an econometric assessment of electricity demand … econometric assessment of electricity demand in...

9 June 2015

An Econometric Assessment of Electricity Demand in the United States Using Panel Data and the Impact of Retail Competition on Prices

By Dr. Agustin J. Ros

This paper was originally

presented at the Rutgers

University Center for

Research in Regulated

Industries, 34th Annual

Eastern Conference.

Introduction

Since the early 1970s electricity demand in the United States has been growing at an average

annual rate of approximately 2%. In that period there have been major developments in the

electricity sector including significant technological changes in generation services and the

development of wholesale and retail competition. In this paper I use panel data covering 72

electricity distribution companies in the United States during the period from 1972–2009 to

econometrically estimate structural demand equations separately for residential, commercial,

and industrial customers and to examine the impact that retail competition has had on

electricity prices.

I find the own-price elasticity of demand for residential, commercial, and industrial consumers

that are generally consistent with the published economics literature, ranging between

-0.382 and -0.613 for residential demand, -0.747 for commercial demand, and ranging

between -0.522 and -0.868 for industrial demand. Regarding retail electricity competition,

I econometrically examine the impact of the restructuring of the retail electricity sector in

the US from the mid-1990s. Since this period and up to 2009, a total of 21 states permitted

retail customers (some states permitting only large industrial customers and some states also

permitting smaller commercial and residential customers) to select their electricity generation

supplier (retail competition) from a firm other than the incumbent electricity distribution

company. As of 2009, 17 and 15 states still permitted retail competition for large and

smaller customers, respectively. I estimate reduced-form static and dynamic price equations

controlling for demand and supply factors, and include a binary variable for those states and

time periods where retail competition was permitted. I test the null hypothesis that retail

competition had no statistically significant impact on real electricity prices. I find that retail

electricity competition is associated with lower electricity prices for each customer class with

the magnitude of the impact being greater for the larger customer classes.

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Literature Review

There is a large economic literature on electricity demand in the US and other countries. Using

data on US residential electricity demand for 1949–1993, Silk and Joutz (1997) find that a

1% increase in electricity prices reduces electricity consumption by -0.62%. With respect to

disposable income, they find a 1% increase in income leads to a 0.82% increase in electricity

consumption. Using data on residential demand for electricity in the US, Dergiades and

Tsoulfidis (2008) find short- and long-run price elasticities of demand to be -0.386% and

-1.065%, respectively. With regard to income, they find short- and long-run income elasticities

of demand to be 0.101% and 0.273%, respectively. Paul, Myers, and Palmer (2009) find

US short-run price elasticities of demand ranging between -0.04 and -0.32 (depending on

customer class and region of the country), and long-run price elasticities of demand ranging

between -0.02 and -1.15 (depending on customer class and region of the country). In the same

paper, they summarize the results from previous studies, which I summarize below in Table 1.

Table 1. Summary of Own-Price Elasticities of Demand from the Literature

Customer Class Reference Short Run Long Run

Residential

Bohi and Zimmerman (1984) (consensus) -0.2 -0.7

Dahl and Roman (2004) -0.23 -0.43

Supawat (2000) -0.21 -0.98

Espey and Espey (2004) -0.35 -0.85

Bernstein and Griffin (2005) -0.24 -0.32

CommercialBohi and Zimmerman (1984) 0 -0.26

Bernstein and Griffin (2005) -0.21 -0.97

Industrial

Bohi and Zimmerman (1984) -0.11 -3.26

Dahl and Roman (2004) -0.14 -0.56

Taylor (1977) -0.22 -1.63

All Dahl and Roman (2004) -0.14 -0.32

Source: Paul, Myers and Palmer (2009), Table 5

Finally, using data from the Korean service sector, Lim and Lim (2014) find the short-

and long-run price elasticities of electricity demand to be -0.421 and -1.002, respectively,

and find short- and long-run income elasticities of electricity demand to be 0.855 and

1.090, respectively.

