Regional economic, demographic, and climatic data are used to analyze residential electricity demand in the United States. Results indicate that electricity is an inferior good for households in the United States. This confirms earlier research compiled using data for less geographically extensive regional and metropolitan markets. The results imply that demographic growth may place fewer pressures on electricity generation capacity than was previously assumed.Residential Electricity Demand, Regional Economics

Contreras, Sergio

Fullerton, Thomas M., Jr.

Roth, Timothy P.

Smith, Wm. Doyle

English

Research Papers in Economics

 
  1 
Empirical Economics Letters 
Volume 8, 2009, Pages 827-832 
 
Regional Evidence regarding U.S. Residential Electricity Consumption 
 
Sergio  Contreras,  Wm.  Doyle  Smith,  Timothy  P.  Roth,  and  Thomas  M. 
Fullerton, Jr.
* 
 
* Corresponding Author, Department of Economics & Finance, University 
of Texas at El Paso, El Paso, TX 79968-0543, USA, Telephone 915-747-
7747, Facsimile 915-747-6282, tomf@utep.edu 
 
Abstract 
 
Regional  economic,  demographic,  and  climatic  data  are  used  to 
analyze residential electricity demand in the United States.  Results indicate 
that electricity is an inferior good for households in the United States.  This 
confirms  earlier  research  compiled  using  data  for  less  geographically 
extensive  regional  and  metropolitan  markets.    The  results  imply  that 
demographic  growth may  place fewer pressures on electricity  generation 
capacity than was previously assumed. 
 
Key Words: Residential Electricity Demand, Regional Economics 
JEL Classification: Q41, Energy Demand; R15, Regional Econometrics 
 
Acknowledgements 
 
Funding support for this research was provided by El Paso Electric 
Company,  Hunt  Communities,  Hunt  Companies,  Inc.,  JPMorgan  Chase 
Bank  of  El  Paso,  a  UTEP  College  of  Business  Administration  Faculty 
Research Grant, and the James A. Baker III Institute for Public Policy at 
Rice  University.    Helpful  suggestions  and  comments  were  provided  by 
Gaspare Genna.  Econometric research assistance was provided by Karen 
Fierro and Emmanuel Villalobos.  
  2 
Regional Evidence regarding U.S. Residential Electricity Consumption 
 
1.  Introduction 
 
Resource constraints have raised questions regarding electric energy 
availability  and  policy  options  in  the  United  States  (Espey  and  Espey, 
2004).    In  that  context,  questions  have  also  been  asked  about  the 
determinants of electricity demand.  To adequately examine gigawatt hour 
(GWH)  consumption  issues  requires  assembling  a  fairly  comprehensive 
regional data set.  This paper utilizes one such sample to study residential 
GWH demand in the United States. 
Data are collected for all 50 states and the District of Columbia.  
Explanatory  variables  include  the  average  price  of  residential  electricity, 
number of households, personal income, heating degree days, and cooling 
degree  days.    Dummy  variables  are  also  included  for  each  of  the  nine 
regions defined by the Census Bureau.   
Section 2 provides a brief overview of previous studies of residential 
GWH demand in the United States.  A discussion of the data, modeling 
approach and empirical results are summarized in Section 3.  Concluding 
remarks follow in Section 4. 
 
2.  Literature Review 
 
Private and public institutions have an interest in determining the 
demand for electricity.  Both private sector electric companies and public 
sector cooperatives frequently employ fairly extensive econometric models 
for short-range and long-run planning purposes (Kamerschen and Porter, 
2004).    Periodic  electricity  shortages  and  rate  hikes  tend  to  heighten 
ongoing interest in this topic (McMahon, 1987; Reiss and White, 2008). 
  Among some of the studies that examine electricity consumption, 
results in Mount, Chapman, and Tyrell (1973) indicate that commercial and 
residential demands are more price elastic than industrial demand.  Taylor 
(1975)  observes  that  marginal  costs  cover  only  part  of  the  needed 
information because of block pricing in the electricity sector.  Halvorsen 
(1975)  concludes  that  empirical  results  using  average  price  measure  are 
likely to be more accurate than those using other approaches.  Roth (1981)  
  3 
uses both marginal and average prices of electricity to estimate electricity 
demand  under  block  pricing  and  concludes  that  electricity  is  an  inferior 
good. 
There  is  widespread  international  agreement  that  residential 
electricity demand is price inelastic (Silk and Joutz, 1997; Fillipini, 1999; 
Holtedahl and Joutz, 2004).  Less consensus exists with respect to income 
elasticity estimates.  Although a number of studies indicate that residential 
electricity is a normal good, many also indicate that its income elasticity is 
fairly low (Dergiades and Tsoulfidis, 2008).  Roth (1981) employs data for 
a single metropolitan market, while many recent efforts rely on nationally 
aggregated data estimates. 
To  shed  additional  light  on  the  issue  of  residential  electricity 
demand, this study takes advantage of a cross-sectional data set for all 50 
states and the District of Columbia.  The explanatory variables are largely 
the same as in Dergiades and Tsoulfidis (2008), although personal income 
is  employed  rather  than  gross  domestic  product.    Empirical  results  are 
summarized below. 
 
