This paper measures the effects of information on residential demand for electricity, using data from a Japanese experiment. In the experiment, households had a continuous-display, electricity use monitoring device installed at their residence. The monitor was designed so that each consumer could easily look at graphs and tables associated with the consumer’s own usage of electricity at any time during the experiment. The panel data were used to estimate a random effects model of electricity and count data models of monitor usage. The results indicate that monitor usage contributed to energy conservation.
AjzenI.BrownT.RosenthalL. (1996). “Information Bias in Contingent Valuation: Effects of Personal Relevance, Quality of Information, and Motivational Orientation,” Journal of Environmental Economics and Management30: 43-57.
2.
BlomquistG.WhiteheadJ. (1998). “Resource Quality Information and Validity of Willingness to Pay in Contingent Valuation,” Resource and Energy Economics20: 179-196.
3.
De PalmaA.MyersG.PapageorgiouY. (1994). “Rational Choice under an Imperfect Ability to Choose,” American Economic Review84: 419-440.
4.
GoldmanC. (1996). “Information and Telecommunications Technologies: The Next Generation of Residential DSM and Beyond,” paper presented at the 1996 ACEEE Summer Study on Energy Efficiency in Buildings.
5.
GreeneW. (1994). “Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models,” Working Paper No. EC-94-10, Stern School of Business, New York University.
6.
GreeneW. (2000). Econometric Analysis. New Jersey: Prentice-Hall.
7.
GroggerJ.CarsonR. (1991). “Models for Truncated Counts,” Journal of Applied Econometrics6: 225-238.
8.
HarveyA. (1976). “Estimating Regression Models with Multiplicative Heteroscedasticity,” Econometrica44: 461-465.
9.
HausmanJ.TrimbleJ. (1984). “Appliance Purchase and Usage Adaptation to a Permanent Time-of-Day Electricity Rate Schedule,” Journal of Econometrics26: 115-139.
10.
HerrigesJ.KuesterK. (1994). “Residential Demand for Electricity under Inverted Block Rates: Evidence from a Controlled Experiment,” Journal of Business and Economic Statistics12: 419-430.
11.
KenkelD.TerzaJ. (2001). “The Effect of Physician Advice on Alcohol Consumption: Count Regression with an Endogenous Treatment Effect,” Journal of Applied Econometrics16: 165-184.
12.
KivetzR.SimonsonI. (2000). “The Effects of Incomplete Information on Consumer Choice,” Journal of Marketing Research37: 427-448.
13.
LawrenceD. (1999). The Economic Value of Information. New York: Springer.
14.
MatsukawaI. (2001). “Household Response to Optional Peak-Load Pricing of Electricity,” Journal of Regulatory Economics20: 249-267.
15.
MatsukawaI.AsanoH.KakimotoH. (2000). “Household Response to Incentive Payments for Load Shifting: A Japanese Time-of-Day Electricity Pricing Experiment,” The Energy Journal21(1): 73-86.
16.
MullahyJ. (1986). “Specification and Testing of Some Modified Count Data Models,” Journal of Econometrics33: 341-365.
17.
MurphyK.TopelR. (1985). “Estimation and Inference in Two-Step Econometric Models,” Journal of Business and Economic Statistics3: 370-379.
18.
SextonR. (1989). “The Conservation and Welfare Effects of Information in a Time-of-Day Pricing Experiment,” Land Economics65: 272-279.
19.
WirlF. (1997). The Economics of Conservation Programs. Boston: Kluwer Academic Publishers.
20.
YatchewA.GrilichesZ. (1985). “Specification Error in Probit Models,” Review of Economics and Statistics67: 134-139.