinterest rates remained at the end of 1998 to early 1999 levels, unsustain1 is a dummy for firmsthat cannot sustain loan repayments for a year (the control group in both these variables are firmswith no outstanding loans), workcap is a dummy for firms that cite lack of working capital financefrom banks contributed to a decline in output after the crisis, dombank is a dummy for firms forwhich domestic bank credit became more restrictive after the crisis.bPanel B compares access to finance before and after the crisis. Declined loan before are firms thatwere declined a loan before the crisis; declined loan after are firms that were declined a loan afterthe crisis; reliance on loan before are firms that relied on loans before the crisis; reliance on loan afterare firms that relied on loans after the crisis; anyloan is a dummy for firms that relied on loanseither before or after the crisis; loandown is a dummy for firms that reduced reliance on loans afterthe crisis.cIn Panel C anydecline is a dummy for firms that were declined a loan either before or after thecrisis; loan10 is a dummy for firms that relied on loans before but not after the crisis; loandown is adummy for firms that reduced reliance on loans after the crisis; unsustain1 is a dummy for firmsthat cannot make loan repayments for at least 1 year; workcap is a dummy for firms for whomoutput decline can be largely accounted for by lack of bank finance for working capital; dombankis a dummy for firms that faced increased restrictions in domestic bank credit after the crisis.nSignificant at 10% or higher.



since previous research documents large differences in terms of trade creditacross industries but little variation within industries (see Ng et al. 1999).

B. Results

We present our results with the two main indicators of financial constraintsdescribed earlier: anydecline and dombank. We run each regression in ourspecification separately for each of these two measures, and report our mainresults in Tables 3 and 4.

We find that on receivables side the amount of goods sold on credit and thelength of time the credit is offered for have declined disproportionately morefor firms that are more financially constrained for both measures. On thediscount terms we find positive but insignificant coefficients on receivablesside.

On the payables side we find that financially constrained firms receive lesscredit from their suppliers (in terms of percent of inputs bought on credit) andthey keep credit for shorter time. They also pay higher cost of credit as thediscount on cash payments has increased after the crisis. These results aresignificant at 5% and above.

In terms of the economic magnitude, our results show that firms that arefinancially constrained receive about 3–4% less goods on credit than those thatare not constrained after the crisis. Because we estimate our model in changes(post-crisis minus pre-crisis), the relevant comparison is the average change intrade credit received, which is in our sample has declined by around 14% (seepayables, percent, all countries in Table 1). While all firms buy less inputs oncredit after the crisis, those that are financially constrained buy nearly 30% lessinputs on credit than the average firm, which is an important difference. Ourresults also show that financially constrained firms also repay their credit totheir suppliers 4 days earlier than not constrained firms, relative to pre-crisis,which shows even larger impact, compared to an average of 7% decline in theoverall sample.

As discussed in the introduction, the results on receivables side have arelatively straightforward interpretation: Firms that face financial constraintshave to cut the credit they provide to their customers. This is consistent withthe previous evidence of liquidity shock chains in Boissay and Gropp (2007)and Raddatz (in press).

The results on the payable side are slightly more difficult to disentanglebecause we do not have any information on the financial position of the firm’ssuppliers. There are two possible cases that might affect the interpretation ofthis finding. First, the suppliers of the financially constrained firms mightthemselves be financially constrained. Note that on average we expect all firmsto become more constrained during the crisis because of the systematic natureof financial crisis. However, our focus is on heterogeneous responses as somefirms are likely to be more constrained than others, which we can isolate withour survey data. Thus, this case implies a high correlation between supplier’s



and customer’s financing constraints, which is consistent with indirectevidence in Raddatz (2009). If this is the case, we can argue that supplierssimply pass on their liquidity shock up the supply chain to their customers,consistent with our first result.

