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Consumer Behavior and Public Policies: Empirical Evidence through VEC model on Brazil’s Automotive Industry

Área 7: Microeconomia e Economia Industrial

Gérson Guilherme Lima Linhares1

Eveline Barbosa Silva Carvalho2

Abstract

The study analyzes the effects on consumer behavior of public policies using empirical evidence on Brazil’s automotive industry. Policies adopted by the Brazilian government to encourage the acquisition of vehicles through tax reduction on industrialized products and bank credit were estimated using an error correction vector model. Simulations on sales change were performed and results showed relevant increase in two different periods. The model showed that the policies caused an increase of 23,2% and 16,8% on sales on the first and second periods analyzed with significant impacts to the market. The Tax on Industrialized Products reduction showed more convincing to make consumers buy vehicles than bank credit. Results lead to the conclusion that the direct impact on demand increase and the artificial incentive to producers may induce wrong management decisions in the long run.

Keywords: Tax on Industrialized Products, Automotive Industry, Error Correction Vector Model. JEL Classification : D03; L62; C32.

Resumo

O estudo analisa os efeitos políticas públicas sobre o comportamento do consumidor, por meio do uso de evidência empírica sobre a indústria automotiva do Brasil. As políticas adotadas pelo governo brasileiro para incentivar a aquisição de veículos por meio da redução do imposto sobre produtos industrializados e de crédito bancário foram estimadas usando um modelo de vetor de correção de erros. As simulações sobre mudança vendas foram realizadas e os resultados mostraram aumento relevante em dois períodos diferentes. O modelo mostrou que as políticas causaram um aumento de 23,2% e 16,8% nas vendas sobre,respectivamente, o primeiro e segundo períodos analisados com impactos significativos para o mercado. A redução do Imposto sobre Produtos Industrializados mostrou mais convincente para fazer os consumidores compram veículos de crédito bancário. Os resultados levam à conclusão de que o impacto direto no aumento da demanda e o incentivo artificial aos produtores pode induzir decisões de gestão erradas no longo prazo.

Palavras-chave: Imposto sobre Produtos Industrializados, Indústria Automobilística, Modelo Vetor de Correção de Erros. Classificação JEL: D03; L62; C32.

1 Master's student of economics at Centro de Aperfeiçoamento de Economistas do Nordeste - Universidade Federal do Ceará (CAEN-UFC) and scholarship holder of CNPQ - Conselho Nacional de Desenvolvimento Científico e Tecnológico. 2 Associate professor at Faculdade de Economia, Administração, Atuária, Contabilidade e Secretariado Executivo - Universidade Federal do Ceará (FEAAC-UFC).

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1. Introduction Public Policies are common around the world and have been present in Brazil for many years. There are many kinds of policies and governments generally apply them to benefit producers in response to pressure from interest groups (Becker, 1983).

The automotive industry is one of those groups that have been in Brazil for more than 50 years and represent such an important market that the share of industrial Gross Domestic Product (GDP) represents around 19% and the number of employees was over 150 thousand in 2012 according to ANFAVEA (2014).

Arguing recession and difficulties to pay salaries and maintain employees this industry lobby the government that as a response apply protectionist policies. In fact, in recent years, government intervened several times in the auto industry.

One form of intervention has been to reduce the tax rates on industrialized products (IPI) for locally manufactured vehicles, in order to stimulate sales internally and avoid the drop in the number of workers employed. Another form of intervention has been through the reduction of the tax on financial transactions (IOF) in loans to stimulate credit by banks to individuals wishing to purchase vehicles.

This study aims to analyze the impact of public policies using as a study case the reduction of the IPI and IOF on the sales of cars and light commercial vehicles produced in Brazil in two periods: between January 2009 and March 2010, which was the period of the first IPI reduction and between June 2012 and December 2012, which was the period of the second IPI reduction. We seek to find out the impacts of these two policies on the sales increase and to what extent this market intervention may impact the sector in the long run.

To achieve the objectives an Error Correction Vector Model (VECM) will be estimated, where the time series on sales, prices, credit and income are in logarithmic form, to facilitate the study of the elasticity of prices, credit and income to sales, and also to obtain long-term relationship between the variables. After that, simulations of sales behavior considering three scenarios will be made for the two referred periods. The first scenario, do not consider IPI reduction, which will be useful to compare the impacts with reduced IPI, the second scenario, considers IPI reduction together with 5% increase in credit grant, which will provide the impact of credit with reduced IPI and the third scenario do not consider IPI reduction but only a 5% increase in credit grant, which will show the impact of the credit without IPI reduction.

One of the justifications for testing the vehicles industry is the fact that despite being such an important market there are few empirical studies on the impacts policies on sales of vehicles.

The study aims to contribute to a better understanding of the quantitative impact of public policies, such as the IPI reduction, adopted by the Brazil´s federal government in recent years in order to boost domestic consumption. However the study does not focus on the consequences of policies in terms of tax revenues or how much it impacts the government in terms of budget. The relevance of this study is to contribute to understanding the consequences in terms of sales which affects consumer decision and company management in the long run.

