No Guarantees, No Trade: How Banks Affect Export Patterns

K.7 No Guarantees, No Trade: How Banks Affect Export Patterns Niepmann, Friederike and Tim Schmidt-Eisenlohr Please cite paper as: Niepmann, Friederike and Tim Schmidt-Eisenlohr (2016). No Guarantees, No Trade: How Banks Affect Export Patterns. International Finance Discussion Papers 1158. http://dx.doi.org/10.17016/IFDP.2016.1158 International Finance Discussion Papers Board of Governors of the Federal Reserve System Number 1158 January 2016

Board of Governors of the Federal Reserve System International Finance Discussion Papers Number 1158 January 2016 No Guarantees, No Trade: How Banks Affect Export Patterns Friederike Niepmann Tim Schmidt-Eisenlohr NOTE: International Finance Discussion Papers are preliminary materials circulated to stimulate discussion and critical comment. References to International Finance Discussion Papers (other than an acknowledgment that the writer has had access to unpublished material) should be cleared with the author or authors. Recent IFDPs are available on the Web at www.federalreserve.gov/pubs/ifdp/. This paper can be downloaded without charge from the Social Science Research Network electronic library at www.ssrn.com.

No Guarantees, No Trade: How Banks Affect Export Patterns* Friederike Niepmann and Tim Schmidt-Eisenlohr† Abstract How relevant are financial instruments to manage risk in international trade for exporting? Employing a unique dataset of U.S. banks’ trade finance claims by country, this paper estimates the effect of shocks to the supply of letters of credit on U.S. exports. We show that a one-standard deviation negative shock to a country’s supply of letters of credit reduces U.S. exports to that country by 1.5 percentage points. This effect is stronger for smaller and poorer destinations. It more than doubles during crisis times, suggesting a non-negligible role for finance in explaining the Great Trade Collapse. Keywords: trade finance, global banks, letter of credit, exports, financial shocks JEL-Codes: F21, F23, F34, G21 *TheauthorswouldespeciallyliketothankGeoffreyBarnesandTylerBodine-Smithforexcellentresearchassistance. For theirhelpfulcomments,theywouldalsoliketothankMaryAmiti,PolAntras,AndrewBernard,GabrielChodorow-Reich,Stijn Claessens, Giancarlo Corsetti, Pablo D’Erasmo, Martin Goetz, Kalina Manova, Atif Mian, Daniel Paravisini, Steve Redding, PeterSchott,PhilippSchnabl,MineZ.Senses,DavidWeinstein,andmembersoftheD.C.tradegroupaswellasparticipants inseminarsatNewYorkFed,SanFranciscoFed,FederalReserveBoard,LMUMunich,IfoInstituteMunich,BankofEngland, University of Oxford, University of Passau, University of Cambridge, Graduate Institute, LSE, and the following conferences: NBERSIInternationalMacro&Finance,SED2015,AEA2015,EconometricSocietyWorldCongress2015,FIRS2014,RMET 2014andCESifoConferenceonMacro,MoneyandInternationalFinance. †The authors are staff economists in the Division of International Finance, Board of Governors of the Federal Reserve System,Washington,D.C.20551U.S.A.Theviewsinthispaperaresolelytheresponsibilityoftheauthor(s)andshouldnotbe interpretedasreflectingtheviewsoftheBoardofGovernorsoftheFederalReserveSystemorofanyotherpersonassociatedwith theFederalReserveSystem. Correspondingauthor: TimSchmidt-Eisenlohr. Email: t.schmidteisenlohr@gmail.com. Address: FederalReserveBoard,ConstitutionAvenue,Washington,DC.Phone: 202-872-7564.

1 Introduction Trading across borders exposes firms to substantial risks. Exporters can mitigate these risks by buying trade finance products from banks, most importantly letters of credit. These services represent a substantial business for the financial sector. In 2012, about 8 percent of U.S. exports or $116 billion were covered by letters of credit.1 World-wide, letters of credit guaranteed more than $2.3 trillion or 12.5 percent of international trade in the same year.2 The 2008/2009 financial crisis has heightened the interest in this business from policy makers and the private sector. There was a perception of a so-called trade finance gap, that is, a potential undersupply of trade finance during the crisis by the financial sector. In responsetothis, manyinternationalorganizationsandtheG20committedtoincreasingtheir trade finance support.3 Special steps were taken in the Basel III capital rules to protect the trade finance business. In the wake of the financial crisis and the European debt crisis, the lower capitalization of banks has led financial institutions to begin refinancing trade finance through securitization. Despite the size and the large interest in this business, there is little evidence on its relevance for international trade. In particular, it remains an open question whether reductions in the supply of letters of credit harm trade. Firms typically have the option to sell and buy without the involvement of banks by settling the trade on pre-payment (cash-in-advance) or post-payment (open account) terms. So a firm that cannot get a letter of credit might simply switch to one of these alternative payment forms without changing its trade volume. However, letters of credit are special in their ability to reduce risk in international trade and potentialsubstitutesare imperfect.4 Antra`sand Foley(forthcoming), forexample, showthat letters of credit are key for the creation of new trade relationships, and Schmidt-Eisenlohr (2013) shows theoretically that switching to alternative payment forms may be very costly. This paper exploits a unique dataset available at the Federal Reserve to show that reductions in the supply of letters of credit by banks have causal effects on U.S. exports. The 1A letter of credit guarantees payment to the exporter against a set of documents presented to the bank that prove the delivery of goods to the importer. Payment choices are discussed in more detail below. 2Thesefiguresarebasedoninformationonletter-of-creditmessagessentthroughSWIFTfromtheSWIFT Institute. 3TheG20,forexamplecommittedtoincreasingitssupportoftradefinanceby$250billionoveratwo-year period. See G20 (2009). 4Trade credit insurance that can be bought in conjunction with open account, for example, is not a good substituteforaletterofcredit. Whileinsuranceshiftsriskfromtheexportertotheinsurer,aletterofcredit reduces the real risk in the economy by providing a commitment device for the importer. For very risky countries, trade credit insurance is therefore often very costly or even unavailable. 1

effects are stronger for smaller and poorer destination countries, where fewer U.S. banks are active, and are twice as big during crisis times than during tranquil times. While earlier studies have focussed on the effects of shocks to the supply of credit, we explore the effects of a reduction in trade-specific financial instruments.5 We term this new channel through which financial shocks are transmitted to the real economy and across borders the letter-ofcredit or risk channel. Besides contributing to the literature on finance and trade, we also add to a set of recent papers that have shown real effects of financial shocks for employment (Chodorow-Reich (2014)), firm liquidity (Khwaja and Mian (2008)) and durable consumption (Mian and Sufi (2010)), among others.6 In carrying out our analysis, we also present several methodological innovations building on the approach proposed by Greenstone et al. (2014) and Amiti and Weinstein (2013) to structurally estimate supply shocks.7 Information on trade finance employed in this paper is from the FFIEC 009 Country Exposure Report that all large U.S. banks are required to file.8 We observe banks’ trade finance claims, which reflect mostly LCs in support of U.S. exports, by destination country at a quarterly frequency over a period of 15 years. The total trade finance claims of all reporting banks account for roughly 20 percent of U.S. exports in 2012. Thus, the trade finance activities captured in the report are sizable relative to trade. Based on these data, we estimate time-varying trade finance supply shocks. To isolate idiosyncratic supply shocks from demand shocks, trade finance growth rates at time 𝑡 in country 𝑐 are regressed on bank-time fixed effects 𝛼 as well as on country-time fixed effects 𝑏𝑡 𝛽 . The estimated bank-time fixed effects 𝛼 correspond to idiosyncratic bank-level supply 𝑐𝑡 𝑏𝑡 shocks. To address potential endogeneity concerns, we estimate bank-time fixed effects separatelyforeachcountry, alwaysdroppingcountry𝑖informationfromthesampletoobtain thebankshocksthatweuseforcountry𝑖. Weshowthatbankshocksarepositivelycorrelated with growth in loans and negatively correlated with banks’ credit default swap spreads. This is evidence that the estimated bank-time fixed effects capture idiosyncrasies in banks’ businessconditions. However, themethodologyalsoallowsthebank-timefixedeffectstopick 5See Amiti and Weinstein (2011), Chor and Manova (2012) and Paravisini et al. (2015). 6On the real effects of financial shocks see also Peek and Rosengren (1997), Peek and Rosengren (2000) and Ashcraft (2005). For a seminal paper on the Great Depression see Bernanke (1983). For papers on the roleofglobalbanksininternationalspillovereffects,see,e.g.,BrunoandShin(2015),CetorelliandGoldberg (2012), Kalemli-Ozcan et al. (2013), and Ongena et al. (2013). 7Weproposeanormalizationtomakebankshockscomparableacrosstime,obtainbankshocksseparately for each country, systematically dropping information on similar countries to counter endogeneity concerns, and demonstrate how sorting into markets can be addressed. We discuss the details of our innovations after introducing the methodology. 8These data were first used in Niepmann and Schmidt-Eisenlohr (2013). 2

up strategic decisions by bank managers to expand or contract the trade finance business. ChangesinthesupplyofLCscanhaveaneffectontradebecauseexportersandimporters cannot easily switch between different banks when they want to settle a transaction based on this instrument. An LC is a means to reduce the risk of a trade, which works as follows: The importer asks a bank in her country to issue an LC. This letter is sent to the exporter. It guarantees that the issuing bank will pay the agreed contract value to the exporter if a set of conditions is fulfilled.9 In addition, a bank in the exporter’s country typically confirms the LC, whereby the confirming bank commits to paying if the issuing bank defaults. Because banksneedtoworkwithcorrespondentbanksabroad, theprovisionofLCsimpliessignificant fixed costs for banks so that the business is highly concentrated with only a few large players. Also, banks learn about the credit- and trustworthiness of their clients over time, and such information is not easily transferable. These factors should make it hard for a firm to switch to another bank when its home bank does not provide the service. When firms are not willing to trade without an LC or adjust quantities because expected profits from trading under alternative payment forms are lower, a reduction in the provision of LCs by a single bank has an effect on exports. The identification strategy pursued in this paper exploits the variation in the importance of banks as providers of LCs across countries. The same reduction in the supply of LCs by a bank should have a bigger effect in markets where the bank has a larger share of the trade finance business. Accordingly, the shock to bank 𝑏 at time 𝑡 is weighted by the market share of bank 𝑏 in country 𝑖 at time 𝑡−2, and these weighted shocks are summed over all banks in the sample. The resulting country-time specific shocks are used to predict exports. The baseline specification tests whether country-level trade finance supply shocks explain the variation in export growth rates controlling for a common time effect and a countryspecific trend in the export growth rate. We find statistically and economically significant effects. A country-level shock of one standard deviation decreases exports, on average, by 1.5 percentage points. We show that below median shocks have larger effects than above median shocks in line with Amiti and Weinstein (2011), which indicates that our identification comes mostly from reductions in the supply of trade finance. Export growth is mostly affected through adjustments in quantities, lending support to the hypothesis that there is trade finance rationing. The identifying assumption that establishes a causal link between supply shocks and ex- 9For example, the issuing bank may promise to pay upon receipt of shipping documents. 3

portsisthattherearenotime-varyingunobservedcountry-specificfactorsthatarecorrelated with both export growth and supply shocks. Given our methodology, two conditions need to hold. First, the estimated shock to the supply of LCs by bank 𝑏, based on information from countries other than country 𝑖, is not correlated with changes in the demand for trade finance and, hence, growth in exports to country 𝑖. Second, banks with positive shocks to their supply of trade finance in period 𝑡 do not sort, at time 𝑡−2, into markets with positive deviations from trend export growth in period 𝑡. We address the first concern in several different ways. First, we systematically exclude countries from the sample on which we estimate the bank-level shocks. In one case, we use information on larger U.S. export destinations to explain export growth to the smaller export destinations. In another exercise, we search for the nearest neighbors of a country in terms of the structure of its U.S. imports and construct the country-level supply shocks without information on these nearest neighbors. In both cases, the results are unchanged. Endogeneity of the country-level supply shocks could only arise if banks specialized in some dimension, e.g. in firms or industries, and if banks’ trade finance market shares were correlated with the export shares of these firms, industries etc. The described checks make it hard to defend the hypothesis that our results could be generated by demand effects. In addition, we show directly that the specialization hypothesis is not supported by the data. Thesecondconcernisrelatedtosorting. BothAmitiandWeinstein(2011)andParavisini etal.(2015)relyontheidentificationassumptionthatthereisnosortingofbanksandfirmsin regard of unobserved trends in their future performance. A key advantage of our estimation strategy is that we can rule out sorting, in our case the sorting of banks into destination markets as described above, because (i) the estimated bank-level shocks are not positively serially correlated and (ii) results are unchanged when different lags of banks’ market shares are used to construct the country-level shocks. This strongly suggests that the link found in this paper is indeed causal. In a quantitative exercise, we evaluate the effect of a negative shock to the trade finance supply of one large bank. A reduction that corresponds to the 10th percentile of the banklevel shock distribution leads to a 1.4-percentage-point decline in total U.S. exports growth. This illustrates that the behavior of a single bank can have an effect in the aggregate due to the high concentration of the business. Another key result of this paper is that banks can affect export patterns. Because banks specialize in confirming and issuing LCs in certain markets, a reduction in the supply of 4

LCs by a single bank has asymmetric effects across destination countries. We show that a shock of the same size to two different banks affects exports to different regions of the world differentially, depending on the markets in which each bank specializes. Hence, the patterns of banks’ global activities determine to which markets shocks are transmitted. In addition, we find that the effect of LC supply shocks are heterogeneous across export destinations and over time. Exports to smaller and poorer destinations where fewer U.S. banks are active decline more when banks reduce their supply of trade finance. We also present evidence that the effect of reductions in the supply of trade finance are stronger during times of financial distress. In a crisis period, the effect more than doubles compared to normal times with a beta coefficient of 13.5 percent. These findings can be explained as follows. Firms use LCs more intensively and are less willing to trade without them when exporting to riskier markets and when economic uncertainty is high. At the same time, it is more difficult for firms to switch to other banks. There are fewer banks active in smaller markets. Moreover, banks may be less willing to expand to new markets and less able to obtain liquidity or to take on more risk during a financial crisis. Together the presented results suggest that the LC channel is quantitatively relevant and that a lack of trade finance can constrain exports especially to the smaller and poorer countries. The key contribution of our paper compared to earlier works on financial shocks and trade is to show that reductions in the supply of trade finance reduce exports through the risk channel, and that this risk channel is more important during times of financial distress and for exports to small and poor countries. Using Japanese matched bank-firm data from 1990 to 2010, Amiti and Weinstein (2011) show that if a bank has a negative shock to its market-to-book value, a firm that lists this bank as its main bank has a drop in exports that is larger than the observed drop in domestic sales. While the authors establish a general link between banks and trade, they cannot test for the heterogeneous effects of shocks across export destinations and cannot directly distinguish between different transmission channels due to data limitations.10 Paravisini et al. (2015) focus on the working capital channel using matched bank-firm data from Peru. Exploiting heterogeneity in the exposure of banks to foreign funding shocks, they find that credit supply shocks reduced exports during the recent financialcrisisbutthatthiseffectwasnotasymmetricacrossexportdestinations.11 DelPrete 10Indirect evidence for the risk channel is provided: exports of firms that have affiliates drop less than exports of stand-alone firms. 11This highlights that the distinction between the working capital channel and the LC channel matters. A reduction in the supply of bank guarantees should have a different effect on trade than a reduction in the supply of general loans. First, working capital needs are independent of payment risk, whereas the risk that 5

and Federico (2014) employ Italian matched bank-firm data that allow them to distinguish between general loans, trade-related loans and guarantees. The authors report that Italian banks mainly reduced their supply of general loans and that this, but not reductions in trade finance per se, lowered Italian exports during the Great Recession. A drawback of that study is the lack of export data by destination so that the authors cannot analyze effects across export markets. Three other papers also focus on the risk channel. Ahn (2013) analyzes the effect of bank balance-sheet shocks on the provision of LCs in 2008/2009 in Colombia. Similar to the results in this paper, he finds that bank balance-sheet items predict the variation in bank-level LC supply. He does not test for the effect of supply shocks on aggregate trade flows, however. Van der Veer (forthcoming) studies the role of trade credit insurance and finds a relationship between the supply of insurance by one large insurer and aggregate trade flows. Auboin and Engemann (2014) exploit data on export insurance from the Berne Union to analyze the effect of insurance on trade. Hale et al. (2013) document that an increase in bank linkages between countries is associated with larger bilateral exports, conjecturing that banks mitigate export risk. The paper is also related to the literature on financial development and trade patterns. Beck (2003) and Manova (2013) show that differences in financial development can generate comparative advantage, confirming a theoretical point first made by Kletzer and Bardhan (1987). The paper is structured as follows. Sections 2 and 3 give background information on banks’ role in trade finance and the data, respectively. Section 4 discusses the empirical strategy. Section 5 presents the results and robustness checks. Section 6 quantifies the aggregate effects of LC supply shocks. Section 7 concludes. the importer defaults determines whether an exporter demands an LC. Moreover, working capital loans are fungible and firms can internally reallocate available funds. LCs, in contrast, are destination specific and can only be obtained from a small number of banks. 6