Table 3 Decline of loan applications

Receivables Payables

Percent Length Discount Percent Length Discount

(1) (2) (3) (4) (5) (6)

Size 0.259 �0.775 0.013 1.034 0.54 �0.075[1.24] [2.31]nn [0.10] [3.34]nnn [1.53] [0.49]

Export �0.409 2.151 �0.19 0.113 0.166 �1.625[0.45] [1.50] [0.43] [0.10] [0.11] [3.57]nnn

Import 0.864 �2.862 �0.109 �0.826 �3.28 �0.108[0.90] [1.84]n [0.22] [0.67] [2.14]nn [0.22]

Anydecline �3.321 �3.346 0.538 �4.041 �4.364 1.117[3.53]nnn [2.21]nn [1.28] [3.58]nnn [3.00]nnn [2.82]nnn

Tha 7.498 7.957 6.695 17.032 8.264 �1.311[5.36]nnn [4.50]nnn [1.50] [8.51]nnn [4.04]nnn [0.98]

Kor 7.427 12.212 7.603 19.927 20.944 0.278[4.21]nnn [4.63]nnn [1.69]n [7.30]nnn [7.42]nnn [0.18]

Phl 10.862 39.099 20.043 �41.423 �13.592 12.494[7.41]nnn [9.66]nnn [4.41]nnn [10.95]nnn [2.04]nn [6.27]nnn

Sector 1 0.769 3.297 0.555 4.078 5.306 1.497[0.58] [1.60] [0.81] [2.27]nn [2.49]nn [1.89]n

Sector 2 2.911 6.296 0.944 4.078 6.676 0.093[2.15]nn [2.70]nnn [1.28] [2.27]nn [3.02]nnn [0.13]

Sector 3 2.308 3.794 �1.598 1.526 10.078 �2.152[1.46] [1.77]n [0.89] [0.56] [4.10]nnn [1.19]

Sector 4 2.492 12.019 �0.127 2.634 11.25 0.338[1.86]n [4.85]nnn [0.24] [1.68]n [5.20]nnn [0.58]

Constant �13.545 �8.414 �7.442 �30.032 �23.64 1.028[6.69]nnn [2.83]nnn [1.63] [9.21]nnn [7.04]nnn [0.60]

Observations 1706 1584 643 1531 1464 511R2 0.05 0.14 0.36 0.34 0.11 0.4

The table shows regressions of change in components of receivables and payables on firms-specific variables. All data are from World Bank survey of establishments on impact of East AsianCrisis (1999). Size is log of sales in 1996, export is a dummy for firms that export their output,import is a dummy for firms that import their inputs, anydecline is a dummy for firms that weredeclined a bank loan before or after the crisis or in both periods, tha is a dummy for Thailand, koris a dummy for Korea, phl is a dummy for Philippines, sector 1 is a dummy for firms that operate ingarments and textiles industry, sector 2 is a dummy for firms that operate in electronics industry,sector 3 is a dummy for firms that operate in foods industry, sector 4 is a dummy for firms thatoperate in autoparts and machinery industry, the omitted sector is chemicals. Robust t-statisticsare in brackets.nSignificant at 10%.nnSignificant at 5%.nnnSignificant at 1%.



An alternative interpretation is possible if suppliers’ financing constraints arenot highly correlated with their customer’s financing constraints. In otherwords, suppliers of firms that we identify as financially constrained might onaverage be no different (at least in their degree of financing constraints) from

Table 4 Restrictions in credit availability from domestic banks



(1) (2) (3) (4) (5) (6)

Size 0.099 �0.761 0.048 0.89 0.493 0.052[0.51] [2.22]nn [0.32] [2.52]nn [1.35] [0.25]

Export �0.212 2.176 �0.407 0.779 0.422 �1.999[0.24] [1.46] [0.80] [0.63] [0.27] [3.77]nnn

Import 0.663 �2.925 0.076 �0.867 �3.445 �0.192[0.72] [1.85]n [0.13] [0.67] [2.17]nn [0.33]

Dombank �2.251 �2.352 0.285 �2.781 �4.113 1.18[2.77]nnn [1.66]n [0.58] [2.45]nn [2.98]nnn [2.35]nn

Tha 7.794 7.709 5.576 17.821 9.372 �0.812[5.26]nnn [4.05]nnn [1.10] [8.47]nnn [4.31]nnn [0.58]

Kor 6.463 11.303 6.298 19.39 21.349 1.344[3.87]nnn [4.30]nnn [1.23] [6.68]nnn [7.48]nnn [0.79]

Phl 11.652 37.957 20.438 �41.498 �17.958 13.503[8.87]nnn [12.37]nnn [4.03]nnn [12.58]nnn [3.34]nnn [7.39]nnn

Sector 1 0.99 3.077 0.105 4.721 5.54 0.476[0.79] [1.48] [0.14] [2.60]nnn [2.53]nn [0.53]