Following this introduction, the paper is divided in six sections. Section two is devoted to the understanding of the study case to be tested, which includes the trajectory of Brazilian automotive industry and policies adopted by the Brazilian government on the sector. Section three presents a literature review on the theoretical framework and empirical studies. In section four, there is a description of methodology including the data with respective sources, as well as methodological procedures adopted to use a Vector Error Correction Model (VECM). Section five presents the model results and Simulations of vehicle sales and at last the final considerations.

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2. Understanding the Automotive Sector and the Policies adopted

This section is divided in two subsections and is devoted to the understanding of the policies adopted by the Brazilian government on the industry chosen do be tested. First, a brief review of the trajectory of Brazilian automotive industry is shown and then policies adopted by the Brazilian government in the automotive sector after the 2008 crises are discussed.

2.1 Trajectory of Brazil’s automotive industry Between 2004 and 2007 Brazil had an average annual growth of 13% in production and

sale of vehicles. However, on September 2008, while the total sales of motor vehicles exceeded in about 30% the sales of the same month in the previous year, the production grew by approximately 20%. (Alvarenga et al., 2010b, p.8).

According to Barros and Pedro (2011), the increase in sales was due to the average income growth of Brazilians, the upward mobility of part of the population to class C, which helped many of them buy the first car, reduction on unemployment levels and better credit access with lower interest rates and longer financing terms.

With the 2008 crisis, there was a sharp reduction in the production and sales of vehicles. In December 2008 vehicle production was 47.1% lower than in November of that same year (Poguetto, 2009).

Figure 1, shows that the number of cars and light commercial licensees, considered as a sales approach, had a large reduction on the second half of 2008 in comparison to the first half. In fact, while in July 2008, there were about 237,000 licensed vehicles in December of the same year only 153,000 vehicles were licensed, representing a reduction of approximately 35.4% in six months.

According to Alvarenga et al. (2010a, 2010b), companies in the automotive industry reacted to the crisis by reducing work shifts and granting collective holidays to workers.

In the first half of 2009, there was a recovery but the number of licensed cars and light commercial vehicles would only overcome the data of July 2008 in June 2009 when nearly 250,000 vehicles were licensed. And during 2010, despite the decline in the first two months, in March 2010 sales reached almost 280,000 units.

Between January 2009 and March 2010 about 208,000 vehicles were sold on average per month, 10% higher than the monthly average in the second half of 2008 (between July and December), when 189,000 vehicles were sold, on average.

In the year 2012, sales between January and May 2012 had a monthly average of a little more than 194 thousand units. However, when considering the period between June and December 2012, 268,000 vehicles were sold on average per month, representing a monthly average increase of 38%.

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Figure 1: Number of cars licensed between January 2006 and December 2012 Source: Prepared by authors using data from ANFAVEA (National Association of Automotive Manufacturers).

With respect to credit, there was a reduction in the volume of grants for the purchase of vehicles during the second half of 2008. While in July 2008 there was approximately R$4.75 billion in credit contracts, in December of the same year there was only approximately R$2.94 billion, representing a reduction of 38% in just six months. Starting in January 2009, there has been a resumption of credit concessions and in June 2009 the value of credit operations reached approximately R$ 5.39 billion, exceeding the month of July 2008 in almost 13.3%.

Credit concessions between January 2009 and March 2010 had a monthly average of R$ 5.574 billion, 56% higher than the monthly average of the second half of 2008, when the monthly average was only R$ 3.561 billion.

During January and May 2012 the loans were on average R$ 7.355 billion, while between June and December 2012, credit concessions averaged R$ 8.152 billion, meaning an increase of approximately 10.8% of the volume of concessions, between these two periods of the year 2012.

Figure 2. Concessions of loans for the purchase of vehicles (In thousands of reais)Source: Prepared by authors using data from Economic Department of the BCB (Central Bank of Brazil).

2.2 Policies adopted by the Brazilian government on the automotive sector Brazilian federal government, led by President Lula, adopted the policy of reducing

the tax on industrialized products (IPI) on new cars through the MP (provisional measure) no. 451/08 as of 12 December 2008, (BRASIL, 2008). This reduction, despite having as limit March 2009, was extended a few times and lasted until the end of March 2010. Tax on Financial Transactions (IOF) for credit to individuals was also reduced from 3% to 1.5% per year, which ended up increasing the credit facilities for the purchase of vehicles.

Alves and Wilbert (2014) reported that the purpose of the IPI reduction was to encourage domestic consumption after the global financial crisis of 2008. In addition, it had the objective of reducing the stock of domestic automakers, allow an increase workers' purchasing power and avoid layoffs in the auto industry.

