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NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

Regional Differences in 

the Price-Elasticity 

of Demand for Energy 

M.A Bernstein and J. Griffin 

RAND Corporation 

Santa Monica, California 

Subcontract Report 

NREL/SR-620-39512 

February 2006 
Regional Differences in 

the Price-Elasticity 

of Demand for Energy 

M.A Bernstein and J. Griffin 

RAND Corporation 

Santa Monica, California 

NREL Technical Monitor: D. Arent 

Prepared under Subcontract No. KACX-4-44454-01 

Subcontract Report 

NREL/SR-620-39512 

February 2006 

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Preface 

About This Analysis 

Each year, the Department of Energy (DOE) requires its research programs to estimate 

the benefits from their research activities. These estimates are part of the programs’ 

annual budget submissions to the DOE, and they are also required under the Government 

Performance and Review Act. Each program in the DOE’s Office of Energy Efficiency 

and Renewable Energy (EERE) is responsible for providing its own assessment of the 

impact of its technology research and development (R&D) programs. For the most part, 

the benefit estimates from each EERE program office are made at the national level, and 

the individual estimates are then integrated through the use of the National Energy 

Modeling System to generate an aggregate set of benefits from the EERE’s various R&D 

programs. 

At the request of the National Renewable Energy Laboratory (NREL), the RAND 

Corporation examined the relationship between energy demand and energy prices with 

the focus on whether the relationships between demand and price differ if these are 

examined at different levels of data resolution. In this case we compare national, 

regional, state, and electric utility levels of data resolution. This study is intended as a 

first step in helping NREL understand the impact that spatial disaggregation of data can 

have on estimating the impacts of their programs. 

This report should be useful to analysts in NREL and other national laboratories, as well 

as to policy nationals at the national level. It may help them understand the complex 

relationships between demand and price and how these might vary across different 

locations in the United States. 

iii 
The RAND Environment, Energy, and Economic Development Program 

This research was conducted under the auspices of the Environment, Energy, and 

Economic Development Program (EEED) within RAND’s Infrastructure, Safety and 

Environment Division. The mission of RAND’s Infrastructure, Safety and Environment 

is to improve the development, operation, use, and protection of society’s essential manmade and natural assets and to enhance the related social assets of safety and security of 

individuals in transit and in their workplaces and community. The EEED research 

portfolio addresses environmental quality and regulation, energy resources and systems, 

water resources and systems, climate, natural hazards and disasters, and economic 

development both domestically and internationally. EEED research is conducted for 

government, foundations, and the private sector. 

Questions or comments about this report should be sent to the project leader, Mark 

Bernstein (mark_bernstein@rand.org). Information about the Environment, Energy, and 

Economic Development Program is available online (www.rand.org/ise/environ). 

Inquiries about EEED projects should be sent to the Program Director 

(ise_eeed@rand.org). 

iv 
Table of Contents 

Preface................................................................................................................................ iii 

About This Analysis ...................................................................................................... iii 

The RAND Environment, Energy, and Economic Development Program ................... iv 

Table of Contents................................................................................................................ v 

Figures and Tables ............................................................................................................ vii 

Executive Summary ........................................................................................................... ix 

Are There Regional Differences in the Price-Demand Relationship? ............................ x 

Acknowledgements.......................................................................................................... xiii 

Chapter 1: Introduction....................................................................................................... 1

Relationship Between Energy Efficiency and Price Elasticity ....................................... 6 

Analytical Approach ....................................................................................................... 7 

Summary of Findings...................................................................................................... 8 

Organization of This Report ........................................................................................... 9 

Chapter 2: Economic Theory, Literature, and Methodological Approach ....................... 10 

Previous Literature on Energy Demand........................................................................ 10 

Estimation Approach .................................................................................................... 12 

Chapter 3: National-Level Results.................................................................................... 16

Residential Electricity Use............................................................................................ 16 

Commercial Electricity ................................................................................................. 18 

Natural Gas ................................................................................................................... 20 

Summary of National-Level Results............................................................................. 22 

Chapter 4: Regional Results.............................................................................................. 24 

Commercial Electricity Results .................................................................................... 30 

Residential Natural Gas ................................................................................................ 32 

Regional Analysis Conclusions .................................................................................... 36 

Chapter 5: State-Level Analysis ....................................................................................... 37 

Residential Electricity................................................................................................... 37 

Commercial Electricity ................................................................................................. 42 

Residential Natural Gas ................................................................................................ 44 

State-Level Conclusions ............................................................................................... 46 

Chapter 6: Utility-Level Analysis..................................................................................... 47 

Chapter 7: Conclusions, Final Thoughts, and Implications of Analysis........................... 49 

....................................................................................................................................... 50 

Should DOE Disaggregate Data for Estimating Energy-Efficiency Programs Benefits?

Price Elasticity of Demand ........................................................................................... 51 

Bibliography ..................................................................................................................... 54 

Appendix A....................................................................................................................... 56 

Details on the Methodology Used to Estimate Elasticities........................................... 56 

Parameter Identification................................................................................................ 58 

Trend Analysis .............................................................................................................. 61 

Appendix B ....................................................................................................................... 63 

Data Sources ................................................................................................................. 63 

Energy Data .................................................................................................................. 63 

Economic Data.............................................................................................................. 63 

v 
Climate Data ................................................................................................................. 63 

Appendix C: Variables and How They Were Constructed ............................................... 64 

Appendix D: Regression Analysis Results ....................................................................... 67 

National-Level Results.................................................................................................. 67 

Residential Electricity............................................................................................... 67 

Commercial Electricity ............................................................................................. 68 

Residential Natural Gas ............................................................................................ 70 

Regional-Level Results................................................................................................. 71 

Residential Electricity............................................................................................... 71 

Commercial Electricity ............................................................................................. 73 

Residential Natural Gas ............................................................................................ 75 

State-Level Results ....................................................................................................... 77 

Residential Electricity............................................................................................... 77 

Commercial Electricity ............................................................................................. 80 

Natural Gas ............................................................................................................... 83 

Utility-level results........................................................................................................ 86 

Results from Energy Use Trend Analysis..................................................................... 90 

Region-Level Results.................................................................................................... 91 

Residential Electricity............................................................................................... 91 

Commercial Electricity ............................................................................................. 92 

Natural Gas ............................................................................................................... 93 

State-Level Results ....................................................................................................... 94 

Residential Electricity............................................................................................... 95 

Commercial Electricity ............................................................................................. 97 

Natural Gas ............................................................................................................... 99 

vi 
Figures and Tables 

Figure 1.1: Relationship of supply and demand with two different demand curves.. 3

Figure 1.2: Impact of a shift in the supply curve ........................................................... 4

Figure 1.3: Impact of a shift in the demand curve......................................................... 5

Figure 3.1: Residential Electricity Prices, Demand, and Intensity, 1977–2003......... 16

Table 3.1: Results of Regression Analysis of Residential Electricity Demand, 1977–

2004........................................................................................................................... 17

Figure 3.2: Commercial Electricity Prices, Demand, and Intensity, 1977–1999....... 19

Table 3.2: Regression Analysis Results for Commercial Electricity Demand .......... 20

Figure 3.3: Residential Natural Gas Prices, Demand, and Intensity, 1977–2003 ..... 21

Table 3.3: Regression Analysis Results for Residential Natural Gas Demand ......... 22

Table 3.4: Price Elasticities for Residential Electricity, Commercial Electricity, and 

Residential Natural Gas at the National Level..................................................... 23

Figure 4.1: DOE Energy Information Agency Census Regions ................................. 24

Figure 4.2: Regional Trends in Per-Capita Residential Electricity-Intensity, 1977–

2004........................................................................................................................... 25

Figure 4.3: Regional Trends in Average Expenditures on Residential Electricity, 

1977–2004................................................................................................................. 27

Figure 4.4: Regional Trends in Average Expenditures on Residential Electricity as a 

Share of Income, 1977–2004................................................................................... 28

Figure 4.5: Estimated Short-Run Residential-Electricity Price Elasticities by Region, 

1977–2004................................................................................................................. 29

Figure 4.6: Estimated Long-Run Residential-Electricity Price Elasticities by Region, 

1977–2004................................................................................................................. 29

Figure 4.7: Regional Trends in Commercial Electricity Use per Square Foot of 

Office Space, 1977–1999 ........................................................................................ 31

Figure 4.8: Short-Run Commercial Electricity Price Elasticities by Region, 1977–

1999........................................................................................................................... 31

Figure 4.9: Long-Run Commercial Electricity Price Elasticities by Region, 1977–

1999........................................................................................................................... 32

Figure 4.10: Natural-Gas Intensity Trends by Region, 1977–2004............................ 33

Figure 4.11: Natural-Gas Price Trends by Region, 1977–2004 .................................. 34

Figure 4.12: Short-Run Natural-Gas Price Elasticities by Region, 1977–2004......... 35

Figure 4.13: Long-Run Natural Gas Price Elasticities by Region, 1977–2004.......... 35

Figure 5.1: State-Level Trends in Residential Electricity Intensity, 1977–2004 ....... 38

Figure 5.2: State-Level Trends in Residential Electricity Expenditures as a Share of 

Income, 1977–2004................................................................................................. 39

Figure 5.3: Estimates of Short-Run Residential Electricity Price Elasticities for Each 

State, 1977–2004..................................................................................................... 39

Figure 5.4: Estimated State-Level Short-Run Price Elasticities for Residential 

Electricity, 1977–2004............................................................................................. 41

Figure 5.5: Estimated State-Level Long-Run Price Elasticities for Residential 

Electricity, 1977–2004............................................................................................. 41

Figure 5.6: Estimated State-Level Trends in Electricity Intensity in the Commercial 

Sector, 1977–1999................................................................................................... 43

vii 
Figure 5.7: Estimated Short Run Elasticities in Electricity Intensity in the 

Commercial Sector at the State Level, 1977–1999 ............................................... 43

Figure 5.8: Trends in Natural-Gas Intensity at the State Level, 1977–2004 ............. 45

Figure 5.9a: Estimated Short-Run Price Elasticities for Natural Gas at the State 

Level, 1977–2004 ..................................................................................................... 45

Figure 5.9b: Short-run Price Elasticities for Natural Gas .......................................... 46

Figure 6.1: Percentage of Utilities in the Sample within Each Region and Percentage 

in Each Region with Significant Elasticities ......................................................... 48

Table C.1: Residential Electricity Regression Analysis Variables ............................. 64

Table C.2: Commercial Electricity Regression Analysis Variables ........................... 65

Table C.3: Residential Natural-Gas Regression Analysis Variables ......................... 66

Table D.1: Regression results from the residential electricity market ...................... 68

Table D.2: Regression results from the commercial electricity market .................... 69

Table D.3: Results from natural gas market regression analysis............................... 70

Table D.4: Estimated short-run price elasticities for the residential electricity 

market. ..................................................................................................................... 72

Table D.5: Estimated long-run price elasticities for the residential electricity 

market. ..................................................................................................................... 72

Table D.6: Short-run price elasticities for commercial electricity with and without 

Tennessee ................................................................................................................. 73

Table D.7: Long-run price elasticity estimates for commercial electricity................ 74

Table D.8: Short run price elasticity for natural gas................................................... 75

Table D.9: Short run price elasticity for natural gas................................................... 76

Table D.10: State-level results for short-run price elasticity. ..................................... 77

Table D.11: Long Run Price Elasticity ......................................................................... 79

Table D.12: Short-run elasticity estimates for commercial electricity....................... 80

Table D.13: Long Run Commercial Electricity Elasticity Estimates......................... 81

Table D.14: Regression results for short run residential natural gas elasticity. ....... 83

Table D.15: Regression results for long-run price elasticities for residential natural 

gas ............................................................................................................................. 84

Table D.16: Short run elasticity estimates for residential electricity at the utility 

level........................................................................................................................... 86

Table D.17: Regional trends in residential electricity energy intensity ..................... 91

Table D.18: Regional trends in residential electricity expenditures .......................... 91

Table D.19: Regional trends in residential electricity expenditures as a share of 

income ...................................................................................................................... 92

Table D.20: Regional trends in commercial energy intensity..................................... 92

Table D.21: Regional energy intensity trends for residential natural gas. ................ 93

Table D.22: Regional trends in natural gas energy expenditures .............................. 93

Table D.23: Annual trends for natural gas expenditures as a share of income ........ 94

Table D.24: Residential electricity energy intensity .................................................... 95

Table D.25: Trends in expenditures on residential electricity as a share of income 96

Table D.26: Estimates of the annual trend in commercial energy intensity.............. 97

Table D.27: Estimated trends for residential natural gas energy intensity............... 99

viii 
Executive Summary 

The Department of Energy (DoE) Office of Energy Efficiency and Renewable Energy 

(EERE) has a portfolio of energy efficiency research and development programs that is 

intended to spur development of energy-efficient technologies. The goal of these 

programs is to decrease costs and improve efficiency of emerging technologies and 

increase the potential for consumers and businesses to adopt them. EERE, under 

requirements of the Government Performance Results Act (GPRA), must estimate the 

benefits of their portfolio of energy efficiency programs. With these estimates of 

benefits, EERE can then assess the cost-effectiveness of its programs and use this 

information in allocating its budget. 

Currently, EERE estimates the benefits of its programs by analyzing their effects using 

the DoE’s National Energy Modeling System (NEMS), a complex model of the U.S. 

energy system. Because the projected benefits of their programs depend heavily on the 

NEMS model, EERE is interested to know if certain assumptions in the NEMS model 

might impact the projected benefits. Specifically, the NEMS model uses data and 

parameters aggregated to the regional and national levels. If, for instance, the data or 

parameters used in the analysis actually vary considerably within a region, then NEMS 

will project biased results and using more disaggregated data—possibly at the state or 

utility level—could improve accuracy of the results. In this study, we examine how 

trends in several measures of the energy market may vary at the state and regional levels 

and in particular how one important parameter used in the NEMS model, price elasticity 

of demand (a measure of how demand responds to price), varies at the national, regional, 

state, and utility levels. With this initial examination, we offer some recommendations 

on whether EERE can improve their benefit estimates by using more disaggregated data 

in analysis of their programs. 

Economic theory says that as energy prices rise, the quantity of energy demanded will 

fall, holding all other factors constant. Price elasticities are typically in the negative 

range, which indicates that demand falls as prices increase or, conversely, that demand 

increases as prices fall. 

ix 
To determine if regional, state, or sub-state characteristics could affect the size of the 

impact from energy-efficiency technologies on energy prices, supply, and consumption, 

we looked at how individual factors—such as climate, supply constraints, energy costs, 

and demand for natural gas—might themselves affect the extent of the impact of energy 

efficiency. 

Are There Regional Differences in the Price-Demand Relationship? 

The object of this study is to determine whether the relationship between prices and 

demand differs at the regional, state, or sub-state level. In this study, we were interested 

solely in determining whether there are geographic differences in the price-demand 

relationship. We did not seek to understand how demand might impact prices and viceversa, although some of our findings provide some insights into these issues. Our focus 

was on finding out whether the state- and regional-level differences were significant 

enough to recommend to the DOE that it should explore disaggregating its data by state 

or region when estimating the potential benefits of energy efficiency. 

We examined three energy-demand components—electricity use in the residential sector, 

natural gas use in the residential sector, and electricity use in the commercial sector—at 

three or four levels of disaggregation of the data, depending on the availability of data. 

For each sector, we looked at national, regional, and state-level results. We also 

examined residential electricity use at the electric-utility level. 

Our analysis indicates that there are regional and state differences in the price-demand 

relationship for electricity and natural gas. We did find, though, that there tends to be 

some consistency in residential electricity use among states within a region and visible 

differences between regions in demand and price trends, particularly for residential 

electricity use and less so for commercial electricity use or residential natural gas use. 

What this implies, for estimating the impact of energy-efficiency technologies, is that the 

DOE may have reason to explore differentiating the impacts of energy efficiency by 

region, at least for residential electricity. There does not seem to be a need, at least in the 

x 
short run, for further disaggregation by geographic area, although more research is 

needed to offer a more conclusive recommendation. 

We also found that the relationship between demand and price is small. That is, demand 

is relatively inelastic to price. We also found that in the past 20 years, this relationship 

has not changed significantly; analyses performed in the 1980s1

 showed approximately 

the same results. These findings might imply that there are few options available to the 

consumer in response to changes in the price of energy, and that price does not respond 

much to changes in demand. On the other hand, because prices were declining in real 

terms over most of the period we studied, the inelasticity of demand may be more of an 

artifact of the lack of price increases. 

However, we now may be witnessing some changes in this area. The past few years have 

seen some increases in energy prices, with some states facing increasing electricity prices 

and all states facing increasing natural gas prices. While it is difficult statistically to 

uncover specific changes in trends, there are signs that demand growth has slowed, 

possibly due to a combination of increasing or flat prices and the economic slowdown of 

the past few years. Although we cannot say specifically that the relationship between 

price and demand might shift in an increasing-price environment, more analysis of recent 

trends may be warranted. 

1

 Bohi, Douglas R., and Mary Beth Zimmerman, “An Update on Econometric Studies of Energy Demand 

Behavior,” Annual Review of Energy, Vol. 9, 1984, pp. 105–154; Dahl, Carol A., “Do Gasoline Demand 

Elasticities Vary?” Land Economics, Vol. 58, No. 3, August 1982, pp. 373–382; and Dahl, Carol A. and 

Thomas Sterner, “Analyzing Gasoline Demand Elasticities: A Survey,” Energy Economics, July 1991, pp. 

203–210. 

xi 

Acknowledgements 

We would like to thank Doug Arent of the National Renewable Energy Laboratory for his 

efforts to fund this study and help guide the development of the analysis. Special thanks 

to Brian Unruh from the Department of Energy and David Loughran of RAND for their 

insightful reviews of the analysis and helping us focus the paper. We also appreciate 

conversations we had with Dan Relles on understanding some of the data issues. We 

appreciate assistance provided to us by staff at the Energy Information Administration, 

and finally thanks to Nancy DelFavero for assistance in editing. 

xiii 

Chapter 1: Introduction 

The Department of Energy (DoE) Office of Energy Efficiency and Renewable Energy 

(EERE) has a portfolio of energy efficiency research and development programs that are 

intended to spur development of energy-efficient technologies. The goal of these 

programs is to decrease costs and improve efficiency of emerging technologies and 

increase the potential for consumers and business to adopt them. EERE, under 

requirements of the Government Performance Results Act (GPRA), must estimate the 

benefits of their portfolio of energy efficiency programs. With these estimates of 

benefits, EERE can then assess the cost-effectiveness of its programs and use this 

information in allocating its budget. 

Currently, EERE estimates the benefits of its programs by analyzing their effects using 

the DoE’s National Energy Modeling System (NEMS), a complex model of the U.S. 

energy system. To make the estimates, DoE runs the NEMS model with traditional 

assumptions about the energy system and uses the results to establish baseline estimates 

of energy use and prices. DoE then introduces into the model the changes to the energy 

system attributable to EERE’s R&D programs and estimates a new set of energy 

demands and prices. EERE uses the differences in the two projections as estimates of the 

impacts of its programs. 

Because the projected benefits of their programs depend heavily on the NEMS model, 

EERE is interested to know if certain assumptions in the NEMS model might impact the 

projected benefits. Specifically, the NEMS model uses data and parameters aggregated 

to the regional and national levels. If, for instance, the data or parameters used in the 

analysis actually vary considerably within a region, then NEMS estimates of the impacts 

of energy efficiency might be misstated. Using more disaggregated data—possibly at the 

state or utility level—could then improve accuracy of the results. In this study, we 

examine how trends in several measures of the energy market may vary at the state and 

regional levels and in particular how one important parameter used in the NEMS model, 

price elasticity of demand (a measure of how demand responds to price), varies at the 

national, regional, state, and utility levels. With this initial examination, we offer some 

1
recommendations on whether EERE can improve their benefit estimates by using more 

disaggregated data in analysis of their programs. 

Geographic Variability in Energy Markets Could Affect DOE Benefit Estimates 

Geographical variation in price-demand relationship and price elasticity has important 

implications for the benefit estimates of EERE’s programs. The NEMS model represents 

energy demand and supply at the regional level and uses one price elasticity for all 

regions. If energy markets vary substantially at the sub-regional level or if price 

elasticities vary across the country, then estimates of the impacts of energy efficiency 

technologies will vary by region and this will not be reflected I the NEMS runs. 

Economic theory says that as energy prices rise, the quantity of energy demanded will 

fall, holding all other factors constant. Economic theory also suggests that consumers’ 

demand for energy is less sensitive to price changes than the demand for many other 

commodities. Economists define consumers’ sensitivity to price changes as a measure of 

price elasticity. Price elasticity is calculated as follows: 

Price Elasticity = %ΔQuantityDe manded

%Δ Pr ice 

In this equation, the numerator and denominator are expressed as a percentage of change. 

Because price elasticity is a ratio of two percentages, it is not expressed as a specific unit 

of measure and can be compared across different commodities. 

Price elasticities are typically in the negative range, which indicates that demand falls as 

prices increase or, conversely, that demand increases as prices fall. Demand elasticities 

are of two types, inelastic and elastic, and the range of each type differs. The range of 

inelastic demand is within absolute values of 0 to 1, and the elastic range begins with 

values greater than 1. These terms can be interpreted intuitively. A commodity with 

inelastic demand has a less than proportional change in demand for a given change in the 

price for the commodity. For instance, if prices increase by 10 percent on a good with a 

price elasticity of –0.20, then demand for the good drops by only 2 percent. In the elastic 

2
range, consumer demand responds with a greater-than-proportional change for a given 

price change. For instance, a good with an elasticity of –1.5 would have a 15 percent 

drop in demand with a 10 percent increase in price. This relationship is pictured in 

Figure 1.1. 

The figure shows a conventional supply curve (S1) and two demand curves with different 

elasticities (D1 and D’1). D1 is less elastic (i.e. steeper) than D’ 1. At equilibrium, both 

demand curves intersect the supply curve at the same point, with price at P1 and quantity 

at Q1. 

Figure 1.1: Relationship of supply and demand with two different demand curves 

If the supply curve shifts inward, which could represent an increase in the price of a fuel 

used to produce electricity such as natural gas, the new equilibrium point would depend 

on which demand curve is used as demonstrated in Figure 1.2. If the demand curve is 

relatively inelastic (D1) then prices would rise and there would be only a small reduction 

in demand (P2, Q2). With the more elastic demand curve (D’1), both the equilibrium 

3
price and the quantity are lower than the more inelastic curve (P’2,Q’2). In the end, the 

difference in the equilibriums would depend on the magnitude in the variation between 

the elasticities. 

Figure 1.2: Impact of a shift in the supply curve 

The price elasticity will also impact results if changes in demand are expected. In figure 

1.3 we show the impact on price and quantity of a shift in the demand curve. In this case 

let’s say demand increases – so the curve shifts outward from D1 to D2. If the supply 

does not change, with a less elastic demand curve the prices and quantity would be higher 

(P2, Q2) than if the demand curve was elastic (P’2, Q’2). Since energy efficiency impacts 

demand first, this picture is very relevant for EERE analysis. The impacts on price and 

quantity of changes in demand will certainly be different with different elasticities. 

4
Figure 1.3: Impact of a shift in the demand curve 

Price elasticities can be used to interpret how consumer demand responds to price 

changes. They also indicate how readily consumers can purchase substitutes for a 

product that has gone up in price and how much consumers value a particular good. 

Price elasticities can be used in this way because of the underlying theory of consumer 

response to price changes. A consumer with a fixed budget in the short term has three 

possible responses to a price change: (1) The consumer can buy another good as a 

substitute; (2) the consumer can buy less of the good with no corresponding purchase of a 

substitute; or (3) the consumer can continue to purchase the same amount of the good and 

reduce expenditures on other goods in his or her consumer bundle. 

In the case of electricity and natural gas (the focus of this study), these commodities have 

a limited degree of substitutability, especially in the short term. For end uses such as 

home heating and cooking, consumers can switch between energy-using systems that use 

electricity or natural gas. However, the consumer may want to purchase a new appliance 

5
that uses the less-expensive energy source. In other uses, such as a power supply for a 

computer, electricity has no substitutes. Nevertheless, the consumer still has the option to 

purchase a more efficient computer and enjoy the same level of service using less 

electricity. Typically, purchasing a more efficient appliance or one that uses a different 

type of fuel requires replacing a relatively expensive item, like a computer or refrigerator, 

and is considered a long-run adjustment by the consumer to high energy prices. 

