Introduction to Econometrics
Worksheet week # 6
1. In 1986 Frederick Schut and Peter VanBergeijk published an article in which they
attempted to see if the pharmaceutical industry practiced international price discrimination
by estimation by estimating a model of the prices of pharmaceuticals in
a cross section of 32 countries. The authors felt that if price discrimination existed,
then the coeﬃcient of per capita income in a properly speciﬁed price equation would
be strongly positive. The reason they felt that the coeﬃcient of per capita income
would measure price discrimination went as follows: the higher the ability to pay,
the lower (in absolute value) the price elasticity of demand for pharmaceuticals and
the higher the price a price discriminator could charge. In addition, the authors expected
that prices would be higher if pharmaceutical patents were allowed and that
prices would be lower if price controls existed, if competition was encouraged, or if
the pharmaceutical market in a country was relatively large. Their estimates were:
Pi = 38.22+ 1.43
0.21)
GDPNi − 0.6
0.22)
CV Ni + 7.31
6.12)
PPi − 15.63
6.93)
DPCi − 11.38
7.16)
IPCi ,
where:
Pi . . . the pharmaceutical price level in the i-th country divided by that
of the United States
GDPNi . . . per capita domestic product in the i-th country divided by that of
the United States
CV Ni . . . per capita volume of consumption of pharmaceuticals in the i-th
country divided by that of the United States
PPi . . . a variable equal to 1 if patents for pharmaceutical products are
recognized in the i-th country and equal to 0 otherwise
DPCi . . . a variable equal to 1 if the i-th country applied strict price controls
and 0 otherwise
IPCi . . . a variable equal to 1 if the i-th country encouraged price competition
and 0 otherwise
(a) Develop and test appropriate hypotheses concerning the regression coeﬃcients
using the t-test at the 5 percent level. Do you think Schut and VanBergeijk
concluded that international price discrimination exists? Why or why not?
(b) Set up 90 percent conﬁdence intervals for each of the estimated slope coeﬃcients.
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2. Using data wage.csv estimate the model describing the impact of education and
experience on wage:
wage = β0 + β1educ + β2exper + β3exper2
+ ε
(a) Import data into Gretl from the csv ﬁle.
(b) Generate variable exper2
.
(c) Why we include this variable into the model?
(d) Estimate model with and without exper2
, compare R2
and R2
adj.
(e) Comment on the signs and signiﬁcance of β1, β2, and β3 in the model with
exper2
.
(f) Test the following hypotheses in the model with exper2
:
- test for the overall signiﬁcance of the regression;
- education has a signiﬁcant impact on wage;
- experience has a signiﬁcant impact on wage.
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