# Effects of Importer Country's GDP in Brazilian Agriculture Exports: Chicken Meat, Coffee, Maize, Soybean and Soybean Cake

Daniel Jesus Xavier, João Reis

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Daniel Jesus Xavier, João Reis. Effects of Importer Country's GDP in Brazilian Agriculture Exports: Chicken Meat, Coffee, Maize, Soybean and Soybean Cake. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2022, Gyeongju, South Korea. pp.352-357, 10.1007/978-3-031-16407-1_42. hal-05386100

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# Effects of Importer Country's GDP in Brazilian Agriculture Exports: Chicken Meat, Coffee, Maize, Soybean and Soybean Cake

Daniel Laurentino de Jesus Xavier ID and João Gilberto Mendes dos Reis (✉) ID

RESUP - Research Group in Supply Chain Management - Postgraduate Program
in Production Engineering, Universidade Paulista, São Paulo, Brazil

daniel.xavier3@aluno.unip.br, joao.reis@docente.unip.br

**Abstract.** It is strategic for countries that produce agricultural goods to identify the factors that can impact the global trade market. Brazil is one of the main agricultural commodities exporters and can benefit from different approaches to trade data analysis. This paper aims to study the correlation between Importer Country's GDP and the Brazilian main agricultural commodities (Soybeans, Sugar Raw Centrifugal, Chicken meat, Soybeans cake, Green Coffee, Cattle boneless meat (beef & veal), and Maize. To do so, we collected exports data from 2011 till 2020 from FAO and their respective Importer Country's GDP from World Bank and analyzed then using Multiple Linear Regression. We found evidence of a positive correlation with Importer Country's GDP from exports of Green Coffee, Soybean and Chicken meat and a negative correlation with Soybean cake and Maize. It was not found correlation on exports of cattle boneless meat (beef & veal) and sugar raw centrifugal.

**Keywords:** Linear regression · Importer Country's GDP · Agricultural commodities

## 1 Introduction

The agribusiness trade is very important to the global economy and represents the main export sector for Brazil. According to data from Brazil's Ministry of Agriculture, Livestock and Supply (MAPA), agribusiness was responsible for almost half of Brazil's exports in 2020, with a 48% share, representing a trade balance surplus of US$ 87.76 billion [1].

The top five Brazilian agribusiness sectors exported year were the Soybean Complex (US$ 35.24 billion and 35% of exports), Meat (US$ 17.16 billion and 17% of exports), Forest Products (US$ 11, 41 billion and 11.3% of exports), Sugar and Alcohol Complex (US$ 9.99 billion and 9.9% of exports) and Cereals, Flours and Preparations (US$ 6.89 billion and 6.8% of exports) [1].
Due to its strategic importance, it is crucial to comprehend which economic factors can impact international agribusiness. De Winne e Peersman [2] examined the macroeconomic consequences of disruptions in global food commodity markets and found evidence of impact on US GDP. Santos et al. [3] studied the correlation between the common stocks of Brazilian agribusiness companies with several factors, including Brazil's GDP. The authors found a negative relationship, even during economic crisis.

Although these studies explore relations between agribusiness and GDP, it is more common to find studies on the GDP of the exporter country, but not so usual to find studies that use Importer Country's GDP, which reinforces the importance of this study.

In this study, we adopted the multiple linear regression technique to understand the relation between Importer Country's GDP and the trade relations over seven important global commodities: Soybean, Soybean Cake, Green Coffee, Cattle Boneless Meat (Beef & Veal), Chicken Meat, Sugar Raw Centrifugal and Maize. We seek to comprehend if the Importer Country's GDP can influence the agribusiness trade. These commodities are chosen because of their importance on Brazil's exports, representing 74% of all agriculture exports from 2011 and 2020.

The data analysis is conducted using Stata and Excel and the measures were coefficient and standardized Error.

This paper is divided as follows: The introduction addresses the article proposal and some background; methodology presents the procedures of the research; results section presents the findings of the research; discussion shows the implications and analysis; and finally the conclusion section points out the final remarks of the study.

## 2 Methodology

To conduct this study, we use the Food and Agriculture Organization of the United Nations (FAO) database to extract data on Brazil's exports of the last decade, from 2011 until 2020, using the amount of trade in US dollars [4]. The FAO database gathers information provided by the countries. The data was exported to a CSV (Comma-separated values) file and later on organized using Microsoft Excel software.

Afterward, we make some amendments to the data settings. Autonomous regions such as Taiwan, Hong Kong, and Macau were sum with mainland China. Moreover, we decided to establish a minimum value of 1,000 US dollars trade per year. We also selected products that represented at least 5% of all agricultural products exported during the period.

