qualitative-1

# qualitative-1 - QUALITATIVE INFORMATION QUALITATIVE models...

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QUALITATIVE INFORMATION QUALITATIVE INFORMATION models with qualitative x - Dummy/Binary Variables Dummy/Binary Variables Models Models models with qualitative y - Linear Probability Models Linear Probability Models - Probit Probit and and Logit Logit Models Models

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QUALITATIVE INFORMATION QUALITATIVE INFORMATION - - 1 1 DUMMY/BINARY VARIABLES MODELS DUMMY/BINARY VARIABLES MODELS
A. Allowing Different Slopes A. Allowing Different Slopes Model 1: # category = 1, binary Model 1: # category = 1, binary where 1. : = E[ wage|female, educ ] - E[ wage|male, educ ] the difference in hourly wage between F and M , given the same level of education. male : the base group in which all comparison are made. is not meaningful since no one has zero wage for all education level. HT: no discrimination against female 00 1 () wage female educ u β δβ = ++ + 1; 0 female female male female →=→= 0 δ 0 0 0 β< 01 0 :0 . : 0 . o Hv s H = <

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2. Reinterpreting the model with log ( wage ) ( )100 : the approximate % difference in hourly wage between F and M , holding other factors fixed. More accurate measure: n l l l 00 1 log( ) ( ) wage female educ βδ β =+ + l 0 δ l l () 0 0 log( ) log( ) 100 exp( ) 1 100% FM M wage wage wage wage wage −=  →=  
Model 2.1: # category > 1, binary Model 2.1: # category > 1, binary : the approximate wage differential between married and non-married, holding education level fixed. limitation: marriage premium ( ) is assumed to be the same for both F and M . n l l l l 00 1 1 log( ) ( ) ( ) wage female married educ βδ δ β =+ + + l 1 l 1

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Model 2.2: # category > 1, binary Model 2.2: # category > 1, binary : the approximate difference in wage relative to single male.
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qualitative-1 - QUALITATIVE INFORMATION QUALITATIVE models...

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