The IPI reduction on vehicles had different levels according to the cubic centimeters(cc) and the used fuel. Vehicles up to a thousand cc (1.0) had the IPI reduced IPI from 7% to zero. Vehicles between one and two thousand cc and gasoline-powered had an IPI reduction from 13% to 6.5%, but flex vehicles between one and two thousand cc had and IPI reduction rate from 11% to 5.5%. For imported vehicles and cars of more than two thousand cubic

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centimeters, the rates had not changed. Commercial vehicles (light commercial) had a reduced IPI rate from 4% to 1%.

In May 2012, government led by Dilma Rousseff reduced again the IPI rates on national vehicles, and the IOF (financial operations tax) for credit to individuals. Including auto loans rates dropped from 2.5% to 1.5% per year, according to Decree No. 7726/12 (BRASIL, 2012). The argument used to adopt the policies was to stimulate economic activity to fight the worsening of the international financial crisis and to avoid layoffs in the auto industry.

Between May and December 2012, the IPI rates on vehicles nationally manufactured had the same reduction as the ones used before, on December 2008: from 7% to 0% (to 1.0 cc vehicles), from 11% to 5.5% (to flex vehicles of more than 1.0 cc and up to 2.0 cc), from 13% to 6.5% (gasoline-powered vehicles of more than 1.0 cc and up to 2.0 cc) and from 4% to 1% to commercial vehicles.

In 2013, the IPI rates on national vehicles were, according to Alves and Wilbert (2014), 2% for cars of 1.0 cc, 7% to flex vehicles of more than 1.0 cc and up to 2.0 cc, 8% to gasoline-powered vehicles of more than 1.0 cc and up to 2.0 cc and 2% for commercial vehicles.

3. Literature ReviewMarket interventions through public policies are a result of pressure from business

groups as stated in Becker (1983), Vieira and Gomes (2014) and Xavier, Bandeira-de-Melo and Marcon (2014) and the efficiency of such policies has been a question addressed by studies such as Bullock (2005).

In fact, public policies have been the objective of researches focusing the reasons and consequences of policies on markets. Shaffer (1995) shows business-government relations developed within a managerial framework to discusses the consequences of public policies for the competitive environment of the firm that determines firm-level responses which include both strategic adaptation and attempts to influence government.

Ball (1995), examines the social costs and benefits of interest groups with political influence. The study concludes that interest groups may not be that bad as the information those groups have may enable governments to choose better policies and this way lobbying can enhance welfare.

Lazzarini (2011), explaining Brazil’s capitalism dynamics, open the discussions for and against government intervention on markets and shows the reasons for the adoption of public policies based on the power of relationships among Brazilian enterprises owners and the government.

Public policies in fact tend to favor powerful groups that pressures government to apply policies arguing in general high costs or economic environment and others reasons that may lead to massive unemployment. This is the case of the vehicles market in the country.

According to Zimmermann, Egan and Poli (2011), Brazil’s strong domestic demand, along with the tax reduction policy made possible the country’s automobile sector to grow compared to other sectors.

The adoption of a policy of tax reduction will have different effects on consumers and producers. On consumers the tax reduction leads to two different effects, the income effect and the substitution effect (Varian, 2006). The income effect happens because the price of cars (considered here as a study case) relative to the price of other products became cheaper with the reduction of tax which represents a change in real income, and for that reason consumers may decide to buy a car instead other products using the opportunity faced which is the substitution effect.

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On the producers’ side, the tax reduction will change producers decisions of reducing the number of employees or closing the facture which probably were the main arguments used to make the government apply the policy of tax reduction and lower the credit rates. The response will be the sales increase which will have an economic impact (Lersh, 2004).

The impact of tax reduction on the vehicles industry was first measured by the Board of macroeconomic studies of the Applied Economic Research Institute (IPEA), that using a simple linear regression model between June 2003 and June 2009, considering the number of cars sold as a function of their prices, income and loans obtained that 13.4% of the cars sold were due to the reduced IPI (DIMAC, 2009).

Alvarenga et al. (2010a, 2010b) also analyzed the IPI reduction and the credit impacts on the sales of vehicles. An econometric time series model was used to analyze the co-integration of series seasonally adjusted of automobiles and light commercial, whole sale price index (IPA) for industrial products, income as measured by GDP at current prices and credit measured by the consolidated concessions of loans for purchase of vehicles. Prices, GDP and credit were deflated by the consumer price index (IPC). The study considered different scenarios: if there had been no IPI reduction in the IPI, if there had been IPI reduction and 5% increase in credit extensions, if there had been no IPI reduction and there was an increase of 5% on credit.

The authors found that 20,7% of the increase in sales of cars between January and November 2009 was due to the IPI reduction. The effect of credit was not negligible and increased with the IPI reduction.

