Abstract
Motivated by the 2008–09 financial crisis and the trade collapse, the paper analyzes the effect of past banking crises (1976–2002) on trade with a focus on African exporters. The paper shows that they are particularly vulnerable to a banking crisis in the countries they export to. It also distinguishes between an income effect (during a banking crisis, income and exports to the country fall) and a disruption effect (a banking crisis disrupts the financing of trade channels). For the average country, the disruption effect is moderate (a deviation from the gravity predicted trade of between 1 and 5 percent). The paper finds however that the disruption effect is much larger and long-lasting for African exporters as the fall in trade (relative to gravity) is 10 to 15 percentage points higher than for other countries in the aftermath of a banking crisis. This vulnerability of African exports in the short run does not come from a composition effect, that is, from the fact that primary exports are disrupted more severely than manufacturing exports. Instead, the paper provides suggestive evidence that the dependence of African countries upon trade finance is an important determinant of their vulnerability to banking crises in partner countries.
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Notes
We have chosen the starting date of the financial crisis in September 2008 because of the Lehman collapse. Whether the financial crisis (but not the economic crisis) ended in April 2009 is more debatable (see IMF, 2009b on this) but our results are the same if we assume that the financial crisis was still going on until the end of our sample.
These results are robust to clustering the standard errors at the country level.
The data come from the IMF Direction of Trade Statistics (DOTS). We keep in the sample only those countries for which at least 5 percent of total exports were directed to these countries before the crisis (between 1990 and 1996). This includes the following SSA countries: Benin, Burkina Faso, Mali, Togo, and Zambia. Non-SSA countries include Australia, Chile, the People's Republic of China, Ecuador, Hong Kong SAR, India, Japan, New Zealand, Panama, Singapore, Taiwan, the United States, and the Republic of Yemen.
As explained very clearly by Amiti and Weinstein (2011), trade credit—contrary to trade finance—has a clear accounting definition, and refer to the value of transactions between a firm and its buyers/sellers where goods and services are provided without an advance or immediate payment (see Amiti and Weinstein, 2011, p. 1843, footnote 1.). Our data do not allow us to test for the relevance of specific forms of trade finance—including trade credit—so we will interpret trade finance in a broad sense in the rest of the paper.
These authors find that financial crises have a negative and long-lasting effect on imports, but no effect on exports. In this paper we also find that the impact is larger on the import side.
With cross-sectional data, a simpler solution is to introduce exporter and importer fixed effects. However, this does not solve the omitted variable problem with panel data, as the variations in the price indices over times are not accounted for.
Using DOTS data, Head, Mayer, and Ries (2010) report a number of examples suggesting that this problem may be important. For instance, the data contain zero trade flows between the Russian Federation, Ukraine and a number of other former USSR countries in the early 1990s. As we use in this paper sectoral bilateral trade data, this issue is a priori even more problematic.
These sectors are based on SITC classification and contain respectively: raw food and live animals, crude material except fuels, and raw fuels. We aggregate these sectors into two broad sectors: raw food and live animals and raw materials. As the SITC and ISIC classifications partly overlap, we dropped some of the subcategories for each of these sectors. More details are provided in the appendix.
Another solution would have been to aggregate the sectoral trade flows from our first data set, but these aggregated trade flows would not be necessarily exhaustive. Our results on the vulnerability of SSA countries to foreign income shocks and banking crises are robust to this aggregation (results available on request), but we preferred working with exhaustive trade flows. The correlation between the DOTS data and our aggregated data is 0.96.
The positive or insignificant coefficients on the contemporaneous crisis variables might be due to the fact that we are not capturing the precise time of the year at which the crisis happen. If crises tend to occur on average toward the end of the calendar year, this coefficient may be capturing increases in trade, for instance due to precrisis credit booms. The coefficient on the exporter crises dummies, not reported, are either slightly positive or insignificant depending on the specification. This less robust impact is also consistent with Abiad, Topalova, and Mishra (2011).
Note that the coefficients on the crisis dummies cannot be directly interpreted as semi-elasticities: the exact percentage change in exporter following a crisis in t in the importer country is (taking for instance Table 1, column (5)) exp (−0.053)−1=−0.051. This makes virtually no difference when the coefficient in low, but the correction becomes more important when the effect is large.
See also Linder (1961).
Primary goods include the following sectors: raw food and live animals, food products, crude material, and raw fuels.
In a related work (Berman and others, 2012) we also find a significantly larger disruption effect for SSA countries (and quantitatively similar, around −15 percent) using aggregate bilateral trade data over a much longer time period (1950–2009).
When including more lags, we find that the additional disruption effect becomes insignificant after seven years.
Similar results are found when using contemporaneous GDP or GDP per capita.
The currency crises dates have been computed from IFS data using the methodology described in Eichengreen and Bordo (2002).
Not reported, the coefficients on the interaction terms between GDP per capita of the exporter and banking crises in the importing country are positive and significant, suggesting that rich countries react more positively to crises in their destination countries.
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Additional information
*Nicolas Berman is Assistant Professor in International Economics at the Graduate Institute of International and Development Studies in Geneva, and CEPR Research Affiliate. Philippe Martin is Professor of Economics at the Department of Economics at Sciences Po (Paris) and CEPR Research Fellow. The authors thank the editor, Pierre-Olivier Gourinchas, and two referees, as well as seminar participants at the WorldBank, the International Growth Centre (LSE-Oxford), the 2009 ERD Conference in Accra, Ghana and at the Banque de France—PSE-CEPII workshop “After the collapse, the reshaping of trade” for very helpful comments and suggestions. Both authors thank the International Growth Centre (grant RA-2009-11-011) for financial support.
Appendix
Appendix
A.I. Primary Goods
We add to the CEPII data information on primary goods bilateral trade from COMTRADE. We consider the following SITC sectors: raw food and live animals (0), crude material, inedible, except fuels (2), mineral fuels lubricants and related materials (3). We do not consider sector 2 as it is already included in the CEPII data. For each of these sectors, we drop some subcategories which are already included in the CEPII data (and therefore do not represent primary goods). To do so we use a concordance Table available on Jon Haveman's website.Footnote 19 This leads us to drop the following SITC categories: 035, 037, 046, 047, 048, 059, 073, 081; 223, 232, 244, 248, 251, 264, 265, 266, 267, 269; 322, 325, 334, 335, 342, 344, 345. Finally, we aggregate categories 2 and 3 to consider raw materials as a whole (see Tables A1 and A2 and Figures A1, A2 to A3).
African Exports During the 2008–09 Crisis
African Exports During the 2008–09 Crisis, by Commodity
Exports to Asian Crisis Countries