Does Increasing Block Pricing Decrease

Energy Use?

Evidence from the Residential Electricity Market

Becka Brolinson

Georgetown University

November 4, 2019

Introduction

Introduction

RQs: Do IBPs decrease total electricity use relative to a flat price? Do

IBPs help low-income households?

In This Paper

Four tasks:

1. Estimate price elasticities of demand by income

2. Calculate a flat price that would raise the same revenue as the

current IBP

3. Compare aggregate energy use under the flat schedule and the IBP

schedule

4. Compare electricity bills by income under both pricing schedules

In This Paper

Four tasks:

1. Estimate price elasticities of demand by income

2. Calculate a flat price that would raise the same revenue as the

current IBP

3. Compare aggregate energy use under the flat schedule and the IBP

schedule

4. Compare electricity bills by income under both pricing schedules

In This Paper

Four tasks:

1. Estimate price elasticities of demand by income

2. Calculate a flat price that would raise the same revenue as the

current IBP

3. Compare aggregate energy use under the flat schedule and the IBP

schedule

4. Compare electricity bills by income under both pricing schedules

In This Paper

Four tasks:

1. Estimate price elasticities of demand by income

2. Calculate a flat price that would raise the same revenue as the

current IBP

3. Compare aggregate energy use under the flat schedule and the IBP

schedule

4. Compare electricity bills by income under both pricing schedules

The Data

Residential Appliance Saturation Study

• 11,745 single-family households in CA in 2003 and 2009

• Household characteristics

• Monthly electricity consumption, 191,851 household-month pairs

Historical rates for CA utilities

• Rates for 2 CA utilities from 2000-2009 PGE & SDGE Rates

The Data

Residential Appliance Saturation Study

• 11,745 single-family households in CA in 2003 and 2009

• Household characteristics

• Monthly electricity consumption, 191,851 household-month pairs

Historical rates for CA utilities

• Rates for 2 CA utilities from 2000-2009 PGE & SDGE Rates

Empirical Challenges

1. Simultaneity

2. Price is a function of quantity:

Identification Strategy

Two Sources of Variation:

1. CA climate zones

2. Changes in price

Two Steps:

1. Compare households across neighboring climate zone borders Maps

2. Simulated instrument for the change in price

Identification Strategy

Two Sources of Variation:

1. CA climate zones

2. Changes in price

Two Steps:

1. Compare households across neighboring climate zone borders Maps

2. Simulated instrument for the change in price

CA Climate Zones

Utility Induced Price Changes

Estimating Equation: 2SLS

Simulated Instrument:

∆ln(kWhit) = β0 + δ∆̂ln(Pit) +10∑j=1

Ditj + β1∆Xit + γi + ηit (1)

• ∆ln(kWhit) = ln(kWhit) − ln(kWhi0)

• ∆ln(P̃it) = ln(Pt(kWhit0 )︸ ︷︷ ︸Price in t ifkWht = kWh0

) − ln( P0(kWhit0 )︸ ︷︷ ︸Price in period 0

) First Stage

• Ditj - Dummy variable equal to 1 if energy use is in decile j , 0

otherwise

• Xit - Weather controls

• γi - border-zone fixed effect

• Similar instrument used Koichiro Ito (AER, 2014)

• Alternative Instruments: Middle Period Consumption & Mean Consumption

Price Elasticities

Income Group Avg Price Elasticity N

Full Sample -0.016 27,144

$0–$49,999 -0.100 7,801

$50,000–$74,999 -0.132 5,913

$75,000–$149,999 -0.165 9,695

>$150,000 -0.427 3,735

More Details: Zone by Zone kWh by Income Main Strategy: Method Comparison

Middle Period: Method Comparison Mean Consumption: Method Comparison

Price Elasticities

Income Group Avg Price Elasticity N

Full Sample -0.016 27,144

$0–$49,999 -0.100 7,801

$50,000–$74,999 -0.132 5,913

$75,000–$149,999 -0.165 9,695

>$150,000 -0.427 3,735

More Details: Zone by Zone kWh by Income Main Strategy: Method Comparison

Middle Period: Method Comparison Mean Consumption: Method Comparison

Finding the Flat Price

Holding R̄ constant:

R̄ =N∑i

(Di (p̄)) ∗ p̄

=N∑i

[Di (pi ) + (p̄ − pi )εiD(pi )

pi]p̄

(2)