With regard to the impact of electricity competition, a number of studies are focused on the

wholesale sector but few studies focus on the retail sector. Regarding the former, Kleit and

Terrell (2001) find that by eliminating technical inefficiencies, gas-fired generation plant could

reduce costs by up to 13%. Fabrizio et al. (2007) found evidence of reduced fuel and nonfuel

expenses in fossil-fueled plants in states that restructured their wholesale markets. Zhang

(2007) finds that, in states that have restructured nuclear-fueled plants, utilization is higher

and operating costs are lower.

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Regarding the impact of retail competition, a recent paper by Su (2014) uses a difference-in-

difference approach to estimate the policy impact for US states that restructured their electricity

retail markets. Su finds that “only residential customers have benefitted from significantly

lower prices but not commercial or industrial customers. Furthermore, this benefit is transitory

and disappears in the long run.” Swadley and Yücel (2011) find that retail competition makes

the market more efficient by lowering the markup of retail prices over wholesale costs, and it

generally appears to lower prices in states with higher customer participation rates in retail choice.

Data

I use several different data sources for my study: (I) FERC Form 1 data that contains information

on residential, commercial, and industrial revenues and sales volume; (II) data from the bureau

of labor statistics on price indices used for deflating prices and other relevant variables; and

(III) inputs and results from a total factor productivity study containing information on cost

indices, and total factor productivity for the 72 US electricity firms that I use in this study.1

In Table 2 below, I provide a description of the variables and data sources used for this study

using average revenue per unit as a proxy for price.2

Table 2. Description of Variables

Variable Name Description Data Source

Lntfp Natural log of index of total factor productivity TFP Study

ln_rpres Natural log of deflated residential revenue per unit sales volume

using US census region cpi and urban consumer as deflator

FERC Form 1, TFP Study, and author’s calculations

ln_rpcom Natural log of deflated commercial revenue per unit sales volume

using US census region cpi and urban consumer as deflator


ln_rpindus Natural log of deflated industrial revenue per unit sales volume using

US census region cpi and urban consumer as deflator


ln_qres Natural log of residential sales volume FERC Form 1, TFP Study, and author’s calculations

ln_qcom Natural log of commercial sales volume FERC Form 1, TFP Study, and author’s calculations

ln_qindus Natural log of industrial sales volume FERC Form 1, TFP Study, and author’s calculations

labprcindex Real labor cost index FERC Form 1 and TFP Study

caprcindex Real capital cost index FERC Form 1 and TFP Study

t_hdd State heating degree day index National Oceanic and Atmospheric Administration

t_cdd State cooling degree day index National Oceanic and Atmospheric Administration

ln_pop Natural log of state population Bureau of Economic Analysis

ln_income Natural log of real state personal income using US census region

cpi and urban consumer as deflator

Bureau of Economic Analysis

ln_price_natural gas Natural log of real state natural gas price index using US census

region cpi and urban consumer as deflator

US Energy Information Administration

compl Indicator variable 1 if competition for large industrial

customers is permitted

Author’s construct

comps Indicator variable 1 if competition

for residential and commercial customers is permitted

Author’s construct

ratecap Indicator variable 1 if state that permitted competition had a rate

cap for residential and commercial customers

Swadley and Yücel (2011) Table 1

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In Table 3 below, I present summary statistics of the variables. Mean values over the period

for deflated residential, commercial, and industry prices were ¢10.02 kWh, ¢8.91 kWh, and

¢6.16 kWh, respectively. And over the time period, there was a downward trend in deflated

prices for all customer classes beginning in the late 1980s and lasting through the early 2000s.

Approximately 14% and 13% of observations of the data reflect retail competition for large and

residential and commercial customers, respectively.