2 A Primer on Trade Finance and Letters of Credit 2.1 The role of banks in facilitating trade When exporters and importers engage in a trade, they have to agree on who finances the transaction and who bears the risk. Banks help both with financing and with mitigating the risk. Themostcommonpaymentformininternationaltradeisanopenaccount. Inthiscase, the exporter produces first and the importer pays after receiving the goods. The exporter pre-finances the working capital, either with funds out of her cash flows or through a loan from a bank. Moreover, she bears the risk that the importer will not pay after receiving the goods.12 To address this commitment problem, banks offer LCs. Figure 1 illustrates how they work. A bank in the importing country issues an LC, which is sent to the exporter. The LC guarantees that the issuing bank will pay the agreed contract value to the exporter if a set of conditions is fulfilled. These conditions typically include delivering a collection of documents to the bank, e.g., shipping documents that confirm the arrival of the goods in the destination country. In most cases, a bank in the exporting country is also involved in the LC transaction. Because there is still a risk that the issuing bank will default on its obligation, the exporter can ask a bank in her country to confirm the LC. The confirming bank thereby agrees to pay the exporter if the issuing bank defaults. To the extent that banks can monitor the transaction, the commitment problem is resolved with an LC, since the exporter is paid only after delivering the goods and the importer commits to paying by making her bank issue an LC.13 International trade is riskier than domestic sales because contracts are harder to enforce across borders. In addition, less information about the reliability of trading partners may be available. Accordingly, LCs are widely used in international trade and are employed to a much smaller extent for domestic sales. Data from the SWIFT Institute on LCs show this. 12Alternatively, the exporter and the importer can agree on cash-in-advance terms. Then, the importer pays the exporter before receiving the goods. In that case, financing is done by the importer who also bears the risk that the exporter may not deliver. 13AnLCcouldbedefinedasapaymentonopenaccountwithabankguarantee. Itissimilartopureopen account in that the exporter still needs to pre-finance the transaction and gets paid only after confirmation of delivery. It differs in that the risk the exporter has to bear is reduced by the guarantee of the bank. Moreover, the importer has to pay a fee to her bank in advance and the requested guarantee might reduce her available credit lines. The financial costs of an LC are therefore higher. See Schmidt-Eisenlohr (2013), Antr`asandFoley(forthcoming)andHoefeleetal.(2013)foramoredetaileddiscussionofdifferentpayment forms. 7

In 2012, around 92 percent of all LCs in support of U.S. sales were related to exports and only 8 percent to domestic activity.14 Essentially all letters of credit supporting U.S. exports are denominated in U.S. dollars. 2.2 Market structure of the business The trade finance business and, in particular, the market for bank guarantees is highly concentrated. Niepmann and Schmidt-Eisenlohr (2013) and Del Prete and Federico (2014) present details on the market structure for the U.S. and Italy, respectively. In 2012, the top 5 banks accounted for 92 percent of all trade finance claims in the U.S. In Italy, the business is similarly concentrated. Only ten Italian banks extend trade guarantees. The high concentration is likely due to high fixed costs. When U.S. banks confirm an LC, theyneedtoworkwithbanksabroadandhaveknowledgeoftheircredit-andtrustworthiness. U.S.banksalsohavetodobackgroundchecksontheircustomerstocomplywithduediligence requirementsandanti-moneylaunderingrulesbeforetheycanengageinanybusinessabroad. They also need to be familiar with the foreign market and the legal environment there. Such knowledge is costly to acquire and not easily transferable. Due to the presence of information asymmetries, the importance of relationships, and the resulting high concentration of the market, it should be difficult for a firm to switch to another bank when its home bank refuses to confirm or issue an LC. Note that there is no alternative method that reduces commitment problems to the same degree. Trade credit insurance, another option for exporters, does not reduce the risk but instead shifts it to another agent, the insurer.15 As a consequence, the price of insurance should increase more with destination country risk than the price of LCs, and insurance may be unavailable in the most risky destinations. If an LC cannot be obtained and trade insurance is very costly or cannot be bought, importers and exporters may not be willing to trade. Then a reduction in the supply of LCs has an effect on trade.16 14These calculations are based on quarterly information about the number of SWIFT MT700 messages that were received by U.S. banks. 15WhenissuingorconfirminganLC,banksactivelyscreendocumentsandmanagetheconditionalpayment totheexporterandtherebyresolvethecommitmentproblem. Tradecreditinsurancealsoimpliesaguarantee of payment but has no direct effect on the underlying commitment problem. This difference can best be seen in a model with risk-neutral firms as in Schmidt-Eisenlohr (2013). There, firms demand LCs but have no reason to buy trade credit insurance. 16Note that there is an effect on trade even if an alternative contract is chosen by a firm. It follows from revealedpreferencesthatwheneverLCsareused,otherpaymentformsgenerateweaklylowerprofits. Hence, areductioninthesupplyofLCscanaffectboththeintensiveandtheextensivemarginsoftrade. Quantities 8

2.3 Public provision of trade finance Mostmultinationaldevelopmentbankstodayrunlargetradefinanceprograms, withtheview that the private sector may not meet the demand. These programs were small at first and often targeted to the least developed countries. However, they were expanded substantially during the 2008/2009 crisis and now also cover many emerging economies.17 The Global Trade Finance Program of the International Finance Organization, which is a part of the World Bank group, for example, now has a $5 billion program that mostly confirms letters of credit through participating private banks.18 Surveys of banks conducted by the International Monetary Fund and the International Chamber of Commerce support the view that the supply of trade finance can constrain international trade. Asmundson et al. (2011) report that 38 percent of large banks said in July 2009 that they were not able to satisfy all their customer needs and 67 percent were not confident that they would be able to meet further increases in trade finance demand in that year. Greater trade finance constraints may also come from increases in prices. According to the same survey, letter of credit prices increased by 28 basis points (bps) over the cost of funds from 2007 q4 to 2008 q4 and by another 23 bps over the cost of funds between 2008 q4 and 2009 q2.19 Banks also reported that their trade-related lending guidelines changed. Every large bank that tightened its guidelines said that it became more cautious with certain countries. Thus, constraints may differ by destination country. As we will show in the next sections, this survey evidence is consistent with the results presented in this paper. 3 Data Description The data on trade finance used in this paper are from the Country Exposure Report (FFIEC 009). U.S. banks and U.S. subsidiaries of foreign banks that have more than $30 million in total foreign assets are required to file this report and have to provide, country by country, information on their trade-finance-related claims with maturity of one year and under. decline as trade finance costs, which represent variable trade costs, go up. If costs become sufficiently large, trade becomes unprofitable. 17In 2009, in the wake of the great recession, the G20 agreed on a $250 billion dollar program over two year to support trade finance. See G20 (2009). 18See IFC (2012) for more details. 19Similar results are obtained in the ICC survey. 42 percent of respondents in a 2009 survey report that theyincreasedtheirpricesforcommerciallettersofcreditissuance,whereas51percentleftpricesunchanged and 7 percent decreased them. LC confirmation also got more expensive. 58 percent of respondents report that they increased their prices, while only 2 percent lowered their fees. 9

Claims are reported quarterly on a consolidated basis; that is, they also include the loans and guarantees extended by the foreign affiliates of U.S. banks. The sample covers the period from the first quarter of 1997 to the second quarter of 2012.20 The statistics are designed to measure the foreign exposures of banks. This information allows regulators to evaluate how U.S. banks would be affected by defaults and crises in foreign countries. Therefore, only information on the claims that U.S. banks have on foreign parties is collected. Loans to U.S. residents and guarantees that back the obligations of U.S. parties are not recorded. While we can rule out based on the reporting instructions that letters of credit in support of U.S. imports or pre-export loans to U.S. exporters are included, it is conceivable that several trade finance instruments that support either U.S. exports, U.S. imports, or third-party trade constitute the data.21 Niepmann and Schmidt-Eisenlohr (2013) provide a detailed discussion and exploration of the data and we provide a summary of the findings here. Their analysis indicates first, that banks’ trade finance claims reflect trade finance in support of U.S. exports, and second, that the main instrument in the data are letters of credit. Beforepresentingindetailevidenceforthesetwoconjectures, weexplainthelinkbetween the reported claims and export values. Suppose that a U.S. bank confirms a letter of credit issued by a bank in Brazil. Then the U.S. bank would suffer a loss in the event that the Brazilian bank defaults on its obligation to pay. Accordingly, the U.S. bank reports claims vis-a`-vis Brazil equivalent to the value of the letter of credit. The value of the letter of credit, in turn, is determined by the value of the goods that the Brazilian firm buys from the U.S exporter. So there is a direct link between claims and the value of the exported goods. Similarly, if an affiliate of a U.S. bank in Brazil issues a letter of credit to a Brazilian importer, the affiliate backs the obligations of the foreign importer. Accordingly, the parent bank, which files the Country Exposure Report on a consolidated basis –meaning that the claims of its affiliate appear on its balance sheet but not the claims on its affiliate, reports the contract value as claims vis-`a-vis Brazil. Since the average maturity of a confirmed letter of credit is 70 days (see ICC (2013)), the stock of claims at the end of a quarter is highly correlated with the flow of exports in that quarter; thus, we compare quarterly stocks with 20Until 2005, banks’ trade finance claims are reported on an immediate borrower basis; that is, a claim is attributed to the country where the contracting counter-party resides. From 2006 onward, claims are given based on the location of the ultimate guarantor of the claim (ultimate borrower basis). This reporting change does not appear to affect the value of banks’ trade finance claims in a systematic way, so we use the entire time series without explicitly accounting for the change. See http://www.ffiec.gov/ for more details. 21Table 1 summarizes which instruments could be included based on the reporting instructions. 10

quarterly trade flows. The data on U.S. trade in goods used in this paper are from the IMF Direction of Trade Statistics. We now turn to the evidence that the FFIEC 009 data largely reflect letters of credit in support of U.S. exports. Consider columns (1) to (3) of table 2, which present the results of OLS regressions, in which the log of banks’ total trade finance claims in quarter 𝑡 in country 𝑐 is regressed on the log of imports from country 𝑐, the log of exports to country 𝑐 and total non-U.S. imports and exports of country 𝑐 at time 𝑡. The second column includes time fixed effects. The third column has both time and country fixed effects. Standard errors are clustered at the destination country level. The estimated coefficients show that banks’ trade finance claims are primarily driven by U.S. exports. While the point estimates associated with U.S. imports, non-U.S. imports and non-U.S. exports are small and insignificant, the coefficient of U.S. exports is large and significant at a 1 percent significance level throughout. The coefficient in column (1) suggests that if U.S. exports rise by 10 percent, banks’ trade finance claims increase by 8.6 percent. A comparison with data from the SWIFT Institute suggests that the main instrument in the FFIEC009 data are letters of credit. SWIFT provides a communications platform to exchange standardized financial messages, which is used by the vast majority of banks in the world. When a letter of credit transaction occurs, the issuing bank in the importer’s country sends a SWIFT MT700 message to the confirming bank in the exporter’s country, specifying the terms of the letter of credit and the parties involved. The SWIFT Institute provided us with the number of monthly MT700 messages received by banks located in the U.S. from 2002 to 2012 by sending country. To the extent that banks’ trade finance claims reflect letters of credit, there should be a close link between the quarterly value of bank claims and the number of SWIFT messages sent within a quarter. Columns (4) to (6) of table 2 show correlations between the two variables. The number of SWIFT messages received by U.S. banks is a strong predictor of banks’ trade finance claims controlling for U.S. exports as well as time and country fixed effects.22 A rise in the number of SWIFT messages by 10 percent increase banks’ trade finance claims by 6 percent according to column (4) of table 2. We also have information on the value of the letters of credit received by U.S. banks from the fourth quarter of 2010 onward. In that quarter, the total value of SWIFT messages accounts for 67 percent of banks’ total trade finance claims, which indicates again that the claims data 22Note that Niepmann and Schmidt-Eisenlohr (2013) also include LC messages sent by U.S. banks to country 𝑖 in the regressions, which reflect LCs issued to U.S. firms that import from origin 𝑖. This variable has zero explanatory power. LCs in support of U.S. imports are not in the data. 11

mostly captures LCs. In addition to the arguments made above, Niepmann and Schmidt-Eisenlohr (2013) show that the claims data behaves in many respects like the MT700 messages. For example, the use of letters of credit by U.S. exporters is expected to be hump-shaped in destination country risk and the authors find that this relationship holds for both banks’ trade finance claims and SWIFT MT700 messages. Thus everything points to letters of credit being the single most important instrument in the data. If bank claims captured other trade finance instruments to a substantial degree, the analysis in this paper would still be valid. The only other instrument in support of U.S. exports that can be included in the data are pre-import loans to foreign firms.23 To the extent that this is the case, the estimated shocks would not necessarily only reflect shocks to the supply of letters of credit but also to credit provided by U.S. banks to foreign importers. Figure2depictstheevolutionofU.S.exportsandbanks’tradefinanceclaimsovertime,as showninNiepmannandSchmidt-Eisenlohr(2013). Tradefinanceclaimspeakedin1997/1998 duringtheAsiancrisisandagainduringthefinancialcrisisin2007-2009.24 Since2010, claims have increased considerably, which is likely due to the low interest rate environment and the retrenchment of European banks from this U.S.-dollar-denominated business, allowing U.S. banks to gain their market shares. The graph clearly indicates that trade finance plays an important role for U.S. firms. In 2012, total trade finance claims of U.S. banks amounted to roughly 20 percent of U.S. exports. Figure 3 shows the distribution of trade finance claims and U.S. exports across world regions in the second quarter of 2012. Regions are ranked in descending order from the left to the right according to their shares in total trade finance. The upper bar displays the trade finance shares of the different regions. The lower bar illustrates regions’ shares in U.S. exports. While around 50 percent of U.S. exports go to high income OECD countries, banks’ trade finance claims in these countries only account for around 20 percent. In contrast, East Asia and the Pacific only receive 11 percent of U.S. exports, but this region’s share in trade finance is twice as large. The figure indicates substantial variation in the extent to which exporters rely on trade guarantees across regions and destination countries, which could lead to asymmetric effects of reductions in the supply of letters of credit. We explore asymmetries 23Credit to U.S. firms cannot be in the data given the reporting instructions. Forfeiting and factoring, which also reduce the risk of a transaction for the exporter, could be included but statisticians at the New York Fed tell us that this is not likely to be the case since U.S. banks are not very active in this business. 24Evidence from Italy and IMF surveys also suggests that trade finance expanded during the recent financial crisis. See Del Prete and Federico (2014) and Asmundson et al. (2011). 12