The products are Soybeans, Sugar Raw Centrifugal, Chicken meat, Soybeans cake, Green Coffee, Cattle Boneless Meat (Beef & Veal), and Maize. These adjustments had the purpose to avoid outlier data and fluctuations that can distort the analysis. Despite these minor modifications our sample represents at least 74% of trade for all categories.
For the GDP information, we use the World Bank database [5] to obtain data from the countries that purchased the selected products. The data were added to the matrix, relating each country to its respective GDP for that year. The generated matrix has as columns: the year, the importer country, each selected product, and the GDP of the importer country in US dollars.

To perform the data analysis, the matrix was loaded into the Stata 16.0 software from StataCorp LLC [6]. The matrix has 1279 lines. Each line is related to a country and a year, from 2011 to 2020, and the columns represent the imports value by-product and the GDP of that year. We applied the multiple linear regression technique, and obtained 63 observations by the model.

According to Fávero [7], “the linear regression technique (simple and multiple) offers, as a priority, the possibility of studying the relationship between one or more explanatory variables, which are presented in linear form, and a quantitative dependent variable”. Applying this technique, we seek to verify if there is a relationship between the GDP of the importer country and the trade value of one or more Brazilian agricultural products.

We use neperian logarithm (LN) on each of the raised variables to generate new logarithmic (e base) variables creating a linear effect. Therefore, Importer Country's GDP is set as LNGDPimp, Soybean Cake is set as LNCake, Green Coffee is set as LNCoffee, Maize is set as LNMaize, Cattle Boneless Meat (Beef & Veal) is set as LNBeef, Chicken Meat is set as LNChiken, Soybean is set as LNSoya and Sugar Raw Centrifugal is set as LNSugar.

$$ \begin{array}{l} LnGDPj = \gamma0 + \beta_1 LNCake + \beta_2 LNCoffee + \beta_3 LNMaize + \beta_4 \\ LNBeef + \beta_5 LNChiken + \beta_6 LNSoya + \beta_7 LNSugar + \varepsilon i \end{array} \quad (1) $$

where, LnGDPj = sample size, N = population, Z = abscissa of the normal standard, p = estimated homogeneity, e = sample error.

At first, we use the sum command in order to obtain statistical information on all variables, dependent and explanatory. This command shows the observation quantities, mean, standard deviation, and minimum and maximum values for each variable. The results are shown in the next section.

Then we use the stepwise command, pr (0.10), and the reg command to perform the regression, using the logarithmic variable of the Importer Country's GDP as the quantitative dependent variable. The logarithmic values of each agricultural product's exports to its respective country are the explanatory variables we used. The results are presented in Sect. 3 and validated according to Gujarati in reference [9]. Table 1 summarizes the statistical data.

## 3 Results

Both Sugar Raw Centrifugal (LnSugar) and Cattle boneless (LnBeef) are not statistically significant and the final equation obtained is represented in Eq. 2.
<table><caption>**Table 1.** Sumarized statistical data</caption><thead><tr><th>Variable</th><th>Obs</th><th>Mean</th><th>Std. Dev.</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>LNCake</td><td>372</td><td>1.052.811</td><td>1.866.948</td><td>6.921.658</td><td>1.464.935</td></tr><tr><td>LNCofee</td><td>598</td><td>9.916.319</td><td>1.690.997</td><td>6.934.397</td><td>1.440.094</td></tr><tr><td>LNmaize</td><td>435</td><td>1.028.425</td><td>176.685</td><td>6.911.747</td><td>1.393.238</td></tr><tr><td>LNBeef</td><td>474</td><td>9.700.381</td><td>19.324</td><td>6.907.755</td><td>1.539.253</td></tr><tr><td>LNchicken</td><td>814</td><td>952.426</td><td>1.778.179</td><td>6.917.706</td><td>142.384</td></tr><tr><td>LnSoya</td><td>308</td><td>112.185</td><td>202.707</td><td>6.910.751</td><td>1.712.877</td></tr><tr><td>LnSugar</td><td>535</td><td>1.038.033</td><td>1.928.087</td><td>6.915.723</td><td>1.442.697</td></tr><tr><td>LNGDPimp</td><td>1,279</td><td>2.500.226</td><td>2.167.128</td><td>1.955.627</td><td>3.069.596</td></tr></tbody></table>

$$ LnGDPj = \gamma_0 + \beta_1 LNCake + \beta_2 LNCofee + \beta_3 LNMaize + \beta_4 \quad (2) \\ LNChicken + \beta_6 LNSoya + \varepsilon_i $$

The number of observations adopted by the model was 63 - which means values without zero for all the categories.

The coefficient of determination $R^2$ was 0.8067. According to Moreira et al. [8], $R^2$ is an indicator of the adopted model fitness to explain the correlations. It can vary from 0 to 1. The closer to one indicates that the model is suitable to explain all the data variability. The result can be interpreted as 80% of Importer Country's GDP can be explained by the variables used (trade products. However, According to Gujarati in reference [9], one of the disadvantages of $R^2$ is the fact that the value naturally grows as we increase the number of dependent variables. As a complement, according to the author, we should also consider the adjusted $R^2$, which is commonly used to evaluate two or more regression models with the same dependent variable. The adjusted $R^2$ we found is 0.7898, or 79% of Importer Country's GDP can be explained by the trade products.