Based on this analysis, consumer demand for electricity and natural gas should be 

relatively unresponsive to price changes in the short term and more responsive to price 

changes in the long term but could differ substantially by region. Demand for these 

goods is generally inelastic in the short term, because a consumer’s main options when 

energy prices change are to vary how he or she uses energy-consuming appliances (e.g., 

adjust a thermostat or turn on fewer lights) or reduce expenditures on other goods. Over 

the longer term, consumers can buy appliances that use a different energy source and/or 

purchase more-efficient appliances. Therefore, price elasticities tend more toward the 

elastic range than the inelastic range in the long term. 

One of the important benefit measures for the EERE programs is the projected energy 

savings from the energy efficiency programs. The diagrams above show that estimating 

the impacts on demand depends on the price elasticities used in the analysis. Therefore, 

if elasticities differ between regions, the model needs to include geographical variation in 

price elasticities to make accurate estimates. The following sections will discuss possible 

reasons for geographic variation in price elasticities and the relationship between energy 

efficient technologies and price elasticity. 

Relationship Between Energy Efficiency and Price Elasticity 

Energy-efficient technologies provide a substitute for energy consumption when energy 

prices increase, which has important implications for the price elasticity of demand in 

energy markets. The price-elasticity of demand measures the percentage change in the 

amount demanded given a percentage change in the price of a good. Overall, this 

measure reflects the value of a good to consumers and the availability of substitutes. 

6
For the goods considered in this study, electricity and natural gas, the availability and 

cost of substitutes vary throughout the country. Constraints in infrastructure cause some 

of the differences in availability. For instance, the states of Maine and Florida have 

limited capacity for natural gas. Therefore, natural gas is a more costly substitute for 

electricity in these states relative to most others. In some cases, policy can drive 

differences in the cost of substitutes. Many states have programs to subsidize adoption of 

energy-efficient technologies, which also creates geographic differences in the cost of a 

substitute to electricity and natural gas. Both cases may cause price elasticities to vary 

across the country. 

The preceding discussion provided reasons why the price elasticity of demand may vary 

and it suggests the direction that price elasticities could change. In areas where the costs 

of substitutes are competitive, price elasticities may increase in absolute magnitude 

(become more elastic) because consumers could more easily switch to substitutes as 

prices increase. Locations where particular energy uses are very valuable, such as air 

conditioning in southern states or winter heating in northern states, could have price 

elasticities smaller in absolute magnitude (more inelastic) because air conditioning and 

heating are so valuable during periods of extreme climate that consumers are unwilling to 

change their use when prices change. Again, both of these driving factors, the cost of 

substitutes and value of energy uses, vary geographically, which suggests price elasticity 

may differ across the country. 

Analytical Approach 

In this study, we analyzed energy demand for three markets—residential electricity, 

commercial electricity, and residential natural gas—and geographical variation in energy 

markets by region, state, and utility (for residential electricity). We assessed how trends 

in energy intensity, per capita energy expenditures, and expenditures as a share of income 

varied across the country. And, since the NEMS model currently uses one national value 

for price elasticity and the preceding discussion suggested some reasons why price 

elasticity might differ geographically, a primary focus of the study was to analyze if price 

elasticities vary at the regional, state, and utility levels. These analyses will help EERE 

7
evaluate whether they need to use more disaggregated analysis in estimating the benefits 

of their programs. 

Summary of Findings 

Our analysis indicates that there are significant regional and state differences in the pricedemand relationship for residential electricity and less so for commercial electricity and 

for residential natural gas. We did find, though, that there tends to be some consistency 

among states within a region and visible differences between regions in consumption and 

price trends. This tendency seems to be particularly strong for residential electricity use. 

It is possible that this relationship is more significant for residential electricity because 

some electricity uses in the home may be more discretionary than commercial or natural 

gas uses. Some electric using appliances can be used less, lights can be switched off and 

more efficient bulbs used. Most commercial business has limited availability to alter 

electricity sue in the short run, and residential natural gas use which is primarily for water 

heating, cooking and heating has less potential for modifications. 

The results imply that the DOE may have reason to explore differentiating the impacts of 

energy efficiency by region, at least for residential electricity. There does not seem to be 

a need, at least in the short run, for further disaggregation by geographic area in the two 

other energy markets, although more research is needed to offer a more conclusive 

recommendation. 

We also found that the relationship between consumption and price is small. That is, 

demand is relatively inelastic to price. We also found that in the past 20 years, this 

relationship has not changed significantly; analyses performed in the 1980s2

 showed 

approximately the same results. These findings might imply that there are few options 

available to the consumer in response to changes in the price of energy, and that price 

does not respond much to changes in demand. On the other hand, because prices were 

2

 Bohi, Douglas R., and Mary Beth Zimmerman, “An Update on Econometric Studies of Energy Demand 

Behavior,” Annual Review of Energy, Vol. 9, 1984, pp. 105-154; Dahl, Carol A., “Do Gasoline Demand 

Elasticities Vary?” Land Economics, Vol. 58, No. 3, August 1982, pp. 373-382; and Dahl, Carol A. and 

Thomas Sterner, “Analyzing Gasoline Demand Elasticities: A Survey,” Energy Economics, July 1991, pp. 

203-210. 

8
declining in real terms over most of the period we studied, the inelasticity of demand may 

be more of an artifact of the lack of price increases. 

However, we now may be witnessing some changes in this area. In the past few years, 

energy prices have increased with some states facing increasing electricity prices and all 

states facing increasing natural gas prices. While it is difficult statistically to uncover 

specific changes in trends, there are signs that demand growth has slowed, possibly due 

to a combination of increasing or flat prices and the economic slowdown of the past few 

years. Although we cannot say specifically that the relationship between price and 

demand might shift in an increasing-price environment, more analysis on recent trends 

may be warranted. 

Organization of This Report 

In Chapter Two, we provide a brief overview of 30 years of literature on the energy 

price-demand relationship and past attempts to estimate price elasticity. We then follow 

with an explanation of the methodology we used in this study. Chapters Three through 

Six present the study results in order by increasing levels of disaggregation of data— 

national-level analysis in Chapter Three, regional-level analysis in Chapter Four, statelevel analysis in Chapter Five, and utility-level analysis for the residential electricity 

sector in Chapter Six, Chapter Seven presents the conclusions derived from the results of 

the study, implications for the DOE and for federal energy-efficiency policy, and 

thoughts for next steps on research topics. The appendixes present methodological details 

and our data sources. 

9
Chapter 2: Economic Theory, Literature, and Methodological Approach 

In this chapter, we present information that we used in producing our findings on energy 

price-demand relationships and the comparative impacts from energy efficiency at the 

national, regional, state, and utility levels. We first provide an overview of some of the 

literature on energy demand, and then describe the model we used to estimate energy 

demand. 

Previous Literature on Energy Demand 

Previous studies have found that energy demand is inelastic in the short run but more 

elastic in the long run. Several studies also found that price elasticities varied across 

locations, but the same general pattern remained (inelastic demand in the short run and 

more-elastic demand in the long run). The energy-demand literature consists of several 

dozen papers and is too voluminous to describe here in detail. Therefore, this section 

focuses on a representative handful of survey articles on this subject. 

Taylor (1975) completed one of the first literature surveys on electricity demand. He 

reviewed the existing studies on residential, commercial, and industrial electricity 

demand. For residential electricity, he reported that short-run price elasticities varied 

from –0.90 to –0.13. Long-run price elasticities ranged from –2.00 to near zero. The 

only study of commercial price elasticities that differentiated between long-run and shortrun elasticities observed a short-run price elasticity of –0.17 and a long run elasticity of 

–1.36. 

Bohi and Zimmerman (1984) conducted another comprehensive review of studies on 

energy demand. They surveyed the existing research on demand in the residential, 

commercial, and industrial sectors for electricity, natural gas, and fuel oil. They also 

reviewed studies on gasoline demand. Bohi and Zimmerman found that the consensus 

estimates for residential electricity price elasticities was –0.2 in the short run and –0.7 in 

the long run. They reported that the range of estimates in commercial electricity was too 

variable to make conclusions about consensus values. For residential natural gas 

10
consumption, they reported consensus values of –0.2 in the short run and –0.3 in the long 

run. 

Bohi and Zimmerman also concluded that the energy price shocks of the 1970s did not 

change the structural characteristics of consumer demand. The studies they reviewed 

include studies from before and after the energy-price shocks in 1974 and 1979. They 

compared studies from the pre– and post–price-shock periods and also reported findings 

from studies that had divided study samples across the various periods to determine if any 

structural changes occurred in energy demand. One hypothesis they tested is that demand 

may become more elastic at higher price levels. Another hypothesis they tested is that 

rapid price changes sensitize consumers to energy demand, causing consumers to change 

their habits to conserve more energy. 

Bohi and Zimmerman did not find much evidence to support their hypotheses. The 

estimated price elasticities from studies before and after the price shocks of the 1970s do 

not differ substantially. However, the authors could not use statistical tests of 

significance to evaluate the differences between price elasticities. In addition, several 

studies reviewed by Bohi and Zimmerman tested whether the price shocks changed the 

structural characteristics of the energy demand equation used to estimate elasticities. 

They found that energy demand decreased significantly after the price shocks. But, their 

analyses did not reveal any change to the structural characteristics of the energy demand 

equation. 

Dahl and Sterner (1991) conducted a comprehensive review of the literature on gasoline 

demand (gasoline demand was not included in our study due to lack of available data). 

However, their review found consensus estimates on price elasticities. Dahl and Sterner 

concluded that the average short-run price elasticity was –0.24, and the average long-run 

price elasticity was –0.80. 

Several previous studies also examined whether energy-price elasticity varied across 

locations. Houthakker et al. (1974) estimated price elasticities for residential electricity 

11
and gasoline and found that elasticities varied across states. They also found some 

correlation between price elasticity and degree of urbanization. Elasticities generally 

became more elastic as the degree of urbanism decreases, except for the most-rural states, 

which had a positive elasticity for both gasoline and residential electricity demand. 

Houthakker et al. did not offer an explanation for this pattern, especially the positive 

elasticity for the most-rural states. 

Maddala et al. (1997) estimated price elasticities in 49 U.S. states (excluding Hawaii) and 

found variation across states. The mean of the estimates was –0.16. The minimum was 

–0.28, and the maximum was –0.06. In the long run, the mean was –0.24, with a 

minimum of –0.87 and a maximum of 0.24. 

Garcia-Cerrutti (2000) estimated price elasticities for residential electricity and natural 

gas demand at the county level in California. For residential electricity, the estimate of 

the mean was –0.17, with a minimum of –0.79 and a maximum of 0.01. 

In summary, previous studies show that price elasticities are generally inelastic in the 

short run and more elastic in the long run. Further, elasticities vary at the state and county 

levels; however, the same general pattern of inelastic demand in the short run and more 

elastic demand in the long run still holds. 

Estimation Approach 

For this study, we used a dynamic demand model developed by Houthakker et al. (1974). 

This model estimates long-run and short-run energy demand by using lagged values of 

the dependent variable along with current and lagged values of energy prices, population, 

economic growth/per capita income, and climate variation. The model estimates shortrun demand using energy prices and quantity demanded in the current period, and it 

estimates long-run demand through changes in the stock of energy-consuming appliances 

reflected by the lagged dependent variable. The technical details of the model and the 

process for making adjustments to reflect long-term demand are described in Appendix 

A. 

12 
We used state-level panel data on residential and commercial electricity consumption and 

residential natural gas consumption in the 48 contiguous U.S. states. The residential 

electricity and natural-gas data span 1977 through 2004. The commercial electricity data 

include only the years 1977 through 1999 because of limitations in economic data 

available from the Bureau of Economic Analysis. We also used a dataset on residential 

electricity consumption at the utility level from 1989 through 1999. The state energy 

data are from the DOE Energy Information Administration’s (EIA) Electric Power 

Annual (see Appendix B for details). This publication contains data on electricity 

consumption and prices by energy-using sector. The natural gas data are from a “U.S. 

Gas Prices” table on the EIA’s Natural Gas Navigator Web site.3

 Finally, the utility data 

set comes from data reported to the DOE on form EIA-861. Submission of this form is a 

mandatory reporting requirement for utilities in the United States. The data on 

demographic and economic variables are from the Bureau of Economic Analysis in the 

Department of Commerce (again, see Appendix B for details). 

The analysis uses a fixed-effects model, which controls for time effects, and a set of 

covariates. The location-specific price elasticity estimates come from interaction terms in 

the model between a location-indicator variable (region, state, or utility) and the variable 

of interest (price or lagged quantity). The estimates on the interaction terms indicate any 

differences between locations in the sample. The final elasticity estimates for each state 

are the sum of the estimate of the main effect and the interaction term for the location. 

The analysis uses hypothesis tests to determine if individual estimates are significantly 

different from zero and if a location is significantly different from the other locations. 

We estimate this model using the following fixed-effect specification: 

QD

i,t = QDi,t-1γ + Xi,tβ + Xi,t-1α + si + yt + εi,t 

3

 Current data on the Web site can be found at table can be found at 

http://tonto.eia.doe.gov/dnav/ng/ng_pri_sum_dcu_nus_m.htm. 

13
where QD

i,t is log energy demand in state i and year t, QDi,t is the lag value of log energy 

demand, Xi,t is a set of measured covariates (e.g., energy prices, population, income, or 

climate) that affect energy demand, and Xi,t-1 is the lag values of the covariates. The 

residual has three components: 

• si is an indicator variable that captures time-invariant differences in energy 

demand across states (“state fixed effects”). 

• yt is an indicator variable that captures time effects common to all states (“year 

fixed effects”). 

• εi,t is a random error term. 

We estimate any spatial differences in the energy demand relationship by adding 

interaction terms between the region or state indicator variables and the regressors of 

interest (price, quantity, and income). These interaction terms allow the estimated 

parameters to vary for each region or state, and we can then determine whether price 

elasticities differ across geographical units. 

The fixed-effects model controls for state-specific time-invariant factors that could bias 

the parameter estimates. The year effects in the model control for any time effects 

common to all states in a particular year, which could bias the parameter estimates. 

These effects control for many potential sources of bias. However, the fixed and year 

effects do not control for state-specific factors that vary through time. If any of these 

factors are correlated with explanatory variables and also affect energy demand, then the 

regression will have biased estimates. 

The fixed-effects model controls for effects specific to each state or utility that do not 

vary through time. An example of such a fixed effect is abundant energy supplies in 

certain states, such as hydroelectric power in the Pacific Northwest states or coal in West 

Virginia. This is a fixed effect because the states have those resources due to 

geographical factors that cannot change in the sample period. These states also tend to 

have much lower energy prices than other states. The fixed-effects model controls for 

14
this particular state-specific effect that does not vary through time and all other fixed 

effects that may or may not be measurable. Without controlling for these effects, the 

effects would bias the results. Appendix A explains the fixed-effects model in more 

detail. 

The model also controls for time trends that affect all the states uniformly. An example 

of a time trend would be the enactment of a new energy-related law or a change in the 

majority political party in Congress. These factors have a constant, national effect, for 

which the model can control using indicator variables for each year. 

The next four chapters present an overview of the results of our analysis of how energy 

prices and demand interact for residential electricity and natural gas and for commercial 

electricity. Details of all the results are presented in Appendix D. Because the purpose of 

this study is to see whether the price-demand relationship differs at the regional or state 

level, we present the results in descending order of dissaggregation—national, then 

regional, then state, and finally utility-level results. Within the chapters, we first discuss 

residential electricity, then commercial electricity, and then residential natural gas. 

15
Chapter 3: National-Level Results 

Residential Electricity Use 

Real electricity prices peaked in the early 1980s in the United States and steadily declined 

until 2000–2001 (see Figure 3.1). In 2001, average electricity prices increased in many 

states, and the figure shows a slight price rise over the past two years in the period 

studied. The figure also shows that residential electricity demand rose steadily during 

this period, although it appears that demand growth may have slowed after 2002. The 

long-term trend is an average annual increase in demand of approximately 2.6 percent. 

Figure 3.1: Residential Electricity Prices, Demand, and Intensity, 1977–2003 

There also was a steady increase in intensity (i.e., per-capita residential electricity use) 

until 2002. The long-term trend in the time series is an average annual increase of 1.5 

percent. Per-capita residential electricity seems to have leveled out over the past few 

years of the period, perhaps due to the flattening of prices and the post-9/11 recession. 

To generate values of the price-demand relationship that we could compare across 

regions and states, we use the functional form described in Chapter Two for estimating 

the price elasticity for residential electricity. Table 3.1 displays the results of our 

16
regression analysis for the residential electricity sector. It presents the coefficients from 

the regression analysis and notes whether the variable is significant. The dependent 

variable is residential electricity demand. The data points represent each state for each 

year in the sample. The independent variables are electricity demand in the previous 

year; average real electricity price in the current and previous years; residential 

disposable income in the current and previous years; population in the current and 

previous year; natural gas price in the current and previous years; and climate measured 

as heating and cooling degree days (see Appendix A for a definition of degree days). 

Definitions of the variables are presented in more detail in Appendix C. Details of the 

regressions are in Appendix D. 

These estimates reflect national-level values. 

Table 3.1: Results of Regression Analysis of Residential Electricity Demand, 1977–2004 

Variable Coefficient Statistically 

Significant 

Electricity demand in previous year .232 Yes 

Electricity price in current year -.243 Yes 

Electricity price in previous year -.129 Yes 

Income in current year .003 No 

Income in previous year .384 Yes 

Population in current year -.225 No 

Population in previous year .827 Yes 

Natural gas price in current year -.005 No 

Natural gas price in previous year .111 Yes 

Climate – heating and cooling degree-days .246 Yes 

The table shows that the estimated short-run price elasticity is –0.2, which is statistically 

significant. The estimated long-run price elasticity is –0.32, and this value is also 

statistically significant. These estimates are consistent with results from the studies of 

residential electricity elasticity, cited in Chapter Two, which were conducted with data 

from earlier years. The survey literature concluded that the residential short-run elasticity 

was near 0.2. 

17
The results also generally show that, except for price, the current-year variables are not 

significant, but the lagged or previous-year variables are statistically significant, 

suggesting that demand for electricity responds after changes occur in factors that 

influence the demand. For example, a consumer’s level of income does not seem to 

impact demand in the same year, but income from one year seems to impact demand in 

the following year. This essentially means that change in income over time impacts 

electricity use, and growing incomes lead to increasing electricity use. Population growth 

has a similar effect. Natural gas prices have an expected result—increasing natural gas 

prices one year lead to increasing electricity demand in the following year. This pattern 

would reflect cases in which people switch from natural gas to electricity for some 

energy-consuming applications, such as heating or cooking. Finally, the more heating 

and cooling degree days there are, the higher the demand for electricity. 

None of these results are unexpected, although what might be somewhat surprising is that 

the basic magnitude of these results has not changed in the past 20 to 30 years. Previous 

analyses done in the late 1980s and early 1990s showed just about the same results. 

Commercial Electricity 

We next examine the price-demand relationship for use of electricity by the commercial 

sector. Some commercial-sector electricity data exhibit trends similar those seen in the 

residential-sector data (see Figure 3.2). Real prices of electricity peaked in the early 

1980s and steadily decreased through the period studied. Demand consistently increased 

throughout the study period. The average annual growth in demand during the period 

was 3.4 percent. Because the data we have for the commercial sector go only to the year 

2000, we do not display recent price increases and do not know how they might have 

impacted demand. 

In Figure 3.2, we show two pictures of commercial electricity intensity. One is electricity 

demand in mWh per dollar of commercial gross state product (GSP)—i.e., the size of the 

commercial electricity sector in economic terms. By this measure, electricity use has 

declined as a ratio of electricity demand to economic output from the commercial sector. 

18
Figure 3.2: Commercial Electricity Prices, Demand, and Intensity, 1977–1999 

The other measure of intensity is electricity use per available square feet of space in the 

commercial sector. By this measure, electric intensity has increased over the period, 

reflecting the rapid growth in demand. This trend implies that the commercial sector, 

while getting more productivity out of electricity on a per-dollar basis, is continuing to 

add electricity loads to buildings, despite the fact that significant amounts of new, and 

ostensibly more-efficient, commercial space was added over the last few years of the 

period illustrated in the figure. 

The relationship among demand, price, and other factors in the commercial sector has 

some similarities to the relationship among demand, price, and other factors in the 

residential sector and also some significant differences. Table 3.2 displays the regression 

analysis results for a regression with the dependent variable being commercial electricity 

demand. The independent variables have a similar construct as the residential model— 

demand in the previous year; prices in the current and previous year; GSP for the 

commercial sector (i.e., income) in the current and previous year; office-space measures 

in square feet in the current and previous year; natural gas prices; and climate. 

19
The commercial electricity regression estimates are also consistent with estimates cited in 

Chapter Two. The short-run price elasticity is –0.21, and the long-run price elasticity 

estimate is –0.97. Previous studies found short-run elasticities somewhere around –0.2. 

Long-run elasticities were more variable, and the survey literature did not report 

consensus values for long-run elasticities. Our long-run estimate of –0.97 is within the 

consensus range for residential electricity and natural-gas demand, however. 

Table 3.2: Regression Analysis Results for Commercial Electricity Demand 

Variable Coefficient Statistically 

Significant 

Electricity demand in previous year .785 Yes 

Electricity price in current year -.209 Yes 

Electricity price in previous year -.148 Yes 

Commercial GSP in current year .155 No 

Commercial GSP in previous year -.039 No 

New floor space in current year .504 No 

New floor space in previous year -.421 No 

Natural gas price in current year -.023 No 

Natural gas price in previous year .049 Yes 

Climate – heating and cooling degree-days .246 Yes 

Interestingly, of the many of the factors that we thought should impact electricity demand 

in the commercial sector, commercial economic output (i.e., GSP) and floor space turned 

out to be not significant. 

Natural Gas 

The patterns for residential natural-gas demand differ from those in the electricity 

markets (see Figure 3.3). Prices peaked in the early 1980s and then again after 2001. 

Demand for natural gas in the short term is more variable than demand for electricity in 

the short term, and there is no real growth in demand over the period that was studied, 

and a recent downward trend perhaps reflects increased prices. 

20
Figure 3.3: Residential Natural Gas Prices, Demand, and Intensity, 1977–2003 

In contrast to residential electricity intensity, natural gas intensity declined during this 

period. The long-term trend during this period was a 0.9 percent decline in intensity 

(defined for this sector as demand per capita for natural gas), reflecting some improved 

energy efficiency and some substitutions away from natural gas. 

The regression estimates also differ from those for the electricity market (see Table 3.2). 

Table 3.3 shows regression results, with the dependent variable being residential natural 

gas prices and the same variables as were used for the residential electricity regression. 

The short-term price elasticity is –0.12, and long-term price elasticity is –0.36. Bohi and 

Zimmerman (1984) reported consensus values of –0.2 in the short term and –0.3 in the 

long term. These values may reflect the fact that there are fewer opportunities for 

consumers to reduce their demand for natural gas in response to price, possibly because 

the use of natural gas in the home (i.e., for air and water heating and cooking) is a 

necessity, whereas turning off some lights or using fewer electric appliances is optional. 

21
Table 3.3: Regression Analysis Results for Residential Natural Gas Demand 

Variable Coefficient Statistically 

Significant 

Natural gas demand in previous year .67 Yes 

Natural gas price in current year -.12 Yes 

Natural gas price in previous year -.08 Yes 

Electricity price in current year .03 No 

Electricity price in previous year .11 Yes 

Income in current year .24 Yes 

Income in previous year .07 No 

Population in current year 1.18 Yes 

Population in previous year -.86 Yes 

Climate – heating and cooling degree-days .27 Yes 

The natural gas results differ from those for electricity. Income in the current year is a 

significant factor in demand for natural gas, whereas income in the previous year is not. 

The reason that previous-year income is significant for electricity could be because 

increased income might lead to consumers buying new appliances that add to the 

electrical load in the following year. In the case of natural gas, by comparison, there a 

that increased income might lead to consumers turning up the thermostat in the winter, 

adding to their current-year natural-gas consumption. The impact of electricity price on 

natural gas demand in the previous year is consistent with what we saw with the impact 

of natural gas price on electricity demand. 

