the retail and distribution gasoline market in brazil - …

Revista Mundi Engenharia, Tecnologia e Gestão. Paranaguá, PR, v.3, n.2, maio de 2018.

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THE RETAIL AND DISTRIBUTION GASOLINE MARKET IN BRAZIL - A STUDYOF VARIATION PRICE BEHAVIORS

A.S.Nascimento Filho1

T.B.Murari2

M.A. Moret3

Abstract: In this paper evaluates the effects in the gasoline prices after the Brazilian downstream oil chain liberation, in late 1990s. That stage meant that the Brazilian govern, that no longer setting the maximum and minimum values of all fuels. For this purpose, the gasoline type C prices were collected from fifteen relevant cities in five economic regions of Brazil, between the years 2005 and 2014. The sequences of computational techniques were applied on these datasets. The stationary and linearity for variation prices time series were analyzed in all cities and, also, the correlations among all cities in order to recognize the times series patterns. Furthermore, the Cumulative Sum control (CUMSUM) chart was used to detect smaller parameter shifts on the distribution time series. Our results reveled distinct patterns for middle of 2005 and the middle of 2006, and also for the first months of 2011 and the middle of 2012. Reinforcing the idea of the Brazilian retail and distribution are governed strongly by exogenous factors. This makes a conventional analysis difficult to be used. Once, the Brazilian downstream fuel chain suggests to be a complexity system. Keywords: gasoline price, statistic tests, CUMSUM analysis.

1 M.Sc., SENAI-CIMATEC, Aloí[email protected]. 2 D.Sc., SENAI-CIMATEC, [email protected]. 3 D.Sc., SENAI-CIMATEC, [email protected].


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1 INTRODUCTION After the oil control changing a new status quo was established in Brazil.

The Brazilian govern no longer setting the maximum and minimum values of

fuel, their derivatives or any requirement of prior official authorization for price

adjustments among others. The main aims were to improve the infrastructure

industries, protect the competition and efficiency of market. For these purpose,

it was created the Brazilian law 8884/94, called Petroleum law, which begun a

new phase in Brazilian downstream oil chains. Three years after, the law 9.478

was published, it created the National Agency of Petroleum, Natural Gas and

Biofuels (ANP), in order to regulate and supervise the petroleum industry, its

derivatives, natural gas and biofuels in Brazil.

Uchoa (2006) points out about the behavior of oil prices in international

trade generate impacts on their derivatives from a global market. In Brazil, this

phenomenon is not different. There, although with a low volume of petroleum

imports, around 5% according to Petrobras (2005) and ANP (2016), the prices

of their derivatives are not detached from the variations of the international oil

market. So that changes in oil prices directly impact socioeconomics (e.g.,

freight cost, vehicle sales, and urban transport, among others). The Brazil has a

larger car fleet, and the gasoline fuel has been the second most consumed fuel,

that coming just behind the diesel oil. Hence, the gasoline has a straight impact

on family budget.

Furthermore, the Brazilian downstream fuel chain segment had been an

easing of entrance since 1993 and according to ANP, in the year of 2013 there

were 38,893 fuel retails, with the main distributions companies such as: BR,

Ipiranga, Chevron, Shell, Esso and Alesat, where they have around 43% of the

market share, and the rest of occupying with small retailers, called “white flags”

(ANP, 2016). According Silva (2014) take account the structure of prices in the

retail and distribution gasoline market in Brazil, it is important to note aspects of

price construction and regional differences that make aggregate analysis less

effective, for understanding the behavior of agents in the formation of prices.

Thus the differences among prices, fuel taxation practices undertaken by states,


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distances, and costs of fuel could be difficult to understanding the gasoline

prices behavior.

The aim of this paper is to evaluate the stochastic process gasoline type

C on retail and distribution market for fifteen cities in five economic regions in

Brazil, between 2005 and 2014. For this purpose, it was suggested a sequence

of steps to support this study, including a stationary and linearity test, a

correlation analysis and Cumulative Sum (CUSUM) control chart processes,

that is suitability to detect smaller parameter shifts in time series (TS). This

work is structured as following: after this introduction, in the section two we can

find materials and methods; third section presents the results and

considerations are in the fourth section.

2 MATERIALS AND METHODS

2.1 Data

In this paper it is evaluated the behavior of price of gasoline type C in

fifteen cities in Brazil between 2005 and 2014 (see the Table 1). Where their

data were collected from weekly survey of prices for retail and distribution

where that is carried out in 555 municipalities. The service is provided monthly

by ANP.

Table 1 –Cities evaluated in every Brazilian regions.

