1 Summary Statistics, Q2 2008–Q2 2009

140



Trade Finance during the Great Trade Collapse



second quarter of 2009. The mean sectoral decline is 25.3 percent for imports

and 20.9 percent for exports. There is considerable heterogeneity across sectors; some sectors even saw an expansion of trade, while others experienced a

large contraction. Thus, a great deal of cross-sectoral variation could potentially be exploited.

Estimation Results

Regarding the results of the regression analysis, table 7.2 presents the results when

the dependent variable is U.S. imports by sector, and table 7.3 presents the results

when the dependent variable is U.S. exports.

Throughout, the tables report the standardized beta coefficients, obtained by

first renormalizing each variable to have a mean of 0 and a standard deviation

of 1. Thus, all the regression coefficients correspond to the number of standard

deviations’ change in the left-hand side variable that would be due to a 1 standard

deviation change in the right-hand side variable. This also implies that the magnitudes of the coefficients are comparable across variables that may have very different scales when not normalized.

The controls for sector size in trade and labor intensity come in as strongly significant across the board. In addition, the main two variables found to be significant in Levchenko, Lewis, and Tesar (2010)—durability and vertical production

linkages—remain strongly significant, with all p-values less than 1 percent in the

case of U.S. imports.

The coefficients on the financial variables are less consistent. Columns (1)

and (2) of each table report the results for the trade credit variables (accounts

payable and accounts receivable). For imports, the coefficients are not significant, and the point estimates are close to zero. For exports, accounts payable is

not significant with a near-zero point estimate, while the accounts receivable

variable is significant at the 10 percent level, but with the “wrong” sign: exports

in sectors that extend trade credit more intensively fell by less.

Columns (3) and (4) of tables 7.2 and 7.3 report the results for the measures of

external finance dependence and asset tangibility. Although for both directions of

trade flows, the Rajan and Zingales (1998) measure of external dependence is

insignificant with a near-zero beta coefficient, asset tangibility is significant, but

once again with the “wrong” sign: sectors with a greater share of tangible assets

should have a relatively easier time getting credit during a crunch; those sectors

also had larger falls in both imports and exports.

Column (5) reports the results for the trade-weighted credit contraction in the

partner countries. Once again, while the coefficient is nearly zero for U.S. imports,

for exports it is significant at 10 percent with the “wrong” sign: exports from the



141



Durable dummy



Share of intrafirm importsa



Average time to ship



Share shipped by vessel



Share shipped by truck and

pipeline



Average distance shipped



TWCC



Asset tangibility



External finance dependence



Accounts receivable/sales



Accounts payable/cost of

goods sold



Dependent variable change

in imports (%)



–0.206***

(0.059)



0.076

(0.085)



(1)



–0.215***

(0.054)



0.056

(0.071)



(2)



–0.194***

(0.048)



0.035

(0.041)



(3)



Table 7.2 U.S. Imports and Financial Variables, Q2 2008–Q2 2009



–0.258***

(0.046)



–0.185***

(0.071)



(4)



–0.185***

(0.050)



–0.008

(0.069)



(5)



–0.193***

(0.047)



0.087

(0.063)



(6)



–0.212***

(0.047)



–0.133**

(0.067)

–0.148**

(0.063)



(7)



0.022

(0.049)

–0.191***

(0.049)



(9)



(continued next page)



–0.220***

(0.046)



–0.123**

(0.058)



(8)



142



b



(2)

–0.195***

(0.044)

–0.073*

(0.038)

–0.073

(0.061)

–0.129**

(0.054)

415

0.122



(1)



–0.200***

(0.042)

–0.092*

(0.047)

–0.076

(0.061)

–0.113**

(0.054)

415

0.124



(4)

–0.154***

(0.047)

–0.027

(0.042)

–0.064

(0.058)

–0.135**

(0.054)

432

0.138



(3)

–0.203***

(0.043)

–0.069*

(0.039)

–0.08

(0.062)

–0.126**

(0.055)

423

0.124

–0.192***

(0.040)

