9 Financial Dependence, Exporter Characteristics, and Recovery

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of its relationship with the financing bank are important determinants of the cost

of financing. In light of this evidence, it is not surprising that after a banking

crisis—when banks face a lack of liquidity, requiring them to restrict credit—only

well-established and better-known firms are likely to get access to credit from the

banks, being able to cover some of the cost of producing and exporting.

Policy Implications

The effect of a banking crisis on different export sectors and products is an important consideration for policy makers as they try to mitigate financial shocks. This

chapter, based on disaggregated data at the product level, helps derive important

implications relevant to policy makers.

First, banking crises seem to hit more-productive exporters less adversely than

less-productive exporters. In line with expectations, small and less-experienced

exporters may not be productive enough to overcome a sharp drop in foreign

demand and, more important, they may also be more affected by short- or longterm credit restrictions. In the first case, small exporters might lack sufficient collateral or credit guarantees; in the second case, exporters with less experience have

not yet built their reputations. In both cases, the policy implication is that if the

objective is to reduce exit of trade relations, the target for policy support should

be relatively small and inexperienced exporters.

Second, although on average size and experience have a significant impact on

the recovery after banking crises, only the latter matters for the recovery of products belonging to industries that are highly dependent on external finance. Consistent with the idea that within-sector heterogeneity matters, this analysis finds

that long- and short-term sectoral financial dependence has an experiencespecific effect. In particular, more experienced exporters reenter faster in financially dependent sectors. This result has important policy implications: for

instance, if the objective is to help trade recover faster after financial disruption,

relatively inexperienced exporters should be targeted to restart foreign operations,

independent of their size.

Annex 12.1: Methodology

The empirical analysis is divided in two main parts. First, the authors estimate a

duration model à la Besedes and Prusa (2004) to study how trade relations are

affected in times of crisis. Second, always using a duration model but this time

only for those products that exited with a banking crisis, the authors analyze how

certain exporter and sectoral characteristics affect the time to recover after banking crises.14



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Except when otherwise indicated in the explanatory note below each table, the

estimations are stratified by exporting country and three-digit International Standard Industrial Classification (ISIC) industry level15—this to allow for a different

hazard function for each country and sector, respectively. In addition, standard

errors are clustered by sector (ISIC three-digit) and country to allow for intraindustry and country correlation in the error terms.

The main explanatory variable for the survival analysis is a banking crisis

dichotomous variable, which takes the value of 1 in those years in which a certain

country has experienced a banking crisis. In addition, a common set of control

variables is included in all regressions. First, variables such as the total number of

countries exporting a certain product to the United States and the total value of

product exports, respectively, serve as controls for the extensive and intensive

margins of competition. Second, to control for the fact that the banking crisis

variable might be capturing a deterioration of demand in the destination country,

a product-specific measure of the growth of U.S. imports is introduced.

With respect to recovery, the authors test whether the size and experience of

export relations at the time of exit affect the number of years it takes to reenter the

export market. In addition, to analyze whether products that exit the export market during a crisis recover at different speeds, according to the sectors to which

they belong, the authors also include an interaction term between long- and

short-term financial dependence indicators and product characteristics. In this

case, too, the total number of countries exporting a certain product to the United

States and the total value of product exports are included as controls. Because it is

not possible to compute these control variables for the subsample of products

where exports have never resumed, their averages are calculated between the first

year after the banking crisis and either the year of reentry or the last year of the

sample, depending on whether exports have resumed.

Some econometric issues related to this empirical methodology are common

to all duration models. First and most important, in the survival analysis, the

authors do not want to artificially record a banking crisis that occurred during a

trade relation as happening at the beginning or at the end of its duration. This

problem is solved by splitting each export relationship at the time of the banking

crisis and assuming that the crisis lasts for one year.

Second, for some export relations, it might be impossible to accurately observe

their beginning or their ending. Whether an export relation that is first observed

at time t actually started at time s < t (left censoring) is unknown. Also unknown

is whether an export relation that is last observed at time T was interrupted at T or

continued after it (right censoring). To control for left censoring, variables are

constructed using trade data from 1991 until 2009. However, the spells that

started in the initial five years of the dataset (1991–95) are excluded from estimations. The Cox model controls for right censoring.



