Trade finance has received special attention during the financial crisis as one of the potential culprits for the great trade collapse. Several researchers have used micro level data to establish the link between trade finance and trade, especially so during the financial crisis, and have found diverting results. This paper analyses the effect of trade credit on trade on a macro level through a whole cycle. We employ Berne Union data on export credit insurance, the most extensive dataset on trade credits available at the moment, for the period of 2005–2011. Using an instrumentation strategy we can identify a significantly positive effect of insured trade credit, as a proxy for trade credits, on trade. The effect of insured trade credit on trade is very strong and remains stable over the cycle, not varying between crisis and non-crisis periods.
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Eaton et al. (2011) find that demand shocks can explain 80 % of the decline in trade and for some countries, like China and Japan, this share is a lot smaller. Hence, a significant share of the trade collapse remains to be explained.
See the Bankers Association of Finance and Trade (BAFT) website's for an expansion of such definition (www.baft-ifsa.org).
Note that we use the term trade credit for credit extended to finance international transactions (not for domestic transactions).
80 % of total credit insured is short-term, only 20 % is long-term (over a year) (IMF-BAFT 2009).
Berne Union website, at www.berneunion.org.
Documents from the G-20 in Cannes (2011) refer to the need to improve statistical information on trade finance (see report of the Development Working Group 2011).
Commercial services such as marketing, design, engineering, maintenance, transportation, telecommunications and computer services are often part of contracts related to the movement of merchandises. For example, the sale of machinery for outward processing manufactures is often associated to installation, maintenance, computer software design, etc. In industries such as natural resource exploration or satellite launching, the service can even hardly be separated from the merchandise itself. Therefore, it seems to make sense to add statistics of commercial service to imports to merchandise. We have nonetheless excluded services for robustness checks purposes, with little change in results.
Note that the data does not include public services.
It can be argued that financial institutions, through the process of "transformation" use short-term resources for long-term loans. This is a fair argument, However, this possibility is limited by prudential ratios, in particular the liquidity and stable funding ratios, from the Basel international framework.
Countries are classified according to their gross national income (GNI). See http://data.worldbank.org/about/country-classifications/country-and-lending-groups Accessed 03.09.2012.
One may argue that the financial crisis already started earlier. However, the real crisis began with the crash of Lehman Brothers in the third quarter of 2008. Nevertheless, in the robustness checks we also use a different definition of the crisis period beginning in the first quarter of 2008. Our results remain basically unchanged.
We do not use the standard gravity equation as we think it is less suited for addressing the endogeneity concerns we have regarding insured trade credits. Furthermore, we do not have bilateral trade credit data but data on short-term insured trade credits by destination countries only. Therefore, we rely with our specification on the classical import estimation equation adding trade finance as an explanatory variable.
Indeed, other fixed effects could have been included to account for such "institutional factors" such as changes in monetary policy in reaction to the financial crisis, which certainly affected liquidity and other variables. The large number of potential fixed effects is linked to the fact that the estimation equations do not derive from a theoretical model, that would have isolated trade finance from the LM (money) function, and discussed its impact on the (income) IS function. We acknowledge that this would have been a very different paper in scope and ambition.
The short-term claims per credit variable has been rescaled to be on similar scales as the rest of the regressors.
To instrument the interaction term we have regressed the endogenous variables insured trade credits on our instruments and the other explanatory variables in the first stage. The predicted value has then been interacted with the crisis dummy.
This is also why we do not take the logarithm of the share of short-term claims paid, as we would lose a large part of our observations otherwise.
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We would like to thank the Berne Union for providing the data on export credit insurance and especially Fabrice Morel for his assistance with the data. Thanks are also due to Patrick Low for his support.
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Auboin, M., Engemann, M. Testing the trade credit and trade link: evidence from data on export credit insurance. Rev World Econ 150, 715–743 (2014). https://doi.org/10.1007/s10290-014-0195-4
- Trade credit
- Financial crisis
- Import estimation