Cross-border trade is risky. Trade partners have to decide who bears the risks associated with each transaction, be it financial, commercial, political, or adverse business risk. But risks can be mitigated. One way of doing so is to use a trade-financed instrument called a ‘letter of credit’ (LC). In an LC-financed transaction, the importer’s bank promises to pay for the goods on behalf of the importer, provided the exporter meets all requirements specified in the contract. Therefore, the risk of non-payment for goods already shipped or the risk of non-delivery of pre-paid goods is eliminated. LCs are costly as they involve bank fees and charges. About 12.5% of world trade is covered by LCs (Niepmann and Schmidt-Eisenlohr 2016).
Trade finance in general, and letters of credit in particular, are an understudied yet important part of international trade institutional infrastructure. Attention to trade finance was first drawn in the aftermath of the Global Crisis when the negative shock to its supply was partially blamed for the Great Trade Collapse (e.g. Auboin 2009, Baldwin and Evenett 2009, Chor and Manova 2012, Niepmann and Schmidt-Eisenlohr 2017, Ahn and Sarmiento 2019). More general studies of trade finance and its implications include Amiti and Weinstein (2011), Paravisini et al. (2015), and Demir and Javorcik (2018).
In our recent paper (Crozet et al. 2022), we introduce a new index capturing product-level LC-intensity and use it to compare the performance of trade in products typically relying on LCs relative to other products during the Covid-19 crisis.
A new index of product-specific intensity of letter of credit use
Based on unique data from Turkey, detailing financial terms of international trade transactions, we construct a measure that captures product-specific reliance on trade insurance. Our measure (LC-Int) captures the incidence of the use of LCs across products, controlling for the characteristics of countries exporting or importing them. LC-Int is available for 1,196 products (defined as distinct four-digit Harmonised System (HS) codes), of which 188 are agricultural and agri-food products.1
The new index shows considerable variation across products even within the same product category. For instance, ‘Silk-worm cocoons suitable for reeling’ (HS5001) are among the products with the highest value of LC-Int, while another product belonging to the same two-digit HS heading ‘Silk waste (including cocoons unsuitable for reeling, yarn waste and gametted stock)’ (HS5003), is among products with the lowest LC-Int value. Similarly, the index value for ‘Live bovine animals’ (HS0102) is in the top decile, while the one for ‘Meat of bovine animals; fresh or chilled’ (HS0201) is only in the third decile. This is further illustrated in Figure 1 which shows the median, lower quartile, and upper quartile values of LC-Int for all products as well as by broad product category. The products with the highest values of LC-Int include metals and minerals (such as ferrous products, tar, crude petroleum oils, pitch coke, etc.), as well as machinery and transport vehicles (e.g. rail locomotives).
Figure 1 Median, 25th, and 75th percentiles of LC-Int, by industry
Notes: The figure shows the median value of LC-Int for each industry. The box sizes show the range from the 25th to the 75th percentiles. Whiskers show the higher (lower) adjacent value, i.e. upper (lower) quartile +(-) 1.5 × interquartile range.
LC-Int exhibits intuitive correlations with other product characteristics. Capital goods and consumer durables use LCs more intensively than other types of goods. LC-Int also increases with average shipment size and fraction of shipments by sea. These patterns are intuitive as trading partners have a greater incentive to insure larger shipments or shipments with longer transit times due to higher risks. In contrast, trading firms have less incentive to insure transactions with more trusted parties, as captured by the degree of relationship stickiness (Martin et al. 2020).
Greater resilience of trade backed by letters of credit during the Covid-19 pandemic
Global trade fell sharply during the first few months of the Covid-19 pandemic: the value of global trade declined by 16% and 18% year-on-year in April and May of 2020, respectively. And US exports saw a decline of 30% and 35% during the same months.
The pandemic created heightened uncertainty, as in its early months governments around the world introduced lockdowns and other restrictions and caused a huge disruption to economic activity. Uncertainty about which firms would survive the downturn increased the risks associated with international trade transactions. Exporters worried about not being paid for the goods they had shipped and importers feared that pre-paid imports would never be delivered.
In our paper, we examine whether products that traditionally rely more on LCs exhibited greater resilience relative other products during that time. The evolution of monthly US exports, illustrated in Figure 2, is consistent with our expectations. The April/May drop in monthly exports is clearly visible. And the dip in US exports of products that traditionally rely more on LCs is milder and the rebound faster.