Sector 2 2.157 3.891 0.445 4.171 5.369 �0.662[1.70]n [1.70]n [0.54] [2.20]nn [2.33]nn [0.79]

Sector 3 2.229 4.008 �3.137 3.891 13.466 �3.078[1.50] [1.84]n [1.73]n [1.44] [5.51]nnn [1.62]

Sector 4 2.638 13.486 �0.205 2.422 12.403 �0.387[1.98]nn [5.32]nnn [0.37] [1.50] [5.61]nnn [0.62]

Constant �12.454 �7.998 �5.96 �29.933 �23.948 0.356[6.32]nnn [2.63]nnn [1.15] [8.35]nnn [6.83]nnn [0.17]

Observations 1755 1534 668 1466 1367 511R2 0.06 0.16 0.43 0.4 0.14 0.44

The table shows regressions of change in components of receivables and payables on firms-specific variables. All data are from the World Bank survey of establishments on impact of EastAsian Crisis (1999). Size is log of sales in 1996, export is a dummy for firms that export theiroutput, import is a dummy for firms that import their inputs, dombank is a dummy for firms forwhich domestic bank credit became more restrictive after the crisis, tha is a dummy for Thailand,kor is a dummy for Korea, phl is a dummy for Philippines, sector 1 is a dummy for firms thatoperate in garments and textiles industry, sector 2 is a dummy for firms that operate in electronicsindustry, sector 3 is a dummy for firms that operate in foods industry, sector 4 is a dummy forfirms that operate in autoparts and machinery industry, the omitted sector is chemicals. Robustt-statistics are in brackets.nSignificant at 10%.nnSignificant at 5%.nnnSignificant at 1%.



suppliers of firms that we identify as unconstrained. In this case, our resultswould suggest that suppliers of financially constrained firms are not willing tolend them their ‘helping hand’ and they withdraw credit from less creditworthyfirms.

While we cannot disentangle which possibility is indeed the case, our resultsdefinitely point to the fact that negative shocks to the supply of bank creditcannot be mitigated by an increase of trade credit. If anything, our resultssuggest that liquidity shocks are passed along the supply chain and the impactof crisis is propagated and possibly amplified. Our results are consistent withprior evidence of correlation of shocks along the supply chains in Boissay andGropp (2007) and Raddatz (in press).

It is interesting to note that in our data larger firms receive more trade creditfrom their suppliers (in terms of percent of inputs bought on credit) and keepcredit for longer number of days.14 Larger firms also get shorter repayment ofcredit they offer to their customers (length of receivables). These results areconsistent with theory and prior evidence which point that because larger firmshave more market power, they can use it to increase their supplier’s willingnessto extend them extra trade credit (Brennan et al., 1988; Giannetti et al.,forthcoming). This market power proves helpful during the crisis times andwhile on average all firms experience declines in trade credit received, largerfirms are less subject to these declines. Larger firms are also able to use theirmarket power on the receivables side, collecting credit from their customers inshorter amount of time. These results help explain the differences in our resultswith Love et al. (2007) who used a sample of large publicly traded firms andfound that trade credit use increased in firms with high proportion of short-term debt.

Our results do not support the argument that larger firms serve as absorbersof liquidity shocks suggested by Boissay and Gropp (2007). It is important tonote that our case is different because we consider the episode of a systemicfinancial crisis, while Boissay and Gropp (2007) study firm behavior in more‘normal times.’ It is important to note that all of our results may not begeneralized beyond the crisis episode.

We also explore other firm characteristics. As the crisis was associated withlarge currency devaluations in all of our countries, we would expect exportingfirms to benefit. We define exporting firms as those with export sales to totalsales ratio of 10% or more after the crisis. In our results, exporting firms receivea lower discount from suppliers, as they are more creditworthy and in a positionof advantage. On the other side, importing firms are likely to be moreconstrained after the crisis. They receive credit for shorter period from their

14 In the two specifications that we report the effect of size on the length of payables is not

significant at conventional levels: the p value is about 11% for anydecline and about 15% for

dombank. However in other specifications (i.e. with loan11 and loan10 or with loandown and

anyloan), the coefficients on firm size are significant at least at 5% level.



suppliers and in turn extend credit to their customers for shorter periods. Otherterms of trade credit are not significantly different for importing firms.