Table 3. Summary Statistics

Variable Obs Mean Std. Dev. Min Max

lntfp 2736 .5056051 .3717154 -.8342119 1.662097

unem 2448 6.16156 2.008683 2.3 15.6

ln_pop 2736 15.59572 .9018602 13.04594 17.42538

ln_rpres 2736 -2.300279 .2935026 -3.328425 -1.22254

ln_rpcom 2736 -2.417624 .3220173 -3.37734 -1.371787

ln_rpindus 2697 -2.786458 .3775694 -4.643488 -1.363757

ln_qres 2736 15.19656 .9528475 12.62801 17.82536

ln_qcom 2736 15.03261 1.052372 11.754 17.66853

ln_qindus 2736 15.02367 1.051302 11.01396 17.18522

labprcindex 2736 .8858641 .2957938 .2484632 3.048455

caprcindex 2736 1.148668 .5397295 .2104876 11.42236

t_hdd 2736 5162.852 2064.818 430 10810

t_cdd 2736 1100.366 862.2685 74 3827

ln_income 2736 10.1229 .2125993 9.519778 10.71989

ln_price_natural gas 2367 2.091865 .1812828 1.358819 2.655835

Compl 2736 .1425439 .349671 0 1

Comps 2736 .1312135 .3376954 0 1

Rate cap 2736 .0811404 .2731004 0 1

Econometric Models

Econometric estimation of US electricity demand

I estimate demand equations for US electricity distribution services for a 72-company panel

sample from 1972 to 2009 to determine the price elasticity of demand, as well as the effects of

other factors. I estimate demand equations separately for residential, commercial, and industrial

electricity demand. In these demand equations, output (sales volume) is the left-hand side

dependent variable. I am measuring how electricity output changes when other variables, such

as price and income, change. The basic model is of the form:

(1) yit = Yit γ + Xitβ + μi + υit

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Where yit is the dependent variable (electricity consumption), Yit is a 1 x g2 vector of

observations on g2 endogenous variables included as covariates (in my demand models, g2 =1

since I assume that price is the only endogenous variables), and these variables are allowed

to be correlated with the υit , Xit is a 1 x k1 vector of observations on the exogenous variables

included as covariates (such as income, population, price of natural gas, and heating and

cooling degree days), γ is a g2 x 1 vector of coefficients, β is a k1 x 1 vector of coefficients,

μi is the individual-level effect (i.e., the unobservable company-level effects), and υit is the

disturbance term.

I estimate demand models for each type of customer using four different estimators: fixed-

effects, random-effects, first-difference, and the Arellano-Bond estimator for dynamic models.

The fixed-effect estimator fits the model after sweeping out the μi by removing the panel-level

means from each variable. The random-effects estimator treats the μi as random variables that

are independent and identically distributed (i.i.d.) over the panels. The first-difference estimator

removes the μi by fitting the model in first differences. The Arellano-Bond model is a linear

dynamic panel-data model that includes p lags of the dependent variables as covariates and

contains unobserved panel-level effects, fixed or random.3

I expect price to be endogenous, and for instruments I use data from the TFP Study of US

distribution companies. Specifically, I use a deflated labor price index and a deflated capital

price index. These two variables reflect changes in input costs for the 72 distribution companies

over the period and are thus good candidates for instruments. In addition to price, I expect

electricity demand to be positively related to population, real income, the state heating degree

day index, the state cooling degree day index, and the deflated price of natural gas.4 To capture

the possibility of changing demand preferences over time, I include three decade

binary variables.

Residential demand models

The first three models are static demand models, while the Arellano-Bond model is a

dynamic demand model. Table 4 contains the results of my static and dynamic residential

demand equations. Since the left-hand side dependent variable is the log of residential demand,

the coefficient on residential prices is the price elasticity of demand. The fixed-effects and

random-effects estimators provide very similar results for the price elasticity of demand and for

all the variables in the model. The price elasticity of demand using the fixed-effects estimator

is estimated to be -0.440 and statistically significant (t statistic = -3.65). The price elasticity

of demand using the random-effects estimator is estimated to be -0.430 and statistically

significant (t statistic = -3.57). When I use the first-difference estimator, the price elasticity

of demand increases to -0.613 (t statistic = -3.23). Finally, when I use the Arellano-Bond

dynamic estimator, the price elasticity of demand is estimated to be -0.382, and is estimated

very precisely.5 The econometric evidence, therefore, supports a price elasticity of demand

for residential customers ranging between -0.382 and -0.613.6 These estimates are within the

range found in the economic literature (see Section 2 above).