Table 2 shows the results of the multiple linear regression technique applied to trade data using logarithmic variables.

<table><caption>**Table 2.** Multiple linear regression results</caption><thead><tr><th>LNGDPimp</th><th>Coef.</th><th>Std. Err.</th><th>t</th><th>P&gt;—t—</th><th>[95% Conf.]</th><th>Interval]</th></tr></thead><tbody><tr><td>LNCake</td><td>-.165979</td><td>.0442037</td><td>-3.75</td><td>0.000</td><td>-.2544954</td><td>-.0774626</td></tr><tr><td>LNCofee</td><td>.5518952</td><td>.0852398</td><td>6.47</td><td>0.000</td><td>.3812054</td><td>.722585</td></tr><tr><td>LNmaize</td><td>-.1252941</td><td>.0593424</td><td>-2.11</td><td>0.039</td><td>-.2441251</td><td>-.006463</td></tr><tr><td>LnSoya</td><td>.3034753</td><td>.0489467</td><td>6.20</td><td>0.000</td><td>.2054613</td><td>.4014894</td></tr><tr><td>LNchicken</td><td>.2923714</td><td>.0513913</td><td>5.69</td><td>0.000</td><td>.189462</td><td>.3952808</td></tr><tr><td>_cons</td><td>1.809.418</td><td>1.231.322</td><td>14.69</td><td>0.000</td><td>156.285</td><td>2.055.986</td></tr></tbody></table>
Data shows that Coffee represents the main correlation with Importer Country's GDP, with a positive coefficient of correlation of 0.552, followed by Soybean (0.303) and Chicken meat (0.292). We also found evidence of a negative correlation with Importer Country's GDP on Soybean cake and Maize, with a coefficient of correlation of -0.165 and -0.125 respectively.

We found evidence that a 1% increase on Importer Country's GDP represents a 0.55% increase on Coffee exports, 0.30% on Soybean exports and 0.29% on Chicken meat exports. On the other hand, a 1% increase on Importer Country's GDP represents a 0.17% decrease on Soybean cake exports and 0.13% on Maize exports to this country.

The information shows that it is possible to use multiple regression technique, to better understand the global trade demand for some agribusiness products. It is intuitive to assume that the more a country increases its GDP, the more it buys all products, but we only found evidence of that correlation on Coffee, Soybean and Chicken Meat.

More counterintuitive is the fact that a negative correlation happens with Soybean Cake and Maize. In other words, the more a country increases its GDP, the less it buys those products. One possible explanation for that is that both Soybean Cake and Maize are used as animal food, so as a country increases its GDP, it can represent a shift in consumer behavior of animal protein, preferring to import those products over local production.

It is quite remarkable that Brazilian farmers can use Importer Country's GDP, as a relevant metric in trade analysis, opening up a field of opportunities for regression studies on the subject.

# 4 Conclusions

Brazil is one of the major agriculture producers and the agribusiness is its main export sector, but international trade is a complex system involving several variables, therefore it is important to apply multiple techniques and different approaches to find insights that might value information for strategic decisions.

In this context, this work analyzed data from Brazilian exports of Soybean, Soybean Cake, Green Coffee, Cattle Boneless Meat (Beef & Veal), Chicken Meat, Sugar Raw Centrifugal and Maize from FAO [4]. We also gathered data on Importer Country's GDP from World Bank [5]. The data refers to 2011 until 2020. The relation between the Brazilian commodities and Importer Country's GDP was analyzed using multiple linear regression technique.

We found evidence of a positive correlation with Importers GDP from exports of Green Coffee, Soybean, and Chicken Meat. It was not found correlation from exports of Cattle Boneless Meat (Beef & Veal) and Sugar Raw Centrifugal. We also found evidence of a negative correlation with Soybean Cake and Maize.

We suggest for further studies, a more detailed approach to beef and sugar commodities, in order to identify which other variables may impact their exports. Another important study would be to understand why Soybean Cake and Maize presents a negative correlation to Importer Country GDP. We also suggest the
use of exported volume as a variable, in order to check if the monetary variation
could affect the results. The results allow us to raise questions for further studies.
The Brazilian agriculture production can properly adapt to world demands? Did
possible international restrictions on agricultural products impact the results?
These questions can be addressed in future research.

**Acknowledgments.** This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES). Finance Code 001.

References

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2. Winne, J.D., Peersman, G.: Macroeconomic effects of disruptions in global food commodity markets: evidence for the United States. Brook. Pap. Econ. Act. **2016**(2), 183-263 (2016)

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