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Trade protection and the role of non-tariff barriers

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Abstract

A growing share of modern trade policy instruments is shaped by non-tariff barriers (NTBs). Based on a structural gravity equation and the recently updated Global Trade Alert database, we empirically investigate the effect of NTBs on imports. Our analysis reveals that the implementation of NTBs reduces imports of affected products by up to 12%. Their trade dampening effect is thus comparable to that of trade defence instruments such as anti-dumping duties. It is smaller for exporters that have a free trade agreement with the importing country. Different types of NTBs affect trade to a different extent. Finally, we investigate the effect of behind-the-border measures, showing that they significantly lower the importer’s market access.

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Notes

  1. Simple averages across all products and countries. Tariff data used in this paper is accessible via the World Integrated Trade Solution database provided by the World Bank and based on UNCTAD’s TRAINS database as well as the WTO’s IDB and CTS database.

  2. See for example studies by Datt et al. (2011), Evenett (2014) or Kee et al. (2013).

  3. The Global Trade Alert (GTA) database was launched in 2009 following the global financial crisis. The following analysis is based on the recent update published in July 2017.

  4. Central Product Classification.

  5. A comprehensive overview of studies that focus on specific non-tariff measures is provided by Ederington and Ruta (2016).

  6. In this analysis less than 1% of all measures have been SPS or TBT measures.

  7. Ghodsi et al. (2017) use the WTO’s I-TIP database.

  8. Section 2 provides details on the estimation strategy.

  9. Data is also available at the HS six-digit product level. However, the affected HS six-digit product code is systematically missing for a subset of observations. See Sect. 3 for details.

  10. Eaton and Kortum (2002) apply the two-step procedure as they are interested in the determinants of exporters’ competitiveness. They use exporter fixed effects derived from a gravity equation and show that technology and human capital are important exporter-specific determinants. Head and Ries (2008) adopt Eaton and Kortum ’s approach to assess country-specific determinants of foreign direct investments. In a very recent approach Agnosteva et al. (2019) use the two-step procedure to estimate systematic unobserved trade barriers based on standard gravity variables and predict pair-fixed effects from a first stage gravity estimation.

  11. For more detailed information on the individual components of the gravity equation the reader is referred to Yotov et al. (2016) and Larch and Wanner (2017).

  12. We use the year of implementation as the starting period and the year of removal as the end period. If the policy was still in place at the beginning of 2015, we set the end date to 2014, the last year covered in our dataset. Only measures that last for at least 1 year are included.

  13. Henn and McDonald (2014) estimate a gravity equation, which does not explicitly model tariffs.

  14. Another solution to the problem of zero trade flows would be to estimate a Heckman selection model, which is a two-step model. It first estimates the likelihood that two economies trade with each other at a product line (extensive margin). Then it assesses the impact of trade policies in a second step conditional on the fact that two economies trade with each other. Alternatively, Tobit models could be estimated, which assume that trade flows are not randomly missing (Head and Mayer 2014). However, these models are biased if trade costs are heteroscedastic. A third alternative would be a two-part PPML model which specifically deals with non-random selection into positive exports (Egger et al. 2011).

  15. For the OLS estimation Eq. 2 is log-linearised, so that it takes an additive form.

  16. See Sect. 3 below.

  17. This command does not permit the additional inclusion of importer–exporter-time fixed effects. However, as shown in Table 7 in the “Appendix”, regression results from using this slightly more relaxed specification are very similar to those of the baseline regression presented in Table 1.

  18. This is in line with Agnosteva et al. (2019) who also use the OLS estimator with bootstrapped standard errors. Head and Ries (2008) use weighted-least squares to account for heteroscedasticity in the error term, while Eaton and Kortum (2002) use the OLS estimator without adjusting for heteroscedasticity.

  19. A full list of countries is provided in Tables 15 in the “Appendix”.

  20. Note that we use lagged dummies of NTBs.

  21. If an official policy document states that a measure is targeted at the agricultural sector and no more detailed information on which types of products are affected could be gained, no affected products at the HS six-digit were identified. However, information about affected products at the CPC three-digit level is complete.

  22. The large majority of NTBs are inward measures. In a robustness check we control for outward measures, which are implemented by the exporting country.

  23. Data on NTBs is still relatively scarce. Most often, researchers rely on data from the TRAINS database, which is collectively published by the WTO, UNCTAD, ITC and the World Bank. It contains information about implemented NTBs at detailed HS six-digit product level, classified according to the UN MAST classification of NTBs. Another common source is the I-TIP database provided by the WTO in cooperation with UNCTAD. It also collects trade policies classified according to the UN MAST classification.

  24. See Evenett and Fritz (2018) for details.

  25. http://i-tip.wto.org/goods/Default.aspx. For a comprehensive list of measures subject to notification, see: https://www.wto.org/english/docs_e/legal_e/33-dnotf_e.htm, last accessed: 25. September 2017.