Summary of National-Level Results 

As we have seen in this chapter, there are similarities and differences between the 

patterns of demand and price when comparing residential electricity, residential natural 

gas, and commercial electricity. Residential electricity use and intensity increased over 

the period we studied, although recent electricity price increases have slowed the growth 

of demand. Natural gas use has been flat, and intensity has declined, and we might see a 

greater decline due to recent natural-gas price increases. Commercial electricity use grew 

rapidly over the period studied, and while electricity as a share of output in the 

commercial sector has declined, electricity use per square foot of office space has 

22
continued to increase. A comparison of estimated price elasticities for the three sectors is 

presented in the Table 3.4. 

Table 3.4: Price Elasticities for Residential Electricity, Commercial Electricity, and Residential 

Natural Gas at the National Level 

Residential Commercial Residential Natural 

Electricity Electricity Gas 

Short-run elasticity -.24 -.21 -.12 

Long-run elasticity -.32 -.97 -.36 

Short-run price elasticities for electricity are similar for residential and commercial 

demand, although it appears that changes in commercial electricity price can have a 

bigger impact in the long term than in the short term. In the short run, natural gas 

demand is less elastic than demand for electricity but is about the same in the long run. 

We used the national-level information presented in this chapter as a starting point for 

determining whether elasticities differ significantly among regions and states. The next 

chapter describes the regional-level results. 

23
Chapter 4: Regional Results 

This chapter describes the results from our analysis of trends in the three energy markets 

(residential electricity, commercial electricity, and residential natural gas) at the regional 

level. The analysis uses the nine census divisions that the DOE Energy Information 

Agency uses in energy modeling and forecasting: New England, Mid-Atlantic, South 

Atlantic, East North Central, East South Central, West North Central, West South 

Central, Mountain, and Pacific (see Figure 4.1).4 

Figure 4.1: DOE Energy Information Agency Census Regions 

In this analysis, we look at regional trends in energy intensity, energy expenditures, and 

expenditures as a share of income to determine if they differ among regions. We then 

4 We excluded Alaska and Hawaii from our analysis because they are unique in their energy uses and 

climate. 

24
reproduce the regressions shown in the national-level analysis in Chapter Three to 

determine if there are significant differences in the price elasticities among regions. 

Residential Electricity 

Of the three markets that we examined in this study, residential electricity shows the most 

regional differentiation. Figures 4.2, 4.3, and 4.4 display trends in residential electricity 

use, expenditures, and expenditures as a share of total income, respectively, for the nine 

DOE census regions. The Figure 4.2 shows regional trends in per-capita residential 

electricity intensity. 

Figure 4.2: Regional Trends in Per-Capita Residential Electricity-Intensity, 1977–2004 

Figure 4.2 shows four categories of trends in intensity–increasing over the period more 

than 1.5 percent on average, increasing between 1 percent and 1.5 percent per year on 

average, increasing less than 1 percent, or declining. Only one region had declining 

electricity intensity--the Pacific. Residential electricity intensity is growing fastest in the 

South Atlantic and East South Central regions. The Middle Atlantic, East North Central, 

and West North Central regions have the next-fastest growth rates. New England and the 

Mountain states have growth rates of less than 1 percent. 

25
It is interesting to note that some commonality exists across contiguous regions. The 

East South Central, West South Central, and South Atlantic regions have experienced the 

most-rapid growth in electricity intensity, perhaps driven by air-conditioning loads and 

rapidly growing populations. The Middle Atlantic and West North Central regions also 

have had increasing air-conditioning loads at levels that did not exist until the late 1980s, 

and they have seen relatively rapid growth in electricity intensity over this period. 

The Pacific Coast, which is dominated by California in its magnitude of electricity use, 

has had declining electricity intensity, possibly due to energy-related building codes that 

are the strictest in the nation and have been in place longer than any others. 

All of these findings might imply that the impact of energy efficiency would be greater in 

areas such as the South in which the intensity of electricity use has been growing more 

rapidly than in other regions and might have less of an impact in the Pacific Coast where 

intensity has been declining. 

Figure 4.3 shows growth trends for average expenditures on residential electricity. The 

figure shows that average expenditures on residential electricity are growing in all 

regions but provides a different picture than residential electricity intensity. Expenditures 

are growing most rapidly in the South Atlantic, East South Central, New England, and 

Pacific Coast regions. The Middle Atlantic and West South Central regions have the 

next-fastest growth rate, while the Mountain, East North Central, and West North Central 

regions have the slowest growth rates. 

In a demand-price relationship, one might expect to see a picture similar to the one for 

electricity intensity--those areas with the most rapid increases in expenditures would have 

declining or slower growth in electricity intensity. While this is true for the Pacific states 

and Northeast, the opposite is true for the South Atlantic and East South Central regions. 

This is the first indication that the regional differences in the demand-price relationship 

might matter when estimating the impact of energy efficiency on other demand changes. 

26
Figure 4.3: Regional Trends in Average Expenditures on Residential Electricity, 1977–2004 

We now look at average expenditures on residential electricity as a share of personal 

income (see Figure 4.4). Although the spread of the numbers is small, there are a few 

interesting findings to note. First, even though expenditures on electricity have been 

rising, the share of electricity as a percentage of income has been declining, meaning that 

incomes are growing faster than electricity use. In the Mountain and Northeast regions, 

the relationship is what we would expect—where expenditures per dollar of income are 

declining rapidly, electricity intensity is growing quickly. We would expect that where 

the expenditures per dollar of income are declining more slowly than in other regions, 

electricity intensity growth would be slower or declining (as is the case in the Pacific 

Coast). But in the South Atlantic and East South Central regions, we find that even 

though the expenditure per dollar of income is not declining as fast as that in other 

regions, electricity intensity is growing more rapidly than in the other regions. This 

finding might be an indication that electricity use in the South Atlantic and East South 

Central regions is relatively insensitive to the cost of using electricity. At the very least, it 

is another indication of regional diversity. We also see some commonality among 

neighboring regions--for example, energy intensity in all the Southern regions is 

declining more slowly than in other regions, while in the mid-Northern regions it is 

declining more rapidly. 

27
Figure 4.4: Regional Trends in Average Expenditures on Residential Electricity as a Share of 

Income, 1977–2004 

One might conclude from Figures 4.2 through 4.4 that there are regional differences in 

the relationship between electricity demand and price and regional differences in the 

trends in electricity usage and expenditures. Using the method described in Chapter 

Two, we estimated the short-run and long-run price elasticities by region, which are 

presented in Figures 4.5 and 4.6. We find that the regional estimates of short-run 

elasticities range from -.04 in the East North Central region to .31 in the South Atlantic 

region. We also present the 95 percent confidence interval for each of the regional 

estimates. Where the confidence intervals do not overlap, we can say the regions are 

significantly different from each other. Where they do overlap, there may be differences, 

but, statistically, it is difficult for us to determine if they are actually distinct. In this 

case, all the confidence intervals overlap to some extent, except for those for the South 

Atlantic and East North Central estimates. Those two regions are the only ones that have 

significant differences in elasticities. 

Long-run demand (see Figure 4.6) is more elastic than short-run demand in each region, 

and while the long-run pattern is relatively similar to the short-run pattern, the East South 

Central region in this case is the most elastic, and the differences between the East South 

28
Central and South Atlantic regions and the East North Central region are statistically 

significant. The other regions differ from one another less for long-run elasticities than 

they do for short-run elasticities. 

Figure 4.5: Estimated Short-Run Residential-Electricity Price Elasticities by Region, 1977–2004 

Figure 4.6: Estimated Long-Run Residential-Electricity Price Elasticities by Region, 1977–2004 

29
When the various pieces of this analysis are brought together, they indicate that the 

relationship between demand and price vary enough by region that estimates of future 

residential electricity use or estimates of the impacts of energy-efficiency programs 

should reflect some of the regional variation. 

Commercial Electricity Results 

While the analysis of the residential electricity sector showed significant regional 

differences, the commercial electricity sector is somewhat less diverse. Our analysis of 

commercial energy intensity found some differences across regions, but the elasticities 

did not differ. The trend in electricity intensity per square foot of office space has been 

moving toward increased intensity, with slower increasing rates in the Pacific Coast and 

East South Central regions (see Figure 4.7). We cannot say that the Pacific Coast region 

is statistically different from zero in terms of commercial electricity intensity, and the 

West Southern Central and East Southern Central regions are significantly lower than 

most of the other regions. This finding indicates that new newly constructed buildings 

may be more energy efficient in some regions than in other regions. It may also indicate 

that the impacts of future improvements in commercial electricity efficiency may be 

larger regions with high growth in energy use, such as New England, the West North 

Central, and the South Atlantic, and might have little additional impact on the Pacific 

Coast region. 

The short-run price elasticities for commercial electricity range from just under -.3 to -.15 

(see Figure 4.8). Figure 4.8 indicates that some differences exist in short-run price 

elasticity estimates across regions, but they are smaller than the differences in such 

estimates across regions in the residential electricity sector. In addition, the commercial 

electricity estimates have considerably greater variance (larger confidence intervals) than 

the residential sector estimates. Given this large variance, there are no significant 

differences among regions. Although we cannot say the regions are statistically different 

from each other, it does appear that the Pacific Coast and East South Central regions are 

30
somewhat more elastic in terms of commercial electricity than the other regions, and one 

might look at these two regions somewhat differently than the others. 

Figure 4.7: Regional Trends in Commercial Electricity Use per Square Foot of Office Space, 

1977–1999 

Figure 4.8: Short-Run Commercial Electricity Price Elasticities by Region, 1977–1999 

31
Figure 4.9 shows that demand is more elastic in the long run than in the short run for the 

commercial electricity sector, but there is even less variation among the regions. The 

estimates shown in Figure 4.9 have large variances, and discerning differences in 

elasticities among the regions is not possible. 

What we can conclude from the above discussion is that there are not many regional 

differences in commercial electricity use. Therefore, estimates of future electricity use at 

the regional level will not be greatly impacted by dissaggregation to the regional level, 

except perhaps for the Pacific Coast and East South Central estimates. Differences in 

elasticities among states are still possible, and those differences are assessed in Chapter 

Five. 

Figure 4.9: Long-Run Commercial Electricity Price Elasticities by Region, 1977–1999 

Residential Natural Gas 

Our analysis of residential natural-gas energy intensity and expenditures on natural gas as 

a share of income shows that there are differences in long-term trends among regions, 

although the trends themselves are small in magnitude. Figures 4.10 and 4.11 show 

32
intensity and price trends by region for natural gas. The largest increase in intensity is in 

the Pacific Coast, driven by Washington and Oregon, and the greatest decline in intensity 

is in the West South Central region, driven by Texas. There are large variations in the 

estimates; therefore, for the most part, we cannot distinguish among trends in intensity in 

the regions. Clearly, though, the Pacific Coast and Mid-Atlantic trends are positive, and 

the rest are negative (with the New England trend being indistinguishable from zero). 

This finding does suggest, however, that improvements in the efficiency of natural-gasusing appliances might have a bigger impact in the Pacific Coast and Middle Atlantic 

regions than they would in most other regions, and that additional improvements in the 

energy efficiency of natural-gas-using appliances in the West South Central region may 

have little impact. 

Figure 4.10: Natural-Gas Intensity Trends by Region, 1977–2004 

The price trends for natural gas provide an interesting picture of the demand-price 

relationships one would expect. Increasing-price trends occurred in the regions with 

declining natural-gas intensity; the Pacific Coast and New England regions, which had 

increases in intensity, had an overall trend of prices not increasing (prices fluctuated 

across the sample for all the regions, but in the Pacific Coast and New England regions, 

the overall average trend was indistinguishable from zero). The sole exception to these 

33
trends is the Middle Atlantic region, which had small increasing price trends and 

increasing intensities. In Washington and Oregon, which were the primary drivers on the 

Pacific Coast for the increasing intensity, there has been some substitution of natural gas 

for electricity for heating purposes, some of which may have been driven by building 

codes that encouraged shifts from electricity for water heating. These results certainly 

indicate that some interesting results should be expected from the elasticity estimates. 

Figure 4.11: Natural-Gas Price Trends by Region, 1977–2004 

Short-run price elasticity for residential natural gas varies from –0.03 for the West South 

Central region to –0.18 for the Pacific Coast (see Figure 4.12). The variance in the 

estimates, as in the commercial sector, is large. The Pacific Coast, again, has the greatest 

elasticity, and its neighboring Mountain region runs a close second. While we cannot say 

that the elasticity in these two regions is significantly different from that of the other 

regions, it may be worth further exploring benefits estimates for these two regions. Of the 

contiguous regions, the southern-state regions are the least elastic, and the northern-state 

regions are in the middle. 

The long-run price-elasticity estimates (see Figure 4.13) are more elastic than the shortterm estimates, with the most inelastic region still the West South Central and the most 

34
elastic still the Pacific Coast. While the variances are large in the long run, too, the 

Pacific Coast and Mountain regions are close to being significantly different from the 

West South Central, and there is a group in the middle with similar elasticities. 

Figure 4.12: Short-Run Natural-Gas Price Elasticities by Region, 1977–2004 

Figure 4.13: Long-Run Natural Gas Price Elasticities by Region, 1977–2004 

35
The results for residential natural gas fall somewhere in between those for residential 

electricity and those for commercial electricity in terms of regional differences. As was 

the case with commercial electricity, there are few discernable differences in trends 

among the regions, but there are more differences in the elasticities than in commercial 

electricity, although still not at the level of significance that was seen in residential 

electricity. It might make a difference in forecasts and estimates if the Pacific Coast and 

Mountain regions are differentiated from the other regions. 

Regional Analysis Conclusions 

The analysis of regional-level differences in the price-demand relationship provides 

different answers for different markets. It seems clear that there are regional differences 

in the residential electricity market, and that estimates of the impact of energy efficiency 

and forecasts of electricity demand could differ significantly if the regional differences 

are taken into account. Clearly, commercial electricity does not appear to differ 

significantly by region; therefore, national-level estimates of commercial electricity 

price-demand relationships are likely to be sufficient for analyzing the impact of energy 

efficiency in the commercial sector. The picture for residential natural gas is somewhat 

different. There is not a lot of variability in energy-use trends over time, which is 

probably why there is not much difference by region in the price-demand relationship. It 

appears that demand responds more to price in two of the regions than in the others, and 

this finding could have some implications for estimates of the benefits of energy 

efficiency, but national level results in this case are probably also sufficient for analyzing 

the impact of energy efficiency in the commercial sector 

36
Chapter 5: State-Level Analysis 

In this chapter, we use a methodology similar to the one that was used for the analysis in 

the previous chapter, but in this case, we differentiate state-level elasticities and trends. 

The state-level analysis consists of an examination of trends and an estimation of shortrun and long-run price elasticities for each state. 

Residential Electricity 

Beginning again with residential electricity, we look at the key trends in energy intensity 

and expenditures as a share of income. In terms of electricity use per capita (see Figure 

5.1), there are only a few states, which are concentrated in the West and New England, 

with trends of small or declining energy intensity over the period studied. As we found 

in the regional-level analysis, the high-growth areas in terms of residential electricity 

usage are concentrated in the South. The significance of Figure 5.1 is in the consistency 

it shows within regions. While the energy intensity trends do vary within each region, 

they do not vary significantly. There are no regions with some states with declining 

intensity and some states with rapidly increasing intensity. This is a first indication that 

the regional-scale analysis might be sufficient for analyzing the impacts of energy 

efficiency. 

We do observe some inter-regional variation in electricity expenditures as a share of 

income (see Figure 5.2). There is some diversity within each of the regions, although in 

most cases, the differences among states in a region are small except for a single state. 

Because the demand is consistent within regions, but the expenditures and prices are not, 

the elasticities in states in each region might vary. 

Next, we take a look at the differences in estimated residential electricity price 

elasticities, which are illustrated in Figure 5.3. Each square-shaped plot point in the 

figure represents a U.S. state, and the vertical lines represent the 95 percent confidence 

interval. Sixteen states have an estimated elasticity that is positive over the period 

studied, although the variance is large enough in most cases that it is difficult to 

37
distinguish it from zero. There are a few possible explanations for this observation. In 

the early years that were studied, when prices were rising, these states saw consistent 

increases in demand, and in the later time period, when prices were declining in real 

terms, these states did not have rapid growth in demand. Therefore, overall, it would 

appear that the demand-price relationship reacts differently in these states than in other 

states. We caution, however, that it is possible that in the future, increases in prices in 

these states would not lead to increases in demand, but that the demand in these states 

would indeed slow or decrease in a manner similar to that in other states (although the 

elasticity might still be substantially less than that in other states). 

Figure 5.1: State-Level Trends in Residential Electricity Intensity, 1977–2004 

38
Figure 5.2: State-Level Trends in Residential Electricity Expenditures as a Share of Income, 

1977–2004 

One other finding of note, illustrated in Figure 5.3, is that there are ten states (represented 

by the squares on the right-hand side of the figure with positive elasticities) that are 

significantly different than 11 states represented by the squares on the left-hand side of 

the figure (all of which have elasticities less than –.2). Given the size of the variances, it 

is difficult to distinguish differences in price elasticities among the other states. 

Figure 5.3: Estimates of Short-Run Residential Electricity Price Elasticities for Each State, 

1977–2004 

39
In Chapter Four, we illustrated significant differences in elasticities among regions. 

When we look at the individual state elasticities, some consistencies within the regions 

emerge. Figure 5.4 shows that several Mountain, West South Central, and West North 

Central states appear to have similar lower-positive or higher-positive price elasticities 

for residential electricity. Nevada, Idaho, Utah, Wyoming, Colorado, New Mexico, 

Oklahoma, Kansas, and Nebraska form a block of states with very inelastic demand (or 

estimated positive elasticities); the price-demand relationship in these states appears to be 

somewhat similar. Another broad region with notable results falls in the middle of the 

country and the Southeast. The group of states from Missouri to Florida has larger-thanaverage price elasticities, with the East South Central and South Atlantic regions showing 

some inter-regional inconsistencies. States within the Middle Atlantic region are 

consistent in terms of elasticities, as are the states in the East North Central region (with 

the exception of Wisconsin). This finding implies that even though there are 

considerable differences among the states in price elasticities, there are some regional 

consistencies. Therefore, disaggregation of data by region might still be sufficient for 

energy-efficiency impact analyses. 

As one might expect, the geographic patterns in long-run price elasticity estimates (see 

Figure 5.5) are similar to those in the short-run price elasticity estimates. The Mountain 

states have inelastic demand, whereas states in the South Atlantic and East South Central, 

Pacific Coast, and New England region have more-elastic demand in the long run. The 

variance in the long-run elasticity estimates is larger than in the short-run estimates, and 

more states exhibit positive long-run price elasticities than positive short-run price 

elasticities. Overall, these findings seem to indicate that over the time period studied, 

electricity demand continued to rise in many of these states, regardless of price. Given 

the prices and demand that were observed over this time period, it is not clear whether 

any conclusions can be made about how long-run demand would react to price increases. 

We can say, again, that there appear to be regional differences, but consistencies among 

states within the regions, in the long-run price-demand relationship. 

40
Figure 5.4: Estimated State-Level Short-Run Price Elasticities for Residential Electricity, 1977–2004 

Overall, the findings presented in this section imply that while regional disaggregation 

will be important for estimating future impacts of energy-efficiency technology and 

forecasting demand for residential electricity, state-level disaggregation may not be 

necessary for that purpose. 

Figure 5.5: Estimated State-Level Long-Run Price Elasticities for Residential Electricity, 1977–2004 

41
Commercial Electricity 

The state-level analysis of the commercial electricity sector reveals a pattern of electricity 

usage similar to that at the regional level – there seems to be some state-level variation in 

electricity use patterns, but few differences in the price-demand relationship. Figure 5.6 

shows trends in commercial electricity use per square foot of office space (i.e., the trends 

in intensity). We see the slowest growth in electricity use in states in the West, although 

a few of those states show a slow growth in intensity. There is some consistency in 

intensity among states within regions. For the most part, states within a region fall into 

one of two consecutive categories of growth. Again, this finding seems to indicate that 

the regional analysis would be sufficient to capture any differences that might exist in 

electricity intensity in the commercial sector. 

The estimated elasticities in commercial-sector intensity are what we might expect from 

the previous sets of analysis. Figure 5.7 shows the estimated state-level short-run 

elasticities. There is not much variation across the states in intensity, except for a few that 

are represented at the left side of the figure. For the most part, the estimated elasticities 

range between –.5 and zero, with a few states with positive elasticity (that is not 

significantly different from zero), and a few states that seem to have more-elastic 

demand. It is interesting to note that for a large number of states, the variance is small, 

which means that the elasticities are well estimated. This is in contrast to the residential 

sector, in which the variance is large for a number of states. There is also more variation 

among the states in the residential-sector analysis in comparison with the commercialsector analysis, which shows little variation among the states. We observed the same sort 

of patterns for the long-run elasticities. 

Clearly, there does not seem to be a reason to disaggregate the analysis for commercial 

electricity to the state level. 

42
Figure 5.6: Estimated State-Level Trends in Electricity Intensity in the Commercial Sector, 

1977–1999 

Figure 5.7: Estimated Short Run Elasticities in Electricity Intensity in the Commercial Sector at the 

State Level, 1977–1999 

43
Residential Natural Gas 

Because the regional-level results for residential natural gas showed little regional 

diversity and a lot of variance in the estimates, it is not surprising that we find basically 

the same results at the state level. There are differences among states, which become 

evident when looking at natural-gas intensity at the state level (see Figure 5.8). What is 

also evident is that there is significant variation among states within regions, with the 

notable exception of the East North Central and the West South Central, where the trends 

in natural gas use per capita are fairly consistent. Otherwise, there is not much in the way 

of observed patterns to note. There are more states in the North that have growing 

natural-gas intensities, but a group of states in the South Atlantic (and Tennessee) also 

have growing intensities. 

Given these findings, it is not surprising that we also find some large differences among 

the states in estimated short-run price elasticities for natural gas (see Figures 5.9a and 

5.9b) along with very large variances among the states. The price elasticities range from 

-.3 to .1, which is quite a broad range, but the variances are so large that we cannot even 

say that states at the extreme low end of the range are statistically different from other 

states. Reflecting what we observed at the regional level, there is not much in the way of 

consistency among states within the regions in terms of price elasticity. There is a group 

of contiguous states ranging from the middle of the country to the East Coast that have 

some similarities in elasticities. But again, because the variances are so large, there is not 

much we can interpret from these results, and there does not seem to be much of a reason 

to assess natural-gas demand and the benefits of energy-efficiency technologies at the 

state level. 

44
Figure 5.8: Trends in Natural-Gas Intensity at the State Level, 1977–2004 

Figure 5.9a: Estimated Short-Run Price Elasticities for Natural Gas at the State Level, 1977–2004 

45
Figure 5.9b: Short-run Price Elasticities for Natural Gas 

State-Level Conclusions 

There are differences among the states in price elasticities and in some trends in energy 

use and other factors, but, for the most part, they are not significant. As was seen with 

the regional analysis, there is a difference between the results of the residential electricity 

analysis and those of commercial electricity and residential natural-gas analyses. For 

residential electricity use, there is enough consistency among states within regions that a 

state-level disaggregation would not likely produce different results than would regionallevel analyses for forecasting the estimated benefits of energy-efficient technologies. 

For commercial electricity and residential natural gas, there is not much consistency 

among states, and there are significant amounts of variance in the estimates; therefore, it 

is not certain that one could use our approach to differentiate states to a degree that would 

be useful in forecasting estimates of energy-efficiency benefits. 

46
Chapter 6: Utility-Level Analysis 

The utility-level analysis posed a number of analytical challenges, which limited the 

conclusions that we were able to draw from our analysis. While consumption and price 

data at the utility level were available in the database that we used, data on other factors 

that are key to the price-demand analysis (such as income and climate) were not. As such, 

we used state-level data in place of the unavailable utility-level data. Nevertheless, we 

continued with the experiment to see if there appear to be significant differences in how 

price and demand respond at the utility-scale level, simply to glean whatever information 

that might contribute to this study. 

We did discover a few interesting things in this analysis. First, there is a lot of variation 

in elasticities among the utilities, which was not unexpected, although the price 

elasticities for about 65 percent of the sample are not statistically significant. Figure 6.1 

illustrates the percentage of the sample of utilities that are in each region (shown in 

Figure 4.1), and of those, the percentage with estimated price elasticities that are 

statistically significant. There are no apparent regional consistencies, other than the 

South Atlantic and East North Central regions having the highest percentage of utilities 

with significant elasticities. For most regions, the percentage of utilities in the region and 

the percentage with significant estimates are very similar. At one end, the East North 

Central region had about 5 percent more utilities with statistically significant price 

elasticities, as a proportion of all utilities in the dataset, than the region’s percent of the 

total number of utilities in the data set, and the Mountain region had more than 5 percent 

fewer utilities with statistically significant price elasticities. 