Region City (symbol)

North Manaus (MA), Belém and Rio Branco (RB)

Northeast Salvador (SSA),Recife (RE) and Fortaleza (FO)

Southeast Rio de Janeiro (RJ), São Paulo (SP) and Belo Horizonte (BH)

Midwest Cuiabá (CB), Brasília (DF) and Goiânia (GO)

South Curitiba (CT), Florianópolis (FP) and Porto Alegre (PA)

Source: author

2.2 Time series evaluation

In general, there are situations where a researcher takes a looked at the

dataset or graphs and rapidly it is chosen a TS analysis method. Sometimes

without recognize how the dataset is governed. That may occur due the

researcher to be an expert in specific tool, and believe that one could solve any


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kind of problems by applying the same tool. Thus, to avoid that situation, first of

all, it is recommended the researcher to observe carefully the characteristics of

the dataset under study. Thereby, before choosing the final method and to

avoid a prejudice, here it is suggested three steps that may help, mainly, new

researchers.

2.2 Procedures

Before applying nonlinear techniques, e.g., those inspired by chaos

theory, in occurrences of phenomena of nature, it is first necessary to know

whether the use of such advanced techniques is justified by the data (Schreiber,

2000). In the Figure 1 depicts the sequence of this suggested verification,

composed by three steps as following:

Figure 1- Strategy used to evaluate the time series behavior.

Source: author

Step 1 - To verify if the TS is stationary or not by use statistical test. Here

we suggest the Augmented Dickey-Fuller (ADF) (Dickey, 1979; Dickey,

1981)


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Step 2 - To verify the linearity behavior. We suggest a surrogate data

analysis test (e.g., Random Shuffle (Monte Carlo) or Fourier Transform

(Schreiber, 2000)).

Step 3 - Finally, to developer statistics study by using an appropriate

technique, such as: ARMA, ARIMA, GARCH, R/S Analysis among others

(Monteiro, 2011; Morettin, 2008).

3. RESULTS

It was evaluated the TS of gasoline type C variation prices for fifteen

cities in Brazil, between 2004 and 2015. Figure 2 depicts the dispersion

behaviors of these cities. It might be observed an imbalanced dispersion in both

markets. For instance, the cities SSA and FP presented large dispersion

whereas DF and RB small one.

In the next subsection it is evaluated the datasets under the stationarity

approaches.

Figure 2- Boxplot shows two capitals with greater variation than any other city (SSA and FP), for

both distribution (a) and retail (b) markets.

Source: author


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3.1 Stationary evaluations

During assesses of these datasets by using the methodology in Figure 1,

there were detected for all datasets a stationary behavior by busing ADF (Dick-

Fuller) with lag − 1 (Dickey, 1979; Dickey, 1981).

3.2 Linear evaluations

Although the distribution dataset either retail analysis shown a stationary

behavior some of those data presented non linearity. The Figure 3 depicts a

surrogate data analysis for RJ, with H0 rejected for this kind of test. Since there

are cases of non-linear behaviors (see Table 2) is necessary to apply other

techniques to better explain the dataset behaviors. In the next subsection we

evaluate these TS with computational complex methods.

Table 2- Cities that presented non-linear behaviors.

Market chain Cities

Retails SSA, RE, BL, RJ, MA, RB, DF and CB

Distribution FO, RE, SP, BH, PA, CT, FP, BL, RB and CB

Source: author

Figure 3- Surrogate data test analysis for RJ distribution time series. Where the solid black

vertical line is the shuffled original time series by Monte Carlo simulation and the red dashed

line is the discriminating statistic between both original data and the surrogate data. If the value

of the statistic is significantly different for the original series than for the surrogate set.

Source: author


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3.3 Application of Statistical Techniques

We shall use complex statistical techniques to evaluate these TS,

because there are some non-linear series on the results found in Section 3.2. It

will be presented two different statistical analyzes in this case study problem.

3.3.1 Spearman’s rank correlation coefficient

The Spearman correlation evaluates the monotonic relationship between

two variables. We calculated the spearman value to find for strong correlations

for each distribution and retail variations series inside each city. The following

equation is used to calculate the Spearman coefficient ρ:

(1)

Where: di is the difference between the ranks of corresponding values Xi and Yi,

both are original TS and n is the number of value in each data set.

Table 3- Distribution versus Retail Spearman Correlation by Brazilian regions.

