EEP 142 Midterm Problems (Industrial Organization with Applications to Agriculture and Natural Resources)

1.(10 points, 2.5 each a) to d)
)In a store, we displayed under the price tag whether the popcorn product was a
low fat popcorn product. We were able to
get data for before and after quantity sold for all popcorn products, in
treatment and in two possible control stores. We also got data on the size of
the popcorn display shelf for all three stores in our data set as shown below.
(2.5 points each a) through d) below)
TABLE
Retail Low Fat Experiment: Summary Statistics

Treated Store

Store C1

Store C2

Average Quantity before(*)

110

100

230

Average Quantity After(*)

130

160

300

Square footage of popcorn shelves

150

149

260

The “Treatment Store” is the store where the
intervention took place; the “Control Stores” are two nearby stores
in the same retail chain, store C1 and store C2. Before period means weeks
before experiment, after period means after experiment. (*) Average across
weeks in Before and Average in Weeks in after period, respectively, average
quantity sold is for all products sold e.g. week one 150 products are sold
total, week 2 100, then average is 125 products).a) Let C1 be the control. Give one reason as to why, given the table above, you choose thatone and not the other to measure the effect of the experiment.b) Compute the effect of our nutritional labels. What do you conclude: do people appear tolike having a low fat label as measured by the responses in total popcorn sold in theabove table?c) Suppose in Store C1 there was new rent-a-DVD display added during that same periodwe ran the experiment. What would be the problem of using C1 as a control.d) Given the problem in c) you have to use C2 as a control. How would you use theinformation available to normalize the data and have somewhat comparable units?2. (5 points, 1 point each below)
Consider the following Stata output pertaining to trying to explain the over
the counter (OTC) price variation (in dollars) in supermarkets in terms of
which class a OTC drug belongs to (coefficients omitted from the printout
below, as there were many classes) and then whether the OTC drug is a generic
(coefficient below), measured by a generic indicator (dummy variable). Source | SS df Number of obs = 413590————-+—————————— F( 34,413555) =12909.53 Model |
4530767.84 34 Prob > F =
0.0000 Residual |
4268897.0 4413555 R-squared =
0.5149————-+—————————— Total
| 8799664.89 413589
—————————————————————————— price | Coef. Std. Err. t P>|t| [95% Conf. Interval]————-+—————————————————————- generic |
-2.338719 .0114159 -204.86
0.000 -2.361094 -2.316344 class_id1 … class_idN omitted _cons
| 7.312916 .0247798
295.12 0.000 7.264349
7.361484
a) What
percent of the price variation in the data can be explained by generic
characteristic and class type?b) What is the average price in the dataset?c) By how much are generics cheaper or more expensive on average?d) Please test the null hypothesis that generic drugs are significantly less expensive.e) If I explain price in terms of class, generic, and also in which stores it is sold at the Rsquared of the regression changes to R-squared = 0.5193.(0.5 points) Can you let me know what percent of the price variation can be explained bylocation, i.e., explained by controlling for at which stores the OTC drugs are sold at (hint: relateto the R squared in the above output)?(0.5 points) Do you think we can conclude that there may not be large differences in pricesacross stores?