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Section Overview: Chapter 6 concerns the issue of customer responsiveness to price changes, which economists call demand elasticity. The law of demand suggests an inverse relationship between price and quantity demanded, but it doesn’t tell us how strongly demand responds to price changes. Consumers may react strongly (elastic) or weakly (inelastic), depending on several factors. Similar concepts apply to changes in income and changes in prices of related products. Chapter 7 also relates to quantifying demand in terms of estimating future demand from past data. OLS is used to estimate demand functions which can be used to extrapolate demand in other periods or areas.
Learning Goals: Upon completing this section, we will understand the qualitative meaning of different values of ownprice, income, and crossprice elasticity. We will be able to calculate elasticity values given data on prices and quantities demanded. We will recognize the impact on total revenue of price changes made in elastic vs. inelastic ranges. We will also be equipped to estimate demand functions using Excel’s OLS and graphing features.
 Let’s examine the history of LSUS undergraduate enrollment vs. its tuition and fees. Go to this link (http://www.lsus.edu/officesandservices/institutionaleffectivenessandplanning/factbook) and look at the PDF “FACT BOOK 2015.” Collect two types of quantity data: the Fall Headcount for undergrads on pg. 6 (9 of the PDF), and the Total (summer, spring, and fall) student credit hour production on pg. 11 (8 of the PDF). Headcount data goes from 19842015, but credit hour data only goes from 19862015.
Next, go here to get tuition data: http://www.lsus.edu/officesandservices/institutionaleffectivenessandplanning/lsusdataprofile, and look at the PDF “LSUS Data Profiles 20112012.” The price (undergraduate fall tuition and fees) data is on pg. 106. You will only need from 1984 through 2011; for the remaining years, use 2012 = $2,472, 2013 = $2,803, 2014 = $3,084, and 2015 = $3,355.
Calculate annual elasticities for both types of quantity variables (i.e., you will have an elasticity of price vs. headcount, and one of price vs. credit hour. You will get an error message in your calculations a few times when the tuition doesn’t change, since the elasticity calculation will be trying to divide by zero. Just delete those in your Excel table. The first headcount elasticity will be calculated based on the 1984 and 1985 values of tuition and headcount and should be about 0.043; the first credit hour elasticity will be based on the 1987 and 1988 values and should be about 0.394). Calculate the average elasticity for headcount (from 19852015), and the average elasticity for credit hour (from 19882015).
Many administrators argue that, to increase revenue to LSUS to cover budget shortfalls, tuition should be raised. Comment on this suggestion, using the evidence you’ve uncovered.

 Copy and paste the following data into Excel:
P  Q 
$130  78 
$110  155 
$90  246 
$70  318 
$50  397 
 Illustration 7.3 (p. 2624) describes timeseries forecasting of new home sales, but you can see that the data is old. Click here (https://www.census.gov/construction/nrs/historical_data/index.html) and download the first table: Houses Sold – Seasonal Factors, Total (Excel file is sold_cust.xls). Look at the monthly data on the “Reg Sold” tab.
Only keep the dates beginning in January 2008, so delete the earlier observations. Keep only the US data, both the seasonally unadjusted monthly (column B) and the seasonally adjusted annual (column G). Make a new column of seasonally adjusted monthly by dividing the annual data by 12. Make a column called “t” similar to the book’s column 4 on page 262 (t will go from 1 to 110 through Feb. 2017); make a t^{2} column too (since, if you look at the data, you can see sales dropping until about mid2011 then rising again; hence the quadratic). Also make a column “D” that is a dummy variable equal to one during the spring and summer months, similar to the book’s column 5.
Determine the correlation between the unadjusted and the adjusted monthly data (=CORREL(unadjust., adjust.) in Excel), and produce scatterplots (with connectors) of both. Do you think making a seasonal adjustment will be useful, given what you observe at this point?
Run four regressions: 1) seasonally unadjusted monthly as the dependent, and t and t^{2} as the independents, 2) seasonally unadjusted monthly as the dependent, and t, t^{2}, and D as the independents, 3) seasonally adjusted monthly as the dependent, and t and t^{2} as the independents, and 4) seasonally adjusted monthly as the dependent, and t, t^{2}, and D as the independents. Discuss your findings, and determine which of the four models is the best for forecasting new home sales. In interpreting your pvalues, remember that, say, 1.0E08 is 1.0 * 10^8, which is 0.00000001. State the equation that would be used to forecast sales.
 Conlan Enterprises has the following demand function:
where Q is the quantity demanded of the product Conlan Enterprises sells, P is the price of that product, M is income, and P_{R} is the price of a related product. The regression results are:
DEPENDENT VARIABLE:  Q  RSQUARE  FRATIO  PVALUE ON F  
OBSERVATIONS:  32  0.7984  36.14  0.0001  
VARIABLE 
PARAMETER
ESTIMATE 
STANDARD
ERROR 
TRATIO 
PVALUE 

INTERCEPT  846.30  76.70  11.03  0.0001  
P  –8.60  2.60  –3.31  0.0026  
M  0.0184  0.0048  3.83  0.0007  
PR  –4.3075  1.230  –3.50  0.0016 
 Discuss whether you think these regression results will generate good sales estimates for Conlan.
Now assume that the income is $10,000, the price of the related good is $40, and Conlan chooses to set the price of its product at $30.
 What is the estimated number of units sold given the data above?
 What are the values for the ownprice, income, and crossprice elasticities?
 If P increases by 5%, what would happen (in percentage terms) to quantity demanded?
 If M increases by 8%, what would happen (in percentage terms) to quantity demanded?
 If P_{R} decreases by 4%, what would happen (in percentage terms) to quantity demanded?
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