Alves and Wilbert (2014) estimated linear regression model, considering the sale of cars a function of average income, available credit to individuals, a time trend variable and IPI as a dummy variable. The authors divided the regression into two periods: from January 2006 to March 2010 and from April 2010 to August 2013. The study found no significant impact of IPI reduction on sales in both periods in contradiction to the results obtained by Alvarenga et al. (2010a, 2010b) and DIMAC (2009).

4. MethodologyTo measure the impacts of a policy we tested two policies applied by the Brazilian

government on the vehicles sector. So, quantitative method is used to test IPI and IOF rate reduction effects on cars and light commercial vehicles produced in Brazil. This section is divided into two subsections. In the first subsection it is shown the model to be used. The second subsection presents the methodological procedures used to define the appropriate model for the variables of interest and used in the simulations.

4.1 Data and used modelThis study adopts a time series model in which the number of vehicles is a function of

price, income and credit concessions, s = f (p, i, c),where:a) s = Sales: car count and new licensees national light commercial (in units). This

variable is used as a proxy for wholesale sales in the domestic market. Source: National Association of Vehicle Manufacturers (ANFAVEA, 2012);

b) p =Price: Wholesale Price Index (IPA) origin - industrial products (motor vehicles, trailers, truck bodies and parts) - monthly) .Source: Getúlio Vargas Foundation (FGV,2015);

c) i= Income: Average nominal income of the main job, actually received in the reference month, for persons 10 years or older, employed during the reference week, by regions. Source: Brazilian Institute of Geography and Statistics (IBGE,2015).

d) c= Credit: consolidated concessions of loans with referential free resources for the purchase of goods vehicles (R$ million monthly). Source: Economic Department of the Brazilian Central Bank (Banco Central do Brasil,2014);

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e) IPC - general: consumer price index, which will be used to deflate the price series, income and credit. Source: Getúlio Vargas Foundation (FGV,2015).

The data is monthly and the period extending from June 2002 to December 2012. The option to deflate by the IPC the price, income and credit series is justified by developments in prices that is understood by the consumer. The statistical software EViews in version 5.0 was used and the series were considered in terms of the logarithm (natural), that is, LSALES, LPRICES, LCREDIT and LINCOME to facilitate the estimation of these variables as that Alvarenga et al. (2010a, 2010b) carried out in his study.

After the seasonal adjustment using the method developed by the US Census Bureau, X-12 ARIMA contained in EViews 5.0 and putting the series in natural logarithm, the series have the following specification: LSALES_SA, LPRICES_SA, LCREDIT_SA , LINCOME_SA.

4.2 Methodological proceduresAccording to Bueno (2011) and Enders (2004), a time series is called weakly stationary

or stationary if:a) |y t|

2<∞ , ∨t ϵ Z, that is, the second off-center point must be finite;b) ( y t )=μ , ∨ t ϵ Z, that is, the average should be constant over time;c)E( yt−μ)( yt− j−μ)=γ j,that is, the variance should be the same in all periods of time

and the autocovariance should depend solely on temporal distance between observations.One of the most popular tests for the presence or absence of unit root is the Augmented

Dickey-Fuller test (ADF). In this test, the null hypothesis is that there is a unit root and the process is not stationary. The alternative hypothesis is that there is no unit root and thus the process would not be stationary. So, the decision rule for the ADF test is:

a) p-value >α , do not reject H 0. There would be a unit root and the process would not be stationary;

b) p-value <α , rejects H 0. Therefore, there would be no unit root and the process would be stationary.

This investigation does not sought to determine the order of integration based on the ADF test considering the presence of structural breaks in the series due to the 2008 crisis. The justification is that the tests of Dickey and Fuller (DF), Dickey Fuller (ADF) and Phillips and Perron (PP) have low power, for "when there is a structural break tests [DF, ADF and PP] lead to biased results in not rejecting the null hypothesis of a unit root when in fact the series is stationary "(Margarido and Medeiros Junior, 2006, p.151). Thus, DF , ADF and PP tests have low power because there is a high probability of committing the error of type 2 (not reject H 0 when H 0 is false).

Here, we used only the KPSS test developed by Kwiatkowski, Phillips, Schmidt and Shin. The KPSS test reverses the null hypothesis and ADF test alternative and may, therefore, "distinguish the unit root series whose data are not conclusive enough" (Bueno, 2011, p.129).

The equation of KPSS test is described in Bueno (2011):y t=μ+δt+x t+μ t , where, x t=xt−1+v t e e t ≡ x t+u t.

The hypotheses of the KPSS test are:a)H 0:σ 2

v= 0, there is not a unit root and the process is stationary;b) H a:σ 2

v>0, there is a unit root and the process is not stationary.Still according to Bueno (2011) the KPSS test statistic is based on Lagrange multiplier

(LM) and formalized as follows:

KPSS= ∑t=1

T S t2

T 2 v̂2 ,

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where Stis the partial sum of the residues, T is the number of observations and v̂2 is the long-term variance. Since the decision rule is the following:

a) KPSS * < KPSSc (α) does not reject H 0. Therefore, there would be no unit root and the process would be stationary;

b) KPSS *> KPSSc (α) rejects H 0. There would be a unit root and the process would not be stationary.