With respect to the income elasticity of demand, the fixed-effects and random-effects

estimators also provide very similar results. The income elasticity of demand using the

fixed-effects estimator is estimated to be 0.366 and statistically significant (t statistic = 5.51).

The income elasticity of demand using the random-effects estimator is estimated to be 0.372

and statistically significant (t statistic = 5.64). When I use the first-difference estimator, the

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income elasticity of demand decreases to 0.105 (t statistic = 1.90). Finally, when I use the

Arellano-Bond dynamic estimator, the income elasticity of demand is estimated to be 0.411

and is estimated very precisely.

To control for changes in quantity demanded over time, I included three decade binary

variables. All the decade variables are positive and statistically significant. Specifically, using

the results from the fixed-effects estimator I find that, compared to the 1970s and holding all

factors constant, residential demand in the 1980s, 1990s, and 2000s was approximately 10%,

12%, and 13% higher, respectively. The similarity in the coefficients suggests that, holding all

factors in the model constant, residential demand has not changed much since 1980.

Other variables in the model are population, the price of natural gas, and the heating and

cooling degree day indices. With respect to population, the four models provide similar results:

a 1% increase in population results in an increase in residential demand ranging between

0.786% and 0.873% and is estimated very precisely (in each case a p-value of less than 0.001).

I find natural gas to be a substitute for residential electricity consumption. A 1% increase in

the real price of natural gas results in an increase in residential electricity consumption of

approximately 0.090%. Finally, I find that the heating and cooling degree days index positively

affects the demand for residential electricity.

Table 4. Estimation of Static 2SLS and Dynamic Residential Demand Equations Using Panel Data

Variable Fixed Effects Random Effects First Difference Arellano-Bond

ln_rpres -.43983922*** -.42958065*** -.10504817***

decade_80s .09833813*** .09856919*** .01954227***

decade_90s .11720025*** .12003327*** .02868456***

decade_2000s .12444248*** .12918732*** .02141042**

ln_pop .87328382*** .85569493*** .21840973***

t_hdd .00003234*** .00003248*** .00002846***

t_cdd .00011367*** .00011673*** .00017076***

ln_income .36623779*** .37236283*** .11290548***

ln_price natural gas .08934869*** .09028773*** .00541858

ln_rpres_cpi D1. -.61326955**

decade_80s D1. .0111662

decade_90s D1. .0131513

decade_2000s D1. .01895704

ln_pop D1. .78628219***

t_hdd D1. .00003095***

t_cdd D1. .00013223***

ln_income D1. .10526433

ln_price natural gas D1. .02681842

ln_qres L1. .72511645***

_cons -3.6963764*** -3.4667861*** .00727446*** -.95904616***

N 2367 2367 2295 2295

Wald χ2 Prob > χ2 0.0000 Prob > χ2 0.0000 Prob > χ2 0.0000 Prob > χ2 0.0000

*p<0.05; **p<0.01; ***p<0.001

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Commercial demand models

Table 5 contains the results of my static and dynamic commercial demand equations. The

fixed-effects and random-effects estimators provide very similar results for the price elasticity

of demand and for all the variables in the model. The price elasticity of demand using the fixed-

effects estimator is estimated to be -0.427 but is not estimated precisely (t statistic = -1.71). The

price elasticity of demand using the random-effects estimator is estimated to be -0.423 and

is also not statistically significant (t statistic = -1.71). When I use the first-difference estimator,

the price elasticity of demand decreases to -0.084 but is estimated with very poor precision (t

statistic = -0.39). Finally, when I use the Arellano-Bond dynamic estimator, the price elasticity of

demand is -0.747 and estimated very precisely.7 The econometric evidence, therefore, supports

a price elasticity of demand for commercial customers of -0.747, also within the findings in the

economics literature (see Section 2).


