Towards a Greener California: An Analysis of Household Variation in Residential Electricity Purchases

Transcription

1 Towards a Greener California: An Analysis of Household Variation in Residential Electricity Purchases Dora L. Costa and Matthew E. Kahn UCLA and NBER

2 Introduction Interesting Time Series and cross-sectional facts: Between 1970 and 2007 residential electricity sales per housing unit increased by 60 percent for the nation but by only 24 percent for California = Rosenfeld Curve In our California region, residential electricity consumption ratio of the (90 th percentile/10 th Percentile) equals 7 in every year from 2000 until 2009 We seek to explain both facts using unique microdata

3 Data Contribution 1. Household/monthly electricity billing data credit bureau data detailing the home s physical attributes and demographic and economic attributes of household 3. Block data on Local Ideology and Prius 4. Data on renovations of homes 5. Data on solar panel installation 6. Data on all home sales in the county Average electricity Price --- current and birth year

4 Empirical Framework Monthly Electricity consumption depends on your; A. Choice of Home, B. Choice of Appliances and home investments, C. Utilization Implicit home production approach Suppose that you only use electricity for your dishwasher Monthly Electricity consumed = f*loads Where f = electricity consumed per load (durable type) Price per load = f*p (p = price per kwh) We recognize that the implicit price per unit of home production depends on choice of durable

5 Framework Continued Households consume electricity for cooling, heating, Kitchen, lights, computer We will not be able to disaggregate electricity consumption into each of these pieces Reduced form approach that aggregates across each of these categories

6 Key OLS Regression Cross-Sectional: ln( kwh) = β + β ( INCOME) β ( HOUSECHAR) 3 β ( TEMP) β ( DEMOG) + + β ( LIBERAL) + + β ( YEARBUILT) + ε β7*ln(price), no credible IV strategy for ln(price) Use Reiss and White s (2005) estimate of -.39 for β7 and re-estimate using the transformed dependent variable, ln(kwh)+0.39*ln(price) 6 4 2

7 Modifications of Cross-Sectional Regression Add information on durables for subset of data ln( kwh) = β + β ( INCOME) + β ( DEMOG) + β ( HOUSECHAR) 3 β ( TEMP) 5 β ( DURABLES) β ( YEARBUILT) ε + β ( LIBERAL) + 4 2

8 Cross-Sectional 2000 IPUMS All of California What effect on current electricity purchases of price in utility district when house built ( birth price)? What happens to year dummies? ln( Elect) = β + β ln("birth" Price) + β ( YearBuilt) + β ( HouseChar) + β ( SESandDemog) + 3 β (PUMA FE) + ε

9 Panel Regressions Sept 2000-Dec 2008 ln( kwh) = β + β (Temp) β (Renovation) 3 β (Price) ε + β (Household FE) β (Media 4 Campaign) + Run separate regressions for renovations, media campaign, and price

10 Hypotheses Tested Using Cross- Sectional Regressions 1. California s residential building codes have reduced electricity consumption 2. Richer households and households who live in larger homes consume more electricity 3. Liberal/environmentalists consume less electricity 4. Liberal/environmentalists have newer durables in their homes 5. Electricity prices in the year the home was built are negatively correlated with current home electricity consumption Each are directly relevant for the Rosenfeld Puzzle

11 Year Built Coefficients, Table 4 Coefficient Year Coefficient on Year Built Confidence Interval

12 Income and Liberal, Table 4 Income elasticity: (0.003) Block hybrid share: (0.441) Block fraction liberal: (0.175) Block faction liberal x temperature: (0.003) Green energy purchaser: (0.004)

13 Controlling for Appliances (Table 5) Liberals purchase less electricity because they have newer appliances and fewer refrigerators New appliances could account for building code patterns only after 2003

14 Birth Year Price and Building Dummies, Table 7 Birth year price: (0.038) Birth year prices can explain some of our building year dummy effects Once control for birth year price: Shrinks pre 1980 birth year dummies by 25-30%

15 Hypotheses Tested Using Panel Regressions (Household Fixed Effects) 1. Do home renovations reduce electricity consumption? 2. Average monthly electricity prices have a small effect on consumption (less price variation than other CA utility areas) 3. Liberal/environmentalists are less likely to increase their electricity consumption when the temperature is higher 4. Are Public Conservation Campaigns effective tools for encouraging conservation? Home aging effects do not matter (thus we can focus on birth year and calendar year effects)

16 Most Renovations Increase Electricity Usage (Table 9): Rebound Effect for HVAC Coefficient Std. Err. Dummy=1 if added Square footage 0.014*** New roof *** New windows New kitchen 0.017*** New HVAC *** New water heater (Average temperature) x (New HVAC) 0.003*** 0.000

17 Prices, Liberals, and Media Campaigns Price: (0.081) Table 9: Temperature x Fraction liberal: (0.001) 12-13% decline during initial phase of campaign (Feb Oct 2001) but no lasting effect

18 Do energy efficiency investments make a home appreciate in value? Table 10 Use hedonic methods where we merge sales transaction data with our electricity consumption data by street address Using the universe of all regional home price sales from , we examine (in Table 10) whether low energy homes sell for a price premium We reject this hypothesis for usage but electric homes sell for lower price More Sexy! We turn to solar panel capitalization 7% (=$24,000) but small sample of solar homes Why the difference? Visible? Expected future tenure in the home

19 Understanding the Time Series Trend: A Decomposition Approach Using our regression coefficients in Table 4 we ask: If in 2008, a random home owner had the 1970 level of income, liberalism, home size, and housing stock age, how would electricity consumption be changed? β2008*(x2008 X1970)

20 β2008*(x2008 X1970) if 1970 head of household age and number of persons, household would use an extra 2.64 kwh per day if 1970 square footage, household would use 2.52 kwh less per day If 1970 income, household would use 0.11 kwh less per day If 1970 housing stock age, household would use an extra 0.82 kwh per day If 1970 fraction liberal, household would use an extra 0.57 kwh per day

21 Accounting for Rosenfeld Curve 4275 kwh difference between California and rest of nation in 2007 Smaller CA homes: 12% of difference Building codes: 7% of difference Not demographics: California household head younger and lives with more family members Liberal: can t tell Variable not comparable across states

22 Conclusion We have used 7 different data sets to offer a consistent explanation for both time series facts and crosssectional facts about California s residential electricity consumption. Time series hinges on aggregation of a heterogeneous set of homes and households; The housing stock is durable capital --- new building codes only gradually affect the average home s electricity consumption. New results from the cross-section --- role of ideology and electricity prices in the birth year