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Eduardo de Rezende Francisco Francisco Aranha Felipe Zambaldi Rafael Goldszmidt FGV-EAESP

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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 21 th , 2006 Campos de Jordão, São Paulo, Brazil. Topics. Introduction

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slide1

Electricity Consumption asa 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

slide2

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

slide3

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
    • 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”

INTRO

METHODS

RESULTS

CONCLUSION

brazilian criterion
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

brazilian criterion1
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
  • Inclusion of high coverage and capillarity indicators or variables with no need of constant update can be useful

INTRO

METHODS

RESULTS

CONCLUSION

consumption of electric energy
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
  • Better characterization of target families (in social-economic terms and purchasing power)

INTRO

METHODS

RESULTS

CONCLUSION

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

slide7

Household Income & Electricity Consumption

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 Investigation
    • Territorial – 456 Weighted Areas (set of census tracts) in São Paulo city
      • Demographic Census 2000 and Electric distribution company households database
  • Methodology
    • income-predicting models (spatial regression models)

INTRO

METHODS

RESULTS

CONCLUSION

research model and postulation of hypotheses
Research Model and Postulation of Hypotheses

Electric Energy Consumption

Household Income

H2

H4

H3

+

+

+

+

H1

Ownership of goods

Posse de Bens

Posse de Bens

Posse de Bens

Posse de Bens

BrazilianEconomicStatus

Head of Family’sEducational Level

  • 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

INTRO

METHODS

RESULTS

CONCLUSION

methodology

São Paulo96 Districts

Methodology
  • Demographic Census + Energy Consumption
  • Analysis unit: Weighted Areas
  • 303,669 sampled households (representing 3,032,095)
  • 3,037,992 residential consumers of AES Eletropaulo

São Paulo13.278 Tracts

São Paulo456 Areas

methodology1
Methodology
  • Demographic Census + Energy Consumption
  • Analysis unit: Weighted Areas
  • Geographic overlay and Spatial Junction

AES Eletropaulo consumers Database

Weighted Areas (IBGE)

ENERGY CONSUMPTIONper Consumer

Average INCOMEper Weighted Area

Spatial Join

INCOME andENERGY CONSUMPTIONper Weighted Areas

methodology2

Brazilian Criterion

Adjusted Brazilian Criterion

Range: 0 to 34 points

Range: 0 to 29 points

Methodology
  • Demographic Census + Energy Consumption
  • Analysis unit: Weighted Areas
  • Geographic overlay and Spatial Junction
  • Creation of Adjusted Brazilian Criteria based on Demographic Census 2000
slide12

observed

predicted

=

2

R

0

.

960

=

2

R

Adjusted

0

.

960

=

2

R

0

.

910

=

2

R

Adjusted

0

.

853

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$)

y: Household Income (R$)

xCBA: Brazilian Economic Criteria

Household Income (R$)

Household Income (R$)

INTRO

Electric Energy Consumption (kWh)

Brazilian Economic Status

METHODS

Kolmogorov-Smirnov test of Normality: 0.129

Kolmogorov-Smirnov test of Normality: 0.171

RESULTS

Non-normality of the residuals

CONCLUSION

slide13

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
results spatial statistics
Results –Spatial Statistics

Spatial Auto-regressive Model

Data set : electric energy

Spatial Weight : areaqueen1.GAL (Queen Graph)

Dependent Variable : LNINCOME Number of Observations: 456

Mean dependent var : 7.46738 Number of Variables : 3

S.D. dependent var : 0.633242 Degrees of Freedom : 453

Lag 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

Moran’s I = 0.07(almost 0)

INTRO

METHODS

  • 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

RESULTS

CONCLUSION

slide15

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

BrazilianEconomicStatus

Household Income

Electric Energy Consumption

RESULTS

CONCLUSION

slide16

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)

slide17

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
  • 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)

BrazilianEconomicStatus

Household Income

Electric Energy Consumption

INTRO

METHODS

RESULTS

CONCLUSION

slide18

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

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