ECO234

1. Consider a very simple "two node" model in which natural gas is produced in region A and is transported by pipeline to region B. The price of natural gas in Region B is $6 per thousand cubic feet (Mcf) and the price of transportation service from Region A to Region B is $1/Mcf. a. If the market for producing gas in region A is perfectly competitive and there is a perfectly elastic supply of transportation service what will be the equilibrium price of natural gas in Region A? b. What will happen if the government places a cap of $4/Mcf on price that producers in Region A can charge for the natural gas they produce? r. 2. There are multiple gas production areas and many consuming areas that are remote from production areas and rely on pipelines to transport gas to them. a. Assume that the price of natural gas at Henry Hub (Louisiana --- a gas producing area) is $5/Mcf and assume that the price of natural gas in Los Angeles (a gas consuming area) is $4/Mcf. Explain how such a price pattern can emerge? b. What incentives are there to expand pipeline capacity from producing areas in the Western U.S. to consuming areas in the Eastern U.S. 3. The United States maintains a Strategic Petroleum Reserve (SPR) that now contains 700 million barrels of crude oil. a. What factors would you take into account to design a policy to determine when and how much oil is released from the SPR? b. How would the expected supply behavior of OPEC affect your policy design?

An electric power system has the following mix of generating capacity installed on its network which is owned by several competing generating firms: Type Marginal Operating Cost Capacity Nuclear $15/Mwh 1000 Mw Coal $25/Mwh 2500 Mw Gas $60/Mwh 1500 Mw Turbine $80/Mwh 500 Mw a. Draw the competitive supply curve for the production of electric energy on this system b. Assume that demand is 3000 Mw and is completely price inelastic in the very short run. What would be the spot price in a perfectly competitive wholesale electricity market? c. Assume that demand is 4000 Mw and is completely price inelastic in the very short run. What would be the spot price in a perfectly competitive wholesale electricity market? d. Assume that demand is 6000 Mw at a price of $80, but that 600 Mw of this demand would be willing to be curtailed for a price of $4000/Mwh or more. What is the perfectly competitive market price in this case? 2. Describe how you would use the information above regarding the attributes of the generating capacity on this system, along with information about actual market prices and supplier behavior to measure whether or not market prices suggest that generators are exercising market power. 3. In New England, spot electricity prices in Maine are often much higher than are spot electricity prices in Boston even though they are part of the same regional network. How can you explain these price differences assume that the market is perfectly competitive.

(20) In section 3.3 the authors' specify a model where there is an interaction between price and income ln Gjt = 0 + 1 ln Pjt + 2 ln Yjt + 3 ln Pjt ln Yjt + j + jt 2 where 3 is the coefficient on the interaction term. a) (10) Derive the price elasticity of demand using this model specification. You should get Ep = 1 + 3 ln Yjt. What would be the expression for the income elasticity of demand? b) (10) Interpret the values of 3 presented in table 7 in the appendix. What do these tell you about the effect of income on the price elasticity of demand? How has this changed from the 1975-80 period to today? 4. (40) (14.444 students only) In section 3.2 the author's find that when the simultaneity bias is accounted for in the period 2001-2006 the estimate for the elasticity of demand changes from -0.042 to -0.077 and this change is statistically significant. The authors' conclude that this is encouraging and that the effects of simultaneity are small relative to other factors. Give a brief (1/2 a page max.) critique of the authors' methodology for addressing the simultaneity bias.

1. (60) In section 2 the authors estimate the following demand equation: ln Gjt = 0 + 1 ln Pjt + 2 ln Yjt + j + jt where Gjt is per capita gasoline consumption in gallons in month j and year t, Pjt is thee real retail price of gasoline in month j and year t, Yjt is real per capita disposable income in month j and year t, j represents unobserved demand factors that vary at the month level and jt is a mean zero error term. a) (10) What have the authors assumed about the price elasticity of demand when they wrote down the demand equation in this form? Remember the price elasticity of demand Ep = G P . P G b) (10) Go to table 1 in the appendix, now assuming the authors have obtained unbiased estimates of the parameters 0,1 and 2 what do they mean? (eg. the coefficient 1 is -0.335 in the period 1975-1980, this represents...) 1 c) (10) Interpret the values of the monthly unobserved demand factors (j )? What are these relative to? What can you say about the yearly pattern of gasoline demand from these coefficients? d) (10) From the information presented in this table calculate the appropriate tstatistics for each of the s to test if it is different from 0. You will need the standard errors for each coefficient which are presented in brackets below the respective coefficient value in the table. For instance the standard error for the coefficient 1 in the period 1975-1980 is 0.024. e) (10) What do the *** next to some of the entries in the table indicate? How are they related to the t-statistics you calculated? f) (10) The table presents the adjusted R-squared statistic for the two regressions. What does this number mean? If we calculated the unadjusted R-squared values, can we say whether these are larger or smaller than the adjusted Rsquared values of 0.84 and 0.94 in this table? 2. (30) In table 2 and table 3 in the appendix, two alternate specifications for the demand equation are compared with the original double-log model. a) (10) Under the linear specification for the period 1975-1980 the coefficient on the Price variable is -7.252. What is the implied elasticity of demand, assuming the linear model, if during a June month per capita demand was 40 gallons, and price was $1.70? b) (10) What is the implied elasticity of demand if during July demand is 5 gallons higher (due to a month specific effect) and price is the same? c) (10) Under the linear demand specification the demand elasticity varies within each period. Therefore the authors calculate an "average" elasticity of demand across each period. Do you think a time weighted or quantity weighted average is more reasonable and why?