3.  Data and Empirical Results 
 
The basic implicit function form of residential GWH demand per 
household is  
 
GWHR/HH = f(PR, INC/HH, HDD, CDD)         (1) 
 
It is specified as a linear equation similar to those in earlier studies (Silk and 
Joutz,  1997;  Filippini,  1999).    Consumption  is  in  gigawatt-hours  per 
household  (GWHR/HH).    Other  variables  include  average  price  per 
kilowatt-hour (KWH), personal income per household, heating degree days, 
and cooling degree days.  Heating degree-days are calculated as differences 
between average temperatures and 65
0F during cool weather days.  Cooling 
degree-days are calculated in the same manner except they measure days 
when energy will be used to cool a residence.  All data employed are from 
official federal and state government agencies.  They are available from the 
authors upon request. 
  
  4 
 
Table 1.  Variable Names 
Mnemonic  Description 
GWHR  Residential Electricity Usage in Gigawatt Hours 
PR    Average Price for Res. Electricity, Cents per Kilowatt Hour 
HH    State Number of Households 
INC    State Personal Income, Thousand Dollars 
HDD    State Heating Degree Days 
CDD    State Cooling Degree Days 
NE    Dummy Variable for New England Census Region 
MIDATL  Dummy Variable for Mid-Atlantic Census Region 
ENC    Dummy Variable for East North Central Census Region 
WNC    Dummy Variable for West North Central Census Region 
SA    Dummy Variable for South Atlantic Census Region 
ES    Dummy Variable for East South Central Census Region 
WSC    Dummy Variable for West South Central Census Region 
MOU    Dummy Variable for Mountain West Census Region 
PAC    Dummy Variable for Pacific Coast Census Region 
 
 
Dummy variables are included for each of the nine regions defined 
by the United States Census Bureau.  A value of one is assigned if a state 
belongs to a region and zero if it does not.  Because the dependent variable 
is logarithmically transformed and regional indicator variables are not, the 
latter  coefficients  are  first  transformed  using  exponential  functions.    To 
avoid matrix singularity, the Pacific Region is excluded from estimation and 
is assigned a value of zero, so its exponential transformation will equal one.  
A negative coefficient for the regional indicator variable will indicate lower 
GWH  purchases  than  the  Pacific  Region;  a  positive  sign  will  indicate 
greater consumption than in the Pacific region. 
Table 1 contains variable names, descriptions, and their respective 
units of measure.  All data are collected for 2002.  Because the variables are 
logarithmically transformed prior to estimation, the regression coefficients 
represent  demand  elasticities.    The  basic  specification  for  GWHR  per 
household is shown in Equation (2). 
  
  5 
Log(GWHR/HH)  =  α0 + α1LogPR + α2Log(INC/HH) + α3LogHDD +  
      α4LogCDD + e        (2) 
 
  Given  the  wide  range  of  market  sizes  in  the  sample,  results  are 
tested  for  heteroscedasticity  (White,  1980).    As  shown  in  Table  2,  test 
statistics  for  per  household  residential  consumption  fall  below  the 
respective F- and Chi-square 1-percent critical values.  Failure to reject the 
null hypothesis of homoscedasticity indicates a fairly uniform pattern  of 
regional per household GWH sales once price and other factors are taken 
into account. 
The price elasticity in Table 2 is -0.59 and satisfies the 5-percent 
significance criterion.  It is slightly more inelastic than the median value 
that has been historically reported for residential electricity sales (Espey and 
Espey, 2004).  It is, however, well within the range of values reported in 
many prior studies. 
The  estimated  income  per  household  elasticity  for  residential 
electricity is -0.44 and also satisfies the significance criterion.  The negative 
sign implies electricity is an inferior  good and  confirms the  conclusions 
reached in earlier region-specific electricity studies such as Lyman (1973) 
and Roth (1981).  This result, however, differs substantially from many of 
the other positive income estimates previously reported (Espey and Espey, 
2004).  It is robust to specification changes, although total personal income 
has a negative sign and total households has a positive sign when those two 
variables are split to allow for parameter heterogeneity (Filippini, 1999). 
Heating degree-days have an elasticity of 0.0004.  The positive sign 
indicates residences increase electricity usage as temperatures falls.  With a 
t-statistic  of  0.012,  the  heating  degree-days  elasticity  coefficient  is  not 
statistically  significant.    Kamerschen  and  Porter  (2004)  report  a  heating 
degree elasticity of 0.08 that is significant.   Filippini (1999) also obtain 
statistically significant heating degree parameters with elasticities that range 
between 0.272 and 0.297.   
 