estimators also provide very similar results. The income elasticity of demand using the fixed-

effects estimator is estimated to be 0.877 and statistically significant (t statistic = 3.90). The

income elasticity of demand using the random-effects estimator is estimated to be 0.883

and statistically significant (t statistic = 3.98). When I use the first-difference estimator, the

income elasticity of demand decreases to 0.448 (t statistic = 4.98). Finally, when I use the

Arellano-Bond dynamic estimator, the income elasticity of demand is estimated to be 0.584

and is estimated very precisely. The econometric evidence thus supports an income elasticity of

residential electricity demand ranging between 0.448 and 0.883. These estimates are within the

range found in the economic literature (see Section 2).


variables. All the decade variables are positive and statistically significant. Specifically, using

the results from the fixed-effects estimator I find that, compared to the 1970s and holding all

factors constant, commercial demand in the 1980s, 1990s, and 2000s was approximately 14%,

23%, and 20% higher, respectively.


cooling degree day indices. With respect to population, a 1% increase in population results

in an increase in commercial demand ranging between 0.472% and 0.767%, depending on

the model, and is estimated precisely, in three cases with a p-value of less than 0.001. I find

evidence that natural gas is a substitute for commercial electricity demand in two of the four

models, with a 1% increase in the real price of natural gas resulting in approximately a 0.10%

increase in commercial demand. And I find some evidence that the heating and cooling degree

days index positively impacts the demand for commercial electricity.

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Table 5. Estimation of Static 2SLS and Dynamic Commercial Demand Equations Using Panel Data


ln_rpcom_cpi -.42668522 -.42263656 -.11108075***

decade_80s .13077288*** .13085258*** .02271251**

decade_90s .20658202*** .20768634*** .02041106*

decade_2000s .1796107** .18139979** -.01235798

ln_pop .66475958*** .65881121*** .11411259***

t_hdd 4.084e-06 3.800e-06 9.531e-06

t_cdd 1.707e-06 2.974e-06 .00008662***

ln_income .87698091*** .8803502*** .08685953**

ln_price natural gas .09677514* .09694503* -.00966809

ln_rpcom_cpi D1. -.08351428

decade_80s D1. .00950299

decade_90s D1. .02985924

decade_2000s D1. .04058238*

ln_pop D1. .47245619*

t_hdd D1. .00001042*

t_cdd D1. .00006827***

ln_income D1. .44778965***

ln_price natural gas D1. -.01570014

ln_qcom L1. .85125672***

_cons -5.5837047*** -5.5164864*** .0167317*** -.7947528

N 2367 2367 2295 2295


*p<0.05; **p<0.01; ***p<0.001

Industrial demand models

Table 6 contains the results of my static and dynamic industrial demand equations. The price

elasticity of demand using the fixed- and random-effects estimator is contrary to economic

theory, showing a positive price elasticity of demand. I therefore rely on the results from the

first-difference and Arellano-Bond models. When I use the first-difference estimator, I find the

price elasticity of demand to be -0.868 (t statistic = -2.80). When I utilize the Arellano-Bond

dynamic estimator, the price elasticity of demand is estimated to be -0.522 and is estimated

precisely.8 The econometric evidence, therefore, supports a price elasticity of demand for

industrial customers between -0.522 and -0.868, also within the findings in the economics

literature (see Section 2).


estimators also provide very similar results. The income elasticity of demand using the

fixed-effects estimator is estimated to be 1.467 and statistically significant (t statistic = 4.95).

The income elasticity of demand using the random-effects estimator is estimated to be 1.456

and statistically significant (t statistic = 5.02). The income elasticity of demand using the

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first-difference estimator is significantly lower at 0.720 (with a student t statistic = 4.26).