In this study the series sales, prices, credit and income are all integrated of order 1, according to the KPSS test performed for the period between June 2002 and December 2012.

Because of the presence of series with a unit root, it is important to check for a long stationary run equilibrium relationship. According to Engle and Granger (1987) series that form a vector X t order (n x 1) are cointegrated of order (d,b) and called for X t ~ CI(d, b) if:

a) all X t elements are I (d), integrated order d; andb) there is a nonzero cointegration vector called for βsuch that:

μt=X t' β I (d−b ) , b>0

According to Bueno (2011), it can be stated that there is a long-term balance between two variables if X t ' B= 0, this is, if the vector establish a linear combination of the variables X t , it follows a common trend, without deviation. Short-term deviations are represented by ut. When two series are cointegrated, the residuals of the regression involving variables is stationary, that is, order 0 and there is a long-term relationship between sets.

Again, according to Bueno (2011), a template vector order(number of lags) Autoregressive , or VAR (ρ) can be represented by the structural formula:

AX t= B0+B1 X t−1+...+B ρ X t−ρ+B εt (1),

where Anxn (matrix of n rows and n columns) is the coefficient matrix that determine the contemporary constraints between the variables X t (endogenous variables) nx1 (n matrix rows and one column); B0nx1 (matrix of rows and one column) is a vector of constants;Bi nxn (matrix of n rows and n columns) is a coefficient matrix; B nxn (matrix of n rows and n columns) is a diagonal matrix of standard deviations ε t nx1 (n matrix of rows and one column) is a vector of error terms that are not correlated with each other contemporaneous or temporally, that is,ε t∼i . i . d .(0 ; In).

From (1) it can be pre-multiplied by the inverse of A, that is, by A−1, in order to obtain the reduced form:

X t=Φ0+∑i=1

p

Φ1 X t−i+e t (2),

Where Φi≡ A−1 Bi, i=0,1...p e B εt ≡ A et. On the other hand, a vector error correction model (VECM) would be a model to correct

a problem of VAR. This problem is the fact that the VAR model only considers differentiated variables I (0) or non-stationary variables. According to Bueno (2011), a VEC(ρ ) model can be written as follows3:

∆ X t=Φ X t−1+∑i=1

p−1

Λ i∆ X t−i+e t (3),

wherein Λi=− ∑j=1+ 1

ρ−1

Φ j , i=1,2 ,... , ρ−1 and Φ=αβ ', if the rank of Φis between 0 and

cointegrating vectors number (r).

3 For a more formal approach is recommended reading Bueno (2011) and Enders (2004).

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The terms α and β are, respectively, the adjustment cointegration matrix and matrix. While α is related to short-term adjustment, β is related to long-term relationship between the variables.

The VEC model has its name due to the fact that is explained by a short-term

component, ∑i=1

ρ−1

Λi ΛX t−i, and a long-term component, Φ X t−1, that, if there is cointegration,

shows long-term relationship between the variables.In this study, as the variables were I (1), it was performed a procedure described by

Johansen and Juselius (1990) to detect the presence or absence of cointegration, that is, the presence or absence of long-term relationship between the variables.

Before carrying out the Johansen and Juselius procedure, the number of lags in the VAR model was determined, which was obtained with the Akaike information criterion (AIC), Hannan Quinn (HQ) and Schwarz (SBC or BIC). This study sought to use the criterion of Schwarz, because AIC tends to overestimate the asymptotic order of the VAR.

For all criteria, the ideal lags number is two and that was that was the number of lags considered for cointegration and considered in the Johansen and Juselius (1990) procedure which is composed of two tests based on a restricted maximum likelihood estimation. The first is the trace test, where hypotheses are:

a)H 0: r = r*4

b)H a:r>r*.The logic of the trace test is that by ordering the eigenvalues, λ i, of Φ matrix in

decreasing order, is tested if there is 0 cointegration vectors against the alternative if there is more than 0 vectors. If not reject H 0, then there is no cointegration vector between the variables of X t vector and reject H 0, we test the existence of more than one vector of cointegration more to the point of not rejecting the null hypothesis that there is r * cointegration vectors.

The second test used is the maximum eigenvalue test, which also has an unconventional distribution, and the hypotheses are:

a)H 0: r=r*; b)H a: r=r*+1The logic of this test is similar to the trace test. First we test the null hypothesis of no

co-integration vector against the alternative that there is one vector. If H 0is not rejected then there is no cointegration vectors and can not use the VEC model. If you reject H 0, then continues the test until H 0is not rejected.The statistics of the maximum eigenvalue test is:

λmax(r , r+1)=−Tln (1− λ̂r+1) (5)Regarding the decision rule, it can be stated that in the trace test if λ trace (r )> λcr itical trace (r )

, then reject the null hypothesis and in the maximum eigenvalue test if λmax(r , r+1)> λcriticalmax(r ,r+1), then the null hypothesis should be rejected.

