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Global value chains and corporate lobbying for trade liberalization

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Abstract

This paper examines the influence of growing global value chain (GVC) integration on the pattern of corporate lobbying for trade liberalization in the United States. We hypothesize that industries with a higher level of foreign content embodied in their exports should be more likely to support trade liberalization. This is because reduced tariff barriers should substantially reduce both the costs of inputs for such industries and the rents that may be obtained through protectionist policies. We test our hypotheses through an examination of the lobbying and election funding activities of the Fortune 500 companies in the U.S. between 2006 and 2012, using the debate over the Trans-Pacific Partnership to orient our analysis. Research findings, which corroborate our main hypotheses, point to the need to revisit conventional models of industry demand for trade liberalization in light of the growing integration of trade, production, and investment activities in the global economy.

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Notes

  1. Osgood (2017, 2018) represent important exceptions.

  2. Foreign value added constitutes more than 20% of the value of final manufacturing output in many countries, reaching more than 50% for some countries and sectors. Similarly, domestic value added accounts for a large share of imported final goods as exported intermediate products could return home embodied in foreign final goods. Blanchard et al. (2017).

  3. See, for example, Chase (2003); Manger (2009).

  4. For studies that address the diversity in GVCs’ organizational forms and governance patterns, see, for example, Antràs and Chor (2013); Baldwin and Venebles (2013); Blanchard et al. (2017); Gereffi et al. (2005).

  5. Both the TiVA dataset and the methods we follow to calculate GVCs using WIOD data (e.g., Wang et al. 2014) consider GVCs as the share of foreign content in exports. We therefore follow the convention and conceive of GVCs as the share of foreign content in exports instead of production.

  6. Baccini et al. (2016) argue that domestic producers of intermediate goods are particularly likely to oppose trade liberalization. It should be noted that in contrast to that study, we are primarily interested in industry demand for trade protection and thus do not specifically address the supply of protectionist policies, or the conditions under which governments pursue trade liberalization in the presence of competing demands for protection and liberalization.

  7. TPP member countries together have a gross domestic product (GDP) of nearly $28 billion and account for about 40% of global GDP and one-third of world trade (Granvill 2017).

  8. A recent survey of global procurement and purchasing executives suggests that the share of respondents who agree that China is a low-cost sourcing destination has gradually declined during the past few years, drop** from 70% in the 2012 survey to below 50% in 2016. The survey additionally points to the importance of China as a center of global supply chains rather than a mere cheap outsourcing destination (CNBC 2017).

  9. See https://www.opensecrets.org/lobby/ (accessed February 9, 2018).

  10. In order to minimize the chances of possible omission, we used a couple of different variations of the term “TPP” in our search, including “TPP,” “Trans-Pacific Partnership,” “Trans-Pacific Partnership (TPP),” and “TPP (Trans-Pacific Partnership).”

  11. The Coalition represents a wide range of industries in the U.S., including agriculture, manufacturing, merchandising, processing, publishing, retailing, transportation and services. Its membership can be found at https://web.archive.org/web/20160211173512/https://www.tppcoalition.org/about/ (accessed February 8, 2018).

  12. See open letter to the Senate signed by numerous companies and associations that form the Trade Benefits America Coalition accessible at http://www.aaei.org/wp-content/uploads/2015/11/Trade-Benefits-America-Coalition-Letter-May-21-2015.pdf and open letter to U.S. Representative Brad Ashford accessible at https://www.tppcoalition.org/wp-content/uploads/2016/07/Coalition-for-TPP-Sign-on-Letters-Nebraska.pdf (accessed February 9, 2018).

  13. There are also eight members of the U.S. Coalition for TPP that nevertheless did not specifically mention the TPP in their lobbying reports. As a robustness check, we coded these firms as having taken “no position” on the TPP. Regression results using this alternative measure of TPP support scale did not change the interpretation of our central findings. These results are available from the authors upon request.