–0.064*

(0.037)

–0.078

(0.061)

–0.122**

(0.051)

435

0.116



(5)

–0.178***

(0.045)

–0.071**

(0.035)

–0.075

(0.059)

–0.121**

(0.058)

436

0.114



(6)

–0.172***

(0.043)

–0.074**

(0.031)

–0.07

(0.061)

–0.114**

(0.055)

436

0.119



(7)



–0.197***

(0.041)

–0.074**

(0.034)

–0.068

(0.062)

–0.124**

(0.052)

434

0.133



(8)



–0.205***

(0.046)

–0.061

(0.037)

–0.078

(0.060)

–0.110**

(0.053)

437

0.112



(9)



Source: Authors’ calculations.

Note: Standardized beta coefficients reported throughout. Robust standard errors are in parentheses. The dependent variable is the percentage reduction in U.S. imports in a sixdigit NAICS category from Q2 2008 to Q2 2009 (year-to-year). The financial variables are described in detail in the text.

a. “Share of intrafirm imports” is total U.S. imports, computed from U.S. Bureau of Economic Analysis multinationals data and averaged over the period 2002–06.

b. “Average downstream use” is the average use output in a sector as an intermediate input in other sectors.

c. “Share in total” is the share of a sector in total U.S. imports.

d. “Elasticity of substitution” between varieties in a sector is sourced from Broda and Weinstein (2006).

e. “Labor intensity” is the compensation of employees as a share of value added, from the U.S. 2002 Benchmark Input-Output Table (BEA 2002).

* significant at 10 percent.

** significant at 5 percent.

*** significant at 1 percent.



Observations

R-squared



Labor intensitye



Elasticity of substitutiond



Share in totalc



Average downstream use



Table 7.2 continued



143



Durable dummy



Share of intrafirm importsa



Average time to ship



Share shipped by vessel



Share shipped by truck

and pipeline



Average distance shipped



TWCC



Asset tangibility



External finance dependence



Accounts receivable/sales



Accounts payable/cost of

goods sold



Dependent variable change

in imports (%)



–0.094

(0.058)



0.012

(0.068)



(1)



–0.137**

(0.055)



0.105*

(0.063)



(2)



–0.100**

(0.050)



0.01

(0.050)



(3)



Table 7.3 U.S. Exports and Financial Variables, Q2 2008–Q2 2009



–0.152***

(0.054)



–0.156**

(0.062)



(4)



–0.082

(0.051)



0.120*

(0.065)



(5)



–0.111**

(0.050)



0.093

(0.064)



(6)



–0.125**

(0.052)



–0.093

(0.062)

–0.083

(0.070)



(7)



0.016

(0.050)

–0.106**

(0.050)



(9)



(continued next page)



–0.104**

(0.050)



–0.042

(0.056)



(8)



144



(1)



–0.098**

(0.042)

–0.191***

(0.067)

–0.049

(0.087)

–0.135**

(0.054)

415

0.097



(2)

–0.090**

(0.043)

–0.194***

(0.064)

–0.042

(0.085)

–0.134***

(0.050)

415

0.106



(3)

–0.100**

(0.043)

–0.189***

(0.067)

–0.049

(0.087)

–0.129**

(0.050)

423

0.098



(4)

–0.054

(0.048)

–0.199***

(0.062)

–0.036

(0.082)

–0.156***

(0.052)

432

0.116



(5)

–0.091**

(0.041)

–0.196***

(0.064)

–0.05

(0.079)

–0.133***

(0.050)

437

0.117



(6)

–0.073*

(0.044)

–0.210***

(0.068)

–0.062

(0.079)

–0.145***

(0.050)

436

0.113



(7)

–0.07

(0.044)

–0.208***

(0.061)

–0.049

(0.081)

–0.156***

(0.050)

436

0.112



(8)

–0.095**

(0.041)

–0.190***

(0.065)

–0.045

(0.083)

–0.143***

(0.050)

436

0.105



(9)

–0.098**

(0.041)

–0.188***

(0.064)

–0.05

(0.083)

–0.145***

(0.050)

437

0.104



Source: Authors’ calculations.

