Trade Complementarity as a Basis for the Natural Trading Partner Hypothesis: A Panel Data Study for Trinidad and Tobago

ABSTRACT

This article examines trade complementarity as a basis for the natural trading partner hypothesis from a sectoral view of trade for the petroleum-based economy of Trinidad and Tobago (T&T) over the period of 2000 to 2015. Gravity modeling and an Intercountries Trade Force (ITF) model are adopted to determine the nature of sectoral trade complementarities in T&T over time. The results shows that trade complementarity and manufacturing trade complementarity significantly contribute to trade for T&T, which is important when choosing trade partners. This provides incentives for manufacturing which can dampen the effect of the Dutch Disease.

KEYWORDS:

• Trade complementarity
• natural trading partner hypothesis
• manufacturing
• Trinidad and Tobago

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website.

Notes

¹ Earlier authors of the NTPH substantiated that a high volume of trade between prospective members in a preferential trade agreement would increase welfare by reducing the effects of trade diversion. However, this may not be the case as trade diversion would increase if the share in intraregional trade is small. Schiff (1997, 2001) established that this is especially applicable where the home country (HC) is small and the partner country (PC) is large. The HC would ultimately lose welfare by continuously losing tariff revenues from the PC by increasing the volume of trade between these countries.

² Krugman (1993) stated that there is a strong tendency for countries with a close geographical proximity to trade more with each other due to lower communication and transportation costs. Deardorff and Stern (1994) also supported proximity as a basis for trade between countries as it would decrease transaction costs. However, this argument, was refuted by Lawrence (1996), Krueger (1999), and Nadav and Kleiman (2008), noting that trade with a distant partner may actually increase welfare since a country’s factor endowments and production structures may be the same as their neighbor’s. There may be no gains by forming a preferential trade agreement (PTA) to the HC from intercontinental trade if there are no significant differences in comparative advantage between partners as countries in closer proximity may be importing the same goods from the rest of the world (Hosein, Khadan, and Seecharan 2017; Krueger 1999; Nadav and Kleiman 2008).

³ The appendix can be found online at www.tandfonline.com/uitj.

⁴ As shown in online Appendix Table A2.

⁵ The resource movement effect is where a boom in the booming tradable sector of the economy draws labor out from the non-tradable and traded sectors of the economy into the booming sector. This causes manufacturing employment to decrease which inherently contracts output via direct deindustrialization.

⁶ The spending effect occurs after windfall gains from the booming sector enter the economy, thereby increasing the demand for services. This would further reduce employment and output from the manufacturing sector at the expense of an expanding services sector due to indirect deindustrialization.

⁷ Data for T&T is only available until 2015.

⁸ Trade intensity measures country i’s exports to country j as a share in total exports in relation to country j’s imports from the world as a share of world imports. It ranges from a value greater than 0 to less than infinity, and a value greater than one represents an intense trade relationship.

⁹ Trade complementarity measures the extent to which the export profile of the reporter country complements or matches the import profile of the partner country. A value of the trade complementarity index closer to 100 indicates that trade complementarity exists between country i and country j, and a value closer to zero indicates that no complementarities are present in trade.

¹⁰ Data on GDP per capita is acquired from the World Development Indicators, and data on distance is acquired using the Center d’Études Prospectives et d’Informations Internationales (CEPII).

¹¹ POLS ignores the panel data nature of the dataset. In contrast, the FE and RE models allow us to capture unobserved heterogeneity among countries that is fixed over time. Also, the FE model allows us to eliminate potential endogeneity due to unobserved heterogeneity. Finally, following Santos Silva and Tenreyro (2006, 2011), the PPML is also used to estimate our model. This method has several advantages over traditional techniques as explained in the recent study by Lorde et al. (2020).

¹² The United Nations Office of the High Representative for the Least Developed Countries, Landlocked Developing Countries, and Small Island Developing States (UN-OHRLLS) categorizes Small Island Developing States (SIDS) as developing countries that face social, economic, and environmental vulnerabilities which are mainly located in the Caribbean, Pacific, Atlantic, Indian Ocean, the Mediterranean, and South China Sea.

¹³ Gravity Index = $\frac{GDPi.GDPj}{Distance}$ where GDP is gross domestic product for country i by j, and distance is measured between country i and j.

¹⁴ FST_ij = $\frac{Tradej\left(w-i\right)t}{Tradejt}$ where Trade_{j(w − i)t} represents trade flows between country j and the world minus country i, while Trade_jt represents the total volume of country j’s trade. This variable reveals the availability of free space to trade with country j.

¹⁵ A common language variable is considered but is found to be highly correlated to the PTA variable. Hence, it is dropped from the model.

¹⁶ For T&T, the variance inflation factor (VIF) for each model was less than 10. For the gravity models, the mean VIF for aggregate trade was 1.44, for energy trade was 1.19, for manufacturing was 1.43, and for agriculture was 1.73. For the ITF models, the mean VIF for aggregate trade was 1.52, for energy trade was 1.30, for manufacturing was 1.51, and for agriculture was 1.50. For the SIDS model, the VIF was 1.50 and for the non-SIDS model, the VIF was 3.41.

¹⁷ For Singapore, the mean VIF for each model was less than 10. For the gravity models, the mean VIF for aggregate trade was 1.34, for energy trade was 1.59, for manufacturing was 1.28, and for agriculture was 1.46. For the ITF models, the mean VIF for aggregate trade was 1.62, for energy trade was 1.65, for manufacturing was 1.59, and for agriculture was 1.72.

¹⁸ The TradeCAN can be used to understand the market positioning for the country and reveal changes to market share and market structure. There are four possibilities. A rising star reflects export areas that are dynamic products and which are gaining market share. A missed opportunity is reflective of areas that have lost market share in dynamic products. Declining stars are reflective of areas that are gaining market share in stagnant export areas. Retreat areas reflect decreasing market share for stagnant export areas.

¹⁹ RCA_ij = (X_ij/ X_it)/(X_wj /X_wt) where X_ij and X_wj are country i’s exports of product j and world exports of product j and X_it and X_wt are country i’s total exports and the world’s total exports.

²⁰ Complementarity significantly impacts aggregate trade and sectoral trade for both T&T and Singapore. In particular, there is a positive and significant relationship between MTC and ETC on trade in both countries. ATC is also found to be positive and insignificant in both countries in predicting agriculture trade.