• Journal of Korea Port Economic Association
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• v.36 no.4
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• pp.93-106
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• 2020

Trade is an important economic activity. In particular, since the establishment of the World Trade Organization (WTO), the scope of trade has been expanding due to events such as the entry of China into the WTO in 2001, the establishment of a multilateral trading system, mitigation and integration of trade barriers, and the establishment of the free trade agreement (FTA). Despite the expansion of the trade market, however, extreme events such as the 2008 global financial crisis, the 2016 Brexit, and the 2018 US-China trade war have had a direct negative impact on the trade market. Therefore, the present this study analyzed the dependence structure between the international shipping freight rate index, a variable representing trade activities, and the trade uncertainty between the US and China. The following is a summary of the analysis results. First, the US-Chinese trade policy uncertainty and international shipping freight rate index presented a Frank copula and rotated Clayton copula 270° distribution, respectively, showing the same distribution structure for each country. Second, the Kendall's tau correlation revealed a negative dependence between the international shipping freight rate index and US-Chinese trade policy uncertainty. The degree of dependence was greater in the combination of uncertainty in China's trade policy and international shipping freight rates. In other words, the dependence of global demand and trade policy uncertainty confirmed that China was stronger than the US. Finally, the tail dependence results revealed that the US-Chinese trade policy uncertainty and international shipping freight rates were independent of each other. This means that extreme events related to the trade policy uncertainty or international shipping rate index were not affected by each other.

• Environmental and Resource Economics Review
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• v.25 no.2
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• pp.199-228
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• 2016

This study investigates the effects of economic factors and forest environments on rural residential area development in seven north central states of the U.S. by focusing on the relative importance of not only economic factors but also forest environments by forest type as core drivers of residential development. An empirical model of locations and magnitudes of population changes since 1950 in the north central region is first constructed, and then a panel model with fixed effects for counties is used to explain population growth by age group over time at the county level. Then a set of three equations is estimated for three major age groups, and a cross-sectional model is estimated for the last time period that regresses county-level environmental amenity variables on fixed effects coefficients for counties. Finally, an equation explaining changes in rural housing density is estimated. The results imply that immigrant age is a key factor influencing the choice of the place of residence and that the effects of environmental amenity factors on population growth and subsequent housing development in a county vary according to the age group.