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Eduardo de Rezende Francisco Francisco Aranha Felipe Zambaldi Rafael Goldszmidt FGV-EAESP Electricity Consumption as a Predictor of Household Income: an.

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Presentation on theme: "Eduardo de Rezende Francisco Francisco Aranha Felipe Zambaldi Rafael Goldszmidt FGV-EAESP Electricity Consumption as a Predictor of Household Income: an."— Presentation transcript:

1 Eduardo de Rezende Francisco Francisco Aranha Felipe Zambaldi Rafael Goldszmidt FGV-EAESP Electricity Consumption as a Predictor of Household Income: an Spatial Statistics approach November 21 th, 2006 Campos de Jordão, São Paulo, Brazil

2 2 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 Topics

3 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. CONCLUSION RESULTS METHODS INTRO (Social and) Economic Classification or Purchasing Power based on indicators Ownership of goods and the head of the familys 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

4 4 Brazilian Criterion Brazil ABA Criterion (1970), ABA-ABIPEME (1982), Almeida and Wickerhausers 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 Source: MATTAR, 1996; ABEP, 2004 CONCLUSION RESULTS METHODS INTRO

5 5 Brazilian Criterion Brazil ABA Criterion (1970), ABA-ABIPEME (1982), Almeida and Wickerhausers 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 dont 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 CONCLUSION RESULTS METHODS INTRO

6 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) Source: FRANCISCO, 2002; IBGE, 2003, 2005; ABRADEE, 2003 CONCLUSION RESULTS METHODS INTRO

7 7 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 spatial regression models income-predicting models (spatial regression models) Household Income & Electricity Consumption CONCLUSION RESULTS METHODS INTRO

8 8 Research Model and Postulation of Hypotheses Electric Energy Consumption Head of Familys Educational Level Brazilian Economic Status Posse de Bens H2 + Household Income Posse de Bens Ownership 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 H3 + H4 + H1 + CONCLUSION RESULTS METHODS INTRO

9 9 Demographic Census + Energy Consumption Analysis unit: Weighted Areas 303,669 sampled households (representing 3,032,095 ) 3,037,992 residential consumers of AES Eletropaulo Methodology São Paulo 96 Districts São Paulo Tracts São Paulo 456 Areas

10 10 Demographic Census + Energy Consumption Analysis unit: Weighted Areas Methodology AES Eletropaulo consumers DatabaseWeighted Areas (IBGE) Average INCOME per Weighted Area ENERGY CONSUMPTION per Consumer INCOME and ENERGY CONSUMPTION per Weighted Areas Geographic overlay and Spatial Junction Spatial Join

11 11 Demographic Census + Energy Consumption Analysis unit: Weighted Areas Geographic overlay and Spatial Junction Creation of Adjusted Brazilian Criteria based on Demographic Census 2000 n Household Income (Average) Electric Energy Consumption (Average) Brazilian Economic Status (Average) Analysis Methods 456 Continum (R$) Continum ( kWh) Continum Pearsons correlation, Linear Regression, Spatial Auto-correlation, Spatial Regression Methodology Brazilian Criterion Range: 0 to 34 points Adjusted Brazilian Criterion Range: 0 to 29 points

12 12 Similar behavior between various representatives of Household Income construct and Electric Energy Consumption construct High correlation and determination coefficient (R 2 ) between Household Income, Electric Energy Consumption and Brazilian Economic Criteria, it grows down for low income territories Results – Traditional Correlation and Regression y: Household Income (R$) x LUZ : Electric Energy Consumption (US$) Household Income (R$) Electric Energy Consumption (kWh) y: Household Income (R$) x CBA : Brazilian Economic Criteria Household Income (R$) Brazilian Economic Status METHODS INTRO CONCLUSION RESULTS Kolmogorov-Smirnov test of Normality: Kolmogorov-Smirnov test of Normality: Non-normality of the residuals observed predicted AdjustedR R AdjustedR R

13 13 Neighborhood Graphs For different neighborhood matrix analyzed, Morans 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 14 Data set : electric energy Spatial Weight : areaqueen1.GAL (Queen Graph) Dependent Variable : LNINCOME Number of Observations: 456 Mean dependent var : Number of Variables : 3 S.D. dependent var : Degrees of Freedom : 453 Lag coeff. (Rho) : R-squared : Log likelihood : Sq. Correlation : - Akaike info criterion : Sigma-square : Schwarz criterion : S.E of regression : Results – Spatial Statistics Spatial Auto-regressive Model METHODS INTRO CONCLUSION RESULTS Morans I = 0.07 (almost 0) 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

15 15 Electric Energy Consumption Brazilian Economic Status Household 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 Conclusions METHODS INTRO CONCLUSION RESULTS

16 16 Managerial Implications Households Census tracts Concentric circles (progressive radius of 125 m) 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.

17 17 Conclusions Energy consumption alone cannot substitute for the Brazilian Criteria Nevertheless, household income forecasts can be enhanced when the electricity bill and the number of residents are included in a regression model of household income against the Brazilian Criteria Among low income households, the level of association between income and electricity consumption was very weak 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 (coefficient of determination R 2 reached more than 0,90) Household Income & Electricity Consumption METHODS INTRO CONCLUSION RESULTS

18 18 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) Electric Energy Consumption Brazilian Economic Status Household Income Household Income & Electricity Consumption METHODS INTRO CONCLUSION RESULTS

19 19 Eduardo de Rezende Francisco, Francisco Aranha, Felipe Zambaldi, Rafael Goldszmidt FGV – EAESP November 21 th 2006, Campos de Jordão, SP, Brazil Electricity Consumption as a Predictor of Household Income: an Spatial Statistics approach Thank You !!!


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