in more detail in section 5. 4 Empirical Approach 4.1 Estimating trade finance supply shocks Inthissection, wediscusstheempiricalstrategytoidentifythecausaleffectofletter-of-credit supply shocks on exports. The challenge in establishing a causal link is to obtain a measure of supply shocks that is exogenous to the demand for LCs. Because we have information on the trade finance claims of U.S. banks by destination country that varies over time, we can estimate time-varying idiosyncratic bank-level supply shocks from the data.25 In line with Greenstone et al. (2014) and Amiti and Weinstein (2013), we estimate the following equation:26 𝑡𝑓 −𝑡𝑓 𝑏𝑐𝑡 𝑏𝑐𝑡−1 Δ𝑡𝑓 = = 𝛼 +𝛽 +𝜖 , (1) 𝑏𝑐𝑡 𝑏𝑡 𝑐𝑡 𝑏𝑐𝑡 𝑡𝑓 𝑏𝑐𝑡−1 where 𝑡𝑓 corresponds to the trade finance claims of bank 𝑏 in country 𝑐 and quarter 𝑡. 𝑏𝑐𝑡 Trade finance growth rates are regressed on bank-time fixed effects 𝛼 and on country-time 𝑏𝑡 fixed effects 𝛽 . If all 𝛽 ’s were included in the regression together with all 𝛼 ’s, the 𝛼 ’s 𝑐𝑡 𝑐𝑡 𝑏𝑡 𝑏𝑡 and 𝛽 ’s would be collinear so one fixed effect must be dropped from the regression in 𝑐𝑡 each quarter. Without an additional step, the estimated bank-time fixed effect would vary dependingonwhichfixedeffectservesasthebasecategoryineachperiodandisomittedfrom the regression. To avoid this, we regress the estimated bank-time fixed effects on time fixed effects and work with the residuals 𝛼^ in place of the estimated 𝛼 ’s. This normalization 𝑏𝑡 𝑏𝑡 sets the mean of 𝛼^ in each period 𝑡 to zero and thereby makes it irrelevant which fixed 𝑏𝑡 effects are left out when equation 1 is estimated. The obtained bank-time fixed effects 𝛼^ correspond to idiosyncratic bank shocks. By 𝑏𝑡 construction, they are independent of country-time specific factors related to the demand for trade finance (and, hence, export growth) that affect all banks in the sample in the same way. To further address the concern that bank shocks might pick up demand effects, bank shocks are estimated for each country separately: the bank shock 𝛼^ for country 𝑖 is 𝑖𝑏𝑡 25Previous works on the effect of finance on trade use proxy variables to identify shocks. Amiti and Weinstein (2011) use banks’ market-to-book values. Paravisini et al. (2015), Del Prete and Federico (2014) and Ahn (2013) exploit the variation in banks’ funding exposures. 26Based on a cross-section observed at two points in time, Greenstone et al. (2014) estimate a model in log differences to obtain bank shocks. Amiti and Weinstein (2013) use a time-series, as we do, but impose adding-up constraints on the shocks. 13

obtained by estimating equation 1 without including observations of country 𝑖. Therefore, 𝛼^ reflects growth in trade finance claims by bank 𝑏 in quarter 𝑡 based on changes in claims 𝑖𝑏𝑡 in all countries except country 𝑖.27 Thenormalizedbank-levelsupplyshocks𝛼^ areusedtoconstructcountry-specificsupply 𝑖𝑏𝑡 shocks as follows: 𝐵 ∑︁ shock = 𝜑 𝛼^ , (2) 𝑖𝑡 𝑖𝑏𝑡−2 𝑖𝑏𝑡 𝑏 where 𝜑 = 𝑡𝑓 𝑖𝑏𝑡−2 . Thus, bank supply shocks are weighted by the share of bank 𝑏 in 𝑖𝑏𝑡−2 ∑︀𝐵𝑡𝑓 𝑏 𝑖𝑏𝑡−2 the total trade finance claims of country 𝑖 at time 𝑡−2 and are summed over all banks in the sample. In section 5.4, we show that results also hold when market shares are lagged by an alternative number of quarters or are averaged over several preceding periods. The effect of trade finance supply shocks on exports is estimated based on the following equation: 𝑋 −𝑋 𝑖𝑡 𝑖𝑡−1 Δ𝑋 = = 𝛾 shock +𝛿 +𝛿 +𝜂 , (3) 𝑖𝑡 𝑖𝑡 𝑡 𝑖 𝑖𝑡 𝑋 𝑖𝑡−1 where 𝑋 denotes U.S. exports to country 𝑖 at time 𝑡. Export growth rates are regressed 𝑖𝑡 on the constructed country-level supply shocks as well as on country fixed effects and time fixed effects. The key coefficient of interest is 𝛾. Under the assumption that the computed country supply shocks are not systematically correlated with unobserved characteristics that vary at the time-country level and are correlatedwithexports, 𝛾 correspondstothecausaleffectoftradefinancesupplyshocksonexport growth. Expressed in formulas, the identification assumption is: 𝐸(( ∑︀𝐵𝜑 𝛼^ )𝜂 ) = 0. 𝑏 𝑖𝑏𝑡−2 𝑖𝑏𝑡 𝑖𝑡 Given the presented strategy, the assumption is satisfied if two conditions hold. First, the estimated shock to the supply of LCs by bank 𝑏, based on information from countries other than country 𝑖, is not correlated with changes in the demand for trade finance and, hence, growth in exports to country 𝑖. Second, banks with positive shocks to their supply of trade finance in period 𝑡 do not sort, at time 𝑡 − 2, into markets with positive deviations from trend export growth in period 𝑡. We discuss possible violations of these conditions in detail in section 5.4. The biggest endogeneity concern that the reader might have is that the estimated bank shocks pick up changes in the supply of or demand for U.S. exports. We 27As indicated, it does not matter which fixed effects are dropped in the estimation of equation 1. In practice, we estimate equation 1 for all countries except Canada and exclude Canada fixed effects. In the regression to obtain bank-time fixed effect that apply to Canada, we exclude France fixed effects. While we estimate equation 1 159 times, dropping one country from the sample actually does not matter. Results are essentially identically if we work with bank-time fixed effects obtained from estimating equation 1 only once based on a sample that includes all countries. 14

present a battery of exercises that strongly suggest that our results come in fact from shocks to the supply of trade finance. We also show that sorting as a driver of our findings can be excluded. Before turning to the description of the data, we also want to discuss the interpretation of 𝛾. 𝛾 captures the effect of a trade finance supply shock to country 𝑖 on export growth to country 𝑖 relative to export growth in all other countries. A positive shock to the supply of trade finance for exports to country 𝑖 may redirect exports to country 𝑖, meaning that exports to country 𝑖 increase at the expense of exports to other countries. Because of such trade diversion, the estimate of 𝛾 should be seen as an upper bound for the direct effect of trade finance supply shocks on exports to country 𝑖.28 4.2 Description of the sample U.S. banks have trade finance claims in practically all countries of the world but only a few out of all banks that file the FFIEC009 report have positive values. For example, in the first quarter of 2012, 18 banks had positive trade finance claims in at least one country whereas 51 banks reported none. Three banks had positive trade finance claims in more than 70 countries while seven banks were active in less than five countries. Over the sample period, banks drop in and out of the dataset and acquire other banks. To account for acquisitions, the trade finance growth rates are calculated in the period of an acquisition based on the sum of the trade finance claims of the acquired bank and the acquiring bank in the previous period. The same adjustment is made when the bank shares 𝜑 are calculated. If a bank 𝑖𝑏𝑡−2 acquired another bank at time 𝑡 or 𝑡−1 we use the country share of the two banks added up to compute bank shares. Bank supply shocks are estimated on a sample in which observations are dropped for which 𝑡𝑓 is zero. If 𝑡𝑓 = 0, trade finance growth rates in quarter 𝑡 have to be dropped 𝑏𝑐𝑡 𝑏𝑐𝑡−1 because they go to infinity. To make the estimation less prone to outliers and keep things symmetric, we also drop negative growth rates of 100 percent. For 8.5 percent of all observations 𝑡𝑓 = 0. The total claims associated with these observations is small, adding 𝑏𝑐𝑡 up to a little more than one percent of the value of total claims in the data.29 We also drop the first and 99th percentiles of the trade finance growth rate distribution based on 28Any standard general equilibrium trade model predicts this kind of trade diversion, that is, trade flows to other countries 𝑗 ̸=𝑖 weakly decrease when trade frictions to country 𝑖 decrease. 29The share is based on the number of non-missing observations for which it is not the case that claims are zero both in period 𝑡 and in period 𝑡−1. 15

the remaining observations, further mitigating the influence of outliers. The dataset used to estimate equation 1 and obtain the bank-time fixed effects has 32,256 observations, covers the period from 1997 q2 to 2012 q2 and includes 107 banks as well 159 countries.30 4.3 Heterogeneity and persistence in banks’ market shares The empirical strategy in this paper requires that the importance of single banks be heterogeneous across destination markets. Otherwise, all countries would be subject to the same shock and we would not be able to identify effects. In addition, it is essential that banks have stable market shares over time, because we use lagged values to compute country shocks. If banks’ market shares were very volatile, then lagged values would not contain useful information about the degree to which bank-level supply shocks affect different countries. The upper panel of table 3 shows summary statistics of 𝜑 , the share of bank 𝑏 in the 𝑏𝑖𝑡 total trade finance claims of all U.S. banks in country 𝑖 at time 𝑡, at different points in time. There is substantial heterogeneity at every date. The average bank share increased from 2000 until 2012, consistent with the observed reduction in the number of banks active in the trade finance business.31 Bank shares range from below 0.1 percent to 100 percent. The standard deviation is 27 percent in the first quarter of 2012. Persistence in banks’ market shares can be reflected in both the intensive and the extensive margin. On the one hand, a bank should account for a stable fraction of a country’s overall trade finance supply over time (intensive margin). On the other hand, there should be no frequent exit and entry of banks into markets (extensive margin). We check whether bank shares are persistent in two different ways. First, we regress the market share 𝜑 of bank 𝑏 in country 𝑖 at time 𝑡 on country-bank fixed effects. These 𝑖𝑏𝑡 fixed effects alone explain more than 77 percent of the variation in bank shares, which implies that there is much cross-sectional variation in banks’ market shares but little time variation. Second, we regress the current market share 𝜑 on its lagged values. Without 𝑖𝑏𝑡 adjusting for mergers and acquisitions, the one-quarter lagged bank share explains around 84 percent of the variation in the current share, as shown in table 4.32 Two-period lagged 30Given that we always drop one country from the sample to estimate equation 1, the sample is slightly different and smaller in each estimation and includes only 158 countries. 31Changes in banks’ market shares over time are slow but substantial. Therefore, we cannot use market shares in the beginning of the sample period and keep them constant over time to obtain country-level shocks. 32If we adjusted for M&As, then persistence would be even higher. 16

values, which are used to construct country supply shocks, still explain around 77 percent of the variation (see the 𝑅2 in column (2)). Column (3) includes bank fixed effects, showing that the high correlation between current and lagged market shares does not come from systematic differences in size across banks. A similar exercise can be conducted for the number of banks 𝑛 that are active in a given 𝑖𝑡 market 𝑖. The lower panel in table 3 shows statistics for this variable. The number of banks operating in a given country fell over the sample period. In the first quarter of 2012, there were at most 14 banks active in a single country. The mean of the variable is 3.6 and the standard deviation is 2.8 in the same quarter. Aregressionofthenumberofbanksincountry𝑖attime𝑡oncountryandtimefixedeffects accounts for more than 76 percent of the variation. As an alternative, similar to before, the number of banks in period 𝑡 is regressed on its lagged values. Table 5 displays the results. The two-quarter lagged number of active banks explains approximately 92 percent of the variation in this variable. 4.4 Exploring the estimated bank-level shocks Thereareatotalof107differentbanksinthesampleforwhichweobtaintradefinancesupply shocks. In the third quarter of 1997, bank shocks for 54 different banks are estimated, down to 18 banks in the second quarter of 2012 due to consolidation in the banking sector. In total, we estimate 325,389 time-country-varying bank shocks from 1997 q2 until 2012 q2.33 Figure 4 shows the distribution of bank shocks, which exhibits significant variation. Table 6 provides the corresponding summary statistics. Figure 5 displays the mean and median normalized bank shock as well as the standard deviation of the bank shocks over time. Note that the mean is by construction equal to zero in each quarter. To check whether the bank shocks, which are estimated without the use of information on country 𝑖, predict trade finance growth in country 𝑖, we run the following regression: Δ𝑡𝑓 = 𝛼^ +𝜉 +𝜉 (+𝜉 )+𝜂 , (4) 𝑖𝑏𝑡 𝑖𝑏𝑡 𝑡 𝑖 𝑖𝑡 𝑖𝑏𝑡 where Δ𝑡𝑓 represents the growth rate of the claims of bank 𝑏 in country 𝑖 in quarter 𝑖𝑏𝑡 𝑡 observed in the data. 𝛼^ is the normalized bank shock of bank 𝑏 at time 𝑡 that was 𝑖𝑏𝑡 33Recall the bank-time fixed effects are estimated for 159 different countries, so for each estimation of equation 1, we estimate around 2,100 bank-time fixed effects. 17

estimated based on equation 1 without including Δ𝑡𝑓 in the sample. The regression results 𝑖𝑏𝑡 are displayed in table 7. The first column excludes fixed effects; the second column includes both time fixed effects 𝜉 and country fixed effects 𝜉 . The third column controls for country- 𝑡 𝑖 time fixed effects 𝜉 . Standard errors are clustered at the bank-time level. The coefficient 𝑖𝑡 on the bank shock is highly significant and positive in all three columns. This shows that the estimated bank shocks based on developments in other countries have strong predictive power for the actual growth of trade finance claims of bank 𝑏 in country 𝑖 at time 𝑡, although they do not explain much of the variation as the low 𝑅2 in column (1) indicates. Next, we investigate whether bank supply shocks are serially correlated. The main goal of this exercise is to document that there is no positive serial correlation. We will use this result in the robustness section 5.4. Table 8 displays results from a regression of the average bank shock 𝛼¯ , which corresponds to the value of 𝛼^ averaged over all countries, 𝑏𝑡 𝑖𝑏𝑡 on its lagged values and time fixed effects. The regression in column (1) includes only the one-quarter lagged bank shock. In column (2), the two-quarter lagged shock is added as a regressor. Column (3) includes one- to four-quarter lagged values of 𝛼¯ . The coefficients of 𝑏𝑡 the one-quarter lagged bank shock is significant and negative but small.34 Finally, we check whether bank shocks are correlated with meaningful bank-level variables.35 Banks allocate funding to business lines and may cut funding as overall conditions worsen. Becausetradefinanceisshorttermandcontractsareliquidatedwithinafewmonths or even weeks, trade finance can be quickly reduced to shrink banks’ overall balance sheet, reduce exposures and improve liquidity. However, banks may also take strategic decisions to grow or contract trade finance for other reasons. Thus, the estimated bank-level shocks may also capture changes in the supply of trade finance that are not closely linked to the current health of the bank. Banks may, for example, decide to contract their operations with foreign entities to refocus on core activities or when due diligence requirements change.36 There is in fact anecdotal evidence that, due to recently elevated due diligence requirements, some banks have reduced their cooperation with foreign banks. Moreover, European banks withdrew from the international trade finance business after the European sovereign debt crisis, which allowed U.S. banks to grow. The empirical strategy pursued in this paper allows us 34Thenegativeserialcorrelationappearstobeparticularlystrongafter2009. Thecoefficientonthelagged shock is not significant during the crisis period. 35Balance-sheetinformationforbanksinthesamplecomesfromtheY-9CandFFIEC031reports. Credit default swap spreads are taken from Markit.com. 36See Working Group on Trade, Debt and Finance (2014) for a summary of recent developments in trade finance after the 2007/2008 financial crisis. 18

