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Technical barriers to trade, product quality and trade margins: firm-level evidence

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Abstract

As tariffs have declined to a low level, the trade literature has paid increasing attention to the impact of non-tariff measures. Unlike tariffs, non-tariff measures could act as both a barrier to trade and a catalyst for quality upgrading. This study examines the effect of technical barriers to trade (TBTs) on trade margins and quality upgrading at the firm level. To do so, we utilise rich Chinese Customs data recording the universe of export transactions from 2000 to 2012, matched with the Annual Survey of Industrial Firms and the World Trade Organization’s Specific Trade Concerns database. We find that TBTs imposition results in higher probability of exit. Surviving exporters enjoy larger sales and charge higher export prices. We also find robust evidence for the quality upgrading effects of TBTs. Firms upgrade their product quality by expanding their research and development and importing more intermediate inputs and capital goods. The positive impact of TBTs on quality upgrading offsets that on price increases, resulting in lower quality-adjusted export prices. This suggests the net welfare-enhancing effect of TBTs for the consumers of imported products. The results hold after controlling for potential endogeneity and across various specifications.

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Notes

  1. In this paper, NTMs and standards are used interchangeably.

  2. In this paper, we interpret the access to better quality products at a lower quality-adjusted price as an improvement in consumer’s welfare. Disdier et al. (2008) also focuses on this welfare-enhancing impact of standards.

  3. The third component, enforcement costs, represents firms’ administrative efforts to comply with the new requirement. For example, firms will need more staff to handle paperwork, prepare for inspections from government officials. This category is less relevant for quality upgrading. As a result, we exclude them from the discussion.

  4. Empirically, Chakraborty (2017) showed that Germany’s ban on an input (Azo dyes) used by Indian textile and leather producers led to investment in high-quality imported raw materials and technology by these firms. In a related study, Chakraborty and Chatterjee (2017) showed a similar finding for dye-makers, who increased technology transfer in response to the ban.

  5. Schmidt and Steingress (2022) also investigates the quality impact of standards. However, their study focuses on the harmonisation of standards across trade partners, whereas our study discusses the introduction of standards by individual trade partners. Morever, they do not explore firm-level aspects.

  6. See, for example, Olper et al. (2014) and Curzi et al. (2020) on food standards. For studies using Chinese transaction-level data, see Beestermöller et al. (2018) on agri-food exports, and Hu et al. (2019) on cigarette lighter exports to the European Union. Chakraborty (2017) and Chakraborty and Chatterjee (2017) focused on one single environmental regulation on dyes.

  7. A rise in export prices can reflect an increase in markups or costs. Nevertheless, data limitation does not allow us to measure firm-level markups. Therefore, we leave this issue for future research.

  8. In our empirical framework, following the existing literature, we have proxied for the presence of a standard, but we cannot measure the stringency of those standards, i.e.how strict they are. Some studies have attempted to compare the stringency of specific standards, in particular maximum residual limits on agricultural and food products, and the impact on trade (see, for example, Hejazi, Grant and Peterson, 2022; Shingal, Ehrich and Foletti, 2021; Li and Beghin, 2012). However, these studies do not examine quality upgrading.

  9. Unfortunately, the lack of data on firm-level production and sales of the importing countries prevents us from investigating this issue further in our empirical analysis. We leave this issue for future research.

  10. The dataset in Excel format can be downloaded from https://www.wto.org/english/res_e/publications_e/wtr12_dataset_e.htm.

  11. Although the STC database covers both SPSs and TBTs, few SPS concerns were raised by China during the period. Therefore, we focus our analysis on TBTs.

  12. This study utilizes data until 2012 when the UK was still a member of the EU. Therefore, in our empirical model and discussion, we treat the UK as an EU country.

  13. We drop around 25,000 wholesalers who account for a quarter of China’s exports. Using the same data, Ahn et al. (2011) identified intermediaries in the same way in order to study wholesale activities.

  14. Squaring the dataset at the firm-HS 6-digit-destination-year level is not workable due to capacity constraint of our computer.