When I use the Arellano-Bond dynamic estimator, the income elasticity of demand is estimated

to be 1.58 and is estimated precisely and closer to the estimates found from the fixed- and

random-effects estimators.


variables. The results are mixed. Three of the four models suggest that industrial electricity

demand was lower in the 1980s (than in the 1970s) but higher in the 1990s and 2000s. The

remaining model, however, suggests that industrial electricity demand was lower in each

decade compared to the 1970s.


cooling degree day indices. With respect to population, the fixed and random effects model

finds that a 1% increase in population results in an increase in industrial demand of 0.361% and

0.338%, respectively while for the first-difference model a 1% increase in population results in

an increase in industrial demand range of 0.882%. I find evidence in the Arellano-Bond model

that natural gas is a complement for industrial electricity consumption. A 1% increase in the real

price of natural gas results in a decrease in industrial electricity consumption of approximately

1.236%. Finally, I do not find strong evidence that the heating and cooling degree days index

impacts the demand for industrial electricity.

Table 6. Estimation of static 2SLS and dynamic industrial demand equations using panel data


ln_rpindus_cpi 1.1752322*** 1.1937324*** -.0342152*

decade_80s -.11297932** -.11340149** -.03659619***

decade_90s .13545005** .14296852** -.06222053***

decade_2000s .07849822 .0928366 -.12470984***

ln_pop .36146288** .33779817** .06730164

t_hdd -7.977e-06 -.00001413 -8.382e-06

t_cdd .00003866 .0000471 2.600e-06

ln_income 1.4671618*** 1.4585598*** .10344492*

ln_price natural gas .13354912 .12156241 -.08090781*

ln_rpindus_cpi D1. -.86765614**

decade_80s D1. .06215827**

decade_90s D1. .06364113*

decade_2000s D1. .07172708*

ln_pop D1. .8819554**

t_hdd D1. -2.597e-06

t_cdd D1. -.0000136

ln_income D1. .71977543***

ln_price natural gas D1. -.02696871

ln_qindus L1. .93404923***

_cons -2.5161967 -1.9718117 -.03033039*** -.92398406

N 2333 2333 2262 2262


*p<0.05; **p<0.01; ***p<0.001

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Econometric Estimation of US Electricity Price Equations

In this section, I estimate reduced-form price equations for residential, commercial, and

electricity customer classes. The left-hand side dependent variable is log of real price and

is the same price variable that I used above for the demand equations. The right-hand side

independent variable comps is a binary variable with a value of one if the state permitted

retail competition for residential and commercial (small) customers. I use this variable for the

residential and commercial price equations. For the industrial price equations, the variable

compl is a binary variable with a value of one if the state permitted retail competition for

industrial (large) consumers.

I estimate fixed and random effects models assuming that comps and compl are exogenous.

In addition, I again estimate dynamic models using the Arellano-Bond estimator and provide

two results: the first treats the competition variable as exogenous, and the second assumes it

is endogenous and uses the lagged values of the competition variable as instruments.

Other independent variables in my reduced-form price equation include binary variables for

the 1980s, 1990s, and 2000s, log of population, log of the firm’s total factor productivity

(lntfp), heating and cooling degree day indices, real personal income, and the log of real price

of natural gas. I also include the variable ratecap to control for the fact that some of the

states that permitted competition imposed a rate cap on electricity prices for small customers

(residential and commercial) for a period of time. Thus, it is important to control for this effect,

otherwise the effect would be included within the comps coefficient. The variable ratecap is a

binary variable with one in those states that permitted competition and had a rate cap in the

year in question.

Residential price equations

Table 7 contains the results of my residential price model. The fixed and random effects

estimators provide very similar results (and each having a p-value of less than 0.001): holding

all other factors constant, residential prices were approximately 8% lower in those states that

permitted retail competition for residential and commercial consumers. The Arellano-Bond

estimator—assuming that comps is exogenous (model 3)—indicates that residential prices

were approximately 3% lower in those states that permitted retail competition for residential

and commercial consumers, but the effect was not significant. When I use the Arellano-Bond

estimator and consider comps as endogenous, I find the impact is practically zero and is not

estimated precisely. Based upon these results, while there is some evidence that residential

electricity competition is associated with lower residential prices, additional work should be

performed on finding suitable instruments for the comps variable in order to ensure that the

parameter estimates for comps are unbiased.