to capture changes in banks’ supply of trade finance for all of these reasons. While we would therefore not expect the balance sheet variables to fully predict the estimated bank-level shocks, it is interesting to understand the extent to which they do. To that end, the mean bank shock 𝛼¯ is regressed on deposit growth, loan growth and the 𝑏𝑡 creditdefaultswapspreadon6-monthsseniorunsecureddebtofbank𝑏attime𝑡. Resultsare displayed in table 9. In all columns, bank and time fixed effects are included and standard errors are clustered at the bank level.37 The results in column (1) and (2) of table 9 indicate that the average bank shock is positively correlated with loan growth. Columns (3) and (4) show that it is negatively correlated with banks’ credit default swap spreads, an implicit measure of banks’ funding costs. The correlations between these bank-level variables and the estimated shocks becomes stronger in the second half of the sample (see columns (2) and (4)).38 4.5 Distribution of country supply shocks In a next step, details on the computed country-level supply shocks Δ𝑡𝑓 are given. In 𝑖𝑡 total, we obtain country shocks for 156 different countries.39 Table 6 displays the summary statistics for this variable. The regressions that are run to estimate the effect of trade finance supply shocks on trade include country fixed effects. Therefore, we control for time-invariant country characteristics that are correlated with export growth and trade finance supply shocks. However, results do not change when country fixed effects are left out as we show in the next section. This is because supply shocks are randomly distributed across countries. To illustrate this, figure 6 plots the distribution of the average value of a dummy variable 𝑑 that takes value 1 if 𝑖𝑡 the supply shock to country 𝑖 in period 𝑡 is above the period-𝑡 median and zero otherwise. In the limit, where time goes to infinity, random assignment would imply that the mean of the dummy goes to 0.5 for every country. In any finite sample, the dummy should be distributed symmetrically around 0.5. Figure 6 shows that this is the case. A correlation between country-level shocks and country characteristics could only arise if banks with above 37Note that reverse causality should not be a problem because trade finance claims constitute a small fraction of banks’ total activities. The median share of trade finance claims in total assets for the banks in our sample is below 1 percent. 38We also included measures of profitability in the regressions, e.g. return on equity and return on assets, but the associated coefficients were not significant. 39The number of countries reduces slightly because lagged bank shares are not observed for all countries for which we can obtain bank-level shocks. 19

or below median shocks were associated with particular countries. Figure 6 indicates that this is not the case and, therefore, that there is no correlation between banks’ market shares 𝜑 and the estimated bank-level shocks 𝛼^ . 𝑖𝑏𝑡 𝑖𝑏𝑡 In the regressions of export growth on country-level supply shocks (equation 3), countries with a population below 250,000, offshore financial centers and observation in the top and bottom one percentile of the export growth rate distribution are excluded from the sample.40 To control for export demand in the destination country, we add a set of variables. This lowers the number of observations further since these variables are not observed for all countries.41 However, the properties of the country-level supply shocks are unchanged as the summary statistics for the shock variable of the reduced sample show (see again table 6). 5 Results 5.1 Baseline Results Table 10 presents the baseline regression results obtained from estimating equation 3.42 Unless stated otherwise, standard errors are bootstrapped for all regressions in this section.43 In column (1), export growth is regressed on trade finance supply shocks and time fixed effects. The estimated effect of supply shocks is positive and significant at a 1 percent significancelevel. Thepositivecoefficientindicatesthatdestinationcountriesthatexperience larger declines in the supply of trade finance exhibit lower export growth rates. In column (2), several independent variables that control for changes in import demand are included in the regression: GDP growth and population growth, the change in the USD exchange rate of the local currency, and growth in non-U.S. imports of country 𝑖 in period 𝑡. In column (3), country fixed effects are added. The inclusion of the additional variables and fixed effects does not affect the magnitude of the estimated coefficient of interest 𝛾. This confirms that trade finance supply shocks are not systematically correlated with time-invariant country characteristics as found in the previous section or with demand factors. 40A list of countries designated as offshore financial centers can be found in the appendix. Niepmann and Schmidt-Eisenlohr(2013)showthatbanks’tradefinanceclaimsinoffshorecentersarebarelycorrelatedwith U.S. exports to these destinations so we drop them since we do not expect a link between trade finance and real activity. 41This reduces the number of countries in the baseline sample to 122. 42The 𝑅2 represents the total 𝑅2 in all tables in the paper. 43Clustering at the country level essentially delivers the same standard errors. 20

Based on the coefficient of 0.0888 displayed in column (3), a country supply shock of one standard deviation increases export growth by 1.5 percentage points. This corresponds to about 5 percent of one standard deviation of export growth rates. As a reference, table 6 provides summary statistics of export growth rates in the sample. We discuss the magnitude of the effect in more detail in section 6. Column(4)oftable10showstheeffectoftradefinancesupplyshocksforaboveandbelow median shocks separately.44 We compute two sets of country-level shocks using either above median or below median bank-level shocks in each period when aggregating shocks up to the country level. The estimated coefficients indicate that above and below median shocks have asymmetric effects. Only the point estimate of shocks below the quarterly median is statistically significant at a 5 percent level. In addition, it is almost three times larger than the coefficient associated with above median shocks.45 This is in line with what one might expect and confirms findings in Amiti and Weinstein (2011). Because a reduction in the supply of LCs typically requires cutting them for existing customers whereas additional supply is more fungible, shocks below the median should have a stronger effect. To explore which banks are responsible for the effect on exports, we compute supply shocks for the five biggest trade finance suppliers and the remaining banks separately and rerun the baseline regression.46 Column (5) of table 10 shows the results. The coefficients on the shocks attributed to the top five banks and the remaining banks are both significant and very similar. It may be surprising that small banks can have an effect in the aggregate. However, smaller banks specialize in certain markets so that they can be large and important for the provision of letters of credit in particular destinations. In column (6), the regression with separate shocks for the top five banks is run on a sample that includes years prior to 2004. Column (7) includes all years beginning with 2004. The sample split highlights that banks other than the top five are responsible for the effect on export growth in the early years of the sample, whereas the top five banks drive the effect in the later years. This finding is likely explained by the fact that the market shares of the top five banks steadily rose over the sample period. Since the banking sector went through a prolonged phase of consolidation, the impact of the top five banks on the total supply of trade finance increased 44Becausewenormalizeshockssothattheirmeaniszero,theabsoluteleveloftheshocksisnotmeaningful. Belowmedianshocksareassociatedwiththosebanksthatcontractedmoreorexpandedlessthanthemedian bank. The opposite holds for above median shocks. 45The estimates are not statistically significantly different from each other, however. 46We take the five bank with the largest trade finance claims over the sample period and also include merged entities that were separate banks in earlier years. 21

as smaller banks exited and the trade finance business became more concentrated. 5.2 Heterogeneous Effects In this section, we explore whether effects differ over time and across countries. In table 11, the sample is split into the crisis and the non-crisis period, respectively. The crisis period goes from the third quarter of 2007 to the second quarter of 2009. The non-crisis period includes all other dates. When the export equation (equation 3) is estimated only on the crisis sample, the effect of letter of credit supply shocks is highly significant at a 1 percent significance level and the point estimate of 𝛾 in column (1) is much larger than for the noncrisis sample in column (2). The shock coefficient of 0.183 in column (1) suggests that a country-level shock of one standard deviation decreases exports by more than 3 percentage points during periods of financial distress. This means that the effect of a reduction in the supply of LCs doubles during a crisis compared to the average effect reported before. To test formally for differences in the effect over time, we include an interaction term between the shock and a dummy variable for the crisis period in column (3) of table 11. The coefficient of the interaction term is significant at a 10 percent level and confirms the differences in magnitudes obtained from the sample split. The effect of LC supply shocks on export growth does not only vary over time but also across export destinations. In columns (4) and (5) of table 11, the effect of LC supply shocks is estimated based on a sample that only includes small and large export destinations, respectively. We define a country to be small if its log exports over the sample period lies below the sample median. Thus the designation of a country into small or large is constant overtime.47 ReductionsinthesupplyofLCsonlyhaveaneffectonsmallexportdestinations. While the point estimate of the shock is highly significant and takes a value of 0.189 for small countries in column (4), it is essentially zero and insignificant when only large export destinations are included in the sample in column (5). The difference is confirmed in column (6), inwhichaninteractiontermbetweentheshockandadummyvariableforsmallcountries is included, although the coefficient of the interaction is only marginally significant at a 12 percent level. In a next step, we jointly investigate differences over time and across countries. Column (7) includes only small countries and the recent crisis period. In column (8), the export equation is estimated on the full sample and includes now both the crisis interaction and the market size interaction. These additional regressions clearly show that the effect of 47Countries designated as small are listed in the data appendix. 22

LC supply shocks on export growth to large countries in normal times is close to zero, while the effect is strongest for exports to small destinations in times of financial distress in the U.S. economy. Then a negative LC supply shock of one standard deviation can lead to a reduction in exports of more than 4 percentage points. To check whether it is really the size of an export market that leads to differences in the effect across countries, we explore alternative sample splits and introduce interaction terms between the shocks and different variables. The evidence suggests that a crucial determinant of the strength of the effect on export growth is the number of U.S. banks that provide LCs for exports to a given destination country as shown in table 12. In column (1), the export regression is estimated only for countries in which less than five U.S. banks are active in quarter 𝑡−1. Column (2) shows the results for countries with at least five banks and column (3) is based on the full sample and adds interaction terms. The effect of LC supply shocks on export growth is clearly larger for countries in which less than five banks are active. The interaction term between a dummy for countries with at least five U.S. banks is large, negative and significant at a 5 percent level. The presented results indicate that export growth to small countries is particularly affected if banks contract their supply of LCs. This is easy to rationalize. First, only a few U.S. banks provide LCs for small destinations.48 If one of the banks active in those markets reduces its supply, it is especially difficult for trading partners to find an alternative. Second, selling to destinations with a weak rule of law might not be profitable for the exporter without an LC since the firm’s implicit cost of conducting the transaction without a guarantee may be high. At the same time, trade insurance, which is an alternative to an LC, is more likely to be unavailable or very costly. Our second finding, that the effect of supply shocks is larger during a crisis period, can also be explained by similar factors. During a period of financial distress, trading partners may find it harder to switch to another bank when the core bank refuses to issue or confirm an LC. Other banks may be less willing to expand their trade finance business to a new market during these times, and banks with existing relationships to intermediaries in a foreign country may not be able to obtain liquidity or may not want to add risk to their balance sheets. At the same time, exporters and importer may be more reluctant to trade without an LC as they are more risk averse. 48Evidence on this is and on how other country characteristics affect the number of active U.S. banks can be found in Table 6 of the working paper version. 23

5.3 Price versus quantity adjustments and dynamic effects To explore in more detail the mechanism through which trade finance supply shocks affect export growth, we study adjustments in prices and quantities separately. If the identified shocks represent contractions and expansions in the supply of trade finance, that is, if there is trade finance rationing, U.S. export growth will mostly respond through changes in traded quantities. A rationing of trade finance should leave export prices unaffected if destination marketsarecompetitive.49 Iftheshocksmainlycapturedchangesinthecostoftradefinance, then they should have a negative effect on observed prices, as importers may pay less to exporters.50 Tochecktheseconjectures, weconstructtheaveragegrowthinunitpricesandtheaverage growth in traded quantities by destination using industry-level export data from the Census Bureau.51 Growth rates of unit prices and quantities are calculated at the HS 10 digit level, which are then weighted by the share of an industry in the total value of exports to country 𝑖 at time 𝑡 and summed over all industries.52 The first two columns of table 13 show the effect of trade finance supply shocks on unit prices for the entire sample period and for the crisis period, respectively; columns (3) and (4) display regression results for quantities. While the effect of trade finance supply shocks on unit prices is close to zero and not significant, there is a positive and significant effect on quantities. This suggests that the adjustment of U.S. exports is mostly due to changes in quantities, lending further support for the mechanism proposed in this paper. Another interesting question is whether the effect of trade finance supply shocks is longterm or transient. In table 14, lags and leads of the shock variable are included in the regressions. We find that the one-quarter lagged trade finance supply shock also explains export growth. Higher order lags and leads are not significant. The negative coefficient of the one-quarter lagged shock suggests that reductions in export growth from a trade finance supply shock in period 𝑡 are partly offset by higher export growth in period 𝑡+1. To better understand this result, we run regressions separately for the crisis sample and the non-crisis 49If U.S. firms had substantial market power in a large number of products and a trade finance expansion made them supply larger quantities, a weak negative correlation between trade finance supply shocks and export prices could arise. 50This could be the case either because exporters and importers bargain over and share rents or because higher financial costs lead to a reduction in import demand, lowering equilibrium prices. 51Industry level trade data with information on quantities is available to us from 2006 onward. 52We drop observation in the bottom and top 10th percentiles of the quantity and unit price growth rate distribution, respectively. 24

sample as shown in columns (4) and (5). While the effects of trade finance supply shocks are in large parts transient in normal times, they are highly persistent in crisis times indicated by the much larger coefficient on the contemporaneous shock (0.17) than on the lagged shock (-0.05) in column (4). Reductions in the supply of trade finance by a single bank may mainly lead to a delay in export transactions until firms have found another bank that can provide the service, but they may harm trade in the long term if they occur during times of financial distress. 5.4 Identification and Robustness In this section, we present several robustness checks. In particular, we address the concern thattheconstructedcountry-levelshockscouldbeendogenoustochangesinexportsupplyor export demand. We also show that the results are robust to lagging banks’ market shares by an alternative number of periods when construction the country-level supply shocks. Combined with the previous observation that the estimated bank-level shocks are not positively serially correlated, this rules out endogeneity due to sorting of banks into markets. Ruling out demand effects If banks were fully specialized along a certain dimension, e.g. in firms or industries, and there was a shock to demand or production of the firm or industry, then the estimated bank shocks could reflect changes in exports. Dropping country 𝑖 information from the sample would not be sufficient to eliminate this endogeneity. To see this, consider the following example. Assume that there are two banks. Bank A specializes in confirming LCs for machinery, and bank B provides guarantees for exports of textiles. Suppose there is a shock to the supply of or the global demand for machinery so that exports in that industry increase. Then bank A faces a higher demand for trade finance and its trade finance claims increase. Because bank A sees an increase in the demand for LCs but not bank B, the estimation strategy could fail to filter out the demand effect, and the increase in the demand for trade finance could show up as a positive shock to bank A’s supply of trade finance. When exports of machinery increase to all destination countries, bank shocks identified without the inclusion of bank A’s trade finance claims in country 𝑖 could still be correlated with exports to country 𝑖. To address such concerns, we restrict the set of countries employed when estimating the bank-level supply shocks. First, we only use observations for the large countries in the sampletoestimateequation1. Thusweobtainbank-timefixedeffectsthatexclusivelyreflect 25

the growth or contraction of trade finance by U.S. banks in the large export destinations, whose imports from the U.S. should be more diversified in terms of firms, industries etc. We use these bank-level shocks to construct country-level supply shocks as before and rerun the regressions on the sample of small and large countries.53 Table 15 presents the results. When the export equation is estimated based on the sample of large countries, the effect of letter of credit supply shocks is close to zero and insignificant (columns (1) and (2)). Thus information on trade finance supplied to large countries does not predict U.S. export growth in large destinations. If the bank-level shocks picked up changes in the demand for trade finance, we should see the opposite. The estimated shocks therefore have to either represent trade finance supply shocks or be uninformative but they cannot be driven by demand-side factors. Column (3) shows that the shocks are able to predict export growth in the small destination countries that were excluded in the construction of the supply shocks. The samples of small and large countries do not systematically vary in their ratio of trade finance claims to U.S. exports, so the hypothesis that our results are driven by the demand for trade finance is hard to defend in consideration of this evidence. While the first robustness check presented above is, in our view, conclusive in regard of the role of demand effects, we do additional exercises that more specifically address concerns about industry and regional specialization. Next, we use the fact that countries differ substantially in the types of goods they import from the U.S. We compute the average share 𝑠¯ of industry 𝑘 in U.S. exports to country 𝑖 over the sample period. Then, we compute 𝑘𝑖 for each country 𝑖 and each country 𝑗 the sum of squared differences in industry shares between the two countries using the following formula: ∑︀𝐾 (𝑠¯ − 𝑠¯ )2. Next, we rank 𝑘=1 𝑘𝑖 𝑘𝑗 countries according to how similar they are to country 𝑖. This information is then used to systematically exclude countries from the sample on which the bank shocks for country 𝑖 are estimated. Specifically, we always exclude the 30 countries that are closest to country 𝑖 (in terms of the industry structure of their U.S. imports) when estimating equation 1 for country 𝑖. The estimated bank-level shocks are aggregated to obtain country-level shocks as before. The results of this exercise are shown in columns (1) to (3) table 16. The effect of letter of credit supply shocks on export growth is still large and highly significant. That is, even when we exclude those countries that are the most similar in terms of the goods they import from the U.S. when we estimate the bank-level shocks, the results still hold. In a third exercise, we exclude yet another set of countries when estimating equation 1. 53For a list of countries designated as small, see the data appendix. 26