5. Model Results and Simulations PerformedThe normalized co-integration vector for the series of sales is shown in Table 1. As

highlighted by Margarido (2004) in the VEC model all variables are on the same side of the system, that is, there would be independent and dependent variables, normalizing one of the variables (LSALES_SA), the other would be deemed independent and therefore, their signals would be reversed. Thus, the interpretation of the signs of the coefficients is made of inverted manner.Table 14 Indicates the number of cointegration vectors.

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Co-integration vector normalized to LSALES_SALSALES_SA LPRICES_SA LCREDIT_SA LINCOME_SA1 2,08872 -0,493761 -3,662485.. (0,78131) (0,17208) (0,59686).. [ 2,67336] [-2,86936] [-6,13625]

Note: The statistics in brackets refer to the deviations of the estimated parameters and statistics in parentesis represent the values p. All variables were significant at 5%.Source: From the results obtained with the software EViews 5.0.

Therefore, the parameterized equation is:LSALES_SA= - 2,088728*LPRICES_SA + 0,493761*LCREDIT_SA +3,662485*LINCOME_SA

Since the variables are in logarithmic form on both sides of the equation, it is possible to interpret them as elasticity in relation to sales (vehicles demand).

Therefore, one can make the following findings: a) The increase of 1% in prices causes a reduction of approximately 2.09% in vehicle sales; b) The increase of 1% in credit leads to an increase of approximately 0.49% in vehicle sales; c) The increase of 1% in income leads to an increase of approximately 3.66% in vehicle sales.Thus, it is important to note that sales are very sensitive to changes in prices and

average income of workers employed and are less sensitive to changes in credit facilities in the analyzed period, that is, between June 2002 and December 2012. The income is the variable that most impacted sales throughout the period and this is probably due to the appreciation of the minimum wage policy promoted in Brazil in recent years.

The results seem to confirm the ones obtained by DIMAC (2009) for low elasticity of sales on credit. The difference of this work in relation to DIMAC (2009) is that the latter used Ordinary Least Squares (OLS) and a sample from June 2003 to June 2009, while the first used the Vector Error Correction Model (VECM).

Concerning the study by Alvarenga et al. (2010a, 2010b), income also showed greater impact in the long run. However there are some differences between this study and the ones performed by those authors regarding the data and methodology. The first is that the data here used cover a longer period (from June 2002 to December 2012). The second is in relation to income since they used GDP as a proxy for income and in this work was used the average nominal income of employed persons. The third is that here sales are inelastic to credit and for Alvarenga et al. (2010a, 2010b) sales are elastic to credit.

Vehicle sales (cars and light commercials) were simulated for two periods. The first period covers the first reduction of the IPI on national vehicles, between January 2009 and March 2010. The second period includes the second reduction of IPI on domestically produced vehicles, from June 2012 to December 2012. The sales performance was simulated for three hypothetical scenarios.

5.1 First Scenario: Impact of IPI reduction

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In the first scenario sales behavior was simulated5 given an increase of 5.85%6 in seasonally adjusted prices and demand. The impact of IPI reduction was first calculated for the period between January 2009 and March 2010.

While in months as November 2009, December 2009, January 2010 and February 2010, sales without IPI reduction approached the sales with IPI reduction in other months as March 2009, June 2009, September 2009 and March 2010 sales with reduction of the IPI were significantly higher. The justification is that on these months consumers anticipated purchase fearing a possible increase in rates.

During this period around 771,000 vehicles were sold, because of the IPI reduction representing about 23.2% of vehicle sales during the period. This clearly shows that the IPI reduction policy was very important for the resumption of sales after the 2008 crisis, confirming the hypothesis of this study as shown in Figure 3.

If only considered the sales between January and November 2009, nearly 620,000 vehicles have been sold because of the IPI reduction, an impact of approximately 18.6%, which is slightly lower than that found by Alvarenga et al. (2010a 2010b)7.

However considering the period between January and June 2009, 387,000 vehicles were sold due to the lower tax rates for cars and light trucks, representing approximately 11.7% of vehicle sales during this period. Therefore, the impact of IPI reduction found in this study was lower but close to what was found by other authors especially in the period between January and November 2009.

Figure 3: Sales with and without IPI reduction between January 2009 and March 2010 (in units)Source: Elaborated by the authors.