The price-elasticity estimates are wide-ranging and have limited precision. The range of 

elasticities for the statistically significant estimates was 1.1 to –1.87. The median was 

–0.57, and the mean was –0.63. 

Size of a utility appears to be correlated with the elasticity estimate. The range of 

elasticity estimates for the largest utilities (the median is –0.25, and the mean is –0.29) is 

47
similar to the range of estimates found in the state analysis. There is greater variability in 

the elasticity estimates observed in the small utilities, which results in a larger range of 

estimates. 

Overall, we cannot conclude much from the utility-level analysis, other than the large 

amount of variation in price elasticities suggests that it may be useful to delve further into 

analyzing utility-level electricity demand. Further analysis may produce information that 

is valuable for planning and estimating energy efficiency at this level. 

Figure 6.1: Percentage of Utilities in the Sample within Each Region and Percentage in Each Region 

with Significant Elasticities 

48
Chapter 7: Conclusions, Final Thoughts, and Implications of Analysis 

This chapter reviews the results of our analysis and their implications and presents our 

recommendations for further analysis. The key findings from this study are as follows: 

• There are state and regional differences in (1) electricity and natural-gas demand, 

(2) the relationship between changes in demand and changes in price (i.e., 

elasticity), and (3) factors that influence demand.

• It is difficult, with the data we have available, to show statistically significant 

differences at all levels for commercial electricity and residential natural gas, though 

our results do indicate there may be regional and state differences in how price and 

demand interact in each of those sectors. 

• We found significant regional differences in the price-demand relationship for 

residential electricity, but also found consistencies in the price-demand relationship 

for residential electricity among states within regions. 

• The price-demand relationships have not changed over the past couple of decades— 

our estimates are about the same as those from studies done in the 1980s. 

• Price elasticity—-i.e., how demand reacts to changes in price overall—has 

continued to be small since the 1980s. 

• Over the periods we examined (1977–2004 for residential electricity and natural 

gas, 1977–1999 for commercial electricity, and 1989–1999 for residential electricity 

at the utility level), some basic trends emerged: Demand for energy overall is 

increasing; in many cases, energy intensity is increasing, but price is decreasing; 

and, while expenditures on energy are increasing, energy expenditures as a share of 

consumers’ income and as a share of commercial sector output are declining. 

• The past few years have seen some changes in these patterns, and it is possible that 

some of these trends and relationships might exhibit further changes. 

49
Should DOE Disaggregate Data for Estimating Energy-Efficiency Programs Benefits? 

The results of this study have a number of implications for the DOE’s decisionmaking 

and policymaking. The basic question that was the impetus for this analysis was whether 

the DOE should disaggregate data (from the national level to the regional, state, or utility 

level) when estimating the benefits of its energy-efficiency programs. The answer to this 

question has a number of components. 

We first made the case that certain factors might affect the impact that energy efficiency 

would have on overall demand. We also made the case that the price-demand 

relationship, or price elasticity, was important for estimating the impact of energyefficiency programs and technology. In examining demand in each sector (residential 

and commercial electricity demand and residential natural-gas demand), we found that 

there are some differences in regional trends—in particular, trends in the intensity of 

energy use. Energy efficiency might have a bigger impact on regions with rapidly 

growing intensity of use than on regions with intensity that is either declining or growing 

slowly. 

In terms of the price-demand relationship, if increasing prices motivate investments in 

energy efficiency, then the impact of energy efficiency might be greater in regions or 

states that are the most elastic (i.e., those with the lowest negative price elasticities). In 

these regions and states, the price-demand relationship is the most robust, and changes in 

price could lead to greater changes in energy efficiency, and vice-versa. Any estimates of 

the impact of energy-efficiency programs will be impacted by price elasticity, and if the 

elasticity differs significantly by region or state, the estimates of the impacts will differ 

accordingly. 

In the case of the residential electricity sector, it is clear that there are regional 

differences. It also seems clear that the elasticities are relatively consistent among states 

within the regions and that, at least for the near term, disaggregating data on energyefficiency programs to the regional level should be sufficient to evaluate the different 

effects that energy efficiency could have in different regions of the country. 

50
The results are less clear for the commercial electricity sector. Few regions appear to 

have significantly different trends in the intensity of electricity use (specifically the 

Pacific Coast and perhaps the West North Central and East South Central regions, which 

have had slower growth). Statistically, it is difficult to distinguish among the other 

regions in terms of intensity of use, and there are no discernable differences in the price 

elasticities between regions. It does seem that the impact of energy efficiency in the 

Pacific Coast would differ from the impact in the rest of the country, and perhaps 

disaggregating Pacific Coast data from the national-level data is all that is needed to 

estimate the impact of certain DOE programs. On the other hand, there is some 

consistency in price elasticities among states within regions, such as what was seen in the 

residential electricity sector, although not to as great an extent. This finding does suggest 

that a state-level analysis would not be necessary in the short term. 

The results are even less clear for the residential natural-gas sector. As in the commercial 

sector, only a couple of regions (again, the Pacific Coast and the West South Central 

region) seem to differ from the rest in all the factors we examined. But there is little 

consistency in the states within the DOE regions and little statistical difference among the 

estimated elasticities for each region. If one uses the estimated elasticities, the impact of 

energy-efficiency programs in the Pacific Coast and in the West South Central region 

would differ if one were to compare the two regions. This makes interpreting the findings 

on residential natural gas use more difficult than interpreting the findings for the other 

energy sectors. One finding of note is that the changes in demand for natural gas are 

smaller than those for electricity over the time period studied, so perhaps national-level 

analysis would be sufficient for determining the impact of energy-efficiency programs on 

demand in the residential natural-gas market. 

Price Elasticity of Demand 

The results on price elasticity are interesting. Our elasticity estimates are no different 

from those from ten to 20 years ago. This indicates that the relationship between price 

and demand has not changed even though (1) 15 to 20 additional years of empirical data 

51
are available; (2) there have been changes and shifts in energy use, in particular the 

introduction of new electricity-using devices; (3) there have been large increases in airconditioning loads; and (4) appliances are more energy efficient than they were 15 to 20 

years ago. 

In addition, the elasticities remained the same over the past two decades—i.e., they 

remained low. In other words, demand did not tend to react much to changes in price. 

There are small, and somewhat consistent, changes, but on the surface it seems that there 

are few options for consumers or commercial businesses to switch to electricity or natural 

gas use in response to energy prices. 

These observations, however, might be driven more by the trends in factors affecting 

intensity than by how consumers react to changes in price. Over the time period studied, 

we observed the following general trends: 

• Energy prices heading downward 

• Energy costs as a share of income also heading downward 

• Energy use rising. 

Given these trends, it is difficult to find significant variations in the price-demand 

relationship, because prices and demand have not varied much. Also, it is difficult to 

achieve improvements in energy efficiency when energy costs continue to decline, 

beyond those that “naturally” occur through technology improvements. 

On the other hand, it is possible that the price-demand relationship is changing. First, just 

anecdotally, when California was facing energy problems in 2000 and 2001, a 

combination of factors led to a significant reduction in residential electricity use, with 

reductions in electricity demand estimated to be as high as 9 percent in response to 

government policy, media coverage, and rising prices. At least in a case such as that, 

consumers will change their demand behavior in the short term in response to energy 

prices and energy policy. 

52
In the past few years, we have witnessed a reversal of the downward price trends and, at 

the same time, we have seen a reversal in the upward trend in electricity intensity in a 

number of states. Overall, prices have not been declining as rapidly, and energy use has 

not been increasing as rapidly either. So, it is possible that with an increasing-price 

regime, one might see a different demand-price relationship than what would be observed 

in a decreasing-price regime. More study and analysis would be needed to uncover these 

trends. 

53
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Wooldridge, Jeffrey, Introductory Econometrics: A Modern Approach, Cincinnati, Ohio: 

South-Western College Publishing, 1994. 

55
Appendix A 

Details on the Methodology Used to Estimate Elasticities 

The primary goal of this study is to measure how the energy-demand relationship varies 

at different levels of spatial aggregation (i.e., at the national, regional, state, and utility 

level). We model the demand relationship as a function of four components: 

• measured variables that vary across states and within states over time—such as 

energy prices, income, population, and climate 

• fixed differences between states—unmeasured variables that do not change in the 

study period but that differ across states 

• an aggregate time trend—one that accounts for unmeasured variables common to 

all states, such as federal policy 

• a random error term that varies across and within states. 

We estimate this model with the following fixed-effects specification: 

QD

i,t = QDi,t-1γ + Xi,tβ + Xi,t-1α + si + yt + εi,t 

where QD

i,t is log energy demand in state i and year t, QDi,t is the lag value of log energy 

demand, Xi,t is a set of measured covariates (e.g., energy prices, population, income, and 

climate) that affect energy demand, and Xi,t-1 is the lag values of the covariates. The 

residual has three components: 

• si is an indicator variable that captures time-invariant differences in energy 

demand across states (“state fixed effects”). 

• yt is an indicator variable that captures time effects common to all states (“year 

fixed effects”). 

• εi,t is a random error term. 

We based this specification on the flow-adjustment model developed by Houthakker et 

Q

al. (1974). In this model, demand is a function of prices, income, population, and climate. 

D* 

i,t = f(Pi,t, PCi,t, Yi,t, Popi,t, Climatei,t) 

where QD* denotes desired demand in time t. The model assumes the following 

adjustment process between periods: 

θ QD 

i,t/ QDi,t-1 = (QD*i,t/ QDi,t-1)

56 
where 0< θ < 1. The estimating equation then becomes the following: 

ln QD

i,t - lnQDi,t-1 = θ lnQD*i,t - θ lnQDi,t-1 

ln QD

i,t = θ lnQD*i,t + lnQDi,t-1 - θ lnQDi,t-1 

ln QD

i,t = θ lnQD*i,t + (1-θ) lnQDi,t-1 

Then, by substituting in a linear function for QD*

i,t , the final form is the following: 

ln QD

i,t = θ ln α + θγ ln pi,t + θβ ln Xi,t + θβ ln Xi,t-1 + (1-θ) ln QDi,t-1. 

In this model, the θ term reflects that current demand (QD

i,t)adjusts partially to changes in 

desired demand (QD*

i,t). Energy demand does not fully adjust in the current period 

because it is a stock-flow process. In this stock-flow process, adjusting the stock usually 

takes more than one period but consumers can control the flow easily in the current 

period. Therefore, demand does not fully adjust within one period to changes in desired 

demand. 

In more tangible terms, the “stock” refers to energy-consuming appliances that a 

consumer owns, such as a car, air conditioner, heater, and stove. The flow is the amount 

that the consumer uses the appliance. In this process, the consumer has immediate 

control over where the thermostat is set or how much he or she drives the car but these 

decisions can only affect energy consumption to a limited degree. If the consumer wants 

larger changes in energy demand, he or she must replace an expensive item like a car, 

heater, or air conditioner, which typically cannot happen immediately. 

This explanation for the partial-adjustment process suggests that an ideal model for 

energy demand would explicitly represent consumer decisions on purchasing energyconsuming appliances and their levels of usage. Taylor (1975) discusses this issue and 

notes that most studies at that time had insufficient data on appliance purchases and usage 

to estimate such a model. Other studies have estimated price elasticity using models of 

this type, such as Dubin and Macfadden (1984). However, data limitations precluded 

estimating a similar model for different spatial scales. Therefore, we proceeded with 

Houthakker et. al.’s reduced-form model, which is commonly used in the literature. 

By estimating the adjustment process (θ), we can estimate both short-run and long-run 

price elasticities. The short-run price elasticity is the long-run price elasticity (γ) 

multiplied by the adjustment factor (θ), which in this model refers to θγ, the estimated 

coefficient on the current period price variable. The long-run elasticity is estimated by 

subtracting the coefficient on the lagged demand variable (1-θ) from one to get an 

estimate of θ and then dividing the coefficient of the current price (θγ) by the estimate of 

θ. 

57
We estimate any spatial differences in the energy-demand relationship by adding 

interaction terms between indicator variables for the spatial unit of interest (region, state, 

or utility) and the regressors of interest (price, quantity, and income). These interaction 

terms allow the estimated parameters to vary, and we can analyze if price elasticities 

differ across geographical units. 

Parameter Identification 

The variables of interest in this study, energy price and quantity, are jointly determined 

by the interaction of energy supply and demand, which creates problems for identifying 

parameters in the demand equation. Ideally, we would model the energy market with a 

system of equations for supply and demand. With a system of equations, we could 

isolate movements in the demand and supply curves and use this variation to estimate the 

parameters in each equation. We were unable to develop a system of equations for each 

spatial level used in the study and instead used a reduced-form model that can identify 

the parameters of the demand equation under the following assumptions: 

• the model includes all the factors that affect energy demand 

• price changes between periods are exogenous 

• the error term does not contain autocorrelation 

The following discussion explains why these assumptions are necessary and then 

examines their plausibility. 

The first assumption is necessary because identifying parameters of the demand equation, 

and more specifically the effect of prices on quantity, requires holding the demand curve 

fixed and allowing shifts in the supply curve to establish the shape of the demand curve. 

If the model was missing a factor that affected demand, then shifts in both demand and 

supply could cause the observed shifts in price and quantity but the model would attribute 

the changes solely to shifts in supply. More simply, the estimates in the demand equation 

could suffer from omitted variables bias. 

The second assumption is required to isolate the effect of price on demand. In a full 

system of equations, changes in price affect demand and feed back into the supply 

equation. Therefore, prices are endogenous and determined by the equilibrium between 

supply and demand. Without a supply equation to capture this feedback, the model 

cannot identify the parameter on the endogenous variable, unless prices enter the system 

exogenously. The following discussion will examine some situations where prices could 

plausibly enter the system as an exogenous variable. 

The final assumption is needed because the lagged demand term can be written as a 

function of past values of the error term. If autocorrelation is present, it creates 

correlation between the error term and the lagged demand variable, which biases 

estimates of the coefficient on lagged demand. The equations below show how 

autocorrelation is a problem. 

58
The model equation is: 

QD

i,t = QDi,t-1γ + Xi,tβ + Xi,t-1α + si + yt + εi,t 

which can be re-written as: 

QD

i,t = γ (QDi,t-2γ + Xi,t-1β + Xi,t-2α + si + yt + εi,t-1) + Xi,tβ + Xi,t-1α + si + yt + εi,t 

By continuing to substitute for the lagged demand term, this term could be re-written as a 

function of initial demand, lagged values of explanatory factors, and, most importantly, 

past values of the error term. Therefore, any autocorrelation in the error term (E(εi,t, εi,s) 

≠ 0 where t ≠ s) will create correlation between the lagged demand term and the error 

term, which will bias estimates of the coefficient on lagged demand, γ. 

The assumptions stated above for parameter identification are strong but not implausible. 

The following discussion addresses each assumption. 

Assumption #1 - Controlling for all factors affecting demand 

The model includes the own-price of the good, price of a substitute, and income, which 

are key variables in microeconomic decisions of demand. The model also controls for 

population and climate, which would also affect energy demand. In addition, the model 

includes lagged values of these factors, which controls for large period-to-period changes 

in explanatory variables. The model also controls for demand in the previous period, 

which in effect controls for the stock of energy-consuming appliances because the stock 

of appliances is unlikely to change significantly from year-to-year. Finally, the model 

includes fixed-effects for each cross-sectional unit and year. 

The fixed-effects control for any unmeasured time-invariant effects on demand 

attributable to the cross-sectional unit. An example of an unmeasured time-invariant 

effect is energy demand patterns in states with federally-administered hydroelectric 

power sectors. Washington, Oregon, and Tennessee have exceptionally high per capita 

electricity use and low average prices. Some of this effect is due to prices, but each of 

these states have electricity markets dominated by large federal power agencies that have 

historically supplied the regions with inexpensive energy. The effect of these agencies is 

difficult to measure, and is likely to differ between states. Therefore, including an 

indicator variable for each state controls for the unique effects that agencies like the 

Bonneville Power Administration or Tennessee Valley Authority have on energy 

demand. In addition, the indicator variable controls for any other unmeasurable variables 

that affect energy demand. The year fixed effects control for any year-to-year variation 

in demand that occurs across all cross-section units. Examples of these effects are 

national legislation, macroeconomic trends, and national-level events that affect energy 

demand (war or terrorism attack). 

59
The explanatory variables comprise a relatively comprehensive set of control variables 

for energy demand. The very high R2

 values (> 0.9) on the regressions indicate that the 

model fits the data well and explains a large amount of the variation in energy demand. 

One area where the model does not control for changes in demand is a state- or crosssection specific factor that changes over time. For instance, if a subset of states 

substantially changes their stock of energy-consuming appliances, then the model may 

not control for this change in demand. This situation contrasts with national consumer 

trends in purchasing new electronics, which the model can control for with year fixed 

effects. 

Assumption #2 – Exogenous energy prices 

The assumption of exogenous energy prices is the strongest assumption but not 

implausible. A public utilities commission that sets consumer rates regulates most 

electricity and natural gas rates. These price schedules do not change regularly and the 

rate setting is not exogenous. Despite these shortcomings, there are portions of a 

consumer’s utility bill that do vary annually and this source of variation is arguably 

exogenous. Most utility bills contain a component that passes through changes in fuel 

prices to customers. Since utility rate schedules do not change regularly, much of the 

period-to-period variation in what consumers actually pay for electricity and natural gas 

is fluctuations in the fuel cost. Because these fuels are typically purchased at prices 

determined on national or world markets, the change in prices from fuel costs is primarily 

exogenous variation. 

Assumption #3 – No autocorrelation in the error term 

The discussion above showed that estimating the model with ordinary least squares 

(OLS) when autocorrelation is present will result in biased estimates. This assumption is 

testable and autocorrelation tests are performed on the regressions in the study. Alternate 

estimation methods are possible, notably instrumental variables and error component 

technique. In previous work, Houthakker et. al. (1974) found that OLS estimates with 

separate intercepts for cross-section units (fixed-effects) produced estimates that were 

comparable to the error component technique. Although, this finding is not generalizable 

to other data sets. Therefore, this analysis includes tests for autocorrelation. 

We follow the test for first-order autocorrelation discussed in Wooldridge (1994; 2002). 

In this test, we run an OLS regression of the dependent variable on the explanatory 

variables and obtain the residuals. We then run an OLS regression of the residuals on the 

explanatory variables and lagged residuals. The coefficient on the lagged residual term is 

a consistent estimate for ρ and the t-statistic on the coefficient of the lagged residual term 

is a valid test for the null hypothesis ρ = 0 (no autocorrelation), where ρ represents the 

coefficient on the lagged error in an AR(1) model. In our results, we present the estimate 

of ρ and the associated t-statistic. 

As stated earlier, an ideal model would include a system of equations to model the 

demand and supply equations of each state’s energy market. The limited scope of this 

60
study excluded an extensive analysis of supply and demand in each state. We followed 

Houthakker’s demand model because it was widely used in the literature, we could 

estimate differences in short- and long-run elasticities, and determine if these parameters 

vary geographically. Under the assumptions stated above, the model will estimate the 

causal effect of prices on energy demand—the focus of this study and measure how this 

effect varies geographically. If these conditions are not met, the estimate will reflect the 

correlation between the observed prices and quantities 

Trend Analysis 

The regional data analysis for each energy market in Chapter Four displays regional 

trends for residential electricity, commercial electricity, and natural gas intensity, 

expenditures, and expenditures as a share of income. We estimate these trends using a 

deterministic time trend of the following form: 

Ln dependent variable = β0 + β1 year + ε

This model fits a linear time trend to our data. In most of the analyses done for this 

study, the trends were linear, and the model was a good fit, which was evidenced by R2 

over 0.9 (the time trend). Appendix D displays the results from the trend analysis. 

61

Appendix B 

Data Sources 

The regression analyses done for this study used panel data for the 48 contiguous states. 

The time periods for each energy market analysis varied because of data limitations. The 

data on the residential electricity and natural gas markets spans 1977 to 2004. The data 

on commercial electricity extends only from 1977 to 1999. The data are from the 

following four sources: the DOE EIA, Bureau of Economic Analysis (BEA) of the 

Department of Commerce, the U.S. Census Bureau, and the National Oceanic and 

Atmospheric Administration (NOAA). 

Energy Data 

The sources for electricity data include the following EIA publications: State Energy 

Data Report 2001, Electric Power Annual, and Electric Power Monthly. By combining 

these data sources, we developed a state-level database of electricity consumption and 

prices for residential and commercial customers. The sources of natural-gas data include 

the following EIA publications: State Energy Data Report 2001, Natural Gas Annual, 

and Natural Gas Monthly. As was done for the electricity sector, for natural gas we 

created a state-level database on consumption and prices for residential customers. 

Economic Data 

We obtained economic data on gross state product, GDP deflator, and population from 

the Department of Commerce Bureau of Economic Analysis (BEA) “Regional Economic 

Accounts” Web site (see www.bea.gov/bea/regional/data.htm). We purchased data on 

commercial floorspace from McGraw-Hill Construction Dodge 

(http://dodge.construction.com). 

Climate Data 

The NOAA publishes state-level data on heating and cooling degree days. The degreeday measures quantify how far the daily average temperature deviates from 65 degrees. 

For instance, if a day’s average temperature is 50 degrees, then the day has 15 heating 

degree days. If the average is 70, then the day has five cooling degree days. We 

constructed an annual climate index by summing heating and cooling degree-day 

measures, which captures annual climate variation in each state. The data on degree days 

are available on the NOAA website 

(http://lwf.ncdc.noaa.gov/oa/documentlibrary/hcs/hcs.html). 

63
Appendix C: Variables and How They Were Constructed 

Table C.1: Residential Electricity Regression Analysis Variables 

Variable How Variable Was Constructed/Data Source 

Residential electricity consumption Electricity consumption (Btus), residential sector (ESRCB), 

1977-1999 

Source: EIA State Energy Data Report (2001) 

Electricity sales (megawatt hours), residential consumers, 

2000-2004 

Source: EIA Electric Power Annual (2003) and Electric 

Power Monthly (2004) 

Real residential electricity prices = Average price of electricity, residential sector (ESRCD), 

1997-1999 

Nominal residential electricity price / Source: EIA State Energy Price and Expenditure Report GDP deflator (2001) 

Average price of electricity, residential consumers, 2000-

2004 

Source: EIA Electric Power Annual (2003) and Electric 

Power Monthly (2004) 

Real residential natural gas prices Average price of natural gas, residential sector (NGRCD), 

1997-1999 

Nominal residential natural gas price / Source: EIA State Energy Price and Expenditure Report GDP deflator (2001) 

Average price of natural gas, residential consumers, 2000-

2004 

Source: EIA Natural Gas Annual (2003) and Natural Gas 

Monthly (2004) 

Population State population 

Source: BEA, Regional Accounts Data, "Annual State 

Personal Income," Population table (no date) 

Real disposable income per capita = Disposable income per capita 

Disposable Income per capita / GDP Source: BEA, Regional Accounts Data, "Annual State deflator Personal Income," Per capita disposable personal income 

table (no date) 

Climate index = Heating degree days, cooling degree days 

Heating degree days + Cooling degree Source: NOAA, National Climatic Data Center, "Heating days and Cooling Degree Data" (no date) 

64
Table C.2: Commercial Electricity Regression Analysis Variables 

Variable How Variable Was Constructed/ Data Source 

Commercial electricity consumption Electricity consumption (Btus), commercial sector (ESCCB) 

1977-1999 

Source: EIA State Energy Data Report (2001) 

Real Commercial Electricity Prices = Average price of electricity, commercial sector (ESCCD) 

1997-1999 

Nominal commercial electricity price / Source: EIA State Energy Price and Expenditure Report GDP deflator (2001) 

Real Commercial Natural Gas Prices Average price of natural gas, commercial sector (NGCCD) 

1997-1999 

Source: EIA State Energy Price and Expenditure Report 

(2001) 

Area of commercial floorspace Data purchased from McGraw-Hill Construction Dodge 

http://dodge.construction.com/—includes data on 

square footage of commercial floor space from 1977 - 

1999 for each sate 

Real gross state product = Gross state product 

Gross state product / GDP deflator Source: BEA, Regional Accounts Data, "Gross State 

Product," (no date) 

Climate index = Heating degree days, Cooling degree days 

Heating degree days + Cooling degree Source: NOAA, National Climatic Data Center, "Heating days and Cooling Degree Data" (no date) 

65
Table C.3: Residential Natural-Gas Regression Analysis Variables 

Variable How Variable Was Constructed/ Data Source 

Residential natural gas consumption Natural gas consumption (Btus), residential sector 

(NGRCB) , 1977-1999 

Source: EIA State Energy Data Report (2001) 

Natural gas sales (thousands of cubic feet), residential 

consumers, 2000-2004 

Source: EIA Natural Gas Annual (2003) and Natural 

Gas Monthly (2004) 

Real residential natural gas prices = Average price of natural gas, residential sector (NGRCD) 

1997-1999 

Nominal residential natural gas price / Source: EIA State Energy Price and Expenditure Report GDP deflator (2001) 

Average price of natural gas, residential consumers, 2000-

2004 

Source: EIA Natural Gas Annual (2003) and Natural Gas 

Monthly (2004) 

Real residential electricity prices = Average price of electricity, residential sector (ESRCD) 

1997-1999 

Nominal residential electricity price / Source: EIA State Energy Price and Expenditure Report GDP deflator (2001) 

Average price of electricity, residential consumers, 2000-

2004 

Source: EIA Electric Power Annual (2003) and Electric 

Power Monthly (2004) 

Population State population 

Source: BEA, Regional Accounts Data, "Annual State 

Personal Income," Population table (no date) 

Real disposable income per capita = Disposable income per capita 

Disposable income per capita / GDP Source: BEA, Regional Accounts Data, "Annual State deflator Personal Income," Per capita disposable personal income 

table (no date) 

Climate index = Heating degree days, cooling degree days 

Heating degree days + Cooling degree Source: NOAA, National Climatic Data Center, "Heating days and Cooling Degree Data" (no date) 

66
Appendix D: Regression Analysis Results 

In this appendix, we present the results from regression analysis at the national, regional, 

state, and utility levels of aggregation. We display the results for each energy market by 

level of aggregation. 

