economic question

E3.2 A consumer is given the chance to buy a baseball card for $1, but he declines the trade. Ifthe consumer is now given the baseball card, will he be willing to sell it for $1? Standard
consumer theory suggests yes, but behavioral economists have found that “ownership” tends
to increase the value of goods to consumers. That is, the consumer may hold out for some
amount more than $1 (for exam- ple, $1.20) when selling the card, even though he was willing
to pay only some amount less than $1 (for example, $0.88) when buying it. Behavioral
economists call this phenomenon the “endowment effect.” John List investigated the endowment effect in a randomized experiment involving sports memorabilia traders at a sports-card
show. Traders were randomly given one of two sports collect- ibles,saygoodA
orgoodB,thathadapproximatelyequalmarketvalue.1Those receiving good A were then given
the option of trading good A for good B with the experimenter; those receiving good B were
given the option of trading good B for good A with the experimenter. Data from the
experiment and a detailed description can be found on the text website, http://
www.pearsonhighered .com/stock_watsonl, in the files Sportscards and
Sportscards_Description.2
a. i. Suppose that, absent any endowment effect, all the subjects prefer good A to good B. What
fraction of the experiment’s subjects would you expect to trade the good that they were given for the
other good? (Hint: Because of random assignment of the two treatments, approximately 50°/o of the
subjects received good A, and 50°/o received good B.)
ii. Suppose that, absent any endowment effect, 50°/o of the subjects prefer good A to good B, and
the other 50°/o prefer good B to good A. What fraction of the subjects would you expect to trade the
good they were given for the other good?
iii. Suppose that, absent any endowment effect, X 0/o of the subjects prefer good A to good B, and
the other (100 – X) 0/o prefer good B to good A. Show that you would expect 50°/o of the subjects
to trade the good they were given for the other good.
b.Using the sports-card data, what fraction of the subjects traded the good they were given? Is the
fraction significantly different from 50°/o? Is there evi- dence of an endowment effect? (Hint:
Review Exercises 3.2 and 3.3.)
c.Some have argued that the endowment effect may be present but that it is likely to disappear as
traders gain more trading experience. Half of the experimental subjects were dealers, and the other
half were nondealers. Dealers have more experience than nondealers. Repeat (b) for dealers and
nondealers. Is there a significant difference in their behavior?
E4.2 On the text website, http://www.pearsonhighered.com/stock_watsonl, you will find the
data file Earnings_and_Height, which contains data on earn- ings, height, and other
characteristics of a random sample of U.S. workers.2
A detailed description is given in Earnings_and_Height_Description, also available on the
website. In this exercise, you will investigate the relationship between earnings and height.
a. What is the median value of height in the sample?
b.i.Estimate average earnings for workers whose height is at most 67 inches.
ii. Estimate average earnings for workers whose height is greater than 67 inches.
iii. On average, do taller workers earn more than shorter workers? How much more? What is a 95°/
o confidence interval for the difference in average earnings?
c. Construct a scatterplot of annual earnings (Earnings) on height (Height). Notice that the points on
the plot fall along horizontal lines. (There are only 23 distinct values of Earnings). Why? (Hint:
Carefully read the detailed data description.)
d. Run a regression of Earnings on Height.
i. What is the estimated slope?
ii. Use the estimated regression to predict earnings for a worker who is 67 inches tall, for a worker
who is 70 inches tall, and for a worker who is 65 inches tall.
e. Suppose height were measured in centimeters instead of inches. Answer the following questions
about the Earnings on Height (in em) regression.
i. What is the estimated slope of the regression?
ii. What is the estimated intercept?
iii. What is the R2?
iv. What is the standard error of the regression?
f. Run a regression of Earnings on Height, using data for female workers only.
i. What is the estimated slope?
ii. A randomly selected woman is 1 inch taller than the average woman in the sample. Would you
predict her earnings to be higher or lower than the average earnings for women in the sample? By
how much?
g. Repeat (f) for male workers.
h. Do you think that height is uncorrelated with other factors that cause earning? That is,do you
think that the regression error term,ui has a conditional mean of 0 given Height (Xi)? (You will
investigate this more in the Earnings and Height exercises in later chapters.)
E5.3 On the text website, http://www.pearsonhighered.com/stock_watsonl, you will find the
data file Birthweight_Smoking, which contains data for a random sample of babies born in
Pennsylvania in 1989. The data include the baby’s birth weight together with various
characteristics of the mother, including whether she smoked during the pregnancy.2 A
detailed description is given in Birthweight_Smoking_ Description, also available on the
website. In this exercise, you will investigate the relationship between birth weight and
smoking during pregnancy.
a. In the sample:
i.
What is the average value of Birthweight for all mothers?
ii.
For mothers who smoke?
iii. For mothers who do not smoke?
b. i. Use the data in the sample to estimate the difference in average birth weight for smoking and
nonsmoking mothers.
ii. What is the standard error for the estimated difference in (i)?
iii. Construct a 95°/o confidence interval for the difference in the average birth weight for smoking
and nonsmoking mothers.
c. Run a regression of Birthweight on the binary variable Smoker.
i.
Explain how the estimated slope and intercept are related to your
answers in parts (a) and (b).
ii.
Explain how the SE(/31) is related to your answer in b(ii).
iii. Construct a 95°/o confidence interval for the effect of smoking on birth weight.
d. Do you think smoking is uncorrelated with other factors that cause low birth weight? That is, do
you think that the regression error term- say, ui – has a conditional mean of 0 given Smoking (XD?
(You will investigate this further in Birthweight and Smoking exercises in later chapters.)