Electricity Consumption as
a Predictor of Household Income:
an Spatial Statistics approach
Eduardo de Rezende Francisco
Francisco Aranha
Felipe Zambaldi
Rafael Goldszmidt
FGV-EAESP
November 21th , 2006
Campos de Jordão,
São Paulo, Brazil
Topics
Introduction
Income and Economic Classification
Brazilian Criterion of Economic Classification
Electricity Consumption
Objectives
Research Methodology
Adopted Model and Postulation of Hypotheses
Selected Databases and Methodology
Results
Conclusions
2
Income and Economic Classification
Income
Indicator usually adopted in studies of Poverty, Living
Conditions and Market
Difficulty in the collection of accurate data on such a
variable (BUSSAB; FERREIRA, 1999)
altered declaration, seasonal changes, refusal etc.
(Social and) Economic Classification or
Purchasing Power based on indicators
INTRO
METHODS
RESULTS
CONCLUSION
Ownership of goods and the head of the family’s
educational level
Supply of durable goods indicates the comfort level
achieved by the family throughout the lifetime
Social Status  Economic Status 
Social-Economic Status
Bottom of Pyramid X “D and E Classes”
3
Brazilian Criterion
Brazil
ABA Criterion (1970), ABA-ABIPEME (1982), Almeida and Wickerhauser’s
Proposal (1991)
CCEB – Brazilian Economic Classification Criterion
Created by ANEP in 1996 and supported by ABEP since 2004
Estimates purchasing power of urban people and families
Economic Classes from a point accumulation system
INTRO
METHODS
RESULTS
CONCLUSION
Source: MATTAR, 1996; ABEP, 2004
4
Brazilian Criterion
Brazil
ABA Criterion (1970), ABA-ABIPEME (1982), Almeida and Wickerhauser’s
Proposal (1991)
CCEB – Brazilian Economic Classification Criterion
Created by ANEP in 1996 and supported by ABEP since 2004
Estimates purchasing power of urban people and families
Economic Classes from a point accumulation system
Use of variables and indicators that don’t have stability
throughout the time and not well discriminate population strata
(PEREIRA, 2004)
It is not suitable for characterizing families which lie on
the extremes of the income distribution
(MATTAR, 1996; SILVA, 2004)
Deeper studies need specializations and adjustments
of Brazilian Criterion
INTRO
METHODS
Inclusion of high coverage and capillarity indicators or variables
with no need of constant update can be useful
RESULTS
CONCLUSION
5
Consumption of Electric Energy
Consumption of Electric Energy can be
a good indicator to better assist process of
characterize customers
Essential Utility
Wide-ranging and Coverage
97.0% of Brazilian households (99.6% in urban areas)
99.9% in São Paulo municipality
High Capillarity
Higher than other utilities (sewer & water, telecom, gas)
A to E Customers
Precision and History
Address, customer geographic location
Monthly collected
History of billing and collection (bad debt management)
Fulfill fundamental part in residential households’ day-by-day – high
influence in welfare of families
INTRO
METHODS
Better characterization of target families (in social-economic
terms and purchasing power)
RESULTS
CONCLUSION
Source: FRANCISCO, 2002; IBGE, 2003, 2005; ABRADEE, 2003
6
Household Income & Electricity Consumption
Analyze the relationship between
Residential Electricity Consumption and
Household Income in the city of São Paulo
OBJ:
Evaluate the potential benefits of:
Adding electricity consumption to the Brazilian Economic
Classification Criteria
Creating an electricity consumption criteria
Level of Investigation
Territorial – 456 Weighted Areas (set of census tracts) in São Paulo city
Demographic Census 2000 and Electric distribution company households database
Methodology
INTRO
income-predicting models (spatial regression models)
METHODS
RESULTS
CONCLUSION
7
Research Model and Postulation of Hypotheses
H4
Electric Energy
Consumption
+
H2
+
Household Income
+ H1
H3
+
Ownership
goods
Posse deofBens
Posse
de Bens
Posse
Possede
deBens
Bens
Head of Family’s
Educational Level
INTRO
Brazilian
Economic
Status
H1:
The higher the score in the Brazilian Criterion (Economic
Classification), the higher the Household Income, in the city of São Paulo
H2:
The higher the consumption of Electric Energy, the higher the
Household Income, in the city of São Paulo
H3:
There is a spatial dependence pattern of Household Income in the city of
São Paulo, with decreasing income in direction Center-Suburbs
H4:
There is a spatial dependence pattern of Electric Energy Consumption in
the city of São Paulo, with decreasing income in direction Center-Suburbs
METHODS
RESULTS
CONCLUSION
8
Methodology
Demographic Census + Energy Consumption
• Analysis unit: Weighted Areas
SãoSão
Paulo
Paulo
São
Paulo
13.278
Tracts
456
Areas
96 Districts
• 303,669 sampled households (representing 3,032,095)
• 3,037,992 residential consumers of AES Eletropaulo
9
Methodology
Demographic Census + Energy Consumption
• Analysis unit: Weighted Areas
• Geographic overlay and Spatial Junction
AES Eletropaulo consumers Database
Weighted Areas (IBGE)
ENERGY CONSUMPTION
per Consumer
Average INCOME
per Weighted Area
Spatial Join
INCOME and
ENERGY CONSUMPTION
per Weighted Areas
10
Methodology
Demographic Census + Energy Consumption
• Analysis unit: Weighted Areas
• Geographic overlay and Spatial Junction
• Creation of Adjusted Brazilian Criteria based on Demographic Census 2000
Brazilian Criterion
Adjusted Brazilian Criterion
Range: 0 to 34 points
Range: 0 to 29 points
n
456
Household
Income (Average)
Continum (R$)
Electric Energy
Consumption
(Average)
Continum ( kWh)
Brazilian Economic
Status (Average)
Analysis Methods
Continum
Pearson’s correlation,
Linear Regression,
Spatial Auto-correlation,
Spatial Regression
11
Results – Traditional Correlation and Regression
Similar behavior between various representatives of Household Income construct
and Electric Energy Consumption construct
High correlation and determination coefficient (R2) between Household
Income, Electric Energy Consumption and Brazilian Economic Criteria, it
grows down for low income territories
y: Household Income (R$)
xLUZ: Electric Energy Consumption (US$)
 1

