Eduardo de Rezende FranciscoEduardo de Rezende FranciscoFrancisco AranhaFrancisco AranhaFelipe ZambaldiFelipe ZambaldiRafael GoldszmidtRafael Goldszmidt

FGV-EAESPFGV-EAESP

Electricity Consumption asElectricity Consumption asa Predictor of Household Income:a Predictor of Household Income:

an Spatial Statistics approachan Spatial Statistics approach

Electricity Consumption asElectricity Consumption asa Predictor of Household Income:a Predictor of Household Income:

an Spatial Statistics approachan Spatial Statistics approach

November 21th , 2006Campos de Jordão,

São Paulo, Brazil

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

TopicsTopics

Income and Economic Classification 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.

CONCLUSION

RESULTS

METHODS

INTRO

(Social and) Economic Classification or Purchasing Power based on indicators

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”

Brazilian CriterionBrazilian Criterion

BrazilABA Criterion (1970), ABA-ABIPEME (1982), Almeida and Wickerhauser’s Proposal (1991)

CCEB – Brazilian Economic Classification CriterionCreated by ANEP in 1996 and supported by ABEP since 2004Estimates purchasing power of urban people and familiesEconomic Classes from a point accumulation system

Source: MATTAR, 1996; ABEP, 2004Source: MATTAR, 1996; ABEP, 2004

CONCLUSION

RESULTS

METHODS

INTRO

Brazilian CriterionBrazilian Criterion

BrazilABA Criterion (1970), ABA-ABIPEME (1982), Almeida and Wickerhauser’s Proposal (1991)

CCEB – Brazilian Economic Classification CriterionCreated by ANEP in 1996 and supported by ABEP since 2004Estimates purchasing power of urban people and familiesEconomic 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

Inclusion of high coverage and capillarity indicators or variables with no need of constant update can be usefulCONCLUSION

RESULTS

METHODS

INTRO

Consumption of Electric EnergyConsumption 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

Better characterization of target families (in social-economic terms and purchasing power)

Source: FRANCISCO, 2002; IBGE, 2003, 2005; ABRADEE, 2003Source: FRANCISCO, 2002; IBGE, 2003, 2005; ABRADEE, 2003

CONCLUSION

RESULTS

METHODS

INTRO

OBJ: Analyze the relationship between Residential Electricity Consumption and Household Income in the city of São Paulo

Evaluate the potential benefits of: Adding electricity consumption to the Brazilian Economic Classification Criteria Creating an electricity consumption criteria

Level of InvestigationTerritorial – 456 Weighted Areas (set of census tracts) in São Paulo city

Demographic Census 2000 and Electric distribution company households database

Methodologyincome-predicting models (spatial regression modelsspatial regression models)

Household Income & Electricity ConsumptionHousehold Income & Electricity Consumption

CONCLUSION

RESULTS

METHODS

INTRO

Research Model and Postulation of Research Model and Postulation of HypothesesHypotheses

Electric Energy Consumption

Head of Family’sEducational Level

BrazilianEconomic

Status

Posse de Bens

H2H2

++Household Income

Posse de BensPosse de BensPosse de BensOwnership of goods

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

H3H3

++

H4H4

++H1H1++

CONCLUSION

RESULTS

METHODS

INTRO

Demographic Census + Energy Consumption

• Analysis unit: Weighted Areas• 303,669 sampled households (representing

3,032,095)

• 3,037,992 residential consumers of AES Eletropaulo

Demographic Census + Energy Consumption

• Analysis unit: Weighted Areas• 303,669 sampled households (representing

3,032,095)

• 3,037,992 residential consumers of AES Eletropaulo

MethodologyMethodology

São Paulo96 Districts

São Paulo96 Districts

São Paulo13.278 Tracts

São Paulo13.278 TractsSão Paulo

456 Areas

São Paulo456 Areas

Demographic Census + Energy Consumption

• Analysis unit: Weighted Areas

Demographic Census + Energy Consumption

• Analysis unit: Weighted Areas

MethodologyMethodology

AES Eletropaulo consumers DatabaseAES Eletropaulo consumers Database

Weighted Areas (IBGE)Weighted Areas (IBGE)

Average INCOMEper Weighted Area

Average INCOMEper Weighted Area

ENERGY CONSUMPTIONper Consumer

ENERGY CONSUMPTIONper Consumer

INCOME andENERGY CONSUMPTION

per Weighted Areas

• Geographic overlay and Spatial Junction• Geographic overlay and Spatial Junction

Spatial JoinSpatial Join

Demographic Census + Energy Consumption

• Analysis unit: Weighted Areas

Demographic Census + Energy Consumption

• Analysis unit: Weighted Areas

• Geographic overlay and Spatial Junction• Geographic overlay and Spatial Junction