North Northeast Southeast Midwest South

0.41144 (BL) 0.56747 (SSA) 0.70028 (RJ) 0.34386 (GO) 0.62846 (FP)

0.14335 (RB) 0.38922 (FO) 0.57311 (SP) 0.17407 (DF) 0.43251 (CT)

0.09588 (MA) 0.18298 (RE) 0.51589 (BH) 0.13175 (CB) 0.33766 (PA)

Source: author

We plotted the top four strongest spearman correlation results to

evaluate the graph pattern (Figure 4). Rio de Janeiro weekly distribution

variations are associated with retail weekly variations.

Figure 4 - Scatterplot for top four strongest spearman correlation results.


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Source: author

3.3.2 Control Charts

Shewhart developed the foundation for the control chart. Shewhart’s

charts are effective for identifying large variations in process parameters, a shift

of 1.5 - sigma or larger on its magnitude, but this chart may take a long time to

identify a small persistent shift in this process (Montgomery, 2001). Cumulative

Sum (CUSUM) control chart has the ability to detect smaller parameter shifts.

This chart plots the cumulative sums of the deviations of the sample values

from a target value.

CUSUM chart is based on a statistic that includes information from

previous samples additionally to current measurements (Page, 1954). The

inclusion of several samples in the cumulative sum results in greater sensitivity

for detecting shifts or trends over the traditional Shewhart charts (Koshti, 2011).

CUSUM control chart is suitable to evaluate the weekly price difference for

gasoline type C, once all evaluated series have a stationary behavior (Section

3.1) and it is difficult to analyze persistent shifts in this series, as it is shown on


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the error bars of the Figure 5. The range between upper and lower error bar

limits of TS means are smaller than 0.003 in the distribution plot and 0.006 in

the retail plot.

Figure 5 - Distribution with bar error for prices variation of retail and distribution, between 2005

and 2014 in Brazil.

Source: author

There are two CUSUM set up parameters used to monitor the process

(BERSIMIS, 2001):

h: For one-sided CUSUM, h is the number of standard deviations between the

center line and the control limits.

k: The upper and lower CUSUMs essentially accumulate deviations from target

that exceed a slack value. k is typically set to be equal to half of the distance

from the target (µ0) and the shifted mean (µ1) that we want to detect (Eq. 2).

(2)

We used h = 4 and k = 0.5 to evaluate this series. In addition, we defined

the target as the historical mean for each city in the CUSUM Chart design

(Figure 6).


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In addition, we generated a Dotplot graph with every out of control point

found on CUSUM chart, divided by year (Figure7). Dot plot is used to evaluate

and compare distributions by plotting the values along a line and useful for

comparing distributions. The x − axis for a Dotplot is divided in small intervals.

Data values falling within each box are represented by dots. We can see many

dots plotted for all cities in two different periods: first, between the middle

of 2005 and the middle of 2006, and second, between first months of 2011 and

the middle of 2012.

According to Cadernos do CADE varejo de Gasolina (2014), Brazilian

market had some acts of concentration between fuel distributors between 2000

and 2012. Specifically, in 2006 we had a fusion between Ale e Satelite groups,

creating the Alesat. In 2012, Alesat acquire the Ello-Puma and Raízen acquire

the Mime group. It could be a starting point to understand the found behavior in

these series.

Figure 6- CUSUM Graph comparison between cities

Source: author


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Figure 7- Cumulative Sum Control Chart Analysis Comparative. Distribution CUSUM out of

control dotplot by year

Source: author

4. FINAL CONSIDERATIONS

In this paper the variation prices of gasoline type C were evaluated for

five economic regions in Brazil. It was followed a three steps methodology

where they were underlying behavior by using a set of techniques. We tested

the stationarity by using ADF (Dickey, 1979; Dickey, 1981) and linearity by

using a surrogate data analysis (Schreiber, 2000). Besides, it was applied

computational techniques such as Spearman correlation, regression dataset

analysis and CUMSUM analysis (Montgomery, 2001) in order to evaluate the

complexity of the datasets. Our finds reinforce the idea that of the Brazilian

downstream fuel chain, retail and distribution are governed by complexities

behavior. Thereby, conventional statistics could be not appropriate economics

data. In this way we suggest, mainly for new researcher, follow the steps of this

paper in order to conduct a research in complex field of science, as economics

issues among others complex disciplines.

Acknowledgements

This work has received financial support from National Petroleum Agency

(ANP/PRH55) - process number: 486100833602013.


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Edição especial - XX ENMC (Encontro Nacional de Modelagem Computacional) e VIII ECTM (Encontro de Ciência e Tecnologia dos Materiais), realizado entre 16 e 19 de outubro de 2017 na cidade de Nova Friburgo – RJ.

Editor – Mateus das Neves Gomes

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