As for the period between June and December 2012, it was found that in the months of June, July, August and October the IPI reduction was much more significant than for the months of September, November and December 2012. Considering August 2012 and October 2012, the significant impact of these months can be explained by the fact that the IPI reduction on national vehicles would end in August and the government extended to October,

5 The simulations were done with the help of the econometric software EViews 5.0..6 This percentage was the same used in the Alvarenga et al. (2010a, 2010b). In the first scenario, Alvarenga et al. (2010a 2010b) mentioned that ANFAVEA calculations showed that 1 percentage point of IPI causes a variation of 0.8% to 0.9% in prices. The authors refereed an average reduction of 6.5 percentage points in the tax rates, meaning that the IPI reduction would lead to lower prices on 5,525% (= 6.5% x 0.85). Thus, a price of 100 without IPI reduction implies that by reducing the price would be 94.475. Already a price of 100 with IPI reduction would imply that without the IPI reduction the price would be 105, 85. Thus, they simulated an increase of 5.85% in prices from January 2009..7 The article Alvarenga et al. (2010a, 2010b) found an impact of 20.7% of sales with IPI reduction between January and November 2009.

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causing an anticipation of purchases by consumers. Because of the IPI reduction 300,000 vehicles were sold, representing almost 16.8% of sales this period, that is, the impact of IPI reduction is 16.8% for this period. Figure 4, shows the situation for the period between June and December 2012.

Figure 4: Sales with and without IPI reduction between June 2002 and December 2012 (in units)Source: Elaborated by the authors.

5.2 Second Scenario: Impact of credit with IPI reductionIn the second scenario, there is a 5% increase on the series of seasonally adjusted credit.

First, was simulated the period between January 2009 and March 2010 and was found that between January and July 2009 the credit impact was greatly reduced. The impact of credit was more significant from in August 2009 and from November 2009 until February 2010.This may be due to the fact that after October 2009 IPI rates were increased gradually while loans still held favorable because of the IOF reduction on credit for individuals.

Between January 2009 and March 2010, because of the credit about 504,000 vehicles were sold. This means, in percentage terms, that there was an increase of approximately 13.2% on sales. If only the period between January 2009 and November 2009, is considered over 236,000 vehicles were sold, corresponding to an increase of 6,2% on sales, which is a very close result to that found by Alvarenga et al. (2010 2010b)8, as shown in figure 5.

8 Alvarenga et al. (2010a, 2010b) found that simulating a 5% increase in credit, there would be an increase of 3.2% in the number of vehicles sold.

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Figure 5: Sales with IPI reduction and with and without 5% increase in credit between January 2009 and March 2010 ( in units)Source: Elaborated by the authors.

As for the period between June and December 2012 it was found that the impact of credit was greatly reduced as shown in figure 6. An explanation for the relatively insignificant impact of credit in August 2012 is that in this month the IPI reduction came to an end, causing significant increase in demand not because of the facilities to obtain cheap credit (reduction of IOF), but to seize the opportunity to anticipate the consumption before a price increase. Between September and December 2012 the impact would be significant.

In both periods it can be seen that the impact of credit with IPI reduction is smaller than the impact of reduced IPI. Thus, we can say that for a share of consumers, if only there was a reduction in the IPI there would be a greater willingness to purchase vehicles as the consumption decisions of these individuals were mainly based on the opportunity to purchase vehicles at a lower price.

June. 2012 July 2012 Aug. 2012 Sept. 2012 Oct. 2012 Nov. 2012 Dec. 20120

50000

100000

150000

200000

250000

300000

350000

Sales with IPI reduction and increased by 5% in credit Sales with IPI reduction Figure 6: Sales with IPI reduction and with and without 5% increase in credit between June 2012 and December 2012 (in units)Source: Elaborated by the authors.

5.3 Third Scenario: Impact of credit without IPI reduction The 3rd scenario considers an increase of 5% in the volume of credit and seasonally

adjusted prices of 5.85% and seeks to find out the impact of the credit if did not occur the IPI reduction.

First, the period between January 2009 and March 2010 was simulated. It was found that first three months presented the greatest impact. According to this scenario, the number of vehicles sold would have increased by about 258 thousand, and in percentage terms, the impact would have been of approximately 9.2%. However if only considered the period between January and November 2009, sales would have increased by close to 210,000 units, representing about 7.5% of sales that would have occurred in the period. Thus, the impact of credit without reducing the IPI would be of 7.5%9 for the period between January and November 2010. Figure 7, shows this situation.

9 Alvarenga et al. (2010a, 2010b) found that simulating a 5% increase in credit, there would be an increase of 3.2% in the number of vehicles sold.

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Figure 7: Sales without IPI reduction and with and without 5% increase in credit between January 2009 and March 2010 (in units)Source: Elaborated by the authors.

As for the period between June and December 2012, it is noticed that, the impact of credit increase without IPI reduction would have been greater in August, November and December 2012. Almost 106,000 more vehicles would have been sold, which would represent a 6.7% increase. Figure 8, shows this situation.

It is noticed that, as well as for the period between January 2009 and March 2010, for the period between June and December 2012, sales obtained were lower than the ones obtained by simulation of sales with reduction IPI and credit enhancement. This is different from the results obtained by Alvarenga et al. (2010a, 2010b), as in that study the impact of credit was increased without reducing the IPI. The differences may be due to the fact that this work performs simulations between January 2009 and March 2010, while the one by Alvarenga et al. (2010a, 2010b) the period used is smaller, between January 2009 and November 200910; and also because the income variable used in this study is the average income of employed persons and not GDP.