T-statistics are calculated by dividing the value of the co-efficient by the standard deviation. They are used to test whether a coefficient is significantly different from 0. For example the first entry in the table below is calculated by 0.615 = 0.662. 0.929 1975-1980 2001-2006 0 -0.662 -2.891 1 -13.958 -4.667 2 4.865 9.138

This indicates that the coefficient is significant at a level of 1%. The t-statistics are used along with the student-t distribution to determine at what level the coefficient is significant .

The adjusted R-squared values represent the percentage of the variation in per-capita consumption is captured by the regression adjusted for the number of variables in the regression. The unadjusted R-squared values will be greater than the adjusted values.

A quantity weighted average seems more reasonable since we are dealing with elasticities. In terms of the amount of quantity demanded across the entire period and how this is affected by the overall price level then additional weight should be attached to periods/months during which there was more demand. In the extreme case where only a couple of gallons are demanded in a month the very small changes which occur during this month should not have as large an impact on the overall demand elasticity as say a month with very large gasoline demand.

3 is -2.879 during 1975-80 and -1.014 during 2001-06. This tells us that the interaction between price and income has become weaker. That is % changes in price and income have relatively less impact on the elasticity of demand wrt the other variable. The negative sign indicates that increases in income decrease the elasticity of demand wrt price, because price elasticities are normally negative this in fact makes demand more elastic. On the other hand increases in prices decrease the elasticity of demand wrt income which is positive here and thus makes it more inelastic.

Read the case study "Pointsec Mobile Technology" under Appendix 1 in this syllabus and write an essay that provides:

When salespeople, construction supervisors, managers, and other employees are away from the workplace, many of them carry mobile devices such as laptop computers and PDAs, often containing valuable, private data related to their jobs. Pointsec (http://www.checkpoint. com/points) provides security systems to pro-

text such data. To bring home the vulnerability of mobile devices, Pointsec decided to share information about the number of such devices left behind in taxis.8

The research involved conducting a survey of taxi drivers. Staff members at Pointsec's public relations firm called major taxi companies in nine cities in Australia, Denmark, Finland, France, Germany, Norway, Sweden, Great Britain, and the United States. Each of the cooperating companies put these interviewers in touch with about one hundred drivers. Drivers were asked how many devices of each typecell phones, PDAs, computers, and so onhad been left in their cab over the preceding six months. From these numbers, they came up with the rate of items left behind. Multiplying by the size

of taxi fleets in each city, the researchers came up with city-by-city numbers: 3.42 cell phones per cab yielded 85,619 cell phones left behind in Chicago, for example. In London, the researchers concluded 63,135 cell phones were left in cabs, a startling increase of 71 percent compared to four years earlier.

Questions

1. Discuss why the sampling method and sample size make these results questionable, even though the numbers were reported as if they were precise.

2. The simple survey method described in the case may have been sufficient as a way to draw attention to the issue of data security. However, if the company was using data on lost mobile devices to predict demand for a product, accuracy might be more significant. Imagine that you have been asked to collect data on mobile devices left in cabs, and you wish to be able to report results with a 95 percent confidence level. How can you improve the sample design and select an appropriate sample size?

Read the case study "Pointsec Mobile Technology" under Appendix 1 in this syllabus and write an essay that provides:

When salespeople, construction supervisors, managers, and other employees are away from the workplace, many of them carry mobile devices such as laptop computers and PDAs, often containing valuable, private data related to their jobs. Pointsec (http://www.checkpoint. com/points) provides security systems to pro-

text such data. To bring home the vulnerability of mobile devices, Pointsec decided to share information about the number of such devices left behind in taxis.8

The research involved conducting a survey of taxi drivers. Staff members at Pointsec's public relations firm called major taxi companies in nine cities in Australia, Denmark, Finland, France, Germany, Norway, Sweden, Great Britain, and the United States. Each of the cooperating companies put these interviewers in touch with about one hundred drivers. Drivers were asked how many devices of each typecell phones, PDAs, computers, and so onhad been left in their cab over the preceding six months. From these numbers, they came up with the rate of items left behind. Multiplying by the size

of taxi fleets in each city, the researchers came up with city-by-city numbers: 3.42 cell phones per cab yielded 85,619 cell phones left behind in Chicago, for example. In London, the researchers concluded 63,135 cell phones were left in cabs, a startling increase of 71 percent compared to four years earlier.

Questions

1. Discuss why the sampling method and sample size make these results questionable, even though the numbers were reported as if they were precise.

2. The simple survey method described in the case may have been sufficient as a way to draw attention to the issue of data security. However, if the company was using data on lost mobile devices to predict demand for a product, accuracy might be more significant. Imagine that you have been asked to collect data on mobile devices left in cabs, and you wish to be able to report results with a 95 percent confidence level. How can you improve the sample design and select an appropriate sample size?

Introductions to the case,

Explanation of the:

Flaws in research design in this study,

Flaws in the interpretation of the outcome of the research

Justify and support your arguments with appropriate in-text citations from peer-reviewed source(s).

All most & 7 references