National-Level Results 

This section shows results for the residential electricity, commercial electricity, and 

residential natural gas markets. In the national level regressions, we estimate the model 

using panel data from the 48 contiguous states. We estimate the following model for 

these regressions: 

QD

i,t = QDi,t-1γ + Xi,tβ + Xi,t-1α + si + yt + εi,t 

where QD

i,t is log energy demand in state i and year t, QDi,t is the lag value of log energy 

demand, Xi,t is a set of measured covariates (e.g. energy prices, population, income, and 

climate) that affect energy demand, and Xi,t-1 is the lag values of the covariates. The si 

term is a state-fixed effect estimated with an indicator variable. The yt term is a yearfixed effect also estimated with an indicator variable and εi,t is a random error term. 

Residential Electricity 

The dependent variable in this regression was the log of electricity sold to residential 

electricity consumers. We controlled for the following variables: 

• Lag value of dependent variable 

• Log of residential electricity price 

• Lag value of log of residential electricity price 

• Log of per capita income 

• Lag value of log of per capita income 

• Log of state population 

• Lag value of log of state population 

• Log of residential natural gas price 

• Lag value of log of residential natural gas price 

• Log of climate index (heating degree days + cooling degree days) 

The residential electricity market regression analysis covers the period from 1977-2004. 

The data from 2001 are excluded from the analysis because EIA had serious errors in the 

data for that year, which they have not corrected yet. 

The results show that lagged quantity has a significant and positive effect on current 

period consumption. Current and lagged electricity prices are significant and negative. 

The estimates indicate that short run price elasticity (-0.24) is inelastic and similar to 

previous estimates in the literature. The income, population, and natural gas variables are 

67
all insignificant in the current period and significant in the lagged period. The lagged 

values are all positive, which is expected. Income and population increases should 

correspond with greater electricity demand. In this case, we consider natural gas a 

substitute for electricity and the positive sign for the cross-price elasticity indicates it is a 

substitute. Finally, the climate index has a significant and positive effect on residential 

electricity demand. 

Table D.1: Regression results from the residential electricity market 

Coef. 

Robust 

Std. Err. t P>|t| [95% Conf. Interval] 

Lag quantity 0.232 0.058 4.03 0 0.119 0.345 

Ln elec price -0.243 0.049 -4.96 0 -0.339 -0.147 

Lag elec price -0.129 0.048 -2.7 0.007 -0.222 -0.035 

Ln income 0.003 0.076 0.04 0.968 -0.146 0.152 

Lag income 0.384 0.073 5.27 0 0.241 0.527 

Ln population -0.225 0.285 -0.79 0.43 -0.783 0.334 

Lag population 0.827 0.307 2.69 0.007 0.225 1.428 

Ln nat gas price -0.005 0.028 -0.16 0.873 -0.06 0.051 

Lag nat gas price 0.111 0.031 3.58 0 0.05 0.172 

Ln climate 0.246 0.026 9.36 0 0.194 0.298 

R2

 = 0.99 

N = 1237 

The adjusted R-squared for this model is very high—approximately 0.99. A high Rsquared is typical with fixed effects models because the state and year effects included in 

the model usually have considerable explanatory power. 

We tested for first-order autocorrelation in the error term. The estimate of ρ was -0.009 

with a t-statistic of -0.69, which indicates first-order correlation is not present. We, 

therefore, conclude that autocorrelation does not affect consistency of the coefficient 

estimates or validity of the standard errors. 

Commercial Electricity 

The dependent variable in this regression was the log of electricity sold to commercial 

electricity consumers. We controlled for the following variables: 

• Lag value of dependent variable 

• Log of commercial electricity price 

• Lag value of log of commercial electricity price 

• Log of gross state product 

• Lag value of log of gross state product 

• Log of commercial floorspace 

68 
• Lag value of log of commercial floorspace 

• Log of commercial natural gas price 

• Lag value of log of commercial natural gas price 

• Log of climate index (heating degree days + cooling degree days) 

Table D.2: Regression results from the commercial electricity market 

Robust 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

Lagged quantity 0.785 0.034 22.81 0 0.717 0.852 

Ln elec price -0.209 0.060 -3.47 0.001 -0.327 -0.091 

Lag elec price 0.148 0.052 2.85 0.004 0.046 0.250 

Ln nat gas price -0.023 0.020 -1.18 0.236 -0.061 0.015 

Lag nat gas price 0.049 0.022 2.19 0.029 0.005 0.093 

Ln commercial GSP 0.155 0.124 1.25 0.211 -0.088 0.398 

Lag commercial GSP -0.039 0.122 -0.32 0.747 -0.279 0.200 

Ln floorspace 0.504 0.339 1.49 0.138 -0.162 1.169 

Lag floorspace -0.421 0.305 -1.38 0.169 -1.020 0.179 

Ln climate 0.233 0.039 5.92 0 0.156 0.310 

R2

= 0.99 

n = 1034 

The commercial electricity market regression analysis covers the period from 1977-1999. 

Later data are not included in the analysis because of consistency problems with gross 

state product data collected by the Bureau of Economic Analysis. In addition, data from 

Tennessee were excluded from this regression. 

The results show that lagged quantity has a significant and positive effect on current 

period consumption. The magnitude is larger than the estimate for residential electricity. 

Current electricity price is significant and negative. The estimate indicates that short run 

price elasticity (-0.21) is also inelastic and similar to previous estimates in the literature. 

The lagged electricity price is positive and significant, which is not expected. The 

estimates for natural gas are insignificant for the current period and significant and 

positive for the lag period. Again, this suggests that natural gas is a substitute but the 

cross price elasticity is small. All of the GSP and floorspace variables were insignificant. 

Finally, the climate index has a significant and positive effect on commercial electricity 

demand. The magnitude is also similar to the residential electricity estimate. 

The adjusted R-squared for this model is also very high—approximately 0.99. This, 

again, indicates the state and year effects included in the model have considerable 

explanatory power. 

We also tested for first-order autocorrelation in the error term. The estimate of ρ was 

0.021 with a t-statistic of 0.47. These results suggest first-order correlation does not 

affect the coefficient estimates and standard errors in this model. 

69
Residential Natural Gas 

The dependent variable in this regression was the log of natural gas sold to residential 

natural gas consumers. We controlled for the following variables: 

• Lag value of dependent variable 

• Log of residential natural gas price 

• Lag value of log of residential natural gas price 

• Log of per capita income 

• Lag value of log of per capita income 

• Log of state population 

• Lag value of log of state population 

• Log of residential electricity price 

• Lag value of log of residential electricity price 

• Log of climate index (heating degree days + cooling degree days) 

Table D.3: Results from natural gas market regression analysis 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

Lag quantity 0.577 0.024 24.44 0 0.531 0.623 

Ln nat gas price -0.132 0.031 -4.24 0 -0.193 -0.071 

Lag nat gas price -0.106 0.031 -3.42 0.001 -0.167 -0.045 

Ln elec price 0.034 0.053 0.64 0.521 -0.070 0.138 

Lag elec price 0.146 0.052 2.8 0.005 0.044 0.248 

Ln income 0.261 0.123 2.13 0.034 0.020 0.503 

Lag income 0.167 0.113 1.48 0.139 -0.054 0.388 

Ln population 1.169 0.449 2.6 0.009 0.287 2.051 

Lag population -0.717 0.449 -1.6 0.11 -1.598 0.163 

Ln climate 0.181 0.042 4.29 0 0.098 0.264 

R2

 = 0.96 

n = 1210 

The residential natural gas market regression analysis covers the period from 1977-2004. 

The regression includes data from all time periods. It excludes the state of Maine from 

the analysis. Gas volumes sold in Maine are very small in absolute terms and relative to 

all other states. Since the absolute volumes traded are small, small changes had large 

effects in % changes and disproportionately affected the price elasticity estimates. Since 

the market there is small compared to the rest of the country, the analysis excludes it. 

The results show that the lagged quantity is significant and the magnitude is similar to the 

estimate in commercial electricity. Natural gas price is significant and negative in the 

current and lagged period. The estimate of short-term price elasticity is –0.132, which is 

smaller in absolute value than the estimates for both electricity markets. The current 

price of electricity is insignificant but the lagged value is positive and significant. This is 

further evidence that electricity and natural gas are substitutes for residential consumers. 

70
The magnitude of the cross price elasticity is also small in this case. The estimates for 

income and population are positive and significant in the current period but insignificant 

in the lagged period. The elasticity for population (1.17) is large relative to the other 

estimates, which indicates population change has a strong effect on demand in this 

market. 

The adjusted R-squared for this model is again very high—approximately 0.96. The 

fixed effects included in the model also have considerable explanatory power for this 

market. 

We tested for first-order autocorrelation and found that it may be present in the error 

term. The estimate of ρ was -0.342 with a t-statistic of -6.75. Based on this result, we 

estimated the model assuming an AR(1) structure in the error term, which should correct 

the standard errors. However, autocorrelation still affects consistency of the estimate on 

the lagged demand term. 

Regional-Level Results 

This section shows regional level results for the residential electricity, commercial 

electricity, and residential natural gas markets. In the regional level regressions, we 

estimate the model using panel data from the 48 contiguous states. We estimate the 

following model for these regressions: 

′ ′ QD

i,t = QDi,t-1γ + Xi,tβ + Xi,t-1α + (ri x QDi,t-1) γ Q + (ri x ln elec pricei,t) β P + ′ (ri x ln incomei,t) β I + si + yt + εi,t 

where QD

i,t is log energy demand in state i and year t, QDi,t is the lag value of log energy 

demand, Xi,t is a set of measured covariates (e.g. energy prices, population, income, and 

climate) that affect energy demand, and Xi,t-1 is the lag values of the covariates. The 

interaction terms interact a region indicator variable with lagged quantity, current prices, ′ and current income. The corresponding region-specific coefficient estimates are (γ + γ Q ) ′ ′ for lagged quantity, (βP + β P) for prices, and (βI + β I) for income. The si term is a statefixed effect estimated with an indicator variable. The yt term is year-fixed effect also 

estimated with an indicator variable and εi,t is a random error term. 

Residential Electricity 

The table shows that demand response in the South Atlantic and East South Central is 

most elastic and the East North Central has the most inelastic demand response. All of 

the estimates are negative and statistically significant, except the East North Central. The 

estimates also indicate regional differences in electricity demand. The estimates for the 

South Atlantic and East South Central have statistically significant differences from the 

East North Central. A Wald Test on the South Atlantic and East North Central 

coefficients rejects the null hypothesis that they are equal (F(1,1130) = 14.59; p = 

0.0001). A Wald Test on the East South Central and East North Central coefficients also 

71
rejects that they are equal (F(1,1130) = 10.37; p=0.0013). Overall, the regression results 

show clear statistically significant differences in price elasticities between the regions. 

Table D.4: Estimated short-run price elasticities for the residential electricity market. 

Short run price elasticity 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

South Atlantic -0.318 0.047 -6.77 0 -0.41 -0.226 

East South Central -0.266 0.071 -3.74 0 -0.405 -0.126 

Mid Atlantic -0.232 0.069 -3.36 0.001 -0.368 -0.096 

Mountain -0.211 0.038 -5.55 0 -0.285 -0.136 

New England -0.192 0.046 -4.2 0 -0.281 -0.102 

Pacific Coast -0.188 0.051 -3.69 0 -0.288 -0.088 

West North Central -0.163 0.054 -3.02 0.003 -0.269 -0.057 

West South Central -0.127 0.051 -2.52 0.012 -0.227 -0.028 

East North Central -0.054 0.053 -1.01 0.312 -0.158 0.051 

We tested for first-order autocorrelation in the error term and the estimate of ρ was -0.003 

with a t-statistic of -0.26. The estimate indicates that first-order autocorrelation does not 

affect the error term and this model. Therefore, autocorrelation does not affect the 

estimate of lagged demand and the inference based on the standard errors is valid. 

Table D.5: Estimated long-run price elasticities for the residential electricity market. 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

East South Central -0.618 0.144 -4.3 0 -0.900 -0.336 

South Atlantic -0.352 0.051 -6.86 0 -0.453 -0.251 

New England -0.325 0.074 -4.37 0 -0.471 -0.179 

Mountain -0.267 0.048 -5.52 0 -0.362 -0.172 

Pacific Coast -0.254 0.078 -3.27 0.001 -0.407 -0.101 

Mid Atlantic -0.247 0.075 -3.28 0.001 -0.395 -0.099 

West North Central -0.244 0.081 -3.01 0.003 -0.403 -0.085 

West South Central -0.174 0.070 -2.48 0.013 -0.311 -0.036 

East North Central -0.058 0.057 -1.02 0.309 -0.169 0.054 

Long run price elasticities are calculated by dividing the coefficient estimate on current 

electricity prices by 1 – the coefficient of lagged quantity. The long-run elasticities are 

larger for all of the regions, which is expected and follows the general findings from 

previous research. The pattern of results is also similar to the short-run elasticity results. 

The East South Central and South Atlantic regions have the most elastic demand and the 

East North Central is the most inelastic. Again, all of the estimates have the expected 

sign and significant, except for the East North Central. 

72
Commercial Electricity 

We used the same regression model to estimate the regional-level commercial electricity 

market. 

Table D.6: Short-run price elasticities for commercial electricity with and without Tennessee 

Short-Run Price Elasticity – with Tennessee 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

East South Central -0.759 0.322 -2.36 0.019 -1.391 -0.127 

Pacific Coast -0.364 0.099 -3.67 0 -0.559 -0.169 

New England -0.273 0.101 -2.71 0.007 -0.470 -0.076 

Mountain -0.258 0.126 -2.04 0.042 -0.505 -0.010 

West South Central -0.250 0.114 -2.19 0.029 -0.475 -0.026 

East North Central -0.237 0.111 -2.13 0.033 -0.455 -0.019 

West North Central -0.233 0.132 -1.76 0.078 -0.491 0.026 

South Atlantic -0.226 0.106 -2.13 0.034 -0.435 -0.017 

Mid Atlantic -0.215 0.081 -2.64 0.009 -0.374 -0.055 

Short-Run Price Elasticity – without Tennessee 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

Pacific Coast -0.306 0.076 -4.04 0 -0.455 -0.158 

East South Central -0.271 0.120 -2.25 0.024 -0.507 -0.035 

New England -0.212 0.079 -2.69 0.007 -0.367 -0.057 

East North Central -0.181 0.089 -2.04 0.042 -0.356 -0.007 

Mid Atlantic -0.180 0.058 -3.11 0.002 -0.293 -0.066 

West South Central -0.179 0.084 -2.12 0.034 -0.345 -0.014 

Mountain -0.178 0.102 -1.74 0.082 -0.377 0.022 

West North Central -0.166 0.109 -1.52 0.128 -0.380 0.048 

South Atlantic -0.158 0.082 -1.94 0.053 -0.318 0.002 

The table shows that the data from Tennessee affect all of the results, especially the East 

South Central region. The EIA appears to have an error in this data series. In 2001, 

Tennessee’s commercial electricity output doubles and then returns to previous levels in 

2002. Due to this apparent error, we excluded Tennessee from the national-level results. 

The estimates in the without Tennessee case are similar to the residential electric market 

except no region is markedly lower than the others. With a much closer range of 

estimates, none of these regional estimates have statistically significant differences 

between them. However, most are significantly different from zero (six out of nine). 

Overall, the estimates suggest that price elasticities vary between regions but the 

magnitude of the differences is not very large. In addition, the differences are difficult to 

detect with a sample of this size. 

73
We tested for first-order autocorrelation in the models including and excluding 

Tennessee. In the model including Tennessee, the estimate of ρ was 0.071 with a tstatistic of 1.18. In the model excluding Tennessee, the estimate of ρ was 0.078 with a tstatistic of 1.26. These estimates suggest first-order autocorrelation was not a problem in 

either model. 

Table D.7: Long-run price elasticity estimates for commercial electricity 

Long-Run Price Elasticity – with Tennessee 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

East South Central -3.106 1.595 -1.95 0.052 -6.236 0.025 

Mid Atlantic -1.737 1.598 -1.09 0.277 -4.872 1.398 

Pacific Coast -1.578 1.018 -1.55 0.121 -3.576 0.419 

New England -1.519 1.118 -1.36 0.175 -3.713 0.676 

South Atlantic -1.508 0.745 -2.02 0.043 -2.969 -0.046 

East North Central -1.156 0.644 -1.8 0.073 -2.419 0.107 

Mountain -0.901 0.448 -2.01 0.044 -1.781 -0.022 

West North Central -0.830 0.573 -1.45 0.148 -1.955 0.294 

West South Central -0.497 0.269 -1.85 0.065 -1.025 0.031 

Long-Run Price Elasticity – without Tennessee 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

Mid Atlantic -1.422 1.149 -1.24 0.216 -3.677 0.832 

Pacific Coast -1.365 0.864 -1.58 0.114 -3.060 0.330 

New England -1.254 0.988 -1.27 0.205 -3.193 0.686 

South Atlantic -1.140 0.604 -1.89 0.059 -2.326 0.045 

East South Central -0.995 0.524 -1.9 0.058 -2.024 0.033 

East North Central -0.882 0.502 -1.76 0.079 -1.866 0.103 

Mountain -0.626 0.351 -1.78 0.075 -1.315 0.063 

West North Central -0.589 0.459 -1.28 0.2 -1.489 0.311 

West South Central -0.371 0.208 -1.78 0.075 -0.779 0.038 

The long-run estimates are considerably larger in absolute magnitude than the short-run 

estimates and also larger than the residential electricity long-run estimates. Comparison 

between the two models shows that including Tennessee increases the magnitude of the 

estimates, especially for the East South Central region. When excluding this state, the 

magnitudes of the estimates drop, but no estimate is statistically significant from zero. 

The confidence intervals show that the variance of the estimates is large and they lack 

precision. 

74
Residential Natural Gas 

Tables D.8 and D.9 compare short-run and long-run elasticity estimates for regressions 

that include and exclude the state of Maine. Maine sells very low volumes of natural gas 

and small changes in the market can have large relative effects. It appears that the 

elasticity estimate is considerably larger in Maine for this reason and comparison across 

the tables shows that including this state has a substantial effect on the results. 

Table D.8: Short run price elasticity for natural gas 

Short-Run Price Elasticity – with Maine 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

New England -0.336 0.064 -5.28 0 -0.461 -0.211 

Mid Atlantic -0.227 0.094 -2.4 0.016 -0.412 -0.042 

Pacific Coast -0.184 0.072 -2.55 0.011 -0.325 -0.043 

Mountain -0.183 0.050 -3.63 0 -0.282 -0.084 

West North Central -0.170 0.053 -3.24 0.001 -0.273 -0.067 

East North Central -0.155 0.062 -2.49 0.013 -0.277 -0.033 

East South Central -0.142 0.071 -2.01 0.045 -0.281 -0.003 

South Atlantic -0.114 0.057 -2 0.046 -0.225 -0.002 

West South Central -0.078 0.068 -1.13 0.258 -0.212 0.057 

Short-Run Price Elasticity – without Maine 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

Mid Atlantic -0.174 0.081 -2.15 0.032 -0.332 -0.015 

Mountain -0.164 0.043 -3.85 0 -0.248 -0.080 

Pacific Coast -0.163 0.062 -2.63 0.009 -0.285 -0.042 

West North Central -0.138 0.044 -3.11 0.002 -0.226 -0.051 

New England -0.127 0.064 -1.98 0.048 -0.253 -0.001 

East North Central -0.120 0.053 -2.26 0.024 -0.225 -0.016 

East South Central -0.100 0.061 -1.64 0.101 -0.219 0.019 

South Atlantic -0.073 0.048 -1.5 0.133 -0.168 0.022 

West South Central -0.049 0.059 -0.84 0.4 -0.165 0.066 

75
Table D.9: Short run price elasticity for natural gas 

Long-Run Price Elasticity – with Maine 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

Pacific Coast -0.630 0.261 -2.41 0.016 -1.142 -0.118 

New England -0.593 0.115 -5.15 0 -0.819 -0.367 

Mid Atlantic -0.469 0.192 -2.44 0.015 -0.847 -0.091 

Mountain -0.440 0.123 -3.57 0 -0.681 -0.198 

East South Central -0.396 0.222 -1.78 0.075 -0.833 0.040 

West North Central -0.298 0.093 -3.19 0.001 -0.481 -0.115 

South Atlantic -0.241 0.122 -1.96 0.05 -0.481 0.000 

East North Central -0.232 0.098 -2.37 0.018 -0.423 -0.040 

West South Central -0.126 0.114 -1.1 0.27 -0.350 0.098 

Long-Run Price Elasticity – without Maine 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

Pacific Coast -0.452 0.173 -2.61 0.009 -0.791 -0.112 

Mountain -0.355 0.092 -3.84 0 -0.536 -0.174 

Mid Atlantic -0.338 0.153 -2.2 0.028 -0.638 -0.037 

New England -0.305 0.158 -1.93 0.054 -0.614 0.005 

East South Central -0.247 0.161 -1.54 0.125 -0.562 0.068 

West North Central -0.220 0.071 -3.11 0.002 -0.358 -0.081 

East North Central -0.171 0.078 -2.19 0.029 -0.323 -0.018 

South Atlantic -0.141 0.095 -1.49 0.136 -0.327 0.045 

West South Central -0.071 0.085 -0.83 0.406 -0.239 0.097 

The results show that including Maine in the analysis increases the absolute magnitude of 

all the elasticity estimates, especially the New England region. Since it is a tiny market 

compared to the other states, we will focus on the results excluding this state and have 

also excluded Maine in the other regressions for this market. 

The residential natural gas estimates are all negative, as expected, but smaller in absolute 

magnitude than the electricity markets. Fewer regions are statistically significant also. 

Five regions are significant for both the short-run and long-run estimates. 

The tests for first-order autocorrelation in the error term suggest autocorrelation exists in 

both models. In the model including Maine, the estimate of ρ was -0.195 with a t-statistic 

of -3.11. In the model excluding Maine, the estimate of ρ was -0.369 with a t-statistic of 

-6.75. In response to these findings, we estimated the results presented above assuming 

an AR(1) structure in the error terms. 

