Open Economy Politics and Trade Preferences Research Paper

Exclusively available on Available only on IvyPanda® Made by Human No AI

Introduction

The first level of OEP reflects micro-level interactions, including how individuals, firms, and industries form preferences and interests. Individual preferences are fundamental in the framework, and actors’ preferences can be deduced from economic theories (Lake, 2009). Two models have been influential in studying how the distributional consequences of trade shape individual preferences based on one’s production profile. The first is the Heckscher-Ohlin (HO) theorem, according to which countries should export and import goods based on their factor endowments. For example, a country with abundant labor and relatively scarce capital factors should export labor-intensive goods and import capital-intensive products and services.

Main body

Based on this theory, Stolper and Samuelson (1941) found out that factor endowment variance in a specific country determines the winner or loser of trade. In developed countries, this has meant that high-skill individuals will support trade openness, and low-skill individuals will support protection. Rogowski (1989) is the first work to test the validity of the Stolper-Samuelson (SS) model domestically. He argued that the SS model resolved conclusively that abundant factors suffer from protection and benefit from the liberalization of trade, and scarce factors benefit from trade and are harmed by trade liberalization (Rogowski, 1989). Rogowski (1989) hypothesizes that if the Stolper-Samuelson theorem is correct, coalitions should form along the factor lines based on the increasing or decreasing international trade.

The validity of the factor-endowment model in the research holds for various assumptions. Frieden utilized different assumptions “about mobility (the “Ricardo-Viner” or “specific factors” model) and collective action to predict coalition formation over international finance policy” (Alt & Gilligan, 1994, p. 167). Scheve and Slaughter (2001) innovatively apply a survey experiment to study individual trade preferences. Their work supports the factor-endowment model. Furthermore, Mayda and Rodrik (2005) find that the factor-endowment model holds worldwide.

The HO model assumes that factors are mobile across sectors. In contrast, the Ricardo-Viner (RV) model assumes that factors are not perfectly mobile. Thus, in the RV model, the distributional consequences of trade fall across industries. Those in import-competing industries suffer from falling incomes, while those in export-oriented industries benefit from trade. Thus, individuals working in import-competing industries would prefer protection over free trade. The findings based on these two models are mixed and inconclusive, but Scheve and Slaughter (2001) argue about the validity of the HO model. Hiscox (2002) finds that when factors are mobile, factor-based cleavages are formed. However, he also finds that when factors are immobile, industry-based coalitions are formed. Hiscox (2002) uses the different scales of return in different areas as a proxy for factor mobility. He argues that a class-based coalition is formed when mobility is high, as Rogowski (1989) suggested. However, when low intra-factory factor mobility is present, the coalition would emerge along the factory lines.

Notably, Irwin (1994) used British voting in 1906 to show that voting behavior was linked to the economic interests of constituents as determined by the international trade performance of the sector in which they are employed. Irwin’s (1994) work is the first to support the RV model using voting behavior in the 1906 British election in which trade was the crucial issue dividing two parties.

Recently, scholars have further examined the heterogeneity of firms and occupations. The logic behind the firm-based model is that only a small portion of firms engage in global economic activities. This makes more productive firms benefit from trade while less productive firms do not. In this case, the distributional consequence falls across firms, and we would expect firm–based cleavages. Kim (2017) found that firms within the same industry have different preferences for trade liberalization based on the extent to which their products are differentiated. “High levels of product differentiation eliminate the collective action problem faced by exporting firms while import-competing firms need not fear product substitution” (Kim, 2017, p.1). Bombardini’s (2005) set-up, firm size determines the benefits of lobbying. “New-new” trade theory attributes an even larger importance to the notion of firm size as it links the benefits from trade reform to firm size. These findings reflect a high variance in the heterogeneity of firms and occupations.

In addition, there is a model which provides an alternative perspective on the issue. According to the Occupation model, the winners and losers of globalization are determined by occupation characteristics (Owen and Johnston 2017). Specifically, the welfare consequences of trade depend upon the degree to which job tasks can be provided from abroad and the degree to which those tasks are competitive internationally. In this model, offshorability and task routineness determine trade preference.

Scholars have examined the non-economic and cultural factors shaping individuals’ preferences toward trade policy. Cultural factors include ideas/values and nootropic considerations. Mansfield and Mutz (2009) found that preferences toward globalization are based on nootropic evaluations rather than an individual’s economic self-interest. In other words, preferences are culturally shaped by people’s perceptions of how the economy is affected. Guisinger (2017) also finds support in favor of the nootropic mechanism. She finds that people’s perceptions about the impacts of trade are based on their beliefs and considerations of the costs and benefits of trade for the community and the country rather than on economic self-interest.

