In this paper we employ a dataset of three dimensions—state, sector, and year—to estimate the short- and long-run price elasticities of state-level electricity demand in the United States. Our sample covers the period 2003-2015. We contribute to the literature by employing instrumental variable estimation approaches, using the between estimator, and pursuing panel specifications that enable us to control for multiple dimensions of fixed effects. We conclude that state-level electricity demand is very price inelastic in the short run, with a same-year elasticity of -0.1.
AlberiniA.FilippiniM. (2011). “Response of Residential Electricity Demand to Price: The Effect of Measurement Error.” Energy Economics33(5): 889-895. https://doi.org/10.1016/j.eneco.2011.03.009.
2.
American Public Power Association (2016). Retail Electric Rates in Deregulated and Regulated States: 2015 Update. Arlington, VA: American Public Power Association.
3.
BaltagiB.H. (2008). Econometric Analysis of Panel Data. 4th ed. West Sussex, UK: John Wiley &Sons.
4.
BaltagiB.H.GriffinJ.M. (1984). “Short and Long Run Effects in Pooled Models.” International Economic Review25(3): 631-645. https://doi.org/10.2307/2526223.
5.
BarroR.J. (2015). “Convergence and Modernisation.” Economic Journal125(585): 911-942. https://doi.org/10.1111/ecoj.12247.
6.
BazziS.ClemensM.A.. (2013). “Blunt Instruments: Avoiding Common Pitfalls in Identifying the Causes of Economic Growth.” American Economic Journal: Macroeconomics5(2): 152-86. https://doi.org/10.1257/mac.5.2.152.
7.
Bloomberg New Energy Finance (2016). New Energy Outlook 2016.
8.
BlundellR.BondS. (1998). “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models.” Journal of Econometrics87(1): 115-143. https://doi.org/10.1016/S0304-4076(98)00009-8.
9.
BorensteinS.BushnellJ. (2015). “The U.S. Electricity Industry After 20 Years of Restructuring.” Annual Review of Economics7: 437-63. https://doi.org/10.1146/annurev-economics-080614-115630.
10.
BreuschT. S.PaganA.R. (1979). “A Simple Test for Heteroscedasticity and Random Coefficient Variation.” Economet-rica47: 1287-1294. https://doi.org/10.2307/1911963.
11.
BunM.J.G.WindmeijerF. (2010). “The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models.” Econometrics Journal13(1): 95-126. https://doi.org/10.1111/j.1368-423X.2009.00299.x.
12.
Bureau of Economic Analysis. (2017). U.S. Economic Accounts, http://www.bea.gov/index.htm.
13.
Bureau of Labor Statistics. (2017). Consumer Price Index. https://www.bls.gov/cpi/.
14.
BurkeP.J.NishitatenoS. (2013). “Gasoline Prices, Gasoline Consumption, and New-Vehicle Fuel Economy: Evidence for a Large Sample of Countries.” Energy Economics36: 363-370. https://doi.org/10.1016/j.eneco.2012.09.008.
15.
BurkePJ.NishitatenoS. (2015). “Gasoline Prices and Road Fatalities: International Evidence.” Economic Inquiry53(3): 1437-1450. https://doi.org/10.1111/ecin.12171.
16.
BurkePJ.YangH. (2016). “The Price and Income Elasticities of Natural Gas Demand: International Evidence.” Energy Economics59: 466-474. https://doi.org/10.1016/j.eneco.2016.08.025.
17.
DelgadoM.S.HendersonD.J.ParmeterC.F. (2014). “Does Education Matter for Economic Growth?” Oxford Bulletin of Economics and Statistics76(3), 334-359. https://doi.org/10.1111/obes.12025.
18.
DergiadesT.TsoulfidisL. (2008). “Estimating Residential Demand for Electricity in the United States, 1965-2006.” Energy Economics30(5): 2722-2730. https://doi.org/10.1016/j.eneco.2008.05.005.
19.
DickeyD.A.FullerW. A. (1979). “Distribution of the Estimators for Autoregressive Time Series with a Unit Root.” Journal of the American Statistical Association74(366): 427-431. https://doi.org/10.2307/2286348.
20.
DietzT.FrankK.A.WhitleyC.T.KellyJ.KellyR. (2015). “Political Influences on Greenhouse Gas Emissions from US States.” Proceedings of the National Academy of Sciences112(27): 8254-8259. https://doi.org/10.1073/pnas.1417806112.
21.
EdwardsR.B. (2016). “Mining Away the Preston Curve.” World Development78, 22-36. https://doi.org/10.1016/). worlddev.2015.10.013.
22.
EllisonG.GlaeserE.L. (1999). “The Geographic Concentration of Industry: Does Natural Advantage Explain Agglomeration?” American Economic Review: Papers and Proceedings89(2): 311-316. https://doi.org/10.1257/aer.89.2.311.
23.
FellH.LiS.PaulA. (2014). “A New Look at Residential Electricity Demand Using Household Expenditure Data.”
24.
International Journal of Industrial Organization33: 37-47. https://doi.org/10.1016/j.ijindorg.2014.02.001.
25.
HaukW.R. (2017). “Endogeneity Bias and Growth Regressions.” Journal of Macroeconomics51: 143-161. https://doi.org/10.1016/j.jmacro.2017.01.001.
26.
HaukW.R.WacziargR. (2009). “A Monte Carlo Study of Growth Regressions.” Journal of Economic Growth14(2): 103-147. https://doi.org/10.1007/s10887-009-9040-3.
27.
HavranekT.IrsovaZ.JandaK. (2012). “Demand for Gasoline is More Price-Inelastic than Commonly Thought.” Energy Economics34: 201-207. https://doi.org/10.1016/j.eneco.2011.09.003.
28.