Trade patterns are more dissimilar, the more different destination countries are in terms of their geographic location and stages of development. Hence, we drop not only information on country 𝑖 but also on the entire region in which country 𝑖 is located to obtain the banklevel shocks 𝛼 that are used to compute the aggregate supply shock of country 𝑖. We split 𝑖𝑏𝑡 countries into eight regions: East Asia and Pacific, Europe and Central Asia, High-income OECD members, High-income non-OECD members, Latin America and the Caribbean, the Middle East and North Africa, South Asia, and Sub-Saharan Africa. The results are qualitatively the same, as columns (4) to (6) of table 16 show. Evidence against specialization of banks in industries We now provide evidence against the hypothesis that banks specialize in particular firms or industries and that such specialization drives our results. Note that specialization would imply that a bank’s share in the total trade finance claims of country 𝑖 is correlated with the country-level export share of the industry in which the bank specializes. In addition, the estimated bank shocks would be correlated with specific industry shocks. To check for evidence of the former relationship, we regress a particular bank’s trade finance shares that vary across countries and over time on the export shares of different industries, which also vary across countries and over time. We split industries into fourteen groups.54 The regression equation reads as follows: 𝜑𝑏 = 𝜎𝑘 industry share𝑘 +𝛿 +𝜓 , (5) 𝑖𝑡 𝑖𝑡 𝑖 𝑖𝑡 where 𝜑𝑏 = 𝑡𝑓 𝑖𝑏𝑡 and 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑠ℎ𝑎𝑟𝑒𝑘 = 𝑋 𝑖𝑘𝑡 . 𝑋 stands for the exports in industry 𝑖𝑡 ∑︀𝐵𝑡𝑓 𝑖𝑡 ∑︀𝐾𝑋 𝑖𝑘𝑡 𝑏 𝑖𝑏𝑡 𝑘 𝑖𝑘𝑡 𝑘 to country 𝑖 at time 𝑡. This regression is estimated for each bank 𝑏 and each industry 𝑘. In a next step, we obtain industry shocks 𝛼 by running the following regression: 𝑘𝑡 Δ𝑋 = 𝛼 +𝛽 +𝜖 , (6) 𝑘𝑖𝑡 𝑘𝑡 𝑖𝑡 𝑘𝑖𝑡 where Δ𝑋 reflect the growth in U.S. exports to destination 𝑖 in industry 𝑘 at time 𝑡. As 𝑘𝑖𝑡 with the bank-level shocks, we regress the estimated industry shocks 𝛼 on time fixed effects 𝑘𝑡 and work with the residuals 𝛼^ . Then we regress the average bank shocks 𝛼¯ , bank by bank, 𝑘𝑡 𝑏𝑡 on the different industry shocks: 𝛼¯𝑏 = 𝜃𝑘 𝛼^𝑘 +𝜉 . (7) 𝑡 𝑡 𝑡 54These are Stone & Glass; Chemicals & Allied Industries; Transportation, Raw Hides, Skins, Leather, & Furs; Miscellaneous; Machinery & Electrical; Wood & Wood Products; Footwear & Headgear; Plastics & Rubbers; Food; Textiles; Mineral Products; Metals; Other. 27

Table 17 displays the results for the five largest banks. Each column presents the estimated coefficientsforaparticularbank. Eachrowreflectsaparticularindustry. Evencolumnsshow thecoefficientsobtainedfromestimatingequation5(𝜎𝑘), oddcolumnsthosefromestimating equation 7 (𝜃𝑘). The table indicates that the trade finance shares of banks do not co-vary systematically with the export shares of particular industries. Also industry shocks do not explain bank shocks. Note that it is not a concern that some of the coefficients in table 17 are positive and significant. Specialization would imply that 𝜎𝑘 and 𝜃𝑘 are both positive and significant for a particular bank 𝑏 and industry 𝑘 but this is never the case. We also ran regressions for the other banks in our sample and regressed the log of the trade finance claims of a particular bank on the log of exports in different industries. Each bank’s trade finance claims are correlated with exports in more than one industry. At the same time, exports of the same industry explain the variation in the trade finance claims of multiple banks. There is no indication that banks specialize and serve only a single industry or large firm and that this could drive the presented results. Beyond the econometric evidence just provided, there are also some basic observations that make it very unlikely that banks specialize in industries or that exports of single firms could drive changes in the trade finance claims of single banks and aggregate export growth rates at the same time. There are only a few banks that provide trade finance, while there are many more firms and industries. While it is true that international trade is highly concentrated, concentration is much lower than in the trade finance business. Bernard et al. (2009) report that in 2000 the top 1 percent of exporters, or 2245 firms, were responsible for 80.9 percent of all U.S. exports. In contrast, in our data in 2012, 5 banks accounted for more than 90 percent of U.S. banks’ trade finance. It is unlikely that idiosyncratic shocks to any of these 2245 firms would matter for the business of any of these very large trade finance suppliers. So the mere fact that the provision of guarantees is concentrated in a few large banks makes specialization improbable. Also, the largest firms are less likely to rely on LCs. Larger firms have longer lasting relationships and are better able to cope with risks, since they are big and can diversify within the firm.55 Moreover, a substantial amount of their trade is intra-firm and does not require bank guarantees. Third, banks should seek to spread trade financing over different industries and firms. On one hand, banks want to diversify risks. On the other hand, the costs associated with gathering LC-relevant information about 55Antr`as and Foley (forthcoming) report that the large U.S. food exporter they study employs letters of credit for only 5.5 percent of its exports. This is substantially smaller than the 8.8 percent found by Niepmann and Schmidt-Eisenlohr (2013) for overall U.S. exports. See Monarch and Schmidt-Eisenlohr (2015) for evidence that relationships of larger firms are systematically longer-lasting. 28

a destination and establishing a network of correspondent banks is likely much higher than the cost of acquiring knowledge about an industry. Country specialization of banks The reader might also be worried that country-level shocks could have feedback effects on banks because these specialize in certain countries and, as a result, are highly exposed to these economies. Suppose, for example, that a bank had most of its business in Argentina. Then, a downturn in Argentina could lead to large losses for that bank and trigger reductions in its supply of trade finance across all markets. To the extent that the Argentina shock moves the bank’s trade finance positions in other countries, wewouldestimateanegativeidiosyncraticshockforthatbank, and, throughtheaggregation of bank-level shocks, could get a negative trade finance supply shock for Argentina. A closer inspection of the data and our results shows that this is not an issue in our application. We find that shocks to the top 5 banks explain export growth in the small destinations. No trade finance position of any of the top 5 banks in the small countries represents more than 2.8 percent of the bank’s total trade finance claims (the mean share is 0.2 percent). Even a full default of one of these small export destination would therefore be insufficient to trigger any quantitatively relevant adjustments at these banks. Sorting The previous discussion addresses concerns that the idiosyncratic bank shocks we obtain could be endogenous to export growth. Any remaining endogeneity between countrylevel shocks and export growth rates must thus come through banks’ market shares. The identification assumption would be violated if banks with positive shocks in period 𝑡 were to provide more trade finance in period 𝑡−2 to markets with positive deviations from trend export growth in period 𝑡. In columns (1), (2) and (3) of table 18, banks’ market shares are lagged by one, three and four quarters, respectively, when computing the country shocks Δ𝑡𝑓 , in contrast to 𝑐𝑡 the two-quarter lags used in the baseline specification. In column (4), four-quarter rolling averages of banks’ market shares lagged by one period are used. In column (5), the yearly average market share of each bank is applied to construct the country-level shocks in the next year. The effect of supply shocks on export growth remains significant at a 10 percent level throughout. Given these results, our identification strategy could be violated only if banks that anticipate growing in period 𝑡 sort, in period 𝑡−1, 𝑡−2, 𝑡−3 and 𝑡−4, into markets with higher deviations from trend export growth in period 𝑡. We have shown in 29

section 4.3 that the estimated bank-level shocks are not positively serially correlated. As results hold independent of the number of lags used for the market share variable, systematic sorting of banks period by period can be ruled out. Placebo test, country time trends and zeros We end the robustness section with a couple of additional checks. In column (1) of table 19, the dependent variable is replaced. InsteadofU.S.exportgrowth,weusegrowthinexportsoftheEU15countriestodestination𝑖 in quarter 𝑡.56 Accordingly, we do not include growth in non-U.S. exports as control variable in the regression. The estimated shock coefficient is close to zero and insignificant. This indicates, on one hand, that the supply shocks are not correlated with the demand for goods from EU countries. On the other hand, it suggests that there is no substitution of EU goods for U.S. goods in response to U.S. trade finance shocks. In column (2), we include countryspecificlineartimetrendsintheregressioninadditiontotimeandcountryfixedeffects. This has essentially no effect on the magnitude and significance of the shock coefficient compared to the baseline result in column (3) of table 10. Thus we can exclude that results are due to an omitted variable that exhibits a time trend and is correlated with both the shocks and export growth.57 In column (3), we present a robustness check that addresses concerns related to our sample selection when estimating bank-time fixed effect. Recall that we deleted observations for which 𝑡𝑓 was equal to zero (see the discussion in section 4.2) when estimating equation 𝑏𝑐𝑡 1. To avoid the exclusion of zeros, we compute growth rates using the formula 2𝑡𝑓 𝑏𝑐𝑑 −𝑡𝑓 𝑏𝑐𝑡−1 𝑡𝑓 +𝑡𝑓 𝑏𝑐𝑑 𝑏𝑐𝑡−1 suggested by Davis et al. (2007). These alternative growth rates are regressed on banktime fixed effects 𝛼 and country-fixed effects 𝛽 . The obtained bank-time fixed effects 𝑏𝑡 𝑐𝑡 are normalized and used to compute country-level shocks as before. Regressions are run equivalently on export growth rates computed as exp growth = 2𝑒𝑥𝑝𝑐𝑡−𝑒𝑥𝑝𝑐𝑡−1. Including 𝑐𝑡 𝑒𝑥𝑝𝑐𝑡+𝑒𝑥𝑝𝑐𝑡−1 observation with zero trade finance at 𝑡 or 𝑡−1 creates noise in our estimation, but results for the crisis period go through as column (3) shows. The estimated effect of trade finance supply shocks is significant at a 10 percent level and comparable to earlier findings with a beta coefficient of around 10 percent. 56The EU15 countries include: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden, the United Kingdom. 57Wealsocheckedthatresultsarerobusttoexcludingtheperiodafter2009,inwhichU.S.banksexpanded their trade finance business in particular in Asia. 30

6 Quantifying the Effect of Supply Shocks In this section, we conduct several experiments to explore the quantitative relevance of trade finance supply shocks for U.S. exports. We will interpret the effect of 𝛾 as the direct effect of trade finance supply shocks on export growth to country 𝑖 in the narrative below. However, as discussed in section 4.1, 𝛾 probably represents an upper bound of this direct effect. We start with the following experiment: we assume that a major trade finance provider experiences a negative supply shock that corresponds to the 10th percentile of the bank shock distribution (a value of -0.426). Using this bank’s market share in each destination country in the fourth quarter of 2011 and export values in the first and second quarters of 2012, the predicted aggregate effect on export growth is calculated as follows: ∑︀𝑁 (𝛾(−0.426)𝜑 𝑋 ) Δ𝑋 = 𝑐=1 𝑐𝑏𝑡−2 𝑐𝑡−1 . (8) 𝑡 𝑋 𝑡−1 We set 𝛾 equal to 0.0888, which corresponds to the estimated coefficient in column (3) of table 10. The calculations predict that such a trade finance supply shock would reduce aggregate U.S. export growth by 1.4 percentage points. It does matter which bank is subject to the shock. In a next step, we choose two large trade finance suppliers and calculate the effect on export growth in selected regions of the world when each of them is hit by the shock described above. Columns (1) and (2) of table 20 show the results. Whereas exports in South Asia would fall by 0.41 percentage point if bank A were hit by the shock (see column (1)), the same relative reduction in trade finance by bank B would reduce exports in this region by 1.86 percentage points (see column (2)). An even stronger asymmetry arises for Sub-Saharan Africa. This example illustrates that banks, through their global operations, can influence export patterns. The same bank shock affects countries differentially, depending on how important the bank is for the provision of LCs in each export market. So far, we focused on what happens when only one of the banks reduces its supply of trade finance. Next, we analyze the effect on exports if all banks were hit by a moderate shock that corresponds to the 25th percentile of the bank-level shock distribution (a value of 𝛼 = −0.245) and roughly to half of the shock considered before. Using the estimated loan 𝑏𝑡 growth coefficient in column (4) of table 10, aggregate U.S. exports would fall by around 2.2 percentage points. According to the results presented in section 5, the effect of letter of 31

credit supply shocks is larger during a crisis period. Based on the coefficient in column (3) of table 11, the effect would double to 4.4 percentage points.58 There is also evidence that the effect of shocks varies across countries. Smaller export markets are more affected than larger markets by a reduction in the supply of trade finance. To account for this, we calculate the effect of supply shocks now based on the estimated relationship in column (8) of table 11 for different regions of the world during crisis times. Compared to other regions, sub-saharan Africa would be hit particularly hard by a reduction in the supply of letters of credit as (4) of table 20 shows, since this region hosts many small export markets. As a final exercise, we compare the effect of an LC supply shock to the effect of an exchange rate shock. According to the estimated coefficient in column (3) of table 11, a 10percent appreciation of the USD against the local currency of the importing country reduces U.S. exports by 2.53 percentage points. Hence, the effect of a negative LC supply shock of one standard deviation during a crisis episode generates the same reduction in trade as an appreciation of the USD by 12.3 percent. The effects of trade finance supply shocks are comparable to those of exchange rate changes.59 The preceding exercises illustrate that trade finance supply shocks are economically relevant for exports. This is particularly true for exports to small countries during times of financial stress, with a beta coefficient of 0.17. The overall role of trade finance for exports is likely larger. Recall that our estimation strategy only identifies effects of trade finance supply shocks through the risk channel. Shocks to the supply of letters of credit should affect trade flows over and above the effects of shocks to the supply of credit identified by Paravisini et al. (2015). Note further that 𝛾 is the elasticity of export growth to bank-specific trade finance supply changes. To disentangle the supply of from the demand for trade finance, the estimated bank-level shocks are purged of any aggregate effects. While necessary for identification, it also means that aggregate trade finance supply shocks, for example during the Great Trade Collapse, cannot be quantified and their effects cannot be estimated. This limitation is not specific to our paper but is a general feature of any cross-sectional estimation strategy like the ones employed by Amiti and Weinstein (2011) and Paravisini et al. (2015). 58Relative to non-crisis times the effect more than doubles. 59We estimate the contemporaneous exchange rate elasticity of U.S. exports. Estimates of the long-run elasticity are typically higher. See, for example Hooper et al. (2000). The beta coefficient of the exchange rate is 5.2 percent, comparable to the beta coefficient of trade finance supply shocks, which is 5 percent. 32