Conclusion

I think the OPE in individuals missed the rise of a non-state actor, such as social media can play a huge role in shaping individual preferences. Nowadays, any social media platform such as TikTok can be used as a soft power to spread misleading information about free trade. For example, Saudi Arabia is trying to bring more FDI to the countries, but a huge amount of misleading information on social media platforms targets uneducated people against that move. So, the rise of these new actors can affect individual decisions. Accordingly, individuals’ beliefs might be wrong because they received the information from low-credibility sources. Mansfield and Mutz (2009) tested Ricardo-Viner against the Heckscher-Olin Models, but neither model holds up empirically because people do not understand how free trade impacts them or their industry. Their attitudes are determined by how they believe trade impacts the country (economy and, in some cases, fear of others – an ethnographic impact). They find that the interpretation of education can be attributed to skill level because their results show that education level has similar impacts on trade preference for people in the workplace and retirees.

References

Alt, J. E., & Gilligan, M. (1994). . Journal of Political Philosophy, 2(2), 165-192, Web.

Bombardini, M. (2005). Essays on international trade policy and international outsourcing [Doctoral dissertation, Massachusetts Institute of Technology].

Guisinger, A. (2017). American opinion on trade: Preferences without politics. Oxford University Press.

Hiscox, M. J. (2002). Interindustry factor mobility and technological change: Evidence on wage and profit dispersion across US industries, 1820–1990. The Journal of Economic History, 62(2), 383-416, Web.

Irwin, D. A. (1994). . The Journal of Law and Economics, 37(1), 75-108, Web.

Kim, I. S. (2017). . American Political Science Review, 111(1), 1-20, Web.

Lake, D. A. (2009). . The Review of International Organizations, 4(3), 219-244, Web.

Mansfield, E. D., & Mutz, D. C. (2009). . International Organization, 63(3), 425-457, Web.

Mayda, A. M., & Rodrik, D. (2005). European Economic Review, 49(6), 1393-1430, Web.

Owen, E., & Johnston, N. P. (2017). . International Organization, 71(4), 665-699, Web.

Rogowski, R. (1989). Commerce and coalitions: How trade affects domestic political alignments. Princeton University Press.

Scheve, K. F., & Slaughter, M. J. (2001). Journal of International Economics, 54(2), 267-292, Web.

Stolper, W. F., & Samuelson, P. A. (1941). . The Review of Economic Studies, 9(1), 58-73, Web.

More related papers Related Essay Examples
Cite This paper
You're welcome to use this sample in your assignment. Be sure to cite it correctly

Reference

IvyPanda. (2024, March 31). Open Economy Politics and Trade Preferences. https://ivypanda.com/essays/open-economy-politics-and-trade-preferences/

Work Cited

"Open Economy Politics and Trade Preferences." IvyPanda, 31 Mar. 2024, ivypanda.com/essays/open-economy-politics-and-trade-preferences/.

References

IvyPanda. (2024) 'Open Economy Politics and Trade Preferences'. 31 March.

References

IvyPanda. 2024. "Open Economy Politics and Trade Preferences." March 31, 2024. https://ivypanda.com/essays/open-economy-politics-and-trade-preferences/.

1. IvyPanda. "Open Economy Politics and Trade Preferences." March 31, 2024. https://ivypanda.com/essays/open-economy-politics-and-trade-preferences/.


Bibliography


IvyPanda. "Open Economy Politics and Trade Preferences." March 31, 2024. https://ivypanda.com/essays/open-economy-politics-and-trade-preferences/.

If, for any reason, you believe that this content should not be published on our website, please request its removal.
Updated:
This academic paper example has been carefully picked, checked and refined by our editorial team.
No AI was involved: only quilified experts contributed.
You are free to use it for the following purposes:
  • To find inspiration for your paper and overcome writer’s block
  • As a source of information (ensure proper referencing)
  • As a template for you assignment
Privacy Settings

IvyPanda uses cookies and similar technologies to enhance your experience, enabling functionalities such as:

  • Basic site functions
  • Ensuring secure, safe transactions
  • Secure account login
  • Remembering account, browser, and regional preferences
  • Remembering privacy and security settings
  • Analyzing site traffic and usage
  • Personalized search, content, and recommendations
  • Displaying relevant, targeted ads on and off IvyPanda

Please refer to IvyPanda's Cookies Policy and Privacy Policy for detailed information.

Required Cookies & Technologies
Always active

Certain technologies we use are essential for critical functions such as security and site integrity, account authentication, security and privacy preferences, internal site usage and maintenance data, and ensuring the site operates correctly for browsing and transactions.

Site Customization

Cookies and similar technologies are used to enhance your experience by:

  • Remembering general and regional preferences
  • Personalizing content, search, recommendations, and offers

Some functions, such as personalized recommendations, account preferences, or localization, may not work correctly without these technologies. For more details, please refer to IvyPanda's Cookies Policy.

Personalized Advertising

To enable personalized advertising (such as interest-based ads), we may share your data with our marketing and advertising partners using cookies and other technologies. These partners may have their own information collected about you. Turning off the personalized advertising setting won't stop you from seeing IvyPanda ads, but it may make the ads you see less relevant or more repetitive.

Personalized advertising may be considered a "sale" or "sharing" of the information under California and other state privacy laws, and you may have the right to opt out. Turning off personalized advertising allows you to exercise your right to opt out. Learn more in IvyPanda's Cookies Policy and Privacy Policy.

1 / 1