Notes: Standardized beta coefficients reported throughout. Robust standard errors are in parentheses. The dependent variable is the percentage reduction in U.S. exports in a sixdigit NAICS category from Q2 2008 to Q2 2009 (year-to-year). The financial variables are described in detail in the text.

a. “Share of intrafirm imports” is total U.S. imports, computed from the U.S. Bureau of Economic Analysis multinationals data and averaged over the period 2002–06.

b. “Average downstream use” is the average use output in a sector as an intermediate input in other sectors.

c. “Share in total” is the share of a sector in total U.S. imports.

d. “Elasticity of substitution” between varieties in a sector is sourced from Broda and Weinstein (2006).

e. “Labor intensity” is the compensation of employees as a share of value added, from the U.S. 2002 Benchmark Input-Output Table (BEA 2002).

* significant at 10 percent.

** significant at 5 percent.

*** significant at 1 percent.



Observations

R-squared



Labor intensitye



Elasticity of substitutiond



Share in totalc



Average downstream useb



Table 7.3 continued



The Role of Trade Finance in the U.S. Trade Collapse: A Skeptic’s View



145



United States fell by less in sectors dominated by trading partners with greater

credit contractions.

Columns (6), (7), and (8) report the results of using shipping lags measures

(average distance shipped, share shipped by truck and pipeline, share shipped by

vessel, and average time to ship). For U.S. exports, these do not seem to matter. For

imports, there is some evidence for the role of shipping lags. Although the simple

average distance shipped is not significant (column [6]), the mode of transportation is. Sectors with higher shares of imports shipped by ocean and other means

(usually truck and pipeline) experienced larger falls than sectors with higher

shares of air shipping (column [7]). Furthermore, sectors with longer shipping

times (column [8]) had larger falls in imports. The magnitudes of the beta coefficients are also economically significant: a 1.0 standard deviation change in share

shipped by ocean is associated with a 0.148 standard deviations’ greater fall in

imports. Similarly, a 1.0 standard deviation change in shipping time leads imports

to fall by 0.123 standard deviations.

One difficulty in interpreting the shipment coefficients is that the mode of

shipping could be an endogenous variable. For instance, firms choose the shipping mode optimally in response to demand volatility (Hummels and Schaur

2010). A second problem is that the mode of shipping is likely to be correlated

with the type of goods (for example, automobiles account for a substantial fraction of the decline in trade and are never shipped by air). Although other industry

characteristics that are explicitly controlled for may sweep out some of this variation, others could be missing from the analysis.

Finally, column (9) reports the results of regressing imports and exports on the

share of intrafirm trade in the sector; although the coefficient has the “right” sign,

it is very close to zero and insignificant.

Conclusions

It is widely recognized that the current global downturn was triggered by a

large-scale financial crisis. At the same time, the world experienced a collapse in

international trade of a magnitude unseen since World War II. If one puts the

two events together, it is a reasonable hypothesis that financial factors contributed to the collapse in trade. However, hard evidence for this has proven elusive. This chapter tests a battery of hypotheses concerning how financial factors

could have affected U.S. imports and exports at the sector level. Overall, the

results show little evidence that financial factors contributed to the trade collapse. This finding is in sharp contrast to the other measures that were found, in

earlier studies, to matter a great deal: vertical production linkages and the role

of durables.



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Trade Finance during the Great Trade Collapse



The remainder of this section highlights some boundaries of this empirical

analysis. First, though there is hardly any effect of financial variables on overall

U.S. import and export volumes in each sector, financial variables could have been

partly responsible for collapses in bilateral trade from individual countries in particular sectors. This possibility is consistent with the results of Chor and Manova

(2010), who found that countries experiencing greater credit contractions

reduced their exports to the United States disproportionately in financially

dependent sectors. These results point out that when one aggregates across partner countries up to sector level, the impact of financial factors on trade volumes

disappears.

