claim that the risk index is the best available proxy for ...nancing needs at the product level.<sup>9</sup> Appendix 8:D provides a list of the CN2 agri-food sector associated with the highest sanitary risk indices, both according to the number of "risky" products–product with a positive Sanitary Risk Index–and the average sanitary risk. The table also gives the total number of alerts for each sector between 2001 and 2005, as well as the most frequent reason for rejection. Not surprisingly, ...shery products, spices emerge as the most "risky" sectors, and thus, the sectors that use the most external ...nance. All together, 373 CN8 codes out of 2146 have a non zero risk index. In most cases, rejections are due to contamination level above the authorised threshold in inspected products.

# 3.2 Trade Duration and Di¤erence in Di¤erence Approach

Our empirical strategy consists of incorporating the Rajan and Zingales (1998) methodology into the survival analysis framework. Our focus is on the long run survival of products in foreign markets. We depart from previous works on ...nance and trade, where the focus is on the short-term year-to-year changes in export status of products or ...rms (Manova, 2008; Berman and Héricourt forth). Survival analysis is probably the most suitable tool to study the impact of ...nancial development on the longer-term exporting status of trade relationships. While our data are initially four dimensional panel data (we observe export by ...rm-destination country-product over time), we reduce the panel dimensions to three, to study the length of trade relationships. This highly detailed level of information is particularly suitable for survival analysis as aggregation may introduce considerable bias, essentially hiding failures. A trade relationship is de...ned as a ...rm-product-destination triplet, and the duration of a trade relationship is de...ned as the time (in years) a triplet has been in existence without interruption. Our variable of interest is the survival-time of ...rm’s export relationships–the time until a trade relationship ends–across products in ...ve African countries. Then, ...rms in our sample are already surviving ...rms. They already incurred sunk entry costs. As a result, our focus is on the determinants of their ability to continuously remain on and not to enter in the market.

Ordinary Least Squares (OLS) are not suitable for duration data, essentially because survival-times are restricted to be positive, and thus, have a skewed distribution. Therefore, we model the survival of trade relationships using a Cox proportional Hazard Model (CPHM). We assume that the duration of exports of product k from ...rm i operating

> 9Since four of the ...ve African countries used in our analysis are also present in the food alert database, this may introduce an endogeneity bias. All together Ghana, Mali, Malawi and Senegal account for 2.6 percent of the food alerts, when an average exporter su¤ers around 90 alerts and an average African exporter gets rejected 12 times on average. We re-estimated the risk index dropping those four countries from the food alert database. The level of correlation between the actual and newly computed risk index is 0.9.

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