76
State-Level Results 

This section shows state-level regression results for the residential electricity, commercial 

electricity, and residential natural gas markets. The state-level regression is similar to the 

region-level model except the interaction terms are at the state level. We estimate the 

following model for these regressions: 

′ ′ QD

i,t = QDi,t-1γ + Xi,tβ + Xi,t-1α + (si x QDi,t-1) γ Q + (si x ln elec pricei,t) β P + ′ (si x ln incomei,t) β I + si + yt + εi,t 

where QD

i,t is log energy demand in state i and year t, QDi,t is the lag value of log energy 

demand, Xi,t is a set of measured covariates (e.g. energy prices, population, income, and 

climate) that affect energy demand, and Xi,t-1 is the lag values of the covariates. The 

interaction terms interact a state indicator variable with lagged quantity, current prices, ′ and current income. The corresponding state-specific coefficient estimates are (γ + γ Q ) ′ ′ for lagged quantity, (βP + β P) for prices, and (βI + β I) for income. The si term is a statefixed effect estimated with an indicator variable. The yt term is year-fixed effect also 

estimated with an indicator variable and εi,t is a random error term. 

Residential Electricity 

Table D.10: State-level results for short-run price elasticity. 

Short run price elasticity 

Region Coeff Std. Error T-stat P-value 95% Conf Interval 

Delaware SA -1.026 0.106 -9.71 0 -1.234 -0.819 

Arkansas WSC -0.618 0.137 -4.51 0 -0.886 -0.349 

Tennessee ESC -0.352 0.137 -2.58 0.01 -0.621 -0.084 

Georgia SA -0.352 0.158 -2.22 0.026 -0.662 -0.041 

New Hampshire NE -0.347 0.086 -4.05 0 -0.516 -0.179 

California PC -0.322 0.101 -3.17 0.002 -0.521 -0.123 

Missouri WNC -0.296 0.118 -2.51 0.012 -0.527 -0.065 

Maine NE -0.275 0.076 -3.61 0 -0.425 -0.126 

Oregon PC -0.258 0.100 -2.57 0.01 -0.455 -0.061 

New Jersey MA -0.231 0.094 -2.47 0.014 -0.415 -0.047 

Florida SA -0.218 0.092 -2.38 0.017 -0.398 -0.039 

Michigan ENC -0.206 0.298 -0.69 0.489 -0.791 0.378 

Mississippi ESC -0.204 0.146 -1.4 0.162 -0.490 0.082 

Alabama ESC -0.190 0.110 -1.72 0.086 -0.407 0.027 

Pennsylvania MA -0.151 0.101 -1.49 0.138 -0.349 0.048 

Virginia SA -0.148 0.174 -0.85 0.398 -0.490 0.195 

South Dakota WNC -0.141 0.123 -1.15 0.25 -0.382 0.099 

Ohio ENC -0.135 0.140 -0.97 0.333 -0.410 0.139 

New York MA -0.125 0.119 -1.06 0.291 -0.358 0.107 

77 
North Carolina SA -0.113 0.115 -0.98 0.326 -0.340 0.113 

Massachusetts NE -0.108 0.105 -1.03 0.304 -0.315 0.098 

Rhode Island NE -0.103 0.092 -1.12 0.262 -0.283 0.077 

Illinois ENC -0.090 0.070 -1.3 0.195 -0.227 0.046 

Connecticut NE -0.090 0.077 -1.17 0.243 -0.240 0.061 

Washington PC -0.079 0.061 -1.3 0.195 -0.199 0.041 

Iowa WNC -0.074 0.128 -0.58 0.562 -0.324 0.176 

Texas WSC -0.062 0.077 -0.81 0.419 -0.213 0.089 

Arizona M -0.059 0.094 -0.63 0.532 -0.243 0.125 

Montana M -0.056 0.119 -0.47 0.637 -0.289 0.177 

Indiana ENC -0.054 0.094 -0.58 0.564 -0.239 0.130 

North Dakota WNC -0.046 0.093 -0.49 0.624 -0.229 0.137 

Oklahoma WSC -0.004 0.080 -0.06 0.956 -0.161 0.152 

Louisiana WSC 0.048 0.071 0.68 0.497 -0.091 0.187 

New Mexico M 0.049 0.099 0.49 0.622 -0.145 0.242 

West Virginia SA 0.052 0.177 0.29 0.769 -0.295 0.398 

Nevada M 0.057 0.073 0.79 0.431 -0.085 0.200 

Kentucky ESC 0.082 0.110 0.75 0.453 -0.133 0.297 

South Carolina SA 0.084 0.100 0.84 0.402 -0.113 0.281 

Idaho M 0.089 0.087 1.02 0.308 -0.082 0.261 

Vermont NE 0.109 0.208 0.52 0.602 -0.300 0.517 

Utah M 0.120 0.073 1.64 0.102 -0.024 0.264 

Kansas WNC 0.128 0.077 1.66 0.097 -0.023 0.280 

Maryland SA 0.136 0.171 0.8 0.427 -0.199 0.471 

Minnesota WNC 0.140 0.134 1.05 0.294 -0.122 0.402 

Wisconsin ENC 0.154 0.085 1.81 0.071 -0.013 0.321 

Nebraska WNC 0.178 0.123 1.46 0.146 -0.062 0.419 

Wyoming M 0.219 0.097 2.27 0.023 0.030 0.409 

Colorado M 0.599 0.129 4.64 0 0.345 0.852 

The results show a wide range in estimates at the state level. Most estimates have the 

expected negative sign, but eleven states are in the positive range. Most of the positive 

estimates are near zero and their confidence intervals include the negative range. 

Wyoming and Colorado are significant, positive, and relatively large in absolute 

magnitude. Delaware and Arkansas have the largest magnitudes in the negative range. 

Between these two ends of the range, thirty states have negative elasticities in the range 

seen in the national- and regional-level results (near 0 to -0.3). In this range, the 

confidence interval for most states includes the national-level estimate (-0.24). 

The states with elasticities in the extreme parts of the range indicate a possible omitted 

variable. Colorado experienced a sharp growth in electricity demand in the early 1980’s, 

which was coincident with a period of rising prices. This short increase is unexplained 

by other regressors in the model. Houthakker et. al. (1974) noticed a correlation between 

rural states and low/positive elasticities. The same pattern occurs in these results. Nearly 

all the states with positive elasticities are predominantly rural. 

78
Table D.11: Long Run Price Elasticity 

Region Coeff Std. Error T-stat P-value 95% Conf Interval 

Delaware SA -0.999 0.093 -10.73 0 -1.182 -0.816 

Arkansas WSC -0.539 0.069 -7.8 0 -0.675 -0.404 

California PC -0.492 0.273 -1.8 0.072 -1.027 0.044 

New Hampshire NE -0.470 0.127 -3.69 0 -0.720 -0.220 

Maine NE -0.437 0.144 -3.03 0.002 -0.720 -0.154 

Tennessee ESC -0.401 0.131 -3.07 0.002 -0.658 -0.145 

Georgia SA -0.313 0.114 -2.75 0.006 -0.536 -0.090 

Missouri WNC -0.263 0.092 -2.86 0.004 -0.444 -0.083 

Florida SA -0.244 0.085 -2.87 0.004 -0.411 -0.077 

Michigan ENC -0.244 0.310 -0.79 0.432 -0.853 0.365 

New Jersey MA -0.240 0.100 -2.41 0.016 -0.436 -0.045 

Mississippi ESC -0.238 0.180 -1.32 0.186 -0.591 0.115 

Oregon PC -0.236 0.098 -2.41 0.016 -0.429 -0.044 

Alabama ESC -0.221 0.114 -1.94 0.053 -0.444 0.003 

Virginia SA -0.184 0.213 -0.86 0.388 -0.601 0.234 

New York MA -0.178 0.169 -1.05 0.293 -0.509 0.154 

South Dakota WNC -0.166 0.152 -1.09 0.277 -0.465 0.133 

Rhode Island NE -0.164 0.162 -1.01 0.313 -0.481 0.154 

Pennsylvania MA -0.163 0.110 -1.49 0.137 -0.379 0.052 

Washington PC -0.161 0.149 -1.08 0.279 -0.453 0.131 

Massachusetts NE -0.150 0.160 -0.93 0.35 -0.464 0.165 

Ohio ENC -0.136 0.138 -0.98 0.327 -0.407 0.136 

Connecticut NE -0.123 0.101 -1.21 0.225 -0.321 0.076 

North Carolina SA -0.109 0.102 -1.06 0.288 -0.310 0.092 

Iowa WNC -0.092 0.161 -0.57 0.568 -0.408 0.224 

Texas WSC -0.081 0.100 -0.81 0.418 -0.276 0.115 

Montana M -0.079 0.174 -0.46 0.648 -0.420 0.261 

Illinois ENC -0.076 0.052 -1.46 0.145 -0.179 0.026 

Arizona M -0.066 0.104 -0.63 0.528 -0.270 0.138 

Indiana ENC -0.056 0.095 -0.59 0.557 -0.243 0.131 

North Dakota WNC -0.055 0.113 -0.49 0.625 -0.277 0.167 

Oklahoma WSC -0.005 0.094 -0.06 0.956 -0.190 0.179 

Nevada M 0.046 0.057 0.81 0.418 -0.066 0.158 

West Virginia SA 0.053 0.179 0.29 0.769 -0.299 0.404 

New Mexico M 0.059 0.119 0.5 0.62 -0.175 0.293 

Louisiana WSC 0.060 0.086 0.7 0.486 -0.108 0.228 

South Carolina SA 0.089 0.106 0.84 0.401 -0.119 0.297 

Idaho M 0.106 0.104 1.02 0.309 -0.098 0.310 

Utah M 0.123 0.075 1.64 0.102 -0.025 0.271 

Kentucky ESC 0.134 0.174 0.77 0.441 -0.207 0.475 

Kansas WNC 0.143 0.084 1.71 0.088 -0.021 0.307 

Minnesota WNC 0.202 0.196 1.03 0.303 -0.183 0.586 

Nebraska WNC 0.206 0.135 1.52 0.129 -0.060 0.471 

Maryland SA 0.206 0.255 0.81 0.419 -0.294 0.706 

79 
Vermont NE 0.281 0.580 0.48 0.629 -0.857 1.419 

Wyoming M 0.296 0.127 2.33 0.02 0.047 0.545 

Wisconsin ENC 0.302 0.183 1.65 0.099 -0.057 0.661 

Colorado M 0.666 0.105 6.36 0 0.461 0.872 

The long run elasticity estimates show greater variability. Only 12 states have 

statistically significant estimates and two of those are positive. Similar to the other 

markets, long run price elasticities are generally greater than the short run estimates. 

We tested for first-order autocorrelation in the state-level residential electricity model and 

found it does not appear to affect the error term. The estimate of ρ was -0.004 with a tstatistic of -0.89. The results indicate that autocorrelation does not affect consistency of 

estimates on the lagged demand term and that inference based on the existing standard 

errors is valid. 

Commercial Electricity 

Table D.12: Short-run elasticity estimates for commercial electricity 

Short Run Commercial Electricity 

[95% 

Coef. Std. Err. t P>|t| Conf. Interval] 

Tennessee -3.363 2.314 -1.45 0.147 -7.90 1.18 

Maryland -1.086 0.946 -1.15 0.251 -2.94 0.77 

Nevada -1.016 0.668 -1.52 0.129 -2.33 0.30 

Michigan -0.948 0.583 -1.63 0.105 -2.09 0.20 

Vermont -0.805 0.212 -3.79 0 -1.22 -0.39 

Alabama -0.656 0.288 -2.28 0.023 -1.22 -0.09 

South Carolina -0.506 0.195 -2.59 0.01 -0.89 -0.12 

Oregon -0.477 0.103 -4.62 0 -0.68 -0.27 

Illinois -0.450 0.144 -3.13 0.002 -0.73 -0.17 

Montana -0.425 0.515 -0.83 0.409 -1.44 0.59 

Rhode Island -0.400 0.107 -3.75 0 -0.61 -0.19 

Idaho -0.337 0.282 -1.2 0.232 -0.89 0.22 

Washington -0.326 0.255 -1.28 0.201 -0.83 0.17 

Massachusetts -0.311 0.099 -3.15 0.002 -0.50 -0.12 

New Jersey -0.310 0.109 -2.85 0.004 -0.52 -0.10 

Iowa -0.309 0.183 -1.69 0.092 -0.67 0.05 

Maine -0.307 0.106 -2.9 0.004 -0.52 -0.10 

Texas -0.281 0.112 -2.51 0.012 -0.50 -0.06 

Arizona -0.246 0.193 -1.27 0.203 -0.63 0.13 

Kansas -0.237 0.113 -2.1 0.036 -0.46 -0.02 

Ohio -0.220 0.215 -1.02 0.306 -0.64 0.20 

California -0.201 0.123 -1.63 0.104 -0.44 0.04 

Connecticut -0.192 0.114 -1.69 0.092 -0.42 0.03 

80
Virginia -0.192 0.138 -1.39 0.164 -0.46 0.08 

Delaware -0.186 0.163 -1.14 0.256 -0.51 0.14 

New Mexico -0.183 0.158 -1.16 0.246 -0.49 0.13 

Minnesota -0.173 0.183 -0.95 0.344 -0.53 0.19 

Mississippi -0.165 0.224 -0.74 0.462 -0.60 0.27 

West Virginia -0.155 0.120 -1.29 0.197 -0.39 0.08 

Utah -0.152 0.140 -1.08 0.279 -0.43 0.12 

New York -0.150 0.086 -1.75 0.081 -0.32 0.02 

Oklahoma -0.108 0.153 -0.7 0.482 -0.41 0.19 

Arkansas -0.108 0.153 -0.7 0.481 -0.41 0.19 

Louisiana -0.098 0.119 -0.83 0.408 -0.33 0.13 

Pennsylvania -0.091 0.073 -1.24 0.216 -0.23 0.05 

Florida -0.070 0.121 -0.58 0.561 -0.31 0.17 

North Dakota -0.055 0.453 -0.12 0.903 -0.94 0.83 

Kentucky -0.053 0.122 -0.44 0.664 -0.29 0.19 

Wisconsin -0.033 0.199 -0.17 0.868 -0.42 0.36 

North Carolina -0.028 0.106 -0.26 0.793 -0.24 0.18 

Missouri -0.022 0.136 -0.16 0.872 -0.29 0.25 

Colorado 0.016 0.140 0.12 0.907 -0.26 0.29 

Wyoming 0.042 0.132 0.32 0.749 -0.22 0.30 

Indiana 0.102 0.174 0.59 0.556 -0.24 0.44 

New Hampshire 0.146 0.341 0.43 0.669 -0.52 0.81 

Nebraska 0.172 0.157 1.1 0.273 -0.14 0.48 

Georgia 0.219 0.117 1.88 0.061 -0.01 0.45 

South Dakota 0.335 0.581 0.58 0.564 -0.80 1.48 

The state-level estimates lack precision. In comparison to the residential data, the 

commercial electricity quantity data have much greater variability, which results in less 

precise estimates for price elasticity. As a result, only nine states have statistically 

significant results. A data error appears to cause the large estimate for Tennessee. This 

data problem was discussed in the regional level section. 

The estimates are distributed more evenly throughout the range compared to residential 

electricity. There are also fewer positive estimates and none of the positive estimates are 

significant. 

Table D.13: Long Run Commercial Electricity Elasticity Estimates 

[95% 

Coef. Std. Err. t P>|t| Conf. Interval] 

Tennessee -10.338 4.001 -2.58 0.01 -18.19 -2.48 

Maryland -7.467 3.332 -2.24 0.025 -14.01 -0.93 

Alabama -4.892 4.255 -1.15 0.251 -13.24 3.46 

Nevada -1.730 0.859 -2.01 0.044 -3.42 -0.04 

Michigan -1.496 0.537 -2.79 0.005 -2.55 -0.44 

Rhode Island -1.315 1.232 -1.07 0.286 -3.73 1.10 

Ohio -1.243 1.500 -0.83 0.407 -4.19 1.70 

81
Washington -1.210 2.205 -0.55 0.583 -5.54 3.12 

Montana -1.177 1.349 -0.87 0.383 -3.82 1.47 

Massachusetts -1.010 0.719 -1.4 0.161 -2.42 0.40 

Vermont -0.899 0.318 -2.83 0.005 -1.52 -0.28 

Illinois -0.804 0.248 -3.24 0.001 -1.29 -0.32 

New Jersey -0.740 0.431 -1.72 0.086 -1.59 0.11 

Oregon -0.678 0.497 -1.36 0.173 -1.65 0.30 

South Carolina -0.623 0.146 -4.28 0 -0.91 -0.34 

Connecticut -0.516 0.540 -0.96 0.34 -1.57 0.54 

Delaware -0.514 0.622 -0.83 0.409 -1.73 0.71 

Iowa -0.493 0.276 -1.79 0.074 -1.04 0.05 

West Virginia -0.489 0.401 -1.22 0.223 -1.28 0.30 

Pennsylvania -0.412 0.493 -0.84 0.404 -1.38 0.56 

Minnesota -0.396 0.459 -0.86 0.389 -1.30 0.51 

Utah -0.394 0.474 -0.83 0.406 -1.32 0.54 

Texas -0.384 0.158 -2.44 0.015 -0.69 -0.07 

Mississippi -0.379 0.550 -0.69 0.491 -1.46 0.70 

New Mexico -0.372 0.468 -0.79 0.428 -1.29 0.55 

Kansas -0.371 0.277 -1.34 0.182 -0.91 0.17 

Idaho -0.366 0.381 -0.96 0.337 -1.11 0.38 

Virginia -0.365 0.297 -1.23 0.22 -0.95 0.22 

Maine -0.348 0.154 -2.26 0.024 -0.65 -0.05 

Arizona -0.330 0.258 -1.28 0.201 -0.84 0.18 

California -0.301 0.266 -1.13 0.259 -0.82 0.22 

New York -0.297 0.257 -1.15 0.249 -0.80 0.21 

Oklahoma -0.147 0.227 -0.65 0.516 -0.59 0.30 

North Dakota -0.145 1.248 -0.12 0.908 -2.59 2.30 

Arkansas -0.132 0.215 -0.62 0.539 -0.55 0.29 

Louisiana -0.130 0.172 -0.76 0.449 -0.47 0.21 

Florida -0.118 0.201 -0.59 0.558 -0.51 0.28 

Kentucky -0.080 0.212 -0.38 0.707 -0.50 0.34 

North Carolina -0.066 0.265 -0.25 0.802 -0.59 0.45 

Missouri -0.057 0.366 -0.16 0.875 -0.78 0.66 

Wisconsin -0.034 0.208 -0.16 0.871 -0.44 0.37 

Colorado 0.038 0.325 0.12 0.907 -0.60 0.68 

Wyoming 0.153 0.470 0.33 0.745 -0.77 1.08 

New Hampshire 0.306 0.579 0.53 0.597 -0.83 1.44 

Georgia 0.327 0.173 1.89 0.059 -0.01 0.67 

Indiana 0.353 0.651 0.54 0.587 -0.92 1.63 

South Dakota 0.434 0.651 0.67 0.505 -0.84 1.71 

Nebraska 0.441 0.354 1.25 0.213 -0.25 1.14 

The long run commercial electricity estimates appear sensitive to the model specification. 

Given this model, when the coefficient of lagged quantity nears one, the denominator of 

the expression decreases and the estimate can become very large. This occurs in the first 

three states on the list: Tennessee, Maryland, and Alabama. 

82
The remaining estimates are generally larger than the short run estimates. The states also 

remain in relatively similar positions to the short run estimates. 

We tested for first-order autocorrelation in the state-level commercial electricity model 

and found it does not appear to affect the error term. The estimate of ρ was 0.018 with a 

t-statistic of 0.50. The results indicate that autocorrelation does not affect consistency of 

estimates on the lagged demand term and that inference based on the existing standard 

errors is valid. 

Natural Gas 

Table D.14: Regression results for short run residential natural gas elasticity. 

Short Run Natural Gas 

[95% 

Coef. Std. Err. t P>|t| Conf. Interval] 

Maine -0.745 0.467 -1.59 0.111 -1.662 0.172 

Vermont -0.281 0.084 -3.35 0.001 -0.445 -0.117 

Illinois -0.229 0.084 -2.72 0.007 -0.394 -0.064 

New Hampshire -0.225 0.093 -2.41 0.016 -0.408 -0.042 

Montana -0.217 0.079 -2.75 0.006 -0.372 -0.062 

South Carolina -0.202 0.141 -1.43 0.154 -0.479 0.076 

New Mexico -0.190 0.111 -1.71 0.088 -0.408 0.028 

Virginia -0.189 0.104 -1.81 0.07 -0.393 0.015 

West Virginia -0.184 0.083 -2.22 0.027 -0.347 -0.021 

North Dakota -0.183 0.063 -2.88 0.004 -0.308 -0.059 

Alabama -0.170 0.103 -1.64 0.101 -0.372 0.033 

Kansas -0.167 0.071 -2.37 0.018 -0.305 -0.028 

Washington -0.166 0.109 -1.53 0.125 -0.380 0.047 

Arkansas -0.151 0.080 -1.89 0.059 -0.308 0.006 

North Carolina -0.149 0.102 -1.46 0.145 -0.350 0.052 

Missouri -0.143 0.068 -2.11 0.035 -0.276 -0.010 

Indiana -0.139 0.063 -2.21 0.027 -0.263 -0.015 

Kentucky -0.137 0.059 -2.31 0.021 -0.253 -0.021 

Ohio -0.127 0.076 -1.68 0.093 -0.276 0.021 

Pennsylvania -0.117 0.089 -1.31 0.19 -0.291 0.058 

South Dakota -0.112 0.077 -1.46 0.144 -0.263 0.039 

Tennessee -0.110 0.101 -1.09 0.277 -0.308 0.088 

Maryland -0.106 0.109 -0.97 0.331 -0.319 0.108 

Colorado -0.102 0.069 -1.48 0.14 -0.237 0.033 

Minnesota -0.100 0.066 -1.52 0.129 -0.229 0.029 

California -0.098 0.119 -0.82 0.41 -0.332 0.135 

Iowa -0.098 0.090 -1.09 0.278 -0.275 0.079 

Wisconsin -0.098 0.066 -1.49 0.138 -0.227 0.031 

Rhode Island -0.085 0.122 -0.7 0.485 -0.323 0.154 

Idaho -0.074 0.076 -0.98 0.329 -0.223 0.075 

Mississippi -0.061 0.080 -0.76 0.448 -0.217 0.096 

Michigan -0.047 0.083 -0.57 0.57 -0.209 0.115 

83 
Utah -0.031 0.108 -0.29 0.771 -0.244 0.181 

Connecticut -0.029 0.128 -0.23 0.819 -0.281 0.222 

Delaware -0.024 0.102 -0.24 0.812 -0.224 0.175 

Oregon -0.024 0.088 -0.27 0.786 -0.198 0.149 

Florida -0.016 0.255 -0.06 0.951 -0.516 0.484 

Texas -0.006 0.111 -0.05 0.958 -0.224 0.212 

Massachusetts -0.005 0.148 -0.04 0.971 -0.295 0.284 

Louisiana 0.009 0.077 0.11 0.909 -0.143 0.161 

Nevada 0.011 0.093 0.12 0.904 -0.172 0.195 

Georgia 0.023 0.107 0.21 0.833 -0.188 0.233 

New York 0.027 0.114 0.24 0.814 -0.197 0.250 

Nebraska 0.034 0.073 0.46 0.642 -0.109 0.177 

Oklahoma 0.050 0.107 0.47 0.641 -0.160 0.260 

New Jersey 0.072 0.115 0.63 0.53 -0.153 0.297 

Wyoming 0.077 0.117 0.66 0.509 -0.152 0.307 

Arizona 0.086 0.150 0.57 0.566 -0.208 0.381 

The short-run estimates are mostly lower in the natural gas market than the electricity 

markets, with the exception of Maine which was discussed earlier. The overall precision 

of the estimates is also limited, which is shown by only ten states with statistically 

significant results. The natural gas market, like the commercial electricity market, had 

much greater variability in demand. Therefore, the limited precision is not surprising. 