In light of historical experience, this finding is not surprising. Relative to the

level of economic activity, the fall in U.S. trade during the 2001 recession was

almost as large as in 2008–09 (Levchenko, Lewis, and Tesar 2010). However, the

2001 recession was not accompanied by a contraction in credit, suggesting that

other mechanisms are probably responsible for falls in cross-border trade during

economic downturns.

Second, although the United States is widely seen as the epicenter of the financial crisis, its financial system is nonetheless one of the deepest and most resilient

in the world. Thus, even if financial factors had no effect on U.S. trade, these factors could have been much more important in other countries with weaker

financial systems. Indeed, in a wide sample of countries, past banking crises did

affect international trade flows (Iacovone and Zavacka 2009).

Third, even if financial characteristics were found to have a significant impact

on international trade volumes, such a result would not necessarily be evidence of

financial factors in international trade specifically because production may have

fallen by just as much in each sector. Thus, a conclusive test of the role of financial

variables in the trade collapse would have to find that financial factors were

responsible for changes in trade over and above the change in output. This critique

applies also to the other existing studies of finance and trade, though it is less of a

problem for the negative results here because a robust effect is not found even on

unadjusted trade volumes.

Notes

1. Data were obtained on all firms in Compustat from 2000 to 2008. These ratios were computed

for each firm in each quarter, and the median value was taken for each firm (across all the quarters for

which data are available). The median value across firms is then taken in each industry. Medians are

taken to reduce the impact of outliers, which tend to be large in firm-level data. Taking means instead

leaves the results unchanged. Because coverage is uneven across sectors, trade credit intensity is calculated over at least 10 firms. This implies that sometimes the level of variation is at the five-, four-, and

even three-digit level, although the trade data are at the six-digit NAICS level of disaggregation. See

Levchenko, Lewis, and Tesar (2010) for more details.



The Role of Trade Finance in the U.S. Trade Collapse: A Skeptic’s View



147



2. The authors are grateful to Davin Chor and Kalina Manova for sharing the interbank rate data

used in Chor and Manova 2010.

3. The authors use 2007 data collected by the U.S. Census Bureau and made available by Peter

Schott on his website: http://www.som.yale.edu/faculty/pks4/sub_international.htm.

4. The authors are grateful to David Hummels and Georg Schaur for computing these measures

using their ocean shipping time data.



References

Amiti, Mary, and David E. Weinstein. 2009. “Exports and Financial Shocks.” Discussion Paper 7590,

Centre for Economic Policy Research, London.

Auboin, Marc. 2009. “Restoring Trade Finance: What the G20 Can Do.” In The Collapse of Global Trade,

Murky Protectionism, and the Crisis: Recommendations for the G20, ed. Richard Baldwin and Simon

Evenett, 75–80. VoxEU.org, E-book. London: Centre for Economic Policy Research.

BEA (U.S. Bureau of Economic Analysis). 2002. U.S. 2002 Benchmark Input-Output Database. BEA,

Washington, DC. http://www.bea.gov/industry/io_benchmark.htm.

Broda, Christian, and David Weinstein. 2006. “Globalization and the Gains from Variety.” The Quarterly Journal of Economics 121 (2): 541–85.

Chor, Davin, and Kalina Manova. 2010. “Off the Cliff and Back? Credit Conditions and International

Trade during the Global Financial Crisis.” Working Paper 16174, National Bureau of Economic

Research, Cambridge, MA.

Hummels, David L. 2007. “Transportation Costs and International Trade in the Second Era of Globalization.” The Journal of Economic Perspectives 21 (3): 131–54.

Hummels, David L., and Georg Schaur. 2010. “Hedging Price Volatility Using Fast Transport.” Journal

of International Economics 82 (1): 15–25.

Iacovone, Leonardo, and Veronika Zavacka. 2009. “Banking Crises and Exports: Lessons from the Past.”

Policy Research Working Paper 5016, World Bank, Washington, DC.

IMF (International Monetary Fund). 2009. “Survey of Private Sector Trade Credit Developments.”