We have seen that reductions in the supply of trade finance by single banks have larger effects during times of financial distress. This is probably because exporters find it harder to switch to other banks, which may be less willing to expand their balance sheets or to cooperate with new banks in foreign countries when uncertainty is high and liquidity limited. Switching might be even harder in the presence of an aggregate reduction in the supply of tradefinance, which, accordingtoindustryreports, happenedin2008. Wethereforeconclude that trade finance, through the risk channel, very likely played a magnifying role in the Great Trade Collapse, with larger effects for exports to the smaller and poorer countries. 7 Conclusions Exploiting data on the trade finance claims of U.S. banks that vary across countries and over time, this paper sheds new light on the effects of financial shocks on trade. While existing studies emphasize the working capital channel, this work provides evidence for the risk channel. We show that shocks to the supply of LCs – a trade-specific, risk-reducing financial instrument – have statistically and economically significant effects on exports. While we follow the strategy of Greenstone et al. (2014) and Amiti and Weinstein (2013) toidentifysupplyshocksfromthedata,wemodifyandaddnewelementstothemethodology. First, we estimate bank shocks over multiple periods and propose a normalization to make bank shocks comparable across time. Second, we obtain bank shocks separately for each country and show how to systematically drop information on similar countries to counter endogeneity concerns that may arise. Third, we demonstrate how sorting into markets can be excluded by jointly looking at serial correlation in bank shocks and by estimating the model using different lags of the market shares. These innovations can be useful for future empirical work. Applying the approach, we find that exports to countries that are poorer and smaller, where fewer U.S. banks are active, are more affected when banks reduce their supply of trade finance. At the same time, changes in supply have much stronger effects during times of financial distress. Another key result of the analysis is that single banks can affect exports in the aggregate. Due to the high concentration of the business, a large negative shock to one of the big U.S. trade finance banks reduces aggregate exports by 1.4 percentage points. This effect more than doubles during times of financial distress. The presented findings suggest that trade finance can constrain exports, especially to the poorer and smaller destinations 33

and during crises episodes. Considering that reductions in the supply of LCs are associated with a contraction in bank lending and a rise in banks’ credit default swap spreads, trade finance may have a role in explaining the collapse in exports to the smaller and poorer countries in 2008/2009. 34

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Data Appendix Data sources ∙ U.S. banks’ trade finance claims: FFIEC009 Report, Statistics Group, New York Fed. ∙ SWIFT MT700 messages received by U.S. banks: the SWIFT Institute. ∙ Quarterly trade data: IMF Direction of Trade Statistics. ∙ Quarterly industry-level trade data: Census Bureau. ∙ Bank balance sheet data: FFIEC031 or Y9c reports. Where available, FFIEC031 information was aggregated up for each Bank Holding Company to match the FFIEC009 reporting level. ∙ Quarterly GDP was obtained from national statistical agencies via Haver Analytics’ Data Link Express (DLX) Software. ∙ Annual population, GDP per capita: World Development Indicators, the World Bank. ∙ Rule of law: World Government Indicators, the World Bank. ∙ Distance: CEPII (see Head et al. (2010)). ∙ Exchange rates: International Financial Statistics, IMF. ∙ Quarterlycreditdefaultswapspreadsonseniorunsecureddebtwithmaturity6months in USD: Markit.com. Matching between ticker names and IDRSSDs was done manually. Quarterly data was obtained by averaging the monthly data. List of countries ∙ Countries designated as offshore financial centers: Netherlands Antilles, Antigua and Barbados, Azerbaijan, Bahrain, Bahamas, Belize, Bermuda, Barbados, Cayman Islands, Cyprus, Dominica, Grenada, Hong Kong, Ireland, Jordan, Lebanon, Macao, Monaco, Maldives, Malta, Mauritius, Seychelles, Vanuatu, Samoa. ∙ Countries designated as small export destinations: Afghanistan, Algeria, Angola, Armenia, Azerbaijan, Bahrain, Bangladesh, Barbados, Belarus, Belize, Benin, Bermuda, 39

Bolivia, Botswana, Brunei Darussalam, Bulgaria, Burkina Faso, Cabo Verde, Cambodia, Cameroon, Democratic Republic of Congo, Republic of the Congo, Cote d’Ivoire, Croatia, Cyprus, Czech Republic, Denmark, Djibouti, Estonia, Ethiopia, Fiji, French Polynesia, Gabon, Georgia, Ghana, Greece, Guinea, Guyana, Haiti, Hungary, Iceland, Iraq, Jordan, Kazakhstan, Kenya, Latvia, Lebanon, Liberia, Libya, Lithuania, Luxembourg, Macau, Macedonia, Madagascar, Malawi, Maldives, Mali, Malta, Mauritania, Mauritius, Mongolia, Morocco, Mozambique, Namibia, Nepal, Netherlands Antilles, New Caledonia, Nicaragua, Oman, Pakistan, Papua New Guinea, Paraguay, Portugal, Qatar, Romania, Rwanda, Samoa, Senegal, Slovakia, Slovenia, Sri Lanka, Sudan, Suriname, Swaziland, Syria, Tanzania, Togo, Trinidad & Tobago, Tunisia, Turkmenistan, Uganda, Ukraine, Uruguay, Uzbekistan, Yemen, Zambia, Zimbabwe. 40

Figure 1: How a letter of credit works Contract Execution 1. Contract 5. Shipment 10. Payment 6. Submit 2. Apply for documents. 11. Release letter of documents. 4. Authenticate credit. 9. Payment letter of credit. 7. Send documents. 3. Send letter 8. Payment of credit. 41

Figure 2: Evolution of aggregate trade finance claims and U.S. exports over time Trade Finance Claims U.S. Goods Exports (Billions of USD) (Billions of USD) 200 450 180 400 160 350 140 300 120 250 100 Goods Exports 200 80 150 60 Trade Finance 100 40 Claims 20 50 0 0 Note: The solid line in the graph shows the aggregate trade finance claims of all reporting U.S. banks overtime. Theyears1997-2000excludedatafromonelargebankthatchangeditstradefinancebusiness fundamentallyinthereportingperiod. ThedottedlinedisplaystheevolutionofaggregateU.S.exports in goods over time. 42

Figure 3: Trade finance and export shares in 2012 q2 by world region 2.28% 20.92% 20.10% 19.81% 17.53% 10.91% 5.75% 2.28% 1.59% 1.38% 25.30% 11.06% 50.65% 6.95% 1.97% 1.03% erahS ecnaniF edarT erahS tropxE Ranked Left to Right by Trade Finance Share Latin America and the Caribbean East Asia and Pacific High Income OECD Members South Asia High Income Non‐OECD Members Europe and Central Asia Sub‐Saharan Africa Middle East and North Africa Note: The upper bar in the graph shows the shares of eight groups of countries in banks’ total trade finance claims in 2012 q2. The lower graphs displays each group’s share in total U.S. goods exports in that quarter. 43

Figure 4: Distribution of bank supply shocks ytisneD 5.1 1 5. 0 -2 0 2 4 6 Bank shock Note: The graph shows the histogram of the 325,389 bank-level shocks that are estimated based on the trade finance data. 44

Figure 5: Mean, median and standard deviation of bank supply shocks over time 5.1 1 5. 0 5.- 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 9 9 9 0 0 0 0 0 0 0 0 0 0 1 1 1 9 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 Date median mean std Note: The solid line in the graph shows the median bank-level supply shock over time. The dashed line displays the mean shock. The dash-dotted line reflects the standard deviation of the bank shocks in each quarter. 45

Figure 6: Evidence for random distribution of shocks across countries ytisneD 4 3 2 1 0 0 .2 .4 .6 .8 1 Distribution of average below and above median counts by country Note: The histogram shows the distribution of country means of a dummy variable that takes value 1 if country 𝑖 is hit by an above median shock in period 𝑡 and zero otherwise. If shocks are randomly distributed across countries, then the means should be distributed symmetrically around 0.5. 46

Table 1: Possible instruments and underlying trade transactions in the data U.S. exports U.S. imports Third party trade Pre-export financing (parent) - X X Pre-import financing (affiliate) X - X LC issuance (affiliate) X - X LC confirmation (parent) X - X Note: 𝑋 indicates thatthis type of tradetransaction could beincluded in theFFIEC 009 data basedon the reporting instructions. 47

atad smialc eht gnirolpxE :2 elbaT )6( )5( )4( )3( )2( )1( )ft(gol rav .ped 𝑡𝑐 ***023.0 ***114.0 ***284.0 ***542.0 ***158.0 ***168.0 ) stropxe(gol tc )601.0( )6050.0( )3740.0( )5950.0( )3090.0( )6880.0( 3910.0 0130.0- 4720.0- ) stropmi(gol tc )8340.0( )1470.0( )2170.0( 9330.0- 5040.0 2760.0 ) stropxe .S.U-non(gol tc )911.0( )241.0( )041.0( 371.0 8350.0- 211.0- ) stropmi .S.U-non(gol tc )271.0( )361.0( )551.0( ***139.0 **712.0- **422.0- ) pac rep PDG(gol tc )232.0( )2090.0( )1980.0( ***382.0 ***636.0 ***195.0 ) segassem TFIWS fo #(gol 𝑡𝑐 )3280.0( )8550.0( )1250.0( seY seY oN seY seY oN EF emiT seY oN oN seY oN oN EF yrtnuoC 383,3 383,3 383,3 995,6 995,6 995,6 snoitavresbO 078.0 996.0 876.0 338.0 955.0 055.0 derauqs-R eht morf segassem tiderc-fo-rettel fo rebmun eht dna edart ,mrof 900 CEIFF eht morf smialc ecnanfi edart neewteb pihsnoitaler eht sezylana elbat sihT :etoN elbairav ehT .yrtnuoc noitanitsed dna retrauq yb sknab .S.U lla fo smialc ecnanfi edart eht fo mus eht fo gol eht si elbairav tnedneped ehT .etutitsnI TFIWS era srorre dradnats deretsulC .𝑡 retrauq ni 𝑐 yrtnuoc morf sknab .S.U yb deviecer segassem )CL( 007TM fo rebmun eht stneserper segassem TFIWS fo # 𝑡𝑐 .level %1 dna %5 ,%01 eht ta ecnacfiingis etoned *** dna ** ,* .sesehtnerap ni 48

Table 3: Summary statistics of banks’ market shares and the number of banks by country date N mean std. min max 𝜑 2000 q1 758 0.151 0.250 0.0003 1 𝑖𝑏𝑡 2006 q1 453 0.256 0.314 0.0003 1 2012 q1 484 0.277 0.324 0.0001 1 n 2000 q1 115 6.591 6.569 1 34 𝑖𝑡 2006 q1 116 3.905 2.871 1 14 2012 q1 134 3.612 2.810 1 13 Note: ThistablereportssummarystatisticsbasedondatafromtheCountryExposureReport(FFIEC009). 𝜑 is the share of the trade finance claims of bank 𝑏 in the total trade finance claims of country 𝑖 at time 𝑖𝑏𝑡 𝑡. 𝑛 is the number of banks with positive trade finance claims in country 𝑖 at time 𝑡. 𝑖𝑡 Table 4: Persistence in banks’ market shares dep. var. 𝜑 (1) (2) (3) 𝑖𝑏𝑡 𝜑 0.913*** 𝑖𝑏𝑡−1 (0.00331) 𝜑 0.880*** 0.831*** 𝑖𝑏𝑡−2 (0.00399) (0.00493) Observations 32,896 29,538 29,538 R-squared 0.836 0.773 0.780 Note: This table analyzes the persistence of banks’ market shares within countries. The dependent variable is the share of the trade finance claims of bank 𝑏 in the trade finance claims of all banks in country 𝑖 at time 𝑡. All regressions include a constant. The regression in column (3) includes bank fixed effects. Robust standard errors are in parentheses. *, ** and *** denote significance at the 10%, 5% and 1% level. Table 5: Persistence in the number of banks active in a market dep. var. n (1) (2) (3) 𝑖𝑡 n 0.956*** 0.690*** 𝑖𝑡−1 (0.00440) (0.0173) n 0.925*** 0.265*** 𝑖𝑡−2 (0.00547) (0.0173) Observations 6,914 6,697 6,587 R-squared 0.947 0.924 0.950 Note: This table analyzes the persistence of the number of banks active in a trade finance market. The dependent variable is the number of banks with positive trade finance claims in country 𝑖 at time 𝑡. All regressions include a constant. Robust standard errors are in parentheses. *, ** and *** denote significance at the 10%, 5% and 1% level. 49

Table 6: Summary statistics (1) (2) (3) (4) (5) variable N mean sd min max trade finance growth Δ𝑡𝑓 32,256 0.225 1.049 -.904 9.8 𝑏𝑐𝑡 bank shock 𝛼^ 325,389 0 0.500 -2.316 5.806 𝑖𝑏𝑡 country-level shock 6,751 0.030 0.171 -0.97 2.202 𝑖𝑡 country-level shock in sample with controls 4,904 0.032 0.174 -0.971 1.813 𝑖𝑡 export growth Δ𝑋 in sample with controls 4,904 0.057 0.309 -.667 2.06 𝑖𝑡 Note: In the first row, summary statistics of trade finance growth rates are given that are observed in the sample that is used to estimate equation 1. The second row provides summary statistics of the normalized bank shocks that are obtained from estimating equation 1, always dropping country 𝑖 from the sample. The summary statistics of the country-level shocks in the third row are for all country-level shocks that are computed. Inthefourthcolumnonlythosecountry-levelshocksareincludedthatareusedintheestimation of equation 3 with controls. The last row displays summary statistics of the corresponding export growth rates. Table 7: Predicting observed trade finance growth rates using bank-level shocks dep. var Δ𝑡𝑓 (1) (2) (3) 𝑖𝑏𝑡 𝛼^ 0.305*** 0.307*** 0.326*** 𝑖𝑏𝑡 (0.0521) (0.0531) (0.0680) Country FE no yes no Time FE no yes no Time×County FE no no yes Observations 32,025 32,025 32,025 R-squared 0.002 0.009 0.142 Note: Thistableanalyzestherelationshipbetweenthecountry-specificbank-levelshock𝛼^ andtheobserved 𝑖𝑏𝑡 growth Δ𝑡𝑓 in bank 𝑏’s trade finance claims in country 𝑖 at time 𝑡. All regressions include a constant. 𝑏𝑖𝑡 Standarderrorsareclusteredatthebank-timelevelandareinparentheses. *,**and***denotesignificance at the 10%, 5% and 1% level. 50