• R̄ Revenue

• Di (p) Demand

• p̄ Flat Rate

• εi Price Elasticity for Income Group i

Example

Example

Example

Aggregate Demand

Year Average Marginal

2003 0.01% -3.43%

2009 0.86% -4.91%

Weighted Average 0.38% -4.12%

Aggregate Demand

Year Average Marginal

2003 0.01% -3.43%

2009 0.86% -4.91%

Weighted Average 0.38% -4.12%

Using Other Elasticities

Income and Electricity Use

Consumer Surplus

$

kWhD

CS

p̄

q̄i

Consumer Surplus

$

kWhD

p1

p2

qi

p̄

q̄i

∆ Use from flat to block

CB

A

Consumer Surplus

Income Group Change in CS ($) Pct. Change N

$0–$49,999 1.22 2.64% 39,540

$50,000–$74,999 4.83 7.48% 60,446

$75,000–$149,999 3.25 4.15% 63,343

>$150,000 -2.02 -1.98% 26,771

Conclusion

• First estimates of price elasticity of energy demand by income group

under average price response assumption

• Price elasticities increase with income from -0.100 to -0.427

• Demand increases by 0.38% for average price response

• Evidence IBP may not be meeting goal of decreasing aggregate

consumption while protecting low income households from increasing

energy prices

Historical Rates

Data

Geography

• Limit the sample to households who live in a zip-code whose

centroid is within 10 km to the nearest climate zone border

Geography

• Limit the sample to households who live in a zip-code whose

centroid is within 10 km to the nearest climate zone border

Geography

• Limit the sample to households who live in a zip-code whose

centroid is within 10 km to the nearest climate zone border

• Generate the same simulated instrument and run the main

estimating equation including border fixed effects

Geography

• Limit the sample to households who live in a zip-code whose

centroid is within 10 km to the nearest climate zone border

• Generate the same simulated instrument and run the main

estimating equation including border fixed effects

Income Group Avg Price Elasticity N

Full Sample -0.100 112835

$0-34,999 -0.043 21466

$35,000- 74,999 -0.0956 35205

$75,000-149,999 -0.0977 39697

$>150,000 -0.114 16467

Geography

• Limit the sample to households who live in a zip-code whose

centroid is within 10 km to the nearest climate zone border

• Generate the same simulated instrument and run the main

estimating equation including border fixed effects

Year Average Marginal

2003 0.17% -0.40%

2009 0.52% -0.69%

Weighted Average 0.33% -0.53%

Back to Empirical Strategy

First Stage Regression

∆ln(Pit) = π0 + π1∆ln(P̃it) + Djt + βXit + γi + τt + ηit (3)

• ∆ln(kWhit) = ln(kWhit) − ln(kWhi0)

• ∆ln(P̃it) = ln(Pt(kWhit0 )︸ ︷︷ ︸Price in t ifkWht = kWh0

) − ln( P0(kWhit0 )︸ ︷︷ ︸Price in period 0

)

First Stage Regression

∆ln(Pit) = π0 + π1∆ln(P̃it) + Djt + βXit + γi + τt + ηit (3)

Dependent Variable is: ∆ln(Pit)

T/X X/R R/S S/X

∆ln(P̃it) 0.836*** 0.681*** 0.507*** 0.749***

s.e. (0.024) (0.016) (0.020) (0.014)

Households 1,719 2,356 1,968 2,766

Observations 27,656 38,124 31,829 44,809

F-Stat 378.33 365.3 303.36 618.13

R-Squared 0.8517 0.7973 0.7742 0.8271

Standard errors in parentheses *p<0.05, **p<0.01,

***p<0.001, s.e. clustered at the household level

Back to Main Strategy

Avg. Price Response Elasticities

Dependent Variable is: ∆ln(kWhit)

Climate Border: T/X X/R R/S S/X

Full Sample -0.144*** -0.248*** -0.300*** -0.203***

(0.0415) (0.0256) (0.0402) (0.0233)

$0-34,999 0.120 -0.110 0.0824 0.0388

(0.0680) (0.103) (0.103) (0.0610)

$35,000- 74,999 -0.265*** -0.253*** -0.315*** -0.218***

(0.0629) (0.0379) (0.0543) (0.0367)

$75,000-149,999 -0.0378 -0.157*** -0.278*** -0.164***

(0.0850) (0.0338) (0.0628) (0.0358)

$>150,000 -0.184** -0.312*** -0.0885 -0.247***

(0.0560) (0.0511) (0.239) (0.0551)

Standard errors in parentheses *p<0.05, **p<0.01,

***p<0.001, standard errors clustered at hh-level

Elasticities

Method Comparison

Income First Diff Sim. Inst. Sim. Inst.