  14. It should be noted that six of these 60 firms are closely linked to a second one in the list, or are spinoffs or subsidiaries. There were several more firms that were associated with other firms, but there were only six firms that we collected data for a second firm they associated with because these were the only firms that also reported a distinct federal election activity spending. CRP, http://opensecrets.org (accessed June 2015).

  15. Firms are legally required to report their campaign finance contributions to the Federal Election Commission (FEC). These reports are reported on by the CRP and include the recipient of the contribution, their office held or contested and the amount of the contribution. CRP, http://opensecrets.org (accessed June 2015).

  16. These committees include the Senate Finance Committee, which has primary congressional jurisdiction on trade matters in the Senate; the Senate Committee on Agriculture, Nutrition, and Forestry; the Senate Commerce, Science, and Transportation; the House Subcommittee on Monetary Policy and Trade; the House subcommittee on Agriculture, Energy and Trade. During the 109th-112th sessions of Congress, these committees encompass 172 members of congress, including 81 members of the House of Representatives and 94 from the Senate. U.S. Congress and Trade Policy Report, available from the Institute for International Economic Policy at The George Washington University, online at https://www.gwu.edu/~iiep/ (accessed September 15, 2015).

  17. For studies that employ similar measures, see Wang et al. (2013), Koopman et al. (2012), and Upward et al. (2013).

  18. For a detailed description of the source data and method used to calculate the TiVA indicators, see online at http://www.oecd.org/sti/ind/tiva/tivasourcesandmethods.htm (accessed June 21, 2016). The fact that the TiVA measures are not available for all the years in the sample results in a substantially reduced number of observations in the models below.

  19. Time-series WIOD data are also only available for the 1995 to 2011 period.

  20. Specifically, GVC_World_TiVait is calculated as follows: EXGR_FVASHc,i,t = (∑p EXGR_FVAc,p,i,t /∑p EXGRc,p,i,t) × 100, where EXGR_FVA refers to the foreign value added content of gross exports and captures the value of imported intermediate goods and services that are embodied in a domestic industry’s exports in year t. The value added can come from any foreign industry upstream in the production chain. Specifically, EXGR_FVAc = V B(c), EXGRc,i, with B(c),c being the column block of B corresponding to country c, with the row block corresponding to c being zero. Country c’s total gross exports for a given industry i is in turn directly calculated from the ICIO system by summing up exports in intermediate goods and services and exports of final demand goods and services. Specifically, EXGRc,i,t = ∑ EXGRc,p,i, t = ∑ (EXGR_INTc,p,i,t + EXGR_FNLc,p,i,t) where EXGR_INTc,p,i,t represents gross exports of intermediate goods and services from domestic industry i in country c to country p in year t, and EXGR_FNLc,p,i, t is gross exports of final demand goods and services in year t, where c and p ∈[1,..,N] and c ≠ p.

  21. Tests using this data have a smaller number of observations because the TiVA database does not provide GVC measures for the years of 2006 and 2007. We use the TiVA database to calculate an industry’s GVC participation with TPP countries because, to the best of our knowledge, this is the only database that offers industry-level GVC data with TPP member countries. We express an industry’s GVC linkages with TPP countries (or China) as the logged value of foreign content in an industry’s exports to TPP countries (or to China) instead of the share of foreign content in such exports as is the case with our other GVC measures because calculating the latter would result in identical shares for individual partner countries using data from the world input-output table.

    Due to the difficulty of identifying the source country of foreign content in a country’s exports to individual partner countries, the World Input-Output Table makes a strong assumption that foreign value added in a country’s imports is used in its domestic production and exports at the same rate in order to better capture a country’s production input. In other words, it is assumed that the share of foreign value added in exports is identical for all partner countries. Specifically, these indicators measure the value of imported intermediate goods and services that are embodied in a domestic industry’s exports and are calculated using the following formula: EXGR_FVAc, p, i, t = 1 – EXGR_DVA c, p, i, t where EXGR_DVA c, p, i, t is the domestic value added content of gross exports, or the value added generated by the exporting industry during its production processes as well as any value added coming from upstream domestic suppliers that is embodied in the exports in year t. EXGR_DVAc,p,i, t = VcBc,cEXGRc,p,i, t Where EXGRc,p,i,t is a Kx1 vector with all entries equal to zero except the one corresponding to industry i in year t.