We also estimate our specification with a foreign equity dummy equal to onefor firms that have foreign equity investors (results are not reported because thisdummy is not available for Indonesia). Firms with foreign equity almost alwaysreceive more trade credit from their suppliers and for more than a week longerthan locally owned firms. Presence of foreign equity may be a signal of goodcredit quality and financial strength. Thus, suppliers are more willing to extendtrade credit, as likelihood of default might be lower for partially foreign-ownedfirms. Compared with domestic firms, these firms also increase extension oftrade credit to customers and for a longer duration. This is likely becauseforeign-owned firms are also less constrained in access to bank finance.

We also find that change in trade credit is significantly different across oursample countries, as all country dummies are significant. The results on countrydummies are consistent with patterns observed in Table 1. Sector dummies arealso statistically significant, especially with respect to length of receivables andpayables. Sectors 2 (electronics) and 4 (autoparts and machinery) receive moretrade credit from their suppliers (in terms of length and percent) and give morecredit to their customers than firms in the chemical industry (which is theomitted category). Firms in sector 1 (garments and textiles) also receive morecredit from their suppliers and allow longer length of repayment to theircustomers (but not significantly different percent of sales). Thus terms of tradecredit vary across sectors. Interestingly enough, we do not find any industryeffect on change in discount terms.15

In sum, we find that after the financial crisis trade credit does not provide asubstitute source of credit to firms that are constrained in access to bank credit.Instead financially constrained firms experience greater contraction in tradecredit on all measures sides – i.e. in terms of reduction in percent of credit,length of credit and increase in discount terms offered. Thus, we find that tradecredit moves in the direction of bank credit and contracts even more for firmsthat are constrained in their access to bank finance. We also find that liquidityshocks are passed along the supply chains, consistent with Boissay and Gropp(2007) and Raddatz (in press).

V. ROBUSTNESS CHECKS

First we check the robustness of our results to using alternative measures offinancing constraints – loan10, loandown, unsustain and workcap – described in thedata section. Instead of presenting individual tables for these four measures, wepresent a summary table that indicates the signs and significance of the coefficients,reported in Table 5. Although there is some variation in the results observed for

15 Only exception is sector 3, food industry, which gave lower discount on credit extended to

customers.



each individual variable, the fairly consistent picture emerges with consistency inthe general pattern, meaning that all significant coefficients for financing variablesgo in the same direction for all the six measures of trade credit.16 Specifically, wefind that firms constrained in their access to finance from banks:

On the payables side:

1 Receive less trade credit from suppliers2 Use trade credit for shorter periods3 Are given higher discounts by their suppliers (i.e. higher cost of trade credit)

On the receivables side:

4 Give out less credit to their customers

Table 5 Summary results on change in trade credit



(1) (2) (3) (4) (5) (6)

Main measuresAnydecline �nnn �nn NS �nnn �nnn 1nnn

Dombank �nnn �n NS �nn �nnn 1nn

Additional measuresLoan10 �nn �n 1n �nn �nn NSLoandown NS �n 1nn �nnn �nnn NSUnsustain1 �nnn NS 1nnn �nnn �nnn 1nnn

Workcap �nnn NS 1nnn �nn �nnn NS

The table summarizes results of regressions of change in components of receivables and payableson financing constraints measures: anydecline is a dummy for firms that were declined a bank loanbefore or after crisis or in both periods, loan10 is a dummy for firms that relied on bank loansbefore but not after the crisis, loandown is a dummy for firms that reduced reliance on bank loansafter the crisis, unsustain1 is a dummy for firms that cannot sustain loan repayments for a year(the control group are firms with no outstanding loans), workcap is a dummy for firms that citelack of working capital finance from banks contributed to a decline in output after the crisis,dombank is a dummy for firms for which domestic bank credit became more restrictive after thecrisis.nSignificant at 10%.nnSignificant at 5%.nnnSignificant at 1%.NS is not significant.

16 All variables conclusively support decline in percent and length of payables. Except for

loandown, all other variables are negatively significant for percent of receivables. Four out of six

variables are negatively significant for length of receivables (workcap and unsustain1 are not).

Except for dombank and anydecline, all other variables are positive and significant for discount

on receivables. The weakest support is for discount on payables, with only half the variables

being positive and significant (workcap, unsustain1 and anydecline), while the rest are

insignificant.