Memorandum, IMF, Washington, DC.

Levchenko, Andrei A., Logan T. Lewis, and Linda L. Tesar. 2010. “The Collapse of International Trade

during the 2008–2009 Crisis: In Search of the Smoking Gun.” IMF Economic Review 58 (2): 214–53.

Love, Inessa, Lorenzo A. Preve, and Virginia Sarria-Allende. 2007. “Trade Credit and Bank Credit:

Evidence from Recent Financial Crises.” Journal of Financial Economics 83(2): 453–69.

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

Economic Review 88 (3): 559–86.



8

Trade Finance in Africa:

A Survey of Firms

John Humphrey



Over the past two decades, development policy has encouraged producers in

developing countries to export labor-intensive manufactures and nontraditional agricultural exports as an effective means of reducing poverty. How did

these industries fare in the wake of the 2008–09 global crisis? Given the

unprecedented financial nature of this crisis and its impact through the banking

system, were exporters from low-income countries hit by cuts in the finance

needed for trade?

Concern has been expressed about this issue by trade specialists and policy

makers from a wide range of international organizations. The final communiqué

of the April 2009 G-20 London Summit identified withdrawal of trade credit as a

factor exacerbating trade declines, and the G-20 leaders committed $250 billion to

support trade finance. Many commentators welcomed this announcement, but

notes of skepticism have also been registered. For example, economist Richard

Baldwin, policy director of the Centre for Economic Policy Research, has suggested that expanding trade finance is an easy option that encounters little political opposition (Baldwin 2009). In fact, whether trade finance has a discernible

effect on levels of trade—and to what extent—is far from clear, as is whether

This paper was written with financial support from the U.K. Department for International Development. Paul Kamau at the Institute for Development Studies, University of Nairobi, and Steve Homer at

Biospartners (www.biospartners.co.uk) interviewed the companies. The author is also grateful to Ian

Sayers at the International Trade Centre in Geneva for providing information about trade credit issues

for developing-country exporters.



149



150



Trade Finance during the Great Trade Collapse



exporters from the poorest countries are affected to the same extent as those in

more-developed countries.

This chapter provides some evidence about whether export-oriented garment

production and high-value export horticulture in Sub-Saharan Africa have experienced problems in obtaining trade finance. The findings are based on telephone

interviews with companies in these two sectors of the African export market.

Trade Finance and How Firms Use it for Trade

Trade finance can take many forms. For simplification, this chapter focuses on

three types, as shown in table 8.1: letters of credit (LCs), domestic bank lending,

and trade credit.

LCs are specifically designed to facilitate trade by providing both finance and

assurances about payment to the exporting company. LCs require confidence and

liquidity to be maintained at various points along the chain of payment—from

the importer to the issuing bank, to the advising or confirming bank, and ultimately to the exporter.

The other two forms of trade finance are extensions of credit facilities that

operate in domestic economies. Companies may use domestic bank lending to

finance both capital investment and working capital. Such lending can be used to

facilitate trade. Similarly, firms extend credit to each other when payment takes

place before or after receipt of goods. Such credit is widely used in domestic transactions. Firms that have well-developed trading relationships may adopt the same

practice. To the extent that sophisticated global value chains linking firms in different countries often involve repeat transactions and long-term relationships,

conducting trade on these terms is not uncommon.

Policy Makers’ Concerns

The possible impact of the global financial crisis on trade finance and the capacity

of developing-country exporters to finance their trade became a salient issue in

the final few months of 2008. The International Chamber of Commerce argued

that uncertainties in global markets were leading firms to be more risk-averse,

shifting from open-account trading to LCs, while financial markets themselves

were providing less trade finance (ICC 2008). These concerns were taken up by

World Trade Organization (WTO) Director-General Pascal Lamy, who

announced the formation of a WTO task force to monitor the issue. The Institute

of International Finance suggested that private financial flows to emerging markets were falling dramatically (IIF 2009). Anecdotal evidence also emerged about

trade credit drying up, international banks becoming less willing to lend, and the