An Empirical Analysis of the Structural Economic Complementarity and the Impact of a Free Trade Agreement Between Kenya and Korea

Abstract

The goals of this Ph.D. research project are motivated by academic and social individual responsibility (ASIR) concerns. The increasing need for a systematic, comprehensive, and consistent approach towards the analysis of economic and trade data that can produce critical and reliable information to help formulate “elastic” policies that can maneuver the contours of the mutating economic, social, and geopolitical environments is a golden opening for scholars to contribute. Through a confirmatory approach, this study aims to contribute. This research project aims to undertake a comprehensive empirical analysis of the economic structural complementarity and the potential impact of a liberalized trade market between Kenya and Korea. To achieve this goal, we carry out three complementary studies. The first study uses economic indicators (the revealed comparative advantage index, trade complementarity index, and trade intensity index). The second study applies a partial equilibrium approach using the gravity model, while the third study applies the computable general equilibrium (CGE) model to evaluate the economy-wide impacts of a free trade agreement between Kenya and Korea. The first empirical study analyzes the economic structural complementarity of Kenya’s and Korea’s economies using economic indicators that include Revealed Comparative Advantage (RCA)indices, the Trade Complementarity Index (TCI), and the Trade Intensity (TI) Indices. In the second study, we aim to estimate country-specific and sector-specific Ad valorem Tariff Equivalents (AVEs) of Non-Tariff Measures (NTMs) in cross-border trade in goods and services for 120 countries in 57 globally traded industries using the gravity model. The study draws bilateral trade data from the Global Trade Analysis Project (GTAP) database version 9, whose base year is 2011. We aggregate the GTAP 140 country/regions into 16 regions. Using the quantity-based border approach, we apply the gravity model by Anderson and van Wincoop (2003, 2004) in which bilateral prices of goods and services have been augmented in the gravity model. In estimating country-specific AVEs of NTMs, the 57 GTAP sectors are aggregated into one sector. The sector-specific empirical analysis of AVEs of NTMs is done for each sector except for one sector (dwelling) due to insufficient trade flow data. Finally, in calculating the country-specific and the sector-specific AVEs of NTMs, we apply the export trade border coefficients together with elasticity of substitution. Empirical findings show that sector-specific AVEs of NTM are higher than country-specific AVEs of NTMs. Expectantly, the agricultural sectors are found to have higher AVEs of NTM values compared to other sectors. The empirical results further confirm that regional trading blocs and industrialized countries face relatively lower trade barriers. The third study aims to evaluate the economic impact of a potential free trade agreement between Kenya and Korea. The study uses a multi-sector, multi-region static GTAP (CGE) approach, using GTAP database version 10, (whose base year is 2014). The GTAP database contains data on 141 regions and 65 traded sectors and 5 endowment commodities, which include land, skilled labor, unskilled labor, capital, and natural resources and capital goods. For ease of computation, the database is aggregated into 16 regions and 57 sectors. The aggregated regions reflect the major trading partners based on economic size and endowment. We develop seven policy scenarios through the implementation of a mix of simulations involving the reduction of trade barriers, including tariffs and non-tariff barriers. Implementation of AVEs of NTMs is calibrated according to, Francois, Miriam, Hanna, Olga, and Patrick (2013), and on 50 percent of the estimated AVEs of NTMs. Simulation results from all scenarios show that GDP change from the baseline range between US$ million 1.89 to 75.63 and 1.00 to 10.38 for Kenya and Korea, respectively. Several regions that experience a mixed GDP growth include Egypt (-0.031 to 0.219), the East African community (-1.625 to 0.852), Sub-Saharan Africa (-0.188 to 0.063) US$ million. However, the United States and the Oil and Petroleum Exporting Countries have no significant change in real GDP, with the remaining economies having a negative GDP growth. In terms of factor prices, there is positive gain from the baseline both in Kenya and Korea except the prices of natural resources for Korea, which have heterogenous responses across all scenarios. The prices of natural resources in Kenya have the highest improvement (1.59%) from baseline followed by skilled labor (0.78%), unskilled labor (0.60%), land 0.43%), and capital 0.40%). However, in Korea, unskilled and skilled labor price change from the baseline is the highest, but it is significantly not different across all scenarios. Welfare for both Kenya and Korea grew significantly from the baseline by up to US$ 147.466 million (a 0.24 share of GDP) and US$ 64.476 million (a 0.005 share of GDP), respectively. The rental rate of return on capital (rorc) is positive for both Kenya and Korea but mostly negative for all other regions. The study has provided concrete evidence of exploitable potential based on the findings of the revealed comparative advantage, the trade complementarity index, and the trade intensity indices. This outcome is collaborated by the second study that quantifies the ad valorem equivalents of non-tariff measures whereby the agricultural sector faces the highest AVEs of NTMs. With significant trade costs detected, the third study uses the CGE approach to simulate trade cost reduction through a bilateral engagement between Kenya and Korea. A liberalized trade market has identified tangible benefits to both countries, such as improvement in GDP, welfare, trade balance, factor prices, among others. However, the study finds that production in some sectors will decline. Liberalization of the services sector will lead to negative welfare for Korea. The static nature of our model does not capture the long-run effects. Therefore, the actual benefits are likely to be underestimated. The contribution of this research is the creation of new datasets on bilateral ad valorem equivalents (AVEs) of non-tariff measures between Kenya and Korea and between the two countries and their global trading partners. The dataset applied in the estimation of the AVEs of NTMs is comprehensive in terms of the variables, sectors, and regions covered. The empirical findings provide insight into the level of non-tariff barriers to trade between Kenya and Korea and their bilateral trading partners. Furthermore, the outcomes of this research work will reinforce a consensus on Poisson Pseudo–Maximum Likelihood (PPML) and Zero Inflated Poison (ZIP) estimators in the estimation of the AVEs of NTMs. Other contributions include new data on RCA, TCI, and TII (EII & MII) indices not elsewhere estimated. According to the author's best knowledge, this is the first comprehensive research work to apply three broad research approaches to analyze the economy-wide impact of a potential Free Trade Agreement (FTA) between Kenya and Korea.