Dummy (or Binary) Explanatory Variables
Supplementary Lecture Notes:
Spring 2007
Dummy variables are qualitative (nonnumerical measures) variables as opposed to quantitative (numerical measures).
A dummy
variable characterizes an observation in terms of one of two possible disjoint categories or ‘qualities’.
In other words, if X is a dummy
variable, then it is a qualitative variable with two categories. For example, X can represent gender where observations have the "quality"
of being male or the "quality" of being female.
X = gender
Î
X = male or
X = female
Some other examples of Dummy variables:
X = employment status
Î
X= employed
or
X = not employed
X = Home ownership status
Î
X = owns home
or
X = does not own home
X = Education
Î
X = college grad
or
X = noncollege grad
In general if X is a Dummy variable then either X exhibits a certain ‘quality’ or X does not exhibit that quality.
The categories or qualities are nonnumerical.
To deal with this, we impose a "quantification" or coding scheme so that the variable takes
on numerical values.
A dummy variable is assigned the value either 0 or 1.
These are numerical codes to distinguish between two
disjoint categories.
Consider the following model
E(Y) =
β
o
+
β
1
X
1
+
β
2
X
2
Where
Y
=
annual salary of professors ($)
X
1
=
teaching experience (years)
X
2
=
gender
= 1 if male and
0 if female
Recall that in general the beta coefficient on a variable, X, tells us the expected change in Y of a one unit change in X, assuming all other
X variables in the model are held constant, i.e., controlling for the other X variables in the model.
Thus
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
This is the end of the preview.
Sign up
to
access the rest of the document.
 Spring '07
 Francisco
 Econometrics, $12.50, $18.30

Click to edit the document details