  26. For trade in goods a country is identified as being affected, if in the year prior to the implementation of the policy, exports of the respective product to the implementing country exceeded one million US-Dollars.

  27. Both aggregation methods have their disadvantages: The problem with trade-weighted averages is that extremely high tariffs with nearly no trade contribute to the weighted average in the same way as zero-tariffs with high volumes of trade. The problem of using the simple averages of tariffs is that tariffs of products with a small import share and a large import share have the same weight.

  28. In contrast to that, usage of TDIs declined over the period of analysis. While 204 TDIs were implemented in 2009, this number dropped to 140 in 2014. Among the different types of TDIs, anti-dumping is by far the most often applied instrument.

  29. Most of the implemented NTBs from the United States are concentrated in the group of public procurement and localisation policies. They account for about 50% of all implemented measures worldwide. Similar, the United States is responsible for close to 40% of all state aid and subsidies measures. To a certain degree this extreme outlier might be driven by the fact that the US government tends to announce each policy separately, while for example European governments tend to announce policies in bundles. We provide a robustness check, excluding the United States from the estimation sample. Results are not driven by this outlier (see Tables 3 and 8 in the “Appendix”).

  30. Table 15 in the “Appendix” provide a list with the number of times a country implemented NTBs and the number of times it has been affected by an NTB.

  31. Percentage change = \((e^{\beta _{NTB}}-1)*100\).

  32. Results for the PPML estimation are reported in Columns (1) and (2) of Table 8.

  33. PPML results in Columns (3) and (4) of Table 8 in the “Appendix”.

  34. Using the PPML estimator, the estimated coefficient of TDIs remains similar in magnitude but turns insignificant. Coefficients for NTBs remain robust (Columns (5) and (6) of Table 8 in the “Appendix”).

  35. Since the GTA database only provides information on NTBs until 2014, the years 2013–2015 are dropped from the sample.

  36. Importer-product-time fixed effects are predicted using the gen(M) option of the ppml_panel_sg command from stata. The gen(M) option produces exponentiated importer-product-time fixed effects (Larch et al. 2017). Therefore, we use the logarithm of the predicted fixed effects in the second stage as the dependent variable. Taking the logarithm excludes all fixed effects that are zero. This is not problematic since fixed effects of the value zero do only occur, if an importer did not import any goods of a respective product at time t. After taking the logarithm, the predicted fixed effects vary between \(-\,22.9\) and 15.53. The average predicted fixed effect is \(-\,0.97\). 147,667 non-zero fixed effects are predicted.

  37. By making this assumption the constructed bilateral structure of the GTA dataset becomes irrelevant. The sample size naturally is much smaller than in the first stage, as the exporter-dimension is dropped. In total 147,667 observations are included.

  38. If the bootstrapping method is used, the estimation is repeated 100 times for different draws from the estimation sample. Each time a sample of the same size of the estimation sample is drawn. Observations can be included more than once in the drawn sample. Bootstrapping ensures that standard errors are estimated consistently.

  39. The estimation equation is thus similar to Eq. (4), but with imports rather than estimated fixed effects as dependent variable and average tariffs as additional control. TDIs are dropped as they cannot be aggregated across exporters.

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Authors and Affiliations

  1. ifo Institute - Leibniz Institute for Economic Research at the University of Munich, Poschinger Str. 5, 81679, Munich, Germany

    Alexander Sandkamp

  2. LMU Munich, Munich, Germany

    Alexander Sandkamp

  3. ifo Institute, CESifo, and TGF, University of Applied Sciences Konstanz, Konstanz, Germany

    Erdal Yalcin

  4. Sinolytics GmbH, Berlin, Germany

    Luisa Kinzius

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  1. Luisa Kinzius
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  3. Erdal Yalcin
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Correspondence to Erdal Yalcin.

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We are grateful to Gabriel Felbermayr, Yoto Yotov, Thomas Zylkin as well as two anonymous referees for fruitful discussions, helpful comments and valuable suggestions.

Appendix

Appendix

See Tables 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 and 15.

Table 5 Overview of types of non-tariff barriers and trade defence instruments
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Table 6 Summary statistics
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Table 7 Excluding exporter–importer-time fixed effects
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Table 8 Robustness checks: Baseline using PPML
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Table 9 Non-tariff barriers and free trade agreements
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Table 10 Contemporaneous NTBs
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Table 11 Contemporaneous and lagged NTBs
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Table 12 Multi-way clustering
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Table 13 Placebo: 2 year leads
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Table 14 Measuring the effect of BTB NTBs
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Table 15 NTBs by Country (2009–2014)
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Kinzius, L., Sandkamp, A. & Yalcin, E. Trade protection and the role of non-tariff barriers. Rev World Econ 155, 603–643 (2019). https://doi.org/10.1007/s10290-019-00341-6

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