  15. Fontagné and Orefice (2018) adopted a similar adjustment for individual export data on French firms.

  16. Even though the EU is considered as a single entity in the STC database, we include EU countries separately in the regression for two reasons. First, the EU accepted new members during our period of study. In 2004, ten countries including Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia entered the EU. Bulgaria and Romania joined the EU in 2007. Second, by including individual EU country, we can control for destination-specific characteristics and demand.

  17. The information on R&D expenditure is only available from 2000 to 2010 and training fee is only available from 2000 to 2007.

  18. In Sect. 4.4, we further examine firm heterogeneity in responses to TBTs. Previous studies based on aggregate data (e.g., Bao and Liu, 2012) disregard how individual exporters with different characteristics adjust to stringent TBTs.

  19. Unresolved concerns at time t are concerns for which related parties have not reported any resolution to the WTO yet, implying the TBT is still in effect at time t.

  20. For instance, Lu et al. (2013) found significant negative impact of antidumping on the extensive margin. Meng et al. (2020) observed resource reallocation from low-quality producers to high-quality ones.

  21. In principle, NTMs are imposed in a non-discriminatory manner. Indeed, over 90% of NTMs are unilateral. They do not specify any individual affected country. Exceptions often fall in SPSs, not TBTs, under special circumstances: for example, when a disease occurs in a specific country and measures are put in place to limit the risk of spreading the disease. These measures, if any, are often temporary.

  22. A scatter plot shows the positive correlation between the number of exporters and market size/attractiveness (measured as GDP and the GDP/distance ratio).

  23. Fontagné and Orefice (2018) also controlled for tariffs in their estimations using IV and lagged TBT dummy. They examine the effects of TBT on the extensive margin, intensive margin, and export price (but not product quality) of French firms.

  24. Productivity premiums of FDI over domestic firms are well documented in the literature. See, for example, Kimura and Kiyota (2006), Tomiura (2007), Antràs and Yeaple (2014), and Cozza et al. (2015).

  25. Existing evidence highlights the importance of distinguishing between processing exporters and non-processing exporters in explaining a firm’s export behaviour (Yu, 2015).

  26. Under this definition, the share of processing trade exporters is 32% (19,789 out of 61,150 firms) in 2000 and it goes down to 10% (20,662 out of 231,791 firms) in 2010, implying a large increase in the relative share of ordinary trade. Alternatively, we assign a processing dummy, which equals 1 if a firm is engaged in processing trade. The key result holds.

  27. Investigating the adjustment across products within firms to TBTs is beyond the scope of our paper. Fontagné and Orefice (2018) focus on multi-destination firms without exploring this dimension but they point out that it deserves future research.

  28. At the first stage regression, the low joint F-stat of these two variables suggest the problem of weak instruments.

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Acknowledgements

We thank Editor-in-Chief Holger Görg and two anonymous referees for their valuable comments and suggestions. This study is conducted as part of the project on ‘Global Market Entry, Survival and Exit of Firms: Understanding the Process and the Effects’ by the Economic Research Institute for ASEAN and East Asia (ERIA). This study is also conducted as a part of the Project “East Asian Industrial Productivity” undertaken at the Research Institute of Economy, Trade and Industry (RIETI). The authors would like to thank Fukunari Kimura, Subash Sasidharan, Tadashi Ito, John Morrow, Shujiro Urata, Chin Hee Hahn, Nobu Yamashita, Kyoji Fukao, Masayuki Morikawa, Makoto Yano, Naohiko Ijiri, Wan Seok Chang, Rashesh Shrestha and other participants at ERIA’s workshops, RIETI seminar, JSIE annual meeting and East Asian Economic Association conference for their helpful comments. The usual disclaimer applies.

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  1. Economic Research Institute for ASEAN and East Asia, Jakarta, Indonesia

    Ha Thi Thanh Doan

  2. Research Institute of Economy, Trade and Industry, Tokyo, Japan

    Hongyong Zhang

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Doan, H.T.T., Zhang, H. Technical barriers to trade, product quality and trade margins: firm-level evidence. Rev World Econ (2023). https://doi.org/10.1007/s10290-023-00514-4

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