  22. Available online at http://www.wiod.org/new_site/home.htm (accessed January 28, 2016).

  23. For a detailed discussion of the construction of the WIOD database and its industry classification, see Timmer 2012.

  24. Using assets or revenues as alternative measures of firm size does not change the interpretation of the main results reported below.

  25. The database can be accessed through Wharton Research Data Services (WRDS) online at https://wrds-web.wharton.upenn.edu/wrds/ (accessed October 10, 2015).

  26. Available online at http://bea.gov/international/direct_investment_multinational_companies_comprehensive_data.htm (accessed January 22, 2016).

  27. The database can be accessed through Wharton Research Data Services (WRDS) online at https://wrds-web.wharton.upenn.edu/wrds/ (accessed October 10, 2015).

  28. We further experimented with coding the dependent variable as a dichotomous variable that equals “1” only if the firm is clearly in support of the TPP and “0” otherwise in order to examine the conditions under which firms are more likely to take a clear position on the issue. The results, while not reported in the paper, are consistent with our central findings.

  29. Available online at https://www.census.gov/econ/concentration.html (accessed January 30, 2016). Since these data are published at five-year intervals (e.g., 1997, 2001, and 2002, etc.), with the most recent data going back only as far as 2007, we had to use 2007 data for the other years in the dataset.

  30. Available online at http://tariffdata.wto.org/Default.aspx?culture=en-US (accessed April 26, 2017). Tariff data is taken from the Tariff Download Facility of the World Trade Organization (WTO).

  31. As labor tends to be immobile between sectors, production sharing within a regional trade agreement should reduce the demand for unskilled labor in high-wage countries, leading to lower wages and heightened unemployment for labor (Chase 2003). Data for this variable are drawn from the Union Membership and Coverage Database compiled from the monthly household Current Population Survey (CPS) and are available online at www.unionstats.com (accessed 16 April 2016). Using the percentage of workers covered by collective bargaining agreements in an industry yielded essentially the same results.

  32. These results are available from the authors upon request. Since these variables have a smaller number of observations due to data availability, including these variables in the model estimates will result in smaller sample sizes.

    Consequently, we choose not to report these results in the Appendix.

  33. Results remain similar when we estimate two-way standard errors clustered at both the firm and year level (Appendices 10D-10F).

  34. For studies that adopt a similar approach, see, for example, Yu (2015).

  35. For example, firms in a given industry may participate in international production chains in a few ways: (1) exporting value-added in intermediate exports used in the production of other countries’ domestically consumed final products; (2) exporting value-added in intermediate exports used by other countries to produce exports directly or indirectly; (3) using other countries’ value-added to produce products for domestic consumption; and (4) using other countries’ value-added to produce products for exports.

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Acknowledgements

We thank **aojun Li for valuable comments on earlier versions of this article, and Clayton Tumlison and Austin Wilkins for research assistance. Ka Zeng gratefully acknowledges financial support from the Chiang-Ching Kuo Foundation for International Scholarly Exchange. Yue Lu acknowledges funding from the National Natural Science Foundation of China (Project number: 71873031; 71503048) and the National Social Science Foundation of China (Project number: 17ZDA098).

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  1. Department of Political Science, University of Arkansas, Fayetteville, AR, 72701, USA

    Ka Zeng & Karen Sebold

  2. China Institute for WTO Studies, University of International Business and Economics, Bei**g, 10029, China

    Yue Lu

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Zeng, K., Sebold, K. & Lu, Y. Global value chains and corporate lobbying for trade liberalization. Rev Int Organ 15, 409–443 (2020). https://doi.org/10.1007/s11558-018-9337-0

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