• Creation of Adjusted Brazilian Criteria based on Demographic Census 2000• Creation of Adjusted Brazilian Criteria based on Demographic Census 2000

nHousehold

Income (Average)

Electric Energy Consumption

(Average)

Brazilian Economic Status (Average)

Analysis Methods

456 Continum (R$) Continum ( kWh) Continum

Pearson’s correlation,Linear Regression,

Spatial Auto-correlation,Spatial Regression

MethodologyMethodology

Brazilian CriterionBrazilian CriterionBrazilian CriterionBrazilian Criterion

Range: 0 to 34 pointsRange: 0 to 34 points

Adjusted Brazilian CriterionAdjusted Brazilian CriterionAdjusted Brazilian CriterionAdjusted Brazilian Criterion

Range: 0 to 29 pointsRange: 0 to 29 points

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

Results – Traditional Correlation and RegressionResults – Traditional Correlation and Regression

1

1

10

98665,001412,08600

1ˆ

8600

1ˆ

LUZ

LUZ

x

x

y

y

y: Household Income (R$)xLUZ: Electric Energy Consumption (US$)

y: Household Income (R$)xLUZ: Electric Energy Consumption (US$)

0

5000

10000

0 100 200 300 400 500 600 700 800

Consumo de Energia Elétrica (kWh)

Ren

da M

édia

Dom

icil

iar

(R$)

Hou

seho

ld I

ncom

e (R

$)

Electric Energy Consumption (kWh)

y: Household Income (R$)xCBA: Brazilian Economic Criteria

y: Household Income (R$)xCBA: Brazilian Economic Criteria

0

5000

10000

5 10 15 20

Classe Econômica Brasil

Ren

da M

édia

Dom

icil

iar

(R$)

Hou

seho

ld I

ncom

e (R

$)

2

2210

30,6936,135763,7512ˆ

ˆ

CBACBA

CBACBA

xxy

xxy

Brazilian Economic StatusMETHODS

INTRO

CONCLUSION

RESULTS Kolmogorov-Smirnov test of Normality: 0.129Kolmogorov-Smirnov test of Normality: 0.129

Kolmogorov-Smirnov test of Normality: 0.171Kolmogorov-Smirnov test of Normality: 0.171

Non-normality of the residuals

observedpredicted

observedpredicted

853.0

910.02

2

AdjustedR

R

960.0

960.02

2

AdjustedR

R

Neighborhood GraphsNeighborhood 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

Data set : electric energy Spatial Weight : areaqueen1.GAL (Queen Graph)Dependent Variable : LNINCOME Number of Observations: 456Mean dependent var : 7.46738 Number of Variables : 3S.D. dependent var : 0.633242 Degrees of Freedom : 453Lag coeff. (Rho) : 0.607507 R-squared : 0.936675 Log likelihood : 171.909 Sq. Correlation : - Akaike info criterion : -337.818 Sigma-square : 0.0253932 Schwarz criterion : -325.451 S.E of regression : 0.159352

Results –Results – Spatial StatisticsSpatial Statistics

Spatial Auto-regressive Model

METHODS

INTRO

CONCLUSION

RESULTS

10,0000009,0000008,0000007,0000006,0000005,000000

lag_predic

0,500000

0,000000

-0,500000

-1,000000

lag_r

esid

u

Moran’s I = 0.07(almost 0)

Moran’s I = 0.07(almost 0)

Use of Neperian Logarithms of dependent and independent variablesResidual error of this model assumed normal distribution pattern and homoskedasticity - Absence of spatial dependence in residuals

Electric Energy Consumption

BrazilianEconomic

StatusHousehold Income

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

ConclusionsConclusions

METHODS

INTRO

CONCLUSION

RESULTS

Managerial ImplicationsManagerial Implications

HouseholdsHouseholds

Census tractsCensus tracts

Concentric circles (progressive radius of 125 m)Concentric circles (progressive radius of 125 m)

Quadricules (1 square kilometer)Quadricules (1 square kilometer)

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.

Next Steps (Future researchs)

Investigation of other statistical modelsGeostatistics, Spatial Econometrics and Hierarchical methods (spatial regression)

To handle heterokedasticity and non-normality in some regression models

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”

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)

Electric Energy Consumption

BrazilianEconomic

Status

Household Income

Household Income & Electricity ConsumptionHousehold Income & Electricity Consumption

METHODS

INTRO

CONCLUSION

RESULTS

Eduardo de Rezende Francisco, Francisco Aranha,Eduardo de Rezende Francisco, Francisco Aranha,Felipe Zambaldi, Rafael GoldszmidtFelipe Zambaldi, Rafael GoldszmidtFGV – EAESP

November 21th 2006 , Campos de Jordão, SP, Brazil

Electricity Consumption as a Predictor of Household Income:an Spatial Statistics approach

Thank You !!!