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Pass-through of trade costs to U.S. import prices

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Abstract

This paper measures the pass-through of trade costs into U.S. import prices by using actual data on duties/tariffs and freight-related costs. The key innovation is to decompose the indirect effects of trade costs (on prices) into the effects on markups, quality and productivity while measuring/interpreting the pass-through of trade costs into welfare. Robust to the consideration of variable versus constant markups, there is evidence for incomplete pass-through, mostly due to the negative indirect effects of trade costs on marginal costs, suggesting that lower trade costs are associated with imports that have higher marginal costs; markups are affected relatively less. When the effects of trade costs on marginal costs are further decomposed into their components, the positive contribution of quality dominates in all cases, followed by the negative effects of productivity, suggesting that lower trade costs are associated with higher-quality imports that have been produced with lower productivity.

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Notes

  1. The following papers have such investigations based on several case studies: Levinsohn (1993), Harrison (1994), Krishna and Mitra (1998), Kim (2000), Bottasso and Sembenelli (2001), Konings et al. (2005), Badinger (2007), De Loecker et al. (2012).

  2. An earlier study by Feenstra (1989) is an exception that focuses on the effects of tariffs on U.S. prices of Japanese cars. Recent studies by Porto (2006), Nicita (2009), and Marchand (2012) have investigated the effects of tariffs on household welfare using demographic data, focusing on Argentina, Mexico, and India, respectively.

  3. After the estimation is done and the parameters/variables are identified, in order to control for outliers in the sample, mostly due to using unit prices as measures of prices, we ignore the estimates of price elasticities and markups that are below and above the 3rd and 97th percentile of the corresponding distributions.

  4. It is important to emphasize that direct effects of trade costs on prices taking a value of 1 is just a definition in this paper. For sure, there may be many other channels that may lead to direct effects ot rade costs on prices taking a value different from 1; e.g., the demand conditions or the share of U.S. in the worldwide sales of the exporter firm may be associated with imperfect pass-through. However, such calculations are out of the scope of this paper due to the lack of corresponding data.

  5. See Behrens and Murata (2007) who formally show the connection between CARA (CRRA) and variable (constant) markups. Also see Yilmazkuday (2013) who has investigated the effects of considering constant versus variable markups on the Law of One Price in a cross-country analysis.

  6. It is important to emphasize that \(\chi _{s,t}^{g}\)’s may also be capturing tastes in utility. Nevertheless, we will test the relation between marginal costs and \(\chi _{s,t}^{g}\)’s, below, and show that they have a positive and statistically significant relation. Under a supplementary assumption of constant returns to scale in production (i.e., marginal costs not depending on the quantity produced through demand shifters), having a statistically significant relation between marginal costs and \(\chi _{s,t}^{g}\)’s confirms our specification. Having a quality measure different from unit values is also in line with Khandelwal (2010) who shows that using unit values as a proxy for quality would lead to biased results.

  7. Alternatively, as in Yilmazkuday (2013), trade equations can be estimated by putting additional structure (e.g., source-and-time fixed effects) on quality parameters. However, we avoid such assumptions/restrictions here by using the methodology in Feenstra (1994).

  8. This approximation holds better especially when \(-1<x<1\) which is the CARA case in this paper where \(x=\alpha ^{g}q_{ds}^{g}\) and gross markups \(\left( \text {given\,by }\mu _{ds}^{g}=\left( 1-\alpha ^{g}q_{ds} ^{g}\right) ^{-1}\right) \) are expected to be higher than \(1\).

  9. The estimation results at the sectoral level are given in Table 10 in the Appendix, where the sector of “Arms and Ammunition” (“Optical, Photographic Instruments”) has the highest median/mean markup under the assumption of variable (constant) markups.

  10. The results are also in line with Yilmazkuday (2013) who estimate median variable (constant) markups of 1.04 \(\left( 4.04\right) \) using a similar methodology but a different cross-country data set covering 4-digit SITC goods. Mandel (2013) also finds significant differences across the cases of variable and constant markups where the median variable (constant) markups are about 1.80 (7.60) across goods.

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Acknowledgments

The author would like to thank Harmen Lehment, Sylvia Kuenne, an anonymous referee, Robert C. Feenstra, and the participants of 2013 International Trade Workshop at Florida International University for their helpful comments and suggestions.

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Authors and Affiliations

  1. Department of Economics, Florida International University, Miami, FL, 33199, USA

    Hakan Yilmazkuday

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Correspondence to Hakan Yilmazkuday.

Appendix

Appendix

See Tables 9 and 10.

Table 9 Descriptive statistics on trade costs
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Table 10 Sectoral estimation results
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Yilmazkuday, H. Pass-through of trade costs to U.S. import prices. Rev World Econ 151, 609–633 (2015). https://doi.org/10.1007/s10290-015-0218-9

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