5 Give credit for shorter periods6 Offer a higher discount to customers (i.e. higher cost of trade credit)

Next we address an important concern with our results that relates to declinein growth opportunities during the crisis. If firm’s growth opportunities decline,

Table 6 Decline of loan applications: export only firms



(1) (2) (3) (4) (5) (6)

Size 0.501 �0.942 �0.071 1.346 0.566 0.153[1.68]n [1.65] [0.46] [3.10]nnn [0.97] [0.92]

Import �0.537 �2.763 �0.586 �1.441 �6.531 �0.58[0.34] [1.12] [1.00] [0.75] [2.48]nn [0.83]

Anydecline �4.205 �2.693 1.202 �3.042 �3.599 1.414[2.96]nnn [1.17] [2.23]nn [2.05]nn [1.68]n [2.50]nn

Tha 8.845 14.051 �1.558 21.075 6.623 �0.352[3.03]nnn [4.44]nnn [0.62] [5.30]nnn [1.80]n [0.14]

Kor 9.313 13.297 �0.765 26.068 18.639 0.862[2.63]nnn [2.98]nnn [0.29] [5.20]nnn [3.82]nnn [0.34]

Phl 9.379 45.835 14.135 �32.677 �7.106 9.153[3.18]nnn [4.92]nnn [4.01]nnn [4.53]nnn [0.84] [2.68]nnn

Sector 1 2.644 1.58 1.173 7.293 2.22 0.74[1.23] [0.49] [1.24] [2.79]nnn [0.72] [0.73]

Sector 2 5.246 7.362 1.802 5.456 6.471 0.586[2.51]nn [2.13]nn [2.19]nn [2.19]nn [2.06]nn [0.72]

Sector 3 3.746 4.11 3.753 �0.618 6.622 �0.895[1.31] [1.07] [1.26] [0.14] [1.85]n [0.55]

Sector 4 5.735 12.912 0.468 5.217 9.645 0.461[2.56]nn [3.45]nnn [0.65] [2.15]nn [2.75]nnn [0.63]

Constant �16.951 �8.079 0.261 �38.432 �17.965 �1.848[4.26]nnn [1.39] [0.09] [6.47]nnn [2.85]nnn [0.70]

Observations 674 613 256 659 630 241R2 0.06 0.13 0.42 0.33 0.08 0.26

The table shows regressions of change in components of receivables and payables on firms-specific variables for the sample of firms that were exporting after the crisis. Exporting firms aredefined as firm that had exports �10% of total sales after the crisis. All data are from World Banksurvey of establishments on impact of East Asian Crisis (1999). Size is log of sales in 1996, import isa dummy for firms that import their inputs, anydecline is a dummy for firms that were declined abank loan before or after the crisis or in both periods, tha is a dummy for Thailand, kor is adummy for Korea, phl is a dummy for Philippines, sector 1 is a dummy for firms that operate ingarments and textiles industry, sector 2 is a dummy for firms that operate in electronics industry,sector 3 is a dummy for firms that operate in foods industry, sector 4 is a dummy for firms thatoperate in autoparts and machinery industry, the omitted sector is chemicals. Robust t-statisticsare in brackets.nSignificant at 10%.nnSignificant at 5%.nnnSignificant at 1%.



they are likely to need less financing both from banks and from trade creditors.Thus, the decline in bank credit and trade credit could be due to decline ingrowth opportunities. While in general it is difficult to measure growthopportunities, in our data we have a nice control group – the exporting firms.Since East Asian crisis was accompanied by large currency devaluations in all of

Table 7 Restrictions in credit availability from domestic banks export only firms



(1) (2) (3) (4) (5) (6)

Size 0.337 �0.93 0.089 1.542 0.544 0.184[1.22] [1.53] [0.48] [2.77]nnn [0.88] [0.88]

Import �0.652 �4.09 �0.246 �1.998 �6.978 �0.541[0.43] [1.53] [0.30] [0.99] [2.50]nn [0.62]

Dombank �3 �0.568 0.935 �3.127 �4.935 1.258[2.34]nn [0.25] [1.47] [1.95]n [2.28]nn [1.79]n

Tha 9.839 13.446 �1.093 22.922 7.648 0.131[3.22]nnn [3.98]nnn [0.45] [5.40]nnn [1.87]n [0.05]

Kor 7.796 12.854 �0.062 28.221 19.169 1.486[2.30]nn [2.85]nnn [0.02] [5.12]nnn [3.76]nnn [0.58]