+ Match

Sim Inst +

Geo

Full Sample -0.101*** -0.133*** -0.201 -0.100***

(0.00749) (0.0105) (0.0127)

$0-34,999 -0.0183 -0.0291 0.006 -0.0430

(0.0234) (0.0379) (0.0517)

$35,000- 74,999 -0.101*** -0.135*** -0.230 -0.0956***

(0.0126) (0.0179) (0.0218)

$75,000-149,999 -0.100*** -0.124*** -0.144 -0.0977***

(0.0126) (0.0161) (0.0208)

$>150,000 -0.106*** -0.123*** -0.205 -0.114***

(0.0161) (0.0198) (0.0242)

Standard errors in parentheses *p<0.05, **p<0.01, ***p<0.001,

s.e. clustered at the household leve

Elasticities

Other Elasticities

Brolinson R&W Ito

Year Average Marginal Average Marginal Average Marginal

2003 -0.28% -0.77% 3.25% -4.74% 0.51% -1.19%

2009 0.23% -1.29% 8.14% -5.05% 1.07% -1.50%

Avg. -0.05% -1.01% 5.51% -4.89% 0.77% -1.33%

Back to My Results

kWh Distribution by Income

Elasticities

Appendix A: Ito Simulated Instrument

Simulated Instrument:

∆ln(kWhit) = α0 + δ∆ln(P̃it) + Djt + βXit + γi + τt + εit (4)

• ∆ln(kWhit) = ln(kWhit) − ln(kWhit−12)

• ∆ln(P̃it) = ln(Pt(kWhit−6)︸ ︷︷ ︸Price in t if

kWht = kWht−6

) − ln( P0(kWhit−6)︸ ︷︷ ︸Price in period 0with use kWhit−6

)

• Djt - Dummy variable equal to 1 if energy use is in decile j , 0

otherwise

Back to Main Strategy

Appendix A: Ito Simulated Instrument Results

Income First Difference Sim. Inst. Sim. Inst.

+ Matching

Sim. Inst.

+ Geo

Full Sample 0.829*** -0.209*** -0.302 -0.151*

(0.0685) (0.0563) (0.0714)

$0-34,999 -0.161 -0.228 -0.371 0.00950

(0.331) (0.253) (0.278)

$35,000- 74,999 0.814*** -0.126 -0.228 -0.182

(0.121) (0.115) (0.158)

$75,000-149,999 0.981*** -0.106 -0.189 -0.0668

(0.0909) (0.0915) (0.114)

$>150,000 0.970*** -0.289* -0.241 -0.0831

(0.154) (0.146) (0.166)

Standard errors in parentheses *p<0.05, **p<0.01, ***p<0.001,

standard errors clustered at the household level to adjust for serial

correlation Elasticities

Appendix B: Mean Cons. Simulated Instrument

Simulated Instrument:

∆ln(kWhit) = α0 + δ∆ln(P̃it) + Dtj + βXit + γi + τt + εit (5)

• ∆ln(kWhit) = ln(kWhit) − ln(kWhi0)

• ∆ln(P̃it) = ln( Pt(kWh)︸ ︷︷ ︸Price in t ifkWht = kWh

) − ln( P0(kWh)︸ ︷︷ ︸Price in period 0

with use kWh

)

• Djt - Dummy variable equal to 1 if energy use is in decile j , 0

otherwise

Back to Main Strategy

Appendix B: Mean Cons. Simulated Instrument Results

Income First Difference Sim. Inst. Sim. Inst.

+ Matching

Sim. Inst.

+ Geo.

Full Sample 0.829*** -0.442*** -0.522 -0.336***

(0.0685) (0.0394) (0.0568)

$0-34,999 -0.161 -1.142*** -1.371 -1.036**

(0.331) (0.246) (0.378)

$35,000- 74,999 0.814*** -0.486*** -0.558 -0.306**

(0.121) (0.0700) (0.112)

$75,000-149,999 0.981*** -0.399*** -0.458 -0.263**

(0.0909) (0.0605) (0.0827)

$>150,000 0.970*** -0.441*** -0.512 -0.425***

(0.154) (0.0825) (0.110)

Standard errors in parentheses *p<0.05, **p<0.01, ***p<0.001,

standard errors clustered at the household level to adjust for serial

correlation Elasticities