Econn-05/Discussions

Analysis of Electricity Demand

Description

This study is aimed at estimating the change in consumer sensitivity to electricity prices in a perfectly competitive market of electricity in the United States. It meant that the electric industry was opened from being a government monopoly to competitiveness where the consumers have the power to choose their electricity provider in the same way as telephone services (Bohi, 2010). A couple of years have passed since the retail electricity market was deregulated which led to an effect on the residential demand for electricity. Therefore, this study focuses on the estimation of changes in price elasticity in the residential electricity market so as to examine changes to the residential electricity rates. Comment by Emil Berendt: The market will probably never be perfectly competitive. Opening up the market, as was done in California, did open it up to competition but there is still a limited number of producers so it is better characterized as an oligopoly.

Elasticity is a term in microeconomics used in measuring the responsiveness to consumer behavior as a result of environmental change (Bohi, 2010). Therefore, this is an analysis which is entirely focused in application of economics skills. Additionally, in the United States, electric utility business in a single state is vested in Public Utility Commission but since power systems cross state lines, it falls under federal regulation. Therefore, this is a technical report targeted to the attention of the National Association of Regulatory Utility Commissioners which represents the State Public Service Commissioners regulating the essential utility services. These services include electricity, oil, water and telecommunications. Since the econometric analysis points out the consumers’ sensitivity to electric prices as a result of the electricity retail market deregulation, it refers to the market’s regulator of the essential commodity.

Literature review

Since electricity is a commodity which is not directly consumed by individuals, its demand is obtained from the flow of services which are provided to a household’s appliances which use it. In the short run, the demand behavior may tolerate the household’s existing electric appliances while in the long run, as a result of consistent price changes, there may be changes in utilization behavior and any other adjustments to the use of electric appliances owned by the household. In this, since the entire analysis focuses on reliability of electricity in the households, maximum likelihood method is appropriate (Li & Racine, 2009). The method provides a stable approach to parameter estimation meaning that the estimates can be developed for a huge electric consumption population. Additionally, the time series analysis method can be used in the short term modelling and forecasting of its price (Li & Racine, 2009). Comment by Emil Berendt: Excellent point about it being a derived demand.

The consumption of electricity and its pricing doesn’t follow a linear manner in households. On the other hand, regression analysis only looks at linear relationships between dependent and independent variables, making it inappropriate in this case. The relationship between electric consumption and pricing in a de-regularized environment where there are many suppliers is curved, it doesn’t observe linearity. From the maximum likelihood method, it becomes possible to forecast the electric load cycles which characterize house hold consumptions in the United States (Li & Racine, 2009). For this reason, the electricity load traded in each household is thereby treated as a separate commodity and model. The load cycle is closely monitored across the households for a particular period of time and the variations noted. The variation is reflected in prices which is thereby used in coming up with empirical models. Comment by Emil Berendt: Citation or evidence? Comment by Emil Berendt: Not necessarily. OLS can be used for log-linear equations. It is true, however, that some nonlinear specifications can only be estimated by search techniques, which includes ML. Comment by Emil Berendt: Can you explain what you mean here?

Data

In conducting empirical work, sample data from a household survey conducted by the National Association of Regulatory Utility Commissioners during the year 2015 and interviewing 10243 households in all the states. The sample size was arrived at after using the variance of total expenditure on electricity load of the households during the year. The data is divided into segments, depending on age, electric appliance stock in the households, household income and expenditure as well as the electricity consumption and use. The last segment contains household’s electricity distribution number so as to be able to match it correctly with its actual consumption from distribution firms. Comment by Emil Berendt: You have a great data source!

The price of electricity in all states was obtained from the electricity distributors. The data from electricity distributors and household survey is generally subjective to age irrespective of income and appliance stock. There is a tendency of most of the households consisting of the young individuals preferring the firms with the lowest load rates while those with the elderly individuals are indifferent to all the electricity suppliers among the states.

References

Bohi, D. R. (2010). Analyzing demand behavior: A study of energy elasticities. Washington, D.C: RFF.

Li, Q. & Racine, J. S. (2009). Nonparametric econometric methods. Bingley: Emerald.

You have a great topic. Keep on working on it.