SECTION 2 QUESTION 3: In 1994, the US joined with Canada and…

SECTION 2 QUESTION 3: In 1994, the US joined with Canada and Mexico in entering the North America Free Trade Agreement (NAFTA). Many feared that US jobs would be lost to Mexico, or that it might at least cause the skill premium in the US to increase. The skill premium is defined as Ws/Wu which is the wage of skilled labor divided by the wage of unskilled labor. Use the HO Model to explain this prediction. Assume for simplicity that there are only two factors, Unskilled labor and skilled labor ,and only two countries, the US and Mexico.   Assuming that the US is skilled labor abundant, answer the following questions i. What should happen to the skill premium (Ws/Wu) in the US and Mexico, according to HO Theory? ii. What alternative explanation for the rising skill premium was discussed in class. State the name and explain what it means. iii. If it was observied that Ws/Wu increased in both Mexico and the US would this be more consistent with the HO Model prediction or the alternative explanation in part iii? Explain.        

Use the following information to answer questions 9-12. Make…

Use the following information to answer questions 9-12. Make all of the regular assumptions of the 2 good/2 country Ricardian Model. The goods are aircraft and motor vehicles. The countries are the US and Japan. Under free trade with the United States, the wage earned if workers make motor vehicles in Japan is higher than the wage earned if workers make aircraft in Japan.  Japan produces

Use the folowing to answer questions 17-20:  The US and Chin…

Use the folowing to answer questions 17-20:  The US and China produce 5 goods: Cars, furniture, textiles, pharmaceuticals, and corn. Labor is the only factor and there is perfect competition and constant marginal product of labor in all countries. The wage in the US is 4 times that of the wage in China.   The table below summarizes the marginal products of labor in each country for each good.   MPL in US MPL in China Cars 32 4 Furniture 10 8 Textiles 3 3 Pharmaceuticals 12 2.5 Corn 10 2