x
yˆ  
  0 1 LUZ 
8600


1
 1

x
yˆ  
 0,01412 0,98665 LUZ 
 8600

2
yˆ   0  1 xCBA   2 xCBA
1
2
yˆ  7512,63  1357,36 xCBA  69,30 xCBA
10000
Income (R$)
Household
Renda
Média Domiciliar
(R$)
Income (R$)
Household
Renda
Média Domiciliar
(R$)
10000
5000
R2  0.910
R2 Adjusted  0.853
METHODS
RESULTS
CONCLUSION
R2  0.960
R2 Adjusted  0.960
5000
0
0
INTRO
observed
predicted
y: Household Income (R$)
xCBA: Brazilian Economic Criteria
0
100
200
300
400
500
600
700
800
Electric
Energy
Consumption
(kWh)
Consumo
de Energia
Elétrica (kWh)
Kolmogorov-Smirnov test of Normality: 0.129
5
10
15
20
Classe Econômica
Brazilian
EconomicBrasil
Status
Kolmogorov-Smirnov test of Normality: 0.171
Non-normality of the residuals
12
Neighborhood Graphs
For different neighborhood matrix analyzed, Moran’s I showed high values (0.78+)
It suggests high influence of neighborhood in Household Income behavior
LISA maps: Increase of income concentration in direction Suburbs-Center. The
same for Electricity consumption
13
Results – Spatial Statistics
Spatial Auto-regressive Model
Data set
Spatial Weight
Dependent Variable
Mean dependent var
S.D. dependent var
Lag coeff.
(Rho)
: electric energy
: areaqueen1.GAL (Queen Graph)
:
LNINCOME Number of Observations:
:
7.46738
Number of Variables
:
:
0.633242
Degrees of Freedom
:
:
0.607507
R-squared
Sq. Correlation
Sigma-square
S.E of regression
:
0.936675 Log likelihood
: 171.909
: Akaike info criterion : -337.818
:
0.0253932 Schwarz criterion
: -325.451
:
0.159352
456
3
453
Moran’s I = 0.07
(almost 0)
lag_residu
0,500000
0,000000
-0,500000
-1,000000
INTRO
METHODS
RESULTS
CONCLUSION
5,000000
6,000000
7,000000
8,000000
9,000000
10,000000
lag_predic
Use of Neperian Logarithms of dependent and independent variables
Residual error of this model assumed normal distribution pattern and
homoskedasticity - Absence of spatial dependence in residuals
14
Conclusions
Use of the mean household electricity consumption, at a
territorial aggregated level, is an excellent regional indicator of
income concentration in the city of São Paulo
INTRO
METHODS
RESULTS
CONCLUSION
Household Income
Brazilian
Economic
Status
Electric Energy
Consumption
15
Managerial Implications
Census tracts
Households
Concentric circles (progressive radius of 125 m)
As it is an easily available,
flexible and monthly updated
information, the electric energy
consumption indicators, when
published widely by energy
distribution companies, can be
useful for strategy formulation
and decision making which use
data of household income
classification, concentration
analysis and prediction.
Quadricules (1 square kilometer)
16
Household Income & Electricity Consumption
Next Steps (Future researchs)
Investigation of other statistical models
Geostatistics, Spatial Econometrics and Hierarchical methods (spatial regression)
To handle heterokedasticity and non-normality in some regression models
Household
Income
Brazilian
Economic
Status
Electric Energy
Consumption
Support for Low Income Microcredit Programs
Inclusion of Household electricity monthly bill in Discriminant analysis models
Replacement of declared Household Income by Mean electricity consumption of region
that locates household of “tomador de crédito”
INTRO
METHODS
RESULTS
CONCLUSION
Validation of territorial results with more updated data, when and if it
is available
Replication in other regions (inside and outside Brazil)
Comparative studies (Europe, Brazil & Latin America)
18
Thank You !!!
Electricity Consumption as a Predictor of Household Income:
an Spatial Statistics approach
Eduardo de Rezende Francisco, Francisco Aranha,
Felipe Zambaldi, Rafael Goldszmidt
FGV – EAESP
November 21th 2006 , Campos de Jordão, SP, Brazil
19