Phl 10.099 40.89 14.157 �31.29 �11.431 10.503[3.51]nnn [5.71]nnn [4.29]nnn [4.77]nnn [1.20] [3.12]nnn

Sector 1 2.358 0.671 0.431 8.27 2.019 �0.283[1.13] [0.20] [0.39] [3.13]nnn [0.64] [0.23]

Sector 2 4.762 5.605 1.536 4.237 5.054 0.415[2.32]nn [1.57] [1.54] [1.64] [1.54] [0.40]

Sector 3 4.385 4.485 3.07 3.573 9.118 �3.258[1.60] [1.18] [0.89] [0.76] [2.40]nn [1.55]

Sector 4 5.784 13.415 0.448 5.032 11.091 �0.081[2.59]nnn [3.50]nnn [0.58] [2.05]nn [3.10]nnn [0.10]

Constant �15.204 �6.885 �0.975 �40.575 �17.099 �2.105[3.85]nnn [1.11] [0.32] [5.89]nnn [2.56]nn [0.75]

Observations 670 566 252 613 574 234R2 0.06 0.14 0.42 0.38 0.11 0.29

The table shows regressions of change in components of receivables and payables on firms-specific variables for the sample of firms that were exporting after the crisis. Exporting firms aredefined as firm that had exports �10% of total sales after the crisis. All data are from the WorldBank survey of establishments on impact of East Asian Crisis (1999). Size is log of sales in 1996,import is a dummy for firms that import their inputs, dombank is a dummy for firms for whichdomestic bank credit became more restrictive after the crisis, tha is a dummy for Thailand, kor is adummy for Korea, phl is a dummy for Philippines, sector 1 is a dummy for firms that operate ingarments and textiles industry, sector 2 is a dummy for firms that operate in electronics industry,sector 3 is a dummy for firms that operate in foods industry, sector 4 is a dummy for firms thatoperate in autoparts and machinery industry, the omitted sector is chemicals. Robust t-statisticsare in brackets.nSignificant at 10%.nnSignificant at 5%.nnnSignificant at 1%.



our crisis countries, the exporters have likely found their growth opportunitiesincreasing, as their products became more competitive. Thus, we reproduce ourmain results on the sample of exporters. In Table 6 we present our results foranydecline measure and in Table 7 we present similar results for dombankmeasure. Despite the fact that our sample is now less than half of the previoussample, we find very similar results. Thus, we can be confident that our resultsare not driven by the omitted growth opportunities.

VI. CONCLUSIONS

In this paper we study the effect of financial crisis on provision of trade credit ina sample of small and medium firms in four East Asian countries. We find thaton average the use of trade credit declines and the cost of trade credit increasesfollowing the crisis. Our main focus is on heterogeneous responses offinancially constrained firms and we find that these firms reduce their useand extension of trade credit after the crisis. Specifically, they buy smallerpercent of inputs on credit, have shorter length of time to repay the credit totheir suppliers and have to pay higher cost for trade credit. They also extend lesscredit to their own customers and charge higher rates. Our results are in linewith the arguments that liquidity shocks are passed along the supply chain,from suppliers to their customers and on to their own customers, thuspropagating the crisis effects.

It is important to keep in mind that our study focuses solely on the time ofthe financial crisis and on the changes in trade credit as a result of crisis.Although understanding firm behavior during the times of financial crisis isvery important, the patterns identified in this study may not apply to the tradecredit behavior of firms during the normal (i.e. non-crisis) times. Clearly, moreresearch on this topic is needed to better understand the differences in tradecredit behavior of firms of different sizes in crisis and non-crisis times.

What we do find is that there is no clear substitution effect during the crisis –i.e. firms constrained by banks are not able to meet the excess demand for fundswith trade credit. Thus, we do not support the idea that trade credit can help tosustain operations of constrained SMEs during the times of the crisis. This hasimplications for firms that may expect to cushion any constraints they face incase of an unexpected crisis by using trade credit. These firms may need toidentify alternative sources of finance or create funds for crisis times that can bedrawn down. Our paper highlights the importance of bank finance for growthof the SME firms, especially in the times of the crisis.

Inessa LoveDevelopment Research GroupThe World [email protected]



mailto:[email protected]

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http://ssrn.com/abstract=1357805

Documents

Trade Credit,Bank Credit and Financial Crisis