Table 8: Testing whether bank-level supply shocks are serially correlated dep. var 𝛼¯ (1) (2) (3) 𝑏𝑡 𝛼¯ -0.0883*** -0.0909** -0.0889** 𝑏𝑡−1 (0.0337) (0.0363) (0.0420) 𝛼¯ 0.0184 -0.00572 𝑏𝑡−2 (0.0292) (0.0294) 𝛼¯ -0.0143 𝑏𝑡−3 (0.0276) 𝛼¯ -0.0214 𝑏𝑡−4 (0.0298) Observations 1,894 1,758 1,545 R-squared 0.012 0.015 0.017 Note: This table tests for serial correlation in the average bank level supply shocks 𝛼¯ , which corresponds 𝑏𝑡 to the value of 𝛼^ averaged over all countries. All regressions include a constant and time fixed effects. 𝑖𝑏𝑡 Robust standard errors are in parentheses. *, ** and *** denote significance at the 10%, 5% and 1% level. Table 9: Correlation of estimated bank shocks with bank-level variables dep. var 𝛼¯ (1) (2) (3) (4) 𝑏𝑡 all after 2006 all after 2006 loan growth 0.333* 0.829*** 𝑏𝑡 (0.184) (0.278) deposit growth 0.0196 -0.271 𝑏𝑡 (0.196) (0.308) CDS spread -0.0838** -0.0862*** 𝑏𝑡 (0.0351) (0.0212) Observations 2,179 469 256 150 R-squared 0.073 0.069 0.343 0.432 Note: Thistableanalyzestherelationshipbetweentheestimatedbankshocksandbank-levelvariables. The dependent variable is the mean bank shock 𝛼¯ , which corresponds to the value of 𝛼^ averaged over all 𝑏𝑡 𝑖𝑏𝑡 countries. CDS spread is the bank-specific current default swap spread of bank 𝑏 at time 𝑡. All regressions include bank and time fixed effects. Standard errors clustered by bank and are in parentheses. *, ** and *** denote significance at the 10%, 5% and 1% level. 51

stluser enilesaB :01 elbaT )7( )6( )5( )4( )3( )2( )1( 2102-4002 3002-7991 𝑋Δ .rav .ped 𝑡𝑖 **8880.0 **8780.0 ***5780.0 kcohs 𝑡𝑖 )1530.0( )0530.0( )1330.0( **951.0 05𝑝 < kcohs 𝑡𝑖 )1270.0( 2060.0 05𝑝 ≥ kcohs 𝑡𝑖 )1040.0( 81300.0- **851.0 *3490.0 sknab rellams kcohs 𝑡𝑖 )9660.0( )0670.0( )1350.0( *901.0 0520.0- **8580.0 sknab 5 pot kcohs 𝑡𝑖 )0850.0( )101.0( )8140.0( ***526.2- 282.2- ***892.2- ***982.2- ***992.2- **725.1htworg .pop 𝑡𝑖 )838.0( )617.1( )127.0( )656.0( )307.0( )706.0( 241.0- 1230.0- 7580.0- 8380.0- 9580.0- 8140.0htworg PDG 𝑡𝑖 )301.0( )821.0( )6960.0( )6970.0( )9960.0( )8470.0( ***954.0- *461.0- ***252.0- ***252.0- ***252.0- ***991.0htworg etarx DSU 𝑡𝑖 )361.0( )8790.0( )6270.0( )3680.0( )3870.0( )1070.0( ***683.0 ***333.0 ***163.0 ***163.0 ***163.0 ***073.0 htworg tropmi .S.U-non 𝑡𝑖 )0470.0( )9960.0( )4050.0( )9150.0( )6350.0( )9150.0( sey sey sey sey sey on on EF yrtnuoC sey sey sey sey sey sey sey EF emiT 997,2 501,2 409,4 409,4 409,4 409,4 753,5 snoitavresbO 311.0 211.0 201.0 201.0 201.0 860.0 940.0 derauqs-R eht si elbairav tnedneped ehT .htworg tropxe .S.U no skcohs ylppus ecnanfi edart fo tcapmi lasuac eht no stluser enilesab ruo stroper elbat sihT :etoN era skcohs naidem woleb dna evobA .kcohs ylppus ecnanfi edart level-yrtnuoc detcurtsnoc eht si 𝑘𝑐𝑜ℎ𝑠 .𝑡 emit ta 𝑖 yrtnuoc ot stropxe .S.U fo etar htworg 𝑡𝑖 gnitagergga yb deniatbo era taht skcohs level-yrtnuoc sedulcni )5( nmuloC .naidem ylretrauq eht woleb ro evoba skcohs level-knab ylno gnisu detcurtsnoc morf sraey eht sedulcni )7( nmuloC .4002 ot roirp sraey lla sedulcni )6( nmuloC .sknab gniniamer eht dna sknab 5 pot eht fo skcohs level-knab eht yletarapes %5 ,%01 eht ta ecnacfiingis etoned *** dna ** ,* .sesehtnerap ni era dna deppartstoob era srorre dradnatS .tnatsnoc a edulcni snoisserger llA .drawno 4002 .level %1 dna 52

I emit revo dna seirtnuoc ssorca htworg tropxe no stceffe suoenegoreteH :11 elbaT )8( )7( )6( )5( )4( )3( )2( )1( lla sisirc & yrtnc llams lla yrtnc egral yrtnc llams lla sisirc on sisirc 𝑋Δ .rav .ped 𝑡𝑖 11400.0 **233.0 2440.0 69100.0- ***981.0 7850.0 1160.0 ***381.0 kcohs 𝑡𝑖 )5240.0( )331.0( )0430.0( )2620.0( )0260.0( )1640.0( )9040.0( )8660.0( **931.0 *321.0 ymmud sisirc × kcohs 𝑡 𝑡𝑖 )1860.0( )4470.0( 6090.0 0080.0 ymmud yrtnc llams × kcohs 𝑖 𝑡𝑖 )7650.0( )6250.0( ***023.2- 931.4- ***523.2- 961.0 ***529.2- ***292.2- **519.1- 587.3htworg .pop 𝑡𝑖 )996.0( )734.4( )896.0( )838.0( )218.0( )096.0( )767.0( )466.2( 2480.0- 731.0- 9780.0- 3440.0- 891.0- 3280.0- 411.0- 5760.0htworg PDG 𝑡𝑖 )5770.0( )424.0( )4770.0( )5470.0( )921.0( )4770.0( )3080.0( )872.0( ***252.0- 710.1- ***152.0- **561.0- **314.0- ***352.0- ***212.0- 745.0htworg etarx DSU 𝑡𝑖 )4670.0( )076.0( )8660.0( )4370.0( )881.0( )9970.0( )8970.0( )573.0( ***063.0 705.0 ***263.0 ***773.0 ***183.0 ***953.0 ***233.0 ***305.0 htworg tropmi .S.U-non 𝑡𝑖 )6640.0( )343.0( )9050.0( )8740.0( )9570.0( )7840.0( )5150.0( )281.0( 409,4 023 409,4 107,2 302,2 409,4 302,4 107 snoitavresbO 301.0 571.0 301.0 822.0 280.0 301.0 990.0 212.0 derauqs-R elbairavtnednepedehT .seirtnuocssorcadnaemitrevohtworgtropxe.S.UnoskcohsylppusecnanfiedartfotceffeehtnisecnereffidrofstsetelbatsihT :etoN sedulcni ylno )1( nmuloC .kcohs ylppus ecnanfi edart level-yrtnuoc detcurtsnoc eht si 𝑘𝑐𝑜ℎ𝑠 .𝑡 emit ta 𝑖 yrtnuoc ot stropxe .S.U fo etar htworg eht si 𝑡𝑖 eno fo eulav a sekat elbairav ymmud sisirc a sedulcni )3( nmuloC .eseht sedulcxe )2( nmuloC .2q 9002 ot 3q 7002 morf doirep sisirc eht gnirud snoitavresbo )5( nmuloC .naidem elpmas eht woleb stropxe .S.U gol htiw snoitanitsed tropxe .S.U llams fo elpmas a no desab si )4( nmuloC .doirep sisirc eht gnirud noitanitsedaotstropxe.S.Ugolfi1foeulavasekat ymmudyrtncllamserehw,elpmasllufehtnodesabsi)6(nmuloC .snoitanitsedtropxeegralylnosedulcni 𝑖 dna elpmas lluf eht sesu )8( nmuloC .doirep sisirc eht gnirud snoitanitsed tropxe llams rof snoitavresbo sedulcni )7( nmuloC .naidem elpmas eht woleb era ** ,* .sesehtnerap ni era dna deppartstoob era srorre dradnatS .stceffe dexfi-yrtnuoc dna -emit ,tnatsnoc a edulcni snoisserger llA .seimmud htob sedulcni .level %1 dna %5 ,%01 eht ta ecnacfiingis etoned *** dna 53

II emit revo dna seirtnuoc ssorca htworg tropxe no stceffe suoenegoreteH :21 elbaT )3( )2( )1( lla sknab # egral sknab # llams 𝑋Δ .rav .ped 𝑡𝑖 *5190.0 6430.0- ***681.0 kcohs 𝑡𝑖 )7740.0( )4130.0( )8550.0( *921.0 ymmud sisirc × kcohs 𝑡 𝑡𝑖 )2570.0( **7290.0ymmud sknab # egral × kcohs 𝑡𝑖 𝑡𝑖 )4640.0( 94600.0 ymmud sknab # egral 𝑡𝑖 )2510.0( ***123.2- 807.0- ***207.2htworg .pop 𝑡𝑖 )007.0( )289.0( )338.0( 2780.0- 6370.0- 511.0htworg PDG 𝑡𝑖 )3970.0( )1360.0( )831.0( ***452.0- ***622.0- *333.0htworg etarx DSU 𝑡𝑖 )5380.0( )4460.0( )102.0( ***953.0 ***423.0 ***414.0 htworg tropmi .S.U-non 𝑡𝑖 )9840.0( )9440.0( )2970.0( 409,4 415,2 093,2 snoitavresbO 301.0 432.0 880.0 derauqs-R elbairavtnednepedehT .emitrevodnaseirtnuocssorcahtworgtropxe.S.UnoskcohsylppusecnanfiedartfotceffeehtnisecnereffidrofstsetelbatsihT :etoN sedulcni ylno )1( nmuloC .kcohs ylppus ecnanfi edart level-yrtnuoc detcurtsnoc eht si 𝑘𝑐𝑜ℎ𝑠 .𝑡 emit ta 𝑖 yrtnuoc ot stropxe .S.U fo etar htworg eht si 𝑡𝑖 si )3( nmuloC .sknab evitca evfi tsael ta htiw snoitanitsed sedulcni )2( nmuloC .1−𝑡 retrauq ni sknab .S.U evfi naht ssel yb devres erew taht snoitanitsed ni smialc ecnanfi edart evitisop dah sknab .S.U evfi tsael ta fi 1 fo eulav eht sekat taht elbairav a si ymmud sknab # egral erehw ,elpmas lluf eht no desab 𝑡𝑖 ni era dna deppartstoob era srorre dradnatS .stceffe dexfi-yrtnuoc dna -emit ,tnatsnoc a edulcni snoisserger llA .esiwrehto orez dna 1−𝑡 emit ta 𝑖 yrtnuoc .level %1 dna %5 ,%01 eht ta ecnacfiingis etoned *** dna ** ,* .sesehtnerap 54

stnemtsujda ytitnauq susrev ecirP :31 elbaT )4( )3( )2( )1( htworg ytitnauq htworg ecirp sisirc elpmas eritne sisirc elpmas eritne *3680.0 **8070.0 76700.0 26800.0 kcohs 𝑡𝑖 )4540.0( )4030.0( )4010.0( )89500.0( 932.1- *219.0- 4790.0 8590.0 htworg .pop 𝑡𝑖 )692.1( )545.0( )192.0( )1190.0( 1380.0- 2770.0- 4420.0- 62900.0 htworg PDG 𝑡𝑖 )271.0( )8170.0( )6830.0( )3710.0( 923.0- **922.0- *5190.0 0740.0 htworg etarx DSU 𝑡𝑖 )312.0( )311.0( )2350.0( )1920.0( **402.0 0060.0 5830.0 3510.0 htworg tropmi .S.U-non 𝑡𝑖 )7970.0( )8640.0( )9320.0( )2110.0( 107 911,2 107 911,2 snoitavresbO 152.0 981.0 933.0 691.0 derauqs-R tnedneped ehT .seititnauq tropxe .S.U no dna htworg ecirp tinu tropxe .S.U no skcohs ylppus ecnanfi edart fo tceffe eht no stluser stroper elbat sihT :etoN nI .𝑡 emit ta 𝑖 yrtnuoc ot stropxe rof level 01SH eht ta detaluclac setar htworg eulav tinu eht fo egareva dethgiew eht si )2( dna )1( snmuloc ni elbairav emit ta 𝑖 yrtnuoc ot stropxe fo level 01SH eht ta detaluclac setar htworg ytitnauq eht fo egareva dethgiew eht si elbairav tnedneped eht ,)4( dna )3( snmuloc detcurtsnoc eht si 𝑘𝑐𝑜ℎ𝑠 .stropxe .S.U latot ni tcudorp evitcepser eht fo erahs eulav eht yb dethgiew era setar htworg 01SH ,selbairav tnedneped htob roF .𝑡 𝑡𝑖 ni era dna deppartstoob era srorre dradnatS .stceffe dexfi-yrtnuoc dna -emit ,tnatsnoc a edulcni snoisserger llA .kcohs ylppus ecnanfi edart level-yrtnuoc .level %1 dna %5 ,%01 eht ta ecnacfiingis etoned *** dna ** ,* .sesehtnerap 55

scimanyD :41 elbaT )5( )4( )3( )2( )1( sisirc on sisirc lla 60500.0kcohs 1+𝑡𝑖 )7330.0( 6560.0 ***371.0 **3570.0 **2470.0 ***0190.0 kcohs 𝑡𝑖 )3240.0( )8260.0( )8430.0( )3430.0( )2530.0( 3860.0- 1450.0- *6950.0- *5460.0- **4270.0kcohs 1−𝑡𝑖 )8140.0( )0270.0( )6430.0( )2430.0( )6430.0( 4720.0 kcohs 2−𝑡𝑖 )3230.0( ***498.1- **257.3- ***192.2- ***090.2- ***072.2htworg .pop 𝑡𝑖 )507.0( )577.1( )266.0( )946.0( )656.0( 711.0- 9930.0- *031.0- 7850.0- 1480.0htworg PDG 𝑡𝑖 )4180.0( )442.0( )3370.0( )3670.0( )2670.0( ***112.0- 415.0- ***092.0- ***342.0- ***942.0htworg etarx DSU 𝑡𝑖 )3770.0( )923.0( )6470.0( )2570.0( )9470.0( ***233.0 ***405.0 ***653.0 ***573.0 ***163.0 htworg tropmi .S.U-non 𝑡𝑖 )5350.0( )961.0( )1150.0( )6250.0( )2150.0( 302,4 107 457,4 576,4 409,4 snoitavresbO 001.0 312.0 601.0 801.0 301.0 derauqs-R 𝑖 yrtnuoc ot stropxe .S.U fo etar htworg eht si elbairav tnedneped ehT .skcohs ylppus ecnanfi edart fo stceffe cimanyd eht no stluser stroper elbat sihT :etoN dna gal tnereffid htiw tub elpmas elohw eht no detamitse era )3( - )1( snmuloC .kcohs ylppus ecnanfi edart level-yrtnuoc detcurtsnoc eht si 𝑘𝑐𝑜ℎ𝑠 .𝑡 emit ta 𝑡𝑖 edulcni snoisserger llA .eseht sedulcxe )5( nmuloC .2q 9002 ot 3q 7002 morf doirep sisirc eht gnirud snoitavresbo sedulcni ylno )4( nmuloC .serutcurts dael dna %5 ,%01 eht ta ecnacfiingis etoned *** dna ** ,* .sesehtnerap ni era dna deppartstoob era srorre dradnatS .stceffe dexfi-yrtnuoc dna -emit ,tnatsnoc a .level %1 56