Table D.15: Regression results for long-run price elasticities for residential natural gas 

Long Run Natural Gas 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

Maine -1.826 0.891 -2.05 0.041 -3.575 -0.078 

Vermont -0.577 0.189 -3.06 0.002 -0.947 -0.207 

New Hampshire -0.430 0.232 -1.86 0.064 -0.885 0.024 

Virginia -0.322 0.179 -1.8 0.072 -0.672 0.028 

South Carolina -0.299 0.248 -1.2 0.228 -0.787 0.188 

Montana -0.287 0.101 -2.83 0.005 -0.486 -0.088 

New Mexico -0.281 0.144 -1.96 0.05 -0.563 0.000 

North Carolina -0.279 0.212 -1.32 0.187 -0.695 0.136 

West Virginia -0.270 0.129 -2.1 0.036 -0.523 -0.018 

Illinois -0.243 0.100 -2.44 0.015 -0.438 -0.047 

North Dakota -0.230 0.087 -2.64 0.009 -0.402 -0.059 

Washington -0.214 0.147 -1.45 0.147 -0.503 0.075 

Missouri -0.174 0.081 -2.16 0.031 -0.332 -0.016 

Kentucky -0.171 0.073 -2.34 0.02 -0.315 -0.028 

Kansas -0.168 0.072 -2.34 0.019 -0.310 -0.027 

Tennessee -0.167 0.169 -0.99 0.323 -0.498 0.165 

Indiana -0.163 0.078 -2.08 0.037 -0.317 -0.009 

Rhode Island -0.163 0.234 -0.7 0.487 -0.622 0.296 

84
Alabama -0.159 0.090 -1.77 0.077 -0.336 0.017 

South Dakota -0.142 0.107 -1.33 0.183 -0.352 0.067 

Arkansas -0.141 0.075 -1.87 0.062 -0.289 0.007 

Maryland -0.134 0.136 -0.99 0.323 -0.400 0.132 

Minnesota -0.133 0.088 -1.5 0.134 -0.306 0.041 

California -0.132 0.166 -0.8 0.425 -0.458 0.193 

Ohio -0.132 0.081 -1.64 0.101 -0.291 0.026 

Pennsylvania -0.124 0.098 -1.27 0.205 -0.315 0.068 

Colorado -0.122 0.075 -1.63 0.104 -0.270 0.025 

Iowa -0.114 0.109 -1.04 0.296 -0.329 0.100 

Wisconsin -0.110 0.075 -1.46 0.145 -0.257 0.038 

Idaho -0.104 0.112 -0.94 0.35 -0.323 0.115 

Mississippi -0.079 0.110 -0.72 0.471 -0.295 0.136 

Michigan -0.075 0.131 -0.57 0.567 -0.333 0.182 

Utah -0.061 0.213 -0.29 0.776 -0.478 0.357 

Connecticut -0.042 0.184 -0.23 0.818 -0.402 0.318 

Delaware -0.036 0.153 -0.24 0.813 -0.337 0.264 

Oregon -0.028 0.104 -0.27 0.788 -0.232 0.176 

Florida -0.020 0.317 -0.06 0.951 -0.642 0.603 

Massachusetts -0.009 0.256 -0.04 0.971 -0.512 0.494 

Texas -0.008 0.159 -0.05 0.958 -0.320 0.303 

Louisiana 0.011 0.093 0.11 0.909 -0.171 0.193 

Nevada 0.021 0.175 0.12 0.905 -0.323 0.365 

Georgia 0.022 0.105 0.21 0.834 -0.185 0.229 

New York 0.029 0.124 0.24 0.812 -0.213 0.272 

Nebraska 0.038 0.081 0.47 0.64 -0.121 0.197 

Oklahoma 0.056 0.120 0.47 0.641 -0.179 0.291 

New Jersey 0.082 0.129 0.63 0.528 -0.172 0.335 

Wyoming 0.092 0.127 0.73 0.465 -0.156 0.341 

Arizona 0.119 0.220 0.54 0.588 -0.312 0.550 

Similar to other markets, the long-run estimates are generally larger than the short run 

estimates. The precision of these estimates is also limited, which is shown by the large 

standard errors and that only ten states have statistically significant estimates. The 

natural gas market also had much greater variation in prices and quantity during this 

period, which appears to affect the results at this level of aggregation. With the exception 

of Maine, the range of estimates is smaller than the electricity markets, which 

corresponds to the generally smaller (in absolute magnitude) values of the estimates when 

compared to the other markets. 

We tested for first-order autocorrelation in the state-level residential natural gas model 

and found it does not appear to affect the error term. The estimate of ρ was -0.12 with a 

t-statistic of -1.75. The results indicate that autocorrelation does not affect consistency of 

estimates on the lagged demand term and that inference based on the existing standard 

errors is valid. 

85
Utility-level results 

Table D.16: Short run elasticity estimates for residential electricity at the utility level 

Utility [95% 

Number region Coef. Std. Err. t P>|t| Conf. Interval] 

182 ENC -1.563 0.472 -3.31 0.001 -2.490 -0.637 

208 ENC -1.081 0.633 -1.71 0.088 -2.323 0.160 

186 ENC -1.061 0.521 -2.04 0.042 -2.082 -0.039 

191 ENC -0.697 0.275 -2.54 0.011 -1.237 -0.158 

18 ENC -0.584 0.279 -2.1 0.036 -1.13 -0.04 

75 ENC -0.480 0.498 -0.96 0.335 -1.46 0.50 

134 ENC -0.392 0.257 -1.53 0.127 -0.896 0.112 

153 ENC -0.314 0.238 -1.32 0.188 -0.781 0.154 

35 ENC -0.250 0.160 -1.57 0.118 -0.56 0.06 

73 ENC -0.243 0.135 -1.8 0.072 -0.51 0.02 

177 ENC -0.217 0.622 -0.35 0.728 -1.438 1.004 

67 ENC -0.167 0.245 -0.68 0.495 -0.65 0.31 

41 ENC -0.150 0.353 -0.43 0.671 -0.84 0.54 

44 ENC -0.137 0.291 -0.47 0.637 -0.71 0.43 

17 ENC -0.131 0.469 -0.28 0.78 -1.05 0.79 

76 ENC -0.105 0.337 -0.31 0.756 -0.77 0.56 

38 ENC -0.097 0.427 -0.23 0.82 -0.93 0.74 

26 ENC -0.091 0.359 -0.25 0.8 -0.80 0.61 

96 ENC -0.082 0.331 -0.25 0.803 -0.73 0.57 

105 ENC -0.070 0.175 -0.4 0.689 -0.41 0.27 

28 ENC -0.045 0.399 -0.11 0.909 -0.83 0.74 

33 ENC -0.033 0.320 -0.1 0.917 -0.66 0.59 

206 ENC -0.004 0.517 -0.01 0.994 -1.019 1.011 

133 ENC 0.024 0.490 0.05 0.96 -0.936 0.985 

207 ENC 0.109 0.355 0.31 0.758 -0.587 0.806 

129 ENC 0.211 0.563 0.37 0.708 -0.894 1.315 

130 ENC 0.952 1.475 0.65 0.519 -1.941 3.845 

103 ESC -1.514 0.410 -3.69 0 -2.32 -0.71 

113 ESC -1.222 0.397 -3.08 0.002 -2.00 -0.44 

47 ESC -1.126 0.675 -1.67 0.096 -2.45 0.20 

120 ESC -1.064 0.625 -1.7 0.089 -2.291 0.163 

30 ESC -1.046 0.689 -1.52 0.129 -2.40 0.31 

198 ESC -0.958 0.414 -2.32 0.021 -1.770 -0.147 

40 ESC -0.884 0.857 -1.03 0.302 -2.56 0.80 

110 ESC -0.766 0.389 -1.97 0.049 -1.53 0.00 

92 ESC -0.680 0.598 -1.14 0.256 -1.85 0.49 

86 ESC -0.528 0.394 -1.34 0.181 -1.30 0.25 

179 ESC -0.499 0.230 -2.17 0.03 -0.950 -0.048 

69 ESC -0.486 0.592 -0.82 0.411 -1.65 0.67 

98 ESC -0.439 0.835 -0.53 0.599 -2.08 1.20 

193 ESC -0.299 0.386 -0.77 0.439 -1.055 0.458 

172 ESC -0.297 0.276 -1.07 0.283 -0.839 0.245 

1 ESC -0.200 0.522 -0.38 0.701 -1.22 0.82 

86 
23 ESC -0.192 0.521 -0.37 0.713 -1.21 0.83 

107 ESC -0.178 0.548 -0.32 0.746 -1.25 0.90 

93 ESC -0.088 0.226 -0.39 0.699 -0.53 0.36 

210 ESC -0.018 0.801 -0.02 0.982 -1.589 1.552 

57 ESC 0.148 0.745 0.2 0.843 -1.31 1.61 

91 ESC 0.317 0.338 0.94 0.348 -0.35 0.98 

112 ESC 0.402 0.612 0.66 0.511 -0.80 1.60 

200 ESC 1.389 0.305 4.55 0 0.790 1.987 

211 M -1.206 0.394 -3.06 0.002 -1.979 -0.433 

94 M -1.084 0.629 -1.72 0.085 -2.32 0.15 

140 M -0.980 0.401 -2.45 0.015 -1.767 -0.194 

155 M -0.696 0.248 -2.81 0.005 -1.183 -0.210 

171 M -0.694 0.586 -1.18 0.237 -1.844 0.456 

184 M -0.663 0.607 -1.09 0.274 -1.854 0.527 

164 M -0.547 0.403 -1.36 0.174 -1.338 0.243 

151 M -0.368 1.027 -0.36 0.72 -2.382 1.647 

53 M -0.325 0.725 -0.45 0.655 -1.75 1.10 

122 M -0.262 0.075 -3.51 0 -0.409 -0.116 

152 M -0.260 0.396 -0.66 0.512 -1.036 0.516 

32 M -0.233 0.649 -0.36 0.72 -1.51 1.04 

5 M -0.221 0.372 -0.59 0.552 -0.95 0.51 

118 M -0.096 0.187 -0.51 0.61 -0.463 0.272 

196 M -0.061 0.305 -0.2 0.842 -0.660 0.538 

25 M -0.014 0.253 -0.06 0.955 -0.51 0.48 

104 M -0.001 0.283 0 0.996 -0.56 0.55 

71 M 0.467 0.298 1.57 0.117 -0.12 1.05 

202 MA -0.800 0.429 -1.87 0.062 -1.641 0.041 

109 MA -0.792 0.625 -1.27 0.205 -2.02 0.43 

37 MA -0.712 0.600 -1.19 0.235 -1.89 0.46 

100 MA -0.639 0.418 -1.53 0.126 -1.46 0.18 

160 MA -0.417 0.383 -1.09 0.277 -1.169 0.336 

125 MA -0.345 0.321 -1.07 0.284 -0.975 0.286 

147 MA -0.341 0.284 -1.2 0.23 -0.897 0.216 

145 MA -0.308 0.477 -0.65 0.518 -1.244 0.627 

213 MA -0.230 0.301 -0.76 0.446 -0.821 0.361 

85 MA -0.177 0.364 -0.49 0.626 -0.89 0.54 

157 MA -0.117 0.475 -0.25 0.805 -1.050 0.815 

49 MA -0.089 0.240 -0.37 0.711 -0.56 0.38 

161 MA -0.003 0.492 -0.01 0.996 -0.967 0.962 

16 MA 0.077 0.478 0.16 0.872 -0.86 1.02 

137 MA 0.099 0.329 0.3 0.763 -0.546 0.744 

146 MA 0.125 0.146 0.86 0.39 -0.161 0.412 

7 MA 0.171 0.180 0.95 0.341 -0.18 0.52 

144 MA 0.302 0.323 0.94 0.35 -0.332 0.936 

126 MA 0.350 0.296 1.18 0.238 -0.231 0.932 

20 NE -0.722 0.421 -1.72 0.086 -1.55 0.10 

119 NE -0.596 0.569 -1.05 0.296 -1.713 0.521 

65 NE -0.546 0.149 -3.65 0 -0.84 -0.25 

22 NE -0.391 0.278 -1.41 0.159 -0.94 0.15 

123 NE -0.360 0.229 -1.57 0.117 -0.809 0.090 

87
194 NE -0.197 0.535 -0.37 0.712 -1.246 0.851 

34 NE -0.196 0.212 -0.93 0.355 -0.61 0.22 

36 NE -0.154 0.348 -0.44 0.659 -0.84 0.53 

14 NE -0.091 0.254 -0.36 0.721 -0.59 0.41 

46 NE -0.019 0.272 -0.07 0.946 -0.55 0.52 

4 NE 0.063 0.416 0.15 0.88 -0.75 0.88 

154 NE 0.072 0.723 0.1 0.92 -1.346 1.491 

10 NE 0.191 0.333 0.57 0.566 -0.46 0.84 

204 NE 0.197 0.400 0.49 0.622 -0.587 0.981 

106 NE 0.849 0.908 0.93 0.35 -0.93 2.63 

139 PC -1.215 0.094 -12.96 0 -1.399 -1.031 

24 PC -0.961 0.035 -27.6 0 -1.03 -0.89 

2 PC -0.770 0.144 -5.35 0 -1.05 -0.49 

173 PC -0.595 0.568 -1.05 0.295 -1.711 0.520 

158 PC -0.488 0.486 -1 0.315 -1.440 0.465 

101 PC -0.471 0.269 -1.75 0.081 -1.00 0.06 

64 PC -0.444 0.324 -1.37 0.171 -1.08 0.19 

166 PC -0.430 0.366 -1.18 0.24 -1.147 0.287 

176 PC -0.279 0.365 -0.76 0.444 -0.995 0.437 

39 PC -0.219 0.228 -0.96 0.336 -0.67 0.23 

188 PC -0.156 0.379 -0.41 0.682 -0.899 0.588 

27 PC -0.119 0.431 -0.28 0.783 -0.96 0.73 

142 PC -0.119 0.335 -0.35 0.724 -0.776 0.539 

170 PC 0.014 0.377 0.04 0.971 -0.726 0.754 

74 PC 0.068 0.303 0.23 0.822 -0.53 0.66 

163 PC 0.144 0.511 0.28 0.778 -0.859 1.147 

201 PC 0.279 0.552 0.5 0.614 -0.805 1.362 

11 PC 0.324 0.181 1.79 0.073 -0.03 0.68 

159 PC 0.404 0.548 0.74 0.461 -0.671 1.479 

115 PC 0.475 0.421 1.13 0.26 -0.35 1.30 

148 PC 0.670 0.402 1.66 0.096 -0.120 1.459 

52 PC 0.756 0.515 1.47 0.142 -0.25 1.77 

197 SA -1.477 0.743 -1.99 0.047 -2.935 -0.020 

178 SA -1.434 0.542 -2.65 0.008 -2.497 -0.371 

29 SA -1.299 0.474 -2.74 0.006 -2.23 -0.37 

63 SA -1.232 0.725 -1.7 0.09 -2.65 0.19 

199 SA -1.150 0.635 -1.81 0.07 -2.396 0.096 

187 SA -1.087 0.525 -2.07 0.039 -2.118 -0.056 

95 SA -1.073 0.621 -1.73 0.085 -2.29 0.15 

31 SA -1.038 0.238 -4.36 0 -1.50 -0.57 

97 SA -1.032 0.621 -1.66 0.097 -2.25 0.19 

127 SA -0.890 0.660 -1.35 0.178 -2.186 0.405 

169 SA -0.884 0.366 -2.42 0.016 -1.602 -0.167 

84 SA -0.878 0.566 -1.55 0.121 -1.99 0.23 

168 SA -0.854 0.290 -2.94 0.003 -1.423 -0.284 

189 SA -0.827 0.607 -1.36 0.173 -2.018 0.363 

83 SA -0.814 0.306 -2.66 0.008 -1.41 -0.21 

15 SA -0.734 0.583 -1.26 0.208 -1.88 0.41 

128 SA -0.686 0.297 -2.31 0.021 -1.270 -0.103 

60 SA -0.678 0.368 -1.84 0.066 -1.40 0.05 

88
48 SA -0.542 0.673 -0.8 0.421 -1.86 0.78 

72 SA -0.540 0.504 -1.07 0.284 -1.53 0.45 

116 SA -0.472 0.298 -1.58 0.113 -1.06 0.11 

190 SA -0.458 0.501 -0.91 0.361 -1.441 0.525 

62 SA -0.447 0.320 -1.4 0.162 -1.07 0.18 

174 SA -0.438 0.541 -0.81 0.418 -1.498 0.623 

66 SA -0.304 0.334 -0.91 0.363 -0.96 0.35 

58 SA -0.299 0.373 -0.8 0.422 -1.03 0.43 

56 SA -0.272 0.366 -0.74 0.458 -0.99 0.45 

82 SA -0.243 0.354 -0.69 0.492 -0.94 0.45 

54 SA -0.195 0.388 -0.5 0.614 -0.96 0.57 

141 SA -0.164 0.102 -1.61 0.108 -0.363 0.036 

214 SA -0.160 0.388 -0.41 0.681 -0.921 0.602 

12 SA -0.129 0.212 -0.61 0.544 -0.55 0.29 

9 SA -0.124 0.426 -0.29 0.772 -0.96 0.71 

175 SA -0.123 0.273 -0.45 0.651 -0.658 0.412 

149 SA -0.004 0.431 -0.01 0.993 -0.850 0.842 

209 SA 0.004 0.473 0.01 0.994 -0.925 0.933 

43 SA 0.038 0.405 0.09 0.925 -0.76 0.83 

59 SA 0.041 0.448 0.09 0.928 -0.84 0.92 

205 SA 0.234 0.383 0.61 0.541 -0.517 0.986 

42 SA 0.241 1.088 0.22 0.825 -1.89 2.38 

162 SA 0.488 0.374 1.31 0.192 -0.245 1.221 

78 WNC -1.746 1.057 -1.65 0.099 -3.82 0.33 

192 WNC -1.127 0.243 -4.64 0 -1.604 -0.651 

131 WNC -0.654 0.332 -1.97 0.049 -1.307 -0.002 

88 WNC -0.622 0.261 -2.39 0.017 -1.13 -0.11 

90 WNC -0.615 0.364 -1.69 0.091 -1.33 0.10 

150 WNC -0.552 0.188 -2.94 0.003 -0.920 -0.184 

114 WNC -0.495 0.749 -0.66 0.509 -1.96 0.97 

132 WNC -0.476 0.447 -1.06 0.287 -1.353 0.401 

77 WNC -0.471 0.336 -1.4 0.162 -1.13 0.19 

183 WNC -0.463 0.115 -4.02 0 -0.688 -0.237 

111 WNC -0.440 0.303 -1.45 0.146 -1.03 0.15 

136 WNC -0.425 0.373 -1.14 0.255 -1.157 0.308 

89 WNC -0.352 0.221 -1.59 0.111 -0.79 0.08 

79 WNC -0.200 0.522 -0.38 0.701 -1.22 0.82 

80 WNC -0.200 0.522 -0.38 0.701 -1.22 0.82 

81 WNC -0.200 0.522 -0.38 0.701 -1.22 0.82 

3 WNC -0.190 0.411 -0.46 0.643 -1.00 0.62 

108 WNC -0.153 0.427 -0.36 0.719 -0.99 0.68 

87 WNC -0.124 0.404 -0.31 0.758 -0.92 0.67 

195 WNC -0.070 0.455 -0.15 0.878 -0.962 0.822 

138 WNC -0.052 0.419 -0.13 0.901 -0.874 0.769 

185 WNC 0.041 0.406 0.1 0.921 -0.757 0.838 

121 WNC 0.179 0.268 0.67 0.504 -0.346 0.704 

99 WNC 0.489 0.717 0.68 0.496 -0.92 1.90 

212 WNC 1.109 0.779 1.42 0.155 -0.420 2.638 

51 WNC 1.404 0.422 3.32 0.001 0.58 2.23 

135 WSC -1.226 0.591 -2.07 0.038 -2.385 -0.067 

89
6 WSC -0.917 0.283 -3.24 0.001 -1.47 -0.36 

215 WSC -0.632 0.354 -1.78 0.075 -1.326 0.063 

102 WSC -0.615 0.344 -1.79 0.074 -1.29 0.06 

124 WSC -0.613 0.254 -2.42 0.016 -1.111 -0.115 

13 WSC -0.517 0.726 -0.71 0.476 -1.94 0.91 

55 WSC -0.485 0.335 -1.45 0.148 -1.14 0.17 

167 WSC -0.484 0.437 -1.11 0.268 -1.340 0.373 

45 WSC -0.464 0.265 -1.75 0.08 -0.98 0.06 

19 WSC -0.450 0.448 -1 0.315 -1.33 0.43 

181 WSC -0.318 0.331 -0.96 0.338 -0.968 0.332 

156 WSC -0.286 0.440 -0.65 0.516 -1.150 0.578 

165 WSC -0.272 0.404 -0.67 0.502 -1.065 0.522 

21 WSC -0.154 0.292 -0.53 0.597 -0.73 0.42 

68 WSC -0.108 0.234 -0.46 0.646 -0.57 0.35 

70 WSC -0.091 0.508 -0.18 0.858 -1.09 0.91 

180 WSC 0.023 0.587 0.04 0.969 -1.129 1.174 

8 WSC 0.066 0.406 0.16 0.872 -0.73 0.86 

203 WSC 0.447 0.205 2.18 0.03 0.044 0.851 

61 WSC 0.452 0.317 1.43 0.154 -0.17 1.07 

50 WSC 0.486 0.427 1.14 0.255 -0.35 1.32 

143 WSC 0.614 0.440 1.39 0.163 -0.250 1.477 

The utility results also have a wide range of price elasticity estimates. The minimum 

value is –1.75 and the maximum is 1.40. In general, the estimates are representative of 

the results from the state-level analysis in residential electricity. Most estimates are 

negative and in the inelastic range. Some are positive in each region. Overall, these 

results suffer from a lack of precision also. Only about 17% of the utilities in the sample 

were statistically significant. Some of this variation in the estimates may be explained by 

the large differences in the size of utilities. 

We tested for first-order autocorrelation in the error term and the results indicate it may 

be present. The estimate of ρ for the utility-level model was -0.32 with a t-statistic of 

-3.27. The results suggest first-order autocorrelation in the error term and we, therefore, 

ran the model to account for an AR(1) structure in the error term. 

Results from Energy Use Trend Analysis 

The trend analysis fits a linear trend to the variable of interest. Many of the trends in the 

data were linear and the model fit well. In some cases, particularly the natural gas 

market, the trends were not linear and the model had a poorer fit. 

This section will now display the trend analysis results first for the region level and then 

at the state level. 

90
Region-Level Results 

The model has the form: 

ln yit = α + yeart β + regioni δi + (regioni x yeart) βi + εit 

The model includes an indicator variable for region and an interaction term between 

region and year. These terms allow the slope of the trend and y-intercept to vary freely 

for each region. 

Residential Electricity 

Table D.17: Regional trends in residential electricity energy intensity 

Coef Std. Err. t P>|t| 95% Conf Interval 

South Atlantic 1.94% 0.08% 25.7 0.0 1.79% 2.09% 

East South Central 1.79% 0.11% 16.8 0.0 1.59% 2.00% 

West South Central 1.59% 0.11% 14.9 0.0 1.38% 1.80% 

West North Central 1.45% 0.08% 18.0 0.0 1.29% 1.61% 

East North Central 1.40% 0.10% 14.6 0.0 1.21% 1.58% 

Mid Atlantic 1.33% 0.12% 10.8 0.0 1.09% 1.57% 

New England 0.91% 0.09% 10.4 0.0 0.73% 1.08% 

Mountain 0.80% 0.08% 10.6 0.0 0.65% 0.95% 

Pacific Coast -0.12% 0.12% -1.0 0.3 -0.36% 0.12% 

Intensity is measured as quantity of residential electricity per capita. The table shows per 

capita electricity use is growing fastest in the South Atlantic and Central regions. Growth 

in per capita electricity use is negligible in the Pacific Coast region. 

Table D.18: Regional trends in residential electricity expenditures 

Coef Std. Err. t P>|t| 95% Conf Interval 

New England 0.717% 0.111% 6.49 0 0.500% 0.934% 

Pacific Coast 0.680% 0.156% 4.35 0 0.373% 0.987% 

East South Central 0.624% 0.135% 4.61 0 0.358% 0.890% 

South Atlantic 0.621% 0.096% 6.48 0 0.433% 0.809% 

West South Central 0.518% 0.135% 3.83 0 0.253% 0.784% 

Mid Atlantic 0.316% 0.156% 2.02 0.04 0.009% 0.623% 

West North Central 0.143% 0.102% 1.4 0.16 -0.058% 0.344% 

East North Central 0.122% 0.121% 1.01 0.32 -0.116% 0.359% 

Mountain 0.008% 0.096% 0.09 0.93 -0.180% 0.196% 

91
Expenditures are growing fastest in the New England and Pacific Coast regions. Growth 

in expenditures is negligible in the Mountain region. Overall, the growth rates are all less 

than 1%. 

