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Trade Adjustment Costs in Developing Countries: - World Bank ...

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278Tibor Besedeš and Thomas J. PrusaFirst, as seen <strong>in</strong> the second chart <strong>in</strong> Figure 17.3, when we restrict ourselves tothe relationships that start large (<strong>in</strong>itial trade values greater than $100,000) wef<strong>in</strong>d better survival; this result is predicted by the model. Said differently, survivalfunctions shift up as we drop small observations. Spells that beg<strong>in</strong> with smalltrade value are at greatest risk. This is true for all product types. For example, fordifferentiated goods the one-year survival rate <strong>in</strong>creases from 69 to 92 per cent.This pattern holds for each product type <strong>in</strong> both time periods studied. As impliedby the model, the larger the <strong>in</strong>itial purchase, the longer the duration for eachproduct type.Second, the estimates provide no evidence that differences among product typesare driven by small observations. Differences among product types grow as weelim<strong>in</strong>ate the smaller-trade observations. When we restrict the sample to onlythose spells with <strong>in</strong>itial transactions exceed<strong>in</strong>g $100,000 the one-year survivalrate is 92 per cent for differentiated and 69 per cent for homogeneous goodswhich compare with 69 and 55 per cent for the benchmark.A limitation of the Kaplan–Meier estimates is that we cannot control for a myriadof factors that might be <strong>in</strong>fluenc<strong>in</strong>g duration. To control for other possible explanationsof survival, we estimate the Cox proportional hazard model. The basicestimation model <strong>in</strong>cludes regressors designed to control for country and productcharacteristics that might <strong>in</strong>fluence duration. Details of these exogenous variablesare found <strong>in</strong> Besedeš and Prusa (2006b); here we note that we control forGDP, ad valorem measure of transportation costs, the tariff rate, the change <strong>in</strong> therelative real exchange rate, the coefficient of variation of unit values, multiplespells, agricultural products, 13 and country fixed effects.The f<strong>in</strong>d<strong>in</strong>gs are reported <strong>in</strong> Table 17.4. In the first column we report the benchmarkestimates based on product level data. We are primarily <strong>in</strong>terested <strong>in</strong> theproduct type estimates. Lett<strong>in</strong>g differentiated products be the benchmark, referencepriced products have a 17 per cent higher hazard and homogeneous goodsa 23 per cent higher hazard. The estimates strongly support what Figure 17.3suggested: namely, product type matters. When we filter out spells that start smallwe f<strong>in</strong>d that our results are not driven by small-value spells. Compared to differentiatedproducts, homogeneous goods face a 71 per cent higher hazard at the$100,000 cutoff level; reference priced products face a 59–155 per cent higherhazard.5. RELATED EMPIRICAL SUPPORTS<strong>in</strong>ce the publication of Besedeš and Prusa (2006a) a grow<strong>in</strong>g literature hasemerged analyz<strong>in</strong>g the duration of export and import trade. Most of it follows theapproach pioneered <strong>in</strong> Besedeš and Prusa (2006a) and summarized <strong>in</strong> this researchnote. The follow<strong>in</strong>g is a brief synopsis of related work <strong>in</strong> this area.13 Agricultural products are generally classified as homogeneous products, and s<strong>in</strong>ce agriculturalproducts are more likely to be subject to weather or disease disruption we <strong>in</strong>clude an agriculturaldummy.

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