10 The article Alvarenga et al. (2010a, 2010b) found that 3.2% of sales that occurred with the IPI reduction are due to credit and that 8.3% of sales that would have occurred without the IPI reduction would be because credit.

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Figure 8: Sales without IPI reduction and with increase of 5% in credit and sales only without IPI reduction in the period between June 2012 and December 2012 (in units)Source: Elaborated by the authors.

It is noticed that, as well as for the period between January 2009 and March 2010, for the period between June and December 2012, sales obtained were lower than the ones obtained by simulation of sales with reduction IPI and credit enhancement. This is different from the results obtained by Alvarenga et al. (2010a, 2010b), as in that study the impact of credit was increased without reducing the IPI. The differences may be due to the fact that this work performs simulations between January 2009 and March 2010, while the one by Alvarenga et al. (2010a, 2010b) the period used is smaller, between January 2009 and November 2009; and also because the income variable used in this study is the average income of employed persons and not GDP.

Table 2 summarizes the results of the three scenarios simulated for the first period (between January 2009 and March 2010) and the second period (between June and December 2012).

Table 2: Results of impacts for IPI reduction, credit with IPI reduction and credit without IPI reduction for the two periods simulated

Percentages for the 1st period (between January 2009 and March 2010)

Percentages for the 2nd period (between June and December 2012)

1st scenario (Impact of IPI reduction) 23,2% 16,8%2nd scenario (Impact of credit with IPI reduction) 13,2% 11,2%3rd scenario (Impact of credit without IPI reduction) 9,2% 6,7%

Source: Elaborated by the authors from the simulations performed in this article.

6. Final Considerations

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This study analyzed the impacts of public policies on markets using as a study case two policies adopted on the national automotive market: reduction of rates on industrialized products (IPI) for nationally manufactured vehicles and reduction of tax on financial transactions (IOF) to stimulate credit to individuals wishing to purchase vehicles.

The investigation covered two periods: between January 2009 and March 2010, which was the first period of IPI reduction, when the world was facing the 2008 financial crisis, and between June and December 2012, which was the second period of IPI reduction.

An econometric model of Vector Error Correction (VEC) for sales series, prices, credit and income was used in logarithmic form and covered the period between June 2002 and December 2012. Results showed that an increase of 1% in prices causes a reduction of approximately 2.09% in vehicle sales; an increase of 1% in credit leads to an increase of approximately 0.49% in vehicle sales and that an increase of 1% in income leads to an increase of approximately 3.66% in vehicle sales.

Thus, sales are very sensitive to changes in prices and average income of workers and are less sensitive to changes in credit facilities in the analyzed period. The highest long-term impact of income on sales may be partially due to minimum wage appreciation policy promoted in Brazil in recent years but may be also due to the income effect of tax reduction policies.

Finally, sales behavior simulations were performed in order to obtain the impacts with reduced IPI only and using bank credit with and without IPI reduction.

From the results obtained from the simulations it can be said that the reduction of IPI was important for the recovery of vehicle sales in both periods. In fact, between January 2009 and March 2010, 23.2% of sales occurred because of the IPI reduction and between June and December 2012, 16.8% of sales were due to the IPI reduction.

Therefore, the IPI reduction was more important for the period immediately after the height of the financial crisis of 2008. Moreover, in 2012, unlike the period of the first IPI reduction of IPI, a significant portion of consumers already had new vehicles acquired because of the first IPI reduction. These consumers would be reluctant to purchase a new vehicle even with a further reduction of IPI, because they were still paying the installments of the financing of the first vehicle, which consumed important part of the monthly income.

In relation to credit with IPI reduction, it also showed a greater impact during the first period of IPI reduction. For this period the impact was 13.2% and in the second period the impact was 11.2%. However, considering the scenario without IPI reduction, the impact of the credit reached 9.2% in the first period and 6.7% in the second period.

Thus, this study showed that policies adopted by the Brazilian government, as the IPI reduction rate on vehicles and IOF reduction on credit extensions to individuals were effective to promote the increase of vehicle domestically produced sales.

So, it can be said that public policies cause effects on markets both on consumers and producers side and also to the government. For the government the tax reductions are in fact a given subsidy which impacts on tax revenues and traffic jam problems in urban cities to be solved. On the consumers side the tax reduction gives an incentive to buy new cars changing purchase possibilities as the tax reductions represents a lowering in prices. On the producers side the market interventions of tax reductions generate an increase on sales that lead to profit increase that may induce a change on production planning even in a bad economic condition as it may wrongly signalize to production increase. This will lead to more pressure on the government to maintain or adopt rate reductions to increase sales and keep employees leading to a vicious cycle.

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