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Third, there are products that exit more than once (multiple spells). The general approach of the literature to control for multiple spells in duration models is

to include in the regressions a multiple spell dummy equal to 1 if the relation has

at least one exit during the sample period. However, to control for the fact that

multiple spells are time-varying within a relation, a different definition of multiple spell is considered, with the construction of a variable equal to the number of

spells before time t. This approach, the authors believe, is theoretically more correct than the standard approach of the literature because it does not consider a

relation to be characterized by multiple spells until its first observed reentry, but

only after it.16

Due to the high level of disaggregation of the dataset, throughout all the analysis the assumption is that there is a representative firm for each trade relation. This

allows the analysis to refer to “experience” and “size” as two measures of heterogeneity among exporters. Because size and experience are not the same (in the

sample, the correlation is 0.19), they capture different characteristics of exporters.

Notes

1. The World Bank’s forecast is 15.7 percent, and the Organisation for Economic Co-operation

and Development’s forecast is 12.3 percent.

2. Using data on U.S. imports at the Harmonized System (HS) 10-digit level of disaggregation

from 157 countries between 1995 and 2009, the authors have extrapolated all relations that were interrupted at the occurrence of a banking crisis in the exporting country.

3. The authors chose the United States as the destination country because the original trade data

used (from the Global Trade Atlas and the Center of International Data at the University of California,

Davis) contains information at the 10-digit level of disaggregation only for trade flows in and out the

United States.

4. After inclusion in the regression of the market share of a product to control for product heterogeneity, results do not change.

5. The variable experience cannot be included in a Cox regression because it is highly correlated

with the duration of a spell, which is the conditioning variable in duration models.

6. The effect of experience should be interpreted with caution, since it captures both the negative

duration effect (the fact that the probability of exit decreases the longer a product has been in the market) and the presence effect (learning by exporting). The authors are only interested in the latter effect,

which has an economic interpretation.

7. The indicator of long-term external financial dependence (EFD) comes from Rajan & Zingales

(1998) and is computed at International Standard Industrial Classification (ISIC) three-digit industry

level. For short-term financial dependence, we use trade credit dependence (TCD) from Levchenko,

Lewis, and Tesar (2009), computed at the North American Industry Classification System (NAICS)

four-digit level (the original measure is from Fisman and Love [2003]). In the data, the correlation

between EFD and TCD is very high and equal to 0.7.

8. For a similar approach, see Besedes (2007), section 3.3.2.

9. Similar results can be shown when using trade credit dependence (TCD).

10. From figure 12.5, it might seem that the variable size is not constant across time. This is controlled for in the regressions by stratifying the sample (see annex 12.1).

11. A Cox regression was also estimated for different groups of export size. The results, available

under request, show that neither the financial dependence variable nor the other control variables have

a size-specific effect.



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12. In the sample, the maximum value of time to recover is 12 years. The assumption is that the

products that have not reentered the export market yet will enter after 15 years. Another assumption is

that they enter after 20 or 30 years and results do not change.

13. To sharpen these conclusions, the authors are planning to perform the same analysis using

firm-level data.

14. The estimated regression is a stratified Cox proportional hazard model of the form hc (t, x, b )

= hc0(t) exp(x' b), where x denotes a series of explanatory variables and b is the vector of coefficients to

be estimated. The baseline hazard hc0(t) represents how the hazard function changes with time and differs for each strata of the sample.

15. When sector-specific variables are included in the regression, the sample is not stratified by

sector.

16. Alternatively, a multiple spell dummy equal to 1 if the relation that is interrupted at time t has

at least one exit at time s < t has been included. Results are qualitatively the same.



References

Álvarez, Roberto. 2007. “Explaining Export Success: Firm Characteristics and Spillover Effects.” World

Development 35 (3): 377–93.

Álvarez, R., Hasan Faruq, and Ricardo López. 2009. “New Products in Export Markets: Learning from

Experience and Learning from Others.” Unpublished manuscript, Indiana University, Indianapolis. https://editorialexpress.com/cgi-bin/conference/download.cgi?db_name=MWM2009&

paper_id=153.

Berman, Nicolas, and Jérôme Héricourt. 2008. “Financial Factors and the Margins of Trade: Evidence

from Cross-Country Firm-Level Data.” Postprint and working papers, Université Paris1 PanthéonSorbonne, Paris.

Besedes, Tibor. 2007. “A Search Cost Perspective on Duration of Trade.” Departmental Working Paper

2006-12, Department of Economics, Louisiana State University, Baton Rouge.

