Exploring the Dynamics of USA-Brazil Commodity Trade

People are always worried that biofuels, which are made from agricultural products, will hurt food supply, which will raise food prices and lead to hunger around the world. This argument in particular assumes that the price of farm goods will go up because biofuel prices are going up. A common theme in recent active research in well-known academic journals about biofuels (Hochman et al., 2014; Kristoufek et al., 2014), agriculture (Myers et al., 2014), and energy (Bastianin et al., 2014a) is the need to rigorously explain this intuitive argument. Using a new way of looking at things, this study looks at how prices have changed recently in the most developed biofuels markets, adding to the conversation. If a country's net exports are going down, it can either devalue its currency or let it devalue. However, the trade balance may keep going down because of adjustment lags in things like production, supply, recognition, and so on. Things may get better later.


Two ideas that use two different methods have been used to test this short-term trend of how the trade balance changes after a currency devaluation. The J-curve was first proposed by Magee in 1973 and tested in the real world by Bahmani-Oskooee in 1985. It is mainly based on a reduced-form trade balance model, various estimation methods, and regression analysis. Backus et al. (1994) came up with the S-curve, which is based on a cross-correlation function between the current terms of trade, also known as the real exchange rate, and past and future values of the trade balance. In 2010, Bahmani-Oskooee and Hagerty did a full review of both ideas and put all the studies they looked at into three groups. This first group looks at all the trade that goes on between a country and the rest of the world. The second group uses trade flows between two countries to get rid of grouping bias. The third group uses trade flows between two countries that are broken down by item to get even less aggregation bias. The amount of disaggregation makes the evidence for both the S-curve and the J-curve stronger. This paper is explain about mostly about what happened in Brazil. Will Brazil's trade balance get better in the future because its currency is falling in value? The J-curve idea has been used in four studies to try to answer this question. In the best case, the effects are mixed.


In 1992, Bahmani-Oskooee and Malixi looked at the J-curve effect in a number of developing countries. Brazil was included in their sample, and they found that it was true in Brazil. The same is true for Gomes and Paz (2005), who only look at Brazil. Moura and Da Silva (2005), who also use data from Brazil, don't find any evidence for the J-curve, though. The three studies use trade data from Brazil and the rest of the world as a whole, so they may have aggregation bias. In order to get rid of any possible bias and find more proof for the J-curve, Bahmani-Oskooee et al. (2012) focus on trade between Brazil and the US, which is its main trading partner, and look at the experiences of 92 different industries. They can support the J-curve in almost one-third of industries by using the bounds-testing approach to cointegration and error-correction modeling. This method tells the difference between the short-run and long-run effects of currency devaluation.


Only two studies have tried to figure out the S-curve for developing countries, but Brazil has used it before. Senhadji (1998) uses aggregate trade data to test the curve for 36 developing countries, but he doesn't include Brazil. Parikh and Shibata (2004), on the other hand, test the curve for 64 developing countries and include Brazil. They find weak support for the S-curve there. These studies use the same method as Backus et al. (1994), who test the curve for 11 OECD countries by adding up all the trade flows between each country and the rest of the world. Bahmani-Oskooee and Ratha (2007a) separate trade flows by trading partners to get rid of aggregation bias. They also give the S-surve in trade between the U.S. and each of its big trading partners more support. Brazil is not listed as a partner, which is a shame. We only look at trade between the U.S. and Brazil in this study and try to find stronger support for the S-curve using trade flows between the two countries. To make our results even stronger, we break down the flows of trade between the two countries and estimate the curve for each of the 95 businesses that trade between them. In 52 of these cases, we find support for the S-curve. In Section 2, we talk about how the S-curve was made to show how we came to our conclusions. 

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