Other significant findings include the impact of lntfp and personal income on residential

electricity prices. In each of the models, an increase in tfp of 1% results in a decrease in

residential prices, ranging from -0.253% to -0.084%. In each of the models, an increase in

personal income results in a decrease in prices, ranging from -0.338% to -0.186%.

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Table 7. Estimation of Static and Dynamic Residential Price Equations Using Panel Data

VariableFixed Effects

(1)

Random Effects

(2)

Arellano-Bond

(3)

Arellano-Bond

(4)

comps -.089*** -.084*** -.008 -.001

ratecap -.105*** -.101*** -.085*** -.081***

decade_80s .045*** .043*** .02*** .02***

decade_90s -.057*** -.065*** .009 .009

decade_2000s -.075*** -.098*** .06*** .056***

ln_pop .053 .067*** -.016 .007

Lntfp -.22*** -.253*** -.087*** -.084***

t_hdd -2.2e-05* -1.2e-05* -6.78e-06 -6.67e-06

t_cdd -4.3e-05*** -5.2e-05*** -2.15e-05* -2.5e-05*

ln_income -.338*** -.258*** -.186*** -.191***

ln_price natural gas -.079*** -.048 .042* .045*

ln_rpres_cpi L1. .773*** .780***

_cons .792 .330 1.61** 1.53**

N 2367 2367 2295 2295

*p<0.05; **p<0.01; ***p<0.001

Commercial price equations

Table 8 contains the results of my commercial price model. The fixed- and random-effects

estimators provide very similar results (each having a p-value of less than 0.001): holding all

other factors constant, commercial prices were approximately 16% lower in those states that

permitted retail competition for residential and commercial consumers. The Arellano-Bond

estimator, assuming that comps is exogenous (model 3), indicates that commercial prices were

approximately 19% lower in those states that permitted retail competition for residential and

commercial consumers (with a p-value of less than 0.001). When I use the Arellano-Bond

estimator and consider comps as endogenous, the impact is approximately 19%. Based upon

these results, there is evidence that commercial electricity competition is associated with lower

commercial prices.

Other significant findings include the impact of lntfp and personal income on residential

electricity prices. In each of the models, an increase in tfp results in a decrease in prices,

ranging from -0.278% to -0.056%. In each of the models, an increase in personal income

results in a decrease in prices, ranging from -0.68% to -0.29%.

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Table 8. Estimation of Static and Dynamic Commercial Price Equations Using Panel Data


(1)

Random Effects

(2)

Arellano-Bond

(3)

Arellano-Bond

(4)

comps -.176*** -.171*** -.038*** -.035***

ratecap -.014 -.010* -.019 -.011

decade_80s .019 .016 .0042 .004

decade_90s -.108*** -.119*** -.008 -.007

decade_2000s -.105*** -.134*** .051*** .05***

ln_pop .075* .079*** .027 .027

lntfp -.236*** -.278*** -.058*** -.056**

t_hdd -2.5e-05** -1.0e-05 -5.7e-06 -5.3e-06

t_cdd -3.2e-05 -4.7e-05* -2.2e-05 -2.3e-05

ln_income -.683*** -.567*** -.296*** -.292***

ln_price natural gas -.087** -.048 .049 .057*

ln_rpcom_cpi L1. .811*** .821***

_cons 3.89*** 2.55*** 2.09*** 2.06***

N 2367 2367 2295 2295

*p<0.05; **p<0.01; ***p<0.001

Table 9 contains the results of my industrial price model. The fixed- and random-effects

estimators provide very similar results (each having a p-value of less than 0.001): holding all

other factors constant, industrial prices were approximately 24% lower in those states that

permitted retail competition for industrial consumers. The Arellano-Bond estimator indicates

that industrial prices were approximately 30% lower in those states that permitted retail

competition for industrial consumers (with a p-value of less than 0.001). When I use the

Arellano-Bond estimator and consider compl as endogenous, the impact is approximately 29%.