Figure 2 The collapse of US exports during the Covid-19 pandemic
Notes: The green bars (right axis) show the monthly US exports during 2019 and 2020. The green and orange lines (left axis) show the year-to-year growth rates of US monthly exports of products with higher than median LC-Int and lower than median LC-Int, respectively.
To formally test our hypothesis, we use monthly data on US and EU15 exports, disaggregated by destination country and four-digit HS product codes, for the period April 2017 to December 2020. We test whether exports of products that typically trade using LCs exhibit a differential trend during the pandemic relative to other products. In the estimation, we include an extensive set of fixed effects to absorb (i) any variation in the year-on-year growth of exports for a given country pair in a given time period (year-month), such as slowdown in the national economy and lockdown, (ii) pre-existing trends in trade flows for a given product and country pair, (iii) change in export supply of a given product from a given origin, and (iv) product-specific seasonality. We also allow for differential monthly trends with respect to other product characteristics such as contract intensity, average shipment size, relationship stickiness, and income elasticity.
Our estimation results confirm our expectation: exports of products that traditionally rely more on LCs were more resilient relative to other products during the pandemic. In particular, a one-standard deviation increase in LC-Int was associated with a 1.3 log-points larger increase in monthly US exports during the pandemic crisis (i.e. period from February through August 2020). The estimated effect is large as it corresponds to about one-fourth of the average annual change in monthly trade flows in our data. The effect estimated for EU15 exports is comparable to the one obtained for the US sample. For both US and EU15 exports, we find that the results are larger for exports destined for countries with a high number of Covid-19 cases.
Validation exercise based on Turkish data
As mentioned earlier, Turkey is unique in that it collects and verifies information on actual financing terms used in each trade transaction. Therefore, as a validation exercise, we conduct additional analysis based just on Turkish data.
We construct monthly share of Turkish exports backed by LCs for each destination-product pair for the 2017-2020 period. Focusing on the share allows us to implicitly control for any factors affect Turkish exports of a particular product to a particular marker in a particular monthly period.
Our findings are consistent with those for the US and EU15 exports. The estimates show that the share of LC-backed exports increased during the Covid crisis by about 0.2 percentage points. This is meaningful, as the average monthly share of LC-backed exports during 2018-2020 was 3% and thus the estimated effect translates into a 7% increase. Moreover, this finding was driven by the destination countries with the above median monthly number of Covid cases.
Ahn, J and M Sarmiento (2019), “Estimating the direct impact of bank liquidity shocks on the real economy: Evidence from letter‐of‐credit import transactions in Colombia”, Review of International Economics 27(5): 1510-1536.
Amiti, M and D E Weinstein (2011), “Exports and Financial Shocks”, The Quarterly Journal of Economics 126(4): 1841-1877.
Auboin, M (2009), “Restoring Trade Finance: What the G20 Can Do”, in R Baldwin and S Evenett (eds.), The Collapse of Global Trade, Murky Protectionism, and the Crisis: Recommendations for the G20, VoxEU.org eBook, 5 March.
Baldwin, R and S Evenett (eds.) (2009), The Collapse of Global Trade, Murky Protectionism, and the Crisis: Recommendations for the G20, VoxEU.org, 5 March.
Chor, D and K Manova (2012), “Off the cliff and back? Credit conditions and international trade during the global financial crisis”, Journal of International Economics 87(1): 117-133.
Crozet, M, B Demir and B Javorcik (2022), “International trade and letters of credit: A double-edged sword in times of crises”, IMF Economic Review, forthcoming.
Demir, B and B Javorcik (2018), “Don’t throw in the towel, throw in trade credit!”, Journal of International Economics 111(C): 177-189.
Martin, J, I Méjean and M Parenti (2020), “Relationship stickiness, international trade, and economic uncertainty”, CEPR Discussion Papers 15609.
Niepmann, F and T Schmidt-Eisenlohr (2016), “Trade finance around the world”, VoxEU.org, 11 June.
Niepmann, F and T Schmidt-Eisenlohr (2017), “International trade, risk and the role of banks”, Journal of International Economics 107(C): pages 111-126.
Paravisini, D, V Rappoport, P Schnabl and D Wolfenzon (2015), “Dissecting the Effect of Credit Supply on Trade: Evidence from Matched Credit-Export Data”, Review of Economic Studies 82(1): 333-359.
1 The data are available at https://www.dropbox.com/s/bnqjlsdnchpo939/LCInt.txt?dl=0