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EViews Illustrated.book Page 258 Monday, February 25, 2013 10:06 AM
258—Chapter 9. Page After Page After Page
We can now use REV just like any
other series. EViews will bring data
in from the Revenue page each time
it’s needed. For example, a scatter
diagram of infant mortality against
per capita revenue shows a slight,
and surprising, positive association.
(The positive association is attributable to the one outlier. Drop Alaska
and the picture shifts to a slight negative relation.)
In this example we’ve used links to
match in a case where there really
was a common identifier, the computer just didn’t know it. Next we
turn to matching up series with fundamentally different identifiers.
Matching When The Identifiers Are Really Different
In this next example, our main data set holds observations on individuals. We’re going to
hook up these individual observations with data specific to each person’s state of residence.
In order to show off more EViews features, we’ll generate the state-by-state data by taking
averages from the individual level data.
For a real problem to work on, we’re going to try to answer whether higher unionization
rates raise wages for everyone, or whether it’s just for union members. We begin with a collection of data, “CPSMar2004Extract.wf1”, taken from the March 2004 Current Population
Survey. We have data for about 100,000 individuals on wage rates (measured in logs,
LNWAGE), education (ED), age (AGE), and whether or not the individual is a union member
(UNION, 1 if union member, 0 if not). The identifier of this data set is the observation number for a particular individual.
Our goal is to regress log wage on education, age, union membership, and the fraction of the
population that’s unionized in the state. The difficulty is that the unionized fraction of the
state’s population is naturally identified by state. We need to find a mechanism to match
individual-identified data with the state-identified data. We’ll do this in several steps.