Based upon these results, there is evidence that industrial electricity competition is associated

with lower industrial prices.

Other significant findings include the impact of tfp and personal income on residential

electricity prices. In each of the models, an increase in tfp results in a decrease in prices,

ranging from -0.263% to -0.067%. In each of the models, an increase in personal income

results in a decrease in prices, ranging from -0.811% to -0.376%.

www.nera.com 13

Table 9. Estimation of Static and Dynamic Industrial Price Equations Using Panel Data


(1)

Random Effects

(2)

Arellano-Bond

(5)

Arellano-Bond

(6)

Compl -.269*** -.263*** -.0741*** -.0674***

decade_80s .0861*** .0844*** .00935 .00762

decade_90s -.0779*** -.0866*** -.00336 -.00385

decade_2000s -.0404 -.0715* .0813*** .0799***

ln_pop .175*** .0967*** -.00951 -.00845

Lntfp -.193*** -.263*** -.075** -.0674**

t_hdd -3.0e-05* -8.8e-06 -3.4e-06 -3.7e-06

t_cdd -4.5e-05 -5.1e-05* -1.1e-05 -1.3e-05

ln_income -.811*** -.616*** -.379*** -.376***

ln_price natural gas -.149*** -.0795 .0317 .0377

ln_rpindus_cpi L1. .791*** .803***

_cons 3.38*** 2.44*** 3.39*** 3.37***

N 2333 2333 2262 2262

*p<0.05; **p<0.01; ***p<0.001

References

T. Dergiades and L. Tsoulfidis, “Estimating residential demand for electricity in the United States,

1965–2006,” Energy Economics, 2008.

K. Lim and S. Lim, “Short- and long-run elasticities of electricity demand in the Korean service

sector,” Energy Policy, 2014.

A. Paul, E. Myers, and K. Palmer, “A Partial Adjustment Model of US Electricity Demand by

Region, Season and Sector,” Resources for the Future Discussion Paper, 2009. Available at

http://www.rff.org/documents/rff-dp-08-50.pdf.

J. Silk and F. Joutz,“Short and long run elasticities in US residential electricity demand: a

co-integration approach,” Energy Economics, 1997.

X. Su, “Have Customers Benefited from Electricity Retail Competition,” Journal of Regulatory

Economics, 2015 (forthcoming).

A. Swadley and M. Yücel, “Did residential electricity rates fall after retail competition? A dynamic

panel analysis,” Energy Policy, 2011.

Notes

1 I was the co-author (with Jeff Makholm) of an expert report entitled Total Factor Productivity in the United States Electricity Sector from 1972–2009. We were expert witnesses on behalf of the Alberta Public Utility Commission in a proceeding in 2012 whose objective was setting tariffs for electricity and gas distribution companies. Our analysis was used to establish the X-factor in a price cap plan. Throughout this paper, I refer to the study as “TFP Study.”

2 I did not have data on actual tariffs for the different customer classes over the time period. Instead, I use average revenue per unit as a proxy for price and assume that some errors exist in variable (EIV). Nevertheless, EIV should not present a significant problem because I treat the price variable as (jointly) endogenous in my structural demand equations, and I explicitly model price in my reduced-form price equations.

3 The model is constructed so that by definition the unobserved panel-level effects are correlated with the lagged dependent variables, thus making standard estimators inconsistent. Arellano and Bond derived a consistent generalized method-of-moments (GMM) estimator for the parameters of the model.

4 I consider natural gas as a substitute for electricity and expect the cross-price elasticity of demand to be positive.

5 The total effect is -.105048/(1-.725116).

6 I also estimate every model by including interaction terms between the price variable and each decade variable to test whether there is evidence that the price elasticity of residential electricity demand changed significantly during the decades. For the fixed-effects model, the price elasticity of demand for the 1980s, 1990s, and 2000s ranged between -0.383 to -0.474, similar to the models without the interaction effects but the parameters were not estimated precisely, none being significant at the 5% level of statistical significance. Results for the random-effects estimator are similar.



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