Besedes, Tibor, and Thomas J. Prusa. 2004. “Surviving the U.S. Import Market: The Role of Product

Differentiation.” Working Paper 10319, National Bureau of Economic Research, Cambridge, MA.

———. 2006a. “Ins, Outs, and the Duration of Trade.” Canadian Journal of Economics 39 (1): 266–95.

———. 2006b. “Product Differentiation and Duration of U.S. Import Trade.” Journal of International

Economics 70 (2): 339–58.

Borgersen, T-A. 2006. “When Experience Matters: The Export Performance of Developing Countries’

SMEs.” Journal of Sustainable Development in Africa 8 (1): 106–18.

Brenton, Paul, Christian Saborowski, and Erik von Uexkull. 2009. “What Explains the Low Survival

Rate of Developing Country Export Flows?” Policy Research Working Paper 4951, World Bank,

Washington, DC.

Center for International Data. Import and Export databases. University of California, Davis.

http://cid.econ.ucdavis.edu.

Fisman, Raymond, and Inessa Love. 2003. “Financial Dependence and Growth Revisited.” Working

Paper 9582, National Bureau of Economic Research, Cambridge, MA.

Fugazza, Marco, and Ana Cristina Molina. 2009. “The Determinants of Trade Survival.” Working Paper

05-2009, Graduate Institute, Geneva.

Global Trade Atlas (database). Global Trade Information Services, Columbia, SC. http://www.gtis

.com/GTA.

Laeven, Luc, and Fabian Valencia. 2008. “Systemic Banking Crises: A New Database.” Working Paper

08/224, International Monetary Fund, Washington, DC.

Levchenko, Andrei A., Logan Lewis, and Linda L. Tesar. 2009. “The Collapse of International Trade

during the 2008–2009 Crisis: In Search of the Smoking Gun.” Working Paper 592, Research Seminar in International Economics, University of Michigan, Ann Arbor.



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Manova, Kalina, Shang-Jin Wei, and Zhiwei Zhang. 2009. “Firm Exports and Multinational Activity

under Credit Constraints.” Unpublished working paper, Stanford University, Palo Alto, CA.

Muûls, Mirabelle. 2008. “Exporters and Credit Constraints: A Firm-Level Approach.” Research series

200809-22, National Bank of Belgium, Brussels.

Petersen, Mitchell A., and Raghuram G. Rajan. 1994. “The Effect of Credit Market Competition

on Lending Relationships.” Working Paper 4921, National Bureau of Economic Research,

Cambridge, MA.

Rajan, Raghuram G., and Luigi Zingales. 1998. “Financial Dependence and Growth.” American

Economic Review 88 (3): 559–86.

Volpe-Martincus, Christian, and Jerónimo Carballo. 2009. “Survival of New Exporters in Developing

Countries: Does It Matter How They Diversify?” Working Paper 140, Inter-American Development

Bank, Washington, DC.



section III



UNDERPINNINGS

OF TRADE

FINANCE

INTERVENTION

DURING

FINANCIAL

CRISES



13

The Theoretical Case

for Trade Finance in

a Liquidity Crisis

Tore Ellingsen and Jonas Vlachos



The economic crisis of 2008 was characterized by a severe contraction of credit,

and the contraction appeared to hit the trade finance sector particularly hard. Is

there a theoretical case for policy intervention to boost trade finance in such a

liquidity crisis?1

The main argument presented here in favor of trade finance intervention during a liquidity crisis is that it mitigates the problems that arise when firms hoard

cash. When cash hoarding occurs, funding for interfirm transactions has greater

social value than other funding because borrowers cannot hoard trade finance.

Thus, the reasons for promoting trade finance are stronger than for promoting

credit in general.

Although these arguments pertain to both domestic and international trade

finance, they are arguably stronger in the international context. Because

international loan enforcement is weaker than domestic enforcement, sellers

are less willing to keep international loans on their books, and it is the seller’s

insistence on immediate payment that creates the demand for liquidity in the

first place.



The authors thank Jean-Pierre Chauffour, Tom Farole, and Leora Klapper for very helpful comments.

This text was previously published as T. Ellingsen, and J. Vlachos “Trade Finance in a Liquidity Crisis”

(Policy Research Working Paper 5136, World Bank, Washington, DC).



