Econ 399 Chapter7a - 7 Dummy Variables Thus far we have...

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7 Dummy Variables Thus far, we have only considered variables with a QUANTITATIVE MEANING -ie: dollars, population, utility, etc. In this chapter we will cover variables with a QUALITATIVE meaning -ie: gender, location, race, specific knowledge or attribute
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7. Dummy Variables 7.1 Describing Qualitative Information 7.2 A Single Dummy Independent Variable 7.3 Using Dummy Variables for Multiple Categories 7.4 Interactions Involving Dummy Variables 7.5 A Binary Dependent Variable: The Linear Probability Model 7.6 More on Policy Analysis and Program Evaluation
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7.1 Describing Qualitative Information Any study where an observation has a quality that can be described as either has/does not have, is/is not, does/does not etc. can be expressed as a DUMMY VARIABLE (DV) or BINARY VARIABLE Ie: -has or does not have a high school diploma -is or is not male -is or is not in Ontario -does or does not smoke
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7.1 Describing Qualitative Information Binary variables generally take on either a zero or one value to make them easier to interpret in regressions. Often the name of the Dummy Variable indicates what value takes a 1: Female = 1 if female = 0 otherwise Single = 1 if single = 0 otherwise
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7.2 Single Dummy Variables -Consider the following model where knowledge of the world is a function of reading and travelling: u Travel Books WKnow 1 0 -where our Dummy Variable, Travel = 1 if you’ve travelled outside Canada and =0 otherwise -delta is therefore the difference in world knowledge between those who have travelled and those who have not, GIVEN the same number of books read
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7.2 Single Dummy Variables -Mathematically, ) | | ( ) , | ( books notravel Wknow E books travel Wknow E -The dummy variable causes an INTERCEPT SHIFT, independent on the number of books read -this inclusion of a dummy variable has no impact on any slopes; the impact of an additional book is the same for a traveller as for a non-traveller
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7.2 Dummy Variable Trap -When two Dummy Variables relating to the same aspect are included, such as travel and notravel, we cause perfect collinearity because travel+notravel=1 -this is the DUMMY VARIABLE TRAP that arises when too many DV’s are included -The DV Trap can also occur when there are too many DV’s relative to the different number of observations
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7.2 All your base are belong
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