Table D.19: Regional trends in residential electricity expenditures as a share of income 

Coef Std. Err. t P>|t| 95% Conf Interval 

Mid Atlantic -1.99% 0.17% -12.08 0 -2.32% -1.67% 

West North Central -1.98% 0.11% -18.32 0 -2.19% -1.77% 

East North Central -1.90% 0.13% -14.83 0 -2.15% -1.65% 

Mountain -1.83% 0.10% -18.07 0 -2.03% -1.63% 

New England -1.82% 0.12% -15.58 0 -2.05% -1.59% 

East South Central -1.80% 0.14% -12.57 0 -2.08% -1.52% 

South Atlantic -1.71% 0.10% -16.9 0 -1.91% -1.51% 

West South Central -1.46% 0.14% -10.24 0 -1.74% -1.18% 

Pacific Coast -1.19% 0.17% -7.21 0 -1.51% -0.87% 

The trends in expenditures as a share of income show that income growth is faster than 

the increase in energy expenditures. Therefore, energy expenditures as a portion of 

household budgets is generally decreasing. The regional differences in the rate of 

decrease vary by about 1%. Expenditures as a share of income are declining fastest in the 

Mid Atlantic at about 2%. Decline is slowest in the Pacific Coast region at 

approximately 1%. 

Commercial Electricity 

Table D.20: Regional trends in commercial energy intensity 

Regional commercial energy intensity trends R-square = 0.57 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

New England 2.32% 0.20% 11.42 0 1.92% 2.72% 

West North Central 2.18% 0.30% 7.15 0 1.58% 2.77% 

South Atlantic 1.97% 0.20% 9.9 0 1.58% 2.36% 

Mid Atlantic 1.78% 0.25% 7.01 0 1.28% 2.28% 

East North Central 1.49% 0.20% 7.58 0 1.10% 1.87% 

Mountain 1.48% 0.29% 5.19 0 0.92% 2.04% 

West South Central 0.96% 0.25% 3.81 0 0.47% 1.45% 

East South Central 0.94% 0.46% 2.04 0.041 0.04% 1.84% 

Pacific Coast 0.25% 0.32% 0.8 0.425 -0.37% 0.88% 

Intensity is measured as quantity of commercial electricity per unit of commercial 

floorspace. The results show statistically significant differences in the annual growth 

rates. The Pacific Coast rate is near zero, whereas the annual growth rates are over 2% in 

New England and the West North Central. All the trend estimates are statistically 

92
significant, except the Pacific Coast region. However, the model fit is only moderate, 

which is shown by the adjusted R-squared of 0.57. 

Natural Gas 

Table D.21: Regional energy intensity trends for residential natural gas. 

Regional Trend in Natural Gas Energy Intensity R-squared 0.4302 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

Pacific Coast 1.09% 0.60% 1.84 0.067 -0.08% 2.26% 

Mid Atlantic 0.45% 0.17% 2.7 0.007 0.12% 0.78% 

New England 0.15% 1.08% 0.14 0.892 -1.97% 2.26% 

Mountain -0.41% 0.39% -1.06 0.288 -1.17% 0.35% 

South Atlantic -0.45% 0.71% -0.63 0.529 -1.85% 0.95% 

East North Central -0.47% 0.19% -2.41 0.016 -0.85% -0.09% 

East South Central -0.57% 0.31% -1.87 0.062 -1.17% 0.03% 

West North Central -0.60% 0.23% -2.66 0.008 -1.05% -0.16% 

West South Central -2.05% 0.28% -7.41 0 -2.60% -1.51% 

In this case, we measured energy intensity as the quantity of natural gas consumed per 

capita. The results show much different regional trends. Residential natural gas energy 

intensity is increasing in the Pacific Coast, Mid Atlantic, and New England regions. The 

trend is a slight decline in the South Atlantic and Central regions, except for the West 

South Central where intensity is declining over 2% per year. 

The model fit is only fair in this case. The r-squared for this model is 0.43. The natural 

gas trends generally have two peaks, which is why the linear fit is limited. 

Table D.22: Regional trends in natural gas energy expenditures 

Regional Trend in Natural Gas Energy Expenditures R-squared 0.3345 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

Pacific Coast 1.01% 0.45% 2.25 0.025 0.13% 1.89% 

Mid Atlantic 0.75% 0.20% 3.85 0 0.37% 1.14% 

East South Central 0.70% 0.34% 2.06 0.04 0.03% 1.36% 

South Atlantic 0.47% 0.64% 0.74 0.458 -0.78% 1.73% 

West North Central 0.14% 0.25% 0.55 0.579 -0.36% 0.64% 

New England -0.04% 1.08% -0.04 0.971 -2.16% 2.08% 

Mountain -0.15% 0.28% -0.53 0.595 -0.71% 0.41% 

East North Central -0.15% 0.23% -0.65 0.517 -0.61% 0.31% 

West South Central -0.56% 0.29% -1.93 0.054 -1.14% 0.01% 

The trend is rising expenditures in the Pacific Coast, Mid Atlantic, East South Central, 

South Atlantic, and West North Central regions. Expenditures are falling in the New 

England, Mountain, East North Central, and West South Central regions. For most 

93
regions, the trend is near or less than 0.5% in absolute magnitude. Therefore, the 

estimates show that expenditures are relatively stable for most people. 

The model fit is marginal in this case. Natural gas prices had several spikes and dropoffs, 

which is a nonlinear pattern. The expenditure data follow the price trend closely. 

Therefore, the linear fit is marginal for this variable. 

Table D.23: Annual trends for natural gas expenditures as a share of income 

R-squared 0.3865 

Regional Trend in Natural Gas Energy Expenditures as Income Share 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

Pacific Coast -0.84% 0.40% -2.09 0.036 -1.62% -0.05% 

Mid Atlantic -1.56% 0.21% -7.44 0 -1.97% -1.15% 

East South Central -1.74% 0.34% -5.06 0 -2.42% -1.07% 

South Atlantic -1.85% 0.65% -2.86 0.004 -3.11% -0.58% 

West North Central -1.98% 0.24% -8.4 0 -2.45% -1.52% 

Mountain -1.99% 0.28% -7.16 0 -2.53% -1.44% 

East North Central -2.17% 0.22% -9.84 0 -2.60% -1.74% 

New England -2.49% 1.00% -2.48 0.013 -4.45% -0.52% 

West South Central -2.53% 0.32% -7.96 0 -3.15% -1.90% 

The results show that expenditures as a share of income are falling in all regions. The 

most rapid decline is in the West South Central. The Pacific Coast trend has the most 

moderate decline. Again, the model fit is only marginal for the reasons stated above. 

State-Level Results 

The model has the form: 

ln yit = α + yeart β + statei δi + (statei x yeart) βi + εit 

The model includes an indicator variable for each state and an interaction term between 

state and year. These terms allow the slope of the trend and y-intercept to vary freely for 

each state. 

94
Residential Electricity 

Table D.24: Residential electricity energy intensity 

Residential Energy Intensity 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

West Virginia 2.51% 0.05% 49.2 0 2.41% 2.61% 

Delaware 2.49% 0.23% 10.81 0 2.04% 2.95% 

Kentucky 2.43% 0.08% 29.32 0 2.26% 2.59% 

Maryland 2.42% 0.13% 18.68 0 2.17% 2.68% 

Mississippi 2.23% 0.08% 26.32 0 2.06% 2.39% 

Alabama 2.18% 0.09% 23.46 0 2.00% 2.36% 

South Carolina 2.16% 0.07% 29.65 0 2.01% 2.30% 

Louisiana 2.14% 0.07% 32.43 0 2.01% 2.27% 

Missouri 2.10% 0.11% 19.84 0 1.89% 2.31% 

Kansas 1.97% 0.13% 14.98 0 1.71% 2.22% 

Georgia 1.95% 0.08% 24.76 0 1.79% 2.10% 

New Mexico 1.88% 0.08% 23.37 0 1.72% 2.04% 

Virginia 1.85% 0.07% 28.03 0 1.72% 1.97% 

Pennsylvania 1.76% 0.05% 36.67 0 1.66% 1.85% 

North Dakota 1.76% 0.12% 14.68 0 1.52% 1.99% 

North Carolina 1.71% 0.06% 27 0 1.59% 1.84% 

Wyoming 1.71% 0.19% 8.93 0 1.33% 2.08% 

Texas 1.64% 0.10% 17.04 0 1.45% 1.83% 

Florida 1.64% 0.08% 20.98 0 1.49% 1.79% 

Massachusetts 1.63% 0.06% 25.06 0 1.50% 1.75% 

Arkansas 1.62% 0.17% 9.55 0 1.29% 1.96% 

Ohio 1.62% 0.04% 38.3 0 1.54% 1.70% 

Indiana 1.62% 0.05% 35.62 0 1.53% 1.71% 

Nebraska 1.61% 0.10% 15.85 0 1.41% 1.81% 

Oklahoma 1.60% 0.11% 14.27 0 1.38% 1.82% 

New Jersey 1.59% 0.06% 27.69 0 1.48% 1.71% 

Arizona 1.52% 0.07% 22.08 0 1.39% 1.66% 

Rhode Island 1.49% 0.05% 31.61 0 1.40% 1.58% 

New York 1.42% 0.05% 30.3 0 1.33% 1.51% 

Michigan 1.40% 0.07% 19.1 0 1.25% 1.54% 

Colorado 1.37% 0.21% 6.44 0 0.95% 1.79% 

Connecticut 1.37% 0.06% 24.85 0 1.26% 1.48% 

Minnesota 1.34% 0.08% 16.31 0 1.18% 1.51% 

Utah 1.22% 0.08% 14.57 0 1.05% 1.38% 

Illinois 1.19% 0.10% 11.66 0 0.99% 1.39% 

Wisconsin 1.18% 0.08% 14.22 0 1.02% 1.34% 

South Dakota 1.16% 0.11% 10.4 0 0.94% 1.37% 

Iowa 1.06% 0.08% 12.6 0 0.90% 1.23% 

Montana 0.79% 0.14% 5.51 0 0.51% 1.07% 

Tennessee 0.69% 0.12% 5.66 0 0.45% 0.93% 

95 
New Hampshire 0.61% 0.10% 6.18 0 0.42% 0.80% 

Maine 0.60% 0.12% 4.82 0 0.35% 0.84% 

Vermont 0.32% 0.17% 1.92 0.055 -0.01% 0.65% 

California 0.28% 0.07% 3.99 0 0.14% 0.42% 

Nevada 0.07% 0.12% 0.59 0.554 -0.16% 0.30% 

Oregon 0.00% 0.08% 0.01 0.994 -0.16% 0.16% 

Idaho -0.01% 0.11% -0.11 0.915 -0.23% 0.20% 

Washington -0.57% 0.16% -3.48 0.001 -0.89% -0.25% 

The results show that per capita residential electricity use is growing quickly in southern 

states. All of the states with a growth rate over 2% are in the South Atlantic and East 

South Central regions. The growth rate is considerably smaller (less than 0.5%) in 

Vermont, California, Nevada, Oregon, Idaho, and Washington. Notably, Oregon, Idaho, 

and Washington have zero growth or declining per capita use. 

Table D.25: Trends in expenditures on residential electricity as a share of income 

Residential Electricity Income Share 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

Utah -2.79% 0.34% -8.1 0 -3.46% -2.11% 

New Jersey -2.47% 0.17% -14.35 0 -2.80% -2.13% 

Tennessee -2.33% 0.12% -19.39 0 -2.57% -2.10% 

Minnesota -2.27% 0.21% -10.71 0 -2.69% -1.85% 

South Dakota -2.22% 0.34% -6.52 0 -2.89% -1.55% 

New Hampshire -2.17% 0.17% -12.68 0 -2.51% -1.84% 

Illinois -2.14% 0.40% -5.38 0 -2.92% -1.36% 

Massachusetts -2.09% 0.19% -11.15 0 -2.46% -1.72% 

Wisconsin -2.01% 0.25% -8.03 0 -2.50% -1.52% 

Virginia -1.98% 0.11% -18.49 0 -2.20% -1.77% 

Colorado -1.98% 0.41% -4.88 0 -2.77% -1.18% 

Nevada -1.97% 0.28% -7.1 0 -2.52% -1.43% 

Arkansas -1.96% 0.19% -10.06 0 -2.34% -1.58% 

Iowa -1.93% 0.25% -7.85 0 -2.41% -1.44% 

Indiana -1.92% 0.22% -8.78 0 -2.35% -1.49% 

Delaware -1.92% 0.12% -16.57 0 -2.15% -1.69% 

Florida -1.86% 0.18% -10.39 0 -2.21% -1.51% 

Rhode Island -1.84% 0.17% -10.55 0 -2.19% -1.50% 

North Dakota -1.81% 0.30% -5.99 0 -2.40% -1.21% 

Arizona -1.78% 0.19% -9.49 0 -2.14% -1.41% 

Nebraska -1.76% 0.21% -8.21 0 -2.18% -1.34% 

Michigan -1.75% 0.09% -20.16 0 -1.92% -1.58% 

Connecticut -1.73% 0.24% -7.33 0 -2.19% -1.26% 

Idaho -1.70% 0.31% -5.46 0 -2.31% -1.09% 

Oregon -1.62% 0.17% -9.37 0 -1.96% -1.28% 

Kentucky -1.61% 0.31% -5.21 0 -2.21% -1.00% 

96
Missouri -1.58% 0.21% -7.69 0 -1.99% -1.18% 

North Carolina -1.58% 0.17% -9.48 0 -1.90% -1.25% 

Alabama -1.55% 0.17% -8.97 0 -1.89% -1.21% 

Vermont -1.50% 0.17% -8.81 0 -1.83% -1.16% 

New Mexico -1.49% 0.27% -5.59 0 -2.02% -0.97% 

Ohio -1.49% 0.19% -7.87 0 -1.86% -1.12% 

Maryland -1.42% 0.20% -7.01 0 -1.82% -1.02% 

Georgia -1.41% 0.12% -12.09 0 -1.64% -1.18% 

Kansas -1.38% 0.35% -3.98 0 -2.05% -0.70% 

Oklahoma -1.35% 0.26% -5.15 0 -1.86% -0.84% 

Pennsylvania -1.33% 0.16% -8.22 0 -1.65% -1.01% 

New York -1.33% 0.14% -9.74 0 -1.59% -1.06% 

Mississippi -1.33% 0.15% -8.7 0 -1.62% -1.03% 

South Carolina -1.29% 0.18% -7.26 0 -1.64% -0.94% 

Texas -1.29% 0.25% -5.22 0 -1.77% -0.80% 

West Virginia -1.12% 0.18% -6.41 0 -1.47% -0.78% 

Washington -1.10% 0.48% -2.27 0.023 -2.04% -0.15% 

Maine -1.04% 0.25% -4.12 0 -1.53% -0.54% 

California -0.73% 0.19% -3.86 0 -1.10% -0.36% 

Louisiana -0.59% 0.39% -1.51 0.131 -1.36% 0.18% 

Wyoming -0.33% 0.51% -0.65 0.517 -1.34% 0.67% 

Montana -0.27% 0.23% -1.14 0.255 -0.73% 0.19% 

The trends are declining in all states but the rates are considerably different. Nine states 

are declining at 2% per year or more. Four states are declining slower than 0.75%. There 

is a relatively even distribution of states between these points. 

Commercial Electricity 

We only estimated trends for commercial electricity energy intensity. We measure 

energy intensity for this variable is the amount of commercial electricity used per unit of 

commercial floorspace. 

Table D.26: Estimates of the annual trend in commercial energy intensity 

Annual Trends - Commercial Energy Intensity (electricity / sq ft flooring) 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

New Hampshire 4.41% 0.21% 20.97 0 3.99% 4.82% 

North Dakota 3.60% 0.34% 10.63 0 2.93% 4.26% 

Wyoming 3.41% 0.45% 7.55 0 2.52% 4.29% 

South Dakota 3.19% 0.18% 17.49 0 2.84% 3.55% 

Vermont 2.89% 0.17% 16.94 0 2.55% 3.22% 

North Carolina 2.61% 0.08% 32.58 0 2.45% 2.77% 

Georgia 2.47% 0.24% 10.1 0 1.99% 2.95% 

Maine 2.45% 0.18% 13.65 0 2.10% 2.81% 

97
Alabama 2.40% 0.29% 8.43 0 1.85% 2.96% 

Delaware 2.35% 0.21% 11.27 0 1.94% 2.76% 

Maryland 2.34% 0.69% 3.41 0.001 0.99% 3.68% 

Mississippi 2.32% 0.27% 8.75 0 1.80% 2.84% 

Michigan 2.24% 0.25% 9.03 0 1.76% 2.73% 

Missouri 2.11% 0.11% 18.8 0 1.89% 2.33% 

West Virginia 2.06% 0.11% 19.46 0 1.85% 2.27% 

New Mexico 2.04% 0.18% 11.63 0 1.69% 2.38% 

Nebraska 2.01% 0.17% 12.11 0 1.68% 2.34% 

Utah 1.99% 0.18% 11.21 0 1.64% 2.33% 

New Jersey 1.95% 0.11% 18.44 0 1.74% 2.16% 

Pennsylvania 1.81% 0.07% 25.32 0 1.67% 1.95% 

Minnesota 1.70% 0.10% 17.38 0 1.51% 1.89% 

Indiana 1.63% 0.12% 13.33 0 1.39% 1.87% 

Rhode Island 1.62% 0.11% 14.08 0 1.39% 1.84% 

Ohio 1.59% 0.14% 11.42 0 1.31% 1.86% 

New York 1.58% 0.07% 21.73 0 1.44% 1.72% 

Colorado 1.57% 0.26% 6.02 0 1.06% 2.08% 

Montana 1.55% 0.54% 2.86 0.004 0.49% 2.61% 

Florida 1.53% 0.09% 16.53 0 1.35% 1.72% 

Kansas 1.41% 0.07% 19.97 0 1.28% 1.55% 

Arkansas 1.38% 0.15% 9.41 0 1.09% 1.66% 

Wisconsin 1.35% 0.10% 13.99 0 1.16% 1.54% 

Kentucky 1.31% 0.07% 17.86 0 1.17% 1.45% 

Texas 1.30% 0.09% 15.27 0 1.13% 1.47% 

Connecticut 1.29% 0.09% 15 0 1.12% 1.46% 

Massachusetts 1.26% 0.08% 15.03 0 1.10% 1.43% 

Virginia 1.22% 0.07% 17.12 0 1.08% 1.36% 

Iowa 1.21% 0.14% 8.48 0 0.93% 1.48% 

South Carolina 1.18% 0.14% 8.62 0 0.91% 1.45% 

Washington 0.86% 0.28% 3.06 0.002 0.31% 1.42% 

Oklahoma 0.69% 0.17% 3.97 0 0.35% 1.03% 

Arizona 0.64% 0.18% 3.58 0 0.29% 0.99% 

Illinois 0.63% 0.14% 4.59 0 0.36% 0.90% 

Oregon 0.56% 0.22% 2.59 0.01 0.14% 0.99% 

Idaho 0.47% 0.18% 2.61 0.009 0.12% 0.83% 

Louisiana 0.47% 0.08% 5.54 0 0.30% 0.64% 

Nevada 0.20% 0.61% 0.34 0.737 -0.98% 1.39% 

California -0.66% 0.10% -6.63 0 -0.86% -0.47% 

Tennessee -2.27% 1.71% -1.33 0.185 -5.64% 1.09% 

The trend is increasing in almost all states and New Hampshire, North Dakota, Wyoming, 

and South Dakota have rapid growth over 3%. Ten states are growing at less than 1%. 

California and Tennessee have negative trends. 

98
Natural Gas 

Table D.27: Estimated trends for residential natural gas energy intensity 

R-square = 0.97 

Natural Gas Energy Intensity Trends 

Coef. Std. Err. t P>|t| [95% Conf. Interval] 

Vermont 3.09% 0.20% 15.5 0 2.70% 3.48% 

Washington 2.66% 0.35% 7.67 0 1.98% 3.34% 

Idaho 2.60% 0.57% 4.59 0 1.49% 3.72% 

Oregon 2.53% 0.34% 7.43 0 1.87% 3.20% 

New Jersey 1.57% 0.13% 11.72 0 1.31% 1.84% 

North Carolina 1.39% 0.21% 6.54 0 0.97% 1.80% 

Tennessee 1.16% 0.17% 6.7 0 0.82% 1.50% 

New Hampshire 0.98% 0.14% 7.06 0 0.71% 1.25% 

Connecticut 0.83% 0.11% 7.31 0 0.61% 1.05% 

Rhode Island 0.81% 0.14% 5.78 0 0.54% 1.09% 

North Dakota 0.60% 0.26% 2.31 0.021 0.09% 1.10% 

Virginia 0.56% 0.20% 2.88 0.004 0.18% 0.95% 

Massachusetts 0.55% 0.18% 3.11 0.002 0.20% 0.90% 

New York 0.44% 0.09% 4.68 0 0.25% 0.62% 

Delaware 0.23% 0.16% 1.42 0.157 -0.09% 0.55% 

Minnesota 0.19% 0.14% 1.37 0.172 -0.08% 0.47% 

South Carolina 0.15% 0.53% 0.28 0.777 -0.88% 1.18% 

South Dakota -0.08% 0.29% -0.28 0.777 -0.65% 0.48% 

Wisconsin -0.09% 0.14% -0.61 0.541 -0.36% 0.19% 

Michigan -0.12% 0.17% -0.69 0.491 -0.45% 0.22% 

Nevada -0.20% 0.22% -0.9 0.367 -0.64% 0.24% 

New Mexico -0.28% 0.17% -1.61 0.109 -0.62% 0.06% 

Wyoming -0.31% 0.34% -0.94 0.349 -0.97% 0.34% 

Montana -0.50% 0.24% -2.08 0.037 -0.96% -0.03% 

Colorado -0.53% 0.23% -2.3 0.022 -0.98% -0.08% 

Indiana -0.61% 0.13% -4.79 0 -0.86% -0.36% 

Iowa -0.62% 0.15% -4.21 0 -0.91% -0.33% 

Illinois -0.63% 0.13% -4.78 0 -0.89% -0.37% 

Georgia -0.64% 0.18% -3.53 0 -0.99% -0.28% 

Pennsylvania -0.66% 0.12% -5.65 0 -0.89% -0.43% 

Maryland -0.72% 0.22% -3.24 0.001 -1.16% -0.28% 

Ohio -0.90% 0.14% -6.6 0 -1.17% -0.63% 

Mississippi -0.98% 0.22% -4.4 0 -1.41% -0.54% 

Nebraska -1.01% 0.14% -7.22 0 -1.29% -0.74% 

Alabama -1.11% 0.15% -7.6 0 -1.40% -0.82% 

West Virginia -1.46% 0.18% -8.05 0 -1.81% -1.10% 

Kentucky -1.52% 0.24% -6.32 0 -1.99% -1.05% 

Oklahoma -1.52% 0.17% -8.92 0 -1.85% -1.18% 

Utah -1.55% 0.30% -5.11 0 -2.15% -0.96% 

99
Arkansas -1.62% 0.13% -12.16 0 -1.89% -1.36% 

Kansas -1.64% 0.19% -8.61 0 -2.02% -1.27% 

Missouri -1.65% 0.14% -11.63 0 -1.93% -1.37% 

California -1.91% 0.16% -11.68 0 -2.23% -1.59% 

Maine -2.15% 2.20% -0.98 0.329 -6.47% 2.17% 

Arizona -2.52% 0.32% -7.92 0 -3.15% -1.90% 

Louisiana -2.59% 0.23% -11.37 0 -3.04% -2.15% 

Texas -2.72% 0.31% -8.76 0 -3.33% -2.11% 

Florida -2.90% 0.37% -7.9 0 -3.61% -2.18% 

The table shows large differences in the trends. Seventeen states have positive trends 

with four states growing over 2% per year. Thirty-one states have declining natural gas 

energy intensity and five states are declining faster than 2% a year. Overall, the results 

show a wide range in the trends for this variable. 

100
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4. TITLE AND SUBTITLE

Regional Differences in the Price-Elasticity of Demand for Energy 

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NREL/SR-620-39512 

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SA01.0100 

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RAND Corporation. 

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At the request of the National Renewable Energy Laboratory (NREL), the RAND Corporation examined the 

relationship between energy demand and energy prices with the focus on whether the relationships between demand 

and price differ if these are examined at different levels of data resolution. In this case, RAND compares national, 

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15. SUBJECT TERMS

NREL; RAND Corporation; energy demand; energy prices; regions; electric utilities; spatial disaggregation; 

residential natural gas; commercial electricity; price elasticity; Doug Arent 

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