HouthakkerH.S. (1980) “Residential Electricity Revisited.” The Energy Journal1(1): 29-41. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol1-No1-4.
29.
ItoK. (2014). “Do Consumers Respond to Marginal or Average Price? Evidence from Nonlinear Electricity Pricing.” American Economic Review104(2): 537-563. https://doi.org/10.1257/aer.104.2.537.
30.
JessoeK.RapsonD. (2014) ‘Knowledge is (Less) Power: Experimental Evidence from Residential Energy Use.” American Economic Review 104(4): 1417-1438. https://doi.org/10.1257/aer.104.4.1417.
31.
KahnM.E.MansurE.T. (2013). “Do Local Energy Prices and Regulation Affect the Geographic Concentration of Employment?” Journal of Public Economics101: 105-114. https://doi.org/10.1016/jjpubeco.2013.03.002.
32.
LabandeiraX.LabeagaJ.M.Lopez-OteroX. (2017). “A Meta-Analysis on the Price Elasticity of Energy Demand.” Energy Policy102: 549-568. https://doi.org/10.1016/j.enpol.2017.01.002.
33.
MaC.SternD.I. (2016). “Long-Run Estimates of Interfuel and Interfactor Elasticities.” Resource and Energy Economics46, 114-130. https://doi.org/10.1016/j.reseneeco.2016.09.003.
34.
MillerM.AlberiniA. (2016). “Sensitivity of Price Elasticity of Demand to Aggregation, Unobserved Heterogeneity, Price Trends, and Price Endogeneity: Evidence from U.S. Data.” Energy Policy97: 235-249. https://doi.org/10.1016/j.enpol.2016.07.031.
35.
National Oceanic and Atmospheric Administration (NOAA) (2016). Climate at a Glance. http://www.ncdc.noaa.gov/cag/time-series/us/11/0/cdd/ytd/12/1990-2016.
36.
NickellS. (1981). “Biases in Dynamic Models with Fixed Effects.” Econometrica49(6): 1417-1426. https://doi.org/10.2307/1911408.
37.
PesaranM.H.SmithR. (1995). “Estimating Long-Run Relationships from Dynamic Heterogeneous Panels.” Journal of Econometrics68(1): 79-113. https://doi.org/10.1016/0304-4076(94)01644-F.
38.
PirotteA. (1999). “Convergence of the Static Estimation Toward the Long Run Effects of Dynamic Panel Data Models.” Economics Letters63(2): 151-158. https://doi.org/10.1016/S0165-1765(99)00023-3.
39.
PirotteA. (2003). “Convergence of the Static Estimation Toward the Long Run Effects of Dynamic Panel Data Models: A Labour Demand Illustration.” Applied Economics Letters10(13): 843-847. https://doi.org/10.1080/1350485032000137486.
40.
RoodmanD. (2009). “How to do xtabond2: An Introduction to Difference and System GMM in Stata.” Stata Journal9(1): 86-136.
41.
RosA.J. (2017). “An Econometric Assessment of Electricity Demand in the United States Using Utility-Specific Panel Data and the Impact of Retail Competition on Prices.” The Energy Journal38(4): 73-99. https://doi.org/10.5547/01956574.38Aaros.
42.
ShinJ.S. (1985). “Perception of Price when Price Information is Costly: Evidence from Residential Electricity Demand.” Review of Economics and Statistics67(4): 591-598. https://doi.org/10.2307/1924803.
43.
SternD.I. (2010). “Between Estimates of the Emissions-Income Elasticity.” Ecological Economics69(11): 2173-2182. https://doi.org/10.1016/j.ecolecon.2010.06.024.
44.
StockJ.H.YogoM. (2005). “Testing for Weak Instruments in Linear IV Regression.” In Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg, eds. AndrewsD.W.K.StockJ.H.80-108. Cambridge, UK: Cambridge University Press. https://doi.org/10.1017/CBO9780511614491.006.
45.
SunY.YuY. (2017). “Revisiting the Residential Electricity Demand in the United States: A Dynamic Partial Adjustment Modelling Approach.” Social Science Journal, 54(3): 295-304. https://doi.org/10.1016/j.soscij.2017.02.004.
46.
United States Census Bureau (2011). Statistical Abstract of the United States: 2012, http://www.census.gov/library/publications/2011/compendia/statab/131ed.html.
47.
United States Census Bureau (2016). Regions and Divisions, https://www.census.gov/geo/reference/gtc/gtc_census_divreg.html.
48.
U.S. Energy Information Administration (2014). Price Elasticities for Energy Use in Buildings of the United States. Washington, DC: U.S. Energy Information Administration.
49.
U.S. Energy Information Administration (2016a). Electricity: Detailed State Data, https://www.eia.gov/electricity/data/state/.
50.
U.S. Energy Information Administration (2016b). U.S. Energy Facts Explained, http://www.eia.gov/energyexplained/?page=us_energy_home.
51.
U.S. Energy Information Administration (2016c). Electricity Explained: Factors Affecting Electricity Prices, http://www.eia.gov/energyexplained/index.cfm?page=electricity_factors_affecting_prices.
52.
U.S. Energy Information Administration (2016e). Natural Gas, https://www.eia.gov/dnav/ng/ng_pri_sum_dcu_nus_m.htm.
53.
U.S. Energy Information Administration (2016f). Electric Power Monthly, http://www.eia.gov/electricity/monthly/.
54.
U.S. Energy Information Administration (2017). Annual Energy Outlook 2017 with Projections to 2050. Washington, DC: U.S. Energy Information Administration.
55.
U.S. Geological Survey (2016). 2015 Minerals Yearbook: Aluminum. https://minerals.usgs.gov/minerals/pubs/commodity/aluminum/.
56.
WooldridgeJ. M. (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge, MA:: MIT Press.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
