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

1. 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

2. 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

3. 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

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 INTRO METHODS RESULTS CONCLUSION Source: MATTAR, 1996; ABEP, 2004

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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)

17. 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

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