Methodology

49 Social Externalities of Fuels

This study aims to analyze socio-economic indicators for the sugarcane, ethanol, oil drilling and oil deriva- tives sectors with respect to job and income creation, as well as regional development. Various indicators are developed to analysis the social benefits of the different kinds of fuels. Taken together, these can guide a comparison between sugarcane ethanol and fossil fuel production in the following aspects:

i Job creation: presenting the evolution of indicators for the labor market, such as the number of workers, educational level, and age;

ii Location of production: identifying the main producing regions and corresponding municipal districts in order to compare the capacity for creating jobs, income and regional development;

iii Estimating the importance of sugarcane and ethanol production in the producing regions, by calculating the location quotient;

iv Measuring and comparing the impact of increased demand for hydrous ethanol, substituting demand for Type C gasoline, on the level of jobs and total income in the Brazilian economy.

fuels, either during the production of raw materials, or during industrial processes, we use both data from PNAD (state level) and RAIS (municipal level).

The state and regional analysis based on the PNAD, especially regarding agricultural activities, demonstrates certain characteristics that should be taken into consideration when interpreting the results, for example:

i The IBGE (2006) considers to be a resident a person who may be temporarily absent from his/her place of dwelling on the date of the interview, for a period not greater than 12 months, by virtue of the nature of his work or in order to be closer to his workplace. This means that the member of a family living in any state in the Northeast region who is temporarily working in another state, in agricultural activities that use this type of migratory labor, will be counted in the Northeast state.

ii Data for the North region includes the agricultural economically active population (EAP) from Tocantins state, but only urban residents in the states of Rondônia, Acre, Amazonas, Roraima, Pará and Amapá (the former North region).

The data for the number of jobs offered by the Input-Output Matrix (IOM) differs from other bases, such as the PNAD and the RAIS, given that the data in the IOM is grouped by sector. This creates the need to estimate the jobs in the missing sectors. The great advantage of this database for the current analysis is that it shows the impact of a demand shock on a specific sector; not only on the number of jobs and level of income in the sector, but also on the other sectors linked to it. This linkage occurs both in sectors connected to the supply chain and in sectors where the demand comes from agents of the supply chain. This gives us an important analytical tool for analyzing the impacts on jobs and income in the Brazilian economy.

2.2Earnings equation

We used information from the PNAD for 2002 through 2007 to analyze the evolution of wages, worker qualification and the number of people employed in the sugarcane, ethanol, petroleum production and pe- troleum derivatives production sectors. To allow for a comparison between income in different years, these will all be expressed in Reais of March 2009, adjusted using the National Consumer Price Index (INCP)^iv. The formula for earnings, with the expansion factor associated with each individual in the sample, is ad- justed by the method of weighted least squares.

In this analysis, we adopt as the dependent variable (Y) the natural logarithm of the principal income of those employed. Under these conditions, the general regression model used is:

Y

= α + Σ β

X

+ u

where α ^and β_i are parameters and u_j is a heteroscedastic random error representing the effect of all the variables that were not considered in the model, obeying the usual statistic properties. The model will be estimated using data from PNAD 2007.

51 Social externalities of fuels

The following explanatory variables are considered:

a A binary variable for gender (S), that assumes the value of 1 for females and 0 for males.

b The person’s age (I) measured in decades.

c The squared variable age chart (I2), considering the fact that income does not vary linearly with age.

If the parameters for age and age squared are indicated by θ1 ^and θ2, respectively, we should have θ₁> 0 ^and θ₂ < 0 and the expected value for Y (and of income) will be greatest when the individual’s age corresponds to – θ₁ /(2 θ₂)

d The educational level (E) of the individual, considering the relation between schooling and wages as a polygon function, to capture the increase in rate of return of education above a certain level. Hence, in models were the existence of a threshold effect is considered, in addition to the E, variable, the variable E=Z_j(E_j–δ) is included, in which δ is the vertex abscissa, in other words, it is the education level in which the return rate becomes greater, and Z_j is a binary variable such that

Z_j =0 para E_j ≤δ e Z_j =1 para E_j >δ

e The logarithm for the number of hours worked per week. The coefficient of this variable will be the elas- ticity of income in relation to the weekly work time.

f A variable will be used to distinguish formally registered employees (the base) and undocumented em- ployees.

g Two binaries to distinguish the person’s color (C): white (the base), black or mulatto, and yellow.

h One binary to distinguish the role of the individual in the family (F): reference person versus a base cat- egory that includes all the remaining conditions (spouse, child, other relative, unofficial member, pensioner, domestic employee, relative of domestic employee).

i Five binaries to distinguish the regions (R): North, Northeast (base), South, Southeast not including São Paulo, Midwest and the state of São Paulo.

j One binary variable to distinguish household situation (D): urban (base) and rural.

k Binaries will be introduced to distinguish the different sectors of activity (SA): sugarcane (base), ethanol production, oil production and petroleum derivatives production.

2.3Formula for the location quotient (QL)

To analyze the relative importance of the sugar-ethanol and petrochemical sectors in the various producing regions, we propose using the location quotient (QL). This identifies the existence of specialization/agglom- eration in the productive activity of the state or region. To this end we use 2000 and 2008 employment data from the RAIS referring to the number of jobs created and the number of establishments. The data on jobs created is used to calculate the location quotients (QL). Information in the RAIS can be viewed by municipality, which permits an analysis of how the various activities are spread throughout the economy.