seirtnuoc llams gnidulcxE :I ssentsuboR :51 elbaT )4( )3( )2( )1( sisirc & yrtnc llams yrtnc llams sisirc & yrtnc egral yrtnc egral 𝑋Δ .rav .ped 𝑡𝑖 381.0 *6080.0 1410.0 47400.0kcohs 𝑡𝑖 )221.0( )1640.0( )4640.0( )5120.0( 139.2- ***088.2- 281.1- 404.0 htworg .pop 𝑡𝑖 )097.3( )139.0( )585.1( )027.0( 641.0- 591.0- 35200.0 5910.0 htworg PDG 𝑡𝑖 )514.0( )321.0( )351.0( )5270.0( *359.0- ***774.0- 7930.0- 9360.0htworg etarx DSU 𝑡𝑖 )594.0( )961.0( )891.0( )4060.0( 183.0 ***723.0 ***574.0 ***493.0 htworg tropmi .S.U-non 𝑡𝑖 )862.0( )3960.0( )9870.0( )6740.0( 553 415,2 283 287,2 snoitavresbO 551.0 770.0 714.0 202.0 derauqs-R era snoitanitsed egral ylno )1( nmuloc nI .skcohs level-knab etamitse ot desu era snoitanitsed tropxe egral ylno nehw stluser eht swohs elbat ehT :etoN htiw elpmas a no desab si )3( nmuloC .dedulcni era 2q 9002 ot 3q 7002 morf sretrauq dna seirtnuoc egral ylno ,)2( nmuloc nI .elpmas eht ni dedulcni dna -emit ,tnatsnoc a edulcni snoisserger llA .doirep sisirc eht dna stekram tropxe llams rof tceffe eht swohs )4( snmuloC .ylno snoitanitsed tropxe llams .level %1 dna %5 ,%01 eht ta ecnacfiingis etoned *** dna ** ,* .sesehtnerap ni era dna deppartstoob era srorre dradnatS .stceffe dexfi-yrtnuoc 57

Table 16: Robustness II: Excluding countries with similar industry trade structure and regions (1) (2) (3) (4) (5) (6) dep. var. Δ𝑋 all crisis no crisis all crisis no crisis 𝑖𝑡 shock 0.0677** 0.126** 0.0515 0.0529** 0.116** 0.0330 𝑖𝑡 (0.0307) (0.0544) (0.0341) (0.0263) (0.0578) (0.0288) pop. growth -2.285*** -3.724 -1.909** -1.487** -3.808 -1.849** 𝑖𝑡 (0.683) (2.653) (0.775) (0.596) (2.536) (0.791) GDP growth -0.0852 -0.0577 -0.115 -0.0357 -0.0709 -0.107 𝑖𝑡 (0.0717) (0.253) (0.0839) (0.0730) (0.274) (0.0827) USD xrate growth -0.250*** -0.534* -0.210** -0.197*** -0.542 -0.209** 𝑖𝑡 (0.0731) (0.279) (0.0849) (0.0698) (0.330) (0.0836) non-U.S. import growth 0.362*** 0.506*** 0.332*** 0.370*** 0.503** 0.332*** 𝑖𝑡 (0.0532) (0.171) (0.0534) (0.0524) (0.199) (0.0532) Observations 4,903 701 4,202 4,902 701 4,201 R-squared 0.102 0.209 0.099 0.068 0.208 0.099 Note: This table reports results of two robustness checks. The first exploits information on the similarity across destinations in terms of the goods they import from the U.S. The bank-level shocks that are used to compute the country-level shocks 𝑠ℎ𝑜𝑐𝑘 for each country 𝑖 are obtained by excluding those 30 countries 𝑖𝑡 thatareclosesttocountry𝑖intermsoftheindustrystructureoftheirU.S.imports(see. columns(1)to(3)). Columns (4) to (6) of the table report results of the second robustness check: the bank-level shocks that are used to compute the country-level shocks that apply to country 𝑖 are estimated without information from any country in country 𝑖’s the region. All regressions include a constant, time- and country-fixed effects. Standard errors are bootstrapped and are in parentheses. *, ** and *** denote significance at the 10%, 5% and 1% level. 58

noitazilaiceps tsniaga ecnedive tceriD :III ssentsuboR :71 elbaT )01( )9( )8( )7( )6( )5( )4( )3( )2( )1( E knaB D knaB C knaB B knaB A knaB skcohS serahS skcohS serahS skcohS serahS skcohS serahS skcohS serahS seirtsudnI 𝑘𝜃 𝑘𝜎 𝑘𝜃 𝑘𝜎 𝑘𝜃 𝑘𝜎 𝑘𝜃 𝑘𝜎 𝑘𝜃 𝑘𝜎 321.1- **924.0 862.0 ***603.0- 921.0 4870.0- 8990.0- 461.0- 135.0- 1550.0seirtsudnI deillA & slacimehC )988.0( )781.0( )255.0( )011.0( )172.0( )8990.0( )732.0( )721.0( )853.0( )102.0( 8230.0- 602.0- 833.0- 801.0 711.0- 933.0- **805.0 9140.0 163.0- 761.1 ffutS dooF )163.0( )975.0( )283.0( )293.0( )702.0( )052.0( )712.0( )504.0( )003.0( )638.0( 192.0 624.8- 746.0- 738.4- 233.0- 518.0 801.0 349.1 8290.0 156.0 raegdaeH & raewtooF )719.0( )587.5( )335.0( )051.3( )272.0( )921.1( )092.0( )015.9( )082.0( )346.2( 286.0 ***464.0- 855.0 371.0- 223.0- 211.0 613.0 9590.0- 203.0- ***893.0 lacirtcelE & yrenihcaM )055.0( )331.0( )214.0( )501.0( )833.0( )7180.0( )586.0( )511.0( )923.0( )901.0( 4150.0- 433.0 214.0 402.0 4210.0- 041.0- 7280.0- 222.0- 2930.0 ***728.0slateM )207.0( )683.0( )834.0( )603.0( )232.0( )591.0( )782.0( )553.0( )762.0( )352.0( 383.0 931.0 68800.0 ***892.0 232.0 721.0 022.0- **832.0 341.0 **162.0stcudorP lareniM )073.0( )151.0( )532.0( )501.0( )931.0( )7490.0( )451.0( )401.0( )891.0( )311.0( 051.0- *674.0- 862.0 443.0- 272.0- 602.0 664.0 **582.0 5590.0- ***408.0 suoenallecsiM )392.0( )852.0( )505.0( )872.0( )982.0( )322.0( )134.0( )731.0( )014.0( )072.0( 401.0 341.0 *165.0- 1490.0 512.0- 2660.0- 302.0 9620.0 203.0- 081.0 rehtO )536.0( )0880.0( )882.0( )401.0( )952.0( )1760.0( )492.0( )581.0( )391.0( )521.0( 90400.0 3110.0- 0570.0 205.0- 842.0 431.0- 574.0- **290.1- 5010.0 882.0srebbuR & scitsalP )963.0( )594.0( )153.0( )364.0( )891.0( )672.0( )213.0( )624.0( )291.0( )916.0( 336.0 304.1- 472.0 884.0- 212.0 367.0 3220.0 721.2 1370.0 551.1 sruF & ,rehtaeL ,snikS ,sediH waR )716.0( )868.1( )315.0( )969.1( )392.0( )094.2( )653.0( )925.1( )871.0( )854.1( 424.0- 483.0 811.0- 8990.0 3660.0- 312.0 9230.0 3830.0- 663.0 ***045.0ssalG & enotS )223.0( )542.0( )483.0( )861.0( )571.0( )472.0( )982.0( )261.0( )022.0( )671.0( 750.1- 942.0 944.0 **353.0- 171.0 *175.0- 021.0- *525.0- 4490.0 7830.0 selitxeT )039.0( )422.0( )915.0( )471.0( )523.0( )713.0( )493.0( )603.0( )554.0( )552.0( 709.0 5360.0 101.0- 0320.0 20500.0 3660.0- 411.0 8740.0- 812.0 **602.0noitatropsnarT )467.0( )501.0( )463.0( )1660.0( )912.0( )7450.0( )775.0( )1260.0( )742.0( )0780.0( 703.1- 143.0 402.0- 073.0- **086.0- 743.0- 2250.0 853.0- 143.0- **448.1 stcudorP dooW & dooW )932.1( )412.1( )817.0( )508.0( )703.0( )547.0( )513.0( )361.1( )004.0( )508.0( ecnanfi edart 5 pot eht rof era detneserp snoisserger ehT .seirtsudni ni sknab fo noitazilaiceps eht rof stset taht kcehc ssentsubor a stroper elbat sihT :etoN fo erahs eht no desserger si 𝑡 emit ta 𝑖 yrtnuoc fo smialc ecnanfi edart latot eht ni erahs s’knab a ,snmuloc ddo nI .E knaB ot A knaB detoned ,sreilppus nI .level yrtnuoc eht ta deretsulc era srorre dradnats dna stceffe dexfi-yrtnuoc edulcni snoisserger esehT .𝑡 emit ta 𝑖 yrtnuoc ot stropxe latot ni 𝑘 yrtsudni srorre dradnats tsuboR .𝑘 yrtsudni htiw detaicossa 𝑘𝛼^ kcohs yrtsudni detamitse eht no desserger si 𝑏 knab fo 𝑏𝛼¯ kcohs level-knab egareva eht ,snmuloc neve 𝑡 𝑡 .level %1 dna %5 ,%01 eht ta ecnacfiingis etoned *** dna ** ,* .tnatsnoc a edulcni snoisserger llA .sesehtnerap ni era 59

Table 18: Robustness IV: Alternative specification of banks’ market shares (1) (2) (3) (4) (5) dep. var. Δ𝑋 1q lag 3q lag 4q lag 4q rolling av. last year’s av 𝑖𝑡 shock 0.0785** 0.0655** 0.0709* 0.0708* 0.0739** 𝑖𝑡 (0.0317) (0.0321) (0.0406) (0.0363) (0.0335) pop. growth -2.288*** -2.295*** -2.282*** -2.267*** -2.289*** 𝑖𝑡 (0.694) (0.686) (0.639) (0.720) (0.728) non-U.S. import growth 0.360*** 0.360*** 0.360*** 0.361*** 0.360*** 𝑖𝑡 (0.0532) (0.0488) (0.0479) (0.0553) (0.0534) GDP growth -0.0862 -0.0845 -0.0862 -0.0868 -0.0861 𝑖𝑡 (0.0785) (0.0724) (0.0762) (0.0715) (0.0805) USD xrate growth -0.254*** -0.251*** -0.251*** -0.250*** -0.254*** 𝑖𝑡 (0.0811) (0.0715) (0.0725) (0.0802) (0.0755) Observations 4,904 4,904 4,904 4,904 4,904 R-squared 0.102 0.101 0.101 0.101 0.102 Note: This table shows that results are robust to the way country-level shocks are constructed. In each column, the variable shock is constructed using different market shares 𝜑 . The dependent variable is the 𝑖𝑡 𝑖𝑏𝑡 growth rate of U.S. exports to country 𝑖 at time 𝑡. 𝑠ℎ𝑜𝑐𝑘 is the constructed country-level trade finance 𝑖𝑡 supplyshock. Incolumn(1), thecountry-levelshocksareconstructedusingonequarterlaggedbankmarket shares. In column (2), three quarters lagged bank market shares are used. In column (3), bank market shares are lagged by four quarters. In column (4), market shares are averaged over the last four quarters. In column (5), a banks’ average market share in the last year is computed and this market share is applied to construct all shocks in the next year. All regressions include a constant, time and country fixed effects. Standard errors are bootstrapped and are in parentheses. *, ** and *** denote significance at the 10%, 5% and 1% level. 60

Table 19: Robustness V: Placebo regressions and other checks (1) (2) (3) dep. var. Δ𝑋 EU15 export growth cntry time trend including zeros 𝑖𝑡 shock 0.00189 0.0858** 0.0862* 𝑖𝑡 (0.0290) (0.0372) (0.0487) pop. growth -0.502 -2.325*** -0.858 𝑖𝑡 (0.575) (0.610) (2.124) non-U.S. import growth 0.359*** 0.142*** 𝑖𝑡 (0.0424) (0.0455) GDP growth -0.101** -0.0963 0.00229 𝑖𝑡 (0.0402) (0.0864) (0.118) USD xrate growth -0.356*** -0.255** -0.245 𝑖𝑡 (0.0486) (0.103) (0.169) Observations 4,916 4,904 782 R-squared 0.168 0.114 0.169 Note: This table shows the results of different robustness checks. In column (1), the dependent variable is growthinexportsbyEU15countriestodestination𝑖. Theregressionincolumn(2)allowsforcountry-specific timetrends. Incolumn(3),thebank-levelshocksthatareusedtocomputethevariable𝑠ℎ𝑜𝑐𝑘 areobtained 𝑐𝑡 from estimating a modified version of equation 1, namely: 2𝑡𝑓𝑏𝑐𝑑−𝑡𝑓𝑏𝑐𝑡−1 = 𝛼 +𝛽 +𝜖 . Regression are 𝑡𝑓𝑏𝑐𝑑+𝑡𝑓𝑏𝑐𝑡−1 𝑏𝑡 𝑐𝑡 𝑏𝑐𝑡 based on observations during the crisis period and the dependent variable is export growth to country 𝑐 at time 𝑡 computed as 2𝑒𝑥𝑝𝑐𝑡−𝑒𝑥𝑝𝑐𝑡−1 . All regressions include a constant, time and country fixed effects. 𝑒𝑥𝑝𝑐𝑡+𝑒𝑥𝑝𝑐𝑡−1 Standard errors are bootstrapped and are in parentheses. *, ** and *** denote significance at the 10%, 5% and 1% level. Table 20: Quantifications Shock to Bank A Shock to Bank B Shock to all banks all times all times crisis times Region (1) (2) (3) East Asia and Pacific -0.469% -1.257% -3.64% Europe and Central Asia -0.536% -1.382% -3.89% South Asia -0.411% -1.861 % -3.74% Sub-Saharan Africa -2.86% -0.375 % -3.97% Note: Columns (1) and (2) of the table show the effect on export growth in different world regions if two different large banks in the U.S. were to reduce its supply of trade finance by a value of -0.426, which corresponds to the 10th percentile of the bank-level shock distribution. To calculate these numbers, the shockcoefficientincolumn(3)oftable10isused. Column(3)displaystheeffectonexportgrowthifallU.S. banksweresubjecttoamoderateshockof-0.245,whichcorrespondstothe25thpercentileofthebank-level shockdistributionduringacrisisepisode. Thecolumnisbasedonthecoefficientsdisplayedincolumn(8)of table11,thatis,theeffectofareductioninthesupplyoftradefinanceisallowedtodifferacrossdestinations with different sizes. 61