235



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A Theory of Trade Finance

Trade finance, broadly defined, is any financial arrangement connected to interfirm

commercial transactions. By this definition, extension of ordinary trade credit is an

example of trade finance. A narrow definition of trade finance is the funding of

individual international commercial transactions by financial intermediaries.

Even under the broader definition, trade finance phenomenon is puzzling at

first glance. Why do firms that do not specialize in financial intermediation

extend credit to other firms? A common explanation is that firms in a business

relationship acquire information about each other that would be expensive

(or even impossible) for banks to obtain. Although plausible, the basic monitoring rationale does not explain why trade finance is provided almost exclusively inkind; if the monitoring advantage is so great, why don’t firms also lend cash to

each other?

One explanation for this pattern is that firms with access to funding to buy

illiquid assets are less tempted to engage in activities that are undesirable from the

investors’ point of view (Burkart and Ellingsen 2004). Because in-kind credit is

expensive to divert to other uses, potential moral hazard problems on the borrower’s side are reduced when trade credit is extended. The important implication

here is that trade credit and other types of credit are complements rather than

substitutes, a prediction supported by evidence in Giannetti, Burkart, and

Ellingsen (2008). Such complementarities suggest that alternative sources of

funding cannot fill the gap when trade credit dries up. Instead, reduced trade

credit will reduce the access to other types of credit as well.2

The narrow definition of trade finance restricts attention to international

transactions that are directly funded by intermediaries. Of course, not all international transactions are intermediated; sometimes the seller keeps the receivable on its own books, as is common for domestic trade credit. However, the

more significant role of intermediaries in international trade informs us that

there are often greater obstacles to international trade credit transactions than

to domestic ones.

The authors’ favored interpretation is that sellers are typically more worried

about strategic default in the case of foreign buyers.3 Thus, sellers tend to insist on

up-front payment from foreigners. When the foreign buyer needs funding, a natural arrangement is to borrow from a bank in the buyer’s own country. That bank,

in turn, for the reasons discussed above, is more willing to provide specific loans

for input purchases than to provide general cash loans. Hence, the obvious solution is for the buyer’s bank to verify the shipment and pay upon delivery to the

seller’s bank while providing a loan to the buyer. In brief, this is the authors’ theory of international trade finance.



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The Case for Policy Intervention

If this theory is correct, what are the arguments for giving priority to trade finance

programs rather than to more general programs aimed at easing credit conditions?

Before answering the question, the authors note that the markets for corporate

funding differ from many other markets. In particular, corporate credit markets

do not have market-clearing prices. Many borrowers would like to have additional

funds at prevailing interest rates, but if their pledgeable returns are smaller than

their full returns, lenders will rationally lend less than the borrowers desire. When

credit constraints bind, it is sometimes (but far from always) justified to intervene

in financial markets (Tirole 2005; Holmstrom and Tirole 2011). At the core of this

argument in favor of an international trade finance program is the insight that it

is more difficult to make credible pledges across borders than within borders.

Benefits of Financial Market Intervention

When a financial crisis turns into a recession, interventions in financial markets

have two beneficial effects. The first direct effect is the value of additional funds to

the financially constrained firms themselves. The second effect, an indirect one,

is the value to the constrained firms’ trading partners of the additional activity

in the constrained firm. For example, when the constrained firm increases its

production, it needs more inputs, and the input suppliers’ profit goes up.

Policies to deal with the current crisis ought to focus on the indirect effect

rather than the direct effect for two reasons. First, the indirect effects are large

during a crisis because of excess capacity. Second, an increase of general credit

provision may not lead to an immediate expansion of production at all because

borrowers are so afraid of being even more heavily constrained in the future that

they simply hoard the additional funds.

A final, and crucial, building block of this argument is that prices are downwardly rigid in the short term. For some reason, sellers cannot or will not immediately reduce their prices despite a high premium on liquidity.4

The assumption here is not that prices are stuck at a level that the buyer is

unwilling to pay, but rather that they are so high that the buyer is unwilling to pay

cash immediately, in view of the high opportunity cost of liquidity. Because of

limited pledgeability, the seller, however, is unwilling to extend the necessary

credit. Also, the opportunity cost of liquidity implies that a general loan to either

party will be hoarded rather than spent on the transaction because the buyer does

not internalize the seller’s benefit when deciding whether to trade. However—and

this is the main point—targeted trade finance loans cannot be used for another

purpose and will thus be used to fund the transaction.