The formula proposed by the IEDI (2002) to calculate the location quotient is:

Using the RAIS database it is possible to verify the level of specialization of the sectors disaggregated to the five-digit level in the municipalities analyzed. According to IEDI (2002), a QL ≥ 1 is interpreted as special- ization in the activity in the region in question. In this section, the Brazilian state to which the municipality belongs is considered as a region.

The following steps are taken to calculate the QL. First, we identify the states with the greatest levels of em- ployment in the production of sugarcane, ethanol, petroleum and petroleum derivatives. For these states, we identify the municipalities in which the relevant activities were present in 2008, and then calculate the QL of each municipality to verify the existence of activity specialization. Next, the QLs are distributed into bands to identify the existence of specialization.

The location quotient has a lower limit equal to zero when there is no activity in the analyzed region. When the activity is present but there is no specialization the QL value lies between zero and one; if there is any specializa- tion the number is greater than one. However, when a QL is greater than one, then the larger the absolute num- ber, the greater the degree of specialization. This study therefore adopted directly comparable value ranges, so that the included municipalities have similar specialization levels. Values between one and five were considered as low specialization; greater than five and less than 10, moderate specialization; with greater than 10 indicat- ing high levels of specialization. For the 15 municipalities in each state that generate most jobs in the sectors under study we show indicators for the number of employees, QL and the average age of the workers.

2.4 Impact on the Brazilian economy: analysis of the input-output matrix

The inter-relation between the sectors producing ethanol and Type C gasoline and the rest of the Brazilian economy generates impacts on the labor market. The input-output matrix analysis of the Brazilian economy can indicate these impacts through the multiplier effects of the activities.

QL_ij= E_ij

E_j● E●●

E●_j

where: the location quotient of sector i in region j E_ij = jobs in sector i in region j

E_j●= ΣE_ij = jobs in sector i in all regions E●_j = ΣE_ij = jobs in all sectors in region j E●●= ΣΣE_ij = jobs in all sectors in all regions

i j j i

53 Social Externalities of Fuels

This study required a greater level of detail of Brazilian economic sectors, highlighting ethanol and gasoline.

Ethanol is one of the sectors used by the IBGE (“Ethanol” sector), but gasoline is included in the “Oil and coke refining” sector. Given the heterogeneity of the products in this sector (in addition to gasoline, they include mineral coal, non-metallic minerals, liquefied petroleum gas, fuel oil, diesel oil and other products), it was necessary to single out gasoline for analysis.

Also, in order to more precisely identify the impacts on the economy, Brazil was divided into the State of São Paulo and the remaining regions . The State of São Paulo was analyzed separately because it accounts for approximately half of the country’s production of ethanol and is therefore the place where the great- est impacts resulting from the substitution of fuel use are to be expected. An inter-regional grid for 2004 constructed by Guilhoto (2009) was used to analyze the data.

The initial simulation was conducted for the “Ethanol” sector, based on a scenario where gasoline is substi- tuted by hydrous ethanol. The increase in demand was calculated equivalent to increases of 5%, 10% and 15% in the volume of hydrous ethanol consumed in each Brazilian state. Using the ratio that establishes the efficiency (in km driven) between the consumption of hydrous ethanol and Type C gasoline equal to 0.70 (UNICA, 2009), we identified the equivalent volume of gasoline saved (not consumed) due to the increase in ethanol volume during the first impact. In other words, multiplying the ethanol volume in each state by the ethanol consumption coefficient in each state.

When the impact is analyzed in terms of value rather than volume, the volume of both products was mul- tiplied by their respective prices^vii. Because the values being used are from 2004, when these fuel prices were subject to different taxes in each Brazilian state, the prices used were the ones for each product in each state, for 2004^viii.

An alternative simulation was carried out to show the impact on jobs and income of substituting gasoline for ethanol – in other words, the impact of an increase in demand for Type C gasoline in detriment to hy- drous ethanol. This simulation looked at substituting 1% of ethanol with Type C gasoline, which would be the highest substitution possible, given the demand for ethanol in the states.

Using the inter-regional input-output matrix described above, we calculated the multipliers that evaluate the impact of a variation in final demand on the economic variables of interest: the number of jobs cre- ated and the value of income. To obtain these results, one must initially calculate the multipliers for the production that is direct, indirect, or induced by family consumption. The multipliers for direct and indirect production determine how much the sectors being analyzed as well as the sectors indirectly affected by it will have to produce in order to satisfy an additional unit of final demand. This multiplier considers fam- ily consumption to be exogenous. On the other hand the multiplier that recognizes the effect induced by family consumption, also known as income effect, takes into consideration the increase of consumption in the economy, due to the growth in family incomes caused by the direct and indirect effect mentioned earlier. Methodologically, this impact is identified by making household consumption endogenous to the input-output matrix.

Using the described multipliers and the employment and income coefficients in the economic sectors, one can calculate the direct, indirect, and induced (income effect) impacts on employment and income levels in the country resulting from the increase in hydrous ethanol demand in place of Type C gasoline. If the net result is positive for the economy, then the substitution of ethanol for gasoline generates more jobs and a greater increase in total income than would occur without it.

The next section describes the results, grouped according to the database and methodologies used: analy- sis of the evolution of socio-economic indicators and of the estimated earnings equation, using the PNAD;

calculation of the location quotients and an analysis of how widespread jobs are distributed, using the RAIS; and finally, estimates of jobs and income created considering the three scenarios analyzed for growth in ethanol demand substituting Type C gasoline.