 
 
 
  
  6 
Table 2.  Estimation Results 
 
Dependent Variable:    LOG(GWHR/HH) 
Method:      Ordinary Least Squares 
Sample Observations:   51 
         
Variable    Coefficient  Std. Error  t-Stat.   Prob. 
         
Constant    -1.7075  0.9457   -1.8054  0.0789 
LOG(PR)    -0.5936  0.1890   -3.1416  0.0032 
LOG(INC/HH)  -0.4377  0.1826   -2.3969  0.0216 
LOG(HDD)    0.0120   0.0303   0.3957   0.6946 
LOG(CDD)    0.0475   0.0402   1.1819   0.2446 
NE      -0.0974  0.1145   -0.8508  0.4002 
MIDATL    -0.0832  0.1355   -0.6141  0.5428 
ENC      -0.1224  0.1128   -1.0842  0.2851 
WNC      -0.0525  0.1130   -0.4649  0.6447 
SA      0.1193   0.1070   1.1152   0.2717 
ESC      0.1200   0.1348   0.8905   0.3788 
WSC      0.1176   0.1315   0.8943   0.3768 
MOU      -0.1941  0.1062   -1.8284  0.0754 
 
R-squared    0.7830   Dependent Variable Mean  -4.4659 
Adjusted R-squared  0.7145   Dep. Var. Standard Dev.  0.2899 
Std. Err. Regression  0.1549   Akaike Inf. Criterion    -0.6764 
Sum Sq. Residuals  0.9119   Schwarz Inf. Criterion    -0.1840 
Log likelihood   30.2476  Hannan-Quinn Inf. Crit.  -0.4882 
F-statistic    11.4268  Durbin-Watson Statistic  2.1706 
Probability (F-stat.)  0.0000      
 
White Heteroscedasticity Test:   
F-statistic    1.2168   Probability, F(12, 38)   0.3076 
Obsvn.*R-squared  14.1569  Probability, Chi-Square(13)  0.2908 
Scaled explained SS  11.7005  Probability, Chi-Square(13)  0.4700 
 
  
  7 
  As anticipated, the Census Region dummy variables carry different 
signs and magnitudes.  None of the t-statistics for the regional indicator 
variables indicate that these parameters are significantly different from zero.  
Collectively, those outcomes imply that residential GWH consumption per 
household does not exhibit much regional heterogeneity after price, income, 
and weather are taken into account. 
 
4.  Conclusion 
 
  This  study  confirms  earlier  work  suggesting  that  pricing  policies 
offer effective tools for encouraging decreased household electricity usage.  
In contrast to a large number of earlier results, the elasticity coefficient for 
income per household is both negative and significant.  That result is robust 
to alternative specifications.  If residential electricity is an inferior good, it 
implies that better income performance alone will not translate into greater 
load pressures for electric utilities.  Weather variables and Census Region 
market  designations  are  not  found  to  improve  model  performance, 
potentially indicating greater household energy usage homogeneity across 
the United States than might otherwise be expected. 
  To  date,  only  a  minority  of  studies  indicate  that  residential 
electricity  is  an  inferior  good.    Accordingly,  additional  research  on  this 
topic would be welcome.  One potential avenue for expansion is to collect 
data for a greater number of years and examine whether panel estimates 
confirm  the  various  results  reported  above.    Empirical  analyses  of 
commercial,  industrial,  public  and  total  electricity  consumption  patterns 
may also be useful from a comparative perspective.  Similarly, empirical 
analyses  using  regional  market  data  from  other  nations  may  provide  a 
stronger contextual base from which to examine this general topic area. 
 
 
 
 
 
 
 
  
  8 
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Electricity in the United States, 1965-2006, Energy Economics, 30, 2722-
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Espey, J.A. and M. Espey, 2004, Turning on the Lights: A Meta Analysis of 
Residential  Electricity  Demand  Elasticities,  Journal  of  Agricultural  & 
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Filippini,  M.,  1999,  Swiss  Residential  Demand  for  Electricity,  Applied 
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Average and Marginal Price Changes and the Demand for Electricity: An Econometric Study,

Estimating Residential Demand for Electricity in the United States, 1965-2006,

Price Elasticities in the Electric Power Industry,

Residential Demand for Electric Energy,

Short and Long Run Elasticities in US Residential Electricity Demand: A Co-integration Approach,

Swiss Residential Demand for Electricity,

The Demand for Electricity: A Survey,

The Demand for Residential,

The LBL Residential Energy Model: An Improved Policy Analysis Tool, Energy Systems and Policy,

Turning on the Lights: A Meta Analysis of Residential Electricity Demand Elasticities,

What Changes Energy Consumption? Prices and Public Pressures,

Regional Evidence regarding U.S. Residential Electricity Consumption

Regional Evidence regarding U.S. Residential Electricity Consumption

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